CN116975689A - Intelligent carbon emission identification and control method and system - Google Patents
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Abstract
The application relates to the technical field of carbon emission calculation, and provides an intelligent carbon emission identification and control method and system, wherein the intelligent carbon emission identification and control method comprises the following steps: constructing a region basic information set; performing data classification on the regional basis information set to generate a direct emission data set and an indirect emission data set; performing similar matching of the regional history data according to the regional task data and the date characteristic data; configuring energy service consumption prediction through a similarity matching result; configuring marginal emission rate according to regional task data, and generating an indirect carbon emission prediction result; configuring direct emission parameters based on the regional task data, the regional history data and the direct emission data set, and generating a direct carbon emission prediction result; carbon emission management of the target monitoring area is performed. The method can solve the technical problem that the accuracy of carbon emission prediction is low due to the fact that ideal calculation indexes are adopted for carbon emission prediction, and can improve the accuracy of carbon emission prediction, so that the management quality of carbon emission is further improved.
Description
Technical Field
The application relates to the technical field of carbon emission calculation, in particular to an intelligent carbon emission identification and control method and system.
Background
The carbon emission amount refers to the total amount of carbon dioxide emitted into the atmosphere due to human activities or natural processes in a certain time and space range, and mainly includes direct emission amount and indirect emission amount. The existing carbon emission calculation mode mainly carries out carbon emission calculation and prediction according to a carbon emission calculation formula, but in the calculation process, the calculation is usually carried out according to the standard value or the average value of calculation indexes issued by related departments, and the calculation is not carried out according to the actual carbon emission condition, so that the accuracy of carbon emission calculation and prediction is lower.
In summary, the prior art has the technical problem that the accuracy of carbon emission prediction is low due to the fact that the ideal calculation index is adopted for carbon emission prediction.
Disclosure of Invention
Based on this, it is necessary to provide an intelligent recognition and control method and system for carbon emission aiming at the technical problems.
The intelligent recognition and control method for carbon emission comprises the following steps: performing regional data interaction on a target monitoring region to construct a regional basic information set, wherein the regional basic information set comprises a carbon emission related activity data set; performing data classification on the regional basis information set to generate a direct emission data set and an indirect emission data set; reading the regional task data of the target monitoring region, and calling the regional history data of the target monitoring region; performing similar matching of the region historical data according to the region task data and the date characteristic data to generate a similar matching result; configuring energy service consumption prediction of the indirect emission data set through the similarity matching result to obtain an energy service consumption prediction result; configuring a marginal emission rate according to the regional task data, and generating an indirect carbon emission prediction result according to the energy service consumption prediction result and the marginal emission rate; configuring direct emission parameters based on the regional task data, the regional history data and the direct emission data set, and generating a direct carbon emission prediction result according to the configuration result; and performing carbon emission management of the target monitoring area through the direct carbon emission prediction result and the indirect carbon emission prediction result.
An intelligent recognition and control system for carbon emissions, comprising:
the regional basic information set construction module is used for executing regional data interaction on the target monitoring region to construct a regional basic information set, wherein the regional basic information set comprises a carbon emission related activity data set;
the data classification module is used for performing data classification on the regional basic information set to generate a direct emission data set and an indirect emission data set;
the regional task data reading module is used for reading regional task data of the target monitoring region and calling regional history data of the target monitoring region;
the similar matching result generation module is used for executing similar matching of the area historical data according to the area task data and the date characteristic data to generate a similar matching result;
the energy service consumption prediction module is used for configuring energy service consumption prediction of the indirect emission data set through the similarity matching result to obtain an energy service consumption prediction result;
An indirect carbon emission prediction result generation module, configured to configure a marginal emission rate according to the regional task data, and generate an indirect carbon emission prediction result according to the energy service consumption prediction result and the marginal emission rate;
the direct carbon emission prediction result generation module is used for configuring direct emission parameters based on the regional task data, the regional historical data and the direct emission data set and generating a direct carbon emission prediction result according to the configuration result;
and the carbon emission management module is used for carrying out carbon emission management of the target monitoring area through the direct carbon emission prediction result and the indirect carbon emission prediction result.
