Strip steel continuous casting and rolling casting plan compilation method, device, equipment and medium

Abstract

The invention provides a strip steel continuous casting and rolling casting plan compilation method, device, equipment and medium, and the method comprises the steps: obtaining the production business information of strip steel continuous casting and rolling, carrying out the first virtual casting division based on the equipment state information, the raw material supply information and the production plan information, and obtaining the initial casting division information; performing casting sequence planning according to the production business information and all producible economic casting sets to obtain an initial casting sequence corresponding to the minimum first order gap, performing casting distribution planning on the initial casting sequence according to the production business information and all virtual time periods to obtain initial casting distribution information, and performing casting distribution planning on the initial casting sequence according to the production business information and all virtual time periods; iteratively updating the initial casting sequence and the initial casting distribution information to obtain a target casting sequence and target casting distribution information so as to generate a casting production plan; according to the method, the compiling process is simplified, the compiling time is shortened, and a more detailed and reasonable casting plan compiling scheme is provided through balanced planning of the production line capacity, the order gap and the cold rolling process capacity.

Description

Technical Field

[0001] The present application relates to the technical field of steel smelting plan scheduling, and in particular to a method, device, equipment and medium for preparing a casting plan for continuous casting and rolling of strip steel. Background Art

[0002] In modern steel production, the connection between steelmaking, continuous casting and rolling is crucial. In addition, due to the diversity and unpredictability of market demand, the formulation of production scheduling has become increasingly complex and critical. Traditional scheduling methods often rely on the experience of schedulers and simple rules, which are difficult to cope with complex production environments and market fluctuations, and there are many problems that need to be solved.

[0003] First, the complexity of steel production scheduling stems from the interaction of multiple data and the diversity of constraints. Schedulers need to process a large amount of production business data, including order information, equipment status, raw material supply, etc. These data need to be effectively integrated and converted into a format suitable for algorithm processing to form an initial data set. When processing these data, problems such as incomplete data and untimely updates may occur, resulting in low quality of the initial data set, which in turn affects the scheduling effect.

[0004] Secondly, the formulation of pouring plans involves the balance of multiple objectives and constraints. Traditional pouring planning methods mainly rely on manual experience, which is not only time-consuming but also difficult to ensure the optimality of the plan. For example, when formulating a pouring sequence, it is necessary to consider multiple indicators such as order fulfillment rate, production line load balance and cost-effectiveness, but there are often conflicts between these indicators. How to optimize these indicators while meeting the production process constraints to form a reasonable pouring plan is a complex optimization problem. At present, manual methods have been unable to cope with the complex and changing production environment and market demand, seriously affecting the production efficiency and cost control of steel mills.

[0005] Furthermore, traditional methods are unable to cope with dynamic changes and complex constraints. Due to changes in the production environment and market demand, production scheduling needs to have a certain degree of flexibility and adaptability. However, traditional manual scheduling methods often lack this flexibility and it is difficult to adjust the schedule in time to cope with changes. In addition, some details and constraints are easily overlooked during manual scheduling, resulting in unsatisfactory scheduling results. For example, in order scheduling, the real-time production status of the production line may be ignored, resulting in a production plan that is not in line with reality.

[0006] In addition, the global optimization of scheduling is difficult, and the coordination between multiple process links is poor. Although the independent optimization of each link may simplify the work of the dispatcher, it reduces the overall production efficiency. For example, in steel production, process links such as steelmaking, continuous casting and rolling need to be closely connected, but traditional methods are difficult to achieve the optimal connection of these links globally, which may lead to uncoordinated production rhythm, increased energy consumption and production costs.

[0007] Finally, the Make Variety Small Batch Production (MVSBP) model brings many challenges in production organization. The MVSBP model requires enterprises to deal with more complex production plans and material requirements plans, but due to the small production batches and many product varieties, it is difficult for enterprises to achieve economies of scale in production, resulting in rising production costs. Logistics management in the production process has become more complicated, and it is necessary to rationally plan the operation sequence and logistics routes to avoid frequent material handling and improve production efficiency. The MVSBP model also increases the difficulty of material management after the order is completed. Various raw materials and parts need to be accurately managed during the production process to ensure timely supply and efficient use, which puts higher requirements on the company's warehousing management and supply chain systems. In addition, enterprises must have the ability to flexibly respond to changes in market demand and improve the response speed and accuracy of production plans through the construction of internal efficiency and information management systems. Summary of the invention

[0008] The present invention provides a method, device, equipment and medium for preparing a casting schedule for continuous casting and rolling of strip steel, so as to solve the technical problems in the above-mentioned existing continuous casting and rolling scheduling of strip steel, such as complex data processing, low scheduling efficiency and poor dynamic adjustment capability, which lead to high production costs.

[0009] In one embodiment of the present application, the present application provides a method for compiling a pouring plan for continuous casting and rolling of strip steel, comprising: obtaining production business information of continuous casting and rolling of strip steel, the production business information including order information, equipment status information, raw material supply information and production plan information; performing a first virtual pouring division based on the equipment status information, the raw material supply information and the production plan information to obtain initial pouring division information, the initial pouring division information including a virtual time period of each first virtual pouring and a set of economically producible pours; performing pouring sequence planning according to the production business information and the initial pouring division information to obtain an initial pouring sequence corresponding to the smallest first order gap, and performing pouring on the initial pouring sequence according to the production business information and all virtual time periods. The method comprises the following steps: performing an allocation planning for each pour to obtain initial pour allocation information; iteratively updating the initial pour sequence and the initial pour allocation information to obtain a target pour sequence and the target pour allocation information to generate a pour production plan, wherein the iterative update comprises updating a neighborhood solution set corresponding to the initial pour sequence, and performing pour allocation planning for the neighborhood solution set; wherein the initial pour allocation information comprises an initial start and stop pouring time corresponding to the maximum initial production line capacity, an initial pour allocation order process corresponding to the minimum initial second order gap, and an initial pour output, wherein the first order gap is used to characterize the gap amount of the order specification group that is lacking in the overall production, and the initial second order gap is used to characterize the initial gap cost of the order specification group that is lacking in the production under the capacity balance constraint of the cold rolling process.

[0010] In one embodiment of the present application, the pouring allocation planning includes: performing pouring time planning for the planned pouring sequence according to the production business information and all virtual time periods to obtain the planned start and stop pouring time corresponding to the maximum planned production line capacity, the pouring time planning is limited by the capacity constraints of the vacuum degassing furnace capacity, the thin specification capacity, and the molten steel output supply, and the pouring sequence planning is limited by the use of crystallizers and support rollers; performing order allocation pouring planning based on the production business information, the pouring sequence to be planned and the planned start and stop pouring time to obtain the planned pouring order process and the planned pouring output corresponding to the minimum planned second order gap, and the order allocation pouring planning is limited by the capacity balance of the cold rolling process; wherein, the pouring sequence to be planned includes the initial pouring sequence, the planned production line capacity includes the initial production line capacity, the planned start and stop pouring time includes the initial start and stop pouring time, the planned second order gap includes the initial second order gap, the planned pouring order process includes the initial pouring order process, and the planned pouring output includes the initial pouring output.

[0011] In one embodiment of the present application, pouring sequence planning is performed according to the production business information and the initial pouring division information to obtain an initial pouring sequence corresponding to the minimum first order gap, including: setting multiple first decision variables, each of the first decision variables including a production line pouring type allocation sequence, a production line pouring preparation time and a first order gap; performing pouring usage constraints on each of the first decision variables to obtain multiple first constraint formulas, the pouring usage constraints including pouring sequence constraints, pouring type continuity constraints, pouring allocation quantity constraints, pouring production time constraints, pouring change preparation time constraints, molten steel consumption constraints, vacuum degassing furnace capacity constraints and first order gap constraints; determining the first order gap as the first optimization target; inputting the production business information and the set of all producible economic pours into the corresponding first constraint formula, solving and obtaining an initial pouring sequence corresponding to the minimum first optimization target, the initial pouring sequence being obtained based on the value of the production line pouring type allocation sequence.

[0012] In one embodiment of the present application, the pouring time of the to-be-planned pouring sequence is planned according to the production business information and all virtual time periods to obtain the planned start and stop pouring time corresponding to the maximum planned production line capacity, including: setting multiple second decision variables, each of the second decision variables including the production line pouring allocation time period, the production line pouring production time period and the production line pouring production time period of the preset type of steel coil; performing continuous casting and rolling capacity constraints on each of the second decision variables to obtain multiple second constraint formulas, the continuous casting and rolling capacity constraints including the time distribution constraint between the production line pouring allocation time period and the production line pouring production time period, the preparation time constraint between two scheduled pours, the production The time distribution constraints between the production period of the line pouring and the period of the production of preset type of steel coils of the production line pouring, the number of production lines for producing preset type of steel coils, the molten steel output supply constraints, the pouring allocation constraints corresponding to the production line period, the time period allocation constraints corresponding to the pouring production line, the pouring production time constraints, and the pouring production sequence constraints; the production line capacity obtained based on the preset pouring output and the production line pouring allocation period is determined as the second optimization target; the production business information, the pouring sequence to be planned and all virtual time periods are input into the corresponding second constraint formula, the value of each second decision variable corresponding to the maximum second optimization target is solved, and the planned start and stop pouring time is converted and generated.

[0013] In one embodiment of the present application, order allocation pouring planning is performed based on the production business information, the to-be-planned pouring sequence and the planned start and stop pouring time to obtain the planned pouring allocation order process and the planned pouring output corresponding to the smallest planned second order gap, including: setting a plurality of third decision variables, each of the third decision variables including an order allocation amount and a third order gap corresponding to different cold rolling process types, the order allocation amount being used to characterize the order amounts corresponding to the cold rolling process types of different specification groups assigned to different second virtual pours, the third order gap being used to characterize the gap amounts of the lack of order specification groups for different second virtual pours, the second virtual pours being obtained by splitting the scheduled pourings across days in the to-be-planned pouring sequence based on the planned start and stop pouring time. To; perform cold rolling process capacity balance constraints on each of the third decision variables to obtain multiple third constraint formulas, wherein the cold rolling process capacity balance constraints include the total quantity constraint of process orders, the third order gap constraint, the order allocation constraint of different cold rolling process types, and the order output constraint of different cold rolling process types; determine the third optimization target according to the third order gap and the pouring time cost, wherein the pouring time cost is used to characterize that the order gap cost of the pouring type with an earlier time is larger; input the production business information, the pouring sequence to be planned, and the planned start and stop pouring time into the corresponding third constraint formula, and solve the value of each of the third decision variables corresponding to the minimum third optimization target to convert and generate the planned pouring allocation order process and the planned pouring output.

