CN116051335A - Environmental carbon management method through big data prediction direction - Google Patents
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Abstract
The invention relates to the field of carbon emission management, in particular to an environmental carbon management method for predicting a direction through big data, which comprises the following steps: dividing regions, collecting carbon emission and carbon absorption in each region through a data collection terminal, and then constructing a region carbon emission model and a region carbon absorption model; constructing a regional carbon emission prediction model, and defining a regional carbon emission threshold; combining the regional carbon emission prediction models to form a comprehensive carbon emission prediction model, and defining a comprehensive carbon emission threshold; and comparing the carbon emission quantity predicted by the carbon emission prediction model with a carbon emission threshold value, and giving out early warning when the predicted carbon emission quantity exceeds the carbon emission threshold value. According to the method, the carbon emission models are respectively constructed for the carbon data in different areas by dividing the areas, and then the area carbon emission models are combined to generate the comprehensive carbon emission model, so that double guarantee of area carbon emission management is realized, and the carbon emission amount of the area is regulated and controlled in time.
Description
Technical Field
The invention relates to the field of carbon emission management, in particular to an environmental carbon management method for predicting a direction through big data.
Background
In the exhaust emission process, the existing energy types are easy to exceed standard and steal, and carbon emission is beyond standard because of untimely equipment updating, and a supervision unit cannot accurately monitor the carbon emission process of an enterprise through the prior art, so that early warning and control of the carbon emission of the enterprise cannot be realized.
Disclosure of Invention
The invention aims to provide an environmental carbon management method for predicting a direction through big data so as to solve the defects in the background technology.
The technical scheme adopted by the invention is as follows:
provided is an environmental carbon management method by predicting a direction by big data, comprising the steps of:
s1.1: dividing areas, and collecting carbon emission and carbon absorption in each area through a data acquisition terminal;
s1.2: constructing a regional carbon emission model and a regional carbon absorption model according to the collected carbon emission and carbon absorption in each region;
s1.3: constructing a regional carbon emission prediction model according to the constructed regional carbon emission model and the regional carbon absorption model, and defining a regional carbon emission threshold;
s1.4: combining the regional carbon emission prediction models to form a comprehensive carbon emission prediction model, and defining a comprehensive carbon emission threshold;
s1.5: and comparing the predicted carbon emission amount of the comprehensive carbon emission prediction model with a comprehensive carbon emission threshold, and sending out early warning when the predicted comprehensive carbon emission amount exceeds the comprehensive carbon emission threshold.
As a preferred technical scheme of the invention: each area defined in the step S1.1 comprises a processor which is used for collecting and processing the carbon emission and carbon absorption in each area, uploading the carbon emission and carbon absorption in each area to the central processing unit and receiving the information of the central processing unit.
As a preferred technical scheme of the invention: the regional carbon emission model constructed in the S1.2 is as shown in the formula (1):
wherein I is the total carbon emission amount of the artificial system in the current area, I is the energy type of the current area, n is various consumption energy sources in the current area, G i The i-th energy consumption amount for the current area,
the carbon emission is consumed for various energy sources in the current area, P is the population size of the current area, I P Respiration carbon emission for current regional population, D i The unit quantity of the ith energy source in the current area is equal to the carbon emission quantity D P And breathing carbon emission for people in the current area.
As a preferred technical scheme of the invention: the regional carbon absorption model constructed in the S1.2 is as shown in the formula (2):
wherein C is the carbon absorption of the current regional ecosystem, j is the current regional ecological type, m is the ecological type of the current region,
total amount of carbon absorption for each ecology type in the current area, F j For the j-th ecological type area of the current area, D j And (5) the unit area uniform carbon absorption of the jth ecological type of the current area.
As a preferred technical scheme of the invention: obtaining a regional carbon emission amount according to the regional carbon emission model and the regional carbon absorption model, as in formula (3):
R x ＝I x -C x (3)
wherein R is x Zone carbon emissions for the x-th zone, I x Total amount of artificial system carbon emissions for the x-th block zone, C x Ecosystem carbon uptake for the x-th block region.
As a preferred technical scheme of the invention: the regional carbon emission prediction model constructed in the S1.3 is as shown in the formula (4):
therein, lnT x Predicting logarithm of carbon emission for an x-th block region, n is various consumption energy sources of the current region, a is a model error coefficient, I x Total amount of artificial system carbon emissions for the x-th block zone, C x Ecosystem carbon uptake for the x-th block region, G ix The ith energy consumption, K, of the xth block area x Producing a total value, P, for the region of the x-th block region x Population scale for the x-th block, a, b, c, d, e are index coefficients.
