Carbon management method in steel production process and electronic device

Abstract

The application provides a carbon management method and an electronic device in a steel production process, and relates to the technical field of steel production. The method can determine actual total carbon performance data of a steel production base according to energy consumption data, actual steel output and a preset emission factor in a current production cycle; predict first predicted total carbon performance data of a next production cycle according to the actual total carbon performance data of a plurality of historical production cycles and a downward trend of the actual total carbon performance data of the current production cycle; and output total managed carbon emission of the steel production base in the next production cycle according to the first predicted total carbon performance data of the next production cycle and recorded planned steel output of the next production cycle, so that the total managed carbon emission is lower than actual carbon emission of a previous cycle, the carbon emission is reduced cycle by cycle, and carbon emission management is sustainable and normative.

Description

Technical Field

[0001] This application relates to the field of steel production technology, and in particular to a carbon management method and electronic device in the steel production process. Background Technology

[0002] With the deepening implementation of the "dual carbon" goals, the construction of the carbon emissions trading market has accelerated significantly. Including the steel industry—the second largest industrial sector in terms of carbon emissions after electricity—into the national carbon market is a key measure to achieve the Nationally Determined Contribution (NDC) target. Against this backdrop, timely and accurate reporting of carbon emission data to the Ministry of Ecology and Environment is not only a basic requirement for fulfilling compliance obligations but also a core basis for enterprises to obtain quota allocations, participate in trading, and manage assets in the carbon market. Therefore, steel production enterprises need to strictly control their carbon emission data.

[0003] Currently, steel companies typically adjust their carbon emission data randomly in the next production cycle if the carbon emissions exceed the limit during a particular production cycle. However, this method of controlling carbon emissions is simplistic, crude, and unregulated, lacking sustainability. Furthermore, if carbon emissions decrease significantly, it can severely impact the steel company's production volume, making it impossible to achieve standardized and effective control over carbon emissions, resulting in low reliability. Summary of the Invention

[0004] This application provides a carbon management method and electronic device for the steel production process, which solves the problem that the existing technology uses carbon emission data to decrease in each production cycle, thus making it impossible to effectively manage and standardize carbon emission data, resulting in low reliability.

[0005] Firstly, this application provides a carbon management method for the steel production process. The method provided by this application includes: Obtain energy consumption data and actual steel output of steel production bases during the current production cycle; Based on energy consumption data, actual steel production, and preset emission factors during the current production cycle, determine the actual total carbon performance data of the steel production base during the current production cycle. Based on the changing trends of actual total carbon performance data from multiple historical production cycles and actual total carbon performance data from the current production cycle, the first predicted total carbon performance data for the next production cycle is predicted, where the changing trend is downward. Based on the first forecast total carbon performance data for the next production cycle and the recorded planned steel production for the next production cycle, output the total controlled carbon emissions of the steel production base in the next production cycle.

[0006] In some implementations, multiple production processes in the steel production process correspond to multiple different types of energy consumption sensors. Energy consumption data within the current production cycle includes multiple energy consumption data collected by these sensors. Based on the energy consumption data within the current production cycle, actual steel production, and a preset emission factor, the actual total carbon performance data of the steel production base is determined, including: The multiple energy consumption data are summarized according to the category of energy consumption sensor corresponding to each data point, resulting in multiple categories of energy consumption data. Based on the energy consumption data of each category and its corresponding emission factor, determine the carbon emissions of each category; By summing up the carbon emissions from each category, the total carbon emissions of the steel production base during the current production cycle can be obtained. The actual total carbon performance data of the steel production base is determined based on the total carbon emissions and actual steel production during the current production cycle.

[0007] In some embodiments, after determining the actual total carbon performance data of a steel production base based on the total carbon emissions and actual steel production during the current production cycle, the method provided in this application further includes: Based on the pre-set basic information of the steel production base, the carbon emissions of each category, the actual total carbon performance data in the current production cycle, the energy consumption data of each category and its corresponding emission factor, carbon market data in a pre-set format is generated. Hash values ​​are generated based on carbon market data, and a correlation is established between the hash values ​​and the carbon market data. Carbon market data carrying hash values ​​is transmitted from the group server to the environmental monitoring server. The hash values ​​are used by the group server and the environmental monitoring server to verify whether the received carbon market data has been tampered with.

[0008] In some implementations, after allocating sub-controlled carbon emissions for the next production cycle to each production process, the method provided in this application further includes: Divide the next production cycle into multiple tracking and assessment periods of equal duration; For each tracking and assessment period, an assessment carbon emission amount is allocated to each production process. The assessment carbon emission amount is equal to the sub-controlled carbon emission amount of the production process in the next production cycle divided by the number of tracking and assessment periods. Obtain the second carbon emissions for each production process; If the second carbon emission of any production process exceeds the allocated assessment carbon emission limit, a carbon emission exceedance warning message will be sent to the administrator terminal. The warning message will include the identifier of the production process and the tracking assessment period for exceeding the allocated assessment carbon emission limit.

