Agricultural waste carbon emission reduction scene simulation method and system based on artificial intelligence

Abstract

The invention discloses an artificial intelligence-based agricultural waste carbon emission reduction scenario simulation method and system, and the method comprises the steps: obtaining multi-source geographic space data of a target region, uniformly fusing the multi-source geographic space data into a grid system with a preset resolution, and carrying out the calculation of the multi-source geographic space data based on a space weighted distribution model, the statistical yield of provincial agricultural products is distributed to each grid of a grid system, and the theoretical resource quantity and the high-precision spatial distribution diagram of the agricultural wastes are obtained in combination with a grass-grain ratio database. Method for determining carbon emission and carbon sink quantitative parameters of agricultural wastes under various utilization paths through dynamic accounting model, simulating carbon emission reduction potential and spatial distribution under different resource utilization scenes based on artificial intelligence model according to carbon emission and carbon sink quantitative parameters of agricultural wastes under various utilization paths, and planting front-end crops and structure and space layout is optimized. A quantifiable and visual scientific decision-making tool is provided for regional low-carbon agricultural planning, and the accuracy and feasibility of an emission reduction strategy are remarkably improved.

Description

Technical Field

[0001] This invention relates to the field of dual-carbon optimization technology, and in particular to an artificial intelligence-based method and system for simulating carbon emission reduction scenarios for agricultural waste. Background Technology

[0002] Currently, developing a low-carbon economy and promoting green transformation have become an international consensus. Agricultural waste, as an abundant and renewable biomass resource, is crucial for achieving deep emission reductions in agriculture and the energy sector through its efficient resource utilization. From a life-cycle perspective, agricultural waste has a naturally "carbon-neutral" characteristic. Its resource utilization can bring significant carbon emission reduction benefits through various means, such as directly replacing fossil fuels and high-carbon materials, forming soil carbon sinks, and avoiding greenhouse gas emissions from natural decomposition or open burning.

[0003] However, the current process of assessing, simulating, and optimizing the carbon emission reduction potential of regional agricultural waste resource utilization still faces a series of severe technical challenges, resulting in low accuracy of assessment results and insufficient decision-making basis, making it difficult to effectively support the formulation of precise emission reduction strategies and industrialization planning. These challenges are mainly reflected in the following aspects: Existing assessment tools are mostly single accounting models, lacking the ability to quickly simulate scenarios and optimize multiple resource utilization paths (such as fertilizer, feed, and fuel). At the same time, there is a disconnect in agricultural system management decisions: the upstream crop planting structure and the downstream waste treatment methods are usually decided in isolation, which makes the formulation of emission reduction strategies often rely on experience, making it difficult to achieve the synergistic maximization of economic costs and emission reduction benefits. Summary of the Invention

[0004] To address the aforementioned problems, this invention provides an artificial intelligence-based method and system for simulating carbon emission reduction scenarios for agricultural waste. This addresses the issue that existing assessment tools mentioned in the background technology are mostly single accounting models, lacking the ability to quickly simulate scenarios and optimize multiple resource utilization paths. Consequently, the formulation of emission reduction strategies often relies on experience, making it difficult to achieve the synergistic maximization of economic costs and emission reduction benefits.

[0005] An artificial intelligence-based method for simulating carbon emission reduction scenarios from agricultural waste includes the following steps: Acquire multi-source geospatial data of the target area and integrate the multi-source geospatial data into a grid system with a preset resolution. The multi-source geospatial data includes agricultural product output statistics, remote sensing crop distribution data, environmental data, and road network data. Based on the spatial weighted allocation model, the provincial agricultural product statistical output is allocated to each grid of the grid system. Combined with the grass-to-grain ratio database, the theoretical resource quantity of agricultural waste and its high-precision spatial distribution map are obtained. A dynamic accounting model is constructed, and the carbon emission and carbon sink quantification parameters of agricultural waste under multiple utilization pathways are determined based on the theoretical resource quantity of agricultural waste and its high-precision spatial distribution map. Based on artificial intelligence models, the carbon emission reduction potential and spatial distribution of agricultural waste under different resource utilization scenarios are simulated according to the carbon emission and carbon sink quantification parameters of agricultural waste under multiple utilization paths. Based on the carbon emission reduction potential and spatial distribution, the front-end crop planting structure and spatial layout are optimized.

[0006] Preferably, the step of acquiring multi-source geospatial data of the target area and uniformly integrating the multi-source geospatial data into a grid system with a preset resolution includes: Multi-source geospatial data of the target area are obtained from statistical data sources, remote sensing data sources, environmental data sources, and geographic data sources, respectively. The format is standardized, coordinate system is unified, and missing values ​​are handled for multi-source geospatial data to obtain preprocessed multi-source geospatial data. A unified basic geographic grid system is constructed within the target area based on the simulation accuracy requirements; The preprocessed multi-source geospatial data are transformed and integrated into the basic geographic grid system through spatial interpolation, resampling, or vector rasterization methods to form a multi-attribute fused grid dataset.

[0007] Preferably, the step of converting and fusing all preprocessed multi-source geospatial data into the basic geographic grid system through spatial interpolation, resampling, or vector rasterization methods to form a multi-attribute fused grid dataset includes: A weighted allocation method based on remote sensing factors is used to distribute statistical data from preprocessed multi-source geospatial data to each grid cell; The cubic convolution resampling method is used to unify the raster data corresponding to remote sensing data and environmental data in the preprocessed multi-source geospatial data to the resolution of the basic geographic grid system; The rasterization method is used to convert the vector data corresponding to the geographic data in the preprocessed multi-source geospatial data into raster data consistent with the basic geographic grid system, generating an intermediate grid data layer with unified spatial reference and resolution; Multiple intermediate grid data layers are overlaid, so that each grid cell is associated with the attribute values ​​of all input data, thereby generating a multi-attribute fused grid dataset.

[0008] Preferably, the step of allocating provincial agricultural product statistical output to various grids of the grid system based on the spatial weighted allocation model, combined with the grass-to-grain ratio database, to obtain the theoretical resource quantity of agricultural waste and its high-precision spatial distribution map, includes: Obtain statistical output data of agricultural products in the province where the target area is located, as well as environmental raster data including vegetation index and soil fertility; Based on the spatial weighted allocation model, the statistical yield data of agricultural products is used as the total control, and environmental raster data including vegetation index and soil fertility are used as the control conditions to allocate the yield to each grid of the basic geographic grid system, generating a spatial distribution map of crop yield. Based on the geographical location and crop type of each grid, query the pre-set regionalized grass-to-grain ratio database to obtain the corresponding grass-to-grain ratio value; Based on the crop yield and the matching grass-to-grain ratio of each grid, the theoretical agricultural waste resource quantity of each grid is calculated. The theoretical resource volume of agricultural waste in all grids is summarized to generate a high-precision spatial distribution map of agricultural waste.

