Country land space carbon emission assessment method

Abstract

The invention relates to the technical field of environmental science and information, in particular to a rural land space carbon emission assessment method, which collects real-time data through a distributed sensor network and an unmanned aerial vehicle remote sensing technology, analyzes and extracts key features by using an adaptive variational auto-encoder and topological data, and effectively identifies a land utilization mode. The graph neural network and the Bayesian optimization generative adversarial network are combined to dynamically evaluate and predict carbon emission, and uncertainty quantitative data are generated; a multi-objective genetic algorithm and a game theory model are used for optimizing land utilization layout, an optimization scheme is executed on a block chain through an intelligent contract and a multi-agent system, and automatic management and dynamic adjustment are achieved; according to the method, the accuracy and the real-time performance of rural land carbon emission evaluation are remarkably improved, and a scientific basis is provided for low-carbon sustainable development.

Description

Technical Field

[0001] The present invention relates to the fields of environmental science and information technology, and in particular to a method for assessing spatial carbon emissions of rural land. Background Art

[0002] Spatial carbon emission assessment of rural land is an important tool for achieving low-carbon rural construction and sustainable development. By assessing the intensity and total amount of carbon emissions, the carbon emission characteristics of different land use types can be identified and optimized, supporting decision makers to make wise choices in land planning and management. This is of great significance for mitigating climate change, improving land use efficiency and improving the ecological environment. In rural areas, accurate carbon emission assessment can guide the rational layout of land use, promote the development of low-carbon agriculture and eco-friendly infrastructure, and ultimately achieve coordinated development of the economy, society and environment.

[0003] The existing rural land spatial carbon emission assessment technology (Chinese invention patent, publication number: CN117217961A, name: a bottom-up rural land spatial carbon emission assessment model) mainly relies on a bottom-up assessment model; this model uses a traditional calculation method, mainly by obtaining land use indicators in land use planning, combining land layout, land scale, land use pattern and land structure and other factors to calculate the carbon emissions of different land types; the specific method includes separate calculations of the carbon emission intensity of unit residential land, commercial construction land, public service facility land, transportation land, etc., and then summarizing them into the total carbon emissions of the entire village.

[0004] However, this technology has several shortcomings. First, the existing models are too dependent on static historical data and average values, lack dynamic adjustment capabilities, and cannot reflect the impact of land use changes on carbon emissions in real time; second, due to the failure to effectively integrate real-time environmental data and socioeconomic factors, the existing methods are insufficient in terms of assessment accuracy and adaptability; finally, the existing technology has limited predictive capabilities for complex land use scenarios, especially in multi-objective optimization and dynamic evaluation, and lacks intelligent analysis methods, resulting in the inability to accurately predict and manage carbon emissions. Summary of the invention

[0005] In view of the many problems existing in the above-mentioned prior arts, the present invention provides a method for assessing spatial carbon emissions of rural land. The present invention realizes accurate assessment and optimization of spatial carbon emissions of rural land by integrating a variety of intelligent technical means; first, data collection is carried out using distributed sensor networks and unmanned aerial vehicle remote sensing technology to monitor environmental parameters and land use changes in real time; then, key features are extracted by adaptive variational autoencoders, and land use patterns are identified in combination with topological data analysis; subsequently, graph neural networks and Bayesian optimization are used to dynamically evaluate and predict carbon emissions to generate uncertainty quantification data; finally, land use layout is optimized through multi-objective genetic algorithms and game theory models, and optimization strategies are executed on the blockchain using smart contracts and multi-agent systems to achieve a comprehensive assessment of carbon emissions; the present invention greatly improves the real-time, accuracy and adaptability of the assessment, and provides a scientific basis for the low-carbon management of rural land.

[0006] The rural land spatial carbon emission assessment method includes the following steps:

[0007] Collect environmental data and land use imagery data in rural areas through distributed sensor networks and UAV remote sensing technology, denoise and format the data to generate a comprehensive initial dataset;

[0008] Performing data cleaning, standardization and feature extraction on the comprehensive initial data set, selecting key features using an adaptive variational autoencoder to generate a selected feature data set; and fusing the selected feature data set with historical carbon emission records and socio-economic data to form a comprehensive feature data set;

[0009] Based on graph neural network and Bayesian optimization combined with generative adversarial network, dynamic carbon emission evaluation and prediction are performed on the comprehensive feature data set to generate prediction results and uncertainty quantification data;

[0010] Optimize land use layout through multi-objective genetic algorithms and game theory models, use smart contracts and multi-agent systems to collaboratively execute optimization plans, and adjust and generate optimization strategies in real time;

[0011] Use optimization strategies to conduct a comprehensive carbon emission assessment of rural land use space, calculate the carbon emission intensity and total amount of each land use type, and generate an assessment report.

[0012] Preferably, data collection is performed through a wireless sensor network, wherein the network nodes are distributed in rural areas to collect environmental parameters in real time, the environmental parameters including temperature, humidity and soil content; the data is denoised to eliminate external noise and formatted to ensure data consistency and availability, thereby generating preliminary processed environmental data.

[0013] Preferably, the UAV remote sensing technology includes using multispectral and thermal imaging cameras to acquire land use images, wherein the images are geometrically corrected to eliminate lens distortion, and then image stitching is performed to form a continuous high-resolution land use map to generate pre-processed remote sensing image data.

[0014] Preferably, the data fusion step includes: using an adaptive variational autoencoder to extract features from a comprehensive initial feature data set, and selecting important features with high correlation therefrom to generate a selected feature data set; fusing the selected feature data set with historical carbon emission records and socioeconomic data, identifying patterns and structures in the data through topological data analysis, and forming a comprehensive feature data set.

