CN117034588A - Industrial carbon emission space simulation method and system based on noctilucent remote sensing and interest points - Google Patents
Industrial carbon emission space simulation method and system based on noctilucent remote sensing and interest points Download PDFInfo
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Abstract
The application discloses an industrial carbon emission space simulation method and system based on noctilucent remote sensing and interest points, wherein the method comprises the following steps: acquiring industrial energy consumption and a corresponding carbon emission factor coefficient; calculating the total annual carbon emission of the industry; acquiring industrial interest point data and noctilucent remote sensing data; calculating a normalized score; acquiring a first index, and constructing a panel data matrix, wherein the first index comprises a night light total value and the industrial annual carbon emission total amount, and the panel data matrix comprises the first index; calculating the weight of each first index; and calculating a space simulation result of the carbon emission. The application realizes the spatial simulation of the industrial carbon emission of the region, improves the accuracy of the spatial distribution result of the carbon emission, and reveals the distribution characteristics and rules of the industrial carbon emission in the geographic space. The application can be widely applied to the technical field of carbon emission treatment.
Description
Technical Field
The application relates to the technical field of carbon emission treatment, in particular to an industrial carbon emission space simulation method and system based on noctilucent remote sensing and interest points.
Background
At present, the climate warming caused by the increase of carbon emission greatly influences the life and health of people, and in order to cope with serious consequences caused by the climate warming, a series of measures for restraining the climate warming and reducing the artificial carbon dioxide emission are needed. The carbon emission accounting system is mainly developed aiming at three types of accounting objects of regional level, industry enterprise level and product level from the statistical perspective, but lacks accurate system accounting, so that carbon emission is distributed cleanly, mechanism is unknown, regulation and control capability is weak, mass carbon emission data of different levels, different scales and different industries lacks uniform reference, and is difficult to integrate and utilize.
The traditional carbon emission accounting system based on statistical data has the defect that the statistical data only provides digital records of relevant elements of a specific area, and the research is not comprehensive enough only through the statistical value. In the related art, the research angle of the carbon emission accounting method is too single, and the precision of the generated carbon emission spatial distribution result is low.
Disclosure of Invention
The following is a summary of the subject matter described in detail herein. This summary is not intended to limit the scope of the claims.
The embodiment of the application provides an industrial carbon emission space simulation method and system based on noctilucent remote sensing and interest points, which can effectively improve the calculation accuracy of a carbon emission space distribution result, thereby revealing the distribution characteristics and rules of industrial carbon emission in a geographic space.
On one hand, the embodiment of the application provides an industrial carbon emission space simulation method based on noctilucent remote sensing and interest points, which comprises the following steps of:
acquiring industrial energy consumption and a corresponding carbon emission factor coefficient;
calculating the total industrial annual carbon emission according to the industrial energy consumption and the corresponding carbon emission factor coefficient;
acquiring industrial interest point data and noctilucent remote sensing data;
calculating a normalized score according to the industrial interest point data and the noctilucent remote sensing data;
acquiring a first index, and constructing a panel data matrix, wherein the first index comprises a night light total value and the industrial annual carbon emission total amount, and the panel data matrix comprises the first index;
according to the panel data matrix, calculating the weight of each first index;
and calculating a spatial simulation result of carbon emission according to the industrial annual carbon emission total amount, the normalized score and the weight.
Further, the calculation formula of the total industrial annual carbon emission is as follows:
wherein CE is the total carbon emission amount of industry year, K i Carbon emission factor coefficient, E, of energy i i The industrial energy consumption of the energy i is p, and the energy type number is p.
Further, the calculating the normalized score according to the industrial interest point data and the noctilucent remote sensing data comprises the following steps:
performing nuclear density analysis on the industrial interest point data to obtain an industrial interest point nuclear density grid image;
analyzing the noctilucent remote sensing data to obtain noctilucent remote sensing images;
calculating the normalized score according to the industrial interest point nuclear density grid image and the noctilucent remote sensing image, wherein the calculation formula of the normalized score is as follows:
wherein I is jk Normalized score at the kth grid for the first index of the jth term, R jk And f is the total pixel number of the raster image of the first index in the j-th item.