[0009] In some embodiments, the method provided in this application further includes: Determine the carbon cost of each production step in the steel production process; Generate and store carbon cost financial data, which includes the identifier, name, carbon unit price, and carbon cost of each production process.

[0010] In some embodiments, the various production steps in the steel production process include a first target step, a second target step, and a third target step, wherein the first target step is used to generate carbon products, and the carbon products are allocated proportionally to the second and third target steps. Determining the carbon cost of each production step in the steel production process includes: Obtain sub-energy consumption data for the first target process in the steel production process within the current production cycle. Based on the sub-energy consumption data of the first target process, the preset carbon emission factor, and the carbon price, determine the carbon cost of the first target process in the current production cycle. The carbon cost of the second target process in the current production cycle is determined based on the carbon cost and the proportion of carbon products allocated to the second target process. The carbon cost of the third target process in the current production cycle is determined based on the carbon cost and the proportion of carbon products allocated to the third target process.

[0011] In some implementations, based on the first predicted total carbon performance data for the next production cycle and the recorded planned steel production for the next production cycle, the total controlled carbon emissions of the steel production base for the next production cycle are output, including: Obtain the second forecast total carbon performance data for the steel industry in the next production cycle from the group's server; The first and second predicted total carbon performance data are weighted and summed to obtain the comprehensive predicted total carbon performance data. Based on the comprehensive forecast of total carbon performance data and the planned steel production for the next production cycle, the total controlled carbon emissions of the steel production base in the next production cycle are output.

[0012] In a second aspect, this application also provides an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein when the processor executes the computer program, the electronic device performs the method provided in the first aspect of this application.

[0013] This application provides a carbon management method and electronic device for steel production. Based on energy consumption data, actual steel output, and preset emission factors in the current production cycle, the method determines the actual total carbon performance data of a steel production base. Based on the downward trend of the actual total carbon performance data from multiple historical production cycles and the current production cycle, it predicts the first predicted total carbon performance data for the next production cycle. This ensures that the first predicted total carbon performance data is lower than the actual total carbon performance data of the current production cycle. Based on the first predicted total carbon performance data for the next production cycle and the recorded planned steel output for the next production cycle, the method outputs the total controlled carbon emissions of the steel production base for the next production cycle. Steel enterprise managers can then refer to the total controlled carbon emissions for carbon emission management in the next production cycle. Because the first predicted total carbon performance data is lower than the actual total carbon performance data of the current production cycle, the total controlled carbon emissions will be lower than the actual carbon emissions of the previous cycle, achieving a gradual decrease in carbon emissions over each production cycle. This ensures sustainable and standardized carbon emission management and effectively controls carbon emissions. Furthermore, since the first predicted total carbon performance data is based on the aforementioned downward trend, steel companies can achieve the total controlled carbon emissions corresponding to the first predicted total carbon performance data through reasonable technological innovation or production management innovation in the next production cycle, with minimal impact on steel company output. Thus, the obtained total controlled carbon emission value is highly reasonable and reliable. Attached Figure Description

[0014] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0015] Figure 1 A schematic diagram illustrating the sequential communication connection between the production base server, the group server, and the environmental monitoring server provided in this embodiment of the application. Figure 2 A flowchart of a carbon management method in the steel production process provided in this application embodiment; Figure 3 Functional block diagram of a carbon management device in the steel production process provided in the embodiments of this application; Figure 4 A circuit module block diagram of an electronic device provided in an embodiment of this application. Detailed Implementation

[0016] To make the objectives, technical solutions, and advantages of the embodiments of this application clearer, the technical solutions of the embodiments of this application will be clearly and completely described below with reference to specific embodiments and corresponding drawings. Obviously, the described embodiments are only a part of the embodiments of this application, and not all of them. All other embodiments obtained by those skilled in the art based on the embodiments of this application without creative effort are within the scope of protection of the embodiments of this application.

[0017] The technical solutions of this application and how they solve the aforementioned technical problems will be described in detail below with specific embodiments. These specific embodiments can be combined with each other, and the same or similar concepts or processes may not be repeated in some embodiments. The embodiments of this application will now be described with reference to the accompanying drawings.

[0018] This application provides a carbon management method for steel production processes, applied to a production base server 101. For example... Figure 1 As shown, there are production base server 101, group server 102, and environmental monitoring server 103. Figure 2 As shown, the method provided in this application embodiment includes: S201: Obtain energy consumption data and actual steel output of the steel production base during the current production cycle.

[0019] The duration of the current production cycle can be one month, one quarter, or one year, and is not limited here.

