CN116882630A

CN116882630A - Intelligent carbon bank assessment method, system and medium based on satellite remote sensing big data

Info

Publication number: CN116882630A
Application number: CN202310881994.1A
Authority: CN
Inventors: 张宸铭; 谢珂
Original assignee: North China University of Water Resources and Electric Power
Current assignee: North China University of Water Resources and Electric Power
Priority date: 2023-07-18
Filing date: 2023-07-18
Publication date: 2023-10-13

Abstract

The embodiment of the application provides a carbon bank intelligent evaluation method, a system and a medium based on satellite remote sensing big data. The method belongs to the technical field of satellite telemetry and carbon black. The method comprises the following steps: performing emission source identification according to satellite remote sensing detection information to obtain carbon source emission characteristic data, plant net carbon quantity data, meteorological thermal kinetic energy characteristic data, carbon emission concentration and volume distribution data, processing to obtain airspace carbon emission detection data, processing to obtain regional carbon enrichment detection data by combining regional carbon emission index data and plant net carbon quantity data, correcting by combining meteorological thermal kinetic energy characteristic data to obtain regional carbon emission negative surplus evaluation index, and comparing with a carbon emission evaluation threshold to judge the carbon emission condition of a region; therefore, information analysis and data processing are carried out on facilities, plant coverage, weather and carbon emission of remote sensing information based on satellite remote sensing big data, regional carbon emission assessment conditions are obtained, and an intelligent technology for carrying out regional carbon emission data processing and assessment through big data technology is realized.

Description

Intelligent carbon bank assessment method, system and medium based on satellite remote sensing big data

Technical Field

The application relates to the technical field of satellite telemetry and carbon black, in particular to a carbon black intelligent evaluation method, system and medium based on satellite telemetry big data.

Background

The carbon satellite is a new means for detecting the emission condition of the carbon nitrogen oxide in the airspace at present, the greenhouse gas content data of the area can be obtained through satellite remote sensing data, and then the area carbon emission data information is obtained, and the analysis and evaluation of the carbon emission condition of a certain area also need to be related to the discharge capacity and the body quantity of a regional carbon source, the influence of meteorological environment interference elements, the carbon absorption condition of vegetation cover and the accumulation of greenhouse gas in the geographic position, and the intelligent technical means for carrying out information analysis and data processing on the element information related to the carbon emission so as to evaluate the carbon emission level condition is lacking at present.

In view of the above problems, an effective technical solution is currently needed.

Disclosure of Invention

The embodiment of the application aims to provide a carbon bank intelligent evaluation method, a system and a medium based on satellite remote sensing big data, which can be used for carrying out information analysis and data processing on facilities, plant coverage, weather and carbon banks of remote sensing information through the satellite remote sensing big data to obtain regional carbon bank evaluation conditions and realize an intelligent technology for carrying out regional carbon bank data processing evaluation through big data technology.

The embodiment of the application also provides a carbon bank intelligent evaluation method based on satellite remote sensing big data, which comprises the following steps:

acquiring satellite remote sensing detection information of a preset area in a preset time period, wherein the satellite remote sensing detection information comprises regional position information, regional facility geophysical prospecting information, airspace meteorological environment information and airspace carbon emission information;

carrying out emission source identification through a preset satellite detection and identification platform according to the regional facility geophysical prospecting information to obtain regional carbon source facility information of the preset area, identifying implantation information of the preset area, and processing according to the implantation information to obtain implantation net carbon quantity data in a preset time period;

inquiring and acquiring carbon source emission characteristic data of each corresponding carbon source facility in a preset time period in a preset area carbon source emission data information base according to the regional carbon source facility information;

extracting meteorological thermal kinetic energy characteristic data according to the airspace meteorological environment information, extracting carbon emission concentration distribution data and carbon emission quantity distribution data according to the airspace carbon emission information, and processing to obtain airspace carbon emission detection data;

processing according to the carbon source emission characteristic data of each carbon source facility object through a preset regional carbon emission index evaluation model to obtain regional carbon emission index data;

Processing the airspace carbon emission detection data and the planting net carbon quantity data according to the regional carbon emission index data through a preset carbon emission detection analysis model to obtain regional carbon enrichment detection data;

correcting the regional carbon-rich detection data according to the meteorological thermal kinetic energy characteristic data in combination with the regional position information and the carbon source emission characteristic data to obtain a regional carbon emission negative surplus evaluation index;

and carrying out threshold comparison according to the regional carbon emission negative surplus evaluation index and a preset carbon emission evaluation threshold value, and judging the carbon emission condition of the preset region in a preset time period according to a threshold value comparison result.

