Dynamic monitoring system for land resources

Abstract

The invention relates to the technical field of environment monitoring, in particular to a land resource dynamic monitoring system which comprises a resource sensing module, a feature extraction module, a land coverage classification module, a dynamic simulation module, a heat island effect analysis module, a decision optimization module, an information interpretation module and an early warning response module. According to the invention, the remote sensing technology and the ground monitoring network are adopted, real-time collection and synchronization of environmental perception data are realized, the principal component analysis and random forest method are applied to feature extraction, the efficiency and accuracy of feature screening are improved, the accuracy of land cover classification is improved, and the deep learning classification technology introduced by the convolutional neural network is improved. According to the method, a multi-agent simulation and system dynamics model is adopted, the practicality of land utilization change dynamic simulation is enhanced, a scientific basis is provided for a heat island effect identification and mitigation strategy by an urban climate model and a geographic space analysis method, and land planning and management decisions are optimized through integration of a multi-objective optimization algorithm and a decision support system.

Description

Technical Field

[0001] This invention relates to the field of environmental monitoring technology, and in particular to a dynamic monitoring system for land resources. Background Technology

[0002] The field of environmental monitoring technology focuses on the continuous or periodic observation and analysis of environmental elements to understand their current state and trends. This includes monitoring air, water, soil, biodiversity, and the environmental impacts of human activities such as urbanization, industrial production, and agriculture. Technological advancements have enabled monitoring methods to evolve from traditional sample collection and field surveys to real-time or near-real-time monitoring using remote sensing, GIS (Geographic Information Systems), satellite imagery, and sensor networks. These technologies provide high-resolution and multi-time-series data, helping scientists, policymakers, and the public to better understand environmental changes, thereby enabling effective resource management and the development of response measures.

[0003] The dynamic monitoring system for land resources is an important branch of environmental monitoring technology, focusing on monitoring and analyzing dynamic changes in land cover, land use, and land quality. The key objective of this system is to provide accurate and timely land resource information, helping decision-makers and managers understand the current status, trends, and potential problems of land resources. By monitoring land resources, sustainability can be assessed, land planning and management can be guided, resource allocation can be optimized, and problems such as land degradation and pollution can be prevented and mitigated. Effective dynamic monitoring of land resources is of great significance for protecting the ecological environment and promoting socio-economic development.

[0004] Traditional land resource monitoring systems typically rely on static data collection and processing methods, lacking real-time updates and synchronization capabilities, leading to reduced data timeliness and accuracy. The absence of efficient feature extraction and deep learning technologies limits the accuracy of classification and prediction, making it difficult to adapt to complex and ever-changing land use conditions. The lack of dynamic simulation and comprehensive analysis of the heat island effect restricts a deeper understanding of land use change and its environmental impact, resulting in a lack of scientific basis for planning and management decisions, inefficient resource allocation, and inaccurate environmental risk assessments. Furthermore, traditional systems exhibit limited capabilities in decision optimization and early warning response, lacking effective strategies to address rapid changes and potential risks, and are insufficient to meet the needs of modern land management. Summary of the Invention

[0005] The purpose of this invention is to address the shortcomings of existing technologies by proposing a dynamic land resource monitoring system.

[0006] To achieve the above objectives, the present invention adopts the following technical solution: the land resource dynamic monitoring system includes a resource perception module, a feature extraction module, a land cover classification module, a dynamic simulation module, a heat island effect analysis module, a decision optimization module, an information interpretation module, and an early warning response module;

[0007] The resource perception module is based on remote sensing technology and ground monitoring network. It uses time series analysis and data synchronization technology to collect and synchronize land, climate and ecological data in real time, and conduct preliminary quality assessment to generate environmental perception data.

[0008] The feature extraction module, based on environmental perception data, uses principal component analysis and random forest methods to perform feature screening and transformation, extract and identify key features affecting land cover and land use change, and evaluate the importance of features to generate a key feature set.

[0009] The land cover classification module uses a convolutional neural network for deep learning classification based on a key feature set to identify and classify land cover types. It also uses cross-validation to analyze the accuracy and stability of the classification and generate a land classification map.

[0010] The dynamic simulation module is based on the land classification map, uses multi-agent simulation and system dynamics model to simulate the dynamic process of land use change, analyze the land resource use efficiency and change trend, assess the environmental impact, and generate a land dynamic change simulation map.

[0011] The urban heat island effect analysis module, based on land dynamic change simulation maps and meteorological data, uses urban climate models and geospatial analysis methods to analyze the spatial distribution and temporal variation of the urban heat island effect, identify influencing factors, assess risk areas, propose mitigation strategies, and generate heat island effect analysis results.

[0012] The decision optimization module, based on the heat island effect analysis results and land dynamic change simulation map, adopts a multi-objective optimization algorithm and decision support system, and takes into account economic, social and environmental factors to formulate the optimal strategy for land planning and management, and conducts effect simulation and evaluation to generate an optimized decision support scheme.

[0013] The information interpretation module is based on an optimized decision support scheme and uses artificial intelligence interpretation technology. It integrates convolutional neural networks, recurrent neural networks and graph neural networks to interpret data, analyze trends and predict future changes, and optimize and adjust the results to generate comprehensive interpretation results.

[0014] The early warning and response module, based on comprehensive interpretation results and dynamic change assessment, adopts early warning models and emergency management strategies to assess and classify potential risks, design and implement early warning and response measures, and generate early warning signals and emergency response strategies.

