CN116739619A - Energy power carbon emission monitoring analysis modeling method and device - Google Patents
Energy power carbon emission monitoring analysis modeling method and device Download PDFInfo
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Abstract
The invention discloses an energy power carbon emission monitoring analysis modeling method and device, in particular to the field of power carbon emission, which are used for solving the problems that the existing carbon emission analysis is usually carried out on a certain area singly, the analysis time is long, and the overall carbon emission trend judgment cannot be directly carried out; according to the method, the carbon emission intensity of each area is calculated, screening early warning is carried out on the areas which do not meet the standard, cluster analysis is carried out on the different areas, the areas which are not in the standard range are screened out, alarming is carried out, finally, the whole area is predicted and evaluated by combining the states of all the areas, and relevant optimization policies are conveniently formulated.
Description
Technical Field
The invention relates to the technical field of electric power carbon emission, in particular to an energy electric power carbon emission monitoring analysis modeling method and device.
Background
Carbon emissions are one of the major greenhouse gases that can accelerate climate change, leading to global climate instability, including more frequent extreme weather events such as heavy rain, floods, drought, forest fires, etc., which can have serious impact on human life, agricultural production, water resources, etc. At the same time, air pollution is a serious problem, and control of carbon emissions can reduce air pollution and related health problems. Therefore, control of carbon emissions is one of the necessary means to protect the earth and human health.
In order to meet the index requirement of carbon emission, a database is required to be built for the carbon emission of each region, and the carbon emission condition of each region is analyzed according to the data in the database, and because the conditions of each region are different, the existing carbon emission analysis generally performs targeted analysis on a certain region independently, the analysis time is long, and the overall carbon emission trend cannot be directly judged.
Disclosure of Invention
In order to overcome the defects in the prior art, the embodiment of the invention provides an energy power carbon emission monitoring analysis modeling method and device, which are characterized in that firstly, the carbon emission intensity of each area is calculated, the areas which do not meet the standard are screened out for early warning, then, the different areas are subjected to clustering analysis according to the respective attribute states, the areas which are not in the standard range are screened out and are alarmed, finally, the whole area is subjected to prediction evaluation by combining the states of all the areas, so that the related optimization policy is conveniently formulated, and the problems in the background technology are solved.
In order to achieve the above purpose, the present invention provides the following technical solutions:
the energy power carbon emission monitoring analysis modeling method comprises the following steps:
step S1, dividing a carbon emission supervision area into a plurality of carbon emission supervision small areas, calculating the carbon emission intensity of each carbon emission supervision small area, judging whether the carbon emission intensity of each carbon emission supervision small area meets the standard or not, and marking and early warning the carbon emission supervision small areas which do not meet the standard;
step S2, calculating carbon emission evaluation coefficients according to regional scale information, population scale information and industrial structure information of the carbon emission supervision small regions meeting the standard, clustering each carbon emission supervision small region according to the carbon emission evaluation coefficients, and determining the carbon emission standard of each carbon emission supervision small region;
s3, analyzing and processing actual carbon emission of each clustered carbon emission supervision small area, screening out carbon emission supervision small areas which do not meet the carbon emission standard, and marking and early warning;
and S4, early warning information in the carbon emission supervision area is obtained, and the future carbon emission trend is determined by combining population flow information of the carbon emission supervision area, so that modeling analysis of a carbon emission database is completed.
In a preferred embodiment, in step S2, the present invention calculates and obtains the carbon emission evaluation coefficients of each carbon emission supervision small area by using a Logistic regression analysis method, which specifically includes the following steps:
before determining the carbon emission evaluation coefficient, all main influencing factors on carbon emission are set as an x set, and each main influencing factor is respectively expressed as { x } 1 、x 2 、...、x n N is the number of main influencing factors, n is a positive integer, and the exponential expression of the Logistic regression analysis method is:
wherein C is a carbon emission evaluation coefficient, Q is a constant term, the magnitude of the adjustment required in the absence of all representative major influencing factors, i.e., Q is all minor influencing factor coefficients, { x 1 、x 2 、...、x n The number of main influencing factors, { g 1 、g 2 、...、g n And the regression coefficients of the main influencing factors are respectively.
In a preferred embodiment, in step S2, after the carbon emission evaluation coefficients of the carbon emission regulatory small regions are calculated and obtained, a K-means clustering algorithm is used to perform cluster analysis on the carbon emission evaluation coefficients of the carbon emission regulatory small regions according to the carbon emission evaluation coefficients, and the carbon emission regulatory small regions are respectively clustered on the cluster center through the cluster analysis, so as to obtain K carbon emission standard clusters.
In a preferred embodiment, in step S2, the specific steps of performing cluster analysis on the carbon emission evaluation coefficients of the respective carbon emission supervision small areas using the k-means clustering algorithm are as follows:
s21, determining the number K of clustering centers of a K-means algorithm by using a contour coefficient method;
and S22, clustering carbon emission evaluation coefficients of all the carbon emission supervision small areas by using a KMEANS algorithm to obtain K carbon emission standard clusters.
In a preferred embodiment, after determining the cluster center where the respective carbon emission control small regions are aggregated in step S2, the carbon emission standard of the respective carbon emission control small regions is determined according to the carbon emission standard corresponding to the cluster center.
In a preferred embodiment, after the clustering obtains K carbon emission standard clusters in step S3, actual carbon emission amounts of the respective carbon emission regulatory small regions in the K carbon emission standard clusters are compared with carbon emission standards corresponding to the clustering center, respectively:
and if the actual carbon emission amount of the carbon emission supervision small area is larger than the corresponding carbon emission standard, marking the carbon emission supervision small area as a high carbon emission supervision small area, and carrying out alarm processing.
