Carbon emission analysis method and device, electronic equipment and storage medium

Abstract

The invention relates to a carbon emission analysis method and device, electronic equipment and a storage medium, and is applied to the technical field of carbon emission. The carbon emission analysis method comprises the following steps: acquiring land utilization data and carbon emission data including at least one land utilization type data in different periods; calculating the carbon emission of each land utilization type in different periods through a preset carbon emission calculation model based on the land utilization data and the carbon emission data; based on the carbon emissions of each land utilization type in different periods, analyzing to obtain an association relationship between the land utilization change and the carbon emissions; and outputting an association relationship between the land utilization change and the carbon emission. By adopting the method provided by the embodiment of the invention, comprehensive, accurate and real-time analysis of the carbon emission can be realized, and the analysis efficiency and analysis effect of the carbon emission are improved.

Description

TECHNICAL FIELD

[0001] The present disclosure relates to the technical field of carbon emission, and particularly relates to a carbon emission analysis method and device, electronic equipment and storage medium. BACKGROUND

[0002] In the related art, land use change is one of the important factors affecting carbon emission. Different land use types, such as forests, farmland, and construction land, often have significant differences in carbon emission characteristics. In order to understand the changes in carbon emission and achieve carbon emission analysis based on land use change, a carbon emission analysis method is urgently needed. SUMMARY

[0003] The present disclosure provides a carbon emission analysis method, device, electronic equipment and storage medium, and the technical solutions of the present disclosure are as follows:

[0004] According to a first aspect of an embodiment of the present disclosure, a carbon emission analysis method is provided, comprising:

[0005] Collecting land use data and carbon emission data in different periods; wherein the land use data comprises at least one land use type data;

[0006] Calculating the carbon emission amount of each land use type in different periods based on the land use data and carbon emission data through a preset carbon emission calculation model;

[0007] Based on the carbon emission amount of each land use type in different periods, the correlation between land use change and carbon emission amount is analyzed;

[0008] Outputting the correlation between land use change and carbon emission amount.

[0009] In a possible implementation, collecting land use data and carbon emission data in different periods comprises:

[0010] Collecting land use type distribution image data in different periods and collecting ecological attribute data of each land;

[0011] Collecting energy consumption data and industrial production data of the land in different periods.

[0012] In a possible implementation, before calculating the carbon emission amount of each land use type in different periods based on the land use data and carbon emission data through a preset carbon emission calculation model, the method comprises:

[0013] Data preprocessing is performed on the land use data and carbon emission data in different periods; wherein the data preprocessing comprises data cleaning, data denoising, and data standardization processing;

[0014] The carbon emission amount of each land use type in different periods is calculated based on the land use data and the carbon emission data by using a preset carbon emission calculation model, including:

[0015] The carbon emission amount of each land use type in different periods is calculated based on the land use data and the carbon emission data after data preprocessing by using a preset carbon emission calculation model.

[0016] In a possible implementation, the carbon emission amount of each land use type in different periods is calculated based on the land use data and the carbon emission data by using a preset carbon emission calculation model, including:

[0017] The carbon emission amount of each land use type in different periods is calculated based on the land use type distribution image data, the ecological attribute data of each land, the energy consumption data of the land, and the industrial production data by using a preset carbon emission calculation model.

[0018] In a possible implementation, the correlation between the land use change and the carbon emission amount is analyzed based on the carbon emission amount of each land use type in different periods, including:

[0019] The correlation between the land use change and the carbon emission amount is obtained by analyzing the carbon emission amount of each land use type in different periods by using a data mining and statistical analysis method.

[0020] In a possible implementation, the correlation between the land use change and the carbon emission amount is output, including at least one of the following:

[0021] The correlation between the land use change and the carbon emission amount is displayed by using a geographic information system software and a data visualization tool.

[0022] A land use carbon emission analysis report is generated based on the correlation between the land use change and the carbon emission amount, and the land use carbon emission analysis report is output.

[0023] According to a second aspect of the embodiments of the present disclosure, a carbon emission analysis device is provided, including:

[0024] A data collection module is configured to collect land use data and carbon emission data in different periods, wherein the land use data includes at least one land use type data.

[0025] A carbon emission calculation module is configured to calculate the carbon emission amount of each land use type in different periods based on the land use data and the carbon emission data by using a preset carbon emission calculation model.

[0026] a data analysis module configured to analyze a correlation between the land use change and the carbon emission based on the carbon emission of each land use type in different periods;

[0027] an output module configured to output the correlation between the land use change and the carbon emission.

[0028] According to a third aspect of the embodiments of the present disclosure, an electronic device is provided, comprising:

[0029] a processor;

[0030] a memory configured to store instructions executable by the processor;

[0031] The processor is configured to execute the instructions to implement the carbon emission analysis method according to any one of the first aspect.

