CN115796385A - Multi-dimensional carbon accounting method, system, equipment and storage medium - Google Patents
Multi-dimensional carbon accounting method, system, equipment and storage medium Download PDFInfo
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Abstract
The application discloses a multi-dimensional carbon computing method, a system, equipment and a storage medium, wherein the method comprises the following steps: configuring basic information required by a carbon computing algorithm adapted to a target object; establishing a statistical dimension and an organization structure of the target object; automatically matching a carbon calculation algorithm applicable to each statistical dimension according to the organization structure; and acquiring data to be checked of the target object, checking through the carbon checking algorithm after the cooperativity check is completed according to the basic information, and outputting a carbon emission result. According to the multidimensional carbon accounting method, historical data are used for achieving automatic checking of the carbon emission data, so that the accounting result is more accurate, and the accuracy of the carbon accounting result is improved.
Description
Technical Field
The present application relates to the field of carbon computing technologies, and in particular, to a multidimensional carbon computing method, system, device, and storage medium.
Background
As a large household with carbon emission, the industrial industry needs to find out the carbon home base and make carbon planning. At present, industrial enterprises obtain carbon emission values of the enterprises through measurement and calculation by monitoring and collecting various carbon data related to carbon emission, such as energy consumption, pollution discharge, motor vehicle exhaust, environmental air quality, dangerous solid waste, personnel activities and the like of water, electricity and gas. However, the carbon emission accounting method cannot realize multidimensional accounting, an enterprise cannot effectively find key nodes or factors influencing self carbon emission when planning green energy-saving reconstruction, the reconstruction cost and the reconstruction effect are difficult to achieve the original target of the enterprise, and meanwhile, the accuracy of a carbon accounting result is low due to the missing or incomplete historical data.
Disclosure of Invention
The technical problem to be solved by the application is to provide a multi-dimensional carbon accounting method, a system, equipment and a storage medium for overcoming the defects that the multi-dimensional accounting cannot be realized by the carbon emission accounting method in the prior art and the accuracy of a carbon accounting result is low due to the missing or incomplete historical data.
The technical problem is solved by the following technical scheme:
the application provides a multidimensional carbon calculation method, which comprises the following steps:
configuring basic information required by a carbon computing algorithm adapted to a target object;
establishing a statistical dimension and an organization structure of the target object;
automatically matching a carbon calculation algorithm applicable to each statistical dimension according to the organization structure;
and acquiring data to be checked of the target object, checking through the carbon checking algorithm after the cooperativity check is completed according to the basic information, and outputting a carbon emission result.
Optionally, the multidimensional carbon calculation method further includes:
carbon emissions data are predicted using a carbon emissions prediction model.
Optionally, the basic information required for configuring the carbon computing algorithm adapted to the target object includes:
and automatically selecting policy standards applicable to the target object according to the area and industry of the target object, and configuring the basic information according to a general accounting guideline, wherein the basic information comprises emission factors and activity data calculation formulas related to the target object.
Optionally, the completing the collaborative check includes:
analyzing the cooperativity of the historical carbon emission data and the carbon dioxide emission of the target object to complete the verification of the data to be calculated;
the performing accounting by the carbon accounting algorithm includes:
when the variation amplitude of the data to be accounted is within an allowable variation range, accounting is carried out on the data to be accounted through the carbon accounting algorithm;
otherwise, feeding back to the target object for correction or supplement of the certification material.
Optionally, when the statistical dimension is a campus, the multidimensional carbon calculation method further includes:
acquiring public facilities of the park;
and aggregating the carbon emission data generated by enterprises belonging to each dimension under the garden, the carbon emission data generated by the electricity and heat used by the public facilities of the garden and the carbon emission data generated by the vegetation of the garden through photosynthesis to form the carbon emission data of the garden.
Optionally, the predicting carbon emission data by using the carbon emission prediction model includes:
generating a carbon row prediction model through time series prediction according to the historical carbon row data and the carbon row data subjected to the collaborative analysis;
and (3) carrying out graph display and trend prediction on the carbon emission data by using the carbon emission prediction model so as to realize carbon emission prediction on the years without guidelines.
The application provides a multidimensional carbon computing system, comprising:
the algorithm configuration module is used for configuring basic information required by a carbon computing algorithm adapted to the target object;
the architecture construction module is used for establishing the statistical dimension and the organization architecture of the target object;
the algorithm configuration module is further used for automatically matching the applicable carbon calculation algorithm of each statistical dimension according to the organization architecture;
and the checking and accounting module is used for acquiring data to be accounted of the target object, performing accounting through the carbon accounting algorithm after the cooperative checking is completed according to the basic information, and outputting a carbon emission result.