[0015] As a further aspect of the present invention, the environmental perception data includes land type data, climate data, ecological data, and human activity data; the key feature set includes land type indicators, climate variability indicators, ecosystem service indicators, and population distribution indicators; the land classification map includes distribution areas and attribute information of multiple land cover types; the land dynamic change simulation map includes land use status change prediction and environmental impact assessment results; the heat island effect analysis results include heat island intensity distribution map, a list of key influencing factors, and a risk area map; the optimization decision support scheme includes land use optimization planning, resource allocation strategies, and environmental protection measures; and the comprehensive interpretation results include data interpretation results, trend prediction analysis, and strategy adjustment schemes.

[0016] As a further aspect of the present invention, the resource sensing module includes a remote sensing submodule, a ground monitoring submodule, a climate sensing submodule, and an ecological sensing submodule;

[0017] The remote sensing submodule is based on satellite images and uses spectral analysis algorithms to preprocess the satellite images, including radiometric correction and atmospheric correction. Then, it uses support vector machine classification and decision tree classification to extract and classify land cover features. It uses maximum likelihood classification to label multiple types of land features and generate a land type remote sensing map.

[0018] The ground monitoring submodule is based on land type remote sensing map, uses geographic information system, and uses the spatial analysis function of geographic information system to draw spatial distribution map of soil moisture. At the same time, it deploys wireless sensor network to monitor soil moisture in real time, collects land quality data and merges it with remote sensing map to generate land quality monitoring data.

[0019] The climate perception submodule is based on land quality monitoring data, uses meteorological models, numerical weather prediction models and climate statistics models to analyze regional climate change, and uses autoregressive moving average and seasonal decomposition trend and seasonal analysis to perform time series climate analysis, extract temperature, precipitation and wind speed information, and generate regional climate information maps.

[0020] The ecological perception submodule is based on regional climate information maps, uses ecological footprint analysis and biodiversity index calculation, calculates the regional ecological footprint using the global ecological footprint network method, and then assesses biodiversity through the Simpson diversity index and Shannon diversity index. It comprehensively assesses the health status and change trend of the ecosystem and generates ecosystem status results.

[0021] As a further aspect of the present invention, the feature extraction module includes a land feature extraction submodule, a climate feature extraction submodule, an ecological feature extraction submodule, and a socio-economic feature extraction submodule;

[0022] The land feature extraction submodule is based on the land type remote sensing map in the environmental perception data. It uses principal component analysis to extract spectral features from the remote sensing image. It reduces the data dimensionality by calculating the covariance matrix of the dataset and extracting key components to generate a land feature dataset.

[0023] The climate feature extraction submodule is based on the regional climate information map in the environmental perception data. It uses time series analysis to extract key climate change indicators, including average temperature, precipitation and wind speed, by analyzing the long-term trends and periodic patterns of historical climate data, and generates a climate feature dataset.

[0024] The ecological feature extraction submodule, based on the ecosystem status results in the environmental perception data, uses ecological model analysis methods to extract ecological features associated with land cover and use by evaluating the ecosystem's productivity, diversity, and stability indicators, and generates an ecological feature dataset.

[0025] The socioeconomic feature extraction submodule, based on human activity information in environmental perception data, uses statistical analysis and socioeconomic models to analyze population distribution, economic activities, and land policy data to extract key socioeconomic features reflecting the impact of human activities on land cover and use, including land use type, economic development level, and policies and regulations, and generates a socioeconomic feature dataset.

[0026] As a further aspect of the present invention, the land cover classification module includes a deep learning classification submodule, a map generation submodule, a classification accuracy evaluation submodule, and a classification result feedback submodule;

[0027] The deep learning classification submodule is based on a key feature set and uses a convolutional neural network to extract features from land cover images. Layer convolution operations extract low- to high-level features of the image in sequence, pooling layers reduce feature dimensionality and retain key information, and fully connected layers make classification decisions based on features to generate preliminary land classification results.

[0028] The map generation submodule uses a geographic information system to draw a land classification map based on the preliminary land classification results. The classification data is rasterized according to geographic coordinates, with each raster representing a land cover type, thus generating a land classification map.

[0029] The classification accuracy evaluation submodule is based on the land classification map and uses cross-validation and confusion matrix to evaluate the accuracy of the classification results. The stability and reliability of the model are tested by repeatedly training and validating the data in multiple parts. The confusion matrix shows the prediction of each category, evaluates the accuracy and error rate of classification, and generates accuracy evaluation results.

[0030] The classification result feedback submodule optimizes the CNN model based on the accuracy evaluation results using a machine learning feedback mechanism. It adjusts the layer parameters and learning rate in the CNN according to the accuracy evaluation results, and improves the generalization ability of the model through data augmentation, and regenerates the optimized land classification map.

[0031] As a further aspect of the present invention, the dynamic simulation module includes a system dynamics submodule, a multi-agent simulation submodule, a land use prediction submodule, and a dynamic change assessment submodule.

[0032] The system dynamics submodule is based on the land classification map and uses system dynamics to simulate land use dynamics. By establishing feedback loops about land resources, population and economic activity factors, it expresses the interaction between variables with difference equations or differential equations, performs continuous time simulation of stock and flow, describes the evolution of land use over time, and generates system dynamic simulation results.

[0033] The multi-agent simulation submodule simulates the behavior of land users based on the system's dynamic simulation results. The agents interact with each other according to their own goals, strategies and land use conditions, simulating the decision-making and interaction behaviors among multiple stakeholders, including farmers, developers and the government. By setting objective functions and behavioral rules for differentiated agents, the module simulates the dynamic reactions of various types of land users under resource constraints and policy changes, generating multi-agent interaction simulation results.

