Method for generating contract resource plan of steel enterprises in multi-product and multi-process scenario

Abstract

The application belongs to the technical field of resource planning, and specifically provides a steel enterprise contract resource plan generation method in a multi-product and multi-process scenario, which comprises the following steps: constructing a metal flow direction model of each process in the whole process upstream and downstream; constructing a multi-dimensional production data archive system; determining monthly production execution data of each process; determining monthly predicted yield and next-month predicted yield based on the production data archive system, the monthly production execution data and the metal flow direction model; establishing a multi-product and multi-variety contract demand resource model, and generating a contract resource plan of each product based on the contract demand resource model, wherein the contract demand resource model is associated with transfer quantity, probe quantity, monthly predicted yield, next-month predicted yield and take-out quantity. The technical scheme provided by the application can improve the continuity of the contract resource plan generation of the steel enterprise in the multi-product and multi-process scenario, and the efficiency and accuracy of the contract resource plan generation.

Description

Technical Field

[0001] This application belongs to the field of resource planning technology, and in particular relates to a method for generating contract resource plans for steel enterprises in a multi-product, multi-process scenario. Background Technology

[0002] For steel production enterprises with multiple products and a complete production process, this section addresses production and contract resource management scenarios. These enterprises produce various products such as hot-rolled plates / coils, medium and heavy plates, cold-rolled coils, and hot-dip galvanized coils, covering multiple production processes including blast furnace, steelmaking, hot rolling, medium and heavy plates, and cold rolling. Contract organization adopts a model of formulating contract resource plans for the following month within the current month. The contract resource demand for the following month needs to be comprehensively calculated based on data such as the output of the current month and the following month, the output of products carried over, the amount of contracts carried over, and the amount of contracts monitored. Because these enterprises have a wide variety of products and long production processes, changes in local demand can lead to overall resource adjustments throughout the entire process. The higher the complexity of the products and processes, the greater the difficulty in resource control. In existing technologies, production forecasting and contract resource planning are generally completed through offline spreadsheets. The lack of data links between different processes makes it impossible to predict inventory fluctuations synchronously. Furthermore, data ledgers rely on offline compilation, which is limited by workload and processing speed. This results in poor continuity and low efficiency in the production forecasting and contract resource planning generation technology of steel enterprises. Therefore, how to standardize the data logic relationship of production forecasting and contract resource calculation, improve the efficiency and accuracy of production forecasting and contract resource planning generation in multi-product and multi-process scenarios, and ensure knowledge transfer are urgent technical problems to be solved. Summary of the Invention

[0003] The embodiments of this application provide a method, apparatus, program product, readable storage medium, and electronic device for generating contract resource plans for steel enterprises in multi-product, multi-process scenarios, thereby improving the efficiency and accuracy of steel enterprise output forecasting and contract resource plan generation in multi-product, multi-process scenarios and ensuring knowledge transfer.

[0004] Other features and advantages of this application will become apparent from the following detailed description, or may be learned in part from practice of this application.

[0005] According to a first aspect of the embodiments of this application, a method for generating contract resource plans for steel enterprises in a multi-product, multi-process scenario is provided. The method comprises: constructing a metal flow model for each upstream and downstream process of the entire process, wherein the metal flow model includes the internal supply flow direction, internal supply quantity, external sales resource quantity, and raw material inventory changes for each process; constructing a multi-dimensional production data archive system, wherein the production data archive system includes historical output data archives, historical contract structure data archives, historical downtime data archives, raw material inventory data archives, output data archives for each product, and carry-over probe data archives for each product; determining the monthly production execution data for each process, wherein the monthly production execution data includes process output, downtime, output, and key contract production progress; determining the monthly predicted output and the next month's predicted output based on the production data archive system, the monthly production execution data, and the metal flow model; establishing a multi-product, multi-variety contract demand resource model, and generating a contract resource plan for each product based on the contract demand resource model, wherein the contract demand resource model is associated with carry-over quantity, probe quantity, monthly predicted output, next month's predicted output, and output.

[0006] In some embodiments of this application, based on the aforementioned scheme, the construction of a metal flow model for each upstream and downstream process of the entire process includes: determining the output, internal supply flow, quantity, and external sales volume of each upstream and downstream process of the entire process to obtain metal flow data for each process, and determining the change in raw material inventory for each process based on the metal flow data; the change in raw material inventory for each process needs to be classified according to the type of raw material, and the expected increase or decrease in raw material inventory is output according to the input-consumption logic relationship; the many-to-many output supply logic relationship for each process is determined based on the metal flow data of each process, the change in raw material inventory for each process, and the expected increase or decrease in raw material inventory; and the metal flow model is determined based on the iron consumption, yield, and many-to-many output supply logic relationship for each process.

[0007] In some embodiments of this application, based on the foregoing scheme, the construction of a multi-dimensional production data archive system includes: establishing historical output data archives for each process, wherein the historical output data archives include the output level of each process, the average output within a rolling cycle, and the optimal output value; establishing historical contract structure data archives for each process, wherein the historical contract structure data archives include key contract attributes and the number of key contracts, wherein the products corresponding to the key contracts are products that affect the capacity utilization of the process; and establishing historical downtime data archives for each process, wherein the historical downtime data archives include at least each process The system includes: planned maintenance time, downtime due to malfunction, planned downtime, and total downtime; establishing raw material inventory data files for each process, including the raw material inventory quantity for each process, the total inventory quantity from each process to the final process, the reasonable inventory range for each process, and the reasonable total inventory quantity from each process to the final process; establishing output data files for each product, including the quantity and category information of scrap, contracted products, surplus materials, and transitional materials; and establishing carryover probe data files for each product, including the carryover quantity and probe quantity for each product.

[0008] In some embodiments of this application, based on the aforementioned scheme, the output level of each process is divided into five levels. The first level includes the historical highest monthly total output and the historical highest daily average output of each process. The second level includes the highest monthly total output and the highest daily average output of each process over the past year. The third level includes the average of the highest monthly total output and the average of the highest daily average output of each process over the past year. The highest monthly total output is defined as the monthly total output over the past year that is greater than the average monthly total output over the past year. The average daily output is the average daily output of each month in the past year that is greater than the average daily output of each month in the past year; the fourth level of output includes the average total monthly output of each process in the past year and the average daily output of each process in the past year; the fifth level of output includes the average low monthly total output of each process in the past year and the average low daily output in the past year, wherein the low monthly total output is the total monthly output of each process that is less than or equal to the average total monthly output of each month in the past year, and the low daily output is the average daily output of each process that is less than or equal to the average daily output of each month in the past year.

[0009] In some embodiments of this application, based on the foregoing scheme, the average daily output for each month is determined by the following formula:

[0010] in, This represents the average daily output for each month. This indicates the total monthly output. This indicates the duration of the pure operation.

[0011] In some embodiments of this application, based on the foregoing scheme, determining the predicted output for the current month and the predicted output for the next month based on the production data archive system, the current month's production execution data, and the metal flow model includes: using the current month's production execution data as reference data for the capacity level of each process in the current month, and combining the historical output data archive, the historical contract structure data archive, and the metal flow model to determine the predicted output of each product in the current month; and determining the predicted output of each product for the next month based on the current month's capacity level of each process, the historical contract structure data archive, and the historical output data archive.

[0012] In some embodiments of this application, based on the foregoing scheme, the method further includes: determining the raw material inventory of each process based on the raw material inventory data file, and using the raw material inventory of each process as reference data for predicting output; based on the reference data, completing the output forecast for the current month and the following month, outputting the raw material inventory level at the end of the month for each process, and determining whether the raw material inventory level at the end of the month exceeds the reasonable inventory level or is below the reasonable inventory level; if the raw material inventory level at the end of the month exceeds the reasonable inventory level or is below the reasonable inventory level, then adjusting the production plan.

[0013] In some embodiments of this application, based on the foregoing scheme, the establishment of a multi-product, multi-variety contract demand resource model includes: determining the carryover volume and the probe volume of the previous month based on the carryover probe data archive; determining the output volume of the current month and the output volume of the next month based on the output data archive; and constructing the following formula as the contract demand resource model based on the predicted output of the current month, the predicted output of the next month, the carryover volume of the previous month, the probe volume of the previous month, the output volume of the current month, and the output volume of the next month:

[0014] in, This indicates the contract demand for the following month. This indicates the projected output for the current month. This indicates the output volume for the current month. This indicates the amount carried over from the previous month. This indicates the probe volume last month. This indicates the contract demand for the current month. This indicates the projected output for the following month. This indicates the output volume for the following month.