[0015] Preferably, the dynamic evaluation step includes: applying a graph neural network to convert the comprehensive feature data set into a graph structure to generate node embedding data; dynamically evaluating the node embedding data in combination with a Bayesian optimization algorithm to form a preliminary prediction result, wherein the node embedding data is calculated by the following formula:

[0016]

[0017] in, Represents the embedding vector of node v at layer k; Represents the set of neighbor nodes of node v; d u and d v are the degrees of nodes u and v respectively; W (k) is the weight matrix of the kth layer; σ is the activation function.

[0018] Preferably, adversarial training is performed in combination with a generative adversarial network to form an adversarial prediction model, which is used to generate prediction results and uncertainty quantification data, wherein the uncertainty quantification estimates the confidence interval of the prediction result through a mixed Gaussian process, and the confidence interval is calculated by the following formula:

[0019]

[0020] Among them, CI is the confidence interval; is the predicted value; z is the critical value of the standard normal distribution; σ is the predicted standard deviation.

[0021] Preferably, the adversarial training process uses an adaptive learning rate, which is dynamically adjusted according to changes in the loss function.

[0022] Preferably, the optimization decision step includes: optimizing the land use layout through a multi-objective genetic algorithm, wherein the multi-objective genetic algorithm initializes a set of candidate solutions and evaluates them through a fitness function, and continuously generates a new generation of candidate solutions through selection, crossover and mutation operations; combining a game theory model to simulate the strategic interaction between different stakeholders to identify the optimal land use strategy, and executing the optimization plan in real time through smart contracts and multi-agent systems.

[0023] Preferably, the optimization is evaluated by a multi-objective function, which is defined as a weighted combination of economic benefits, carbon emissions and ecological benefits, and the optimization target value is calculated by the following formula:

[0024] F=α·R-β·E+γ·S

[0025] Among them, F is the optimization target value, R is the economic benefit, E is the carbon emission, S is the ecological benefit, and α, β, and γ are weight coefficients.

[0026] Preferably, the smart contract is combined with the multi-agent system and executed on a blockchain platform, wherein the smart contract automatically executes the optimization plan and dynamically adjusts the optimization strategy according to the real-time feedback data provided by the sensor network.

[0027] Compared with the prior art, the advantages and beneficial effects of the present invention are:

[0028] The present invention realizes the real-time collection and processing of environmental data through distributed sensor networks and UAV remote sensing technology, which can dynamically reflect the immediate impact of land use changes on carbon emissions and significantly improve the real-time and accuracy of data;

[0029] The present invention uses adaptive variational autoencoders and topological data analysis to achieve accurate extraction and fusion of feature data, enhance the prediction ability of the evaluation model, and accurately identify the impact of key factors on carbon emissions;

[0030] The present invention realizes dynamic assessment and prediction of carbon emissions through graph neural network and Bayesian optimization combined with generative adversarial network, which can provide accurate carbon emission prediction results and uncertainty quantification data in complex scenarios and support multi-objective optimization decision-making;

[0031] The present invention optimizes land use layout through multi-objective genetic algorithms and game theory models, realizes the rational allocation and utilization of land resources, achieves the optimal balance among multiple stakeholders, and promotes sustainable development;

[0032] The present invention realizes the automatic management and dynamic adjustment of optimization schemes through the execution of smart contracts and multi-agent systems on the blockchain platform, improves the transparency and flexibility of scheme execution, and ensures the effective implementation of real-time optimization strategies;

[0033] The present invention conducts carbon emission assessment on rural land through a comprehensive optimization strategy, realizes accurate calculation of carbon emission intensity and total amount of each land use type, provides decision makers with detailed assessment reports and scientific guidance, and supports the implementation of low-carbon sustainable development strategies. BRIEF DESCRIPTION OF THE DRAWINGS

[0034] Figure 1 It is a schematic diagram of the process of the present invention;

[0035] Figure 2 It is a flow chart of data collection and processing in the present invention;

[0036] Figure 3 It is a flow chart of feature extraction and fusion in the present invention;

[0037] Figure 4 This is a flowchart for optimizing and executing the present invention. DETAILED DESCRIPTION

[0038] Hereinafter, embodiments of the present disclosure will be described with reference to the accompanying drawings. However, it should be understood that these descriptions are exemplary only and are not intended to limit the scope of the present disclosure. In the following detailed description, for ease of explanation, many specific details are set forth to provide a comprehensive understanding of the embodiments of the present disclosure. However, it is obvious that one or more embodiments may also be implemented without these specific details. In addition, in the following description, descriptions of known structures and technologies are omitted to avoid unnecessary confusion of the concepts of the present disclosure.

[0039] The terms used herein are only for describing specific embodiments and are not intended to limit the present disclosure. The terms "comprise", "include", etc. used herein indicate the existence of the features, steps, operations and / or components, but do not exclude the existence or addition of one or more other features, steps, operations or components.

[0040] All terms (including technical and scientific terms) used herein have the meanings commonly understood by those skilled in the art, unless otherwise defined. It should be noted that the terms used herein should be interpreted as having a meaning consistent with the context of this specification, and should not be interpreted in an idealized or overly rigid manner.

