Steelmaking cost calculation method, device, equipment and storage medium

Abstract

The present application provides a kind of steelmaking cost calculation method, device, equipment and storage medium, it is related to steelmaking technical field.The method comprises: obtaining iron consumption task quantity and the yield of at least one target steel grade;Match the iron consumption task quantity with the iron consumption task quantity gradient interval in the benchmark database, extract the steel material consumption standard value corresponding to the matched iron consumption task quantity gradient interval;Each target steel grade is matched with the steel grade in the benchmark database, to obtain the alloy material consumption standard value, slag consumption standard value and auxiliary material consumption standard value corresponding to each target steel grade;Steel material consumption standard value, alloy material consumption standard value, slag consumption standard value and auxiliary material consumption standard value are multiplied by the corresponding yield respectively and summed up, to obtain the steelmaking cost corresponding to the iron consumption task quantity and the yield of each target steel grade.The present application supports the yield structure of multiple steel grades, can flexibly handle the cost accounting under different iron consumption task quantity, improves the efficiency of steelmaking cost prediction and plan formulation.

Description

Technical Field

[0001] This invention relates to the field of steelmaking technology, and in particular to a method, apparatus, equipment and storage medium for calculating steelmaking costs. Background Technology

[0002] In the steel smelting industry, cost accounting is a core component of production planning and operational decision-making. Traditional steelmaking cost calculation methods often rely on manual experience or simple arithmetic averages, which are not only inefficient and prone to errors but also ill-suited to the complex production scenarios characterized by diverse steel grades and frequent fluctuations in iron consumption. With the development of information technology, some existing technologies attempt to dynamically calculate steelmaking costs by establishing material balance and heat balance models. For example, some technical solutions calculate the different proportions of materials and scrap output required to smelt the target amount of molten steel based on the material and heat balance of steelmaking. Combined with the unit price of materials, the steelmaking cost is obtained, and then linked with the steel market price and the target amount of molten steel to predict revenue. Finally, the material structure with the lowest production cost and highest total gross profit is selected.

[0003] These methods improve the scientific nature of the calculations through mechanistic models, but they still have obvious shortcomings in practical applications: each cost calculation requires a complete material balance and heat balance iterative calculation for a given iron consumption level, steel type and production structure. When the production plan includes multiple steel types or requires rapid evaluation of costs under different iron consumption tasks, the calculation process becomes very cumbersome and the response speed is difficult to meet the requirements of dynamic scheduling. Summary of the Invention

[0004] This invention provides a method, apparatus, equipment, and storage medium for calculating steelmaking costs, in order to solve the problems of cumbersome calculation process and slow response speed in steelmaking cost accounting.

[0005] In a first aspect, embodiments of the present invention provide a method for calculating steelmaking costs, including: Obtain the iron consumption target and the production output of at least one target steel grade; The iron consumption task quantity is matched with the iron consumption task quantity gradient interval in the benchmark database, and the steel material consumption standard value corresponding to each target steel grade within the matched iron consumption task quantity gradient interval is extracted. The benchmark database includes steel material consumption standard values ​​divided by iron consumption task quantity gradient interval and steel grade, as well as alloy material consumption standard values, slag material consumption standard values ​​and auxiliary material consumption standard values ​​divided by steel grade. Each target steel grade is matched with the steel grades in the benchmark database to obtain the standard values ​​for alloy material consumption, slag material consumption, and auxiliary material consumption for each target steel grade. Multiply the standard values ​​for steel material consumption, alloy material consumption, slag material consumption, and auxiliary material consumption by their respective outputs and sum them to obtain the iron consumption target and the steelmaking cost corresponding to the output of each target steel grade.

[0006] In one possible implementation, before matching the iron loss task quantity with the iron loss task quantity gradient interval in the benchmark database, the following is also included: Obtain historical smelting data; the smelting data includes actual iron consumption, steel grade, actual consumption of steel materials, actual consumption of alloy materials, actual consumption of slag materials, and actual consumption of auxiliary materials; Based on the distribution range of each actual iron loss value, multiple iron loss task volume gradient intervals are divided; For each iron consumption task gradient interval, based on the smelting data of the historical furnace for each steel grade falling within the iron consumption task gradient interval, material balance calculation and heat balance calculation are performed respectively to obtain the standard value of steel material consumption for each steel grade within the iron consumption task gradient interval, and store it in the benchmark database. For each steel grade, based on the smelting data of the corresponding historical furnace batches, the standard values ​​of alloy material consumption, slag material consumption, and auxiliary material consumption are statistically calculated or obtained through material balance calculations and stored in the benchmark database.

[0007] In one possible implementation, for each iron consumption task gradient interval, based on the historical furnace smelting data of each steel grade falling within that iron consumption task gradient interval, material balance calculations and heat balance calculations are performed separately to obtain the standard values ​​of steel material consumption for each steel grade within that iron consumption task gradient interval, including: For each iron consumption task gradient interval, calculate the average value of the actual steel material consumption of each steel grade in the historical heats that falls within the iron consumption task gradient interval, and use the heat balance equation to correct the deviation caused by the fluctuation of molten iron temperature. Use the corrected average value as the standard value of steel material consumption for each steel grade within the iron consumption task gradient interval.

[0008] In one possible implementation, the deviation caused by fluctuations in molten iron temperature is corrected using the heat balance equation, including: Based on the time spent in the tundish, each steel grade is divided into different tundish types; among them, the tundish types include ordinary high-quality steel and special steel; For each steel grade, the absorption rate corresponding to that steel grade is calculated based on the blowing loss and casting loss of the corresponding historical heats. For each type of tundish, the average absorption rate of each steel grade included in that tundish type is calculated to obtain the absorption rate of that tundish type; For each iron consumption task gradient interval, the weighted average of the absorption rate of each ladle type is calculated based on the distribution ratio of different ladle types in the historical heats falling within that iron consumption task gradient interval, thus obtaining the absorption rate of that iron consumption task gradient interval. For each iron consumption task gradient interval, the absorption rate of that iron consumption task gradient interval is used as a correction coefficient and substituted into the heat balance equation to obtain the corrected standard value of steel material consumption.