Further, the step of obtaining the first index and constructing a panel data matrix includes the following steps:
calculating the noctilucent remote sensing data to obtain the total night light value;
constructing a panel data matrix, wherein the calculation formula of the panel data matrix is as follows:
wherein r is ij And (3) for the panel data value of the first index in the j-th item in the i-th year, m is the total number of years of the panel data, and n is the total number of the first indexes.
Further, the calculating the weight of each first index according to the panel data matrix includes the following steps:
calculating a standardized index according to the panel data matrix;
calculating the entropy value of each first index according to the standardized index;
and calculating the weight of each first index according to the entropy value.
Further, the calculation formula of the standardized index is as follows:
or,
wherein x is ij Max, which is the value of the first index in the ith year after normalization j {r ij The value of the first index of item j, min j {r ij The value of the first index in the j-th item is the minimum value, r ij A panel data value at the ith year for the first indicator of item j.
Further, the calculation formula of the entropy value is as follows:
in the formula e j For the entropy value, p of the first index of item j ij The index value specific gravity of the first index in the ith year is the index value specific gravity of the jth item and is p ij When=0, p ij ·lnp ij =0, n is the total number of the first indices, k is a coefficient;
wherein m is the number of rows of the panel data matrix, namely the total number of years of the data;
wherein p is ij The index value specific gravity of the first index in the ith year, x ij The value of the first index in the ith year after normalizing the panel data, and m is the number of rows of the panel data matrix.
Further, the calculation formula of the weight is as follows:
wherein w is j A weight value e of the first index of the j-th item j And (3) the entropy value of the first index in the j-th item, and n is the total number of the first indexes.
Further, the calculation formula of the spatial simulation result of carbon emission is as follows:
wherein F is k Is the comprehensive weight value, w, of the kth grid j For the weight of the first index of item j, I jk Normalized score at kth grid for the first index of jth term, n being the total number of the first indices;
in CE k For the carbon emission in the kth grid after the space formation, CE is the total industrial annual carbon emission of the research area where the grid is located, F k The comprehensive weight value of the kth grid is calculated, and u is the total number of grids.
On the other hand, the embodiment of the application provides an industrial carbon emission space simulation system based on noctilucent remote sensing and interest points, which comprises the following components:
the first module is used for acquiring industrial energy consumption and corresponding carbon emission factor coefficients;
the second module is used for calculating the total industrial annual carbon emission according to the industrial energy consumption and the corresponding carbon emission factor coefficient;
the third module is used for acquiring industrial interest point data and noctilucent remote sensing data;
a fourth module, configured to calculate a normalized score according to the industrial interest point data and the noctilucent remote sensing data;
a fifth module, configured to obtain a first indicator, and construct a panel data matrix, where the first indicator includes a total night light value and a total industrial annual carbon emission amount, and the panel data matrix includes the first indicator;
a sixth module, configured to calculate a weight of each of the first indicators according to the panel data matrix;
and a seventh module, configured to calculate a spatial simulation result of carbon emission according to the total industrial annual carbon emission, the normalized score and the weight.
The application has the following beneficial effects:
according to the application, based on noctilucent remote sensing and interest point data, firstly, the industrial energy consumption and the corresponding carbon emission factor coefficient are acquired, the industrial annual carbon emission total amount is calculated, then the industrial interest point data and noctilucent remote sensing data are acquired, the normalized score is calculated, then the first index is acquired, a panel data matrix is constructed, the weight of each first index is calculated, and finally, the space simulation result of carbon emission is calculated, so that the space simulation of industrial carbon emission in a region is realized, the accuracy of the carbon emission space distribution result is improved, and the distribution characteristics and rules of industrial carbon emission in a geographic space are disclosed.
Additional features and advantages of the application will be set forth in the description which follows, and in part will be obvious from the description, or may be learned by practice of the application. The objectives and other advantages of the application will be realized and attained by the structure particularly pointed out in the written description and claims hereof as well as the appended drawings.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings required for the description of the embodiments will be briefly described below, and it is apparent that the drawings in the following description are only some embodiments of the present application, and other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.
