Steel production energy consumption data processing method and device, medium and electronic equipment

Abstract

The invention provides a steel production energy consumption data processing method and device, a medium and electronic equipment. The method comprises the steps that production information and energy metering information of all working procedures in the steel production process are obtained, the production information comprises product identifiers and corresponding production time periods, and the energy metering information comprises energy medium types and corresponding consumption; performing time-space synchronization processing on the production information and the energy metering information, and establishing an association mapping relation between the production information and the energy metering information; calculating single-process energy consumption of each piece of secondary product in each process based on the association mapping relation and the material flow characteristics of each process; and according to the single-process energy consumption and the process circulation logic of steel production, constructing an energy consumption circulation model, and calculating the accumulated energy consumption of each product through the energy consumption circulation model. According to the invention, the accuracy of steel production energy consumption data processing can be improved.

Description

Methods, devices, media and electronic equipment for processing energy consumption data in steel production Technical Field

[0001] This application relates to the field of steel production data processing technology, and more specifically, to a method, apparatus, medium, and electronic equipment for processing steel production energy consumption data. Background Technology

[0002] As a pillar industry of the national economy, the steel industry is also a resource-intensive and energy-intensive sector, with energy consumption accounting for a significant proportion of its production costs. The steel mill production process involves a wide variety of energy media, including coal gas, natural gas, electricity, steam, oxygen, nitrogen, compressed air, and water. Furthermore, the production process encompasses multiple key steps such as steelmaking, refining, continuous casting, hot rolling, and cold rolling, resulting in complex energy consumption distribution and a massive total energy consumption.

[0003] Under the current policy background of normalized implementation of "dual carbon" targets, steel companies that fail to achieve refined energy consumption management will not only face increasing carbon tax compliance risks, but will also hinder their green transformation process. From the perspective of cost control, with frequent fluctuations in energy prices in recent years, accurately identifying key high-energy-consuming links such as hot-rolling furnaces and implementing targeted energy-saving measures has become the core path for steel companies to reduce production costs and increase profit margins. At the market competition level, the demand for green and low-carbon steel in high-end manufacturing sectors such as automobiles is increasing year by year. Accurate energy consumption data at the product level, as the core basis for green product certification, directly determines the competitiveness of enterprises in the high-end market. Therefore, achieving refined energy consumption accounting at the steel product level has become an inevitable choice for the sustainable development of steel companies.

[0004] Therefore, how to improve the accuracy of energy consumption data processing in steel production and achieve refined energy consumption accounting at the product level has become an urgent technical problem to be solved in the field of energy consumption data processing in steel production. Summary of the Invention

[0005] The embodiments of this application provide a method, apparatus, computer program product or computer program, computer-readable storage medium, or electronic device for processing energy consumption data in steel production, which can at least to some extent improve the accuracy of energy consumption data processing in steel production.

[0006] 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.

[0007] According to one aspect of the embodiments of this application, a method for processing energy consumption data in steel production is provided. The method includes: acquiring production information and energy metering information for each process in the steel production process, wherein the production information includes product identification and corresponding production time period, and the energy metering information includes energy medium type and corresponding consumption; performing spatiotemporal synchronization processing on the production information and energy metering information to establish an association mapping relationship between the two; calculating the single-process energy consumption of each product in each process based on the association mapping relationship and the material flow characteristics of each process; constructing an energy consumption flow model based on the single-process energy consumption and the process flow logic of steel production, and calculating the cumulative energy consumption of each product through the energy consumption flow model.

[0008] In some embodiments of this application, based on the aforementioned scheme, the spatiotemporal synchronization processing of the production information and energy metering information includes: performing time calibration on the production system server that collects the production information and the metering system server that collects the energy metering information to maintain a unified time reference; preprocessing the production information and energy metering information to remove abnormal data and fill in missing data; and collecting the preprocessed production information and energy metering information at a preset collection frequency, wherein the preset collection frequency does not exceed a set collection frequency.

[0009] In some embodiments of this application, based on the aforementioned scheme, the step of collecting preprocessed production information and energy metering information according to a preset collection frequency includes: if the production system uploads the precise start and end time of production for each product, and the collection frequency of the metering system server meets the preset collection frequency, then the corresponding energy metering information is matched based on the precise start and end time of production; if the production system does not upload the precise start and end time of production for each product, or the collection frequency of the metering system does not meet the preset collection frequency, then the minimum time segment for batch production is determined, the total energy consumption within the minimum time segment is obtained, and the energy consumption is allocated according to the weight ratio of each product.

[0010] In some embodiments of this application, based on the aforementioned scheme, the step of calculating the single-process energy consumption of each product in each process based on the associated mapping relationship and the material flow characteristics of each process includes: if the input materials and output products of each process have a one-to-one correspondence, then the single-process energy consumption of each product is determined based on the energy metering information of each process in the associated mapping relationship; if the input materials and output products of each process have a one-to-many or many-to-one relationship, then the single-process energy consumption of each product is calculated based on the total energy consumption of each process in the associated mapping relationship and according to the weight ratio of each product.

[0011] In some embodiments of this application, based on the aforementioned scheme, the step of calculating the single-process energy consumption of each product in each process based on the associated mapping relationship and the material flow characteristics of each process further includes: for processes with many-to-one relationships, if there are new materials that are not input from upstream, then the new materials are assigned a default energy consumption value, which is the historical average energy consumption of the same steel grade or a preset benchmark value.

[0012] In some embodiments of this application, based on the foregoing scheme, the energy consumption flow model is a directed graph model, wherein: vertices e in the vertex set of the directed graph model... i,j Defined as the j-th product in the i-th process, the weight w of the vertex is... k (e i,j ) is defined as the single-process energy consumption of the j-th product in the i-th process using the k-th energy medium; the arcs (e) in the arc set of the directed graph model are... i,j ,e i+1,l ) is defined as a single product e i+1,l From product e i,j Processing and generating, the weight w of the arc k (e i,j ,e i+1,l Defined as a single product e i,j Energy consumption allocated to each product e i+1,l The proportion.