[0020] Specifically, the methods for obtaining energy consumption data can be as follows: Step A1: Acquire multiple energy consumption data collected by multiple different types of energy consumption sensors during the current production cycle.

[0021] For example, multiple different types of energy consumption sensors may include smart meters, gas meters, and weighing sensors deployed on-site in various processes such as sintering, ironmaking, steelmaking, and rolling in the steel production process. The specific energy consumption data collected includes: (1) Sintering process: Weighing sensors collect the amount of ore used for mixing (t), coke powder used (t), anthracite used (t), and finished sintered ore output (t); gas meters collect the ignition gas consumption (m³); and smart meters collect the main exhaust power consumption of the sintering machine (kWh); (2) Ironmaking process: Weighing sensors collect the amount of pellet ore used (t), coke used (t), pulverized coal injection (t), blast furnace gas production (m³), and molten iron output (t); gas meters collect the hot blast stove gas consumption (m³); and smart meters collect the blast furnace blast power consumption (kWh); (3) (3) Steelmaking process: The weighing sensor collects the amount of molten iron entering the furnace (t), the amount of scrap steel added (t), the output of steel billet (t), the gas meter collects the amount of converter gas recovered (m³), and the oxygen consumption (m³), and the smart meter collects the electric arc furnace power consumption (kWh); (4) Steel rolling process: The gas meter collects the gas consumption in the heating of steel billet, the smart meter collects the power consumption in the heating of steel billet, the smart meter collects the mill power consumption (kWh), the weighing sensor collects the output of finished steel (t), and the weight of carbon-containing materials (including limestone (CaCO3 content), dolomite (MgCO3 content), etc.).

[0022] Step A2: Summarize the multiple energy consumption data according to the category of the energy consumption sensor corresponding to each data point to obtain multiple different categories of energy consumption data.

[0023] For example, energy consumption data of several different categories are divided into fossil fuel energy consumption (such as the amount of blended ore used (t), the amount of coke powder used (t), the amount of anthracite used (t), etc.), electricity consumption, and industrial process energy consumption (such as the consumption of limestone (containing CaCO3), the consumption of dolomite (containing MgCO3), etc.).

[0024] S202: Based on energy consumption data, actual steel production, and preset emission factors during the current production cycle, determine the actual total carbon performance data of the steel production base during the current production cycle.

[0025] S202 is specifically implemented as follows: Step B1: Determine the carbon emissions for each category based on the energy consumption data and corresponding emission factors for each category.

[0026] For example, the carbon emissions from fossil fuels = ∑(AD_i × EF_i), where AD_i is the activity data for the i-th fossil fuel (activity data equals consumption multiplied by the corresponding preset lower heating value), and EF_i is the preset emission factor for the i-th fossil fuel (emission factor for the i-th fossil fuel = carbon content per unit calorific value of the i-th fossil fuel × preset carbon oxidation rate × 44 / 12). For example, the carbon emissions from coke = 28.435 GJ / t × 0.0295 tC / GJ × 0.93 × 44 / 12 = 2.86 tCO2 / t coke. The carbon emissions from industrial processes (i.e., carbon-containing feedstocks) = ∑(AD_j × EF_j), where AD_j is the consumption of the j-th carbon-containing feedstock, and EF_j is the emission factor of the carbon-containing feedstock. For example, EF_limestone = 0.440 tCO2 / t limestone, and the carbon emissions of electricity = electricity consumption × electricity emission factor, where the electricity emission factor is the latest national power grid average emission factor released by the Ministry of Ecology and Environment (e.g., 0.5703 tCO2 / MWh).

[0027] For example, if the sintering process consumes 1000t of coke, then the carbon emissions from fossil fuels in the sintering process = 1000 × 2.86 (i.e., the emission factor corresponding to coke energy consumption) = 2860 tCO2; if the ironmaking process consumes 5000t of coke and 2000t of pulverized coal, then the carbon emissions from fossil fuels in the ironmaking process = 5000 × 2.86 (i.e., the emission factor corresponding to coke energy consumption) + 2000 × 2.093 (i.e., the emission factor corresponding to pulverized coal energy consumption) = 14300 + 4186 = 18486 tCO2 Step B2: Summarize the carbon emissions of each category to obtain the total carbon emissions of the steel production base during the current production cycle.

[0028] For example, by summarizing carbon emissions from fossil fuels, carbon emissions from industrial processes (i.e., carbon-containing raw materials), and carbon emissions from electricity, the total carbon emissions of a steel production base during the current production cycle can be obtained.

[0029] Step B3: Determine the actual total carbon performance data of the steel production base based on the total carbon emissions and actual steel production during the current production cycle.

[0030] For example, dividing the total carbon emissions during the current production cycle (e.g., 9 million tons of CO2) by the actual steel production (5 million tons) yields the actual total carbon performance data of the steel production base (e.g., 1.8 tons of CO2 / ton of steel).