In a preferred embodiment, in step S4, the ratio of the high carbon emission supervision small area to the total carbon emission supervision small area is calculated and is calibrated as RR, the population flow value in the carbon emission supervision area is obtained and is calibrated as PM, and the future risk coefficient RC is obtained according to the formula calculation of the ratio RR of the population flow value PM to the high carbon emission supervision small area to the total carbon emission supervision small area, wherein the specific calculation expression is as follows;
RC＝ln(αRR+βPM+1)
Wherein, alpha and beta are respectively preset proportionality coefficients of the ratio RR of the small area of high carbon emission supervision to the small area of total carbon emission supervision and the population flow value PM, and alpha > beta >0.
In a preferred embodiment, in step S4, the future risk factor RC is compared with a standard risk threshold:
if the future risk coefficient RC is greater than or equal to the standard risk threshold, marking the carbon emission supervision area as a high risk area, and carrying out early warning prompt;
if the future risk coefficient RC is less than the standard risk threshold, the carbon emission regulatory region is marked as a low risk region.
An energy electricity carbon emission monitoring analysis modeling device, comprising: the system comprises a data acquisition and analysis module, a clustering evaluation module, an early warning module and a carbon emission trend prediction module;
the data acquisition and analysis module divides the carbon emission supervision area into a plurality of carbon emission supervision small areas, calculates the carbon emission intensity of each carbon emission supervision small area, and judges whether the carbon emission intensity of each carbon emission supervision small area meets the standard;
the clustering evaluation module calculates carbon emission evaluation coefficients of the carbon emission supervision small areas meeting the standard, clusters each carbon emission supervision small area according to the carbon emission evaluation coefficients, and determines the carbon emission standard of each carbon emission supervision small area;
The early warning module is used for respectively analyzing and processing actual carbon emission of the clustered carbon emission supervision small areas and carrying out marking early warning;
the carbon emission trend prediction module acquires early warning information in a carbon emission supervision area, and determines future carbon emission trend by combining population flow information of the carbon emission supervision area to complete modeling analysis of a carbon emission database.
In a preferred embodiment, the cluster evaluation module calculates the carbon emission evaluation coefficient of the carbon emission regulatory small area meeting the standard by the following steps:
before determining the carbon emission evaluation coefficient, all main influencing factors on carbon emission are set as an x set, and each main influencing factor is respectively expressed as { x } 1 、x 2 、...、x n N is the number of main influencing factors, n is a positive integer, and the exponential expression of the Logistic regression analysis method is:
wherein C is a carbon emission evaluation coefficient, Q is a constant term, the magnitude of the adjustment required in the absence of all representative major influencing factors, i.e., Q is all minor influencing factor coefficients, { x 1 、x 2 、...、x n The number of main influencing factors, { g 1 、g 2 、...、g n And the regression coefficients of the main influencing factors are respectively.
In a preferred embodiment, the cluster evaluation module performs cluster analysis on the carbon emission evaluation coefficients of the carbon emission supervision small areas by adopting a K-means clustering algorithm according to the carbon emission evaluation coefficients after calculating and acquiring the carbon emission evaluation coefficients of the carbon emission supervision small areas, and respectively gathers the carbon emission supervision small areas on a cluster center through the cluster analysis to obtain K carbon emission standard clusters.
In a preferred embodiment, the clustering evaluation module performs the following specific steps of clustering analysis on the carbon emission evaluation coefficients of each carbon emission supervision small area by adopting a k-means clustering algorithm:
determining the number K of clustering centers of a K-means algorithm by using a contour coefficient method;
and clustering carbon emission evaluation coefficients of each carbon emission supervision small region by using a KMEANS algorithm to obtain K carbon emission standard clusters.
In a preferred embodiment, the cluster evaluation module determines the carbon emission standard of each carbon emission supervision small area according to the carbon emission standard corresponding to the cluster center after determining the cluster center collected by each carbon emission supervision small area.
In a preferred embodiment, after the early warning module obtains K carbon emission standard clusters according to the clustering, comparing the actual carbon emission amounts of the carbon emission supervision small areas in the K carbon emission standard clusters with the carbon emission standards corresponding to the clustering center respectively:
and if the actual carbon emission amount of the carbon emission supervision small area is larger than the corresponding carbon emission standard, marking the carbon emission supervision small area as a high carbon emission supervision small area, and carrying out alarm processing.
In a preferred embodiment, the early warning module calculates the ratio of the high carbon emission supervision small area to the total carbon emission supervision small area, and marks the ratio as RR, obtains the population flow value in the carbon emission supervision area, marks the population flow value as PM, and calculates and obtains the future risk coefficient RC through a formula according to the ratio RR of the population flow value PM to the high carbon emission supervision small area to the total carbon emission supervision small area, wherein the specific calculation expression is as follows;
RC＝ln(αRR+βPM+1)
Wherein, alpha and beta are respectively preset proportionality coefficients of the ratio RR of the small area of high carbon emission supervision to the small area of total carbon emission supervision and the population flow value PM, and alpha > beta >0.
In a preferred embodiment, the early warning module compares the future risk factor RC with a standard risk threshold:
if the future risk coefficient RC is greater than or equal to the standard risk threshold, marking the carbon emission supervision area as a high risk area, and carrying out early warning prompt;
if the future risk coefficient RC is less than the standard risk threshold, the carbon emission regulatory region is marked as a low risk region.