[0034] The land use prediction submodule is based on the results of multi-agent interaction simulation. It uses time series analysis and trend prediction to predict future land use changes. By analyzing historical land use data, it identifies trends and periodic patterns of land use changes and applies these patterns to future scenario predictions. By establishing models, it predicts land use at different time points or under different conditions in the future and generates land use trend prediction results.

[0035] The dynamic change assessment submodule assesses land use change and its environmental impact based on land use trend prediction results. It uses ecological footprint and ecosystem service value assessment methods to quantify the ecological and socio-economic impacts of land use change, analyzes the impact of land change on biodiversity, water resources, carbon storage environmental factors, and the potential impact of change on socio-economic conditions, and generates an environmental impact assessment of land dynamic change.

[0036] As a further aspect of the present invention, the heat island effect analysis module includes a climate simulation submodule, a spatial analysis submodule, a first risk assessment submodule, and a response strategy submodule;

[0037] The climate simulation submodule uses a numerical weather prediction model to simulate the climate conditions of urban areas based on land dynamic change simulation maps and meteorological data. By calculating the physical state of the atmosphere, it simulates the distribution of climate elements, including temperature, humidity, and wind speed, and generates an urban climate simulation map.

[0038] The spatial analysis submodule is based on the urban climate simulation map and uses geospatial analysis technology to depict the spatial distribution of the heat island effect. Through GIS technology including spatial interpolation and hotspot analysis, the climate simulation data is transformed into a spatial layer in the geographic information system to draw the intensity and range of the urban heat island effect and generate a heat island spatial distribution map.

[0039] The first risk assessment submodule is based on the spatial distribution map of the heat island, uses environmental risk assessment methods to assess the impact and risk level of the heat island effect, analyzes the population density, infrastructure sensitivity and ecological vulnerability of the heat island area, identifies risk areas and potential impacts, and generates heat island risk assessment results.

[0040] The aforementioned response strategy submodule, based on the heat island risk assessment results, uses urban planning and environmental management methods to formulate strategies to mitigate the heat island effect, analyzes the impact of urban layout, green coverage, and building materials, and proposes adjustment plans, including increasing urban green space, improving building design, and formulating heat island effect mitigation strategies.

[0041] As a further aspect of the present invention, the decision optimization module includes a strategy simulation submodule, a multi-objective optimization submodule, a decision analysis submodule, and a strategy evaluation submodule;

[0042] The strategy simulation submodule uses system simulation technology to simulate strategies based on the heat island effect analysis results and land dynamic change simulation map. This includes creating a rule-based model to simulate the implementation effects of multiple land planning and management strategies, and generating a strategy simulation analysis map by referring to economic, social and environmental factors.

[0043] The multi-objective optimization submodule is based on the strategy simulation analysis graph, uses multi-objective optimization algorithm to formulate the optimal strategy, sets the objective function of multiple strategies, including maximizing economic benefits, minimizing environmental impact and optimizing social benefits, and uses genetic algorithm or particle swarm optimization to balance the objective function and select the optimal strategy scheme.

[0044] The decision analysis submodule is based on optimized strategy schemes. It uses a decision support system to analyze the feasibility and impact of multiple strategy schemes. By integrating historical data, simulation results and expert opinions, it compares and analyzes the advantages and disadvantages of differentiated strategy schemes, as well as the impact of multiple schemes on the economy, society and environment, and generates decision analysis results.

[0045] The strategy evaluation submodule evaluates the established land planning strategies based on the decision analysis results using effect evaluation methods. It compares the changes in economic, social, and environmental indicators before and after the strategy implementation, and uses cost-benefit analysis and life cycle assessment to quantify the actual impact and lasting effects of the strategies, generating strategy effect evaluation results.

[0046] As a further aspect of the present invention, the information interpretation module includes a deep learning interpretation submodule, a multi-source data fusion submodule, a prediction trend analysis submodule, and a feedback learning adjustment submodule;

[0047] The deep learning interpretation submodule is based on an optimized decision support scheme and uses deep learning algorithms to extract features. It captures local features through convolutional layers, processes sequence data through recurrent layers to analyze temporal dynamics, processes non-Euclidean structure data through layers, performs in-depth analysis of data structure and correlation, performs pattern recognition, and generates data feature interpretation results.

[0048] The multi-source data fusion submodule integrates data based on the data feature interpretation results and uses a data fusion algorithm. It combines data features from multiple sources through feature-level fusion technology and integrates decision results from multiple decision items through decision-level fusion technology to generate a comprehensive data profile.

[0049] The predicted trend analysis submodule is based on comprehensive data profiling, uses time series analysis and machine learning algorithms to predict trends, captures the time dependence of data through an autoregressive model, identifies and utilizes the statistical regularity of historical data through a moving average model, analyzes and predicts data change trends, performs trend analysis, and generates trend prediction analysis results.

[0050] The feedback learning adjustment submodule optimizes the model based on the trend prediction analysis results using a feedback learning strategy. It updates the model by receiving new data points through online learning, incrementally adjusts the model to match data changes, adjusts the parameters and structure of the prediction model, performs continuous optimization and adaptive adjustment, and generates optimization adjustment interpretation results.

[0051] As a further aspect of the present invention, the early warning response module includes an early warning signal issuance submodule, a second risk assessment submodule, an emergency response plan submodule, and a response effect evaluation submodule;

[0052] The warning signal issuing submodule, based on comprehensive interpretation results and dynamic change assessment, uses a Bayesian network algorithm to identify potential risks, calculates the conditional probabilities between multiple risk factors, comprehensively analyzes and forms a risk map, formulates warning signal rules, executes risk level classification and warning signal initiation, and generates potential risk warning signals.

