Enterprise carbon emission calculation and evaluation method and system and storage medium

Abstract

The invention relates to the technical field of carbon emission calculation, in particular to an enterprise carbon emission calculation evaluation method and system and a storage medium. The method comprises the following steps: collecting boundary material inflow information and material outflow information of an enterprise production system to obtain material flow data; establishing a material balance equation based on the material flow data, and tracking a flow path of carbon elements through the material balance equation to obtain a carbon element balance table; and according to preset standard state thermodynamic parameters and chemical reaction equations, performing combustion condition and stoichiometric unit fuel combustion on the carbon element balance table to generate carbon emission, and obtaining direct carbon emission data. According to the method, direct and indirect carbon emission and carbon sink contribution are integrated, the total carbon emission of the enterprise is dynamically summarized and counteracted, net carbon emission data are generated, and a quantitative basis is provided for a carbon neutralization target of the enterprise.

Description

Technical Field

[0001] The present invention relates to the technical field of carbon emission calculation, and particularly to a method, a system and a storage medium for calculating and evaluating enterprise carbon emissions. Background Art

[0002] Carbon emissions refer to the total amount of greenhouse gases directly or indirectly generated by human activities. The most important gas among them is carbon dioxide, so the term "carbon emissions" is often used to refer to carbon dioxide emissions. The accounting of carbon emissions is an important support for achieving carbon emission reduction goals and participating in international climate change negotiations. It can directly quantify carbon emission data and find potential emission reduction links and methods by analyzing carbon emission data in each link. Enterprise carbon emissions refer to all carbon dioxide emissions generated by enterprises during the production, activities and services, expressed in the form of carbon dioxide equivalent.

[0003] However, the methods for calculating and evaluating enterprise carbon emissions often have the following problems: Many existing methods only focus on some direct carbon emissions (such as fuel combustion), while ignoring the flow path of carbon elements, indirect carbon emissions and potential carbon sink effects during the production process, resulting in one-sided evaluation results. In traditional methods, the calculation of carbon emissions mostly uses static models, ignoring the dynamic characteristics of enterprise production and energy use and the differences in the energy structure of regional power grids, and cannot meet the needs of complex industrial scenarios. Summary of the Invention

[0004] Based on this, it is necessary for the present invention to provide a method, a system and a storage medium for calculating and evaluating enterprise carbon emissions to solve at least one of the above technical problems.

[0005] To achieve the above object, a method for calculating and evaluating enterprise carbon emissions includes the following steps:

[0006] Step S1: Collect the boundary material inflow information and material outflow information of the enterprise production system to obtain material flow data, where the material flow information includes raw material feed quantity, fuel consumption quantity and auxiliary material usage quantity, and the material outflow information includes product output quantity, waste gas emission quantity and solid waste generation quantity;

[0007] Step S2: Establish a material balance equation based on the material flow data, and trace the flow path of carbon elements through the material balance equation to obtain a carbon element balance table;

[0008] Step S3: Calculate the carbon emissions generated by unit fuel combustion based on combustion conditions and stoichiometry for the carbon element balance table according to the preset standard state thermodynamic parameters and chemical reaction equations to obtain direct carbon emission data;

[0009] Step S4: Obtain the enterprise's electricity consumption data, and according to the pre-obtained electricity energy structure data of the regional power grid; determine the coal consumption coefficient for power generation based on the weighted average method according to the enterprise's electricity consumption data and the electricity energy structure data, and convert the electricity consumption into carbon emissions to obtain the indirect carbon emissions data;

[0010] Step S5: Calculate the carbon storage based on the biomass equation and the root-shoot ratio model for the preset enterprise greening data to obtain the carbon storage data; perform carbon sink estimation and correction processing on the carbon storage data according to the carbon element balance sheet to obtain the carbon sink fixation data;

[0011] Step S6: Summarize the total carbon emissions based on the direct carbon emissions data and the indirect carbon emissions data to obtain the enterprise's total carbon emissions data; generate the enterprise's carbon sink contribution data based on the carbon sink fixation data; perform carbon emission offset processing on the enterprise's total carbon emissions data and the enterprise's carbon sink contribution data to obtain the enterprise's net carbon emissions data.

[0012] By collecting the material inflow and outflow data of the enterprise production system, the present invention can comprehensively grasp the quantities of raw materials, fuels, and auxiliary materials used by the enterprise during the production process. At the same time, it also understands the product output, waste gas emissions, and solid waste generation. These data are the basis for carbon emission calculations, providing comprehensive material flow information and making subsequent carbon emission analyses more accurate. Through the material balance equation, the flow path of carbon elements during the enterprise production process can be traced, thereby forming a carbon element balance sheet. This process can clearly show the input, transformation, and output of carbon elements within the enterprise, ensuring the comprehensiveness and accuracy of carbon emission calculations. Based on thermodynamic parameters and chemical reaction equations, and on the theory of combustion conditions and stoichiometry, the carbon emissions generated by the combustion of unit fuel are calculated. This can help enterprises quantify the direct carbon emissions during the production process, especially the emissions caused by fuel consumption, which is of great significance for enterprise carbon management. By obtaining the enterprise's electricity consumption data and the energy structure information of the power grid, the weighted average method is used to calculate the coal consumption coefficient for power generation, thereby converting the electricity consumption into carbon emissions. This method can accurately estimate the indirect carbon emissions caused by external power supply, ensuring that enterprises do not overlook the impact of external factors when calculating the total carbon emissions. By calculating the carbon storage brought by enterprise greening based on the biomass equation and the root-shoot ratio model and correcting the carbon sink data, the carbon sink contribution of the enterprise can be estimated, helping to reduce the carbon emissions of the enterprise. This process helps enterprises reasonably consider the positive effects of greening and carbon storage when calculating the net carbon emissions and increase the carbon sink contribution. By aggregating the direct carbon emissions and indirect carbon emissions, the total carbon emissions of the enterprise are obtained. Adding the contribution of the carbon sink fixation amount and performing carbon emission offset processing, the net carbon emissions of the enterprise are finally obtained. This step can provide a comprehensive carbon emission result, helping enterprises understand their overall environmental impact and take corresponding emission reduction measures. Through the above steps, enterprises can comprehensively and accurately calculate their carbon emissions and make corrections through methods such as carbon sinks, finally obtaining accurate net carbon emission data. This not only helps enterprises formulate environmental policies that meet carbon emission standards but also provides a scientific basis for enterprise carbon emission reduction, promoting enterprises to transform towards low-carbon and green production, which is in line with the current globally advocated sustainable development and carbon neutrality goals.

[0013] The present invention also provides an enterprise carbon emission calculation and evaluation system for implementing the above-mentioned enterprise carbon emission calculation and evaluation method. The enterprise carbon emission calculation and evaluation system includes:

[0014] A material flow collection module for collecting the boundary material inflow information and material outflow information of the enterprise production system to obtain material flow data, where the material flow information includes the raw material feed quantity, fuel consumption quantity, and auxiliary material usage quantity, and the material outflow information includes the product output quantity, waste gas emission quantity, and solid waste generation quantity;

[0015] A carbon element balance calculation module, which is used to establish a material balance equation based on material flow data, and track the flow path of carbon elements through the material balance equation to obtain a carbon element balance table;

[0016] A direct carbon emission measurement module, which is used to calculate the carbon emissions generated by the combustion of unit fuel based on combustion conditions and stoichiometry for the carbon element balance table according to the preset standard state thermodynamic parameters and chemical reaction equations, and obtain direct carbon emission data;

[0017] An indirect carbon emission conversion module, which is used to obtain the enterprise's electricity consumption data, and according to the pre-obtained electricity energy structure data of the regional power grid; determine the coal consumption coefficient for power generation based on the weighted average method according to the enterprise's electricity consumption data and the electricity energy structure data, and convert the electricity consumption into carbon emissions to obtain indirect carbon emission data;

[0018] A carbon sink estimation module, which is used to calculate the carbon storage based on the biomass equation and the root-shoot ratio model for the preset enterprise greening data to obtain carbon storage data; perform carbon sink estimation correction processing on the carbon storage data according to the carbon element balance table to obtain carbon sink fixation data;

[0019] A net carbon emission offset module, which is used to summarize the total carbon emissions according to the direct carbon emission data and the indirect carbon emission data to obtain the enterprise's total carbon emission data; generate the enterprise's carbon sink contribution data according to the carbon sink fixation data; perform carbon emission offset processing on the enterprise's total carbon emission data and the enterprise's carbon sink contribution data to obtain the enterprise's net carbon emission data.

[0020] The present invention also provides a storage medium, including:

[0021] A memory, which is used to store a computer program;

[0022] A processor, which is used to implement the above-mentioned enterprise carbon emission calculation and evaluation method when executing the computer program.

[0023] The present invention provides a comprehensive and accurate carbon emission assessment system for enterprises through the collaborative work of each module, helping enterprises effectively track and optimize their carbon footprints and promoting the achievement of sustainable development goals. First, the material flow collection module collects information on the inflow and outflow of boundary materials in the enterprise production system to obtain the usage amounts of raw materials, fuels, and auxiliary materials, as well as the production amounts of products, waste gases, and solid wastes. This data collection provides basic data support for subsequent links and ensures the accuracy of carbon emission calculations. The carbon element balance calculation module establishes a material balance equation based on the material flow data, tracks the flow path of carbon elements, and generates a carbon element balance sheet. This process ensures the comprehensive monitoring and accurate assessment of carbon emissions by precisely calculating the input and output of carbon elements, avoiding carbon emission errors caused by miscalculation or incorrect calculation. The direct carbon emission measurement module combines thermodynamic parameters and chemical reaction equations to calculate the combustion conditions and stoichiometry of the carbon element balance sheet, obtaining the direct carbon emissions. This module can quantify the direct carbon emissions in the enterprise production process based on the combustion characteristics of fuels, helping enterprises accurately grasp the carbon emissions in their production links. The indirect carbon emission conversion module determines the coal consumption coefficient for power generation by using the weighted average method by obtaining the enterprise's electricity consumption data and combining it with the electricity energy structure data of the regional power grid, and converts the electricity consumption into carbon emissions. This step provides clear indirect carbon emission data for enterprises, helping them understand the impact of their electricity use on carbon emissions and providing a basis for reducing electricity consumption and optimizing the energy structure. The carbon sink estimation module processes the enterprise's greening data, calculates the carbon storage through the biomass equation and the root-shoot ratio model, and corrects the carbon storage data according to the carbon element balance sheet, finally obtaining the carbon sink fixation amount. This module provides data support for the enterprise's carbon storage capacity, enabling it to evaluate the carbon sink benefits of greening projects on the basis of carbon emission control and enhancing its sustainable development ability. Finally, the net carbon emission offset module calculates the total carbon emissions of the enterprise by comprehensively considering the direct carbon emissions and indirect carbon emissions, generates carbon sink contribution data in combination with the carbon sink fixation amount, and performs carbon emission offset processing to obtain the enterprise's net carbon emissions. This module provides a clear picture of the enterprise's carbon emissions, helps it optimize its carbon emission management strategy, reduce overall carbon emissions, meet environmental protection requirements, and promote the enterprise to achieve green development. Overall, through the collaborative work of these modules, enterprises can achieve precise carbon emission monitoring, carbon emission reduction, and carbon sink optimization, providing important data support and decision-making basis for achieving low-carbon development goals. BRIEF DESCRIPTION OF THE DRAWINGS

[0024] Other features, objects, and advantages of the present invention will become more apparent by reading the detailed description of the non-limiting embodiments with reference to the following drawings:

[0025] Figure 1 It is a schematic flow chart of the steps of the enterprise carbon emission calculation and assessment method of the present invention;

[0026] Figure 2 is Figure 1 a detailed process flow diagram of step S1 in

[0027] Figure 3 is Figure 1 a detailed process flow diagram of step S2 in Specific implementation manners

[0028] The technical method of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are part of the embodiments of the present invention, rather than all of the embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts fall within the scope of protection of the present invention.