[0041] like Figure 1-Figure 2 As shown in the figure, the rural land spatial carbon emission assessment method includes the following steps:

[0042] Collect environmental data and land use imagery data in rural areas through distributed sensor networks and UAV remote sensing technology, denoise and format the data to generate a comprehensive initial dataset;

[0043] Preferably, data collection is performed through a wireless sensor network, wherein the network nodes are distributed in rural areas to collect environmental parameters in real time, the environmental parameters including temperature, humidity and soil content; the data is denoised to eliminate external noise and formatted to ensure data consistency and availability, thereby generating preliminary processed environmental data.

[0044] Data collection is performed through a distributed wireless sensor network, where these sensor nodes are deployed in key areas of the countryside to monitor environmental parameters such as temperature, humidity, and soil content in real time. Wireless sensor networks have the characteristics of low power consumption and efficient communication, making them suitable for long-term environmental monitoring in rural areas. The data collected by these sensors in real time are denoised to remove the noise generated by environmental interference during the collection process, thereby improving the accuracy and reliability of the data. In addition, formatting ensures the compatibility and consistency of data from different sources, laying the foundation for further analysis of the data. After these processing steps, preliminary processed environmental data are finally generated, which will provide direct support for the construction of a comprehensive initial dataset.

[0045] UAV remote sensing technology acquires high-resolution land use image data by carrying multispectral and thermal imaging cameras. UAVs can flexibly fly at different altitudes and areas to quickly capture land use conditions over large areas. The acquired image data is geometrically corrected to eliminate lens distortion, and continuous high-resolution maps are generated through image stitching technology. These image data provide visual and data support for analyzing land use changes and determining the carbon emission intensity of land use types.

[0046] Through the above methods, the generation of a comprehensive initial dataset integrates multimodal data in rural areas, providing a panoramic perspective and accurate data basis for assessing carbon emissions. This dataset not only contains detailed records of current environmental conditions and land use, but also reveals potential land use change trends and their impact on carbon emissions through subsequent analysis.

[0047] For example, in a specific embodiment, the nodes of the sensor network can be deployed around and in the central area of ​​a typical rural farmland to monitor changes in temperature and humidity to assess the contribution of soil respiration to carbon emissions. At the same time, drones monitor crop growth and farmland use patterns through regular flights to determine the impact of different crops and management practices on carbon emissions. In this case, the combination of environmental data provided by sensors and image data provided by drones can not only help identify high-carbon emission areas, but also recommend best management practices to reduce carbon footprints by analyzing the impact of different crop management methods on carbon emissions.

[0048] In summary, through the combined application of advanced sensor networks and remote sensing technology, this invention effectively collects and processes rural environmental and land use data, forms a comprehensive initial data set, and provides a solid foundation for subsequent carbon emission assessment. The effect of this process significantly improves the accuracy of the assessment and the ability to respond to dynamic changes in rural land carbon emissions, enabling decision makers to make more scientific planning and management decisions based on data.

[0049] Preferably, the UAV remote sensing technology includes using multispectral and thermal imaging cameras to acquire land use images, wherein the images are geometrically corrected to eliminate lens distortion, and then image stitching is performed to form a continuous high-resolution land use map to generate pre-processed remote sensing image data.

[0050] In this invention, UAV remote sensing technology plays a key role, and its core lies in the use of multispectral and thermal imaging cameras to obtain high-resolution land use images. These images provide rich data information for evaluating the impact of land use patterns and changes on carbon emissions. Specifically, drones can fly flexibly in rural areas, and the multispectral cameras they carry can capture spectral information of different wavelengths, which can reveal different properties of the land surface and vegetation status. Thermal imaging cameras are used to detect changes in surface temperature and provide data support for analyzing surface heat radiation and its potential contribution to carbon emissions.

[0051] The acquired image data is first processed through a geometric correction step to eliminate geometric distortion caused by lens distortion and changes in the flight attitude of the drone. This step ensures the spatial accuracy of the image, so that each pixel can be accurately mapped to the geographic coordinates, ensuring the accuracy of subsequent analysis. The geometrically corrected image can accurately reflect the terrain characteristics and land use conditions, which is the basis for land use change analysis and carbon emission estimation.

[0052] The geometrically corrected images need to be stitched to form a continuous high-resolution land use map. Image stitching is the process of seamlessly integrating adjacent image segments, which can cover large rural areas and provide panoramic land use information. During the stitching process, there may be differences in brightness and color difference in the edge areas of the image, and image processing algorithms are usually required to perform color balance and brightness adjustment to ensure the overall consistency of the stitched image.

[0053] The generated preprocessed remote sensing image data forms a high-precision, continuous land use map through the above processing steps. These maps not only show the current land use situation, but can also be used to identify and monitor the changing trends of land use, thereby assessing the potential impact of these changes on carbon emissions.

[0054] For example, in practical applications, drones can regularly conduct remote sensing monitoring of a rural area to track crop planting cycles and land use changes. Multispectral images can help identify different crop types and their growth conditions, while thermal imaging data can identify soil moisture changes and heat distribution. This information helps analyze the impact of different agricultural management practices on carbon emissions. By regularly comparing land use maps of different periods, the patterns of land use changes can be discovered and their impact on carbon emissions can be inferred.

[0055] The application of UAV remote sensing technology has significantly improved the efficiency and accuracy of carbon emission assessment of rural land use. Through high-resolution multispectral and thermal imaging data, this technical means can describe land use characteristics in detail, provide accurate input data for carbon emission models, and help decision makers better manage land resources and formulate low-carbon development strategies.