[0013] In some embodiments of this application, based on the foregoing scheme, calculating the total cumulative energy consumption of each product throughout the entire process using the energy consumption flow model includes: calculating the cumulative energy consumption of each product in each process based on the following formula:

[0014]

[0015] Among them, f k (e i,j ) indicates a product e i,j Total energy consumption throughout the entire process; w k (e i,j ) indicates a product e i,j Energy consumption per process; w k (e i-1,h ,e i,j ) indicates a product e i-1,h Energy consumption allocated to each product e i,j The proportion; f k (e i-1,h ) indicates a product e i-1,h Total energy consumption throughout the entire process; N - (e i,j ) indicates a product e i,jThe set of parent nodes, i.e., those used to produce the next product e. i,j The collection of all products in the previous process.

[0016] According to one aspect of the embodiments of this application, an energy consumption data processing device for steel production is provided. The device includes: an acquisition unit, configured to acquire production information and energy metering information of each process in the steel production process, wherein the production information includes product identification and corresponding production time period, and the energy metering information includes energy medium type and corresponding consumption; a processing unit, configured to perform spatiotemporal synchronization processing on the production information and energy metering information to establish an association mapping relationship between the two; a first calculation unit, configured to calculate the single-process energy consumption of each product in each process based on the association mapping relationship and the material flow characteristics of each process; and a second calculation unit, configured to construct an energy consumption flow model based on the single-process energy consumption and the process flow logic of steel production, and calculate the cumulative energy consumption of each product through the energy consumption flow model.

[0017] According to one aspect of the embodiments of this application, a computer program product or computer program is 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.

[0018] According to one aspect of the embodiments of this application, a computer-readable storage medium is provided, on which a computer program is stored, which, when executed by a processor, implements the method described in the above embodiments.

[0019] According to one aspect of the embodiments of this application, an electronic device is provided, including: one or more processors; and a storage device for storing one or more programs, which, when executed by the one or more processors, cause the one or more processors to perform the method described in the above embodiments.

[0020] Based on the technical solution proposed in this application, by accurately locating energy consumption data to each individual product, the accuracy of energy consumption data processing in steel production can be improved, enabling refined energy consumption accounting at the product level. This addresses the industry pain point of knowing only the total energy consumption of a process but not the energy consumption of an individual product, providing core data support for green product certification. Simultaneously, by establishing a correlation mapping between production information and energy metering information through spatiotemporal synchronous processing, it ensures a one-to-one correspondence between energy consumption data and the production process, avoiding accounting errors caused by data misalignment and improving the reliability of energy consumption data. Furthermore, by designing differentiated calculation logic based on the material flow characteristics of each process, it is compatible with various production scenarios such as one-input-one-output, one-input-multiple-output, and multiple-input-one-output, with a wide range of applications. Moreover, through the energy consumption flow model, it realizes the energy consumption transfer calculation from the initial furnace batch to the final product, allowing for reverse tracing of the energy consumption contribution of each process, providing precise direction for locating high-consumption links and reducing energy consumption.

[0021] 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

[0022] 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:

[0023] Figure 1 shows a flowchart of a steel production energy consumption data processing method according to an embodiment of this application;

[0024] Figure 2 shows a directed graph of processes in a steel production process according to an embodiment of this application;

[0025] Figure 3 shows a block diagram of an energy consumption data processing apparatus for steel production according to an embodiment of this application;

[0026] Figure 4 shows a schematic diagram of the structure of a computer system suitable for implementing the electronic device of the present application. Detailed Implementation

[0027] Exemplary embodiments will now be described more fully with reference to the accompanying drawings. However, these exemplary embodiments can be implemented in many forms and should not be construed as limited to the examples set forth herein; rather, they are provided to make this application more comprehensive and complete, and to fully convey the concept of the exemplary embodiments to those skilled in the art.

[0028] 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.

[0029] 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.

[0030] The flowchart shown in the attached diagram is for illustrative purposes only and does not necessarily include all content and operations / steps, nor does it 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 actual situation.

[0031] It should be noted that "multiple" in this article refers to two or more. "And / or" describes the relationship between related objects, indicating that three relationships can exist. For example, A and / or B can represent: A alone, A and B simultaneously, or B alone. The character " / " generally indicates that the preceding and following related objects have an "or" relationship.

[0032] It should 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.

[0033] As a pillar industry of the national economy, the steel industry is also a resource-intensive and energy-intensive sector, with energy consumption accounting for a significant proportion of its production costs. The steel mill production process involves a wide variety of energy media, including coal gas, natural gas, electricity, steam, oxygen, nitrogen, compressed air, and water. Furthermore, the production process encompasses multiple key steps such as steelmaking, refining, continuous casting, hot rolling, and cold rolling, resulting in complex energy consumption distribution and a massive total energy consumption.

[0034] Under the current policy background of normalized implementation of "dual carbon" targets, steel companies that fail to achieve refined energy consumption management will not only face increasing carbon tax compliance risks, but will also hinder their green transformation process. From the perspective of cost control, with frequent fluctuations in energy prices in recent years, accurately identifying key high-energy-consuming links such as hot-rolling furnaces and implementing targeted energy-saving measures has become the core path for steel companies to reduce production costs and increase profit margins. At the market competition level, the demand for green and low-carbon steel in high-end manufacturing sectors such as automobiles is increasing year by year. Accurate energy consumption data at the product level, as the core basis for green product certification, directly determines the competitiveness of enterprises in the high-end market. Therefore, achieving refined energy consumption accounting at the steel product level has become an inevitable choice for the sustainable development of steel companies.

[0035] In this context, this application proposes a data processing scheme for energy consumption in steel production to improve the accuracy of energy consumption data processing in steel production and achieve refined energy consumption accounting at the level of steel products.

[0036] The implementation details of the technical solutions in the embodiments of this application are described below:

[0037] Referring to Figure 1, a flowchart of a steel production energy consumption data processing method according to an embodiment of this application is shown. The method can be executed by a computing device with computing capabilities.

[0038] As shown in Figure 1, this method for processing energy consumption data in steel production includes at least steps 110 to 140, which are detailed below:

[0039] Step 110: Obtain production information and energy metering information for each process in the steel production process. The production information includes product identification and corresponding production time period, and the energy metering information includes energy medium type and corresponding consumption.

