CN117591619A - Method, system, equipment and medium for identifying double high-temperature hot spot grids of polluted carbon - Google Patents
Method, system, equipment and medium for identifying double high-temperature hot spot grids of polluted carbon Download PDFInfo
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Abstract
The invention provides a method, a system, equipment and a medium for identifying a polluted carbon double-high-hot-spot grid, which relate to the technical field of atmosphere monitoring and comprise the following steps: dividing a target monitoring area into a plurality of grid cells; acquiring atmospheric pollutant intensity data and greenhouse gas intensity data of the area; respectively obtaining atmospheric pollutant intensity data of each grid cell, sequencing from high to low, and screening the previous grid cells which accord with a set threshold as pollutant high-value area grids; respectively obtaining greenhouse gas intensity data of each grid unit, respectively calculating greenhouse gas intensity enhancement values, sequencing from high to low, and screening the previous grid units which accord with a set threshold as grids of a greenhouse gas high-value region; and matching the pollutant high-value area grid with the greenhouse gas high-value area grid, and taking the high-value area grid overlapped in space as a pollution carbon dual-high grid. The invention improves the recognition accuracy of the grid with the double high hot spots of the polluted carbon and strengthens the cooperative monitoring and treatment means of the polluted carbon.
Description
Technical Field
The invention belongs to the technical field of atmosphere monitoring, and particularly relates to a method, a system, equipment and a medium for identifying a double-high-hot-spot grid of polluted carbon.
Background
In the field of pollutant monitoring and treatment, the atmospheric hot spot grid technology achieves remarkable effect. The hot spot grid technology combines meteorological data and air quality monitoring data through satellite remote sensing monitoring means, and divides urban pollution into different grid areas at high incidence, and small micro monitoring stations are built on each grid area to carry out air quality key supervision according to the height of real-time monitoring air quality data. For greenhouse gas monitoring, mainly means such as satellite monitoring, ground monitoring and navigation monitoring are relied on, besides satellites, the greenhouse gas ground monitoring, the navigation monitoring and the like mainly exist in part of carbon monitoring evaluation test point cities, and for cities without equipment, the greenhouse gas monitoring system is weak, and the alternative method is that the satellite remote sensing technology is used for carrying out change analysis and high-value area analysis.
Greenhouse gas emission and pollutant emission have high homology, and the current carbon-sewage cooperative treatment work is mainly in the policy making and measure research stage, and how to monitor and assist in treatment is also a newer research field. The regional pollution carbon collaborative monitoring technology currently has the following problems:
1. the atmospheric pollutant hot spot grid technology can lock a pollutant high emission area to a kilometer grid, but the traceability analysis is also lacking;
2. greenhouse gas monitoring techniques have been demonstrated to monitor the concentration enhancement caused by high emissions sources, but lack techniques similar to atmospheric hot spot grids to enhance sheet differentiation and management, only with long time-series concentration variation analysis and regional spatial distribution;
3. unlike atmospheric pollutants, as a long life cycle gas, a greenhouse gas concentration high value zone may exist in a large area of the high value zone, so that it is difficult to locate a real emission source only through the high value zone, and a reliable tracing algorithm and auxiliary data are required to be combined.
Therefore, the method for cooperatively monitoring the polluted carbon can simultaneously improve the monitoring level of pollutants and greenhouse gases and strengthen the cooperative monitoring treatment means of the polluted carbon.
Disclosure of Invention
In view of this, the embodiment of the application provides a method for identifying a grid with two high hot spots of carbon dioxide, so as to achieve the purposes of improving the accuracy of identifying the grid with two high hot spots of carbon dioxide and strengthening the cooperative monitoring and treatment means of carbon dioxide.
The embodiment of the application provides the following technical scheme: a method for identifying a polluted carbon double-high-temperature spot grid comprises the following steps:
dividing a target monitoring area into a plurality of grid cells;
acquiring satellite remote sensing monitoring data of the target monitoring area, wherein the satellite remote sensing monitoring data comprise atmospheric pollutant intensity data and greenhouse gas intensity data;
according to the position of the region corresponding to the satellite remote sensing monitoring data, matching the atmospheric pollutant intensity data with each grid cell to obtain atmospheric pollutant intensity data corresponding to each grid cell, sorting the atmospheric pollutant intensity data corresponding to each grid cell from high to low, and screening the grid cells which are in front and accord with a set threshold as pollutant high-value region grids;
according to the position of the region corresponding to the satellite remote sensing monitoring data, matching the greenhouse gas intensity data with each grid cell to obtain greenhouse gas intensity data corresponding to each grid cell, calculating to obtain a greenhouse gas intensity enhancement value corresponding to each grid cell according to the greenhouse gas intensity data corresponding to each grid cell, sorting the greenhouse gas intensity enhancement values from high to low, and screening the grid cells which are in front of the sorting and accord with a set threshold value as greenhouse gas high-value region grids;
and matching the pollutant high-value area grid with the greenhouse gas high-value area grid, and taking the high-value area grid overlapped in space as a carbon pollution double high grid.