[0009] Preferably, the spatial weighted allocation model is implemented using the following formula: ; in, Let this be the theoretical yield of crop k within grid (i,j). The comprehensive weighting factor is represented as grid (i,j). Let the total provincial-level statistical output of crop k be denoted as ; The comprehensive weighting factor Calculated using the following formula: ; in, This represents the planting area weight of crop k within grid (i,j). Represented as vegetation productivity weights obtained based on the normalization of remote sensing vegetation indices. This is represented as the soil fertility weight obtained based on the normalization treatment of soil organic matter content; The theoretical resource volume of agricultural waste for each grid is calculated using the following formula: ; in, This represents the theoretical amount of agricultural waste resources for crop k within grid (i,j). This represents the grass-to-grain ratio of crop k obtained from a regionalized grass-to-grain ratio database.

[0010] Preferably, the construction of the dynamic accounting model, based on the theoretical resource quantity of agricultural waste and its high-precision spatial distribution map, determines the carbon emission and carbon sink quantification parameters of agricultural waste under multiple utilization pathways, including: Define the system boundary of the dynamic accounting model, which includes carbon emission items and carbon emission reduction items; wherein, the carbon emission items include carbon emissions from storage and transportation. Carbon emissions from processing The carbon reduction items include returning carbon to the soil. Emission reductions from replacing fossil fuels Emission reduction from alternative fertilizer production Emission reduction from alternative feed production And carbon emissions avoided under the baseline scenario ; The theoretical resource quantity of agricultural waste and the agricultural waste resource quantity of each grid cell in the high-precision spatial distribution map are used as the basic input parameters of the dynamic accounting model. Based on the basic input parameters and geographical location attributes of each grid cell, the corresponding process sub-model and dynamic emission factor library are called to calculate carbon emission items and carbon emission reduction items in parallel. The system aggregates the calculation results of all carbon emission items and carbon emission reduction items in each grid cell, outputs the net carbon emission reduction of the grid cell using a predefined net emission reduction calculation formula, and summarizes the results to generate a spatial distribution map of carbon emission reduction in the target area.

[0011] Preferably, the formula for calculating the net emission reduction is: ; Where S represents the net carbon emission reduction of a single grid cell; The baseline scenario avoids carbon emissions Using the natural dumping and open burning of agricultural waste as the baseline, the specific calculation formula is as follows: ; ; ; in, This represents the carbon emission baseline for open burning and natural dumping of agricultural waste within grid (i,j). This represents the methane emissions from open burning and natural dumping of agricultural waste within grid (i,j). This represents the nitrous oxide emissions from open burning and natural dumping of agricultural waste within grid (i,j); This represents the theoretical amount of agricultural waste suitable for open burning within grid (i,j). The methane emission factor for open burning of agricultural waste is 2.7, expressed in g (grams per cubic meter). / kg waste) refers to the number of grams (g) of methane (CH4) produced per kilogram (kg) of agricultural waste burned in the open. Expressed as the combustion factor, with a value of 0.8-0.9, it represents the fraction of the total combustible mass of waste that actually undergoes combustion; it is an efficiency coefficient or proportionality factor. This represents the theoretical amount of agricultural waste suitable for natural disposal within grid (i,j). The methane emission factor, expressed as a value from 1 to 10, represents the methane emission factor from naturally deposited agricultural waste. This represents the global warming potential of methane. The value of 0.07 represents the nitrous oxide emission factor from open burning of agricultural waste. The nitrous oxide emission factor, expressed as that from naturally deposited agricultural waste, ranges from 0 to 0.6. This is expressed as the global warming potential of nitrous oxide. carbon emissions from storage and transportation The dynamic accounting is specifically as follows: Based on the geographical location of grid cells, the optimal transportation distance to the nearest processing center is determined through GIS network analysis; Based on the optimal distance and vehicle type, the location-based transportation carbon emissions are calculated using fuel consumption per kilometer and emission factors. : ; in, Let represent the carbon emissions from transportation within grid (i,j), and d represent the optimal transportation distance. This is expressed as the fuel emission factor for transport vehicles. The value is expressed as fuel consumption per kilometer for the type of transport vehicle, and L represents the vehicle's weight limit parameter.

[0012] Preferably, the artificial intelligence-based model simulates the carbon reduction potential and spatial distribution under different resource utilization scenarios based on the carbon emission and carbon sink quantification parameters of agricultural waste under multiple utilization pathways, and optimizes the front-end crop planting structure and spatial layout based on the carbon reduction potential and spatial distribution, including: Define at least one scenario for the resource utilization of agricultural waste based on user input, and set utilization ratio parameters for each scenario, such as fertilizer, feed, and fuel. By inputting the proportional parameters into a pre-trained artificial intelligence model, and based on the spatial distribution map of the theoretical resource quantity of agricultural waste, the carbon emission reduction potential and its spatial distribution map under each scenario are simulated and calculated. With regional carbon emission reduction benefits and / or economic costs as the optimization objectives, based on the carbon emission reduction potential and spatial distribution obtained from simulation, a multi-objective optimization algorithm is used to perform reverse optimization of the front-end crop planting structure and spatial layout to generate an optimized planting plan. Output an optimized planting plan, which includes optimized crop planting area, types, and spatial configuration suggestions.

[0013] Preferably, the step of inputting the proportional parameter into a pre-trained artificial intelligence model to simulate and calculate the carbon reduction potential and its spatial distribution map under each scenario based on the theoretical resource quantity spatial distribution map of agricultural waste includes: The proportional parameters and the spatial distribution map of the theoretical resource quantity of agricultural waste will be input into the pre-trained artificial intelligence model. Using an artificial intelligence model, the agricultural waste resources in each grid are allocated to different utilization pathways: Based on the pre-defined sub-model and dynamic emission factor for each utilization path, the net carbon emission reduction of each grid under a given scenario is calculated. By aggregating the calculation results of all grids, a spatial distribution map of carbon emission reduction potential under the simulated scenario is generated.