[0053] The second risk assessment submodule, based on potential risk warning signals, uses a neural network algorithm to conduct in-depth risk analysis, analyzes the characteristics and patterns of risk data, performs a comprehensive assessment of the likelihood and impact of risks, and generates a comprehensive risk assessment result.

[0054] The emergency response plan submodule, based on the comprehensive risk assessment results, uses fault tree analysis to plan emergency measures. By identifying and assessing potential failure points and their interrelationships, it plans resource allocation and action steps based on risk type and urgency, generating an emergency response strategy plan.

[0055] The response effectiveness evaluation submodule is based on emergency response strategy planning, uses utility analysis methods to monitor and evaluate the effectiveness, quantitatively analyzes the utility and value of response measures, performs an overall response efficiency and adaptability assessment, and generates a comprehensive response effectiveness evaluation.

[0056] Compared with the prior art, the advantages and positive effects of the present invention are as follows:

[0057] This invention utilizes remote sensing technology and ground monitoring networks to achieve real-time collection and synchronization of environmental perception data, improving data timeliness and accuracy. The application of principal component analysis and random forest methods in feature extraction enhances the efficiency and accuracy of feature selection, promoting the precision of land cover classification. Deep learning classification technology introduced by convolutional neural networks, along with multi-agent simulation and system dynamics models, enhances the practicality of dynamic land use change simulation and improves prediction reliability. Urban climate models and geospatial analysis methods provide a scientific basis for identifying and mitigating the urban heat island effect. The integration of multi-objective optimization algorithms and decision support systems optimizes land planning and management decisions, improving planning efficiency. Furthermore, the comprehensive information interpretation and trend prediction of different types of neural networks significantly enhances the accuracy and timeliness of early warning responses. Attached Figure Description

[0058] Figure 1 This is a system flowchart of the present invention;

[0059] Figure 2 This is a schematic diagram of the system framework of the present invention;

[0060] Figure 3 This is a flowchart of the resource perception module of the present invention;

[0061] Figure 4 This is a flowchart of the feature extraction module of the present invention;

[0062] Figure 5 This is a flowchart of the land cover classification module of the present invention;

[0063] Figure 6 This is a flowchart of the dynamic simulation module of the present invention;

[0064] Figure 7 This is a flowchart of the heat island effect analysis module of the present invention;

[0065] Figure 8 This is a flowchart of the decision optimization module of the present invention;

[0066] Figure 9 This is a flowchart of the information interpretation module of the present invention;

[0067] Figure 10 This is a flowchart of the early warning response module of the present invention. Detailed Implementation

[0068] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the invention.

[0069] In the description of this invention, it should be understood that the terms "length," "width," "upper," "lower," "front," "rear," "left," "right," "vertical," "horizontal," "top," "bottom," "inner," and "outer," etc., indicating orientation or positional relationships, are based on the orientation or positional relationships shown in the accompanying drawings and are only for the convenience of describing the invention and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, or be constructed and operated in a specific orientation, and therefore should not be construed as a limitation of the invention. Furthermore, in the description of this invention, "a plurality of" means two or more, unless otherwise explicitly specified.

[0070] Example 1

[0071] Please see Figures 1 to 2 The land resource dynamic monitoring system includes a resource perception module, a feature extraction module, a land cover classification module, a dynamic simulation module, a heat island effect analysis module, a decision optimization module, an information interpretation module, and an early warning response module.

[0072] The resource perception module is based on remote sensing technology and ground monitoring network. It uses time series analysis and data synchronization technology to collect and synchronize land, climate and ecological data in real time, and conduct preliminary quality assessment to generate environmental perception data.

[0073] The feature extraction module, based on environmental perception data, uses principal component analysis and random forest methods to screen and transform features, extract and identify key features affecting land cover and land use change, and evaluate the importance of features to generate a key feature set.

[0074] The land cover classification module uses a convolutional neural network for deep learning classification based on a key feature set to identify and classify land cover types. Cross-validation is used to analyze the accuracy and stability of the classification and generate a land classification map.

[0075] The dynamic simulation module is based on land classification maps and uses multi-agent simulation and system dynamics models to simulate the dynamic process of land use change, analyze the land resource use efficiency and change trend, assess environmental impact, and generate a land dynamic change simulation map.

[0076] The urban heat island effect analysis module is based on land dynamic change simulation map and meteorological data. It uses urban climate model and geospatial analysis methods to analyze the spatial distribution and temporal variation of urban heat island effect, identify influencing factors, assess risk areas, propose mitigation strategies, and generate heat island effect analysis results.

[0077] The decision optimization module, based on the analysis results of the heat island effect and the simulation map of land dynamic changes, adopts a multi-objective optimization algorithm and a decision support system, and takes into account economic, social and environmental factors to formulate the optimal strategy for land planning and management, and conducts effect simulation and evaluation to generate an optimized decision support scheme.

[0078] The information interpretation module is based on an optimized decision support scheme. It uses artificial intelligence interpretation technology, integrates convolutional neural networks, recurrent neural networks and graph neural networks to interpret data, analyze trends and predict future changes, and optimize and adjust the results to generate comprehensive interpretation results.

[0079] The early warning and response module, based on comprehensive interpretation results and dynamic change assessment, adopts early warning models and emergency management strategies to assess and classify potential risks, design and implement early warning and response measures, and generate early warning signals and emergency response strategies.