[0040] In this application, production information is the foundation for achieving precise correlation of energy consumption. Product identifiers uniquely distinguish each product, such as furnace number L20251001, slab number S20251001-01, and hot-rolled coil number H20251001-01, ensuring that energy consumption data can be accurately linked to specific products. Production time periods refer to the start and end times of each product's corresponding process. For example, the production time period for furnace L20251001 in the steelmaking process is 2025092610:00:00-2025092610:28:00, providing a time reference for subsequent spatiotemporal matching of energy consumption and the production process.

[0041] In this application, energy metering information is the core data source for energy consumption calculation. The energy medium type can include various energy media commonly used in steel plant production, such as coal gas, natural gas, electricity, steam, oxygen, nitrogen, compressed air, and water. The corresponding consumption refers to the total consumption of each type of energy medium within a specific time period. For example, the coal gas consumption at metering point M-CONV01 from 2025092610:00:00 to 2025092610:28:00 is 2250 Nm³. 3 .

[0042] Referring again to Figure 1, in step 120, the production information and energy metering information are processed for spatiotemporal synchronization to establish a correlation mapping relationship between them.

[0043] In this application, considering that the production system and energy metering system in steel production usually operate independently, there may be problems such as inconsistent server times and asynchronous data acquisition, leading to an inaccurate match between energy consumption data and the production process. Therefore, this application ensures a unique correspondence between the production behavior and energy consumption of the same product within the same production period through time calibration and data preprocessing, i.e., an associative mapping relationship. For example, the steelmaking production period (10:00-10:28) of furnace L20251001 is correlated with the gas consumption (2250 Nm³) of metering point M-CONV01 during that period. 3 Establish a mapping.

[0044] In this application, step 120, which involves performing spatiotemporal synchronization processing on the production information and energy metering information, can be executed according to steps 121 to 123 as follows:

[0045] Step 121: Perform time calibration between the production system server that collects the production information and the metering system server that collects the energy metering information to ensure that they maintain a unified time reference.

[0046] Step 122: Preprocess the production information and energy metering information, remove abnormal data and fill in missing data.

[0047] Step 123: Collect preprocessed production information and energy metering information according to a preset collection frequency, wherein the preset collection frequency does not exceed the set collection frequency.

[0048] In this application, production systems (such as MES systems) and energy metering systems (such as EMS systems) are typically provided by different equipment vendors, and the local time of the servers may differ. If data is directly accessed, a mismatch between production and energy consumption periods will occur (for example, production takes place from 10:00 to 10:30, but energy consumption data is recorded from 10:05 to 10:35). Therefore, by calibrating time and unifying all servers to the same time standard, it is possible to ensure that the time dimension of production activities and energy consumption is completely consistent, laying the foundation for subsequent data correlation.

[0049] To enable those skilled in the art to better understand this application, the following description is provided in conjunction with some specific embodiments.

[0050] For example, in one specific embodiment, the production system server (MES-STEEL-001) and the metering system server (EMS-GAS-001) connect to the factory clock server (CLK-IND-001) via Network Time Protocol (NTP) for millisecond-level time calibration. Before calibration, the MES server time was 2025092610:00:02.3, and the EMS server time was 2025092610:00:01.8. After calibration, both times are unified to 2025092610:00:02.0, with a time deviation of ≤±0.5s, ensuring consistency in the time dimension of subsequent data.

[0051] In actual production, data acquisition may be abnormal (e.g., production parameters exceeding the process range, instantaneous changes in energy consumption data) or incomplete (e.g., production data not uploaded for a certain period, interruption of energy consumption records) due to factors such as sensor malfunctions and network interruptions. Directly using such data would severely affect the accuracy of calculations. Therefore, data preprocessing to remove invalid and abnormal data and supplement key missing data can ensure the integrity and rationality of the data.

[0052] For example, in a specific embodiment, the production information records the thickness of slab S20251002-01 as 200mm, while the thickness range for continuous casting of this steel grade is 220-225mm. This data is an outlier. After removing it, the thickness is supplemented to 222.5mm by referring to the thicknesses of other slabs (S20251002-02, S20251002-03) in the same furnace (222mm, 223mm). In the energy metering information, the gas flow rate at metering point M-CONV02 suddenly drops to 0Nm³ between 2025092614:05:00 and 2025092614:05:20. 3 , and the flow rate before and after the time period (145 Nm 3 / h, 152Nm 3The difference ( / h) was significant and determined to be an instantaneous interference value. The average value of the preceding and following 15 minutes was used ((145×15+152×15) / 30=148.5Nm). 3 / h) Complete the energy consumption data for this period; for the missing width data of hot-rolled coil H20251003-01, refer to the widths of the other 3 hot-rolled coils in the same batch (1502mm, 1505mm, 1503mm) and fill in 1503mm.

[0053] In this application, considering that the sampling frequency directly affects the real-time performance and matching accuracy of the data—for example, a sampling frequency that is too low will result in the inability to capture energy consumption fluctuations within a short period, while a sampling frequency that is too high will increase the system's data processing pressure—this application sets a preset sampling frequency (not exceeding the set sampling frequency, such as not exceeding 5 seconds / time). This ensures data accuracy while balancing the system's operating load and guarantees efficient integration of production information and energy metering information.

[0054] For example, in a specific embodiment, the preset collection frequency is set to 5 seconds / time. The production system collects information such as product identification and production status every 5 seconds, and the metering system collects energy medium type and consumption data every 5 seconds. After preprocessing, the system connects the production information and energy metering information at the same time point at a frequency of 5 seconds / time. For example, the production status (in steelmaking) of furnace L20251001 collected at 10:00:00 on 20250926 is compared with the gas consumption (1.2 Nm³) at metering point M-CONV01. 3 The data collected at 10:00:05 on September 26, 2025, showed the production status (in steelmaking) and corresponding gas consumption (1.3 Nm³) for this furnace batch. 3 Connect with the system to ensure real-time data synchronization.