FIG. 1 is a flow chart of an industrial carbon emission space simulation method based on noctilucent remote sensing and interest points provided by an embodiment of the application;
FIG. 2 is a graph showing the carbon emission coefficient of energy consumption according to an embodiment of the present application;
FIG. 3 is a flowchart of calculating the weight of each first index according to the panel data matrix in the industrial carbon emission space simulation method based on noctilucent remote sensing and interest points according to the embodiment of the present application;
FIG. 4 is a schematic diagram of industrial carbon emission spatialization indicators and weights within 2020 within a certain area according to an embodiment of the present application;
fig. 5 is a schematic diagram of an industrial carbon emission space simulation system based on noctilucent remote sensing and interest points according to an embodiment of the present application.
Detailed Description
Embodiments of the present application are described in detail below, examples of which are illustrated in the accompanying drawings, wherein like or similar reference numerals refer to like or similar elements or elements having like or similar functions throughout. The embodiments described below by referring to the drawings are illustrative only and are not to be construed as limiting the application.
In order to make the objects, technical solutions and advantages of the present application more apparent, the present application will be described in further detail with reference to the accompanying drawings and specific embodiments. The step numbers in the following embodiments are set for convenience of illustration only, and the order between the steps is not limited in any way, and the execution order of the steps in the embodiments may be adaptively adjusted according to the understanding of those skilled in the art.
Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs. The terminology used in the embodiments of the application is for the purpose of describing embodiments of the application only and is not intended to be limiting of the application.
As shown in fig. 1, the embodiment of the application provides an industrial carbon emission space simulation method based on noctilucent remote sensing and interest points, which comprises the following steps:
step S11, obtaining industrial energy consumption and corresponding carbon emission factor coefficients.
Specifically, 7 kinds of industrial energy terminal consumption data of industrial departments in the region 2016-2020 are collected, and industrial energy consumption and corresponding carbon emission factor coefficients can be obtained from the industrial energy terminal consumption data, wherein 7 kinds of industries such as textile industry, petroleum, coal and other fuel processing industry, chemical raw material and chemical product manufacturing industry, nonmetallic mineral product industry, automobile manufacturing industry, computer, communication and other electronic equipment manufacturing industry, electric power, thermal production and supply industry and the like in the region 2016-2020 are targeted.
And step S12, calculating the total annual carbon emission of the industry according to the industrial energy consumption and the corresponding carbon emission factor coefficient.
Specifically, the calculation of carbon emission adopts a carbon emission factor method, and according to the carbon emission coefficient table of various fossil energy sources in 2006 greenhouse gas emission list as shown in fig. 2, the raw coal, gasoline, diesel oil, fuel oil, thermal power, electric power and other energy source terminal consumption amounts are selected to estimate the carbon dioxide emission amount generated by the regional industrial energy consumption according to the regional energy terminal consumption data, wherein the calculation formula is as follows:
wherein CE is the total industrial annual carbon emission, and the unit is t; k (K) i Is the carbon emission factor coefficient of the energy source i, the unit is (10 4 t carbon)/(10 4 t standard coal), K i The values are derived from the default values of the carbon emission calculation guidelines, the raw data units are J, and are converted into standard coal in accordance with the statistical data units, and the conversion coefficient is1 multiplied by 10 4 t standard coal is equal to 2.93×10 5 GJ；E i The industrial energy consumption of the energy i is10 in units calculated by standard coal 4 t is; p is the number of energy species.
Further, the total carbon emission of seven kinds of industries was calculated year by year according to formula (1). The total carbon emissions of seven types of industries 2016-2020 were calculated to be 3272.83, 3569.63, 3662.27, 3749.86, 3776.10 ten thousand tons, respectively.
And S13, acquiring industrial interest point data and noctilucent remote sensing data.
Specifically, the method comprises the steps of obtaining the interest point data of 7 types of industries in a certain area in 2020 by calling a software development kit of an Internet map software provider, and obtaining the global noctilucent remote sensing data of NPP-VIIRS in 2016-2020.
And S14, calculating a normalized score according to the industrial interest point data and the noctilucent remote sensing data.