As a preferred technical scheme of the invention: the comprehensive carbon emission prediction model in the S1.4 is as shown in the formula (5):
wherein R is the predicted carbon emission of the x-th block region, T x Predicting carbon emission for the x-th block region, x being the x-th block region, y being the total number of divided regions, ω x Is a weight coefficient, wherein omega x Satisfy the following requirements
As a preferred technical scheme of the invention: and in S1.3 and S1.4, a regional carbon emission threshold and a comprehensive carbon emission threshold are defined, the carbon emission quantity predicted by the carbon emission prediction model is compared with the carbon emission threshold, and when the predicted carbon emission quantity exceeds the carbon emission threshold, an early warning is sent out through a visual screen.
As a preferred technical scheme of the invention: in the step S1.3, when the predicted regional carbon emission exceeds the regional carbon emission threshold, an early warning is sent out through a visual screen connected with the processor, and the visual screen connected with the processor displays the carbon emission of different energy types in the region according to the carbon emission level.
As a preferred technical scheme of the invention: in the step S1.4, when the predicted integrated carbon emission exceeds the integrated carbon emission threshold, an early warning is sent out through a visual screen connected with the central processing unit, and the visual screen connected with the central processing unit respectively displays the carbon emission of each area and the carbon emission of different energy types in each area in an arrangement mode according to the carbon emission order.
Compared with the prior art, the environmental carbon management method based on the big data prediction direction has the beneficial effects that:
by dividing the regions, carbon emission models are respectively constructed for the carbon data in different regions, and then the region carbon emission models are combined to generate a comprehensive carbon emission model, so that double guarantee of regional carbon emission management is realized, and the carbon emission of the region is regulated and controlled in time.
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FIG. 1 is a flow chart of a method of a preferred embodiment of the present invention.
Detailed Description
It should be noted that, under the condition of no conflict, the embodiments of the present embodiments and features in the embodiments may be combined with each other, and the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and obviously, the described embodiments 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.
Referring to fig. 1, a preferred embodiment of the present invention provides an environmental carbon management method for predicting a direction by big data, comprising the steps of:
s1.1: dividing areas, and collecting carbon emission and carbon absorption in each area through a data acquisition terminal; s1.2: constructing a regional carbon emission model and a regional carbon absorption model according to the collected carbon emission and carbon absorption in each region;
s1.3: constructing a regional carbon emission prediction model according to the constructed regional carbon emission model and the regional carbon absorption model, and defining a regional carbon emission threshold;
s1.4: combining the regional carbon emission prediction models to form a comprehensive carbon emission prediction model, and defining a comprehensive carbon emission threshold;
s1.5: and comparing the carbon emission quantity predicted by the carbon emission prediction model with a carbon emission threshold value, and giving out early warning when the predicted carbon emission quantity exceeds the carbon emission threshold value.
Each area defined in the step S1.1 comprises a processor which is used for collecting and processing the carbon emission and carbon absorption in each area, uploading the carbon emission and carbon absorption in each area to the central processing unit and receiving the information of the central processing unit.
The regional carbon emission model constructed in the S1.2 is as shown in the formula (1):
wherein I is the total carbon emission amount of the artificial system in the current area, I is the energy type of the current area, n is various consumption energy sources in the current area, G i The i-th energy consumption amount for the current area,
the carbon emission is consumed for various energy sources in the current area, P is the population size of the current area, I P Respiration carbon emission for current regional population, D i The unit quantity of the ith energy source in the current area is equal to the carbon emission quantity D P And breathing carbon emission for people in the current area.
The regional carbon absorption model constructed in the S1.2 is as shown in the formula (2):
wherein C is the carbon absorption of the current regional ecosystem, j is the current regional ecological type, m is the ecological type of the current region,
total amount of carbon absorption for each ecology type in the current area, F j For the j-th ecological type area of the current area, D j And (5) the unit area uniform carbon absorption of the jth ecological type of the current area.
Obtaining a regional carbon emission amount according to the regional carbon emission model and the regional carbon absorption model, as in formula (3):
R x ＝I x -C x (3)
wherein R is x Zone carbon emissions for the x-th zone, I x Total amount of artificial system carbon emissions for the x-th block zone, C x Ecosystem carbon uptake for the x-th block region.