[0055] Based on the technical solutions in steps 121 to 123 above, server time calibration achieves a unified time base, eliminating time discrepancies between different systems and preventing mismatches between energy consumption and production processes caused by time misalignment. This provides a reliable time basis for establishing correlation mapping relationships and further improves the accuracy of energy consumption accounting. Simultaneously, by eliminating abnormal data and supplementing missing data, the randomness of data collection in actual production is addressed, ensuring that both production information and energy metering information used for accounting meet the requirements of completeness and rationality, reducing the impact of data quality issues on the accounting results. Furthermore, setting a reasonable preset collection frequency, while ensuring data real-time performance and matching accuracy, avoids excessive system load caused by data redundancy, ensuring the efficiency of the connection between production information and energy metering information, and providing a guarantee for the real-time performance of subsequent energy consumption calculations.

[0056] In step 123 above, the step of collecting preprocessed production information and energy metering information according to a preset collection frequency can be performed according to step 1231 or step 1232 as follows:

[0057] In step 1231, if the production system uploads the precise start and end time of each product and the collection frequency of the metering system server meets the preset collection frequency, then the corresponding energy metering information is matched based on the precise start and end time of production.

[0058] In this application, when the production system has refined data acquisition capabilities (capable of outputting the precise production time period for a single product) and the metering system has a sufficiently high acquisition frequency (capable of capturing energy consumption changes within that time period), a direct time matching mode can be adopted. That is, energy metering data within the precise start and end time of production can be directly extracted as the energy consumption data for the corresponding process of that product.

[0059] For example, in a specific embodiment, the Manufacturing System (MES) of a steel plant's cold rolling production line can upload the precise start and end times of production for each cold-rolled coil (e.g., the production period for cold-rolled coil C20251001-01-01 is 2025093014:10:00-2025093014:50:00), and the gas collection frequency of the metering system (EMS) is 5 seconds / time (meeting the preset collection frequency). At this time, the cumulative gas consumption of metering point M-COLD01 during the period of 14:10:00-14:50:00 is directly extracted as 594 Nm³. 3 A matching relationship is established with cold-rolled coil C20251001-01-01 for subsequent single-process energy consumption calculation.

[0060] In step 1232, if the production system does not upload the precise start and end time of production for each product, or the collection frequency of the metering system does not meet the preset collection frequency, then the minimum time segment for batch production is determined, the total energy consumption within the minimum time segment is obtained, and the energy consumption is allocated according to the weight ratio of each product.

[0061] In this application, when the data acquisition accuracy of the production system is insufficient (only providing the production period for batches, unable to distinguish the precise time for individual products), or the acquisition frequency of the metering system is low (unable to accurately match the production period for individual products), a "minimum time segment + weight allocation" model is adopted. The "minimum time segment" refers to the shortest time range encompassing the production process of the target batch of products, ensuring that the energy consumption within this period is primarily used for the production of that batch of products. Since the energy consumption of steel products is strongly correlated with weight, allocating the total energy consumption according to weight percentage can maximize the rationality of the allocation result.

[0062] For example, in a specific embodiment, the production system of a small steel plant can only record the production time period of a batch of heats (e.g., 3 heats of steel are produced within 2025092808:00:00-2025092812:00:00, but the precise start and end times of each heat cannot be distinguished). The gas sampling frequency of the metering system is 1 minute / time (which does not meet the preset sampling frequency of 5 seconds / time). At this time, the minimum time segment is determined to be 08:00:00-12:00:00, during which the total gas consumption of metering point M-CONV02 is 6750 Nm³. 3 The weights of the three heats of steel are 120t, 115t, and 125t respectively, with a total weight of 360t. Therefore, the energy consumption allocated to the first heat of steel is 6750 × (120 / 360) = 2250 Nm³. 3 The second furnace = 6750 × (115 / 360) = 2156.25 Nm 3 The third furnace = 6750 × (125 / 360) = 2343.75 Nm 3 .

[0063] In this application, addressing the differences between high-precision and ordinary data acquisition equipment in steel mills, a differentiated interface logic is designed. This logic fully leverages the advantages of high-precision equipment to achieve accurate time matching while providing a reasonable energy allocation scheme for ordinary equipment, thus expanding the scope of application and reducing equipment upgrade costs for enterprises. Simultaneously, high-precision calculation can be achieved through direct time matching. By utilizing minimum time segment limits and weight-based allocation, energy consumption cross-interference caused by batch production can be minimized, ensuring high accuracy in calculation results under both scenarios and meeting product-level energy consumption calculation requirements. Furthermore, both interface logics are based on the existing data acquisition system of steel mills, requiring no additional complex equipment or software. Enterprises can directly select the corresponding mode based on existing data conditions, making operation simple, implementation cost-effective, and highly feasible.

[0064] Referring again to Figure 1, in step 130, based on the aforementioned correlation mapping relationship and the material flow characteristics of each process, the single-process energy consumption of each product in each process is calculated.

[0065] In this application, the material flow patterns differ across different processes. For example, steelmaking and refining processes may involve one input and one output, while continuous casting, hot rolling, and cold rolling processes may involve a split / joint configuration of one input and multiple outputs or multiple inputs and one output. This application employs differentiated energy consumption calculation logic tailored to the different material flow characteristics, ensuring the accuracy of energy consumption for each process. For instance, energy consumption in steelmaking is directly calculated based on production time periods, while the total energy consumption in continuous casting is allocated based on slab weight.

[0066] In this application, the calculation of the single-process energy consumption of each product in each process based on the associated mapping relationship and the material flow characteristics of each process can be performed according to the following steps 131 or 132:

[0067] In step 131, if there is a one-to-one correspondence between the input materials and output products of each process, the single-process energy consumption of each product is determined based on the energy metering information of each process in the associated mapping relationship.

[0068] In this application, a one-to-one correspondence means that in a single process, one input material is processed to produce only one output product, without any splitting or splicing. For example, in the steelmaking process, one furnace of molten iron (input material) is processed to produce one furnace of molten steel (output product). In the refining process, one furnace of molten steel (input material) is refined to still become one furnace of molten steel (output product). The energy consumption of such processes is entirely used for the processing of a single product. Therefore, the energy metering information of this process can be directly extracted from the associated mapping relationship as the single-process energy consumption of that product. The calculation logic is simple and accurate.