[0015] According to a second aspect of the embodiments of this application, a device for generating contract resource plans for steel enterprises in a multi-product, multi-process scenario is provided. The device comprises: a first construction unit for constructing a metal flow model for each upstream and downstream process of the entire process, the metal flow model including the internal supply flow direction, internal supply quantity, external sales resource quantity, and raw material inventory changes for each process; and a second construction unit for constructing a multi-dimensional production data archive system, the production data archive system including historical output data archives, historical contract structure data archives, historical downtime data archives, raw material inventory data archives, and output data archives for each product. The system includes: a carry-over probe data archive; a determination unit for determining the monthly production execution data for each process, including process output, downtime, output, and key contract production progress; a prediction unit for determining the monthly and next month's predicted output based on the production data archive system, the monthly production execution data, and the metal flow model; and a generation unit for establishing a multi-product, multi-variety contract demand resource model and generating contract resource plans for each product based on the contract demand resource model, wherein the contract demand resource model is associated with carry-over volume, probe volume, monthly predicted output, next month's predicted output, and output.

[0016] According to a third aspect of the embodiments of this application, a computer program product is provided, the computer program product including computer instructions stored in a computer-readable storage medium and adapted to be read and executed by a processor to cause a computer device having the processor to perform an operation as described in any of the embodiments of the first aspect above.

[0017] According to a fourth aspect of the embodiments of this application, a computer-readable storage medium is provided, the computer-readable storage medium storing at least one computer program instruction, the at least one computer program instruction being loaded and executed by a processor to perform the operation performed by the method described in any of the embodiments of the first aspect above.

[0018] According to a fifth aspect of the present application, an electronic device is provided, the electronic device including one or more processors and one or more memories, the one or more memories storing at least one computer program instruction, the at least one computer program instruction being loaded and executed by the one or more processors to perform the operation performed by the method as described in any of the first aspect embodiments above.

[0019] Based on the technical solution proposed in this application, a metal flow model is constructed to analyze and integrate the internal supply flow, quantity, and external sales resources of each upstream and downstream process in the entire steel enterprise process. This ensures that subsequent production forecasting and contract resource planning are based on the balance of material supply and demand throughout the entire process, avoiding material supply and demand mismatches caused by independent analysis of single processes, and making resource planning highly compatible with the actual material flow in production. Simultaneously, it provides multi-dimensional data support for production forecasting and contract resource accounting. The constructed production data archive system stores production data from multiple dimensions, including output, contract structure, downtime, inventory, production losses, and production-sales linkage, enabling subsequent analysis and forecasting work to be conducted in a comprehensive manner. With historical data available, biases caused by subjective judgment are significantly reduced, improving the comprehensiveness and reliability of data references. In addition, integrating historical data archives, current month production execution data, and metal flow models into the production forecasting process ensures that the production forecasts for the current and following months accurately reflect the actual capacity potential of the process. Finally, by linking indicators related to contract resource demand, such as carryover volume, probe volume, current month's forecasted production, next month's forecasted production, and output volume, through the contract resource demand model, core influencing factors in the production and sales linkage are integrated, making the accounting dimensions of contract resource demand more comprehensive and thus effectively improving the efficiency of contract resource planning.

[0020] It should be understood that the above general description and the following detailed description are exemplary and explanatory only, and do not limit this application. Attached Figure Description

[0021] The accompanying drawings, which are incorporated in and form part of this specification, illustrate embodiments consistent with this application and, together with the description, serve to explain the principles of this application. It is obvious that the drawings described below are merely some embodiments of this application, and those skilled in the art can obtain other drawings based on these drawings without any inventive effort. In the drawings: Figure 1 A flowchart illustrating a method for generating a steel enterprise contract resource plan in a multi-product, multi-process scenario according to one embodiment of this application is shown. Figure 2 A block diagram of a device for generating a contract resource plan for a steel enterprise in a multi-product, multi-process scenario, according to one embodiment of this application, is shown. Figure 3 A schematic diagram of the structure of an electronic device according to one embodiment of this application is shown. Detailed Implementation

[0022] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0023] Furthermore, the described features, structures, or characteristics can be combined in any suitable manner in one or more embodiments. Numerous specific details are provided in the following description to give a thorough understanding of embodiments of this application. However, those skilled in the art will recognize that the technical solutions of this application can be practiced without one or more of the specific details, or other methods, components, apparatuses, steps, etc., can be employed. In other instances, well-known methods, apparatuses, implementations, or operations are not shown or described in detail to avoid obscuring various aspects of this application.

[0024] The block diagrams shown in the accompanying drawings are merely functional entities and do not necessarily correspond to physically independent entities. That is, these functional entities can be implemented in software, in one or more hardware modules or integrated circuits, or in different network and / or processor devices and / or microcontroller devices.

[0025] The flowcharts shown in the accompanying drawings are merely illustrative and do not necessarily include all content and operations / steps, nor do they necessarily have to be performed in the described order. For example, some operations / steps can be broken down, while others can be combined or partially combined; therefore, the actual execution order may change depending on the specific circumstances.

[0026] It should also be noted that the terms "first," "second," etc., in the specification, claims, and accompanying drawings of this application are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such uses of these terms can be interchanged where appropriate so that the embodiments of this application described herein can be implemented in orders other than those illustrated or described.

[0027] For steel production enterprises with multiple products and a complete production process, this section addresses production and contract resource management scenarios. These enterprises produce various products such as hot-rolled plates / coils, medium and heavy plates, cold-rolled coils, and hot-dip galvanized coils, covering multiple production processes including blast furnace, steelmaking, hot rolling, medium and heavy plates, and cold rolling. Contract organization adopts a model of formulating contract resource plans for the following month within the current month. The contract resource demand for the following month needs to be comprehensively calculated based on data such as the output of the current month and the following month, the output of products carried over, the amount of contracts carried over, and the amount of contracts monitored. Because these enterprises have a wide variety of products and long production processes, changes in local demand can lead to overall resource adjustments throughout the entire process. The higher the complexity of the products and processes, the greater the difficulty in resource control. In existing technologies, production forecasting is generally completed through offline tables, and the lack of data links between different processes makes it impossible to predict inventory fluctuations synchronously. Furthermore, data ledgers rely on offline processing, which is limited by workload and processing speed, resulting in low efficiency in generating contract resource plans for steel enterprises. Based on this, this application proposes a method for generating contract resource plans for steel enterprises in multi-product, multi-process scenarios, in order to improve the efficiency and accuracy of production forecasting and contract resource plan generation for steel enterprises in multi-product, multi-process scenarios, and ensure knowledge transfer.

[0028] Next, we will combine Figure 1 This application provides a detailed explanation of the method for generating contract resource plans (CRS) for steel companies in multi-product, multi-process scenarios.

[0029] See Figure 1 The flowchart illustrates a method for generating a steel enterprise contract resource plan in a multi-product, multi-process scenario according to one embodiment of this application. Figure 1 As shown, the method may include at least the following steps 110 to 150: Step 110: Construct a metal flow model for each upstream and downstream process of the entire process. The metal flow model includes the internal supply flow direction, internal supply quantity, external sales resource quantity, and raw material inventory change of each process.

[0030] Step 120: Construct a multi-dimensional production data archive system, which includes historical output data archives for each process, historical contract structure data archives, historical downtime data archives, raw material inventory data archives, output data archives for each product, and carry-over probe data archives.

[0031] Step 130: Determine the monthly production execution data for each process. The monthly production execution data includes process output, downtime, output volume, and production progress of key contracts.

[0032] Step 140: Determine the predicted output for the current month and the predicted output for the next month based on the production data archive system, the production execution data for the current month, and the metal flow model.

[0033] Step 150: Establish a contract demand resource model for multiple products and varieties, and generate contract resource plans for each product based on the contract demand resource model. The contract demand resource model is associated with carry-over volume, probe volume, current month's predicted output, next month's predicted output, and output volume.

[0034] In this application, the metal flow model is the core foundation of the entire process of material flow in steel enterprises. To build the metal flow model, it is necessary to sort out the material transfer relationship between the upstream and downstream processes of the entire process, clarify the material flow direction and specific internal supply quantity of each process, and at the same time calculate the amount of resources that each process can sell externally, so as to sort out the entire process of metal material flow.

[0035] In this application, the multi-dimensional production data archive system serves as the data support for subsequent output forecasting and contract resource planning. It includes historical output data archives, historical contract structure data archives, historical downtime data archives, raw material inventory data archives, output data archives for each product, and carryover probe data archives. The historical output data archive records output-related data from past production processes, providing historical evidence of the inherent capacity level and capacity utilization of each process, and offering a reference for judging the capacity potential of each process. The historical contract structure data archive includes the contract structure corresponding to each process, and focuses on recording the attributes and quantity of key contracts that affect process capacity, allowing subsequent output forecasting to be adjusted accordingly based on the actual impact of the contract structure on capacity. The historical downtime data archive records various downtime-related information from past processes. Downtime is a crucial factor affecting the actual capacity utilization of a process; establishing a historical downtime data archive provides data support for calculating the actual operating time of processes and analyzing factors interfering with capacity utilization.