The invention relates to an energy power carbon emission monitoring analysis modeling method and a device thereof, which have the technical effects and advantages that:
according to the method, the carbon emission intensity of each area is calculated, screening early warning is carried out on the areas which do not meet the standard, then clustering analysis is carried out on the different areas according to the respective attribute states, the areas which are not in the standard range are screened out and alarm is carried out, finally, the state of all the areas is combined, prediction evaluation is carried out on the whole area, and relevant optimization policies are conveniently formulated;
according to the invention, the carbon emission evaluation coefficients of all the carbon emission supervision small areas are comprehensively calculated, and the carbon emission state of all the carbon emission supervision small areas can be more accurately analyzed by comparing and analyzing the carbon emission supervision small areas with corresponding carbon emission standards according to the carbon emission evaluation coefficients, so that the defect that the supervision areas are required to be singly analyzed one by one in the existing carbon emission monitoring means is avoided, the similarity of all the carbon emission supervision small areas can be known while the monitoring is more rapid and convenient, and the follow-up adjustment of the overall structure in the carbon emission supervision areas is facilitated;
According to the invention, the high carbon emission supervision small area and the future population flow condition are combined, so that the carbon emission standard reaching risk of the carbon emission supervision area in the future period can be comprehensively analyzed, the future can be comprehensively predicted according to the actual carbon emission condition, and when the risk is high, the method can help related personnel to carry out overall optimization adjustment in advance.
Drawings
FIG. 1 is a flow chart of an energy power carbon emission monitoring analysis modeling method;
fig. 2 is a schematic diagram of an energy power carbon emission monitoring analysis modeling device.
Detailed Description
The following description of the embodiments of the present invention will be made clearly and completely with reference to the accompanying drawings, in which it is apparent that the embodiments described are only some embodiments of the present invention, but not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
According to the method for monitoring, analyzing and modeling the carbon emission of the energy power, firstly, the carbon emission intensity of each area is calculated, screening and early warning are carried out on the areas which do not meet the standard, then cluster analysis is carried out on the different areas according to the respective attribute states, the areas which are not in the standard range are screened out, alarming is carried out, finally, the whole area is predicted and evaluated according to the states of all the areas, and relevant optimization policies such as energy structure optimization, environmental protection measure enhancement and the like are conveniently formulated.
It should be noted that, the carbon emission supervision area or the carbon emission supervision small area in the present disclosure may represent various meanings, for example, the carbon emission supervision area may be various cities, and the carbon emission supervision small area may be a district under each city, for example, the carbon emission supervision area may be a district under each city, and at this time, the carbon emission supervision small area is an industrial park under the district, etc., which is not limited herein, and may be substituted by a suitable scenario according to actual needs.
Example 1
FIG. 1 shows a flow chart of an energy power carbon emission monitoring analysis modeling method, which comprises the following steps:
step S1, dividing a carbon emission supervision area into a plurality of carbon emission supervision small areas, calculating the carbon emission intensity of each carbon emission supervision small area, judging whether the carbon emission intensity of each carbon emission supervision small area meets the standard or not, and marking and early warning the carbon emission supervision small areas which do not meet the standard.
And S2, calculating carbon emission evaluation coefficients according to the regional scale information, the population scale information and the industrial structure information by using the carbon emission supervision small regions meeting the standard, clustering each carbon emission supervision small region according to the carbon emission evaluation coefficients, and determining the carbon emission standard of each carbon emission supervision small region.
And S3, respectively analyzing and processing the actual carbon emission of each clustered carbon emission supervision small area, screening out the carbon emission supervision small areas which do not meet the carbon emission standard, and carrying out marking and early warning.
And S4, early warning information in the carbon emission supervision area is obtained, and the future carbon emission trend is determined by combining population flow information of the carbon emission supervision area, so that modeling analysis of a carbon emission database is completed.
Specifically, because the actual carbon emission supervision area is larger, the industrial structures of all areas in the interior are also dissimilar, and the energy structures are divided into a plurality of small areas for analysis, so that the accurate grasp of the carbon emission supervision area is more facilitated. Therefore, in step S1, the carbon emission control region is first divided into several carbon emission control small regions.
Carbon emission intensity refers to the amount of carbon dioxide (CO 2) emitted per unit GDP (total domestic production), typically measured in ton carbon dioxide equivalents per ten thousand GDP. It reflects the relationship between economic development and carbon emissions, i.e., the amount of energy consumed per GDP and the amount of carbon emissions emitted. The decrease in carbon emission intensity means that economic development can be achieved at the cost of less carbon emissions, and that economic development in that region or country is more sustainable. Therefore, the carbon emission intensity has a certain index meaning, the carbon emission intensity of each carbon emission supervision small area is calculated and compared with the critical carbon emission intensity, if the carbon emission intensity of the carbon emission supervision small area is larger than the critical carbon emission intensity, the carbon emission supervision small area unit GDP is proved to be out of standard, the carbon emission is not in accordance with the low carbon requirement, at the moment, the carbon emission supervision small area is marked as a high carbon emission supervision small area, alarm processing is carried out on the high carbon emission supervision small area, and related staff is prompted to carry out optimization adjustment on the high carbon emission supervision small area.
It should be noted that, the carbon emission intensity is calculated by weighted average according to the contribution degree of each industry of the carbon emission supervision small area to the GDP and the corresponding carbon emission amount, and specifically can be calculated according to the following steps:
step 11, calculating the proportion of each industry production value in the small carbon emission supervision area to GDP, for example, assuming that the first industry production value in a certain city is 1000 ten thousand yuan, the second industry production value is 5000 ten thousand yuan, the third industry production value is 9000 ten thousand yuan, and the total GDP production value is 15000 ten thousand yuan, the first industry production value is 6.67% (1000/15000), the second industry production value is 33.33% (5000/15000), and the third industry production value is 60% (9000/15000);
and step S12, calculating the carbon emission of each industry. For example, assuming that the first industrial carbon emission amount of the region is 100 tons, the second industrial carbon emission amount is 500 tons, and the third industrial carbon emission amount is 1000 tons, the first industrial carbon emission amount is 6.67 tons (1006.67%), the second industrial carbon emission amount is 166.67 tons (50033.33%), and the third industrial carbon emission amount is 600 tons (1000×60%);
step S13, calculating a weighted average carbon emission intensity. According to the data of step S11 and step S12, the weighted average carbon emission intensity is: (the ratio of the carbon emission of the first industry to the ratio of the carbon emission of the second industry to the ratio of the carbon emission of the third industry to the ratio of the carbon emission of the first industry to the ratio of the carbon emission of the second industry to the ratio of the carbon emission of the third industry)/GDP total yield. Substituting the numerical value to obtain: (6.670.0667+166.670.3333+6000.6)/15000= 0.0549 tons/ten thousand yuan.
