Optimized scheduling method for carbon emission reduction system

Abstract

The invention relates to the field of carbon emission reduction, in particular to an optimal scheduling method for a carbon emission reduction system. The method comprises the following steps: dividing a monitoring area into scheduling blocks, obtaining remote sensing data, obtaining area remote sensing data and area scheduling block data, and generating an industrial carbon emission curve based on the data; non-industrial remote sensing data is acquired from the regional remote sensing data, and natural carbon consumption model construction is performed on the regional scheduling block data based on the non-industrial remote sensing data; drawing a soil carbon content influence curve and a building density temperature curve for the regional scheduling block data based on non-industrial remote sensing data, and correcting natural carbon consumption model data based on a hyperbolic curve to obtain a non-industrial carbon consumption model; and performing optimization scheduling on the regional scheduling block data based on the industrial carbon emission curve and the non-industrial carbon consumption model to obtain a carbon emission reduction optimization scheduling result. The remote sensing data of the analysis area can be obtained, the priority sequence is generated, and optimal scheduling of carbon emission reduction is facilitated.

Description

Technical Field

[0001] The present invention relates to the field of carbon emission reduction, and in particular to a method for optimizing and scheduling a carbon emission reduction system. Background Art

[0002] At this stage, the continuous growth of carbon emissions has caused huge negative impacts on the environment. In order to meet this challenge, it is urgent to take measures to reduce carbon emissions; carbon emission reduction can not only slow down the pace of climate change, but also help improve air quality and the health of the ecosystem; carbon emission reduction helps to reduce the overall concentration of greenhouse gases and reduce regional weather extremes, thereby maintaining regional ecological monitoring. Secondly, reducing carbon emissions can promote sustainable development and promote the development and application of clean energy technologies; an effective carbon emission reduction system, including accurate emissions monitoring and reasonable emission reduction target setting, can achieve effective carbon emission reduction; however, at this stage, due to the lack of analysis of building-related parameters of carbon emissions, it is impossible to obtain an accurate carbon emission reduction sequence, resulting in the inability to guide the scheduling of the carbon emission reduction system, and it is impossible to avoid the waste of emission reduction resources and low emission reduction efficiency, as well as the excessive time consumption required to achieve carbon emission reduction targets. Summary of the invention

[0003] Based on this, it is necessary for the present invention to provide a method for optimizing the scheduling of a carbon emission reduction system to solve at least one of the above technical problems.

[0004] To achieve the above purpose, the optimization scheduling method based on the carbon emission reduction system includes the following steps:

[0005] Step S1: acquiring regional remote sensing data of the monitoring area to obtain regional remote sensing data; dividing the monitoring area into equal-area scheduling blocks to obtain regional scheduling block data; drawing an industrial carbon emission curve for the regional remote sensing data and the regional scheduling block data to obtain an industrial carbon emission curve;

[0006] Step S2: acquiring non-industrial regional remote sensing data from regional remote sensing data to obtain non-industrial remote sensing data; constructing a natural carbon consumption model for regional scheduling block data based on the non-industrial remote sensing data to obtain a natural carbon consumption model;

[0007] Step S3: Based on the non-industrial remote sensing data, the building density temperature curve is plotted for the regional scheduling block data to obtain the building density temperature curve; based on the non-industrial remote sensing data, the soil carbon content influence curve is plotted for the regional scheduling block data to obtain the soil carbon content influence curve; based on the soil carbon content influence curve and the building density temperature curve, the natural carbon consumption model is corrected to obtain the non-industrial carbon consumption model;

[0008] Step S4: Generate a carbon emission reduction priority sequence for the regional scheduling block data based on the industrial carbon emission curve and the non-industrial carbon consumption model to obtain a carbon emission reduction priority sequence; optimize the regional scheduling block data based on the carbon emission reduction priority sequence to obtain a carbon emission reduction optimization scheduling result.

[0009] By acquiring remote sensing data of the monitoring area and performing equal-area scheduling and block division, the present invention can comprehensively grasp the industrial layout and carbon emission spatial distribution characteristics in the region, draw industrial carbon emission curves, and intuitively show the changing trends of industrial carbon emission intensity in different regions and different time periods, providing a scientific basis for the subsequent accurate identification of key areas for carbon emission reduction and the formulation of differentiated emission reduction strategies; by extracting remote sensing data of non-industrial areas, it is possible to analyze the natural element information of vegetation coverage and water distribution, and construct a natural carbon consumption model in combination with regional scheduling block data. The model can quantitatively evaluate the natural carbon sink capacity of different regions, provide basic data for the scientific accounting of regional carbon emissions, and provide basic data for the subsequent optimization of carbon emission reduction sequences; using remote sensing data of non-industrial areas, the building density temperature curve and soil The carbon content impact curve can reflect the impact of urban heat island effect on carbon emissions and the carbon sequestration potential of soil carbon pools respectively. By incorporating these factors into the natural carbon consumption model for data correction, a more accurate non-industrial carbon consumption model can be constructed to comprehensively reflect the impact of various natural and human factors on carbon emissions in the region. By integrating the industrial carbon emission curve and the non-industrial carbon consumption model, the net carbon emission value of each scheduling block can be calculated, and then a carbon reduction priority sequence can be generated. By obtaining emission reduction area information and generating a carbon reduction sequence based on relevant information, the scientific method can accurately identify key areas for carbon emission reduction, optimize scheduling based on the sequence, and maximize the benefits of emission reduction measures, achieve maximum utilization of emission reduction resources, improve emission reduction efficiency, and reduce the time to achieve carbon reduction targets.

[0010] Preferably, step S1 comprises the following steps:

[0011] Step S11: acquiring regional remote sensing data of the monitoring area to obtain regional remote sensing data;

[0012] Step S12: Divide the monitoring area into equal-area scheduling blocks to obtain regional scheduling block data;

[0013] Step S13: performing block industrial building regional data analysis on the regional scheduling block data based on the regional remote sensing data to obtain block industrial building regional data;

[0014] Step S14: Draw an industrial carbon emission curve for the regional scheduling block data based on the block industrial building area data to obtain an industrial carbon emission curve.

[0015] By acquiring remote sensing data of the monitoring area, the present invention can comprehensively and intuitively understand the land use, vegetation distribution, human activities and factory distribution in the area, which is conducive to grasping the overall situation of the region and providing basic data support for subsequent analysis and modeling. The monitoring area is divided into equal-area scheduling blocks to ensure the refinement and comparability of subsequent analysis. Equal-area blocks can balance the land attributes and characteristics of different regions, making the comparison and analysis between blocks more accurate, and providing a reliable spatial basis for optimized scheduling. The block industrial building area data obtained through regional remote sensing data analysis can accurately grasp the industrial distribution of each block in the monitoring area. This analysis method can effectively identify and distinguish industrial activities, which is beneficial for subsequent carbon emissions. Quantification and visualization provide key information; drawing industrial carbon emission curves can intuitively present the differences in industrial carbon emissions in different blocks. Through this visual representation, decision makers can quickly identify high-emission areas and formulate targeted emission reduction strategies based on emission trends. These curves can also help verify the effectiveness of carbon emission reduction measures, ensure that emission reduction work is targeted and effective, and carbon emission curves can also be used to predict future emissions. By simulating different scenarios to guide long-term planning and decision-making, it can analyze the building-related parameters of carbon emissions, obtain accurate carbon emission reduction sequences, and guide the scheduling of carbon emission reduction systems, avoid waste of emission reduction resources and low emission reduction efficiency, and reduce the excessive time required to achieve carbon emission reduction targets.

[0016] Preferably, step S13 comprises the following steps:

[0017] Step S131: acquiring regional remote sensing texture from regional remote sensing data to obtain regional remote sensing texture;

[0018] Step S132: extracting industrial area range features from the regional remote sensing texture to obtain industrial area range texture;

[0019] Step S133: extracting industrial area building features from the regional remote sensing texture to obtain industrial area building texture;

[0020] Step S134: Based on the industrial area range texture and the industrial area building texture, the regional scheduling block data is segmented into industrial building area identification to obtain segmented industrial building area data.

[0021] The present invention can capture the surface features and material composition information of the monitoring area by acquiring regional remote sensing textures. Texture analysis can effectively identify and distinguish different land use forms, and provide basic data for subsequent industrial area scope and building feature extraction; industrial area scope feature extraction of remote sensing textures can effectively identify and define the scope of industrial activities in the monitoring area. This step can distinguish industrial areas from non-industrial areas, and provide accurate scope definition for block industrial building area data analysis; this step focuses on the extraction of building features in industrial areas in remote sensing textures. By analyzing the building textures in industrial areas, industrial buildings can be identified and classified, and the density and distribution of industrial activities can be understood; by combining the industrial area scope texture and industrial area building textures, can accurately identify and divide industrial areas in regional scheduling block data. This method takes into account the scope and density of industrial activities, ensures the accuracy and comprehensiveness of the block industrial building area data, and the identification of block industrial building area data is conducive to the targeted assessment of industrial carbon emissions and provides a basis for the subsequent construction and optimization of carbon emission models; the fine processing and feature extraction of remote sensing textures effectively identify industrial activities in the monitoring area. These steps comprehensively consider the industrial scope and building characteristics, and provide accurate and detailed information for the analysis of block industrial building area data. This method provides a reliable basis for the quantification and modeling of industrial carbon emissions, which is conducive to optimizing carbon emission reduction strategies and management in the monitoring area.

[0022] Preferably, step S14 comprises the following steps:

[0023] Step S141: positioning the carbon emission source buildings on the block industrial building area data to obtain the industrial carbon emission source building positioning data;

[0024] Step S142: performing emission source density calculation on the industrial carbon emission source building location data to obtain industrial carbon emission source building density data;

[0025] Step S143: monitoring carbon emissions of industrial carbon emission source building location data to obtain industrial carbon emission data;

[0026] Step S144: Calculate the mean carbon emission value of industrial areas based on the industrial carbon emission source building density data to obtain the mean carbon emission value of industrial areas;

[0027] Step S145: Calculating the block industrial building area ratio of the regional scheduling block data based on the block industrial building area data to obtain the block industrial building area ratio;

[0028] Step S146: Draw an industrial carbon emission curve for the mean carbon emission value of the industrial area and the proportion of the industrial building area by block to obtain an industrial carbon emission curve.