[0014] In one embodiment of the present application, the initial pouring order sequence and the initial pouring order allocation information are iteratively updated to obtain a target pouring order sequence and target pouring order allocation information, including: generating multiple neighborhood pouring order sequences of the initial pouring order sequence in multiple preset pouring order neighborhood structures; performing pouring order allocation planning for each of the neighborhood pouring order sequences based on the production business information and all virtual time periods to obtain multiple neighborhood pouring order allocation information and multiple neighborhood planning values; comparing each of the neighborhood planning values ​​and the initial planning value corresponding to the initial pouring order allocation information to obtain current optimal information, the current optimal information including the iterative pouring order sequence and iterative pouring order allocation information corresponding to the optimal planning value; if the current iteration does not meet the preset iteration condition, the iterative pouring order sequence in the current optimal information is used as the initial pouring order sequence, the iterative pouring order allocation information in the current optimal information is used as the initial pouring order allocation information, and the multiple neighborhood pouring order sequences of the initial pouring order sequence generated in multiple preset pouring order neighborhood structures are repeatedly executed. A pouring order sequence is defined as a domain pouring order sequence. Based on the production business information and all virtual time periods, pouring order allocation planning is performed for each pair of the neighborhood pouring order sequences to obtain a plurality of neighborhood pouring order allocation information and a plurality of neighborhood planning values. The neighborhood planning values ​​and the initial planning values ​​corresponding to the initial pouring order allocation information are compared to obtain the current optimal information. If the current iteration does not meet the preset iteration conditions, the iterative pouring order sequence in the current optimal information is used as the initial pouring order sequence, and the iterative pouring order allocation information in the current optimal information is used as the initial pouring order allocation information, until the current iteration meets the preset iteration conditions. If the current iteration meets the preset iteration conditions, the iterative pouring order sequence is used as the target pouring order sequence, and the iterative pouring order allocation information is used as the target pouring order allocation information. The neighborhood planning value is obtained based on the maximum neighborhood production line capacity and the minimum neighborhood second order gap in the neighborhood pouring order allocation information, and the initial planning value is obtained based on the maximum initial production line capacity and the minimum initial second order gap.

[0015] In one embodiment of the present application, a first virtual pouring division is performed based on the equipment status information, the raw material supply information and the production plan information to obtain initial pouring division information, including: dividing each production line into time periods according to the production plan information to obtain the idle time periods of each production line; estimating the number of first virtual pours based on the idle time periods of each production line to obtain virtual time periods of multiple first virtual pours; and determining a set of economically producible pours for each first virtual pour based on the equipment status information and the raw material supply information.

[0016] In one embodiment of the present application, the present application provides a device for compiling a pouring plan for continuous casting and rolling of strip steel, including: obtaining production business information of continuous casting and rolling of strip steel, the production business information including order information, equipment status information, raw material supply information and production plan information; a pouring division module, used to perform a first virtual pouring division based on the equipment status information, the raw material supply information and the production plan information, and obtain initial pouring division information, the initial pouring division information includes a virtual time period of each first virtual pouring and a set of economically producible pourings; an initial planning module, used to perform pouring sequence planning according to the production business information, obtain an initial pouring sequence corresponding to the smallest first order gap, and perform pouring allocation planning on the initial pouring sequence according to the production business information. an iterative planning module for iteratively updating the initial pouring sequence and the initial pouring allocation information to obtain a target pouring sequence and the target pouring allocation information to generate a pouring production plan, wherein the iterative update includes updating a neighborhood solution set corresponding to the initial pouring sequence and performing pouring allocation planning on the neighborhood solution set; wherein the initial pouring allocation information includes an initial start-stop pouring time corresponding to the maximum initial production line capacity, an initial pouring allocation order process and an initial pouring output corresponding to the minimum initial second order gap, wherein the first order gap is used to characterize the gap amount of the overall production lacking an order specification group, and the initial second order gap is used to characterize the initial gap cost of the production pouring lacking an order specification group under the capacity balance constraint of the cold rolling process.

[0017] In one embodiment of the present application, the present application provides an electronic device, which includes: one or more processors; a storage device for storing one or more programs, and when the one or more programs are executed by the one or more processors, the electronic device implements the method for preparing a casting plan for continuous casting and rolling of strip steel as described in any of the above embodiments.

[0018] In one embodiment of the present application, the present application provides a computer-readable storage medium having a computer program stored thereon. When the computer program is executed by a processor of a computer, the computer executes the method for preparing a casting plan for continuous casting and rolling of strip steel as described in any of the above embodiments.

[0019] Beneficial effects of embodiments of the present invention: The present invention provides a method, device, equipment and medium for preparing a casting plan for continuous casting and rolling of strip steel. The embodiments of the present invention obtain an initial casting sequence when the gap amount of the overall production order specification group lacking is minimized, and perform casting allocation planning on the basis of the initial casting sequence to obtain the initial start and stop casting time when the initial production line capacity is maximum, the initial casting allocation order process and the initial casting output when the gap cost of the initial production casting order specification group lacking is minimum. After the initialization is completed, the initial casting sequence and the initial casting allocation information are iteratively updated and optimized through the neighborhood solution set update of the initial casting sequence to obtain the target casting sequence and the target casting allocation information, which simplifies the formulation process of the casting scheduling plan for continuous casting and rolling of strip steel, significantly shortens the planning time, and provides a more detailed and reasonable scheduling solution through the balanced planning of the production line capacity, order gap and cold rolling process capacity, which can effectively meet order requirements, flexibly respond to market fluctuations, ensure high-efficiency specification inventory, improve the accuracy of response delivery, and bring significant economic benefits.

[0020] It is to be understood that the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the invention. BRIEF DESCRIPTION OF THE DRAWINGS

[0021] The drawings herein are incorporated into the specification and constitute a part of the specification, showing embodiments consistent with the present application, and together with the specification, are used to explain the principles of the present application. Obviously, the drawings described below are only some embodiments of the present application, and for those of ordinary skill in the art, other drawings can be obtained based on these drawings without creative work. In the drawings:

[0022] Figure 1 A schematic flow chart of a method for preparing a casting plan for continuous casting and rolling of strip steel according to an embodiment of the present application is shown;

[0023] Figure 2 A schematic diagram showing the effect of time interval division according to an embodiment of the present application is shown;

[0024] Figure 3 A schematic diagram showing the effect of pouring sequence planning according to an embodiment of the present application is shown;

[0025] Figure 4 A schematic diagram showing the effect of pouring time planning according to an embodiment of the present application is shown;

[0026] Figure 5 A schematic diagram showing the effect of order gap settlement according to an embodiment of the present application is shown;

[0027] Figure 6A schematic diagram of a process of variable neighborhood search according to an embodiment of the present application is shown;

[0028] Figure 7 A schematic diagram showing the effects of the delete and insert operators according to an embodiment of the present application is shown;

[0029] Figure 8 A schematic diagram showing the effects of the deletion and insertion operators according to another embodiment of the present application is shown;

[0030] Fig. 9 A schematic diagram of the implementation of a method for preparing a casting schedule for continuous casting and rolling of strip steel according to an embodiment of the present application is shown;

[0031] Fig.10 A block diagram of a device for preparing a casting schedule for continuous casting and rolling of strip steel according to an embodiment of the present application is shown;

[0032] Fig.11 A schematic diagram of the structure of a computer system suitable for implementing an electronic device of an embodiment of the present application is shown. DETAILED DESCRIPTION

[0033] The following describes the embodiments of the present application through specific examples, and those skilled in the art can easily understand other advantages and effects of the present application from the contents disclosed in this specification. The present application can also be implemented or applied through other different specific embodiments, and the details in this specification can also be modified or changed in various ways based on different viewpoints and applications without departing from the spirit of the present application. It should be noted that the following embodiments and features in the embodiments can be combined with each other without conflict.

[0034] It should be noted that the illustrations provided in the following embodiments are only schematic illustrations of the basic concept of the present application, and thus the drawings only show components related to the present application rather than being drawn according to the number, shape and size of components in actual implementation. In actual implementation, the type, quantity and proportion of each component may be changed at will, and the component layout may also be more complicated.

[0035] In the following description, a large number of details are discussed to provide a more thorough explanation of the embodiments of the present application. However, it is obvious to those skilled in the art that the embodiments of the present application can be implemented without these specific details. In other embodiments, well-known structures and devices are shown in the form of block diagrams rather than in detail to avoid making the embodiments of the present application difficult to understand.

[0036] In the related art, the data processing in the strip continuous casting and rolling scheduling is complex, the scheduling efficiency is low, and the dynamic adjustment capability is poor, which leads to the problem of high production costs.

[0037] In order to solve the above technical problems, the present application provides a method, device, equipment and medium for preparing a casting plan for continuous casting and rolling of strip steel. The implementation details of the technical solution of the embodiment of the present application are elaborated in detail below.

[0038] See also Figure 1 , Figure 1 FIG. 1 is a flow chart of a method for preparing a casting schedule for continuous casting and rolling of strip steel according to an embodiment of the present application. Figure 1 As shown, in an exemplary embodiment, the method for preparing a casting schedule for continuous casting and rolling of strip steel includes at least steps S110 to S140, which are described in detail as follows:

[0039] Step S110, obtaining production business information of strip continuous casting and rolling.

[0040] Among them, production business information includes order information, equipment status information, raw material supply information and production plan information.

[0041] In one embodiment of the present application, the production plan information includes the published casting plan, section plan, maintenance shutdown plan, crystallizer commissioning plan, support roller commissioning plan, and daily steel production plan; the equipment status information includes equipment production capacity, crystallizer position rules, support roller position rules, current crystallizer status, and current support roller status, so as to obtain the steel types and specifications recommended or prohibited for production at each stage of the life cycle, so as to obtain the standard castings that cannot be produced in each time period and the standard castings recommended for production; the raw material supply information includes the vacuum degassing (RH) furnace capacity limit and molten steel production supply information.

