An Optimization Scheduling Method for Carbon Emission Reduction System

Abstract

The present invention relates to the field of carbon emission reduction, and particularly to an optimized scheduling method for a carbon emission reduction system. The method includes the following steps: dividing the monitoring area into scheduling blocks and obtaining remote sensing data to obtain regional remote sensing data and regional scheduling block data, and generating an industrial carbon emission curve based on the above data; obtaining non-industrial remote sensing data from the regional remote sensing data, and constructing a natural carbon consumption model for the regional scheduling block data based on the non-industrial remote sensing data; drawing a soil carbon content impact curve and a building density temperature curve for the regional scheduling block data based on the non-industrial remote sensing data and correcting the natural carbon consumption model data based on a hyperbola to obtain a non-industrial carbon consumption model; performing optimized scheduling on the regional scheduling block data based on the industrial carbon emission curve and the non-industrial carbon consumption model to obtain an optimized carbon emission reduction scheduling result. The present invention can obtain and analyze regional remote sensing data, generate a priority sequence, and is beneficial to the optimized scheduling of carbon emission reduction.

Description

Technical Field

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

[0002] At the present stage, the continuous growth of carbon emissions has had a huge negative impact on the environment. To address this challenge, it is urgent to take measures to reduce carbon emissions; carbon emission reduction can not only slow down the rate of climate change, but also contribute to improving air quality and the health of the ecosystem; carbon emission reduction helps to reduce the overall concentration of greenhouse gases and slow down regional weather extremization, thereby maintaining regional ecological monitoring. Secondly, reducing carbon emissions can promote sustainable development and drive the development and application of clean energy technologies; an effective carbon emission reduction system includes accurate emissions monitoring, reasonable emission reduction target setting, and achieving effective carbon emission reduction; however, at the present stage, due to the lack of analysis of building-related parameters of carbon emissions, accurate carbon emission reduction sequences cannot be obtained, resulting in guiding the scheduling of the carbon emission reduction system, and the problems of waste of emission reduction resources, low emission reduction efficiency, and excessive time consumption required to complete the carbon emission reduction target cannot be avoided. Summary of the Invention

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

[0004] To achieve the above object, based on the optimized scheduling method for a carbon emission reduction system, the following steps are included:

[0005] Step S1: Obtain regional remote sensing data for the monitoring area to obtain regional remote sensing data; perform equal-area scheduling block division on the monitoring area to obtain regional scheduling block data; draw an industrial carbon emission curve based on the regional remote sensing data and the regional scheduling block data to obtain an industrial carbon emission curve;

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

[0007] Step S3: Draw a building density temperature curve based on the non-industrial remote sensing data for the regional scheduling block data to obtain a building density temperature curve; draw a soil carbon content influence curve based on the non-industrial remote sensing data for the regional scheduling block data to obtain a soil carbon content influence curve; perform data correction on the natural carbon consumption model based on the soil carbon content influence curve and the building density temperature curve to obtain a non-industrial carbon consumption model;

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

[0009] By obtaining the remote sensing data of the monitoring area and performing equal-area dispatch block division, the present invention can comprehensively grasp the industrial layout and the spatial distribution characteristics of carbon emissions within the area, draw the industrial carbon emission curve, and can intuitively show the changing trends of industrial carbon emission intensities in different regions and different time periods, providing a scientific basis for subsequent accurately identifying key areas for carbon emission reduction and formulating differentiated emission reduction strategies; by extracting the remote sensing data of the non-industrial area, it is possible to analyze natural element information such as vegetation cover and water body distribution, and construct a natural carbon consumption model in combination with the regional dispatch block data. This model can quantitatively evaluate the natural carbon sink capacity of different regions, provide basic data for scientifically accounting for regional carbon emissions, and provide basic data for subsequent optimizing the carbon emission reduction sequence; using the remote sensing data of the non-industrial area to draw the building density temperature curve and the soil carbon content influence curve can respectively reflect the impact of the urban heat island effect on carbon emissions and the carbon sequestration potential of the soil carbon pool. Incorporating these factors into the natural carbon consumption model for data correction can construct a more accurate non-industrial carbon consumption model to comprehensively reflect the impacts of various natural and human factors within the area on carbon emissions; integrating the industrial carbon emission curve and the non-industrial carbon consumption model can calculate the net carbon emission value of each dispatch block, and then generate a carbon emission reduction priority sequence. Through the scientific method of obtaining emission reduction area information and generating the carbon emission reduction sequence based on relevant information, it can accurately identify key areas for carbon emission reduction, perform optimized dispatch based on this sequence to maximize the benefits of emission reduction measures, achieve the maximum utilization of emission reduction resources, improve emission reduction efficiency, and reduce the time to complete the carbon emission reduction target.

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

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

[0012] Step S12: Perform equal-area dispatch block division on the monitoring area to obtain regional dispatch block data;

[0013] Step S13: Analyze the data of the block industrial building area for the regional dispatch block data based on the regional remote sensing data to obtain the data of the block industrial building area;

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

[0015] By obtaining 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. This is conducive to grasping the overall situation of the region and providing basic data support for subsequent analysis and modeling. Conducting equal-area scheduling and block division of the monitoring area can ensure the refinement and comparability of subsequent analysis. Equal-area block division 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. Through the block industrial building area data obtained from the analysis of regional remote sensing data, the industrial distribution of each block within the monitoring area can be accurately grasped. This analysis method can effectively identify and distinguish industrial activities, providing key information for subsequent carbon emission quantification and visualization. Drawing industrial carbon emission curves can visually present the industrial carbon emission differences between different blocks. Through this visual representation, decision-makers can quickly identify high-emission areas and formulate targeted emission reduction strategies based on the emission trends. These curves can also help verify the effectiveness of carbon emission reduction measures, ensuring that the emission reduction work is targeted and effective. The carbon emission curves can also be used to predict future emission situations, guiding long-term planning and decision-making by simulating different scenarios. By analyzing the building-related parameters of carbon emissions, accurate carbon emission reduction sequences can be obtained, completing the guidance for the scheduling of the carbon emission reduction system, avoiding waste of emission reduction resources and low emission reduction efficiency, and reducing the excessive time consumption required to achieve the carbon emission reduction target.

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

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

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

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

[0020] Step S134: Based on the industrial area range texture and the industrial area building texture, identify the block industrial building area from the regional scheduling block data to obtain the block industrial building area data.

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

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

[0023] Step S141: Locate the carbon emission source buildings in the data of segmented industrial building areas to obtain industrial carbon emission source building location data;

[0024] Step S142: Calculate the emission source density for the industrial carbon emission source building location data to obtain industrial carbon emission source building density data;

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

[0026] Step S144: Calculate the average industrial area carbon emissions for the industrial carbon emission data based on the industrial carbon emission source building density data to obtain the average industrial area carbon emissions;

[0027] Step S145: Calculate the proportion of the segmented industrial building area for the regional scheduling segmented data based on the data of segmented industrial building areas to obtain the proportion of the segmented industrial building area;

[0028] Step S146: Plot an industrial carbon emission curve for the average industrial area carbon emissions and the proportion of the segmented industrial building area to obtain an industrial carbon emission curve.