[0032] According to a fourth aspect of the embodiments of the present disclosure, a storage medium is provided, when instructions in the storage medium are executed by a processor of an electronic device, the electronic device is enabled to perform the carbon emission analysis method according to any one of the first aspect.

[0033] According to a fifth aspect of the embodiments of the present disclosure, a computer program product is provided, comprising a computer program, when the computer program is executed by a processor, the carbon emission analysis method according to any one of the first aspect is implemented.

[0034] The embodiments of the present disclosure provide at least the following beneficial effects:

[0035] In the embodiments of the present disclosure, land use data and carbon emission data in different periods are collected, the land use data comprising data of at least one land use type; a carbon emission calculation model is preset, and based on the land use data and the carbon emission data, carbon emission of each land use type in different periods is calculated; based on the carbon emission of each land use type in different periods, a correlation between the land use change and the carbon emission is analyzed; and the correlation between the land use change and the carbon emission is output. In this way, based on the land use data and the carbon emission data, combined with the calculation model, comprehensive, accurate and real-time analysis of carbon emission can be realized, and the carbon emission analysis efficiency and analysis effect are improved.

[0036] It should be understood that the foregoing general description and the following detailed description are only exemplary and explanatory, and cannot limit the present disclosure. BRIEF DESCRIPTION OF DRAWINGS

[0037] The accompanying drawings, which are incorporated into and form part of the specification, illustrate embodiments consistent with the present disclosure and, together with the specification, serve to explain the principles of the present disclosure, and do not constitute an improper limitation on the present disclosure.

[0038] Figure 1 is a flow chart of a carbon emission analysis method according to an example embodiment.

[0039] Figure 2 is a block diagram of a carbon emission analysis device according to an example embodiment.

[0040] Figure 3 is a block diagram of an electronic device according to an example embodiment. DETAILED DESCRIPTION

[0041] In order for those skilled in the art to better understand the technical solutions of the present disclosure, the technical solutions in the embodiments of the present disclosure will be described clearly and completely below in conjunction with the drawings.

[0042] It should be noted that the terms "first", "second", etc. in the specification and claims of the present disclosure and the above-mentioned drawings are used to distinguish similar objects, and do not necessarily describe a specific order or sequence. It should be understood that the data used in this way can be interchanged under appropriate circumstances, so that the embodiments of the present disclosure described herein can be implemented in an order other than those illustrated or described herein. The implementation described in the following example embodiments does not represent all implementations consistent with the present disclosure. Rather, they are merely examples of devices and methods consistent with some aspects of the present disclosure as detailed in the appended claims.

[0043] As known from the background, land use change is one of the important factors affecting carbon emissions. Different land use types, such as forests, farmland, and construction land, have significantly different carbon emission characteristics. In related technologies, although there are some carbon emission analysis methods, in terms of carbon emission analysis of land use, at least the following deficiencies exist: it is difficult to comprehensively and accurately obtain land use related data, which limits the accuracy and reliability of the analysis results; some analysis systems lack effective integration and deep mining of multi-source data, making it difficult to achieve fine analysis of carbon emissions; in the analysis process, the dynamic monitoring and real-time updating capability of land use change is weak, and it is difficult to reflect the latest changes in carbon emissions in a timely manner.

[0044] Based on this, the present proposal provides a carbon emission analysis method, device, electronic equipment and storage medium, which can collect land use data including at least one type of land use data and carbon emission data at different periods; calculate the carbon emission of each type of land use at different periods based on the land use data and the carbon emission data through a preset carbon emission calculation model; analyze the correlation between land use change and carbon emission based on the carbon emission of each type of land use at different periods; and output the correlation between land use change and carbon emission. In this way, based on the land use data and the carbon emission data, the comprehensive, accurate and real-time analysis of carbon emission can be realized by combining the calculation model, and the carbon emission analysis efficiency and effect are improved.

[0045] The carbon emission analysis method, device, electronic equipment and storage medium provided by the embodiments of the present disclosure will be described in detail below with reference to the accompanying drawings.

[0046] Figure 1 is a flowchart of a carbon emission analysis method provided by an embodiment of the present disclosure. The carbon emission analysis method can be applied to a server, for example, a single server or a server cluster. As shown in Figure 1 , the carbon emission analysis method can include the following steps.

[0047] In step S101, land use data and carbon emission data at different periods are collected.

[0048] The land use data includes at least one type of land use data.