[0069] In step 132, if the input materials and output products of each process have a one-to-many or many-to-one relationship, then based on the total energy consumption of each process in the associated mapping relationship, the single-process energy consumption of each product is calculated according to the weight ratio of each product.

[0070] In this application, a one-to-many relationship refers to the processing of one input material into multiple output products. For example, in a continuous casting process, one furnace of molten steel (input) is cast into three slabs (output). Another example is in a cold rolling and pickling process, where one hot-rolled coil (input) is rolled into three cold-rolled coils (output).

[0071] In this application, a many-to-one relationship refers to the processing of multiple input materials to generate one output product. For example, in the cold rolling and welding process, three cold-rolled coils (inputs) are welded into one large-diameter cold-rolled coil (output). The total energy consumption of such processes needs to be shared by multiple output products or multiple input materials. Since the energy consumption of steel products is strongly positively correlated with weight (the greater the weight, the more energy is required for processing), allocating the total energy consumption according to weight ratio is the most reasonable method, which can ensure that the energy consumption allocation result of each product conforms to the actual production situation.

[0072] In this application, differentiated calculation logic is designed for different material flow relationships to avoid a one-size-fits-all accounting approach, ensuring that the energy consumption calculation results for a single process are consistent with the actual production process and improving the accuracy of energy consumption data. Specifically, one-to-one correspondences are handled by directly extracting data, while one-to-many and many-to-one relationships are handled by weight-based allocation. Neither logic requires complex algorithms, facilitating system implementation and manual verification. For many-to-one relationships, this calculation logic not only outputs the energy consumption of a single process for the product but also traces the energy contribution ratio of each input material, providing a clear data source for subsequent cumulative energy consumption calculations throughout the entire process, and facilitating the identification of energy consumption anomalies in individual materials.

[0073] In step 130 as shown in Figure 1, the calculation of the single-process energy consumption of each product in each process based on the associated mapping relationship and the material flow characteristics of each process can also be performed as follows: Step 133:

[0074] Step 133: For the process with the many-to-one relationship, if there is a new material that is not an upstream input, then the new material is assigned a default energy consumption value, which is the historical average energy consumption value or a preset benchmark value for the same steel grade.

[0075] In this application, "new materials not from upstream input" refers to materials added to auxiliary processing in a many-to-one process, in addition to the input materials from the preceding process. Examples include filler welding materials added to ensure joint strength in a cold rolling and welding process, and alloy additives added to adjust the steel composition in a hot rolling process. These new materials are not transferred from preceding processes and have no corresponding upstream energy consumption data, but they consume energy during processing. Ignoring their energy consumption would lead to an underestimation of the single-process energy consumption of the output product.

[0076] Therefore, this application can assign a default energy consumption value to newly added materials, and the default value can be determined in the following two ways:

[0077] The first type is the historical average energy consumption of the same steel grade, which is the statistical average energy consumption data of the same steel grade in the same process and the same type of newly added materials, to ensure that the default value matches the actual production level.

[0078] The second method is to preset a baseline value, which is a fixed energy consumption value set according to industry standards and process requirements. This is suitable for scenarios where there is no historical data to refer to. By assigning default energy consumption values ​​to new materials, their energy consumption is included in the total energy consumption of the process for allocation, ensuring the completeness and accuracy of energy consumption calculation for a single process.

[0079] For example, in a specific embodiment, taking the cold rolling welding process as an example, the input of a certain cold rolling welding process is two preceding cold-rolled coils (C20251003-01, weight 15t; C20251003-02, weight 15t). To ensure the splicing quality, a filler welding material (not a new material added from the upstream input) is added, weighing 1t. The output is one welded coil (C20251003-03, weight 31t). The total power consumption of this process is 930kWh, and the historical average energy consumption of filler welding material of the same steel grade (Q355B) in the welding process is 30kWh / t (i.e., the default energy consumption).

[0080] Based on the technical solution in step 133 above, it can be ensured that the total energy consumption of the process includes the energy contribution of all materials involved in the processing, realizing complete accounting of energy consumption for a single process and avoiding data distortion due to omissions. Simultaneously, it provides two default value setting methods: historical average and preset benchmark value, which not only fits the actual production situation of enterprises but also provides a solution for scenarios without historical data, balancing flexibility and practicality. Furthermore, the default energy consumption value for newly added materials is consistent with the energy consumption calculation logic of other materials, and all are incorporated into the weight proportion allocation system, ensuring the logical coherence and data consistency of energy consumption calculation for a single process, laying a reliable foundation for subsequent cumulative energy consumption calculation throughout the entire process.

[0081] Referring again to Figure 1, in step 140, an energy consumption flow model is constructed based on the single-process energy consumption and the process flow logic of steel production, and the cumulative energy consumption of each product is calculated through the energy consumption flow model.

[0082] In this application, steel production is a continuous process, with the product sequentially undergoing processes such as steelmaking, refining, continuous casting, hot rolling, and cold rolling. The energy consumption of the preceding processes is transferred to the subsequent processes. In this case, an energy consumption flow model can be used to simulate this transfer process, integrating the energy consumption of each process in series to ultimately obtain the total energy consumption of each product from raw materials to finished product, i.e., the cumulative energy consumption.

[0083] In this application, the energy consumption flow model can be a directed graph model.

[0084] In this application, the directed graph model consists of a set of vertices (V) and a set of arcs (A), denoted as D = (V, A), where vertices represent products, arcs represent the processing flow of products, and weights represent energy consumption data. This structure can simulate the process flow and energy consumption transfer of steel production.

[0085] In this application, vertex e in the vertex set of the directed graph model can be... i,j Defined as the j-th product in the i-th process, the weight w of the vertex is... k (e i,j) is defined as the single-process energy consumption of the j-th product in the i-th process using the k-th energy medium.

[0086] Referring to Figure 2, a directed graph showing the processes in a steel production flow according to an embodiment of this application is illustrated. As shown in Figure 2, for example, i = 1 represents the steelmaking process, and j = 1 represents the product of the first heat of the steelmaking process, i.e., e. 1,1 This refers to the first furnace of molten steel in the steelmaking process. For example, w1(e 1,1 ) = 2250 Nm 3 e represents the first furnace of molten steel in the steelmaking process. 1,1 The energy consumption of the first type of energy medium (gas) in a single process is 2250 Nm³. 3 .