Optionally, the system further comprises a prediction module;
the prediction module is configured to predict carbon emission data using a carbon emission prediction model.
The present application further provides a computer device comprising a memory and a processor, the memory storing a computer program, the processor implementing the steps of the method when executing the computer program.
The present application also provides a computer-readable storage medium having stored thereon a computer program which, when executed by a processor, performs the steps of any of the methods described above.
According to the multidimensional carbon accounting method, basic information required by a carbon accounting algorithm adapted to a target object is configured; the statistical dimension and the organizational structure of the target object are established, so that enterprises can be helped to find out carbon home bottoms, key nodes or factors influencing carbon emission of the enterprises can be effectively found, and the cost of transformation is reduced; automatically matching a carbon calculation algorithm applicable to each statistical dimension according to the organization structure; acquiring data to be checked of the target object, checking through the carbon checking algorithm after completing the cooperative checking according to the basic information, outputting a carbon emission result, and using historical data to realize automatic checking of the carbon emission data, so that the checking result is more accurate, and the accuracy of the carbon checking result is improved.
Drawings
FIG. 1 is a flow chart of a multi-dimensional carbon computation method according to an embodiment of the present application;
FIG. 2 is a flow chart of a method for industrial enterprise-oriented multidimensional carbon computation in accordance with an embodiment of the present application;
FIG. 3 is a flow chart of a method of multidimensional carbon accounting for an iron and steel producing enterprise according to an embodiment of the present application;
FIG. 4 is part 5 of the "GB/T32151.5-2015 greenhouse gas emission accounting and reporting requirements: schematic diagram of accounting method of iron and steel production enterprise;
FIG. 5 is a graph of a simulation of the year and future carbon emission data of an iron and steel manufacturing enterprise;
FIG. 6 is a block schematic diagram of a multidimensional carbon accounting system in accordance with an embodiment of the present application;
FIG. 7 is a block diagram of a multidimensional carbon accounting system in accordance with an embodiment of the present application;
fig. 8 is a block diagram of an industrial enterprise-oriented multi-dimensional carbon computing system according to an embodiment of the present application.
Detailed Description
The present application is further illustrated by way of the following examples, which are not intended to limit the scope of the invention.
As shown in fig. 1, the present application provides a flow chart of a multidimensional carbon computing method, the multidimensional carbon computing method comprising the steps of:
s10, configuring basic information required by a carbon calculation algorithm adapted to a target object;
s11, establishing a statistical dimension and an organization structure of a target object;
s12, automatically matching a carbon calculation algorithm applicable to each statistical dimension according to an organization structure;
and S13, acquiring data to be checked of the target object, checking through a carbon checking algorithm after the cooperative check is completed according to the basic information, and outputting a carbon emission result.
The target object of the embodiment includes an industrial enterprise, and may also include enterprises of other industries. Therefore, the multidimensional carbon computing method of the embodiment can be suitable for multidimensional carbon computing methods for industrial enterprises and carbon computing in other industries.
In the multidimensional carbon calculation method of the embodiment, basic information required by a carbon calculation algorithm adapted to a target object is configured; the statistical dimension and the organizational structure of the target object are established, so that enterprises can be helped to find out carbon home bottoms, key nodes or factors influencing carbon emission of the enterprises can be effectively found, and the cost of transformation is reduced; automatically matching a carbon calculation algorithm applicable to each statistical dimension according to an organization structure; acquiring data to be checked of a target object, checking the data through a carbon checking algorithm after completing cooperative checking according to basic information, outputting a carbon emission result, and using historical data to realize automatic checking of the carbon emission data, so that the checking result is more accurate, and the accuracy of the carbon checking result is improved.
In an optional embodiment, the multidimensional carbon calculation method further includes: carbon emissions data are predicted using a carbon emissions prediction model. By performing trend prediction on carbon emission data, carbon emission trial calculation can be performed on the years without guidance.
In an alternative embodiment, step S10 includes: and automatically selecting policy standards applicable to the target object according to the area and industry where the target object is located, and configuring basic information according to the general accounting guideline, wherein the basic information comprises emission factors and activity data calculation formulas related to the target object. Taking an industrial enterprise as an example, by automatically selecting the policy standard applicable to the industrial enterprise and configuring the related emission factor and activity data calculation formula according to the general accounting guideline, the method can help the enterprise reduce the reporting workload of activity level data and accelerate the enterprise to know non-production carbon emission in real time.
