A method for calculating carbon accounting for industrial production

Abstract

The present invention discloses a method for calculating the amount of carbon accounting for industrial production, which belongs to the field of industrial production technology and includes the following steps: S1, data collection and processing: collecting and processing real-time data of industrial production carbon emission monitoring; S2, model construction and optimization: constructing and optimizing the industrial production carbon accounting amount calculation model, and determining the optimal industrial production carbon accounting amount calculation model; S3, carbon accounting amount calculation: calculating the industrial production carbon accounting amount according to the optimal industrial production carbon accounting amount calculation model, and determining the industrial production carbon accounting amount calculation result. The present invention solves the existing problems of being unable to perform carbon accounting amount calculation for industrial production, being unable to timely understand the carbon emission situation of industrial production and managing it, resulting in poor management effect of industrial production carbon emissions. The present invention can effectively calculate the carbon accounting amount for industrial production, can timely understand the carbon emission situation of industrial production, can timely manage it, and can improve the management effect of industrial production carbon emissions.

Description

Technical Field

[0001] The present invention relates to the technical field of industrial production, and in particular to a method for calculating carbon accounting amount for industrial production. Background Art

[0002] Industrial production primarily takes place in factories, where workers use power (fuel, electricity) and machinery to transform raw materials into products. A single raw material can produce different products, while a single product can be made from the processing, assembly, or combination of multiple raw materials. Industrial production activities directly and indirectly emit carbon dioxide and its equivalent gases into the atmosphere, requiring carbon accounting. Carbon emissions accounting is a fundamental prerequisite for effectively implementing carbon reduction efforts and promoting a green economic transition, and it is a crucial support for actively participating in international negotiations on climate change.

[0003] Chinese patent publication number CN116298093A discloses a detection system and method for greenhouse gas emissions from industrial production, including a chimney, with evenly distributed exhaust pipes fixedly connected to the outside of the chimney, gas concentration sensors fixedly connected to the inside of the exhaust pipes, a top tube fixedly connected to the top of the chimney, a motor provided inside the top tube, a threaded rod fixedly connected to the end of the motor main shaft, a threaded sleeve spirally connected to the outside of the threaded rod, a fixed sleeve fixedly connected to the outside of the threaded sleeve, a support rod fixedly connected to the outside of the fixed sleeve, and a retaining cylinder fixedly connected to the other end of the support rod; in rainy weather, the motor is continuously started and moved downward, the upper unit exhaust device is in close contact with the lower unit exhaust device, the upper unit exhaust hole is staggered with the lower unit exhaust hole, to ensure that external rainwater is not easy to fall, and greenhouse gases are discharged through the exhaust pipe, and the gas concentration sensor inside the exhaust pipe can accurately detect the concentration of greenhouse gases; however, this patent has the following defects:

[0004] The existing carbon accounting cannot be effectively calculated for industrial production, which makes it impossible to timely understand the carbon emissions of industrial production and to manage them in a timely manner, resulting in poor results in industrial production carbon emissions management. Summary of the Invention

[0005] The purpose of the present invention is to provide a method for calculating the carbon accounting amount for industrial production, which can effectively calculate the carbon accounting amount for industrial production, timely understand the carbon emissions of industrial production, manage it in a timely manner, improve the carbon emission management effect of industrial production, and solve the problems raised in the above background technology.

[0006] To achieve the above object, the present invention provides the following technical solutions:

[0007] A method for calculating carbon accounting for industrial production, comprising the following steps:

[0008] S1. Data collection and processing:

[0009] Collect and process the real-time data of industrial production carbon emission monitoring to determine the characteristic data of industrial production carbon emission monitoring;

[0010] S2. Model construction and optimization:

[0011] Construct a deep learning-based industrial production carbon accounting calculation model, optimize the deep learning-based industrial production carbon accounting calculation model, and determine the optimal deep learning-based industrial production carbon accounting calculation model;

[0012] S3. Calculation of carbon accounting amount:

[0013] According to the optimal industrial production carbon accounting calculation model based on deep learning, the industrial production carbon emission monitoring characteristic data is mined and analyzed, the industrial production carbon accounting amount is calculated, and the industrial production carbon accounting amount calculation results based on deep learning are determined. The industrial production carbon accounting amount calculation report is transmitted to industrial production management personnel in a visual form.

[0014] Preferably, in S1, real-time data of industrial production carbon emission monitoring is collected, and the following operations are performed:

[0015] Continuously monitor and collect real-time data on the concentration of carbon dioxide and its equivalent gases emitted directly and indirectly into the atmosphere from fossil fuel combustion, electricity consumption, and heat consumption in industrial production activities to obtain data on industrial production carbon emission concentrations;

[0016] Continuously monitor and collect real-time data on the flow rate of carbon dioxide and its equivalent gases emitted directly and indirectly into the atmosphere during fossil fuel combustion, electricity consumption, and heat consumption in industrial production activities to obtain data on the flow rate of industrial production carbon emissions;

[0017] Among them, based on the industrial production carbon emission concentration data and the industrial production carbon emission flow rate data, the real-time data of industrial production carbon emission monitoring is determined.