The intelligent carbon emission identification and control method and system can solve the technical problem that in the prior art, carbon emission prediction accuracy is low due to the fact that ideal calculation indexes are adopted for carbon emission prediction, and firstly, a regional basic information set of a target monitoring region is obtained, wherein the regional basic information set comprises a carbon emission related activity data set; then, carrying out data classification on the regional basic information set to obtain a direct emission data set and an indirect emission data set; acquiring regional task data and regional history data of the target monitoring region; performing similar matching on the regional historical data according to the regional task data and the date characteristic data, and configuring energy service consumption prediction of the indirect emission data set according to a similar matching result; configuring a marginal emission rate according to the regional task data, and multiplying the energy service consumption prediction result by the marginal emission rate to obtain a product which is an indirect carbon emission prediction result; configuring direct emission parameters based on the regional task data, the regional history data and the direct emission data set, and calculating according to configuration results to obtain direct carbon emission prediction results; and finally, carrying out carbon emission management on the target monitoring area according to the direct carbon emission prediction result and the indirect carbon emission prediction result. The method can improve the accuracy of carbon emission prediction, thereby further improving the quality of carbon emission management.
The foregoing description is only an overview of the present application, and is intended to be implemented in accordance with the teachings of the present application in order that the same may be more clearly understood and to make the same and other objects, features and advantages of the present application more readily apparent.
Drawings
FIG. 1 is a schematic flow chart of an intelligent identification and control method for carbon emission;
FIG. 2 is a schematic diagram showing a flow of correction calculation of a direct carbon emission prediction result in an intelligent carbon emission recognition and control method according to the present application;
FIG. 3 is a schematic flow chart of the generated granularity evaluation result in the intelligent recognition and control method of carbon emission;
fig. 4 is a schematic structural diagram of an intelligent recognition and control system for carbon emission.
Reference numerals illustrate: the system comprises a regional basic information set construction module 1, a data classification module 2, a regional task data reading module 3, a similarity matching result generation module 4, an energy service consumption prediction module 5, an indirect carbon emission prediction result generation module 6, a direct carbon emission prediction result generation module 7 and a carbon emission management module 8.
Detailed Description
The present application will be described in further detail with reference to the drawings and examples, in order to make the objects, technical solutions and advantages of the present application more apparent. It should be understood that the specific embodiments described herein are for purposes of illustration only and are not intended to limit the scope of the application.
As shown in fig. 1, the present application provides an intelligent recognition and control method for carbon emission, comprising:
step S100: performing regional data interaction on a target monitoring region to construct a regional basic information set, wherein the regional basic information set comprises a carbon emission related activity data set;
specifically, the method provided by the application is used for accurately predicting the carbon emission of the target monitoring area, and further carrying out carbon emission management on the target monitoring area according to the carbon emission prediction result, so that the quality of the carbon emission management of the target monitoring area is improved.
Firstly, a carbon emission management database of a target monitoring area is connected, the target monitoring area refers to a target area to be subjected to carbon emission management, the area data of the target monitoring area is extracted through the carbon emission management database, and an area basic information set is obtained, wherein the area basic information set comprises a carbon emission related activity data set, and the carbon emission related activity data set refers to related activity data such as a carbon emission mode, a carbon emission amount, a carbon emission time and the like of carbon emission in the target monitoring area. The carbon emission means includes means for burning fossil fuel, burning biomass, directly generating carbon dioxide emissions in an industrial process, indirectly generating carbon dioxide emissions using electricity, indirectly generating carbon dioxide emissions using heat, etc., and the carbon emission amount refers to the total amount of carbon dioxide generated by the carbon emission means. By constructing the regional basis information set, raw data support is provided for carbon emission prediction and management of the next step target monitoring region.
Step S200: performing data classification on the regional basis information set to generate a direct emission data set and an indirect emission data set;
specifically, the carbon emission related activity data set in the regional basis information set is subjected to data classification according to the carbon emission type, and the carbon emission related activity data set is divided into a direct emission data set and an indirect emission data set. Wherein, the direct emission refers to the emission of carbon dioxide generated directly by burning fossil fuel, biomass or industrial process, and the like, and comprises the direct emission modes of energy, industry, traffic, and the like; the indirect emission refers to emission of carbon dioxide indirectly generated by using energy services such as electricity, heat, steam and the like, and the indirect emission mainly comes from the electricity production department. By obtaining the direct emission dataset and the indirect emission dataset, data support is provided for direct carbon emission prediction and indirect carbon emission prediction for the next step.
Step S300: reading the regional task data of the target monitoring region, and calling the regional history data of the target monitoring region;
specifically, regional task data of the target monitoring region is read, the regional task data refers to real-time tasks of a plurality of carbon emission points in the target monitoring region, and the carbon emission points refer to factories, enterprises, vehicles and the like which directly or indirectly generate carbon dioxide emission, for example: when a thermal power plant, a chemical plant, or the like is used as the carbon emission point, the real-time task of the carbon emission point is the power generation amount of the thermal power plant at 100 kilowatts of the current day. And calling regional historical data of the target monitoring region, wherein the regional historical data comprises historical tasks, historical carbon emission amounts, historical carbon emission related activity data and the like which refer to a plurality of carbon emission points in the target monitoring region, and the historical tasks and the historical carbon emission amounts have corresponding relations.
