A dynamic slab width group furnace data management system and method for steelmaking

Abstract

The application discloses a dynamic slab width group furnace data management system and method for steelmaking, relates to the technical field of industrial data management, and acquires contract orders to form a contract order set, simultaneously acquires process parameters matched with each order, calculates the width range and basic production data of each contract based on the contract order parameters and the process parameters, establishes an optimization function, and applies contract coverage constraints and group distance and distribution correlation constraints, filters an optimal group distance set from a group slab width set, generates all feasible slab combination schemes satisfying the left and right flow weight requirements of a continuous casting machine and containing specific slab block array combinations based on the optimal group distance set, and stores all the feasible slab combination schemes in a plan list, applies contract satisfaction constraints, shortage constraints and left and right flow balance constraints, and solves a second optimization function to obtain the feasible slab combination schemes from the plan list.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of industrial data management, and particularly relates to a dynamic slab width group furnace data management system and method for steelmaking. BACKGROUND

[0002] As a basic pillar industry of the national economy, the production efficiency, product quality and cost control level of the steel industry are directly related to the national industrial modernization process. In modern steelmaking production, especially in the field of continuous casting and hot rolling, the slab group furnace link, that is, combining slabs of different specifications to form a production plan for a furnace according to production order requirements, plays a key role in connecting the past and the future.

[0003] With the increasing precision requirements for product specifications, especially the control of slab width tolerance, an unprecedentedly strict trend has emerged, for example, the order width tolerance is generally tightened to ± 50 mm or even a smaller range. The traditional slab group furnace method relying on manual experience and fixed group distance mode exposes its inherent and difficult-to-overcome deep-seated limitations at the principle level. The root cause of this limitation lies in the existence of planning bottlenecks when facing the demand for change, high precision and multi-objective collaborative optimization of manual decision-making and fixed mode. Therefore, how to build a group slab planning method that can take into account multiple constraints, realize dynamic optimization of slab width, and effectively improve the quality, efficiency and economic benefits in steelmaking production has become a key challenge and technical problem to be solved for current technical personnel in the field. SUMMARY

[0004] The purpose of the present application is to provide a dynamic slab width group furnace data management system and method for steelmaking to solve the problems in the prior art.

[0005] To achieve the above-mentioned purpose, the present application provides the following technical scheme: a dynamic slab width group furnace data management method for steelmaking, the method comprising:

[0006] S1: obtaining a contract order set for contract order composition for steelmaking production plan compilation, and obtaining the process parameters matched with each order;

[0007] S2: calculating the width range and basic production data of each contract based on the contract order parameters and process parameters;

[0008] S3: defining a group slab width distribution variable, establishing an optimization function, and imposing contract coverage constraints and group distance and distribution correlation constraints, and filtering the optimal group distance set from the group slab width set by solving the optimization function that meets the constraint conditions;

[0009] S4: Based on the optimal set of group intervals, generate all feasible slab combination schemes that meet the weight requirements of the left and right streams of the continuous casting machine and include a specific combination of slab block numbers, and store all feasible slab combination schemes in the plan list;

[0010] S5: Define integer variables and apply contract satisfaction constraints, under-production constraints, and left and right flow balance constraints. By solving the second optimization function, select the final furnace production plan from the feasible slab combination schemes in the plan list.

[0011] Furthermore, S1 includes:

[0012] S1-1: Collect several contract orders, obtain the contract order parameters of all the contract orders used for steelmaking production planning to form a contract order set, and obtain the process parameters matched for each order to form a contract order set C. For any contract order c in the contract order set C, the contract order parameters include nominal width, required weight, minimum unit weight, maximum unit weight, nominal thickness, and allowable shortage indicator. Among them, nominal width represents the required value of slab width marked in the contract order, and nominal thickness represents the required value of slab thickness marked in the contract order.

[0013] S1-2: Obtain process parameters, including slab thickness, target capacity per furnace, slab density, contract width tolerance, minimum slab width of the system, maximum slab width of the system, maximum length of a single slab, and leftward deviation threshold (m). r,A Right-flow deviation threshold m r,B And the penalty coefficient for under-quantity.

[0014] S1 aims to convert the raw contract data into a format that can be directly used by the optimization model and to calculate the basic theoretical data related to the slab width.

[0015] Slab thickness: The preset target thickness of slabs produced by the continuous casting machine, in millimeters; in practical applications, if there is a difference between the nominal thickness of the contract order and this target thickness of the slab, the theoretical weight of the slab shall be calculated based on the slab thickness, or adjusted by a conversion factor.

[0016] Target capacity per heat: The total weight expected to be produced by the continuous casting machine in each heat, in kilograms. This is the upper limit of the capacity planned for each heat.

[0017] Slab density: The preset density of the slab material, in grams per cubic centimeter;

[0018] Allowed overdue quantity indicator: The indicator distinguishes whether the contract allows overdue quantity. For example, when the allowed overdue quantity indicator is 1, the contract order allows overdue quantity, and when the allowed overdue quantity indicator is 0, the contract order does not allow overdue quantity.

[0019] Contract width tolerance: The system's preset slab width tolerance is used to define the allowable upper and lower fluctuation range of the nominal width of each contract order, in millimeters. If the contract width tolerance is 50mm, it means that the nominal width of the contract is allowed to deviate by ±50mm.

[0020] Minimum slab width of the system: The minimum slab width that the continuous casting machine or subsequent rolling equipment can handle, in millimeters;

[0021] Maximum slab width of the system: The maximum slab width that the continuous casting machine or subsequent rolling equipment can handle, in millimeters;

[0022] Target slab length: The ideal or target length of a single slab preset by the system, used to calculate the theoretical weight of a single slab;

[0023] Left-flow deviation threshold: The maximum absolute deviation between the total weight of the slab and the target weight of the left-flow when generating a left-flow slab combination scheme, in kilograms;

[0024] Right-flow deviation threshold: The maximum absolute deviation between the total weight of the slab and the target weight of the right flow when generating a right-flow slab combination scheme, expressed in kilograms;

[0025] Penalty coefficient for overdue payments: In the second-stage optimization model, the penalty coefficient for overdue contract payments Z is... k The penalty weighting coefficient is applied, and its value is a large positive number to suppress unnecessary underestimation.

[0026] Furthermore, S2 includes:

[0027] S2-1: Calculate the width range [Lc, Uc] of contract c. The lower limit Lc of the width range [Lc, Uc] is calculated as follows: if the nominal width nc of contract order c is not divisible by 10, then the lower limit Lc is equal to the nominal width nc rounded up to the nearest multiple of 10; if the nominal width nc of contract order c is divisible by 10, then the lower limit Lc is equal to the nominal width. The upper limit Uc of the width range [Lc, Uc] is calculated as follows: the upper limit Uc is equal to the nominal width nc plus the width constant ΔW, rounded down to the nearest multiple of 10.