[0080] Environmental perception data includes land type data, climate data, ecological data, and human activity data. Key feature sets include land type indicators, climate variability indicators, ecosystem service indicators, and population distribution indicators. Land classification maps include distribution areas and attribute information for multiple land cover types. Land dynamic change simulation maps include land use status change predictions and environmental impact assessment results. Heat island effect analysis results include heat island intensity distribution maps, lists of key influencing factors, and risk area maps. Optimization decision support schemes include land use optimization planning, resource allocation strategies, and environmental protection measures. Comprehensive interpretation results include data interpretation results, trend prediction analysis, and strategy adjustment schemes.

[0081] The resource perception module utilizes remote sensing technology and a ground-based monitoring network to achieve real-time collection and synchronization of land, climate, and ecological data. Based on time-series analysis and data synchronization techniques, it ensures the timeliness and accuracy of the data. Remote sensing technology uses satellites, airships, and other vehicles to acquire high-resolution land images, while the ground-based monitoring network acquires real-time meteorological and ecological data through sensors, weather stations, and other equipment. This data includes land type, temperature, precipitation, vegetation index, etc. Through preliminary quality assessment, noise or outliers are removed, generating high-quality environmental perception data.

[0082] The feature extraction module, based on environmentally sensed data, employs principal component analysis (PCA) and random forest methods for feature selection and transformation. PCA is used for dimensionality reduction and to extract the main directions of change in the data, while random forest assesses the importance of features by constructing multiple decision trees. These two methods yield a key feature set, including land type indicators, climate variability indicators, ecosystem service indicators, and population distribution indicators. These feature sets reflect the key characteristics of land cover and land use change.

[0083] The land cover classification module utilizes a key feature set and employs a convolutional neural network (CNN) for deep learning classification. CNNs effectively process image data, learning feature hierarchies to identify and classify land cover types. Cross-validation is used to analyze classification accuracy and stability, generating a land classification map that includes the distribution areas and attribute information of multiple land cover types.

[0084] The dynamic simulation module, based on land classification maps, employs multi-agent simulation and system dynamics models to simulate the dynamic process of land use change. Multi-agent simulation considers the behaviors of different decision-makers, while the system dynamics model simulates the influencing factors and trends of land use change. By analyzing land resource use efficiency and trends, and assessing environmental impacts, a land dynamic change simulation map is generated, including land use status change predictions and environmental impact assessment results.

[0085] The urban heat island effect analysis module is based on land dynamics simulation maps and meteorological data, employing urban climate models and geospatial analysis methods. The urban climate model considers the formation mechanism of the urban heat island effect, while geospatial analysis methods are used to identify the spatial distribution and temporal variation of the heat island effect. By analyzing heat island intensity distribution maps, lists of key influencing factors, and risk area maps, mitigation strategies are proposed.

[0086] The decision optimization module, based on the analysis results of the urban heat island effect and the simulation map of land dynamic changes, uses multi-objective optimization algorithms and a decision support system to formulate optimal strategies for land planning and management. Taking into account economic, social, and environmental factors, it generates optimized decision support schemes, including land use optimization planning, resource allocation strategies, and environmental protection measures, and performs effect simulations and evaluations.

[0087] The information interpretation module, based on an optimized decision support scheme, employs artificial intelligence interpretation technology, integrating convolutional neural networks, recurrent neural networks, and graph neural networks for data interpretation. It analyzes trends and predicts future changes, optimizing and adjusting the results. Finally, it generates comprehensive interpretation results, including data interpretation findings, trend prediction analysis, and strategy adjustment plans.

[0088] The early warning and response module, based on comprehensive interpretation of results and dynamic change assessment, employs early warning models and emergency management strategies to evaluate and classify potential risks. It designs and implements early warning and response measures, generates early warning signals and emergency response strategies, and improves the ability to identify and respond to potential risks.

[0089] Please see Figure 3 The resource perception module includes a remote sensing submodule, a ground monitoring submodule, a climate perception submodule, and an ecological perception submodule.

[0090] The remote sensing submodule is based on satellite imagery and uses spectral analysis algorithms to preprocess the satellite imagery, including radiometric and atmospheric correction. Then, it uses support vector machine classification and decision tree classification to extract and classify land cover features. It uses maximum likelihood classification to label multiple land cover types and generate a land type remote sensing map.

[0091] The ground monitoring submodule is based on land type remote sensing maps and uses a geographic information system. It uses the spatial analysis function of the geographic information system to draw a spatial distribution map of soil moisture. At the same time, it deploys a wireless sensor network to monitor soil moisture in real time, collects land quality data and merges it with the remote sensing map to generate land quality monitoring data.

[0092] The climate perception submodule is based on land quality monitoring data, uses meteorological models, numerical weather prediction models and climate statistics models to analyze regional climate change, and uses autoregressive moving average and seasonal decomposition trend and seasonal analysis to conduct time series climate analysis, extract temperature, precipitation and wind speed information, and generate regional climate information maps.

[0093] The ecological perception submodule is based on regional climate information maps, uses ecological footprint analysis and biodiversity index calculation, calculates the regional ecological footprint using the global ecological footprint network method, and then assesses biodiversity through the Simpson diversity index and Shannon diversity index. It comprehensively assesses the health status and change trend of the ecosystem and generates ecosystem status results.

[0094] The remote sensing submodule acquires land information from satellite imagery and performs preprocessing using spectral analysis algorithms, including radiometric and atmospheric corrections. First, radiometric correction converts the radiometric units in the satellite imagery into reflectance, eliminating the effects of daily airflow and the Earth's atmosphere. Then, atmospheric correction further reduces the impact of atmospheric absorption and scattering on the image. These two preprocessing steps ensure higher accuracy and comparability of the satellite imagery.