Step S400: performing similar matching of the region historical data according to the region task data and the date characteristic data to generate a similar matching result;
specifically, date feature data of the regional task data is acquired, wherein the date feature data refers to a specific date of regional task execution, for example: the date characteristic data is the next sunday, assuming that the carbon emission amount on the whole day of the next sunday needs to be predicted. Then, similarity matching is performed in the area history data according to the area task data and the date characteristic data, and area history data with the same time period is firstly matched in the area history data according to the date characteristic data, for example: and if the date characteristic data is the next Tuesday, matching the area history data with the date of Tuesday in the area history data. And then carrying out similarity analysis according to the regional historical data of the regional task data in a plurality of same time periods, wherein the similarity analysis method can carry out similarity analysis by using a method of Jaccard correlation coefficients, wherein the greater the Jaccard correlation coefficients are, the higher the similarity between two samples is represented, the Jaccard correlation coefficient analysis method is a common technical means for a person skilled in the art, the development is not carried out, and the regional historical data with the largest Jaccard correlation coefficients are used as a similarity matching result. By performing similar matching on the area history data according to the area task data and the date characteristic data, the accuracy of the next energy service consumption prediction can be improved.
Step S500: configuring energy service consumption prediction of the indirect emission data set through the similarity matching result to obtain an energy service consumption prediction result;
specifically, the energy service consumption prediction of the indirect emission data set is configured according to the historical energy service consumption in the similarity matching result, wherein the energy service consumption refers to the energy service used by a certain department or activity in a certain time, such as electric quantity, heat quantity, steam quantity and the like, and an energy service consumption prediction result is obtained, and is equal to the historical energy service consumption in the similarity matching result. And by obtaining the energy service consumption prediction result, data support is provided for the calculation of the next step of indirect carbon emission prediction result.
Step S600: configuring a marginal emission rate according to the regional task data, and generating an indirect carbon emission prediction result according to the energy service consumption prediction result and the marginal emission rate;
specifically, according to the regional task data, an energy service type is obtained, wherein the energy service type comprises electricity quantity, heat quantity, steam quantity and the like, and a marginal emission rate is configured according to the energy service type, and the marginal emission rate refers to carbon dioxide emission quantity corresponding to unit energy service, for example: carbon dioxide emissions produced per unit of electricity. Wherein the marginal emission rate can be obtained from standard values or average values issued by the relevant departments.
Obtaining an indirect carbon emission prediction result calculation formula: indirect carbon emission prediction result=energy service consumption prediction result×marginal emission rate; and calculating the energy service consumption prediction result and the marginal emission rate according to the indirect carbon emission prediction result calculation formula to generate an indirect carbon emission prediction result. The accuracy of the indirect carbon emission prediction result can be improved by performing the similarity matching to obtain the energy service consumption prediction result and calculating the indirect carbon emission prediction result according to the energy service consumption prediction result.
Step S700: configuring direct emission parameters based on the regional task data, the regional history data and the direct emission data set, and generating a direct carbon emission prediction result according to the configuration result;
in particular, the direct energy service type and activity data, which refers to the amount of energy or substances consumed or produced by a certain department or activity over a certain time, such as fuel usage, raw material usage, product yield, etc., are obtained from the regional tasks and the direct emission data set. And then carrying out similarity matching on the regional task data and the direct emission data set and the regional historical data, and obtaining an emission factor and an oxidation rate according to the regional historical data with the highest similarity in the similarity matching result. The emission factor refers to the carbon dioxide emission amount corresponding to the unit activity data, such as the carbon dioxide emission amount generated by unit energy consumption or unit product output. The oxidation rate refers to the proportion of carbon element in the fuel that is completely converted to carbon dioxide during combustion, where the oxidation rate generally depends on the type of fuel and the combustion conditions, and can be assumed to be generally 100%.
Constructing a direct carbon emission prediction result calculation formula: direct carbon emission prediction result = activity data x emission factor x oxidation rate; and calculating the activity data, the emission factor and the oxidation rate according to the direct carbon emission prediction result calculation formula to obtain a direct carbon emission prediction result.