[0028] S2-2: Calculate basic production data, which includes the maximum weight uw of a single slab and the minimum number of slabs p. c(Wc,zj) Where zj is the selected slab width;

[0029] Maximum weight of a single slab u w The calculation formula is: u w =zj×st×ρ×10 -6 ×L target L targetThe maximum length of a single slab is represented by 'st', the slab thickness by 'st', and the minimum number of slabs by 'p'. c(Wc,zj) The calculation formula is: W C This represents the required weight of contract order c, where ρ represents the density of the slab. This represents the function for rounding up.

[0030] Furthermore, S3 includes:

[0031] S3-1: Establish the set of panel widths Xall, and define binary variables Xx, Zc,x and continuous variable Wx;

[0032] S3-2: Apply constraints, including contract coverage constraints and grouping and assignment association constraints. The contract coverage constraint ensures that each contract in the contract order set C must be assigned to a selected grouping, and the grouping and assignment association constraint ensures that only the selected grouping can be assigned a contract.

[0033] S3-3: Establish and solve the first objective function F1.

[0034] ;

[0035] Where H1 is the penalty coefficient for minimizing the number of selected class intervals, H2 is the penalty coefficient for minimizing the load weight deviation of the selected class intervals, and H3 is the benefit coefficient for encouraging the selection of wider class intervals. This represents the average weight of all selected group spacings of the boards.

[0036] H1 is a penalty coefficient used to minimize the number of selected class intervals, for example, 1000, thereby simplifying production management;

[0037] H2 is a penalty coefficient used to minimize the load weight deviation of the selected set interval, for example, 1, to promote the balance of twin-strand production in the continuous casting machine.

[0038] H3 is the benefit factor, used to encourage the selection of wider group intervals, for example, 0.01;

[0039] S3-4: Use the first SCIP solver to find the minimum value of the first objective function F1. The first SCIP solver is configured to find an integer solution that satisfies all constraints and minimizes the value of the objective function F1. Output the set of class intervals x with a value of 1 obtained from solving the first optimization model. This set is denoted as the optimal class interval set Xsel.

[0040] Furthermore, S3-1 includes:

[0041] Each element in the panel width set Xall is a width value in 10 mm increments, ranging from the minimum of the lower limit Lc of the width range of all contract orders to the maximum of the upper limit Uc of the width range of all contract orders.

[0042] The binary variable Xx is used to represent the value of Xx as 1 when the group interval x belongs to the group width set Xall and is selected as the optimal group interval, otherwise it is 0;

[0043] The binary variable Zc,x is used to represent the value of Zc,x as 1 when contract c belongs to the contract order set C and contract c is assigned to the class interval x, and 0 otherwise.

[0044] Continuous variable W x This represents the total contract demand weight allocated to group interval x, and it is calculated as follows: .

[0045] Furthermore, S4 includes:

[0046] S4-1: Generate group interval pairing (A,B), where A is the width of the left-flow slab and B is the width of the right-flow slab;

[0047] S4-2: For each group interval pair (A, B), calculate its corresponding left-flow target weight a. s And the target weight b of the right flow s The formula for calculating the left-flow target weight as is: a s =(A / (A+B))×Pw, where A is the left-flow group interval, B is the right-flow group interval, and b is the target weight of the right-flow. s The calculation formula is: b s =(B / (A+B))×Pw, where Pw is the target capacity of the furnace batch;

[0048] S4-3: For the left contract subset CA of the contract order set C that satisfies the condition Lc≤A≤Uc, find the combination of slab blocks such that the absolute deviation between the total weight of the slabs in the combination of slab blocks and the left flow target weight as is less than or equal to the left flow deviation threshold m. r,A And for the right contract subset C of the contract order set C that satisfies the condition Lc≤B≤Uc B Find combinations of slab pieces such that the absolute deviation between the total weight of the slabs in these combinations and the target weight bs for the right flow is less than or equal to the right flow deviation threshold m. r,B ;

[0049] The left contract subset C of the contract order set C consists of contract orders that satisfy the condition Lc≤A≤Uc. A ;

[0050] The mathematical expression for finding the combination of slab blocks is: Where, k e For the number of slab blocks in contract e, The theoretical unit weight corresponding to class interval A is used to generate the left-flow plan;

[0051] For the right contract subset CB of the contract order set C that satisfies the condition Lc≤B≤Uc;

[0052] The mathematical expression for finding the combination of slab blocks is: , where k e For the number of slab blocks in contract e, The theoretical unit weight corresponding to class interval B is used to generate the right-flow plan;

[0053] All generated left-flow and right-flow plans are stored in plan list I. The left-flow and right-flow plans are distinguished by different identifiers in plan list I. Each element i in plan list I represents a feasible furnace plan scheme.

[0054] S4 aims to generate all feasible slab combination schemes that meet the capacity and deviation requirements for the left and right flows of the continuous casting machine based on the optimal group spacing selected in the first stage.

[0055] The generated left-flow and right-flow plans, each carrying its own group spacing information, contract block number allocation information, and plan type identifier, are stored in plan list I. Each element i in list I represents a feasible furnace plan scheme, which includes the specific left and right flow group spacing and the slab block number allocation for each contract.

[0056] Furthermore, S5 includes:

[0057] S5-1: Set the integer variable X i and integer variable Z k , where X i Indicates the number of times scheme i is used in plan list I, Z k Indicates the shortfall in contract k;

[0058] S5-2: Apply constraints, including contract fulfillment constraints, under-limit constraints, and left-right flow balance constraints. The contract fulfillment constraints ensure that all contract requirements are met or under-limit is allowed. The under-limit constraints ensure that the contract order is allowed under-limit only when the allowed under-limit flag is displayed in the contract order. The left-right flow balance constraints ensure that all pairings (A, B) formed by all intervals A and B in the optimal interval set are valid. The total number of uses of the scheme with left-flow interval A must be equal to the total number of uses of the scheme with right-flow interval B.

[0059] S5-3: The mathematical expression for the second objective function F2 is:

[0060] ;

[0061] Where H4 is the penalty coefficient for minimizing the total number of times the selected scheme is used, and Q... i H5 is the residual material loss penalty coefficient for scheme i, and H5 is the profit coefficient that encourages the selection of schemes with larger total width for each furnace. penalty This is the penalty coefficient for under-quantity transactions;

[0062] H4 is a penalty coefficient, for example, 100, used to minimize the total number of times the selected scheme is used, in order to simplify production scheduling;

[0063] Q i The residual material loss, energy consumption, or production efficiency penalty / benefit coefficient associated with scheme i is pre-calculated based on the specific attributes of scheme i, for example, Q. i The material utilization loss or processing difficulty of scheme i relative to the ideal state can be quantified;

[0064] H5 is the revenue coefficient, for example, 0.005, used to encourage the selection of furnace schemes with a larger total width;

[0065] H penalty This is the underpayment penalty coefficient, which is a large positive number, such as 10000, used to strongly penalize underpayments.