[0009] In one possible implementation, for each steel grade, based on the smelting data of the corresponding historical heats, the standard values ​​for alloy material consumption, slag material consumption, and auxiliary material consumption are statistically analyzed or calculated through material balance to obtain the standard values ​​for that steel grade, including: Based on carbon content, steel grades are classified into different carbon content types; these include low-carbon steel, medium-low carbon steel, and medium-high carbon steel. For each steel grade, the element addition amount corresponding to the steel grade is determined based on the element residual amount in the historical heats at the end of smelting, and the ferroalloy usage per ton of steel grade is determined in combination with the effective element content of each ferroalloy. Based on the process requirements of each steel grade, determine the slag consumption per ton of steel and the auxiliary material consumption per ton of steel for that steel grade. For each carbon content type, the average consumption of ferroalloy per ton of steel for each steel grade included in that carbon content type is taken as the standard value of alloy material consumption for that carbon content type; the average consumption of slag per ton of steel for each steel grade included in that carbon content type is taken as the standard value of slag material consumption for that carbon content type; and the average consumption of auxiliary materials per ton of steel for each steel grade included in that carbon content type is taken as the standard value of auxiliary materials for that carbon content type.

[0010] In one possible implementation, the benchmark database also includes energy cost benchmarks categorized by steel type; Before matching the iron loss task volume with the iron loss task volume gradient interval in the benchmark database, the following steps are also included: Based on the actual consumption of energy media in historical furnace cycles, the average unit energy consumption is statistically calculated using steel grade as the grouping variable to obtain the benchmark energy cost value for each steel grade, and then stored in the benchmark database.

[0011] In one possible implementation, the benchmark database also includes benchmark values ​​for equipment cost by steel type; Before matching the iron loss task volume with the iron loss task volume gradient interval in the benchmark database, the following steps are also included: Based on the operating time and depreciation allocation of smelting equipment in historical furnace cycles, the average unit equipment cost for each steel grade is calculated using steel grade as the grouping variable. The benchmark cost value of equipment for each steel grade is then obtained and stored in the benchmark database.

[0012] Secondly, embodiments of the present invention provide a steelmaking cost calculation device, comprising: The acquisition module is used to acquire the iron consumption task quantity and the output of at least one target steel grade; The iron consumption matching module is used to match the iron consumption task quantity with the iron consumption task quantity gradient interval in the benchmark database, and extract the steel material consumption standard value corresponding to each target steel grade within the matched iron consumption task quantity gradient interval; wherein, the benchmark database includes steel material consumption standard values ​​divided by iron consumption task quantity gradient interval and steel grade, as well as alloy material consumption standard values, slag material consumption standard values ​​and auxiliary material consumption standard values ​​divided by steel grade. The steel grade matching module is used to match each target steel grade with the steel grades in the benchmark database to obtain the standard values ​​of alloy material consumption, slag material consumption, and auxiliary material consumption for each target steel grade. The cost calculation module is used to multiply the standard values ​​of steel material consumption, alloy material consumption, slag material consumption, and auxiliary material consumption by their corresponding output and sum them to obtain the iron consumption target and the steelmaking cost corresponding to the output of each target steel grade.

[0013] Thirdly, embodiments of the present invention provide an electronic device, including a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to implement the method described in the first aspect or any possible implementation thereof.

[0014] Fourthly, embodiments of the present invention provide a computer-readable storage medium storing a computer program that, when executed by a processor, implements the method described in the first aspect or any possible implementation thereof.

[0015] The steelmaking cost calculation method, apparatus, equipment, and storage medium provided in this invention pre-construct a benchmark database containing gradient ranges of iron consumption targets and standard values ​​for steel material consumption, as well as standard values ​​for steel grades, alloy materials, slag materials, and auxiliary materials consumption. During actual calculations, only the iron consumption target and the output of the target steel grade need to be obtained. The iron consumption target is matched with the gradient range to extract the standard values ​​for steel material consumption. Each target steel grade is matched with the steel grades in the database to extract the corresponding standard values ​​for alloy, slag, and auxiliary materials consumption. Finally, all consumption standard values ​​are multiplied by the output and summed to obtain the consumption of various materials as the total cost. This method moves the complex material balance and heat balance calculations to the database construction stage. Online calculations only involve table lookups, matching, and multiplication operations, resulting in fast calculation speed and timely response. Simultaneously, it supports multiple steel grade output structures, enabling flexible handling of cost accounting under different iron consumption targets. Under complex production plans involving multiple steel grades, it can significantly improve the efficiency of steelmaking cost prediction and planning, providing a convenient decision-making tool for production scheduling. Attached Figure Description

[0016] Figure 1 This is a flowchart illustrating the implementation of the steelmaking cost calculation method provided in this embodiment of the invention. Figure 2 This is a graph showing the changing trends of steel material consumption and iron consumption provided in an embodiment of the present invention; Figure 3 This is a schematic diagram of the steelmaking cost calculation device provided in an embodiment of the present invention; Figure 4 This is a schematic diagram of an electronic device provided in an embodiment of the present invention. Detailed Implementation

[0017] The embodiments of the present invention will now be described in detail with reference to the accompanying drawings.

[0018] See Figure 1 The document illustrates a flowchart of the steelmaking cost calculation method provided in an embodiment of the present invention, which is described in detail below: Step 101: Obtain the iron consumption task quantity and the production of at least one target steel grade.

[0019] In this embodiment, the iron consumption target refers to the number of tons of molten iron consumed to produce one ton of qualified steel within the target cycle, expressed in tons of molten iron per ton of steel. This iron consumption target can be directly input by the user according to the production plan, or it can be automatically calculated by the system based on the ratio of total molten iron to total steel production. The output of the target steel grade can be an absolute output value, or it can be an absolute output calculated by combining the proportion of each steel grade to the total output with the total output. By obtaining this basic data, input parameters are provided for subsequent matching of consumption standards.

[0020] Step 102: Match the iron consumption task quantity with the iron consumption task quantity gradient interval in the benchmark database, and extract the steel material consumption standard value corresponding to each target steel grade within the matched iron consumption task quantity gradient interval; wherein, the benchmark database includes steel material consumption standard values ​​divided by iron consumption task quantity gradient interval and steel grade, as well as alloy material consumption standard values, slag material consumption standard values ​​and auxiliary material consumption standard values ​​divided by steel grade.

[0021] In this embodiment, the benchmark database is pre-built and includes at least the standard values ​​of steel material consumption divided by iron consumption task gradient intervals and steel grades, as well as the standard values ​​of alloy materials, slag materials, and auxiliary materials divided by steel grade. The iron consumption task gradient interval is a continuous interval divided according to a preset step size (e.g., 10 kg per ton of steel) based on the distribution range of historical actual iron consumption values. During matching, the system first determines which gradient interval the input iron consumption task falls into, and then directly reads the pre-stored standard values ​​of steel material consumption for each target steel grade within that interval. The physical meaning of this standard value is: the total amount of molten iron, scrap steel, and iron-containing recycled materials required to produce one ton of qualified steel at that iron consumption level. Since there is an approximately linear relationship between iron consumption and steel material consumption, storing standard values ​​through gradient intervals avoids complex material balance iterations in each calculation, significantly improving calculation speed.