[0018] Preferably, in S1, collecting real-time data on industrial production carbon emissions monitoring also includes:

[0019] Extract the real-time monitored carbon dioxide concentration ratio value;

[0020] Obtaining a carbon dioxide concentration index parameter according to the carbon dioxide concentration ratio value;

[0021] The carbon dioxide concentration index parameter is obtained by the following formula:

[0022]

[0023] Among them, S represents the concentration index parameter of carbon dioxide; n represents the number of times the carbon dioxide concentration data is collected; P ci Indicates the concentration ratio of carbon dioxide corresponding to the i-th concentration data collection; P ck Indicates the preset carbon dioxide concentration ratio reference value; P cmax and P cmin Indicates the maximum and minimum values ​​of the carbon dioxide concentration ratio during the n-time data collection process; P cz Indicates the median value of the carbon dioxide concentration ratio during the n-time data collection process;

[0024] Comparing the carbon dioxide concentration index parameter with a preset concentration index parameter threshold;

[0025] When the concentration index parameter of carbon dioxide exceeds the preset concentration index parameter threshold, the data collection frequency of the flow rate of carbon dioxide and its equivalent gas is adjusted.

[0026] Preferably, when the concentration index parameter of carbon dioxide exceeds the preset concentration index parameter threshold, the data collection frequency of the flow rate of carbon dioxide and its equivalent gas is adjusted, including:

[0027] When the concentration index parameter of the carbon dioxide exceeds the preset concentration index parameter threshold, extracting the current flow rate of the carbon dioxide and its equivalent gas;

[0028] Extract the flow rate data corresponding to carbon dioxide from historical detection records;

[0029] Retrieve the data collection frequency of the current flow rate of carbon dioxide and its equivalent gas;

[0030] Adjusting the data collection frequency of the current flow rate of carbon dioxide and its equivalent gas according to the current flow rate of carbon dioxide and its equivalent gas and the flow rate data corresponding to carbon dioxide in combination with the carbon dioxide concentration index parameter;

[0031] The data acquisition frequency of the adjusted flow rate is obtained by the following formula:

[0032]

[0033] Where F represents the data collection frequency of the flow rate after adjustment; f represents the data collection frequency of the flow rate before adjustment; S represents the concentration index parameter of carbon dioxide; S y Indicates the preset concentration index parameter threshold; V b Indicates the flow rate standard deviation of the flow rate data corresponding to carbon dioxide in the historical detection records in addition to the current flow rate of carbon dioxide and its equivalent gas; V sIndicates the current flow rate of carbon dioxide and its equivalent gas; V ck Indicates the preset reference value of the flow rate of carbon dioxide; V max and V min Indicates the flow rate corresponding to the maximum and minimum values ​​of the carbon dioxide concentration ratio during the n-time data collection process.

[0034] Preferably, in S1, the real-time data of industrial production carbon emission monitoring is processed by performing the following operations:

[0035] Obtain real-time data on industrial production carbon emissions monitoring;

[0036] Clean and process real-time data from industrial production carbon emissions monitoring, including:

[0037] Conduct consistency checks on real-time data on industrial production carbon emissions monitoring;

[0038] Check whether the real-time data of industrial production carbon emission monitoring meets the requirements based on the reasonable value range and mutual relationship of each parameter in the real-time data of industrial production carbon emission monitoring;

[0039] Remove inconsistent data that is beyond the normal range, logically unreasonable, or contradictory in real-time industrial production carbon emission monitoring data;

[0040] Process invalid and missing values ​​in real-time data of industrial production carbon emission monitoring;

[0041] According to the calculation requirements of carbon accounting for industrial production, the real-time data of industrial production carbon emission monitoring are checked one by one to determine whether there are invalid values ​​and missing values ​​in the real-time data of industrial production carbon emission monitoring;

[0042] Remove invalid and missing values ​​in the real-time data of industrial production carbon emission monitoring that are useless for calculating the carbon accounting amount used in industrial production;

[0043] Determine real-time industrial production carbon emission monitoring data that is useful for calculating industrial production carbon accounting quantities.

[0044] Preferably, in said S1, the real-time data of industrial production carbon emission monitoring is processed, and the following operations are further performed:

[0045] Obtain real-time industrial production carbon emission monitoring data that is useful for calculating industrial production carbon accounting quantities;

[0046] Convert and process the real-time data of industrial production carbon emission monitoring that is useful for calculating the carbon accounting amount of industrial production, and eliminate the dimensional differences between the real-time data of industrial production carbon emission monitoring;

[0047] Determine standardized real-time data for industrial production carbon emissions monitoring;

[0048] Extract features from standardized real-time data of industrial production carbon emissions monitoring;

[0049] Extract features that reflect the calculation of carbon accounting for industrial production;

[0050] Determine the characteristic data for industrial production carbon emission monitoring.

