CN115346431B - Method, device and equipment for generating carbon emission map and storage medium - Google Patents
Method, device and equipment for generating carbon emission map and storage medium Download PDFInfo
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Abstract
The present disclosure provides a method, an apparatus, a device and a storage medium for generating a carbon emission map, which relates to the technical field of artificial intelligence, and the concrete implementation scheme is as follows: acquiring first carbon emission data and first land data corresponding to each first land patch in an area to be detected; determining a plurality of second land patches corresponding to each first land patch from the respective land patches of the plurality of reference regions; acquiring second land data of each second land patch, and generating a land data image according to each second land data and the first land data; inputting first carbon emission data and first land data of the area to be detected into the model so as to determine carbon emission characteristics corresponding to each first land patch of the area to be detected; and marking the carbon emission characteristics in the land data image to generate a carbon emission map of the area to be detected. The system can help decision makers and researchers to provide basic data support for regional environment governance analysis and policy suggestions according to regional resource allocation and industry development.
Description
Technical Field
The disclosure relates to the technical field of artificial intelligence, in particular to a method, a device, equipment and a storage medium for generating a carbon emission map.
Background
Along with the continuous development of society, cities are more and more prosperous, but the prosperous cities also bring a large amount of carbon dioxide emission, carbon dioxide is emitted when different objects are produced, transported, used and part of products are recycled, carbon dioxide is greenhouse gas, the emission of carbon dioxide is generally considered as the most main reason of global warming, the environment is seriously influenced, and in order to regulate and control the emission of carbon dioxide, a low-carbon life style is encouraged to be adopted to reduce the carbon emission; on the other hand, the balance is achieved by certain carbon offset measures. Global carbon emission reduction and carbon neutralization targets depend on timely, accurate and reliable dynamic monitoring and policy evaluation of carbon emission, so that a new space-time quantitative characterization paradigm of carbon emission is urgently needed to be established.
Decision makers and researchers need to perform regional resource allocation and industry development according to specific carbon emission situations, and therefore how to provide a carbon emission map to help the researchers provide reliable and accurate carbon emission information is a problem to be solved urgently. In the related art, the carbon emission image includes only the amount of carbon emission, which is very single.
Disclosure of Invention
The disclosure provides a method, an apparatus, a device and a storage medium for generating a carbon emission map.
According to a first aspect of the present disclosure, there is provided a method of generating a carbon emission map, including:
acquiring first carbon emission data and first land data corresponding to each first land patch in an area to be detected;
determining a plurality of second land patches corresponding to each first land patch from respective land patches of a plurality of reference regions;
acquiring second land data of each second land patch, and generating a land data image of the area to be detected according to each second land data and the first land data;
inputting first carbon emission data and first land data of the area to be detected into a carbon analysis neural network model generated by pre-training so as to determine carbon emission characteristics corresponding to each first land patch of the area to be detected;
and marking each carbon emission characteristic in the land data image to generate a carbon emission map of the area to be detected.
According to a second aspect of the present disclosure, there is provided a generation apparatus of a carbon emission map, including:
the first acquisition module is used for acquiring first carbon emission data and first land data corresponding to each first land patch in the area to be detected;
a first determination module for determining a plurality of second land patches corresponding to each first land patch from respective land patches of a plurality of reference regions;
the second acquisition module is used for acquiring second land data of each second land patch and generating a land data image of the area to be detected according to each second land data and the first land data;
the second determination module is used for inputting the first carbon emission data and the first land data of the area to be detected into a carbon analysis neural network model generated by pre-training so as to determine the carbon emission characteristics corresponding to each first land patch of the area to be detected;
and the generation module is used for marking each carbon emission characteristic in the land data image so as to generate a carbon emission map of the area to be detected.
An embodiment of a third aspect of the present disclosure provides a computer device, including: the present invention relates to a computer program product, and a computer program stored on a memory and executable on a processor, which when executed by the processor performs a method as set forth in an embodiment of the first aspect of the present disclosure.
A fourth aspect of the present disclosure is directed to a non-transitory computer-readable storage medium storing a computer program, which when executed by a processor implements the method as set forth in the first aspect of the present disclosure.
A fifth aspect of the present disclosure provides a computer program product, which when executed by an instruction processor performs the method provided in the first aspect of the present disclosure.
The generation method, the device and the equipment of the carbon emission map provided by the disclosure have at least the following beneficial effects:
in the embodiment of the disclosure, first carbon emission data and first land data corresponding to each first land patch in a region to be detected are firstly acquired, then a plurality of second land patches corresponding to each first land patch are determined from each land patch of a plurality of reference regions, then second land data of each second land patch are acquired, a land data image of the region to be detected is generated according to each second land data and the first land data, then the first carbon emission data and the first land data of the region to be detected are input into a carbon analysis neural network model generated by pre-training to determine a carbon emission characteristic corresponding to each first land patch of the region to be detected, and then each carbon emission characteristic is labeled in the land data image to generate a carbon emission map of the region to be detected. Therefore, by combining the second land patch information associated with the first land patch and generating the land data image of the region to be detected according to the second land data corresponding to the reference region and the first land data, the timeliness and accuracy of carbon emission accounting, monitoring and analysis can be effectively improved after the carbon emission characteristics are combined in the land data image. The system can help decision makers and researchers to track and quantify the whole process of the carbon emission source according to local conditions according to regional resource allocation and industry development, and provides basic data support for regional environment governance cooperative mechanism analysis and policy suggestion.