[0022] Step 103: Match each target steel grade with the steel grades in the benchmark database to obtain the standard values ​​for alloy material consumption, slag material consumption, and auxiliary material consumption for each target steel grade.

[0023] In this embodiment, the steel grades in the benchmark database can be specific steel grades or steel types categorized according to process characteristics (such as carbon content type or tundish type). During matching, the system retrieves the pre-calculated unit consumption standard for that steel grade from the database based on the steel grade name or code entered by the user. The alloy material consumption standard value includes the amount of ferroalloys and deoxidizers used per ton of steel; the slag material consumption standard value includes the amount of slag-forming agents such as lime and dolomite used per ton of steel; and the auxiliary material consumption standard value includes the amount of other auxiliary materials used per ton of steel. In this way, for any given steel grade, its material consumption benchmark can be obtained without repeatedly performing thermodynamic calculations.

[0024] Step 104: Multiply the standard values ​​of steel material consumption, alloy material consumption, slag material consumption, and auxiliary material consumption by the corresponding output and sum them to obtain the iron consumption target and the steelmaking cost corresponding to the output of each target steel grade.

[0025] In this embodiment, the standard consumption value of steel material obtained in step 102 and the standard consumption values ​​of alloy material, slag material, and auxiliary material corresponding to each steel grade obtained in step 103 are multiplied by the corresponding output and summed to obtain the total steelmaking cost. The total steelmaking cost can be the required amount of material or the purchase amount corresponding to the amount of material.

[0026] The specific calculation method is as follows: Let the total steel output Q equal the sum of the output qi of each target steel grade; the total consumption of steel materials equals the standard value of steel material consumption multiplied by the total steel output; the total consumption of alloy materials equals the sum of the standard value of alloy material consumption for each steel grade multiplied by its output; the same applies to slag and auxiliary materials. Multiply the total consumption of the above materials by their respective market unit price (which can be obtained in real time from an external system or set by the user), and add all the products together to obtain the total cost. Through this step, a direct conversion from production tasks to costs is achieved, and simultaneous calculation of any number of steel grades is supported.

[0027] This invention, through its pre-constructed benchmark database containing gradient ranges of iron consumption targets and standard values ​​for steel material consumption, as well as standard values ​​for steel grades, alloy materials, slag materials, and auxiliary materials consumption, In actual calculations, only the iron consumption target and the target steel grade's output need to be obtained. The iron consumption target is matched with the gradient range to extract the standard values ​​for steel material consumption. Each target steel grade is matched with the steel grades in the database to extract the corresponding standard values ​​for alloy materials, slag materials, and auxiliary materials consumption. Finally, all consumption standard values ​​are multiplied by the output and summed to obtain the consumption of each type of material as the total cost. This method moves the complex material balance and heat balance calculations to the database construction stage. Online calculations only involve table lookups, matching, and multiplication operations, resulting in fast calculation speed and timely response. Simultaneously, it supports multiple steel grade output structures, enabling flexible handling of cost accounting under different iron consumption targets. In complex production plans involving multiple steel grades, it significantly improves the efficiency of steelmaking cost prediction and planning, providing a convenient decision-making tool for production scheduling.

[0028] In one possible implementation, before matching the iron loss task quantity with the iron loss task quantity gradient interval in the benchmark database, the following is also included: Obtain historical smelting data; the smelting data includes actual iron consumption, steel grade, actual consumption of steel materials, actual consumption of alloy materials, actual consumption of slag materials, and actual consumption of auxiliary materials; Based on the distribution range of each actual iron loss value, multiple iron loss task volume gradient intervals are divided; For each iron consumption task gradient interval, based on the smelting data of the historical furnace for each steel grade falling within the iron consumption task gradient interval, material balance calculation and heat balance calculation are performed respectively to obtain the standard value of steel material consumption for each steel grade within the iron consumption task gradient interval, and store it in the benchmark database. For each steel grade, based on the smelting data of the corresponding historical furnace batches, the standard values ​​of alloy material consumption, slag material consumption, and auxiliary material consumption are statistically calculated or obtained through material balance calculations and stored in the benchmark database.

[0029] In this embodiment, a benchmark database needs to be pre-built before matching the iron loss task quantity with the iron loss task quantity gradient interval. This construction process includes: First, obtain historical smelting data for each heat. This data specifically includes the actual iron consumption for each heat, the type of steel produced, the actual consumption of steel materials (molten iron, scrap steel, pig iron blocks), and the actual consumption of various alloy materials, slag materials, and auxiliary materials. This data is typically derived from the steel plant's manufacturing execution system or process control system.

[0030] Then, based on the distribution range of the actual iron consumption value, multiple iron consumption task gradient intervals are divided according to a fixed step size (such as 5 kg or 10 kg per ton of steel), covering all iron consumption values ​​from the lowest to the highest. For each gradient interval, all historical furnaces whose actual iron consumption values ​​fall within this interval are collected, and material balance calculations and heat balance calculations are performed on the smelting data of these furnaces respectively.

[0031] Material balance calculations are based on the conservation of iron, while heat balance calculations are based on the equality of heat input and output. By combining these two balances, the theoretically reasonable consumption of steel materials within the given iron consumption range can be derived. This consumption amount is then stored in the database as the standard value for steel material consumption within that range. Simultaneously, for each specific steel grade, historical furnace data for all heats of that steel grade are collected, and the average consumption per ton of alloy materials, slag materials, and auxiliary materials is statistically analyzed. Alternatively, the theoretical optimal value is obtained through material balance calculations and stored in the database as the standard consumption value for that steel grade.

[0032] In this way, the benchmark database integrates historical statistical patterns and mechanistic models, making it both reliable and physically interpretable.

[0033] In one possible implementation, for each iron consumption task gradient interval, based on the historical furnace smelting data of each steel grade falling within that iron consumption task gradient interval, material balance calculations and heat balance calculations are performed separately to obtain the standard values ​​of steel material consumption for each steel grade within that iron consumption task gradient interval, including: For each iron consumption task gradient interval, calculate the average value of the actual steel material consumption of each steel grade in the historical heats that falls within the iron consumption task gradient interval, and use the heat balance equation to correct the deviation caused by the fluctuation of molten iron temperature. Use the corrected average value as the standard value of steel material consumption for each steel grade within the iron consumption task gradient interval.

