CN115271265A - Electric energy carbon flow analysis method and system based on carbon satellite data - Google Patents
Electric energy carbon flow analysis method and system based on carbon satellite data Download PDFInfo
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Abstract
The invention relates to the technical field of electric energy carbon emission analysis, in particular to an electric energy carbon flow analysis method and system based on carbon satellite data. Firstly, selecting a plurality of target position points and carbon dioxide concentration values of all the target position points from a target geographical area in carbon satellite data; then, obtaining a first concentration change characteristic vector based on the carbon dioxide concentration values of the target position points and the position relation; then obtaining a second concentration change characteristic vector based on the electric energy carbon emission and environmental parameter data of the target geographical area; and finally, according to the difference between the first concentration change characteristic vector and the second concentration change characteristic vector, correcting the circulation of the electric energy carbon in the target geographic area to obtain the corrected electric energy carbon emission. According to the method, the electric energy carbon emission of the target geographical area is corrected based on the carbon dioxide concentration data of the target geographical area in the carbon satellite data, so that the prediction accuracy of the electric energy carbon emission of the target geographical area can be improved.
Description
Technical Field
The invention relates to the technical field of electric energy carbon emission analysis, in particular to an electric energy carbon flow analysis method and system based on carbon satellite data.
Background
The power industry is a high energy consumption industry, about 75% of generated energy in China is thermal power generation, fossil fuel is required to be combusted in the thermal power generation, and a large amount of carbon dioxide is generated, so that the flow conversion analysis of electric energy carbon in the power generation process is very necessary. However, currently, the prediction accuracy of the electric energy carbon emission in the industry still has a small error, and how to improve the prediction accuracy of the electric energy carbon emission by improving the circulation analysis process of the electric energy carbon becomes a technical problem that needs to be solved by technicians in the field.
Disclosure of Invention
In order to overcome at least the above disadvantages in the prior art, the present invention provides a method for analyzing carbon flux of electric energy based on carbon satellite data, the method comprising:
acquiring carbon satellite data, wherein the carbon satellite data comprises different geographical areas and carbon dioxide concentration data corresponding to the different geographical areas;
selecting a target geographical area from the carbon satellite data, selecting a plurality of target position points in the target geographical area according to a preset rule, and acquiring carbon dioxide concentration values of the target position points;
obtaining a first concentration change feature vector of carbon dioxide in the target geographic area based on the carbon dioxide concentration values of the target location points and the position relation between the target location points;
acquiring the electric energy carbon emission amount of the target geographical area and the environmental parameter data of the target geographical area, and obtaining a second concentration change characteristic vector of carbon dioxide when the electric energy carbon circulates in the target geographical area based on the electric energy carbon emission amount and the environmental parameter data;
and according to the difference between the first concentration change characteristic vector and the second concentration change characteristic vector, correcting the circulation of the electric energy carbon in the target geographical area to obtain the corrected electric energy carbon emission.
In one possible implementation manner, the step of selecting a target geographic area from the carbon satellite data, selecting a plurality of target location points in the target geographic area according to a preset rule, and acquiring carbon dioxide concentration values of the plurality of target location points includes:
selecting a target geographic area in the carbon satellite data;
determining a target geographical sub-area with the highest carbon dioxide concentration value in the target geographical area, and selecting a target position point in the target geographical sub-area as a first target position point;
selecting a plurality of position points as second target position points along a plurality of different position directions by taking the first target position point as a center, wherein each position direction comprises at least two second target position points;
and acquiring the first target position point and a carbon dioxide concentration value corresponding to the target position point in the carbon satellite data.
In a possible implementation manner, the step of obtaining a first concentration variation feature vector of carbon dioxide in the target geographic area based on carbon dioxide concentration values of a plurality of the target location points and a position relationship between the plurality of the target location points includes:
taking the first target position point as a starting point, and acquiring second target position points distributed in different position directions;
calculating to obtain carbon dioxide concentration variation parameters in different position directions according to the carbon dioxide concentration values of the first target position points and the second target position points distributed in different position directions, wherein the carbon dioxide concentration variation parameters are the ratio of the carbon dioxide concentration difference between the adjacent target position points to the distance between the adjacent target position points;
and obtaining a first concentration change feature vector of the carbon dioxide in the target geographic area based on the carbon dioxide concentration change parameter and the corresponding position direction.
In a possible implementation manner, the step of obtaining the carbon emission amount of the electric energy in the target geographic area and the environmental parameter data of the target geographic area, and obtaining a second concentration change feature vector of the carbon dioxide when the electric energy carbon circulates in the target geographic area based on the carbon emission amount of the electric energy and the environmental parameter data includes:
when the target geographic area is a thermal power plant, acquiring the power generation amount of the target geographic area;
calculating the electric energy carbon emission of the target geographical area based on the generated energy of the target geographical area;
acquiring environment parameter data of the target geographic area, wherein the environment parameter data comprises air temperature, wind power and wind direction;
inputting the electric energy carbon emission amount and the environmental parameter data of the target geographic area into a trained electric energy carbon concentration prediction model for prediction to obtain carbon dioxide concentration change prediction parameters of carbon dioxide concentrations in different position directions of the target geographic area, and obtaining a second concentration change feature vector of carbon dioxide during circulation in the target geographic area based on the carbon dioxide concentration change prediction parameters and the corresponding position directions.
In a possible implementation manner, the step of calculating the carbon emission of the electric energy based on the electric energy generated by the target geographic area includes:
calculating the quality of the required fossil fuel based on the power generation amount of the target geographic area;
calculating a first electrical energy carbon emission amount of carbon dioxide generated by burning the required fossil fuel;
calculating a second electrical energy carbon emission amount of carbon dioxide generated during the desulfurization of the required fossil fuel;
and calculating the carbon emission of the electric energy of the target geographical area according to the carbon emission of the first electric energy and the carbon emission of the second electric energy.