[0049] In the embodiments of the present disclosure, when performing carbon emission analysis, land use data and carbon emission data at different periods can be collected first. For example, collecting land use data and carbon emission data at different periods can include collecting land use type distribution image data at different periods, collecting ecological attribute data of each type of land, collecting energy consumption data and industrial production data of land at different periods. As an example, land use data at different periods, different geographical locations and different land use types can be collected. Different periods can be different time periods, different seasons, etc.; different land use types can be forest, grassland, farmland, construction land and other types of land use. As a specific example, land use type distribution image data at different periods can be obtained by satellite remote sensing, covering forest, grassland, farmland, construction land and other types of land use. For example, high-resolution remote sensing images can be obtained from a satellite remote sensing data provider regularly (e.g., once a month), and the image resolution can be high enough to distinguish different land use types; professional remote sensing image receiving equipment and software are used for data receiving and storage.

[0050] The basic geographic data of the land, such as geographic location and topography, can be collected by a geographic information system (GIS), and the soil carbon content and vegetation growth conditions can be collected by field monitoring stations. Meanwhile, carbon emission data related to carbon emissions, such as energy consumption data and industrial production data, can be collected. For example, the latest basic geographic data, including topographic data and zoning data, can be obtained, and field monitoring stations can be set up in different land use type areas, and professional personnel can be arranged to conduct field measurements regularly (for example, monthly) to record carbon emission data such as soil carbon content, vegetation height, and coverage. In addition, for example, energy consumption reports and industrial production statistics can be collected, and the data can be updated regularly, for example, once every quarter.

[0051] In step S102, the carbon emission amount of each land use type in different periods is calculated based on the land use data and the carbon emission data by a preset carbon emission calculation model.

[0052] In the embodiments of the present disclosure, after the land use data and the carbon emission data are obtained, the carbon emission amount of each land use type in different periods can be calculated by a preset carbon emission calculation model. For example, the land use data and the carbon emission data can be input into the preset carbon emission calculation model, and the land use data and the carbon emission data can be analyzed and calculated by the preset carbon emission model to obtain the carbon emission amount of each land use type in different periods. For example, according to the carbon emission characteristics of different land use types, a corresponding carbon emission calculation model (i.e., the preset carbon emission model) can be established. For example, for forest land, the preset carbon emission calculation model can calculate the carbon emission amount according to the vegetation growth amount, the change in carbon storage, and forest fires and other factors; for farmland, the preset carbon emission calculation model can calculate the carbon emission amount by considering the use of chemical fertilizers, the energy consumption of agricultural machinery, and soil respiration and other factors; and for construction land, the preset carbon emission calculation model can calculate the carbon emission amount based on building energy consumption, transportation carbon emission, and other factors. In this way, the carbon emission amount of different land use types in different periods can be accurately calculated.

[0053] As an example, the preset carbon emission calculation model can be, for example, a carbon emission coefficient method, a carbon footprint method, a LEAP (Long Range Energy Alternatives Planning System), a space-time simulation model (which combines time and space dimensions to predict the impact of land use change on carbon emissions), a structural equation model (SEM), a machine learning model (such as an ARIMA-BP neural network model), and the like.

[0054] In step S103, the correlation between the land use change and the carbon emission is analyzed based on the carbon emission of each land use type in different periods.

[0055] In the embodiments of the present disclosure, after the carbon emission of each land use type in different periods is calculated, the correlation between the land use change and the carbon emission can be analyzed, including analyzing the change trend of the carbon emission of different regions, different time scales and different land use types. As an example, the correlation between the land use change and the carbon emission can include the influence of the land use type change on the carbon emission, for example, the land use types such as farmland, forest land and grassland play an important role in carbon absorption, while the expansion of construction land usually increases carbon emission; the correlation between the spatio-temporal characteristics of the land use change and the carbon emission, for example, the correlation between different land use types and the carbon emission in different periods in different regions.

[0056] In step S104, the correlation between the land use change and the carbon emission is output.

[0057] In the embodiments of the present disclosure, after the correlation between the land use change and the carbon emission is analyzed, the correlation between the land use change and the carbon emission can be output. As an example, the correlation between the land use change and the carbon emission can be displayed. As an example, the correlation between the land use change and the carbon emission can be displayed by using geographic information system software and data visualization tools. For example, the correlation between the land use change and the carbon emission can be displayed by using geographic information system (GIS) software and data visualization tools (such as Echarts), for example, the carbon emission data can be displayed in the form of a map, and different colors can be used to represent different carbon emission levels; a time series chart can be generated to show the change trend of the carbon emission of different land use types over time.