[0031] Furthermore, after step B3, the method provided in this application embodiment further includes: Step B4: Based on the preset basic information of the steel production base (such as the name and number of the steel production base), the carbon emissions of each category, the actual total carbon performance data in the current production cycle, the energy consumption data of each category and its corresponding emission factor, generate carbon market data in a preset format.

[0032] Step B5: Generate hash values ​​based on carbon market data and establish a correlation between the hash values ​​and the carbon market data.

[0033] Step B6: The carbon market data carrying the hash value is transmitted through the group server 102 to the environmental monitoring server 103 for review by the regulatory personnel corresponding to the environmental monitoring server 103. The hash value is used by the group server 102 and the environmental monitoring server 103 to verify whether the received carbon market data has been tampered with (thus improving the reliability and security of the transmitted carbon market data). Furthermore, if tampering occurs, the data transmission node where the tampering occurred is recorded, thereby achieving full-chain data traceability.

[0034] Among them, the group server 102 can verify whether the carbon market data includes the basic information of the preset steel production base (such as the name and number of the steel production base), the carbon emissions of each category, the actual total carbon performance data in the current production cycle, the energy consumption data of each category and its corresponding emission factors, and identify whether there is abnormal data. If the corresponding sub-segment content is missing or there is abnormal data, the carbon market data will be automatically returned to the production base server 101.

[0035] S203: Based on the changing trends of the actual total carbon performance data of multiple historical production cycles and the actual total carbon performance data of the current production cycle, predict the first predicted total carbon performance data for the next production cycle, wherein the changing trend is downward.

[0036] For example, the actual total carbon performance data for the previous two historical production cycles were 1.84 tCO2 / t and 1.82 tCO2 / t, respectively, and the actual total carbon performance data for the current production cycle is 1.80 tCO2 / t, showing an arithmetic decreasing trend. Therefore, the first predicted total carbon performance data for the next production cycle is 1.78 tCO2 / t.

[0037] S204: Based on the first forecast total carbon performance data for the next production cycle and the recorded planned steel production for the next production cycle, output the total controlled carbon emissions of the steel production base in the next production cycle.

[0038] For example, if the first predicted total carbon performance data is 1.78 CO2 / t, and the planned steel output for the next production cycle is 5 million tons of steel, then the total controlled carbon emissions for the next production cycle will be 5 million tons multiplied by 1.78, which equals 8.9 million tons of CO2. This represents a reduction of 200,000 tons of CO2 compared to the current production cycle, or a reduction rate of 2.22%.

[0039] Specifically, S204 can be implemented as follows: Obtain the second predicted total carbon performance data for the steel industry in the next production cycle from the group server 102 (e.g., carbon emissions per ton of steel: 1.78 tCO2 / t). Weight the first and second predicted total carbon performance data to obtain the comprehensive predicted total carbon performance data. For example, according to the formula: Comprehensive predicted total carbon performance data = α × Second predicted total carbon performance data + β × First predicted total carbon performance data, where α is the first weighting coefficient (e.g., α = 0.4) and β is the second weighting coefficient (e.g., α = 0.6). Based on the comprehensive predicted total carbon performance data and the recorded planned steel output for the next production cycle, output the total controlled carbon emissions of the steel production base in the next production cycle. This approach ensures higher reliability of the obtained comprehensive predicted total carbon performance data, and consequently, higher reliability of the corresponding total controlled carbon emissions for the next production cycle.

[0040] In summary, the carbon management method for steel production provided in this application can determine the actual total carbon performance data of a steel production base based on energy consumption data, actual steel output, and preset emission factors in the current production cycle. Based on the downward trend of the actual total carbon performance data from multiple historical production cycles and the actual total carbon performance data of the current production cycle, a first predicted total carbon performance data for the next production cycle is predicted. This ensures that the first predicted total carbon performance data is lower than the actual total carbon performance data of the current production cycle. Based on the first predicted total carbon performance data for the next production cycle and the planned steel output for the next production cycle, the total controlled carbon emissions of the steel production base in the next production cycle are output. Thus, steel enterprise managers can refer to the total controlled carbon emissions to manage carbon emissions in the next production cycle. Because the first predicted total carbon performance data is lower than the actual total carbon performance data of the current production cycle, the total controlled carbon emissions will be lower than the actual carbon emissions of the previous cycle, achieving a gradual decrease in carbon emissions over each production cycle. This ensures sustainable and standardized carbon emission management and effectively controls carbon emissions. Furthermore, since the first predicted total carbon performance data is based on the aforementioned downward trend, steel companies can achieve the total controlled carbon emissions corresponding to the first predicted total carbon performance data through reasonable technological innovation or production management innovation in the next production cycle, with minimal impact on steel company output. Thus, the obtained total controlled carbon emission value is highly reasonable and reliable.