[0042] In one embodiment of the present application, the cross-section planning is used to characterize the production plan of steel coils with different cross-sections produced by each production line in each time period. Steel mills usually formulate one per month, namely a monthly cross-section planning. Steel coils with different cross-sections are also steel bars with different widths.

[0043] In one embodiment of the present application, the crystallizer and support rollers need to be overhauled after a certain number of castings are produced; and for a period of time before and after the replacement of the crystallizer or support rollers and the overhaul, there are restrictions on the type, thickness and width of the strip steel that can be produced due to the production capacity of the machine.

[0044] In one embodiment of the present application, the standard casting sequence is used to characterize the optimal output combination for producing steel coils of different steel grades, cross-sections, and thicknesses while taking into account production capacity and economic benefits in any casting sequence. In the actual production process of the steel plant, the standard casting sequences for different steel grades, cross-sections, and thicknesses are different.

[0045] In one embodiment of the present application, due to the capacity limitation of the upstream steelmaking RH furnace, the steel mill has a limit on the number of production lines that can produce steel coils that need to pass through the RH furnace in the same period.

[0046] In one embodiment of the present application, the order information includes the order number, the order delivery date, the ordered steel grade number, the order width, the order thickness, the required output, whether to pass the vacuum degassing (RH) furnace, and the required downstream cold rolling process type, as shown in the following table:

[0047] Table 1 Order information

[0048]

[0049] In one embodiment of the present application, the maintenance shutdown plan includes the production line to be maintained, the maintenance start time, the maintenance end time and the maintenance reason, and the maintenance reason includes the replacement of the crystallizer and the support roller. The maintenance shutdown plan is shown in the following table:

[0050] Table 2 Maintenance shutdown plan

[0051] Production Line Maintenance start time Maintenance end time Reason for maintenance Production Line 1# 2024 / 1 / 4 0:00 2024 / 1 / 4 23:59 Replacement of crystallizer Production Line 1# 2024 / 1 / 21 0:00 2024 / 1 / 21 23:59 Replacement of crystallizer Production Line 1# 2024 / 1 / 1 0:00 2024 / 1 / 1 7:59 Replace the support roller Production Line 1# 2024 / 1 / 11 0:00 2024 / 1 / 11 7:59 Replace the support roller Production Line 1# 2024 / 1 / 21 0:00 2024 / 1 / 21 7:59 Replace the support roller 1# Production Line 2024 / 1 / 31 0:00 2024 / 1 / 31 7:59 Replace the support roller Production Line 2# 2024 / 1 / 15 0:00 2024 / 1 / 15 23:59 Replacement of crystallizer Production Line 2# 2024 / 1 / 6 8:00 2024 / 1 / 6 15:59 Replace the support roller Production Line 2# 2024 / 1 / 16 8:00 2024 / 1 / 16 15:59 Replace the support roller Production Line 2# 2024 / 1 / 26 8:00 2024 / 1 / 26 15:59 Replace the support roller 3# Production Line 2024 / 1 / 9 0:00 2024 / 1 / 9 23:59 Replacement of crystallizer 3# Production Line 2024 / 1 / 26 0:00 2024 / 1 / 26 23:59 Replacement of crystallizer 3# Production Line 2024 / 1 / 9 2:00 2024 / 1 / 9 9:59 Replace the support roller 3# Production Line 2024 / 1 / 19 2:00 2024 / 1 / 19 9:59 Replace the support roller 3# Production Line 2024 / 1 / 29 2:00 2024 / 1 / 29 9:59 Replace the support roller

[0052] In one embodiment of the present application, the current crystallizer status includes the number of times the crystallizers of different production lines have been used, the number of furnaces produced, the number of tons produced, and the number of kilometers produced. The current crystallizer status is shown in the following table:

[0053] Table 3 Current crystallizer status

[0054] Production Line Use pouring times Number of furnaces produced Tons produced Kilometers produced 1# Production Line 2 16 5000 2km Production Line 2# 3 20 7000 3km 3# Production Line 4 30 7000 4km

[0055] In one embodiment of the present application, the current support roller status includes the number of rolled furnaces, rolled tonnage, and rolled mileage of support rollers of different production lines. The current support roller status is shown in the following table:

[0056] Table 4 Current support roller status

[0057] Production Line Number of rolled furnaces Tonnage rolled Mileage covered (km) Production Line 1# 10 3000 3 Production Line 2# 13 4000 3.5 3# Production Line 15 4500 4

[0058] In one embodiment of the present application, the monthly cross-section planning includes the start time of production, the end time of production and the cross-section width of production of different production lines. The monthly cross-section planning is shown in the following table:

[0059] Table 5 Section Planning

[0060] Production Line Start production time End of production time Section width (mm) 1# Production Line 2024 / 1 / 1 0:00 2024 / 1 / 4 0:00 1250 1# Production Line 2024 / 1 / 5 0:00 2024 / 1 / 20 23:59 1250 1# Production Line 2024 / 1 / 22 0:00 2024 / 1 / 31 23:59 1250 Production Line 2# 2024 / 1 / 1 0:00 2024 / 1 / 14 23:59 1500 Production Line 2# 2024 / 1 / 16 0:00 2024 / 1 / 31 23:59 1500 3# Production Line 2024 / 1 / 1 0:00 2024 / 1 / 8 23:59 1250 3# Production Line 2024 / 1 / 10 0:00 2024 / 1 / 25 23:59 1219 3# Production Line 2024 / 1 / 27 0:00 2024 / 1 / 31 23:59 1219

[0061] In one embodiment of the present application, the current production performance is obtained through the published pouring plan. The current production performance includes the economic pouring ID of different production lines, the start pouring time, the end pouring time and the current pouring status, as shown in the following table:

[0062] Table 6 Current production performance

[0063]

[0064]

[0065] In one embodiment of the present application, the daily steel production plan is shown in the following table:

[0066] Table 7 Daily steel production plan

[0067] date Steel production 2024 / 1 / 1 15000 2024 / 1 / 2 16000 2024 / 1 / 3 15000

[0068] In one embodiment of the present application, the economic casting is used to characterize the strip steel product sequence and the best output that meet the production organization requirements and have the greatest benefits for different types of steel coil products in an actual production casting. Among them, the economic casting can summarize steel coil products of different steel grades, sections, and thickness ranges in the long term based on sales conditions and production equipment capabilities, thereby forming a specification group sequence template, that is, a rolling process template.

[0069] Table 8 Sample composition of products required for economic pouring

[0070]

[0071] Step S120, performing a first virtual pouring division based on the equipment status information, the raw material supply information and the production plan information to obtain initial pouring division information.

[0072] The initial pouring order division information includes the virtual time period of each first virtual pouring order and the set of economically producible pouring orders.

[0073] In one embodiment of the present application, a first virtual pouring division is performed based on equipment status information, raw material supply information and production plan information to obtain initial pouring division information, including: dividing each production line into time periods according to the production plan information to obtain idle time periods for each production line; estimating the number of first virtual pours based on the idle time periods of each production line to obtain virtual time periods for multiple first virtual pours; and determining a set of economically producible pours for each first virtual pour based on the equipment status information and raw material supply information.

[0074] In one embodiment of the present application, the time occupied by the published plan and the remaining time are excluded through the monthly section planning, maintenance shutdown plan, crystallizer commissioning plan, and support roller commissioning plan. The time nodes at which the above information changes in all production lines during the planning period are marked, and all time nodes are used as the basis for dividing time to divide the time periods. For example, production line 1 needs to be overhauled from 0:00 to 23:59 on January 1, 2024, then 0:00 on January 1 to 0:00 on January 4 is a time period.

[0075] In one embodiment of the present application, see Figure 2 , Figure 2 FIG. 1 shows a schematic diagram of the effect of time interval division according to an embodiment of the present application. Figure 2 As shown, the planning period of production line 2# is divided into two periods because of the replacement of support rollers. The number of first virtual pours of the production line is estimated based on the idle time corresponding to the idle time of the production line. For example, production line 1# has only one idle time. According to the idle time corresponding to the idle time, the maximum number of pours is estimated based on the replacement time of 1 hour and the pouring time of 8 hours. A maximum of 8 first virtual pours can be estimated and sorted, and different production processes are marked with different colors. For example, the scheduled pours are marked with green, and the process of changing support rollers is marked with yellow. Considering the limitation of production capacity, the set of economic pours that can be produced for each first virtual pour is formed according to the raw material supply information and equipment status information.

[0076] In one embodiment of the present application, the suitable economic pouring times of different production lines are divided according to their production capacities as shown in the following table:

[0077] Table 9 Economic pouring times corresponding to production line capacity

[0078] Production Line Economic Times ID 1# Production Line JC001, JC002, JC003, JC008 Production Line 2# JC001, JC002, JC003, JC005 3# Production Line JC004, JC006, JC007, JC009

[0079] In one embodiment of the present application, the equipment production capacity includes the maximum production days, kilometers and tons of each crystallizer and each support roller, as shown in the following table:

[0080] Table 10 Equipment production capacity

[0081] Device Name Production days Kilometers (km) Tonnage Crystallizer 15 7000 100000 Support roller 10 4500 65000

[0082] In one embodiment of the present application, the crystallizer position rule includes the economic casting times that can be produced by the crystallizer in different production periods according to the production product restrictions during the crystallizer life cycle, as shown in the following table:

[0083] Table 11 Crystallizer Position Rules

[0084]

[0085]

[0086] In one embodiment of the present application, the support roller position rule includes the type restriction of the produced products according to the different rolling mileages of the support rollers and the weight of the rolled steel coils, and obtains the economic castings that can be produced by the different rolling mileages and rolling tonnages of the support rollers, as shown in the following table:

[0087] Table 12 Support roller position rules

[0088]

[0089] In one embodiment of the present application, the economic pouring times that cannot be produced after maintenance are shown in the following table:

[0090] Table 13 Economic pouring times that cannot be produced after maintenance

[0091] Watering times Cannot produce economical times The first pour JC001, JC002, JC003 The second pouring JC001, JC002 The second pouring JC001

[0092] Step S130, performing pouring sequence planning according to the production business information and the initial pouring division information to obtain the initial pouring sequence corresponding to the smallest first order gap, and performing pouring allocation planning on the initial pouring sequence according to the production business information and all virtual time periods to obtain the initial pouring allocation information.