Specifically, according to the industrial interest point data and noctilucent remote sensing data, calculating a normalized score value, including the following steps:
performing nuclear density analysis on the industrial interest point data to obtain an industrial interest point nuclear density grid image;
specifically, performing nuclear density analysis on the interest point data of each industry by using ArcGIS10.6 software to obtain an industry interest point nuclear density grid image;
analyzing the noctilucent remote sensing data to obtain noctilucent remote sensing images;
specifically, preprocessing and cutting noctilucent remote sensing data to obtain an NPP-VIIRS noctilucent remote sensing image in 2016-2020 in the area;
calculating a normalized score according to the industrial interest point nuclear density grid image and the noctilucent remote sensing image;
specifically, grid pixel value normalization is carried out on the industrial interest point nuclear density grid image and the noctilucent remote sensing image to obtain a normalized score, and the calculation formula of the normalized score is as follows:
wherein I is jk Normalized score at the kth grid for the jth first indicator, R jk And f is the total pixel number of the raster image of the jth first index.
Step S15, acquiring a first index, and constructing a panel data matrix, wherein the first index comprises a night light total value and an industry annual carbon emission total amount, and the panel data matrix comprises the first index.
Specifically, a first index is acquired, and a panel data matrix is constructed, which comprises the following steps:
calculating noctilucent remote sensing data to obtain a night light total value;
specifically, according to noctilucent remote sensing data, calculating a noctilucent remote sensing night light total value of the area by using a pixel statistics data function of ArcGIS 10.6;
constructing a panel data matrix;
specifically, the total night light value and the total annual carbon emission of each industry are combined to form a panel data matrix, and the calculation formula of the panel data matrix is as follows:
wherein r is ij The panel data value of the j-th first index in the i-th year is represented by m, which is the total number of years of the panel data, and n is the total number of the first indexes.
And S16, calculating the weight of each first index according to the panel data matrix.
Specifically, an entropy weight method is adopted, the weight of the index can be determined according to the information provided by the user, and the more the information provided by one index is, the lower the entropy is, and the greater the weight of the index is; the method is applied as an important evaluation method, can overcome the negative influence caused by the subjectivity of the artificial determination weight and the overlapping of multi-index variable information, and has strong objectivity. As shown in fig. 3, the weight of each first index is calculated according to the panel data matrix, which comprises the following steps:
step S301, calculating a standardized index according to the panel data matrix.
Specifically, the setting angles of the various first indexes are different, resulting in a variation in the trend and unit of change of the data. In order to exclude the influence of the index dimension difference on the result, it is necessary to unify the main data to a unified value without a unit of measurement. The extremum method is adopted to unify the first index, and because the night light and the carbon emission have positive correlation, the indexes of the embodiment are all forward indexes, and the calculation formula of the standardized indexes is as follows:
or,
wherein x is ij Max, which is the value of the j-th first index in the i-th year after normalization j {r ij The value of the j-th first index is the maximum value, min j {r ij The value of the first index of the j is the minimum value, r ij The panel data value at the i-th year for the j-th first index. The formula (4) is a forward index, and the higher the value of the index is, the more important the expression is; equation (5) is a negative indicator, and indicates that the lower the indicator is, the more important the expression is.
Step S302, calculating the entropy value of each first index according to the standardized index.
Specifically, the calculation formula of the entropy value is as follows:
in the formula e j Entropy value of the j-th first index, p ij Index value specific gravity of the j-th first index in the i-th year and when p ij When=0, p ij ·lnp ij =0, n is the total number of first indices, k is the coefficient;
wherein m is the number of rows of the panel data matrix, namely the total number of years of the data;
wherein p is ij Index value specific gravity of the jth first index in the ith year, x ij The value of the j-th first index in the i-th year after the normalization of the panel data is achieved, and m is the number of rows of the panel data matrix.
Step S303, calculating the weight of each first index according to the entropy value.
Specifically, the weight of each first index is calculated according to the formula (9), the industrial carbon emission spatialization evaluation index system and the weight are shown in fig. 4, the sum of the weights of the first indexes is1, and the calculation formula of the weights is as follows:
wherein w is j A weight value of the j-th first index, e j The entropy value of the j-th first index, and n is the total number of the first indexes.
And S17, calculating a spatial simulation result of the carbon emission according to the total industrial annual carbon emission, the normalized score and the weight.