[0029] In addition, the accompanying drawings are only schematic diagrams of the present invention and are not necessarily drawn to scale. The same reference numerals in the drawings represent the same or similar parts, and thus repeated descriptions thereof will be omitted. Some of the block diagrams shown in the drawings are functional entities and do not necessarily correspond to physically or logically independent entities. The functional entities can be implemented in software, or in one or more hardware modules or integrated circuits, or in different networks and / or processor methods and / or microcontroller methods.

[0030] It should be understood that although terms such as "first" and "second" may be used herein to describe various units, these units should not be limited by these terms. These terms are only used to distinguish one unit from another. For example, without departing from the scope of the exemplary embodiments, the first unit may be referred to as the second unit, and similarly the second unit may be referred to as the first unit. The term "and / or" used herein includes any and all combinations of one or more of the listed associated items.

[0031] To achieve the above object, please refer to Figures 1 to 3 , the present invention provides an enterprise carbon emission calculation and evaluation method, and the method includes the following steps:

[0032] Step S1: Collect the boundary material inflow information and material outflow information of the enterprise production system to obtain material flow data, where the material flow information includes raw material feed quantity, fuel consumption, and auxiliary material usage, and the material outflow information includes product output quantity, waste gas emission quantity, and solid waste generation quantity;

[0033] In an embodiment of the present invention, for the production system of a chemical enterprise, devices such as flow meters, mass sensors, and on-line chemical analyzers are first deployed and installed at the raw material inlet, fuel delivery pipeline, auxiliary material feeding point, product output end, waste gas emission pipeline, and solid waste treatment equipment respectively. These devices collect data such as feed quantity, consumption quantity, and emission quantity in real time. For example, the enterprise annually feeds 5000 tons of methanol and 3000 tons of ethylene, consumes 2000 tons of natural gas as fuel, and uses 50 tons of catalyst auxiliary materials annually. For the material outflow, an infrared gas analyzer is installed to on-line monitor the concentration of CO 2 in the waste gas. The results show that the annual CO 2 emission in the waste gas is 12000 tons. At the same time, the annual output of waste catalyst in solid waste is measured to be 20 tons by a weighing meter. The annual output of the product polyethylene is measured to be 7000 tons through a weighing system. All material flow data are automatically uploaded to the database of the enterprise environmental management information system for storage and classification processing, forming a complete material flow data set.

[0034] Step S2: Establish a material balance equation based on the material flow data, and trace the flow path of carbon elements through the material balance equation to obtain a carbon element balance table;

[0035] In an embodiment of the present invention, based on the material flow data collected in Step S1, a material balance equation of the enterprise is constructed: input quantity = output quantity + loss quantity, with carbon elements as the tracking object. For methanol (CH 3 OH), the mass proportion of carbon elements in the molecule is 37.5%, that is, each ton of methanol contains 0.375 tons of carbon; for ethylene (C 2 H 4 ), the carbon mass proportion is 85.7%. The carbon mass proportion in natural gas as fuel is 75%. By performing mass balance calculations on the carbon content of raw material feeding, fuel consumption, product output, waste gas and solid waste emissions respectively, the following results are obtained: the total annual carbon input is 1875 tons of methanol, 2571 tons of ethylene, and 1500 tons of natural gas, with a total carbon input of 5946 tons; the annual carbon output is 4900 tons in products, 330 tons in waste gas, and 8 tons in solid waste, with a total carbon output of 5238 tons, and the difference of 708 tons is classified as system loss or other unknown flow directions. Based on the above data, a carbon element balance table is formed, which clearly marks the carbon distribution ratio and flow direction of each logistics node.

[0036] Step S3: According to the preset standard state thermodynamic parameters and chemical reaction equations, calculate the carbon emissions generated by the combustion of unit fuel based on the combustion conditions and stoichiometry for the carbon element balance table to obtain direct carbon emission data;

[0037] In an embodiment of the present invention, relevant data on natural gas combustion are extracted from the carbon element balance table obtained in Step S2, and combined with the chemical reaction formula for natural gas combustion under standard conditions CH4 +2O 2 →CO 2 +2H 2 O. According to stoichiometric calculations, when 1 cubic meter of natural gas is completely burned, 2.75 kg of CO is generated 2 , and at the same time, based on the known lower calorific value of natural gas being 35.8 MJ / m 3 , it can be deduced that the carbon emissions generated per ton of natural gas burned is 2750 kg of CO 2 . The annual fuel consumption of natural gas by the enterprise is 2000 tons, and the carbon content in natural gas in the carbon balance sheet is 1500 tons. After calculation, the direct carbon emissions are 2000 tons of natural gas × 2750 kg of CO 2 / ton of natural gas = 5500 tons of CO 2 . Further combined with the combustion efficiency adjustment factor (assumed to be 99%), the adjusted direct carbon emissions are 5445 tons of CO 2 .

[0038] Step S4: Obtain the enterprise's electricity consumption data, and based on the pre-obtained electricity energy structure data of the regional power grid; determine the coal consumption coefficient for power generation using the weighted average method according to the enterprise's electricity consumption data and the electricity energy structure data, and convert the electricity consumption into carbon emissions to obtain the indirect carbon emissions data;

[0039] In the embodiment of the present invention, the annual electricity consumption data of the enterprise is obtained as 15 million kWh. From the electricity energy structure data of the regional power grid, it is known that: the proportion of coal-fired power generation is 60%, the proportion of natural gas power generation is 20%, and the proportion of renewable energy power generation is 20%. The coal consumption coefficient of coal-fired power generation is 0.8 kg / kWh, the coal consumption coefficient of natural gas power generation is 0.5 kg / kWh, and the carbon emission coefficient of renewable energy power generation is 0. Using the weighted average method to calculate the comprehensive coal consumption coefficient of the regional power grid: 0.6×0.8 + 0.2×0.5 + 0.2×0 = 0.58 kg / kWh. Then multiply the annual electricity consumption of the enterprise by the comprehensive coal consumption coefficient to obtain the indirect carbon emissions of 15 million kWh × 0.58 kg / kWh = 8700 tons of CO 2 .

[0040] Step S5: Calculate the carbon storage based on the biomass equation and the root-shoot ratio model for the preset enterprise greening data to obtain the carbon storage data; perform carbon sink estimation and correction processing on the carbon storage data according to the carbon element balance sheet to obtain the carbon sink fixation data;

[0041] In the embodiment of the present invention, a detailed investigation is carried out on the greening area of the enterprise. The total greening area is 20 hectares, and the main vegetation type is fast-growing poplar trees. Select an individual poplar tree with a breast diameter of 25 cm as a representative sample, and use the biomass equation W = 0.1×D 2.5 to calculate the biomass of a single poplar tree, where D is the breast diameter. Substitute D = 25 cm to get W = 0.1×252.5 = 157 kg. The root-shoot ratio of the poplar tree is 1:4. The above-ground biomass is 125.6 kg, and the below-ground biomass is 31.4 kg. Calculated according to the carbon storage coefficient of 50%, the carbon storage of a single poplar tree is 78.5 kg. There are 8,000 plants in the greening area, and the total carbon storage is 78.5×8,000 = 628 tons of carbon. Considering the plant metabolic loss ratio of 3% in the carbon balance table, the corrected carbon sink fixation data is 609 tons of carbon.

[0042] Step S6: Summarize the total carbon emissions based on the direct carbon emission data and the indirect carbon emission data to obtain the enterprise's total carbon emission data; generate the enterprise's carbon sink contribution data based on the carbon sink fixation data; perform carbon emission offset processing on the enterprise's total carbon emission data and the enterprise's carbon sink contribution data to obtain the enterprise's net carbon emission data.

[0043] In the embodiment of the present invention, the direct carbon emissions of 5,445 tons calculated in step S3 and the indirect carbon emissions of 8,700 tons calculated in step S4 are added together to obtain the enterprise's total carbon emissions of 14,145 tons of CO 2 . At the same time, the carbon sink fixation data obtained from step S5 is 609 tons, which is used as the carbon sink contribution value for carbon emission offset processing. Finally, by subtracting the carbon sink fixation from the total carbon emissions, the enterprise's net carbon emissions are calculated as 14,145 tons - 609 tons = 13,536 tons of CO 2 . The enterprise generates an annual carbon emission report based on this net carbon emission, detailing the measures and effects of carbon emission reduction and carbon sink increase, for submitting a carbon emission management plan to the regulatory agency.

[0044] By collecting the material inflow and outflow data of the enterprise production system, it is possible to comprehensively grasp the quantities of raw materials, fuels, and auxiliary materials used by the enterprise during the production process. At the same time, it is also possible to understand the situation of product output, waste gas emissions, and solid waste generation. These data are the basis for carbon emission calculations, providing comprehensive material flow information and making subsequent carbon emission analyses more accurate. Through the material balance equation, it is possible to trace the flow path of carbon elements during the enterprise production process, thereby forming a carbon element balance sheet. This process can clearly show the input, transformation, and output of carbon elements within the enterprise, ensuring the comprehensiveness and accuracy of carbon emission calculations. Based on thermodynamic parameters and chemical reaction equations, and on the theory of combustion conditions and stoichiometry, the carbon emissions generated by the combustion of unit fuel are calculated. This can help the enterprise quantify the direct carbon emissions during the production process, especially the emissions caused by fuel consumption, which is of great significance for the enterprise's carbon management. By obtaining the enterprise's electricity consumption data and the energy structure information of the power grid, the weighted average method is used to calculate the coal consumption coefficient for power generation, thereby converting the electricity consumption into carbon emissions. This method can accurately estimate the indirect carbon emissions caused by external power supply, ensuring that the enterprise does not ignore the impact of external factors when calculating the total carbon emissions. By calculating the carbon storage brought about by the enterprise's greening based on the biomass equation and the root-shoot ratio model, and correcting the carbon sink data, the carbon sink contribution of the enterprise can be estimated, helping to reduce the enterprise's carbon emissions. This process helps the enterprise reasonably consider the positive effects of greening and carbon storage when calculating the net carbon emissions, increasing the carbon sink contribution. By aggregating the direct carbon emissions and indirect carbon emissions, the total carbon emissions of the enterprise are obtained. Adding the contribution of the carbon sink fixation amount and performing carbon emission offset processing, the net carbon emissions of the enterprise are finally obtained. This step can provide a comprehensive carbon emission result, helping the enterprise understand its overall environmental impact and take corresponding emission reduction measures. Through the above steps, the enterprise can comprehensively and accurately calculate its carbon emissions, and make corrections through methods such as carbon sinks, and finally obtain accurate net carbon emission data. This not only helps the enterprise formulate environmental policies that meet carbon emission standards, but also provides a scientific basis for the enterprise's carbon emission reduction, promoting the enterprise's transformation towards low-carbon and green production, which is in line with the current global advocacy of sustainable development and carbon neutrality goals.

[0045] Preferably, step S1 includes the following steps:

[0046] Step S11: Obtain the process flow diagram of the enterprise production system, identify and number the system boundary nodes of the process flow diagram, so as to obtain the material flow monitoring point data, where the material flow monitoring point data includes the spatial position data of the raw material inlet, fuel supply port, auxiliary material feeding port, product outlet, waste gas emission port, and solid waste collection port;

[0047] In an embodiment of the present invention, for example, for a cement manufacturing enterprise, first, a process flow chart of its production system is obtained through on-site investigation and process analysis, including main links such as raw material processing, clinker burning, and finished product packaging. Key material flow nodes are marked on the process flow chart, including raw material inlet (such as the inlet of the limestone conveyor belt), fuel supply port (coal powder supply pipeline), auxiliary material feeding port (such as gypsum and slag addition ports), product outlet (finished cement bagging outlet), waste gas emission port (kiln tail chimney), and solid waste collection port (such as waste catalyst collection point). The spatial position data of each node is measured using GIS positioning technology. Specifically, for example, the inlet of the limestone conveyor belt is located in the southeast corner of the factory area, with coordinates (100.5, 200.3), and the kiln tail chimney is located in the northwest corner of the factory area, with coordinates (50.2, 300.7). Subsequently, each node is uniquely numbered. For example, the inlet is numbered P1, the fuel supply port is numbered P2, and the waste gas emission port is numbered P5, thus forming a data table of material flow monitoring points containing information such as node type, number, and spatial position.