[0095] Next, support vector machine (SVM) classification and decision tree classification were used for land cover feature extraction and classification. SVM separated different land types by constructing a hyperplane, while the decision tree partitioned the image layer by layer using a tree structure. Finally, maximum likelihood classification was used to label the various land cover classes, generating a land type remote sensing map. The parameter tuning and model training processes for both SVM and decision trees ensured accurate land cover classification.

[0096] In the ground monitoring submodule, land type remote sensing maps are used, and spatial analysis is performed using a geographic information system (GIS) to create a spatial distribution map of soil moisture. Simultaneously, a wireless sensor network is deployed to monitor soil moisture in real time, enabling high-frequency data collection. This real-time data is fused with the remote sensing maps to generate land quality monitoring data. The spatial analysis capabilities of the GIS ensure accurate visualization of soil moisture, while the real-time monitoring via the wireless sensor network provides more refined land quality data.

[0097] The climate perception submodule uses land quality monitoring data and meteorological models for climate analysis. First, it simulates future meteorological conditions using numerical weather prediction models to obtain forecast data. Second, it analyzes regional climate change trends using climate statistical models. Simultaneously, it extracts climate information such as temperature, precipitation, and wind speed through trend and seasonal analysis using autoregressive moving averages and seasonal decomposition. These operations generate a regional climate information map, providing accurate meteorological data support for subsequent modules.

[0098] The ecological perception submodule assesses ecosystem status based on regional climate information maps, employing ecological footprint analysis and biodiversity index calculations. It utilizes the global ecological footprint network method to calculate the regional ecological footprint and assess human impacts on the ecosystem. Simultaneously, it quantitatively assesses biodiversity using the Simpson Diversity Index and the Shannon Diversity Index. By integrating this information, a comprehensive assessment of the ecosystem's health and trends is conducted, generating ecosystem status results. This integrated assessment contributes to the development of effective ecological protection and restoration strategies.

[0099] In the remote sensing submodule, consider a satellite image with a resolution of 1 meter, containing red, green, and blue bands. Using a spectral analysis algorithm, radiometric correction is first performed to calculate reflectance. Then, atmospheric correction is performed to eliminate atmospheric influence. Next, support vector machines and decision trees are used for classification, and the model is trained by adjusting parameters such as the kernel function and tree depth. Finally, the image is labeled using maximum likelihood classification to generate a land type remote sensing map in GeoTIFF file format.

[0100] In the ground monitoring submodule, it is assumed that a wireless sensor network is deployed over an area of ​​1000 square meters, collecting soil moisture data every 15 minutes. Using a geographic information system (GIS), spatial analysis is performed on this data to generate a spatial distribution map of soil moisture in raster image format. Simultaneously, the real-time monitoring data from the wireless sensor network is fused with the remote sensing image to generate land quality monitoring data in CSV file format.

[0101] The climate perception submodule uses one year's worth of land quality monitoring data and employs numerical weather prediction models to simulate meteorological conditions for the coming week, generating meteorological forecast data. It analyzes regional climate change trends using climate statistical models, extracting temperature, precipitation, and wind speed information. Simultaneously, it uses autoregressive moving averages and seasonal decomposition methods to perform time-series climate analysis. Finally, it generates regional climate information maps in chart and table formats.

[0102] In the ecological perception submodule, the ecological footprint is calculated using the global ecological footprint network method based on regional climate information maps. Simultaneously, the Simpson diversity index and the Shannon diversity index are used to calculate biodiversity.

[0103] Please see Figure 4 The feature extraction module includes a land feature extraction submodule, a climate feature extraction submodule, an ecological feature extraction submodule, and a socio-economic feature extraction submodule.

[0104] The land feature extraction submodule is based on the land type remote sensing map in the environmental perception data. It uses principal component analysis to extract spectral features from the remote sensing image. It reduces the data dimensionality by calculating the covariance matrix of the dataset and extracting key components to generate a land feature dataset.

[0105] The climate feature extraction submodule is based on the regional climate information map in the environmental perception data. It uses time series analysis to extract key climate change indicators, including average temperature, precipitation and wind speed, by analyzing the long-term trends and periodic patterns of historical climate data, and generates a climate feature dataset.

[0106] The ecological feature extraction submodule, based on the ecosystem status results in the environmental perception data, adopts ecological model analysis methods to extract ecological features associated with land cover and use by assessing the ecosystem's productivity, diversity, and stability indicators, and generates an ecological feature dataset.

[0107] The socioeconomic feature extraction submodule, based on human activity information in environmental perception data, uses statistical analysis and socioeconomic models to analyze population distribution, economic activities, and land policy data to extract key socioeconomic features that reflect the impact of human activities on land cover and use, including land use type, economic development level, and policies and regulations, and generates a socioeconomic feature dataset.

[0108] In the land feature extraction submodule, spectral feature extraction is performed using remote sensing images of land types from environmental perception data, employing principal component analysis (PCA). First, the remote sensing images are preprocessed, and the covariance matrix of the dataset is calculated. Then, PCA extracts key components, reduces data dimensionality, and retains the most representative spectral information to generate a land feature dataset. During PCA, mathematical operations are performed on the spectral information of each pixel, and principal component extraction is completed by calculating eigenvectors and eigenvalues, ultimately generating the dimensionality-reduced land feature dataset.

[0109] The generated land feature dataset contains the most critical spectral features from the original remote sensing images, which helps to more accurately describe the spectral information of land cover and provides more accurate feature data for subsequent land use and cover classification.