According to an embodiment of the present application, the pollutant high-value area grid and the greenhouse gas high-value area grid are matched, and the high-value area grid which is overlapped in space is used as a carbon pollution double high grid, and further including:
selecting a grid cell with a distance within a set distance range from the edge of the carbon dioxide double high grid, and combining the selected grid cell with the carbon dioxide double high grid to serve as a target grid cell;
determining multiple multi-source auxiliary data according to emission source identification influence factors;
and respectively calculating the total score of the multiple multi-source auxiliary data of each target grid unit according to the set score rule of each multi-source auxiliary data, sorting the total score from high to low, screening the target grid units which are sorted in front and accord with the set threshold, and taking the screened target grid units as the final sewage carbon double high-hot spot grid.
According to one embodiment of the present application, the plurality of multi-source assistance data includes: land utilization data, traffic road network data, enterprise point location POI data, night light data and ground surface temperature data of the target monitoring area.
According to an embodiment of the present application, according to the greenhouse gas intensity data corresponding to each grid cell, the calculating to obtain the strong greenhouse gas intensity increment corresponding to each grid cell includes:
and starting convolution calculation on each grid unit in the target monitoring area one by one, and respectively calculating the difference value between the greenhouse gas intensity background value of the convolution matrix and the greenhouse gas intensity value of each grid unit to obtain the greenhouse gas intensity value corresponding to each grid unit.
According to one embodiment of the present application, the greenhouse gas intensity background value of the convolution matrix is the lowest greenhouse gas intensity value of the grid in the convolution matrix.
According to one embodiment of the present application, NO 2 Satellite remote sensing monitoring data is used as the atmospheric pollutant intensity data, and CO is used as the atmospheric pollutant intensity data 2 Satellite remote sensing monitoring data is used as the greenhouse gas intensity data.
According to one embodiment of the present application, the target monitoring area is divided into a plurality of grid cells, and further includes:
each grid cell is provided with a unique geographic number for index querying.
The application also provides a dirty carbon dual high-hot spot grid identification system, including:
the grid dividing module is used for dividing the target monitoring area into a plurality of grid units;
the data acquisition module is used for acquiring satellite remote sensing monitoring data of the target monitoring area, wherein the satellite remote sensing monitoring data comprise atmospheric pollutant intensity data and greenhouse gas intensity data;
the first screening module is used for matching the atmospheric pollutant intensity data with each grid unit according to the position of the area corresponding to the satellite remote sensing monitoring data to obtain atmospheric pollutant intensity data corresponding to each grid unit, sorting the atmospheric pollutant intensity data corresponding to each grid unit from high to low, and screening the grid units which are in front of the sorting and accord with a set threshold value as pollutant high-value area grids;
the second screening module is used for matching the greenhouse gas intensity data with each grid cell according to the region position corresponding to the satellite remote sensing monitoring data, obtaining greenhouse gas intensity data corresponding to each grid cell, calculating according to the greenhouse gas intensity data corresponding to each grid cell, obtaining a greenhouse gas intensity enhancement value corresponding to each grid cell, sorting the greenhouse gas intensity enhancement values from high to low, and screening the grid cells which are in front and accord with a set threshold value as greenhouse gas high-value region grids;
and the third screening module is used for matching the pollutant high-value area grid with the greenhouse gas high-value area grid, and taking the high-value area grid which is overlapped in space as a carbon pollution double high grid.
The application also provides computer equipment, which comprises a memory, a processor and a computer program stored on the memory and capable of running on the processor, wherein the method for identifying the dirty carbon double-high-hot-spot grid is realized when the processor executes the computer program.
The application also provides a computer readable storage medium storing a computer program for executing the method for identifying the dirty carbon double high-hot spot grid.