[0058] Alternatively, a land use carbon emission analysis report can be generated based on the correlation between the land use change and the carbon emission; and the land use carbon emission analysis report can be output. As an example, a land use carbon emission report can be generated based on the correlation between the land use change and the carbon emission, including land use carbon emission spatio-temporal characteristic analysis, carbon emission hotspot analysis, carbon emission barycenter migration path characteristic analysis and influence factor driving mechanism analysis.

[0059] In the embodiments of the present disclosure, the correlation between the land use change and the carbon emission amount is obtained by collecting land use data and carbon emission data of different periods, the carbon emission amount of each land use type in different periods is calculated based on the land use data and the carbon emission data by using a preset carbon emission calculation model, and the correlation between the land use change and the carbon emission amount is output. In this way, the comprehensive, accurate and real-time analysis of carbon emission can be realized based on the land use data and the carbon emission data in combination with the calculation model, and the carbon emission analysis efficiency and effect are improved.

[0060] In a possible implementation, before the carbon emission amount of each land use type in different periods is calculated based on the land use data and the carbon emission data by using a preset carbon emission calculation model, the following steps are included.

[0061] The land use data and the carbon emission data of different periods are subjected to data preprocessing, and the data preprocessing includes data cleaning, data denoising and data standardization processing.

[0062] The carbon emission amount of each land use type in different periods is calculated based on the land use data and the carbon emission data by using a preset carbon emission calculation model.

[0063] The carbon emission amount of each land use type in different periods is calculated based on the land use data and the carbon emission data after data preprocessing by using a preset carbon emission calculation model.

[0064] In the embodiments of the present disclosure, before the carbon emission amount of each land use type in different periods is calculated, the land use data and the carbon emission data can be subjected to data preprocessing. For example, the land use data and the carbon emission data of different periods are subjected to data cleaning, data denoising and data standardization processing. For example, the remote sensing image data can be subjected to geometric correction, so that the geometric deformation in the image is eliminated according to the ground control point data, and the accuracy of the position of the ground object in the image is ensured; the remote sensing image data can be subjected to radiation correction, so that the brightness and contrast of the image are adjusted, and the radiation error caused by factors such as atmospheric scattering is removed, so as to improve the image quality. For the geographic data, the GIS software is used for format conversion, and the data is unified into a standard format that can be recognized by the system, for example, the data processing script can be written to identify and remove the abnormal values in the data, such as the soil carbon content data that deviates from the normal range. The geographic data and other monitoring data are subjected to format unification and abnormal value processing, so as to ensure the accuracy and consistency of the data, and provide a reliable data basis for subsequent analysis.

[0065] In a possible implementation, the carbon emission amount of each land use type in different periods is calculated based on the land use data and the carbon emission data by using a preset carbon emission calculation model.

[0066] The carbon emission amount of each land use type in different periods is calculated based on the distribution image data of each land use type, the ecological attribute data of each land, the energy consumption data of the land, and the industrial production data, through a preset carbon emission calculation model.

[0067] In the embodiments of the present disclosure, when calculating the carbon emission amount of each land use type in different periods, the calculation can be performed according to the distribution image data of each land use type, the ecological attribute data of each land, the energy consumption data of the land, and the industrial production data. As a specific example, for forest land type, the carbon absorption and carbon emission amount of the forest can be calculated according to the field monitored vegetation growth data and the satellite remote sensing obtained forest coverage area change data, combined with the information such as the number and area of forest fires, by using a forest ecosystem carbon cycle model (i.e., a preset carbon emission calculation model corresponding to the forest land type). For example, the carbon accumulation amount of the vegetation per year is calculated according to a vegetation growth model, and the carbon emission caused by the fire is calculated according to a fire loss model. For cultivated land type, the carbon emission in the process of using fertilizer can be calculated according to the collected fertilizer usage data, according to the content of carbon elements in the fertilizer and the chemical reaction principle; the carbon emission of agricultural machinery operation can be calculated according to the energy consumption data (such as diesel usage) of the agricultural machinery, combined with the energy carbon emission coefficient; at the same time, the soil carbon emission amount can be calculated by using the soil respiration monitoring data, considering the influence of factors such as soil temperature and humidity on soil respiration. For construction land type, the carbon emission amount of the building can be calculated according to the building energy consumption data (such as power and natural gas consumption), according to the carbon emission coefficient of different energy; the traffic carbon emission amount can be calculated through the traffic flow monitoring data and the carbon emission coefficient of different vehicle types; and the carbon emission amounts of various types of construction land are summarized to obtain the carbon emission amount of the construction land type.

[0068] In a possible implementation, based on the carbon emission amount of each land use type in different periods, the correlation between the land use change and the carbon emission amount is analyzed, including:

[0069] The carbon emission amount of each land use type in different periods is analyzed through data mining and statistical analysis method, to obtain the correlation between the land use change and the carbon emission amount.