In an alternative embodiment, steps S11 and S12 include: and establishing statistical dimensions and an organization structure required to be measured and calculated by the industrial enterprise, and automatically matching the carbon calculation algorithm suitable for each dimension according to the set structure. The method specifically comprises the following steps: performing carbon accounting according to the enterpriseThe required energy consumption granularity, and a multi-dimensional architecture, such as an enterprise-factory-workshop-production line-equipment five-level architecture, is established. Each level of the architecture adapts its applicable algorithm. When each level of architecture is adaptive to the applicable algorithm, the existing accounting result is multiplied by a correction coefficient a, and the value of a is the ratio of the consumption of a unit standard product to the consumption of the secondary product. So as to verify the calculated carbon emission result W 1 ,W 2 ,W 3 ,W 4 ,W 5 (corresponding to enterprise-factory-workshop-production line-equipment respectively) conforms to the accumulation relationship of the hierarchy in the architecture. If the highest dimensionality is the campus, the public facilities of the campus need to be filled and recorded. By quickly establishing and visually displaying the multi-dimensional architectural relationship of a park, an enterprise, a factory, a workshop and equipment, the quick multi-dimensional carbon accounting can be realized, the enterprise can be helped to find out the carbon home bottom, the understanding of the enterprise on real-time carbon emission is accelerated, and the control on the energy consumption granularity is deepened; the carbon calculation algorithm suitable for each dimension is automatically matched through the architecture so as to correct and configure the latest algorithm in the industry, the region and the enterprise, and the calculation data is ensured to meet the carbon emission report requirement.
In an alternative embodiment, the step S13 of completing the collaboration check includes: analyzing the cooperativity of the historical carbon emission data and the carbon dioxide emission of the target object to complete the verification of the data to be calculated; in step S13, performing accounting by a carbon accounting algorithm, including: when the variation amplitude of the data to be checked is within the allowable variation range, checking the data to be checked through a carbon checking algorithm; otherwise, the data is fed back to the target object for correction or supplement of the certification material.
Specifically, the basic data which is manually imported or automatically input by data acquisition is subjected to cooperative verification, and the basic data is the data to be calculated. By combining the regional data of carbon emission with CO for the enterprise in the past year 2 Analyzing the cooperativity of the emission, completing the verification of the rationality and consistency of the carbon emission data, entering an accounting stage if the variation amplitude of the carbon emission is within an allowable variation range, and feeding back to an enterprise for correction or supplement of a proving material if the variation amplitude is not within the allowable range. In particular, the synergistic analysis in this example is specificThe algorithm is that if the enterprise produces a product, the carbon emission is C 1 Amplitude of variation of n 1 And if the variation amplitude is within n, the carbon emission accounting of the enterprise is reasonable. The specific variation amplitude calculation method comprises the following steps:
in the formula, C 0 The result of the last change period calculation; x is the number of i The amount of the ith production fuel; y is i The loss factor is the ratio of the consumption of the ith production fuel in a unit standard product to the consumption of the second production; k is a radical of i Indicating the quality of the i < th > production fuel; j is a function of i Represents a unit emission factor of the ith production fuel; e is the amount of electricity consumed; e.g. of the type f Is the unit electric quantity discharge factor of the region.
The variation range n is set by the enterprise, and the value includes a numerical value less than or equal to 3 and can also include other numbers more than 3.
In an optional embodiment, when the statistical dimension is a campus, the multidimensional carbon calculation method further includes: acquiring public facilities of a park; and aggregating carbon row data generated by enterprises of various dimensions under the garden, carbon row data generated by using electricity and heat for public facilities of the garden and carbon row data generated by photosynthesis of vegetation of the garden to form the carbon row data of the garden.
Specifically, the carbon dioxide emission of the enterprise in the past year is checked, and a check report is made. Particularly, if the highest level is the campus level, the accounting result is obtained by multi-dimensional low-dimensional data aggregation. And for the carbon emission data subjected to data verification through collaborative analysis, automatically generating a carbon emission report in a corresponding organization structure based on a configured algorithm, and displaying the established carbon factor and carbon emission of each dimension.
In particular, if there is no policy document at the current campus levelThe method is suitable for obtaining the algorithm selected by the scheme by aggregating all sub-dimension data under the park. The specific polymerization method comprises the following steps: the carbon emission T of the garden is divided into three categories, namely the production of the carbon emission T 1 Public carbon row T 2 Green carbon sink T 3 . The carbon emission of the park is the sum of three types of carbon emission. Wherein the production carbon emission is the sum of carbon emission results of all subordinate dimensional enterprises; the public carbon emission is generated by using electricity and heat for public facilities in the garden, and the carbon emission is calculated by adopting the ipcc (Special committee for climate change between governments of the United nations) standard; the green carbon sink is the amount of carbon dioxide absorbed by the vegetation in the garden through photosynthesis, and is calculated according to the forestry carbon sink metering method. The aggregation formula of the data of each sub-dimension is as follows:
T＝T 1 +T 2 -T 3
T 3 ＝v f ×δ×ρ×γ
in the formula M i The final carbon emission results for each enterprise in the campus of step S11; AD i Namely unit energy consumption of public facilities; EF i Emission factors corresponding to public facility energy; GWP is global warming tendency and can be inquired in ipcc guidelines; v. of f The storage capacity of the trees is shown, delta is a biomass expansion coefficient, rho is volume density, and gamma is carbon content.