As shown in fig. 2, in one embodiment, step S700 of the present application further includes:
step S710: setting a sampling frequency, and performing quality sampling on the combustion raw materials of the direct emission data set through the sampling frequency to generate a quality sampling result;
step S720: performing raw material image acquisition of the combustion raw materials through an image acquisition device to generate a raw material image set;
specifically, setting a sampling frequency, which can be set by those skilled in the art based on practical situations, wherein the higher the sampling frequency, the higher the sampling result accuracy and the longer the consumed sampling time; the lower the sampling frequency, the lower the sampling result accuracy, the shorter the sampling time consumed, for example: the sampling frequency is set to sample once per hour. And performing quality analysis on the combustion raw materials in the direct emission data set according to the sampling frequency, wherein the quality analysis refers to the detection of the combustion quality of the combustion raw materials, and the larger the ratio of the combustion raw materials capable of being fully combusted is, the better the quality is, for example: when the combustion raw material is coal, the higher the carbon content therein, the better the quality, and the quality sampling result is obtained, the quality sampling result being represented by the quality grade of the combustion raw material, wherein the better the quality of the combustion raw material, the higher the quality grade.
Then, the raw material image acquisition is carried out on the combustion raw material through an image acquisition device, wherein the image acquisition device is equipment with a high-definition image acquisition function, for example: and an industrial camera for obtaining a raw material image set.
Step S730: performing raw material granularity evaluation on the raw material image set to generate granularity evaluation results;
as shown in fig. 3, in one embodiment, step S730 of the present application further includes:
step S731: configuring a boundary interval threshold value, and carrying out boundary identification on the raw material image set through the boundary interval threshold value to generate a boundary identification result;
step S732: performing closed boundary analysis on the boundary recognition result to determine a non-closed boundary region;
step S733: performing closed fitting according to the contour trend of the non-closed boundary region, and generating a contour fitting result according to the fitting result and a closed boundary analysis result;
step S734: performing granularity identification based on the contour fitting result to obtain a granularity identification result;
step S735: performing uniformity evaluation based on the contour fitting result to generate a uniformity recognition result;
step S736: and generating a granularity evaluation result through the granularity identification result and the uniformity identification result.
Specifically, a boundary interval threshold is set for the raw material image set, where the boundary interval threshold refers to a specification average value of raw material images, for example: when the raw material image is a coal image, the boundary separation threshold value refers to the average value of the radius of the coal block, and if the average value radius of the coal block is 3 cm, the boundary separation threshold value is a circle with the radius of 3 cm. And then carrying out boundary recognition on the raw material image set according to the boundary interval threshold value, namely obtaining the raw material boundary which accords with the boundary interval threshold value in the raw material image set, and obtaining a boundary recognition result. And then carrying out closed boundary analysis on the boundary recognition result, wherein the closed boundary refers to judging whether the boundary in the boundary recognition result is closed or not, wherein the boundary recognition result is closed under the condition of no shielding, is non-closed under the condition of shielding by other raw materials, and determines a non-closed boundary area and a closed boundary area.
And performing closed fitting according to the contour trend of the non-closed boundary region, wherein the closed fitting refers to drawing the residual boundary according to the existing boundary of the non-closed boundary region and the shape characteristics of the combustion raw material, and obtaining the fitting result of the non-closed boundary region. And then generating a contour fitting result according to the fitting result and the closed boundary analysis result, wherein the contour fitting result refers to the boundary contour of the original material image in the original material image set. By performing closed fitting of the non-closed areas on the raw material images, the accuracy of the contour fitting result can be improved, and the accuracy of the granularity identification result can be improved.
Carrying out particle size identification on the combustion raw material according to the contour fitting result to obtain a particle size identification result, wherein the smaller the particle size of the combustion raw material is, the greater the reliability that the combustion raw material can be fully combusted is, and the greater the value of the particle size identification result is; performing uniformity evaluation according to the contour fitting result, wherein the uniformity evaluation can be performed by calculating the variance of the contour fitting result, and the smaller the variance value of the contour fitting result is, the higher the uniformity of the contour fitting result is, the greater the reliability of the fully-combustible raw material is, and the greater the value of the uniformity recognition result is; and adding and summing the value of the granularity identification result and the value of the uniformity identification result, and taking the obtained sum as a granularity evaluation result. The larger the value of the granularity evaluation result is, the better the combustion effect of the combustion raw material is represented.
Step S740: generating a log state factor based on the quality sampling result and the granularity evaluation result;
in one embodiment, step S740 of the present application further includes:
step S741: continuously monitoring the combustion raw material, sampling a combustion sample, and recording combustion control parameters;
Step S742: executing the component analysis of the combustion sample, and compensating the component analysis result through the combustion control parameter to generate result supervision data;
step S743: and compensating the log state factor through the result supervision data.
Specifically, the quality grade of the quality sampling result and the granularity evaluation value of the granularity evaluation result are weighted, the weight ratio of the quality sampling result and the granularity evaluation result can be calculated through a variation coefficient method, wherein the greater the degree of influence on the full combustion of the combustion raw material is, the greater the weight ratio is, the variation coefficient method is a weight calculation method commonly used in the prior art, the description is omitted, and the weighted calculation result is taken as a raw material state factor.