[0036] In this application, the raw material inventory data file includes the raw material inventory of each process and the total inventory from each process to the final process, as well as the reasonable inventory range of each process. This serves as the basis for ensuring production continuity and subsequent adjustments to production arrangements. The product output data file records the quantity and type of scrap, contracted products, surplus materials, and transitional materials generated during the production process. These materials occupy process capacity but do not participate in formal contract delivery and are important deduction data in subsequent contract resource accounting. The product carryover probe data file records the carryover amount and probe quantity of each product, which is important data for accurately calculating the actual demand for contract resources.

[0037] In this application, determining the monthly production execution data for each process is to establish the correlation between historical production data and future output forecasts. The extracted monthly production execution data represents the phased actual data of each process's monthly production and operation, directly reflecting the actual production status of the process in that month. Specifically, it can include process output, downtime, output volume, and key contract production progress. Process output reflects the actual production results completed in the current month and is a fundamental indicator for judging the actual capacity utilization of each process in that month. Downtime refers to the actual downtime of each process in the current month, reflecting interruptions in the production process and serving as an important indicator for revising capacity forecasts. Output volume refers to the actual quantity of output generated by each process in the current month, reflecting the material loss level in production and providing actual data for reducing monthly output. Key contract production progress refers to the actual completion progress of key contracts for each process in the current month, reflecting the production status of the products corresponding to the key contracts and thus determining the impact of these products on the capacity of related processes.

[0038] In this application, the predicted monthly output is determined by using the monthly production execution data as a reference for the monthly capacity level of each process, combining the historical capacity patterns of the processes reflected in the historical output data archives, the impact of key contracts on capacity reflected in the historical contract structure data archives, and using the full-process material flow logic of the metal flow model as a constraint. The overall predicted monthly output can be determined by comprehensive calculation, so that the prediction results are both in line with the actual production progress of the month and in line with the historical capacity characteristics of the processes and the requirements of the supply and demand balance of materials throughout the process.

[0039] In this application, the predicted output for the following month is based on the actual capacity level reflected by the production execution data of each process in the current month, combined with the historical contract structure data archive to predict the impact of the contract structure on the process capacity in the following month, and at the same time, the capacity pattern of the historical output data archive is referenced, and the full-process material flow requirements of the metal flow model are matched to comprehensively determine the output.

[0040] This application constructs a metal flow model to analyze and integrate the internal supply flow, quantity, and external sales resources of all upstream and downstream processes in a steel enterprise. This ensures that subsequent production forecasting and contract resource planning are based on the balance of material supply and demand throughout the entire process, avoiding material supply and demand mismatches caused by independent analysis of single processes. This ensures a high degree of alignment between resource planning and actual production material flow. Simultaneously, it provides multi-dimensional data support for production forecasting and contract resource accounting. The constructed production data archive system stores production data from multiple dimensions, including output, contract structure, downtime, inventory, production losses, and production-sales linkage, providing historical data for subsequent analysis and forecasting. Data availability significantly reduces biases caused by subjective judgments, enhancing the comprehensiveness and reliability of data references. Furthermore, integrating historical data archives, current month's production execution data, and metal flow models during production forecasting ensures that the production forecasts for the current and following months accurately reflect the actual capacity potential of each process. Finally, by linking carryover quantities, probe quantities, current month's forecasted production, next month's forecasted production, and output quantities—indicators related to contract resource demand—through the contract demand resource model, core influencing factors in the production and sales linkage processes are integrated, making the accounting dimensions of contract resource demand more comprehensive and effectively improving the efficiency of contract resource planning generation.

[0041] In this application, the proposed solution can reduce contract carryover and probe volume, ensure timely and full contract delivery, and improve customer satisfaction. By establishing a rigorous data logic between multiple processes, it can not only ensure the accuracy of the current contract resource plan, but also guarantee knowledge transfer.

[0042] In this application, the proposed solution can also improve the accuracy of the contract resource plan for the following month, reduce contract carryover and probe volume, reduce economic losses, achieve multi-product, multi-process production line capacity and metal flow balance, and improve the accuracy of the contract resource plan.

[0043] In step 110 above, the construction of the metal flow model for each upstream and downstream process of the entire process can be specifically performed according to steps 111 to 114 as follows: Step 111: Determine the output, internal supply flow, quantity, and external sales volume of each upstream and downstream process in the entire process, obtain the metal flow data of each process, and determine the change in raw material inventory of each process based on the metal flow data.

[0044] Step 112: The changes in raw material inventory for each process need to be classified according to the type of raw material. Based on the input and consumption logic, the expected increase or decrease in raw material inventory is output by type.

[0045] Step 113: Determine the many-to-many production supply logic relationship for each process based on the metal flow data of each process, the change in raw material inventory of each process, and the expected increase or decrease in raw material inventory.

[0046] Step 114: Determine the metal flow model based on the iron consumption, yield, and many-to-many output supply logic of each process.

[0047] In this application, determining the correspondence between output and supply between processes is a fundamental prerequisite for constructing a metal flow model. This requires clearly identifying the downstream supply processes corresponding to the output materials of each process in the entire steel enterprise process, and clarifying the material supply matching relationship between upstream and downstream processes, including one-to-one, one-to-many, and many-to-one supply and output relationships. Determining the internal supply flow direction and quantity of each process, and defining the remaining portion of each process's capacity after deducting its corresponding internal supply quantity as the external sales resource quantity, quantifies the internal supply materials from each process to its corresponding downstream process based on a clear understanding of the inter-process supply correspondence. The flow direction and specific internal supply quantity are determined. Then, based on the actual capacity of each process, the internal supply quantity required by that process to external processes is deducted, and the remaining capacity is the external sales resource quantity that the process can sell externally. This enables the quantitative division of the internal supply and external sales volume of each process's capacity. The construction of the metal flow direction model for each upstream and downstream process is to integrate the three core data types determined above: the correspondence between output and supply between each process, the internal supply direction and quantity of each process, and the external sales resource quantity of each process. The data-driven mathematical logic relationship of metal flow is built according to the upstream and downstream sequence of the entire process to obtain the metal flow direction model.

[0048] In step 120 above, the construction of a multi-dimensional production data archive system can be specifically performed according to steps 121 to 126 as follows: Step 121: Establish historical production data archives for each process. The historical production data archives include the production level of each process, the average production within the rolling cycle, and the optimal production value.

[0049] Step 122: Establish historical contract structure data archives for each process. The historical contract structure data archives include key contract attributes and key contract quantities. The products corresponding to the key contracts are those that affect the production capacity of the process.

[0050] Step 123: Establish historical downtime data archives for each process. The historical downtime data archives shall include at least the planned maintenance time, fault downtime, planned downtime, and total downtime for each process.

[0051] Step 124: Establish raw material inventory data files for each process. The raw material inventory data files include the raw material inventory quantity for each process, the total inventory quantity from each process to the final process, the reasonable inventory range for each process, and the reasonable total inventory quantity from each process to the final process.

[0052] Step 125: Establish output data files for each product, including the quantity and category information of scrap, contracted products, surplus materials, and transitional materials.

[0053] Step 126: Establish a carryover probe data file for each product. The carryover probe data file includes the carryover amount and probe quantity for each product.

[0054] In this application, the establishment of historical contract structure data archives for each process can specifically involve first identifying the quantity of key contracts for varieties and specifications that affect production capacity, such as galvanized outer plates, zinc-aluminum-magnesium coatings, and extreme specifications. Generally, the production capacity loss for these contracts is 20%-40%, which seriously affects the production capacity level of this process. The key contracts need to be defined by variety according to contract attribute columns, such as manufacturing product classification, steel grade code, coating type code, etc.

[0055] In this application, a historical downtime data archive is established for each process. The historical downtime may include planned maintenance time, downtime due to fault, planned downtime, and total downtime. This time is used as input for statistically analyzing the process operation time; pure operation time = calendar time - total downtime. The downtime due to fault serves as flexible evaluation data for the capacity utilization level of each process. In this application, establishing raw material inventory data files for each process specifically includes the raw material quantity and total inventory at each node of the entire process. A product typically requires 5-6 processes from raw material processing to finished product. To reduce capacity losses due to material shortages and downtime at each process, and to ensure continuous and stable production at each process, the raw material inventory at each process, as well as the raw material inventory from each intermediate node to the final process, should be at a reasonable inventory level. Before establishing raw material inventory data files for multiple processes, it is necessary to define the reasonable inventory quantity for each process and from each node to the final process; the reasonable inventory is the minimum inventory quantity required to ensure continuous and stable production at each process. In this application, the establishment of output data files for each product specifically includes scrap, contracted goods, surplus materials, and transitional materials. These materials occupy process capacity but are not delivered under formal contracts, meaning they do not contribute to formal contract resources. Formal contracts are those submitted through the contract resource plan, with the sales department organizing contract delivery and the production department organizing production accordingly. Outputs, such as scrap, contracted goods, surplus materials, and transitional materials generated during the production process corresponding to formal contracts, cannot be delivered under formal contracts and require corresponding spot contracts based on finished goods. The quantity of outputs needs to be deducted from the contract resource plan. In this application, the establishment of carry-over probe data files for each product specifically refers to the carry-over quantity and probe quantity of the formal contracts from the previous month. Before establishing multiple product carry-over probe data files, it is necessary to define the time from production to approval for shipment and from approval for shipment to settlement for each product. Based on the principle of settlement at the end of the current month's contract, the production time node for each product is calculated backwards, and this node serves as the carry-over probe statistical node. Once the carry-over probe nodes for each product are determined, the production cycle of each product's contract for the current month is the period from the carry-over probe node of the previous month to the carry-over probe node of the current month. In this application, the historical production data archive is a data archive reflecting the historical production capacity of each process, including three types of data: production level, average production within a rolling cycle, and optimal production value. Among them, the production level is a multi-dimensional quantitative result of the historical production capacity of the process, which can intuitively reflect the degree of capacity utilization of the process under different production conditions and is the core basis for judging the basic capacity of the process. The average production within a rolling cycle is the average production calculated by selecting continuous production cycles of the process, which can avoid the fluctuation deviation caused by accidental factors and accurately reflect the stable level of capacity utilization of the process. The optimal production value is the production performance achieved by the process under optimal production conditions in the historical production process, which can reflect the capacity improvement potential of the process. The rolling cycle can be the past six months or the past three months, and this application does not make a specific limitation on it.