[0014] An artificial intelligence-based scenario simulation system for carbon emission reduction in agricultural waste, comprising: The fusion module is used to acquire multi-source geospatial data of the target area and integrate the multi-source geospatial data into a grid system with a preset resolution. The multi-source geospatial data includes agricultural product output statistics, remote sensing crop distribution data, environmental data, and road network data. The acquisition module is used to allocate the provincial agricultural product statistical output to each grid of the grid system based on the spatial weighted allocation model, and combine the grass-to-grain ratio database to obtain the theoretical resource quantity of agricultural waste and its high-precision spatial distribution map. The determination module is used to construct a dynamic accounting model. Based on the theoretical resource quantity of agricultural waste and its high-precision spatial distribution map, the dynamic accounting model determines the carbon emission and carbon sink quantification parameters of agricultural waste under multiple utilization pathways. The optimization module is used to simulate the carbon reduction potential and spatial distribution under different resource utilization scenarios based on the carbon emission and carbon sink quantification parameters of agricultural waste under multiple utilization paths using artificial intelligence models, and to optimize the front-end crop planting structure and spatial layout based on the carbon reduction potential and spatial distribution.

[0015] Other features and advantages of the invention will be set forth in the following description, and will be apparent in part from the description, or may be learned by practicing the invention. The objects and other advantages of the invention may be realized and obtained by means of the structures particularly pointed out in the written description and the accompanying drawings.

[0016] The technical solution of the present invention will be further described in detail below with reference to the accompanying drawings and embodiments. Attached Figure Description

[0017] The accompanying drawings are provided to further illustrate the invention and form part of the specification. They are used together with the embodiments of the invention to explain the invention and do not constitute a limitation thereof.

[0018] Figure 1 A flowchart illustrating the workflow of an artificial intelligence-based method for simulating carbon emission reduction scenarios from agricultural waste, as provided by this invention. Figure 2 Another flowchart of the artificial intelligence-based agricultural waste carbon reduction scenario simulation method provided by the present invention; Figure 3 This is another flowchart illustrating the scenario simulation method for carbon reduction of agricultural waste based on artificial intelligence provided by the present invention. Figure 4 This is a schematic diagram of the structure of an artificial intelligence-based agricultural waste carbon reduction scenario simulation system provided by the present invention. Detailed Implementation

[0019] Exemplary embodiments will now be described in detail, examples of which are illustrated in the accompanying drawings. When the following description relates to the drawings, unless otherwise indicated, the same numerals in different drawings denote the same or similar elements. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with this disclosure. Rather, they are merely examples of apparatuses and methods consistent with some aspects of this disclosure as detailed in the appended claims.

[0020] An AI-based method for simulating carbon reduction scenarios from agricultural waste, such as... Figure 1 As shown, it includes the following steps: Step S101: Obtain multi-source geospatial data of the target area and integrate the multi-source geospatial data into a grid system with a preset resolution. The multi-source geospatial data includes agricultural product output statistics, remote sensing crop distribution data, environmental data, and road network data. Step S102: Based on the spatial weighted allocation model, the provincial agricultural product statistical output is allocated to each grid of the grid system. Combined with the grass-to-grain ratio database, the theoretical resource quantity of agricultural waste and its high-precision spatial distribution map are obtained. Step S103: Construct a dynamic accounting model. Based on the theoretical resource quantity of agricultural waste and its high-precision spatial distribution map, determine the carbon emission and carbon sink quantification parameters of agricultural waste under multiple utilization pathways using the dynamic accounting model. Step S104: Based on the artificial intelligence model, simulate the carbon reduction potential and spatial distribution under different resource utilization scenarios according to the carbon emission and carbon sink quantification parameters of agricultural waste under multiple utilization paths, and optimize the front-end crop planting structure and spatial layout according to the carbon reduction potential and spatial distribution.

[0021] In this embodiment, the preset resolution refers to the cell size of the regular geographic grid that is pre-set according to the simulation accuracy requirements and data availability. In a preferred embodiment of the present invention, this resolution can be preset to 1 km × 1 km. This grid system, as a unified basic geographic grid system, is the benchmark framework for all spatial data fusion, analysis, and result display. In this embodiment, the spatial weighted allocation model is the core model for spatially discretizing provincial statistical output into each grid. In this embodiment, the grass-to-rice ratio database is a categorized lookup table. Its regionalization is reflected in the fact that it is divided into major agricultural ecological zones in China; its categorization is reflected in the fact that specific grass-to-rice ratio values ​​are assigned to the main crop types (such as corn, rice, wheat, etc.) within each zone. This database is constructed by consulting national and industry standards and publicly published literature, ensuring the authority of the parameters and regional differences. In this embodiment, the dynamic accounting model is a modular accounting system composed of multiple process sub-models (such as the transport emission sub-model and the soil carbon sink sub-model) and a dynamic emission factor library. It achieves refined accounting through grid-level parallel computing.

[0022] The working principle of the above technical solution is as follows: First, acquire multi-source geospatial data of the target area and integrate it into a grid system with a preset resolution. This multi-source geospatial data includes agricultural product output statistics, remote sensing crop distribution data, environmental data, and road network data. Second, based on a spatial weighted allocation model, allocate provincial agricultural product output statistics to various grids within the grid system. Third, combine this with a grass-to-grain ratio database to obtain the theoretical resource quantity of agricultural waste and its high-precision spatial distribution map. Fourth, construct a dynamic accounting model to determine the carbon emission and carbon sink quantification parameters of agricultural waste under various utilization pathways based on the theoretical resource quantity and high-precision spatial distribution map. Fifth, use an artificial intelligence model to simulate the carbon reduction potential and spatial distribution under different resource utilization scenarios based on the carbon emission and carbon sink quantification parameters of agricultural waste under various utilization pathways. Finally, optimize the front-end crop planting structure and spatial layout based on the carbon reduction potential and spatial distribution.

[0023] The beneficial effects of the above technical solution are as follows: through multi-source data fusion and AI optimization, a high-precision, dynamic, and spatially explicit simulation of the carbon emission reduction potential of agricultural waste is achieved, providing a quantifiable and visualized scientific decision-making tool for regional low-carbon agricultural planning, significantly improving the accuracy and feasibility of emission reduction strategies, and solving the problem mentioned in the existing technology that the existing assessment tools are mostly single accounting models, lacking the ability to quickly simulate scenarios and optimize multiple resource utilization paths, which often leads to the formulation of emission reduction strategies relying on experience and making it difficult to achieve the synergistic maximization of economic costs and emission reduction benefits.