[0048] Step S12: Install flow measurement devices at each monitoring point according to the material flow monitoring point data and perform calibration processing to obtain the detection accuracy data of the flow measurement devices;

[0049] In an embodiment of the present invention, according to the monitoring point data generated in step S11, an electronic belt scale is installed at the raw material inlet numbered P1 to monitor the conveying volume of limestone in real time, an ultrasonic flowmeter is installed at the fuel supply port numbered P2 to monitor the coal powder conveying speed, and a differential pressure flowmeter is installed at the waste gas emission port numbered P5 to monitor the waste gas flow rate. After installation, each flow measurement device is calibrated using a standard calibration tool. For example, standard weights of known weights (such as 50 kg and 100 kg) are placed on the electronic belt scale to verify the measurement accuracy; for the ultrasonic flowmeter, the error between the detected value and the actual flow rate is calibrated through a standard fluid flow device, and the result shows that its detection error is less than 1%. After calibration, the detection accuracy data of each flow measurement device is recorded. For example, the detection error of the electronic belt scale is ±0.5%, and the detection error of the ultrasonic flowmeter is ±0.8%, and the accuracy data is archived and stored for subsequent data processing.

[0050] Step S13: Set the data acquisition time interval and sampling frequency according to the detection accuracy data, and perform continuous data acquisition processing on the monitoring points to obtain the original material flow data, where the original material flow data includes the instantaneous flow values of each monitoring point at different time points;

[0051] According to the detection accuracy data obtained in step S12, the present invention embodiment sets the data acquisition time interval to 30 seconds and the sampling frequency to 1 Hz to ensure efficient data acquisition within the allowable accuracy range. At the raw material inlet numbered P1, continuous monitoring is carried out through an electronic belt scale, and the instantaneous limestone flow rate is recorded every 30 seconds. The data includes a timestamp and a flow value. For example, a record at a certain moment is: time "2024-12-31 10:00:30", and the instantaneous flow rate is 500 tons per hour. At the fuel supply port numbered P2, the pulverized coal flow rate is collected through an ultrasonic flowmeter, and similar data formats are recorded. The flow rate data of all monitoring points are synchronously collected and uploaded to the enterprise data center server to form an original material flow dataset. For example, the original data for a certain day includes multiple records at each point: P1 (500 tons per hour, 480 tons per hour), P2 (50 tons per hour, 48 tons per hour), P5 (3000 cubic meters per hour, 2950 cubic meters per hour).

[0052] Step S14: Conduct statistical tests and data repair processing on the outliers and missing values in the original material flow data, so as to obtain the corrected material flow data, where the data repair processing smooths the outliers using the moving average method and supplements the missing values using the interpolation method.

[0053] In the present invention embodiment, from the original material flow data obtained in step S13, outlier detection is first performed. For example, the 3σ principle is used to determine whether the flow value exceeds three standard deviation ranges of the mean. The instantaneous flow rate of 600 tons per hour at a certain sampling data such as P1 exceeds the reasonable range and is marked as an outlier. Subsequently, the moving average method is used to smooth the outlier. For example, the average value of the previous and subsequent five sampling points is taken as the correction value, and after repair, the data is adjusted to 490 tons per hour. For the missing value caused by equipment failure during the sampling process, for example, the flow rate data of P2 is null at a certain time point, the interpolation method is used to supplement it. The calculation method is to take the mean value of the previous and subsequent two valid data. Assuming that the previous and subsequent values are 50 tons per hour and 48 tons per hour respectively, the supplemented value is 49 tons per hour. After repair, the corrected material flow data forms a complete form including time, node number, and flow value, providing accurate basic data for subsequent analysis.

[0054] The present invention can accurately and real-time monitor and correct the material flow data in its production process, so as to ensure the accurate calculation and management of environmental indicators such as carbon emissions. First, step S11 helps to clarify the key monitoring points of each material flow, such as raw material inlet, fuel supply port, auxiliary material feeding port, product outlet, waste gas emission port and solid waste collection port, by obtaining the process flow chart of the enterprise production system and identifying and numbering the system boundary nodes. The spatial position data of these monitoring points provides a clear reference framework for subsequent data collection and monitoring, which helps to achieve full-process monitoring, non-blind-spot data collection and carbon emission analysis. Secondly, in step S12, flow measurement devices are installed and calibrated according to the material flow monitoring point data to ensure the data collection accuracy of each monitoring point and ensure that the material flow information reflected is true and accurate. The implementation of this link not only improves the reliability of the monitoring equipment, but also provides a scientific measurement standard for data analysis. Next, in step S13, appropriate acquisition time intervals and sampling frequencies are set according to the detection accuracy data, and then continuous data acquisition is carried out. This high-frequency dynamic data acquisition can ensure that the changes in material flow during the production process are reflected in real time, and the instantaneous flow data of each monitoring point at different time points can be obtained. These original data provide an important basis for subsequent carbon emission calculation and other environmental impact analysis. Through continuous monitoring, the enterprise can accurately track and control each link of the production system, and ensure the accuracy and timeliness of data from the source. Finally, in step S14, statistical tests and repair processes are carried out on the outliers and missing values in the original material flow data. The moving average method is used to smooth the outliers, and the interpolation method is used to supplement the missing values. This repair process can effectively improve the integrity and reliability of the data, and ensure the accuracy of the material flow data, so as not to affect the subsequent analysis results due to data missing or deviation. To sum up, through these steps, the enterprise can obtain comprehensive and accurate material flow data, and ensure the data quality through data repair and continuous monitoring, so as to provide solid data support for carbon emission calculation, resource utilization efficiency analysis and environmental management decision-making. The implementation of these steps not only improves the accuracy and timeliness of data collection, but also enhances the monitoring ability of the enterprise in the process of realizing green production and carbon neutrality goals, and provides a scientific basis for the sustainable development of the enterprise.

[0055] Preferably, step S2 includes the following steps:

[0056] Step S21: Classify and summarize various material flows and perform unit conversion processing according to the material flow data, so as to obtain standard material flow data;

[0057] In an embodiment of the present invention, taking a chemical enterprise as an example, for the material flow data obtained from its production system, first, raw materials, fuels, auxiliary materials, products, waste gases, and solid wastes are classified according to their material properties. For example, pulverized coal is classified as fuel, and limestone is classified as raw material. The flow units of each material are uniformly converted. All flow data are uniformly converted into standard measurement units. For example, the conveying amount of pulverized coal is converted from 50 tons / hour to kilograms / second, and the conversion formula is 50×1000 / 3600, and the result is 13.89 kilograms / second. The waste gas flow is converted from cubic meters / hour to standard cubic meters / second, and the conversion coefficient is calculated using the temperature and pressure conditions of the standard state. After all unit conversions are completed, a standard material flow data table containing material classification, flow value, and timestamp is generated, providing basic data for subsequent analysis.

[0058] Step S22: Perform elemental composition analysis on the carbon-containing substances entering the system according to the standard material flow data, so as to obtain the carbon content data of the substances flowing into the system;

[0059] In an embodiment of the present invention, according to the standard material flow data generated in step S21, elemental composition analysis is performed on the raw materials, fuels, and auxiliary materials entering the system. Taking pulverized coal as an example, its carbon element content is detected by an elemental analyzer, and the carbon content of the pulverized coal is obtained as 80%. According to the standard flow data of the pulverized coal (13.89 kilograms / second), the carbon content flowing into the system is calculated as 13.89×0.8 = 11.11 kilograms / second. The same detection is performed on limestone. Assuming its carbon element content is 10% and the flow rate is 1000 kilograms / second, the carbon content is 1000×0.1 = 100 kilograms / second. The detection data of all carbon-containing substances are summarized to generate a carbon content data table of the substances flowing into the system, which includes the substance name, flow rate, carbon content ratio, and carbon content value.

[0060] Step S23: Perform elemental composition analysis on the carbon-containing substances flowing out of the system according to the standard material flow data, so as to obtain the carbon content data of the substances flowing out of the system;

[0061] In an embodiment of the present invention, according to the standard material flow data generated in step S21, carbon elemental composition analysis is performed on the products, waste gases, and solid wastes flowing out of the system. Taking waste gas as an example, the CO 2 content is detected by an on-line gas chromatograph, and the result is 20%. Combining with the waste gas flow data (100 standard cubic meters / second), according to the density of 0.001977 kilograms / standard cubic meter (CO 2The carbon content is calculated as 100×0.001977×0.2 = 0.03954 kg / s in terms of density. Similarly, for solid waste (such as coal cinder), its carbon element content is detected through elemental analysis. Assuming it is 5% and the flow rate is 50 kg / s, then the carbon content is 50×0.05 = 2.5 kg / s. The carbon content data of all carbon-containing substances are summarized to generate a carbon content data table of material outflow.

[0062] Step S24: Based on the carbon content data of material inflow and the carbon content data of material outflow, establish a carbon element conservation equation at the system level, thereby obtaining an initial carbon element balance table;

[0063] In the embodiment of the present invention, based on the carbon content data of material inflow and the carbon content data of material outflow, a carbon element conservation equation at the system level is established. Assume that the total carbon content input to the system is limestone (100 kg / s) + pulverized coal (11.11 kg / s), and the total input carbon is 111.11 kg / s; the total carbon content of the outflow is waste gas (0.03954 kg / s) + solid waste (2.5 kg / s), and the preliminary calculated total output carbon is 2.53954 kg / s. Take the difference between the input carbon and the output carbon as the adjustment benchmark to generate an initial carbon element balance table, which records the carbon input, output, and difference of each substance.

[0064] Step S25: According to the process flow chart and the initial carbon element balance table, perform process path identification processing on the carbon element conversion process inside the system, thereby obtaining a carbon element conversion path diagram, where the process path identification includes the analysis of the morphological changes and flow directions of carbon elements in physical change processes and chemical reaction processes;

[0065] In the embodiment of the present invention, combined with the initial carbon element balance table in step S24 and the enterprise process flow chart, the conversion path of carbon elements inside the system is identified. For example, in the cement manufacturing process, pulverized coal burns in the kiln to be converted into CO 2 , and limestone decomposes into CaO and CO 2 at high temperature. Through chemical reaction equations (such as C + O 2 → CO 2 ), analyze the flow direction and morphological changes of carbon elements. The flow direction identification path from pulverized coal to CO 2 is "pulverized coal → combustion in the kiln → CO in the waste gas 2 ". Use the material balance equation to calculate each path node, generate a carbon element conversion path diagram, and record the carbon input, output, and conversion relationships of each node.

[0066] Step S26: Through the carbon element conversion path diagram, perform material balance check processing on each carbon element conversion node, thereby obtaining carbon conversion dynamic balance correction data, where the material balance check specifically uses an iterative calculation method to balance and adjust the carbon element income and expenditure differences at different nodes;

[0067] In the embodiment of the present invention, material balance verification is performed on each node in the carbon element conversion path diagram generated in step S25. For example, for the node of "coal powder combustion generates CO 2 ", the initial data shows that the input coal powder carbon is 11.11 kg / s, and the CO 2 carbon generated by combustion is 10 kg / s, there is a carbon income and expenditure difference. The iterative calculation method is used to balance and adjust the difference. Assuming the initial adjustment coefficient is 0.8, the carbon element income and expenditure values of each node are gradually updated according to the conservation equation until the input and output errors of all nodes are less than 0.01 kg / s. After the verification is completed, a carbon conversion dynamic balance correction data table is generated.

[0068] Step S27: According to the carbon conversion dynamic balance correction data, statistical processing is performed on the input amount, conversion amount, loss amount, and output amount of carbon elements of various carbon-containing substances in the distribution of carbon elements in the system, so as to obtain the final carbon element balance table.

[0069] In the embodiment of the present invention, according to the carbon conversion dynamic balance correction data generated in step S26, the input amount, conversion amount, loss amount, and output amount of carbon elements of various carbon-containing substances in the system are statistically calculated. For example, in the cement production system, the input amount of carbon element of coal powder is 11.11 kg / s, the conversion amount is 10 kg / s, and the loss amount (unburned part) is 1.11 kg / s; the CO 2 output amount in the waste gas is 10 kg / s, and the carbon output amount in the solid waste is 2.5 kg / s. All the data are summarized to form the final carbon element balance table, clarifying the distribution and flow direction of various carbon elements, and providing a basis for subsequent carbon emission accounting.