It should be understood that the statements in this section do not necessarily identify key or critical features of the embodiments of the present disclosure, nor do they limit the scope of the present disclosure. Other features of the present disclosure will become apparent from the following description.
Drawings
The drawings are included to provide a better understanding of the present solution and are not to be construed as limiting the present disclosure. Wherein:
FIG. 1 is a schematic flow chart diagram of a method of generating a carbon emission map provided by the present disclosure;
FIG. 2 is a block diagram of a carbon emission map generation apparatus according to the present disclosure;
FIG. 3 is a block diagram of an electronic device used to implement a method of generating a carbon emission map of an embodiment of the present disclosure.
Detailed Description
Exemplary embodiments of the present disclosure are described below with reference to the accompanying drawings, in which various details of the embodiments of the disclosure are included to assist understanding, and which are to be considered as merely exemplary. Accordingly, those of ordinary skill in the art will recognize that various changes and modifications of the embodiments described herein can be made without departing from the scope and spirit of the present disclosure. Also, descriptions of well-known functions and constructions are omitted in the following description for clarity and conciseness.
The method for generating a carbon emission map provided by the present disclosure may be performed by the apparatus for generating a carbon emission map provided by the present disclosure, and may also be performed by an electronic device provided by the present disclosure, where the electronic device may include, but is not limited to, a desktop computer, a tablet computer, and other terminal devices, and the method for generating a carbon emission map provided by the present disclosure is performed by the apparatus for generating a carbon emission map provided by the present disclosure, without limiting the present disclosure.
A method of generating a carbon emission map provided by the present disclosure is described in detail below with reference to the accompanying drawings.
Fig. 1 is a schematic flow chart of a method for generating a carbon emission map according to an embodiment of the present disclosure.
As shown in fig. 1, the method of generating the carbon emission map may include the steps of:
step 101, acquiring first carbon emission data and first land data corresponding to each first land patch in an area to be detected.
The region to be measured may be an urban region, a rural region, a county and city region, a provincial region, or a set of a plurality of provincial regions (e.g., shanghai, zhejiang and Shanghai), which is not limited herein.
It should be noted that land patches refer to relatively homogeneous non-linear regions that are different from the surrounding background. Time and space speckling is common in various systems of different levels in nature. It reflects intra-and inter-system similarities or dissimilarities.
The first land patch can be any land patch in the area to be detected, the area to be detected can be composed of a plurality of land patches, and the characteristics of each land patch are different. The region to be detected can be divided geographically in advance, so that the region to be detected is divided according to natural attributes, building characteristics, industrial structures and community characteristics of the region to be detected, and the region to be detected is divided into a plurality of land patches with different characteristics. For example, a forest area is a land patch, a wetland area is a land patch, and the carbon emission amount of some land patches is high and some land patches are few.
Specifically, first carbon emission data and first land data corresponding to each first land patch in the area to be detected can be obtained from the big data.
The first land data can be the land utilization condition of the first land patch, and the first land data can be determined according to GIS urban land utilization current situation vector data, such as paddy field, cultivated land, grassland, construction land or unused land. Therefore, the method can be used for conveniently and quickly and effectively analyzing and judging the change rule of the land in the area and the influence of the change of the human production life and the living environment on the land utilization.
The first land data may further include, but is not limited to, land use intensity, land area, land building type, land utilization rate, industrial distribution of land area, VOC emission amount corresponding to land area, and electricity usage.
Specifically, the land use behaviors in a certain period of time can be divided into a plurality of types according to different purposes, modes and the like of the land use behaviors of human beings, and the system framework (comprising type names, identification standards, relations among the types and the like) with certain structural relations is formed by the types. A hierarchical structure is generally employed. By acquiring multi-source satellite images such as Sentinel, landsat, GF, ZY and the like covering the whole Chinese situation in a vegetation growing season, combining field investigation and other auxiliary data, analyzing the geometric shape, color feature, texture feature and spatial distribution condition of ground objects, establishing a unified interpretation mark, and obtaining land utilization vector data by adopting a full-digital man-machine interaction operation method.
The first carbon emission data can be determined according to the yearbook energy balance table corresponding to the region to be detected.
In the yearbook energy balance sheet (standard amount), the unit of carbon emission data (terminal consumption amount) is ten thousand tons of standard coal.
Alternatively, the carbon emission data of the carbon emission target of each first land patch may be collected and the average carbon emission data of each first land patch may be calculated, for example, atmospheric CO for each first land patch may be calculated 2 The content concentration is monitored in real time to obtain real-time carbon content in different areas, the vegetation area of the green forest of each first land patch is monitored respectively, carbon absorption is calculated, real-time actual carbon emission changes in different areas are reflected, then AI intelligent analysis processing is carried out on average carbon emission data and real-time carbon emission change data of each first land patch, and CO of a monitored city is calculated 2 High emission area and high emission factor, and monitoring urban CO 2 And (4) a low-emission area and a low-emission factor, and comprehensively determining first carbon emission data.
It can be understood that on the premise of acquiring the first land data and the first carbon emission data, a carbon emission map corresponding to the patches of different land utilization types of the subdivision industry of the grade city can be quickly constructed through a simpler processing process, so that the specific problem that the carbon emission data of different land utilization types are implemented on the patches of the land utilization is solved, and the method has important significance for indicating the carbon emission conditions of different subdivision land utilization types in the corresponding area. Then, by spreading and drawing the carbon emission data on a two-dimensional plane map, workers can carry out expansion analysis on the corresponding carbon emission conditions of different industrial types in different places; based on the carbon emission map, decision makers and researchers can track and quantify the whole process of the carbon emission source according to local conditions according to regional resource allocation and industry development, and basic data support is provided for regional environment governance cooperative mechanism analysis and policy suggestion.