Optionally, in the method for intelligently evaluating carbon emissions based on satellite remote sensing big data according to the embodiment of the present application, the performing emission source identification according to the regional facility geophysical prospecting information through a preset satellite detection and identification platform to obtain regional carbon source facility information of the preset area, identifying implantation information of the preset area, and performing processing according to the implantation information to obtain implantation net carbon quantity data in a preset time period includes:

carrying out emission source identification through a preset satellite detection and identification platform according to the regional facility geophysical prospecting information, and acquiring regional carbon source facility object information of the preset region, wherein the regional carbon source facility object information comprises carbon source facility object category information, carbon row object identification positioning information and carbon row activity information;

Performing implantation and coverage identification on the geophysical prospecting information of the regional facilities through the preset satellite detection and identification platform to obtain implantation and coverage information of the preset area;

and extracting the data of the effective area and the data of the unit net carbon quantity of the planting and covering according to the planting and covering information, and processing to obtain the data of the net carbon quantity of the planting and covering in a preset area in the preset time period.

Optionally, in the method for intelligently evaluating carbon emissions based on satellite remote sensing big data according to the embodiment of the present application, the querying and obtaining carbon source emission characteristic data corresponding to each carbon source facility in a preset time period from a preset area carbon emissions data information base according to the regional carbon source facility information includes:

inquiring in a carbon bank data information base of a preset area according to the carbon source setting object category information, the carbon bank setting object identification positioning information and the carbon bank activity information, and acquiring carbon source emission characteristic data corresponding to each carbon source setting object in the preset time period;

the carbon source emission characteristic data comprises attribute displacement coefficient, emission activity intensity data and unit carbon emission index data.

Optionally, in the method for intelligently evaluating carbon emissions based on satellite remote sensing big data according to the embodiment of the present application, the extracting weather thermal kinetic energy characteristic data according to the airspace weather environment information, extracting carbon emissions concentration distribution data and carbon emissions quantity distribution data according to the airspace carbon emissions information, and processing to obtain airspace carbon emissions detection data includes:

Extracting meteorological and thermal kinetic energy characteristic data including thermal radiation data, temperature change temperature difference data and air flow dynamic data according to the airspace meteorological environment information;

extracting carbon emission concentration distribution data and carbon emission quantity distribution data according to the airspace carbon emission information, and processing according to the carbon emission concentration distribution data and the carbon emission quantity distribution data to obtain airspace carbon emission detection data in the preset time period;

the airspace carbon bank detection dataWherein y is _e For carbon emission concentration distribution data, m _b For carbon emission mass distribution data, +.>Presetting a characteristic coefficient.

Optionally, in the method for intelligently evaluating carbon emissions based on satellite remote sensing big data according to the embodiment of the present application, the processing is performed according to the carbon source emission characteristic data of each carbon source facility object through a preset regional carbon emission index evaluation model, to obtain regional carbon emission index data, including:

processing through a preset regional carbon emission index evaluation model according to the attribute displacement coefficient, emission activity intensity data and unit carbon emission index data of each carbon source facility object to obtain regional carbon emission index data of the preset region;

the calculation formula of the regional carbon discharge index data is as follows:

Wherein K is _d Is regional carbon emission index data, s _hi For the property displacement coefficient of the ith carbon source facility, G _ri Emission activity intensity data for the ith carbon source facility, b _pi The index data of the unit carbon emission of the ith carbon source facility is obtained, n is the number of the carbon source facilities in the preset area,is a preset characteristic coefficient.

Optionally, in the method for intelligently evaluating carbon emissions based on satellite remote sensing big data according to the embodiment of the present application, the step of processing the airspace carbon emissions detection data and the net carbon implantation amount data according to the regional carbon emissions index data by a preset carbon emissions detection analysis model to obtain regional carbon enrichment detection data includes:

inputting the regional carbon emission index data into a preset carbon emission detection analysis model according to the regional carbon emission index data in combination with the airspace carbon emission detection data and the net carbon implantation amount data to perform calculation processing, so as to obtain regional carbon enrichment detection data of a preset region in the preset time period;

the calculation formula of the regional carbon-rich detection data is as follows:

wherein E is _d For regional carbon-rich detection data, I _c For airspace carbon array detection data, F _p K for planting net carbon data _d Is the regional carbon emission index data,and (3) for presetting a carbon index correction factor, wherein mu and delta are preset characteristic coefficients.