[0051] Preferably, in S2, a deep learning-based industrial production carbon accounting calculation model is constructed, and the following operations are performed:

[0052] Collect historical data on industrial production carbon emissions monitoring based on the calculation requirements of industrial production carbon accounting;

[0053] Divide the historical data of industrial production carbon emission monitoring to determine the training set and test set for carbon accounting calculation;

[0054] Select a convolutional neural network model framework suitable for calculating carbon accounting for industrial production;

[0055] Based on the carbon accounting calculation training set, the selected convolutional neural network model framework suitable for industrial production carbon accounting calculation is trained;

[0056] Determine the industrial production carbon accounting calculation model based on deep learning.

[0057] Preferably, in S2, the industrial production carbon accounting calculation model based on deep learning is optimized, and the following operations are performed:

[0058] Obtain a deep learning-based industrial production carbon accounting calculation model;

[0059] Based on the carbon accounting calculation test set, the performance test and evaluation of the industrial production carbon accounting calculation model based on deep learning was conducted;

[0060] Determine the performance test and evaluation results of the industrial production carbon accounting calculation model;

[0061] Based on the performance test and evaluation results of the industrial production carbon accounting calculation model, the industrial production carbon accounting calculation model based on deep learning was mined and analyzed;

[0062] Determine the parameter adjustment optimization scheme based on the industrial production carbon accounting calculation model;

[0063] According to the parameter adjustment optimization scheme based on the industrial production carbon accounting calculation model, the parameters of the industrial production carbon accounting calculation model based on deep learning are adjusted and optimized;

[0064] Determine the optimal deep learning-based industrial production carbon accounting calculation model.

[0065] Preferably, in S3, the characteristic data of industrial production carbon emission monitoring is mined and analyzed to calculate the industrial production carbon accounting amount, and the following operations are performed:

[0066] Obtaining industrial production carbon emission monitoring characteristic data;

[0067] Input the characteristic data of industrial production carbon emission monitoring into the optimal industrial production carbon accounting calculation model based on deep learning;

[0068] Based on the optimal industrial production carbon accounting calculation model based on deep learning, the characteristic data of industrial production carbon emission monitoring is mined and analyzed to calculate the industrial production carbon accounting amount;

[0069] Determine the calculation results of industrial production carbon accounting based on deep learning.

[0070] Preferably, in S3, the industrial production carbon accounting calculation report is transmitted to the industrial production management personnel in a visual form, and the following operations are performed:

[0071] Obtain the calculation results of industrial production carbon accounting based on deep learning;

[0072] Generate an industrial production carbon accounting calculation report based on the deep learning-based industrial production carbon accounting calculation results and combined with the industrial production carbon emission monitoring characteristic data;

[0073] The industrial production carbon accounting calculation report is transmitted to industrial production management personnel in a visual form, so that the industrial production management personnel can make timely adjustments and management of industrial production carbon emissions based on the industrial production carbon accounting calculation report.

[0074] Compared with the prior art, the present invention has the following beneficial effects:

[0075] The present invention collects real-time data on industrial production carbon emission monitoring, processes the real-time data on industrial production carbon emission monitoring, determines characteristic data on industrial production carbon emission monitoring, constructs an industrial production carbon accounting quantity calculation model based on deep learning according to the calculation requirements of industrial production carbon accounting quantity, optimizes the industrial production carbon accounting quantity calculation model based on deep learning, determines the optimal industrial production carbon accounting quantity calculation model based on deep learning, mines and analyzes the characteristic data on industrial production carbon emission monitoring according to the optimal industrial production carbon accounting quantity calculation model based on deep learning, calculates the industrial production carbon accounting quantity, determines the industrial production carbon accounting quantity calculation result based on deep learning, and generates an industrial production carbon accounting quantity calculation report, transmits the industrial production carbon accounting quantity calculation report to industrial production management personnel in a visual form, so that the industrial production management personnel can make timely adjustments and management on the industrial production carbon emission situation, effectively calculate the carbon accounting quantity of industrial production, timely understand the industrial production carbon emission situation, and timely manage it, thereby improving the industrial production carbon emission management effect. BRIEF DESCRIPTION OF THE DRAWINGS

[0076] Figure 1 The figure is a flow chart of the method for calculating the carbon accounting amount for industrial production according to the present invention. DETAILED DESCRIPTION

[0077] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.