Compared with the prior art, the beneficial effects that above-mentioned at least one technical scheme that this description embodiment adopted can reach include at least: according to the embodiment of the invention, the characteristics of atmospheric pollutants and greenhouse gases are comprehensively considered, the dual-high grids of the polluted carbon are screened out through satellite monitoring data, the source matching is further carried out by means of multi-source auxiliary data, the control range is reduced, and a decision basis is provided for traceability analysis and source management. In addition, the embodiment of the invention considers the long life cycle characteristics of the greenhouse gas, calculates the relative enhancement value of the greenhouse gas concentration, and can weaken the influence of the long life cycle of the greenhouse gas as the greenhouse gas intensity data. Therefore, the method provided by the embodiment of the invention can be used as a powerful means for collaborative monitoring of regional carbon pollutants, makes up the blank of collaborative monitoring of the carbon pollutants at the present stage, and assists in analyzing the emission characteristics of the atmospheric pollutants and greenhouse gases and performing supervision.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings that are needed in the embodiments will be briefly described below, and it is obvious that the drawings in the following description are only some embodiments of the present application, and that other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.
FIG. 1 is a schematic flow chart of a method for identifying a grid with two high hot spots on polluted carbon according to a first embodiment of the present invention;
FIG. 2 is a schematic flow chart of a method for identifying a grid with two high hot spots on polluted carbon according to a second embodiment of the present invention;
FIG. 3 is NO in an embodiment of the invention 2 Satellite remote sensing monitoring data schematic diagram;
FIG. 4 is a CO in an embodiment of the invention 2 Satellite remote sensing monitoring data schematic diagram;
FIG. 5 is NO in an embodiment of the invention 2 A high-value area grid screening result schematic diagram;
FIG. 6 is CO in an embodiment of the invention 2 A high-value area grid screening result schematic diagram;
FIG. 7 is a graph showing the results of a dual high grid screening of contaminated carbon in an embodiment of the present invention;
FIG. 8 is a schematic diagram of land utilization data in an embodiment of the invention;
FIG. 9 is a schematic diagram of traffic network data in an embodiment of the invention;
FIG. 10 is a schematic illustration of enterprise point of interest POI data in an embodiment of the invention;
FIG. 11 is a schematic diagram of night light data in an embodiment of the invention;
FIG. 12 is a schematic representation of surface temperature data in an embodiment of the invention;
FIG. 13 is a diagram of grid source score results in an embodiment of the invention;
FIG. 14 is a schematic diagram of the final result of matching the grid of the dual high hot spots of the contaminated carbon in an embodiment of the present invention;
FIG. 15 is a block diagram of a dirty carbon dual high hot spot grid identification system in accordance with an embodiment of the present invention;
fig. 16 is a schematic structural view of the computer device of the present invention.
Detailed Description
Embodiments of the present application are described in detail below with reference to the accompanying drawings.
Other advantages and effects of the present application will become apparent to those skilled in the art from the present disclosure, when the following description of the embodiments is taken in conjunction with the accompanying drawings. It will be apparent that the described embodiments are only some, but not all, of the embodiments of the present application. The present application may be embodied or carried out in other specific embodiments, and the details of the present application may be modified or changed from various points of view and applications without departing from the spirit of the present application. It should be noted that the following embodiments and features in the embodiments may be combined with each other without conflict. All other embodiments, which can be made by one of ordinary skill in the art based on the embodiments herein without making any inventive effort, are intended to be within the scope of the present application.
As shown in fig. 1, an embodiment of the present invention provides a method for identifying a grid with two high hot spots on contaminated carbon, including:
s101, dividing a target monitoring area into a plurality of grid cells;
in the step, the target monitoring area is divided into grids, and each grid unit is provided with a unique geographic number for index inquiry. In the specific implementation, the target area is divided into a plurality of grids according to various factors such as the resolution of a data source, the supervision requirement and the like, the size of each grid corresponds to a certain-size area of the ground, for example, 3km is 3km, 1km is 1km, and each grid corresponds to a unique number, so that indexing is facilitated.