[0056] like Figure 3 As shown, the comprehensive initial data set is cleaned, standardized and feature extracted, and an adaptive variational autoencoder is used to select key features to generate a selected feature data set; and the selected feature data set is fused with historical carbon emission records and socio-economic data to form a comprehensive feature data set;

[0057] Preferably, the data fusion step includes: using an adaptive variational autoencoder to extract features from a comprehensive initial feature data set, and selecting important features with high correlation therefrom to generate a selected feature data set; fusing the selected feature data set with historical carbon emission records and socioeconomic data, identifying patterns and structures in the data through topological data analysis, and forming a comprehensive feature data set.

[0058] In the present invention, data cleaning, standardization and feature extraction of the comprehensive initial data set is one of the key steps, the purpose of which is to ensure the accuracy and availability of the data and provide high-quality input for subsequent analysis and evaluation. In this step, data cleaning is first performed to remove noise and outliers to improve the accuracy and consistency of the data. Next, through standardization, the data is scaled to a uniform scale to eliminate the dimensional differences between different data sources, thereby improving the learning ability of the model.

[0059] Feature extraction is an important part of the data processing process. In the present invention, an adaptive variational autoencoder (VAE) is used to extract features from the comprehensive initial data set. The adaptive variational autoencoder is a deep learning model, the core of which is to learn the latent representation of data through an encoder-decoder structure to identify important features in the data. Compared with traditional feature extraction methods, VAE can adaptively select features and has greater flexibility and accuracy. Through the feature extraction of VAE, features that are highly correlated with carbon emissions can be selected from the comprehensive initial data set to generate a selected feature data set, which provides key information input for subsequent evaluations.

[0060] After the selected feature dataset is generated, it is fused with historical carbon emission records and socioeconomic data to form a comprehensive feature dataset. This process is carried out through topological data analysis. Topological data analysis is a technique for analyzing multidimensional data structures. It can reveal potential patterns and structures in the data and help identify the complex relationship between rural land use and carbon emissions. Through data fusion and topological analysis, the comprehensive feature dataset can reflect the multi-level information of rural areas and provide a more comprehensive perspective for carbon emission assessment.

[0061] For example, in one embodiment, it is assumed that the comprehensive initial data set contains land use image data and real-time environmental monitoring data in different seasons. The features extracted by the VAE model may include crop growth characteristics in a specific season, changes in land use types, and changing trends in climate conditions. These features are combined with historical carbon emission data and local economic activity data through fusion to form a comprehensive feature data set. Through topological analysis, the data set can reveal the impact of specific crop planting patterns on carbon emissions, and how changes in socio-economic activities affect land use and its carbon emission characteristics.

[0062] This data processing step significantly improves the accuracy and in-depth analysis capabilities of carbon emission assessments for rural land use. Through the combined application of adaptive variational autoencoders and topological data analysis, the present invention can effectively extract and fuse multi-level features from complex data sets, thereby providing strong support for carbon emission assessments in rural areas. This not only helps to assess current carbon emission levels, but also identifies key factors that affect carbon emissions, providing a scientific basis for decision makers to formulate sustainable land management strategies.

[0063] like Figure 4 As shown, based on graph neural network and Bayesian optimization combined with generative adversarial network, dynamic carbon emission evaluation and prediction are performed on the comprehensive feature data set to generate prediction results and uncertainty quantification data;

[0064] Preferably, the dynamic evaluation step includes: applying a graph neural network to convert the comprehensive feature data set into a graph structure to generate node embedding data; dynamically evaluating the node embedding data in combination with a Bayesian optimization algorithm to form a preliminary prediction result, wherein the node embedding data is calculated by the following formula:

[0065]

[0066] in, Represents the embedding vector of node v at layer k; Represents the set of neighbor nodes of node v; d u and d v are the degrees of nodes u and v respectively; W (k) is the weight matrix of the kth layer; Λ is the activation function.

[0067] In this paper, the key analysis step is to use Graph Neural Network (GNN) and Bayesian optimization combined with Generative Adversarial Network (GAN) to dynamically evaluate and predict the comprehensive feature data set. This step aims to capture the complex dynamic relationship between land use patterns, environmental factors and their impact on carbon emissions, and make accurate carbon emission predictions on this basis.

[0068] Graph neural network is a deep learning model that can process graph structured data. In the present invention, GNN is used to convert the comprehensive feature data set into a graph structure, where each node represents a land use unit or a specific area, and the edge represents the spatial or functional association between these units. In this way, GNN can effectively capture the complex interactive relationship between land use. Node embedding data is the feature representation of each node after GNN encodes it. These representations are iteratively updated through the network hierarchy, so that each node can obtain the information of its neighboring nodes, thereby reflecting the dynamic characteristics of the entire land use network.

[0069] The above formula describes how to use the information of neighboring nodes to update the embedding vector of each node, so that each node not only contains its own information, but also comprehensively considers the characteristics of its neighborhood. This information aggregation mechanism enables GNN to perform well when processing data with complex dependencies.

[0070] After the node embedding data is generated, it is dynamically evaluated in combination with Bayesian optimization. Bayesian optimization is a strategy for global optimization that can efficiently search for optimal solutions in high-dimensional space. In the present invention, Bayesian optimization is used to adjust the parameters and model structure of the graph neural network to maximize the accuracy and robustness of the prediction. Through this optimization process, the system can accurately predict carbon emissions in a dynamic environment.

[0071] Generative adversarial networks are used to further enhance the robustness and predictive power of the model. GAN consists of a generator and a discriminator. The generator tries to create synthetic data similar to real data, while the discriminator learns to distinguish between real data and synthetic data. Through this adversarial training, the model can better cope with the noise and uncertainty of the data, thereby improving the accuracy and stability of the prediction results.