[0078] In order to solve the existing problem of being unable to effectively calculate carbon emissions from industrial production, which makes it impossible to timely understand the carbon emissions from industrial production and manage them in a timely manner, resulting in poor management of industrial carbon emissions, please refer to Figure 1 , this embodiment provides the following technical solutions:

[0079] A method for calculating carbon accounting for industrial production, comprising the following steps:

[0080] S1. Data collection and processing:

[0081] Collect and process the real-time data of industrial production carbon emission monitoring to determine the characteristic data of industrial production carbon emission monitoring;

[0082] In this embodiment, real-time data on industrial production carbon emissions monitoring is collected and the following operations are performed:

[0083] Continuously monitor and collect real-time data on the concentration of carbon dioxide and its equivalent gases emitted directly and indirectly into the atmosphere from fossil fuel combustion, electricity consumption, and heat consumption in industrial production activities to obtain data on industrial production carbon emission concentrations;

[0084] Continuously monitor and collect real-time data on the flow rate of carbon dioxide and its equivalent gases emitted directly and indirectly into the atmosphere during fossil fuel combustion, electricity consumption, and heat consumption in industrial production activities to obtain data on the flow rate of industrial production carbon emissions;

[0085] Among them, based on the industrial production carbon emission concentration data and the industrial production carbon emission flow rate data, the real-time data of industrial production carbon emission monitoring is determined.

[0086] Specifically, in S1, collecting real-time data on industrial production carbon emissions monitoring also includes:

[0087] Extract the real-time monitored carbon dioxide concentration ratio value;

[0088] Obtaining a carbon dioxide concentration index parameter according to the carbon dioxide concentration ratio value;

[0089] The carbon dioxide concentration index parameter is obtained by the following formula:

[0090]

[0091] Among them, S represents the concentration index parameter of carbon dioxide; n represents the number of times the carbon dioxide concentration data is collected; P ci Indicates the concentration ratio of carbon dioxide corresponding to the i-th concentration data collection; P ck Indicates the preset carbon dioxide concentration ratio reference value; P cmax and P cmin Indicates the maximum and minimum values ​​of the carbon dioxide concentration ratio during the n-time data collection process; P cz Indicates the median value of the carbon dioxide concentration ratio during the n-time data collection process;

[0092] Comparing the carbon dioxide concentration index parameter with a preset concentration index parameter threshold;

[0093] When the concentration index parameter of carbon dioxide exceeds the preset concentration index parameter threshold, the data collection frequency of the flow rate of carbon dioxide and its equivalent gas is adjusted.

[0094] The technical effect of the above-mentioned technical solution is that, by real-time monitoring of the carbon dioxide concentration ratio, the solution can instantly obtain carbon emissions information from the production process, which is crucial for timely responding to environmental changes and taking appropriate measures. The extracted concentration index parameter is calculated using the above-mentioned calculation formula, which takes into account multiple factors such as the number of data collections, the concentration value of each collection, the preset reference value, the maximum value, the minimum value, and the median value, thereby improving the accuracy and representativeness of the data. The solution includes a step for comparing the calculated carbon dioxide concentration index parameter with a preset threshold, which provides a basis for dynamic monitoring and management. When the concentration index parameter exceeds the preset threshold, the solution automatically adjusts the frequency of data collection for the flow rate of carbon dioxide and its equivalent gas. This means that when emissions are abnormal or potential risks increase, the system can collect data more frequently, allowing for more accurate assessment of emissions and the implementation of appropriate emission reduction measures. Through precise monitoring and dynamic adjustment, this solution helps companies promptly identify abnormal carbon emissions and take effective measures to reduce unnecessary emissions, which is of great significance for achieving environmental protection and energy conservation and emission reduction goals. Long-term precise monitoring and dynamic management also help companies optimize production processes and procedures, improve energy efficiency, and reduce resource waste. This solution enables intelligent processing and analysis of carbon emissions data through calculation formulas and preset thresholds, reducing the need for manual intervention and judgment. The automated adjustment of data collection frequency also improves work efficiency and response speed, enabling companies to more effectively manage carbon emissions.

[0095] In summary, this technical solution provides a comprehensive and effective means of monitoring carbon emissions in industrial production processes through real-time monitoring, precise calculation, dynamic adjustment and intelligent management, helping enterprises achieve the goals of environmental protection, energy conservation and emission reduction, and sustainable development.

[0096] Specifically, when the concentration index parameter of carbon dioxide exceeds the preset concentration index parameter threshold, the data collection frequency of the flow rate of carbon dioxide and its equivalent gas is adjusted, including:

[0097] When the concentration index parameter of the carbon dioxide exceeds the preset concentration index parameter threshold, extracting the current flow rate of the carbon dioxide and its equivalent gas;

[0098] Extract the flow rate data corresponding to carbon dioxide from historical detection records;

[0099] Retrieve the data collection frequency of the current flow rate of carbon dioxide and its equivalent gas;

[0100] Adjusting the data collection frequency of the current flow rate of carbon dioxide and its equivalent gas according to the current flow rate of carbon dioxide and its equivalent gas and the flow rate data corresponding to carbon dioxide in combination with the carbon dioxide concentration index parameter;

[0101] The data acquisition frequency of the adjusted flow rate is obtained by the following formula:

[0102]

[0103] Where F represents the data collection frequency of the flow rate after adjustment; f represents the data collection frequency of the flow rate before adjustment; S represents the concentration index parameter of carbon dioxide; S y Indicates the preset concentration index parameter threshold; V b Indicates the flow rate standard deviation of the flow rate data corresponding to carbon dioxide in the historical detection records in addition to the current flow rate of carbon dioxide and its equivalent gas; V s Indicates the current flow rate of carbon dioxide and its equivalent gas; V ck Indicates the preset reference value of the flow rate of carbon dioxide; V max and V min Indicates the flow rate corresponding to the maximum and minimum values ​​of the carbon dioxide concentration ratio during the n-time data collection process.