In step S2, since the area scale information, population scale information, and industry structure information of the respective carbon emission regulatory small areas conforming to the carbon emission intensity standard may all be different, so that the carbon emission standards thereof may be different, in step S2, it is necessary to cluster-divide the respective carbon emission regulatory small areas conforming to the carbon emission intensity standard according to their respective attribute states, so as to more accurately screen whether the respective carbon emission regulatory small areas conform to the carbon emission requirements.
Specifically, the area scale information includes a floor scale value of each carbon emission supervision small area, where the floor scale value refers to an area of each carbon emission supervision small area, and the larger the floor scale value is, the corresponding carbon emission index is increased. Population scale information includes population density values for each carbon emission regulatory small region, and the greater the population density value, the greater the carbon dioxide emissions generated, and thus, the corresponding carbon emission index is increased. The industrial structure information includes energy utilization information, industry type information, etc., and the corresponding carbon emission index is different for different industrial structure information.
In an alternative example, for facilitating analysis, the invention calculates and obtains the carbon emission evaluation coefficient of each carbon emission supervision small area by using a Logistic regression analysis method, and performs cluster analysis on the carbon emission evaluation coefficient.
Specifically, before determining the carbon emission evaluation coefficient, all main influencing factors on carbon emission are set as x sets, and each main influencing factor is respectively expressed as { x } 1 、x 2 、...、x n N is the number of main influencing factors, n is a positive integer, and the exponential expression of the Logistic regression analysis method is:
wherein C is a carbon emission evaluation coefficient, Q is a constant term, i.e., the magnitude of the adjustment required in the absence of all representative major influencing factors, i.e., Q is all minor influencing factor coefficients, { x 1 、x 2 、...、x n The variable (number of main influencing factors), { g 1 、g 2 、...、g n And is the regression coefficient of each variable.
The calculated value range of the carbon emission evaluation coefficient is (0, 1), and the larger the carbon emission evaluation coefficient is, the larger the incremental influence of the carbon emission supervision small region on the carbon emission is, and the carbon emission standard of the carbon emission supervision small region is increased.
Among the major contributors to carbon emissions include, but are not limited to: whether large area, medium area, small area, densely populated area, whether clean energy is the primary energy, and whether the secondary industry is the primary industry.
It should be noted that, whether the carbon emission monitoring area is a large area, whether the carbon emission monitoring area is a medium area, and whether the carbon emission monitoring area is a small area are determined according to different scenes set according to the carbon emission monitoring area, for example, if the carbon emission monitoring area is a jurisdiction and the carbon emission monitoring area is each community, the size determination of that area is performed according to the community scale determination standard, and if the carbon emission monitoring area is each enterprise, the carbon emission monitoring area is divided according to the enterprise scale standard, and is not specifically set herein. Similarly, densely populated settings are also changed differently from carbon emission regulatory small area settings scenarios.
It is easily known to those skilled in the art that whether clean energy is used as a main energy source is an important factor affecting carbon emission, and thus needs to be considered separately, and similarly, the second industry is industry, which is generally the industry that contributes most to carbon emission. This is because industrial production requires consuming a large amount of energy, including fossil fuel and the like, resulting in a large amount of carbon emissions, and thus, an influence factor of industry needs to be considered alone.
In this embodiment, the value of the regression coefficient is set according to the influence of the main influencing factor on the carbon emission, and when the main influencing factor appears to cause the improvement of the carbon emission standard, the regression coefficient g >0; when the major influencing factors appear to result in a reduction of the carbon emission standard, the regression coefficient g <0.
The larger the apparent area size, the denser the population, and the larger the carbon emission standard in the case where the second industry is the main industry, and the smaller the carbon emission standard in the case where the clean energy is the main energy.
The logic factors for calculating the carbon emission evaluation coefficient C of the invention comprise the following components: firstly, an index, namely, a factor causing the change of the carbon emission standard (the invention refers to the influence of regional scale information, population scale information and industry structure information on the carbon emission standard); the weight of the indexes, namely the proportion of each main influencing factor; thirdly, an operation equation, namely, a result is obtained through what mathematical operation process, and the carbon emission evaluation coefficient C is obtained through the operation of the operation equation by using the indexes with the respective weights.
Performing data conversion and processing on the special environment obtained in the sample, and converting the special environment into a data language which can be identified by computer software; secondly, carrying out Logistic regression analysis on the evaluation factors by using SPSS software, and screening out factors and weights thereof which have important correlation with the results; and thirdly, carrying the evaluation factors and the weights into a Logistic regression equation to operate, so as to obtain a result.
Q is a constant term, and Q is a specific meaning of all the fine influencing factor coefficients: the main influencing factors collected in the application are representative influencing factors, and the influence of the main influencing factors on the carbon emission standard is large, however, in the actual use process, other non-representative fine influencing factors (such as urban degree and the like) also influence the carbon emission standard, and the influence is small, so that the Logistic regression analysis method is corrected by setting a constant term Q, and when the representative main influencing factors are not present, the carbon emission evaluation coefficient C is determined by the constant term Q.
It should be noted that, the samples adopted in the Logistic regression analysis method of the present application can be adjusted and selected according to the setting of the actual carbon emission supervision area. And the calculated carbon emission evaluation coefficients are different, the corresponding carbon emission standards are also different, and the standard setting is different according to the selection of the actual samples.