[0072] For example, in a specific embodiment, it is assumed that the impact of different land use types (such as farmland, forest land, and building land) on carbon emissions in a rural area needs to be evaluated. By constructing the graph structure data of the area, GNN is applied to obtain the node embedding of each land use. These embedding vectors reflect the land use characteristics of each area and its relationship with neighboring areas. The model parameters are adjusted through Bayesian optimization to finally form a preliminary carbon emission prediction result. On this basis, GAN generates diversified sample data to further improve the model's adaptability to various changing scenarios and ensure the reliability of the prediction results.

[0073] In summary, through the application of graph neural network and Bayesian optimization combined with generative adversarial network, this invention can make full use of the spatial and functional relationship of land use data when evaluating carbon emissions from rural land, and provide accurate carbon emission prediction and uncertainty quantification. This combination not only enhances the prediction ability of the model, but also provides data support for optimizing land use strategies, helping decision makers make more sustainable management decisions.

[0074] Preferably, adversarial training is performed in combination with a generative adversarial network to form an adversarial prediction model, which is used to generate prediction results and uncertainty quantification data, wherein the uncertainty quantification estimates the confidence interval of the prediction result through a mixed Gaussian process, and the confidence interval is calculated by the following formula:

[0075]

[0076] Among them, CI is the confidence interval; is the predicted value; z is the critical value of the standard normal distribution; σ is the predicted standard deviation. This formula represents the range of the predicted results, and the probability that the predicted value falls within this interval at a certain confidence level.

[0077] In the present invention, the Generative Adversarial Network (GAN) is used for adversarial training to form an adversarial prediction model, which is a key step to improve the accuracy and reliability of prediction results. The core of the Generative Adversarial Network is to improve the model's predictive ability and adaptability to data uncertainty through the adversarial process between the generator and the discriminator.

[0078] In the present invention, the generator of GAN attempts to generate synthetic data similar to real data, while the discriminator learns how to distinguish between real data and generated data. Through this adversarial training process, the model can learn subtle patterns and features in the data, thereby improving the robustness of the prediction model. The adversarial prediction model is thus formed, which can maintain a high prediction accuracy in the face of noise and outliers.

[0079] Generative adversarial networks are particularly suitable for processing complex and diverse data sets, such as different land use patterns in rural land and their corresponding carbon emission characteristics. In this application scenario, GAN can help the model learn diverse carbon emission patterns and trends by generating land use data under different scenarios, so as to better cope with the uncertainties in the real world.

[0080] An important aspect of the prediction results is the uncertainty quantification, that is, the estimation of the prediction accuracy. In the present invention, uncertainty quantification is achieved by a mixture of Gaussian processes (GP). Mixture of Gaussian processes is a powerful statistical tool that can capture uncertainty in complex data and provide accurate confidence intervals.

[0081] By estimating the confidence interval of the prediction results through the mixed Gaussian process, the present invention not only provides the prediction value, but also quantifies the uncertainty of the prediction, providing a basis for risk assessment for decision makers. For example, in practical applications, suppose we use an adversarial prediction model to predict the carbon emissions of a certain region in a specific season. By generating data under different scenarios, GAN helps the model grasp more change patterns, while the mixed Gaussian process provides a confidence interval for each prediction result, showing the range of changes in the prediction value at different confidence levels. In this way, decision makers can make more robust land management decisions based on different confidence levels.

[0082] Combining the application of generative adversarial networks and mixed Gaussian processes, this paper not only improves the prediction accuracy and stability of the model, but also provides richer information for the interpretation of prediction results through uncertainty quantification. This method effectively supports the scientific management of rural carbon emissions and the formulation of low-carbon development strategies.

[0083] Preferably, the adversarial training process uses an adaptive learning rate, which is dynamically adjusted according to changes in the loss function.

[0084] In the present invention, the use of adaptive learning rate in the adversarial training process is a strategy to improve model training efficiency and prediction accuracy. Adaptive learning rate refers to dynamically adjusting the learning rate according to the change of the loss function during the training process in order to more effectively update the weight parameters of the model.

[0085] In adversarial training, the generator and discriminator of the Generative Adversarial Network (GAN) improve their respective performance through continuous adversarial iterations. The generator tries to generate realistic synthetic data, while the discriminator tries to identify the difference between these synthetic data and real data. In order to maintain stability and efficiency in this complex training process, the introduction of adaptive learning rate is particularly important.

[0086] The core principle of adaptive learning rate is to dynamically adjust the learning rate according to the change of loss function. When the loss function changes greatly, it means that the update direction of the model parameters is not clear enough. At this time, reducing the learning rate can avoid excessive adjustment caused by large step size; when the loss function changes tend to be stable, increasing the learning rate can accelerate the convergence process and improve training efficiency. This dynamic adjustment mechanism makes the GAN training process more flexible and robust, and helps to overcome the common oscillation and instability problems in the training process.

[0087] In the specific application of rural land carbon emission assessment, the use of adaptive learning rate can help the model adapt to complex environmental data changes more quickly and improve the ability to capture the dynamic relationship between land use patterns and carbon emissions. For example, when simulating land use and carbon emission data in a certain area, the complexity of the data may make it difficult for the model to find a stable convergence path in the initial stage. Through adaptive learning rate, the model can reduce the learning rate in the early stage to fine-tune the parameters, gradually enter a stable state, and then increase the learning rate to converge quickly, ultimately achieving higher prediction accuracy and better model stability.