And burning the burning raw material, and continuously monitoring a burning result, wherein the burning result is residual substances after burning, and the burning rate of burning the burning raw material can be judged after the burning result is analyzed. And sampling the combustion raw material, obtaining a combustion sample, and recording combustion control parameters of the combustion sample during combustion, wherein the combustion control parameters comprise parameters such as combustion temperature, oxygen content and the like. And then, carrying out component analysis on the combustion sample, wherein the component analysis refers to analysis on the residual proportion of combustible in the combustion sample, wherein the smaller the residual proportion is, the better the sufficient combustion effect of the combustion sample is represented, the component analysis result of the combustion sample is obtained, and the component analysis result is compensated according to the combustion control parameter, and when the combustion control parameter is poor, for example: when the combustion temperature is lower or the oxygen content is lower during combustion, the residual proportion of combustible in the component analysis result can be properly reduced, and result supervision data can be obtained. And finally correcting the log state factor according to the result supervision data, for example: when the log control factor is large, but the ratio of the remaining combustible in the result supervision data is relatively high, the result of the log control factor is inaccurate, and the value of the log control factor needs to be reduced. The obtained result supervision data is used for compensating the raw material state factors, so that the accuracy of obtaining the raw material state factors can be improved, and the accuracy of obtaining the direct carbon emission prediction result is improved.
Step S750: and correcting and calculating the direct carbon emission prediction result according to the raw material state factor.
In one embodiment, step S750 of the present application further includes:
step S751: performing control parameter similarity matching of combustion control through the regional task data and the regional historical data to obtain a control parameter similarity matching result;
step S752: determining the oxidation rate based on the raw material state factor and the control parameter similarity matching result;
step S753: and correcting the direct carbon emission prediction result based on the oxidation rate after calculation.
Specifically, the regional historical data is subjected to similar matching according to the regional task data, and combustion control parameters of the regional historical data with the highest similarity coefficient in the similarity matching result are extracted to be used as control parameter similar matching results. And then determining the actual oxidation rate according to the log state factor and the control parameter similarity matching result, wherein the larger the value of the log state factor is, the higher the parameter quality of the control parameter similarity matching result is, such as: when the combustion temperature is high and the oxygen content is high, the higher the oxidation rate is; the smaller the value of the log state factor, the lower the parameter quality of the control parameter similarity matching result, such as: when the combustion temperature is low and the oxygen content is low, the lower the oxidation rate is, the calculation mode of the oxidation rate can be set based on actual conditions, and the actual oxidation rate is obtained. The actual oxidation rate is then substituted for the oxidation rate in the direct carbon emission prediction result calculation formula, which has an oxidation rate value of 100%. Namely, the calculation formula of the direct carbon emission prediction result is modified as follows: direct carbon emission prediction result = activity data x emission factor x actual oxidation rate; and then, calculating a direct carbon emission prediction result according to the modified direct carbon emission prediction result calculation formula to obtain a direct carbon emission prediction result.
By correcting the direct carbon emission prediction result according to the oxidation rate obtained by actual calculation, the accuracy of obtaining the direct carbon emission prediction result can be improved, and the accuracy of obtaining the carbon emission prediction result of the target monitoring area can be further improved.
Step S800: and performing carbon emission management of the target monitoring area through the direct carbon emission prediction result and the indirect carbon emission prediction result.
In one embodiment, step S800 of the present application further includes:
step S810: carrying out carbon emission peak prediction according to the direct carbon emission prediction result and the indirect carbon emission prediction result to obtain a peak prediction result;
step S820: invoking carbon emission constraint of the target monitoring area, and performing constraint evaluation based on the carbon emission constraint and the peak prediction result;
step S830: and adjusting the task adjustment of the target monitoring area according to the constraint evaluation result.
Specifically, the direct carbon emission prediction result and the indirect carbon emission prediction result are added in accordance with a carbon emission prediction period, and the specific period of time of a certain day of the carbon emission prediction period may be set according to actual conditions, such as: and obtaining a peak value prediction result in a carbon emission prediction time period from 14 hours to 16 hours of the next Tuesday, wherein the peak value prediction result refers to the sum of a direct carbon emission prediction result and an indirect carbon emission prediction result in the carbon emission prediction time period.