[0087] In this application, the arcs (e) in the arc set of the directed graph model can also be included. i,j ,e i+1,l Defined as a single product e i+1,l From product e i,j Processing and generating, the weight w of the arc k (e i,j ,e i+1,l Defined as a single product e i,j Energy consumption allocated to each product e i+1,l The proportion. As shown in Figure 2, for example, arc (e 1,1 ,e 2,1 ) indicates the first slab e in the continuous casting process. 2,1 From the first furnace of molten steel in the steelmaking process e 1,1 Casting. For example, the first furnace of molten steel e 1,1 Energy consumption allocated to one slab e 2,1 The ratio is 100%, used to calculate the energy transfer from preceding products to subsequent products.

[0088] In this application, by transforming the complex steel production process into a structured directed graph model, the product flow path from the initial process to subsequent processes can be clearly displayed, making energy consumption transfer relationships visible and facilitating understanding and traceability. Simultaneously, by defining vertex weights (i.e., single-process energy consumption) and arc weights (i.e., allocation ratios), a clear calculation basis for energy consumption flow is provided, avoiding logical confusion in the energy consumption transfer process and ensuring the orderliness and accuracy of cumulative energy consumption calculation. Furthermore, this directed graph model can flexibly add vertices and arcs according to the actual number of processes in steel production, such as adding cold rolling or heat treatment processes, without reconstructing the model structure. It possesses good scalability and can adapt to the differences in production processes of different steel mills.

[0089] In this application, the calculation of the total energy consumption of each product throughout the entire process using the energy consumption flow model can be based on the following formula (1):

[0090]

[0091] Among them, f k (e i,j ) indicates a product e i,j Total energy consumption throughout the entire process; w k (e i,j ) indicates a product e i,j Energy consumption per process; w k (e i-1,h ,e i,j ) indicates a product e i-1,h Energy consumption allocated to each product e i,j The proportion; f k (e i-1,h ) indicates a product e i-1,h Total energy consumption throughout the entire process; N - (e i,j ) indicates a product e i,j The set of parent nodes, i.e., those used to produce the next product e. i,j The collection of all products in the previous process.

[0092] In this application, the calculation logic of the above formula (1) is that the cumulative energy consumption of the product of the i-th process is equal to the single process energy consumption of this process + the cumulative energy consumption of each preceding parent node product × the corresponding apportionment ratio, that is, the energy consumption of this process + the upstream transmission energy consumption. The energy consumption of the whole process is accumulated through iterative calculation.

[0093] For example, in a specific embodiment, in the converter and refining processes (furnace-level energy consumption), gas consumption is calculated based on the production time of each furnace. Specifically, in the converter process, furnace L20251001's converter process time is 10:00:00-10:45:00, and the cumulative gas consumption at metering point M-CONV01 is 2250 Nm³. 3 That is, the energy consumption per furnace in the converter process = 2250 Nm 3 In the refining process, furnace L20251001, refining time was 10:55:00-11:35:00, and the cumulative gas consumption at metering point M-REF01 was 1400 Nm³. 3 That is, the energy consumption per furnace in the refining process = 1400 Nm 3 .

[0094] In the continuous casting process (slab-level energy consumption, 1 heat → 3 slabs, allocated by weight), the total gas consumption of the continuous casting process is: 1800 Nm³ at metering point M-CC01 from 11:45:00 to 12:30:00. 3Assuming the three slabs weigh equally (40.0t each), the energy consumption for continuous casting of a single slab = total energy consumption × (weight of a single slab / weight of a furnace) = 1800 × (40.0 / 120.0) = 600 Nm 3 That is, the energy consumption of each slab (e.g., S20251001-01) in the continuous casting process is 600 Nm. 3 .

[0095] In the hot rolling process (energy consumption per hot-rolled coil, 1 slab → 1 hot-rolled coil, allocated by weight), the total gas consumption for the hot rolling process is: 2376 Nm³ at metering point M-HOT01 from 13:00:00 to 13:40:00. 3 (Corresponding to 3 hot-rolled coils, total weight 39.6 × 3 = 118.8t). Therefore, the hot-rolling energy consumption of a single hot-rolled coil = total energy consumption × (weight of a single hot-rolled coil / total weight of 3 hot-rolled coils) = 2376 × (39.6 / 118.8) = 792 Nm 3 That is, the energy consumption of each hot-rolled coil (e.g., H20251001-01) in the hot rolling process is 792 Nm. 3 .

[0096] In the cold rolling process (energy consumption per cold-rolled coil, 1 hot-rolled coil → 3 cold-rolled coils, allocated by weight), the total gas consumption of the cold rolling process is: 594 Nm³ at metering point M-COLD01 from 14:10:00 to 14:50:00. 3 (Corresponding to 1 hot-rolled coil and 3 cold-rolled coils, the total weight is 13.2 × 3 = 39.6t). Therefore, the cold-rolling energy consumption of a single cold-rolled coil = total energy consumption × (weight of a single cold-rolled coil / weight of a hot-rolled coil) = 594 × (13.2 / 39.6) = 198 Nm 3 That is, the energy consumption of each cold-rolled coil (e.g., C20251001-01-01) in the cold rolling process is 198 Nm. 3 .

[0097] Then, the cumulative energy consumption can be calculated according to the product's unit process. This can be done by using the principle of "summing the energy consumption of each process" to calculate the cumulative energy consumption for different material types.

[0098] Step 1, Cumulative Energy Consumption per Furnace: Cumulative Energy Consumption per Furnace = Energy Consumption of Converter Process + Energy Consumption of Refining Process = 2250 + 1400 = 3650 Nm 3 (Furnace number L20251001).

[0099] Step 2, cumulative energy consumption of slabs: Cumulative energy consumption of slabs = Energy consumption of heat run allocated to slabs + Energy consumption of continuous casting process; Energy consumption of heat run allocated to a single slab: (Converter energy consumption + Refining energy consumption) × (Weight of a single slab / Weight of heat run) = (2250 + 1400) × (40.0 / 120.0) = 1216.67 Nm 3Cumulative energy consumption of slab = 1216.67 + 600 = 1816.67 Nm 3 (e.g., slab S20251001-01).