The reference region may be a region similar or similar to the region to be measured, for example, if the region to be measured is a provincial city, the reference region may also be a provincial city, and if the region to be measured is a county city with high economy, the reference region may also be a relatively similar county city region, and the area and the land structure need to be relatively similar. It should be noted that the reference area may be automatically matched by the apparatus according to the type of the area to be measured, so as to provide a closer reference area.
Alternatively, the reference area may be any area, and is not limited herein.
Wherein the second land patch is any land patch in any reference area.
Alternatively, the apparatus may first determine a land use type, a land construction type, a land use rate, a historical annual carbon emission, historical annual energy consumption data, a historical annual VOC emission, a land area for each of the respective land patches in each reference area.
Wherein, the VOC is volatile organic compounds. The historical annual energy consumption data can be historical annual energy consumption, for example, the historical annual energy consumption data of city A is 1185.5 hundred million kilowatt hours, the historical annual VOC emission is 491007.1 kg, the land utilization rate is 75.36%, the land area is 606.519 square kilometers, the land utilization type is cultivated land, urban and rural residential sites and industrial and mining sites are taken as main bodies, and grassland, water areas, forest lands and unused land are taken as auxiliary bodies.
Optionally, if the land use type and the land construction type of any land patch are the same as the land use type and the land construction type of the first land patch, calculating a first difference between the land use rates of any land patch and the first land patch. Further, the apparatus may calculate a second difference between the historical annual carbon emissions of the any land patch and the first land patch, calculate a third difference between the historical annual energy consumption data of the any land patch and the first land patch, calculate a fourth difference between the historical annual VOC emissions of the any land patch and the first land patch, and calculate a fifth difference between the land area of the any land patch and the first land patch.
It should be noted that if the land use type and the land construction type between two land patches are the same, the two land patches are relatively consistent and have relatively high comparability, so that when the land use type and the land construction type between the first land patch and any land patch are the same, the device can continue to compare any land patch with other aspects of the first land patch.
Optionally, if the first difference, the second difference, the third difference, the fourth difference, and the fifth difference all satisfy preset conditions, determining that the any land patch is a second land patch corresponding to the first land patch.
As a possible implementation manner, if a first difference is smaller than a preset land utilization difference threshold, a second difference is smaller than a preset historical annual carbon emission difference threshold, a third difference is smaller than a preset historical annual energy consumption difference threshold, a fourth difference is smaller than a preset historical annual VOC emission difference threshold, and a fifth difference is smaller than a preset land area difference threshold, the first difference, the second difference, the third difference, the fourth difference, and the fifth difference may all satisfy a preset condition, and the device may determine that any land patch is a second land patch corresponding to the first land patch.
It should be noted that, in general, there is a certain relationship between the land area and the land utilization rate, the carbon emission amount in the historical years, the energy consumption data in the historical years, and the VOC emission amount in the historical years, and the larger the land area is, the higher the land utilization rate, the carbon emission amount in the historical years, the energy consumption data in the historical years, and the VOC emission amount in the historical years are, the smaller the land area is, the lower the land utilization rate, the carbon emission amount in the historical years, the energy consumption data in the historical years, and the VOC emission amount in the historical years are.
Thus, as a possible implementation method, the device may determine, according to a preset first mapping relationship table, a second mapping relationship corresponding to the land utilization type and the land building type of any land patch from the respective first mapping relationships included in the first mapping relationship table, then determine, based on the second mapping relationship, a first reference difference threshold corresponding to a fifth difference, a second reference difference threshold corresponding to a fifth difference, a third reference difference threshold corresponding to the fifth difference, and a fourth reference difference threshold corresponding to the fifth difference, and then determine any land patch as a second land patch corresponding to the first land patch in the case that the first difference is smaller than the first reference difference threshold, the second difference is smaller than the second reference difference threshold, the third difference is smaller than the third reference difference threshold, and the fourth difference is smaller than the fourth reference difference threshold.
It can be understood that there are differences between the carbon emission, the land utilization rate and the VOC emission corresponding to different land utilization types and land building types, for example, the carbon emission, the land utilization rate and the VOC emission, and the annual energy consumption data of land patches such as deserts, forests, lakes, etc. are very low, and even if the land area is large, the proportional relationship between the land area and the land utilization rate, the historical annual carbon emission, the historical annual energy consumption data, and the historical annual VOC emission may be different.
Therefore, a first mapping relation table may be predetermined for recording a plurality of first mapping relations, for example, sometimes the land area is increased by one time, and the land utilization rate, the historical annual carbon emission amount, the historical annual energy consumption data, and the historical annual VOC emission amount may be unchanged or changed very little, such a mapping relation may be used as the first type of first mapping relation, and sometimes the land area is increased by one time, while the land utilization rate, the historical annual carbon emission amount, the historical annual energy consumption data, and the historical annual VOC emission amount may be increased by 1.5 times or other multiples, and the change amount is relatively large, and at this time, the mapping relation may be used as the second type of first mapping relation, which may be many, and is not limited herein.
Further, each land use type and land building type correspond to one mapping relationship, and the device can determine a corresponding second mapping relationship according to the current land use type and land building type. The second mapping relation is determined according to the land utilization type and the land building type of any land patch of the area to be measured.
Then, the device may determine, according to the second mapping relationship, a first reference difference threshold, a second reference difference threshold, a third reference difference threshold, and a fourth reference difference threshold corresponding to a fifth difference of the current land area.