[0104] The technical effect of the above-mentioned technical solution is that, by real-time monitoring of the carbon dioxide concentration ratio, the solution can instantly obtain carbon emissions information from the production process, which is crucial for timely responding to environmental changes and taking appropriate measures. The extracted concentration index parameter is calculated using the above-mentioned calculation formula, which takes into account multiple factors such as the number of data collections, the concentration value of each collection, the preset reference value, the maximum value, the minimum value, and the median value, thereby improving the accuracy and representativeness of the data. The solution includes a step for comparing the calculated carbon dioxide concentration index parameter with a preset threshold, which provides a basis for dynamic monitoring and management. When the concentration index parameter exceeds the preset threshold, the solution automatically adjusts the frequency of data collection for the flow rate of carbon dioxide and its equivalent gas. This means that when emissions are abnormal or potential risks increase, the system can collect data more frequently, allowing for more accurate assessment of emissions and the implementation of appropriate emission reduction measures. Through precise monitoring and dynamic adjustment, this solution helps companies promptly identify abnormal carbon emissions and take effective measures to reduce unnecessary emissions, which is of great significance for achieving environmental protection and energy conservation and emission reduction goals. Long-term precise monitoring and dynamic management also help companies optimize production processes and procedures, improve energy efficiency, and reduce resource waste. This solution enables intelligent processing and analysis of carbon emissions data through calculation formulas and preset thresholds, reducing the need for manual intervention and judgment. The automated adjustment of data collection frequency also improves work efficiency and response speed, enabling companies to more effectively manage carbon emissions.

[0105] In summary, this technical solution provides a comprehensive and effective means of monitoring carbon emissions in industrial production processes through real-time monitoring, precise calculation, dynamic adjustment and intelligent management, helping enterprises achieve the goals of environmental protection, energy conservation and emission reduction, and sustainable development.

[0106] In this embodiment, the real-time data of industrial production carbon emission monitoring is processed by performing the following operations:

[0107] Obtain real-time data on industrial production carbon emissions monitoring;

[0108] Clean and process real-time data from industrial production carbon emissions monitoring, including:

[0109] Conduct consistency checks on real-time data on industrial production carbon emissions monitoring;

[0110] Check whether the real-time data of industrial production carbon emission monitoring meets the requirements based on the reasonable value range and mutual relationship of each parameter in the real-time data of industrial production carbon emission monitoring;

[0111] Remove inconsistent data that is beyond the normal range, logically unreasonable, or contradictory in real-time industrial production carbon emission monitoring data;

[0112] Process invalid and missing values ​​in real-time data of industrial production carbon emission monitoring;

[0113] According to the calculation requirements of carbon accounting for industrial production, the real-time data of industrial production carbon emission monitoring are checked one by one to determine whether there are invalid values ​​and missing values ​​in the real-time data of industrial production carbon emission monitoring;

[0114] Remove invalid and missing values ​​in the real-time data of industrial production carbon emission monitoring that are useless for calculating the carbon accounting amount used in industrial production;

[0115] Identify real-time industrial production carbon emission monitoring data that is useful for calculating industrial production carbon accounting quantities;

[0116] Obtain real-time industrial production carbon emission monitoring data that is useful for calculating industrial production carbon accounting quantities;

[0117] Convert and process the real-time data of industrial production carbon emission monitoring that is useful for calculating the carbon accounting amount of industrial production, and eliminate the dimensional differences between the real-time data of industrial production carbon emission monitoring;

[0118] Determine standardized real-time data for industrial production carbon emissions monitoring;

[0119] Extract features from standardized real-time data of industrial production carbon emissions monitoring;

[0120] Extract features that reflect the calculation of carbon accounting for industrial production;

[0121] Determine the characteristic data for industrial production carbon emission monitoring.

[0122] It should be noted that the real-time data of industrial production carbon emission monitoring is obtained, cleaned and processed, and the real-time data of industrial production carbon emission monitoring that is useful for the calculation of industrial production carbon accounting quantity is determined; the real-time data of industrial production carbon emission monitoring that is useful for the calculation of industrial production carbon accounting quantity is converted and processed to eliminate the dimensional differences between the real-time data of industrial production carbon emission monitoring, and determine the standardized real-time data of industrial production carbon emission monitoring; feature extraction is performed on the standardized real-time data of industrial production carbon emission monitoring to extract features that can reflect the calculation of industrial production carbon accounting quantity, and determine the characteristic data of industrial production carbon emission monitoring; it is convenient for the subsequent mining and analysis of the characteristic data of industrial production carbon emission monitoring according to the optimal deep learning-based industrial production carbon accounting quantity calculation model, calculate the industrial production carbon accounting quantity, and determine the calculation results of the industrial production carbon accounting quantity.