Optionally, in the method for intelligently evaluating carbon emissions based on satellite remote sensing big data according to the embodiment of the present application, the correcting the regional carbon-rich detection data according to the meteorological thermal kinetic energy characteristic data in combination with the regional position information and the carbon source emission characteristic data to obtain a regional carbon emissions negative surplus evaluation index includes:

inquiring and obtaining an area carbon array aggregation and dispersion interference coefficient of the preset area in the area carbon array data information base according to the regional position information;

correcting the regional carbon-rich detection data according to the emission activity intensity data of the preset region and the regional carbon emission gathering and scattering interference coefficient and the meteorological thermal kinetic energy characteristic data to obtain a regional carbon emission negative interference assessment index;

the correction calculation formula of the regional carbon emission negative surplus evaluation index is as follows:

wherein N is _f Assessment index for regional carbon emission negative, g _c 、z _u 、r _t Respectively thermal radiation data, temperature change and temperature difference data and air flow power data G _R To discharge activity intensity data, f _u Is the interference coefficient of regional carbon emission aggregation and dispersion, E _d And delta, epsilon, eta and theta are preset characteristic coefficients for the regional carbon-rich detection data.

In a second aspect, an embodiment of the present application provides a carbon black intelligent assessment system based on satellite remote sensing big data, the system comprising: the system comprises a memory and a processor, wherein the memory comprises a program of a carbon bank intelligent evaluation method based on satellite remote sensing big data, and the program of the carbon bank intelligent evaluation method based on the satellite remote sensing big data realizes the following steps when being executed by the processor:

Optionally, in the intelligent carbon grid assessment system based on satellite remote sensing big data according to the embodiment of the present application, the performing emission source identification according to the regional facility geophysical prospecting information through a preset satellite detection identification platform to obtain regional carbon source facility information of the preset area, identifying implantation information of the preset area, and performing processing according to the implantation information to obtain implantation net carbon quantity data in a preset time period includes:

In a third aspect, an embodiment of the present application further provides a computer readable storage medium, where the computer readable storage medium includes a carbon bank intelligent evaluation method program based on satellite remote sensing big data, where the carbon bank intelligent evaluation method program based on satellite remote sensing big data implements the steps of the carbon bank intelligent evaluation method based on satellite remote sensing big data as described in any one of the above.

As can be seen from the above, the method, the system and the medium for intelligent evaluation of carbon emissions based on satellite remote sensing big data provided by the embodiments of the present application perform emission source identification by acquiring satellite remote sensing information to acquire regional carbon source facility information and implantation information, acquire implantation net carbon quantity data, acquire carbon source emission characteristic data according to the regional carbon source facility information, extract weather thermal kinetic energy characteristic data, carbon emission concentration distribution data and carbon emission object quantity distribution data, process to acquire airspace carbon emission detection data, process the carbon source emission characteristic data to acquire regional carbon emission index data, process to acquire regional carbon enrichment detection data by combining airspace carbon emission detection data and implantation net carbon quantity data, process to acquire regional carbon emission negative-filling evaluation index by combining weather thermal kinetic energy characteristic data and carbon source emission characteristic data, and then compare with a preset carbon emission evaluation threshold value to determine the carbon emission condition of a region; therefore, information analysis and data processing are carried out on facilities, plant coverage, weather and carbon emission of remote sensing information based on satellite remote sensing big data, regional carbon emission assessment conditions are obtained, and an intelligent technology for carrying out regional carbon emission data processing and assessment through big data technology is realized.

Additional features and advantages of the application will be set forth in the description which follows, and in part will be apparent from the description, or may be learned by practice of the embodiments of the application. The objectives and other advantages of the application will be realized and attained by the structure particularly pointed out in the written description and claims thereof as well as the appended drawings.

Drawings

In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings that are needed in the embodiments of the present application will be briefly described below, it should be understood that the following drawings only illustrate some embodiments of the present application and should not be considered as limiting the scope, and other related drawings can be obtained according to these drawings without inventive effort for a person skilled in the art.

FIG. 1 is a flowchart of a carbon black intelligent evaluation method based on satellite remote sensing big data provided by an embodiment of the application;

FIG. 2 is a flowchart of a method for intelligently evaluating carbon emissions based on satellite remote sensing big data to obtain regional carbon source facility information and plant coverage net carbon quantity data according to an embodiment of the present application;

FIG. 3 is a flowchart of acquiring carbon source emission characteristic data according to the method for intelligently evaluating carbon emissions based on satellite remote sensing big data provided by the embodiment of the application;

Fig. 4 is a schematic structural diagram of a carbon bank intelligent evaluation system based on satellite remote sensing big data according to an embodiment of the present application.