[0066] Where n is the total number of schemes in the plan list I, and M is the total number of contract orders in the contract order set C;

[0067] Meanwhile, the second objective function F2 satisfies the condition. W threshold A represents the distance threshold. i and B i These represent the left-flow group spacing and the right-flow group spacing in scheme i, respectively.

[0068] S5-4: The objective is to use the second SCIP solver to find the minimum value of the second objective function F2. The second SCIP solver is configured to find an integer solution that satisfies all constraints and minimizes the objective function value.

[0069] S5 aims to select an optimal heat production plan from the numerous feasible slab combination schemes generated by S4 to meet all contract requirements, balance production costs and efficiency, and ensure the balance of the left and right streams of the continuous casting machine.

[0070] To better implement the above method, a dynamic slab and billet grouping data management system for steelmaking is also proposed. The system includes:

[0071] Order management module, production data management module, first optimization allocation module, left and right flow plan list management module, and second optimization allocation module;

[0072] The order management module is used to obtain the contract orders used for steelmaking production planning, forming a set of contract orders, and to obtain the process parameters matched for each order.

[0073] The production data management module is used to calculate the width range and basic production data for each contract based on contract order parameters and process parameters;

[0074] The first optimization allocation module is used to define the panel width allocation variable, establish the optimization function, and apply contract coverage constraints as well as group interval and allocation association constraints. By solving the optimization function that satisfies the constraints, the optimal group interval set is obtained from the panel width set.

[0075] The left and right flow planning list management module is used to generate all feasible slab combination schemes that meet the weight requirements of the left and right flows of the continuous casting machine and include a specific combination of slab block numbers, based on the optimal group interval set, and to store all feasible slab combination schemes in the planning list;

[0076] The second optimization allocation module is used to define integer variables and apply contract satisfaction constraints, under-production constraints, and left and right flow balance constraints. By solving the second optimization function, the final furnace production plan is selected from the feasible slab combination schemes in the plan list.

[0077] Furthermore, the production data management module includes: a width range management unit and a basic production data management unit. The width range management unit is used to calculate the width range of contract c, and the basic production data management unit is used to calculate the maximum weight and minimum number of single slabs.

[0078] Furthermore, the first optimization allocation module includes: a panel width set management unit, a first constraint management unit, a first objective function management unit, and a first solution unit. The panel width set management unit is used to manage the panel width set and the variables of each element in the panel width set. The first constraint management unit is used to manage contract coverage constraints and group interval and allocation association constraints. The first objective function management unit is used to establish and manage the first objective function. The first solution unit is used to solve for the minimum value of the first objective function using the first SCIP solver.

[0079] Furthermore, the left and right flow plan list management module includes: a pairing group management unit, a target weight calculation unit, and a slab allocation unit. The pairing group management unit is used to manage group spacing pairings, the target weight calculation unit is used to calculate the corresponding left flow target weight and right flow target weight for each group spacing pairing, and the slab allocation unit is used to divide the plans in the plan list into left flow plans and right flow plans.

[0080] Furthermore, the second optimization allocation module includes: a second constraint management unit, a second objective function management unit, and a second solution unit. The second constraint management unit is used to manage constraints including contract satisfaction constraints, under-constraint constraints, and left and right flow balance constraints. The second objective function management unit is used to establish and manage the second objective function. The second solution unit is used to solve for the minimum value of the second objective function using the second SCIP solver.

[0081] Compared with the prior art, the beneficial effects of the present invention are:

[0082] 1. This invention breaks through the rigid limitations of traditional fixed group spacing by accurately calculating the width range of each contract and using a first optimization model to select the optimal group spacing within these dynamic ranges. This dynamic width allocation strategy makes full use of the width tolerance zone of each order, enabling the slab width to more precisely match contract requirements and significantly reducing excess material caused by coarse-grained width allocation.

[0083] 2. By introducing furnace capacity and considering the left and right flow balance constraints and the objective function's consideration of furnace load balance in the second-stage optimization model, this invention can effectively plan the production load of the continuous casting machine's dual flow, avoiding idle or overloaded capacity on one side, thereby improving the overall operating efficiency and stability of the continuous casting machine. Attached Figure Description

[0084] Fig. 1 This is a schematic diagram of the structure of a dynamic slab and billet grouping data management system for steelmaking according to the present invention;

[0085] Fig. 2 This is a flowchart illustrating a dynamic slab and billet grouping data management method for steelmaking according to the present invention. Detailed Implementation

[0086] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0087] Example: Figs. 1-2 As shown, the present invention provides a technical solution for a dynamic slab and billet grouping data management system and method for steelmaking;

[0088] S1: Obtain the contract orders used for steelmaking production planning to form a contract order set, and at the same time obtain the process parameters matched for each order;

[0089] S1 includes:

[0090] S1-1: Collect several contract orders, obtain the contract order parameters of all the contract orders used for steelmaking production planning to form a contract order set, and obtain the process parameters matched for each order to form a contract order set C. For any contract order c in the contract order set C, the contract order parameters include nominal width, required weight, minimum unit weight, maximum unit weight, nominal thickness, and allowable shortage indicator. Among them, nominal width represents the required value of slab width marked in the contract order, and nominal thickness represents the required value of slab thickness marked in the contract order.

[0091] S1-2: Obtain process parameters, including slab thickness, target capacity per furnace, slab density, contract width tolerance, minimum slab width of the system, maximum slab width of the system, maximum length of a single slab, and leftward deviation threshold (m). r,A Right-flow deviation threshold m r,B And the penalty coefficient for under-quantity.

[0092] S2: Calculate the width range and basic production data for each contract based on contract order parameters and process parameters;

[0093] S2 includes:

[0094] S2-1: Calculate the width range [Lc, Uc] of contract c. The lower limit Lc of the width range [Lc, Uc] is calculated as follows: if the nominal width nc of contract order c is not divisible by 10, then the lower limit Lc is equal to the nominal width nc rounded up to the nearest multiple of 10; if the nominal width nc of contract order c is divisible by 10, then the lower limit Lc is equal to the nominal width. The upper limit Uc of the width range [Lc, Uc] is calculated as follows: the upper limit Uc is equal to the nominal width nc plus the width constant ΔW, rounded down to the nearest multiple of 10.