[0093] Among them, the initial pouring allocation information includes the initial start and stop pouring time corresponding to the maximum initial production line capacity, the initial pouring allocation order process and the initial pouring output corresponding to the minimum initial second order gap. The first order gap is used to characterize the gap amount of the overall production lacking order specification groups, and the initial second order gap is used to characterize the initial gap cost of the production pouring lacking order specification groups under the capacity balance constraint of the cold rolling process.

[0094] In one embodiment of the present application, pouring allocation planning includes: performing pouring time planning for the planned pouring sequence according to production business information and all virtual time periods, obtaining the planned start and stop time corresponding to the maximum planned production line capacity, the pouring time planning is limited by the capacity constraints of the vacuum degassing furnace capacity, the thin specification capacity, and the molten steel output supply, and the pouring sequence planning is limited by the use of crystallizers and support rollers; performing order allocation pouring planning based on production business information, the pouring sequence to be planned and the planned start and stop time, obtaining the planned pouring allocation order process and the planned pouring output corresponding to the minimum planned second order gap, and the order allocation pouring planning is limited by the capacity balance of the cold rolling process; wherein, the pouring sequence to be planned includes the initial pouring sequence, the planned production line capacity includes the initial production line capacity, the planned start and stop time includes the initial start and stop time, the planned second order gap includes the initial second order gap, the planned pouring allocation order process includes the initial pouring allocation order process, and the planned pouring output includes the initial pouring output.

[0095] In one embodiment of the present application, pouring sequence planning is performed according to production business information and initial pouring division information to obtain an initial pouring sequence corresponding to the minimum first order gap, including: setting multiple first decision variables, each first decision variable including a production line pouring type allocation sequence, a production line pouring preparation time and a first order gap; performing pouring usage constraints on each first decision variable to obtain multiple first constraint formulas, the pouring usage constraints including pouring sequence constraints, pouring type continuity constraints, pouring allocation quantity constraints, pouring production time constraints, pouring change preparation time constraints, molten steel consumption constraints, vacuum degassing furnace capacity constraints and first order gap constraints; determining the first order gap as the first optimization target; inputting the production business information and the set of all producible economic pours into the corresponding first constraint formula, solving and obtaining the initial pouring sequence corresponding to the minimum first optimization target, the initial pouring sequence is obtained based on the value of the production line pouring type allocation sequence.

[0096] In one embodiment of the present application, see Figure 3 , Figure 3 FIG. 1 shows a schematic diagram of the effect of pouring sequence planning according to an embodiment of the present application. Figure 3 As shown, an economic pour is arranged for different first virtual pours according to the order information and the set of all producible economic pours, and multiple specification groups are allocated in each first virtual pour. For example, the economic pour corresponding to M4 is arranged in pour 1 of production line 1#, and specification group 1 of 60 tons and specification group 2 of 50 tons are allocated.

[0097] In one embodiment of the present application, the specification group is used to characterize a collection of steel coils of the same or similar steel grade, within a preset width range, and a preset thickness range. The division of specification groups is usually closely related to actual production. In actual production, steel coils with the same production process, similar production resource consumption, and similar benefits are often divided into the same specification group.

[0098] In one embodiment of the present application, the production line pouring type allocation sequence is The symbol is used to indicate that if production line l is scheduled to produce economic pouring order m at pouring order position w, it is 1.

[0099] In one embodiment of the present application, the pouring position is used to represent the serial number identification of the first virtual pouring, wherein the serial numbers of the first virtual pourings of different production lines can be counted separately.

[0100] In one embodiment of the present application, the pouring preparation time of the production line is An identifier used to indicate the pouring preparation time from production line l not being scheduled to pouring position w. The unit can be hours.

[0101] In one embodiment of the present application, the first order gap is obtained by p∈[0,+∞) is used to represent the order quantity gap of specification group p. The unit can be tons.

[0102] In one embodiment of the present application, the pouring sequence constraint is used to indicate that in each idle period of each production line, the next pouring position can be arranged only after the previous pouring position has arranged the economic pouring position, as shown in the following formula:

[0103]

[0104] in, To schedule the economic pouring order m for production line l at pouring order position w, The economic pouring order m is scheduled for production line l at pouring position w+1, J w is the set of economic pouring times that can be scheduled for pouring time position w, J w+1 is the set of economic pouring times that can be scheduled for pouring time position w+1, is the set of pouring positions of production line l in idle period n, N l is the set of idle time periods of production line l, and L is the set of production lines.

[0105] In one embodiment of the present application, the economic casting times that can be scheduled are limited by section planning, crystallizers, support rollers, and production capacity after maintenance. For example, the first casting time after maintenance can only produce steel coils with a thickness of more than 1.2 mm.

[0106] In one embodiment of the present application, the pouring time usage constraint also includes a pouring time type continuity constraint, such as there are no more than two consecutive economic pouring times of a preset type in the same idle period, as shown in the following formula:

[0107]

[0108] in, The economic pouring order m is scheduled for production line l at pouring order position w+k, where A is the economic pouring order of the preset type. is the set of pouring positions of production line l in idle period n, L is the set of production lines, N l is the set of idle time periods of production line l.

[0109] In one embodiment of the present application, the preset type of economic pouring is a set of thin gauge pouring, as shown in the following table:

[0110] Table 14 Thin gauge pouring times collection

[0111] Economic Times ID Casting category ID JC001 A JC002 A JC003 A

[0112] In one embodiment of the present application, the pouring allocation quantity constraint is used to indicate that only one economic pouring can be arranged at a pouring position, as shown in the following formula:

[0113]

[0114] in, To schedule the economic pouring order m for production line l at pouring order position w, J w is the set of economic pouring positions that can be scheduled for pouring position w, W is the set of pouring positions of all production lines, and L is the set of production lines.

[0115] In one embodiment of the present application, the pouring production duration constraint is used to indicate that the sum of the duration of all pourings produced in each idle period and the corresponding pouring preparation duration cannot exceed the total duration of the idle period, as shown below:

[0116]

[0117] in, is the pouring preparation time from production line l that has not been scheduled to pouring position w, is the set of pouring positions of production line l in idle period n, T m The time it takes to pour m times into the economy, Scheduling the economic pouring order m for production line l at pouring order position w, L n is the length of idle period n, N l is the set of idle time periods of production line l, J w is the set of economic pouring positions w that can be scheduled, and L is the set of production lines.

[0118] In one embodiment of the present application, T m The unit can be hours.

[0119] In one embodiment of the present application, the pouring preparation time constraint is used to characterize that any pouring preparation time is greater than a preset time, as shown in the following formula:

[0120]

[0121] in, To schedule the economic pouring order m for production line l at pouring order position w, J w is the set of economic pouring times that can be scheduled for pouring time position w, The economic pouring order m, h is the production line l at pouring position w+1 preset For the preset duration, is the pouring preparation time from production line l that has not been scheduled to pouring position w+1, is the set of pouring positions of production line l in idle period n, N l is the set of idle time periods of production line l, and L is the set of production lines.

[0122] In one embodiment of the present application, h preset Can be set to 1 hour.

[0123] In one embodiment of the present application, the molten steel consumption constraint is used to characterize that the provided molten steel is consumed as much as possible, as shown in the following formula:

[0124]

[0125] Among them, S m The amount of iron used for economic casting times m is: S is the economic pouring order m for production line l at pouring position w. plan is the planned total amount of molten steel during the planning period, L is the set of production lines, and W is the set of pouring positions of all production lines.

[0126] In one embodiment of the present application, S m and S plan The unit used may be tons.

[0127] In one embodiment of the present application, the vacuum degassing furnace capacity is used to indicate that the amount of steel coils that need to pass through the RH furnace for the economic casting scheduled in the planning period is less than or equal to the total refining capacity of the RH furnace in the planning period, as shown in the following formula:

[0128]

[0129] Among them, E m The total number of specification groups that need to pass through the RH furnace for economic casting times m, The economic pouring order m is arranged for production line l at pouring position w, E plan is the total refining capacity of the RH furnace during the planning period, L is the set of production lines, W is the set of pouring positions of all production lines, and J w is the set of economically schedulable pouring sequences for pouring sequence position w.

[0130] In one embodiment of the present application, E plan and E m The unit used may be tons.

[0131] In one embodiment of the present application, the first order gap constraint is as shown in the following formula:

[0132]

[0133] Among them, E mp is the demand for specification group p in economic pouring order m, The economic pouring order m is arranged for production line k at pouring position w, H op is the order quantity of specification group p in the order information, z p is the order quantity gap of specification group p, L is the set of production lines, W is the set of pouring positions of all production lines, J w is the set of economically schedulable pouring positions w, and P is the set of specification groups p.

[0134] In one embodiment of the present application, E mp , H op 、z p The unit used may be tons.

[0135] In one embodiment of the present application, the first optimization objective is determined as follows:

[0136]

[0137] Among them, z p is the order quantity gap of specification group p, in order to minimize the order gap of all specification groups p.

[0138] In one embodiment of the present application, see Figure 4 , Figure 4 FIG. 1 shows a schematic diagram of the effect of pouring time planning according to an embodiment of the present application. Figure 4 As shown, when the pouring sequence of each production line has been determined, the start and end time of each pouring is planned.

[0139] In one embodiment of the present application, a pouring time planning is performed on a planned pouring sequence according to production business information and all virtual time periods to obtain a planned start and stop pouring time corresponding to the maximum planned production line capacity, including: setting multiple second decision variables, each second decision variable including a production line pouring allocation time period, a production line pouring production time period, and a production line pouring production time period of a preset type of steel coil; performing continuous casting and rolling capacity constraints on each second decision variable to obtain multiple second constraint formulas, the continuous casting and rolling capacity constraints including a time allocation constraint between a production line pouring allocation time period and a production line pouring production time period, a preparation time constraint between two scheduled pours, a production line pouring The second optimization target is the time allocation constraint between the production period and the period of the production line pouring the preset type of steel coil, the number of production lines for the pouring of the preset type of steel coil, the molten steel output supply constraint, the pouring allocation constraint corresponding to the production line period, the time period allocation constraint corresponding to the pouring production line, the pouring production time constraint, and the pouring production sequence constraint; the production line capacity obtained based on the preset pouring output and the production line pouring allocation period is determined as the second optimization target; the production business information, the pouring sequence to be planned and all virtual time periods are input into the corresponding second constraint formula, the value of each second decision variable corresponding to the maximum second optimization target is solved, and the planned start and stop pouring time is converted and generated.