[0034] In this embodiment, when determining the standard value of steel consumption for each gradient range of iron consumption task, the method of mean value plus temperature correction is adopted.

[0035] Specifically, for all historical heats of the same steel grade falling within the same iron consumption gradient range, the average actual consumption of steel feedstock for that steel grade is first calculated. Then, the deviation caused by fluctuations in molten iron temperature is corrected using the heat balance equation. According to historical data, a 10-degree Celsius change in molten iron temperature affects steel feedstock consumption by approximately 2 to 3 kilograms per ton of steel. Therefore, a heat balance correction term needs to be calculated based on the difference between the actual molten iron temperature and the standard temperature for each heat. This correction term is then added to or subtracted from the average value to obtain the standard value after eliminating temperature interference. Finally, the corrected average value is used as the standard value for steel feedstock consumption for that steel grade within that range. This method is simple and efficient, avoiding complex calculations for each heat.

[0036] In one possible implementation, the deviation caused by fluctuations in molten iron temperature is corrected using the heat balance equation, including: Based on the time spent in the tundish, each steel grade is divided into different tundish types; among them, the tundish types include ordinary high-quality steel and special steel; For each steel grade, the absorption rate corresponding to that steel grade is calculated based on the blowing loss and casting loss of the corresponding historical heats. For each type of tundish, the average absorption rate of each steel grade included in that tundish type is calculated to obtain the absorption rate of that tundish type; For each iron consumption task gradient interval, the weighted average of the absorption rate of each ladle type is calculated based on the distribution ratio of different ladle types in the historical heats falling within that iron consumption task gradient interval, thus obtaining the absorption rate of that iron consumption task gradient interval. For each iron consumption task gradient interval, the absorption rate of that iron consumption task gradient interval is used as a correction coefficient and substituted into the heat balance equation to obtain the corrected standard value of steel material consumption.

[0037] In this embodiment, the tundish type is classified according to the continuous casting time in the tundish: steel grades with a continuous casting time of 24 hours or more are classified as ordinary high-quality steel, and those with a continuous casting time of less than 24 hours are classified as special steel. Different tundish types correspond to different casting losses; special steel typically has greater tundish casting residue and billet head and tail trimming losses.

[0038] First, for each steel grade, the iron content absorption rate is calculated based on historical heat loss data and casting loss data. This rate is the ratio of qualified steel production to the total amount of iron content entering the furnace. Then, for each ladle type, the absorption rates of all steel grades within that type are statistically summarized (e.g., by taking the median or robust mean) to obtain the absorption rate for that ladle type.

[0039] For each iron consumption task gradient interval, the proportion of the number of heats of ordinary high-quality steel and special steel in the historical heats falling into the interval is statistically analyzed. The weighted average of the absorption rates of the two types of ladles is calculated using this proportion as the weight, and this average is taken as the absorption rate of the interval.

[0040] Finally, the absorption rate is used as a correction factor in the heat balance equation to readjust the standard value of steel material consumption. For example, if the proportion of special steel is high and the absorption rate is low in a certain range, the heat balance calculation will show that the amount of iron fed into the furnace needs to be increased to achieve the same steel output, thus appropriately increasing the standard value of steel material consumption for that range.

[0041] In this way, the impact of the type of intermediate packaging on the yield is quantified and incorporated into the standard value, making the standard value more reflective of the material loss characteristics in actual production.

[0042] In one possible implementation, for each steel grade, based on the smelting data of the corresponding historical heats, the standard values ​​for alloy material consumption, slag material consumption, and auxiliary material consumption are statistically analyzed or calculated through material balance to obtain the standard values ​​for that steel grade, including: Based on carbon content, steel grades are classified into different carbon content types; these include low-carbon steel, medium-low carbon steel, and medium-high carbon steel. For each steel grade, the element addition amount corresponding to the steel grade is determined based on the element residual amount in the historical heats at the end of smelting, and the ferroalloy usage per ton of steel grade is determined in combination with the effective element content of each ferroalloy. Based on the process requirements of each steel grade, determine the slag consumption per ton of steel and the auxiliary material consumption per ton of steel for that steel grade. For each carbon content type, the average consumption of ferroalloy per ton of steel for each steel grade included in that carbon content type is taken as the standard value of alloy material consumption for that carbon content type; the average consumption of slag per ton of steel for each steel grade included in that carbon content type is taken as the standard value of slag material consumption for that carbon content type; and the average consumption of auxiliary materials per ton of steel for each steel grade included in that carbon content type is taken as the standard value of auxiliary materials for that carbon content type.

[0043] In this embodiment, when determining the standard values ​​for the consumption of alloys, slags, and auxiliary materials for each steel grade, the materials are categorized by carbon content type and their average values ​​are calculated.

[0044] First, steel grades are categorized into three types based on their finished carbon content range: low-carbon steel (carbon content less than 0.10%), medium-low carbon steel (carbon content between 0.10% and 0.40%), and medium-high carbon steel (carbon content between 0.41% and 0.80%). For each specific steel grade, the net increment of each alloying element required to achieve the target composition is calculated based on the residual element content (such as residual silicon, manganese, phosphorus, and sulfur) at the end of the historical heats in the converter smelting process. Then, combining the effective element content of each ferroalloy (e.g., the effective silicon content in ferrosilicon is 75%) and the market price (calculated per 1% effective content), the ferroalloy combination with the best cost-effectiveness is selected, and the amount of ferroalloy used per ton of steel is calculated. Simultaneously, the amount of deoxidizer (such as aluminum or calcium silicate) per ton of steel is determined based on the deoxidation and calcification processes of the steel grade. The theoretical usage of slag and auxiliary materials is obtained through heat balance calculations based on process requirements (such as slag basicity and MgO content).

[0045] After completing the calculations for all specific steel grades, for all steel grades within the same carbon content type, the ferroalloy consumption per ton of steel is simply averaged arithmetically to obtain the standard value for alloy material consumption for that carbon content type; similarly, the slag consumption is averaged to obtain the standard value for slag consumption for that type; and the auxiliary material consumption is averaged to obtain the standard value for auxiliary material consumption for that type. In practical applications, when calculating the consumption of a specific steel grade, as long as its carbon content type is known, the standard value for that type can be directly retrieved. This classification method significantly reduces the database storage requirements, and since the smelting behavior of steel grades within the same carbon content type is similar, the error introduced by averaging is within an acceptable range.

[0046] In one possible implementation, the benchmark database also includes energy cost benchmarks categorized by steel type; Before matching the iron loss task volume with the iron loss task volume gradient interval in the benchmark database, the following steps are also included: Based on the actual consumption of energy media in historical furnace cycles, the average unit energy consumption is statistically calculated using steel grade as the grouping variable to obtain the benchmark energy cost value for each steel grade, and then stored in the benchmark database.