[0123] S2. Model construction and optimization:

[0124] Construct a deep learning-based industrial production carbon accounting calculation model, optimize the deep learning-based industrial production carbon accounting calculation model, and determine the optimal deep learning-based industrial production carbon accounting calculation model;

[0125] In this embodiment, a deep learning-based industrial production carbon accounting calculation model is constructed, and the following operations are performed:

[0126] Collect historical data on industrial production carbon emissions monitoring based on the calculation requirements of industrial production carbon accounting;

[0127] Divide the historical data of industrial production carbon emission monitoring to determine the training set and test set for carbon accounting calculation;

[0128] Select a convolutional neural network model framework suitable for calculating carbon accounting for industrial production;

[0129] Based on the carbon accounting calculation training set, the selected convolutional neural network model framework suitable for industrial production carbon accounting calculation is trained;

[0130] Determine the industrial production carbon accounting calculation model based on deep learning.

[0131] In this embodiment, the industrial production carbon accounting calculation model based on deep learning is optimized, and the following operations are performed:

[0132] Obtain a deep learning-based industrial production carbon accounting calculation model;

[0133] Based on the carbon accounting calculation test set, the performance test and evaluation of the industrial production carbon accounting calculation model based on deep learning was conducted;

[0134] Determine the performance test and evaluation results of the industrial production carbon accounting calculation model;

[0135] Based on the performance test and evaluation results of the industrial production carbon accounting calculation model, the industrial production carbon accounting calculation model based on deep learning was mined and analyzed;

[0136] Determine the parameter adjustment optimization scheme based on the industrial production carbon accounting calculation model;

[0137] According to the parameter adjustment optimization scheme based on the industrial production carbon accounting calculation model, the parameters of the industrial production carbon accounting calculation model based on deep learning are adjusted and optimized;

[0138] Determine the optimal deep learning-based industrial production carbon accounting calculation model.

[0139] S3. Calculation of carbon accounting amount:

[0140] According to the optimal industrial production carbon accounting calculation model based on deep learning, the industrial production carbon emission monitoring characteristic data is mined and analyzed, the industrial production carbon accounting amount is calculated, and the industrial production carbon accounting amount calculation results based on deep learning are determined. The industrial production carbon accounting amount calculation report is transmitted to industrial production management personnel in a visual form.

[0141] In this embodiment, the industrial production carbon emission monitoring feature data is mined and analyzed to calculate the industrial production carbon accounting amount, and the following operations are performed:

[0142] Obtaining industrial production carbon emission monitoring characteristic data;

[0143] Input the characteristic data of industrial production carbon emission monitoring into the optimal industrial production carbon accounting calculation model based on deep learning;

[0144] Based on the optimal industrial production carbon accounting calculation model based on deep learning, the characteristic data of industrial production carbon emission monitoring is mined and analyzed to calculate the industrial production carbon accounting amount;

[0145] Determine the calculation results of industrial production carbon accounting based on deep learning.

[0146] In this embodiment, the industrial production carbon accounting calculation report is transmitted to the industrial production management personnel in a visual form, and the following operations are performed:

[0147] Obtain the calculation results of industrial production carbon accounting based on deep learning;

[0148] Generate an industrial production carbon accounting calculation report based on the deep learning-based industrial production carbon accounting calculation results and combined with the industrial production carbon emission monitoring characteristic data;

[0149] The industrial production carbon accounting calculation report is transmitted to industrial production management personnel in a visual form, so that the industrial production management personnel can make timely adjustments and management of industrial production carbon emissions based on the industrial production carbon accounting calculation report.

[0150] In summary, by collecting real-time data of industrial production carbon emission monitoring, processing the real-time data of industrial production carbon emission monitoring, determining the characteristic data of industrial production carbon emission monitoring, and according to the calculation requirements of industrial production carbon accounting quantity, constructing an industrial production carbon accounting quantity calculation model based on deep learning, optimizing the industrial production carbon accounting quantity calculation model based on deep learning, determining the optimal industrial production carbon accounting quantity calculation model based on deep learning, mining and analyzing the industrial production carbon emission monitoring characteristic data according to the optimal industrial production carbon accounting quantity calculation model based on deep learning, calculating the industrial production carbon accounting quantity, determining the industrial production carbon accounting quantity calculation results based on deep learning, and generating an industrial production carbon accounting quantity calculation report, transmitting the industrial production carbon accounting quantity calculation report to industrial production management personnel in a visual form, so that industrial production management personnel can make timely adjustments to the industrial production carbon emission situation, effectively calculate the carbon accounting quantity of industrial production, timely understand the industrial production carbon emission situation, and manage it in a timely manner, which can improve the effect of industrial production carbon emission management.