In a possible implementation manner, before the step of obtaining the carbon emission amount of the electric energy in the target geographic area and the environmental parameter data of the target geographic area, and obtaining a second concentration variation feature vector of carbon dioxide when the electric energy carbon circulates in the target geographic area based on the carbon emission amount of the electric energy and the environmental parameter data, the method further includes a step of training the electric energy carbon concentration prediction model, where the step includes:
obtaining historical electric energy carbon concentration distribution information of a plurality of different sample geographical areas, historical electric energy carbon emission of the different sample geographical areas and environmental parameter data of the different sample geographical areas, wherein the historical electric energy carbon concentration distribution information of each sample geographical area comprises a sample position point located in the sample geographical area and an electric energy carbon concentration value of the sample position point;
analyzing the position relation of sample position points in any sample geographical area according to the historical electric energy carbon concentration distribution information of the sample geographical area, determining a sample position central point in the sample geographical area, and obtaining a plurality of associated sample position points distributed along the same position direction by taking the sample position central point as a starting point;
calculating to obtain carbon dioxide concentration change sample parameters in different position directions based on the electric energy carbon concentration values of a plurality of associated sample position points distributed in the same position direction;
taking carbon dioxide concentration change sample parameters in different position directions in the sample geographic area, environmental parameter data of the sample geographic area and historical electric energy emission of the sample geographic area as training samples;
inputting carbon dioxide concentration change sample parameters in different position directions in the sample geographical area and environment parameter data of the sample geographical area into a neural network model as input data, outputting electric energy carbon emission prediction quantity of the sample geographical area, calculating a loss function value of the model based on the electric energy carbon emission prediction quantity and historical electric energy carbon emission quantity of the sample geographical area, adjusting model parameters of the neural network model when the loss function value is not less than a preset function threshold value until the loss function value is less than the preset function threshold value, and taking the neural network model as a trained electric energy carbon concentration prediction model.
In a possible implementation manner, the step of correcting the circulation of the electric energy carbon in the target geographic area according to a difference between the first concentration variation eigenvector and the second concentration variation eigenvector to obtain a corrected electric energy carbon emission amount includes:
calculating a difference value between carbon dioxide concentration change parameters in the same position direction in the first concentration change characteristic vector and the second concentration change characteristic vector to obtain a carbon dioxide concentration difference characteristic vector;
inputting the carbon oxide concentration difference characteristic vector into a pre-trained electric energy carbon emission correction model to correct the electric energy carbon emission to obtain the correction amount of the electric energy carbon emission;
and obtaining the corrected carbon emission amount of the electric energy based on the carbon emission amount of the electric energy in the target geographical area and the correction amount of the carbon emission amount of the electric energy.
In one possible implementation, the method further includes a step of training the electric energy carbon emission correction model, which includes:
taking the carbon dioxide concentration difference feature vector, the electric energy carbon emission amount of the target geographic area and the electric energy carbon emission difference value between the actual electric energy carbon emission amount of the target geographic area as training samples;
inputting the difference value of the electric energy carbon emission between the carbon dioxide concentration difference characteristic vector and the actual electric energy carbon emission of the target geographic area into a deep learning model for training, enabling the deep learning model to be converged in a mode of iteratively updating model parameters in the deep learning model, and taking the converged deep learning model as the electric energy carbon emission correction model.
In a possible implementation manner, the deep learning model includes an input layer, a pooling layer, a comparison layer, a feedback layer, and an output layer, the step of inputting the difference value of carbon dioxide concentration difference between the feature vector and the actual carbon emission of the target geographic area into the deep learning model for training, and iteratively updating model parameters in the deep learning model to converge the deep learning model, and using the converged deep learning model as the electric carbon emission correction model includes:
inputting the carbon dioxide concentration difference characteristic vector from the input layer, and inputting the carbon dioxide concentration difference characteristic vector into the comparison layer after the pooling treatment of the pooling layer;
the electrical energy carbon emission difference value is input to the comparison layer via the input layer;
and the comparison layer performs subtraction processing on the data obtained by the pooling processing and the electric energy carbon emission difference value, compares the subtraction result with a preset threshold value to judge whether the deep learning model is converged, adjusts parameters in the input layer and the pooling layer through the feedback layer when the deep learning model is not converged, repeats the steps until the deep learning model is converged, and obtains the electric energy carbon emission correction model by taking the value output from the output layer as the correction quantity of the electric energy carbon emission when the deep learning model is converged.
The invention also provides an electric energy carbon flow analysis system based on carbon satellite data, which comprises:
the first acquisition module is used for acquiring carbon satellite data, wherein the carbon satellite data comprises different geographical areas and carbon dioxide concentration data corresponding to the different geographical areas;
the second acquisition module is used for selecting a target geographical area from the carbon satellite data, selecting a plurality of target position points in the target geographical area according to a preset rule, and acquiring carbon dioxide concentration values of the target position points;
the first calculation module is used for obtaining a first concentration change feature vector of carbon dioxide in the target geographic area based on the carbon dioxide concentration values of the target location points and the position relation among the target location points;
the second calculation module is used for acquiring the electric energy carbon emission of the target geographic area and the environmental parameter data of the target geographic area, and obtaining a second concentration change characteristic vector of carbon dioxide when the electric energy carbon circulates in the target geographic area based on the electric energy carbon emission and the environmental parameter data;
and the correction module is used for correcting the circulation of the electric energy carbon in the target geographic area according to the difference between the first concentration change characteristic vector and the second concentration change characteristic vector to obtain the corrected electric energy carbon emission.