[0070] Through the above steps, enterprises can accurately and comprehensively track and analyze the flow of carbon elements in the production system, providing a scientific basis for carbon emission monitoring and control. First, in step S21, various material flows are classified, summarized, and unit-converted to obtain standard material flow data, which provides a unified and standardized data basis for subsequent carbon element analysis, ensuring comparability and data consistency among different material flows. Steps S22 and S23 respectively perform elemental composition analysis on the carbon content of material inflows and outflows to obtain material inflow carbon content data and material outflow carbon content data. The implementation of these two steps helps to accurately grasp the carbon content of various materials in the system, thus providing core data support for the calculation of carbon emissions. Then, in step S24, based on the material inflow and outflow carbon content data, a carbon element conservation equation at the system level is established to obtain an initial carbon element balance sheet. Through this step, enterprises can comprehensively master the input and output of carbon elements in the entire production process, identify the balance state of carbon elements, and provide a preliminary basis for subsequent carbon management decisions. Step S25 then identifies the process path of the carbon element conversion process through the process flow diagram and the initial carbon element balance sheet. This link can help identify the conversion path of carbon elements in the production process, reveal their morphological changes and flow directions in physical and chemical reaction processes, further refine the behavior analysis of carbon elements, and provide detailed process information for carbon emission optimization. Step S26 analyzes the carbon element conversion path diagram and performs material balance verification on each conversion node to ensure the balance of carbon income and expenditure differences at each node, thereby obtaining carbon conversion dynamic balance correction data. This link can effectively correct the deviations in the preliminary data and further improve the accuracy of carbon element balance analysis. Finally, in step S27, based on the carbon conversion dynamic balance correction data, statistical processing is performed on the input, conversion, loss, and output amounts of carbon elements to generate a final carbon element balance sheet, providing comprehensive and accurate carbon emission data, which provides a key basis for the carbon management and emission optimization of enterprises. Generally speaking, these steps provide enterprises with a scientific and systematic carbon element monitoring and analysis framework. Through precise data analysis and identification of conversion paths, not only the accuracy of carbon emission data is improved, but also enterprises are helped to deeply understand the dynamic changes of carbon elements in the production process, thus achieving more efficient and accurate carbon emission control. The implementation of this process helps enterprises to adopt more targeted optimization measures in the process of achieving the carbon neutrality goal, promoting green production and sustainable development.

[0071] Preferably, step S24 includes the following steps:

[0072] Step S241: Calculate the carbon element flux of each feed stream according to the material inflow carbon content data, so as to obtain the carbon element input flux data. The flux calculation specifically adopts the method of multiplying the material flow by the carbon content, and unifies the data of different time scales into annual fluxes;

[0073] In an embodiment of the present invention, taking a certain iron and steel plant as an example, according to the carbon content data of the material inflow, the carbon element flux of all raw material and fuel feed streams is calculated. Taking pulverized coal as an example, its average flow rate is 50 tons per hour and the carbon content is 80%, then its hourly flux is 50×0.8 = 40 tons per hour. When converting the hourly flux to the annual flux, calculated based on the annual operating time of 8000 hours, the annual flux is 40×8000 = 320000 tons per year. Similarly, for limestone (flow rate of 100 tons per hour and carbon content of 10%), the calculated annual flux is 100×0.1×8000 = 80000 tons per year. The same calculation is performed for all feed streams to generate a carbon element input flux data table, including the name of each substance, the carbon content ratio, the hourly flux, and the annual flux.

[0074] Step S242: Perform carbon element flux calculation processing on each discharge stream according to the carbon content data of the material outflow, so as to obtain carbon element output flux data, where the flux calculation is specifically an annual flux conversion based on time-weighted average of the material flow rate volatility;

[0075] In an embodiment of the present invention, for the carbon content data of the material outflow, carbon element flux calculation is performed and converted to the annual flux. Taking CO in the waste gas 2 as an example, its flow rate fluctuates. Using the instantaneous flow rate data recorded by the on-line monitoring system, the annual flux calculation is performed by the time-weighted average method. Assuming the monitoring data are 100, 110, and 90 standard cubic meters per second respectively, perform weighted average on them, and the flow rate is (100 + 110 + 90) / 3 = 100 standard cubic meters per second. Using the density of CO 2 being 1.977 kg per standard cubic meter and the carbon content being 27.27%, the annual flux calculation is 100×1.977×0.2727×8000 = 431080 tons per year. Other material outflow streams are processed in the same way to generate a carbon element output flux data table.

[0076] Step S243: Perform dynamic monitoring processing on the cumulative change of carbon element in the system based on the process flow diagram, so as to obtain carbon element cumulative amount data;

[0077] In an embodiment of the present invention, the cumulative change of carbon element in the system is dynamically monitored based on the process flow diagram. For example, in a certain chemical reactor, the carbon input and output of each stream are monitored in real time through sensors. Assuming the initial value of the carbon element cumulative amount in the reactor is 100 tons, the input flux is 10 tons per hour, and the output flux is 9 tons per hour, then the cumulative amount after 1 hour is 100 + 10 - 9 = 101 tons. The data for each hour is accumulated to generate a dynamic cumulative amount curve of carbon element in the reactor, and the cumulative change data is stored as a time series for subsequent analysis.

[0078] Step S244: Establish a carbon element balance equation based on the law of conservation of mass according to the carbon element input flux data, carbon element output flux data, and carbon element accumulation data, so as to obtain preliminary carbon element conservation relationship data;

[0079] In the embodiment of the present invention, a carbon element balance equation based on the law of conservation of mass is established according to the input flux, output flux, and accumulation data. Taking an industrial system as an example, the input carbon flux is 400,000 tons / year, the output carbon flux is 380,000 tons / year, and the change in accumulation is 20,000 tons / year. Then the balance equation is: input flux (400,000) - output flux (380,000) = accumulation change (20,000). Substitute all flux data into the balance equation to generate a preliminary carbon element conservation relationship data table, including the input, output, and accumulation change values of each material flow in the system.

[0080] Step S245: Evaluate the reliability intervals of the data of each item of the equation coefficients in the carbon element conservation relationship data based on the error transfer theory, so as to obtain coefficient correction factor data;

[0081] In the embodiment of the present invention, the reliability of each coefficient in the carbon element conservation relationship data is evaluated. Using the error transfer theory and combining the detection errors of the flow sensor and the elemental analyzer, the reliability intervals of the input flux and the output flux are evaluated. For example, the measurement error of the input flux is ±1%, and the measurement error of the output flux is ±2%. Then the reliability interval of the input flux of 400,000 tons / year is 396,000 - 404,000 tons / year, and the reliability interval of the output flux of 380,000 tons / year is 372,400 - 387,600 tons / year. Store the evaluation results as coefficient correction factor data and use them for subsequent parameter optimization.

[0082] Step S246: Perform parameter optimization processing on the carbon element conservation relationship data according to the coefficient correction factor data, so as to obtain an initial carbon element balance table, where the parameter optimization specifically uses the least squares method to dynamically adjust each coefficient.

[0083] In the embodiment of the present invention, the least squares method is used to optimize the parameters in the carbon element conservation relationship data. Assume that the initial balance equation is input flux (400,000) - output flux (380,000) = accumulation change (20,000). According to the correction factor obtained by the error transfer theory, adjust the input flux to 398,000 tons / year and the output flux to 379,500 tons / year. Calculate the optimized accumulation change to be 19,800 tons / year by the least squares method. Continuously adjust each coefficient until the equation error is minimized, and finally generate an optimized initial carbon element balance table, recording the adjusted input, output, and accumulation change data, laying a foundation for subsequent in-depth analysis.

[0084] The present invention can achieve precise calculation and dynamic monitoring of the flow of carbon elements, providing high-precision data support and decision-making basis for carbon emission management. The carbon element fluxes of the substances flowing in and out are calculated respectively to obtain the flux data of carbon element input and output. In these steps, the calculation of carbon element flux adopts the method of multiplying the material flow by the carbon content, ensuring that the quantitative analysis of carbon input and output has high precision. Through annual flux conversion, data of different time scales are unified into annual fluxes, making the analysis results have unity and comparability. This data processing method can help enterprises comprehensively master the carbon input and output of various materials in the production process, providing a reliable basis for subsequent carbon emission accounting and control. By dynamically monitoring the cumulative change of carbon elements in the system based on the process flow chart, the cumulative amount data of carbon elements is obtained. This link can track the accumulation change of carbon elements in the production system in real time, reveal the dynamic distribution and flow trend of carbon elements, and provide strong support for enterprises to identify potential carbon emission risks. At the same time, based on the carbon element input flux, output flux and cumulative amount data, a carbon element balance equation based on the law of conservation of mass is established to obtain preliminary carbon element conservation relationship data. The establishment of this equation not only reflects the conservation of carbon elements in the production process, but also lays a theoretical foundation for further optimizing the carbon emission management strategy. The reliability interval of the error transfer theory is evaluated for each coefficient in the carbon element conservation relationship data to obtain coefficient correction factor data. Through this link, enterprises can evaluate the errors in the data, identify potential calculation deviations, and provide a scientific basis for subsequent data correction. By optimizing the parameters of the carbon element conservation relationship data according to the coefficient correction factor and dynamically adjusting each coefficient using the least squares method, an initial carbon element balance sheet is obtained. This optimization process can effectively improve the accuracy of the carbon element conservation relationship and further enhance the accuracy and reliability of carbon emission calculation. In summary, these steps help enterprises deeply analyze and track the flow of carbon elements in the production process through precise carbon element flux calculation, cumulative amount dynamic monitoring and the establishment of a carbon element balance equation based on the law of conservation of mass. At the same time, through error correction and parameter optimization, the high reliability and precision of carbon emission data are ensured. The implementation of these steps provides strong technical support for enterprises to achieve precise carbon emission monitoring, optimize carbon management strategies and promote green production, and ultimately helps enterprises make greater progress in environmental protection goals and sustainable development.

[0085] Preferably, step S26 includes the following steps:

[0086] Step S261: Identify and classify the key nodes in the carbon element conversion path diagram in the system to obtain a carbon element conversion node list. The types of key nodes include physical conversion nodes, chemical reaction nodes, and storage and accumulation nodes. Each node has a unique identification number and corresponding process parameters.

[0087] In an embodiment of the present invention, taking the methanol production process of a certain chemical enterprise as an example, based on the carbon element conversion path diagram, by analyzing the material flow path, the key nodes in the system are identified. First, the gasifier is determined as a physical conversion node, and its process parameters include an operating temperature of 1200 °C and a pressure of 2.5 MPa. Secondly, the synthesis reactor is determined as a chemical reaction node, and its main reaction is CO and H 2 to generate CH3OH, and the reaction parameters include a temperature of 250 °C and a pressure of 5 MPa. Finally, the raw material warehouse and the finished product storage tank are identified as storage and accumulation nodes, and their storage capacities and annual turnover volumes are recorded. The above nodes are numbered through unique identification numbers, such as Node 1 (gasifier), Node 2 (reactor), and Node 3 (storage tank), to generate a carbon element conversion node list.

[0088] Step S262: Perform carbon element flux accounting processing on the in-out logistics of each node according to the carbon element conversion node list to obtain node-level carbon element balance data.

[0089] In an embodiment of the present invention, for each node in the node list, the carbon element flux of its in-out logistics is accounted. For example, for the gasifier Node 1, the input materials include coal (flow rate 100 tons / hour, carbon content 80%) and air, and the output logistics is raw syngas (flow rate 80 tons / hour, carbon content 50%). The input carbon flux is 100×0.8 = 80 tons / hour, and the output carbon flux is 80×0.5 = 40 tons / hour. The carbon element flux is accounted for all nodes in a similar method and recorded as a node-level carbon element balance data table, including the node number, the names of the in-out logistics, and the corresponding carbon fluxes.