[0110] In the climate feature extraction submodule, climate features are extracted based on the regional climate information map in the environmental sensing data using time series analysis. By analyzing the long-term trends and periodic patterns of historical climate data, key climate change indicators, such as average temperature, precipitation, and wind speed, are extracted using time series analysis methods. This includes trend analysis and periodic analysis, such as using moving averages and decomposition methods, to extract representative climate change features from historical data.

[0111] The generated climate feature dataset contains key indicators of long-term regional climate trends and periodic changes, providing representative and interpretable data for analyzing climate change and helping to predict future climate change trends.

[0112] In the ecological feature extraction submodule, ecological features are extracted using ecological model analysis methods based on the ecosystem status results from environmental perception data. Ecological features related to land cover and use are extracted by assessing indicators such as ecosystem productivity, diversity, and stability. This involves the operation and parameter adjustment of ecological models, such as ecological productivity models and diversity index calculations.

[0113] The generated ecological feature dataset contains key indicators such as ecosystem health and diversity, which helps to assess the relationship between land use and the ecological environment and provides an important reference for formulating sustainable land use policies.

[0114] In the socioeconomic feature extraction submodule, human activity information from environmental perception data is utilized, and statistical analysis and socioeconomic models are employed to extract socioeconomic features. Data on population distribution, economic activity, and land policies are analyzed to extract key socioeconomic features reflecting the impact of human activities on land cover and use. These include factors such as land use type, economic development level, and policies and regulations.

[0115] The generated socioeconomic feature dataset contains factors influencing human activities on land use, which helps to understand the relationship between socioeconomics and land use and provides data support for the formulation of comprehensive land management policies.

[0116] In the land feature extraction submodule, assuming a remote sensing image of land type containing data in red, green, and blue bands with a resolution of 5 meters, principal component analysis (PCA) is used to process the remote sensing image, calculate the covariance matrix, and extract principal components. Three principal components representing spectral information are obtained through calculation. The final generated land feature dataset is saved in CSV file format, containing principal component information for each pixel.

[0117] In the climate feature extraction submodule, a historical climate data table containing annual temperature, precipitation, and wind speed is assumed. Using time series analysis methods, through moving averages and periodic decomposition, the long-term trends and seasonal variation patterns of annual average temperature, precipitation, and wind speed are extracted. The generated climate feature dataset is saved in Excel spreadsheet format.

[0118] In the ecological feature extraction submodule, based on the ecosystem state results, ecological productivity, diversity, and stability indicators are considered. The ecological productivity model and diversity index calculation formula are used to calculate the ecosystem's productivity and diversity, respectively. The final generated ecological feature dataset is saved in JSON format, containing a description of the ecosystem for each indicator.

[0119] In the socioeconomic feature extraction submodule, population distribution, economic development level, and land policy data are used as a foundation. Statistical analysis and socioeconomic models are employed to analyze population density, GDP indicators, and land policy scores, respectively. The resulting socioeconomic feature dataset is stored in a database, containing various socioeconomic factors from different regions.

[0120] Please see Figure 5The land cover classification module includes a deep learning classification submodule, a map generation submodule, a classification accuracy evaluation submodule, and a classification result feedback submodule.

[0121] The deep learning classification submodule uses a convolutional neural network to extract features from land cover images based on a key feature set. Layer convolution operations extract low- to high-level features of the image sequentially, pooling layers reduce feature dimensionality while retaining key information, and fully connected layers make classification decisions based on features to generate preliminary land classification results.

[0122] The map generation submodule uses a geographic information system to draw a land classification map based on the preliminary land classification results. The classification data is rasterized according to geographic coordinates, with each raster representing a land cover type, thus generating a land classification map.

[0123] The classification accuracy evaluation submodule is based on the land classification atlas. It uses cross-validation and confusion matrix to evaluate the accuracy of the classification results. The stability and reliability of the model are tested by dividing the data into multiple parts and repeatedly training and validating. The confusion matrix shows the prediction performance of each category, evaluates the classification accuracy and error rate, and generates accuracy evaluation results.

[0124] The classification result feedback submodule optimizes the CNN model based on the accuracy evaluation results using a machine learning feedback mechanism. It adjusts the layer parameters and learning rate in the CNN according to the accuracy evaluation results, and improves the generalization ability of the model through data augmentation, and regenerates the optimized land classification map.

[0125] In the deep learning classification submodule, a convolutional neural network (CNN) is used to extract features from land cover images based on a key feature set. First, the CNN extracts low- to high-level features from the image through layer convolution operations, abstracting and representing image information layer by layer. Subsequent pooling layers reduce feature dimensionality while preserving key information, accelerating computation and preventing overfitting. Finally, a fully connected layer makes classification decisions based on the features, generating preliminary land classification results.

[0126] The convolutional layers of a CNN extract feature information from an image through a sliding window, the pooling layers use max pooling or average pooling to reduce the dimensionality of the features, and the fully connected layers use the Softmax function to perform multi-class classification, thus obtaining preliminary land classification results.

[0127] The generated preliminary land classification results specify the corresponding land cover type for each pixel, providing basic classification data for subsequent land use planning and resource management.

[0128] In the map generation submodule, a land classification map is drawn using a geographic information system based on the preliminary land classification results. The classification data is rasterized according to geographic coordinates, with each raster representing a land cover type, ultimately generating a land classification map.

[0129] By correlating the preliminary classification results with geographic coordinates, a land classification map in raster units is drawn and saved as a raster dataset for subsequent use in geographic information systems and spatial analysis applications.

[0130] The generated land classification map shows the distribution of various types of land cover in the region, providing a visual reference for decision-making in land planning, resource management, and environmental protection.