[0140] In one embodiment of the present application, the production line pouring time allocation period is The symbol is used to indicate that if the production line t allocates the scheduled pouring j in the sequence of pourings to be planned, it is 1. The allocation period is used to indicate the schedulable time period in the planning period. The division interval of the allocation period is smaller than the division interval of the virtual period, and the period interval can be divided into 1 hour.

[0141] In one embodiment of the present application, the production line pouring production period is The identifier is used to indicate that production line l starts to produce the scheduled pouring j in the sequence of pourings to be planned in allocation period t, and the value is 1.

[0142] In one embodiment of the present application, the production line produces a preset type of steel coil during a casting period. The flag is used to indicate that if the scheduled pouring j in the pouring sequence to be planned produces a steel coil with a specific carbon content in the allocation period of production line l and t, the value is 1.

[0143] In one embodiment of the present application, the time allocation constraints between the production line pouring allocation period and the production line pouring production period are as follows:

[0144]

[0145] in, Arrange the scheduled pouring j in the sequence of pourings to be planned for production line l in the allocation period t′, T j is the production time of the scheduled pouring time j, The production line l starts production in the allocation period t and has scheduled pouring times j. T is the set of allocation periods that can be arranged in the planning period. J is the set of scheduled pouring times in the sequence of pouring times to be planned corresponding to all production lines. L is the set of production lines. For production line l, the scheduled pouring time j is arranged in the allocation period t+1. For production line l, the scheduled pouring times j are arranged in the allocation period t. Production line l starts production in the allocation period t+1 and has scheduled pouring times j.

[0146] In one embodiment of the present application, the preparation time constraint between two poured times is used to indicate that the interval between poured times is at least greater than the minimum preparation time of the pouring times, as shown below:

[0147]

[0148] in, The production line l starts production in the allocation period t and has been poured j times. The production line l starts production in the allocation period t′ and has been poured for the jth time, t j is the production time of the jth pouring, T g The minimum preparation time for pouring, J l is the set of scheduled pouring times of production line l, T is the set of allocation time periods that can be arranged during the planning period, and L is the set of production lines.

[0149] In one embodiment of the present application, the time allocation constraint between the production period of a production line and the period of producing a preset type of steel coil of the production line is as follows:

[0150]

[0151] Among them, N j ' is the duration of producing the preset type of steel coil in the casting time j, The production line l starts production in the allocation period t and has been poured j times. For production line l to produce a preset type of steel coil in the jth cast in the t′ allocation period, B j ′ is the start time of the production of the preset type of steel coil in the scheduled pouring time j, T is the set of allocation time periods that can be arranged in the planning period, J is the set of scheduled pouring times in the sequence of pouring times to be planned corresponding to all production lines, L is the set of production lines, Produce a preset type of steel coil for production line l in the jth cast in the t allocation period.

[0152] In one embodiment of the present application, the preset type of steel coil includes a steel coil with a preset carbon content.

[0153] In one embodiment of the present application, the number of production lines for producing a preset type of steel coil in a casting is constrained for characterization as follows:

[0154]

[0155] in, The production line l produces a preset type of steel coil in the scheduled pouring j in the allocation period t, L is the set of production lines, J is the set of scheduled pouring times in the to-be-planned pouring sequence corresponding to all production lines, N L is the maximum number of production lines that can produce the preset type of steel coils at the same time, and T is the set of allocation time periods that can be arranged during the planning period.

[0156] In one embodiment of the present application, the molten steel output supply constraint is as follows:

[0157]

[0158] Among them, Q j is the hourly output of the jth pouring, For production line l, the number of pouring times j is arranged in the allocation period t, Q d is the molten steel output on day d, T d is the set of allocation time periods belonging to d days, D is the number of planning days in the planning period, L is the set of production lines, and J is the set of scheduled pouring times in the pouring sequence to be planned corresponding to all production lines.

[0159] In one embodiment of the present application, the pouring order allocation constraint corresponding to the production line time period is used to indicate that any production line can only arrange at most one scheduled pouring order belonging to the production line in any allocation period of the planning period, as shown in the following formula:

[0160]

[0161] in, The production line l starts production in the allocation period t and has been poured j times, J l is the set of scheduled pouring times of production line l, T is the set of allocation time periods that can be arranged during the planning period, L is the set of production lines, The scheduled pouring times j are arranged for production line l in allocation period t.

[0162] In one embodiment of the present application, the time period allocation constraint corresponding to the pouring production line is used to indicate that any scheduled pouring can only be arranged at most once within the planning time of the production line to which the scheduled pouring belongs, as shown in the following formula:

[0163]

[0164] in, is the number of times j that production line l starts production in allocation period t, T is the set of allocation periods that can be arranged in the planning period, and J l is the set of poured times of production line l, and L is the production line set.

[0165] In one embodiment of the present application, the pouring production time constraint is used to indicate that the last scheduled pouring may exceed the planned period, and the planned production duration of the scheduled pouring in the planned period is greater than or equal to the actual production duration of the pouring, as shown in the formula:

[0166]

[0167] in, The production line l has arranged the scheduled pouring time j in the allocation period t, T is the set of allocation periods that can be arranged in the planning period, J l is the set of poured times of production line l, L is the set of production lines, T j is the actual production time of the scheduled pouring time j.

[0168] In one embodiment of the present application, the pouring production sequence constraint is used to indicate that the pouring production start time that is ranked first is earlier, as shown in the formula:

[0169]

[0170] in, The production line l starts production at the allocation period t′ and has been poured j times 1 , The production line l starts production at the allocation period t′ and has been poured j times 2 , T is the set of allocation time periods that can be arranged during the planning period, J l It is the set of poured times of production line l.

[0171] In one embodiment of the present application, the second optimization objective is determined as follows:

[0172]

[0173] Among them, Q j is the hourly output of the jth pouring, The scheduled pouring times j are arranged for production line l in allocation period t, where L is the set of production lines, T is the set of allocation periods that can be arranged during the planning period, and J is the set of scheduled pouring times in the to-be-planned pouring sequence corresponding to all production lines.

[0174] In one embodiment of the present application, order allocation pouring planning is performed based on production business information, a pouring sequence to be planned, and a planned pouring start and stop time to obtain a planned pouring allocation order process and a planned pouring output corresponding to the smallest planned second order gap, including: setting multiple third decision variables, each third decision variable including an order allocation amount and a third order gap corresponding to different cold rolling process types, the order allocation amount is used to characterize the order amount corresponding to the cold rolling process type assigned to different specification groups for different second virtual pours, the third order gap is used to characterize the gap amount of the order specification group lacking in different second virtual pours, and the second virtual pour is obtained by splitting the scheduled pourings across days in the pouring sequence to be planned based on the planned start and stop time. To; perform cold rolling process capacity balance constraints on each third decision variable to obtain multiple third constraint formulas, the cold rolling process capacity balance constraints include the total quantity constraint of process orders, the third order gap constraint, the order allocation constraint of different cold rolling process types, and the order output constraint of different cold rolling process types; determine the third optimization target according to the third order gap and the pouring time cost, the pouring time cost is used to characterize that the order gap cost of the pouring type with an earlier time is larger; input the production business information, the pouring sequence to be planned, and the planned start and stop pouring time into the corresponding third constraint formula, and solve the value of each third decision variable corresponding to the minimum third optimization target to convert and generate the planned pouring allocation order process and the planned pouring output.

[0175] In one embodiment of the present application, see Figure 5 , Figure 5 The effect diagram of order gap settlement according to an embodiment of the present application is shown. Figure 5 As shown, the order gap calculation of the cold rolling capacity balance is taken into account, and modeling is performed on the premise that the scheduled pouring times across days are split into two second virtual pouring times on a daily basis to allocate orders to each second virtual pouring time.

[0176] In one embodiment of the present application, by x im ,y im 、z im , k im Identifies the order allocation corresponding to different cold rolling process types. im The order quantity for the flat coils of specification group i assigned to the second virtual pouring m, y imThe order quantity for pickled coils of specification group i assigned to the second virtual pouring m, z im Used to characterize the order quantity of galvanized coils assigned to the second virtual pouring m of specification group i, k im Used to characterize the order quantity of straight hair rolls assigned to the second virtual pouring time m by specification group i.

[0177] In one embodiment of the present application, the third order gap is obtained by q im An identifier used to characterize that the second virtual pour m lacks an order gap for specification group i.

[0178] In one embodiment of the present application, the total process order quantity constraint is used to characterize that the sum of the flattened coil order quantity, pickled coil order quantity, galvanized coil order quantity and straight-shipped coil order quantity of specification group i assigned to any second virtual pouring time m is less than the quantity of specification group i required by the second virtual pouring time m, as shown in the following formula:

[0179]

[0180] Among them, x im The order quantity of the flat coil assigned to the second virtual pouring m for specification group i, y im is the order quantity of pickled coils assigned to the second virtual pouring m by specification group i, z im is the order quantity of the galvanized coils assigned to the second virtual pouring m by specification group i, k im is the order quantity of the straight hair rolls assigned to the second virtual pouring time m by specification group i, Q im The quantity of specification group i required for the known second virtual pouring time m, I is the set of specification groups, and M is the set of all second virtual pouring times.

[0181] In one embodiment of the present application, the third order gap constraint is used to characterize the relationship between the order gap of the second virtual pouring m lacking the specification group i and the actual gap, as shown below:

[0182]

[0183] Among them, Q im is the known quantity of specification group i required for the second virtual pouring m, x im The order quantity of the flat coil assigned to the second virtual pouring m for specification group i, y im is the order quantity of pickled coils assigned to the second virtual pouring m by specification group i, z im is the order quantity of the galvanized coils assigned to the second virtual pouring m by specification group i, k im is the order quantity of the straight hair rolls assigned to the second virtual pouring time m by specification group i, q im The second virtual pouring m lacks the order gap of specification group i, I is the specification group set, and M is the set of all second virtual pourings.

[0184] In one embodiment of the present application, the order allocation constraints for different cold rolling process types include order allocation constraints for flat-rolled coils, order allocation constraints for pickled coils, order allocation constraints for galvanized coils, and order allocation constraints for straight-rolled coils.