[0100] Step 3, cumulative energy consumption of hot-rolled coil: Cumulative energy consumption of hot-rolled coil = Cumulative energy consumption of slab + Energy consumption of hot rolling process = 1816.67 + 792 = 2608.67 Nm 3 (e.g., hot-rolled coil H20251001-01).

[0101] Step 4, Cumulative Energy Consumption of Cold-Rolled Coil: Cumulative Energy Consumption of Cold-Rolled Coil = Energy Consumption of Hot-Rolled Coil Amounted to Cold-Rolled Coil + Energy Consumption of Cold Rolling Process; Energy Consumption of Hot-Rolled Coil Amounted to a Single Cold-Rolled Coil: Cumulative Energy Consumption of Hot-Rolled Coil × (Weight of a Single Cold-Rolled Coil / Weight of a Hot-Rolled Coil) = 2608.67 × (13.2 / 39.6) = 869.56 Nm 3 Cumulative energy consumption of cold-rolled coil = 869.56 + 198 = 1067.56 Nm 3 (e.g., cold-rolled coil C20251001-01-01).

[0102] Based on the technical solution proposed in this application, by accurately locating energy consumption data to each individual product, the accuracy of energy consumption data processing in steel production can be improved, enabling refined energy consumption accounting at the product level. This addresses the industry pain point of knowing only the total energy consumption of a process but not the energy consumption of an individual product, providing core data support for green product certification. Simultaneously, by establishing a correlation mapping between production information and energy metering information through spatiotemporal synchronous processing, it ensures a one-to-one correspondence between energy consumption data and the production process, avoiding accounting errors caused by data misalignment and improving the reliability of energy consumption data. Furthermore, by designing differentiated calculation logic based on the material flow characteristics of each process, it is compatible with various production scenarios such as one-input-one-output, one-input-multiple-output, and multiple-input-one-output, with a wide range of applications. Moreover, through the energy consumption flow model, it realizes the energy consumption transfer calculation from the initial furnace batch to the final product, allowing for reverse tracing of the energy consumption contribution of each process, providing precise direction for locating high-consumption links and reducing energy consumption.

[0103] The following describes an embodiment of the apparatus described in this application, which can be used to execute the steel production energy consumption data processing method 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 steel production energy consumption data processing method described above in this application.

[0104] Figure 3 shows a block diagram of an energy consumption data processing apparatus for steel production according to an embodiment of this application.

[0105] Referring to FIG3, a steel production energy consumption data processing device 300 according to an embodiment of the present application includes an acquisition unit 301, a processing unit 302, a first calculation unit 303, and a second calculation unit 304.

[0106] The system includes: an acquisition unit 301, used to acquire production information and energy metering information for each process in the steel production process; the production information includes product identification and corresponding production time period; and the energy metering information includes energy medium type and corresponding consumption. A processing unit 302 is used to perform spatiotemporal synchronization processing on the production information and energy metering information, establishing a correlation mapping relationship between the two. A first calculation unit 303 is used to calculate the single-process energy consumption of each product in each process based on the correlation mapping relationship and the material flow characteristics of each process. A second calculation unit 304 is used to construct an energy consumption flow model based on the single-process energy consumption and the process flow logic of steel production, and calculate the cumulative energy consumption of each product through the energy consumption flow model.

[0107] In some embodiments of this application, based on the foregoing scheme, the processing unit 302 is configured to: perform time calibration on the production system server that collects the production information and the metering system server that collects the energy metering information to maintain a unified time reference; preprocess the production information and energy metering information, remove abnormal data and fill in missing data; and collect the preprocessed production information and energy metering information at a preset collection frequency, wherein the preset collection frequency does not exceed the set collection frequency.

[0108] In some embodiments of this application, based on the aforementioned scheme, the processing unit 302 is configured as follows: if the production system uploads the precise start and end time of production for each product, and the collection frequency of the metering system server meets the preset collection frequency, then the corresponding energy metering information is matched based on the precise start and end time of production; if the production system does not upload the precise start and end time of production for each product, or the collection frequency of the metering system does not meet the preset collection frequency, then the minimum time segment for batch production is determined, the total energy consumption within the minimum time segment is obtained, and the energy consumption is allocated according to the weight ratio of each product.

[0109] In some embodiments of this application, based on the foregoing scheme, the first calculation unit 303 is configured as follows: if the input materials and output products of each process are in a one-to-one correspondence, then the single-process energy consumption of each product is determined based on the energy metering information of each process in the associated mapping relationship; if the input materials and output products of each process are in a one-to-many or many-to-one relationship, then the single-process energy consumption of each product is calculated based on the total energy consumption of each process in the associated mapping relationship and according to the weight ratio of each product.

[0110] In some embodiments of this application, based on the foregoing scheme, the first calculation unit 303 is further configured to: for the process with the many-to-one relationship, if there is a new material that is not an upstream input, then assign a default energy consumption value to the new material, wherein the default energy consumption value is the historical average value or a preset benchmark value of the energy consumption corresponding to the same steel grade.

[0111] In some embodiments of this application, based on the foregoing scheme, the energy consumption flow model is a directed graph model, wherein: vertices e in the vertex set of the directed graph model... i,j Defined as the j-th product in the i-th process, the weight w of the vertex is... k (e i,j ) is defined as the single-process energy consumption of the j-th product in the i-th process using the k-th energy medium; the arcs (e) in the arc set of the directed graph model are... i,j ,e i+1,l Defined as a single product e i+1,l From product e i,j Processing and generating, the weight w of the arc k (e i,j ,e i+1,l Defined as a single product e i,j Energy consumption allocated to each product e i+1,l The proportion.

[0112] In some embodiments of this application, based on the foregoing scheme, the second calculation unit 304 is further configured to calculate the cumulative energy consumption of each product in each process based on the following formula:

[0113]

[0114] Among them, f k (e i,j ) indicates a product e i,j Total energy consumption throughout the entire process; w k (e i,j ) indicates a product e i,j Energy consumption per process; w k (e i-1,h ,e i,j ) indicates a product e i-1,h Energy consumption allocated to each product e i,j The proportion; f k (e i-1,h ) indicates a product e i-1,h Total energy consumption throughout the entire process; N - (e i,j ) indicates a product e i,j The set of parent nodes, i.e., those used to produce the next product e. i,j The collection of all products in the previous process.