[0070] In embodiments of the present disclosure, the carbon emissions of each land use type in different periods can be analyzed in depth by data mining and statistical analysis methods, and the correlation between land use change and carbon emissions can be analyzed. For example, statistical analysis software (such as SPSS (Statistical Package for the Social Sciences) and R language) can be used to analyze the correlation of carbon emission data and study the correlation between land use change and carbon emissions. For example, the quantitative relationship between the decrease of forest area and the increase of carbon emissions can be analyzed. Using data mining algorithms (such as decision tree algorithms), the variation law and influencing factors of carbon emissions in different regions and different time scales can be mined.

[0071] To make the carbon emission analysis method, device, electronic equipment and storage medium provided by the embodiments of the present disclosure clearer, the following will be described in conjunction with specific examples. The carbon emission analysis method provided by the embodiments of the present disclosure can include the following processes:

[0072] Step 1, data collection: periodically (such as once a month) obtain high-resolution remote sensing images from a satellite remote sensing data provider, and the image resolution reaches a level that can clearly distinguish different land use types. Professional remote sensing image receiving equipment and software are used for data receiving and storage.

[0073] Obtain the latest basic geographic data, including topographic data, administrative division data, etc.; set up field monitoring stations in different land use type areas, and arrange professional personnel to conduct field measurement every month to record soil carbon content, vegetation height, coverage, etc.

[0074] Collect energy consumption reports, industrial production statistics and other social and economic data, and update them every quarter.

[0075] Step 2, data preprocessing: use remote sensing image processing software to perform geometric correction on the remote sensing images, eliminate geometric distortion in the images according to ground control point data, and ensure the accuracy of the location of features in the images. Perform radiation correction to adjust the brightness and contrast of the image and remove radiation errors caused by factors such as atmospheric scattering.

[0076] For geographic data, use GIS software to convert the format to a standard format that can be recognized by the system. By writing a data processing script, identify and remove outliers in the data, such as soil carbon content data that deviate significantly from the normal range.

[0077] Step 3, Carbon Emission Calculation: For forest land, using the forest ecosystem carbon cycle model, based on the data of vegetation growth monitoring and satellite remote sensing of forest coverage area changes, combined with information such as the number and area of forest fires, calculate the carbon absorption and carbon emissions of the forest. For example, according to the vegetation growth model, calculate the carbon accumulation of vegetation each year, and according to the fire loss model, calculate the carbon emissions caused by fire.

[0078] For cultivated land, according to the collected data of fertilizer use, according to the content of carbon elements in fertilizer and chemical reaction principle, calculate the carbon emissions in the process of fertilizer use. According to the energy consumption data of agricultural machinery (such as diesel consumption), combined with the carbon emission coefficient of energy, calculate the carbon emissions of agricultural machinery operation. At the same time, using soil respiration monitoring data, considering the influence of soil temperature, humidity and other factors on soil respiration, calculate the soil carbon emissions.

[0079] For construction land, according to the building energy consumption data (such as power, natural gas consumption), according to the carbon emission coefficient of different energy to calculate the carbon emissions of building. Through the traffic flow monitoring data and the carbon emission coefficient of different vehicle types, calculate the traffic carbon emissions. Sum up the carbon emissions of various types of construction land.

[0080] Step 4, Data Analysis: Use statistical analysis software (such as SPSS, R language) to analyze the correlation of carbon emission data, and study the correlation between land use change and carbon emission. For example, analyze the quantitative relationship between the decrease of forest area and the increase of carbon emission.

[0081] Use data mining algorithm (such as decision tree algorithm) to mine the change rule and influencing factors of carbon emission in different regions and different time scales.

[0082] Use geographic information system (GIS) software and data visualization tools (such as Echarts) to display the carbon emission data in the form of map, different colors represent different carbon emission levels; Generate time series chart to show the trend of carbon emission of different land use types with time.

[0083] Generate analysis report of land use carbon emission, generate analysis report according to the analysis results.

[0084] Step 5, Dynamic monitoring and updating: Establish real-time data monitoring mechanism, check satellite remote sensing data receiving system periodically (such as every day), and obtain the latest remote sensing image in time. When changes in land use types are found (such as deforestation, cultivated land converted to construction land, etc.), immediately start the data updating process. According to the newly obtained data, re-process the data, calculate the carbon emissions, and analyze the data, etc. operation, to ensure that the carbon emission analysis results in the system are always the latest, which can reflect the real-time state of land use and carbon emission.