Detailed Description

The following description of the embodiments of the present application will be made clearly and completely with reference to the accompanying drawings, in which it is apparent that the embodiments described are only some embodiments of the present application, but not all embodiments. The components of the embodiments of the present application generally described and illustrated in the figures herein may be arranged and designed in a wide variety of different configurations. Thus, the following detailed description of the embodiments of the application, as presented in the figures, is not intended to limit the scope of the application, as claimed, but is merely representative of selected embodiments of the application. All other embodiments, which can be made by a person skilled in the art without making any inventive effort, are intended to be within the scope of the present application.

It should be noted that like reference numerals and letters refer to like items in the following figures, and thus once an item is defined in one figure, no further definition or explanation thereof is necessary in the following figures. Meanwhile, in the description of the present application, the terms "first", "second", and the like are used only to distinguish the description, and are not to be construed as indicating or implying relative importance.

Referring to fig. 1, fig. 1 is a flowchart of a carbon bank intelligent evaluation method based on satellite remote sensing big data according to some embodiments of the application. The intelligent carbon bank assessment method based on the satellite remote sensing big data is used in terminal equipment, such as a computer, a mobile phone terminal and the like. The intelligent carbon bank assessment method based on the satellite remote sensing big data comprises the following steps:

s101, acquiring satellite remote sensing detection information of a preset area in a preset time period, wherein the satellite remote sensing detection information comprises regional position information, regional facility geophysical prospecting information, airspace meteorological environment information and airspace carbon emission information;

s102, carrying out emission source identification through a preset satellite detection and identification platform according to the regional facility geophysical prospecting information to obtain regional carbon source facility information of the preset area, identifying implantation information of the preset area, and processing according to the implantation information to obtain implantation net carbon quantity data in a preset time period;

s103, inquiring and obtaining carbon source emission characteristic data corresponding to each carbon source facility in the preset time period in a preset area carbon source data information base according to the regional carbon source facility information;

s104, extracting meteorological thermal kinetic energy characteristic data according to the airspace meteorological environment information, extracting carbon emission concentration distribution data and carbon emission quantity distribution data according to the airspace carbon emission information, and processing to obtain airspace carbon emission detection data;

S105, processing according to the carbon source emission characteristic data of each carbon source facility through a preset regional carbon emission index evaluation model to obtain regional carbon emission index data;

s106, processing the airspace carbon bank detection data and the planting net carbon quantity data according to the regional carbon discharge index data through a preset carbon bank detection analysis model to obtain regional carbon enrichment detection data;

s107, correcting the regional carbon-rich detection data according to the meteorological thermal kinetic energy characteristic data in combination with the regional position information and the carbon source emission characteristic data to obtain a regional carbon emission negative surplus evaluation index;

s108, comparing the threshold value with a preset carbon number evaluation threshold value according to the regional carbon number negative surplus evaluation index, and judging the carbon number condition of the preset region in a preset time period according to a threshold value comparison result.

It should be noted that, in order to implement an intelligent means for performing regional carbon emission assessment on information obtained by satellite telemetry, analysis processing is performed according to the information of the telemetry on the regional position, carbon source facility, carbon emission and absorption capacity, meteorological environment and carbon emission to obtain assessment on carbon emission conditions, regional carbon emission detection data are obtained by obtaining the regional positioning elevation position of a preset region in a preset time period, detection of regional facilities and objects and vegetation, airspace meteorological environment and satellite remote sensing information of airspace carbon emission, then emission source identification is performed through a satellite detection identification platform to obtain regional carbon source facility information, implantation information processing is identified to obtain implantation net carbon quantity data, carbon source emission characteristic data corresponding to each carbon source facility are obtained by inquiring in a preset regional carbon emission data information base according to regional carbon source facility information, meteorological thermal kinetic energy characteristic data are extracted, airspace carbon emission concentration distribution data and carbon emission object distribution data are processed according to each carbon source carbon emission facility, regional carbon emission characteristic data are processed according to obtain regional carbon emission index data of each carbon source, then, carbon emission detection data are combined with each carbon emission characteristic data is processed through a measurement model to obtain regional carbon emission index data, and carbon emission threshold value data are compared with carbon emission threshold value data in a preset region, and carbon emission threshold value data is obtained by comparing the regional carbon emission threshold value data with the preset region carbon emission threshold value data, and carbon emission threshold value is estimated according to the regional carbon emission characteristic data and the threshold value is finally assessed, the regional carbon emission condition is good, otherwise, the regional carbon emission condition exceeds the standard.