[0029] The present invention can accurately identify and define the carbon emission source buildings in each block within the monitoring area by locating the carbon emission source buildings in the block industrial building area data. This helps to clarify the source of carbon emissions and provides a basis for subsequent emission calculations and density analysis; calculating the density of industrial carbon emission source buildings can effectively evaluate the intensity of industrial activities. Through this step, high-density carbon emission source building areas can be identified, thereby helping decision makers determine key target areas for carbon emission reduction; monitoring industrial carbon emission source buildings can obtain emission data for each emission source. This step provides a basis for quantifying and evaluating industrial carbon emissions, which is conducive to the subsequent calculation of carbon emissions at the block and regional levels; calculating the mean carbon emissions of industrial areas can comprehensively evaluate the emission level of industrial activities. This step takes into account the density of industrial carbon emission source buildings and the regional scale, and provides a standardized indicator for comparing emissions in different blocks and regions; Calculating the proportion of industrial building areas in each block can analyze the relationship between industrial and non-industrial activities in the monitored area. This step provides a basis for evaluating the degree of industrialization and potential carbon emission contributions of different blocks; drawing industrial carbon emission curves can intuitively present the carbon emission trends and distribution of industrial activities. This step effectively displays the overall situation of industrial carbon emissions in the monitored area through visualization, and provides clear guidance for carbon emission reduction strategies; by calculating density, monitoring emissions, calculating means and proportions, a quantitative assessment of carbon emissions in industrial areas is provided, and drawing carbon emission curves provides an effective means for intuitively understanding the distribution of industrial carbon emissions. These steps jointly construct an industrial carbon emission analysis framework, providing data support and feasible paths for carbon emission reduction decisions in the monitored area.

[0030] Preferably, step S2 comprises the following steps:

[0031] Step S21: extracting non-industrial regional feature textures from regional remote sensing data to obtain non-industrial feature textures;

[0032] Step S22: acquiring non-industrial regional remote sensing data based on non-industrial feature textures to obtain non-industrial remote sensing data;

[0033] Step S23: Draw a vegetation carbon consumption curve for the non-industrial remote sensing data to obtain a vegetation carbon consumption curve;

[0034] Step S24: Draw a water body carbon consumption curve for the non-industrial remote sensing data to obtain a water body carbon consumption curve;

[0035] Step S25: construct a natural carbon consumption model for the regional scheduling block data based on the vegetation carbon consumption curve and the water body carbon consumption curve to obtain a natural carbon consumption model.

[0036] The present invention can effectively identify and distinguish natural landscape elements, such as vegetation and water bodies, in the monitoring area by extracting characteristic textures of non-industrial areas; this step can provide a basis for obtaining non-industrial remote sensing data, which is helpful for accurately defining and analyzing natural environment characteristics; obtaining non-industrial remote sensing data can exclude the impact of industrial activities and focus on analyzing the role of the natural environment in carbon consumption. This step provides a direct data source for building a natural carbon consumption model, ensuring the accuracy and effectiveness of the model; drawing vegetation carbon consumption curves can quantify and visualize the absorption and storage of carbon elements by vegetation. Vegetation is an important factor in natural carbon consumption. This step helps to evaluate the carbon consumption capacity of different types of vegetation and provides a basis for optimizing the natural carbon consumption model; drawing water body carbon consumption curves can be used to classify Analyze and present the role of water in the carbon cycle. Water can store and transport carbon. Studying it can better understand the flow of carbon in the natural environment. This step provides key information about water for the natural carbon consumption model. By combining vegetation and water carbon consumption curves, a comprehensive natural carbon consumption model can be constructed. This model takes into account the contribution of different elements in the natural environment to carbon consumption, and provides a scientific basis for analyzing and predicting the carbon reduction potential in non-industrial areas. The construction of a natural carbon consumption model can provide decision-making support for carbon reduction strategies and help optimize the positive impact of the natural environment in the carbon cycle. It effectively evaluates the role of the natural environment in carbon consumption. From feature texture extraction to building a natural carbon consumption model, these steps ensure the accurate identification and quantification of natural environmental factors.

[0037] Preferably, step S23 includes the following steps:

[0038] Step S231: performing vegetation characteristic color analysis on non-industrial remote sensing data to obtain vegetation characteristic color data;

[0039] Step S232: performing vegetation distribution positioning on the non-industrial remote sensing data based on the vegetation characteristic color data to obtain vegetation distribution data;

[0040] Step S233: performing non-industrial vegetation coverage analysis on the vegetation distribution data to obtain the non-industrial vegetation coverage;

[0041] Step S234: acquiring vegetation carbon consumption data from the vegetation distribution data to obtain vegetation carbon consumption data;

[0042] Step S235: Draw a vegetation carbon consumption curve for the non-industrial vegetation coverage rate based on the vegetation carbon consumption data to obtain a vegetation carbon consumption curve.

[0043] The present invention can effectively identify and extract the unique color features of vegetation by performing vegetation characteristic color analysis on non-industrial remote sensing data. This step utilizes the characteristics of the vegetation reflectance spectrum in the remote sensing image to provide basic data for subsequent vegetation distribution positioning and analysis; the acquisition of vegetation characteristic color data helps to accurately identify and locate the vegetation distribution in the monitoring area. By analyzing the characteristic color, vegetation and non-vegetation areas can be distinguished, and the scope and boundaries of vegetation coverage can be determined; calculating the non-industrial vegetation coverage rate can quantitatively evaluate the vegetation distribution in the monitoring area. This step calculates the proportion of land area covered by vegetation by analyzing the vegetation distribution data, which is used to evaluate the natural carbon consumption of vegetation. The effect of vegetation carbon consumption can be quantitatively measured; obtaining vegetation carbon consumption data can quantify the role of vegetation in the carbon cycle. This step provides key data for drawing vegetation carbon consumption curves by analyzing the carbon absorption and storage capacity of vegetation; drawing vegetation carbon consumption curves can intuitively present the trend and potential of vegetation carbon consumption. This step visualizes vegetation carbon consumption data to show the positive impact of vegetation in the carbon cycle. Vegetation carbon consumption curves provide a basis for evaluating and comparing vegetation carbon consumption efficiency in different regions, and provide important parameters for the construction of natural carbon consumption models; it provides comprehensive and accurate vegetation information for the construction of natural carbon consumption models, ensuring that the model takes into account the key role of vegetation in the carbon cycle. This method provides a scientific basis for optimizing carbon reduction strategies.

[0044] Preferably, step S24 comprises the following steps:

[0045] Step S241: performing water body smoothing texture recognition on non-industrial remote sensing data to obtain non-industrial water body texture;

[0046] Step S242: extracting water algae texture from non-industrial water texture to obtain water algae texture;

[0047] Step S243: performing water algae scale analysis on non-industrial water textures based on water algae textures to obtain water algae scale data;

[0048] Step S244: performing water coverage analysis on non-industrial remote sensing data based on non-industrial water body textures to obtain non-industrial water body coverage;

[0049] Step S245: Calculate the water area based on the water coverage rate to obtain water area data;

[0050] Step S246: Calculate water body carbon consumption data based on the water body algae scale data and water body area data to obtain water body carbon consumption data;

[0051] Step S247: Draw a water body carbon consumption curve for the non-industrial water body coverage based on the water body carbon consumption data to obtain a water body carbon consumption curve.

[0052] The present invention can effectively distinguish and extract water body features by identifying the smooth texture of water bodies in non-industrial remote sensing data. The surface of a water body often has a unique smooth texture. This step aims to accurately identify the water body area and distinguish it from other features of land and vegetation; extracting water body algae texture from non-industrial water body texture can capture the unique texture characteristics of algae in the water body. Algae growth will affect the texture and color of the water body. This step helps to identify and analyze the existence and distribution of algae in the water body; analyzing the scale of water body algae texture can evaluate the abundance and coverage of algae in the water body. This step quantifies the presence of algae to provide basic data for subsequent calculation of water body carbon consumption; analyzing non-industrial water body texture can accurately calculate and evaluate water body coverage. This step aims to distinguish water from other elements of land and vegetation and quantify their The proportion of land area covered; calculating the area of ​​water bodies can provide key parameters for water body carbon consumption analysis. The carbon consumption capacity of water bodies is related to the size of the area. This step provides an important indicator for the subsequent calculation of water body carbon consumption; by combining the water body algae scale data and water body area data, the carbon consumption level of water bodies can be calculated. This step comprehensively analyzes the impact of algae presence on carbon consumption and the contribution of water body area to the carbon cycle; drawing water body carbon consumption curves can intuitively present the carbon consumption trend and potential of water bodies. This step effectively demonstrates the role of water bodies in the carbon cycle through visualization, providing an important reference for the construction of natural carbon consumption models; providing comprehensive and accurate water body information for natural carbon consumption models, ensuring that the model takes into account the unique carbon cycle process of water bodies. This method provides a scientific basis for optimizing carbon reduction strategies.

[0053] Preferably, step S3 comprises the following steps:

[0054] Step S31: performing regional temperature data analysis on non-industrial remote sensing data to obtain regional temperature data;

[0055] Step S32: performing regional bare land analysis on the non-industrial remote sensing data to obtain non-industrial bare land data;

[0056] Step S33: Drawing a building density temperature curve for the regional scheduling block data based on the non-industrial remote sensing data and the regional temperature data to obtain a building density temperature curve;

[0057] Step S34: analyzing the effect of temperature on soil carbon content of non-industrial bare land data based on regional temperature data to obtain data on the effect of temperature on soil carbon content;

[0058] Step S35: fitting the building density temperature curve based on the temperature effect on soil carbon content data to obtain a soil carbon content effect curve;

[0059] Step S36: Based on the soil carbon content influence curve and the building density temperature curve, the natural carbon consumption model is corrected to obtain a non-industrial carbon consumption model.