[0041] In addition, in some embodiments, after S204, the method provided in this application embodiment may further include: Step C1: Divide the next production cycle into multiple tracking and assessment periods of equal duration.

[0042] For example, if the production cycle is 1 year, then each period is 1 month; if the production cycle is 1 month, then each period is 1 week.

[0043] Step C2: Allocate assessment carbon emissions for each tracking and assessment period, where the assessment carbon emissions are equal to the total controlled carbon emissions for the next production cycle divided by the number of tracking and assessment periods.

[0044] For example, if the total controlled carbon emissions for one year are 8.9 million tons of CO2, then the monthly assessed carbon emissions are 8.9 million tons of CO2 divided by 12, which equals 74,100 tons of CO2. Understandably, for example, if the carbon emissions from the sintering process account for 25% of the total carbon emissions of a steel production base, then that sintering process bears a 25% carbon reduction target.

[0045] Step C3: Obtain the total carbon emissions of the steel production base during the tracking and assessment period.

[0046] Step C4: If the total carbon emissions exceed the allocated assessment carbon emissions, send a warning message about the carbon emissions exceeding the limit to the administrator terminal.

[0047] Based on the above steps C1-C2, early warning and control of carbon emissions at specific time points can be achieved, thereby ensuring high reliability in keeping carbon emissions within the total controlled carbon emissions for the next production cycle.

[0048] Alternatively, in other embodiments, the method provided in this application further includes: acquiring sub-energy consumption data of multiple production processes in the steel production process during the current production cycle; and determining the first carbon emission amount of each production process during the current production cycle based on the sub-energy consumption data of the multiple production processes and a preset carbon emission factor. The determination of the first carbon emission amount of each production process during the current production cycle is similar in principle to steps B1-B2 described above, and will not be elaborated upon here.

[0049] Following S204, the method provided in this application embodiment further includes: Step D1: Based on the proportion of total controlled carbon emissions and the first carbon emissions of each production process in the current production cycle, allocate sub-controlled carbon emissions for the next production cycle to each production process for staff reference. This decomposes the total controlled carbon emissions into multiple sub-controlled carbon emissions and allocates them to each production process, ensuring high reliability in meeting the carbon emission requirements of the next production cycle within the total controlled carbon emissions.

[0050] In addition, after step D1, the method provided in this application embodiment further includes: Step D2: Divide the next production cycle into multiple tracking and assessment time periods of equal duration.

[0051] The principle of step D2 is similar to that of step C1 described above, and will not be repeated here.

[0052] Step D3: For each tracking and assessment period, allocate assessment carbon emissions to each production process. The assessment carbon emissions are equal to the sub-controlled carbon emissions of the production process in the next production cycle divided by the number of tracking and assessment periods.

[0053] The principle of step D3 is similar to that of step C2 described above, and will not be repeated here.

[0054] Step D4: Obtain the second carbon emissions for each production process.

[0055] The principle of step D4 is similar to that of step C3 described above, and will not be repeated here.

[0056] Step D5: If the second carbon emission of any production process exceeds the allocated assessment carbon emission limit, send a carbon emission exceedance warning to the administrator terminal. The warning message includes the identifier of the production process and the tracking assessment period for exceeding the allocated assessment carbon emission limit.

[0057] Based on steps D1-D4 above, the total controlled carbon emissions are now decomposed into multiple sub-controlled carbon emissions and allocated to each production process. This ensures that the carbon emissions in the next production cycle are reliably within the total controlled carbon emissions. Furthermore, it enables early warning and control of carbon emissions at each production process at specific time points, thus ensuring that the carbon emissions in the next production cycle are reliably within the total controlled carbon emissions.

[0058] In addition, the method provided in this application embodiment also includes: determining the carbon cost of each production process in the steel production process; generating and storing carbon cost financial data, wherein the carbon cost financial data includes the identifier, name, carbon unit price and carbon cost of each production process, which can be used by staff to view the carbon cost of each production process later.

[0059] Specifically, the steel production process includes a first target process, a second target process, and a third target process. The first target process generates carbon products, which are then allocated proportionally to the second and third target processes. Methods for determining the carbon cost of each production process in steel production can include: Step E1: Obtain the sub-energy consumption data of the first target process in the steel production process during the current production cycle.

[0060] Step E2: Determine the carbon cost of the first target process in the current production cycle based on the sub-energy consumption data of the first target process, the preset carbon emission factor, and the carbon price.

[0061] Step E3: Determine the carbon cost of the second target process in the current production cycle based on the carbon cost and the proportion of carbon products allocated to the second target process.

[0062] Step E4: Determine the carbon cost of the third target process in the current production cycle based on the carbon cost and the proportion of carbon products allocated to the third target process.