[0047] In this embodiment, the benchmark database also includes energy cost benchmark values ​​categorized by steel type. When constructing the database, based on the actual consumption of each energy medium (such as electricity, oxygen, natural gas, and compressed air) in historical furnace runs, and using steel type as the grouping variable, the average unit energy consumption for each steel type under normal production conditions is calculated. For example, how many kilowatt-hours of electricity and how many cubic meters of oxygen are consumed per ton of steel. These average values ​​are stored in the database as the energy cost benchmark value for that steel type. When calculating the total cost in step 104, the output of each target steel type is multiplied by its energy cost benchmark value, then multiplied by the corresponding energy unit price, and added to the total cost. This provides a more comprehensive reflection of smelting costs.

[0048] In one possible implementation, the benchmark database also includes benchmark values ​​for equipment cost by steel type; Before matching the iron loss task volume with the iron loss task volume gradient interval in the benchmark database, the following steps are also included: Based on the operating time and depreciation allocation of smelting equipment in historical furnace cycles, the average unit equipment cost for each steel grade is calculated using steel grade as the grouping variable. The benchmark cost value of equipment for each steel grade is then obtained and stored in the benchmark database.

[0049] In this embodiment, the benchmark database also includes equipment cost benchmarks categorized by steel grade. Equipment costs include the operation and maintenance costs of smelting equipment, depreciation amortization, etc. During construction, based on the actual operating time of each furnace in historical heat runs (such as converter blowing time and refining furnace processing time), combined with the hourly maintenance costs and depreciation rates of the equipment, a statistical average is performed for each steel grade to obtain the equipment cost amortization value per ton of steel. For example, a certain steel grade requires a longer refining time, so its equipment cost benchmark value is relatively higher. These benchmark values ​​are stored in the database, multiplied by the output of each steel grade during cost calculation, and summed to make the cost structure more complete and facilitate refined cost analysis.

[0050] In one specific implementation, this method constructs a benchmark database and performs cost calculations according to the following steps.

[0051] First, all steel grades involved in production are classified in multiple dimensions. The first dimension is based on the continuous casting time in the tundish: steel grades with a continuous casting time of 24 hours or more are classified as general-purpose steel, while those with a continuous casting time of less than 24 hours are classified as special steel. This classification distinguishes between the difference in casting residue and billet head / tail cutting losses, as special steel has a shorter tundish life, resulting in greater casting residue losses than general-purpose steel. The second dimension is based on the carbon content range of the finished product: steel grades with a carbon content less than 0.10% are classified as low-carbon steel, those with a carbon content between 0.10% and 0.40% are classified as medium-low carbon steel, and those with a carbon content between 0.41% and 0.80% are classified as medium-high carbon steel. This classification distinguishes between different endpoints in converter smelting; low-carbon steel typically has a lower endpoint carbon content and a higher total iron content in the slag, resulting in greater blowing losses; high-carbon steel has a higher endpoint carbon content and a lower total iron content in the slag, resulting in less blowing losses. By combining these two dimensions, each type of steel exhibits similar smelting loss characteristics.

[0052] Next, the specific material consumption baseline for each steel grade is determined. For alloy materials, the main alloying elements required for each steel grade are listed, including carbon, silicon, manganese, chromium, boron, titanium, aluminum, molybdenum, vanadium, etc. Based on the residual amounts of each element at the converter smelting endpoint, the net increment of each element required to achieve the target finished product composition is calculated. Combining the effective element content of each ferroalloy (e.g., the effective silicon content in ferrosilicon is 75%), and comparing the market price per 1% of effective content of different ferroalloys, the ferroalloy type and its addition amount with the best cost-performance ratio are selected. At the same time, based on the deoxidation and calcification processes of the steel grade, the amount of deoxidizer such as aluminum or calcium silicate per ton of steel is determined. The ferroalloy and deoxidizer per ton of steel consumption are added together to obtain the standard value of alloy material consumption per ton of steel for that steel grade. For slag and auxiliary materials, under the premise of meeting the requirements for steel purity, harmful element control, smooth production, and controlled billet quality, the theoretical addition amount of lime, dolomite, fluorite, etc., is obtained through heat balance calculation, and a lower limit control standard is implemented. This standard allows process personnel to manually fine-tune it according to the actual situation. The above calculation results are used as the standard values ​​for slag and auxiliary material consumption per ton of steel for this steel grade.

[0053] Next, the correspondence between iron consumption targets and standard steel material consumption values ​​is established. Historical smelting data from various furnaces are statistically analyzed, and steel grades are categorized and summarized according to different carbon contents. Based on the principle of actual production and consumption of self-circulating materials, detailed calculations of heat balance and material balance are performed to identify the correlation between changes in iron consumption and corresponding changes in steel material consumption. Linear analysis is then conducted using line graphs in Excel to facilitate the setting of subsequent conditional relationship formulas. The resulting graph is shown below. Figure 2 The graph shows the trend of steel material consumption and iron consumption. The left vertical axis represents steel material consumption per ton of steel, the right vertical axis represents iron consumption per ton of steel, and the horizontal axis represents the serial number. Based on the possible range of iron consumption targets, gradient intervals are divided in 10 kg / ton steel increments, covering the complete range from 800 kg / ton of steel to 1040 kg / ton of steel. For each iron consumption target gradient interval, material balance and heat balance calculations are performed, taking into account the differences in blowing and casting losses for different carbon content steel grades. When performing heat balance calculations under different iron consumption targets, a certain heat surplus is required in the smelting process, controlled between 100,000 kJ and 120,000 kJ. The calculations consider the iron-containing material absorption rate corresponding to different carbon content steel grades; the absorption rate reflects the total yield from the iron-containing material entering the furnace to the qualified steel product. By combining material balance and heat balance calculations, the gradient range of iron consumption, the per-ton steel consumption of molten iron, scrap steel, and iron-containing recycled materials for this carbon content steel grade are obtained. The sum of these three is the steel material consumption. The calculation results are stored separately according to carbon content type.