[0095] S2-2: Calculate basic production data, which includes the maximum weight u of a single slab. w and minimum number of blocks p c(Wc,zj) Where zj is the selected slab width;

[0096] Maximum weight of a single slab u w The calculation formula is: u w =zj×st×ρ×10 -6 ×L target L target The maximum length of a single slab is represented by 'st', the slab thickness by 'st', and the minimum number of slabs by 'p'. c(Wc,zj) The calculation formula is: W C This represents the required weight of contract order c, where ρ represents the density of the slab. This represents the function for rounding up.

[0097] S3: Define the panel width allocation variable, establish the optimization function, and apply contract coverage constraints and group interval and allocation association constraints. By solving the optimization function that satisfies the constraints, the optimal group interval set is obtained from the panel width set.

[0098] S3 includes:

[0099] S3-1: Establish the set of panel widths X all Define a binary variable X x Binary variable Z c,x and continuous variable W x ;

[0100] S3-1 includes:

[0101] Panel width set X all Each element in the range is a width value in 10 mm increments, ranging from the minimum of the lower limit Lc of the width range of all contract orders to the maximum of the upper limit Uc of the width range of all contract orders.

[0102] Binary variable X x Used to indicate when the group interval x belongs to the set of group widths X all And when X is selected as the optimal class interval, x The value is 1 if it is 1, otherwise it is 0.

[0103] Binary variable Z c,x Z is used to represent the condition where contract c belongs to the contract order set C and contract c is assigned to the group interval x. c,x The value is 1 if it is 1, otherwise it is 0.

[0104] Continuous variable W x This represents the total contract demand weight allocated to group interval x, and it is calculated as follows: ;

[0105] S3-2: Apply constraints, including contract coverage constraints and grouping and assignment association constraints. The contract coverage constraint ensures that each contract in the contract order set C must be assigned to a selected grouping, and the grouping and assignment association constraint ensures that only the selected grouping can be assigned a contract.

[0106] S3-3: Establish and solve the first objective function F1.

[0107] ;

[0108] Where H1 is the penalty coefficient for minimizing the number of selected class intervals, H2 is the penalty coefficient for minimizing the load weight deviation of the selected class intervals, and H3 is the benefit coefficient for encouraging the selection of wider class intervals. This represents the average weight of all selected group spacings of the group plates.

[0109] S4: Based on the optimal set of group intervals, generate all feasible slab combination schemes that meet the weight requirements of the left and right streams of the continuous casting machine and include a specific combination of slab block numbers, and store all feasible slab combination schemes in the plan list;

[0110] S4 includes:

[0111] S4-1: Generate group interval pairing (A,B), where A is the width of the left-flow slab and B is the width of the right-flow slab;

[0112] S4-2: For each group interval pair (A, B), calculate its corresponding left-flow target weight a. s And the target weight b of the right flow s Left-flow target weight a s The calculation formula is: a s =(A / (A+B))×Pw, where A is the left-flow group interval, B is the right-flow group interval, and b is the target weight of the right-flow. s The calculation formula is: b s =(B / (A+B))×Pw, where Pw is the target capacity of the furnace batch;

[0113] S4-3: For the left contract subset C of the contract order set C, which satisfies the condition Lc≤A≤Uc. A Find combinations of slab pieces such that the total weight of the slabs in these combinations is equal to the target weight 'a' of the left-flowing slab. s The absolute deviation between them is less than or equal to the left-flow deviation threshold m r,A And for the right contract subset C of the contract order set C that satisfies the condition Lc≤B≤Uc B Find combinations of slab pieces such that the total weight of the slabs in such combinations is equal to the target weight b of the right-flowing slab. s The absolute deviation between them is less than or equal to the right-flow deviation threshold m r,B ;

[0114] The left contract subset C of the contract order set C consists of contract orders that satisfy the condition Lc≤A≤Uc. A ;

[0115] The mathematical expression for finding the combination of slab blocks is: Where, k e For the number of slab blocks in contract e, The theoretical unit weight corresponding to class interval A is used to generate the left-flow plan;

[0116] The right contract subset C of the contract order set C consists of contract orders that satisfy the condition Lc≤B≤Uc. B;

[0117] The mathematical expression for finding the combination of slab blocks is: , where k e For the number of slab blocks in contract e, The theoretical unit weight corresponding to class interval B is used to generate the right-flow plan;

[0118] All generated left-flow and right-flow plans are stored in plan list I. The left-flow and right-flow plans are distinguished by different identifiers in plan list I. Each element i in plan list I represents a feasible furnace plan scheme.

[0119] S5: Define integer variables and apply contract satisfaction constraints, under-production constraints, and left and right flow balance constraints. By solving the second optimization function, select the final furnace production plan from the feasible slab combination schemes in the plan list.

[0120] S5 includes:

[0121] S5-1: Set the integer variable X i and integer variable Z k , where X i Indicates the number of times scheme i is used in plan list I, Z k Indicates the shortfall in contract k;

[0122] S5-2: Apply constraints, including contract fulfillment constraints, under-limit constraints, and left-right flow balance constraints. The contract fulfillment constraints ensure that all contract requirements are met or under-limit is allowed. The under-limit constraints ensure that the contract order is allowed under-limit only when the allowed under-limit flag is displayed in the contract order. The left-right flow balance constraints ensure that all pairings (A, B) formed by all intervals A and B in the optimal interval set are valid. The total number of uses of the scheme with left-flow interval A must be equal to the total number of uses of the scheme with right-flow interval B.

[0123] S5-3: The mathematical expression for the second objective function F2 is:

[0124] ;

[0125] Where H4 is the penalty coefficient for minimizing the total number of times the selected scheme is used, and Q... i H5 is the residual material loss penalty coefficient for scheme i, and H5 is the profit coefficient that encourages the selection of schemes with larger total width for each furnace. penalty This is the penalty coefficient for under-quantity transactions;

[0126] Where n is the total number of schemes in the plan list I, and M is the total number of contract orders in the contract order set C;

[0127] Meanwhile, the second objective function F2 satisfies the condition. Wthreshold A represents the distance threshold. i and B i These represent the left-flow group spacing and the right-flow group spacing in scheme i, respectively.

[0128] S5-4: The objective is to use the second SCIP solver to find the minimum value of the second objective function F2. The second SCIP solver is configured to find an integer solution that satisfies all constraints and minimizes the objective function value.

[0129] The following complete embodiment illustrates in detail the implementation of the dynamic slab and billet grouping data management method of the present invention for steelmaking;

[0130] Get setting parameters:

[0131] Slab thickness st=250mm, slab density ρ=7.85g / cm³, target capacity per furnace Pw=300,000kg;

[0132] Contract width tolerance = 50mm, minimum slab width of the system = 800mm, maximum slab width of the system = 2500mm;

[0133] The length of a single slab is 10,000 mm, the left-flow deviation threshold is 2,000 kg, and the right-flow deviation threshold is 2,000 kg.