S102, acquiring satellite remote sensing monitoring data of the target monitoring area, wherein the satellite remote sensing monitoring data comprise atmospheric pollutant intensity data and greenhouse gas intensity data;
the method comprehensively considers the characteristics of atmospheric pollutants and greenhouse gases, screens out the dual-high grids of the carbon pollutants through satellite monitoring data, and provides decision basis for traceability analysis and source management. In the specific implementation of this step, NO is added to 2 Satellite remote sensing monitoring data is used as the atmospheric pollutant intensity data, and CO is used as the atmospheric pollutant intensity data 2 Satellite remote sensing monitoring data is used as the greenhouse gas intensity data.
S103, matching the atmospheric pollutant intensity data with each grid cell according to the position of the area corresponding to the satellite remote sensing monitoring data to obtain atmospheric pollutant intensity data corresponding to each grid cell, sorting the atmospheric pollutant intensity data corresponding to each grid cell from high to low, and screening the grid cells which are in front and accord with a set threshold value as pollutant high-value area grids;
when the method is specifically implemented, the atmospheric pollutant intensity data are extracted to grids, each grid acquires corresponding unique atmospheric pollutant intensity data, ranking is carried out from high to low according to the pollutant concentration, a certain threshold (such as percentage or grid number) is set, and the first N grids are taken as pollutant high-value area grids.
S104, matching the greenhouse gas intensity data with each grid cell according to the position of the region corresponding to the satellite remote sensing monitoring data, obtaining greenhouse gas intensity data corresponding to each grid cell, calculating according to the greenhouse gas intensity data corresponding to each grid cell, obtaining a greenhouse gas intensity enhancement value corresponding to each grid cell, sorting the greenhouse gas intensity enhancement values from high to low, and screening the grid cells which are in front of the sorting and accord with a set threshold value as greenhouse gas high-value region grids;
in this embodiment, convolution calculation is started for each grid unit in the target monitoring area one by one, and the difference between the greenhouse gas intensity background value in the convolution matrix and the greenhouse gas intensity value of each grid unit is calculated respectively, so as to obtain the greenhouse gas intensity value corresponding to each grid unit.
In the step, the characteristic of the long life cycle of the greenhouse gas is innovatively considered, the local background concentration is calculated through a convolution traversal algorithm, the relative concentration of the greenhouse gas is calculated to be enhanced, and the effect of the long life cycle of the greenhouse gas can be weakened as greenhouse gas intensity data.
In specific implementation, firstly, extracting greenhouse gas intensity data to grids, each grid acquires corresponding unique greenhouse gas intensity data, then performing convolution traversal, firstly, determining a small grid matrix with n-n number, determining a local background value corresponding to the grid matrix, subtracting the background value from the concentration value of a central grid to obtain a concentration enhancement value of the central grid, then performing step 1, calculating grid by grid until the last grid of a target area is calculated, finally, ranking the calculated concentration enhancement data from top to bottom, setting a threshold value (percentage or grid number), and taking the first M grids as grids of a greenhouse gas high-value area.
S105, matching the pollutant high-value area grid with the greenhouse gas high-value area grid, and taking the high-value area grid which is overlapped in space as a carbon pollution double high grid.
In the specific implementation, the intersection of the pollutant and the greenhouse gas high-value area is extracted according to the high-value area grid screening results of S103 and S104, and the pollutant carbon double-high grid screening result is obtained.
As shown in fig. 2, in one embodiment of the present invention, the pollutant high-value area grid and the greenhouse gas high-value area grid are matched, and the high-value area grid that is spatially overlapped is used as a carbon pollution dual-high grid, and further includes:
selecting a grid cell with a distance within a set distance range from the edge of the carbon dioxide double high grid, and combining the selected grid cell with the carbon dioxide double high grid to serve as a target grid cell; determining multiple multi-source auxiliary data according to emission source identification influence factors; and respectively calculating the total score of the multiple multi-source auxiliary data of each target grid unit according to the set score rule of each multi-source auxiliary data, sorting the total score from high to low, screening the target grid units which are sorted in front and accord with the set threshold, and taking the screened target grid units as the final sewage carbon double high-hot spot grid.
The present embodiment performs source matching based on multi-source auxiliary data. In specific implementation, considering the diffusion characteristics of atmospheric pollutants and greenhouse gases, the high-value area grid may not correspond to the emission source, and in order to further improve the accuracy of the result of the dual high-hot spot grid of the polluted carbon, the multiple multi-source auxiliary data adopted in the embodiment include: land utilization data, traffic road network data, enterprise point location POI data, night light data and ground surface temperature data of the target monitoring area; constructing a grid score system by using the data, wherein each grid score has the following calculation formula:
S total score ＝S Land use +S Traffic network +S Enterprise POI data +S Night light data +S Surface temperature product
The scoring rule for each multi-source auxiliary data set in this embodiment is shown in table 1.