[0054] After completing the above calculations, for each carbon content type, a linear analysis was performed on the steel consumption values ​​calculated for different iron consumption task ranges and the corresponding iron consumption values. The analysis revealed that for every 10 kg / ton increase or decrease in iron consumption, steel consumption decreased or increased by a relatively fixed margin, but this margin may have inflection points across the entire range of iron consumption changes. Therefore, linear analysis was used to identify these inflection points, dividing the iron consumption range into several linear segments. Within each linear segment, a linear function was used to describe the relationship between iron consumption and steel consumption. In the computer system, conditional judgment formulas, such as the IF or IFS functions in Excel, were used to automatically determine the corresponding linear segment based on the input iron consumption task and calculate the corresponding steel consumption value. In this way, for any given iron consumption task, the standard value of steel consumption can be quickly obtained without re-performing heat balance and material balance iterations.

[0055] In the construction of the aforementioned benchmark database, constraints for optimizing proportions and reducing losses were specifically incorporated. When determining the standard value for steel material consumption, priority was given to including iron-containing return materials generated within the smelting plant that do not require outsourcing for processing, such as self-produced scrap steel, slag steel, and cold-pressed briquettes. The actual usable inventory of these return materials was used as the upper limit to minimize the use of purchased molten iron and purchased scrap steel. Regarding alloy material selection, while meeting the requirements for molten steel purity and finished product composition, the correlation between the alloy carbon increase and the final carbon content of the converter and the finished product was comprehensively considered to ensure that the carbon content of the finished product meets process requirements. Simultaneously, the market price of different ferroalloys per 100% of their effective content was compared, and the lowest price was selected. For slag and auxiliary materials, while ensuring molten steel purity, control of harmful elements, smooth production, and billet quality, the lower-to-middle limit control standards of the process requirements were implemented to avoid excessive slag formation leading to iron loss and heat waste.

[0056] In actual cost calculation using this method, the user inputs the iron consumption task for the target period and the output of each target steel grade. The system first calculates the standard value of steel material consumption based on the iron consumption task using the piecewise linear model described above. Then, based on the carbon content category and ladle category of each target steel grade, it extracts pre-calculated standard values ​​of alloy materials, slag materials, and auxiliary materials from the benchmark database. The total steel material consumption equals the standard value of steel material consumption multiplied by the total steel output. The total alloy material consumption equals the sum of the standard values ​​of alloy material consumption for each steel grade multiplied by its output; the same applies to slag materials and auxiliary materials. Multiplying the total consumption of each type of material by its corresponding unit price and summing the results yields the total steelmaking cost. If the user needs to evaluate cost differences under different iron consumption task levels, the system can batch calculate and plot the relationship curve between iron consumption and total cost, automatically marking the optimal iron consumption value with the lowest cost. If the user needs to optimize the steel grade structure, the system can use the unit cost of each steel grade as a coefficient, with the total output and the upper and lower limits of each steel grade's output as constraints, to construct a linear programming model and solve for the lowest-cost output allocation scheme.

[0057] Through the above methods, this approach enables rapid calculation of steelmaking costs in scenarios involving multiple steel grades and varying iron consumption. It also incorporates a material ratio optimization strategy, which can reduce the cost of steel materials and alloys while ensuring smelting process requirements are met. It should be understood that the sequence numbers of the steps in the above embodiments do not imply an order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of this invention.

[0058] The following are device embodiments of the present invention. For details not described in detail, please refer to the corresponding method embodiments described above.

[0059] Figure 3 A schematic diagram of the steelmaking cost calculation device provided in an embodiment of the present invention is shown. For ease of explanation, only the parts related to the embodiment of the present invention are shown, and are described in detail below: like Figure 3 As shown, the steelmaking cost calculation device 3 includes: The acquisition module 31 is used to acquire the iron consumption task quantity and the output of at least one target steel grade; The iron consumption matching module 32 is used to match the iron consumption task quantity with the iron consumption task quantity gradient interval in the benchmark database, and extract the steel material consumption standard value corresponding to each target steel grade within the matched iron consumption task quantity gradient interval; wherein, the benchmark database includes steel material consumption standard values ​​divided by iron consumption task quantity gradient interval and steel grade, as well as alloy material consumption standard values, slag material consumption standard values ​​and auxiliary material consumption standard values ​​divided by steel grade. The steel grade matching module 33 is used to match each target steel grade with the steel grades in the benchmark database to obtain the standard values ​​of alloy material consumption, slag material consumption, and auxiliary material consumption for each target steel grade. The cost calculation module 34 is used to multiply the standard values ​​of steel material consumption, alloy material consumption, slag material consumption, and auxiliary material consumption by the corresponding output and sum them to obtain the iron consumption task and the steelmaking cost corresponding to the output of each target steel grade.

[0060] In one possible implementation, the steelmaking cost calculation device 3 further includes a calculation module for: Before matching the iron consumption task quantity with the iron consumption task quantity gradient range in the benchmark database, the smelting data of historical furnaces is obtained; the smelting data includes the actual iron consumption value, steel grade, actual consumption of steel material, actual consumption of alloy material, actual consumption of slag material, and actual consumption of auxiliary materials. Based on the distribution range of each actual iron loss value, multiple iron loss task volume gradient intervals are divided; For each iron consumption task gradient interval, based on the smelting data of the historical furnace for each steel grade falling within the iron consumption task gradient interval, material balance calculation and heat balance calculation are performed respectively to obtain the standard value of steel material consumption for each steel grade within the iron consumption task gradient interval, and store it in the benchmark database. For each steel grade, based on the smelting data of the corresponding historical furnace batches, the standard values ​​of alloy material consumption, slag material consumption, and auxiliary material consumption are statistically calculated or obtained through material balance calculations and stored in the benchmark database.

[0061] In one possible implementation, the computation module is specifically used for: For each iron consumption task gradient interval, calculate the average value of the actual steel material consumption of each steel grade in the historical heats that falls within the iron consumption task gradient interval, and use the heat balance equation to correct the deviation caused by the fluctuation of molten iron temperature. Use the corrected average value as the standard value of steel material consumption for each steel grade within the iron consumption task gradient interval.

[0062] In one possible implementation, the computation module is specifically used for: Based on the time spent in the tundish, each steel grade is divided into different tundish types; among them, the tundish types include ordinary high-quality steel and special steel; For each steel grade, the absorption rate corresponding to that steel grade is calculated based on the blowing loss and casting loss of the corresponding historical heats. For each type of tundish, the average absorption rate of each steel grade included in that tundish type is calculated to obtain the absorption rate of that tundish type; For each iron consumption task gradient interval, the weighted average of the absorption rate of each ladle type is calculated based on the distribution ratio of different ladle types in the historical heats falling within that iron consumption task gradient interval, thus obtaining the absorption rate of that iron consumption task gradient interval. For each iron consumption task gradient interval, the absorption rate of that iron consumption task gradient interval is used as a correction coefficient and substituted into the heat balance equation to obtain the corrected standard value of steel material consumption.