[0029] Through the carbon emission source building positioning of the block industrial building area data, the present invention can accurately identify and define the carbon emission source buildings in each block within the monitoring area. This helps to clarify the sources of carbon emissions and provides a basis for subsequent emission calculation and density analysis; calculating the industrial carbon emission source building density can effectively evaluate the intensity of industrial activities. Through this step, high-density carbon emission source building areas can be identified, thus helping decision-makers to determine the key target areas for carbon emission reduction; monitoring the industrial carbon emission source buildings can obtain the emission data of each emission source. This step provides a basis for quantifying and evaluating industrial carbon emissions and is conducive to calculating the carbon emissions at the block and regional levels subsequently; calculating the average value of industrial area carbon emissions can comprehensively evaluate the emission level of industrial activities. This step takes into account the industrial carbon emission source building density and regional scale and provides a standardized index for comparing the emission situations of different blocks and regions; calculating the proportion of the block industrial building area can analyze the relationship between industrial activities and non-industrial activities in the monitoring area. This step provides a basis for evaluating the industrialization degree and potential carbon emission contribution of different blocks; drawing the industrial carbon emission curve can visually present the carbon emission trend and distribution of industrial activities. This step effectively displays the overall situation of industrial carbon emissions in the monitoring area through visualization means and provides clear guidance for carbon emission reduction strategies; through calculating density, monitoring emissions, calculating the average value and proportion, a quantitative evaluation of industrial area carbon emissions is provided, and drawing the carbon emission curve provides an effective means for intuitively understanding the industrial carbon emission distribution. These steps together construct an industrial carbon emission analysis framework, providing data support and feasible paths for carbon emission reduction decisions in the monitoring area.

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

[0031] Step S21: Extract the non-industrial area feature texture from the regional remote sensing data to obtain the non-industrial feature texture;

[0032] Step S22: Based on the non-industrial feature texture, obtain the non-industrial remote sensing data by acquiring the regional remote sensing data of the non-industrial area;

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

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

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

[0036] By extracting the characteristic textures of non-industrial areas, the present invention can effectively identify and distinguish natural landscape elements within the monitoring area, such as vegetation and water bodies; this step can provide a basis for obtaining non-industrial remote sensing data, contributing to the accurate definition and analysis of natural environmental characteristics; obtaining non-industrial remote sensing data can exclude the influence of industrial activities and focus on analyzing the role of the natural environment in carbon consumption, this step provides a direct data source for constructing the natural carbon consumption model, ensuring the accuracy and effectiveness of the model; plotting the vegetation carbon consumption curve can quantify and visualize the absorption and storage of carbon elements by vegetation, and 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; plotting the water body carbon consumption curve can analyze and present the role of water bodies in the carbon cycle, water bodies can store and transport carbon elements, and studying them can better understand the carbon flow in the natural environment, this step provides key information about water bodies for the natural carbon consumption model; by combining the vegetation and water body carbon consumption curves, a comprehensive natural carbon consumption model can be constructed, this model takes into account the contributions of different elements in the natural environment to carbon consumption, providing a scientific basis for analyzing and predicting the carbon emission reduction potential of non-industrial areas, the construction of the natural carbon consumption model can provide decision-making support for carbon emission reduction strategies and help optimize the positive impact of the natural environment in the carbon cycle; effectively evaluating the role of the natural environment in carbon consumption, from characteristic texture extraction to constructing the 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: Analyze the characteristic colors of vegetation in the non-industrial remote sensing data to obtain vegetation characteristic color data;

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

[0040] Step S233: Analyze the non-industrial vegetation coverage rate of the vegetation distribution data to obtain the non-industrial vegetation coverage rate;

[0041] Step S234: Obtain the vegetation carbon consumption data from the vegetation distribution data;

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

[0043] The present invention can effectively identify and extract the unique color features of vegetation by analyzing the vegetation characteristic colors of non-industrial remote sensing data. This step utilizes the characteristics of the vegetation reflection 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 colors, the vegetation and non-vegetation areas can be distinguished, and the scope and boundary 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 the land area covered by vegetation by analyzing the vegetation distribution data, providing a quantitative indicator for evaluating the impact of vegetation on natural carbon consumption; obtaining vegetation carbon consumption data can quantify the role of vegetation in the carbon cycle. This step provides key data for drawing the vegetation carbon consumption curve by analyzing the carbon absorption and storage capacity of vegetation; drawing the vegetation carbon consumption curve can visually present the carbon consumption trend and potential of vegetation. This step visualizes the vegetation carbon consumption data, demonstrating the positive impact of vegetation in the carbon cycle. The vegetation carbon consumption curve provides a basis for evaluating and comparing the vegetation carbon consumption efficiency in different regions and provides important parameters for constructing the natural carbon consumption model; it provides comprehensive and accurate vegetation information for constructing the natural carbon consumption model, 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 emission reduction strategies.

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

[0045] Step S241: Identify the water body smooth texture of the non-industrial remote sensing data to obtain the non-industrial water body texture;

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

[0047] Step S243: Analyze the water body algae scale of the non-industrial water body texture based on the water body algae texture to obtain the water body algae scale data;

[0048] Step S244: Analyze the water body coverage rate of the non-industrial remote sensing data based on the non-industrial water body texture to obtain the non-industrial water body coverage rate;

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

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

[0051] Step S247: Draw the water body carbon consumption curve based on the water body carbon consumption data for the non-industrial water body coverage rate to obtain the 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 water bodies often has a unique smooth texture. This step aims to accurately identify the water body area and distinguish it from other features such as land and vegetation; extracting the water body algae texture from non-industrial water body textures can capture the unique texture features of the presence of algae in the water body. The growth of algae will affect the texture and color of the water body. This step helps to identify and analyze the presence and distribution of algae in the water body; analyzing the scale of the water body algae texture can evaluate the abundance and coverage of algae in the water body. This step provides basic data for subsequent calculation of water body carbon consumption by quantifying the presence of algae; analyzing non-industrial water body textures can accurately calculate and evaluate the water body coverage rate. This step aims to distinguish water bodies from other elements such as land and vegetation and quantify the proportion of the land area they cover; calculating the water body area can provide key parameters for water body carbon consumption analysis. The carbon consumption capacity of the water body is related to the area size. This step provides an important indicator for subsequent calculation of water body carbon consumption; by combining the water body algae scale data and the water body area data, the carbon consumption level of the water body can be calculated. This step comprehensively analyzes the impact of the presence of algae on carbon consumption and the contribution of the water body area to the carbon cycle; plotting the water body carbon consumption curve can visually present the carbon consumption trend and potential of the water body. This step effectively demonstrates the role of the water body in the carbon cycle through visualization means and provides an important reference for constructing a natural carbon consumption model; it provides comprehensive and accurate water body information for the natural carbon consumption model, ensuring that the model takes into account the unique carbon cycle process of the water body. This method provides a scientific basis for optimizing carbon emission reduction strategies.