The regional carbon emission prediction model constructed in the S1.3 is as shown in the formula (4):
therein, lnT x Predicting logarithm of carbon emission for an x-th block region, n is various consumption energy sources of the current region, a is a model error coefficient, I x Total amount of artificial system carbon emissions for the x-th block zone, C x Ecosystem carbon uptake for the x-th block region, G ix The ith energy consumption, K, of the xth block area x Producing a total value, P, for the region of the x-th block region x Population scale for the x-th block, a, b, c, d, e are index coefficients.
The comprehensive carbon emission prediction model in the S1.4 is as shown in the formula (5):
wherein R is the predicted carbon emission of the x-th block region, T x Predicting carbon emission for the x-th block region, x being the x-th block region, y being the total number of divided regions, ω x Is a weight coefficient, wherein omega x Satisfy the following requirements
And in the step S1.3 and the step S1.4, a regional carbon emission threshold and a comprehensive carbon emission threshold are defined, the carbon emission quantity predicted by the carbon emission prediction model is compared with the carbon emission threshold, and when the predicted carbon emission quantity exceeds the carbon emission threshold, an early warning is sent out through a visual screen.
In the step S1.3, when the predicted regional carbon emission exceeds the regional carbon emission threshold, an early warning is sent out through a visual screen connected with the processor, and the carbon emission of different energy types in the region is displayed in a arraying manner according to the carbon emission level.
In the step S1.4, when the predicted integrated carbon emission exceeds the integrated carbon emission threshold, an early warning is sent out through a visual screen connected with the central processing unit, and the visual screen connected with the central processing unit respectively displays the carbon emission of each area and the carbon emission of different energy types in each area in an arrangement mode according to the carbon emission order.
In this embodiment, taking environmental carbon management of a city as an example, the city is divided into five areas, namely an east area, a west area, a south area, a north area and a middle area.
The carbon emission amount mixed carbon absorption amounts of the east region, the west region, the south region, the north region and the middle region are respectively collected through the data collection terminals of the regions, and regional carbon emission models are respectively constructed according to the formula (1):
wherein I is the total carbon emission amount of the artificial system in the current area, I is the energy type of the current area, n is various consumption energy sources in the current area, G i The i-th energy consumption amount for the current area,
the carbon emission is consumed for various energy sources in the current area, P is the population size of the current area, I P Respiration carbon emission for current regional population, D i The unit quantity of the ith energy source in the current area is equal to the carbon emission quantity D P And breathing carbon emission for people in the current area.
The regional carbon absorption model is as in formula (2):
wherein C is the carbon absorption of the current regional ecosystem, j is the current regional ecological type, m is the ecological type of the current region,
total amount of carbon absorption for each ecology type in the current area, F j For the j-th ecological type area of the current area, D j And (5) the unit area uniform carbon absorption of the jth ecological type of the current area.
Regional carbon emissions, as in formula (3):
R x ＝I x -C x (3)
wherein R is x Zone carbon emissions for the x-th zone, I x Total amount of artificial system carbon emissions for the x-th block zone, C x Ecosystem carbon uptake for the x-th block region.
Obtaining regional carbon emission R of east region, west region, south region, north region and middle region 1 、R 2 、R 3 、R 4 And R is 5 。
And according to a regional carbon emission prediction model as (4):
therein, lnT x Predicting logarithm of carbon emission for an x-th block region, n is various consumption energy sources of the current region, a is a model error coefficient, I x Total amount of artificial system carbon emissions for the x-th block zone, C x Ecosystem carbon uptake for the x-th block region, G ix The ith energy consumption, K, of the xth block area x Producing a total value, P, for the region of the x-th block region x Population scale for the x-th block, a, b, c, d, e are index coefficients.
The method comprises the steps of predicting the carbon emission of five areas, comparing the carbon emission with the carbon emission threshold of the five areas, realizing real-time detection of the carbon emission of the five areas, when the carbon emission of a western area is detected to exceed the carbon emission threshold, carrying out early warning on a visual screen connected with a processor of the western area, and arranging and displaying the carbon emission of various energy types of the western area in a sequence from high to low, so that the energy types with high carbon emission can be conveniently and rapidly positioned, and corresponding measures can be taken, wherein the energy type with the highest carbon emission arrangement of the western area is the carbon emission of an automobile, and the method such as a limited-running parking space can be adopted for reducing the carbon emission of the automobile.