The first reference difference threshold may be a land utilization rate corresponding to the land area determined according to the second mapping relationship, that is, the mapping relationship between the land area and the land utilization rate, and since the current value is the fifth difference value, that is, the difference value between the land area of any land patch and the second land patch area, the first reference difference threshold may be a reference difference threshold of the land utilization rate corresponding to the fifth difference value.
The second reference difference threshold may be a historical annual carbon emission amount corresponding to the determined land area according to a second mapping relationship, that is, a mapping relationship between the land area and the historical annual carbon emission amount, and since the current value is a fifth difference value, that is, a difference value between the land area of any land patch and the second land patch area, the second reference difference threshold may be a reference difference threshold of the annual carbon emission amount corresponding to the fifth difference value.
The third reference difference threshold may be historical annual energy consumption data corresponding to the determined land area according to a second mapping relationship, that is, a mapping relationship between the land area and the historical annual energy consumption data, and since the current value is a fifth difference value, that is, a difference value between the land area of any land patch and the second land patch area, the third reference difference threshold may be a reference difference threshold of the historical annual energy consumption data corresponding to the fifth difference value.
The fourth reference difference threshold may be a historical annual VOC emission amount corresponding to the determined land area according to the second mapping relationship, that is, a mapping relationship between the land area and the historical annual VOC emission amount, and thus the fourth reference difference threshold may be a reference difference threshold of the land utilization rate corresponding to the historical annual VOC emission amount.
The first land data and the second land data comprise greenhouse gas concentration data, energy processing data, industrial data, transportation data, agricultural data, land utilization data and resident life data of the region to be detected, wherein the greenhouse gas concentration data, the energy processing data, the industrial data, the transportation data, the agricultural data, the land utilization data and the resident life data are acquired by a remote sensing satellite. Wherein the greenhouse gas concentration data comprises carbon dioxide (CO) 2 ) Concentration data, methane (CH) 4 ) Concentration data, nitrous oxide (N) 2 O) concentration data, perfluorocarbon (PFCs) concentration data, hydrofluorocarbon (HFCs) concentration data, sulfur hexafluoride (SF) 6 ) Concentration data and nitrogen trifluoride (NF) 3 ) Concentration data.
Wherein the energy processing data comprises energy processing production equipment position and floor space, thermal radiation flux density of an energy processing area and other related data. The industrial data comprise relevant data such as industrial wastewater water body distribution area, wastewater organic matter concentration, building floor area, building height and the like, first transportation data highway distribution area, railway distribution area, airport area, flying equipment quantity, distribution positions of medium and large ships, ship running tracks and the like. The first agricultural data comprises relevant data such as the position, the distribution area, the vegetation type, the soil water content, the vegetation structure parameter, the vegetation physiological parameter, the phenological parameter and the like of a paddy field/dry land in agriculture. The first land utilization data comprises relevant data such as forest fire passing area, burning duration, vegetation type, forest biomass, dead organic matters, forest decomposition floor area, forest growth stage and forest growth condition. The first resident life data comprise relevant parameters such as garbage occupation area, black and odorous water body distribution area, sewage organic matter concentration and the like. The first carbon sink data comprises relevant parameters such as forest carbon sink/cultivated land carbon sink/grassland carbon sink/wetland carbon sink, distribution, vegetation type, vegetation structure parameters (such as tree height), vegetation seepage parameters (such as water content and chlorophyll content), phenological parameters, biomass, productivity and the like, and relevant parameters such as type, coverage, biomass and the like of ocean carbon sink (organisms).
Optionally, the device may determine a land utilization rate change curve, land building type change trend information, and a carbon emission change curve corresponding to each second land patch according to the land data of each second land patch in each year, then predict a target land utilization rate, a target land building type, and a target carbon emission corresponding to the area to be measured according to the land utilization rate, the land building type, and the carbon emission corresponding to each first land patch in the area to be measured, and the land utilization rate change curve, the land building type change trend information, and the carbon emission change curve corresponding to each second land patch, and then generate a land data image corresponding to the area to be measured based on the first land data, and the target land utilization rate, the target land building type, and the target carbon emission.
The initial land data image of the region to be detected may be generated according to the first land data of the land patch corresponding to each region to be detected, that is, the comprehensive information image of each land patch of the region to be detected may be generated, for example, each first land data may be labeled in the initial land data image.
Specifically, the device may determine, in combination with the annual land data of each second land patch, a land utilization rate change curve, land structure type change trend information, and a carbon emission change curve corresponding to each second land patch, and then determine, according to the current land utilization rate, land structure type, and carbon emission corresponding to the first land patch, a position of the information corresponding to each first land patch on the land utilization rate change curve, the land structure type change trend information, and the carbon emission change curve, so as to be used for predicting information of the subsequent first land patch, such as a target land utilization rate, a target land structure type, and a target carbon emission, where the target land utilization rate, the target land structure type, and the target carbon emission are predicted data. The device can label the target land utilization rate, the target land building type and the target carbon emission in the initial land data image, so that the land data image can be obtained.
And 104, inputting the first carbon emission data and the first land data of the area to be detected into a carbon analysis neural network model generated by pre-training so as to determine the carbon emission characteristics corresponding to each first land patch of the area to be detected.
The carbon emission characteristics can include carbon emission, emission proportion, emission risk prompt, carbon emission change trend, early warning information, carbon emission object proportion and carbon emission reason contained in the first land patch.