[0056] In this application, the historical contract structure data archive is a data archive that associates contract structure with process capacity, recording key contract attributes and the number of key contracts. The products corresponding to the key contracts are those that affect the process capacity utilization. The production of these products will cause an increase or decrease in process capacity, and are key elements affecting capacity in the contract structure. The key contract attributes are the characteristics that determine the products corresponding to the key contracts, such as manufacturing product classification, steel grade code, coating type code, etc. The number of key contracts is the specific production scale of each type of key contract in historical production, which can reflect the actual impact of the production proportion of key contracts on process capacity.

[0057] In this application, the historical downtime data archive is used to calculate the actual operating time of processes and analyze factors that interfere with production capacity. It records at least four types of time data: planned maintenance time, fault downtime, planned downtime, and total downtime. Among them, the planned maintenance time is the time scheduled for equipment maintenance and repair in each process to ensure the normal operation of equipment, which is a downtime that can be planned in advance; the fault downtime is the downtime caused by unforeseen factors such as sudden equipment failure or production abnormalities, which is an unplanned sudden downtime; the planned downtime is the non-maintenance downtime caused by production planning needs such as production arrangement and material allocation, which is also a downtime that can be planned in advance; the total downtime is the sum of all types of downtime, including planned maintenance, fault downtime, and planned downtime, within a certain production cycle, and is the main parameter for calculating the pure operating time of each process.

[0058] In this application, the raw material inventory data archive is a data archive for controlling the material inventory status of the entire steel enterprise process and ensuring production continuity. It records inventory data such as the raw material inventory of each process, the total inventory of each process up to the final process, the reasonable inventory range of each process, and the reasonable total inventory of each process up to the final process. Among them, the raw material inventory of each process is the real-time material inventory of the process itself, which can reflect the material supply status of a single process. The total inventory of each process up to the final process is the sum of all material inventory from the beginning of the process to the final process of the entire production process, which can reflect the material flow and reserve status of the process in the entire process. The reasonable inventory range of each process is the material inventory range that can ensure continuous and stable production of each process, and is the standard for judging whether the inventory of a single process is reasonable. The reasonable total inventory of each process up to the final process is the minimum material inventory range that can ensure continuous temperature production throughout the entire process from the process to the final process, and is the standard for judging whether the total inventory of the entire process is reasonable.

[0059] In this application, the product output data files for each product are data files used to calculate the actual effective capacity of the process and to statistically analyze production losses. They record the quantity and category information of scrap, contracted products, surplus materials, and transitional materials. Among them, scrap are non-conforming products generated during the production process that have no value for delivery under a formal contract; contracted products are products that do not meet the standards of a formal contract but can be delivered through negotiation; surplus materials are leftover materials generated during the production process; and transitional materials are transitional materials generated during the production switchover phase. All four types of materials are products that occupy process capacity but cannot be delivered under a formal contract.

[0060] In this application, the carry-over probe data archives for each product are data archives that balance the production and sales relationship of steel enterprises and connect production with contracts. They record the carry-over volume and probe volume of each product. Before establishing multiple product carry-over probe data archives, it is necessary to define the nodes from production to delivery and from delivery to settlement for each product. Based on the principle of settling the contract for the current month at the end of the month, the production time node of each product is deduced backward, and this node is used as the carry-over probe statistical node. After the carry-over probe nodes for each product are determined, the production cycle of the contract for each product in the current month is the period from the carry-over probe node of the previous month to the carry-over probe node of the current month. Furthermore, the carry-over probe volumes for each product are diverse, and the statistical nodes are fixed. After sorting out the rules, the system module performs statistical analysis by product and node.

[0061] In this application, historical data archives are constructed from six dimensions: output, contracts, downtime, inventory, output, and carry-over probes. This provides rich, accurate, and reliable data support for subsequent output forecasting and contract resource planning. The content of each archive is matched to the different needs of output forecasting and contract resource planning, enabling subsequent data analysis and model calculations to accurately call the corresponding data, improving the efficiency and accuracy of data use, and thus improving the efficiency of steel companies in generating contract resource plans.

[0062] In step 121 above, the output level of each process can be divided into five levels, and the specific grading criteria can be implemented according to steps 1211 to 1215 as follows: Step 1211: The first-level output level includes the historical highest monthly total output of each process and the historical highest daily average output of each process.

[0063] Step 1212: The second-level production level includes the highest monthly total output of each process in the past year and the highest daily average output of each process in the past year.

[0064] Step 1213: The third-level output level includes the average of the highest monthly total output of each process over the past year and the average of the highest daily average output over the past year. The highest monthly total output is the monthly total output that is greater than the average monthly total output over the past year, and the highest daily average output is the daily average output that is greater than the average daily average output over the past year.

[0065] Step 1214: The fourth level of output includes the average monthly total output of each process over the past year, and the average daily output of each process over the past year.

[0066] Step 1215: The fifth level of output includes the average of the lowest monthly total output of each process over the past year and the average of the lowest daily average output over the past year. The lowest monthly total output is the monthly total output that is less than or equal to the average monthly total output over the past year, and the lowest daily average output is the daily average output that is less than or equal to the average daily average output over the past year.

[0067] In this application, the output level of each process can be specifically represented by T-value levels. The T-value levels for each process are divided into five levels, T1 to T5, where T1 is the historical record level; T2 is the best level in the past year; T3 is the average of T4 and above in the past year; T4 is the average level in the past year; and T5 is the average of T4 and below in the past year. The first level of output is T1, the second level is T2, the third level is T3, the fourth level is T4, and the fifth level is T5. The average level within the rolling cycle refers to the average of the past 6 months, the average of the past 3 months, the best in the past 6 months, and the best in the past 3 months. The historical output data archive serves as key foundational data for the annual and monthly planned levels of each process, as well as for monthly output forecasting.

[0068] In this application, the first-level output level is the highest historical level of process capacity utilization, including the highest historical monthly total output and the highest historical daily average output of each process. The highest historical monthly total output refers to the maximum monthly total output among all production months, representing the highest historical scale of the overall monthly capacity output of the process. The highest historical daily average output refers to the maximum daily average output among all production months since the process started production, representing the highest historical level of process capacity efficiency per unit of pure operating time. The first-level output level can intuitively reflect the theoretical maximum potential of process capacity utilization and provide a long-term reference for process capacity improvement planning.

[0069] In this application, the second-level output level is the highest capacity level of the process within the past year's production cycle, including the highest monthly total output of each process in the past year and the highest daily average output of each process in the past year. The highest monthly total output in the past year refers to the maximum monthly total output of the process in the most recent twelve production months, representing the highest scale of the overall monthly capacity output of the process in the recent period. The highest daily average output in the past year refers to the maximum daily average output of the process in the most recent twelve production months, representing the highest level of capacity efficiency per unit of pure working time of the process in the recent period. The second-level output level is more realistically referential than the historical highest level and can reflect the optimal capacity state that the process can achieve under current production conditions.

[0070] In this application, the third-level output level is a stable average level under good capacity utilization over the past year, including the average of the highest monthly total output of each process over the past year and the average of the highest daily average output over the past year. Specifically, for the average of the highest monthly total output of each process over the past year, firstly, the average monthly total output of the process over the past twelve production months is calculated; then, from the twelve monthly total outputs, the monthly total outputs greater than the average monthly total output over the past year are selected and defined as the high monthly total output; finally, the average of the high monthly total outputs is calculated, which is the high monthly total output of each process over the past year. The average of total output; similarly, for the average of high daily output over the past year, it is first necessary to calculate the average daily output of the process over the past twelve production months, and then select the daily output that is greater than the average daily output of the past twelve production months from the twelve daily outputs, which is defined as high daily output. Finally, the average of all high daily outputs is calculated to obtain the average of high daily output over the past year; the third level of output is the capacity level that the process can stably achieve when production conditions are good and there are no major interfering factors, and it is a reference for setting capacity targets in output forecasting.