[0063] In one possible implementation, the computation module is specifically used for: Based on carbon content, steel grades are classified into different carbon content types; these include low-carbon steel, medium-low carbon steel, and medium-high carbon steel. For each steel grade, the element addition amount corresponding to the steel grade is determined based on the element residual amount in the historical heats at the end of smelting, and the ferroalloy usage per ton of steel grade is determined in combination with the effective element content of each ferroalloy. Based on the process requirements of each steel grade, determine the slag consumption per ton of steel and the auxiliary material consumption per ton of steel for that steel grade. For each carbon content type, the average consumption of ferroalloy per ton of steel for each steel grade included in that carbon content type is taken as the standard value of alloy material consumption for that carbon content type; the average consumption of slag per ton of steel for each steel grade included in that carbon content type is taken as the standard value of slag material consumption for that carbon content type; and the average consumption of auxiliary materials per ton of steel for each steel grade included in that carbon content type is taken as the standard value of auxiliary materials for that carbon content type.

[0064] In one possible implementation, the benchmark database also includes energy cost benchmarks categorized by steel type; The calculation module is also used for: Before matching the iron consumption task quantity with the iron consumption task quantity gradient range in the benchmark database, based on the actual consumption of energy media in historical furnace cycles, the average unit energy consumption is calculated using steel grade as the grouping variable to obtain the benchmark energy cost value corresponding to each steel grade, and then stored in the benchmark database.

[0065] In one possible implementation, the benchmark database also includes benchmark values ​​for equipment cost by steel type; The calculation module is also used for: Before matching the iron consumption task quantity with the iron consumption task quantity gradient range in the benchmark database, based on the operating time and maintenance cost depreciation allocation of smelting equipment in historical furnace cycles, the average unit equipment cost of each steel type is calculated using steel type as the grouping variable. The benchmark value of equipment cost corresponding to each steel type is obtained and stored in the benchmark database.

[0066] This invention, through its pre-constructed benchmark database containing gradient ranges of iron consumption targets and standard values ​​for steel material consumption, as well as standard values ​​for steel grades, alloy materials, slag materials, and auxiliary materials consumption, In actual calculations, only the iron consumption target and the target steel grade's output need to be obtained. The iron consumption target is matched with the gradient range to extract the standard values ​​for steel material consumption. Each target steel grade is matched with the steel grades in the database to extract the corresponding standard values ​​for alloy materials, slag materials, and auxiliary materials consumption. Finally, all consumption standard values ​​are multiplied by the output and summed to obtain the consumption of each type of material as the total cost. This method moves the complex material balance and heat balance calculations to the database construction stage. Online calculations only involve table lookups, matching, and multiplication operations, resulting in fast calculation speed and timely response. Simultaneously, it supports multiple steel grade output structures, enabling flexible handling of cost accounting under different iron consumption targets. In complex production plans involving multiple steel grades, it significantly improves the efficiency of steelmaking cost prediction and planning, providing a convenient decision-making tool for production scheduling.

[0067] Figure 4 This is a schematic diagram of an electronic device provided in an embodiment of the present invention. For example... Figure 4As shown, the electronic device 4 in this embodiment includes a processor 40 and a memory 41. The memory 41 stores a computer program 42. When the processor 40 executes the computer program 42, it implements the steps in the various method embodiments described above. Alternatively, when the processor 40 executes the computer program 42, it implements the functions of each module / unit in the various device embodiments described above.

[0068] For example, computer program 42 may be divided into one or more modules / units, which are stored in memory 41 and executed by processor 40 to complete the present invention. The one or more modules / units may be a series of computer program instruction segments capable of performing a specific function, which describe the execution process of computer program 42 in electronic device 4.

[0069] Electronic device 4 may include, but is not limited to, processor 40 and memory 41. Those skilled in the art will understand that... Figure 4 This is merely an example of electronic device 4 and does not constitute a limitation on electronic device 4. It may include more or fewer components than shown, or combine certain components, or different components. For example, electronic device 4 may also include input / output devices, network access devices, buses, etc.

[0070] The processor 40 can be a central processing unit (CPU), or other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor can be a microprocessor or any conventional processor.

[0071] The memory 41 can be an internal storage unit of the electronic device 4, such as a hard disk or RAM. The memory 41 can also be an external storage device of the electronic device 4, such as a plug-in hard disk, Smart Media Card (SMC), Secure Digital (SD) card, or Flash Card. Furthermore, the memory 41 can include both internal and external storage units of the electronic device 4. The memory 41 is used to store the computer program 42 and other programs and data required by the electronic device 4. The memory 41 can also be used to temporarily store data that has been output or will be output.

[0072] For the sake of simplicity and clarity, only the above-described functional modules / units are used as examples. In practical applications, the functions described above can be assigned to different functional modules / units as needed. These modules / units can be implemented in hardware, software, or a combination of both.

[0073] This invention also provides a computer-readable storage medium storing a computer program. When the computer program is executed by a processor, it implements the methods described in the above-described method embodiments.

[0074] This invention also provides a computer program product, including a computer program. When the computer program is executed by a processor, it implements the methods described in the above-described method embodiments.

[0075] Computer programs include computer program code, which can be in the form of source code, object code, executable files, or certain intermediate forms. Computer-readable media can include: any entity or device capable of carrying computer program code, recording media, USB flash drives, portable hard drives, magnetic disks, optical disks, computer memory, read-only memory (ROM), random access memory (RAM), electrical carrier signals, telecommunication signals, and software distribution media, etc.

[0076] In the above embodiments, the descriptions of each embodiment have their own emphasis. Parts not detailed or described in a particular embodiment can be referred to in the relevant descriptions of other embodiments. Unless otherwise specified or in conflict with logic, the terminology and / or descriptions between different embodiments are consistent and can be referenced interchangeably. Technical features in different embodiments can be combined to form new embodiments based on their inherent logical relationships.

[0077] The above-described embodiments are only used to illustrate the technical solutions of the present invention, and are not intended to limit it. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention, and should all be included within the protection scope of the present invention.

Claims

1. A method for calculating steelmaking costs, characterized in that, include: Obtain the iron consumption target and the production output of at least one target steel grade; The iron consumption task quantity is matched with the iron consumption task quantity gradient interval in the benchmark database, and the steel material consumption standard value corresponding to each target steel grade within the matched iron consumption task quantity gradient interval is extracted; wherein, the benchmark database includes steel material consumption standard values ​​divided by iron consumption task quantity gradient interval and steel grade, as well as alloy material consumption standard values, slag material consumption standard values ​​and auxiliary material consumption standard values ​​divided by steel grade. Each target steel grade is matched with the steel grades in the benchmark database to obtain the standard values ​​for alloy material consumption, slag material consumption, and auxiliary material consumption for each target steel grade. The steel consumption standard value, alloy material consumption standard value, slag material consumption standard value, and auxiliary material consumption standard value are multiplied by their respective output values ​​and summed to obtain the iron consumption target and the steelmaking cost corresponding to the output of each target steel grade.