[0085] Figure 2 is a block diagram of a carbon emission analysis device provided by an embodiment of the present disclosure. Referring to Figure 2 , the carbon emission analysis device 200 can include:

[0086] a data collection module 210, configured to collect land use data and carbon emission data in different periods; wherein the land use data comprises at least one land use type data;

[0087] a carbon emission calculation module 220, configured to calculate carbon emission amounts of each land use type in different periods based on the land use data and carbon emission data by using a preset carbon emission calculation model;

[0088] a data analysis module 230, configured to analyze a correlation between land use change and carbon emission amount based on the carbon emission amounts of each land use type in different periods;

[0089] an output module 240, configured to output the correlation between the land use change and the carbon emission amount.

[0090] In a possible implementation, the data collection module 210 is configured to:

[0091] collect distribution image data of each land use type in different periods, and collect ecological attribute data of each land;

[0092] collect energy consumption data and industrial production data of the land in different periods.

[0093] In a possible implementation, the carbon emission analysis device 200 further includes a data preprocessing module, configured to:

[0094] perform data preprocessing on the land use data and carbon emission data in different periods; wherein the data preprocessing comprises data cleaning, data denoising, and data standardization processing;

[0095] the carbon emission calculation module 220 is configured to:

[0096] calculate carbon emission amounts of each land use type in different periods based on the land use data and carbon emission data after data preprocessing by using a preset carbon emission calculation model.

[0097] In a possible implementation, the carbon emission calculation module 220 is configured to:

[0098] calculate carbon emission amounts of each land use type in different periods based on distribution image data of each land use type, ecological attribute data of each land, energy consumption data and industrial production data of the land by using a preset carbon emission calculation model.

[0099] In one possible implementation, the data analysis module 230 is configured to:

[0100] By using data mining and statistical analysis methods, the carbon emissions of each land use type in different periods are analyzed to obtain the correlation between land use change and carbon emissions.

[0101] In one possible implementation, the output module 240 is configured to perform at least one of the following:

[0102] Demonstrate the relationship between land use change and carbon emissions using geographic information system software and data visualization tools;

[0103] A land use carbon emission analysis report is generated based on the correlation between the land use change and the carbon emission; and the land use carbon emission analysis report is output.

[0104] To make the carbon emission analysis device provided by the embodiment of the present disclosure clearer, a specific example is used below to illustrate.

[0105] Data Collection Module: This module is used to collect various data related to land use and carbon emissions. This includes satellite remote sensing to obtain image data on land use distribution over different time periods, covering various types of land, such as forest, grassland, cultivated land, and construction land. It also uses a geographic information system (GIS) to collect basic geographic data, such as the land's location and topography. Field monitoring stations collect data on soil carbon content and vegetation growth. Furthermore, it collects socioeconomic data related to carbon emissions, such as energy consumption and industrial production data.

[0106] Data preprocessing module: Cleans, denoises, and standardizes collected data. For remote sensing image data, geometric correction and radiometric correction are performed to improve image quality. Geographic data and other monitoring data are formatted and outliers are processed to ensure accuracy and consistency, providing a reliable data foundation for subsequent analysis.

[0107] Carbon Emission Calculation Module: This module establishes corresponding carbon emission calculation models based on the carbon emission characteristics of different land use types. For example, for forest land, carbon emissions are calculated based on factors such as vegetation growth, carbon storage changes, and forest fires. For cultivated land, carbon emissions are calculated based on factors such as fertilizer use, agricultural machinery energy consumption, and soil respiration. For construction land, carbon emissions are calculated based on building energy consumption and transportation carbon emissions. This module accurately calculates the carbon emissions of different land use types at different time points.

[0108] Data analysis module: using data mining and statistical analysis methods, the calculated carbon emission data is analyzed in depth. The correlation between land use change and carbon emission is mined, and the change trend of carbon emission in different regions and different time scales is analyzed. At the same time, the analysis results are presented in an intuitive visual way, such as generating carbon emission map, time series chart, etc., and according to the user's demand, the analysis report of land use carbon emission is generated synchronously, including the analysis of spatio-temporal characteristics of land use carbon emission, carbon emission hotspot analysis, carbon emission gravity center migration path characteristic analysis and influence factor driving mechanism analysis, etc.

[0109] Dynamic monitoring module: continuously and real-time monitoring the land use change, by regularly obtaining the latest remote sensing data and other monitoring data, updating the data in the system in time. When the land use type changes, quickly recalculate the carbon emission, and update the analysis results and visual display, to ensure that the system can reflect the dynamic changes of carbon emission based on land use in real time.

[0110] As to the device in the above embodiment, the specific manner in which each module performs an operation has been described in detail in the embodiment related to the method, and will not be described in detail here.

[0111] According to embodiments of the present disclosure, the present disclosure also provides an electronic device, a readable storage medium and a computer program product.