For example, the carbon emission supervision small area is set as a factory, and the carbon emission standards of various factories can be determined according to the national standard "Industrial Enterprise Unit product energy consumption Limit" of China.
In an alternative example, five levels of factory carbon emission standards are set, and then the carbon emission standards of each carbon emission regulatory small region are shown in the following table in combination with the carbon emission evaluation coefficients:
| carbon emission evaluation coefficient | Carbon emission standard |
| ≥0.7999 | Grade 5 |
| 0.5999～0.7998 | Grade 4 |
| 0.3999～0.5998 | 3 grade |
| 0.1999～0.3998 | Level 2 |
| 0.0001～0.1998 | Level 1 |
In the table, the carbon emission standards 1 to 5 are respectively corresponding to the five set plant carbon emission grades, and the carbon emission standard 5 is highest.
It should be noted that, since the standard of carbon emission varies greatly with the policy, the present invention is only illustrated, and the specific carbon emission standard is set according to the actual situation and is not repeated here.
In step S2, after the carbon emission evaluation coefficients of the carbon emission supervision small areas are calculated and obtained, clustering analysis is carried out on the carbon emission standards of the carbon emission supervision small areas by adopting a k-means clustering algorithm according to the carbon emission evaluation coefficients. The method comprises the following specific steps:
s21, determining the number K of clustering centers of a K-means algorithm by using a contour coefficient method;
and S22, clustering the data by using a KMEANS algorithm to obtain K carbon emission standard clusters.
The specific procedure of step S21 is as follows:
step S211, given a K value, clustering the data by using a KMEANS algorithm, and calculating a contour coefficient of a carbon emission evaluation coefficient of each carbon emission supervision small region, wherein the specific calculation expression is as follows:
S(i)＝[b(i)-a(i)]/max{a(i),b(i)}
where S (i) is a profile factor, a (i) is an average distance between the carbon emission evaluation factor of each carbon emission regulatory small region and all other points in the same cluster, and b (i) is a minimum value between the carbon emission evaluation factor of each carbon emission regulatory small region and the average distance between the carbon emission evaluation factor of each carbon emission regulatory small region and all other points in all other clusters.
Step S212, for each cluster, calculating the average profile coefficient of all carbon emission evaluation coefficients of the cluster to obtain the average profile coefficient of the cluster;
step S213, calculating the average value of the average contour coefficients of all clusters to obtain the average contour coefficient under the current K value;
step S214, repeating the steps to calculate average contour coefficients under different K values;
in step S215, the K value with the largest average profile factor is selected as the final K value.
Thus, by clustering, individual carbon emission regulatory small regions are clustered on a certain cluster center.
In step S3, after the K carbon emission standard clusters are obtained by the clustering, the actual carbon emission amounts of the carbon emission supervision small areas in the K carbon emission standard clusters are compared with the carbon emission standards corresponding to the clustering center, so as to determine whether the carbon emission of each carbon emission supervision small area exceeds the standard.
If the actual carbon emission is larger than the corresponding carbon emission standard, the carbon emission in the carbon emission supervision small area exceeds the standard, the carbon emission supervision small area is marked as a high carbon emission supervision small area, alarm processing is carried out, and relevant staff are prompted to carry out optimization adjustment on the carbon emission supervision small area.
According to the invention, the carbon emission evaluation coefficients of the carbon emission supervision small areas are comprehensively calculated, and the carbon emission supervision small areas are clustered uniformly and are subjected to comparative analysis according to the carbon emission evaluation coefficients, so that the carbon emission state of each carbon emission supervision small area can be more accurately analyzed, and marked alarm is carried out, thereby avoiding the defect that the supervision areas are required to be individually analyzed one by one in the existing carbon emission monitoring means, being more rapid and convenient, knowing the similarity of each carbon emission supervision small area, and facilitating the subsequent adjustment of the integral structure in the carbon emission supervision area.
In step S4, the early warning information in the carbon emission supervision area refers to the information marked as the high carbon emission supervision small area, the ratio of the high carbon emission supervision small area to the total carbon emission supervision small area is calculated and calibrated as RR, and obviously, the larger the ratio of the high carbon emission supervision small area to the total carbon emission supervision small area is, the larger the future carbon emission risk in the carbon emission supervision area is indicated, meanwhile, population flow information in the carbon emission supervision area, namely population flow value, is required to be obtained, and calibrated as PM, the larger the population flow value is, the larger the carbon emission requirement is, and the larger the carbon emission risk is, otherwise, the risk of carbon emission is correspondingly reduced if the population flow value is negative, namely population flows out; therefore, in step S4, the future risk coefficient RC is obtained by calculation according to the ratio RR of the population flowing value PM to the high carbon emission regulatory small region to the total carbon emission regulatory small region through a formula, and the specific calculation expression is as follows:
RC＝ln)αRR+βPM+1)
Wherein, alpha and beta are respectively preset proportionality coefficients of the ratio RR of the small area of high carbon emission supervision to the small area of total carbon emission supervision and the population flow value PM, and alpha > beta >0.
Comparing the future risk coefficient RC with a standard risk threshold value to determine how future risk conditions in the carbon emission supervision area are:
if the future risk coefficient RC is larger than or equal to the standard risk threshold, the fact that the carbon emission in the carbon emission supervision area in a future period is not up to standard is larger is indicated, the carbon emission supervision area is marked as a high risk area, early warning prompt is carried out, and relevant personnel are prompted to carry out overall optimization adjustment;
if the future risk coefficient RC is smaller than the standard risk threshold, the carbon emission is less in the carbon emission supervision area for a period of time in the future, and the carbon emission supervision area is marked as a low risk area.
According to the invention, the high carbon emission supervision small area and the future population flow condition are combined, so that the carbon emission standard reaching risk of the carbon emission supervision area in the future period can be comprehensively analyzed, the future can be comprehensively predicted according to the actual carbon emission condition, and when the risk is high, the method can help related personnel to carry out overall optimization adjustment in advance.