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

[0054] Step S31: Analyze the regional temperature data of the non-industrial remote sensing data to obtain the regional temperature data;

[0055] Step S32: Analyze the regional bare land of the non-industrial remote sensing data to obtain the non-industrial bare land data;

[0056] Step S33: Draw the 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 the building density temperature curve;

[0057] Step S34: Analyze the temperature impact on soil carbon content for the non-industrial bare land data based on the regional temperature data to obtain the temperature impact on soil carbon content data;

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

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

[0060] Through the analysis of regional temperature data from non - industrial remote sensing data, the present invention can accurately obtain the spatial distribution information of surface temperature, providing basic data for subsequent analysis of the impact of temperature on soil carbon content and building density. By analyzing regional bare land from non - industrial remote sensing data, land without vegetation cover can be identified, clarifying the area where temperature affects soil carbon content and improving the accuracy of subsequent analysis. By combining regional temperature data and regional scheduling block data to draw the building density - temperature curve, the temperature differences in different building density areas can be intuitively displayed, revealing the influence law of urbanization on regional temperature. Based on regional temperature data, the analysis of the impact of temperature on soil carbon content for non - industrial bare land data can quantify the degree of influence of temperature on soil carbon content, which is a key link in constructing the non - industrial carbon consumption model. Using the data of the impact of temperature on soil carbon content to fit the building density - temperature curve can establish a connection between the impact of urbanization on temperature and changes in soil carbon content, constructing a soil carbon content impact curve and providing 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, incorporating the impact of urbanization on the carbon cycle into the model to construct a more accurate and practical non - industrial carbon consumption model. By systematically analyzing factors such as temperature, land use, building density, and soil carbon content in non - industrial areas, a more comprehensive and accurate carbon consumption model is constructed. This model not only considers natural factors but also incorporates the impact of urbanization, thus better guiding carbon emission reduction scheduling.

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

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

[0063] Step S332: Based on the building texture geometric features, perform building identification on the regional scheduling block data to obtain building data;

[0064] Step S333: Based on the building data, calculate the building density of the regional scheduling block data to obtain the building density;

[0065] Step S334: Based on the building density and regional temperature data, draw the building density - temperature curve for the regional scheduling block data to obtain the building density - temperature curve.

[0066] Through the extraction of the geometric features of building textures from non-industrial remote sensing data, the present invention can identify and analyze the building layout and form within a region. This step helps to obtain information on the scale, shape, and orientation of buildings within the region, providing basic data support for subsequent building identification and density calculation; identifying the region based on the geometric features of building textures can effectively extract building information from remote sensing data and accurately obtain the number, size, and location data of buildings within the region. This step is crucial for understanding the building distribution within the study area and subsequent density calculation and temperature analysis; calculating the building density based on the building identification results can quantitatively analyze the density of buildings within the region, providing an important basis for evaluating the urban heat island effect and optimizing the urban spatial layout; combining the building density and regional temperature data to plot the building density-temperature curve can visually display the relationship between building density and temperature, revealing the formation mechanism of the urban heat island effect. Based on the analysis of the heat island effect, obtaining the correlation between building density and temperature is conducive to providing a basic temperature discrimination benchmark for subsequent analysis of the impact of temperature on vegetation changes and soil carbon content changes within the region.

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

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

[0069] Step S42: Obtain equally divided industrial emission block data by equally dividing the industrial carbon emission sequence;

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

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

[0072] Step S45: Optimize the dispatch of the regional dispatch block data based on the carbon emission reduction priority sequence to obtain the carbon emission reduction optimized dispatch 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 within a region, provide more accurate data support for subsequent carbon emission reduction work, avoid the errors caused by equal distribution, and make the emission reduction strategy more targeted. By equally dividing the industrial carbon emission sequence, regions with similar carbon emission amounts can be grouped into the same category, which is convenient for subsequent implementation of differentiated emission reduction measures for regions with different emission levels, improving the efficiency and accuracy of carbon emission reduction. By generating a carbon consumption sequence based on the non-industrial carbon consumption model, the dynamic balance between carbon emissions and carbon absorption within the region can be more comprehensively considered, avoiding only focusing on industrial emission reduction while ignoring other carbon sink resources, and providing a basis for formulating more comprehensive and scientific carbon emission reduction strategies. By generating a carbon emission reduction priority sequence, the priority of carbon emission reduction can be scientifically determined according to the carbon emission intensity and emission reduction potential factors of different regions, and limited resources can be invested in the regions with the greatest emission reduction benefits, maximizing the overall carbon emission reduction efficiency. By optimizing the regional scheduling based on the carbon emission reduction priority sequence, the resource allocation, industrial layout, and energy structure of different regions can be adjusted, thereby effectively reducing the overall regional carbon emissions. The emission reduction work is targeted and effective, and the carbon emission curve can also be used to predict future emissions, guiding long-term planning and decision-making by simulating different scenarios. This step combines remote sensing data with carbon emission analysis, providing a quantitative method and decision-making support for monitoring industrial carbon emission management within the region. BRIEF DESCRIPTION OF THE DRAWINGS

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

[0075] Figure 1 It is a schematic flowchart of the steps of the method for optimizing the scheduling of the carbon emission reduction system of the present invention;

[0076] Figure 2 is Figure 1 a detailed schematic flowchart of step S3 in

[0077] Figure 3 is Figure 2 a detailed schematic flowchart of step S33 in DETAILED DESCRIPTION OF THE EMBODIMENTS

[0078] The technical method of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are part of the embodiments of the present invention, rather than all of them. All other embodiments obtained by those skilled in the art based on the embodiments of the present invention without creative efforts belong to 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 drawings denote the same or similar parts, and thus repeated descriptions thereof will be omitted. Some of the block diagrams shown in the drawings are functional entities and do not necessarily correspond to physically or logically independent entities. The functional entities may be implemented in software form, or in one or more hardware modules or integrated circuits, or 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 only used to distinguish one unit from another. 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 the above object, please refer to Figures 1 to 3 , the present invention provides an optimized scheduling method for a carbon emission reduction system, and the method includes the following steps:

[0082] Step S1: Obtain regional remote sensing data for the monitoring area to obtain regional remote sensing data; divide the monitoring area into equal-area scheduling blocks to obtain regional scheduling block data; draw an industrial carbon emission curve based on the regional remote sensing data and the regional scheduling block data to obtain an industrial carbon emission curve;

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

[0084] Step S3: Draw a building density temperature curve for the regional scheduling block data based on the non-industrial remote sensing data to obtain a building density temperature curve; draw a soil carbon content impact curve for the regional scheduling block data based on the non-industrial remote sensing data to obtain a soil carbon content impact curve; correct the data of the natural carbon consumption model based on the soil carbon content impact curve and the building density temperature curve to obtain a 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; perform optimized scheduling on the regional scheduling block data based on the carbon emission reduction priority sequence to obtain a carbon emission reduction optimized scheduling result.