TABLE 1
In the practical operation of the embodiment, in order to achieve higher calculation speed, the scores of all grids of the whole target area can be calculated uniformly, and then the target area can be screened according to the distance. Specifically, according to the grid score condition, matching with the result of the dual high grid of the polluted carbon obtained in the step S105, ranking the dual high grid of the polluted carbon obtained in the step S105 and the grid units within 10 km from the range of the dual high grid of the polluted carbon according to the score, and leaving the grid with the score greater than 3 as a final screening result of the dual high grid of the polluted carbon.
According to the embodiment of the invention, the source related data such as land utilization, night light, road network, enterprises and the like are considered, so that the source analysis work is facilitated, and compared with the method for carrying out source analysis by using an atmospheric diffusion model, the method is simpler and faster, and the accuracy of identifying the pollution carbon emission source is higher.
The invention is further described in connection with specific embodiments. Take the example of the dirty carbon cooperative hotspot grid identification of a certain province.
Step 1, dividing the urban area into grids, wherein each grid corresponds to a unique value number.
Specifically, grids covering the urban area are established, each grid has a unique geographic number, and index inquiry can be performed according to the number. In this case, the grid is 3km by 3km in size and the total number of grids is 909.
Step 2: and acquiring satellite monitoring data of the atmospheric pollutants and the greenhouse gases in the city.
Specifically, the satellite monitoring data of the city is obtained, which mainly comprises the satellite remote sensing monitoring data of atmospheric pollutants and CO 2 Satellite remote sensing monitors data. In this case, obtain NO 2 The satellite monitoring data are used as atmospheric pollutant intensity data to obtain CO 2 The data are shown in fig. 3 and 4 (both processed as month mean data) as greenhouse gas intensity data.
Step 3: matching the high-value grids to grid data according to the atmospheric pollutant intensity data, and ranking the high-value grids.
Specifically, atmospheric contaminant intensity data is extracted into grids, each of which will acquire corresponding unique contaminant intensity data in terms of contaminant concentrationThe degree is ranked from high to low, a certain threshold (such as percentage or grid number) is set, and the first N grids are taken as pollutant high-value area grids. In this embodiment, there are a total of 909 grids, in terms of NO 2 Sorting from high to low, screening out NO 2 The first 20% concentration grid is approximately 180 grids. The screening results are shown in FIG. 5.
Step 4: and matching the green house gas intensity data to corresponding grids, calculating a concentration enhancement value of each grid, and calculating a ranking.
Specifically, greenhouse gas intensity data is extracted into grids, each of which will acquire corresponding unique greenhouse gas intensity data. In this embodiment, a 5*5 grid region (i.e. 12km x 12km region) template is used, and convolution calculation is started from the first grid of the first line of the city, that is, the background values of all grids in the region are calculated, in this case, the background value takes the lowest concentration value of the grids in the region, and then the central grid concentration enhancement value of the region is calculated according to the following formula:
ΔCO2＝CO2-CO2 min(i-2,i+2)
wherein i represents an ith grid; by traversing all grids of the city, sorting from high to low according to grid data of concentration enhancement values, taking the first 20% of grids and about 180 grids as CO 2 The high value region identification result is shown in fig. 6.
Step 5: and (3) taking the intersection of the results of the step (3) and the step (4) to obtain a screening result of the double high grids of the polluted carbon. The calculation result is shown in fig. 7 by the spatial intersection algorithm.
Step 6: and performing source matching according to the multi-source auxiliary data.
Specifically, land utilization data, traffic road network data, enterprise point location POI data, night light data and ground surface temperature data of the city are obtained, and a grid scoring system is constructed. Part of the data is shown in fig. 8-12.
According to the auxiliary data, the scores are calculated for each grid of the city grid by grid, and the grid source score result is shown in fig. 13. And matching the grid source scoring result with the dirty carbon double-high grid screening result to obtain a final dirty carbon double-high hot spot grid matching result, as shown in fig. 14.