[0090] Step S263: Perform statistical analysis processing on the carbon element income and expenditure differences of each node according to the node-level carbon element balance data to obtain node balance error data.

[0091] In an embodiment of the present invention, according to the node-level carbon element balance data, the carbon element income and expenditure differences of each node are statistically analyzed. Taking Node 1 as an example, its input carbon flux is 80 tons / hour, and the output carbon flux is 40 tons / hour. Assuming that there is a 20% carbon loss in the process, the carbon income and expenditure difference is 80 - 40 - 80×20% = 24 tons / hour. The statistical analysis includes calculating the difference values of all nodes and classifying them into material loss, measurement error, or other uncertain factors, and generating a node balance error data table, including the node number, the income and expenditure difference, and the error source classification.

[0092] Step S264: Establish a node balance adjustment model based on the principle of minimum variance based on the node balance error data, so as to obtain optimized adjustment coefficient data, where the node balance adjustment model differentiates data with different reliability levels by setting an error weight factor;

[0093] In the embodiment of the present invention, based on the node balance error data, a node balance adjustment model is established. Based on the principle of minimum variance, an error weight factor is set to differentiate data with different reliability levels. For example, for the input flow rate data of gasifier node 1, the weight factor is set to 0.9 based on the flow meter accuracy (±1%), while for the output raw syngas flow rate data, since its measurement error is larger (±5%), the weight factor is set to 0.5. Through error weight adjustment, the adjusted model is more in line with the actual situation, and an optimized adjustment coefficient data table is generated.

[0094] Step S265: Perform iterative correction processing on the node-level carbon element balance data according to the optimized adjustment coefficient data, so as to obtain node balance data, where the process of iterative correction is specifically to set a convergence threshold, and stop the iteration when the relative error between two adjacent calculation results is less than a preset value;

[0095] In the embodiment of the present invention, iterative correction processing is performed on the node-level carbon element balance data according to the optimized adjustment coefficient data. Taking gasifier node 1 as an example, the initial income and expenditure difference is 24 tons / hour, and the adjustment coefficient is 0.8. After one iteration, the correction value is 24×0.8 = 19.2 tons / hour. The convergence threshold is set to 1%, and the iteration stops when the relative error between two adjacent iteration results is less than 1%. For example, the result after the first correction is 19.2 tons, and the result after the second correction is 19.1 tons, meeting the convergence condition. The final node balance data is 19.1 tons, and the correction is completed.

[0096] Step S266: Perform system integration processing on the carbon element balance of the system through the node balance data, so as to obtain carbon conversion dynamic balance correction data.

[0097] In the embodiment of the present invention, system integration processing is performed on the carbon element balance of the system through the node balance data. For example, the balance data of the gasifier, reactor, and storage tank are summarized to calculate the total carbon input, total carbon output, and cumulative change amount of the entire system. Assuming that the input carbon flux of the gasifier is 80,000 tons / year and the output flux is 40,000 tons / year, after the node data of the reactor and storage tank are balance-corrected, the total income and expenditure difference of the carbon element in the system is ±2%, meeting the actual operation requirements. Finally, carbon conversion dynamic balance correction data is generated for subsequent optimization analysis and process adjustment.

[0098] The present invention can more accurately identify and optimize the conversion and flow processes of carbon elements in the production system, thereby effectively improving the accuracy and reliability of carbon emission management. By identifying and classifying key nodes in the carbon element conversion path diagram, a list of carbon element conversion nodes is obtained, providing basic data support for subsequent carbon flow monitoring and analysis. The types of key nodes include physical conversion nodes, chemical reaction nodes, and storage accumulation nodes. These nodes have unique identification numbers and corresponding process parameters, which help to clarify the roles and characteristics of different nodes in the carbon element flow process and provide clear objectives for further analysis and optimization. Based on the list of carbon element conversion nodes, carbon element flux accounting is carried out for the incoming and outgoing logistics of each node to obtain node-level carbon element balance data. This process can deeply analyze the contribution of each node in the carbon element flow process and ensure the accurate quantification of carbon emissions from each node, providing the necessary quantitative basis for subsequent carbon management decisions. Step S263 performs statistical analysis on the carbon element income and expenditure differences of the node-level carbon element balance data to obtain node balance error data. Through this step, enterprises can identify the unbalanced parts of each node in the carbon element income and expenditure, discover potential carbon emission risk points, and thus provide a scientific basis for optimization and adjustment. Based on the node balance error data, a node balance adjustment model is established, and the minimum variance principle is used to calculate the optimization adjustment coefficient. By setting an error weight factor, differential processing is performed on data with different reliability levels to ensure more accurate and reliable adjustment of key nodes. The node-level carbon element balance data is iteratively corrected using the optimization adjustment coefficient, and finally, the optimized node balance data is obtained. By setting a convergence threshold, it is ensured that the iteration stops after reaching the predetermined error range, thus ensuring the efficiency and accuracy of the correction process. Through systematic integration processing of the node balance data, carbon conversion dynamic balance correction data is finally obtained. This data can reflect the dynamic balance situation of the entire system in the carbon element conversion process, provide accurate carbon emission data for enterprises, and provide scientific support for subsequent carbon emission control measures and optimization. Through the implementation of these steps, enterprises can more comprehensively and accurately master the flow and conversion of carbon elements in the production system, realizing dynamic monitoring and optimization of carbon emissions from the micro-node to the system level. This method can effectively identify and correct carbon element income and expenditure differences, ensure the reliability and accuracy of carbon emission data, and provide a solid data foundation for enterprises to formulate efficient carbon emission management strategies and promote green production.

[0099] Preferably, step S3 includes the following steps:

[0100] Step S31: Perform thermodynamic equilibrium analysis on the Gibbs free energy and reaction enthalpy change of the combustion process according to the preset standard state thermodynamic parameters and the carbon element balance sheet, so as to obtain reaction spontaneity data;

[0101] In an embodiment of the present invention, taking coal combustion as an example, the thermodynamic equilibrium of the combustion process is analyzed by using preset standard-state thermodynamic parameters, such as the Gibbs free energy of formation (ΔG) and enthalpy change (ΔH) at 25°C and 1 atm. Assuming that the main components of coal are carbon (C) and a small amount of volatile matter, the combustion reaction is C + O 2 → CO 2 . By looking up the table, ΔG = -394.36 kJ / mol and ΔH = -393.51 kJ / mol are obtained. The input carbon amount in the carbon element balance sheet is compared with the theoretical reaction amount, and the Gibbs free energy change and enthalpy change of the reaction are calculated to judge the spontaneity of the combustion reaction. The result shows that ΔG < 0, and the reaction is spontaneous, obtaining the reaction spontaneity data.

[0102] Step S32: Based on the combustion reaction mechanism, perform stoichiometric analysis on the combustion process of hydrocarbons in the material flow data to obtain chemical reaction equation data, where the stoichiometric analysis includes balancing the complete combustion reaction equation and the partial combustion reaction equation;

[0103] In an embodiment of the present invention, aiming at the combustion reaction mechanism, taking natural gas combustion as an example, it is determined that its main component is methane (CH 4 ), and stoichiometric analysis is performed on the hydrocarbons in the material flow data. For complete combustion, the reaction equation is CH 4 + 2O 2 → CO 2 + 2H 2 O, ensuring that the number of atoms on both sides of the reaction equation is conserved. For partial combustion, the reaction equation is 2CH 4 + 3O 2 → 2CO + 4H 2 O. When balancing the reaction equation, the working conditions of insufficient oxygen supply need to be comprehensively considered. Through the stoichiometric analysis of the actual operation conditions, the corresponding complete and partial combustion reaction equation data are obtained.

[0104] Step S33: Perform component analysis on the carbon content of the fuel according to the carbon element balance sheet and the chemical reaction equation data to obtain the fuel carbon content data, where the component analysis is specifically to determine the mass fraction of carbon element in the unit fuel by using the elemental analysis method;

[0105] In an embodiment of the present invention, taking the raw coal used in a coal-fired power plant as an example, according to the carbon element balance sheet and the above chemical reaction equation, the elemental analyzer is used to perform component analysis on the carbon content in the coal sample. In specific operations, a certain amount of coal sample (such as 1 g) is weighed, and through high-temperature combustion and gas analysis method, the mass of the generated CO 2 is measured. Assuming that 2.67 g of CO 2 is generated by burning 1 g of coal sample, calculate the carbon mass as 2.67×12 / 44 = 0.727 g. Divide the mass of carbon element by the mass of the coal sample to obtain a carbon content of 72.7%. Based on this, a data table of fuel carbon content is formed.

[0106] Step S34: Perform multi-physical field coupling analysis and processing on the temperature field, pressure field, and oxygen concentration field during the combustion process based on reaction spontaneity data, so as to obtain combustion condition parameter data;

[0107] In the embodiment of the present invention, based on reaction spontaneity data, multi-physical field coupling analysis is performed on the temperature field, pressure field, and oxygen concentration field involved in the coal combustion process. A combustion chamber model is established using CFD (Computational Fluid Dynamics) software, and boundary conditions (temperature 1200°C, pressure 1.5 atm, oxygen concentration 21%) are set. Through simulation calculations, the temperature gradient, pressure distribution, and oxygen concentration change curves at different positions in the combustion chamber are obtained. The results show that in the high-temperature region, the oxygen concentration decreases significantly, the pressure increases with the increase in temperature, and the generated multi-physical field data is used to optimize the combustion condition parameters.

[0108] Step S35: Perform kinetic simulation processing on the oxidation reaction of carbon element through the combustion condition parameter data and chemical reaction equation data, so as to obtain carbon oxidation rate data;

[0109] In the embodiment of the present invention, a kinetic model of the carbon element oxidation reaction is established through the combustion condition parameter data and chemical reaction equations. Assuming the combustion temperature is 1200°C and the oxygen excess coefficient is 1.1, the reaction rate constant k = Aexp(-Ea / RT) is calculated using the Arrhenius equation, where the activation energy Ea = 125 kJ / mol and the frequency factor A = 1.2×10 7 s -1 . Combining the combustion reaction rate and oxygen concentration distribution, calculate the carbon oxidation rate, and assume the calculation result is 95%. The results show that the oxidation rate of carbon element is positively correlated with temperature and oxygen concentration, and carbon oxidation rate data is obtained.

[0110] Step S36: Establish a calculation model for the amount of carbon dioxide generated based on stoichiometric relationships based on the fuel carbon content data and carbon oxidation rate data, so as to obtain direct carbon emission data.

[0111] In the embodiment of the present invention, taking the actual working conditions of a coal-fired power plant as an example, a calculation model for the generation amount of CO 2 is established based on the fuel carbon content data and carbon oxidation rate data. Assuming the coal carbon content is 72.7% and the oxidation rate is 95%, the mass of CO 2 generated by the combustion of unit mass of coal is fuel mass × carbon content × oxidation rate × (44 / 12). For the combustion of 1 ton of coal, CO 2The generated amount is 1×72.7%×95%×(44 / 12) = 2.53 tons. Based on this, the direct carbon emissions of a coal-fired power plant consuming 1 million tons of coal annually are 2.53×10 6 tons, forming direct carbon emission data for subsequent carbon emission assessments.