Optionally, the apparatus may first obtain an initial training data set, where the initial training data set includes historical land data and historical carbon emission data corresponding to a plurality of reference areas, where the historical carbon emission data includes carbon emission data corresponding to each land patch of each reference area, where the carbon emission data of each land patch includes carbon emission data of a population corresponding to the land patch, carbon emission data of an industrial fossil fuel, carbon emission data of a living carbon, carbon emission data of a traffic and carbon emission data of a disaster accident, where the historical land data includes a land utilization rate, carbon absorption intensity data of green forest vegetation, industrial structure data, a land area, a land patch distribution, and a VOC emission amount corresponding to each land patch corresponding to each reference area; and performing data desensitization and data cleaning on the initial training data set, and training an initial carbon analysis neural network model by using the initial training data set subjected to data desensitization and data cleaning to obtain the trained carbon analysis neural network model.
It should be noted that through the above training process, the sensitive fields in the initial training data set can be processed without affecting the accuracy of the data analysis result, thereby reducing the data sensitivity and reducing the personal privacy risk. The data cleaning is carried out on the initial training data set, so that repeated and redundant data in the initial training data set can be screened and eliminated, missing parts are supplemented completely, and incorrect data is corrected or deleted, so that the reliability, the integrity and the effective availability of the initial training data set are guaranteed.
It can be understood that, through the training process, the trained carbon analysis neural network model can be subjected to data analysis and processing by combining the first land data and the first carbon emission data, so that the carbon emission characteristics obtained by analyzing the land distribution, land area and land utilization type of each land patch in the area to be tested, the carbon absorption intensity of the green forest vegetation and the carbon emission object of each land patch in the area to be tested can reflect the carbon emission composition, namely the emission proportion, of each land patch. Such as 20% of population carbon emissions, 30% of industrial fossil fuel carbon emissions, 15% of life carbon emissions, 30% of traffic carbon emissions, and 5% of disaster accident carbon emissions, but not limited thereto. In addition, the carbon emission characteristics can also comprise a carbon emission early warning prompt for each current land patch, the reasonable carbon emission intensity and the carbon emission distribution of each current land patch in the area to be detected can be calculated through green forest vegetation carbon absorption intensity, land distribution, land area, land utilization type and industry type information in the land data, for example, if the carbon emission occupancy of industrial fossil fuel is higher than the emission degree adaptive to the land data, the current carbon emission intensity is higher, the early warning prompt needs to be carried out, the carbon emission of different collected objects can be conveniently obtained when the carbon emission data is analyzed, so that observation and analysis are convenient, meanwhile, the carbon emission data change of different objects in the future can be calculated through comparing and analyzing the current carbon emission data and the past carbon emission data, and further planning and management of carbon emission in cities are facilitated.
Because it has combined greenery vegetation carbon absorption intensity, can obtain the actual carbon emission in city, the accurate measurement to carbon emission and carbon absorption has been increased, be favorable to accurate and stable to carbon emission data analysis, and through knowing the area of greenery vegetation, can obtain whether the afforestation level in city is up to standard, simultaneously through the greenery vegetation carbon absorption contrast with the normal ability carbon absorption of same area greenery vegetation and monitoring area, judge whether the growing environment of greenery vegetation is intact in the city, help the growth and the treatment of greenery vegetation in the city.
And 105, marking each carbon emission characteristic in the land data image to generate a carbon emission map of the area to be detected.
Specifically, the device can mark the carbon emission characteristics corresponding to each land patch at the position corresponding to the land patch in the land data image respectively, so as to generate a carbon emission map, and the carbon emission map is displayed on a terminal, so that monitoring personnel can check the carbon emission map.
In the embodiment of the disclosure, first carbon emission data and first land data corresponding to each first land patch in a region to be detected are firstly acquired, then a plurality of second land patches corresponding to each first land patch are determined from each land patch of a plurality of reference regions, then second land data of each second land patch are acquired, a land data image of the region to be detected is generated according to each second land data and the first land data, then the first carbon emission data and the first land data of the region to be detected are input into a carbon analysis neural network model generated by pre-training to determine a carbon emission characteristic corresponding to each first land patch of the region to be detected, and then each carbon emission characteristic is labeled in the land data image to generate a carbon emission map of the region to be detected. Therefore, by combining the second land patch information associated with the first land patch and generating the land data image of the region to be detected according to the second land data corresponding to the reference region and the first land data, the timeliness and accuracy of carbon emission accounting, monitoring and analysis can be effectively improved after the carbon emission characteristics are combined in the land data image. The system can help decision makers and researchers to track and quantify the whole process of the carbon emission source according to local conditions according to regional resource allocation and industry development, and provides basic data support for regional environment governance cooperative mechanism analysis and policy suggestion.
Fig. 2 is a block diagram of a carbon emission map generation apparatus according to an embodiment of the present disclosure.
As shown in fig. 2, the carbon emission map-based generating device 200 may include:
the first acquisition module 210 is configured to acquire first carbon emission data and first land data corresponding to each first land patch in the area to be detected;
a first determination module 220 for determining a plurality of second land patches corresponding to each of the first land patches from respective land patches of a plurality of reference regions;
a second obtaining module 230, configured to obtain second land data of each second land patch, and generate a land data image of the area to be detected according to each second land data and the first land data;
a second determining module 240, configured to input the first carbon emission data and the first land data of the region to be detected into a carbon analysis neural network model generated by pre-training, so as to determine a carbon emission characteristic corresponding to each first land patch of the region to be detected;
a generating module 250, configured to label each carbon emission feature in the land data image to generate a carbon emission map of the area to be detected.