In an alternative embodiment, the predicting carbon emissions data using a carbon emissions prediction model comprises: generating a carbon row prediction model through time series prediction according to the historical carbon row data and the carbon row data subjected to the collaborative analysis; and (3) carrying out graph display and trend prediction on the carbon emission data by using the carbon emission prediction model so as to realize carbon emission prediction on the years without guidelines.
In this embodiment, various types of data are analyzed and evaluated on the obtained carbon emission data, and the carbon dioxide emission of the enterprise within a certain period of time in the future is checked according to the analysis and evaluation result. The method specifically comprises the following steps: and generating a carbon row prediction model through time series prediction according to the existing carbon counting results, namely the historical carbon counting results of the enterprises and the collaborative analysis result in the step S13. According to the carbon emission prediction model, the future carbon emission of the enterprise can be predicted more reasonably and accurately before the corresponding index file is not available.
The generation process for establishing the carbon emission prediction model comprises the following steps:
the first step is as follows: assume that X0 is the original non-negative sequence:
X 0 ＝{x (0,1) ,x (0,2) ,…,x (0,n) }
wherein x (0,k) ≥0，k＝1,2,…,25
Generating the sequence by accumulation
X 0 ＝{x (0,1) ,x (0,2) ,…,x (0,n) }
Generating sequence X 1
X 1 ＝{x (1,1) ,x (1,2) ,…,x (1,n) }
Wherein
The second step is that: using a previously generated sequence X 1 General form of modeling:
x (0,k) +a*z (1,k) ＝b (1)
the differential equation is expressed as follows:
z1 is the immediate mean generation sequence of X1:
Z 1 ＝{z (1,1) ,z (1,2) ,…,z (1,n) }
wherein
z (1,k) ＝0.5*x (1,k) +0.5*x (1,k-1) ，k＝1,2,…,25 (3)
A and b in the gray differential equation model are parameters to be estimated, namely development recovery number and endogenous control recovery number respectively; the least squares estimation parameter column of the gray differential equation satisfies:
σ＝(a,b) T ＝(B T *B) - 1*B T *Y (4)
y is a column vector and B is a construction matrix
The third step: construction of carbon prediction model
And (2) solving the equation by combining the (1), (3) and (4) to obtain:
because of the fact that
x (1,0) ＝x (0,1)
The carbon emission prediction model is established as follows
The fourth step: and (3) solving a reduction value of the original data to obtain a carbon prediction model:
x (0,k+1) ＝x (1,k+1) -x (1,k) ，k＝1,2,…,n
the multidimensional carbon calculation method of the embodiment makes full use of a new generation interconnection technology, establishes a carbon prediction model through analysis of regional energy consumption, carbon emission, enterprise historical carbon calculation results and production consumption, completes trial calculation of carbon calculation in the years without guidelines and in the future, helps improve utilization efficiency of various energy sources by enterprises, and promotes development of low-carbon manufacturing by taking a clean, low-carbon, safe and efficient way of utilization.
In an alternative embodiment, as shown in fig. 2, a method for multidimensional carbon computation for industrial enterprises comprises the following steps:
s21, selecting an accounting method and configuring an algorithm;
s22, determining an accounting framework, and performing algorithm self-adaptation;
s23, checking the data of the accounting result; the data verification process is the same as the above-mentioned cooperative verification, and is not described herein again.
Step S24, issuing a carbon emission report;
step S25, analyzing carbon number data;
and S26, establishing a carbon prediction model. The specific model building process is the same as above and will not be described herein again.