Specifically, the score I is normalized according to the total industrial annual carbon emission CE jk Weight w j The calculation formula of the spatial simulation result of the carbon emission is as follows:
wherein F is k Is the comprehensive weight value, w, of the kth grid j Is the weight of the j-th first index, I jk Normalized score of the jth first index at the kth grid, n being the total number of first indexes;
in CE k For the carbon emission in the kth grid after the space formation, CE is the total industrial annual carbon emission of the research area where the grid is located, F k The comprehensive weight value of the kth grid is calculated, and u is the total number of grids.
The embodiment of the application has the beneficial effects that: the application integrates the geographical space big data of interest points based on noctilucent remote sensing traditional data, realizes the spatial simulation of carbon emission through a geographical space information technology, focuses on the dimension of space and specific industry scale, establishes high-spatial-resolution carbon dioxide carbon emission space grid data, helps to establish emission lists and space databases of specific areas, industries and departments, and reveals the distribution characteristics and rules of industrial carbon emission in the geographical space.
In addition, the embodiment of the application realizes the carbon emission space simulation of the industrial level in the area by combining the space geographic big data and the geographic information space analysis technology on the basis of the statistical data, can establish an industrial carbon emission space data set of the area, provides key basic data support for industrial green transformation upgrading, optimizing an industrial structure and promoting industrial low-carbon development, and has wide application prospect in the fields of space low-carbon control, accurate carbon reduction and the like.
As shown in fig. 5, the embodiment of the present application further provides an industrial carbon emission space simulation system based on noctilucent remote sensing and interest points, including:
a first module 51 for obtaining industrial energy consumption and a corresponding carbon emission factor coefficient;
a second module 52 for calculating an industrial annual total carbon emission based on the industrial energy consumption and the corresponding carbon emission factor coefficient;
a third module 53, configured to obtain industrial interest point data and noctilucent remote sensing data;
a fourth module 54, configured to calculate a normalized score according to the industrial interest point data and the noctilucent remote sensing data;
a fifth module 55, configured to obtain a first index, and construct a panel data matrix, where the first index includes a total night light value and a total industrial annual carbon emission, and the panel data matrix includes the first index;
a sixth module 56, configured to calculate a weight of each first index according to the panel data matrix;
seventh module 57 is configured to calculate a spatial simulation result of carbon emissions according to the total industrial annual carbon emissions, the normalized score and the weight.
It can be seen that the content in the above method embodiment is applicable to the system embodiment, and the functions specifically implemented by the system embodiment are the same as those of the method embodiment, and the beneficial effects achieved by the method embodiment are the same as those achieved by the method embodiment.
While the preferred embodiment of the present application has been described in detail, the present application is not limited to the above embodiment, and various equivalent modifications and substitutions can be made by those skilled in the art without departing from the spirit of the present application, and these equivalent modifications and substitutions are intended to be included in the scope of the present application as defined in the appended claims.
Claims (10)
1. The industrial carbon emission space simulation method based on noctilucent remote sensing and interest points is characterized by comprising the following steps of:
acquiring industrial energy consumption and a corresponding carbon emission factor coefficient;
calculating the total industrial annual carbon emission according to the industrial energy consumption and the corresponding carbon emission factor coefficient;
acquiring industrial interest point data and noctilucent remote sensing data;
calculating a normalized score according to the industrial interest point data and the noctilucent remote sensing data;
acquiring a first index, and constructing a panel data matrix, wherein the first index comprises a night light total value and the industrial annual carbon emission total amount, and the panel data matrix comprises the first index;
according to the panel data matrix, calculating the weight of each first index;
and calculating a spatial simulation result of carbon emission according to the industrial annual carbon emission total amount, the normalized score and the weight.
2. The method for simulating industrial carbon emission space based on noctilucent remote sensing and interest points according to claim 1, wherein the calculation formula of the industrial annual carbon emission total is as follows:
wherein CE is the total carbon emission amount of industry year, K i Carbon emission factor coefficient, E, of energy i i The industrial energy consumption of the energy i is p, and the energy type number is p.