[0151] It should be noted that, in this document, relational terms such as first and second, etc., are used only to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any actual relationship or order between these entities or operations. Moreover, the terms "comprises," "comprising," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that includes a list of elements includes not only those elements but also other elements not explicitly listed, or elements inherent to such process, method, article, or apparatus.

[0152] While embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions, and variations may be made to these embodiments without departing from the principles and spirit of the invention, and that the scope of the invention is defined by the appended claims and their equivalents.

Claims

1. A method for calculating carbon accounting for industrial production, characterized in that: The steps include: S1. Data collection and processing: Collect and process the real-time data of industrial production carbon emission monitoring to determine the characteristic data of industrial production carbon emission monitoring; S2. Model construction and optimization: Construct a deep learning-based industrial production carbon accounting calculation model, optimize the deep learning-based industrial production carbon accounting calculation model, and determine the optimal deep learning-based industrial production carbon accounting calculation model; S3. Calculation of carbon accounting amount: Based on the optimal industrial production carbon accounting calculation model based on deep learning, the characteristic data of industrial production carbon emission monitoring is mined and analyzed to calculate the industrial production carbon accounting amount, determine the industrial production carbon accounting amount calculation result based on deep learning, and transmit the industrial production carbon accounting amount calculation report to industrial production management personnel in a visual form; In S1, collecting real-time data on industrial production carbon emissions monitoring also includes: Extract the real-time monitored carbon dioxide concentration ratio value; Obtaining a carbon dioxide concentration index parameter according to the carbon dioxide concentration ratio value; The carbon dioxide concentration index parameter is obtained by the following formula: Among them, S represents the concentration index parameter of carbon dioxide; n represents the number of times the carbon dioxide concentration data is collected; P ci Indicates the concentration ratio of carbon dioxide corresponding to the i-th concentration data collection; P ck Indicates the preset carbon dioxide concentration ratio reference value; P cmax and P cmin Indicates the maximum and minimum values ​​of the carbon dioxide concentration ratio during the n-time data collection process; P cz Indicates the median value of the carbon dioxide concentration ratio during the n-time data collection process; Comparing the carbon dioxide concentration index parameter with a preset concentration index parameter threshold; When the concentration index parameter of carbon dioxide exceeds the preset concentration index parameter threshold, the data collection frequency of the flow rate of carbon dioxide and its equivalent gas is adjusted.

2. The method for calculating carbon accounting for industrial production according to claim 1, characterized in that: In S1, real-time data on industrial production carbon emissions monitoring is collected and the following operations are performed: Continuously monitor and collect real-time data on the concentration of carbon dioxide and equivalent gases emitted directly and indirectly into the atmosphere from fossil fuel combustion, electricity consumption, and heat consumption in industrial production activities to obtain data on industrial production carbon emission concentrations; Continuously monitor and collect real-time data on the flow rate of carbon dioxide and its equivalent gases emitted directly and indirectly into the atmosphere during fossil fuel combustion, electricity consumption, and heat consumption in industrial production activities to obtain data on the flow rate of industrial production carbon emissions; Among them, based on the industrial production carbon emission concentration data and the industrial production carbon emission flow rate data, the real-time data of industrial production carbon emission monitoring is determined.

3. The method for calculating carbon accounting for industrial production according to claim 2, characterized in that: When the concentration index parameter of carbon dioxide exceeds the preset concentration index parameter threshold, the data collection frequency of the flow rate of carbon dioxide and its equivalent gas is adjusted, including: When the concentration index parameter of the carbon dioxide exceeds the preset concentration index parameter threshold, extracting the current flow rate of the carbon dioxide and its equivalent gas; Extract the flow rate data corresponding to carbon dioxide from historical detection records; Retrieve the data collection frequency of the current flow rate of carbon dioxide and its equivalent gas; Adjusting the data collection frequency of the current flow rate of carbon dioxide and its equivalent gas according to the current flow rate of carbon dioxide and its equivalent gas and the flow rate data corresponding to carbon dioxide in combination with the carbon dioxide concentration index parameter; The data acquisition frequency of the adjusted flow rate is obtained by the following formula: Where F represents the data collection frequency of the flow rate after adjustment; f represents the data collection frequency of the flow rate before adjustment; S represents the concentration index parameter of carbon dioxide; S y Indicates the preset concentration index parameter threshold; V b Indicates the flow rate standard deviation of the flow rate data corresponding to carbon dioxide in the historical detection records in addition to the current flow rate of carbon dioxide and its equivalent gas; V s Indicates the current flow rate of carbon dioxide and its equivalent gas; V ck Indicates the preset reference value of the flow rate of carbon dioxide; V max and V min Indicates the flow rate corresponding to the maximum and minimum values ​​of the carbon dioxide concentration ratio during the n-time data collection process.