[0024] In one embodiment, such as Figure 2 As shown, the step of acquiring multi-source geospatial data of the target area and integrating the multi-source geospatial data into a grid system with a preset resolution includes: Step S201: Obtain multi-source geospatial data of the target area from statistical data sources, remote sensing data sources, environmental data sources, and geographic data sources respectively; Step S202: Standardize the format, unify the coordinate system, and handle missing values ​​of the multi-source geospatial data to obtain preprocessed multi-source geospatial data; Step S203: Construct a unified basic geographic grid system within the target area according to the simulation accuracy requirements; Step S204: The preprocessed multi-source geospatial data is converted and integrated into the basic geographic grid system through spatial interpolation, resampling or vector rasterization methods to form a multi-attribute fused grid dataset.

[0025] In this embodiment, the statistical data sources include provincial, municipal, and county-level agricultural statistical yearbooks, which are used to obtain agricultural product output data classified by crop. Remote sensing data sources include multispectral and high-resolution optical satellites, used to generate crop type distribution maps, vegetation indices, land surface temperature, and land cover classification products; Environmental data sources include meteorological observation stations, soil survey databases, and reanalysis data products, used to obtain data on temperature, precipitation, solar radiation, soil type, and soil organic matter content; Geographic data sources include digital elevation models, administrative boundary maps, and road network data.

[0026] The beneficial effects of the above technical solution are as follows: by standardizing the process, heterogeneous multi-source geospatial data are unified under the same spatial grid framework, which solves the problem of mismatch between data format, coordinate system and scale, provides a high-quality and consistent spatial dataset for subsequent accurate analysis, and lays the data foundation for high-precision simulation.

[0027] In one embodiment, the process of converting and fusing all preprocessed multi-source geospatial data into a basic geographic grid system through spatial interpolation, resampling, or vector rasterization methods to form a multi-attribute fused grid dataset includes: A weighted allocation method based on remote sensing factors is used to distribute statistical data from preprocessed multi-source geospatial data to each grid cell; The cubic convolution resampling method is used to unify the raster data corresponding to remote sensing data and environmental data in the preprocessed multi-source geospatial data to the resolution of the basic geographic grid system; The rasterization method is used to convert the vector data corresponding to the geographic data in the preprocessed multi-source geospatial data into raster data consistent with the basic geographic grid system, generating an intermediate grid data layer with unified spatial reference and resolution; Multiple intermediate grid data layers are overlaid, so that each grid cell is associated with the attribute values ​​of all input data, thereby generating a multi-attribute fused grid dataset.

[0028] In this embodiment, the spatial allocation strategy for configuring statistical data is as follows: the total statistical volume at the administrative division level is allocated based on the area weight or comprehensive weight factor of each grid unit within the administrative division. In this embodiment, the resampling strategy configured for raster data is as follows: When the resolution of the original raster data is higher than that of the basic geographic grid system, bilinear interpolation or cubic convolution is used for resampling to smoothly transition the pixel values. When the resolution of the original raster data is lower than that of the basic geographic grid system, the nearest neighbor assignment method is used for resampling to preserve the classification or discrete value characteristics of the original data. In this embodiment, the rasterization strategy configured for vector data is as follows: For point-based vector data, the direct point assignment method is used to assign the attribute values ​​of the points to the grid cells in which they reside. For linear vector data, use the line density calculation method or the centerline rasterization method to generate a line density raster or a centerline position raster. For planar vector data, the dominance of the planar region method is adopted, which assigns the dominant attribute or area-weighted attribute value of the planar region to the grid cells covered by the planar region.

[0029] The beneficial effects of the above technical solution are as follows: the optimal spatial transformation strategy is adopted for different types of data (statistical, raster, vector), which not only ensures the rationality of the spatial distribution of statistical data, but also preserves the continuous distribution characteristics of remote sensing and environmental data and the topological relationship of geographic data to the greatest extent, thus ensuring the scientificity and accuracy of the fused dataset.

[0030] In one embodiment, such as Figure 3 As shown, the method of allocating provincial agricultural product output statistics to various grids in a grid system based on a spatial weighted allocation model, combined with a grass-to-grain ratio database, to obtain the theoretical resource quantity of agricultural waste and its high-precision spatial distribution map, includes: Step S301: Obtain the statistical output data of agricultural products in the province where the target area is located, as well as environmental raster data including vegetation index and soil fertility. Step S302: Based on the spatial weighted allocation model, the total amount of agricultural product statistical output data is used as the control, and environmental raster data including vegetation index and soil fertility is used as the control conditions to allocate the output to each grid of the basic geographic grid system, thereby generating a spatial distribution map of crop output. Step S303: Based on the geographical location and crop type of each grid, query the preset regionalized grass-to-grain ratio database to obtain the corresponding grass-to-grain ratio value; Step S304: Based on the crop yield and the matching grass-to-grain ratio of each grid, calculate the theoretical agricultural waste resource quantity of each grid. Step S305: Summarize the theoretical agricultural waste resource quantity of all grids and generate a high-precision spatial distribution map of agricultural waste.

[0031] The beneficial effects of the above technical solution are as follows: by combining macro-statistical output with high-resolution environmental factors for spatial scale allocation, and then combining it with a regionalized grass-to-grain ratio database, it achieves accurate estimation from provincial output to grid resource quantity, completely changing the status quo of insufficient spatial accuracy of traditional resource assessment methods, and generating a high-precision agricultural waste resource distribution map that can guide actual operation and maintenance.

[0032] In one embodiment, the spatial weighted allocation model is implemented using the following formula: ; in, Let this be the theoretical yield of crop k within grid (i,j). The comprehensive weighting factor is represented as grid (i,j). Let the total provincial-level statistical output of crop k be denoted as ; The comprehensive weighting factor Calculated using the following formula: ; in, This represents the planting area weight of crop k within grid (i,j). Represented as vegetation productivity weights obtained based on the normalization of remote sensing vegetation indices. This is represented as the soil fertility weight obtained based on the normalization treatment of soil organic matter content; The theoretical resource volume of agricultural waste for each grid is calculated using the following formula: ; in, This represents the theoretical amount of agricultural waste resources for crop k within grid (i,j). This represents the grass-to-grain ratio of crop k obtained from a regionalized grass-to-grain ratio database.

[0033] The beneficial effects of the above technical solution are as follows: by introducing vegetation index and soil fertility as comprehensive weighting factors, the spatial allocation of yield not only depends on the planting area, but also reflects the actual growth status of crops and land productivity, making the resource quantity estimation results more in line with reality.