[0063] For example, assuming the first target process is coking, the second target process is ironmaking, and the third target process is sintering, the coking process produces 5000 tons of coke with a total carbon cost of 1,200,000 yuan. Of this, 4000 tons of coke are supplied to the ironmaking process and 1000 tons to the sintering process. Therefore, the carbon cost received by the ironmaking process from the coking process is 1,200,000 × (4000 / 5000) = 960,000 yuan, and the carbon cost received by the sintering process from the coking process is 1,200,000 × (1000 / 5000) = 240,000 yuan. The carbon costs of other processes can be obtained by multiplying carbon emissions by the carbon price.

[0064] Additionally, please see Figure 3 This application also provides a carbon management device for steel production. It should be noted that the basic principle and technical effects of the carbon management device for steel production provided in this application are the same as those in the above embodiments. For the sake of brevity, any parts not mentioned in this application can be referred to the corresponding content in the above embodiments. The device provided in this application includes a data acquisition unit, a carbon performance data determination unit, a carbon performance data prediction unit, and a carbon emission output unit. The data acquisition unit is used to acquire energy consumption data and actual steel output of the steel production base during the current production cycle. The carbon performance data determination unit is used to determine the actual total carbon performance data of the steel production base based on energy consumption data, actual steel output, and preset emission factors during the current production cycle. The carbon performance data prediction unit is used to predict the first predicted total carbon performance data for the next production cycle based on the changing trend of the actual total carbon performance data of multiple historical production cycles and the actual total carbon performance data of the current production cycle, wherein the changing trend is downward. The carbon emissions output unit is used to output the total controlled carbon emissions of the steel production base in the next production cycle based on the first predicted total carbon performance data of the next production cycle and the recorded planned steel output of the next production cycle.

[0065] In some implementations, multiple production processes in the steel production process correspond to multiple different types of energy consumption sensors. The carbon performance data determination unit is specifically used to summarize multiple energy consumption data according to the categories of energy consumption sensors corresponding to the multiple energy consumption data, to obtain multiple different categories of energy consumption data; to determine the carbon emissions of each category according to the energy consumption data of each category and its corresponding emission factor; to summarize the carbon emissions of each category to obtain the total carbon emissions of the steel production base in the current production cycle; and to determine the actual total carbon performance data of the steel production base based on the total carbon emissions in the current production cycle and the actual steel output.

[0066] In some embodiments, the apparatus provided in this application further includes: The carbon market data generation unit is used to generate carbon market data in a preset format based on the basic information of the steel production base, the carbon emissions of each category, the actual total carbon performance data in the current production cycle, the energy consumption data of each category and its corresponding emission factor. The association establishment unit is used to generate hash values ​​based on carbon market data and establish associations between the hash values ​​and carbon market data. The data transmission unit is used to transmit carbon market data carrying hash values ​​to the environmental monitoring server 103 via the group server 102. The hash values ​​are used by the group server 102 and the environmental monitoring server 103 to verify whether the received carbon market data has been tampered with.

[0067] In some embodiments, the apparatus provided in this application further includes: Time period division unit, used to divide the next production cycle into multiple tracking and assessment time periods of equal duration; The carbon emission allocation unit is used to allocate assessment carbon emissions to each production process for each tracking and assessment period. The assessment carbon emissions are equal to the sub-controlled carbon emissions of the production process in the next production cycle divided by the number of tracking and assessment periods. The carbon emission acquisition unit is used to acquire the second carbon emission of each production process. The data sending unit is used to send a warning message about carbon emission exceeding the limit to the administrator terminal when the second carbon emission of any production process exceeds the allocated assessment carbon emission limit. The warning message carries the identifier of the production process and the tracking assessment period for exceeding the allocated assessment carbon emission limit.

[0068] In some embodiments, the apparatus provided in this application further includes: The carbon cost determination unit is used to determine the carbon cost of each production process in the steel production process. The financial data generation unit is used to generate and store carbon cost financial data, which includes the identifier, name, carbon unit price, and carbon cost of each production process.

[0069] In some embodiments, the steel production process includes a first target process, a second target process, and a third target process. The first target process generates carbon products, which are proportionally allocated to the second and third target processes. The carbon cost determination unit is specifically used to acquire sub-energy consumption data of the first target process in the steel production process during the current production cycle, and determine the carbon cost of the first target process during the current production cycle based on the sub-energy consumption data, a preset carbon emission factor, and a carbon price. Based on the carbon cost and the proportion of carbon products allocated to the second target process, the unit determines the carbon cost of the second target process during the current production cycle. Based on the carbon cost and the proportion of carbon products allocated to the third target process, the unit determines the carbon cost of the third target process during the current production cycle.