[0112] Figure 3 A schematic block diagram of an example electronic device 300 that can be used to implement embodiments of the present disclosure is shown. The electronic device 300 is intended to represent various forms of digital computers, such as laptops, desktops, workstations, personal digital assistants, servers, blade servers, mainframes, and other appropriate computers. The electronic device can also represent various forms of mobile devices, such as personal digital assistants, cellular telephones, smartphones, wearable devices, and other similar computing devices. The components shown here, their connections and relationships, and their functions, are meant to be examples only, and are not meant to limit implementations of the present disclosure described and / or claimed in this document.

[0113] As shown in Figure 3 The electronic device 300 includes a computing unit 301 that can perform various appropriate actions and processes in accordance with a computer program stored in a read-only memory (ROM) 302 or a computer program loaded from a storage unit 308 into a random access memory (RAM) 303. In the RAM 303, various programs and data required for the operation of the device 300 can also be stored. The computing unit 301, the ROM 302, and the RAM 303 are connected to each other through a bus 304. An input / output (I / O) interface 305 is also connected to the bus 304.

[0114] A plurality of components in the electronic device 300 are connected to the I / O interface 305, including: an input unit 306, such as a keyboard, a mouse, etc.; an output unit 307, such as various types of displays, speakers, etc.; a storage unit 308, such as a magnetic disk, an optical disk, etc.; and a communication unit 309, such as a network card, a modem, a wireless communication transceiver, etc. The communication unit 309 allows the electronic device 300 to exchange information / data with other devices through a computer network, such as the Internet, and / or various telecommunication networks.

[0115] The computing unit 301 can be various general and / or special-purpose processing components with processing and computing capabilities. Some examples of the computing unit 301 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various special-purpose artificial intelligence (AI) computing chips, various computing units running machine learning model algorithms, a digital signal processor (DSP), and any appropriate processor, controller, microcontroller, etc. The computing unit 301 performs various methods and processes described above, such as the carbon emission analysis method. For example, in some embodiments, the carbon emission analysis method can be implemented as a computer software program, which is tangibly embodied in a machine-readable medium, such as the storage unit 308. In some embodiments, part or all of the computer program can be loaded and / or installed onto the electronic device 300 via the ROM 302 and / or the communication unit 309. When the computer program is loaded onto the RAM 303 and executed by the computing unit 301, one or more steps of the carbon emission analysis method described above can be performed. Alternatively, in other embodiments, the computing unit 301 can be configured to perform the carbon emission analysis method by any other appropriate means, such as by means of firmware.

[0116] Various implementations of the systems and techniques described above herein can be realized in digital electronic circuitry, integrated circuitry, a field programmable gate array (FPGA), an application specific integrated circuit (ASIC), a system on a chip (SOC), a programmable logic device (CPLD), computer hardware, firmware, software, and / or combinations thereof. These various implementations can include implementation in one or more computer programs that are executable and / or interpretable on a programmable system including at least one programmable processor, which can be special or general purpose, coupled to receive data and instructions from, and to transmit data and instructions to, a storage system, at least one input device, and at least one output device.

[0117] Program code for carrying out methods of the present disclosure can be written in any combination of one or more programming languages. The program code can be provided to a processor or controller of a general purpose computer, special purpose computer, or other programmable data processing apparatus to produce a machine, such that the program code, when executed by the processor or controller, produces the functions / operations specified in the flowcharts and / or block diagrams. The program code can be executed entirely on a machine, partially on a machine, partially on a machine as a standalone software package, or entirely on a remote machine or server.

[0118] In the context of the present disclosure, a machine-readable medium can be a tangible medium that contains or stores a program for use by or in connection with an instruction execution system, apparatus, or device. The machine-readable medium can be a machine-readable signal medium or a machine-readable storage medium. A machine-readable medium can include but is not limited to an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any suitable combination of the foregoing. More specific examples of the machine-readable storage medium will include one or more lines of electrical connections, portable computer disks, hard disk drives, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or Flash memory), optical fibers, portable compact disc read-only memories (CD-ROMs), optical storage devices, magnetic storage devices, or any suitable combination of the foregoing.

[0119] To provide for interaction with a user, the systems and techniques described here can be implemented on a computer having a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to the user and a keyboard and a pointing device (e.g., a mouse or a trackball) by which the user can provide input to the computer. Other kinds of devices can be used to provide for interaction with a user as well; for example, feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user can be received in any form, including acoustic, speech, or tactile input.