Referring to fig. 2, fig. 2 is a flowchart of a method for obtaining regional carbon source facility information and plant coverage net carbon quantity data according to a satellite remote sensing big data-based carbon emission intelligent evaluation method in some embodiments of the application. According to the embodiment of the application, the emission source identification is performed through a preset satellite detection and identification platform according to the regional facility geophysical prospecting information to obtain regional carbon source facility information of the preset area, the implantation information of the preset area is identified, and the implantation net carbon amount data in a preset time period is obtained by processing according to the implantation information, specifically comprising the following steps:

s201, carrying out emission source identification through a preset satellite detection and identification platform according to the regional facility geophysical prospecting information, and acquiring regional carbon source facility object information of the preset area, wherein the regional carbon source facility object information comprises carbon source facility object category information, carbon row object identification positioning information and carbon row activity information;

s202, performing plant coverage identification on the geophysical prospecting information of the regional facilities through the preset satellite detection and identification platform, and acquiring plant coverage information of the preset area;

s203, extracting the data of the effective area and the data of the unit net carbon amount of the implantation and covering according to the implantation and covering information, and processing to obtain the data of the net carbon amount of the implantation and covering of the preset area in the preset time period.

It should be noted that, in order to measure the carbon emission situation of the area in the time period, the actual carbon emission situation of the area needs to be definitely and collected, because there are non-carbon source facility objects without carbon emission in the area, and carbon source facility objects with carbon emission such as oil refineries, thermal power plants, heating plants, fuel oil car body quantity, carbon emission factories and agriculture, etc., the category, location and activity situation of the carbon source facilities and objects need to be identified, and meanwhile, because the vegetation coverage area in the area has a carbon absorption function, the carbon absorption capacity of the vegetation coverage area in the area needs to be known, so as to obtain the data of carbon purification of the carbon absorption, therefore, the regional carbon source facility information is identified by a preset satellite detection identification platform, the regional carbon source facility information including the category information of the carbon source facility objects, the identification positioning information of the carbon source carbon emission facility objects and the activity information of the carbon source carbon emission is identified by the preset satellite detection identification platform, and the vegetation coverage situation is obtained at the same time, and then the vegetation coverage area data and the unit time are extracted according to the vegetation coverage area data, namely, the carbon absorption capacity is calculated, and the carbon coverage area is calculated as the net coverage area data of the carbon unit time unit and the carbon absorption capacity is calculated in the carbon purification formula:

Wherein F is _p To plant and cover the net carbon data, C _w To plant and cover the effective area data, k _c And (3) taking the data as unit net carbon quantity data, wherein T is a time endpoint node of a preset time period, dt is time differentiation, and mu is a preset characteristic coefficient.

Referring to fig. 3, fig. 3 is a flowchart of a method for acquiring carbon source emission characteristic data according to an intelligent evaluation method of carbon emission based on satellite remote sensing big data in some embodiments of the application. According to the embodiment of the application, the carbon source emission characteristic data of each carbon source facility in the preset time period is obtained by inquiring in a preset area carbon source emission data information base according to the regional carbon source facility information, specifically:

s301, inquiring in a carbon bank data information base of a preset area according to the carbon source setting object category information, the carbon bank setting object identification positioning information and the carbon bank activity information, and acquiring carbon source emission characteristic data corresponding to each carbon source setting object in the preset time period;

s302, the carbon source emission characteristic data comprise attribute displacement coefficient, emission activity intensity data and unit carbon emission index data.

After identifying the carbon source facility objects in the area, the carbon source emission characteristic data of each carbon source facility object in a preset area is obtained by inquiring the carbon source emission characteristic information of each carbon source in the relevant information base according to the type information, the carbon source identification positioning information and the carbon source activity level information of the carbon source facility objects, namely, the carbon source emission characteristic data of each carbon source facility object in a preset time period is obtained, namely, the carbon source emission characteristic data of each carbon source facility object in the preset time period is obtained according to the type, the facility object positioning identification and the carbon source activity level information, such as the carbon source activity condition information of scale factories of various emission types, fuel vehicles of various emission amounts, three-product processing factories of various carbon sources and the like in a time period, the carbon source emission characteristic data corresponding to each carbon source facility object can be obtained by inquiring the information base, wherein the attribute displacement coefficient is the carbon source emission characteristic data corresponding to the carbon source emission characteristic data in the time period, and the carbon source emission characteristic data corresponding to the carbon source emission characteristic data in the time period is the carbon source emission index data corresponding to the carbon source emission characteristic data of the carbon source emission unit, and the carbon source emission characteristic data corresponding to the carbon source emission characteristic data.