[0071] In this application, the fourth-level output level is the average production capacity level of the process over the past year under normal conditions. It can reflect the basic production capacity performance of the process under normal production conditions and without special favorable or unfavorable factors. It includes the average monthly total output of each process over the past year and the average daily output of each process over the past year. The average monthly total output over the past year refers to the arithmetic mean of the total monthly output of the process over the past twelve production months, which can represent the normal benchmark scale of the overall monthly production capacity output of the process in the recent period. The average daily output over the past year refers to the arithmetic mean of the average daily output of the process over the past twelve production months, which represents the normal benchmark level of the process's unit pure operating time capacity efficiency in the recent period.

[0072] In this application, the fifth-level output level is the stable average level of the process under poor capacity utilization over the past year. This includes the average of the lowest monthly total output for each process over the past year, and the average of the lowest daily average output over the past year. Specifically, to calculate the average of the lowest monthly total output over the past year, the average monthly total output for the twelve production months of that process is first calculated. Then, from the twelve monthly total outputs, monthly total outputs lower than the average monthly total output are selected and defined as low monthly total outputs. Finally, the average of all low monthly total outputs is calculated, which represents the average output over the past year. The average of low monthly total output is used to calculate the average of low daily output over the past year. To calculate the average of daily output for the twelve production months of the past year, the average of daily output for the process is calculated. Then, from the twelve average daily outputs, the average daily outputs that are lower than the average of the average daily outputs are selected and defined as low daily outputs. Finally, the average of all low daily outputs is calculated to obtain the average of low daily outputs over the past year. The fifth level of output represents the minimum production capacity of the process when production conditions are limited, there are machine downtime due to malfunctions, insufficient raw material supply and other interference factors, and sets a lower limit reference for production capacity forecasting.

[0073] In this application, the first to fifth level of production levels can be used as reference data for the annual and monthly planned levels and monthly production forecasts of each process. The fourth level of production level can be used as the monthly planned level of each process, that is, the reasonable level that each process can achieve. The third level of production level can be used as the monthly target level of each process, that is, the production capacity that each process can achieve when production conditions are good and there are no major interfering factors. This application does not make specific limitations on this.

[0074] In this application, process capacity is divided into five levels from high to low, which can match the capacity reference requirements under different production scenarios and provide multi-level capacity reference data for output forecasting. When forecasting output for the current month and the following month, the appropriate capacity level can be selected as a reference by combining the actual situation such as the production execution data, contract structure, and downtime plan for the current month, so that the output forecast values ​​are more in line with the actual production conditions and improve the accuracy of the forecast. In addition, the total monthly output reflects the overall monthly capacity output of the process, and the average daily output reflects the capacity efficiency per unit time, which can comprehensively and objectively reflect the actual capacity status of the process, further improving the accuracy of the forecast and the efficiency of steel companies in generating contract resource plans.

[0075] In steps 1213 to 1215 above, the average daily output for each month can be determined by the following formula (1): (1) in, This represents the average daily output for each month. This indicates the total monthly output. This indicates the duration of the pure operation.

[0076] In this application, the monthly net operating time of each process in a steel enterprise is affected by factors such as planned maintenance, downtime due to malfunctions, and production arrangements, resulting in significant differences in net operating time across different months. If the daily average output is calculated directly based on the number of natural days, the data will be distorted due to the presence of non-production time, failing to accurately reflect the actual capacity efficiency of the process. This application uses the net operating time of the process in actual production as the core accounting benchmark, and through standardized conversion logic, accurately calculates the daily average output of each process in each month, ensuring that the calculation results truly reflect the production capacity of the process per unit time. At the same time, it enables the comparability of daily average output between different months and different processes, providing reliable data for subsequent capacity analysis and forecasting.

[0077] In this application, the average daily output for each month is the result of formula calculation, representing the capacity output level of the process per unit of pure working hours during the production process in that month. It is also a key indicator reflecting the actual capacity efficiency of the process, and can provide an accurate efficiency reference for subsequent classification of five levels of output and calculation of the actual capacity potential of the process.

[0078] In this application, the total monthly output refers to the total amount of qualified products actually produced by the process within the monthly production cycle. It is a basic indicator reflecting the monthly capacity output of the process. This data, based on the actual statistical value of the process's production, directly reflects the production results of the process in the current month and is the core basic data for calculating the average daily output.

[0079] In this application, the pure operating time refers to the total time that the process actually carries out production operations in the current month. It is the effective production time after deducting the total downtime from the calendar time, which can make the calculation of the average daily output based entirely on the actual production time of the process, ensuring the authenticity of the results. The total downtime can include at least the planned maintenance time, the fault downtime, and the planned downtime.

[0080] In step 140 above, the determination of the predicted output for the current month and the predicted output for the next month based on the production data archive system, the production execution data for the current month, and the metal flow model can be specifically performed according to steps 141 to 142 as follows: Step 141: Use the production execution data of the current month as reference data for the capacity level of each process in the current month, and combine the historical output data archive, historical contract structure data archive and the metal flow model to determine the predicted output of each product in the current month.

[0081] Step 142: Determine the predicted output of each product for the following month based on the current month's production capacity level of each process, the historical contract structure data archive, and the historical output data archive.

[0082] In this application, the monthly production execution data includes process output, downtime, output volume, and key contract production progress. This data represents the actual data generated during the process's production that month, directly reflecting the current capacity utilization status of the process and serving as a reference for determining the monthly capacity level. The historical output data archive includes the five-level output levels for each process, the average output within the rolling cycle, and the optimal output value. It records the historical capacity performance of the process under different production conditions. Combining this with the monthly production execution data allows for the calibration of the randomness of short-term execution data based on historical patterns. Combined with the process's conventional capacity level in the historical output archive, it is possible to reasonably predict the process's capacity recovery after a fault is resolved, making output forecasts more closely aligned with the process's actual capacity potential. The historical contract structure data archive includes key contract attributes and... The quantity and key contracts correspond to products that affect the capacity utilization of the process. The records show the capacity change patterns of the process under different contract structures. By combining this with the monthly production execution data, the production forecast can be adjusted in a targeted manner based on the actual production progress and proportion of key contracts in the current month, matching the corresponding capacity impact data in the archive. This ensures that the production forecast aligns with the actual impact of the contract structure on capacity. The metal flow model clarifies the output and supply correspondence of each process in the entire upstream and downstream process, the internal supply flow and quantity, and the external sales resource volume. Incorporating this model when determining the monthly forecast output can avoid material supply and demand mismatch caused by independent production forecasts for single processes. This ensures that the forecast output of each process and each product meets the actual needs of material supply and internal supply throughout the entire process, so that the production forecast does not deviate from the actual material flow.

[0083] In this application, the capacity level of each process in a given month is the actual capacity utilization reflected by the production execution data for that month. This includes a comprehensive assessment of the current equipment status, technological level, operational capabilities, raw material supply, and other real-world production conditions. Using this as the basis for the production forecast of the following month allows the forecast to reflect the actual production status of the processes that will follow, preventing the production forecast from deviating from the current capacity and ensuring the rationality of the production forecast.

[0084] Based on the technical solution proposed in this application, the method may further perform the following steps 210 to 230: Step 210: Based on the raw material inventory data file, determine the raw material inventory of each process, and use the raw material inventory of each process as reference data for predicting output.

[0085] Step 220: Based on the reference data, complete the production forecast for the current month and the next month, output the raw material inventory level at the end of the month for each process, and determine whether the raw material inventory level at the end of the month exceeds the reasonable inventory level or falls short of the reasonable inventory level.

[0086] Step 230: If the raw material inventory level at the end of the month exceeds the reasonable inventory level, or falls short of the reasonable inventory level, then adjust the production plan.

[0087] In this application, the raw material inventory data file records the raw material inventory of each process, the total inventory of each process up to the final process, and the reasonable inventory range corresponding to each process and the entire process. This is the basis for determining the actual inventory status and judging whether the inventory is reasonable. Based on this file, the current actual value of raw material inventory of each process can be accurately extracted, and the upper and lower limits of inventory control for each process can be clearly defined, providing quantitative data for subsequent inventory status judgment and production forecast reference.

[0088] In this application, when determining the predicted output of each product in the current month and the following month, the actual value of raw material inventory extracted from each process is used as an important reference data. Together with the production data archive system, the production execution data of the current month, and the metal flow model, it constitutes the reference basis for output prediction. Since there is a direct linkage between process output and raw material inventory, an increase in the output of the upstream supply process will lead to an increase in raw material inventory, and an increase in the output of the downstream consumption process will lead to a decrease in raw material inventory. Therefore, in the process of output prediction, it is necessary to combine the whole process material flow logic of the metal flow model to perform inventory balance verification on the predicted output.