[0120] The systems and techniques described herein can be implemented in a computing system that includes a back end component (e.g., as a data server), or that includes a middleware component (e.g., an application server), or that includes a front end component (e.g., a user computer having a graphical user interface or a Web browser through which a user can interact with an implementation of the systems and techniques described herein), or any combination of such back end, middleware, or front end components. The components of the system can be interconnected by any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include a local area network (LAN), a wide area network (WAN), the Internet, and a blockchain network.

[0121] The computer system can include clients and servers. This relationship can be. The servers are generally remote from the users and can be accessed via the Internet using a communication network. The relationship can be a client-server relationship, where the servers are the data servers and the clients are users' computers. The servers can be cloud servers, also known as cloud computing servers or cloud hosts, which are a host product in the cloud computing service system. The servers can also be servers of a distributed system, or servers combined with a blockchain.

[0122] It should be understood that various forms of flow shown above can be used with orders of steps reordered, steps added, or steps deleted. For example, the steps described in the present disclosure can be performed in parallel, in series, or in a different order, as long as the desired results of the technical solutions of the present disclosure are achieved, and the present disclosure is not limited herein.

[0123] The above detailed description does not constitute a limitation on the protection scope of the present disclosure. Those skilled in the art should understand that various modifications, combinations, sub-combinations, and substitutions can be made according to design requirements and other factors. Any modifications, equivalent replacements, and improvements within the spirit and principles of the present disclosure should be included in the protection scope of the present disclosure.

Claims

1. A carbon emission analysis method, characterized in that: include: Collecting land use data and carbon emission data for different periods; wherein the land use data includes at least one type of land use data; Calculate the carbon emissions of each land use type in different periods based on the land use data and carbon emission data using a preset carbon emission calculation model; Based on the carbon emissions of each land use type in different periods, the correlation between land use change and carbon emissions was analyzed; Output the correlation between the land use change and carbon emissions.

2. The carbon emission analysis method according to claim 1, characterized in that: Collect land use data and carbon emission data for different periods, including: Collecting distribution image data of each land use type at different periods and collecting ecological attribute data of each type of land; Energy consumption data and industrial production data of the land at different periods are collected.

3. The carbon emission analysis method according to claim 1, characterized in that: Before calculating the carbon emissions of each land use type in different periods based on the land use data and carbon emission data by using a preset carbon emission calculation model, the method includes: Performing data preprocessing on the land use data and carbon emission data of the different periods; wherein the data preprocessing includes data cleaning, data denoising, and data standardization; The carbon emission calculation model is preset to calculate the carbon emissions of each land use type in different periods based on the land use data and the carbon emission data, including: By using a preset carbon emission calculation model, based on the land use data and carbon emission data after data preprocessing, the carbon emissions of each land use type in different periods are calculated.

4. The carbon emission analysis method according to claim 2, characterized in that: The carbon emission calculation model is preset to calculate the carbon emissions of each land use type in different periods based on the land use data and the carbon emission data, including: By presetting the carbon emission calculation model, based on the distribution image data of each land use type, the ecological attribute data of each land, the energy consumption data of the land, and the industrial production data, the carbon emissions of each land use type in different periods are calculated.

5. The carbon emission analysis method according to claim 1, characterized in that: The correlation between land use change and carbon emissions is analyzed based on the carbon emissions of each land use type in different periods, including: By using data mining and statistical analysis methods, the carbon emissions of each land use type in different periods are analyzed to obtain the correlation between land use change and carbon emissions.

6. The carbon emission analysis method according to claim 5, characterized in that: The outputting of the correlation between the land use change and carbon emissions includes at least one of the following: Demonstrate the relationship between land use change and carbon emissions using geographic information system software and data visualization tools; Generate a land use carbon emission analysis report based on the correlation between the land use change and carbon emissions; Output the land use carbon emission analysis report.

7. A carbon emission analysis device, characterized in that: include: A data collection module, configured to collect land use data and carbon emission data for different periods; wherein the land use data includes at least one type of land use data; A carbon emission calculation module, configured to calculate the carbon emissions of each land use type in different periods based on the land use data and the carbon emission data using a preset carbon emission calculation model; A data analysis module is used to analyze the correlation between land use change and carbon emissions based on the carbon emissions of each land use type in different periods; The output module is used to output the correlation relationship between the land use change and carbon emissions.

8. An electronic device, characterized in that: include: processor; a memory for storing instructions executable by the processor; The processor is configured to execute the instructions to implement the carbon emission analysis method according to any one of claims 1 to 6.

9. A storage medium, when instructions in the storage medium are executed by a processor of an electronic device, enables the electronic device to execute the carbon emission analysis method according to any one of claims 1 to 6.

10. A computer program product comprising a computer program / instructions, characterized in that When the computer program / instructions are executed by a processor, the carbon emission analysis method according to any one of claims 1 to 6 is implemented.

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