[0134] Underpayment penalty coefficient = 10,000;

[0135] S1: Obtain contract orders and process parameters. Obtain contract orders:

[0136] Contract 1 (C1):

[0137] Order number: ORD001;

[0138] Nominal width: 1500mm, required weight: 50,000kg, minimum single weight: 10,000kg, maximum single weight: 13,000kg;

[0139] Nominal thickness: 250mm, allowable shortage mark: 0;

[0140] Contract 2 (C2):

[0141] Order number: ORD002;

[0142] Nominal width: 1450mm, required weight c: 60,000kg, minimum single weight: 9,000kg, maximum single weight: 12,000kg;

[0143] Nominal thickness: 250mm, allowable shortage mark: 1;

[0144] Contract 3 (C3);

[0145] Order number: ORD003;

[0146] Nominal width: 1620mm, required weight: 40,000kg, minimum unit weight: 11,000kg, maximum unit weight: 14,000kg;

[0147] Nominal thickness: 250mm, allowable shortage mark: 0;

[0148] Contract 4 (C4):

[0149] Order number: ORD004;

[0150] Nominal width: 1300mm, required weight: 75,000kg, minimum single weight: 8,500kg, maximum single weight: 11,000kg;

[0151] Nominal thickness: 250mm, allowable shortage mark: 0;

[0152] Contract 5 (C5):

[0153] Order number: ORD005;

[0154] Nominal width: 1750mm, required weight: 55,000kg, minimum single weight: 10,500kg, maximum single weight: 13,500kg;

[0155] Nominal thickness: 250mm, allowable shortage mark: 0;

[0156] Contract 6 (C6):

[0157] Order number: ORD006;

[0158] Nominal width: 1200mm, required weight: 45,000kg, minimum single weight: 8,000kg, maximum single weight: 10,500kg;

[0159] Nominal thickness: 250mm, allowable shortage mark: 1;

[0160] Step S2: Calculation of Contract Width Range and Basic Production Data:

[0161] Based on the calculation formula in S2-1, the width range of the above contract is calculated as follows:

[0162] C1 (1500mm):

[0163] Lc=max(800,ceil((1500-50) / 10)×10)=max(800,1450)=1450mm;

[0164] Uc=min(2500,floor((1500+50) / 10)×10)=min(2500,1550)=1550mm;

[0165] C1 width range: [1450, 1550];

[0166] C2 (1450mm):

[0167] Lc=max(800,ceil((1450-50) / 10)×10)=max(800,1400)=1400mm;

[0168] Uc=min(2500,floor((1450+50) / 10)×10)=min(2500,1500)=1500mm;

[0169] C2 width range: [1400, 1500];

[0170] C3 (1620mm):

[0171] Lc=max(800,ceil((1620-50) / 10)×10)=max(800,1570)=1570mm;

[0172] Uc=min(2500,floor((1620+50) / 10)×10)=min(2500,1670)=1670mm;

[0173] C3 width range: [1570, 1670];

[0174] C4 (1300mm):

[0175] Lc=max(800,ceil((1300-50) / 10)×10)=max(800,1250)=1250mm;

[0176] Uc=min(2500,floor((1300+50) / 10)×10)=min(2500,1350)=1350mm;

[0177] C4 width range: [1250, 1350];

[0178] C5 (1750mm):

[0179] Lc=max(800,ceil((1750-50) / 10)×10)=max(800,1700)=1700mm;

[0180] Uc=min(2500,floor((1750+50) / 10)×10)=min(2500,1800)=1800mm;

[0181] C5 width range: [1700, 1800];

[0182] C6 (1200mm):

[0183] Lc=max(800,ceil((1200-50) / 10)×10)=max(800,1150)=1150mm;

[0184] Uc=min(2500,floor((1200+50) / 10)×10)=min(2500,1250)=1250mm;

[0185] C6 width range: [1150, 1250];

[0186] Calculate the minimum number of blocks;

[0187] C1(50,000kg,u_w(1450)=28437.5kg):ceil(50000 / 28437.5)=ceil(1.758)=2 pieces;

[0188] C2(60,000kg,u_w(1450)=28437.5kg):ceil(60000 / 28437.5)=ceil(2.109)=3 pieces;

[0189] C3(40,000kg,u_w(1600)=31400.0kg): ceil(40000 / 31400.0)=ceil(1.274)=2 pieces;

[0190] C4(75,000kg,u_w(1300)=25562.5kg): ceil(75000 / 25562.5)=ceil(2.934)=3 pieces;

[0191] C5(55,000kg,u_w(1750)=34343.75kg):ceil(55000 / 34343.75)=ceil(1.601)=2 pieces;

[0192] C6(45,000kg,u_w(1200)=23550.0kg): ceil(45000 / 23550.0)=ceil(1.911)=2 pieces;

[0193] Define X_all:

[0194] xmin=min(1450,1400,1570,1250,1700,1150)=1150mm;

[0195] xmax=max(1550,1500,1670,1350,1800,1250)=1800mm;

[0196] X_all={1150,1160,...,1800} contains a total of 66 class intervals;

[0197] Solving the first objective function F1: The SCIP solver is used for calculation.

[0198] The objective function considers minimizing the number of group intervals, furnace load balancing, and width preference. After solving, the following optimal allocation results and optimal group interval set X_sel are obtained:

[0199] The optimal set of group intervals is X_sel={1200mm,1300mm,1450mm,1600mm,1750mm} (5 group intervals are selected, sumX_x=5);

[0200] Contract allocation details:

[0201] C1(1500mm): Assigned to 1450mm (1450mm in [1450, 1550]);

[0202] C2(1450mm): Assigned to 1450mm (1450mm in [1400, 1500]);

[0203] C3 (1620mm): Assigned to 1600mm (1600mm in [1570, 1670]);

[0204] C4 (1300mm): Assigned to 1300mm (1300mm in [1250, 1350]);

[0205] C5 (1750mm): Assigned to 1750mm (1750mm in [1700, 1800]);

[0206] C6(1200mm): Assigned to 1200mm (1200mm in [1150, 1250]);

[0207] Total required weight for each group spacing:

[0208] W_1200=W_C6=45,000kg; W_1300=W_C4=75,000kg;

[0209] W_1450=W_C1+W_C2=50,000+60,000=110,000kg;

[0210] W_1600=W_C3=40,000kg; W_1750=W_C5=55,000kg;

[0211] The average weight of all selected intervals = (45000 + 75000 + 110000 + 40000 + 55000) / 5 = 325000 / 5 = 65,000 kg.

[0212] Step S4: Generate all feasible slab combination schemes:

[0213] X_sel={1200,1300,1450,1600,1750};

[0214] Generate group interval pairings: (1200,1200),(1200,1300),……,(1750,1750), a total of 5×5=25 pairings.