[0060] By performing regional temperature data analysis on non-industrial remote sensing data, the present invention can accurately obtain the spatial distribution information of surface temperature, and provide basic data for subsequent analysis of the impact of temperature on soil carbon content and building density; by performing regional bare land analysis on non-industrial remote sensing data, land without vegetation cover can be identified, and the area where temperature affects soil carbon content can be clarified, thereby improving the accuracy of subsequent analysis; by combining regional temperature data with regional scheduling block data to draw a building density temperature curve, the temperature difference between areas with different building densities can be intuitively displayed, revealing the influence of urbanization on regional temperature; based on regional temperature data, the impact of temperature on soil carbon content on non-industrial bare land data can be analyzed, and the degree of influence of temperature on soil carbon content can be quantified, which is an important factor in building a non-industrial carbon consumption model. The key link of the model; using the data of temperature affecting soil carbon content to fit the building density temperature curve, the impact of urbanization on temperature can be linked to changes in soil carbon content, and a soil carbon content impact curve can be constructed to provide more refined data support for the model; finally, based on the soil carbon content impact curve and the building density temperature curve, data correction is performed on the natural carbon consumption model, the impact of urbanization on the carbon cycle can be incorporated into the model, and a more accurate and realistic non-industrial carbon consumption model can be constructed; by systematically analyzing the temperature, land use, building density and soil carbon content factors in non-industrial areas, a more comprehensive and accurate carbon consumption model is constructed, which not only takes into account natural factors, but also incorporates the impact of urbanization, and can therefore better guide carbon emission reduction scheduling.

[0061] Preferably, step S33 includes the following steps:

[0062] Step S331: extracting building texture geometric features from non-industrial remote sensing data to obtain building texture geometric features;

[0063] Step S332: performing building recognition on the regional scheduling block data based on building texture geometric features to obtain building data;

[0064] Step S333: Calculate the building density of the regional scheduling block data based on the building data to obtain the building density;

[0065] Step S334: Draw a building density temperature curve for the regional scheduling block data based on the building density and regional temperature data to obtain a building density temperature curve.

[0066] The present invention can identify and analyze the layout and form of buildings in a region by extracting geometric features of building textures from non-industrial remote sensing data. This step helps to obtain the scale, shape and orientation information of buildings in the region, and provides basic data support for subsequent building identification and density calculation; by identifying the region based on the geometric features of building textures, it is possible to effectively extract building information from remote sensing data and accurately obtain the number, size and location data of buildings in the region, which is crucial for understanding the distribution of buildings in the study area, as well as subsequent density calculation and temperature analysis; by calculating the building density based on the building identification results, it is possible to quantitatively analyze the density of buildings in the region, and provide an important basis for evaluating the urban heat island effect and optimizing the urban spatial layout; by drawing a building density temperature curve in combination with building density and regional temperature data, it is possible to intuitively display the relationship between building density and temperature, and reveal the formation mechanism of the urban heat island effect. Based on the analysis of the heat island effect, the relationship between building density and temperature is obtained, which is conducive to providing a basic temperature discrimination benchmark for the subsequent analysis of the impact of changes in vegetation in the region due to temperature and changes in soil carbon content due to temperature.

[0067] Preferably, step S4 comprises the following steps:

[0068] Step S41: constructing an industrial carbon emission sequence for the regional scheduling block data based on the industrial carbon emission curve to obtain an industrial carbon emission sequence;

[0069] Step S42: performing industrial emission equal block acquisition on the industrial carbon emission sequence to obtain industrial emission equal block data;

[0070] Step S43: Generate a carbon consumption sequence for the industrial emission equal block data based on the non-industrial carbon consumption model to obtain a carbon consumption sequence;

[0071] Step S44: generating a carbon emission reduction priority sequence for the industrial carbon emission sequence and the carbon consumption sequence to obtain a carbon emission reduction priority sequence;

[0072] Step S45: Optimize the regional scheduling block data based on the carbon emission reduction priority sequence to obtain the carbon emission reduction optimization scheduling result.

[0073] By constructing an industrial carbon emission sequence based on the industrial carbon emission curve, the present invention can more accurately depict the dynamic change trend of industrial carbon emissions in a region, provide more accurate data support for subsequent carbon emission reduction work, avoid the error caused by average distribution, and make the emission reduction strategy more targeted; by equally dividing the industrial carbon emission sequence, regions with similar carbon emissions can be divided into the same group, which is convenient for subsequent differentiated emission reduction measures for regions with different emission levels, thereby improving the efficiency and accuracy of carbon emission reduction; generating a carbon consumption sequence based on a non-industrial carbon consumption model can more comprehensively consider the dynamic balance of carbon emissions and carbon absorption in the region, avoid focusing only on industrial emission reduction and ignoring other carbon sink resources, and provide more comprehensive and scientific carbon emission reduction strategies. Provide a basis for the strategy; Generate a carbon emission reduction priority sequence, which can scientifically determine the priority of carbon emission reduction according to the carbon emission intensity and emission reduction potential factors of different regions, invest limited resources in areas with the greatest emission reduction benefits, and maximize the overall carbon emission reduction efficiency; Regional scheduling optimization based on the carbon emission reduction priority sequence can achieve adjustments to the resource allocation, industrial layout, and energy structure of different regions, thereby effectively reducing the overall carbon emissions of the region, making emission reduction work targeted and effective, and the carbon emission curve can also be used to predict future emissions, by simulating different scenarios to guide long-term planning and decision-making. This step combines remote sensing data with carbon emission analysis, and provides a quantitative method and decision-making support for industrial carbon emission management in the monitoring area. BRIEF DESCRIPTION OF THE DRAWINGS

[0074] Other features, objects and advantages of the present invention will become more apparent from the detailed description of non-limiting embodiments thereof made with reference to the following drawings:

[0075] Figure 1 A schematic diagram of the steps of the method for optimizing the scheduling of the carbon emission reduction system of the present invention;

[0076] Figure 2 for Figure 1 Detailed step flow diagram of step S3;

[0077] Figure 3 for Figure 2 Detailed step flow chart of step S33 in FIG. DETAILED DESCRIPTION

[0078] The technical method of the present invention is described clearly and completely below in conjunction with the accompanying drawings. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by technicians in this field without creative work are within the scope of protection of the present invention.

[0079] In addition, the accompanying drawings are only schematic illustrations of the present invention and are not necessarily drawn to scale. The same reference numerals in the figures represent the same or similar parts, and their repeated description will be omitted. Some of the block diagrams shown in the accompanying drawings are functional entities and do not necessarily correspond to physically or logically independent entities. The functional entities can be implemented in software form, or implemented in one or more hardware modules or integrated circuits, or implemented in different networks and / or processor methods and / or microcontroller methods.

[0080] It should be understood that, although the terms "first", "second", etc. may be used herein to describe various units, these units should not be limited by these terms. These terms are used only to distinguish one unit from another unit. For example, without departing from the scope of the exemplary embodiments, the first unit may be referred to as the second unit, and similarly the second unit may be referred to as the first unit. The term "and / or" used herein includes any and all combinations of one or more of the listed associated items.

[0081] To achieve this, please refer to Figures 1 to 3 The present invention provides a method for optimizing and scheduling a carbon emission reduction system, the method comprising the following steps:

[0082] Step S1: acquiring regional remote sensing data of the monitoring area to obtain regional remote sensing data; dividing the monitoring area into equal-area scheduling blocks to obtain regional scheduling block data; drawing an industrial carbon emission curve for the regional remote sensing data and the regional scheduling block data to obtain an industrial carbon emission curve;

[0083] Step S2: acquiring non-industrial regional remote sensing data from regional remote sensing data to obtain non-industrial remote sensing data; constructing a natural carbon consumption model for regional scheduling block data based on the non-industrial remote sensing data to obtain a natural carbon consumption model;

[0084] Step S3: Based on the non-industrial remote sensing data, the building density temperature curve is plotted for the regional scheduling block data to obtain the building density temperature curve; based on the non-industrial remote sensing data, the soil carbon content influence curve is plotted for the regional scheduling block data to obtain the soil carbon content influence curve; based on the soil carbon content influence curve and the building density temperature curve, the natural carbon consumption model is corrected to obtain the non-industrial carbon consumption model;

[0085] Step S4: Generate a carbon emission reduction priority sequence for the regional scheduling block data based on the industrial carbon emission curve and the non-industrial carbon consumption model to obtain a carbon emission reduction priority sequence; optimize the regional scheduling block data based on the carbon emission reduction priority sequence to obtain a carbon emission reduction optimization scheduling result.

[0086] In the embodiment of the present invention, please refer to Figure 1 FIG. 1 is a schematic diagram of the steps of the method for optimizing the scheduling of a carbon emission reduction system according to the present invention. In this example, the method for optimizing the scheduling of a carbon emission reduction system includes the following steps:

[0087] Step S1: acquiring regional remote sensing data of the monitoring area to obtain regional remote sensing data; dividing the monitoring area into equal-area scheduling blocks to obtain regional scheduling block data; drawing an industrial carbon emission curve for the regional remote sensing data and the regional scheduling block data to obtain an industrial carbon emission curve;

[0088] In an embodiment of the present invention, by utilizing satellite remote sensing technology, multi-spectral, high-resolution remote sensing image data of the monitoring area is obtained, and the area is divided into a number of scheduling blocks of equal area according to the area and data characteristics of the study area. The distribution, scale, and energy consumption meter data of the industries in the region, as well as the industrial land information extracted from the remote sensing images, are combined to construct an industrial carbon emission curve to reflect the relationship between industrial carbon emissions and time or other related factors.

[0089] Step S2: acquiring non-industrial regional remote sensing data from regional remote sensing data to obtain non-industrial remote sensing data; constructing a natural carbon consumption model for regional scheduling block data based on the non-industrial remote sensing data to obtain a natural carbon consumption model;

[0090] In an embodiment of the present invention, remote sensing data of non-industrial areas, such as forests, grasslands, and water bodies, are identified and extracted from the acquired regional remote sensing data using land use classification technology. Based on the remote sensing data of non-industrial areas, combined with regional scheduling block data, a natural carbon consumption model is constructed. The model can adopt a light energy utilization model and an ecological process model, and use vegetation index and water body data in remote sensing data as model input parameters to calculate the carbon absorption of the natural ecosystem in each scheduling block, and finally obtain a natural carbon consumption model.

[0091] Step S3: Based on the non-industrial remote sensing data, the building density temperature curve is plotted for the regional scheduling block data to obtain the building density temperature curve; based on the non-industrial remote sensing data, the soil carbon content influence curve is plotted for the regional scheduling block data to obtain the soil carbon content influence curve; based on the soil carbon content influence curve and the building density temperature curve, the natural carbon consumption model is corrected to obtain the non-industrial carbon consumption model;

[0092] In an embodiment of the present invention, by utilizing remote sensing data of non-industrial areas, building land information is extracted, and combined with regional scheduling block data, the building density in each scheduling block is calculated, the thermal infrared band information in the remote sensing data is used to invert the surface temperature, and combined with meteorological data, a relationship curve between the building density and temperature of each scheduling block is constructed, and remote sensing data of non-industrial areas is used to construct a spatial distribution model of soil carbon content in combination with soil type range data and terrain data, and a soil carbon content influence curve of each scheduling block is drawn to reflect the influence of soil carbon content on carbon absorption. The constructed building density temperature curve and soil carbon content influence curve are used to correct the data of the constructed natural carbon consumption model, for example, considering the influence of the urban heat island effect on carbon absorption, and the influence of different soil types on carbon absorption, and finally a more accurate non-industrial carbon consumption model is obtained.