As shown in fig. 15, the present application further provides a dirty carbon dual high-hot spot grid identification system 200, including:
a grid dividing module 201 for dividing the target monitoring area into a plurality of grid cells;
a data acquisition module 202, configured to acquire satellite remote sensing monitoring data of the target monitoring area, where the satellite remote sensing monitoring data includes atmospheric pollutant intensity data and greenhouse gas intensity data;
the first screening module 203 is configured to match the atmospheric contaminant intensity data with each grid cell according to the location of the area corresponding to the satellite remote sensing monitoring data, obtain atmospheric contaminant intensity data corresponding to each grid cell, sort the atmospheric contaminant intensity data corresponding to each grid cell from high to low, and screen the grid cell which meets the set threshold before sorting as a contaminant high-value area grid;
the second screening module 204 is configured to match the greenhouse gas intensity data with each grid cell according to the region position corresponding to the satellite remote sensing monitoring data, obtain greenhouse gas intensity data corresponding to each grid cell, calculate and obtain a greenhouse gas intensity enhancement value corresponding to each grid cell according to the greenhouse gas intensity data corresponding to each grid cell, sort the greenhouse gas intensity enhancement values from high to low, and screen the grid cell which is in front and meets the set threshold as a greenhouse gas high-value region grid;
and the third screening module 205 is configured to match the pollutant high-value area grid with the greenhouse gas high-value area grid, and use the high-value area grid that is spatially coincident as a carbon pollution dual-high grid.
In a specific implementation, the third screening module 205 is further configured to use, as a target grid cell, a grid cell whose distance from the dual-high grid of the contaminated carbon is within a set distance range; determining multiple multi-source auxiliary data according to emission source identification influence factors; according to the set score rule of each multi-source auxiliary data, calculating the total score of the multi-source auxiliary data of each target grid unit, sorting the total score from high to low, screening target grid units which are in front and accord with a set threshold, combining the screened target grid units with the sewage carbon double high grid, and jointly serving as a final sewage carbon double high hot spot grid.
In specific implementation, the second screening module 204 starts convolution calculation for each grid unit in the target monitoring area one by one, and calculates a difference value between a greenhouse gas intensity background value in the convolution matrix and a greenhouse gas intensity value of each grid unit, so as to obtain the strong value-added greenhouse gas intensity corresponding to each grid unit.
In particular, the meshing module 201 is further configured to set a unique geographic number for each mesh unit, for performing index query.
In one embodiment, a computer device is provided, as shown in fig. 16, including a memory 301, a processor 302, and a computer program stored on the memory and capable of running on the processor, where the processor implements any of the foregoing methods for identifying a dual high-hot-spot grid of contaminated carbon when the computer program is executed.
In particular, the computer device may be a computer terminal, a server or similar computing means.
In this embodiment, a computer-readable storage medium is provided, in which a computer program for executing any of the foregoing methods for identifying a dual high-hot-spot grid of contaminated carbon is stored.
In particular, computer-readable storage media, including both permanent and non-permanent, removable and non-removable media, may be used to implement information storage by any method or technology. The information may be computer readable instructions, data structures, modules of a program, or other data. Examples of computer-readable storage media include, but are not limited to, phase-change memory (PRAM), static Random Access Memory (SRAM), dynamic Random Access Memory (DRAM), other types of Random Access Memory (RAM), read Only Memory (ROM), electrically Erasable Programmable Read Only Memory (EEPROM), flash memory or other memory technology, compact disc read only memory (CD-ROM), digital Versatile Disks (DVD) or other optical storage, magnetic cassettes, magnetic tape disk storage or other magnetic storage devices, or any other non-transmission medium, which can be used to store information that can be accessed by a computing device. Computer-readable storage media, as defined herein, does not include transitory computer-readable media (transmission media), such as modulated data signals and carrier waves.
It will be apparent to those skilled in the art that the modules or steps of the embodiments of the invention described above may be implemented in a general purpose computing device, they may be concentrated on a single computing device, or distributed across a network of computing devices, they may alternatively be implemented in program code executable by computing devices, so that they may be stored in a storage device for execution by computing devices, and in some cases, the steps shown or described may be performed in a different order than what is shown or described, or they may be separately fabricated into individual integrated circuit modules, or a plurality of modules or steps in them may be fabricated into a single integrated circuit module. Thus, embodiments of the invention are not limited to any specific combination of hardware and software.
The foregoing is merely specific embodiments of the present application, but the scope of the present application is not limited thereto, and any changes or substitutions easily conceivable by those skilled in the art within the technical scope of the present application should be covered in the scope of the present application. Therefore, the protection scope of the present application shall be subject to the protection scope of the claims.