The invention also provides a server, which comprises a processor, a computer readable storage medium and a communication interface, wherein the computer readable storage medium, the communication interface and the processor are connected through a bus system, the communication interface is used for being in communication connection with a carbon satellite, the computer readable storage medium is used for storing programs, instructions or codes, and the processor is used for executing the programs, the instructions or the codes in the computer readable storage medium so as to execute the electric energy carbon flow analysis method based on the carbon satellite data.
An embodiment of the present invention provides a computer-readable storage medium, where instructions are stored in the computer-readable storage medium, and when the instructions are executed, the computer is enabled to execute a method for analyzing a carbon flux of electric energy based on carbon satellite data in any one of the foregoing possible implementation manners.
In the embodiment of the invention, firstly, carbon satellite data is obtained, a target geographical area is selected from the carbon satellite data, and a plurality of target position points and carbon dioxide concentration values of all the target position points are selected in the target geographical area; then, obtaining a first concentration change characteristic vector based on the carbon dioxide concentration values of the target position points and the position relation among the target position points; then, obtaining a second concentration change characteristic vector based on the electric energy carbon emission and the environmental parameter data of the target geographical area; and finally, according to the difference between the first concentration change characteristic vector and the second concentration change characteristic vector, the circulation of the electric energy carbon in the target geographic area is corrected, and the corrected electric energy carbon emission is obtained. According to the scheme, the electric energy carbon emission of the target geographical area is corrected based on the carbon dioxide concentration data of the target geographical area in the carbon satellite data, and the accuracy of the carbon dioxide concentration data in the carbon satellite data is high, so that the prediction accuracy of the electric energy carbon emission of the target geographical area can be improved through the correction.
Drawings
To more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings required to be called in the embodiments are briefly described below, it should be understood that the following drawings only illustrate some embodiments of the present invention, and therefore should not be considered as limiting the scope, and those skilled in the art can also obtain other related drawings based on the drawings without creative efforts.
Fig. 1 is a schematic flow chart of a method for analyzing carbon flux of electric energy based on carbon satellite data according to an embodiment of the present invention;
fig. 2 is a schematic functional module diagram of an electric energy carbon flow analysis system based on carbon satellite data according to an embodiment of the present invention;
fig. 3 is a schematic structural framework diagram of a server for implementing the above-described method for analyzing carbon satellite data-based power carbon transfer, according to an embodiment of the present invention.
Detailed Description
The present invention is described in detail below with reference to the drawings, and the specific operation methods in the method embodiments can also be applied to the apparatus embodiments or the system embodiments.
In order to solve the technical problem in the foregoing background art, fig. 1 is a schematic flow chart of an electric energy carbon flow analysis method based on carbon satellite data according to an embodiment of the present invention, the electric energy carbon flow analysis method based on carbon satellite data according to the embodiment may be executed by a server, and the electric energy carbon flow analysis method based on carbon satellite data is described in detail below with reference to fig. 1.
And S10, acquiring carbon satellite data.
The carbon satellite is a global atmospheric carbon dioxide observation scientific experiment satellite, can monitor the carbon dioxide emission conditions in different seasons and different regions on the earth, has the carbon dioxide concentration monitoring precision of up to four parts per million (4 ppm), and has the resolution of up to 1KM x 2KM or even higher.
In this embodiment, the server may obtain carbon satellite data monitored by the carbon satellite by communicating with the carbon satellite, wherein the carbon satellite data may include different geographic regions and carbon dioxide concentration data corresponding to the different geographic regions.
And S20, selecting a target geographical area from the carbon satellite data, selecting a plurality of target position points in the target geographical area according to a preset rule, and acquiring carbon dioxide concentration values of the plurality of target position points.
In this step, the target geographical area may be an area with a large carbon dioxide emission amount, and exemplarily, the target geographical area may be an area corresponding to a thermal power plant, wherein an area selected by the target geographical area may be larger than an area actually occupied by the thermal power plant, so that a situation that the target geographical area cannot completely cover the thermal power plant due to the monitoring accuracy of the carbon satellite itself may be avoided.
In the present embodiment, selecting the plurality of target location points according to the preset rule may be selecting the plurality of target location points according to a set azimuth, and the set azimuth may be 8 azimuths, including true east, southeast, true south, southwest, true west, northwest, true north and northeast, for example.
Step S30, obtaining a first concentration change feature vector of the carbon dioxide in the target geographic area based on the carbon dioxide concentration values of the target location points and the position relation among the target location points.
In this step, the first concentration variation feature vector may include a plurality of feature components, each feature component representing a variation in carbon dioxide concentration in the same direction, for example, when the number of directions is 8, the first concentration variation feature vector may include 8 feature components, each feature component including a direction parameter and a variation in carbon dioxide concentration in a corresponding direction.
And S40, acquiring the electric energy carbon emission of the target geographic area and the environmental parameter data of the target geographic area, and obtaining a second concentration change characteristic vector of the carbon dioxide when the electric energy carbon circulates in the target geographic area based on the electric energy carbon emission and the environmental parameter data.
In this embodiment, the second concentration variation feature vector is obtained based on the electrical energy carbon emission amount of the target geographic area and the environmental parameter data of the target geographic area, the second concentration variation feature vector and the first concentration variation feature vector have feature components with the same dimension, and the direction parameter of the feature component in the second concentration variation feature vector corresponds to the direction parameter of the feature component in the first concentration variation feature vector, that is, if the first concentration variation feature vector includes feature components in 8 directions, the second concentration variation feature vector also includes corresponding feature components in 8 directions.