[0086] In the embodiments of the present invention, please refer to Figure 1 As shown in the figure, it is a schematic flowchart of the steps of the carbon emission reduction system optimization scheduling method of the present invention. In this example, the carbon emission reduction system optimization scheduling method includes the following steps:

[0087] Step S1: Obtain regional remote sensing data for the monitoring area to obtain regional remote sensing data; divide the monitoring area into equal-area scheduling blocks to obtain regional scheduling block data; draw an industrial carbon emission curve based on the regional remote sensing data and the regional scheduling block data to obtain an industrial carbon emission curve;

[0088] In the embodiments of the present invention, by using satellite remote sensing technology, multi-spectral and high-resolution remote sensing image data of the monitoring area are obtained. According to the area and data characteristics of the research area, the area is divided into several scheduling blocks with equal areas. Combining the distribution, scale, energy consumption data of industries in the area, and the industrial land information extracted from the remote sensing images, an industrial carbon emission curve is constructed to reflect the relationship between industrial carbon emissions and time or other relevant factors.

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

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

[0091] Step S3: Draw a building density temperature curve based on the non-industrial remote sensing data for the regional scheduling block data to obtain a building density temperature curve; draw a soil carbon content influence curve based on the non-industrial remote sensing data for the regional scheduling block data to obtain a soil carbon content influence curve; correct the data of the natural carbon consumption model based on the soil carbon content influence curve and the building density temperature curve to obtain a non-industrial carbon consumption model;

[0092] In the embodiment of the present invention, by using remote sensing data of non-industrial areas, building land information is extracted, and combined with regional scheduling block data, the building density within each scheduling block is calculated. The surface temperature is inverted using the thermal infrared band information in the remote sensing data, and combined with meteorological data, a relationship curve between the building density and temperature of each scheduling block is constructed. Using the remote sensing data of non-industrial areas, combined with soil type range data and terrain data, a spatial distribution model of soil carbon content is constructed, and an influence curve of soil carbon content for each scheduling block is drawn to reflect the influence of soil carbon content on carbon absorption. Using the constructed building density-temperature curve and soil carbon content influence curve, data correction is performed on 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, and obtain a carbon emission reduction priority sequence; perform optimal scheduling on the regional scheduling block data based on the carbon emission reduction priority sequence to obtain an optimized carbon emission reduction scheduling result.

[0094] In the embodiment of the present invention, by integrating the obtained industrial carbon emission curve and the obtained non-industrial carbon consumption model, the net carbon emission of each scheduling block is calculated, that is, the difference between the industrial carbon emission and the non-industrial carbon consumption. All scheduling blocks are sorted according to the magnitude of the net carbon emission to generate a carbon emission reduction priority sequence, and the blocks with higher net carbon emissions are preferentially reduced. 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 optimal scheduling strategy is formulated. For example, more stringent emission reduction measures are implemented for high-emission blocks, and finally effective control and optimal scheduling of regional carbon emissions are achieved.

[0095] By obtaining 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 spatial distribution characteristics of carbon emissions in the area, draw an industrial carbon emission curve, and can intuitively show the changing trends of industrial carbon emission intensities in different regions and different time periods, providing a scientific basis for subsequent accurately identifying key areas for carbon emission reduction and formulating differentiated emission reduction strategies; by extracting remote sensing data of non-industrial areas, information on natural elements such as vegetation cover and water body distribution can be analyzed, and a natural carbon consumption model can be constructed in combination with the regional scheduling and block data. This model can quantitatively evaluate the natural carbon sink capacity of different regions, provide basic data for scientifically accounting for regional carbon emissions, and provide basic data for subsequent optimizing the carbon emission reduction sequence; by using remote sensing data of non-industrial areas to draw a building density-temperature curve and a soil carbon content impact curve, the impact of the urban heat island effect on carbon emissions and the carbon sequestration potential of the soil carbon pool can be respectively reflected. Incorporating these factors into the natural carbon consumption model for data correction can construct a more accurate non-industrial carbon consumption model, comprehensively reflecting the impacts of various natural and human factors on carbon emissions in the region; 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 emission reduction priority sequence can be generated. Through the scientific method of obtaining emission reduction area information and generating a carbon emission reduction sequence based on relevant information, key areas for carbon emission reduction can be accurately identified, and optimized scheduling can be carried out based on this sequence to maximize the benefits of emission reduction measures, achieve the maximum utilization of emission reduction resources, improve emission reduction efficiency, and reduce the time to complete the carbon emission reduction target.

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

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

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

[0099] Step S12: Perform equal-area scheduling and block division on the monitoring area to obtain regional scheduling and block data;

[0100] In an embodiment of the present invention, by according to factors such as the area, shape, and geographical features of the monitoring area, the monitoring area is divided into several scheduling blocks with equal areas. During the division process, GIS software can be used for grid division operations, and the geographical location, boundary information, and area size attribute information of each scheduling block are recorded.

[0101] Step S13: Analyze the block industrial building area data of the regional dispatching block data based on the regional remote sensing data to obtain the block industrial building area data;

[0102] In the embodiment of the present invention, by using the regional remote sensing data, combined with the land use data and distribution data auxiliary data, industrial area identification and extraction are carried out for each dispatching block, the 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 dispatching block data based on the block industrial building area data to obtain the industrial carbon emission curve.

[0104] In the embodiment of the present invention, by based on the obtained block industrial building area data, determine the emission sources for the block industrial areas, after obtaining the emission source information, obtain the emission data of the emission sources. For example, by obtaining the remote sensing spectral data around the emission sources, and obtaining the carbon emission data of the carbon emission source buildings through analyzing the absorption spectrum of carbon emission substances, establish an industrial carbon emission model, use this model to calculate the industrial carbon emissions of each dispatching block at different time periods, and draw the industrial carbon emission curve of each dispatching block.

[0105] By obtaining the remote sensing data of the monitoring area, the present invention can comprehensively and intuitively understand the land use, vegetation distribution and human activities, and factory distribution in this area, which is conducive to grasping the overall situation of the region and providing basic data support for subsequent analysis and modeling. Dividing the monitoring area into equal-area dispatching blocks can 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 dispatching; through the block industrial building area data obtained by regional remote sensing data analysis, the industrial distribution of each block in the monitoring area can be accurately grasped. This analysis method can effectively identify and distinguish industrial activities and provide key information for subsequent carbon emission quantification and visualization; drawing the industrial carbon emission curve can intuitively present the industrial carbon emission differences of different blocks. Through this visual representation, decision-makers can quickly identify high-emission areas and formulate targeted emission reduction strategies according to the emission trends. These curves can also help verify the effectiveness of carbon emission reduction measures, ensure that the emission reduction work is targeted and achieves actual results. The 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, providing a quantitative method and decision support for industrial carbon emission management in the monitoring area.