[0112] The present invention provides a systematic analysis and accurate simulation for calculating carbon emissions during the combustion process, significantly improving the prediction accuracy and reliability of carbon emissions. Through the analysis of thermodynamic parameters under standard conditions and the carbon element balance sheet, the Gibbs free energy and reaction enthalpy change data of the combustion reaction can be obtained, and then thermodynamic equilibrium analysis can be carried out to help enterprises understand the spontaneity of the reaction. This data provides a basis for the subsequent optimization of the combustion process, can evaluate the spontaneity of the reaction under different combustion conditions, so as to conduct refined management of energy use and avoid unnecessary energy waste. In terms of stoichiometric analysis, based on the combustion reaction mechanism, a detailed analysis of hydrocarbons in the material flow data is carried out to generate complete chemical reaction equation data. This process provides a clear mathematical description for the transformation of each substance during the combustion process by balancing the complete combustion and partial combustion reaction equations, ensuring the accuracy and operability of the reaction calculation. These reaction equations provide a theoretical basis for the subsequent calculation of carbon emissions, and can ensure that the carbon emission contribution of each material flow is accurately evaluated. Through the carbon element balance sheet and chemical reaction equation data, the component analysis of the fuel carbon content is carried out, and the carbon mass fraction in the fuel is accurately determined by using the element analysis method. This analysis can help enterprises comprehensively understand the carbon content of the fuel, provide core data support for accurately calculating carbon emissions. Especially in the scenario of using multiple fuels, the accuracy of the component analysis can significantly affect the final calculation result of carbon emissions. Based on the reaction spontaneity data, a multi-physical field coupling analysis of the temperature field, pressure field and oxygen concentration field during the combustion process is carried out to obtain combustion condition parameter data. The coupling analysis of multiple physical fields can simulate a more realistic combustion condition, reflect the combustion efficiency and carbon emission characteristics under different conditions, and provide data basis for optimization in actual operation. Combining the combustion condition parameter data and the chemical reaction equation data, a kinetic simulation of the carbon oxidation reaction is carried out to obtain carbon oxidation rate data. This data provides a detailed simulation of the carbon oxidation process, helps to optimize the combustion efficiency and reduce carbon emissions. According to the fuel carbon content data and the carbon oxidation rate data, a calculation model for the amount of carbon dioxide generated is established, and finally the direct carbon emission data is obtained. This model integrates data from multiple aspects such as fuel composition, combustion reaction and oxidation efficiency, enabling enterprises to accurately calculate the direct carbon emissions during the combustion process, and providing a scientific basis for the formulation of carbon emission control and emission reduction measures. Overall, the above steps provide a comprehensive and systematic method for accurately quantifying carbon emissions through multi-dimensional processing such as thermodynamic analysis, chemical reaction simulation and physical field coupling, which not only helps enterprises achieve accurate carbon emission monitoring, but also lays a solid foundation for the realization of environmental protection and carbon emission reduction goals.

[0113] Preferably, step S4 includes the following steps:

[0114] Step S41: Obtain the list of power-consuming equipment in the enterprise production system, and perform statistical analysis on the power parameters and operating time of each device according to the equipment list to obtain the equipment power load data;

[0115] In an embodiment of the present invention, in a manufacturing enterprise, first obtain a list of all power-consuming equipment through the enterprise's energy management system, including production equipment, lighting, air conditioning, and other office equipment. After sorting out the equipment list, obtain the power parameters of each device (for example, the power of the production line equipment is 150 kW, and the power of the air conditioner is 5 kW) and the operating time of the equipment (such as the production equipment runs for 12 hours per day, and the air conditioner runs for 8 hours per day). Multiply the equipment power by its operating time to obtain the power load of each device. For example, the power load of a certain production line equipment is 150 kW × 12 h = 1800 kWh / day, and the power load of the air conditioner is 5 kW × 8 h = 40 kWh / day. Through such statistical analysis, the power load data of each device in the enterprise is obtained.

[0116] Step S42: Obtain the power source data of the regional power grid, and perform energy structure analysis on the power generation of different types of power generation facilities in the power source data to obtain the power source structure proportion data;

[0117] In an embodiment of the present invention, according to the power grid in the region where the enterprise is located, obtain the power source data, including the power generation data of different types of power generation facilities such as coal power, natural gas power, nuclear power, hydropower, and wind power. Assume that coal power accounts for 50% of the total power generation, natural gas power accounts for 20%, hydropower accounts for 15%, wind power accounts for 10%, and nuclear power accounts for 5%. Analyze these data according to the power generation of each type of power source, and calculate the proportion of different types of power sources in the entire power supply system according to the data of the actual power generation facilities (for example, coal power 50%, natural gas power 20%, hydropower 15%, etc.), so as to obtain the power source structure proportion data.

[0118] Step S43: Perform weighted calculation on the standard coal consumption coefficients of different power generation types based on the energy conversion efficiency and power transmission and distribution loss rates of various power generation facilities according to the power source structure proportion data, so as to determine the power generation coal consumption coefficient;

[0119] In the embodiments of the present invention, based on the power generation source data of the regional power grid, the energy conversion efficiency and transmission and distribution loss rate of various power generation facilities are first determined. For example, the energy conversion efficiency of coal-fired power is 38%, that of natural gas power is 45%, that of hydropower and wind power is 90% (assuming 90% since there is no fuel consumption), and that of nuclear power is 33%. Assume that the standard coal consumption coefficient of coal-fired power is 350 g / kWh and that of natural gas power is 250 g / kWh. According to these data, using the weighted calculation method, combined with the energy conversion efficiency and transmission and distribution loss rate of different power generation sources, the weighted adjustment of the coal consumption coefficient for power generation is carried out. For example, the weighted coal consumption coefficients of coal-fired power and natural gas power can be obtained by multiplying the power generation of each power generation type by the corresponding standard coal consumption coefficient and then dividing by the total power generation, to obtain the weighted coal consumption coefficient for power generation of the entire power grid.

[0120] Step S44: Perform standard coal equivalent conversion processing on the power consumption according to the equipment power consumption load data and the coal consumption coefficient for power generation, so as to obtain the standard coal consumption data;

[0121] In the embodiments of the present invention, after obtaining the equipment power consumption load data and the coal consumption coefficient for power generation, the standard coal equivalent conversion of the power consumption is carried out. Assume that the annual power consumption of an enterprise is 2 million kWh, and the weighted coal consumption coefficient for power generation calculated above is 350 g / kWh. Using this coefficient, first calculate the standard coal amount consumed by the equipment power consumption: 2 million kWh × 350 g / kWh = 70,000,000 g = 70,000 kg of standard coal. In this way, the power consumption of the enterprise is converted into standard coal equivalent data.

[0122] Step S45: Perform stoichiometric conversion processing on the carbon content in the standard coal based on the standard coal consumption data, so as to obtain the carbon emission equivalent data;

[0123] In the embodiments of the present invention, according to the obtained standard coal consumption data, through stoichiometric conversion, the carbon content in the standard coal is calculated. The carbon content of standard coal is generally 70% (that is, each kilogram of standard coal contains 0.7 kg of carbon). Assume that the annual standard coal consumption of the enterprise is 70,000 kg, then according to the stoichiometric formula, the carbon emission is 70,000 kg × 0.7 = 49,000 kg of carbon. Through this calculation, the carbon emission equivalent data is obtained.

[0124] Step S46: Perform time accumulation statistics on the annual indirect carbon emissions of the enterprise according to the carbon emission equivalent data, so as to obtain the indirect carbon emission data.

[0125] After obtaining the annual carbon emission equivalent data in the embodiments of the present invention, time accumulation statistics are performed to obtain the annual indirect carbon emissions of the enterprise. For example, assuming that the carbon emission equivalents of the enterprise per month are 5000 kg, 5200 kg, 5300 kg, etc., by accumulating these data, the annual indirect carbon emissions are obtained. For example, the cumulative carbon emissions for the whole year of 12 months are 5000 kg + 5200 kg +... + 5300 kg = 63,600 kg. Through this statistical process, the annual indirect carbon emission data is obtained.

[0126] Through precise calculation and comprehensive analysis, the present invention provides a complete set of indirect carbon emission assessment methods for enterprises, which can effectively monitor and reduce the environmental impact of energy consumption. First, by obtaining the list of power-consuming equipment in the enterprise production system and statistically analyzing the power and operating time of each equipment, the power consumption load data of the equipment is obtained. This data lays the foundation for subsequent energy consumption calculations, can accurately reflect the power demand of each equipment, and helps enterprises identify high-energy-consuming equipment, thereby optimizing energy management and reducing electricity costs. Further, by obtaining the power source data of the regional power grid and combining the power generation amounts of different power generation types, energy structure analysis is carried out to obtain the power source structure proportion data. This analysis can help enterprises understand the composition of power sources, especially the proportion of renewable energy and fossil energy in the power grid, provides clear background information for carbon emission assessment, and enables enterprises to take corresponding emission reduction measures under different energy structures. Based on the power source structure proportion data, combined with the energy conversion efficiency and power transmission and distribution loss rates of various power generation facilities, weighted calculations are carried out to determine the coal consumption coefficient for power generation. Through this calculation, the energy consumption corresponding to power consumption can be accurately estimated, which makes the subsequent carbon emission calculation more accurate and provides an important reference for enterprises when choosing energy usage methods. Combining the equipment power consumption load data with the coal consumption coefficient for power generation, standard coal equivalent conversion is carried out to obtain the standard coal consumption data. This step ensures cross-industry and cross-field comparison of energy consumption, enables the consumption of different types of energy to be unified to the benchmark of standard coal, and facilitates quantitative analysis of environmental impacts. By performing stoichiometric conversion on the carbon content in standard coal, the carbon emission equivalent data is obtained. This conversion ensures that the carbon emissions brought about by the consumption of different types of energy can be accurately calculated, thereby providing data support for carbon emission management and emission reduction targets. By performing time accumulation statistics on the carbon emission equivalent data, the annual indirect carbon emission data of the enterprise is obtained. This cumulative statistical method helps enterprises regularly evaluate the carbon emission trend and provides a basis for setting and implementing long-term carbon emission reduction targets. Generally speaking, the effect of the above steps is to provide enterprises with precise carbon emission monitoring tools through systematic data collection, analysis and conversion, ensuring the scientificity and efficiency of enterprises in energy use and carbon emission management.

[0127] Preferably, step S5 includes the following steps:

[0128] Step S51: Perform vegetation type identification processing on the spatial distribution of arbors, shrubs and lawns according to the preset enterprise greening data, so as to obtain vegetation distribution map data;

[0129] In the embodiment of the present invention, in an enterprise park, according to the preset greening data, including the distribution of arbors, shrubs and lawns in the park, through remote sensing technology or on-site survey, the spatial distribution information of vegetation is obtained. Use geographic information system (GIS) software to analyze the spatial distribution of vegetation in the park and identify different types of vegetation areas. Arbor areas are identified by the height and shape of the trees, shrub areas are identified by the density and shape of the shrubs, and lawn areas are identified by the coverage and ground distribution of the lawns, and finally vegetation distribution map data is generated. For example, the arbor area covers 50,000 m 2 , the shrub area covers 20,000 m 2 , and the lawn area covers 30,000 m 2 . This vegetation distribution map data provides basic information for subsequent vegetation feature analysis.

[0130] Step S52: Perform biometric measurement processing on the diameter at breast height and tree height of arbors, the crown width and height of shrubs, and the coverage of lawns based on the vegetation distribution map data, so as to obtain vegetation morphological parameter data;

[0131] In the embodiment of the present invention, based on the vegetation distribution map data, the biological characteristics of arbors, shrubs and lawns in the park are measured. For arbors, through lidar (LiDAR) technology or manual measurement, the diameter at breast height and tree height of each arbor are obtained. For example, the measured diameter at breast height of an arbor is 20 cm and the tree height is 12 m. For shrubs, the crown width and height of each shrub are obtained through on-site measurement. Suppose the crown width of a certain shrub is 3 m and the tree height is 2 m. For lawns, a ground coverage measurement instrument or visual estimation is used to estimate its coverage. Suppose the coverage of the lawn is 80%. These measurement results generate vegetation morphological parameter data, including arbor diameter at breast height, tree height, shrub crown width, tree height, and lawn coverage.

[0132] Step S53: Perform quantitative calculation processing on the above-ground biomass based on the vegetation morphological parameter data according to the preset biomass equation, and perform conversion processing on the underground biomass based on the preset root-shoot ratio model, so as to obtain above-ground biomass data and underground biomass data;

[0133] In the embodiment of the present invention, according to the measured vegetation morphological parameter data, the preset biomass equation is used to quantitatively calculate the above-ground biomass of the vegetation in the park. For arbors, the biomass equation of diameter at breast height and tree height is used, for example: above-ground biomass = 0.1 × diameter at breast height 2× tree height. For the above-mentioned arbors, the above-ground biomass is calculated as 0.1 × (20 cm) 2 × 12 m = 48 kg. For shrubs, the biomass equation using crown width and height is used for calculation. For example: above-ground biomass = 0.15 × crown width 2 × height. For a certain shrub, its biomass is 0.15 × (3 m) 2 × 2 m = 2.7 kg. For lawns, the above-ground biomass is calculated according to the formula of coverage and growth density. For example: above-ground biomass = 0.25 × coverage × lawn area. Assuming the lawn area is 30,000 m 2 , then the above-ground biomass of the lawn is 0.25 × 80% × 30,000 m 2 = 6,000 kg. The below-ground biomass is converted through the root-shoot ratio model. Assuming the root-shoot ratio is 0.25, the below-ground biomass is 0.25 times the above-ground biomass. The below-ground biomass of arbors is 48 kg × 0.25 = 12 kg, the below-ground biomass of shrubs is 2.7 kg × 0.25 = 0.675 kg, and the below-ground biomass of lawns is 6,000 kg × 0.25 = 1,500 kg.