According to the embodiment of the invention, the meteorological thermal kinetic energy characteristic data is extracted according to the airspace meteorological environment information, the carbon emission concentration distribution data and the carbon emission quantity distribution data are extracted according to the airspace carbon emission information, and the airspace carbon emission detection data are obtained through processing, specifically:

It should be noted that, for the influence of the weather environment of the airspace in which the area is located on the carbon emission assessment, the weather thermal kinetic energy characteristic data, which is characteristic data reflecting the heat, the temperature difference and the air flow of the weather environment, is processed and extracted, and the characteristic data has influence on the aggregation or dispersion of the carbon emission, so that the weather thermal kinetic energy characteristic data is required to be extracted to further process the influence assessment of the weather environment on the carbon emission of the area, and meanwhile, for detecting the carbon emission condition of the area in a preset time period, the carbon emission concentration distribution data and the carbon emission object quantity distribution data, that is, the concentration distribution data and the volume distribution data of the carbon emission such as the carbon nitrogen oxide, of the airspace are required to be extracted according to the airspace carbon emission information obtained by remote sensing, and the airspace carbon emission detection data in the preset time period, that is, the detection data of the carbon emission condition is obtained by integrating the distribution data is obtained according to the carbon emission concentration distribution data and the carbon emission object quantity distribution data.

According to the embodiment of the invention, the carbon source emission characteristic data according to the carbon source facilities is processed through a preset regional carbon source emission index evaluation model to obtain regional carbon source emission index data, specifically:

wherein K is _d Is regional carbon emission index data, s _hi For the property displacement coefficient of the ith carbon source facility, G _ri Emission activity intensity data for the ith carbon source facility, b _pi The index data of the unit carbon emission of the ith carbon source facility is obtained, n is the number of the carbon source facilities in the preset area,and the characteristic coefficient is preset (the characteristic coefficient is obtained by inquiring a regional carbon bank data information base).

After obtaining the carbon emission characteristic data of the regional carbon source, obtaining the actual index condition of the regional carbon source carbon emission activity for evaluating the carbon emission index of the regional carbon source in the time period, and performing aggregation processing according to the attribute emission coefficient, emission activity intensity data and unit carbon emission index data of each carbon source facility object through a preset regional carbon emission index evaluation model to obtain regional carbon emission index data of a preset region.

According to the embodiment of the invention, the regional carbon-rich detection data is obtained by processing the regional carbon-discharge index data in combination with the airspace carbon-discharge detection data and the net carbon implantation amount data through a preset carbon-discharge detection analysis model, specifically comprising the following steps:

wherein E is _d For regional carbon-rich detection data, I _c For airspace carbon array detection data, F _p K for planting net carbon data _d Is the regional carbon emission index data,and (3) for presetting the carbon index correction factors, mu and delta are preset characteristic coefficients (the carbon index correction factors and the characteristic coefficients are obtained by inquiring a regional carbon bank data information base).

It should be noted that, in order to detect the actual carbon emission and the purification effect of the preset area in the preset time period, that is, measure how much or how little the carbon emission is in the time period, the area carbon emission index data is combined with the airspace carbon emission detection data and the plant-covered carbon quantity data, and the area carbon-rich detection data of the preset area in the preset time period is obtained by inputting the preset carbon emission detection analysis model for calculation processing, where the detection data reflects the quality of the carbon emission and the purification cycle effect of the area in the time period.

According to the embodiment of the invention, the regional carbon-rich detection data is corrected according to the meteorological thermal kinetic energy characteristic data in combination with the regional position information and the carbon source emission characteristic data to obtain a regional carbon emission negative surplus evaluation index, which specifically comprises:

wherein N is _f Assessment index for regional carbon emission negative, g _c 、z _u 、r _t Respectively thermal radiation data, temperature change and temperature difference data and air flow power data G _R To discharge activity intensity data, f _u Is the interference coefficient of regional carbon emission aggregation and dispersion, E _d And delta, epsilon, eta and theta are preset characteristic coefficients for the regional carbon-rich detection data (the characteristic coefficients are obtained by inquiring a regional carbon bank data information base).