[0034] In one embodiment, the construction of a dynamic accounting model, which determines the carbon emission and carbon sink quantification parameters of agricultural waste under multiple utilization pathways based on the theoretical resource quantity and high-precision spatial distribution map of agricultural waste, includes: Define the system boundary of the dynamic accounting model, which includes carbon emission items and carbon emission reduction items; wherein, the carbon emission items include carbon emissions from storage and transportation. Carbon emissions from processing The carbon reduction items include returning carbon to the soil. Emission reductions from replacing fossil fuels Emission reduction from alternative fertilizer production Emission reduction from alternative feed production And carbon emissions avoided under the baseline scenario ; The theoretical resource quantity of agricultural waste and the agricultural waste resource quantity of each grid cell in the high-precision spatial distribution map are used as the basic input parameters of the dynamic accounting model. Based on the basic input parameters and geographical location attributes of each grid cell, the corresponding process sub-model and dynamic emission factor library are called to calculate carbon emission items and carbon emission reduction items in parallel. The system aggregates the calculation results of all carbon emission items and carbon emission reduction items in each grid cell, outputs the net carbon emission reduction of the grid cell using a predefined net emission reduction calculation formula, and summarizes the results to generate a spatial distribution map of carbon emission reduction in the target area.

[0035] In this embodiment, the process sub-models include, but are not limited to: a soil carbon sink calculation model for returning to the field, a fossil energy alternative emission reduction model, a transportation route optimization model, and a processing carbon emission query model based on an emission factor database.

[0036] The beneficial effects of the above technical solution are as follows: by establishing a full life cycle and multi-path carbon accounting framework and realizing grid-level parallel computing, the problem of deviation of the traditional static emission factor method accounting results from reality is solved, and the true carbon footprint and emission reduction benefits under different regions and different management methods can be dynamically and accurately reflected.

[0037] In one embodiment, the formula for calculating the net emission reduction is: ; Where S represents the net carbon emission reduction of a single grid cell; The baseline scenario avoids carbon emissions Using the natural dumping and open burning of agricultural waste as the baseline, the specific calculation formula is as follows: ; ; ; in, This represents the carbon emission baseline for open burning and natural dumping of agricultural waste within grid (i,j). This represents the methane emissions from open burning and natural dumping of agricultural waste within grid (i,j). This represents the nitrous oxide emissions from open burning and natural dumping of agricultural waste within grid (i,j); This represents the theoretical amount of agricultural waste suitable for open burning within grid (i,j). The methane emission factor for open burning of agricultural waste is 2.7, expressed in g (grams per cubic meter). / kg waste) refers to the number of grams (g) of methane (CH4) produced per kilogram (kg) of agricultural waste burned in the open. Expressed as the combustion factor, with a value of 0.8-0.9, it represents the fraction of the total combustible mass of waste that actually undergoes combustion; it is an efficiency coefficient or proportionality factor. This represents the theoretical amount of agricultural waste suitable for natural disposal within grid (i,j). The methane emission factor, expressed as a value from 1 to 10, represents the methane emission factor from naturally deposited agricultural waste. This represents the global warming potential of methane. The value of 0.07 represents the nitrous oxide emission factor from open burning of agricultural waste. The nitrous oxide emission factor, expressed as that from naturally deposited agricultural waste, ranges from 0 to 0.6. This is expressed as the global warming potential of nitrous oxide. carbon emissions from storage and transportation The dynamic accounting is specifically as follows: Based on the geographical location of grid cells, the optimal transportation distance to the nearest processing center is determined through GIS network analysis; Based on the optimal distance and vehicle type, the location-based transportation carbon emissions are calculated using fuel consumption per kilometer and emission factors. : ; in, Let represent the carbon emissions from transportation within grid (i,j), and d represent the optimal transportation distance. This is expressed as the fuel emission factor for transport vehicles. The value is expressed as fuel consumption per kilometer for the type of transport vehicle, and L represents the vehicle's weight limit parameter.

[0038] The beneficial effects of the above technical solution are: it enables the accounting results to respond to the actual optimization of the collection, storage and transportation layout, and greatly improves the spatial accuracy and practical guidance value of carbon accounting results.

[0039] In one embodiment, the AI-based model simulates the carbon reduction potential and spatial distribution under different resource utilization scenarios based on carbon emission and carbon sink quantification parameters of agricultural waste under multiple utilization pathways, and optimizes the front-end crop planting structure and spatial layout based on the carbon reduction potential and spatial distribution, including: Define at least one scenario for the resource utilization of agricultural waste based on user input, and set utilization ratio parameters for each scenario, such as fertilizer, feed, and fuel. By inputting the proportional parameters into a pre-trained artificial intelligence model, and based on the spatial distribution map of the theoretical resource quantity of agricultural waste, the carbon emission reduction potential and its spatial distribution map under each scenario are simulated and calculated. With regional carbon emission reduction benefits and / or economic costs as the optimization objectives, based on the carbon emission reduction potential and spatial distribution obtained from simulation, a multi-objective optimization algorithm is used to perform reverse optimization of the front-end crop planting structure and spatial layout to generate an optimized planting plan. Output an optimized planting plan, which includes optimized crop planting area, types, and spatial configuration suggestions.

[0040] In this embodiment, the core function of the pre-trained artificial intelligence model is to map the spatial distribution of agricultural waste resources input from the front end and the utilization ratio parameters set by the user into optimization suggestions for the crop planting structure and spatial layout at the front end, and in this process, quickly and accurately simulate the distribution of carbon emission reduction potential under various scenarios.

[0041] Model Properties and Tasks: The artificial intelligence model is essentially an integration of a multi-objective optimization solver and a spatial allocation predictor. It is trained to solve a complex constrained optimization problem with the objective function of maximizing net carbon emission reductions in the region (and / or minimizing economic costs). The decision variables are the crop planting type and area of ​​each grid cell, and the constraints include total land resources, water resource limitations, crop rotation systems, etc.

[0042] Feasible choices for model architecture: As the carrier for implementing the above functions, the artificial intelligence model can be selected from, but is not limited to, the following types of models or combinations of models: Evolutionary algorithm-based optimization frameworks, such as genetic algorithms (GA) and particle swarm optimization (PSO), use population individual codes to represent different planting schemes, and the fitness function is the carbon emission reduction benefit calculated by a dynamic accounting model.

[0043] In a deep reinforcement learning model, the agent treats each grid as part of the environmental state, the action space is crop planting decisions, and the reward function is the carbon emission reduction increment.

[0044] Integrated prediction-optimization two-stage model: First, a prediction model (such as random forest, gradient boosting tree or neural network) is used to quickly estimate the carbon emission reduction potential under a given planting scheme, and then an optimizer is coupled to search for the scheme.