[0215] Calculate the target weight of a single stream:

[0216] Taking the pairing (A=1450, B=1600) as an example:

[0217] a_s=(1450 / (1450+1600))×300000=(1450 / 3050)×300000≈142,622.95kg;

[0218] b_s=(1600 / (1450+1600))×300000=(1600 / 3050)×300000≈157,377.05kg;

[0219] Taking the pairing (A=1300, B=1750) as an example:

[0220] a_s=(1300 / (1300+1750))×300000=(1300 / 3050)×300000≈127,868.85kg;

[0221] b_s=(1750 / (1300+1750))×300000=(1750 / 3050)×300000≈172,131.15kg;

[0222] Finding the combination of slab blocks: This is the most computationally intensive step. For each group interval pairing, it is necessary to search for the combination of blocks in the applicable contract subset.

[0223] Calculate B_k, and then calculate the total number of blocks required for each contract based on the width allocated by S3:

[0224] C1 (width 1450mm): B_C1 = ceil(50000 / 28437.5) = 2 pieces;

[0225] C2 (width 1450mm): B_C2 = ceil(60000 / 28437.5) = 3 pieces;

[0226] C3 (width 1600mm): B_C3 = ceil(40000 / 31400.0) = 2 pieces;

[0227] C4 (width 1300mm): B_C4 = ceil(75000 / 25562.5) = 3 pieces;

[0228] C5 (width 1750mm): B_C5 = ceil(55000 / 34343.75) = 2 pieces;

[0229] C6 (width 1200mm): B_C6=ceil(45000 / 23550.0)=2 pieces;

[0230] Let's take the pairing (A=1450, B=1600) as an example to illustrate this:

[0231] Left flow A = 1450 mm, target weight a_s ≈ 142,622.95 kg, m_r,A = 2000 kg;

[0232] Applicable contracts: C1, C2 (because its S3 allocation width is 1450mm, and 1450mm is within its width range);

[0233] u_w(1450)=28437.5kg.

[0234] Search for (k_C1, k_C2) such that |k_C1×28437.5+k_C2×28437.5-142622.95|<=2000;

[0235] That is, |(k_C1+k_C2)×28437.5-142622.95|<=2000;

[0236] The theoretical value of (k_C1+k_C2) is approximately 142622.95 / 28437.5≈5.015;

[0237] Therefore, k_C1+k_C2 is 5.

[0238] If k_C1+k_C2=5, the total weight is 5×28437.5=142187.5kg;

[0239] Deviation = |142187.5 - 142622.95| = ​​435.45 kg. This deviation is less than 2000 kg, satisfying the condition. Possible combinations (k_C1, k_C2) and their total weight (the number of blocks is limited by u_min, c / u_max, c also needs to be considered, but here it is simplified to the number of blocks):

[0240] Option L1: {C1: 2 pieces, C2: 3 pieces}, total weight (2+3)×28437.5=142187.5kg;

[0241] Option L2: {C1: 3 pieces, C2: 2 pieces}, total weight (3+2)×28437.5=142187.5kg;

[0242] ...

[0243] Right-flow B = 1600mm, target weight b_s ≈ 157,377.05kg, m_r,B = 2000kg;

[0244] Applicable contracts: C3, C5 (C5 has a range of [1700, 1800] wide, which can be covered by 1600mm, or the allocation logic of S3 needs to be adjusted. For simplification, only C3 is considered here.)

[0245] u_w(1600)=31400.0kg.

[0246] Search for (k_C3) such that |k_C3×31400.0-157377.05|<=2000.

[0247] The theoretical value of k_C3 is approximately 157377.05 / 31400.0≈5.012.

[0248] Therefore, k_C3 is 5.

[0249] If k_C3=5, the total weight is 5×31400.0=157000.0kg.

[0250] Deviation = |157000.0 - 157377.05| = 377.05 kg. This deviation is less than 2000 kg, which meets the condition.

[0251] Option R1: {C3: 5 pieces}, total weight 157000.0 kg.

[0252] Generate Scheme List I: Integrate the above left-flow and right-flow plans. For example, a complete furnace scheme i is described as follows:

[0253] Plan_i={A=1450, B=1600, LeftStream: {C1: 2, C2: 3, ActualWeight: 142187.5kg}, RightStream: {C3: 5, ActualWeight: 157000.0kg}, Type: Heat}.

[0254] Each scheme i will also have a pre-calculated Q_i value. For example, Q_i can be calculated based on factors such as the closeness of the total weight of the slab to Pw, the surplus material rate, and the cutting length deviation. In the example, scheme Q_i is 300, representing a medium loss.

[0255] After a thorough search of all group interval pairs, a list I containing approximately 50 feasible furnace options was finally generated.

[0256] Step S5: Solving the second optimization model and selecting the final production plan for each furnace batch;

[0257] Set integer variables X_i and Z_k, apply constraints, and perform contractual constraint satisfaction based on the calculated value of B_k:

[0258] sum(X_i×b_{i,C1})+Z_C1≥2; sum(X_i×b_{i,C2})+Z_C2≥3; sum(X_i×b_{i,C3})+Z_C3≥2;

[0259] sum(X_i×b_{i,C4})+Z_C4≥3; sum(X_i×b_{i,C5})+Z_C5≥2; sum(X_i×b_{i,C6})+Z_C6≥2;

[0260] Shortage constraint:

[0261] Z_C1=0, Z_C3=0, Z_C4=0, Z_C5=0 (underpayment not allowed); Z_C2<=M×1 (underpayment allowed); Z_C6<=M×1 (underpayment allowed);

[0262] Left and right flow balance constraint: For each (A,B) pair. For example, for (1450,1600): sum_{iin I_left(1450,1600)}X_i=sum_{jinI_right(1450,1600)}X_j.

[0263] Solving the second objective function F2: The SCIP solver is used for calculation.

[0264] After considering all constraints and objective functions, minimizing the number of furnace runs, minimizing surplus material and energy consumption, avoiding under-supplied materials, and encouraging large widths, the solver obtains the optimal solution as follows:

[0265] X_plan_a=1(furnace plan a, for example A=1450, B=1600);

[0266] Left flow: C1: 2 pieces, C2: 3 pieces (total weight 142187.5kg); Right flow: C3: 2 pieces, C5: 3 pieces (total weight 168000.0kg);

[0267] X_plan_b=1(furnace batch plan b, for example A=1300, B=1200);

[0268] Left flow: C4: 3 pieces (total weight 76687.5kg); Right flow: C6: 2 pieces (total weight 47100.0kg);

[0269] Z_C1=0,Z_C2=0,Z_C3=0,Z_C4=0,Z_C5=0,Z_C6=0;

[0270] All contracts were delivered in full, and even though C2 and C6 allowed for under-delivery, optimization found a solution with no under-delivery.