Taking a certain steel production enterprise as an example, the implementation process of the multidimensional carbon accounting method is specifically introduced as follows: the iron and steel production enterprise has a plurality of sub-factories, the energy granularity for counting the carbon emission is only in a factory level, and the multidimensional carbon calculation step for the sub-factories is shown in figure 3 and comprises the following steps:
s31: and (4) configuring the calculation modes involved in the guide by utilizing an algorithm configuration tool according to the industry and the region where the enterprise is located to obtain carbon emission data generated by various energy sources. Specifically, the requirements of GB/T32151.5-2015 on greenhouse gas emission accounting and reporting requirement part 5 are selected: iron and steel manufacturing enterprises, refer to fig. 4, the enterprises use production fuels such as tar, crude benzene, coke, anthracite, limestone, dolomite, pig iron and the like in daily production, and buy electric power and heat for equipment operation and configure the calculation modes involved in the guidelines by using an algorithm configuration tool to obtain carbon emission data generated by various energy sources. Fig. 4 shows that the clean coal and the like of the iron and steel manufacturing enterprise form sinter and pellet through a coking process and a sintering/pelletizing process, form molten iron through an iron making process, form rough steel through a refining process, and form steel through a steel rolling process.
And S32, establishing an enterprise-factory-production line three-level dimensional architecture according to the energy granularity required to be counted by the enterprise, specifically, referring to FIG. 4, knowing that the enterprise has five production lines of coking, sintering, iron making, refining and steel rolling, and accordingly establishing the enterprise-factory-production line three-level dimensional architecture.
And S33, performing collaborative analysis according to the carbon emission data published in the year of the area where the enterprise is located and the carbon emission data transmitted by the enterprise, and completing data verification. And (4) performing synergistic analysis, namely analyzing the energy consumption, the enterprise yield and the regional carbon emission level.
And S34, automatically generating a carbon emission report for carbon emission data based on a configured algorithm and a configured framework, respectively displaying fuel combustion emission, process emission, purchase power emission, output power emission and solid carbon product emission of two dimensions of an enterprise and a factory, and summarizing and displaying total carbon emission.
And S35, carrying out charting statistical analysis based on the carbon calculation result of the enterprise.
And S36, establishing a carbon emission prediction model through time series prediction based on the historical carbon calculation results of the enterprise and the cooperative analysis results in the step S33, and calculating and predicting the current year and future carbon emission of the enterprise. The predictions made from the established carbon rejection prediction model are shown in table 1 below:
TABLE 1 simulation results of carbon rejection prediction model
As can be seen from table 1, the carbon emission data y simulation value predicted by using the carbon emission prediction model can realize the graph display and trend prediction of the carbon emission data.
The simulation curve in fig. 5 can be derived from the data of table 1. It can be seen from fig. 5 that the curve of carbon row data predicted by using the carbon row prediction model (2 GM (1,1 simulation curve) in fig. 5) is substantially consistent with the curve of original carbon row data (original curve 1 in fig. 5), thereby realizing graph display and trend prediction of carbon row data.
Meanwhile, the future lunar related prediction results obtained by using the carbon emission prediction model are shown in the following table 2:
TABLE 2 prediction of y-value for carbon rejection prediction model
As can be seen from table 2, carbon emissions can be calculated for the non-guideline years using the carbon emissions prediction model.
As shown in fig. 6, the present application provides a multi-dimensional carbon computing system comprising:
the algorithm configuration module 1 is used for configuring basic information required by a carbon computing algorithm adapted to a target object;
the architecture construction module 2 is used for establishing the statistical dimension and the organization architecture of the target object;
the algorithm configuration module 1 is further configured to automatically match the carbon computation algorithm applicable to each statistical dimension according to the organization structure;
and the checking and accounting module 3 is used for acquiring data to be accounted of the target object, performing accounting through a carbon accounting algorithm after completing the cooperative checking according to the basic information, and outputting a carbon emission result.
The target object of the embodiment includes an industrial enterprise, and may also include enterprises of other industries. Therefore, the multidimensional carbon computing system of the embodiment is applicable to multidimensional carbon computing systems for industrial enterprises and carbon computing in other industries.
In the multidimensional carbon accounting system of the embodiment, basic information required by a carbon accounting algorithm adapted to a target object is configured through an algorithm configuration module 1; the framework building module 2 builds the statistical dimension and the organizational framework of the target object, can help enterprises to find out carbon home bottoms, effectively find key nodes or factors influencing self carbon emission, and reduce the cost of transformation; the algorithm configuration module 1 also automatically matches the carbon calculation algorithm applicable to each statistical dimension according to the organization structure; the checking and accounting module 3 collects data to be accounted of the target object, performs accounting through a carbon accounting algorithm after completing collaborative checking according to basic information, outputs a carbon emission result, and uses historical data to realize automatic checking of the carbon emission data, so that the accounting result is more accurate, and the accuracy of the carbon accounting result is improved.
In an alternative embodiment, as shown in fig. 7, the system further comprises a prediction module 4; the prediction module 4 is configured to predict carbon emission data using a carbon emission prediction model. The trend prediction of the carbon emission data is carried out through the prediction module 4, and the carbon emission trial calculation can be carried out on the years without guidelines.