It should be noted that, in order to accurately reflect the detection and evaluation result of the regional carbon emission situation, the influence of the regional geographic position and the meteorological environment on the carbon emission accumulation or dispersion needs to be considered, because the geographic position, the altitude and the meteorological circulation of different regions have larger influence on the carbon emission, the regional carbon-rich detection data needs to be weighted and modified according to the interference data of the geographic position and the influence data of the thermal force, the temperature change and the aerodynamic force of the meteorological environment, so as to improve the accuracy of the carbon emission detection and evaluation result, the acquired regional position information of the region is queried in the regional carbon emission data information base to obtain the corresponding regional carbon emission interference coefficient, the interference coefficients of different regional positions have variability, the regional carbon-rich detection data is modified according to the emission activity intensity data and the interference coefficient and the meteorological thermal kinetic energy characteristic data, the regional carbon emission negative evaluation index is obtained, and the carbon emission detection technology is obtained based on the facilities, the plants, the weather and the carbon emission objects of the remote sensing information is analyzed and the data processing, and the accuracy of the regional carbon emission detection and evaluation result is improved.

As shown in fig. 4, the invention also discloses a carbon bank intelligent evaluation system 4 based on satellite remote sensing big data, which comprises a memory 41 and a processor 42, wherein the memory comprises a carbon bank intelligent evaluation method program based on satellite remote sensing big data, and the following steps are realized when the carbon bank intelligent evaluation method program based on satellite remote sensing big data is executed by the processor:

According to the embodiment of the invention, the emission source identification is performed through a preset satellite detection and identification platform according to the regional facility geophysical prospecting information to obtain regional carbon source facility information of the preset area, the implantation information of the preset area is identified, and the implantation net carbon amount data in a preset time period is obtained by processing according to the implantation information, specifically comprising the following steps:

According to the embodiment of the invention, the carbon source emission characteristic data of each carbon source facility in the preset time period is obtained by inquiring in a preset area carbon source emission data information base according to the regional carbon source facility information, specifically:

wherein K is _d Is regional carbon emission index data, s _hi For the property displacement coefficient of the ith carbon source facility, G _ri Emission activity intensity data for the ith carbon source facility, b _pi The index data of the unit carbon emission of the ith carbon source facility is obtained, n is the number of the carbon source facilities in the preset area,for presetting characteristic coefficient (characteristic coefficient passing region carbon data informationLibrary query acquisition).

The third aspect of the present invention provides a readable storage medium, where the readable storage medium includes a carbon bank intelligent evaluation method program based on satellite remote sensing big data, where the carbon bank intelligent evaluation method program based on satellite remote sensing big data implements the steps of the carbon bank intelligent evaluation method based on satellite remote sensing big data as described in any one of the above.

The invention discloses a carbon emission intelligent evaluation method, a system and a medium based on satellite remote sensing big data, which are characterized in that emission source identification is carried out by acquiring satellite remote sensing detection information to acquire regional carbon source facility information and planting information, planting net carbon quantity data is acquired, carbon source emission characteristic data is acquired according to the regional carbon source facility information, then meteorological thermal kinetic energy characteristic data, carbon emission concentration distribution data and carbon emission object quantity distribution data are extracted, airspace carbon emission detection data are acquired through processing, regional carbon emission index data are acquired through processing the carbon source emission characteristic data, regional carbon enrichment detection data are acquired through processing by combining the airspace carbon emission detection data and the planting net carbon quantity data, regional carbon emission negative surplus evaluation index is acquired through correction processing by combining the meteorological thermal kinetic energy characteristic data and the carbon source emission characteristic data, and then the regional carbon emission negative surplus evaluation index is compared with a preset carbon emission evaluation threshold value to judge the regional carbon emission condition; therefore, information analysis and data processing are carried out on facilities, plant coverage, weather and carbon emission of remote sensing information based on satellite remote sensing big data, regional carbon emission assessment conditions are obtained, and an intelligent technology for carrying out regional carbon emission data processing and assessment through big data technology is realized.

In the several embodiments provided by the present application, it should be understood that the disclosed apparatus and method may be implemented in other ways. The above described device embodiments are only illustrative, e.g. the division of the units is only one logical function division, and there may be other divisions in practice, such as: multiple units or components may be combined or may be integrated into another system, or some features may be omitted, or not performed. In addition, the various components shown or discussed may be coupled or directly coupled or communicatively coupled to each other via some interface, whether indirectly coupled or communicatively coupled to devices or units, whether electrically, mechanically, or otherwise.

The units described above as separate components may or may not be physically separate, and components shown as units may or may not be physical units; can be located in one place or distributed to a plurality of network units; some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

In addition, each functional unit in each embodiment of the present application may be integrated in one processing unit, or each unit may be separately used as one unit, or two or more units may be integrated in one unit; the integrated units may be implemented in hardware or in hardware plus software functional units.