[0093] Step S4: Generate a carbon emission reduction priority sequence for the regional scheduling block data based on the industrial carbon emission curve and the non-industrial carbon consumption model to obtain a carbon emission reduction priority sequence; optimize the regional scheduling block data based on the carbon emission reduction priority sequence to obtain a carbon emission reduction optimization scheduling result.

[0094] In an embodiment of the present invention, by integrating the acquired industrial carbon emission curve and the obtained non-industrial carbon consumption model, the net carbon emission value of each scheduling block is calculated, that is, the difference between industrial carbon emissions and non-industrial carbon consumption, and all scheduling blocks are sorted according to the size of the net carbon emission value to generate a carbon emission reduction priority sequence, with priority given to blocks with higher net carbon emission values. According to the carbon emission reduction target, the carbon emission characteristics of the scheduling blocks, and the carbon emission correlation factors between regions, a carbon emission reduction optimization scheduling strategy is formulated, such as implementing more stringent emission reduction measures for high-emission blocks, ultimately achieving effective control and optimized scheduling of regional carbon emissions.

[0095] By acquiring remote sensing data of the monitoring area and performing equal-area scheduling and block division, the present invention can comprehensively grasp the industrial layout and carbon emission spatial distribution characteristics in the region, draw industrial carbon emission curves, and intuitively show the changing trends of industrial carbon emission intensity in different regions and different time periods, providing a scientific basis for the subsequent accurate identification of key areas for carbon emission reduction and the formulation of differentiated emission reduction strategies; by extracting remote sensing data of non-industrial areas, it is possible to analyze the natural element information of vegetation coverage and water distribution, and construct a natural carbon consumption model in combination with regional scheduling block data. The model can quantitatively evaluate the natural carbon sink capacity of different regions, provide basic data for the scientific accounting of regional carbon emissions, and provide basic data for the subsequent optimization of carbon emission reduction sequences; using remote sensing data of non-industrial areas, the building density temperature curve and soil The carbon content impact curve can reflect the impact of urban heat island effect on carbon emissions and the carbon sequestration potential of soil carbon pools respectively. By incorporating these factors into the natural carbon consumption model for data correction, a more accurate non-industrial carbon consumption model can be constructed to comprehensively reflect the impact of various natural and human factors on carbon emissions in the region. By integrating the industrial carbon emission curve and the non-industrial carbon consumption model, the net carbon emission value of each scheduling block can be calculated, and then a carbon reduction priority sequence can be generated. By obtaining emission reduction area information and generating a carbon reduction sequence based on relevant information, the scientific method can accurately identify key areas for carbon emission reduction, optimize scheduling based on the sequence, and maximize the benefits of emission reduction measures, achieve maximum utilization of emission reduction resources, improve emission reduction efficiency, and reduce the time to achieve carbon reduction targets.

[0096] Preferably, step S1 comprises the following steps:

[0097] Step S11: acquiring regional remote sensing data of the monitoring area to obtain regional remote sensing data;

[0098] In an embodiment of the present invention, satellite remote sensing technology is used to collect data from the target monitoring area to obtain high-resolution, multi-temporal remote sensing image data. The data source can select aerial remote sensing data. The data types obtained include multispectral images, hyperspectral images, and radar images. In the data acquisition process, cloud coverage, data quality, and temporal resolution factors need to be considered, and appropriate image data is selected for preprocessing, including geometric correction, radiation correction, and image enhancement operations.

[0099] Step S12: Divide the monitoring area into equal-area scheduling blocks to obtain regional scheduling block data;

[0100] In an embodiment of the present invention, the monitoring area is divided into a number of scheduling blocks of equal area according to the area, shape, and geographical characteristics of the monitoring area. During the division process, GIS software can be used to perform grid division operations and record the geographical location, boundary information, and area size attribute information of each scheduling block.

[0101] Step S13: performing block industrial building regional data analysis on the regional scheduling block data based on the regional remote sensing data to obtain block industrial building regional data;

[0102] In an embodiment of the present invention, by utilizing regional remote sensing data, combined with land use data and distribution data auxiliary data, industrial area identification and extraction are performed on each scheduling block, and areas related to industrial land, factories, facilities and industrial activities are identified, and their spatial distribution, area size, and shape feature information are extracted.

[0103] Step S14: Draw an industrial carbon emission curve for the regional scheduling block data based on the block industrial building area data to obtain an industrial carbon emission curve.

[0104] In an embodiment of the present invention, the emission source of the blocked industrial area is determined based on the obtained block industrial building area data, and the emission data of the emission source is acquired after the emission source information is obtained. For example, by acquiring remote sensing spectral data around the emission source, and analyzing the absorption spectrum of carbon emission substances to obtain the carbon emission data of the carbon emission source building, an industrial carbon emission model is established, and the model is used to calculate the industrial carbon emissions of each scheduling block in different time periods, and draw the industrial carbon emission curve of each scheduling block.

[0105] By acquiring remote sensing data of the monitoring area, the present invention can comprehensively and intuitively understand the land use, vegetation distribution, human activities and factory distribution in the area, which is conducive to grasping the overall situation of the region and providing basic data support for subsequent analysis and modeling. The monitoring area is divided into equal-area scheduling blocks to ensure the refinement and comparability of subsequent analysis. Equal-area blocks can balance the land attributes and characteristics of different regions, making the comparison and analysis between blocks more accurate, and providing a reliable spatial basis for optimizing scheduling. The block industrial building area data obtained through regional remote sensing data analysis can accurately grasp the industrial distribution of each block in the monitoring area. This analysis method can be Effectively identify and distinguish industrial activities, providing key information for subsequent carbon emission quantification and visualization; drawing industrial carbon emission curves can intuitively present the differences in industrial carbon emissions in different blocks. Through this visual representation, decision makers can quickly identify high-emission areas and formulate targeted emission reduction strategies based on emission trends. These curves can also help verify the effectiveness of carbon emission reduction measures, ensure that emission reduction work is targeted and effective, and carbon emission curves can also be used to predict future emissions and guide long-term planning and decision-making by simulating different scenarios. This step combines remote sensing data with carbon emission analysis to provide a quantitative method and decision-making support for industrial carbon emission management in the monitoring area.

[0106] Preferably, step S13 comprises the following steps:

[0107] Step S131: acquiring regional remote sensing texture from regional remote sensing data to obtain regional remote sensing texture;

[0108] In an embodiment of the present invention, the acquired regional remote sensing data is preprocessed, including geometric correction, radiation correction, and image enhancement operations to eliminate data errors and highlight texture information, and a suitable texture analysis method, such as a gray-level co-occurrence matrix, is selected to calculate the preprocessed remote sensing data. According to the selected method, corresponding parameters are set to extract texture features at different scales and directions, and the calculated texture feature values ​​are used to form a regional remote sensing texture image. Each pixel corresponds to a texture feature value to characterize the texture information of the pixel and its neighborhood.

[0109] Step S132: extracting industrial area range features from the regional remote sensing texture to obtain industrial area range texture;

[0110] In an embodiment of the present invention, by utilizing the acquired regional remote sensing texture and combining it with the characteristics of the industrial area, range feature extraction is performed, texture features that are sensitive to the industrial area, such as roughness, contrast, and directionality, are selected, and feature vectors are constructed. Using existing industrial area sample data, a classifier, such as a support vector machine, is trained to distinguish between industrial areas and other types of land objects. The trained classifier is used to classify the remote sensing texture of the entire area to obtain a preliminary identification result of the industrial area. The classification result is processed to eliminate noise to obtain the final industrial area range texture.

[0111] Step S133: extracting industrial area building features from the regional remote sensing texture to obtain industrial area building texture;

[0112] In an embodiment of the present invention, by further extracting the characteristics of industrial area buildings based on the regional remote sensing texture, selecting texture features that are sensitive to buildings according to the characteristics of industrial buildings, such as shape, size, and edge density, edge detection is used to extract building contour information in the regional remote sensing texture, and feature calculations are performed on the extracted building contours, such as aspect ratio, area, and perimeter. A feature vector is constructed in combination with the texture features, and a machine learning method is used to classify the building features, identify the buildings in the industrial area, and represent the identification results as industrial area building textures.

[0113] Step S134: Based on the industrial area range texture and the industrial area building texture, the regional scheduling block data is segmented into industrial building area identification to obtain segmented industrial building area data.

[0114] In an embodiment of the present invention, the industrial area range texture and the industrial area building texture are fused as the basis for identifying the block industrial building area. According to the regional scheduling block data, the regional remote sensing texture is divided into a number of sub-areas. For each sub-area, its statistical features on the industrial area range texture and the industrial area building texture are calculated respectively. A feature vector is constructed using these statistical features and input into a classifier. Industrial area identification is performed on each sub-area, and the identification results are integrated into the regional scheduling block data to obtain the final block industrial building area data.

[0115] The present invention can capture the surface features and material composition information of the monitoring area by acquiring regional remote sensing textures. Texture analysis can effectively identify and distinguish different land use forms, and provide basic data for subsequent industrial area scope and building feature extraction; industrial area scope feature extraction of remote sensing textures can effectively identify and define the scope of industrial activities in the monitoring area. This step can distinguish industrial areas from non-industrial areas, and provide accurate scope definition for block industrial building area data analysis; this step focuses on the extraction of building features in industrial areas in remote sensing textures. By analyzing the building textures in industrial areas, industrial buildings can be identified and classified, and the density and distribution of industrial activities can be understood; by combining the industrial area scope texture and industrial area building textures, can accurately identify and divide industrial areas in regional scheduling block data. This method takes into account the scope and density of industrial activities, ensures the accuracy and comprehensiveness of the block industrial building area data, and the identification of block industrial building area data is conducive to the targeted assessment of industrial carbon emissions and provides a basis for the subsequent construction and optimization of carbon emission models; the fine processing and feature extraction of remote sensing textures effectively identify industrial activities in the monitoring area. These steps comprehensively consider the industrial scope and building characteristics, and provide accurate and detailed information for the analysis of block industrial building area data. This method provides a reliable basis for the quantification and modeling of industrial carbon emissions, which is conducive to optimizing carbon emission reduction strategies and management in the monitoring area.