3. The method for simulating industrial carbon emission space based on noctilucent remote sensing and interest points according to claim 1, wherein the calculating normalized score according to the industrial interest point data and the noctilucent remote sensing data comprises the following steps:
performing nuclear density analysis on the industrial interest point data to obtain an industrial interest point nuclear density grid image;
analyzing the noctilucent remote sensing data to obtain noctilucent remote sensing images;
calculating the normalized score according to the industrial interest point nuclear density grid image and the noctilucent remote sensing image, wherein the calculation formula of the normalized score is as follows:
wherein I is jk Normalized score at the kth grid for the first index of the jth term, R jk And f is the total pixel number of the raster image of the first index in the j-th item.
4. The method for simulating industrial carbon emission space based on noctilucent remote sensing and interest points according to claim 1, wherein the step of obtaining the first index and constructing the panel data matrix comprises the following steps:
calculating the noctilucent remote sensing data to obtain the total night light value;
constructing a panel data matrix, wherein the calculation formula of the panel data matrix is as follows:
wherein r is ij And (3) for the panel data value of the first index in the j-th item in the i-th year, m is the total number of years of the panel data, and n is the total number of the first indexes.
5. The method for simulating industrial carbon emission space based on noctilucent remote sensing and interest points according to claim 1, wherein the calculating the weight of each first index according to the panel data matrix comprises the following steps:
calculating a standardized index according to the panel data matrix;
calculating the entropy value of each first index according to the standardized index;
and calculating the weight of each first index according to the entropy value.
6. The method for simulating industrial carbon emission space based on noctilucent remote sensing and interest points according to claim 5, wherein the calculation formula of the standardized index is as follows:
or,
wherein x is ij Max, which is the value of the first index in the ith year after normalization j {r ij The value of the first index of item j, min j {r ij The value of the first index in the j-th item is the minimum value, r ij A panel data value at the ith year for the first indicator of item j.
7. The method for simulating industrial carbon emission space based on noctilucent remote sensing and interest points according to claim 5, wherein the calculation formula of the entropy value is as follows:
in the formula e j For the entropy value, p of the first index of item j ij The index value specific gravity of the first index in the ith year is the index value specific gravity of the jth item and is p ij When=0, p ij ·lnp ij =0, n is the total number of the first indices, k is a coefficient;
wherein m is the number of rows of the panel data matrix, namely the total number of years of the data;
wherein p is ij The index value specific gravity of the first index in the ith year, x ij The value of the first index in the ith year after normalizing the panel data, and m is the number of rows of the panel data matrix.
8. The method for simulating industrial carbon emission space based on noctilucent remote sensing and interest points according to claim 5, wherein the weight is calculated according to the following formula:
wherein w is j A weight value e of the first index of the j-th item j And (3) the entropy value of the first index in the j-th item, and n is the total number of the first indexes.
9. The method for simulating the industrial carbon emission space based on noctilucent remote sensing and interest points according to claim 1, wherein the calculation formula of the carbon emission space simulation result is as follows:
wherein F is k Is the comprehensive weight value, w, of the kth grid j For the weight of the first index of item j, I jk Normalized score at kth grid for the first index of jth term, n being the total number of the first indices;
in CE k For the carbon emission in the kth grid after the space formation, CE is the total industrial annual carbon emission of the research area where the grid is located, F k The comprehensive weight value of the kth grid is calculated, and u is the total number of grids.
10. Industry carbon emission space simulation system based on night light remote sensing and interest point, its characterized in that includes:
the first module is used for acquiring industrial energy consumption and corresponding carbon emission factor coefficients;
the second module is used for calculating the total industrial annual carbon emission according to the industrial energy consumption and the corresponding carbon emission factor coefficient;
the third module is used for acquiring industrial interest point data and noctilucent remote sensing data;
a fourth module, configured to calculate a normalized score according to the industrial interest point data and the noctilucent remote sensing data;
a fifth module, configured to obtain a first indicator, and construct a panel data matrix, where the first indicator includes a total night light value and a total industrial annual carbon emission amount, and the panel data matrix includes the first indicator;
a sixth module, configured to calculate a weight of each of the first indicators according to the panel data matrix;
and a seventh module, configured to calculate a spatial simulation result of carbon emission according to the total industrial annual carbon emission, the normalized score and the weight.
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