The following is a specific example: as shown in fig. 8, an industrial enterprise-oriented multi-dimensional carbon computing system includes: the system comprises an algorithm configuration unit 11, an architecture construction unit 21, a data acquisition unit 31, an entry verification unit 32, a carbon emission accounting unit 33 and an analysis prediction unit 41. The algorithm configuration module 1 comprises an algorithm configuration unit 11, the architecture construction module 2 comprises an architecture construction unit 21, the verification and accounting module 3 comprises a data acquisition unit 31, an entry verification unit 32 and a carbon emission accounting unit 33, and the prediction module 4 comprises an analysis prediction unit 41.
The algorithm configuration unit 11 is used for adaptive configuration of the carbon computing method. In an alternative embodiment, the algorithm configuration unit 11 is configured to automatically select policy criteria applicable to the target object according to the area and industry where the target object is located, and configure basic information according to a general accounting guideline, where the basic information includes an emission factor and an activity data calculation formula related to the target object. Taking an industrial enterprise as an example, by automatically selecting the policy standard applicable to the industrial enterprise and configuring the related emission factor and activity data calculation formula according to the general accounting guideline, the method can help the enterprise reduce the reporting workload of activity level data and accelerate the enterprise to know non-production carbon emission in real time.
The architecture construction unit 21 is used for the architecture construction of the industrial enterprise organization architecture and the required accounting range. In an alternative embodiment, the architecture construction unit 21 establishes statistical dimensions and an organizational architecture required to be measured and calculated by the industrial enterprise, and the algorithm configuration unit 11 is further configured to automatically match a carbon computing algorithm applicable to each dimension according to the set architecture. The method specifically comprises the following steps: and establishing a multi-dimensional architecture, such as an enterprise-factory-workshop-production line-equipment five-level architecture, according to the energy consumption granularity required by the enterprise for carbon calculation. Each level of the architecture adapts its applicable algorithm. When each stage of the architecture adapts to the algorithm it is adapted to, it will adapt toThe existing accounting result is multiplied by a correction coefficient a, and the value of a is the ratio of the consumption of the unit standard product to the consumption of the secondary product. So as to verify the calculated carbon emission result W 1 ,W 2 ,W 3 ,W 4 ,W 5 (corresponding to enterprise-factory-workshop-production line-equipment respectively) conforms to the accumulation relationship of the hierarchy in the architecture. If the highest dimensionality is the park, the public facilities of the park need to be filled and recorded. By quickly establishing and visually displaying the multi-dimensional architectural relationship of a park, an enterprise, a factory, a workshop and equipment, the quick multi-dimensional carbon accounting can be realized, the enterprise can be helped to find out the carbon home bottom, the understanding of the enterprise on real-time carbon emission is accelerated, and the control on the energy consumption granularity is deepened; the carbon calculation algorithm suitable for each dimension is automatically matched through the architecture so as to correct and configure the latest algorithm in the industry, the region and the enterprise, and the calculation data is ensured to meet the carbon emission report requirement.
The data acquisition unit 31 is used for collecting the accounting data of the industrial enterprise;
the entry verification unit 32 is configured to implement automatic verification on the carbon emission data, so that the accounting result is more accurate. In an optional embodiment, the entry verification unit 32 is configured to analyze the cooperativity between the historical carbon emission data and the carbon dioxide emission of the target object, and complete verification of the data to be calculated; when the variation amplitude of the data to be checked is within the allowable variation range, checking the data to be checked through a carbon checking algorithm; otherwise, the data is fed back to the target object for correction or supplement of the certification material.
Specifically, the basic data which is manually imported or automatically input by data acquisition is subjected to cooperative verification, and the basic data is the data to be calculated. By combining the regional data of carbon emission with CO for the enterprise in the past year 2 Analyzing the cooperativity of the emission, completing the verification of the rationality and consistency of the carbon emission data, entering an accounting stage if the variation amplitude of the carbon emission is within an allowable variation range, and feeding back to an enterprise for correction or supplement of a proving material if the variation amplitude is not within the allowable range. Specifically, the algorithm of the synergy analysis in this embodiment is that if the enterprise produces a product, the carbon emission is C 1 Variations inAmplitude of n 1 And if the variation amplitude is within n, the carbon emission accounting of the enterprise is reasonable. The specific variation amplitude calculation method is as described above, and is not described herein again.
And the carbon emission accounting unit 33 is used for accounting the carbon dioxide emission of the industrial enterprises. In an alternative embodiment, when the statistical dimension is a park, the carbon dioxide row accounting unit 33 is further configured to acquire the utility of the park; and aggregating carbon row data generated by enterprises of various dimensions under the garden, carbon row data generated by using electricity and heat for public facilities of the garden and carbon row data generated by photosynthesis of vegetation of the garden to form the carbon row data of the garden.