The data of the five areas are simultaneously uploaded to a central processing unit, and the central processing unit is used for predicting the model according to the comprehensive carbon emission, wherein the model is shown as the formula (5):
wherein T is x Predicting carbon emission for the x-th block region, x being the x-th block region, y being the total number of divided regions, ω x Is a weight coefficient, wherein omega x Satisfy the following requirements
And carrying out combined prediction on the regional carbon emission models of the five regions, sending out early warning through a visual screen connected with the central processing unit when the predicted comprehensive carbon emission exceeds the comprehensive carbon emission threshold, and respectively displaying the carbon emission of each region and the carbon emission of different energy types in each region in a arraying manner according to the carbon emission level sequence by the visual screen connected with the central processing unit. Such as a north zone, a south zone, a west zone, an east zone and a middle zone, which are arranged in order of high and low carbon emissions. And then the early warning that the carbon emission prediction is higher is transmitted to the north area, and the north area can timely save energy and reduce emission for adjustment. And double guarantees are formed through the regional carbon emission prediction model and the comprehensive carbon emission prediction model, so that the carbon emission of the region is managed.
It will be evident to those skilled in the art that the invention is not limited to the details of the foregoing illustrative embodiments, and that the present invention may be embodied in other specific forms without departing from the spirit or essential characteristics thereof. The present embodiments are, therefore, to be considered in all respects as illustrative and not restrictive, the scope of the invention being indicated by the appended claims rather than by the foregoing description, and all changes which come within the meaning and range of equivalency of the claims are therefore intended to be embraced therein. Any reference sign in a claim should not be construed as limiting the claim concerned.
Furthermore, it should be understood that although the present disclosure describes embodiments, not every embodiment is provided with a separate embodiment, and that this description is provided for clarity only, and that the disclosure is not limited to the embodiments described in detail below, and that the embodiments described in the examples may be combined as appropriate to form other embodiments that will be apparent to those skilled in the art.
Claims (8)
1. An environmental carbon management method for predicting a direction through big data is characterized in that: the method comprises the following steps:
s1.1: dividing areas, and collecting carbon emission and carbon absorption in each area through a data acquisition terminal;
s1.2: constructing a regional carbon emission model and a regional carbon absorption model according to the collected carbon emission and carbon absorption in each region;
s1.3: constructing a regional carbon emission prediction model according to the constructed regional carbon emission model and the regional carbon absorption model, and defining a regional carbon emission threshold;
s1.4: combining the regional carbon emission prediction models to form a comprehensive carbon emission prediction model, and defining a comprehensive carbon emission threshold;
s1.5: comparing the predicted carbon emission amount of the comprehensive carbon emission prediction model with a comprehensive carbon emission threshold, and sending out early warning when the predicted comprehensive carbon emission amount exceeds the comprehensive carbon emission threshold;
the regional carbon emission model constructed in the S1.2 is as shown in the formula (1):
wherein I is the total carbon emission amount of the artificial system in the current area, I is the energy type of the current area, n is various consumption energy sources in the current area, G i The i-th energy consumption amount for the current area,
the carbon emission is consumed for various energy sources in the current area, P is the population size of the current area, I P Respiration carbon emission for current regional population, D i The unit quantity of the ith energy source in the current area is equal to the carbon emission quantity D P And breathing carbon emission for people in the current area.
The regional carbon absorption model constructed in the S1.2 is as shown in the formula (2):
wherein C is the carbon absorption of the current regional ecosystem, j is the current regional ecological type, m is the ecological type of the current region,
total amount of carbon absorption for each ecology type in the current area, F j For the j-th ecological type area of the current area, D j And (5) the unit area uniform carbon absorption of the jth ecological type of the current area.
2. The method for environmental carbon management by big data prediction direction according to claim 1, wherein: each area defined in the step S1.1 comprises a processor which is used for collecting and processing the carbon emission and carbon absorption in each area, uploading the carbon emission and carbon absorption in each area to the central processing unit and receiving the information of the central processing unit.
3. The method for environmental carbon management by big data prediction direction according to claim 1, wherein: obtaining a regional carbon emission amount according to the regional carbon emission model and the regional carbon absorption model, as in formula (3):
R x ＝I x -C x (3)
wherein R is x Zone carbon emissions for the x-th zone, I x Total amount of artificial system carbon emissions for the x-th block zone, C x Ecosystem carbon uptake for the x-th block region.