Optionally, the first determining module is specifically configured to:
determining the land utilization type, land building type, land utilization rate, carbon emission in historical years, energy consumption data in historical years, VOC emission in historical years and land area of each land patch in each reference area;
if the land utilization type and the land building type of any land patch are the same as those of the first land patch, calculating a first difference value between the land utilization rates of the any land patch and the first land patch;
calculating a second difference between the historical annual carbon emissions of the any land patch and the first land patch;
calculating a third difference between the historical annual energy consumption data for the any land patch and the first land patch;
calculating a fourth difference between said historical annual VOC emissions of said any land patch and said first land patch, and calculating a fifth difference between said land area of said any land patch and said first land patch;
and if the first difference, the second difference, the third difference, the fourth difference and the fifth difference all meet preset conditions, determining that any land patch is a second land patch corresponding to the first land patch.
Optionally, the second determining module is further configured to:
obtaining an initial training data set, wherein the initial training data set comprises historical land data and historical carbon emission data corresponding to a plurality of reference regions,
wherein the historical carbon emission data comprises carbon emission data corresponding to each land patch for each reference region,
wherein the carbon emission data of each land patch comprises population carbon emission data, industrial fossil fuel carbon emission data, living carbon emission data, traffic carbon emission data and disaster accident carbon emission data corresponding to the land patch,
the historical land data comprises land utilization rate, green forest vegetation carbon absorption intensity data, industry structure data, land area, land patch distribution and VOC emission corresponding to each land patch corresponding to each reference region;
and performing data desensitization and data cleaning on the initial training data set, and training an initial carbon analysis neural network model by using the initial training data set subjected to data desensitization and data cleaning to obtain the trained carbon analysis neural network model.
Optionally, the first determining module is further configured to include:
determining a second mapping relation corresponding to the land utilization type and the land building type of any land patch from each first mapping relation contained in a first mapping relation table according to a preset first mapping relation table;
determining a first reference difference threshold corresponding to the fifth difference, a second reference difference threshold corresponding to the fifth difference, a third reference difference threshold corresponding to the fifth difference, and a fourth reference difference threshold corresponding to the fifth difference based on the second mapping;
determining any land patch as a second land patch corresponding to the first land patch if the first difference is less than the first reference difference threshold, the second difference is less than the second reference difference threshold, the third difference is less than the third reference difference threshold, and the fourth difference is less than the fourth reference difference threshold.
Optionally, the second obtaining module 230 is specifically configured to:
determining a land utilization rate change curve, land building type change trend information and a carbon emission change curve corresponding to each second land patch according to the annual land data of each second land patch;
predicting a target land utilization rate, a target land building type and a target carbon emission corresponding to the area to be detected according to the land utilization rate, the land building type and the carbon emission corresponding to each first land patch in the area to be detected, and a land utilization rate change curve, land building type change trend information and a carbon emission change curve corresponding to each second land patch;
and generating a land data image corresponding to the area to be detected based on the first land data, the target land utilization rate, the target land building type and the target carbon emission.
In the embodiment of the disclosure, first carbon emission data and first land data corresponding to each first land patch in a region to be detected are firstly acquired, then a plurality of second land patches corresponding to each first land patch are determined from each land patch of a plurality of reference regions, then second land data of each second land patch are acquired, a land data image of the region to be detected is generated according to each second land data and the first land data, then the first carbon emission data and the first land data of the region to be detected are input into a carbon analysis neural network model generated by pre-training to determine a carbon emission characteristic corresponding to each first land patch of the region to be detected, and then each carbon emission characteristic is labeled in the land data image to generate a carbon emission map of the region to be detected. Therefore, by combining the second land patch information associated with the first land patch and generating the land data image of the region to be detected according to the second land data corresponding to the reference region and the first land data, the timeliness and accuracy of carbon emission accounting, monitoring and analysis can be effectively improved after the carbon emission characteristics are combined in the land data image. The system can help decision makers and researchers to track and quantify the whole process of the carbon emission source according to local conditions according to regional resource allocation and industry development, and provides basic data support for regional environment governance cooperative mechanism analysis and policy suggestion.
FIG. 3 illustrates a block diagram of an exemplary computer device suitable for use to implement embodiments of the present disclosure. The computer device 12 shown in fig. 3 is only one example and should not bring any limitations to the functionality or scope of use of the embodiments of the present disclosure.
As shown in FIG. 3, computer device 12 is in the form of a general purpose computing device. The components of computer device 12 may include, but are not limited to: one or more processors or processing units 16, a system memory 28, and a bus 18 that couples various system components including the system memory 28 and the processing unit 16.
A program/utility 40 having a set (at least one) of program modules 42 may be stored, for example, in memory 28, such program modules 42 including, but not limited to, an operating system, one or more application programs, other program modules, and program data, each of which examples or some combination thereof may comprise an implementation of a network environment. Program modules 42 generally carry out the functions and/or methodologies of the embodiments described in this disclosure.
The processing unit 16 executes various functional applications and data processing, for example, implementing the methods mentioned in the foregoing embodiments, by running a program stored in the system memory 28.
In the description of the present specification, reference to the description of "one embodiment," "some embodiments," "an example," "a specific example," or "some examples" or the like means that a particular feature, structure, material, or characteristic described in connection with the embodiment or example is included in at least one embodiment or example of the present disclosure. In this specification, the schematic representations of the terms used above are not necessarily intended to refer to the same embodiment or example. Furthermore, the particular features, structures, materials, or characteristics described may be combined in any suitable manner in any one or more embodiments or examples. Furthermore, various embodiments or examples and features of different embodiments or examples described in this specification can be combined and combined by one skilled in the art without contradiction.