[0089] In this application, after completing the preliminary calculation of the projected output for each product in the current and following months, it is necessary to predict the changes in raw material inventory at each process stage after the projected output is realized, and confirm whether the changed inventory value is within the reasonable range specified in the raw material inventory data file. If the prediction results indicate that the inventory will exceed the reasonable upper limit or fall below the reasonable lower limit, the preliminary projected output needs to be fine-tuned until the predicted inventory status meets the requirements of the reasonable range. The final determined projected output not only matches the capacity utilization and contract production needs, but also ensures the dynamic stability of the inventory.

[0090] In this application, situations requiring adjustments to the production plan also include planned reductions in production of individual products after achieving cost-effectiveness balance; passive reductions in production of individual products based on cost-effectiveness ranking when billet production is insufficient; and situations where a series of annual shutdowns lead to capacity losses in the final process, resulting in planned over- or under-stocking of raw material inventory. These adjustments can be linked for two consecutive months: if inventory increases or decreases in one month, inventory decreases or increases in the following month, restoring a reasonable inventory level within two months.

[0091] In this application, inventory judgment and production reference are based on raw material inventory data files. The inventory level is used as a reference data for predicting production, so as to avoid the problems of raw material inventory backlog or material shortage caused by improper production planning. This can reduce the material storage costs caused by excessive inventory and avoid the process shutdown caused by material shortage due to excessive inventory, thus ensuring the production continuity of each process.

[0092] In step 150 above, the establishment of a multi-product, multi-variety contract demand resource model can be specifically performed according to steps 151 to 153 as follows: Step 151: Based on the carryover probe data file, determine the carryover amount and probe amount of the previous month.

[0093] Step 152: Based on the product output data file, determine the product output volume for the current month and the product output volume for the following month.

[0094] Step 153, based on the predicted output for the current month, the predicted output for the next month, the carryover from the previous month, the probe output from the previous month, the output brought in for the current month, and the output brought in for the next month, construct the following formula (2) as the contract demand resource model: (2) in, This indicates the contract demand for the following month. This indicates the projected output for the current month. This indicates the output volume for the current month. This indicates the amount carried over from the previous month. This indicates the probe volume last month. This indicates the contract demand for the current month. This indicates the projected output for the following month. This indicates the output volume for the following month.

[0095] In this application, the carryover volume from the previous month refers to the quantity of products that were produced in the previous production cycle but not yet delivered under formal contracts and carried over to the current cycle. This reflects the lag in the production and sales connection of steel enterprises in the previous cycle. These products will occupy the capacity absorption space of the current cycle and are relevant indicators that need to be deducted in the calculation of contract demand. The probe volume from the previous month refers to the quantity of products that were produced in the previous production cycle but have no corresponding formal contracts. This reflects the disconnect between the production and sales connection of steel enterprises in the previous cycle. These products can directly match the contract demand of the current cycle without the need for new contracts and are relevant indicators that need to be deducted in the calculation of contract demand.

[0096] In this application, the monthly output volume refers to the total amount of various outputs that will be generated during the production process corresponding to the monthly forecast output. This part of the material occupies the process capacity but cannot be delivered according to the formal contract. Therefore, it needs to be deducted from the monthly forecast output to obtain the effective output that can actually be delivered to the formal contract in the current month. The next month output volume refers to the total amount of various outputs that will be generated during the production process in the next month based on the historical data in the output data archive and the production plan of the next month's forecast output. Its deduction logic is the same as that of the monthly output volume. It needs to be deducted from the next month's forecast output to obtain the effective deliverable output in the next month.

[0097] In this application, all key parameters affecting contract resource quantities, such as carryover volume, probe volume, output volume, and forecasted output for the current and next month, are incorporated into the model's calculation. This establishes a correlation between each key parameter and contract demand, resolving issues such as incomplete parameter consideration, ambiguous calculation logic, and large errors in manual calculations in traditional steel enterprise contract resource planning. This makes the calculation of contract demand more accurate and efficient, enabling rapid updates to contract demand based on changes in output forecast data, output data, and carryover probe data. This allows for dynamic adjustments to the contract resource plan and effectively improves the efficiency of steel enterprise contract resource plan generation.

[0098] Next, we will elaborate on the method for generating contract resource plans for steel enterprises in multi-product, multi-process scenarios proposed in this application, using some specific embodiments.

[0099] Example: Taking the contract resource calculation for galvanized products in month M as an example, the production forecasting module shows a total of 5 parallel galvanizing production lines capable of producing the same product, namely DA, DB, DD, DE, and DK. Their raw materials are supplied by three acid-rolling mills and an 18-roll mill, namely SA, SB, SC, and SJ; internal supply is also provided to the downstream color coating process CA, with the internal supply volume determined by the monthly market demand for pure zinc coating in color coating. The remaining capacity is sold externally. Galvanized raw material inventory is categorized and classified into the post-rolling warehouse, which consists entirely of cold-rolled coils. The production forecasting module maintains the post-rolling warehouse at a reasonable inventory level through unified balancing.

[0100] Production data archives for DA, DB, DD, DE, and DK galvanizing processes, taking the daily average T3 (third-level production) as an example; The key contracts for galvanizing processes in DA, DB, DD, DE, and DK include four types: galvanized outer sheet, aluminum-magnesium coating, thin material less than 0.6mm, and ultra-strong DP steel. The contract structure data file is as follows, taking aluminum-magnesium coating as an example. The aluminum-magnesium coating contract production process includes two processes: DD and DK. The average daily T3 (third-level production) output of aluminum-magnesium coating is 1,385 tons and 1,368 tons, respectively. The average daily T3 (third-level production) for the galvanizing processes of other contracts DA, DB, DD, DE, and DK is 1419 tons, 1141 tons, 1626 tons, 1487 tons, and 1970 tons, respectively. The total average daily T3 (third-level production) for galvanizing processes 1-5 is 7510 tons.

[0101] The pure operation time for the DA, DB, DD, DE, and DK galvanizing processes in the data archive module is 29.5 days, 30 days, 28.7 days, 30 days, and 28.5 days, respectively. In the production forecast module, the aluminum-magnesium coating production volume for M-1 is 13,000 tons for DD and 15,000 tons for DK, based on the production line roll schedule. The monthly output for the DA, DB, DD, DE, and DK galvanizing processes in M-1 is as follows: DA = 29.5 × 1419 = 42,000 tons; DB = 30 × 1141 = 34,000 tons; DD = 1.3 + (28.7 - 1.3 × 10000 / 1385) × 1626 = 44,000 tons; DE = 30 × 1487 = 45,000 tons; DK = 1.5 + (28.5 - 1.5 × 10000 / 1368) × 1970 = 49,000 tons; In January, the total output of galvanizing processes DA, DB, DD, DE, and DK was 169,000 tons.

[0102] In month M, the production of aluminum-magnesium coatings DD and DK is 12,000 tons and 14,000 tons respectively; the production forecasts for DA, DB, DD, DE, and DK processes are 41,000 tons, 33,000 tons, 42,000 tons, 43,000 tons, and 47,000 tons respectively, totaling 206,000 tons (the calculation method is the same as that for month M-1, calculated jointly by the production forecast module and the data archive module).

[0103] The data archive module shows that the output of processes DA, DB, DD, DE, and DK is T3 (third-level production level) for aluminum-magnesium contracts and other contracts, which are 0.2 million tons and 1.6 million tons respectively. Note that the output is expected to be small. Data archive module DA, DB, DD, DE, DK process M-2 month carryover volume: aluminum-magnesium contract 0.3 million tons, probe volume 0 tons; other contract carryover volume 1.75 million tons, probe volume 1 million tons; The data archive module DA, DB, DD, DE, and DK processes had 144,000 tons of contracts received in January, including 25,000 tons of aluminum and magnesium contracts and 119,000 tons of other contracts. The data archive modules DA, DB, DD, DE, and DK processes M-1 and M all supplied 0.2 million tons of color coating within the month; In the Contract Resource Planning module, for processes DA, DB, DD, DE, and DK, the carryover / probe quantity for month M-1 is calculated as follows: (Carryover quantity for month M-2 - Probe quantity for month M-2 + Contract quantity for month M-1) - (Production output for month M-1 - Internal supply of color coating - Outgoing products). A positive carryover / probe quantity indicates contract carryover, while a negative quantity indicates contract probe.

[0104] Aluminum-magnesium contract M-1 month carryover / probe volume = (0.3 - 0 + 2.5) - (1.3 + 1.5 - 0.2) = 0.2 million tons Other contracts carried over to month M-1 = (1.75 - 1 + 11.9) - (16.9 - 1.3 - 1.5 - 0.2 - 1.6) = 0.35 million tons; Contract Resource Planning Module DA, DB, DD, DE, DK Process M Month Contract Demand = M Month Output - M - 1 Month Carryover / Probe Quantity - Internal Color Coating - Output Quantity.