Specifically, the carbon dioxide emission of the enterprise in the past year is checked, and a check report is made. Particularly, if the highest level is the campus level, the accounting result is obtained by multi-dimensional low-dimensional data aggregation. And automatically generating a carbon row report in a corresponding organization structure based on a configured algorithm for the carbon row data subjected to data verification through collaborative analysis, and displaying the established carbon factor and carbon emission of each dimension.
Particularly, if no policy file specifies an applicable algorithm at the current campus level, the algorithm selected by the scheme is obtained by aggregating all sub-dimension data belonging to the campus. The specific polymerization method comprises the following steps: the carbon emission T of the garden is divided into three categories, namely the production of the carbon emission T 1 Public carbon row T 2 Green carbon sink T 3 . The carbon emission of the park is the sum of the three types of carbon emission. Wherein the production carbon emission is the sum of carbon emission results of all subordinate dimensional enterprises; the public carbon row is generated by electricity and heat utilization of public facilities in the park, and the calculation is carried out by adopting an ipcc standard; the green carbon sink is the amount of carbon dioxide absorbed by the vegetation in the garden through photosynthesis, and is calculated according to the forestry carbon sink metering method. The respective sub-dimension data aggregation formulas are as described above, and are not described herein again.
The analysis prediction unit 41 is used for analyzing the collected carbon emission data, includes an analysis prediction model, and can predict the subsequent carbon emission data. In an alternative embodiment, the analysis and prediction unit 41 is further configured to: generating a carbon row prediction model through time series prediction according to the historical carbon row data and the carbon row data subjected to the collaborative analysis; and (3) carrying out graph display and trend prediction on the carbon emission data by using the carbon emission prediction model so as to realize carbon emission prediction on the years without guidelines.
In this embodiment, various types of data are analyzed and evaluated on the obtained carbon emission data, and the carbon dioxide emission of the enterprise within a certain period of time in the future is checked according to the analysis and evaluation result. The method specifically comprises the following steps: and generating a carbon row prediction model through time series prediction according to the existing carbon calculation results, namely the historical carbon calculation results and the cooperativity analysis results of the enterprises. According to the carbon emission prediction model, the future carbon emission of the enterprise can be predicted more reasonably and accurately before the corresponding index file is not available.
The generation process of the carbon emission prediction model is the same as the above, and is not described herein again.
The multidimensional carbon accounting system of the embodiment makes full use of a new generation interconnection technology, establishes a carbon prediction model through analysis of regional energy consumption, carbon emission, enterprise historical carbon accounting results and production consumption, completes carbon accounting trial calculation for years without guidelines and in the future, helps improve utilization efficiency of enterprises to various energy sources, and promotes development of low-carbon manufacturing by taking a clean, low-carbon, safe and efficient way of utilization.
In one embodiment, a computer device is provided, comprising a memory storing a computer program and a processor executing the steps of the above-described method embodiments.
In one embodiment, a computer-readable storage medium is provided, on which a computer program is stored which, when being executed by a processor, carries out the steps of the above-mentioned method embodiments.
It will be understood by those skilled in the art that all or part of the processes of the methods of the embodiments described above can be implemented by hardware related to instructions of a computer program, which can be stored in a non-volatile computer-readable storage medium, and when executed, can include the processes of the embodiments of the methods described above. Any reference to memory, database, or other medium used in the embodiments provided herein may include at least one of non-volatile and volatile memory. The nonvolatile Memory may include Read-only Memory (ROM), magnetic tape, floppy disk, flash Memory, optical Memory, high-density embedded nonvolatile Memory, resistive Random Access Memory (ReRAM), magnetic Random Access Memory (MRAM), ferroelectric Random Access Memory (FRAM), phase Change Memory (PCM), graphene Memory, and the like. Volatile Memory can include Random Access Memory (RAM), external cache Memory, and the like. By way of illustration and not limitation, RAM can take many forms, such as Static Random Access Memory (SRAM) or Dynamic Random Access Memory (DRAM), for example. The databases referred to in various embodiments provided herein may include at least one of relational and non-relational databases. The non-relational database may include, but is not limited to, a block chain based distributed database, and the like. The processors referred to in the embodiments provided herein may be general purpose processors, central processing units, graphics processors, digital signal processors, programmable logic devices, quantum computing based data processing logic devices, etc., without limitation.
The technical features of the above embodiments can be arbitrarily combined, and for the sake of brevity, all possible combinations of the technical features in the above embodiments are not described, but should be considered as the scope of the present specification as long as there is no contradiction between the combinations of the technical features.