Furthermore, the terms "first", "second" and "first" are used for descriptive purposes only and are not to be construed as indicating or implying relative importance or implicitly indicating the number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include at least one such feature. In the description of the present disclosure, "a plurality" means at least two, e.g., two, three, etc., unless explicitly specifically limited otherwise.
Any process or method descriptions in flow charts or otherwise described herein may be understood as representing modules, segments, or portions of code which include one or more executable instructions for implementing steps of a custom logic function or process, and alternate implementations are included within the scope of the preferred embodiment of the present disclosure in which functions may be executed out of order from that shown or discussed, including substantially concurrently or in reverse order, depending on the functionality involved, as would be understood by those reasonably skilled in the art of the embodiments of the present disclosure.
The logic and/or steps represented in the flowcharts or otherwise described herein, e.g., an ordered listing of executable instructions that can be considered to implement logical functions, can be embodied in any computer-readable medium for use by or in connection with an instruction execution system, apparatus, or device, such as a computer-based system, processor-containing system, or other system that can fetch the instructions from the instruction execution system, apparatus, or device and execute the instructions. For the purposes of this description, a "computer-readable medium" can be any means that can contain, store, communicate, propagate, or transport the program for use by or in connection with the instruction execution system, apparatus, or device. More specific examples (a non-exhaustive list) of the computer-readable medium would include the following: an electrical connection (electronic device) having one or more wires, a portable computer diskette (magnetic device), a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber device, and a portable compact disc read-only memory (CDROM). Additionally, the computer-readable medium could even be paper or another suitable medium upon which the program is printed, as the program can be electronically captured, via for instance optical scanning of the paper or other medium, then compiled, interpreted or otherwise processed in a suitable manner if necessary, and then stored in a computer memory.
It should be understood that portions of the present disclosure may be implemented in hardware, software, firmware, or a combination thereof. In the above embodiments, the various steps or methods may be implemented in software or firmware stored in memory and executed by a suitable instruction execution system. If implemented in hardware, as in another embodiment, any one or combination of the following techniques, which are known in the art, may be used: a discrete logic circuit having a logic gate circuit for implementing a logic function on a data signal, an application specific integrated circuit having an appropriate combinational logic gate circuit, a Programmable Gate Array (PGA), a Field Programmable Gate Array (FPGA), or the like.
It will be understood by those skilled in the art that all or part of the steps carried out in the method of implementing the above embodiments may be implemented by hardware related to instructions of a program, which may be stored in a computer readable storage medium, and the program, when executed, includes one or a combination of the steps of the method embodiments.
In the embodiment of the disclosure, a plurality of contents to be recommended of a plurality of categories and interest features of a user are firstly acquired, a first rating label is determined according to the proportion of the contents to be recommended in the category to which the contents belong, a second rating label is determined according to the matching degree of the contents to be recommended and the interest features of the user, a third rating label is determined according to the feature information of the user and the matching degree of the contents to be recommended, and a target recommended content is determined according to the first rating label, the second rating label and the third rating label corresponding to the contents to be recommended. Therefore, according to the set of algorithm rules, the content which the user wants to see can be actively pushed to the user, the recommended content contains the interest characteristics of the user, the accurate delivery of the recommended content with high heat and interest to the user is realized, and the click rate of the content is directly embodied to be greatly improved.
In addition, functional units in the embodiments of the present disclosure may be integrated into one processing module, or each unit may exist alone physically, or two or more units are integrated into one module. The integrated module can be realized in a hardware mode, and can also be realized in a software functional module mode. The integrated module, if implemented in the form of a software functional module and sold or used as a stand-alone product, may also be stored in a computer readable storage medium.
The storage medium mentioned above may be a read-only memory, a magnetic or optical disk, etc. Although embodiments of the present disclosure have been shown and described above, it is understood that the above embodiments are exemplary and should not be construed as limiting the present disclosure, and that variations, modifications, substitutions and alterations may be made in the above embodiments by those of ordinary skill in the art within the scope of the present disclosure.
Claims (9)
1. A method of generating a carbon emission map, comprising:
acquiring first carbon emission data and first land data corresponding to each first land patch in an area to be detected;
determining a plurality of second land patches corresponding to each of the first land patches from respective land patches of a plurality of reference regions;
acquiring second land data of each second land patch, and generating a land data image of the area to be detected according to each second land data and the first land data;
inputting first carbon emission data and first land data of the area to be detected into a carbon analysis neural network model generated by pre-training so as to determine carbon emission characteristics corresponding to each first land patch of the area to be detected;
marking each carbon emission characteristic in the land data image to generate a carbon emission map of the area to be detected;
the second land data comprises land data of the second land patch for a plurality of years, and the land data image of the region to be detected is generated according to each second land data and the first land data, and comprises the following steps:
determining a land utilization rate change curve, land building type change trend information and a carbon emission change curve corresponding to each second land patch according to the annual land data of each second land patch;
predicting a target land utilization rate, a target land building type and a target carbon emission corresponding to the area to be detected according to the land utilization rate, the land building type and the carbon emission corresponding to each first land patch in the area to be detected, and a land utilization rate change curve, land building type change trend information and a carbon emission change curve corresponding to each second land patch;
and generating a land data image corresponding to the area to be detected based on the first land data, the target land utilization rate, the target land building type and the target carbon emission.