[0185] In one embodiment of the present application, the flat roll order allocation constraint is used to indicate that the sum of the flat roll order quantities allocated to all second virtual pours is less than the total flat roll order quantity of the specification group, as shown below:

[0186]

[0187] Among them, x im is the order quantity of the flat coil assigned to the second virtual pouring m by specification group i, H i is the total number of flat coil orders for specification group i, M is the set of all second virtual pouring times, and I is the set of specification groups.

[0188] In one embodiment of the present application, the pickling roll order allocation constraint is used to indicate that the sum of the pickling roll order quantities allocated to all second virtual pours is less than the total pickling roll order quantity of the specification group, as shown below:

[0189]

[0190] Among them, y im is the order quantity of pickled coils assigned to the second virtual pouring m by specification group i, B i is the total number of pickling coil orders for specification group i, M is the set of all second virtual pouring times, and I is the set of specification groups.

[0191] In one embodiment of the present application, the galvanized coil order allocation constraint is used to indicate that the sum of the galvanized coil order quantities allocated to all second virtual pours is less than the total galvanized coil order quantity D of specification group i. i , as shown below:

[0192]

[0193] Among them, z im is the order quantity of galvanized coils assigned to the second virtual pouring m by specification group i, D i is the total number of galvanized coil orders for specification group i, M is the set of all second virtual pouring times, and I is the set of specification groups.

[0194] In one embodiment of the present application, the order allocation constraint of the direct hair roll is used to indicate that the sum of the direct hair roll order quantities allocated to all second virtual pours is less than the total direct hair roll order quantity of specification group i, as shown below:

[0195]

[0196] Among them, kim is the order quantity of the straight hair rolls assigned to the second virtual pouring time m by specification group i, X i is the total number of direct-hair volume orders of specification group i, M is the set of all second virtual pouring times, and I is the set of specification groups.

[0197] In one embodiment of the present application, the order production constraints of different cold rolling process types include flat-rolled coil allocation production constraints, pickled coil allocation production constraints, and galvanized coil allocation production constraints.

[0198] In one embodiment of the present application, the flat roll allocation production constraint is used to indicate that the flat roll order quantity allocated to all second virtual pours belonging to day d is less than the flat production capacity upper limit of day d, as shown below:

[0199]

[0200] Among them, x im is the order quantity of the flat coil assigned to the second virtual pouring m by specification group i, H d is the upper limit of leveling capacity for d days, M d is the set of second virtual pours belonging to day d, I is the set of specification groups, and D is the number of planned days in the planning period.

[0201] In one embodiment of the present application, the pickling roll allocation production constraint is used to characterize that the pickling roll order quantity obtained by all second virtual pours belonging to day d is less than the pickling capacity upper limit of day d.

[0202]

[0203] Among them, y im is the order quantity of pickled coils assigned to the second virtual pouring m by specification group i, B d is the upper limit of pickling capacity on day d, M d is the set of second virtual pours belonging to day d, I is the set of specification groups, and D is the number of planned days in the planning period.

[0204] In one embodiment of the present application, the galvanizing allocation output constraint is used to characterize that the galvanizing coil order quantity obtained by all second virtual pours belonging to day d is less than the galvanizing capacity upper limit of day d.

[0205]

[0206] Among them, z im is the order quantity of galvanized coils assigned to the second virtual pouring m by specification group i, D d is the upper limit of galvanizing capacity on day d, M d is the set of second virtual pours belonging to day d, I is the set of specification groups, and D is the number of planned days in the planning period.

[0207] In one embodiment of the present application, the third optimization objective is determined as follows:

[0208]

[0209] Among them, q im For the second virtual pouring m, there is a lack of order gaps for specification group i, C m is the order gap cost of the second virtual pouring m, M is the set of all second virtual pourings, and I is the set of specification groups to minimize the order gap cost.

[0210] In one embodiment of the present application, the order gap cost of the second virtual pour is used to represent that the order gap cost of the pour earlier in time is larger, so as to ensure that the pour earlier in time is filled first.

[0211] Step S140, iteratively updating the initial pouring sequence and the initial pouring allocation information to obtain the target pouring sequence and the target pouring allocation information to generate a pouring production plan.

[0212] The iterative update includes updating the neighborhood solution set corresponding to the initial casting sequence and performing casting allocation planning on the neighborhood solution set.

[0213] In one embodiment of the present application, the optimal solution, that is, the target pouring sequence, is determined by the optimal planning value, and the optimal planning value can be obtained by performing a weighted summation method or a multi-objective optimization method on the maximum production line capacity and the minimum second order gap.

[0214] In one embodiment of the present application, the initial pouring order sequence and the initial pouring order allocation information are iteratively updated to obtain the target pouring order sequence and the target pouring order allocation information, including: generating multiple neighborhood pouring order sequences of the initial pouring order sequence in multiple preset pouring order neighborhood structures; performing pouring order allocation planning for each neighborhood pouring order sequence pair based on production business information and all virtual time periods to obtain multiple neighborhood pouring order allocation information and multiple neighborhood planning values; comparing each neighborhood planning value and the initial planning value corresponding to the initial pouring order allocation information to obtain the current optimal information, the current optimal information including the iterative pouring order sequence and iterative pouring order allocation information corresponding to the optimal planning value; if the current iteration does not meet the preset iteration condition, the iterative pouring order sequence in the current optimal information is used as the initial pouring order sequence, the iterative pouring order allocation information in the current optimal information is used as the initial pouring order allocation information, and the multiple neighborhood pouring order sequences generating the initial pouring order sequence in the multiple preset pouring order neighborhood structures are repeatedly executed. Domain pouring order sequence, based on production business information and all virtual time periods, pouring order allocation planning is performed for each neighborhood pouring order sequence pair respectively, multiple neighborhood pouring order allocation information and multiple neighborhood planning values ​​are obtained, and each neighborhood planning value and the initial planning value corresponding to the initial pouring order allocation information are compared to obtain the current optimal information. If this iteration does not meet the preset iteration conditions, the iterative pouring order sequence in the current optimal information is used as the initial pouring order sequence, and the iterative pouring order allocation information in the current optimal information is used as the initial pouring order allocation information step, until this iteration meets the preset iteration conditions; if this iteration meets the preset iteration conditions, the iterative pouring order sequence is used as the target pouring order sequence, and the iterative pouring order allocation information is used as the target pouring order allocation information; wherein, the neighborhood planning value is obtained based on the largest neighborhood production line capacity and the smallest neighborhood second order gap in the neighborhood pouring order allocation information, and the initial planning value is obtained based on the largest initial production line capacity and the smallest initial second order gap.

[0215] In one embodiment of the present application, the to-be-planned pouring sequence also includes the neighboring pouring sequence, the planned production line capacity also includes the neighboring production line capacity, the planned start and stop pouring times also include the neighboring start and stop pouring times, the planned second order gap also includes the neighboring second order gap, the planned pouring order allocation process also includes the neighboring pouring order allocation process, and the planned pouring output also includes the neighboring pouring output.

[0216] In one embodiment of the present application, see Figure 6 , Figure 6 FIG. 2 shows a schematic diagram of a process of variable neighborhood search according to an embodiment of the present application. Figure 6As shown in the figure, parameter initialization: set key parameters such as search depth, number of iterations, and solution neighborhood size to determine the search framework; construct the initial solution: for each production line, use positive integer variables to represent the order of the initial pouring sequence, use positive integer variables to represent the economic pouring, and the integer pair [1,2] represents the first scheduled pouring of the production line with the code 2 of the economic pouring; generate neighborhood solution sets: in each preset pouring neighborhood structure, by modifying the encoding of the initial solution, form the corresponding neighborhood solution set in each preset pouring neighborhood structure, and the preset pouring neighborhood structure includes insertion / deletion operators; local search algorithm: in each neighborhood, the candidate solutions are selected through pouring allocation planning. Conduct local search; Performance evaluation: Use planned output and order gap as the evaluation basis for the pouring plan, that is, the smallest target second order gap and the largest target production line capacity for performance evaluation; Select and update solutions: If a better solution is found in a neighborhood, move to that neighborhood to continue searching, otherwise switch to the next neighborhood; Whether the maximum number of iterations has been reached: If the maximum number of iterations has been reached or the fitness value is no longer significantly improved, output the optimal solution and obtain the target pouring sequence and target pouring allocation information; if the maximum number of iterations has not been reached or the fitness value is still significantly improved, generate a new neighborhood solution set based on the current solution to continue searching for the optimal solution.

[0217] In one embodiment of the present application, the deletion and insertion operations randomly select variables for deletion, remove any part of the initial solution, explore new potential solutions, change the pouring sequence order of each production line or replace the standard pouring sequence that does not meet the constraints, and generate a new pouring sequence. After deletion, the insertion operation is used to reallocate these parts or new elements, perform local search on the solution after the deletion operation, apply local optimization strategies to refine the solution, and seek local improvements to the solution. Please refer to Figure 7 and Figure 8 , Figure 7 A schematic diagram showing the effects of the delete and insert operators according to an embodiment of the present application is shown. Figure 8 FIG. 2 shows a schematic diagram of the effects of the delete and insert operators according to another embodiment of the present application. Figure 7 and Figure 8 As shown, Figure 7 Delete the economic pouring number coded as 2 at the third position, and add the economic pouring number coded as 1 at the original third position; Figure 8 Delete the economic pouring times coded as 1 in the 1st and 2nd positions, and add the economic pouring times coded as 1 in the original 5th and 6th positions.

[0218] In one embodiment of the present application, generating a pouring production plan includes: converting the target pouring sequence and target pouring allocation information into a preset format for display on a preset terminal to facilitate planners to view and modify the pouring plan and form the current pouring plan.

[0219] In one embodiment of the present application, the preset format includes a Gantt chart format, which displays the types and time schedules of production runs of different production lines.