[0105] The contracted demand for aluminum-magnesium coatings in month M = 1.2 + 1.4 - 0.2 - 0.2 = 2.2 tons; Other coatings contract demand in month M = 20.6 - 2.8 - 0.35 - 0.2 - 1.6 = 156,500 tons; The progress monitoring system can monitor the daily average output, T-value level, and maintenance duration of DA, DB, DD, DE, and DK processes. This, in turn, assists in evaluating the accuracy of output forecasts for each process in M-1 and M months, and allows for timely adjustments to the contract requirements for M month.

[0106] Based on the technical solution proposed in this application, a metal flow model is constructed to analyze and integrate the internal supply flow, quantity, and external sales resources of each upstream and downstream process in the entire steel enterprise process. This ensures that subsequent production forecasting and contract resource planning are based on the balance of material supply and demand throughout the entire process, avoiding material supply and demand mismatches caused by independent analysis of single processes, and making resource planning highly compatible with the actual material flow in production. Simultaneously, it provides multi-dimensional data support for production forecasting and contract resource accounting. The constructed production data archive system stores production data from multiple dimensions, including output, contract structure, downtime, inventory, production losses, and production-sales linkage, enabling subsequent analysis and forecasting work to be conducted in a comprehensive manner. With historical data available, biases caused by subjective judgment are significantly reduced, improving the comprehensiveness and reliability of data references. In addition, integrating historical data archives, current month production execution data, and metal flow models into the production forecasting process ensures that the production forecasts for the current and following months accurately reflect the actual capacity potential of the process. Finally, by linking indicators related to contract resource demand, such as carryover volume, probe volume, current month's forecasted production, next month's forecasted production, and output volume, through the contract resource demand model, core influencing factors in the production and sales linkage are integrated, making the accounting dimensions of contract resource demand more comprehensive and thus effectively improving the efficiency of contract resource planning.

[0107] The following describes an embodiment of the apparatus described in this application, which can be used to execute the method for generating contract resource plans for steel enterprises in a multi-product, multi-process scenario as described in the above embodiments of this application. For details not disclosed in the apparatus embodiments of this application, please refer to the embodiments of the method for generating contract resource plans for steel enterprises in a multi-product, multi-process scenario described in the above applications.

[0108] Figure 2 A block diagram of a device for generating a contract resource plan for a steel company in a multi-product, multi-process scenario, according to one embodiment of this application, is shown.

[0109] Reference Figure 2According to one embodiment of this application, a steel enterprise contract resource planning generation device 200 in a multi-product, multi-process scenario includes: a first construction unit 201, a second construction unit 202, a determination unit 203, a prediction unit 204, and a generation unit 205.

[0110] The first construction unit 201 is used to construct a metal flow model for each upstream and downstream process of the entire process. The metal flow model includes the internal supply flow direction, internal supply quantity, external sales resource quantity, and raw material inventory changes for each process. The second construction unit 202 is used to construct a multi-dimensional production data archive system. The production data archive system includes historical output data archives, historical contract structure data archives, historical downtime data archives, raw material inventory data archives, output data archives for each product, and carry-over probe data archives for each process. The determination unit 203 is used to determine the current status of each process. Monthly production execution data, including process output, downtime, output, and key contract production progress; prediction unit 204, used to determine the predicted output for the current month and the predicted output for the next month based on the production data archive system, the monthly production execution data, and the metal flow model; generation unit 205, used to establish a contract demand resource model for multiple products and varieties, and generate contract resource plans for each product based on the contract demand resource model, wherein the contract demand resource model is associated with carryover volume, probe volume, predicted output for the current month, predicted output for the next month, and output.

[0111] In some embodiments of this application, based on the aforementioned scheme, the first construction unit 201 is configured to: determine the output, internal supply flow, quantity, and external sales volume of each upstream and downstream process in the entire process, obtain metal flow data for each process, and determine the change in raw material inventory for each process based on the metal flow data; the change in raw material inventory for each process needs to be classified according to the type of raw material, and output the expected increase or decrease in raw material inventory according to the input and consumption logic relationship; determine the many-to-many output supply logic relationship for each process based on the metal flow data of each process, the change in raw material inventory for each process, and the expected increase or decrease in raw material inventory; and determine the metal flow model based on the iron consumption, yield rate, and many-to-many output supply logic relationship for each process.

[0112] In some embodiments of this application, based on the foregoing scheme, the second construction unit 202 is configured to: establish historical output data archives for each process, the historical output data archives including the output level of each process, the average output within the rolling cycle, and the optimal output value; establish historical contract structure data archives for each process, the historical contract structure data archives including key contract attributes and key contract quantities, the products corresponding to the key contracts being products that affect the capacity utilization of the process; establish historical downtime data archives for each process, the historical downtime data archives including at least the planned maintenance time, fault downtime, planned downtime, and total downtime for each process; establish raw material inventory data archives for each process, the raw material inventory data archives including the raw material inventory quantity of each process, the total inventory quantity from each process to the final process, the reasonable inventory range of each process, and the reasonable total inventory quantity from each process to the final process; establish output data archives for each product, the output data archives including the quantity and category information of scrap, contracted products, surplus materials, and transitional materials; and establish carry-over probe data archives for each product, the carry-over probe data archives including the carry-over quantity and probe quantity of each product.

[0113] In some embodiments of this application, based on the aforementioned scheme, the output level of each process is divided into five levels. The first level includes the historical highest monthly total output and the historical highest daily average output of each process. The second level includes the highest monthly total output and the highest daily average output of each process over the past year. The third level includes the average of the highest monthly total output and the average of the highest daily average output of each process over the past year. The highest monthly total output is defined as the monthly total output over the past year that is greater than the average monthly total output over the past year. The average daily output is the average daily output of each month in the past year that is greater than the average daily output of each month in the past year; the fourth level of output includes the average total monthly output of each process in the past year and the average daily output of each process in the past year; the fifth level of output includes the average low monthly total output of each process in the past year and the average low daily output in the past year, wherein the low monthly total output is the total monthly output of each process that is less than or equal to the average total monthly output of each month in the past year, and the low daily output is the average daily output of each process that is less than or equal to the average daily output of each month in the past year.

[0114] In some embodiments of this application, based on the foregoing scheme, the average daily output for each month is determined by the following formula:

[0115] in, This represents the average daily output for each month. This indicates the total monthly output. This indicates the duration of the pure operation.

[0116] In some embodiments of this application, based on the aforementioned scheme, the base prediction unit is configured to: use the production execution data of the current month as reference data for the capacity level of each process in the current month, combine the historical output data archive, the historical contract structure data archive and the metal flow model to determine the predicted output of each product in the current month; and determine the predicted output of each product in the following month based on the capacity level of each process in the current month, the historical contract structure data archive and the historical output data archive.

[0117] In some embodiments of this application, based on the foregoing scheme, the device further includes a determination unit, which is configured to: determine the raw material inventory of each process based on the raw material inventory data file, and use the raw material inventory of each process as reference data for predicted output; based on the reference data, complete the output prediction for the current month and the following month, output the raw material inventory level at the end of the month for each process, and determine whether the raw material inventory level at the end of the month exceeds the reasonable inventory level or is below the reasonable inventory level; if the raw material inventory level at the end of the month exceeds the reasonable inventory level or is below the reasonable inventory level, then adjust the production plan.

[0118] In some embodiments of this application, based on the foregoing scheme, the generation unit 205 is configured to: determine the carry-over amount and the probe amount of the previous month based on the carry-over probe data file; determine the output amount of the current month and the output amount of the next month based on the output data file; and construct the following formula as a contract demand resource model based on the predicted output of the current month, the predicted output of the next month, the carry-over amount of the previous month, the probe amount of the previous month, the output amount of the current month, and the output amount of the next month:

[0119] in, This indicates the contract demand for the following month. This indicates the projected output for the current month. This indicates the output volume for the current month. This indicates the amount carried over from the previous month. This indicates the probe volume last month. This indicates the contract demand for the current month. This indicates the projected output for the following month. This indicates the output volume for the following month.

[0120] As another embodiment of this application, a computer program product or computer program is also provided, which includes computer instructions stored in a computer-readable storage medium. A processor of a computer device reads the computer instructions from the computer-readable storage medium and executes the computer instructions, causing the computer device to perform the methods described in the above embodiments.

[0121] As another embodiment of this application, a computer-readable storage medium is also provided. This computer-readable storage medium may be included in the electronic device described in the above embodiments; or it may exist independently and not assembled into the electronic device. The aforementioned computer-readable storage medium carries one or more programs, which, when executed by the electronic device, cause the electronic device to perform the methods described in the above embodiments.

[0122] Based on the same inventive concept, embodiments of this application also provide an electronic device. (Reference) Figure 3 The diagram illustrates the structure of an electronic device according to one embodiment of this application. The electronic device includes one or more memories 304, one or more processors 302, and at least one computer program (program code) stored in the memories 304 and executable on the processors 302. When the processors 302 execute the computer program, they implement the method described above.