Invoking a carbon emission constraint in a carbon emission prediction time period of the target monitoring region, wherein the carbon emission constraint refers to an allowable maximum carbon emission amount in the carbon emission prediction time period. Judging the peak value predicted result according to the carbon emission constraint, when the peak value predicted result is greater than or equal to the carbon emission constraint, obtaining a difference value of the peak value predicted result minus the carbon emission constraint, taking the difference value as a carbon emission predicted deviation value, and adjusting an area task of the target monitoring area according to the predicted deviation value, for example: when the peak value prediction result is greater than or equal to the carbon emission constraint, the carbon emission prediction deviation value is 5 tons of carbon dioxide, and the calculated 5 tons of carbon dioxide is about 5000 kilowatt-hours, so that the power generation amount of the thermal power plant in the regional task is reduced by 5000 kilowatt-hours. By performing task adjustment on the target monitoring area, the carbon emission management quality of the target monitoring area can be improved.
In one embodiment, step S800 of the present application further includes:
step S840: carrying out real-time monitoring on carbon emission in the target monitoring area;
step S850: executing the predictive evaluation of the direct carbon emission predictive result and the indirect carbon emission predictive result based on the real-time monitoring result to generate predictive compensation;
Step S860: and carrying out predictive management of subsequent carbon emission through the predictive compensation.
Specifically, the target monitoring area is monitored in real time for carbon emission, and a real-time monitoring result, namely, a real-time monitoring carbon emission, is obtained, wherein the real-time monitoring result comprises a direct carbon emission and an indirect carbon emission. Subtracting the direct carbon emission predicted result from the direct carbon emission amount to obtain a direct carbon emission predicted deviation, and taking the ratio of the direct carbon emission predicted deviation to the direct carbon emission predicted result as a direct carbon emission predicted deviation rate; subtracting the indirect carbon emission predicted result from the indirect carbon emission amount to obtain an indirect carbon emission predicted deviation, and taking the ratio of the indirect carbon emission predicted deviation to the indirect carbon emission predicted result as an indirect carbon emission predicted deviation rate; taking the direct carbon emission prediction deviation rate and the indirect carbon emission prediction deviation rate as prediction compensation. And finally, carrying out predictive management of the subsequent carbon emission according to the predictive compensation, namely subtracting the deviation value in the predictive compensation from the carbon emission predicted result in the subsequent carbon emission prediction to obtain the subsequent carbon emission predicted result. By comparing the carbon emission quantity with the carbon emission prediction result according to the real-time monitoring, the prediction compensation is obtained, and the accuracy of the subsequent carbon emission prediction result can be improved. The method solves the technical problem of lower accuracy of carbon emission prediction caused by the fact that ideal calculation indexes are adopted for carbon emission prediction in the prior art, and can improve the accuracy of carbon emission prediction, so that the management quality of carbon emission is further improved.
In one embodiment, as shown in FIG. 4, an intelligent carbon emission identification and control system is provided, comprising: the system comprises a regional basic information set construction module 1, a data classification module 2, a regional task data reading module 3, a similarity matching result generation module 4, an energy service consumption prediction module 5, an indirect carbon emission prediction result generation module 6, a direct carbon emission prediction result generation module 7 and a carbon emission management module 8, wherein:
the regional basic information set construction module 1 is used for executing regional data interaction on a target monitoring region to construct a regional basic information set, wherein the regional basic information set comprises a carbon emission related activity data set;
a data classification module 2, wherein the data classification module 2 is used for performing data classification on the regional basic information set to generate a direct emission data set and an indirect emission data set;
the regional task data reading module 3 is used for reading regional task data of the target monitoring region and calling regional history data of the target monitoring region;
the similar matching result generation module 4 is used for executing similar matching of the area history data according to the area task data and the date characteristic data to generate a similar matching result;
The energy service consumption prediction module 5 is used for configuring the energy service consumption prediction of the indirect emission data set according to the similarity matching result to obtain an energy service consumption prediction result;
an indirect carbon emission prediction result generation module 6, wherein the indirect carbon emission prediction result generation module 6 is configured to configure a marginal emission rate according to the regional task data, and generate an indirect carbon emission prediction result according to the energy service consumption prediction result and the marginal emission rate;
a direct carbon emission prediction result generation module 7, wherein the direct carbon emission prediction result generation module 7 is configured to configure a direct emission parameter based on the regional task data, the regional history data and the direct emission data set, and generate a direct carbon emission prediction result according to the configuration result;
a carbon emission management module 8, wherein the carbon emission management module 8 is configured to perform carbon emission management of the target monitoring area by the direct carbon emission prediction result and the indirect carbon emission prediction result.