[0220] In one embodiment of the present application, see Fig. 9 , Fig. 9 FIG. 1 is a schematic diagram showing an implementation of a method for preparing a casting schedule for continuous casting and rolling of a strip steel according to an embodiment of the present application. Fig. 9 As shown, step S910, obtain and process business data, and obtain the initial data set under the constraint of production line capacity: perform the first virtual pouring division based on equipment status information, raw material supply information and production plan information to obtain the initial pouring division information, which includes the virtual time period of each first virtual pouring and the set of economically producible pouring times; step S920, production order processing, summarize and count the number of orders on hand according to steel type, section, thickness, whether to follow up with cold rolling and cold rolling process; step S930, with the goal of minimizing order gaps, input the initial data set, and construct pouring sequence planning: perform pouring sequence planning based on production business information and initial pouring division information to obtain the initial pouring sequence corresponding to the smallest first order gap; step S940, construct pouring time planning based on the pouring sequence to obtain fitness 1: perform pouring time planning for the planned pouring sequence based on the production business information and all virtual time periods to obtain the planned production line capacity corresponding to the largest planned production line capacity. Start and stop pouring time; step S950, obtain fitness evaluation 2 by satisfying the balance of steel coil types in different subsequent processes: perform order allocation pouring planning based on production business information, the pouring sequence to be planned and the planned start and stop pouring time, and obtain the planned pouring allocation order process and the planned pouring output corresponding to the smallest planned second order gap; step S960, continuously optimize and generate new pouring sequences through variable neighborhood search algorithm: generate multiple neighborhood pouring sequences of the initial pouring sequence in multiple preset pouring neighborhood structures; step S970, repeat steps S940-S960 until the optimal sequence is generated: iteratively update the initial pouring sequence and the initial pouring allocation information to obtain the target pouring sequence and the target pouring allocation information, the iterative update includes updating the neighborhood solution set corresponding to the initial pouring sequence, and performing pouring allocation planning on the neighborhood solution set; step S980, present the final plan schedule on the front-end system to the corresponding production planner: display the generated pouring production plan;

[0221] See also Fig.10 , Fig.10 The block diagram of a casting schedule preparation device for strip steel continuous casting and rolling according to an embodiment of the present application is shown. The device can also be applied to other exemplary implementation environments and specifically configured in other equipment. This embodiment does not limit the implementation environment to which the device is applied.

[0222] like Fig.10 As shown, a device 1000 for preparing a casting plan for continuous casting and rolling of strip steel according to an embodiment of the present application includes: an information acquisition module 1001 , a casting division module 1002 , an initial planning module 1003 and an iterative planning module 1004 .

[0223] Information acquisition module 1001, used to acquire production business information of strip steel continuous casting and rolling, the production business information includes order information, equipment status information, raw material supply information and production plan information;

[0224] The pouring order division module 1002 is used to perform first virtual pouring order division based on equipment status information, raw material supply information and production plan information to obtain initial pouring order division information, wherein the initial pouring order division information includes a virtual time period of each first virtual pouring order and a set of producible economic pouring orders;

[0225] The initial planning module 1003 is used to plan the pouring sequence according to the production business information, obtain the initial pouring sequence corresponding to the smallest first order gap, and plan the pouring allocation for the initial pouring sequence according to the production business information to obtain the initial pouring allocation information;

[0226] Iterative planning module 1004, used for iteratively updating the initial pouring sequence and the initial pouring allocation information to obtain the target pouring sequence and the target pouring allocation information to generate a pouring production plan, wherein the iterative update includes updating the neighborhood solution set corresponding to the initial pouring sequence and performing pouring allocation planning on the neighborhood solution set;

[0227] Among them, the initial pouring allocation information includes the initial start and stop pouring time corresponding to the maximum initial production line capacity, the initial pouring allocation order process and the initial pouring output corresponding to the minimum initial second order gap. The first order gap is used to characterize the gap amount of the overall production lacking order specification groups, and the initial second order gap is used to characterize the initial gap cost of the production pouring lacking order specification groups under the capacity balance constraint of the cold rolling process.

[0228] It should be noted that the device for preparing a casting plan for continuous casting and rolling of steel strip provided in the above embodiment and the method for preparing a casting plan for continuous casting and rolling of steel strip provided in the above embodiment belong to the same concept, wherein the specific manner in which each module and unit performs operations has been described in detail in the method embodiment and will not be repeated here. In actual application, the device for preparing a casting plan for continuous casting and rolling of steel strip provided in the above embodiment can allocate the above functions to different functional modules as needed, that is, divide the internal structure of the device into different functional modules to complete all or part of the functions described above, and this is not limited here.

[0229] An embodiment of the present application also provides an electronic device, comprising: one or more processors; a storage device for storing one or more programs, and when the one or more programs are executed by one or more processors, the electronic device implements the casting plan preparation method for continuous casting and rolling of strip steel provided in the above-mentioned embodiments.

[0230] See also Fig.11 , Fig.11 The structure diagram of the computer system suitable for implementing the electronic device of the embodiment of the present application is shown. It should be noted that: Fig.11 The computer system 1100 of the electronic device shown is only an example and should not bring any limitation to the functions and scope of use of the embodiments of the present application.

[0231] like Fig.11 As shown, the computer system 1100 includes a central processing unit (CPU) 1101, which can perform various appropriate actions and processes according to the program stored in the read-only memory (ROM) 1102 or the program loaded from the storage part 1108 to the random access memory (RAM) 1103, such as executing the method in the above embodiment. In the RAM 1103, various programs and data required for system operation are also stored. The CPU 1101, the ROM 1102 and the RAM 1103 are connected to each other through the bus 1104. The input / output (I / O) interface 1105 is also connected to the bus 1104.

[0232] The following components are connected to the I / O interface 1105: an input section 1106 including a keyboard, a mouse, etc.; an output section 1107 including a cathode ray tube (CRT), a liquid crystal display (LCD), etc., and a speaker, etc.; a storage section 1108 including a hard disk, etc.; and a communication section 1109 including a network interface card such as a LAN (Local Area Network) card, a modem, etc. The communication section 1109 performs communication processing via a network such as the Internet. A drive 1110 is also connected to the I / O interface 1105 as needed. A removable medium 1111, such as a magnetic disk, an optical disk, a magneto-optical disk, a semiconductor memory, etc., is installed on the drive 1110 as needed so that a computer program read therefrom is installed into the storage section 1108 as needed.

[0233] In particular, according to an embodiment of the present application, the process described above with reference to the flowchart can be implemented as a computer software program. For example, an embodiment of the present application includes a computer program product, which includes a computer program carried on a computer-readable medium, and the computer program includes a computer program for executing the method shown in the flowchart. In such an embodiment, the computer program can be downloaded and installed from a network through a communication section 1109, and / or installed from a removable medium 1111. When the computer program is executed by a central processing unit (CPU) 1101, various functions defined in the system of the present application are executed.

[0234] It should be noted that the computer-readable medium shown in the embodiment of the present application can be a computer-readable signal medium or a computer-readable storage medium or any combination of the above two. The computer-readable storage medium can be, for example, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, device or device, or any combination of the above. More specific examples of computer-readable storage media can include, but are not limited to: an electrical connection with one or more wires, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM), a flash memory, an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the above. In the present application, a computer-readable signal medium may include a data signal propagated in a baseband or as part of a carrier wave, wherein a computer-readable computer program is carried. This propagated data signal can take a variety of forms, including but not limited to an electromagnetic signal, an optical signal, or any suitable combination of the above. A computer-readable signal medium may also be any computer-readable medium other than a computer-readable storage medium, which may send, propagate or transmit a program for use by or in conjunction with an instruction execution system, apparatus or device. A computer program contained on a computer-readable medium may be transmitted using any appropriate medium, including but not limited to: wireless, wired, etc., or any suitable combination of the above.

[0235] The flowchart and block diagram in the accompanying drawings illustrate the possible architecture, functions and operations of the system, method and computer program product according to various embodiments of the present application. Wherein, each box in the flowchart or block diagram can represent a module, a program segment, or a part of the code, and the above-mentioned module, program segment, or a part of the code contains one or more executable instructions for realizing the specified logical function. It should also be noted that in some alternative implementations, the functions marked in the box can also occur in a different order from the order marked in the accompanying drawings. For example, two boxes represented in succession can actually be executed substantially in parallel, and they can sometimes be executed in the opposite order, depending on the functions involved. It should also be noted that each box in the block diagram or flowchart, and the combination of boxes in the block diagram or flowchart can be implemented with a dedicated hardware-based system that performs a specified function or operation, or can be implemented with a combination of dedicated hardware and computer instructions.

[0236] The units involved in the embodiments described in the present application can be implemented by software or by hardware, and the units described can also be set in a processor. Among them, the names of these units do not constitute a limitation on the units themselves under certain circumstances. Therefore, the technical solution according to the implementation mode of the present application can be embodied in the form of a software product, which can be stored in a non-volatile storage medium (which can be a CD-ROM, a USB flash drive, a mobile hard disk, etc.) or on a network, including a number of instructions to enable a computing device (which can be a personal computer, a server, a touch terminal, or a network device, etc.) to execute the method according to the implementation mode of the present application.

[0237] Another aspect of the present application provides a computer-readable storage medium on which a computer program is stored. When the computer program is executed by a processor of a computer, the computer executes the casting schedule compilation method for continuous casting and rolling of strip steel provided in the above embodiments. The computer-readable storage medium may be included in the electronic device described in the above embodiments, or may exist independently without being assembled into the electronic device.

[0238] In the above embodiments, unless otherwise specified, by using serial numbers such as "first" and "second" to describe common objects, it only means that they refer to different instances of the same object, rather than indicating that the objects being described must adopt a given order, whether in time, space, sorting or any other way.

[0239] The above embodiments are merely illustrative of the principles and effects of the present application, and are not intended to limit the present application. Anyone familiar with the technology may modify or change the above embodiments without violating the spirit and scope of the present application. Therefore, all equivalent modifications or changes made by a person of ordinary skill in the art without departing from the spirit and technical ideas disclosed in the present application shall still be covered by the claims of the present application.