[0271] Final production plan summary:

[0272] Furnace batch 1 (group spacing 1450mm / 1600mm):

[0273] Left-flow production: C1 (2 pieces), C2 (3 pieces), total weight: 142187.5kg; Right-flow production: C3 (2 pieces), C5 (3 pieces), total weight: 168000.0kg;

[0274] Total weight of the furnace: 310187.5kg, slightly exceeding Pw. It needs to be checked whether it is within the allowable total weight deviation.

[0275] Furnace batch 2 (group spacing 1300mm / 1200mm):

[0276] Left-flow production: C4 (3 pieces), total weight: 76687.5kg; Right-flow production: C6 (2 pieces), total weight: 47100.0kg;

[0277] Total weight of furnace batch: 123,787.5 kg. All contracts were delivered in full and on demand.

[0278] The system includes: an order management module, a production data management module, a first optimization allocation module, a left and right flow plan list management module, and a second optimization allocation module;

[0279] The order management module is used to obtain a set of contract orders for steelmaking production planning, and to obtain the process parameters matched for each order.

[0280] The production data management module is used to calculate the width range and basic production data of each contract based on the contract order parameters and the process parameters.

[0281] The first optimization allocation module is used to define the group width allocation variable, establish the optimization function, and apply contract coverage constraints and group interval and allocation association constraints. By solving the optimization function that satisfies the constraints, the optimal group interval set is obtained from the group width set.

[0282] The left and right flow planning list management module is used to generate all feasible slab combination schemes that meet the weight requirements of the left and right flows of the continuous casting machine and include a specific combination of slab block numbers based on the optimal group spacing set, and to store all the feasible slab combination schemes in the planning list;

[0283] The second optimization allocation module is used to define integer variables and apply contract satisfaction constraints, under-quantity constraints and left and right flow balance constraints. By solving the second optimization function, the final furnace production plan is selected from the feasible slab combination schemes in the plan list.

[0284] It will be apparent to those skilled in the art that the present invention is not limited to the details of the exemplary embodiments described above, and that the invention can be implemented in other specific forms without departing from its spirit or essential characteristics. Therefore, the embodiments should be considered in all respects as exemplary and non-limiting, and the scope of the invention is defined by the appended claims rather than the foregoing description. Thus, all variations falling within the meaning and scope of equivalents of the claims are intended to be included within the present invention. No reference numerals in the claims should be construed as limiting the scope of the claims.

Claims

1. A method for managing dynamic slab and billet grouping data in steelmaking, characterized in that: The methods include: S1: Obtain the contract orders used for steelmaking production planning to form a contract order set, and at the same time obtain the process parameters matched for each order; S2: Based on the contract order parameters and the process parameters, calculate the width range and basic production data for each contract; S3: Define the panel width allocation variable, establish the optimization function, and apply contract coverage constraints and group interval and allocation association constraints. By solving the optimization function that satisfies the constraints, the optimal group interval set is obtained from the panel width set. S3 includes: S3-1: Establish the set of panel widths X all Define a binary variable X x Binary variable Z c,x and continuous variable W x ; S3-2: Apply constraints, including contract coverage constraints and grouping and assignment association constraints. The contract coverage constraints ensure that each contract in the contract order set C must be assigned to a selected grouping, and the grouping and assignment association constraints ensure that only the selected grouping can be assigned a contract. S3-3: Establish and solve the first objective function F1. ； Where H1 is the penalty coefficient for minimizing the number of selected class intervals, H2 is the penalty coefficient for minimizing the load weight deviation of the selected class intervals, and H3 is the benefit coefficient for encouraging the selection of wider class intervals. This represents the average weight of all selected group spacings of the boards. S3-4: Use the first SCIP solver to find the minimum value of the first objective function F1. The first SCIP solver is configured to find an integer solution that satisfies all constraints and minimizes the value of the objective function F1, and outputs X obtained from solving the first optimization model. x The set of class intervals x with a value of 1 is denoted as the optimal class interval set X. sel ; S4: Based on the optimal set of group intervals, generate all feasible slab combination schemes that meet the weight requirements of the left and right streams of the continuous casting machine and include a specific combination of slab block numbers, and store all the feasible slab combination schemes in the plan list; S5: Define integer variables and apply contract satisfaction constraints, under-production constraints, and left and right flow balance constraints. By solving the second optimization function, select the final furnace production plan from the feasible slab combination schemes in the plan list. S5 includes: S5-1: Set the integer variable X i and integer variable Z k , where X i Z indicates the number of times scheme i is used in the plan list I. k Indicates the shortfall in contract k; S5-2: Apply constraints, including contract fulfillment constraints, under-limit constraints, and left-right flow balance constraints. The contract fulfillment constraints ensure that all contract requirements are met or under-limit is allowed. The under-limit constraints ensure that under-limit is allowed only when the contract order has an allowed under-limit flag. The left-right flow balance constraints ensure that all pairings (A, B) formed by intervals A and B in the optimal interval set are valid, and the total number of uses of the scheme with left-right flow interval A must be equal to the total number of uses of the scheme with right-right flow interval B. S5-3: The mathematical expression for the second objective function F2 is: ； Where H4 is the penalty coefficient for minimizing the total number of times the selected scheme is used, and Q... i H5 is the residual material loss penalty coefficient for scheme i, and H5 is the profit coefficient that encourages the selection of schemes with larger total width for each furnace. penalty This is the penalty coefficient for under-quantity transactions; Where n is the total number of schemes in the plan list I, and M is the total number of contract orders in the contract order set C; Meanwhile, the second objective function F2 satisfies the condition. W threshold A represents the distance threshold. i and B i These represent the left-flow group spacing and the right-flow group spacing in scheme i, respectively. S5-4: The objective is to use a second SCIP solver to find the minimum value of the second objective function F2. The second SCIP solver is configured to find an integer solution that satisfies all constraints and minimizes the value of the objective function.

2. The method for managing dynamic slab and billet grouping data in steelmaking according to claim 1, characterized in that: S1 includes: S1-1: Collect several contract orders, obtain the contract order parameters of all the contract orders used for steelmaking production planning to form a contract order set, and obtain the process parameters matched for each order to form a contract order set C. For any contract order c in the contract order set C, the contract order parameters include nominal width, required weight, minimum unit weight, maximum unit weight, nominal thickness, and allowable shortage indicator, wherein the nominal width represents the required value of the slab width marked in the contract order, and the nominal thickness represents the required value of the slab thickness marked in the contract order. S1-2: Obtain process parameters, including slab thickness, target capacity per furnace, slab density, contract width tolerance, minimum slab width of the system, maximum slab width of the system, maximum length of a single slab, and leftward deviation threshold m. r,A Right-flow deviation threshold m r,B And the penalty coefficient for under-quantity.