In one embodiment, the system further comprises:
the quality sampling result module is used for setting sampling frequency, and performing quality sampling on the combustion raw materials of the direct emission data set through the sampling frequency to generate a quality sampling result;
The raw material image acquisition module is used for acquiring the raw material image of the burning raw material through the image acquisition device to generate a raw material image set;
the raw material granularity evaluation module is used for evaluating the raw material granularity of the raw material image set and generating a granularity evaluation result;
the raw material state factor generation module is used for generating a raw material state factor based on the quality sampling result and the granularity evaluation result;
and the correction calculation module is used for correcting and calculating the direct carbon emission prediction result according to the raw material state factor.
In one embodiment, the system further comprises:
the boundary recognition module is used for configuring a boundary separation threshold value, and carrying out boundary recognition on the raw material image set through the boundary separation threshold value to generate a boundary recognition result;
the closed boundary analysis module is used for carrying out closed boundary analysis on the boundary recognition result and determining a non-closed boundary area;
the contour fitting result generation module is used for carrying out closed fitting according to the contour trend of the non-closed boundary area and generating a contour fitting result according to the fitting result and the closed boundary analysis result;
The granularity identification module is used for carrying out granularity identification based on the contour fitting result to obtain a granularity identification result;
the uniformity evaluation module is used for evaluating uniformity based on the contour fitting result and generating a uniformity recognition result;
and the granularity evaluation result generation module is used for generating granularity evaluation results according to the granularity identification result and the uniformity identification result.
In one embodiment, the system further comprises:
the combustion control parameter recording module is used for continuously monitoring the combustion raw material, sampling a combustion sample and recording the combustion control parameters;
the result supervision data generation module is used for executing component analysis of the combustion sample, carrying out component analysis result compensation through the combustion control parameters and generating result supervision data;
and the raw material state factor compensation module is used for compensating the raw material state factor through the result supervision data.
In one embodiment, the system further comprises:
the control parameter similarity matching module is used for performing control parameter similarity matching of combustion control through the regional task data and the regional historical data to obtain a control parameter similarity matching result;
the oxidation rate determining module is used for determining the oxidation rate together based on the raw material state factor and the control parameter similarity matching result;
and the direct carbon emission prediction result correction module is used for correcting the direct carbon emission prediction result after calculating based on the oxidation rate.
In one embodiment, the system further comprises:
the carbon emission peak prediction module is used for predicting a carbon emission peak according to the direct carbon emission prediction result and the indirect carbon emission prediction result to obtain a peak prediction result;
the constraint evaluation module is used for calling the carbon emission constraint of the target monitoring area and performing constraint evaluation based on the carbon emission constraint and the peak prediction result;
and the task adjustment module is used for adjusting task adjustment of the target monitoring area according to the constraint evaluation result.
In one embodiment, the system further comprises:
the real-time monitoring module is used for monitoring the carbon emission of the target monitoring area in real time;
a predictive evaluation module for performing predictive evaluation of the direct carbon emission prediction result and the indirect carbon emission prediction result based on the real-time monitoring result, generating a predictive compensation;
and the prediction management module is used for performing prediction management of subsequent carbon emission through the prediction compensation.
In summary, the application provides an intelligent carbon emission identification and control method and system, which have the following technical effects:
1. the method solves the technical problem that the accuracy of carbon emission prediction is low due to the fact that ideal calculation indexes are adopted for carbon emission prediction in the prior art, and can improve the accuracy of carbon emission prediction, so that the management quality of carbon emission is further improved.
2. The accuracy of the indirect carbon emission prediction result can be improved by performing the similarity matching to obtain the energy service consumption prediction result and calculating to obtain the indirect carbon emission prediction result, and the accuracy of the direct carbon emission prediction result can be improved by correcting the direct carbon emission prediction result according to the oxidation rate obtained by actual calculation, so that the accuracy of the target monitoring area carbon emission prediction result is further improved.
3. By comparing the real-time monitored carbon emission with the carbon emission prediction result, prediction compensation is obtained, and the accuracy of the subsequent carbon emission prediction result can be improved, so that the quality of the subsequent carbon emission management of the target monitoring area is improved.
The technical features of the above embodiments may be arbitrarily combined, and all possible combinations of the technical features in the above embodiments are not described for brevity of description, however, as long as there is no contradiction between the combinations of the technical features, they should be considered as the scope of the description.
The above examples illustrate only a few embodiments of the application, which are described in detail and are not to be construed as limiting the scope of the application. It should be noted that it will be apparent to those skilled in the art that several variations and modifications can be made without departing from the spirit of the application, which are all within the scope of the application. Accordingly, the scope of protection of the present application is to be determined by the appended claims.