[0116] Preferably, step S14 comprises the following steps:

[0117] Step S141: positioning the carbon emission source buildings on the block industrial building area data to obtain the industrial carbon emission source building positioning data;

[0118] In an embodiment of the present invention, the type, scale, location, and energy consumption information of the main industries in the region are collected based on the industrial emission source database, and the block industrial building area data are combined to match the collected industrial enterprise information with the corresponding plots of land using GIS spatial analysis technology. The spatial location information and the corresponding carbon emissions of all emission sources are integrated together based on the emission factors and activity level data of different types of industrial enterprises to form industrial carbon emission source building positioning data.

[0119] Step S142: performing emission source density calculation on the industrial carbon emission source building location data to obtain industrial carbon emission source building density data;

[0120] In an embodiment of the present invention, by obtaining the industrial carbon emission source building positioning data, selecting the distance weight function and then using the spatial analysis method of the kernel density estimation method, the number of carbon emission source buildings or carbon emissions per unit area of ​​each plot of land is calculated to obtain the industrial carbon emission source building density data.

[0121] Step S143: monitoring carbon emissions of industrial carbon emission source building location data to obtain industrial carbon emission data;

[0122] In an embodiment of the present invention, by selecting appropriate carbon emission monitoring methods according to different types of emission sources, such as remote sensing monitoring and model estimation, carbon emission monitoring is performed on the located industrial emission sources. For emission sources that can be directly measured, their emission data can be monitored in real time by installing sensors; for emission sources that are difficult to measure directly, their emissions can be estimated by collecting energy consumption data and establishing emission models, and the carbon emission data obtained by different monitoring methods are integrated to obtain complete industrial carbon emission data.

[0123] Step S144: Calculate the mean carbon emission value of industrial areas based on the industrial carbon emission source building density data to obtain the mean carbon emission value of industrial areas;

[0124] In an embodiment of the present invention, by combining the building density data of industrial carbon emission sources and the industrial carbon emission data, the average carbon emissions per unit area of ​​each plot, that is, the average carbon emissions of the industrial area, is calculated. The specific calculation method can be the weighted average method.

[0125] Step S145: Calculating the block industrial building area ratio of the regional scheduling block data based on the block industrial building area data to obtain the block industrial building area ratio;

[0126] In an embodiment of the present invention, by utilizing the block industrial building area data and the regional scheduling block data, the area ratio of the industrial area in each scheduling block, that is, the block industrial building area ratio, is calculated. The specific calculation method is to add up the areas of all plots belonging to the industrial area in each scheduling block, and then divide it by the total area of ​​the scheduling block.

[0127] Step S146: Draw an industrial carbon emission curve for the mean carbon emission value of the industrial area and the proportion of the industrial building area by block to obtain an industrial carbon emission curve.

[0128] In the embodiment of the present invention, an industrial carbon emission curve is drawn by calculating the average carbon emission value of the industrial area and the proportion of the industrial building area by block, with time as the horizontal axis and the carbon emission of the industrial area as the vertical axis.

[0129] The present invention can accurately identify and define the carbon emission source buildings in each block within the monitoring area by locating the carbon emission source buildings in the block industrial building area data. This helps to clarify the source of carbon emissions and provides a basis for subsequent emission calculations and density analysis; calculating the density of industrial carbon emission source buildings can effectively evaluate the intensity of industrial activities. Through this step, high-density carbon emission source building areas can be identified, thereby helping decision makers determine key target areas for carbon emission reduction; monitoring industrial carbon emission source buildings can obtain emission data for each emission source. This step provides a basis for quantifying and evaluating industrial carbon emissions, which is conducive to the subsequent calculation of carbon emissions at the block and regional levels; calculating the mean carbon emissions of industrial areas can comprehensively evaluate the emission level of industrial activities. This step takes into account the density of industrial carbon emission source buildings and the regional scale, and provides a standardized indicator for comparing emissions in different blocks and regions; Calculating the proportion of industrial building areas in each block can analyze the relationship between industrial and non-industrial activities in the monitored area. This step provides a basis for evaluating the degree of industrialization and potential carbon emission contributions of different blocks; drawing industrial carbon emission curves can intuitively present the carbon emission trends and distribution of industrial activities. This step effectively displays the overall situation of industrial carbon emissions in the monitored area through visualization, and provides clear guidance for carbon emission reduction strategies; by calculating density, monitoring emissions, calculating means and proportions, a quantitative assessment of carbon emissions in industrial areas is provided, and drawing carbon emission curves provides an effective means for intuitively understanding the distribution of industrial carbon emissions. These steps jointly construct an industrial carbon emission analysis framework, providing data support and feasible paths for carbon emission reduction decisions in the monitored area.

[0130] Preferably, step S2 comprises the following steps:

[0131] Step S21: extracting non-industrial regional feature textures from regional remote sensing data to obtain non-industrial feature textures;

[0132] In an embodiment of the present invention, by defining the categories of non-industrial areas, such as vegetation and water bodies, and collecting corresponding remote sensing image sample data, the regional remote sensing data is preprocessed, including geometric correction, radiation correction, and image enhancement operations to eliminate data errors and highlight the differences in spectral characteristics of different types of land objects, and a suitable feature extraction method is selected, such as spectral index calculation, texture analysis, and principal component analysis, to extract features that are sensitive to distinguishing different types of land objects, construct feature vectors, and use machine learning methods to classify and train the remote sensing image sample data to obtain a classification model that can identify the characteristics of non-industrial areas, and apply it to the entire regional remote sensing data to obtain non-industrial feature texture data.

[0133] Step S22: acquiring non-industrial regional remote sensing data based on non-industrial feature textures to obtain non-industrial remote sensing data;

[0134] In an embodiment of the present invention, regional remote sensing data is classified by utilizing non-industrial feature texture data, pixels belonging to non-industrial areas are extracted, and remote sensing data of non-industrial areas are formed. The original remote sensing data can be masked according to the classification results, pixel values ​​belonging to non-industrial areas are retained, and other pixel values ​​are set to specific values. The Bayesian averaging method can be used to integrate the results of multiple classifiers to obtain remote sensing data of non-industrial areas.

[0135] Step S23: Draw a vegetation carbon consumption curve for the non-industrial remote sensing data to obtain a vegetation carbon consumption curve;

[0136] In an embodiment of the present invention, vegetation coverage areas are identified from remote sensing data of non-industrial areas, for example, threshold segmentation is performed using the normalized vegetation index indicator, and vegetation biomass data of different time periods are collected or simulated according to the vegetation type and growth cycle of the study area, such as leaf area index and net primary productivity, and a relationship model between vegetation biomass and carbon consumption is established. For example, the carbon conversion coefficient is used to convert biomass into carbon consumption, and a vegetation carbon consumption curve is drawn with time as the horizontal axis and vegetation carbon consumption as the vertical axis to reflect the carbon absorption or release of vegetation in different time periods.

[0137] Step S24: Draw a water body carbon consumption curve for the non-industrial remote sensing data to obtain a water body carbon consumption curve;

[0138] In an embodiment of the present invention, water body areas are identified from remote sensing data of non-industrial areas, for example, threshold segmentation is performed using water body index indicators, and according to the water body type and hydrological characteristics of the study area, the dissolved organic carbon and particulate organic carbon index data of the water body in different time periods are simulated, and a relationship model between the water body carbon index and the carbon consumption is established. For example, the carbon conversion coefficient is used for estimation, and a water body carbon consumption curve is drawn with time as the horizontal axis and the water body carbon consumption as the vertical axis to reflect the carbon absorption or release of the water body in different time periods.

[0139] Step S25: construct a natural carbon consumption model for the regional scheduling block data based on the vegetation carbon consumption curve and the water body carbon consumption curve to obtain a natural carbon consumption model.

[0140] In an embodiment of the present invention, a natural carbon consumption model at a regional scale is constructed by integrating the vegetation carbon consumption curve and the water body carbon consumption curve. The specific method can be to associate the spatial distribution information of different types of natural carbon sinks with the corresponding carbon consumption curves according to the regional scheduling block data, for example, by using a geographic information system to perform spatial overlay analysis to finally obtain a natural carbon consumption model.

[0141] The present invention can effectively identify and distinguish natural landscape elements, such as vegetation and water bodies, in the monitoring area by extracting characteristic textures of non-industrial areas; this step can provide a basis for obtaining non-industrial remote sensing data, which is helpful for accurately defining and analyzing natural environment characteristics; obtaining non-industrial remote sensing data can exclude the impact of industrial activities and focus on analyzing the role of the natural environment in carbon consumption. This step provides a direct data source for building a natural carbon consumption model, ensuring the accuracy and effectiveness of the model; drawing vegetation carbon consumption curves can quantify and visualize the absorption and storage of carbon elements by vegetation. Vegetation is an important factor in natural carbon consumption. This step helps to evaluate the carbon consumption capacity of different types of vegetation and provides a basis for optimizing the natural carbon consumption model; drawing water body carbon consumption curves can be used to classify Analyze and present the role of water in the carbon cycle. Water can store and transport carbon. Studying it can better understand the flow of carbon in the natural environment. This step provides key information about water for the natural carbon consumption model. By combining vegetation and water carbon consumption curves, a comprehensive natural carbon consumption model can be constructed. This model takes into account the contribution of different elements in the natural environment to carbon consumption, and provides a scientific basis for analyzing and predicting the carbon reduction potential in non-industrial areas. The construction of a natural carbon consumption model can provide decision-making support for carbon reduction strategies and help optimize the positive impact of the natural environment in the carbon cycle. It effectively evaluates the role of the natural environment in carbon consumption. From feature texture extraction to building a natural carbon consumption model, these steps ensure the accurate identification and quantification of natural environmental factors.