[0123] Among them, Figure 3 In this document, a bus architecture (represented by bus 300) is used. Bus 300 may include any number of interconnected buses and bridges, linking various circuits including one or more processors represented by processor 302 and memory represented by memory 304. Bus 300 may also link various other circuits such as peripheral devices, voltage regulators, and power management circuits, which are well known in the art and therefore will not be described further herein. Bus interface 305 provides an interface between bus 300 and receiver 301 and transmitter 303. Receiver 301 and transmitter 303 may be the same element, i.e., a transceiver, providing a unit for communicating with various other devices over a transmission medium. Processor 302 is responsible for managing bus 300 and general processing, while memory 304 can be used to store data used by processor 302 during operation.

[0124] The functions described herein may be implemented in hardware, software executed by a processor, firmware, or any combination thereof. If implemented in software executed by a processor, the functions may be stored as one or more instructions or codes on or transmitted via a computer-readable medium. Other examples and embodiments are within the scope and spirit of this application and the appended claims. For example, due to the nature of software, the functions described above may be implemented using software executed by a processor, hardware, firmware, hardwired, or any combination thereof. Furthermore, the functional units may be integrated into a single processing unit, or each unit may exist physically separately, or two or more units may be integrated into a single unit.

[0125] In the several embodiments provided in this application, it should be understood that the disclosed technical content can be implemented in other ways. The device embodiments described above are merely illustrative; for example, the division of units can be a logical functional division, and in actual implementation, there may be other division methods. For instance, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the displayed or discussed mutual coupling, direct coupling, or communication connection may be through some interfaces; the indirect coupling or communication connection between units or modules may be electrical or other forms.

[0126] The units described as separate components may or may not be physically separate. Similarly, the components of the control device may or may not be physical units; they may be located in one place or distributed across multiple units. Some or all of the units can be selected to achieve the purpose of this embodiment, depending on actual needs.

[0127] If the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of this application. The aforementioned storage medium includes various media capable of storing program code, such as a USB flash drive, read-only memory (ROM), random access memory (RAM), portable hard drive, magnetic disk, or optical disk.

[0128] The above description is merely an embodiment of this application and is not intended to limit this application. Various modifications and variations can be made to this application by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of this application should be included within the scope of the claims of this application.

Claims

1. A method for generating contract resource plans (CRS) for steel enterprises in a multi-product, multi-process scenario, characterized in that, The method includes: Construct a metal flow model for each upstream and downstream process in the entire process. The metal flow model includes the internal supply flow, internal supply quantity, external sales resource quantity, and raw material inventory change of each process. Construct a multi-dimensional production data archive system, which includes historical output data archives for each process, historical contract structure data archives, historical downtime data archives, raw material inventory data archives, output data archives for each product, and carry-over probe data archives. Determine the monthly production execution data for each process, including process output, downtime, output volume, and production progress of key contracts; Based on the production data archive system, the monthly production execution data and the metal flow model, the predicted output for the current month and the predicted output for the next month are determined. Establish a contract demand resource model for multiple products and varieties, and generate contract resource plans for each product based on the contract demand resource model. The contract demand resource model is associated with carry-over volume, probe volume, current month's predicted output, next month's predicted output, and output volume.

2. The method according to claim 1, characterized in that, The construction of the metal flow model for all upstream and downstream processes in the entire process includes: Determine the output, internal supply flow, quantity, and external sales volume of each process in the entire upstream and downstream process, obtain the metal flow data of each process, and determine the change in raw material inventory of each process based on the metal flow data; The changes in raw material inventory for each process need to be categorized according to raw material type. Based on the input-consumption logic, the expected increase or decrease in raw material inventory should be output by type. Based on the metal flow data of each process, the changes in raw material inventory of each process, and the expected increase or decrease in raw material inventory, the many-to-many output supply logic relationship of each process is determined. The metal flow model is determined based on the iron consumption, yield, and many-to-many output supply logic of each process.

3. The method according to claim 1, characterized in that, The construction of a multi-dimensional production data archive system includes: Establish historical output data archives for each process, including the output level of each process, the average output within the rolling cycle, and the optimal output value; Establish a historical contract structure data archive for each process. The historical contract structure data archive includes key contract attributes and key contract quantity. The products corresponding to the key contracts are those that affect the capacity utilization of the process. Establish a historical downtime data archive for each process. The historical downtime data archive shall include at least the planned maintenance time, fault downtime, planned downtime and total downtime for each process. Establish raw material inventory data files for each process. The raw material inventory data files include the raw material inventory quantity for each process, the total inventory quantity from each process to the final process, the reasonable inventory range for each process, and the reasonable total inventory quantity from each process to the final process. Establish output data files for each product, including the quantity and category information of scrap, contracted products, surplus materials, and transitional materials; Establish a carryover probe data file for each product, which includes the carryover quantity and probe quantity for each product.

4. The method according to claim 3, characterized in that, The output levels of each process are divided into five levels, among which, The first level of output includes the highest historical monthly total output of each process and the highest historical daily average output of each process; The second level of output includes the highest monthly total output of each process over the past year, and the highest daily average output of each process over the past year. The third level of output includes the average of the highest monthly total output of each process over the past year and the average of the highest daily average output over the past year. The highest monthly total output is the monthly total output that is greater than the average monthly total output over the past year, and the highest daily average output is the daily average output that is greater than the average daily average output over the past year. The fourth level of output includes the average monthly total output of each process over the past year, and the average daily output of each process over the past year. The fifth level of output includes the average of the lowest monthly total output of each process over the past year and the average of the lowest daily average output over the past year. The lowest monthly total output is the monthly total output that is less than or equal to the average monthly total output over the past year, and the lowest daily average output is the daily average output that is less than or equal to the average daily average output over the past year.

5. The method according to claim 4, characterized in that, The average daily output for each month is determined by the following formula: in, This represents the average daily output for each month. This indicates the total monthly output. This indicates the duration of the pure operation.

6. The method according to claim 3, characterized in that, The process of determining the predicted output for the current month and the predicted output for the following month based on the production data archive system, the production execution data for the current month, and the metal flow model includes: The production execution data for the current month is used as reference data for the capacity level of each process in the current month. Combined with the historical production data archive, historical contract structure data archive and the metal flow model, the predicted output of each product in the current month is determined. The predicted output of each product in the following month is determined based on the production capacity level of each process in the current month, the historical contract structure data archive, and the historical output data archive.

7. The method according to claim 3, characterized in that, The method further includes: Based on the raw material inventory data file, the raw material inventory of each process is determined, and the raw material inventory of each process is used as reference data for predicting output. Based on the reference data, complete the production forecast for the current month and the next month, output the raw material inventory level at the end of the month for each process, and determine whether the raw material inventory level at the end of the month exceeds the reasonable inventory level or is below the reasonable inventory level. If the raw material inventory level at the end of the month exceeds the reasonable inventory level, or falls short of the reasonable inventory level, the production plan will be adjusted.

8. The method according to claim 3, characterized in that, The establishment of a multi-product, multi-variety contract demand resource model includes: Based on the carryover probe data archive, determine the carryover amount and probe amount of the previous month; Based on the aforementioned product output data file, determine the product output volume for the current month and the product output volume for the following month; Based on the predicted output for the current month, the predicted output for the next month, the carryover from the previous month, the probe output from the previous month, the output brought in for the current month, and the output brought in for the next month, the following formula is constructed as the contract demand resource model: in, This indicates the contract demand for the following month. This indicates the projected output for the current month. This indicates the output volume for the current month. This indicates the amount carried over from the previous month. This indicates the probe volume last month. This indicates the contract demand for the current month. This indicates the projected output for the following month. This indicates the output volume for the following month.

9. A device for generating contract resource plans for steel enterprises in a multi-product, multi-process scenario, characterized in that, The device includes: The first construction unit is used to construct a metal flow model for each process in the upstream and downstream of the entire process. The metal flow model includes the internal supply flow, internal supply quantity, external sales resource quantity, and raw material inventory change of each process. The second construction unit is used to construct a multi-dimensional production data archive system, which includes historical output data archives for each process, historical contract structure data archives, historical downtime data archives, raw material inventory data archives, output data archives for each product, and carry-over probe data archives. The determination unit is used to determine the monthly production execution data for each process, which includes process output, downtime, output volume, and production progress of key contracts. The prediction unit is used to determine the predicted output for the current month and the predicted output for the next month based on the production data archive system, the production execution data for the current month, and the metal flow model. The generation unit is used to establish a contract demand resource model for multiple products and varieties, and to generate a contract resource plan for each product based on the contract demand resource model. The contract demand resource model is associated with carry-over volume, probe volume, current month's predicted output, next month's predicted output, and output volume.

10. An electronic device, characterized in that, The electronic device includes one or more processors and one or more memories, wherein at least one piece of program code is stored in the one or more memories, and the at least one piece of program code is loaded and executed by the one or more processors to implement the method as described in any one of claims 1 to 8.

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