2. The steelmaking cost calculation method according to claim 1, characterized in that, Before matching the iron loss task quantity with the iron loss task quantity gradient interval in the benchmark database, the method further includes: Obtain smelting data from historical furnace cycles; wherein, the smelting data includes actual iron consumption, steel grade, actual consumption of steel materials, actual consumption of alloy materials, actual consumption of slag materials, and actual consumption of auxiliary materials; Based on the distribution range of each actual iron loss value, multiple iron loss task volume gradient intervals are divided; For each iron consumption task gradient interval, based on the smelting data of the historical furnace for each steel grade falling within the iron consumption task gradient interval, material balance calculation and heat balance calculation are performed respectively to obtain the standard value of steel material consumption for each steel grade within the iron consumption task gradient interval, and store it in the benchmark database. For each steel grade, based on the smelting data of the corresponding historical furnace batches, the standard values ​​of alloy material consumption, slag material consumption, and auxiliary material consumption for that steel grade are statistically analyzed or calculated through material balance, and stored in the benchmark database.

3. The steelmaking cost calculation method according to claim 2, characterized in that, For each iron consumption target gradient interval, based on the historical furnace smelting data of each steel grade falling within that interval, material balance calculations and heat balance calculations are performed to obtain the standard values ​​of steel material consumption for each steel grade within that iron consumption target gradient interval, including: For each iron consumption task gradient interval, calculate the average value of the actual steel material consumption of each steel grade in the historical heats that falls within the iron consumption task gradient interval, and use the heat balance equation to correct the deviation caused by the fluctuation of molten iron temperature. Use the corrected average value as the standard value of steel material consumption for each steel grade within the iron consumption task gradient interval.

4. The steelmaking cost calculation method according to claim 3, characterized in that, The method of correcting deviations caused by fluctuations in molten iron temperature using the heat balance equation includes: Based on the time spent in the tundish, each steel grade is divided into different tundish types; among which, the tundish types include ordinary high-quality steel and special steel; For each steel grade, the absorption rate corresponding to that steel grade is calculated based on the blowing loss and casting loss of the corresponding historical heats. For each type of tundish, the average absorption rate of each steel grade included in that tundish type is calculated to obtain the absorption rate of that tundish type; For each iron consumption task gradient interval, the weighted average of the absorption rate of each ladle type is calculated based on the distribution ratio of different ladle types in the historical heats falling within that iron consumption task gradient interval, thus obtaining the absorption rate of that iron consumption task gradient interval. For each iron consumption task gradient interval, the absorption rate of that iron consumption task gradient interval is used as a correction coefficient and substituted into the heat balance equation to obtain the corrected standard value of steel material consumption.

5. The steelmaking cost calculation method according to claim 2, characterized in that, For each steel grade, based on the smelting data of the corresponding historical heats, the standard values ​​for alloy material consumption, slag material consumption, and auxiliary material consumption are statistically calculated or obtained through material balance calculations, including: Based on carbon content, steel grades are classified into different carbon content types; wherein, the carbon content types include low carbon steel, medium-low carbon steel, and medium-high carbon steel. For each steel grade, the element addition amount corresponding to the steel grade is determined based on the element residual amount in the historical heats at the end of smelting, and the ferroalloy usage per ton of steel grade is determined in combination with the effective element content of each ferroalloy. Based on the process requirements of each steel grade, determine the slag consumption per ton of steel and the auxiliary material consumption per ton of steel for that steel grade; For each carbon content type, the average consumption of ferroalloy per ton of steel for each steel grade included in that carbon content type is taken as the standard value of alloy material consumption for that carbon content type; the average consumption of slag per ton of steel for each steel grade included in that carbon content type is taken as the standard value of slag material consumption for that carbon content type; and the average consumption of auxiliary materials per ton of steel for each steel grade included in that carbon content type is taken as the standard value of auxiliary materials for that carbon content type.

6. The steelmaking cost calculation method according to claim 1, characterized in that, The benchmark database also includes energy cost benchmarks categorized by steel type; Before matching the iron loss task quantity with the iron loss task quantity gradient interval in the benchmark database, the method further includes: Based on the actual consumption of energy media in historical furnace cycles, the average unit energy consumption is statistically calculated using steel grade as the grouping variable to obtain the energy cost benchmark value corresponding to each steel grade, and stored in the benchmark database.

7. The steelmaking cost calculation method according to claim 1, characterized in that, The benchmark database also includes benchmark values ​​for equipment costs categorized by steel type; Before matching the iron loss task quantity with the iron loss task quantity gradient interval in the benchmark database, the method further includes: Based on the operating time and depreciation allocation of smelting equipment in historical furnace cycles, the average unit equipment cost for each steel grade is calculated using steel grade as the grouping variable. The benchmark equipment cost value for each steel grade is then obtained and stored in the benchmark database.

8. A steelmaking cost calculation device, characterized in that, include: The acquisition module is used to acquire the iron consumption task quantity and the output of at least one target steel grade; The iron consumption matching module is used to match the iron consumption task quantity with the iron consumption task quantity gradient interval in the benchmark database, and extract the steel material consumption standard value corresponding to each target steel grade within the matched iron consumption task quantity gradient interval; wherein, the benchmark database includes steel material consumption standard values ​​divided by iron consumption task quantity gradient interval and steel grade, as well as alloy material consumption standard values, slag material consumption standard values ​​and auxiliary material consumption standard values ​​divided by steel grade. The steel grade matching module is used to match each target steel grade with the steel grades in the benchmark database to obtain the standard values ​​of alloy material consumption, slag material consumption, and auxiliary material consumption for each target steel grade. The cost calculation module is used to multiply the standard values ​​of steel material consumption, alloy material consumption, slag material consumption, and auxiliary material consumption by their corresponding outputs and sum them up to obtain the steelmaking cost corresponding to the iron consumption target and the output of each target steel grade.

9. An electronic device, characterized in that, It includes a memory and a processor, the memory storing a computer program, and the processor executing the computer program to implement the method as described in any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a computer program that, when executed by a processor, implements the method as described in any one of claims 1 to 7.

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