[0088] The application of adaptive learning rate in the adversarial training process significantly improves the training efficiency and effect of the model. By dynamically adjusting the learning rate, the present invention can more flexibly respond to different training stages and data complexity, ensuring that the generative adversarial network can effectively learn valuable features from the data. This not only improves the accuracy of carbon emission assessment of rural land, but also provides more reliable decision support for optimizing land management strategies.

[0089] Optimize land use layout through multi-objective genetic algorithms and game theory models, use smart contracts and multi-agent systems to collaboratively execute optimization plans, and adjust and generate optimization strategies in real time;

[0090] Preferably, the optimization decision step includes: optimizing the land use layout through a multi-objective genetic algorithm, wherein the multi-objective genetic algorithm initializes a set of candidate solutions and evaluates them through a fitness function, and continuously generates a new generation of candidate solutions through selection, crossover and mutation operations; combining a game theory model to simulate the strategic interaction between different stakeholders to identify the optimal land use strategy, and executing the optimization plan in real time through smart contracts and multi-agent systems.

[0091] In this invention, optimizing land use layout through multi-objective genetic algorithm and game theory model is an important link to achieve low-carbon development and sustainable management. The optimization process aims to balance multiple objectives such as environment, economy and society, and realize efficient allocation and utilization of land resources through intelligent technology.

[0092] Multi-objective genetic algorithm is an evolutionary computing technology dedicated to solving complex optimization problems. Its core principle is to simulate the process of natural selection and biological evolution, and continuously improve candidate solutions through genetic operations (such as selection, crossover, and mutation). In the present invention, the multi-objective genetic algorithm is used to optimize land use layout. Specifically, the algorithm first initializes a set of candidate solutions, each of which represents a possible land use configuration. These solutions are evaluated through a fitness function, which is usually measured based on indicators such as carbon emissions, economic benefits, and ecological protection. The algorithm gradually evolves a set of optimized land use layout solutions by selecting excellent candidate solutions, cross-combining to generate new solutions, and introducing mutations to maintain diversity.

[0093] In order to better reflect the decision-making process in the real world, game theory models are introduced to simulate the strategic interactions between different stakeholders. Game theory provides a mathematical framework for analyzing competition and cooperation strategies, which can help identify the best strategy combination for different entities (such as farmers, governments, and enterprises) in land use decision-making. By simulating the strategic interactions between these entities, the model can identify the equilibrium solution under multi-objective optimization, that is, the optimal land use strategy.

[0094] At the optimization scheme execution level, the combination of smart contracts and multi-agent systems provides technical support for real-time dynamic adjustments. Smart contracts are automated protocols based on blockchain technology that can automatically execute contract terms when specific conditions are met. In the present invention, smart contracts are used to automatically execute optimization schemes to ensure that the decisions of various stakeholders can be implemented efficiently and fairly. The multi-agent system consists of a group of autonomous and collaborative agents, each of which can represent a decision-making subject or a specific land use unit. Through the multi-agent system, each agent can respond to environmental changes and policy adjustments in real time under the framework of smart contracts, thereby realizing dynamic optimization strategy adjustments.

[0095] For example, in one embodiment, suppose we need to optimize land use in a rural area to achieve the goals of reducing carbon emissions, improving economic benefits, and protecting ecology. The multi-objective genetic algorithm first generates different land use plans and evaluates them through fitness indicators such as carbon emission intensity, economic output, and ecological impact. The game theory model simulates the interaction between local governments, farmers, and environmental protection organizations to identify the optimal plan for balancing interests. The plan is automatically implemented through smart contracts, and the multi-agent system adjusts the strategy based on real-time feedback during the implementation of the plan to cope with uncertainties such as climate change or market demand fluctuations.

[0096] In summary, through the combination of multi-objective genetic algorithm and game theory model, as well as the execution of smart contracts and multi-agent systems, the present invention achieves the optimization of rural land use layout. This method not only improves the scientificity and accuracy of carbon emission assessment, but also promotes the sustainable use of land resources, and provides decision makers with an effective tool for rational planning under multi-objective constraints.

[0097] Preferably, the optimization is evaluated by a multi-objective function, which is defined as a weighted combination of economic benefits, carbon emissions and ecological benefits, and the optimization target value is calculated by the following formula:

[0098] F=α·R-β·E+γ·S

[0099] Among them, F is the optimization target value, R is the economic benefit, E is the carbon emission, S is the ecological benefit, and α, β, and γ are weight coefficients.

[0100] In this formula, economic benefits R are usually measured by indicators such as income and output value brought by land use. Carbon emissions E evaluates the greenhouse gas emissions brought by land use, and it is usually hoped to minimize them to reduce the negative impact on the environment. Ecological benefits S consider the contribution of land use to ecological indicators such as biodiversity, soil health and water resource protection.

[0101] The weight coefficients α, β, and γ are key factors in the multi-objective function. They are used to adjust the relative importance of each objective in the optimization process. For example, the larger the value of α, the higher the priority of economic benefits in the optimization decision; on the contrary, if you want to pay more attention to the carbon emission reduction effect, you can increase the weight of β, and so on. These weight coefficients are usually determined by policymakers or stakeholders based on actual needs and strategic goals.

[0102] In practical applications, multi-objective functions can help decision makers weigh the contradictions between different objectives. For example, in the land use optimization of a rural area, if the weight of α is large, the optimization process will tend to choose land use options that can bring higher economic returns, such as using more land for the cultivation of high-value crops. However, such decisions may lead to increased carbon emissions and reduced ecological benefits. In order to achieve a more balanced development, decision makers may adjust the values ​​of β and γ so that carbon emissions and ecological benefits are given higher priority in decision-making, and ultimately choose a more sustainable land use strategy.