[0115] 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.

[0116] 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.

[0117] Based on the same inventive concept, this application also provides an electronic device. Referring to FIG4, a schematic diagram of a computer system suitable for implementing the electronic device of this application is shown. The electronic device includes one or more memories 404, one or more processors 402, and at least one computer program (program code) stored in the memory 404 and executable on the processor 402. When the processor 402 executes the computer program, it implements the method described above.

[0118] In Figure 4, the bus architecture (represented by bus 400) includes any number of interconnected buses and bridges, linking various circuits including one or more processors represented by processor 402 and memory represented by memory 404. Bus 400 can also link various other circuits such as peripherals, voltage regulators, and power management circuits, which are well known in the art and therefore will not be described further herein. Bus interface 405 provides an interface between bus 400 and receiver 401 and transmitter 403. Receiver 401 and transmitter 403 may be the same element, a transceiver, providing a unit for communicating with various other devices over a transmission medium. Processor 402 is responsible for managing bus 400 and general processing, while memory 404 can be used to store data used by processor 402 during operation.

[0119] 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.

[0120] 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.

[0121] 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.

[0122] 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, including 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 USB flash drives, read-only memory (ROM), random access memory (RAM), portable hard drives, magnetic disks, or optical disks.

[0123] 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 processing energy consumption data in steel production, characterized in that, The method includes: acquiring production information and energy metering information for each process in the steel production process, wherein the production information includes product identification and corresponding production time period, and the energy metering information includes energy medium type and corresponding consumption; performing spatiotemporal synchronization processing on the production information and energy metering information to establish a correlation mapping relationship between the two; calculating the single-process energy consumption of each product in each process based on the correlation mapping relationship and the material flow characteristics of each process; constructing an energy consumption flow model based on the single-process energy consumption and the process flow logic of steel production, and calculating the cumulative energy consumption of each product through the energy consumption flow model.

2. The method according to claim 1, characterized in that, The process of performing spatiotemporal synchronization processing on the production information and energy metering information includes: performing time calibration on the production system server that collects the production information and the metering system server that collects the energy metering information to maintain a unified time reference; preprocessing the production information and energy metering information to remove abnormal data and fill in missing data; and collecting the preprocessed production information and energy metering information at a preset collection frequency, wherein the preset collection frequency does not exceed the set collection frequency.

3. The method according to claim 2, characterized in that, The step of collecting preprocessed production information and energy metering information according to a preset collection frequency includes: if the production system uploads the precise start and end time of production for each product and the collection frequency of the metering system server meets the preset collection frequency, then the corresponding energy metering information is matched based on the precise start and end time of production; if the production system does not upload the precise start and end time of production for each product, or the collection frequency of the metering system does not meet the preset collection frequency, then the minimum time segment for batch production is determined, the total energy consumption within the minimum time segment is obtained, and the energy consumption is allocated according to the weight ratio of each product.

4. The method according to claim 1, characterized in that, The calculation of the single-process energy consumption of each product in each process based on the associated mapping relationship and the material flow characteristics of each process includes: if the input materials and output products of each process have a one-to-one correspondence, then the single-process energy consumption of each product is determined based on the energy metering information of each process in the associated mapping relationship; if the input materials and output products of each process have a one-to-many or many-to-one relationship, then the single-process energy consumption of each product is calculated based on the total energy consumption of each process in the associated mapping relationship and according to the weight ratio of each product.

5. The method according to claim 4, characterized in that, The calculation of single-process energy consumption for each product in each process based on the associated mapping relationship and the material flow characteristics of each process also includes: for processes with many-to-one relationships, if there are new materials that are not input from upstream, then the new materials are assigned a default energy consumption value, which is the historical average or preset benchmark value of the energy consumption of the same steel grade.

6. The method according to claim 1, characterized in that, The energy consumption flow model is a directed graph model, wherein: vertex e in the vertex set of the directed graph model i,j Defined as the j-th product in the i-th process, the weight w of the vertex is... k (e i,j ) is defined as the single-process energy consumption of the j-th product in the i-th process using the k-th energy medium; the arcs (e) in the arc set of the directed graph model are... i,j ,e i+1,l Defined as a single product e i+1,l From product e i,j Processing and generating, the weight w of the arc. k (e i,j ,e i+1,l Defined as a single product e i,j Energy consumption allocated to each product e i+1,l The proportion.

7. The method according to claim 6, characterized in that, The calculation of the total energy consumption of each product throughout the entire process using the energy consumption flow model includes: calculating the total energy consumption of each product in each process based on the following formula: Among them, f k (e i,j ) indicates a product e i,j Total energy consumption throughout the entire process; w k (e i,j ) indicates a product e i,j Energy consumption per process; w k (e i-1,h ,e i,j ) indicates a product e i-1,h Energy consumption allocated to each product e i,j The proportion; f k (e r-1,h ) indicates a product e r-1,h Total energy consumption throughout the entire process; N - (e i,j ) indicates a product e i,j The set of parent nodes, i.e., those used to produce the next product e. i,j The collection of all products in the previous process.

8. A steel production energy consumption data processing device, characterized in that, The device includes: an acquisition unit for acquiring production information and energy metering information of each process in the steel production process, wherein the production information includes product identification and corresponding production time period, and the energy metering information includes energy medium type and corresponding consumption; a processing unit for performing spatiotemporal synchronization processing on the production information and energy metering information to establish a correlation mapping relationship between the two; a first calculation unit for calculating the single-process energy consumption of each product in each process based on the correlation mapping relationship and the material flow characteristics of each process; and a second calculation unit for constructing an energy consumption flow model based on the single-process energy consumption and the process flow logic of steel production, and calculating the cumulative energy consumption of each product through the energy consumption flow model.

9. A computer program product, characterized in that, The computer program product includes 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 the method as described in any one of claims 1 to 7.

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 7.

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