And S50, correcting the circulation of the electric energy carbon in the target geographical area according to the difference between the first concentration change characteristic vector and the second concentration change characteristic vector to obtain the corrected electric energy carbon emission.
In this step, the carbon dioxide concentration variation in the same direction in the first concentration variation eigenvector and the second concentration variation eigenvector may be subjected to subtraction processing to obtain a difference between the first concentration variation eigenvector and the second concentration variation eigenvector, and the flow of the electric energy carbon in the target geographic area may be corrected based on the difference. The first concentration change characteristic vector corresponds to a carbon dioxide concentration change parameter actually monitored in the target geographical area, and the second concentration change characteristic vector corresponds to a carbon dioxide concentration change parameter predicted in the target geographical area.
According to the technical scheme, the electric energy carbon emission of the target geographical area is corrected based on the carbon dioxide concentration data of the target geographical area in the carbon satellite data, and due to the fact that the precision of the carbon dioxide concentration data in the carbon satellite data is high, the prediction precision of the electric energy carbon emission of the target geographical area can be improved through the correction.
Further, in the present embodiment, step S20 may be implemented in the following manner.
First, a target geographic region is selected in the carbon satellite data.
Optionally, the target geographical area may be selected from the carbon satellite data by manual selection, and for example, when the target geographical area is a thermal power farm, the target geographical area may be obtained by selection along a geographical contour of the thermal power farm; the manner of selecting the target geographical area may also be selected in the carbon satellite data by searching for the name of the target geographical area, which is found by entering the name in the carbon satellite data, for example, when the name of the target geographical area is XX fire farm.
Then, a target geographical sub-area with the highest carbon dioxide concentration value is determined in the target geographical area, and a target position point is selected from the target geographical sub-area to serve as a first target position point.
Based on the carbon dioxide concentration data corresponding to the target geographical area, a target geographical sub-area with the highest carbon dioxide concentration value can be determined from the target geographical area, and generally, when the target geographical area is a thermal power plant, the target geographical sub-area with the highest carbon dioxide concentration value corresponds to a chimney area of a power plant. And selecting a target location point in the target geographic sub-region as a first target location point, which may be, for example, a geometric center point in the target geographic sub-region.
Then, with the first target position point as the center, selecting a plurality of position points along a plurality of different position directions as second target position points, wherein each position direction comprises at least two second target position points.
In the present embodiment, the different position directions may be 8 orientations, namely true east, southeast, true south, southwest, true west, northwest, true north and northeast, for example.
And then, acquiring the first target position point and the corresponding carbon dioxide concentration value of the target position point in the carbon satellite data.
Further, in the present embodiment, step S30 may be implemented in the following manner.
Firstly, taking the first target position point as a starting point, and acquiring second target position points distributed in different position directions.
And then, calculating to obtain carbon dioxide concentration variation parameters in different position directions according to the carbon dioxide concentration values of the first target position point and the second target position points distributed in different position directions in the carbon satellite data.
Wherein the carbon dioxide concentration variation parameter is the ratio of the carbon dioxide concentration difference between the adjacent target position points to the distance between the adjacent target position points.
And then, obtaining a first concentration change characteristic vector of the carbon dioxide in the target geographic area based on the carbon dioxide concentration change parameter and the corresponding position direction.
Each position direction and the carbon dioxide concentration variation parameter corresponding to the position direction form a characteristic component, and when the position direction is 8, the first concentration variation characteristic vector comprises 8 characteristic components.
Further, in the present embodiment, step S40 may be implemented in the following manner.
Firstly, when the target geographical area is a thermal power plant, the power generation amount of the target geographical area is obtained.
And secondly, calculating the electric energy carbon emission of the target geographical area based on the generated energy of the target geographical area.
Illustratively, the process of calculating the electrical energy carbon emissions is as follows: the quality of the required fossil fuel can be calculated through the power generation amount of the target geographical area, the first electric energy carbon emission amount of carbon dioxide generated by burning the required fossil fuel and the second electric energy carbon emission amount of the carbon dioxide generated during the desulfurization of the required fossil fuel are calculated, and the electric energy carbon emission amount of the target geographical area is calculated according to the first electric energy carbon emission amount and the second electric energy carbon emission amount.
Thirdly, acquiring environment parameter data of the target geographic area, wherein the environment parameter data comprises air temperature, wind power and wind direction;
and finally, inputting the electric energy carbon emission amount and the environmental parameter data of the target geographic area into a trained electric energy carbon concentration prediction model for prediction to obtain carbon dioxide concentration change prediction parameters of the carbon dioxide concentration in different position directions of the target geographic area, and obtaining a second concentration change feature vector of the carbon dioxide during circulation in the target geographic area based on the carbon dioxide concentration change prediction parameters and the corresponding position directions.
In the embodiment of the present application, a step of training the electric energy carbon concentration prediction model may be further included, and the step may be implemented in the following manner.
Firstly, historical electric energy carbon concentration distribution information of a plurality of different sample geographical areas, historical electric energy carbon emission of the different sample geographical areas and environmental parameter data of the different sample geographical areas are obtained, wherein the historical electric energy carbon concentration distribution information of each sample geographical area comprises a sample position point located in the sample geographical area and an electric energy carbon concentration value of the sample position point.
Then, for the historical electric energy carbon concentration distribution information of any sample geographic area, performing position relation analysis on sample position points in the sample geographic area, determining a sample position center point in the sample geographic area, and obtaining a plurality of associated sample position points distributed along the same position direction by taking the sample position center point as a starting point, wherein the position direction can comprise 8 directions of true east, southeast, true south, southwest, true west, northwest, true north and northeast.