Example 2
The foregoing embodiment 1 describes in detail an energy power carbon emission monitoring analysis modeling method of the present invention, and in order to implement the method involved in the foregoing embodiment 1, the present embodiment describes an energy power carbon emission monitoring analysis modeling apparatus, as shown in fig. 2, which includes: the system comprises a data acquisition and analysis module, a clustering evaluation module, an early warning module and a carbon emission trend prediction module;
the data acquisition and analysis module divides the carbon emission supervision area into a plurality of carbon emission supervision small areas, calculates the carbon emission intensity of each carbon emission supervision small area, and judges whether the carbon emission intensity of each carbon emission supervision small area meets the standard;
the clustering evaluation module calculates carbon emission evaluation coefficients of the carbon emission supervision small areas meeting the standard, clusters each carbon emission supervision small area according to the carbon emission evaluation coefficients, and determines the carbon emission standard of each carbon emission supervision small area;
the early warning module is used for respectively analyzing and processing actual carbon emission of the clustered carbon emission supervision small areas and carrying out marking early warning;
the carbon emission trend prediction module acquires early warning information in a carbon emission supervision area, and determines future carbon emission trend by combining population flow information of the carbon emission supervision area to complete modeling analysis of a carbon emission database.
In a preferred embodiment, the cluster evaluation module calculates the carbon emission evaluation coefficient of the carbon emission regulatory small area meeting the standard by the following steps:
before determining the carbon emission evaluation coefficient, all main influencing factors on carbon emission are set as an x set, and each main influencing factor is respectively expressed as { x } 1 、x 2 、...、x n And n is related to the main influenceThe number of factors, n is a positive integer, and the exponential expression of the Logistic regression analysis method is:
wherein C is a carbon emission evaluation coefficient, Q is a constant term, the magnitude of the adjustment required in the absence of all representative major influencing factors, i.e., Q is all minor influencing factor coefficients, { x 1 、x 2 、...、x n The number of main influencing factors, { g 1 、g 2 、...、g n And the regression coefficients of the main influencing factors are respectively.
In a preferred embodiment, the cluster evaluation module performs cluster analysis on the carbon emission evaluation coefficients of the carbon emission supervision small areas by adopting a K-means clustering algorithm according to the carbon emission evaluation coefficients after calculating and acquiring the carbon emission evaluation coefficients of the carbon emission supervision small areas, and respectively gathers the carbon emission supervision small areas on a cluster center through the cluster analysis to obtain K carbon emission standard clusters.
In a preferred embodiment, the clustering evaluation module performs the following specific steps of clustering analysis on the carbon emission evaluation coefficients of each carbon emission supervision small area by adopting a k-means clustering algorithm:
determining the number K of clustering centers of a K-means algorithm by using a contour coefficient method;
and clustering carbon emission evaluation coefficients of each carbon emission supervision small region by using a KMEANS algorithm to obtain K carbon emission standard clusters.
In a preferred embodiment, the cluster evaluation module determines the carbon emission standard of each carbon emission supervision small area according to the carbon emission standard corresponding to the cluster center after determining the cluster center collected by each carbon emission supervision small area.
In a preferred embodiment, after the early warning module obtains K carbon emission standard clusters according to the clustering, comparing the actual carbon emission amounts of the carbon emission supervision small areas in the K carbon emission standard clusters with the carbon emission standards corresponding to the clustering center respectively:
and if the actual carbon emission amount of the carbon emission supervision small area is larger than the corresponding carbon emission standard, marking the carbon emission supervision small area as a high carbon emission supervision small area, and carrying out alarm processing.
In a preferred embodiment, the early warning module calculates the ratio of the high carbon emission supervision small area to the total carbon emission supervision small area, and marks the ratio as RR, obtains the population flow value in the carbon emission supervision area, marks the population flow value as PM, and calculates and obtains the future risk coefficient RC through a formula according to the ratio RR of the population flow value PM to the high carbon emission supervision small area to the total carbon emission supervision small area, wherein the specific calculation expression is as follows;
RC＝ln(αRR+βPM+1)
Wherein, alpha and beta are respectively preset proportionality coefficients of the ratio RR of the small area of high carbon emission supervision to the small area of total carbon emission supervision and the population flow value PM, and alpha > beta >0.
In a preferred embodiment, the early warning module compares the future risk factor RC with a standard risk threshold:
if the future risk coefficient RC is greater than or equal to the standard risk threshold, marking the carbon emission supervision area as a high risk area, and carrying out early warning prompt;
if the future risk coefficient RC is less than the standard risk threshold, the carbon emission regulatory region is marked as a low risk region.
The above formulas are all formulas with dimensions removed and numerical values calculated, the formulas are formulas with a large amount of data collected for software simulation to obtain the latest real situation, and preset parameters in the formulas are set by those skilled in the art according to the actual situation.
It will be clear to those skilled in the art that, for convenience and brevity of description, the above apparatus embodiments may refer to the specific working procedure of the above described method embodiments, which are not described in detail herein.
The above embodiments may be implemented in whole or in part by software, hardware, firmware, or any other combination. When implemented in software, the above-described embodiments may be implemented in whole or in part in the form of a computer program product. The computer program product comprises one or more computer instructions or computer programs. When the computer instructions or computer program are loaded or executed on a computer, the processes or functions described in accordance with embodiments of the present application are produced in whole or in part. The computer may be a general purpose computer, a special purpose computer, a network of computers, or other programmable devices. The computer instructions may be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another computer-readable storage medium, for example, the computer instructions may be transmitted from one website site, computer, server, or data center to another website site, computer, server, or data center by wired (e.g., infrared, wireless, microwave, etc.). The computer readable storage medium may be any available medium that can be accessed by a computer or a data storage device such as a server, data center, etc. that contains one or more sets of available media. The usable medium may be a magnetic medium (e.g., floppy disk, hard disk, magnetic tape), an optical medium (e.g., DVD), or a semiconductor medium. The semiconductor medium may be a solid state disk.