4. The method for calculating carbon accounting for industrial production according to claim 2, characterized in that: In S1, the real-time data of industrial production carbon emission monitoring is processed by performing the following operations: Obtain real-time data on industrial production carbon emissions monitoring; Cleaning and processing of real-time data from industrial production carbon emissions monitoring, including: Conduct consistency checks on real-time data on industrial production carbon emissions monitoring; Check whether the real-time data of industrial production carbon emission monitoring meets the requirements based on the reasonable value range and mutual relationship of each parameter in the real-time data of industrial production carbon emission monitoring; Remove inconsistent data that is beyond the normal range, logically unreasonable, or contradictory in real-time industrial production carbon emission monitoring data; Process invalid and missing values ​​in real-time data of industrial production carbon emission monitoring; According to the calculation requirements of carbon accounting for industrial production, the real-time data of industrial production carbon emission monitoring are checked one by one to determine whether there are invalid values ​​and missing values ​​in the real-time data of industrial production carbon emission monitoring; Remove invalid and missing values ​​in the real-time data of industrial production carbon emission monitoring that are useless for calculating the carbon accounting amount used in industrial production; Determine real-time industrial production carbon emission monitoring data that is useful for calculating industrial production carbon accounting quantities.

5. The method for calculating carbon accounting for industrial production according to claim 4, characterized in that: In S1, the real-time data of industrial production carbon emission monitoring is processed, and the following operations are also performed: Obtain real-time industrial production carbon emission monitoring data that is useful for calculating industrial production carbon accounting quantities; Convert and process the real-time data of industrial production carbon emission monitoring that is useful for calculating the carbon accounting amount of industrial production, and eliminate the dimensional differences between the real-time data of industrial production carbon emission monitoring; Determine standardized real-time data for industrial production carbon emissions monitoring; Extract features from standardized real-time data of industrial production carbon emissions monitoring; Extract features that reflect the calculation of carbon accounting for industrial production; Determine the characteristic data for industrial production carbon emission monitoring.

6. The method for calculating carbon accounting for industrial production according to claim 5, characterized in that: In S2, a deep learning-based industrial production carbon accounting calculation model is constructed, and the following operations are performed: Collect historical data on industrial production carbon emissions monitoring based on the calculation requirements of industrial production carbon accounting; Divide the historical data of industrial production carbon emission monitoring to determine the training set and test set for carbon accounting calculation; Select a convolutional neural network model framework suitable for calculating carbon accounting for industrial production; Based on the carbon accounting calculation training set, the selected convolutional neural network model framework suitable for industrial production carbon accounting calculation is trained; Determine the industrial production carbon accounting calculation model based on deep learning.

7. The method for calculating carbon accounting for industrial production according to claim 6, characterized in that: In S2, the industrial production carbon accounting calculation model based on deep learning is optimized, and the following operations are performed: Obtain a deep learning-based industrial production carbon accounting calculation model; Based on the carbon accounting calculation test set, the performance test and evaluation of the industrial production carbon accounting calculation model based on deep learning was conducted; Determine the performance test and evaluation results of the industrial production carbon accounting calculation model; Based on the performance test and evaluation results of the industrial production carbon accounting calculation model, the industrial production carbon accounting calculation model based on deep learning was mined and analyzed; Determine the parameter adjustment optimization scheme based on the industrial production carbon accounting calculation model; According to the parameter adjustment optimization scheme based on the industrial production carbon accounting calculation model, the parameters of the industrial production carbon accounting calculation model based on deep learning are adjusted and optimized; Determine the optimal deep learning-based industrial production carbon accounting calculation model.

8. The method for calculating carbon accounting for industrial production according to claim 7, characterized in that: In S3, the characteristic data of industrial production carbon emission monitoring is mined and analyzed to calculate the industrial production carbon accounting amount, and the following operations are performed: Obtaining industrial production carbon emission monitoring characteristic data; Input the characteristic data of industrial production carbon emission monitoring into the optimal industrial production carbon accounting calculation model based on deep learning; Based on the optimal industrial production carbon accounting calculation model based on deep learning, the characteristic data of industrial production carbon emission monitoring is mined and analyzed to calculate the industrial production carbon accounting amount; Determine the calculation results of industrial production carbon accounting based on deep learning.

9. The method for calculating carbon accounting for industrial production according to claim 8, characterized in that: In S3, the industrial production carbon accounting calculation report is transmitted to the industrial production management personnel in a visual form, and the following operations are performed: Obtain the calculation results of industrial production carbon accounting based on deep learning; Generate an industrial production carbon accounting calculation report based on the deep learning-based industrial production carbon accounting calculation results and combined with the industrial production carbon emission monitoring characteristic data; The industrial production carbon accounting calculation report is transmitted to industrial production management personnel in a visual form, so that the industrial production management personnel can make timely adjustments and management of industrial production carbon emissions based on the industrial production carbon accounting calculation report.

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