[0070] In some implementations, the carbon emission output unit is specifically used to obtain the second predicted total carbon performance data of the steel industry in the next production cycle from the group server 102; to perform a weighted summation of the first predicted total carbon performance data and the second predicted total carbon performance data to obtain the comprehensive predicted total carbon performance data; and to output the total controlled carbon emissions of the steel production base in the next production cycle based on the comprehensive predicted total carbon performance data and the recorded planned steel output for the next production cycle.

[0071] Figure 4 This is a schematic diagram of the structure of an electronic device according to one embodiment of this application. Please refer to it. Figure 4 At the hardware level, the electronic device includes a processor, and optionally also includes an internal bus, a network interface, and memory. The memory may include main memory, such as high-speed random-access memory (RAM), or non-volatile memory, such as at least one disk drive. Of course, the electronic device may also include other hardware required for other business operations.

[0072] The processor, network interface, and memory can be interconnected via an internal bus, which can be an ISA (Industry Standard Architecture) bus, a PCI (Peripheral Component Interconnect) bus, or an EISA (Extended Industry Standard Architecture) bus, etc. This bus can be divided into address bus, data bus, control bus, etc. For ease of representation, Figure 4 The symbol is represented by a single double-headed arrow, but this does not mean that there is only one bus or one type of bus.

[0073] Memory is used to store programs. Specifically, programs may include program code, which includes computer operation instructions. Memory may include main memory and non-volatile memory, and provides instructions and data to the processor.

[0074] The processor reads the corresponding computer program from non-volatile memory into memory and then runs it, forming a carbon management device for the steel production process at the logical level. The processor executes the program stored in memory and performs the method provided in the above embodiments of this application.

[0075] The methods described in the embodiments of this application can be applied to a processor or implemented by a processor. The processor may be an integrated circuit chip with signal processing capabilities. During implementation, each step of the above method can be completed by integrated logic circuits in the processor's hardware or by instructions in software form. The processor can be a general-purpose processor, including a Central Processing Unit (CPU), a Network Processor (NP), etc.; it can also be a Digital Signal Processor (DSP), an Application Specific Integrated Circuit (ASIC), a Field-Programmable Gate Array (FPGA), or other programmable logic devices, discrete gate or transistor logic devices, or discrete hardware components. It can implement or execute the methods, steps, and logic block diagrams disclosed in the embodiments of this application. The general-purpose processor can be a microprocessor or any conventional processor. The steps of the methods disclosed in the embodiments of this application can be directly manifested as execution by a hardware decoding processor, or execution by a combination of hardware and software modules in the decoding processor. The software module can reside in a mature storage medium in the field, such as random access memory, flash memory, read-only memory, programmable read-only memory, electrically erasable programmable memory, or registers. This storage medium is located in memory, and the processor reads information from the memory and, in conjunction with its hardware, completes the steps of the above method.

[0076] The electronic device can also perform Figure 2 The method, and the implementation of carbon management devices in the steel production process. Figure 2 The functions of the embodiments shown are not described in detail here.

[0077] Of course, in addition to software implementation, the electronic device in this application embodiment does not exclude other implementation methods, such as logic devices or a combination of hardware and software, etc. That is to say, the execution subject of the following processing flow is not limited to each logic unit, but can also be hardware or logic devices.

[0078] Furthermore, embodiments of this application also propose a computer-readable storage medium that stores one or more programs, each program including instructions that, when executed by an electronic device comprising multiple applications, enable the electronic device to perform the methods provided in the above embodiments of this application. Computer-readable media, including both permanent and non-permanent, removable and non-removable media, can store information using any method or technology. Information can be computer-readable instructions, data structures, program modules, or other data. Examples of computer storage media include, but are not limited to, phase-change memory (PRAM), static random-access memory (SRAM), dynamic random-access memory (DRAM), other types of random-access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), or any other non-transfer medium that can be used to store information accessible by a computing device.

[0079] It should also be noted that the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes said element.

[0080] In summary, the above descriptions are merely preferred embodiments of this application and are not intended to limit the scope of protection of this application. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of this application should be included within the scope of protection of this application.

Claims

1. A carbon management method in the steel production process, characterized in that, The method includes: Obtain energy consumption data and actual steel output of steel production bases during the current production cycle; Based on the energy consumption data, actual steel output, and preset emission factors during the current production cycle, the actual total carbon performance data of the steel production base during the current production cycle is determined. Based on the recorded total carbon performance data from multiple historical production cycles and the changing trend of the total carbon performance data from the current production cycle, a first predicted total carbon performance data for the next production cycle is predicted, wherein the changing trend is a downward trend. Based on the first predicted total carbon performance data for the next production cycle and the recorded planned steel production for the next production cycle, the total controlled carbon emissions of the steel production base for the next production cycle are output.