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

[0107] Step S131: Obtain regional remote sensing texture from regional remote sensing data to get regional remote sensing texture;

[0108] In the embodiment of the present invention, by preprocessing the obtained regional remote sensing data, including geometric correction, radiometric correction, and image enhancement operations, to eliminate data errors and highlight texture information, select a suitable texture analysis method, such as the gray-level co-occurrence matrix, calculate the preprocessed remote sensing data, and set corresponding parameters according to the selected method to extract texture features at different scales and directions. The calculated texture feature values form a regional remote sensing texture image, and each pixel point corresponds to a texture feature value, which is used to characterize the texture information of the pixel point and its neighborhood.

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

[0110] In the embodiment of the present invention, by using the obtained regional remote sensing texture, combine the characteristics of the industrial area to extract range features, select texture features sensitive to the industrial area, such as roughness, contrast, and directionality, construct a feature vector, use the existing industrial area sample data to train a classifier, such as a support vector machine, to distinguish the industrial area from other land cover types, use the trained classifier to classify the entire regional remote sensing texture to get the preliminary recognition result of the industrial area, and process the classification result to eliminate noise to get the final industrial area range texture.

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

[0112] In the embodiment of the present invention, based on the regional remote sensing texture, further extract the features of industrial area buildings. According to the characteristics of industrial buildings, select texture features sensitive to buildings, such as shape, size, and edge density. Use edge detection to extract the building contour information in the regional remote sensing texture, calculate the features of the extracted building contour, such as aspect ratio, area, and perimeter, and construct a feature vector in combination with texture features. Use machine learning methods to classify the building features, identify the buildings in the industrial area, and represent the recognition result as industrial area building texture.

[0113] Step S134: Identify the block industrial building area for the regional scheduling block data based on the industrial area range texture and the industrial area building texture to get the block industrial building area data.

[0114] In the embodiments of the present invention, by fusing the industrial area range texture and the industrial area building texture as the basis for identifying the segmented industrial building areas, and according to the regional scheduling segmented data, the regional remote sensing texture is divided into several sub-areas. For each sub-area, its statistical features on the industrial area range texture and the industrial area building texture are calculated respectively. Feature vectors are constructed using these statistical features and input into a classifier to identify the industrial areas for each sub-area. The identification results are integrated into the regional scheduling segmented data to obtain the final segmented industrial building area data.

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

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

[0117] Step S141: Locate the carbon emission source buildings in the segmented industrial building area data to obtain the industrial carbon emission source building location data;

[0118] In the embodiment of the present invention, according to the industrial emission source database, the types, scales, locations, and energy consumption information of the main industries in the region are collected. Combining with the data of the industrial building areas divided into blocks, using the GIS spatial analysis technology, the information of industrial enterprises collected is matched with the corresponding plots. According to the emission factors and activity level data of different types of industrial enterprises, the spatial location information of all emission sources and the corresponding carbon emissions are integrated together to form the industrial carbon emission source building location data.

[0119] Step S142: Calculate the emission source density of the industrial carbon emission source building location data to obtain the industrial carbon emission source building density data;

[0120] In the embodiment of the present invention, through the obtained industrial carbon emission source building location data, select the distance weight function and then use the spatial analysis method of kernel density estimation to calculate the number of carbon emission source buildings or carbon emissions per unit area of each plot to obtain the industrial carbon emission source building density data.

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

[0122] In the embodiment of the present invention, according to different types of emission sources, select appropriate carbon emission monitoring methods, such as remote sensing monitoring and model estimation, to monitor the carbon emissions of 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 directly measure, their emissions can be estimated by collecting energy consumption data and establishing emission models. Integrate the carbon emission data obtained by different monitoring methods to obtain the complete industrial carbon emission data.

[0123] Step S144: Calculate the average carbon emissions of the industrial area based on the industrial carbon emission source building density data to obtain the average carbon emissions of the industrial area;

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

[0125] Step S145: Calculate the proportion of the industrial building area divided into blocks of the regional scheduling divided data based on the data of the industrial building areas divided into blocks to obtain the proportion of the industrial building area divided into blocks;

[0126] In an embodiment of the present invention, by using the data of the industrial building areas divided into blocks and the data of the area scheduling divided into blocks, the proportion of the area of the industrial areas in each scheduling block is calculated, that is, the proportion of the industrial building areas divided into blocks. The specific calculation method is to sum up the areas of all the plots belonging to the industrial areas in each scheduling block, and then divide by the total area of the scheduling block.

[0127] Step S146: Draw an industrial carbon emission curve based on the average value of industrial carbon emissions and the proportion of the industrial building areas divided into blocks to obtain the industrial carbon emission curve.

[0128] In an embodiment of the present invention, based on the calculated average value of industrial carbon emissions and the proportion of the industrial building areas divided into blocks, with time as the abscissa and the industrial carbon emissions as the ordinate, an industrial carbon emission curve is drawn.

[0129] Through the positioning of carbon emission source buildings for the data of the industrial building areas divided into blocks, the present invention can accurately identify and define the carbon emission source buildings in each block within the monitoring area. This helps to clarify the sources of carbon emissions and provides a basis for subsequent calculation of emissions 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, thus helping decision-makers to determine the key target areas for carbon emission reduction; monitoring the industrial carbon emission source buildings can obtain the emissions data of each emission source. This step provides a basis for quantifying and evaluating industrial carbon emissions and is conducive to subsequent calculation of carbon emissions at the block and regional levels; calculating the average value of industrial carbon emissions 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 index for comparing the emission situations of different blocks and regions; calculating the proportion of the industrial building areas divided into blocks can analyze the relationship between industrial activities and non-industrial activities in the monitoring area. This step provides a basis for evaluating the industrialization degree and potential carbon emission contribution of different blocks; drawing the industrial carbon emission curve can visually present the carbon emission trend and distribution of industrial activities. This step effectively shows the overall situation of industrial carbon emissions in the monitoring area through visualization means and provides clear guidance for carbon emission reduction strategies; through calculating density, monitoring emissions, calculating the average value and proportion, a quantitative evaluation of industrial carbon emissions is provided, and drawing the carbon emission curve provides an effective means for intuitively understanding the distribution of industrial carbon emissions. These steps together construct an industrial carbon emission analysis framework, providing data support and feasible paths for carbon emission reduction decisions in the monitoring area.

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

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

[0132] In the embodiment of the present invention, by defining the categories of non-industrial areas, such as vegetation and water bodies, and collecting the corresponding remote sensing image sample data, preprocessing the regional remote sensing data, including geometric correction, radiometric correction, and image enhancement operations, to eliminate data errors and highlight the spectral feature differences of different land cover types, selecting appropriate feature extraction methods, such as spectral index calculation, texture analysis, and principal component analysis, extracting features sensitive to distinguishing different land cover types, constructing a feature vector, using machine learning methods to classify and train the remote sensing image sample data, obtaining a classification model capable of identifying the features of non-industrial areas, and applying it to the entire regional remote sensing data to obtain non-industrial feature texture data.