[0045] Training Data and Process: The model is trained based on a paired dataset of "planting schemes - carbon reduction benefits" generated from history or simulation. This dataset is constructed by generating a large number of random or rule-based crop planting spatial layout schemes. For each scheme, the spatial distribution and total amount of its corresponding carbon reduction potential are calculated using steps S102 and S103 (i.e., the spatial allocation and dynamic accounting model) described above. The goal of training is to enable the model to learn the complex mapping relationship from planting schemes to carbon reduction benefits, so that new schemes can be quickly evaluated and optimized in application. The model training follows standard machine learning procedures, including data partitioning, loss function minimization (such as mean squared error MSE or a custom multi-objective loss), and iterative training using gradient descent algorithms until convergence.

[0046] Convergence criteria: The convergence criteria for training a model can adopt the conventional standards for training and optimizing models in this field, such as: the loss function value on the validation set no longer decreases significantly in several consecutive iterations (the rate of change is lower than a preset threshold), or the preset maximum number of iterations is reached.

[0047] In this embodiment, carbon emission reduction benefit refers to the total net carbon emission reduction of the target area calculated by the method of the present invention, and its unit is carbon dioxide equivalent. In this embodiment, the mechanism described by reverse optimization is as follows: the spatial distribution map of carbon emission reduction potential output by the end-of-pipe scenario simulation is used as a feedback signal. With maximizing this potential as one of the objectives, a multi-objective optimization algorithm is adopted to reverse calculate and suggest how the crop planting area structure and spatial layout at the front end should be adjusted. This process takes into account real constraints such as land resources and water resources, and realizes intelligent decision-making backtracking from "waste management" to "agricultural production".

[0048] In this embodiment, the optimized planting scheme is a set of specific data recommendations, including but not limited to: recommended crop types to be increased / decreased, recommended adjustments to the planting area on the spatial grid, and the expected theoretical carbon emission reduction increment. In this embodiment, multi-objective optimization algorithms are the core technology for achieving system optimization. The multi-objective aspect is specifically defined in this invention as the cooperative optimization of at least two conflicting objectives: Objective 1: Maximize the benefits of regional carbon emission reduction, quantified as total net carbon emission reduction; Objective 2: Minimize the total economic cost of the system, including costs for storage, transportation, and processing; The algorithm provides users with multiple optimal solutions that balance emissions reduction and cost by searching the Pareto optimal solution set.

[0049] The beneficial effects of the above technical solution are as follows: it links and optimizes the end-of-pipe waste management scenario with the front-end main crop production layout, breaks through the traditional decision-making model in which the two links are disconnected, and realizes a leap in intelligent decision-making from emission reduction potential analysis to source planting plan formulation through AI model, thereby improving the level of intelligence and scientificity of decision-making.

[0050] In one embodiment, the step of inputting a proportional parameter into a pre-trained artificial intelligence model to simulate and calculate the carbon reduction potential and its spatial distribution map under each scenario based on the theoretical resource quantity spatial distribution map of agricultural waste includes: The proportional parameters and the spatial distribution map of the theoretical resource quantity of agricultural waste will be input into the pre-trained artificial intelligence model. Using an artificial intelligence model, the agricultural waste resources in each grid are allocated to different utilization pathways: Based on the pre-defined sub-model and dynamic emission factor for each utilization path, the net carbon emission reduction of each grid under a given scenario is calculated. By aggregating the calculation results of all grids, a spatial distribution map of carbon emission reduction potential under the simulated scenario is generated.

[0051] The beneficial effects of the above technical solution are as follows: by inputting proportional parameters and resource distribution maps into a pre-trained AI model, rapid, batch, and automated simulations of carbon emission reduction effects under different management scenarios are achieved. This enables decision-makers to intuitively compare the advantages and disadvantages of multiple options, significantly improving the efficiency of scenario analysis and strategy formulation.

[0052] In one embodiment, this embodiment also discloses an artificial intelligence-based agricultural waste carbon reduction scenario simulation system, such as... Figure 4 As shown, the system includes: The fusion module 401 is used to acquire multi-source geospatial data of the target area and integrate the multi-source geospatial data into a grid system with a preset resolution. The multi-source geospatial data includes agricultural product output statistics, remote sensing crop distribution data, environmental data, and road network data. The acquisition module 402 is used to allocate the provincial agricultural product statistical output to each grid of the grid system based on the spatial weighted allocation model, and combine the grass-to-grain ratio database to obtain the theoretical resource quantity of agricultural waste and its high-precision spatial distribution map. Module 403 is used to construct a dynamic accounting model. Based on the theoretical resource quantity of agricultural waste and its high-precision spatial distribution map, the dynamic accounting model determines the carbon emission and carbon sink quantification parameters of agricultural waste under multiple utilization pathways. The optimization module 404 is used to simulate the carbon reduction potential and spatial distribution under different resource utilization scenarios based on the carbon emission and carbon sink quantification parameters of agricultural waste under multiple utilization paths using an artificial intelligence model, and to optimize the front-end crop planting structure and spatial layout based on the carbon reduction potential and spatial distribution.

[0053] The working principle and beneficial effects of the above technical solution have been explained in the method embodiments, and will not be repeated here.

[0054] Those skilled in the art should understand that the "first" and "second" in this invention simply refer to different application stages.

[0055] Other embodiments of this disclosure will readily occur to those skilled in the art upon consideration of the specification and practice of the disclosure herein. This application is intended to cover any variations, uses, or adaptations of this disclosure that follow the general principles of this disclosure and include common knowledge or customary techniques in the art not disclosed herein. The specification and examples are to be considered exemplary only, and the true scope and spirit of this disclosure are indicated by the following claims.

[0056] It should be understood that this disclosure is not limited to the precise structures described above and shown in the accompanying drawings, and various modifications and changes can be made without departing from its scope. The scope of this disclosure is limited only by the appended claims.