Claims (10)
1. A method for identifying a grid with double high hot spots of polluted carbon is characterized by comprising the following steps:
dividing a target monitoring area into a plurality of grid cells;
acquiring satellite remote sensing monitoring data of the target monitoring area, wherein the satellite remote sensing monitoring data comprise atmospheric pollutant intensity data and greenhouse gas intensity data;
according to the position of the region corresponding to the satellite remote sensing monitoring data, matching the atmospheric pollutant intensity data with each grid cell to obtain atmospheric pollutant intensity data corresponding to each grid cell, sorting the atmospheric pollutant intensity data corresponding to each grid cell from high to low, and screening the grid cells which are in front and accord with a set threshold as pollutant high-value region grids;
according to the position of the region corresponding to the satellite remote sensing monitoring data, matching the greenhouse gas intensity data with each grid cell to obtain greenhouse gas intensity data corresponding to each grid cell, calculating to obtain a greenhouse gas intensity enhancement value corresponding to each grid cell according to the greenhouse gas intensity data corresponding to each grid cell, sorting the greenhouse gas intensity enhancement values from high to low, and screening the grid cells which are in front of the sorting and accord with a set threshold value as greenhouse gas high-value region grids;
and matching the pollutant high-value area grid with the greenhouse gas high-value area grid, and taking the high-value area grid overlapped in space as a carbon pollution double high grid.
2. The method for identifying a dual high-hot spot grid for contaminated carbon according to claim 1, wherein the contaminant high-value area grid and the greenhouse gas high-value area grid are matched, and the high-value area grid which is overlapped in space is used as the dual high-value grid for contaminated carbon, further comprising:
selecting a grid cell with a distance within a set distance range from the edge of the carbon dioxide double high grid, and combining the selected grid cell with the carbon dioxide double high grid to serve as a target grid cell;
determining multiple multi-source auxiliary data according to emission source identification influence factors;
and respectively calculating the total score of the multiple multi-source auxiliary data of each target grid unit according to the set score rule of each multi-source auxiliary data, sorting the total score from high to low, screening the target grid units which are sorted in front and accord with the set threshold, and taking the screened target grid units as the final sewage carbon double high-hot spot grid.
3. The method for identifying a dual high-hot spot grid of contaminated carbon according to claim 2, wherein the plurality of multi-source auxiliary data comprises: land utilization data, traffic road network data, enterprise point location POI data, night light data and ground surface temperature data of the target monitoring area.
4. The method for identifying a grid of dual high hot spots on carbon dioxide according to claim 1, wherein the step of calculating the greenhouse gas intensity value corresponding to each grid cell according to the greenhouse gas intensity data corresponding to each grid cell comprises the steps of:
and starting convolution calculation on each grid unit in the target monitoring area one by one, and respectively calculating the difference value between the greenhouse gas intensity background value of the convolution matrix and the greenhouse gas intensity value of each grid unit to obtain the greenhouse gas intensity value corresponding to each grid unit.
5. The method for identifying a grid of two high-temperature hot spots on carbon as recited in claim 4, wherein the background value of the greenhouse gas intensity of the convolution matrix is the minimum value of the greenhouse gas intensity of the grid in the convolution matrix.
6. The method for identifying the grid of the double high-temperature hot spots of the polluted carbon as claimed in claim 1, wherein NO 2 Satellite remote sensing monitoring data is used as the atmospheric pollutant intensity data, and CO is used as the atmospheric pollutant intensity data 2 Satellite remote sensing monitoring data is used as the greenhouse gas intensity data.
7. The method for identifying a grid of a dual high-temperature spot on a carbon soot according to claim 1, wherein the target monitoring area is divided into a plurality of grid cells, further comprising:
each grid cell is provided with a unique geographic number for index querying.