4. A method of environmental carbon management by big data prediction direction according to claim 3, characterized in that: the regional carbon emission prediction model constructed in the S1.3 is as shown in the formula (4):
therein, lnT x Predicting logarithm of carbon emission for an x-th block region, n is various consumption energy sources of the current region, a is a model error coefficient, I x Total amount of artificial system carbon emissions for the x-th block zone, C x Ecosystem carbon uptake for the x-th block region, G ix The ith energy consumption, K, of the xth block area x Producing a total value, P, for the region of the x-th block region x Population scale for the x-th block, a, b, c, d, e are index coefficients.
5. The method for environmental carbon management by big data prediction direction according to claim 4, wherein: the comprehensive carbon emission prediction model in the S1.4 is as shown in the formula (5):
wherein R is the predicted carbon emission of the x-th block region, T x Predicting carbon emission for the x-th block region, x being the x-th block region, y being the total number of divided regions, ω x Is a weight coefficient, wherein omega x Satisfy the following requirements
6. The method for environmental carbon management by big data prediction direction according to claim 1, wherein: and in the step S1.3 and the step S1.4, a regional carbon emission threshold and a comprehensive carbon emission threshold are defined, the carbon emission quantity predicted by the carbon emission prediction model is compared with the carbon emission threshold, and when the predicted carbon emission quantity exceeds the carbon emission threshold, an early warning is sent out through a visual screen.
7. The method for environmental carbon management by big data prediction direction according to claim 6, wherein: in the step S1.3, when the predicted regional carbon emission exceeds the regional carbon emission threshold, an early warning is sent out through a visual screen connected with the processor, and the visual screen connected with the processor displays the carbon emission of different energy types in the region according to the carbon emission level.
8. The method for environmental carbon management by big data prediction direction according to claim 7, wherein: in the step S1.4, when the predicted integrated carbon emission exceeds the integrated carbon emission threshold, an early warning is sent out through a visual screen connected with the central processing unit, and the visual screen connected with the central processing unit respectively displays the carbon emission of each area and the carbon emission of different energy types in each area in an arrangement mode according to the carbon emission order.
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| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN116957356A (en) * | 2023-07-25 | 2023-10-27 | 中国地质科学院岩溶地质研究所 | Scenic spot carbon neutralization management method and system based on big data |
Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| KR20160072861A (en) * | 2014-12-15 | 2016-06-24 | 주식회사 미래엔에스 | System for displaying carbon emission on the region planing model |
| CN109190211A (en) * | 2018-08-17 | 2019-01-11 | 中国科学院地理科学与资源研究所 | A method of appraising and deciding area alignment space |
| CN113962468A (en) * | 2021-10-29 | 2022-01-21 | 杭州青橄榄网络技术有限公司 | Energy consumption monitoring and statistics-based energy consumption carbon emission management method and system |
| CN114240463A (en) * | 2021-12-21 | 2022-03-25 | 合肥前卫科技有限公司 | Carbon emission monitoring and management system based on big data |
| CN115392757A (en) * | 2022-08-31 | 2022-11-25 | 中国长江三峡集团有限公司 | Quantitative evaluation method and device for carbon emission of town sewage treatment facility |
-
2022
- 2022-12-26 CN CN202211673689.5A patent/CN116051335A/en active Pending
Patent Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| KR20160072861A (en) * | 2014-12-15 | 2016-06-24 | 주식회사 미래엔에스 | System for displaying carbon emission on the region planing model |
| CN109190211A (en) * | 2018-08-17 | 2019-01-11 | 中国科学院地理科学与资源研究所 | A method of appraising and deciding area alignment space |
| CN113962468A (en) * | 2021-10-29 | 2022-01-21 | 杭州青橄榄网络技术有限公司 | Energy consumption monitoring and statistics-based energy consumption carbon emission management method and system |
| CN114240463A (en) * | 2021-12-21 | 2022-03-25 | 合肥前卫科技有限公司 | Carbon emission monitoring and management system based on big data |
| CN115392757A (en) * | 2022-08-31 | 2022-11-25 | 中国长江三峡集团有限公司 | Quantitative evaluation method and device for carbon emission of town sewage treatment facility |
Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN116957356A (en) * | 2023-07-25 | 2023-10-27 | 中国地质科学院岩溶地质研究所 | Scenic spot carbon neutralization management method and system based on big data |
| CN116957356B (en) * | 2023-07-25 | 2024-01-02 | 中国地质科学院岩溶地质研究所 | Scenic spot carbon neutralization management method and system based on big data |
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