[0142] Preferably, step S23 includes the following steps:

[0143] Step S231: performing vegetation characteristic color analysis on non-industrial remote sensing data to obtain vegetation characteristic color data;

[0144] In an embodiment of the present invention, by acquiring non-industrial remote sensing data of the target area, the data source can select multispectral satellite images, and use remote sensing image processing software to pre-process the remote sensing data, including radiation correction, geometric correction, and atmospheric correction. According to the spectral characteristics of the vegetation, bands sensitive to vegetation, such as near-infrared bands and red light bands, are selected to construct vegetation indices, such as normalized vegetation indexes. By analyzing the differences between different vegetation types in the vegetation index image, characteristic color information of different vegetation types is extracted, such as NDVI value ranges, hues, and saturation of different vegetation types, and this information is stored as vegetation characteristic color data.

[0145] Step S232: performing vegetation distribution positioning on the non-industrial remote sensing data based on the vegetation characteristic color data to obtain vegetation distribution data;

[0146] In an embodiment of the present invention, non-industrial remote sensing data is classified by utilizing the obtained vegetation characteristic color data in combination with an image classification algorithm. The vegetation characteristic color data can be used as a training sample to train a classifier to identify different vegetation types. The classifier is used to classify the entire remote sensing image to obtain the vegetation type to which each pixel belongs, locate each phase element, and output it as vegetation distribution data.

[0147] Step S233: performing non-industrial vegetation coverage analysis on the vegetation distribution data to obtain the non-industrial vegetation coverage;

[0148] In an embodiment of the present invention, vegetation distribution data is used to count the area proportions of different vegetation types in each analysis unit. The analysis unit is based on grid units. According to the definition, the proportion of the vegetation area in each analysis unit to the total area is calculated, which is the non-industrial vegetation coverage rate.

[0149] Step S234: acquiring vegetation carbon consumption data from the vegetation distribution data to obtain vegetation carbon consumption data;

[0150] In an embodiment of the present invention, the biomass of different vegetation types in each analysis unit is calculated according to a biomass estimation model for different vegetation types, such as a model based on remote sensing data. The biomass is converted into carbon storage according to the carbon conversion coefficient. The carbon absorption and carbon release of the vegetation type in each analysis unit are estimated using vegetation growth models, such as a photosynthesis model and a respiration model. The carbon absorption is subtracted from the carbon release to obtain the vegetation carbon consumption data in each analysis unit.

[0151] Step S235: Draw a vegetation carbon consumption curve for the non-industrial vegetation coverage rate based on the vegetation carbon consumption data to obtain a vegetation carbon consumption curve.

[0152] In an embodiment of the present invention, by integrating non-industrial vegetation coverage data and vegetation carbon consumption data and making the non-industrial vegetation coverage data and vegetation carbon consumption data correspond to each other, a vegetation carbon consumption curve is drawn, with the horizontal axis being the non-industrial vegetation coverage rate and the vertical axis being the vegetation carbon consumption.

[0153] The present invention can effectively identify and extract the unique color features of vegetation by performing vegetation characteristic color analysis on non-industrial remote sensing data. This step utilizes the characteristics of the vegetation reflectance spectrum in the remote sensing image to provide basic data for subsequent vegetation distribution positioning and analysis; the acquisition of vegetation characteristic color data helps to accurately identify and locate the vegetation distribution in the monitoring area. By analyzing the characteristic color, vegetation and non-vegetation areas can be distinguished, and the scope and boundaries of vegetation coverage can be determined; calculating the non-industrial vegetation coverage rate can quantitatively evaluate the vegetation distribution in the monitoring area. This step calculates the proportion of land area covered by vegetation by analyzing the vegetation distribution data, which is used to evaluate the natural carbon consumption of vegetation. The effect of vegetation carbon consumption can be quantitatively measured; obtaining vegetation carbon consumption data can quantify the role of vegetation in the carbon cycle. This step provides key data for drawing vegetation carbon consumption curves by analyzing the carbon absorption and storage capacity of vegetation; drawing vegetation carbon consumption curves can intuitively present the trend and potential of vegetation carbon consumption. This step visualizes vegetation carbon consumption data to show the positive impact of vegetation in the carbon cycle. Vegetation carbon consumption curves provide a basis for evaluating and comparing vegetation carbon consumption efficiency in different regions, and provide important parameters for the construction of natural carbon consumption models; it provides comprehensive and accurate vegetation information for the construction of natural carbon consumption models, ensuring that the model takes into account the key role of vegetation in the carbon cycle. This method provides a scientific basis for optimizing carbon reduction strategies.

[0154] Preferably, step S24 comprises the following steps:

[0155] Step S241: performing water body smoothing texture recognition on non-industrial remote sensing data to obtain non-industrial water body texture;

[0156] In an embodiment of the present invention, by targeting the spectral characteristics of the water body, a band that is sensitive to the water body, such as a near-infrared band and a short-wave infrared band, is selected to construct a water body index, such as a normalized water body index. The water body index image is used in combination with an image segmentation algorithm, such as threshold segmentation and edge detection, to extract the water body area. The texture analysis algorithm, such as a gray-level co-occurrence matrix, is used to extract texture features of the water body area to obtain non-industrial water body texture data.

[0157] Step S242: extracting water algae texture from non-industrial water texture to obtain water algae texture;

[0158] In an embodiment of the present invention, the differences in texture characteristics between water algae and other water areas are analyzed through non-industrial water texture data. The water algae area exhibits texture color and is different from the reflectance spectrum. The non-industrial water texture data is classified to distinguish the water algae texture from other water textures.

[0159] Step S243: performing water algae scale analysis on non-industrial water textures based on water algae textures to obtain water algae scale data;

[0160] In an embodiment of the present invention, the water algae areas are identified and extracted by utilizing the extracted water algae texture data in combination with image processing technology. The area of ​​each area is calculated based on the number of pixels and image resolution of each water algae area, and the area of ​​all water algae areas is statistically analyzed to obtain water algae scale data.

[0161] Step S244: performing water coverage analysis on non-industrial remote sensing data based on non-industrial water body textures to obtain non-industrial water body coverage;

[0162] In an embodiment of the present invention, by using spatial analysis technology and water body mapping technology, water body areas are identified and divided, and the proportion of water body area in each analysis unit to the total area is counted. The non-industrial water body coverage rate can be calculated by dividing the total non-industrial coverage data by the water body area data.

[0163] Step S245: Calculate the water area based on the water coverage rate to obtain water area data;

[0164] In an embodiment of the present invention, by obtaining the non-industrial water body coverage rate in combination with the non-industrial remote sensing data of the target area, the proportion of the non-industrial area is analyzed as the total area of ​​each analysis unit, and the area of ​​the water body in each analysis unit is calculated to obtain the water body area data.

[0165] Step S246: Calculate water body carbon consumption data based on the water body algae scale data and water body area data to obtain water body carbon consumption data;

[0166] In an embodiment of the present invention, the biomass of water algae is calculated based on the water algae scale data, the biomass is converted into carbon storage based on the carbon conversion coefficient, the carbon absorption and carbon release of the water algae are calculated using the water body ecosystem model, the carbon absorption and carbon release of the water body are calculated based on the water body area data and the water body carbon cycle model, and the carbon absorption of the water algae and the water body is subtracted from the carbon release to obtain the water body carbon consumption data.

[0167] Step S247: Draw a water body carbon consumption curve for the non-industrial water body coverage based on the water body carbon consumption data to obtain a water body carbon consumption curve.

[0168] In an embodiment of the present invention, the obtained non-industrial water body coverage data and the obtained water body carbon consumption data are integrated, and the non-industrial water body coverage data and the water body carbon consumption data are used to construct a corresponding data set to draw a water body carbon consumption curve, in which the horizontal axis is the non-industrial water body coverage rate and the vertical axis is the water body carbon consumption.

[0169] The present invention can effectively distinguish and extract water body features by identifying the smooth texture of water bodies in non-industrial remote sensing data. The surface of a water body often has a unique smooth texture. This step aims to accurately identify the water body area and distinguish it from other features of land and vegetation; extracting water body algae texture from non-industrial water body texture can capture the unique texture characteristics of algae in the water body. Algae growth will affect the texture and color of the water body. This step helps to identify and analyze the existence and distribution of algae in the water body; analyzing the scale of water body algae texture can evaluate the abundance and coverage of algae in the water body. This step quantifies the presence of algae to provide basic data for subsequent calculation of water body carbon consumption; analyzing non-industrial water body texture can accurately calculate and evaluate water body coverage. This step aims to distinguish water from other elements of land and vegetation and quantify their The proportion of land area covered; calculating the area of ​​water bodies can provide key parameters for water body carbon consumption analysis. The carbon consumption capacity of water bodies is related to the size of the area. This step provides an important indicator for the subsequent calculation of water body carbon consumption; by combining the water body algae scale data and water body area data, the carbon consumption level of water bodies can be calculated. This step comprehensively analyzes the impact of algae presence on carbon consumption and the contribution of water body area to the carbon cycle; drawing water body carbon consumption curves can intuitively present the carbon consumption trend and potential of water bodies. This step effectively demonstrates the role of water bodies in the carbon cycle through visualization, providing an important reference for the construction of natural carbon consumption models; providing comprehensive and accurate water body information for natural carbon consumption models, ensuring that the model takes into account the unique carbon cycle process of water bodies. This method provides a scientific basis for optimizing carbon reduction strategies.

[0170] Preferably, step S3 comprises the following steps:

[0171] Step S31: performing regional temperature data analysis on non-industrial remote sensing data to obtain regional temperature data;

[0172] Step S32: performing regional bare land analysis on the non-industrial remote sensing data to obtain non-industrial bare land data;

[0173] Step S33: Drawing a building density temperature curve for the regional scheduling block data based on the non-industrial remote sensing data and the regional temperature data to obtain a building density temperature curve;

[0174] Step S34: analyzing the effect of temperature on soil carbon content of non-industrial bare land data based on regional temperature data to obtain data on the effect of temperature on soil carbon content;

[0175] Step S35: fitting the building density temperature curve based on the temperature effect on soil carbon content data to obtain a soil carbon content effect curve;

[0176] Step S36: Based on the soil carbon content influence curve and the building density temperature curve, the natural carbon consumption model is corrected to obtain a non-industrial carbon consumption model.