[0133] Step S22: Based on the non-industrial feature texture, obtain non-industrial area remote sensing data from the regional remote sensing data, and obtain non-industrial remote sensing data;

[0134] In the embodiment of the present invention, by using the non-industrial feature texture data, classifying the regional remote sensing data, extracting the pixels belonging to the non-industrial area, forming the non-industrial area remote sensing data, the original remote sensing data can be masked according to the classification result, retaining the pixel values belonging to the non-industrial area, and setting other pixel values to a specific value. The Bayesian averaging method can be used to integrate the results of multiple classifiers to obtain the non-industrial area remote sensing data.

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

[0136] In the embodiment of the present invention, by identifying the vegetation-covered area from the non-industrial area remote sensing data, such as using the normalized difference vegetation index (NDVI) index for threshold segmentation, according to the vegetation type and growth cycle of the study area, collecting or simulating the vegetation biomass data at different time periods, such as leaf area index (LAI) and net primary productivity (NPP), establishing a relationship model between the vegetation biomass and the carbon consumption, such as using the carbon conversion coefficient to convert the biomass into the carbon consumption, with time as the abscissa and the vegetation carbon consumption as the ordinate, plotting the vegetation carbon consumption curve to reflect the carbon absorption or release of the vegetation at different time periods.

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

[0138] In an embodiment of the present invention, by identifying a water body area from non-industrial area remote sensing data, for example, using a water body index indicator for threshold segmentation, according to the water body type and hydrological characteristics of the research area, simulating the water body dissolved organic carbon and particulate organic carbon index data for different time periods, establishing a relationship model between the water body carbon index and the carbon consumption amount, for example, using a carbon conversion coefficient for estimation, with time as the abscissa and the water body carbon consumption amount as the ordinate, plotting a water body carbon consumption curve to reflect the carbon absorption or release situation of the water body at different time periods.

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

[0140] In an embodiment of the present invention, by integrating the vegetation carbon consumption curve and the water body carbon consumption curve, a natural carbon consumption model at the regional scale is constructed. 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, using a geographic information system for spatial overlay analysis to finally obtain a natural carbon consumption model.

[0141] The present invention can effectively identify and distinguish natural landscape elements in the monitoring area, such as vegetation and water bodies, by extracting the characteristic textures of non-industrial areas; this step can provide a basis for obtaining non-industrial remote sensing data, helping to accurately define and analyze the characteristics of the natural environment; obtaining non-industrial remote sensing data can exclude the influence of industrial activities and focus on analyzing the role of the natural environment in carbon consumption. This step provides a direct data source for constructing a natural carbon consumption model, ensuring the accuracy and effectiveness of the model; plotting the vegetation carbon consumption curve 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; plotting the water body carbon consumption curve can analyze and present the role of the water body in the carbon cycle. The water body can store and transport carbon elements, and studying it can better understand the carbon flow in the natural environment. This step provides key information about the water body for the natural carbon consumption model; by combining the vegetation and water body carbon consumption curves, a comprehensive natural carbon consumption model can be constructed. This model considers the contributions of different elements in the natural environment to carbon consumption, provides a scientific basis for analyzing and predicting the carbon emission reduction potential of non-industrial areas, and the construction of the natural carbon consumption model can provide decision-making support for carbon emission reduction strategies, helping to optimize the positive impact of the natural environment in the carbon cycle; effectively evaluating the role of the natural environment in carbon consumption, from feature texture extraction to constructing 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: Conduct vegetation characteristic color analysis on non-industrial remote sensing data to obtain vegetation characteristic color data;

[0144] In the embodiment of the present invention, by acquiring non-industrial remote sensing data of the target area, the data source can select multispectral satellite images. Using remote sensing image processing software, preprocess the remote sensing data, including radiometric correction, geometric correction, and atmospheric correction. According to the spectral characteristics of vegetation, select the bands sensitive to vegetation, such as the near-infrared band and the red band, to construct a vegetation index, such as the normalized difference vegetation index (NDVI). By analyzing the differences of different vegetation types on the vegetation index image, extract the characteristic color information of different vegetation types, such as the NDVI value range, hue, and saturation of different vegetation types, and store this information as vegetation characteristic color data.

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

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

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

[0148] In the embodiment of the present invention, through the vegetation distribution data, statistically analyze the area proportion of different vegetation types in each analysis unit. The analysis unit is based on grid units. According to the definition, calculate the proportion of the vegetation area in the total area in each analysis unit, which is the non-industrial vegetation coverage.

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

[0150] In the embodiment of the present invention, according to the biomass estimation model of different vegetation types, such as the model based on remote sensing data, calculate the biomass of different vegetation types in each analysis unit. According to the carbon conversion coefficient, convert the biomass into carbon storage. Use the vegetation growth model, such as the photosynthesis model and the respiration model, to estimate the carbon absorption and carbon release of the vegetation type in each analysis unit. Subtract the carbon release from the carbon absorption to obtain the vegetation carbon consumption data of each analysis unit.

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

[0152] In the embodiment of the present invention, the non-industrial vegetation coverage rate data and the vegetation carbon consumption data are integrated and the non-industrial vegetation coverage rate data and the vegetation carbon consumption data are made to correspond to each other, and a vegetation carbon consumption curve is drawn, with the non-industrial vegetation coverage rate as the abscissa and the vegetation carbon consumption as the ordinate.

[0153] The present invention can effectively identify and extract the unique color characteristics of vegetation by analyzing the vegetation characteristic colors of non-industrial remote sensing data. This step utilizes the characteristics of the vegetation reflection 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 colors, the vegetation area and non-vegetation area can be distinguished, and the scope and boundary 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 the land area covered by vegetation by analyzing the vegetation distribution data, providing a quantitative index for evaluating the impact of vegetation on natural carbon consumption; obtaining the vegetation carbon consumption data can quantify the role of vegetation in the carbon cycle. This step provides key data for drawing the vegetation carbon consumption curve by analyzing the carbon absorption and storage capacity of vegetation; drawing the vegetation carbon consumption curve can visually present the carbon consumption trend and potential of vegetation. This step visualizes the vegetation carbon consumption data, demonstrating the positive impact of vegetation in the carbon cycle. The vegetation carbon consumption curve provides a basis for evaluating and comparing the vegetation carbon consumption efficiency in different regions and provides important parameters for constructing a natural carbon consumption model; it provides comprehensive and accurate vegetation information for constructing a natural carbon consumption model, 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 emission reduction strategies.

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

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

[0156] In the embodiment of the present invention, according to the spectral characteristics of the water body, bands sensitive to the water body are selected, such as the near-infrared band and the short-wave infrared band, to construct a water body index, such as the normalized water body index. Using the water body index image and combining with an image segmentation algorithm, such as threshold segmentation and edge detection, the water body area is extracted. Using a texture analysis algorithm, such as the gray-level co-occurrence matrix, texture features of the water body area are extracted to obtain non-industrial water body texture data.

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

[0158] In an embodiment of the present invention, by using non-industrial water body texture data, the differences in texture features between water body algae and other water body regions are analyzed. The water body algae region shows different texture colors and reflection spectra from other regions. The non-industrial water body texture data is classified to distinguish the water body algae texture from other water body textures.