[0134] Step S54: Perform carbon content conversion processing on the vegetation carbon storage according to the above-ground biomass data and the below-ground biomass data, so as to obtain the carbon storage data;

[0135] In the embodiment of the present invention, after obtaining the above-ground biomass and below-ground biomass data, through carbon content conversion, these data are converted into carbon storage data. According to the fact that the carbon content of plants is usually about 50% of the above-ground biomass and below-ground biomass, first, the carbon content conversion of the above-ground biomass and below-ground biomass of arbors, shrubs and lawns is carried out. For example, the above-ground carbon storage of arbors is 48 kg × 50% = 24 kg of carbon, the above-ground carbon storage of shrubs is 2.7 kg × 50% = 1.35 kg of carbon, and the above-ground carbon storage of lawns is 6,000 kg × 50% = 3,000 kg of carbon. The carbon storage of the underground part is also calculated according to the carbon content of 50%. The underground carbon storage of arbors is 12 kg × 50% = 6 kg of carbon, the underground carbon storage of shrubs is 0.675 kg × 50% = 0.3375 kg of carbon, and the underground carbon storage of lawns is 1,500 kg × 50% = 750 kg of carbon. Finally, the carbon storage data of each type of vegetation are obtained.

[0136] Step S55: Perform differential calculation processing on the annual carbon storage change through the carbon storage data, so as to obtain the initial carbon sink fixation amount data;

[0137] In an embodiment of the present invention, differential calculations are performed on the carbon storage amounts at different time points based on the carbon storage data within a year to obtain the annual carbon sink change data. Suppose the annual carbon storage amount in the enterprise's greening area is 2,000 kg of carbon at the beginning of the year and 3,500 kg of carbon at the end of the year. The annual carbon storage change is obtained by calculating the difference between the end of the year and the beginning of the year. For example, the annual carbon sink change amount is 3,500 kg - 2,000 kg = 1,500 kg. This differential calculation process provides preliminary carbon sink fixation amount data for subsequent carbon sink calculations.

[0138] Step S56: Based on the initial carbon sink fixation amount data and the carbon element balance sheet, perform a balance correction process for the carbon sink calculation result in terms of carbon sink calculation and system carbon balance, so as to obtain the final carbon sink fixation amount data.

[0139] In an embodiment of the present invention, based on the carbon sink fixation amount data obtained from preliminary calculations and the previously constructed carbon element balance sheet, the final carbon sink fixation amount data is obtained through a balance correction process of carbon sink calculation and system carbon balance. First, compare the preliminary carbon sink fixation amount data with the input and output data in the carbon element balance sheet to determine whether there are inconsistencies or areas that need to be corrected. Suppose the preliminary carbon sink fixation amount is 1,500 kg, but during the balance correction process, through system carbon balance calibration, the actual carbon sink fixation amount is found to be 1,450 kg. This correction amount reflects the influence of other environmental factors in the system (such as climate change, soil carbon storage change, etc.) on the carbon sink fixation amount, and finally obtains the final carbon sink fixation amount data of the enterprise.

[0140] The present invention provides an enterprise with a scientific and accurate method for evaluating the carbon sink fixation amount, which can effectively calculate the carbon storage and carbon sink effect brought by greening, and helps the enterprise formulate more reasonable carbon emission reduction and carbon storage strategies. First, through the preset enterprise greening data, the spatial distribution of arbors, shrubs and lawns is identified for vegetation type to obtain the vegetation distribution map data. This provides detailed greening coverage for subsequent carbon storage calculation, ensures the accuracy of the data, and provides a regional reference for the carbon storage calculation of different types of vegetation. Based on the vegetation distribution map data, the breast diameter and tree height of arbors, the crown width and height of shrubs, and the coverage of lawns are measured for biological characteristics, and the vegetation morphological parameter data is obtained. Through accurate morphological data, the growth status and coverage characteristics of each type of vegetation can be quantified, providing a reliable basis for biomass calculation. This step ensures a detailed evaluation of various plants in the enterprise greening area, avoiding carbon storage errors caused by inaccurate data. According to the vegetation morphological parameter data, the aboveground biomass is calculated using the preset biomass equation, and the underground biomass is converted through the root-shoot ratio model, so as to obtain the aboveground biomass and underground biomass data. This step further optimizes the determination of carbon storage through accurate biomass calculation, ensuring the comprehensiveness and accuracy of carbon storage evaluation, and fully considering the carbon storage above and below the ground. According to the aboveground and underground biomass data, the carbon content conversion is carried out to obtain the vegetation carbon storage data. This calculation step can provide the enterprise with accurate data on the carbon fixation ability of the green belt through a scientific carbon content conversion method, providing a basis for the enterprise's carbon emission reduction and carbon footprint calculation. By performing the differential calculation of the annual carbon storage change on the carbon storage data, the initial carbon sink fixation amount data is obtained. This step helps the enterprise monitor the change trend of carbon storage every year, facilitating the evaluation of the long-term effect of greening measures and further optimizing the carbon sink management strategy. Combining the initial carbon sink fixation amount data with the carbon element balance sheet, a balance correction based on carbon sink calculation and system carbon balance is carried out to obtain the final carbon sink fixation amount data. This correction process ensures the accuracy of the calculation results, takes into account the influence of the external environment and system carbon balance, and further improves the scientificity and accuracy of carbon sink evaluation. Generally speaking, through these steps, the enterprise can comprehensively and accurately evaluate and optimize the carbon storage in its greening area, providing strong data support for achieving the carbon neutrality goal.

[0141] The present invention also provides an enterprise carbon emission calculation and evaluation system for implementing the above-mentioned enterprise carbon emission calculation and evaluation method. The enterprise carbon emission calculation and evaluation system includes:

[0142] A material flow collection module is used to collect the boundary material inflow information and material outflow information of an enterprise's production system to obtain material flow data. The material flow information includes raw material feed quantity, fuel consumption, and auxiliary material usage, and the material outflow information includes product output quantity, waste gas emission, and solid waste generation;

[0143] A carbon element balance calculation module is used to establish a material balance equation based on the material flow data and trace the flow path of carbon elements through the material balance equation to obtain a carbon element balance table;

[0144] A direct carbon emission measurement module is used to calculate the carbon emissions generated per unit fuel combustion based on combustion conditions and stoichiometry for the carbon element balance table according to preset standard state thermodynamic parameters and chemical reaction equations to obtain direct carbon emission data;

[0145] An indirect carbon emission conversion module is used to obtain the enterprise's electricity consumption data and according to the pre-obtained electricity energy structure data of the regional power grid; determine the coal consumption coefficient for power generation based on the weighted average method according to the enterprise's electricity consumption data and the electricity energy structure data, and convert the electricity consumption into carbon emissions to obtain indirect carbon emission data;

[0146] A carbon sink estimation module is used to calculate the carbon storage based on the biomass equation and the root-shoot ratio model for the preset enterprise greening data to obtain carbon storage data; perform carbon sink estimation correction processing on the carbon storage data according to the carbon element balance table to obtain carbon sink fixation data;

[0147] A net carbon emission offset module is used to summarize the total carbon emissions according to the direct carbon emission data and the indirect carbon emission data to obtain the enterprise's total carbon emission data; generate the enterprise's carbon sink contribution data according to the carbon sink fixation data; perform carbon emission offset processing on the enterprise's total carbon emission data and the enterprise's carbon sink contribution data to obtain the enterprise's net carbon emission data.

[0148] The present invention also provides a storage medium, including:

[0149] A memory for storing a computer program;

[0150] A processor for implementing the above-mentioned enterprise carbon emission calculation and evaluation method when executing the computer program.

[0151] The present invention provides an enterprise with a comprehensive and accurate carbon emission assessment system through the collaborative work of each module, helping the enterprise effectively track and optimize its carbon footprint and promoting the achievement of sustainable development goals. First, the material flow collection module collects information on the inflow and outflow of boundary materials in the enterprise's production system to obtain the usage amounts of raw materials, fuels, and auxiliary materials, as well as the production amounts of products, waste gases, and solid wastes. This data collection provides basic data support for subsequent links and ensures the accuracy of carbon emission calculations. The carbon element balance calculation module establishes a material balance equation based on the material flow data, tracks the flow path of carbon elements, and generates a carbon element balance sheet. This process ensures the comprehensive monitoring and accurate assessment of carbon emissions by precisely calculating the input and output of carbon elements, avoiding carbon emission errors caused by miscalculation or incorrect calculation. The direct carbon emission measurement module combines thermodynamic parameters and chemical reaction equations to calculate the combustion conditions and stoichiometry of the carbon element balance sheet, and obtains the direct carbon emissions. This module can quantify the direct carbon emissions in the enterprise's production process based on the combustion characteristics of fuels, helping the enterprise accurately grasp the carbon emissions in its production links. The indirect carbon emission conversion module determines the coal consumption coefficient for power generation by obtaining the enterprise's electricity consumption data and combining the electricity energy structure data of the regional power grid, and converts the electricity consumption into carbon emissions. This step provides clear indirect carbon emission data for the enterprise, helps the enterprise understand the impact of its electricity use on carbon emissions, and provides a basis for reducing electricity consumption and optimizing the energy structure. The carbon sink estimation module processes the enterprise's greening data, calculates the carbon storage through the biomass equation and the root-shoot ratio model, and corrects the carbon storage data based on the carbon element balance sheet to finally obtain the carbon sink fixation amount. This module provides data support for the enterprise's carbon storage capacity, enabling it to evaluate the carbon sink benefits of greening projects on the basis of carbon emission control and enhancing its sustainable development ability. Finally, the net carbon emission offset module calculates the total carbon emissions of the enterprise by integrating the direct carbon emissions and indirect carbon emissions, generates carbon sink contribution data in combination with the carbon sink fixation amount, and performs carbon emission offset processing to obtain the enterprise's net carbon emissions. This module provides a clear carbon emission situation for the enterprise, which can help it optimize its carbon emission management strategy, reduce the overall carbon emissions, meet environmental protection requirements, and promote the enterprise to achieve green development. Overall, through the collaborative work of these modules, the enterprise can achieve precise carbon emission monitoring, carbon emission reduction, and carbon sink optimization, providing important data support and decision-making basis for the achievement of low-carbon development goals.

[0152] Therefore, from any perspective, the embodiments should be regarded as exemplary and non-limiting. The scope of the present invention is defined by the appended claims rather than the above description. Therefore, all changes falling within the meaning and scope of the equivalent elements of the application documents are intended to be encompassed within the present invention.

[0153] The above are only specific embodiments of the present invention, enabling those skilled in the art to understand or implement the present invention. Various modifications to these embodiments will be obvious to those skilled in the art, and the general principles defined herein can be implemented in other embodiments without departing from the spirit or scope of the present invention. Therefore, the present invention will not be limited to these embodiments shown herein, but rather will conform to the widest scope consistent with the principles and novel features invented herein.