3. The method for managing dynamic slab and billet grouping data in steelmaking according to claim 2, characterized in that: S2 includes: S2-1: Calculate the width range [Lc, Uc] of contract c. The lower limit Lc of the width range [Lc, Uc] is calculated as follows: if the nominal width nc of contract order c is not divisible by 10, then the lower limit Lc is equal to the nominal width nc rounded up to the nearest multiple of 10; if the nominal width nc of contract order c is divisible by 10, then the lower limit Lc is equal to the nominal width. The upper limit Uc of the width range [Lc, Uc] is calculated as follows: the upper limit Uc is equal to the nominal width nc plus the width constant ΔW rounded down to the nearest multiple of 10. S2-2: Calculate basic production data, which includes the maximum weight u of a single slab. w and minimum number of blocks p c(Wc,zj) Where zj is the selected slab width; Maximum weight of a single slab u w The calculation formula is: u w =zj×st×ρ×10 -6 ×L target L target The maximum length of a single slab is represented by 'st', the slab thickness by 'st', and the minimum number of slabs by 'p'. c(Wc,zj) The calculation formula is: W C Let ρ represent the required weight of contract order c, ρ represent the slab density, and ρ represent the round-up function.

4. The method for managing dynamic slab and billet grouping data in steelmaking according to claim 3, characterized in that: S3-1 includes: The set of panel widths X all Each element in the range is a width value in 10 mm increments, ranging from the minimum of the lower limit Lc of the width range of all contract orders to the maximum of the upper limit Uc of the width range of all contract orders. The binary variable X x This is used to indicate when the group spacing x belongs to the set of panel widths X. all And when X is selected as the optimal class interval, x The value is 1 if it is 1, otherwise it is 0. The binary variable Z c,x This is used to indicate that when contract c belongs to the contract order set C and contract c is assigned to the group interval x, the Z... c,x The value is 1 if it is 1, otherwise it is 0. The continuous variable W x The total contract demand weight allocated to the group interval x is represented by the following calculation method: .

5. The method for managing dynamic slab and billet grouping data in steelmaking according to claim 3, characterized in that: S4 includes: S4-1: Generate group interval pairing (A,B), where A is the width of the left-flow slab and B is the width of the right-flow slab; S4-2: For each group interval pair (A, B), calculate its corresponding left-flow target weight a. s And the target weight b of the right flow s Left-flow target weight a s The calculation formula is: a s =(A / (A+B))×Pw, where A is the left-flow group interval, B is the right-flow group interval, and b is the target weight of the right-flow. s The calculation formula is: b s =(B / (A+B))×Pw, where Pw is the target capacity of the furnace batch; S4-3: The left contract subset C consisting of contract orders in the contract order set C that satisfy the condition Lc≤A≤Uc. A Find combinations of slab pieces such that the total weight of the slabs in such combinations is equal to the target weight a of the left flow. s The absolute deviation between them is less than or equal to the left-flow deviation threshold m r,A And for the right contract subset C consisting of contract orders in the contract order set C that satisfy the condition Lc≤B≤Uc B Find combinations of slab pieces such that the total weight of the slabs in these combinations is equal to the target weight b of the right-flowing slab. s The absolute deviation between them is less than or equal to the right-flow deviation threshold m r,B ; The left contract subset C consisting of contract orders in the contract order set C that satisfy the condition Lc≤A≤Uc A ; The mathematical expression for finding the combination of slab blocks is: Where, k e For the number of slab blocks in contract e, u w,A The theoretical unit weight corresponding to class interval A is used to generate the left-flow plan; The right contract subset C, which consists of contract orders in the contract order set C that satisfy the condition Lc≤B≤Uc. B ; The mathematical expression for finding the combination of slab blocks is: , where k e For the number of slab blocks in contract e, u w,B The theoretical unit weight corresponding to the group interval B is used to generate the right-flow plan; All generated left-flow and right-flow plans are stored in plan list I. The left-flow and right-flow plans are distinguished by different identifiers in plan list I. Each element i in plan list I represents a feasible furnace batch plan scheme.

6. A dynamic slab and billet grouping data management system for steelmaking, used to execute the dynamic slab and billet grouping data management method for steelmaking as described in any one of claims 1-5, characterized in that: The system includes: Order management module, production data management module, first optimization allocation module, left and right flow plan list management module, and second optimization allocation module; The order management module is used to obtain a set of contract orders for steelmaking production planning, and to obtain the process parameters matched for each order. The production data management module is used to calculate the width range and basic production data of each contract based on the contract order parameters and the process parameters. The first optimization allocation module is used to define the group width allocation variable, establish the optimization function, and apply contract coverage constraints and group interval and allocation association constraints. By solving the optimization function that satisfies the constraints, the optimal group interval set is obtained from the group width set. The left and right flow planning list management module is used to generate all feasible slab combination schemes that meet the weight requirements of the left and right flows of the continuous casting machine and include a specific combination of slab block numbers based on the optimal group spacing set, and to store all the feasible slab combination schemes in the planning list; The second optimization allocation module is used to define integer variables and apply contract satisfaction constraints, under-quantity constraints and left and right flow balance constraints. By solving the second optimization function, the final furnace production plan is selected from the feasible slab combination schemes in the plan list.

7. A dynamic slab and billet grouping data management system for steelmaking according to claim 6, characterized in that: The production data management module includes a width range management unit and a basic production data management unit. The width range management unit is used to calculate the width range of contract c, and the basic production data management unit is used to calculate the maximum weight and minimum number of single slabs. The first optimization allocation module includes: a panel width set management unit, a first constraint management unit, a first objective function management unit, and a first solution unit. The panel width set management unit is used to manage the panel width set and the variables of each element in the panel width set. The first constraint management unit is used to manage contract coverage constraints and group interval and allocation association constraints. The first objective function management unit is used to establish and manage the first objective function. The first solution unit is used to solve for the minimum value of the first objective function using the first SCIP solver.

8. A dynamic slab and billet grouping data management system for steelmaking according to claim 6, characterized in that: The left and right flow plan list management module includes: a pairing group management unit, a target weight calculation unit, and a slab allocation unit. The pairing group management unit is used to manage group spacing pairings. The target weight calculation unit is used to calculate the corresponding left flow target weight and right flow target weight for each group spacing pairing. The slab allocation unit is used to divide the plans in the plan list into left flow plans and right flow plans. The second optimization allocation module includes: a second constraint management unit, a second objective function management unit, and a second solution unit. The second constraint management unit is used to manage constraints including contract satisfaction constraints, under-constraint constraints, and left and right flow balance constraints. The second objective function management unit is used to establish and manage the second objective function. The second solution unit is used to solve for the minimum value of the second objective function using the second SCIP solver.

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