2. The method according to claim 1, characterized in that, The steel production process involves multiple production steps, each corresponding to a different type of energy consumption sensor. The energy consumption data for the current production cycle includes multiple data points collected by these sensors. Determining the actual total carbon performance data of the steel production base based on the energy consumption data for the current production cycle, actual steel production, and a preset emission factor includes: The multiple energy consumption data are summarized according to the category of the energy consumption sensor corresponding to each of the multiple energy consumption data, resulting in multiple categories of energy consumption data; Based on the energy consumption data of each category and its corresponding emission factor, determine the carbon emissions of each category; The total carbon emissions of the steel production base during the current production cycle are obtained by summing up the carbon emissions of each category. The actual total carbon performance data of the steel production base is determined based on the total carbon emissions and actual steel production during the current production cycle.

3. The method according to claim 2, characterized in that, After determining the actual total carbon performance data of the steel production base based on the total carbon emissions and actual steel production during the current production cycle, the method further includes: Based on the preset basic information of the steel production base, the carbon emissions of each category, the actual total carbon performance data in the current production cycle, the energy consumption data of each category and its corresponding emission factor, carbon market data in a preset format is generated. A hash value is generated based on the carbon market data, and a correlation is established between the hash value and the carbon market data. The carbon market data carrying the hash value is transmitted from the group server to the environmental monitoring server, wherein the hash value is used by the group server and the environmental monitoring server to verify whether the received carbon market data has been tampered with.

4. The method according to claim 1, characterized in that, After outputting the total controlled carbon emissions of the steel production base in the next production cycle, the method further includes: Divide the next production cycle into multiple tracking and assessment periods of equal duration; A carbon emission assessment amount is allocated to each of the tracking and assessment periods, wherein the carbon emission assessment amount is equal to the total controlled carbon emission amount of the next production cycle divided by the number of tracking and assessment periods; Obtain the total carbon emissions of the steel production base during the tracking and assessment period; If the total carbon emissions exceed the allocated assessment carbon emissions, a warning message indicating excessive carbon emissions will be sent to the administrator terminal.

5. The method according to claim 1, characterized in that, The method further includes: Obtain sub-energy consumption data for multiple production processes in the steel production process during the current production cycle; Based on the sub-energy consumption data of the multiple production processes and the preset carbon emission factor, determine the first carbon emission amount of each of the production processes in the current production cycle; After outputting the total controlled carbon emissions of the steel production base for the next production cycle, the method further includes: Based on the ratio of the total controlled carbon emissions to the first carbon emissions of each of the production processes in the current production cycle, a sub-controlled carbon emissions amount for the next production cycle is allocated to each of the production processes.

6. The method according to claim 5, characterized in that, After allocating sub-controlled carbon emissions for the next production cycle to each of the aforementioned production processes, the method further includes: Divide the next production cycle into multiple tracking and assessment periods of equal duration; For each of the tracking and assessment periods, an assessment carbon emission amount is allocated to each of the production processes, wherein the assessment carbon emission amount is equal to the sub-controlled carbon emission amount of the production process in the next production cycle divided by the number of tracking and assessment periods. Obtain the second carbon emissions for each production process; If the second carbon emission of any production process exceeds the allocated assessment carbon emission limit, a carbon emission exceedance warning message is sent to the administrator terminal. The warning message carries the identifier of the production process and the tracking assessment period for exceeding the allocated assessment carbon emission limit.

7. The method according to claim 1, characterized in that, The method further includes: Determine the carbon cost of each production step in the steel production process; Generate and store carbon cost financial data, wherein the carbon cost financial data includes the identifier, name, carbon unit price, and carbon cost of each production process.

8. The method according to claim 7, characterized in that, The steel production process includes a first target process, a second target process, and a third target process. The first target process generates carbon products, which are proportionally allocated to the second and third target processes. Determining the carbon cost of each production process in the steel production process includes: Obtain sub-energy consumption data for the first target process in the steel production process during the current production cycle. Based on the sub-energy consumption data of the first target process, the preset carbon emission factor, and the carbon price, determine the carbon cost of the first target process in the current production cycle; The carbon cost of the second target process in the current production cycle is determined based on the carbon cost and the proportion of carbon products allocated to the second target process. The carbon cost of the third target process in the current production cycle is determined based on the carbon cost and the proportion of carbon products allocated to the third target process.

9. The method according to any one of claims 1-8, characterized in that, The method, based on the first predicted total carbon performance data for the next production cycle and the recorded planned steel production for the next production cycle, outputs the total controlled carbon emissions of the steel production base for the next production cycle, including: Obtain the second forecast total carbon performance data for the steel industry in the next production cycle from the group's server; The first predicted total carbon performance data and the second predicted total carbon performance data are weighted and summed to obtain the comprehensive predicted total carbon performance data. Based on the comprehensive forecast of total carbon performance data and the planned steel production for the next production cycle, the total controlled carbon emissions of the steel production base in the next production cycle are output.

10. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the computer program, it causes the electronic device to perform the method as described in any one of claims 1 to 9.

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