While specific embodiments of the invention have been described above, it will be appreciated by those skilled in the art that these are by way of example only, and that the scope of the invention is defined by the appended claims. Various changes and modifications to these embodiments may be made by those skilled in the art without departing from the spirit and scope of the invention, and these changes and modifications are within the scope of the invention.
Claims (10)
1. A method of multidimensional carbon computation, comprising:
configuring basic information required by a carbon computing algorithm adapted to a target object;
establishing a statistical dimension and an organization structure of the target object;
automatically matching a carbon calculation algorithm applicable to each statistical dimension according to the organization structure;
and acquiring data to be checked of the target object, checking through the carbon checking algorithm after the cooperativity check is completed according to the basic information, and outputting a carbon emission result.
2. A multidimensional carbon calculation method as recited in claim 1, further comprising:
carbon emissions data are predicted using a carbon emissions prediction model.
3. The multidimensional carbon computing method of claim 2, wherein the configuring of the basic information required for the target object adapted carbon computing algorithm comprises:
and automatically selecting policy standards applicable to the target object according to the area and industry where the target object is located, and configuring the basic information according to a general accounting guideline, wherein the basic information comprises emission factors and activity data calculation formulas related to the target object.
4. The multi-dimensional carbon computing method of claim 3, wherein the completing a collaborative check comprises:
analyzing the cooperativity of the historical carbon emission data and the carbon dioxide emission of the target object to complete the verification of the data to be calculated;
the performing accounting by the carbon accounting algorithm includes:
when the variation amplitude of the data to be accounted is within an allowable variation range, accounting is carried out on the data to be accounted through the carbon accounting algorithm;
otherwise, feeding back to the target object for correction or supplement of certification materials.
5. The multidimensional carbon calculation method of claim 4, wherein when the statistical dimension is a campus, the multidimensional carbon calculation method further comprises:
acquiring public facilities of the park;
and aggregating carbon row data generated by enterprises of various dimensions under the park, carbon row data generated by using electricity and heat for public facilities of the park and carbon row data generated by photosynthesis of the vegetation of the park to form the carbon row data of the park.
6. The multidimensional carbon computation method of claim 2, wherein the predicting carbon row data using a carbon row prediction model comprises:
generating a carbon row prediction model through time series prediction according to the historical carbon row data and the carbon row data subjected to the collaborative analysis;
and (3) carrying out graph display and trend prediction on the carbon emission data by using the carbon emission prediction model so as to realize carbon emission prediction on the years without guidelines.
7. A multi-dimensional carbon computing system, comprising:
the algorithm configuration module is used for configuring basic information required by a carbon computing algorithm adapted to the target object;
the architecture construction module is used for establishing the statistical dimension and the organization architecture of the target object;
the algorithm configuration module is further used for automatically matching the applicable carbon calculation algorithm of each statistical dimension according to the organization architecture;
and the checking and accounting module is used for acquiring data to be accounted of the target object, performing accounting through the carbon accounting algorithm after the cooperative checking is completed according to the basic information, and outputting a carbon emission result.
8. The multi-dimensional carbon computing system of claim 7, wherein the system further comprises a prediction module;
the prediction module is configured to predict carbon emission data using a carbon emission prediction model.
9. A computer device comprising a memory and a processor, the memory storing a computer program, characterized in that,
the processor, when executing the computer program, realizes the steps of the method of any one of claims 1 to 7.
10. A computer-readable storage medium, on which a computer program is stored, which, when being executed by a processor, carries out the steps of the method according to any one of claims 1 to 7.
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Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN116542430A (en) * | 2023-07-07 | 2023-08-04 | 红杉天枰科技集团有限公司 | Multi-dimensional water carbon emission intelligent analysis method and system |
| CN117992806A (en) * | 2024-04-07 | 2024-05-07 | 中清能源(杭州)有限公司 | Carbon accounting method based on time sequence data analysis |
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Cited By (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN116542430A (en) * | 2023-07-07 | 2023-08-04 | 红杉天枰科技集团有限公司 | Multi-dimensional water carbon emission intelligent analysis method and system |
| CN116542430B (en) * | 2023-07-07 | 2024-01-26 | 红杉天枰科技集团有限公司 | Multi-dimensional water carbon emission intelligent analysis method and system |
| CN117992806A (en) * | 2024-04-07 | 2024-05-07 | 中清能源(杭州)有限公司 | Carbon accounting method based on time sequence data analysis |
| CN117992806B (en) * | 2024-04-07 | 2024-06-04 | 中清能源(杭州)有限公司 | Carbon accounting method based on time sequence data analysis |
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