[0159] Step S243: Based on the water body algae texture, analyze the scale of water body algae in the non-industrial water body texture to obtain water body algae scale data;

[0160] In an embodiment of the present invention, by using the extracted water body algae texture data and combining image processing techniques, the water body algae regions are identified and extracted. According to the number of pixels in each water body algae region and the image resolution, the area of each region is calculated, and the areas of all water body algae regions are statistically analyzed to obtain water body algae scale data.

[0161] Step S244: Based on the non-industrial water body texture, analyze the water body coverage rate of the non-industrial remote sensing data to obtain the non-industrial water body coverage rate;

[0162] In an embodiment of the present invention, by using spatial analysis techniques and water body mapping techniques, the water body regions are identified and divided, and the proportion of the water body area in the total area of each analysis unit is statistically analyzed. The non-industrial water body coverage rate can be obtained by calculating the non-industrial total coverage data divided by the water body region data.

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

[0164] In an embodiment of the present invention, based on the obtained non-industrial water body coverage rate and combined with the non-industrial remote sensing data of the target area, the proportion area 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 water body area data.

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

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

[0167] Step S247: Based on the water body carbon consumption data, draw a water body carbon consumption curve for the non-industrial water body coverage rate to obtain the water body carbon consumption curve.

[0168] In the embodiment of the present invention, the obtained non-industrial water body coverage rate data and the obtained water body carbon consumption data are integrated, and the non-industrial water body coverage rate data and the water body carbon consumption data are used to construct a corresponding data set, and a water body carbon consumption curve is drawn. The abscissa is the non-industrial water body coverage rate, and the ordinate 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 water bodies often has a unique smooth texture. This step aims to accurately identify the water body area and distinguish it from other features such as land and vegetation; extracting the water body algae texture from the non-industrial water body texture can capture the unique texture features of the presence 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 presence and distribution of algae in the water body; analyzing the scale of the water body algae texture can evaluate the abundance and coverage degree of algae in the water body. This step provides basic data for subsequent calculation of water body carbon consumption by quantifying the presence of algae; analyzing the non-industrial water body texture can accurately calculate and evaluate the water body coverage rate. This step aims to distinguish water bodies from other elements such as land and vegetation and quantify the proportion of the land area they cover; calculating the water body area can provide key parameters for water body carbon consumption analysis. The carbon consumption capacity of water bodies is related to the area size. This step provides an important index for subsequent calculation of water body carbon consumption; by combining the water body algae scale data and the water body area data, the carbon consumption level of the water body can be calculated. This step comprehensively analyzes the impact of the presence of algae on carbon consumption and the contribution of the water body area to the carbon cycle; drawing the water body carbon consumption curve can visually present the carbon consumption trend and potential of the water body. This step effectively demonstrates the role of water bodies in the carbon cycle through visualization means and provides an important reference for constructing a natural carbon consumption model; it provides comprehensive and accurate water body information for the natural carbon consumption model, ensuring that the model takes into account the unique carbon cycle process of water bodies. This method provides a scientific basis for optimizing carbon emission reduction strategies.

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

[0171] Step S31: Analyze the regional temperature data of the non-industrial remote sensing data to obtain the regional temperature data;

[0172] Step S32: Analyze the non-industrial bare land of the non-industrial remote sensing data to obtain the non-industrial bare land data;

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

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

[0175] Step S35: Fit the data of the building density-temperature curve based on the data of the impact of temperature on soil carbon content to obtain the curve of the impact of soil carbon content;

[0176] Step S36: Correct the data of the natural carbon consumption model based on the curve of the impact of soil carbon content and the building density-temperature curve to obtain the non-industrial carbon consumption model.

[0177] As an embodiment of the present invention, referring to Figure 2 shown, it is Figure 1 the detailed step flow schematic diagram of step S3 in

[0178] Step S31: Analyze the regional temperature data for non-industrial remote sensing data to obtain the regional temperature data;

[0179] In the embodiment of the present invention, by using the radiative transfer model, the remote sensing image is corrected for the atmosphere to eliminate the influence of the atmosphere on the retrieval of the surface temperature, select the appropriate thermal infrared band, and calculate the surface temperature image according to the surface temperature retrieval algorithm. The surface temperature image is subjected to spatial interpolation and filtering processing to obtain the regional temperature data.

[0180] Step S32: Analyze the regional bare land for non-industrial remote sensing data to obtain the non-industrial bare land data;

[0181] In the embodiment of the present invention, by using the obtained remote sensing image data, select the appropriate band combination, such as the near-infrared, red, and blue bands, construct the normalized difference vegetation index vegetation index image, classify the image according to the vegetation index threshold to distinguish the vegetation-covered area and the non-vegetation-covered area, and extract the non-industrial bare land data by using the high-resolution remote sensing image.

[0182] Step S33: Draw the 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 the building density-temperature curve;

[0183] In the embodiment of the present invention, by dividing the region into several regular grids, counting the building area and the total area in each grid, calculating the building density of each grid, extracting the average temperature value in each grid, taking the building density as the abscissa and the average temperature as the ordinate, drawing a scatter plot, and using the regression analysis method to fit the relationship curve between the building density and the temperature, that is, the building density-temperature curve.

[0184] Step S34: Analyze the temperature impact on soil carbon content based on the regional temperature data for the non-industrial bare land data to obtain the temperature impact on soil carbon content data;

[0185] In the embodiment of the present invention, by collecting the measured soil carbon content data of the region and spatially matching different temperature ranges with the regional temperature data, grouping the soil carbon content data, analyzing the change trend of soil carbon content in different temperature ranges, using statistical analysis methods, establishing a relationship model between temperature and soil carbon content, quantifying the impact of temperature on soil carbon content, and obtaining the temperature impact on soil carbon content data.

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

[0187] In the embodiment of the present invention, through data fusion of the temperature impact on soil carbon content data and the building density temperature curve, according to the building density temperature curve, converting the temperature values corresponding to different building densities into corresponding soil carbon content prediction values, and constructing a relationship curve between building density and soil carbon content, that is, the soil carbon content impact curve.

[0188] Step S36: Perform data correction on the natural carbon consumption model based on the soil carbon content impact curve and the building density temperature curve to obtain the non-industrial carbon consumption model.

[0189] In the embodiment of the present invention, by selecting a suitable natural carbon consumption model, localizing and calibrating the parameters of the model according to the actual situation of the research region, using the obtained soil carbon content impact curve and building density temperature curve as input parameters, and performing data correction on the natural carbon consumption model, finally obtaining the non-industrial carbon consumption model.