Claims (8)
1. The intelligent recognition and control method for the carbon emission is characterized by comprising the following steps of:
performing regional data interaction on a target monitoring region to construct a regional basic information set, wherein the regional basic information set comprises a carbon emission related activity data set;
Performing data classification on the regional basis information set to generate a direct emission data set and an indirect emission data set;
reading the regional task data of the target monitoring region, and calling the regional history data of the target monitoring region;
performing similar matching of the region historical data according to the region task data and the date characteristic data to generate a similar matching result;
configuring energy service consumption prediction of the indirect emission data set through the similarity matching result to obtain an energy service consumption prediction result;
configuring a marginal emission rate according to the regional task data, and generating an indirect carbon emission prediction result according to the energy service consumption prediction result and the marginal emission rate;
configuring direct emission parameters based on the regional task data, the regional history data and the direct emission data set, and generating a direct carbon emission prediction result according to the configuration result;
and performing carbon emission management of the target monitoring area through the direct carbon emission prediction result and the indirect carbon emission prediction result.
2. The method of claim 1, wherein the method further comprises:
setting a sampling frequency, and performing quality sampling on the combustion raw materials of the direct emission data set through the sampling frequency to generate a quality sampling result;
Performing raw material image acquisition of the combustion raw materials through an image acquisition device to generate a raw material image set;
performing raw material granularity evaluation on the raw material image set to generate granularity evaluation results;
generating a log state factor based on the quality sampling result and the granularity evaluation result;
and correcting and calculating the direct carbon emission prediction result according to the raw material state factor.
3. The method of claim 2, wherein the method further comprises:
configuring a boundary interval threshold value, and carrying out boundary identification on the raw material image set through the boundary interval threshold value to generate a boundary identification result;
performing closed boundary analysis on the boundary recognition result to determine a non-closed boundary region;
performing closed fitting according to the contour trend of the non-closed boundary region, and generating a contour fitting result according to the fitting result and a closed boundary analysis result;
performing granularity identification based on the contour fitting result to obtain a granularity identification result;
performing uniformity evaluation based on the contour fitting result to generate a uniformity recognition result;
and generating a granularity evaluation result through the granularity identification result and the uniformity identification result.
4. The method of claim 2, wherein the method further comprises:
continuously monitoring the combustion raw material, sampling a combustion sample, and recording combustion control parameters;
executing the component analysis of the combustion sample, and compensating the component analysis result through the combustion control parameter to generate result supervision data;
and compensating the log state factor through the result supervision data.
5. The method of claim 2, wherein the method further comprises:
performing control parameter similarity matching of combustion control through the regional task data and the regional historical data to obtain a control parameter similarity matching result;
determining the oxidation rate based on the raw material state factor and the control parameter similarity matching result;
and correcting the direct carbon emission prediction result based on the oxidation rate after calculation.
6. The method of claim 1, wherein the method further comprises:
carrying out carbon emission peak prediction according to the direct carbon emission prediction result and the indirect carbon emission prediction result to obtain a peak prediction result;
invoking carbon emission constraint of the target monitoring area, and performing constraint evaluation based on the carbon emission constraint and the peak prediction result;
And adjusting the task adjustment of the target monitoring area according to the constraint evaluation result.
7. The method of claim 1, wherein the method further comprises:
carrying out real-time monitoring on carbon emission in the target monitoring area;
executing the predictive evaluation of the direct carbon emission predictive result and the indirect carbon emission predictive result based on the real-time monitoring result to generate predictive compensation;
and carrying out predictive management of subsequent carbon emission through the predictive compensation.
8. An intelligent carbon emission identification and control system, characterized by the steps for performing any one of the methods for intelligent carbon emission identification and control as set forth in claims 1-7, said system comprising:
the regional basic information set construction module is used for executing regional data interaction on the target monitoring region to construct a regional basic information set, wherein the regional basic information set comprises a carbon emission related activity data set;
the data classification module is used for performing data classification on the regional basic information set to generate a direct emission data set and an indirect emission data set;
the regional task data reading module is used for reading regional task data of the target monitoring region and calling regional history data of the target monitoring region;
The similar matching result generation module is used for executing similar matching of the area historical data according to the area task data and the date characteristic data to generate a similar matching result;
the energy service consumption prediction module is used for configuring energy service consumption prediction of the indirect emission data set through the similarity matching result to obtain an energy service consumption prediction result;
an indirect carbon emission prediction result generation module, configured to configure a marginal emission rate according to the regional task data, and generate an indirect carbon emission prediction result according to the energy service consumption prediction result and the marginal emission rate;
the direct carbon emission prediction result generation module is used for configuring direct emission parameters based on the regional task data, the regional historical data and the direct emission data set and generating a direct carbon emission prediction result according to the configuration result;
and the carbon emission management module is used for carrying out carbon emission management of the target monitoring area through the direct carbon emission prediction result and the indirect carbon emission prediction result.
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