And then, calculating to obtain carbon dioxide concentration change sample parameters in different position directions based on the electric energy carbon concentration values of a plurality of associated sample position points distributed in the same position direction.
The carbon dioxide concentration change sample parameter is the ratio of the carbon dioxide concentration difference between adjacent sample position points to the distance between the adjacent sample position points.
Then, taking carbon dioxide concentration change sample parameters in different position directions in a sample geographical area, environmental parameter data of the sample geographical area and historical electric energy emission of the sample geographical area as training samples;
and finally, inputting carbon dioxide concentration change sample parameters in different position directions in a sample geographical area and environmental parameter data of the sample geographical area into a neural network model as input data, outputting electric energy carbon emission prediction quantity of the sample geographical area, calculating a loss function value of the model based on the electric energy carbon emission prediction quantity and historical electric energy carbon emission quantity of the sample geographical area, adjusting the model parameters of the neural network model when the loss function value is not less than a preset function threshold value until the loss function value is less than the preset function threshold value, and taking the neural network model as a trained electric energy carbon concentration prediction model.
Further, in the present embodiment, step S50 may be implemented in the following manner.
Firstly, calculating a difference value between carbon dioxide concentration variation parameters in the same position direction in the first concentration variation characteristic vector and the second concentration variation characteristic vector to obtain a carbon dioxide concentration difference characteristic vector.
And then inputting the characteristic vector of the difference of the carbon oxide concentration into a pre-trained electric energy carbon emission correction model to correct the electric energy carbon emission so as to obtain the correction amount of the electric energy carbon emission.
And then, obtaining the corrected carbon emission of the electric energy based on the carbon emission of the electric energy in the target geographical area and the correction amount of the carbon emission of the electric energy.
In this embodiment of the application, the method for analyzing the carbon transfer of the electrical energy based on the carbon satellite data may further include a step of training the electrical energy carbon emission correction model, which may be implemented as follows.
Firstly, the difference value of the carbon dioxide concentration difference characteristic vector, the electric energy carbon emission of the target geographical area and the actual electric energy carbon emission of the target geographical area is used as a training sample.
And then inputting the difference value of the electric energy carbon emission between the carbon dioxide concentration difference characteristic vector and the actual electric energy carbon emission of the target geographic area into a deep learning model for training, enabling the deep learning model to be converged in a mode of iteratively updating model parameters in the deep learning model, and taking the converged deep learning model as the electric energy carbon emission correction model.
In this embodiment, the deep learning model includes an input layer, a pooling layer, a comparison layer, a feedback layer, and an output layer, and this step can be implemented as follows.
Firstly, inputting the carbon dioxide concentration difference characteristic vector from the input layer, and inputting the carbon dioxide concentration difference characteristic vector into the comparison layer after the pooling layer is subjected to pooling treatment.
Then, the electrical energy carbon emission difference value is input to the comparison layer via the input layer.
And then, the comparison layer performs subtraction processing on the data obtained by pooling processing and the electric energy carbon emission difference value, compares the subtraction result with a preset threshold value to judge whether the deep learning model converges, adjusts parameters in the input layer and the pooling layer through the feedback layer when the deep learning model does not converge, repeats the steps until the deep learning model converges, and obtains the electric energy carbon emission correction model by taking the value output from the output layer as the correction amount of the electric energy carbon emission when the deep learning model converges.
Through the design, the electric energy carbon emission of the target geographical area can be corrected based on the carbon dioxide concentration data of the target geographical area in the carbon satellite data, and due to the fact that the precision of the carbon dioxide concentration data in the carbon satellite data is high, the prediction precision of the electric energy carbon emission of the target geographical area can be improved through the correction.
Referring to fig. 2, fig. 2 illustrates a schematic diagram of functional modules of the electrical energy carbon flow analysis system 100 based on carbon satellite data provided in this embodiment, and this embodiment may divide the functional modules of the electrical energy carbon flow analysis system 100 based on carbon satellite data according to the above method embodiments, that is, the following functional modules corresponding to the electrical energy carbon flow analysis system 100 based on carbon satellite data may be used to execute the above method embodiments. The system 100 for analyzing the carbon satellite data-based power carbon transfer may include a first obtaining module 110, a second obtaining module 120, a first calculating module 130, a second calculating module 140, and a correcting module 150, and the functions of the functional modules of the system 100 for analyzing the carbon satellite data-based power carbon transfer are described in detail below with reference to fig. 2.
A first acquisition module 110 for acquiring carbon satellite data.
The carbon satellite is a global atmospheric carbon dioxide observation scientific experiment satellite, can monitor the carbon dioxide emission conditions in different seasons and different regions on the earth, has the carbon dioxide concentration monitoring precision of up to four parts per million (4 ppm), and has the resolution of up to 1KM x 2KM or even higher.
In this embodiment, the first obtaining module 110 may obtain carbon satellite data monitored by a carbon satellite through communication with the carbon satellite, where the carbon satellite data may include different geographic areas and carbon dioxide concentration data corresponding to the different geographic areas.
The first obtaining module 110 may be configured to perform the step S10, and as for a detailed implementation of the first obtaining module 110, reference may be made to the detailed description of the step S10.
The second obtaining module 120 is configured to select a target geographic area from the carbon satellite data, select a plurality of target location points in the target geographic area according to a preset rule, and obtain carbon dioxide concentration values of the plurality of target location points.
In this step, the target geographical area may be an area with a large carbon dioxide emission amount, and exemplarily, the target geographical area may be an area corresponding to a thermal power plant, wherein an area selected by the target geographical area may be larger than an area actually occupied by the thermal power plant, so that a situation that the target geographical area cannot completely cover the thermal power plant due to the monitoring accuracy of the carbon satellite itself may be avoided.