Claims

1. An artificial intelligence-based agricultural waste carbon emission reduction scenario simulation method, characterized in that, The method comprises the following steps: obtaining multi-source geospatial data of a target area, and uniformly fusing the multi-source geospatial data into a grid system with a preset resolution, wherein the multi-source geospatial data comprises agricultural product yield statistical data, remote sensing crop distribution data, environmental data and road network data; based on a spatial weighted distribution model, distributing provincial agricultural product statistical yield to each grid of the grid system, combining a straw-to-grain ratio database, obtaining a theoretical resource amount of agricultural waste and a high-precision spatial distribution map thereof; constructing a dynamic accounting model, and determining carbon emission and carbon sink quantification parameters of agricultural waste under multiple utilization paths based on the theoretical resource amount of agricultural waste and the high-precision spatial distribution map thereof through the dynamic accounting model; based on an artificial intelligence model, simulating carbon emission reduction potential and spatial distribution under different resource utilization scenarios according to the carbon emission and carbon sink quantification parameters of agricultural waste under multiple utilization paths, and optimizing front-end crop planting structure and spatial layout according to the carbon emission reduction potential and spatial distribution. 2.The artificial intelligence-based agricultural waste carbon emission reduction scenario simulation method according to claim 1, characterized in that, The method comprises the following steps: obtaining multi-source geospatial data of a target area, and uniformly fusing the multi-source geospatial data into a grid system with a preset resolution, wherein the multi-source geospatial data comprises agricultural product yield statistical data, remote sensing crop distribution data, environmental data and road network data; based on a spatial weighted distribution model, distributing provincial agricultural product statistical yield to each grid of the grid system, combining a straw-to-grain ratio database, obtaining a theoretical resource amount of agricultural waste and a high-precision spatial distribution map thereof; constructing a dynamic accounting model, and determining carbon emission and carbon sink quantification parameters of agricultural waste under multiple utilization paths based on the theoretical resource amount of agricultural waste and the high-precision spatial distribution map thereof through the dynamic accounting model; based on an artificial intelligence model, simulating carbon emission reduction potential and spatial distribution under different resource utilization scenarios according to the carbon emission and carbon sink quantification parameters of agricultural waste under multiple utilization paths, and optimizing front-end crop planting structure and spatial layout according to the carbon emission reduction potential and spatial distribution. 3.The AI-based agricultural waste carbon emission reduction scenario simulation method according to claim 2, characterized in that, The method comprises the following steps: obtaining multi-source geospatial data of a target area, and uniformly fusing the multi-source geospatial data into a grid system with a preset resolution, wherein the multi-source geospatial data comprises agricultural product yield statistical data, remote sensing crop distribution data, environmental data and road network data; based on a spatial weighted distribution model, distributing provincial agricultural product statistical yield to each grid of the grid system, combining a straw-to-grain ratio database, obtaining a theoretical resource amount of agricultural waste and a high-precision spatial distribution map thereof; constructing a dynamic accounting model, and determining carbon emission and carbon sink quantification parameters of agricultural waste under multiple utilization paths based on the theoretical resource amount of agricultural waste and the high-precision spatial distribution map thereof through the dynamic accounting model; based on an artificial intelligence model, simulating carbon emission reduction potential and spatial distribution under different resource utilization scenarios according to the carbon emission and carbon sink quantification parameters of agricultural waste under multiple utilization paths, and optimizing front-end crop planting structure and spatial layout according to the carbon emission reduction potential and spatial distribution. 4.The AI-based agricultural waste carbon emission reduction scenario simulation method according to claim 1, wherein, The method comprises the following steps: obtaining multi-source geospatial data of a target area, and uniformly fusing the multi-source geospatial data into a grid system with a preset resolution, wherein the multi-source geospatial data comprises agricultural product yield statistical data, remote sensing crop distribution data, environmental data and road network data; based on a spatial weighted distribution model, distributing provincial agricultural product statistical yield to each grid of the grid system, combining a straw-to-grain ratio database, obtaining a theoretical resource amount of agricultural waste and a high-precision spatial distribution map thereof; constructing a dynamic accounting model, and determining carbon emission and carbon sink quantification parameters of agricultural waste under multiple utilization paths based on the theoretical resource amount of agricultural waste and the high-precision spatial distribution map thereof through the dynamic accounting model; based on an artificial intelligence model, simulating carbon emission reduction potential and spatial distribution under different resource utilization scenarios according to the carbon emission and carbon sink quantification parameters of agricultural waste under multiple utilization paths, and optimizing front-end crop planting structure and spatial layout according to the carbon emission reduction potential and spatial distribution. The method comprises the following steps: obtaining multi-source geospatial data of a target area, and uniformly fusing the multi-source geospatial data into a grid system with a preset resolution, wherein the multi-source geospatial data comprises agricultural product yield statistical data, remote sensing crop distribution data, environmental data and road network data; based on a spatial weighted distribution model, distributing provincial agricultural product statistical yield to each grid of the grid system, combining a straw-to-grain ratio database, obtaining a theoretical resource amount of agricultural waste and a high-precision spatial distribution map thereof; constructing a dynamic accounting model, and determining carbon emission and carbon sink quantification parameters of agricultural waste under multiple utilization paths based on the theoretical resource amount of agricultural waste and the high-precision spatial distribution map thereof through the dynamic accounting model; based on an artificial intelligence model, simulating carbon emission reduction potential and spatial distribution under different resource utilization scenarios according to the carbon emission and carbon sink quantification parameters of agricultural waste under multiple utilization paths, and optimizing front-end crop planting structure and spatial layout according to the carbon emission reduction potential and spatial distribution. Based on the spatially weighted allocation model, the statistical yield data of agricultural products is taken as the total control, and the environmental grid data of vegetation index and soil fertility are taken as the control conditions to allocate the yield to each grid of the basic geographic grid system, and a spatial distribution map of crop yield is generated; According to the geographic location and crop type of each grid, a preset regionalized straw-grain ratio database is queried to obtain the corresponding straw-grain ratio value; Based on the crop yield of each grid and the matched straw-grain ratio value, the agricultural waste physical resource quantity of each grid is calculated; The agricultural waste physical resource quantity of each grid is calculated by the following formula: 5.The AI-based agricultural waste carbon emission reduction scenario simulation method according to claim 4, characterized in that, The dynamic accounting model is constructed, and the carbon emission and carbon sink quantification parameters of agricultural waste under multiple utilization paths are determined based on the theoretical resource quantity of agricultural waste and the high-precision spatial distribution map thereof by the dynamic accounting model, including: ； wherein, Yijk represents the theoretical yield of crop k in grid (i,j), Wij represents the combined weight factor for grid (i,j), Yk represents the provincial statistical total yield of crop k; The combined weight factor is calculated by the following equation: ； wherein, represents a planting area weight of crop k in grid (i, j), represents a weight of vegetation productivity based on normalized processing of remote sensing vegetation index, represents a weight of soil fertility based on normalized processing of soil organic matter content; The theoretical resource quantity of agricultural waste and the agricultural waste resource quantity of each grid unit in the high-precision spatial distribution map are taken as the basic input parameters of the dynamic accounting model; ； wherein, represents the amount of agricultural waste physical theory resource for crop k within grid (i,j), represents the crop k grass valley ratio obtained from the regionalized grass valley ratio database. 