Claims

1. A method for calculating and evaluating corporate carbon emissions, characterized in that: The following steps are involved: Step S1: Collecting the boundary material inflow information and material outflow information of the enterprise production system to obtain material flow data, wherein the material flow information includes the raw material feed amount, fuel consumption and auxiliary material usage, and the material outflow information includes the product output, waste gas emission and solid waste generation; Step S2: establishing a material balance equation based on the material flow data, and tracing the flow path of the carbon element through the material balance equation to obtain a carbon element balance table; Step S3: According to the preset standard state thermodynamic parameters and chemical reaction equations, the carbon element balance table is calculated based on the combustion conditions and stoichiometry of the unit fuel combustion to obtain direct carbon emission data; Step S4: obtaining the enterprise power consumption data, based on the pre-acquired regional power grid power energy structure data; Determine the power generation coal consumption coefficient based on the weighted average method according to the enterprise's power consumption data and power energy structure data, and convert power consumption into carbon emissions to obtain indirect carbon emissions data; Step S5: Calculate the carbon storage of the preset enterprise greening data based on the biomass equation and the root-to-shoot ratio model to obtain carbon storage data; perform carbon sink estimation and correction processing on the carbon storage data according to the carbon element balance table to obtain carbon sink fixed amount data; Step S6: Summarize the total carbon emissions according to the direct carbon emissions data and the indirect carbon emissions data to obtain the total carbon emissions data of the enterprise; Generate corporate carbon sink contribution data based on carbon sink fixed amount data; perform carbon emission offset processing on corporate total carbon emission data and corporate carbon sink contribution data to obtain corporate net carbon emission data.

2. The enterprise carbon emission calculation and evaluation method according to claim 1 is characterized in that: Step S1 includes the following steps: Step S11: Obtain the process flow chart of the enterprise production system, identify and number the system boundary nodes of the process flow chart, and obtain material flow monitoring point data, wherein the material flow monitoring point data includes the spatial position data of the raw material feed port, the fuel supply port, the auxiliary material delivery port, the product discharge port, the exhaust gas discharge port, and the solid waste collection port; Step S12: installing a flow metering device at each monitoring point according to the material flow monitoring point data and performing calibration processing, thereby obtaining detection accuracy data of the flow metering device; Step S13: setting the data collection time interval and sampling frequency according to the detection accuracy data, and performing continuous data collection processing on the monitoring points to obtain original material flow data, wherein the original material flow data includes the instantaneous flow value of each monitoring point at different time points; Step S14: Perform statistical tests and data repair processing on the abnormal values ​​and missing values ​​in the original material flow data to obtain corrected material flow data, wherein the data repair processing uses a moving average method to smooth the abnormal values ​​and uses an interpolation method to supplement the missing values.

3. The enterprise carbon emission calculation and evaluation method according to claim 2 is characterized in that: Step S2 includes the following steps: Step S21: Classify and summarize various material flows and convert the units according to the material flow data, so as to obtain standard material flow data; Step S22: performing elemental composition analysis on the carbon-containing material entering the system according to the standard material flow data, thereby obtaining the carbon content data of the material inflow; Step S23: performing elemental composition analysis on the carbon-containing material flowing out of the system according to the standard material flow data, thereby obtaining the material outflow carbon content data; Step S24: establishing a system-level carbon conservation equation based on the carbon content data of the material inflow and the carbon content data of the material outflow, thereby obtaining an initial carbon balance table; Step S25: performing process path identification processing on the carbon element conversion process within the system according to the process flow chart and the initial carbon element balance table, thereby obtaining a carbon element conversion path chart, wherein the process path identification includes the morphological change and flow direction analysis of the carbon element in the physical change process and the chemical reaction process; Step S26: performing material balance verification processing on each carbon element conversion node through the carbon element conversion path diagram, thereby obtaining carbon conversion dynamic balance correction data, wherein the material balance verification specifically uses an iterative calculation method to balance and adjust the carbon element income and expenditure differences of different nodes; Step S27: Perform statistical processing on the carbon input, conversion, loss and output of various carbon-containing substances based on the carbon conversion dynamic balance correction data to obtain a final carbon balance table.

4. The enterprise carbon emission calculation and evaluation method according to claim 3 is characterized in that: Step S24 includes the following steps: Step S241: performing carbon element flux calculation processing on each feed stream according to the carbon content data of the material inflow, thereby obtaining carbon element input flux data, wherein the flux calculation specifically adopts the product method of material flow and carbon content, and the data of different time scales are uniformly converted into annual flux; Step S242: performing carbon element flux calculation processing on each outflow stream according to the outflow carbon content data of the material, thereby obtaining carbon element output flux data, wherein the flux calculation is specifically based on the material flow volatility time-weighted average to perform annual flux conversion; Step S243: dynamically monitoring the cumulative changes of carbon elements in the system based on the process flow chart, thereby obtaining carbon element cumulative amount data; Step S244: establishing a carbon element balance equation based on the law of conservation of mass according to the carbon element input flux data, the carbon element output flux data and the carbon element accumulation data, thereby obtaining preliminary carbon element conservation relationship data; Step S245: performing reliability interval evaluation on each coefficient of the equation in the carbon element conservation relationship data based on the error propagation theory, thereby obtaining coefficient correction factor data; Step S246: Parameter optimization processing is performed on the carbon element conservation relationship data according to the coefficient correction factor data, so as to obtain an initial carbon element balance table, wherein the parameter optimization is specifically to dynamically adjust each coefficient using the least square method.

5. The enterprise carbon emission calculation and evaluation method according to claim 4 is characterized in that: Step S26 includes the following steps: Step S261: performing identification and classification processing on the key nodes in the system of the carbon element conversion path diagram, thereby obtaining a carbon element conversion node list, wherein the types of key nodes include physical conversion nodes, chemical reaction nodes and storage accumulation nodes, and each node has a unique identification number and corresponding process parameters; Step S262: performing carbon element flux accounting processing on the inflow and outflow logistics of each node according to the carbon element conversion node list, thereby obtaining node-level carbon element balance data; Step S263: performing statistical analysis on the carbon element income and expenditure differences of each node according to the node-level carbon element balance data, thereby obtaining node balance error data; Step S264: establishing a node balance adjustment model based on the minimum variance principle based on the node balance error data, thereby obtaining optimized adjustment coefficient data, wherein the node balance adjustment model performs differentiated processing on data of different reliability levels by setting an error weight factor; Step S265: performing iterative correction processing on the node-level carbon element balance data according to the optimized adjustment coefficient data, thereby obtaining the node balance data, wherein the iterative correction process specifically includes setting a convergence threshold, and stopping the iteration when the relative error between two adjacent calculation results is less than a preset value; Step S266: Perform system integration processing on the carbon element balance of the system through the node balance data, so as to obtain carbon conversion dynamic balance correction data.

6. The enterprise carbon emission calculation and evaluation method according to claim 5 is characterized in that: Step S3 includes the following steps: Step S31: performing thermodynamic equilibrium analysis on the Gibbs free energy and reaction enthalpy change of the combustion process according to preset standard state thermodynamic parameters and a carbon element balance table, thereby obtaining reaction spontaneity data; Step S32: performing stoichiometric analysis on the combustion process of hydrocarbons in the material flow data based on the combustion reaction mechanism, thereby obtaining chemical reaction equation data, wherein the stoichiometric analysis includes balancing of the complete combustion reaction equation and the partial combustion reaction equation; Step S33: performing component analysis on the carbon content of the fuel according to the carbon element balance table and the chemical reaction equation data, thereby obtaining fuel carbon content data, wherein the component analysis specifically is to determine the mass fraction of the carbon element in the unit fuel by using an elemental analysis method; Step S34: performing multi-physics field coupling analysis and processing on the temperature field, pressure field and oxygen concentration field of the combustion process based on the reaction spontaneity data, thereby obtaining combustion condition parameter data; Step S35: Performing kinetic simulation processing on the oxidation reaction of carbon element by using the combustion condition parameter data and the chemical reaction equation data, thereby obtaining carbon oxidation rate data; Step S36: A carbon dioxide generation calculation model based on a stoichiometric relationship is established based on the fuel carbon content data and the carbon oxidation rate data, thereby obtaining direct carbon emission data.

7. The enterprise carbon emission calculation and evaluation method according to claim 6 is characterized in that: Step S4 includes the following steps: Step S41: Obtain a list of electrical equipment in the enterprise production system, and perform statistical analysis on the power parameters and operating time of each device according to the list of electrical equipment, thereby obtaining equipment power load data; Step S42: acquiring power source data of the regional power grid, and performing energy structure analysis and processing according to the power generation of different types of power generation facilities in the power source data, thereby obtaining power structure proportion data; Step S43: performing weighted calculation processing on the standard coal consumption coefficients of different power generation types based on the energy conversion efficiency and transmission and distribution loss rate of various power generation facilities according to the power structure proportion data, thereby determining the power generation coal consumption coefficient; Step S44: converting the power consumption into standard coal equivalent according to the equipment power load data and the power generation coal consumption coefficient, thereby obtaining standard coal consumption data; Step S45: performing a stoichiometric conversion process on the carbon content in the standard coal based on the standard coal consumption data, thereby obtaining carbon emission equivalent data; Step S46: Perform time-accumulated statistics on the annual indirect carbon emissions of the enterprise according to the carbon emission equivalent data, so as to obtain indirect carbon emission data.

8. The enterprise carbon emission calculation and evaluation method according to claim 7 is characterized in that: Step S5 includes the following steps: Step S51: performing vegetation type recognition processing on the spatial distribution of trees, shrubs and lawns according to preset enterprise greening data, thereby obtaining vegetation distribution map data; Step S52: Based on the vegetation distribution map data, biometric measurement is performed on the breast diameter and tree height of trees, the crown width and height of shrubs, and the coverage of lawns, thereby obtaining vegetation morphological parameter data; Step S53: quantitatively calculating the aboveground biomass based on the preset biomass equation according to the vegetation morphological parameter data, and converting the underground biomass based on the preset root-shoot ratio model, thereby obtaining aboveground biomass data and underground biomass data; Step S54: converting the carbon content of vegetation carbon storage according to the aboveground biomass data and the underground biomass data, thereby obtaining carbon storage data; Step S55: performing differential calculation processing on the annual carbon storage change through the carbon storage data, thereby obtaining initial carbon sink fixed amount data; Step S56: performing a balance correction process based on carbon sink calculation and system carbon balance on the carbon sink calculation result according to the initial carbon sink fixed amount data and the carbon element balance table, so as to obtain the final carbon sink fixed amount data.

9. A corporate carbon emissions calculation and evaluation system, characterized in that: Used to execute the enterprise carbon emission calculation and evaluation method according to claim 1, the enterprise carbon emission calculation and evaluation system comprises: The material flow collection module is used to collect the boundary material inflow information and material outflow information of the enterprise production system to obtain material flow data, where the material flow information includes the raw material feed amount, fuel consumption and auxiliary material usage, and the material outflow information includes product output, waste gas emission and solid waste generation; The carbon element balance calculation module is used to establish a material balance equation based on material flow data, and to track the flow path of the carbon element through the material balance equation to obtain a carbon element balance table; The direct carbon emission calculation module is used to calculate the carbon emission per unit fuel combustion based on the combustion conditions and stoichiometry of the carbon element balance table according to the preset standard state thermodynamic parameters and chemical reaction equations to obtain direct carbon emission data; The indirect carbon emission conversion module is used to obtain the enterprise's electricity consumption data, and according to the pre-acquired regional power grid's electric energy structure data; according to the enterprise's electricity consumption data and the electric energy structure data, the power generation coal consumption coefficient is determined based on the weighted average method, and the electricity consumption is converted into carbon emissions to obtain indirect carbon emission data; The carbon sink estimation module is used to calculate the carbon storage of the preset enterprise greening data based on the biomass equation and the root-to-shoot ratio model to obtain carbon storage data; perform carbon sink estimation and correction processing on the carbon storage data according to the carbon element balance table to obtain carbon sink fixed amount data; The net carbon emission offset module is used to summarize the total carbon emissions based on the direct carbon emission data and the indirect carbon emission data to obtain the total carbon emission data of the enterprise; generate the enterprise carbon sink contribution data based on the carbon sink fixed amount data; and perform carbon emission offset processing on the enterprise total carbon emission data and the enterprise carbon sink contribution data to obtain the enterprise net carbon emission data.

10. A storage medium, characterized in that: include: Memory for storing computer programs; A processor, configured to implement the enterprise carbon emission calculation and assessment method as described in any one of claims 1 to 8 when executing the computer program.

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