Claims

1. A method for preparing a casting schedule for continuous casting and rolling of strip steel, characterized in that: The method comprises: Acquire production business information of strip steel continuous casting and rolling, wherein the production business information includes order information, equipment status information, raw material supply information and production plan information; Performing first virtual pouring division based on the equipment status information, the raw material supply information and the production plan information to obtain initial pouring division information, wherein the initial pouring division information includes a virtual time period of each first virtual pouring and a set of producible economic pouring times; Performing pouring sequence planning according to the production business information and the initial pouring order division information to obtain an initial pouring sequence corresponding to the smallest first order gap, and performing pouring order allocation planning on the initial pouring sequence according to the production business information and all virtual time periods to obtain initial pouring order allocation information; Iteratively updating the initial pouring sequence and the initial pouring allocation information to obtain a target pouring sequence and the target pouring allocation information to generate a pouring production plan, wherein the iterative updating includes updating a neighborhood solution set corresponding to the initial pouring sequence and performing pouring allocation planning on the neighborhood solution set; Among them, the initial pouring allocation information includes the initial start and stop pouring time corresponding to the maximum initial production line capacity, the initial pouring allocation order process and the initial pouring output corresponding to the minimum initial second order gap, the first order gap is used to characterize the gap amount of the overall production lacking order specification group, and the initial second order gap is used to characterize the initial production pouring lacking order specification group gap cost under the capacity balance constraint of the cold rolling process.

2. The method for preparing a casting schedule for continuous casting and rolling of strip steel according to claim 1, characterized in that: The pouring distribution planning includes: Performing pouring time planning for the planned pouring sequence according to the production business information and all virtual time periods, obtaining the planned start and stop pouring time corresponding to the maximum planned production line capacity, wherein the pouring time planning is limited by the capacity of the vacuum degassing furnace, the capacity of the thin gauge, and the capacity limit of the molten steel output supply, and the pouring sequence planning is limited by the use of the crystallizer and the support roller; Based on the production business information, the to-be-planned pouring sequence and the planned pouring start and stop time, order allocation pouring planning is performed to obtain the planned pouring order process and the planned pouring output corresponding to the smallest planned second order gap, wherein the order allocation pouring planning is subject to the capacity balance of the cold rolling process; Among them, the pouring sequence to be planned includes the initial pouring sequence, the planned production line capacity includes the initial production line capacity, the planned start and stop pouring time includes the initial start and stop pouring time, the planned second order gap includes the initial second order gap, the planned pouring order allocation process includes the initial pouring order allocation process, and the planned pouring output includes the initial pouring output.

3. The method for preparing a casting schedule for continuous casting and rolling of strip steel according to claim 2, characterized in that: Performing pouring sequence planning according to the production business information and the initial pouring order division information to obtain an initial pouring sequence corresponding to the smallest first order gap includes: Setting a plurality of first decision variables, each of which includes a production line pouring type allocation sequence, a production line pouring preparation time, and a first order gap; Performing a casting use constraint on each of the first decision variables to obtain a plurality of first constraint formulas, wherein the casting use constraint includes a casting sequence constraint, a casting type continuity constraint, a casting allocation quantity constraint, a casting production time constraint, a casting change preparation time constraint, a molten steel consumption constraint, a vacuum degassing furnace capacity constraint, and a first order gap constraint; Determining the first order gap as a first optimization target; The production business information and all producible economic pouring sequence sets are input into the corresponding first constraint formula, and the initial pouring sequence corresponding to the minimum first optimization target is solved, and the initial pouring sequence is obtained based on the value of the production line pouring type allocation sequence.

4. The method for preparing a casting schedule for continuous casting and rolling of strip steel according to claim 2, characterized in that: The pouring sequence to be planned is planned according to the production business information and all virtual time periods to obtain the planned start and stop pouring time corresponding to the maximum planned production line capacity, including: Setting a plurality of second decision variables, each of which comprises a production line pouring allocation period, a production line pouring production period, and a production line pouring production period of a preset type of steel coil; A continuous casting and rolling capacity constraint is performed on each of the second decision variables to obtain a plurality of second constraint formulas, wherein the continuous casting and rolling capacity constraint includes a time allocation constraint between the casting allocation period of the production line and the casting production period of the production line, a preparation time constraint between two scheduled castings, a time allocation constraint between the casting production period of the production line and the period of the casting production of a preset type of steel coil of the production line, a production line quantity constraint for casting production of a preset type of steel coil, a molten steel output supply constraint, a casting allocation constraint corresponding to the production line period, a time period allocation constraint corresponding to the casting production line, a casting production time constraint, and a casting production sequence constraint; Determining the production line capacity obtained based on the preset pouring output and the pouring allocation period of the production line as the second optimization target; The production business information, the pouring sequence to be planned and all virtual time periods are input into the corresponding second constraint formula, the values ​​of each second decision variable corresponding to the maximum second optimization target are solved, and the planned start and stop pouring times are converted and generated.

5. The method for preparing a casting schedule for continuous casting and rolling of strip steel according to claim 2, characterized in that: The order allocation pouring planning is performed based on the production business information, the pouring sequence to be planned, and the planned pouring start and stop time, to obtain the planned pouring allocation order process and the planned pouring output corresponding to the smallest planned second order gap, including: A plurality of third decision variables are set, each of which includes an order allocation amount corresponding to different cold rolling process types and a third order gap, the order allocation amount is used to characterize the order amount corresponding to the cold rolling process type assigned to different second virtual pours by different specification groups, the third order gap is used to characterize the gap amount of the lack of order specification groups in different second virtual pours, and the second virtual pour is obtained by splitting the scheduled pours across days in the pouring sequence to be planned based on the planned start and stop time; Performing a cold rolling process capacity balance constraint on each of the third decision variables to obtain a plurality of third constraint formulas, wherein the cold rolling process capacity balance constraint includes a total quantity constraint of process orders, a third order gap constraint, an order allocation constraint of different cold rolling process types, and an order output constraint of different cold rolling process types; Determine a third optimization target according to the third order gap and the pouring time cost, wherein the pouring time cost is used to indicate that the order gap cost of the pouring type with an earlier time is larger; The production business information, the pouring sequence to be planned and the planned pouring start and stop time are input into the corresponding third constraint formula, and the value of each third decision variable corresponding to the minimum third optimization objective is solved to convert and generate the planned pouring allocation order process and the planned pouring output.

6. The method for preparing a casting schedule for continuous casting and rolling of strip steel according to any one of claims 2 to 5, characterized in that: Iteratively updating the initial pouring sequence and the initial pouring allocation information to obtain a target pouring sequence and target pouring allocation information, including: Generating a plurality of neighborhood pouring order sequences of the initial pouring order sequence in a plurality of preset pouring order neighborhood structures respectively; Based on the production business information and all virtual time periods, pouring order allocation planning is performed on each of the neighboring pouring order sequence pairs to obtain multiple neighboring pouring order allocation information and multiple neighboring planning values; Compare the neighborhood planning values ​​and the initial planning values ​​corresponding to the initial pouring order allocation information to obtain current optimal information, wherein the current optimal information includes an iterative pouring order sequence and iterative pouring order allocation information corresponding to the optimal planning value; If the current iteration does not meet the preset iteration conditions, the iterative pouring sequence in the current optimal information is used as the initial pouring sequence, the iterative pouring allocation information in the current optimal information is used as the initial pouring allocation information, and the multiple neighborhood pouring sequences of the initial pouring sequence are generated in multiple preset pouring neighborhood structures repeatedly, and pouring allocation planning is performed on each of the neighborhood pouring sequence pairs based on the production business information and all virtual time periods to obtain multiple neighborhood pouring allocation information and multiple neighborhood planning values, and the initial planning value corresponding to each of the neighborhood planning values ​​and the initial pouring allocation information is compared to obtain the current optimal information. If the current iteration does not meet the preset iteration conditions, the iterative pouring sequence in the current optimal information is used as the initial pouring sequence, and the iterative pouring allocation information in the current optimal information is used as the initial pouring allocation information step until the current iteration meets the preset iteration conditions; If the current iteration satisfies the preset iteration condition, the iterative pouring sequence is used as the target pouring sequence, and the iterative pouring allocation information is used as the target pouring allocation information; Among them, the neighborhood planning value is obtained based on the maximum neighborhood production line capacity and the minimum neighborhood second order gap in the neighborhood pouring allocation information, and the initial planning value is obtained based on the maximum initial production line capacity and the minimum initial second order gap.

7. The method for preparing a casting schedule for continuous casting and rolling of strip steel according to any one of claims 1 to 5, characterized in that: Performing a first virtual pouring division based on the equipment status information, the raw material supply information and the production plan information to obtain initial pouring division information includes: Divide each production line into time periods according to the production plan information to obtain the idle time periods of each production line; The number of first virtual pouring times is estimated based on the idle time period of each production line to obtain multiple virtual time periods of the first virtual pouring times; A set of producible economic pouring times for each first virtual pouring time is determined according to the equipment status information and the raw material supply information.

8. A casting schedule preparation device for continuous casting and rolling of strip steel, characterized in that: include: An information acquisition module is used to acquire production business information of strip steel continuous casting and rolling, wherein the production business information includes order information, equipment status information, raw material supply information and production plan information; A pouring order division module, configured to perform first virtual pouring order division based on the equipment status information, the raw material supply information and the production plan information to obtain initial pouring order division information, wherein the initial pouring order division information includes a virtual time period of each first virtual pouring order and a set of producible economic pouring orders; An initial planning module is used to plan a pouring sequence according to the production business information to obtain an initial pouring sequence corresponding to the smallest first order gap, and to plan pouring allocation for the initial pouring sequence according to the production business information to obtain initial pouring allocation information; An iterative planning module, used for iteratively updating the initial pouring sequence and the initial pouring allocation information to obtain a target pouring sequence and the target pouring allocation information to generate a pouring production plan, wherein the iterative update includes updating a neighborhood solution set corresponding to the initial pouring sequence and performing pouring allocation planning on the neighborhood solution set; Among them, the initial pouring allocation information includes the initial start and stop pouring time corresponding to the maximum initial production line capacity, the initial pouring allocation order process and the initial pouring output corresponding to the minimum initial second order gap, the first order gap is used to characterize the gap amount of the overall production lacking order specification group, and the initial second order gap is used to characterize the initial production pouring lacking order specification group gap cost under the capacity balance constraint of the cold rolling process.

9. An electronic device, characterized in that: The electronic device comprises: one or more processors; A storage device for storing one or more programs, which, when executed by the one or more processors, enables the electronic device to implement the method for preparing a casting plan for continuous casting and rolling of strip steel as described in any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that: A computer program is stored thereon, and when the computer program is executed by a processor of a computer, the computer is caused to execute a casting schedule preparation method for continuous casting and rolling of strip steel as claimed in any one of claims 1 to 7.

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