[0190] Through the analysis of regional temperature data from non-industrial remote sensing data, the present invention can accurately obtain the spatial distribution information of surface temperature, providing basic data for subsequent analysis of the impact of temperature on soil carbon content and building density; through the analysis of regional bare land from non-industrial remote sensing data, land without vegetation cover can be identified, the area where temperature affects soil carbon content can be clarified, and the accuracy of subsequent analysis can be improved; by combining regional temperature data and regional scheduling block data to draw a building density-temperature curve, the temperature differences in areas with different building densities can be intuitively displayed, revealing the influence law of urbanization on regional temperature; based on regional temperature data, the analysis of the impact of temperature on soil carbon content for non-industrial bare land data can quantify the degree of influence of temperature on soil carbon content, which is a key link in constructing a non-industrial carbon consumption model; using the data of the impact of temperature on soil carbon content to perform data fitting on the building density-temperature curve can establish a connection between the impact of urbanization on temperature and changes in soil carbon content, constructing a soil carbon content impact curve 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, incorporating the impact of urbanization on the carbon cycle into the model, and constructing a more accurate and practical non-industrial carbon consumption model; by systematically analyzing factors such as temperature, land use, building density, and soil carbon content in non-industrial areas, a more comprehensive and accurate carbon consumption model is constructed. This model not only considers natural factors but also incorporates the impact of urbanization, thus better guiding carbon emission reduction scheduling.

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

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

[0193] Step S332: Based on the building texture geometric features, perform building identification on the regional scheduling block data to obtain building data;

[0194] Step S333: Based on the building data, calculate the building density of the regional scheduling block data to obtain the building density;

[0195] Step S334: Based on the building density and the regional temperature data, draw a building density-temperature curve for the regional scheduling block data to obtain the building density-temperature curve.

[0196] As an embodiment of the present invention, referring to Figure 3 as shown, it is Figure 2 a detailed step flow diagram of step S33 in

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

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

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

[0200] In the embodiment of the present invention, by using the geometric features of building texture, the random forest method is selected to construct a building recognition model. The existing building sample data is used to train and verify the model, optimize the model parameters, and improve the recognition accuracy of the model. The trained model is applied to the remote sensing images of the entire study area, and each pixel is classified to identify the building and non-building areas to obtain building data.

[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 the embodiment of the present invention, the study area is divided into several regular grids as the basic units for spatial analysis. The total area of 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 weighting method can be used to fuse building data with different resolutions to obtain the building density.

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

[0204] In the embodiment of the present invention, by performing spatial matching on the calculated building density data and the regional temperature data to ensure that each grid cell has corresponding building density and temperature values, a scatter plot is drawn with the building density as the abscissa and the average temperature as the ordinate, and the curve fitting method is used to fit the relationship curve between the building density and the temperature, that is, the building density-temperature curve.

[0205] Through the extraction of the geometric features of building textures from non-industrial remote sensing data, the present invention can identify and analyze the building layout and form within a region. This step helps to obtain information on the scale, shape, and orientation of buildings within the region, providing basic data support for subsequent building identification and density calculation; identifying the region based on the geometric features of building textures can effectively extract building information from remote sensing data and accurately obtain the number, size, and location data of buildings within the region. This step is crucial for understanding the building distribution within the research region and subsequent density calculation and temperature analysis; calculating the building density based on the building identification results can quantitatively analyze the density of buildings within the region, providing an important basis for evaluating the urban heat island effect and optimizing the urban spatial layout; combining the building density and regional temperature data to draw a building density-temperature curve can visually display the relationship between building density and temperature, revealing the formation mechanism of the urban heat island effect. Based on the analysis of the heat island effect, obtaining the correlation between building density and temperature is conducive to providing a basic temperature discrimination benchmark for subsequent analysis of the impact of temperature changes on vegetation and soil carbon content changes in the region.

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

[0207] Step S41: Construct 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 the embodiment of the present invention, by model prediction, the region is divided into several regular grids as the basic units for spatial analysis. According to the industrial proportion information within each grid and in combination with the industrial carbon emission curve, the industrial carbon emissions of each grid at different time nodes are calculated, and finally the industrial carbon emission sequence of each grid is obtained.

[0209] Step S42: Obtain equally divided industrial emission block data by equally dividing the industrial carbon emission sequence;

[0210] In the embodiment of the present invention, by sorting the regional scheduling block data based on the industrial carbon emission sequence, then dividing multiple blocks according to the carbon emissions, then obtaining the industrial carbon emissions within each block, extracting the blocks with the same carbon emissions between different blocks, constructing the blocks with the same carbon emissions into groups, and then constructing the group data into a set, the equally divided industrial emission block data is finally obtained.

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

[0212] In an embodiment of the present invention, by collecting relevant data of equal sub-blocks of each industrial emission, such as vegetation coverage rate, soil area, water area, and building density, inputting these data into a non-industrial carbon consumption model, calculating the carbon consumption capacity of each sub-block in different time periods, considering the influence of dynamic factors on carbon consumption, dynamically adjusting the model results, and re-sorting the calculated carbon consumption capacity data according to the consumed energy, a carbon consumption sequence of each sub-block is formed.

[0213] Step S44: Generate 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, through time alignment and spatial matching of the industrial carbon emission sequence and the carbon consumption sequence, calculate the net carbon emission of each sub-block in each time period, that is, the industrial carbon emission minus the carbon consumption, sort the net carbon emissions to determine the sequence, and re-sort the carbon consumption of the sub-blocks with the same carbon emission data in the sequence to obtain a carbon emission reduction priority scheduling sequence.

[0215] Step S45: Optimally schedule the regional scheduling sub-block data based on the carbon emission reduction priority sequence to obtain a carbon emission reduction optimized scheduling result.

[0216] In an embodiment of the present invention, according to the carbon emission reduction priority sequence, first perform carbon emission reduction adjustment on the adjustable load within the time block with a higher net carbon emission. Through the carbon emission reduction adjustment, optimize the scheduling of the carbon emission reduction system, and finally obtain a carbon emission reduction optimized scheduling result.

[0217] The present invention constructs an industrial carbon emission sequence based on the industrial carbon emission curve, which can more accurately depict the dynamic change trend of industrial carbon emissions in the region, provide more accurate data support for subsequent carbon emission reduction work, avoid the errors caused by equal 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 adoption of differentiated emission reduction measures for regions with different emission levels, improving the efficiency and accuracy of carbon emission reduction; generating a carbon consumption sequence based on the non-industrial carbon consumption model can more comprehensively consider the dynamic balance of carbon emissions and carbon absorption in the region, avoid only focusing on industrial emission reduction while ignoring other carbon sink resources, and provide a basis for formulating more comprehensive and scientific carbon emission reduction strategies; generating a carbon emission reduction priority sequence 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 the regions with the greatest emission reduction benefits, and maximize the overall carbon emission reduction efficiency; optimizing the regional scheduling based on the carbon emission reduction priority sequence can adjust the resource allocation, industrial layout, and energy structure of different regions, thereby effectively reducing the overall regional carbon emissions, making the emission reduction work targeted and achieving actual results. The carbon emission curve 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, providing a quantitative method and decision-making support for monitoring industrial carbon emission management in the region.

[0218] The above are only specific embodiments of the present invention, enabling those skilled in the art to understand or implement the present invention. Various modifications to these embodiments will be obvious to those skilled in the art, and the general principles defined herein can 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 these embodiments shown herein, but rather to the broadest scope consistent with the principles and novel features disclosed 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; 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.

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.

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