Those of ordinary skill in the art will appreciate that the various illustrative modules and algorithm steps described in connection with the embodiments disclosed herein may be implemented as electronic hardware, or combinations of computer software and electronic hardware. Whether such functionality is implemented as hardware or software depends upon the particular application and design constraints imposed on the solution. Skilled artisans may implement the described functionality in varying ways for each particular application, but such implementation decisions should not be interpreted as causing a departure from the scope of the present application.
In the several embodiments provided in the present application, it should be understood that the disclosed apparatus, apparatus and method may be implemented in other manners. For example, the apparatus embodiments described above are merely illustrative, and for example, the division of the modules is merely a logical function division, and there may be additional divisions when actually implemented, for example, multiple modules or components may be combined or integrated into another apparatus, or some features may be omitted or not performed. Alternatively, the coupling or direct coupling or communication connection shown or discussed with each other may be an indirect coupling or communication connection via some interfaces, devices or modules, which may be in electrical, mechanical, or other forms.
In addition, each functional module in each embodiment of the present application may be integrated into one processing module, or each module may exist alone physically, or two or more modules may be integrated into one module.
The foregoing is merely illustrative of the present application, and the present application is not limited thereto, and any person skilled in the art will readily recognize that variations or substitutions are within the scope of the present application. Therefore, the protection scope of the present application shall be subject to the protection scope of the claims.
Finally: the foregoing description of the preferred embodiments of the application is not intended to limit the application to the precise form disclosed, and any such modifications, equivalents, and alternatives falling within the spirit and principles of the application are intended to be included within the scope of the application.
Claims (16)
1. The energy power carbon emission monitoring analysis modeling method is characterized by comprising the following steps of;
dividing the carbon emission supervision area into a plurality of carbon emission supervision small areas, calculating the carbon emission intensity of each carbon emission supervision small area, and judging whether the carbon emission intensity of each carbon emission supervision small area meets the standard;
Calculating a carbon emission evaluation coefficient of the carbon emission supervision small region meeting the standard, clustering each carbon emission supervision small region according to the carbon emission evaluation coefficient, and determining the carbon emission standard of each carbon emission supervision small region;
analyzing and processing actual carbon emission of each class of clustered carbon emission supervision small areas respectively, and marking and early warning;
and acquiring early warning information in the carbon emission supervision area, and determining future carbon emission trend by combining population flow information of the carbon emission supervision area to complete modeling analysis of a carbon emission database.
2. The method for monitoring, analyzing and modeling the carbon emission of energy power according to claim 1, which is characterized in that: the carbon emission evaluation coefficient is calculated in the carbon emission supervision small area meeting the standard, and the specific process is as follows:
before determining the carbon emission evaluation coefficient, all main influencing factors on carbon emission are set as an x set, and each main influencing factor is respectively expressed as x 1 、x 2 、...、x n N is the number of main influencing factors, n is a positive integer, and the exponential expression of the Logistic regression analysis method is as follows:
wherein C is a carbon emission evaluation coefficient, Q is a constant term, the magnitude of the adjustment required in the absence of all representative major influencing factors, i.e., Q is a coefficient of all minor influencing factors, x 1 、x 2 、...、x n G is the number of main influencing factors 1 、g 2 、...、g n Regression coefficients for each of the major influencing factors.
3. The method for monitoring, analyzing and modeling the carbon emission of energy power according to claim 2, which is characterized in that: after the carbon emission evaluation coefficients of all the carbon emission supervision small areas are obtained through calculation, carrying out cluster analysis on the carbon emission evaluation coefficients of all the carbon emission supervision small areas by adopting a K-means clustering algorithm according to the carbon emission evaluation coefficients, and respectively gathering all the carbon emission supervision small areas on a cluster center through cluster analysis to obtain K carbon emission standard clusters.
4. The method for monitoring, analyzing and modeling the carbon emission of energy power according to claim 3, wherein the method comprises the following steps: the specific steps of carrying out cluster analysis on the carbon emission evaluation coefficients of each carbon emission supervision small region by adopting a k-means clustering algorithm are as follows:
determining the number K of clustering centers of a K-means algorithm by using a contour coefficient method;
and clustering carbon emission evaluation coefficients of each carbon emission supervision small region by using a KMEANS algorithm to obtain K carbon emission standard clusters.
5. The method for monitoring, analyzing and modeling the carbon emission of energy power according to claim 3, wherein the method comprises the following steps: after the cluster centers gathered by the carbon emission supervision small areas are determined, the carbon emission standard of each carbon emission supervision small area is determined according to the carbon emission standard corresponding to the cluster centers.
6. The method for monitoring, analyzing and modeling the carbon emission of energy power according to claim 5, which is characterized in that: after the K carbon emission standard clusters are obtained by clustering, comparing the actual carbon emission amount of each carbon emission supervision small area in the K carbon emission standard clusters with the carbon emission standard corresponding to the clustering center:
and if the actual carbon emission amount of the carbon emission supervision small area is larger than the corresponding carbon emission standard, marking the carbon emission supervision small area as a high carbon emission supervision small area, and carrying out alarm processing.
7. The method for monitoring, analyzing and modeling the carbon emission of energy power according to claim 6, which is characterized in that: calculating the ratio of the high carbon emission supervision small area to the total carbon emission supervision small area, calibrating the ratio as RR, obtaining population flowing value in the carbon emission supervision area, calibrating the population flowing value as PM, and calculating and obtaining future risk coefficient RC through a formula according to the population flowing value PM and the ratio RR of the high carbon emission supervision small area to the total carbon emission supervision small area, wherein the specific calculation expression is as follows;
RC＝lnαRR+βPM+1
wherein, alpha and beta are respectively preset proportionality coefficients of the ratio RR of the small area of high carbon emission supervision to the small area of total carbon emission supervision and the population flow value PM, and alpha > beta >0.