In the present embodiment, selecting the plurality of target location points according to the preset rule may be selecting the plurality of target location points according to a set azimuth, and the set azimuth may be 8 azimuths, including true east, southeast, true south, southwest, true west, northwest, true north and northeast, for example.
The second obtaining module 120 may be configured to perform the step S20, and as for a detailed implementation of the second obtaining module 120, reference may be made to the detailed description of the step S20.
The first calculating module 130 is configured to obtain a first concentration change feature vector of carbon dioxide in the target geographic area based on the carbon dioxide concentration values of the plurality of target location points and the position relationship between the plurality of target location points.
In this embodiment, the first concentration change feature vector may include a plurality of feature components, each feature component representing a change in carbon dioxide concentration in the same direction, for example, when the number of directions is 8, the first concentration change feature vector may include 8 feature components, each feature component including a direction parameter and a change amount in carbon dioxide concentration in a corresponding direction.
The first calculating module 130 may be configured to perform the step S30, and as for a detailed implementation of the first calculating module 130, reference may be made to the detailed description of the step S30.
The first calculating module 140 is configured to obtain an electric energy carbon emission amount of the target geographic area and environment parameter data of the target geographic area, and obtain a second concentration change feature vector of carbon dioxide when the electric energy carbon circulates in the target geographic area based on the electric energy carbon emission amount and the environment parameter data.
In this embodiment, the second concentration variation feature vector is obtained based on the electrical energy carbon emission amount of the target geographic area and the environmental parameter data of the target geographic area, the second concentration variation feature vector and the first concentration variation feature vector have feature components with the same dimension, and the direction parameter of the feature component in the second concentration variation feature vector corresponds to the direction parameter of the feature component in the first concentration variation feature vector, that is, if the first concentration variation feature vector includes feature components in 8 directions, the second concentration variation feature vector also includes corresponding feature components in 8 directions.
The second calculating module 140 may be configured to perform the step S40, and as for a detailed implementation manner of the second calculating module 140, reference may be made to the detailed description of the step S40.
And the correcting module 150 is configured to correct the circulation of the electrical energy carbon in the target geographic area according to the difference between the first concentration change feature vector and the second concentration change feature vector, so as to obtain the corrected electrical energy carbon emission.
In this embodiment, the correction module 150 may perform subtraction processing on the carbon dioxide concentration variation amounts in the same direction in the first concentration variation feature vector and the second concentration variation feature vector to obtain a difference between the first concentration variation feature vector and the second concentration variation feature vector, and correct the flow of the electric energy carbon in the target geographic area based on the difference. The first concentration change characteristic vector corresponds to a carbon dioxide concentration change parameter actually monitored in the target geographical area, and the second concentration change characteristic vector corresponds to a carbon dioxide concentration change parameter predicted in the target geographical area.
The modification module 150 may be configured to perform the step S50, and for a detailed implementation of the modification module 150, reference may be made to the detailed description of the step S50.
It should be noted that the division of each module of the above apparatus is only a logical division, and all or part of the actual implementation may be integrated into one physical entity or may be physically separated. And these modules may all be implemented in software (e.g., open source software) invoked by the processing element. Or may be implemented entirely in hardware. And part of the modules can be realized in the form of calling software by the processing element, and part of the modules can be realized in the form of hardware. For example, the modification module 150 may be a separate processing element, or may be integrated into a chip of the apparatus, or may be stored in a memory of the apparatus in the form of program code, and a processing element of the apparatus calls and executes the functions of the modification module 150. Other modules are implemented similarly. In addition, all or part of the modules can be integrated together or can be independently realized. The processing element described herein may be an integrated circuit having signal processing capabilities. In implementation, each step of the above method or each module above may be implemented by an integrated logic circuit of hardware in a processor element or an instruction in the form of software.
Referring to fig. 3, fig. 3 is a schematic diagram of a hardware structure of a server 10 for implementing the above method for analyzing the carbon flow of electric energy based on carbon satellite data according to the embodiment of the present disclosure, where the server 10 may be implemented on a cloud server. As shown in fig. 3, the server 10 may include a processor 101, a computer-readable storage medium 102, a bus 103, and a communication interface 104.
In a specific implementation process, at least one processor 101 executes computer-executable instructions (e.g., modules shown in fig. 2) stored in the computer-readable storage medium 102, so that the processor 101 may execute the method for carbon satellite data-based power carbon transfer analysis according to the above method embodiment, where the processor 101, the computer-readable storage medium 102, and the communication interface 104 are connected by the bus 103, and the processor 101 may be configured to control the transceiving action of the communication interface 104.
For a specific implementation process of the processor 101, reference may be made to the above-mentioned method embodiments executed by the server 10, which implement similar principles and technical effects, and this embodiment is not described herein again.
The computer-readable storage medium 102 may comprise high-speed RAM memory and may also include non-volatile storage NVM, such as at least one disk memory.
The bus 103 may be divided into an address bus, a data bus, a control bus, and the like. For ease of illustration, the buses in the figures of the present invention are not limited to only one bus or one type of bus.
In addition, the embodiment of the invention also provides a readable storage medium, wherein the readable storage medium stores computer execution instructions, and when a processor executes the computer execution instructions, the method for analyzing the carbon flow of the electric energy based on the carbon satellite data is realized.