2. The method of claim 1, wherein said determining a plurality of second land patches from respective land patches of a plurality of reference regions corresponding to each of said first land patches comprises:
determining the land utilization type, land building type, land utilization rate, carbon emission in historical years, energy consumption data in historical years, VOC emission in historical years and land area of each land patch in each reference area;
if the land utilization type and the land building type of any land patch are the same as those of the first land patch, calculating a first difference value between the land utilization rates of the any land patch and the first land patch;
calculating a second difference between the historical annual carbon emissions of the any land patch and the first land patch;
calculating a third difference between the historical annual energy consumption data for the any land patch and the first land patch;
calculating a fourth difference between said historical annual VOC emissions of said any land patch and said first land patch,
and calculating a fifth difference between the area of land of said any land patch and said first land patch;
and if the first difference, the second difference, the third difference, the fourth difference and the fifth difference all meet preset conditions, determining that any land patch is a second land patch corresponding to the first land patch.
3. The method according to claim 2, wherein if the first difference, the second difference, the third difference, the fourth difference and the fifth difference all satisfy a preset condition, determining that any one of the land patches is a second land patch corresponding to the first land patch comprises:
determining a second mapping relation corresponding to the land utilization type and the land building type of any land patch from each first mapping relation contained in a first mapping relation table according to a preset first mapping relation table;
determining a first reference difference threshold corresponding to the fifth difference, a second reference difference threshold corresponding to the fifth difference, a third reference difference threshold corresponding to the fifth difference, and a fourth reference difference threshold corresponding to the fifth difference based on the second mapping;
determining any land patch as a second land patch corresponding to the first land patch if the first difference is less than the first reference difference threshold, the second difference is less than the second reference difference threshold, the third difference is less than the third reference difference threshold, and the fourth difference is less than the fourth reference difference threshold.
4. The method of claim 1, wherein prior to said inputting the first carbon emissions data and the first land data for the area under test into the pre-trained carbon analysis neural network model, further comprising:
obtaining an initial training data set, wherein the initial training data set comprises historical land data and historical carbon emission data corresponding to a plurality of reference regions,
wherein the historical carbon emission data comprises carbon emission data corresponding to each land patch for each reference region,
wherein the carbon emission data of each land patch comprises population carbon emission data, industrial fossil fuel carbon emission data, living carbon emission data, traffic carbon emission data and disaster accident carbon emission data corresponding to the land patch,
the historical land data comprises land utilization rate, green forest vegetation carbon absorption intensity data, industry structure data, land area, land patch distribution and VOC emission corresponding to each land patch corresponding to each reference region;
and carrying out data desensitization and data cleaning on the initial training data set, and training the initial carbon analysis neural network model by using the initial training data set subjected to data desensitization and data cleaning so as to obtain the trained carbon analysis neural network model.
5. An apparatus for generating a carbon emission map, comprising:
the first acquisition module is used for acquiring first carbon emission data and first land data corresponding to each first land patch in the area to be detected;
a first determination module for determining a plurality of second land patches corresponding to each of the first land patches from respective land patches of a plurality of reference regions;
the second acquisition module is used for acquiring second land data of each second land patch and generating a land data image of the area to be detected according to each second land data and the first land data;
the second determination module is used for inputting the first carbon emission data and the first land data of the area to be detected into a carbon analysis neural network model generated by pre-training so as to determine the carbon emission characteristics corresponding to each first land patch of the area to be detected; and the generating module is used for marking each carbon emission characteristic in the land data image so as to generate a carbon emission map of the area to be detected.
6. The apparatus of claim 5, wherein the first determining module is specifically configured to:
determining the land utilization type, land building type, land utilization rate, carbon emission in historical years, energy consumption data in historical years, VOC emission in historical years and land area of each land patch in each reference area;
if the land utilization type and the land building type of any land patch are the same as those of the first land patch, calculating a first difference value between the land utilization rates of the any land patch and the first land patch;
calculating a second difference between the historical annual carbon emissions of the any land patch and the first land patch;
calculating a third difference between the historical annual energy consumption data for the any land patch and the first land patch;
calculating a fourth difference between said historical annual VOC emissions of said any land patch and said first land patch,
and calculating a fifth difference between the area of land of said any land patch and said first land patch;
and if the first difference, the second difference, the third difference, the fourth difference and the fifth difference all meet preset conditions, determining that any land patch is a second land patch corresponding to the first land patch.
7. The apparatus of claim 5, wherein the second determining module is further configured to:
obtaining an initial training data set, wherein the initial training data set comprises historical land data and historical carbon emission data corresponding to a plurality of reference regions,
wherein the historical carbon emission data comprises carbon emission data corresponding to each land patch for each reference region,
wherein the carbon emission data of each land patch comprises population carbon emission data, industrial fossil fuel carbon emission data, life carbon emission data, traffic carbon emission data and disaster accident carbon emission data corresponding to the land patch,
the historical land data comprises land utilization rate, green forest vegetation carbon absorption intensity data, industry structure data, land area, land patch distribution and VOC emission corresponding to each land patch corresponding to each reference region;
and performing data desensitization and data cleaning on the initial training data set, and training an initial carbon analysis neural network model by using the initial training data set subjected to data desensitization and data cleaning to obtain the trained carbon analysis neural network model.
8. A computer device comprising a memory and a processor, the memory having stored thereon a computer program operable on the processor, wherein the processor, when executing the computer program, performs the steps of the method of any of claims 1 to 4.
9. A computer-readable storage medium, on which a computer program is stored, which, when being executed by a processor, carries out the steps of the method of any one of claims 1 to 4.
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