[0103] A specific implementation may include optimizing the configuration of an agricultural land, taking into account the economic benefits of food production, carbon emissions during farmland management, and the ecological benefits of wetlands around the farmland. By adjusting the weight coefficients in the multi-objective function, decision makers can find an optimal land use plan that can maintain food production, effectively reduce carbon emissions, and protect wetland ecology.

[0104] In summary, the multi-objective function plays a vital role in this invention. It not only provides a systematic way to comprehensively evaluate the advantages and disadvantages of different land use options, but also helps decision makers find the optimal balance between multiple objectives, thereby supporting the low-carbon sustainable development strategy of rural areas.

[0105] Preferably, the smart contract is combined with the multi-agent system and executed on a blockchain platform, wherein the smart contract automatically executes the optimization plan and dynamically adjusts the optimization strategy according to the real-time feedback data provided by the sensor network.

[0106] In the present invention, the execution of smart contracts combined with multi-agent systems on the blockchain platform provides an efficient and transparent management mechanism for land use optimization. The core of the present invention is to utilize the distributed and tamper-proof characteristics of blockchain technology, combined with the automated execution capabilities of smart contracts and the flexibility of multi-agent systems, to achieve real-time dynamic adjustment of optimization schemes.

[0107] Smart contracts are automated protocols stored on the blockchain that do not require human intervention and can automatically execute related operations once preset conditions are met. In this invention, smart contracts are used to implement land use optimization strategies, such as allocating land resources, adjusting crop planting plans, or implementing environmental protection measures. This automated feature not only improves execution efficiency, but also ensures transparency and traceability of operations, helping to reduce human errors and corruption risks.

[0108] A multi-agent system consists of a group of collaborative agents that can represent different decision-making entities or land use units. Each agent collects and analyzes data within its jurisdiction and works together under the framework of smart contracts to achieve global optimization. The advantage of a multi-agent system lies in its distributed processing capabilities, which can quickly respond to environmental changes and policy adjustments, and provide real-time support for land use optimization.

[0109] The sensor network provides real-time feedback data for smart contracts and multi-agent systems. The network consists of various sensors deployed in rural areas to monitor environmental parameters such as soil moisture, temperature, air quality, etc. in real time. These data are aggregated and uploaded to the blockchain platform to ensure the integrity and authenticity of the data. Smart contracts dynamically adjust optimization strategies based on these real-time data. For example, when sensors detect that the soil moisture in a specific area drops to a certain threshold, smart contracts can automatically trigger irrigation strategies to ensure stable crop growing conditions.

[0110] In a specific embodiment, consider the integrated land use optimization problem of a rural community that includes farmland, orchards, and nature reserves. Smart contracts are programmed to automatically adjust land use strategies based on real-time environmental data and policy priorities. For example, when air quality sensors detect high concentrations of particulate matter in the air, smart contracts can reduce the frequency of mechanical operations on farmland to reduce dust. At the same time, the agents of the multi-agent system will assess the water needs of the orchard and adjust the irrigation plan in real time to avoid production losses due to drought.

[0111] In summary, the combination of smart contracts and multi-agent systems on the blockchain provides an advanced technical means for the spatial carbon emission assessment and optimization of rural land use. This solution significantly improves the flexibility and transparency of land management through automation and real-time data feedback, and provides strong support for achieving low-carbon and sustainable land use. This method is not only suitable for carbon emission assessment in rural areas, but also provides a reference for resource optimization management in other fields.

[0112] Use optimization strategies to conduct a comprehensive carbon emission assessment of rural land use space, calculate the carbon emission intensity and total amount of each land use type, and generate an assessment report.

[0113] In this invention, using optimization strategies to conduct a comprehensive carbon emission assessment of rural land is a key step to ultimately achieve land management optimization and low-carbon development. The core of this process is to comprehensively analyze the carbon emission characteristics of various land use types, calculate their carbon emission intensity and total amount, and then generate a detailed assessment report to provide a scientific basis for decision makers.

[0114] First, the application of optimization strategies is based on the previous multi-objective genetic algorithm, game theory model, and comprehensive decision-making of smart contracts and multi-agent systems. Through these optimization methods, the present invention can identify the best configuration schemes for different land use types in different situations, ensuring that carbon emissions are minimized while maximizing economic and ecological benefits. Specifically, land use types can include agricultural land, forest land, wetlands, construction land, etc., and each type will produce different carbon emissions during use and management.

[0115] The principle of assessing carbon emission intensity is to quantify the carbon emissions per unit area of ​​land in a specific time period. Through real-time monitoring data from sensor networks, combined with remote sensing images and historical records, the carbon emission intensity of each land use type can be accurately calculated. For example, the carbon emission intensity of farmland may be closely related to factors such as crop type, fertilizer application, and farming methods, while the carbon absorption of forest land is affected by tree species, coverage, and management measures.

[0116] The calculation of total carbon emissions is based on the area of ​​each land use type and its corresponding carbon emission intensity. This process takes into account the interactive effects of different land uses. For example, the presence of wetlands can partially offset the carbon emissions of surrounding farmland through the carbon sink effect.

[0117] When generating the assessment report, the present invention not only provides the carbon emission intensity and total amount of each land use type, but also predicts the potential carbon emission reduction effect under different optimization strategies. The assessment report is presented through visual means, such as charts and maps, to intuitively reflect the current status and future scenarios of carbon emissions of rural land.