In summary, in the technical solution provided in the embodiment of the present invention, first, carbon satellite data is obtained, a target geographic area is selected from the carbon satellite data, and a plurality of target location points and carbon dioxide concentration values of the target location points are selected in the target geographic area; then, obtaining a first concentration change characteristic vector based on the carbon dioxide concentration values of the target position points and the position relation among the target position points; then, obtaining a second concentration change characteristic vector based on the electric energy carbon emission and the environmental parameter data of the target geographical area; and finally, according to the difference between the first concentration change characteristic vector and the second concentration change characteristic vector, correcting the circulation of the electric energy carbon in the target geographical area to obtain the corrected electric energy carbon emission. According to the scheme, the electric energy carbon emission of the target geographic area is corrected based on the carbon dioxide concentration data of the target geographic area in the carbon satellite data, and due to the fact that the precision of the carbon dioxide concentration data in the carbon satellite data is high, the prediction precision of the electric energy carbon emission of the target geographic area can be improved through the correction.
The foregoing description has been directed to specific embodiments of this disclosure. Other embodiments are within the scope of the following claims. In some cases, the actions or steps recited in the claims may be performed in a different order than in the embodiments and still achieve desirable results. In addition, the processes depicted in the accompanying figures do not necessarily require the particular order shown, or sequential order, to achieve desirable results. In some embodiments, multitasking and parallel processing may also be possible or may be advantageous.
Claims (10)
1. A method for analyzing carbon flux of electric energy based on carbon satellite data is characterized by comprising the following steps:
acquiring carbon satellite data, wherein the carbon satellite data comprises different geographical areas and carbon dioxide concentration data corresponding to the different geographical areas;
selecting a target geographical area from the carbon satellite data, selecting a plurality of target position points in the target geographical area according to a preset rule, and acquiring carbon dioxide concentration values of the target position points;
obtaining a first concentration change feature vector of carbon dioxide in the target geographic area based on the carbon dioxide concentration values of the target location points and the position relationship among the target location points;
acquiring the electric energy carbon emission amount of the target geographical area and the environmental parameter data of the target geographical area, and obtaining a second concentration change characteristic vector of carbon dioxide when the electric energy carbon circulates in the target geographical area based on the electric energy carbon emission amount and the environmental parameter data;
and according to the difference between the first concentration change characteristic vector and the second concentration change characteristic vector, correcting the circulation of the electric energy carbon in the target geographical area to obtain the corrected electric energy carbon emission.
2. The method for carbon satellite data-based power carbon transfer analysis according to claim 1, wherein said step of selecting a target geographical area from said carbon satellite data, selecting a plurality of target location points in said target geographical area according to a predetermined rule, and obtaining carbon dioxide concentration values of said plurality of target location points comprises:
selecting a target geographic area in the carbon satellite data;
determining a target geographical sub-area with the highest carbon dioxide concentration value in the target geographical area, and selecting a target position point in the target geographical sub-area as a first target position point;
selecting a plurality of position points as second target position points along a plurality of different position directions by taking the first target position point as a center, wherein each position direction comprises at least two second target position points;
and acquiring the first target position point and a carbon dioxide concentration value corresponding to the target position point in the carbon satellite data.
3. A method as claimed in claim 2, wherein said step of obtaining a first concentration variation eigenvector of carbon dioxide in said target geographic region based on carbon dioxide concentration values of a plurality of said target location points and a positional relationship between said plurality of said target location points comprises:
taking the first target position point as a starting point, and acquiring second target position points distributed in different position directions;
calculating to obtain carbon dioxide concentration variation parameters in different position directions according to the carbon dioxide concentration values of the first target position points and the second target position points distributed in different position directions, wherein the carbon dioxide concentration variation parameters are the ratio of the carbon dioxide concentration difference between the adjacent target position points to the distance between the adjacent target position points;
and obtaining a first concentration change feature vector of the carbon dioxide in the target geographic area based on the carbon dioxide concentration change parameter and the corresponding position direction.
4. The method for analyzing carbon satellite data-based electric energy carbon flux analysis according to claim 3, wherein the step of obtaining the electric energy carbon emission amount of the target geographical area and the environmental parameter data of the target geographical area, and obtaining a second concentration variation eigenvector of carbon dioxide when electric energy carbon is flux in the target geographical area based on the electric energy carbon emission amount and the environmental parameter data comprises:
when the target geographic area is a thermal power plant, acquiring the power generation amount of the target geographic area;
calculating the electric energy carbon emission of the target geographical area based on the generated energy of the target geographical area;
acquiring environment parameter data of the target geographic area, wherein the environment parameter data comprises air temperature, wind power and wind direction;
inputting the electric energy carbon emission amount and the environmental parameter data of the target geographic area into a trained electric energy carbon concentration prediction model for prediction to obtain carbon dioxide concentration change prediction parameters of carbon dioxide concentrations in different position directions of the target geographic area, and obtaining a second concentration change feature vector of carbon dioxide during circulation in the target geographic area based on the carbon dioxide concentration change prediction parameters and the corresponding position directions.
5. The method for analyzing carbon satellite data-based carbon transfer of electric energy according to claim 4, wherein the step of calculating the carbon emission of electric energy of the target geographical area based on the electric energy generation of the target geographical area comprises:
calculating the quality of the required fossil fuel based on the power generation amount of the target geographic area;
calculating a first electric energy carbon emission amount of carbon dioxide generated by burning the required fossil fuel;
calculating a second electrical energy carbon emission amount of carbon dioxide generated during the desulfurization of the required fossil fuel;
and calculating the carbon emission of the electric energy of the target geographical area according to the carbon emission of the first electric energy and the carbon emission of the second electric energy.