6.The AI-based agricultural waste carbon emission reduction scenario simulation method according to claim 1, wherein, Based on the basic input parameters of each grid unit and the geographic location attribute thereof, corresponding process sub-models and a dynamic emission factor library are called, and carbon emission items and carbon emission reduction items are calculated in parallel; define a system boundary of a dynamic accounting model, the system boundary comprising carbon emission items and carbon emission reduction items; wherein the carbon emission items comprise carbon emissions from collection, storage and transportation and processing process carbon emissions , and the carbon emission reduction items comprise carbon sinks of returned field soil , carbon emission reduction amount of alternative fossil energy , carbon emission reduction amount of alternative fertilizer production , carbon emission reduction amount of alternative feed production and carbon emissions avoided by a baseline scenario ; The accounting results of all carbon emission items and carbon emission reduction items of each grid unit are aggregated, the net carbon emission reduction quantity of the grid unit is output through a predefined net emission reduction quantity calculation formula, and a carbon emission reduction quantity spatial distribution map of the target region is generated. The net emission reduction quantity calculation formula is: Wherein, S represents the net carbon emission reduction quantity of a single grid unit; 7.The artificial intelligence-based agricultural waste carbon emission reduction scenario simulation method according to claim 6, characterized in that, Based on the geographic location of the grid unit, the optimal transportation distance to the nearest processing center is determined through GIS network analysis; ； The carbon emission reduction potential and spatial distribution under different resource utilization scenarios are simulated according to the carbon emission and carbon sink quantification parameters of agricultural waste under multiple utilization paths based on the artificial intelligence model, and the front-end crop planting structure and spatial layout are optimized according to the carbon emission reduction potential and spatial distribution, including: Carbon emissions of the baseline scenario of line-avoidance The specific calculation formula is: ； ； ； wherein, represents the carbon emission baseline for open burning and natural piling of agricultural waste within grid (i,j), represents the methane emission amount for open burning and natural piling of agricultural waste within grid (i,j), represents the nitrous oxide emission amount for open burning and natural piling of agricultural waste within grid (i,j); represents the physical amount of agricultural waste suitable for open burning within grid (i,j), represents the methane emission factor for open burning of agricultural waste, with a value of 2.7, in units of (g CH4 / kg waste) represents the grams (g) of methane (CH4) produced per kilogram (kg) of agricultural waste open burned, represents the combustion factor, with a value of 0.8-0.9, representing the fraction of the total combustible mass that actually undergoes combustion in the waste, which is an efficiency coefficient or proportionality factor, represents the physical amount of agricultural waste suitable for natural piling within grid (i,j), represents the methane emission factor for natural piling of agricultural waste, with a value of 1-10, represents the global warming potential of methane, represents the nitrous oxide emission factor for open burning of agricultural waste, with a value of 0.07, represents the nitrous oxide emission factor for natural piling of agricultural waste, with a value of 0-0.6, represents the global warming potential of nitrous oxide; The collection and transportation of carbon emissions The dynamic accounting of the carbon emissions is specified as follows: At least one agricultural waste resource utilization scenario is defined according to user input, and utilization proportion parameters of fertilizerization, feed, and fuelization are set for each scenario; Transportation carbon emissions based on spatial location are calculated from the optimal distance and the vehicle type's kilometer oil consumption and emission factors : ； wherein, represents the transport carbon emissions within grid (i,j), d represents the optimal transport distance, represents the fuel emission factor for the transport vehicle, represents the vehicle type's km fuel consumption, L represents the vehicle transport weight limit parameter. 8.The AI-based agricultural waste carbon emission reduction scenario simulation method according to claim 1, wherein, The utilization proportion parameters are input into the pre-trained artificial intelligence model, and the carbon emission reduction potential and spatial distribution map under each scenario are simulated and calculated based on the theoretical resource quantity spatial distribution map of agricultural waste; Taking the regional carbon emission reduction benefit and / or economic cost as the optimization target, the front-end crop planting structure and spatial layout are inversely optimized and solved based on the simulated carbon emission reduction potential and spatial distribution by using a multi-objective optimization algorithm, and an optimized planting scheme is generated; The optimized planting scheme is output, and the planting scheme includes optimized crop planting area, type, and spatial configuration suggestions. The utilization proportion parameters are input into the pre-trained artificial intelligence model, and the carbon emission reduction potential and spatial distribution map under each scenario are simulated and calculated based on the theoretical resource quantity spatial distribution map of agricultural waste, including: ​ 9.The AI-based agricultural waste carbon emission reduction scenario simulation method according to claim 8, wherein, ​ The proportion parameter and the theoretical resource amount spatial distribution map of agricultural waste are input into a pre-trained artificial intelligence model; The artificial intelligence model is used to distribute the resource amount of agricultural waste in each grid to different utilization paths; Based on the preset accounting sub-model and dynamic emission factor for each utilization path, the net carbon emission reduction amount of each grid under a given scenario is calculated; The calculation results of all grids are aggregated to generate a carbon emission reduction potential spatial distribution map under the simulated scenario.

10. An artificial intelligence-based agricultural waste carbon emission reduction scenario simulation system, characterized by, The system comprises: A fusion module is configured to obtain multi-source geospatial data of a target region, and uniformly fuse the multi-source geospatial data into a grid system with a preset resolution, wherein the multi-source geospatial data comprises agricultural product yield statistical data, remote sensing crop distribution data, environmental data and road network data; An acquisition module is configured to distribute provincial agricultural product statistical yield to each grid of the grid system based on a spatial weighted distribution model, and obtain a theoretical resource amount of agricultural waste and a high-precision spatial distribution map thereof in combination with a straw-to-vegetable ratio database; A determination module is configured to construct a dynamic accounting model, and determine carbon emission and carbon sink quantification parameters of agricultural waste under multiple utilization paths based on the theoretical resource amount of agricultural waste and the high-precision spatial distribution map thereof by using the dynamic accounting model; An optimization module is configured to simulate carbon emission reduction potential and spatial distribution under different resource utilization scenarios based on the carbon emission and carbon sink quantification parameters of agricultural waste under the multiple utilization paths by using an artificial intelligence model, and optimize front-end crop planting structure and spatial layout according to the carbon emission reduction potential and spatial distribution.

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