8. A dirty carbon dual high-hot spot grid identification system, comprising:
the grid dividing module is used for dividing the target monitoring area into a plurality of grid units;
the data acquisition module is used for acquiring satellite remote sensing monitoring data of the target monitoring area, wherein the satellite remote sensing monitoring data comprise atmospheric pollutant intensity data and greenhouse gas intensity data;
the first screening module is used for matching the atmospheric pollutant intensity data with each grid unit according to the position of the area corresponding to the satellite remote sensing monitoring data to obtain atmospheric pollutant intensity data corresponding to each grid unit, sorting the atmospheric pollutant intensity data corresponding to each grid unit from high to low, and screening the grid units which are in front of the sorting and accord with a set threshold value as pollutant high-value area grids;
the second screening module is used for matching the greenhouse gas intensity data with each grid cell according to the region position corresponding to the satellite remote sensing monitoring data, obtaining greenhouse gas intensity data corresponding to each grid cell, calculating according to the greenhouse gas intensity data corresponding to each grid cell, obtaining a greenhouse gas intensity enhancement value corresponding to each grid cell, sorting the greenhouse gas intensity enhancement values from high to low, and screening the grid cells which are in front and accord with a set threshold value as greenhouse gas high-value region grids;
and the third screening module is used for matching the pollutant high-value area grid with the greenhouse gas high-value area grid, and taking the high-value area grid which is overlapped in space as a carbon pollution double high grid.
9. A computer device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, wherein the processor implements the method of identifying a dirty carbon dual high hot spot grid of any one of claims 1 to 7 when the computer program is executed by the processor.
10. A computer-readable storage medium, characterized in that the computer-readable storage medium stores a computer program for executing the dirty carbon dual high hot spot grid identification method according to any one of claims 1 to 7.
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Citations (8)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN108648127A (en) * | 2018-04-28 | 2018-10-12 | 北京航空航天大学 | A kind of urban air pollution hot spot region locking means |
| CN109284706A (en) * | 2018-09-12 | 2019-01-29 | 北京英视睿达科技有限公司 | Hot spot grid agglomeration of industries area recognizing method based on Multi-sensor satellite remote sensing |
| CN109490477A (en) * | 2018-11-19 | 2019-03-19 | 北京英视睿达科技有限公司 | The method and device of atmosphere pollution hot spot grid is determined based on satellite remote sensing date |
| CN109506809A (en) * | 2018-11-19 | 2019-03-22 | 北京英视睿达科技有限公司 | The method and device of atmosphere pollution hot spot grid is determined based on surface temperature |
| CN109543935A (en) * | 2018-10-09 | 2019-03-29 | 北京英视睿达科技有限公司 | Environmental data processing method based on hot spot grid |
| CN112034094A (en) * | 2020-07-10 | 2020-12-04 | 海南星瞰信息咨询中心(有限合伙) | Pollution source prevention and control hotspot grid supervision method and system |
| CN114878748A (en) * | 2022-05-07 | 2022-08-09 | 国网四川省电力公司电力科学研究院 | CO (carbon monoxide) 2 Method and system for monitoring discharge amount |
| WO2023206984A1 (en) * | 2022-04-27 | 2023-11-02 | 河北先河环保科技股份有限公司 | Inversion method and apparatus for carbon dioxide emission, and device and storage medium |
-
2023
- 2023-11-23 CN CN202311578964.XA patent/CN117591619B/en active Active
Patent Citations (8)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN108648127A (en) * | 2018-04-28 | 2018-10-12 | 北京航空航天大学 | A kind of urban air pollution hot spot region locking means |
| CN109284706A (en) * | 2018-09-12 | 2019-01-29 | 北京英视睿达科技有限公司 | Hot spot grid agglomeration of industries area recognizing method based on Multi-sensor satellite remote sensing |
| CN109543935A (en) * | 2018-10-09 | 2019-03-29 | 北京英视睿达科技有限公司 | Environmental data processing method based on hot spot grid |
| CN109490477A (en) * | 2018-11-19 | 2019-03-19 | 北京英视睿达科技有限公司 | The method and device of atmosphere pollution hot spot grid is determined based on satellite remote sensing date |
| CN109506809A (en) * | 2018-11-19 | 2019-03-22 | 北京英视睿达科技有限公司 | The method and device of atmosphere pollution hot spot grid is determined based on surface temperature |
| CN112034094A (en) * | 2020-07-10 | 2020-12-04 | 海南星瞰信息咨询中心(有限合伙) | Pollution source prevention and control hotspot grid supervision method and system |
| WO2023206984A1 (en) * | 2022-04-27 | 2023-11-02 | 河北先河环保科技股份有限公司 | Inversion method and apparatus for carbon dioxide emission, and device and storage medium |
| CN114878748A (en) * | 2022-05-07 | 2022-08-09 | 国网四川省电力公司电力科学研究院 | CO (carbon monoxide) 2 Method and system for monitoring discharge amount |
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