6. The method for carbon satellite data-based power carbon transfer analysis according to claim 4, wherein before the step of obtaining the power carbon emission amount of the target geographical area and the environmental parameter data of the target geographical area, and obtaining a second concentration variation feature vector of carbon dioxide when power carbon is transferred in the target geographical area based on the power carbon emission amount and the environmental parameter data, the method further comprises a step of training the power carbon concentration prediction model, the step comprising:
obtaining historical electric energy carbon concentration distribution information of a plurality of different sample geographical areas, historical electric energy carbon emission of the different sample geographical areas and environmental parameter data of the different sample geographical areas, wherein the historical electric energy carbon concentration distribution information of each sample geographical area comprises a sample position point located in the sample geographical area and an electric energy carbon concentration value of the sample position point;
analyzing the position relation of sample position points in any sample geographical area according to the historical electric energy carbon concentration distribution information of the sample geographical area, determining a sample position central point in the sample geographical area, and obtaining a plurality of associated sample position points distributed along the same position direction by taking the sample position central point as a starting point;
calculating to obtain carbon dioxide concentration change sample parameters in different position directions based on the electric energy carbon concentration values of a plurality of associated sample position points distributed in the same position direction;
taking carbon dioxide concentration change sample parameters in different position directions in the sample geographic area, environmental parameter data of the sample geographic area and historical electric energy emission of the sample geographic area as training samples;
inputting carbon dioxide concentration change sample parameters in different position directions in the sample geographical area and environment parameter data of the sample geographical area into a neural network model as input data, outputting electric energy carbon emission prediction quantity of the sample geographical area, calculating a loss function value of the model based on the electric energy carbon emission prediction quantity and historical electric energy carbon emission quantity of the sample geographical area, adjusting model parameters of the neural network model when the loss function value is not less than a preset function threshold value until the loss function value is less than the preset function threshold value, and taking the neural network model as a trained electric energy carbon concentration prediction model.
7. The carbon satellite data-based electric energy carbon flux analysis method as claimed in claim 6, wherein said step of correcting the flux of the electric energy carbon in the target geographical area according to the difference between the first concentration variation eigenvector and the second concentration variation eigenvector to obtain the corrected electric energy carbon emission amount comprises:
calculating a difference value between carbon dioxide concentration variation parameters in the same position direction in the first concentration variation characteristic vector and the second concentration variation characteristic vector to obtain a carbon dioxide concentration difference characteristic vector;
inputting the carbon oxide concentration difference characteristic vector into a pre-trained electric energy carbon emission correction model to correct the electric energy carbon emission to obtain the correction amount of the electric energy carbon emission;
and obtaining the corrected carbon emission amount of the electric energy based on the carbon emission amount of the electric energy in the target geographical area and the correction amount of the carbon emission amount of the electric energy.
8. A method for carbon satellite data-based power carbon flux analysis according to claim 7, further comprising the step of training said power carbon emission correction model, comprising:
taking the carbon dioxide concentration difference characteristic vector, the electric energy carbon emission difference value between the electric energy carbon emission of the target geographic area and the actual electric energy carbon emission of the target geographic area as a training sample;
inputting the difference value of the electric energy carbon emission between the carbon dioxide concentration difference characteristic vector and the actual electric energy carbon emission of the target geographic area into a deep learning model for training, enabling the deep learning model to be converged in a mode of iteratively updating model parameters in the deep learning model, and taking the converged deep learning model as the electric energy carbon emission correction model.
9. The method according to claim 8, wherein the deep learning model comprises an input layer, a pooling layer, a comparison layer, a feedback layer and an output layer, and the step of inputting the difference value of carbon dioxide concentration difference between the feature vector and the actual carbon emission of the target geographic area into the deep learning model for training, iteratively updating model parameters in the deep learning model to converge the deep learning model, and using the converged deep learning model as the modified model of carbon emission of the power comprises:
inputting the carbon dioxide concentration difference characteristic vector from the input layer, and inputting the carbon dioxide concentration difference characteristic vector into the comparison layer after the pooling layer is subjected to pooling treatment;
the electrical energy carbon emission difference value is input to the comparison layer via the input layer;
and the comparison layer performs subtraction processing on the data obtained by the pooling processing and the electric energy carbon emission difference value, compares the subtraction result with a preset threshold value to judge whether the deep learning model is converged, adjusts parameters in the input layer and the pooling layer through the feedback layer when the deep learning model is not converged, repeats the steps until the deep learning model is converged, and obtains the electric energy carbon emission correction model by taking the value output from the output layer as the correction quantity of the electric energy carbon emission when the deep learning model is converged.
10. An electric energy carbon flow analysis system based on carbon satellite data, characterized in that the system comprises:
the carbon satellite data acquisition system comprises a first acquisition module, a second acquisition module and a third acquisition module, wherein the first acquisition module is used for acquiring carbon satellite data, and the carbon satellite data comprises different geographic areas and carbon dioxide concentration data corresponding to the different geographic areas;
the second acquisition module is used for selecting a target geographical area from the carbon satellite data, selecting a plurality of target position points in the target geographical area according to a preset rule, and acquiring carbon dioxide concentration values of the target position points;
the first calculation module is used for obtaining a first concentration change feature vector of carbon dioxide in the target geographic area based on carbon dioxide concentration values of the target location points and a position relation among the target location points;
the second calculation module is used for acquiring the electric energy carbon emission of the target geographic area and the environmental parameter data of the target geographic area, and obtaining a second concentration change characteristic vector of carbon dioxide when the electric energy carbon circulates in the target geographic area based on the electric energy carbon emission and the environmental parameter data;
and the correction module is used for correcting the circulation of the electric energy carbon in the target geographic area according to the difference between the first concentration change characteristic vector and the second concentration change characteristic vector to obtain the corrected electric energy carbon emission.
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