8. The method for monitoring, analyzing and modeling the carbon emission of energy power according to claim 7, wherein the method comprises the following steps: comparing the future risk coefficient RC with a standard risk threshold:
if the future risk coefficient RC is greater than or equal to the standard risk threshold, marking the carbon emission supervision area as a high risk area, and carrying out early warning prompt;
if the future risk coefficient RC is less than the standard risk threshold, the carbon emission regulatory region is marked as a low risk region.
9. An energy power carbon emission monitoring analysis modeling device, which is characterized by comprising: the system comprises a data acquisition and analysis module, a clustering evaluation module, an early warning module and a carbon emission trend prediction module;
the data acquisition and analysis module divides the carbon emission supervision area into a plurality of carbon emission supervision small areas, calculates the carbon emission intensity of each carbon emission supervision small area, and judges whether the carbon emission intensity of each carbon emission supervision small area meets the standard;
the clustering evaluation module calculates carbon emission evaluation coefficients of the carbon emission supervision small areas meeting the standard, clusters each carbon emission supervision small area according to the carbon emission evaluation coefficients, and determines the carbon emission standard of each carbon emission supervision small area;
The early warning module is used for respectively analyzing and processing actual carbon emission of the clustered carbon emission supervision small areas and carrying out marking early warning;
the carbon emission trend prediction module acquires early warning information in a carbon emission supervision area, and determines future carbon emission trend by combining population flow information of the carbon emission supervision area to complete modeling analysis of a carbon emission database.
10. The method for monitoring, analyzing and modeling the carbon emission of energy power according to claim 9, wherein the method comprises the following steps: the clustering evaluation module calculates a carbon emission evaluation coefficient of a carbon emission supervision small area meeting the standard, and the specific process is as follows:
before determining the carbon emission evaluation coefficient, all main influencing factors on carbon emission are set as an x set, and each main influencing factor is respectively expressed as x 1 、x 2 、...、x n N is the number of main influencing factors, n is a positive integer, and the exponential expression of the Logistic regression analysis method is as follows:
wherein C is a carbon emission evaluation coefficient, Q is a constant term, the magnitude of the adjustment required in the absence of all representative major influencing factors, i.e., Q is a coefficient of all minor influencing factors, x 1 、x 2 、...、x n G is the number of main influencing factors 1 、g 2 、...、g n Regression coefficients for each of the major influencing factors.
11. The method for monitoring, analyzing and modeling the carbon emission of energy power according to claim 10, wherein the method comprises the following steps: and the clustering evaluation module is used for performing clustering analysis on the carbon emission evaluation coefficients of the carbon emission supervision small areas by adopting a K-means clustering algorithm according to the carbon emission evaluation coefficients after calculating and acquiring the carbon emission evaluation coefficients of the carbon emission supervision small areas, and respectively gathering the carbon emission supervision small areas on a clustering center through the clustering analysis to obtain K carbon emission standard clusters.
12. The method for monitoring, analyzing and modeling the carbon emission of energy power according to claim 11, wherein the method comprises the following steps: the clustering evaluation module adopts a k-means clustering algorithm to perform clustering analysis on the carbon emission evaluation coefficients of each carbon emission supervision small area, and comprises the following specific steps:
determining the number K of clustering centers of a K-means algorithm by using a contour coefficient method;
and clustering carbon emission evaluation coefficients of each carbon emission supervision small region by using a KMEANS algorithm to obtain K carbon emission standard clusters.
13. The method for monitoring, analyzing and modeling the carbon emission of energy power according to claim 11, wherein the method comprises the following steps: and the clustering evaluation module is used for determining the carbon emission standard of each carbon emission supervision small area according to the carbon emission standard corresponding to the clustering center after determining the clustering center gathered by each carbon emission supervision small area.
14. The method for monitoring, analyzing and modeling the carbon emission of energy power according to claim 13, wherein the method comprises the following steps: after the early warning module acquires K carbon emission standard clusters according to the clusters, comparing the actual carbon emission amount of each carbon emission supervision small area in the K carbon emission standard clusters with the carbon emission standard corresponding to the cluster center:
and if the actual carbon emission amount of the carbon emission supervision small area is larger than the corresponding carbon emission standard, marking the carbon emission supervision small area as a high carbon emission supervision small area, and carrying out alarm processing.
15. The method for monitoring, analyzing and modeling the carbon emission of energy power according to claim 14, wherein the method comprises the following steps: the early warning module calculates the ratio of the high carbon emission supervision small area to the total carbon emission supervision small area, marks the ratio as RR, obtains the population flow value in the carbon emission supervision area, marks the population flow value as PM, and calculates and obtains the future risk coefficient RC through a formula according to the ratio RR of the population flow value PM to the total carbon emission supervision small area, wherein the specific calculation expression is as follows;
RC＝lnαRR+βPM+1
wherein, alpha and beta are respectively preset proportionality coefficients of the ratio RR of the small area of high carbon emission supervision to the small area of total carbon emission supervision and the population flow value PM, and alpha > beta >0.
16. The method for monitoring, analyzing and modeling the carbon emission of energy power according to claim 15, wherein the method comprises the following steps: the early warning module compares the future risk coefficient RC with a standard risk threshold value:
if the future risk coefficient RC is greater than or equal to the standard risk threshold, marking the carbon emission supervision area as a high risk area, and carrying out early warning prompt;
if the future risk coefficient RC is less than the standard risk threshold, the carbon emission regulatory region is marked as a low risk region.
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