[0177] As an embodiment of the present invention, refer to Figure 2 As shown, Figure 1 Detailed step flow diagram of step S3 in the embodiment, step S3 includes the following steps:

[0178] Step S31: performing regional temperature data analysis on non-industrial remote sensing data to obtain regional temperature data;

[0179] In an embodiment of the present invention, by utilizing a radiation transfer model, atmospheric correction is performed on remote sensing images to eliminate the influence of the atmosphere on the surface temperature inversion, a suitable thermal infrared band is selected, and a surface temperature image is calculated based on a surface temperature inversion algorithm. The surface temperature image is spatially interpolated and filtered to obtain regional temperature data.

[0180] Step S32: performing regional bare land analysis on the non-industrial remote sensing data to obtain non-industrial bare land data;

[0181] In an embodiment of the present invention, by utilizing the acquired remote sensing image data, a suitable band combination, such as near-infrared, red light and blue light bands, is selected to construct a normalized difference vegetation index vegetation index image, and the image is classified according to the vegetation index threshold to distinguish between vegetation covered areas and non-vegetation covered areas, and non-industrial bare land data is extracted using high-resolution remote sensing images.

[0182] Step S33: Drawing a building density temperature curve for the regional scheduling block data based on the non-industrial remote sensing data and the regional temperature data to obtain a building density temperature curve;

[0183] In an embodiment of the present invention, an area is divided into several regular grids, the building area and the total area in each grid are counted, the building density of each grid is calculated, the average temperature value in each grid is extracted, a scatter plot is drawn with the building density as the horizontal axis and the average temperature as the vertical axis, and a regression analysis method is used to fit the relationship curve between the building density and the temperature, that is, the building density-temperature curve.

[0184] Step S34: analyzing the effect of temperature on soil carbon content of non-industrial bare land data based on regional temperature data to obtain data on the effect of temperature on soil carbon content;

[0185] In an embodiment of the present invention, measured data on soil carbon content in a region are collected and spatially matched with regional temperature data in different temperature ranges, the soil carbon content data are grouped, the trend of soil carbon content changes in different temperature ranges is analyzed, and a relationship model between temperature and soil carbon content is established using statistical analysis methods. The effect of temperature on soil carbon content is quantified, and data on the effect of temperature on soil carbon content are obtained.

[0186] Step S35: fitting the building density temperature curve based on the temperature effect on soil carbon content data to obtain a soil carbon content effect curve;

[0187] In an embodiment of the present invention, data fusion is performed through the temperature-affected soil carbon content data and the building density temperature curve. According to the building density temperature curve, the temperature values ​​corresponding to different building densities are converted into corresponding soil carbon content prediction values, and a relationship curve between building density and soil carbon content, i.e., the soil carbon content influence curve, is constructed.

[0188] Step S36: Based on the soil carbon content influence curve and the building density temperature curve, the natural carbon consumption model is corrected to obtain a non-industrial carbon consumption model.

[0189] In an embodiment of the present invention, by selecting a suitable natural carbon consumption model, the parameters of the model are locally calibrated according to the actual situation of the study area, the obtained soil carbon content influence curve and building density temperature curve are used as input parameters, the natural carbon consumption model is data corrected, and finally a non-industrial carbon consumption model is obtained.

[0190] By performing regional temperature data analysis on non-industrial remote sensing data, the present invention can accurately obtain the spatial distribution information of surface temperature, and provide basic data for subsequent analysis of the impact of temperature on soil carbon content and building density; by performing regional bare land analysis on non-industrial remote sensing data, land without vegetation cover can be identified, and the area where temperature affects soil carbon content can be clarified, thereby improving the accuracy of subsequent analysis; by combining regional temperature data with regional scheduling block data to draw a building density temperature curve, the temperature difference between areas with different building densities can be intuitively displayed, revealing the influence of urbanization on regional temperature; based on regional temperature data, the impact of temperature on soil carbon content on non-industrial bare land data can be analyzed, and the degree of influence of temperature on soil carbon content can be quantified, which is an important factor in building a non-industrial carbon consumption model. The key link of the model; using the data of temperature affecting soil carbon content to fit the building density temperature curve, the impact of urbanization on temperature can be linked to changes in soil carbon content, and a soil carbon content impact curve can be constructed to provide more refined data support for the model; finally, based on the soil carbon content impact curve and the building density temperature curve, data correction is performed on the natural carbon consumption model, the impact of urbanization on the carbon cycle can be incorporated into the model, and a more accurate and realistic non-industrial carbon consumption model can be constructed; by systematically analyzing the temperature, land use, building density and soil carbon content factors in non-industrial areas, a more comprehensive and accurate carbon consumption model is constructed, which not only takes into account natural factors, but also incorporates the impact of urbanization, and can therefore better guide carbon emission reduction scheduling.

[0191] Preferably, step S33 includes the following steps:

[0192] Step S331: extracting building texture geometric features from non-industrial remote sensing data to obtain building texture geometric features;

[0193] Step S332: performing building recognition on the regional scheduling block data based on building texture geometric features to obtain building data;

[0194] Step S333: Calculate the building density of the regional scheduling block data based on the building data to obtain the building density;

[0195] Step S334: Draw a building density temperature curve for the regional scheduling block data based on the building density and regional temperature data to obtain a building density temperature curve.

[0196] As an embodiment of the present invention, refer to Figure 3 As shown, Figure 2 FIG. 1 is a schematic diagram of a detailed step flow chart of step S33 in FIG. 1 . In this embodiment, step S33 includes the following steps:

[0197] Step S331: extracting building texture geometric features from non-industrial remote sensing data to obtain building texture geometric features;

[0198] In an embodiment of the present invention, by utilizing a gray-level co-occurrence matrix and a local binary pattern texture analysis method, texture features of buildings in an image, such as contrast, are extracted; by utilizing an edge detection and a shape analysis geometric feature extraction method, geometric features of buildings, such as area, perimeter, and shape index, are extracted; the extracted texture features and geometric features are combined to construct a multidimensional feature vector as a basis for building recognition.

[0199] Step S332: performing building recognition on the regional scheduling block data based on building texture geometric features to obtain building data;

[0200] In an embodiment of the present invention, by utilizing the geometric features of building textures and selecting the random forest method, a building recognition model is constructed, the model is trained and verified using existing building sample data, the model parameters are optimized, the recognition accuracy of the model is improved, the trained model is applied to the remote sensing images of the entire study area, each pixel is classified, buildings and non-building areas are identified, and building data is obtained.

[0201] Step S333: Calculate the building density of the regional scheduling block data based on the building data to obtain the building density;

[0202] In an embodiment of the present invention, the study area is divided into a number of regular grids as the basic units of spatial analysis, the total area of ​​the buildings in each grid is counted, and the ratio of the building area to the total area of ​​the grid is calculated to obtain the building density of each grid. The area weighted method can be used to fuse building data of different resolutions to obtain the building density.

[0203] Step S334: Draw a building density temperature curve for the regional scheduling block data based on the building density and regional temperature data to obtain a building density temperature curve.

[0204] In an embodiment of the present invention, the calculated building density data is spatially matched with the regional temperature data to ensure that each grid unit has corresponding building density and temperature values. A scatter plot is drawn with building density as the horizontal axis and average temperature as the vertical axis, and a curve fitting method is used to fit the relationship curve between building density and temperature, that is, the building density temperature curve.

[0205] The present invention can identify and analyze the layout and form of buildings in a region by extracting geometric features of building textures from non-industrial remote sensing data. This step helps to obtain the scale, shape and orientation information of buildings in the region, and provides basic data support for subsequent building identification and density calculation; by identifying the region based on the geometric features of building textures, it is possible to effectively extract building information from remote sensing data and accurately obtain the number, size and location data of buildings in the region, which is crucial for understanding the distribution of buildings in the study area, as well as subsequent density calculation and temperature analysis; by calculating the building density based on the building identification results, it is possible to quantitatively analyze the density of buildings in the region, and provide an important basis for evaluating the urban heat island effect and optimizing the urban spatial layout; by drawing a building density temperature curve in combination with building density and regional temperature data, it is possible to intuitively display the relationship between building density and temperature, and reveal the formation mechanism of the urban heat island effect. Based on the analysis of the heat island effect, the relationship between building density and temperature is obtained, which is conducive to providing a basic temperature discrimination benchmark for the subsequent analysis of the impact of changes in vegetation in the region due to temperature and changes in soil carbon content due to temperature.

[0206] Preferably, step S4 comprises the following steps:

[0207] Step S41: constructing an industrial carbon emission sequence for the regional scheduling block data based on the industrial carbon emission curve to obtain an industrial carbon emission sequence;

[0208] In an embodiment of the present invention, the region is divided into several regular grids according to model prediction as the basic units of spatial analysis. The industrial carbon emissions of each grid at different time nodes are calculated based on the industrial proportion information in each grid and the industrial carbon emission curve, and finally the industrial carbon emission sequence of each grid is obtained.

[0209] Step S42: performing industrial emission equal block acquisition on the industrial carbon emission sequence to obtain industrial emission equal block data;

[0210] In an embodiment of the present invention, the regional scheduling block data is sorted based on the industrial carbon emission sequence, and then multiple blocks are divided according to the carbon emissions. The industrial carbon emissions within each block are obtained, and the blocks with the same carbon emissions between different blocks are extracted. The same carbon emissions are constructed into groups, and the group data are constructed into sets, and finally the industrial emission equal block data is obtained.

[0211] Step S43: Generate a carbon consumption sequence for the industrial emission equal block data based on the non-industrial carbon consumption model to obtain a carbon consumption sequence;

[0212] In an embodiment of the present invention, relevant data of each equal block of industrial emissions, such as vegetation coverage, soil area, water area and building density, are collected and input into a non-industrial carbon consumption model to calculate the carbon consumption capacity of each block in different time periods. The impact of dynamic factors on carbon consumption is considered, and the model results are dynamically adjusted. The calculated carbon consumption capacity data are re-sorted according to energy consumption to form a carbon consumption sequence for each block.

[0213] Step S44: generating a carbon emission reduction priority sequence for the industrial carbon emission sequence and the carbon consumption sequence to obtain a carbon emission reduction priority sequence;

[0214] In an embodiment of the present invention, the industrial carbon emission sequence and the carbon consumption sequence are time-aligned and spatially matched to calculate the net carbon emissions of each block in each time period, that is, the industrial carbon emissions minus the carbon consumption. The net carbon emissions are sorted to determine the sequence, and the carbon consumption of the blocks with the same carbon emission data in the sequence is re-sorted to obtain a carbon emission reduction priority scheduling order.

[0215] Step S45: Optimize the regional scheduling block data based on the carbon emission reduction priority sequence to obtain the carbon emission reduction optimization scheduling result.