[0118] For example, in a specific embodiment, assume that a rural area contains 1,000 hectares of agricultural land, 500 hectares of forest land, and 200 hectares of wetlands. Through optimization strategy analysis, after conservative farming and weight loss measures are adopted in farmland, its carbon emission intensity is reduced from 3.5 tons / hectare to 2.8 tons / hectare. By enhancing the carbon sequestration capacity of forest land, it is estimated that 5,000 tons of carbon dioxide can be absorbed each year. In this context, the assessment report will compare the changes in carbon emissions under different periods and different management strategies.

[0119] Using optimization strategies to conduct a comprehensive carbon emissions assessment of rural land can not only provide carbon emissions from current land use, but also provide scientific guidance for future land management strategies. This process helps to improve the carbon management capacity of rural areas and achieve sustainable development and low-carbon transformation goals. Through detailed assessment reports, decision makers can better understand the long-term impacts of different land use options and make wise resource allocation and policy formulation accordingly.

[0120] Those skilled in the art will appreciate that the embodiments of the present application may be provided as methods, systems or computer program products. Therefore, the present application may take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware.

[0121] The above are only embodiments of the present application and are not intended to limit the present application. For those skilled in the art, the present application may have various changes and variations. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application should be included in the scope of the claims of the present application.

Claims

1. A method for assessing spatial carbon emissions of rural land, characterized in that: The following steps are involved: Collect environmental data and land use imagery data in rural areas through distributed sensor networks and UAV remote sensing technology, denoise and format the data to generate a comprehensive initial dataset; Performing data cleaning, standardization and feature extraction on the comprehensive initial data set, selecting key features using an adaptive variational autoencoder to generate a selected feature data set; and fusing the selected feature data set with historical carbon emission records and socio-economic data to form a comprehensive feature data set; Based on graph neural network and Bayesian optimization combined with generative adversarial network, dynamic carbon emission evaluation and prediction are performed on the comprehensive feature data set to generate prediction results and uncertainty quantification data; Optimize land use layout through multi-objective genetic algorithms and game theory models, use smart contracts and multi-agent systems to collaboratively execute optimization plans, and adjust and generate optimization strategies in real time; Use optimization strategies to conduct a comprehensive carbon emission assessment of rural land use space, calculate the carbon emission intensity and total amount of each land use type, and generate an assessment report.

2. The method according to claim 1, characterized in that Data collection is performed through a wireless sensor network, wherein the network nodes are distributed in rural areas to collect environmental parameters in real time, including temperature, humidity and soil content; the data is denoised to eliminate external noise and formatted to ensure data consistency and availability, thereby generating preliminary processed environmental data.

3. The method according to claim 1, characterized in that The UAV remote sensing technology includes using multispectral and thermal imaging cameras to acquire land use images, wherein the images are geometrically corrected to eliminate lens distortion, and then image stitching is performed to form a continuous high-resolution land use map to generate pre-processed remote sensing image data.

4. The method according to claim 1, characterized in that: The data fusion step includes: using an adaptive variational autoencoder to extract features from a comprehensive initial feature data set, and selecting important features with high correlation to generate a selected feature data set; fusing the selected feature data set with historical carbon emission records and socioeconomic data, identifying patterns and structures in the data through topological data analysis, and forming a comprehensive feature data set.

5. The method according to claim 1, characterized in that The dynamic evaluation step includes: applying a graph neural network to convert the comprehensive feature data set into a graph structure to generate node embedding data; combining a Bayesian optimization algorithm to dynamically evaluate the node embedding data to form a preliminary prediction result, wherein the node embedding data is calculated by the following formula: in, Represents the embedding vector of node v at layer k; Represents the set of neighbor nodes of node v; d u and d v are the degrees of nodes u and v respectively; W (k) is the weight matrix of the kth layer; σ is the activation function.

6. The method according to claim 5, characterized in that Combined with the generative adversarial network for adversarial training, an adversarial prediction model is formed. The adversarial prediction model is used to generate prediction results and uncertainty quantification data. Among them, uncertainty quantification estimates the confidence interval of the prediction result through a mixed Gaussian process, and the confidence interval is calculated by the following formula: Among them, CI is the confidence interval; is the predicted value; z is the critical value of the standard normal distribution; σ is the predicted standard deviation.

7. The method according to claim 6, characterized in that The adversarial training process uses an adaptive learning rate, which is dynamically adjusted according to changes in the loss function.

8. The method according to claim 1, characterized in that The optimization decision-making steps include: optimizing the land use layout through a multi-objective genetic algorithm, wherein the multi-objective genetic algorithm initializes a set of candidate solutions and evaluates them through a fitness function, and continuously generates a new generation of candidate solutions through selection, crossover and mutation operations; combining a game theory model to simulate the strategic interaction between different stakeholders to identify the optimal land use strategy, and executing the optimization plan in real time through smart contracts and multi-agent systems.

9. The method according to claim 8, characterized in that The optimization is evaluated by a multi-objective function, which is defined as a weighted combination of economic benefits, carbon emissions and ecological benefits. The optimization target value is calculated by the following formula: F=α·R-β·E+γ·S Among them, F is the optimization target value, R is the economic benefit, E is the carbon emission, S is the ecological benefit, and α, β, and γ are weight coefficients.

10. The method according to claim 8, characterized in that The smart contract is combined with the multi-agent system and executed on the blockchain platform, wherein the smart contract automatically executes the optimization plan and dynamically adjusts the optimization strategy according to the real-time feedback data provided by the sensor network.

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