Those of ordinary skill in the art will appreciate that: all or part of the steps for implementing the above method embodiments may be implemented by hardware related to program instructions, and the foregoing program may be stored in a readable storage medium, where the program, when executed, performs steps including the above method embodiments; and the aforementioned storage medium includes: a mobile storage device, a Read-only memory (ROM), a random access memory (RAM, random Access Memory), a magnetic disk or an optical disk, or the like, which can store program codes.

Alternatively, the above-described integrated units of the present invention may be stored in a readable storage medium if implemented in the form of software functional modules and sold or used as separate products. Based on such understanding, the technical solution of the embodiments of the present invention may be embodied in essence or a part contributing to the prior art in the form of a software product stored in a storage medium, including several instructions for causing a computer device (which may be a personal computer, a server, or a network device, etc.) to execute all or part of the methods described in the embodiments of the present invention. And the aforementioned storage medium includes: a removable storage device, ROM, RAM, magnetic or optical disk, or other medium capable of storing program code.

Claims

1. The intelligent carbon bank assessment method based on the satellite remote sensing big data is characterized by comprising the following steps of:

2. The intelligent carbon bank assessment method based on satellite remote sensing big data according to claim 1, wherein the acquiring the regional carbon source facility information of the preset region by performing emission source identification through a preset satellite detection and identification platform according to the regional facility geophysical prospecting information, identifying the implantation information of the preset region, and processing according to the implantation information to obtain implantation net carbon quantity data in a preset time period comprises:

3. The intelligent carbon bank assessment method based on satellite remote sensing big data according to claim 2, wherein the inquiring and obtaining the carbon source emission characteristic data of each carbon source facility in the preset time period according to the regional carbon source facility information in a preset regional carbon bank data information base comprises the following steps:

4. The intelligent carbon bank assessment method based on satellite remote sensing big data according to claim 3, wherein the steps of extracting weather thermal kinetic energy characteristic data according to the airspace weather environment information, extracting carbon bank concentration distribution data and carbon bank object quantity distribution data according to the airspace carbon bank information, and processing to obtain airspace carbon bank detection data include:

5. The intelligent carbon emission assessment method based on satellite remote sensing big data according to claim 4, wherein the processing is performed according to the carbon emission characteristic data of each carbon source facility through a preset regional carbon emission index assessment model to obtain regional carbon emission index data, and the method comprises the following steps:

6. The intelligent carbon bank assessment method based on satellite remote sensing big data according to claim 5, wherein the processing of the regional carbon bank detection data and the net carbon implantation amount data according to the regional carbon discharge index data by a preset carbon bank detection analysis model to obtain regional carbon-rich detection data comprises the following steps:

7. The intelligent carbon emission assessment method based on satellite remote sensing big data according to claim 6, wherein the correcting the regional carbon-rich detection data according to the meteorological thermal kinetic energy characteristic data in combination with the regional position information and the carbon source emission characteristic data to obtain a regional carbon emission negative surplus assessment index comprises the following steps:

wherein N is _f Assessment index for regional carbon emission negative, g _c 、z _u 、r _t Respectively thermal radiation data, temperature change and temperature difference data and air flow power data G _R Is a rowPut activity intensity data, f _u Is the interference coefficient of regional carbon emission aggregation and dispersion, E _d And delta, epsilon, eta and theta are preset characteristic coefficients for the regional carbon-rich detection data.

8. The intelligent carbon bank assessment system based on the satellite remote sensing big data is characterized by comprising: the system comprises a memory and a processor, wherein the memory comprises a program of a carbon bank intelligent evaluation method based on satellite remote sensing big data, and the program of the carbon bank intelligent evaluation method based on the satellite remote sensing big data realizes the following steps when being executed by the processor:

9. The intelligent carbon bank assessment system based on satellite remote sensing big data according to claim 8, wherein the acquiring the regional carbon source facility information of the preset region by performing emission source identification through a preset satellite detection and identification platform according to the regional facility geophysical prospecting information, identifying the implantation information of the preset region, and processing according to the implantation information to obtain implantation net carbon quantity data in a preset time period comprises:

10. A computer readable storage medium, wherein the computer readable storage medium includes a carbon bank intelligent evaluation method program based on satellite remote sensing big data, and when the carbon bank intelligent evaluation method program based on satellite remote sensing big data is executed by a processor, the steps of the carbon bank intelligent evaluation method based on satellite remote sensing big data according to any one of claims 1 to 8 are implemented.