[0216] In an embodiment of the present invention, carbon emission reduction adjustments are made to adjustable loads in time blocks with higher net carbon emission values ​​according to a carbon emission reduction priority sequence. Through the carbon emission reduction adjustments, optimal scheduling of the carbon emission reduction system is achieved, and ultimately a carbon emission reduction optimized scheduling result is obtained.

[0217] By constructing an industrial carbon emission sequence based on the industrial carbon emission curve, the present invention can more accurately depict the dynamic change trend of industrial carbon emissions in a region, provide more accurate data support for subsequent carbon emission reduction work, avoid the error caused by average distribution, and make the emission reduction strategy more targeted; by equally dividing the industrial carbon emission sequence, regions with similar carbon emissions can be divided into the same group, which is convenient for subsequent differentiated emission reduction measures for regions with different emission levels, thereby improving the efficiency and accuracy of carbon emission reduction; generating a carbon consumption sequence based on a non-industrial carbon consumption model can more comprehensively consider the dynamic balance of carbon emissions and carbon absorption in the region, avoid focusing only on industrial emission reduction and ignoring other carbon sink resources, and provide more comprehensive and scientific carbon emission reduction strategies. Provide a basis for the strategy; Generate a carbon emission reduction priority sequence, which can scientifically determine the priority of carbon emission reduction according to the carbon emission intensity and emission reduction potential factors of different regions, invest limited resources in areas with the greatest emission reduction benefits, and maximize the overall carbon emission reduction efficiency; Regional scheduling optimization based on the carbon emission reduction priority sequence can achieve adjustments to the resource allocation, industrial layout, and energy structure of different regions, thereby effectively reducing the overall carbon emissions of the region, making emission reduction work targeted and effective, and the carbon emission curve can also be used to predict future emissions, by simulating different scenarios to guide long-term planning and decision-making. This step combines remote sensing data with carbon emission analysis, and provides a quantitative method and decision-making support for industrial carbon emission management in the monitoring area.

[0218] The above description is only a specific embodiment of the present invention, so that those skilled in the art can understand or implement the present invention. Various modifications to these embodiments will be apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the present invention. Therefore, the present invention will not be limited to the embodiments shown herein, but should conform to the widest scope consistent with the principles and novel features invented herein.

Claims

1. A method for optimizing and scheduling a carbon emission reduction system, characterized in that: The following steps are involved: Step S1: acquiring regional remote sensing data of the monitoring area to obtain regional remote sensing data; dividing the monitoring area into equal-area scheduling blocks to obtain regional scheduling block data; drawing an industrial carbon emission curve for the regional remote sensing data and the regional scheduling block data to obtain an industrial carbon emission curve; Step S2: acquiring non-industrial regional remote sensing data for regional remote sensing data to obtain non-industrial remote sensing data; constructing a natural carbon consumption model for regional scheduling block data based on the non-industrial remote sensing data to obtain a natural carbon consumption model; Step S3: Based on the non-industrial remote sensing data, the building density temperature curve is plotted for the regional scheduling block data to obtain the building density temperature curve; based on the non-industrial remote sensing data, the soil carbon content influence curve is plotted for the regional scheduling block data to obtain the soil carbon content influence curve; based on the soil carbon content influence curve and the building density temperature curve, the natural carbon consumption model is corrected to obtain the non-industrial carbon consumption model; Step S4: Generate a carbon emission reduction priority sequence for the regional scheduling block data based on the industrial carbon emission curve and the non-industrial carbon consumption model to obtain a carbon emission reduction priority sequence; optimize the regional scheduling block data based on the carbon emission reduction priority sequence to obtain a carbon emission reduction optimization scheduling result.

2. The method for optimizing and scheduling a carbon emission reduction system according to claim 1, characterized in that: Step S1 includes the following steps: Step S11: acquiring regional remote sensing data of the monitoring area to obtain regional remote sensing data; Step S12: Divide the monitoring area into equal-area scheduling blocks to obtain regional scheduling block data; Step S13: performing block industrial building regional data analysis on the regional scheduling block data based on the regional remote sensing data to obtain block industrial building regional data; Step S14: Draw an industrial carbon emission curve for the regional scheduling block data based on the block industrial building area data to obtain an industrial carbon emission curve.

3. The method for optimizing and scheduling a carbon emission reduction system according to claim 2, characterized in that: Step S13 includes the following steps: Step S131: acquiring regional remote sensing texture from regional remote sensing data to obtain regional remote sensing texture; Step S132: extracting industrial area range features from the regional remote sensing texture to obtain industrial area range texture; Step S133: extracting industrial area building features from the regional remote sensing texture to obtain industrial area building texture; Step S134: Based on the industrial area range texture and the industrial area building texture, the regional scheduling block data is segmented into industrial building area identification to obtain segmented industrial building area data.

4. The method for optimizing and scheduling a carbon emission reduction system according to claim 2, characterized in that: Step S14 includes the following steps: Step S141: positioning the carbon emission source buildings on the divided industrial building area data to obtain the industrial carbon emission source building positioning data; Step S142: performing emission source density calculation on the industrial carbon emission source building location data to obtain industrial carbon emission source building density data; Step S143: monitoring carbon emissions of industrial carbon emission source building location data to obtain industrial carbon emission data; Step S144: Calculate the mean carbon emission value of industrial areas based on the industrial carbon emission source building density data to obtain the mean carbon emission value of industrial areas; Step S145: Calculating the block industrial building area ratio of the regional scheduling block data based on the block industrial building area data to obtain the block industrial building area ratio; Step S146: Draw an industrial carbon emission curve for the mean carbon emission value of the industrial area and the proportion of the industrial building area by block to obtain an industrial carbon emission curve.

5. The method for optimizing and scheduling a carbon emission reduction system according to claim 1, characterized in that: Step S2 includes the following steps: Step S21: extracting non-industrial regional feature textures from regional remote sensing data to obtain non-industrial feature textures; Step S22: acquiring non-industrial regional remote sensing data based on non-industrial feature textures to obtain non-industrial remote sensing data; Step S23: Draw a vegetation carbon consumption curve for the non-industrial remote sensing data to obtain a vegetation carbon consumption curve; Step S24: Draw a water body carbon consumption curve for the non-industrial remote sensing data to obtain a water body carbon consumption curve; Step S25: construct a natural carbon consumption model for the regional scheduling block data based on the vegetation carbon consumption curve and the water body carbon consumption curve to obtain a natural carbon consumption model.

6. The method for optimizing and scheduling a carbon emission reduction system according to claim 5, characterized in that: Step S23 includes the following steps: Step S231: performing vegetation characteristic color analysis on non-industrial remote sensing data to obtain vegetation characteristic color data; Step S232: performing vegetation distribution positioning on the non-industrial remote sensing data based on the vegetation characteristic color data to obtain vegetation distribution data; Step S233: performing non-industrial vegetation coverage analysis on the vegetation distribution data to obtain the non-industrial vegetation coverage; Step S234: acquiring vegetation carbon consumption data from the vegetation distribution data to obtain vegetation carbon consumption data; Step S235: Draw a vegetation carbon consumption curve for the non-industrial vegetation coverage rate based on the vegetation carbon consumption data to obtain a vegetation carbon consumption curve.

7. The method for optimizing and scheduling a carbon emission reduction system according to claim 5, characterized in that: Step S24 includes the following steps: Step S241: performing water body smoothing texture recognition on non-industrial remote sensing data to obtain non-industrial water body texture; Step S242: extracting water algae texture from non-industrial water texture to obtain water algae texture; Step S243: performing water algae scale analysis on non-industrial water textures based on water algae textures to obtain water algae scale data; Step S244: performing water coverage analysis on non-industrial remote sensing data based on non-industrial water body textures to obtain non-industrial water body coverage; Step S245: Calculate the water area based on the water coverage rate to obtain water area data; Step S246: Calculate water body carbon consumption data based on the water body algae scale data and water body area data to obtain water body carbon consumption data; Step S247: Draw a water body carbon consumption curve for the non-industrial water body coverage based on the water body carbon consumption data to obtain a water body carbon consumption curve.

8. The method for optimizing and scheduling a carbon emission reduction system according to claim 1, characterized in that: Step S3 includes the following steps: Step S31: performing regional temperature data analysis on non-industrial remote sensing data to obtain regional temperature data; Step S32: performing regional bare land analysis on the non-industrial remote sensing data to obtain non-industrial bare land data; Step S33: Drawing a building density temperature curve for the regional scheduling block data based on the non-industrial remote sensing data and the regional temperature data to obtain a building density temperature curve; Step S34: analyzing the effect of temperature on soil carbon content of non-industrial bare land data based on regional temperature data to obtain data on the effect of temperature on soil carbon content; Step S35: fitting the building density temperature curve based on the temperature effect on soil carbon content data to obtain a soil carbon content effect curve; Step S36: Based on the soil carbon content influence curve and the building density temperature curve, the natural carbon consumption model is corrected to obtain a non-industrial carbon consumption model.

9. The method for optimizing and scheduling a carbon emission reduction system according to claim 8, characterized in that: Step S33 includes the following steps: Step S331: extracting building texture geometric features from non-industrial remote sensing data to obtain building texture geometric features; Step S332: performing building recognition on the regional scheduling block data based on building texture geometric features to obtain building data; Step S333: Calculate the building density of the regional scheduling block data based on the building data to obtain the building density; Step S334: Draw a building density temperature curve for the regional scheduling block data based on the building density and regional temperature data to obtain a building density temperature curve.

10. The method for optimizing and scheduling a carbon emission reduction system according to claim 1, characterized in that: Step S4 includes the following steps: Step S41: constructing an industrial carbon emission sequence for the regional scheduling block data based on the industrial carbon emission curve to obtain an industrial carbon emission sequence; Step S42: performing industrial emission equal block acquisition on the industrial carbon emission sequence to obtain industrial emission equal block data; Step S43: Generate a carbon consumption sequence for the industrial emission equal block data based on the non-industrial carbon consumption model to obtain a carbon consumption sequence; Step S44: generating a carbon emission reduction priority sequence for the industrial carbon emission sequence and the carbon consumption sequence to obtain a carbon emission reduction priority sequence; Step S45: Optimize the regional scheduling block data based on the carbon emission reduction priority sequence to obtain the carbon emission reduction optimization scheduling result.

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