[0131] In the classification accuracy evaluation submodule, cross-validation and a confusion matrix are used to evaluate the accuracy of the classification results based on the land classification atlas. The stability and reliability of the model are tested by dividing the data into multiple parts for training and validation. The confusion matrix shows the prediction performance for each category, evaluating the classification accuracy and error rate.

[0132] The dataset is divided into training and validation sets using cross-validation. The classification performance of the model is evaluated using the validation set, and the classification accuracy is quantitatively evaluated based on the confusion matrix.

[0133] The generated accuracy assessment results provide a performance evaluation of the land classification model, providing a basis for evaluating the model's classification accuracy and stability.

[0134] In the classification result feedback submodule, the CNN model is optimized using a machine learning feedback mechanism based on the accuracy evaluation results. The layer parameters and learning rate in the CNN are adjusted according to the evaluation results, and data augmentation is used to improve the model's generalization ability, ultimately regenerating the optimized land classification map.

[0135] By adjusting the hyperparameters and learning rate of the CNN model, and combining data augmentation techniques such as mirroring and rotation, the model is retrained to improve its adaptability to different data and generate an optimized land classification map.

[0136] The optimized land classification map is more accurate than the preliminary results, improves the model's ability to generalize to new data, and provides more reliable data support for land classification.

[0137] For the deep learning classification submodule, when using a convolutional neural network (CNN) to extract features from land cover images, we assume a set of satellite image data containing different land types, each image being 256x256 pixels in size, with a total of 10 categories. A CNN structure is used for training, including multiple convolutional layers, pooling layers, and fully connected layers. After training, preliminary land classification results are obtained.

[0138] In the map generation submodule, based on the preliminary land classification results, a geographic information system is used to perform map rasterization. The classification result of each pixel is mapped to geographic coordinates to generate a rasterized land classification map.

[0139] For the classification accuracy evaluation submodule, cross-validation is used to divide the dataset into training and validation sets, and the model's accuracy is evaluated on the validation set. The confusion matrix is ​​used to analyze the model's classification results, and the accuracy and error rate are calculated.

[0140] Finally, in the classification result feedback submodule, the CNN model is optimized based on the accuracy evaluation results, adjusting hyperparameters such as learning rate and batch size, and retrained using data augmentation techniques. This generates an optimized land classification map, improving the model's classification accuracy and generalization ability.

[0141] Please see Figure 6 The dynamic simulation module includes a system dynamics submodule, a multi-agent simulation submodule, a land use prediction submodule, and a dynamic change assessment submodule.

[0142] The system dynamics submodule is based on the land classification map and uses system dynamics to simulate land use dynamics. By establishing feedback loops about land resources, population and economic activity factors, it expresses the interaction between variables with difference equations or differential equations, performs continuous time simulation of stock and flow, describes the evolution of land use over time, and generates system dynamic simulation results.

[0143] The multi-agent simulation submodule simulates the behavior of land users based on the system's dynamic simulation results. The agents interact with each other according to their own goals, strategies and land use conditions, simulating the decision-making and interaction behaviors among multiple stakeholders, including farmers, developers and the government. By setting objective functions and behavioral rules for differentiated agents, the module simulates the dynamic reactions of various types of land users under resource constraints and policy changes, generating multi-agent interaction simulation results.

[0144] The land use prediction submodule is based on the results of multi-agent interaction simulation. It uses time series analysis and trend prediction to predict future land use changes. By analyzing historical land use data, it identifies trends and periodic patterns of land use changes and applies these patterns to future scenario predictions. By building models, it predicts land use at different time points or under different conditions in the future and generates land use trend prediction results.

[0145] The dynamic change assessment submodule assesses land use change and its environmental impacts based on land use trend prediction results. It uses ecological footprint and ecosystem service value assessment methods to quantify the ecological and socio-economic impacts of land use change, analyzes the impact of land change on biodiversity, water resources, carbon storage environmental factors, and the potential impact of change on socio-economic conditions, and generates an environmental impact assessment of land dynamic change.

[0146] In the land use dynamics simulation submodule, a system dynamics method based on land classification maps is used to simulate continuous-time land use change. First, a land classification map is used as the basic data, which includes different types of land resources, such as farmland, urban land, and forest. These land types are treated as state variables of the system in the system dynamics model.

[0147] System dynamics models describe the interactions between variables by establishing feedback loops concerning land resources, population, and economic activity. These loops are represented by difference equations or systems of differential equations to simulate the evolution of land use over time. Variables include the quantity of land resources, population size, and level of economic activity, while the relationships between them are defined by parameters and equations in the model. By performing continuous-time simulations of stock and flow, system dynamics simulation results on the dynamic evolution of land use can be generated.

[0148] The advantage of system dynamics lies in its ability to capture the interactions between elements in complex systems and simulate their evolution over time. The specific implementation process of the model includes parameter setting, equation establishment, and numerical solution. Parameter setting can be optimized by fitting historical data or incorporating expert experience. Equation establishment involves the rate of change of different land use elements, which requires a deep understanding of the relationships between the elements in the system. The numerical solution process uses numerical integration methods, such as the Euler method or the fourth-order Runge-Kutta method, to simulate the evolution of the system state over time.

[0149] Dynamic land use simulation can reveal the land use status of the system at different points in time, including the area and distribution of various land types. This provides fundamental data for subsequent decision-making, enabling governments, research institutions, and others to better understand land use trends.

[0150] In the multi-agent simulation submodule, based on the system dynamics simulation results, a multi-agent simulation method is used to simulate the behavior of land users. Agents represent various land users, such as farmers, developers, and the government, and interact according to their own goals, strategies, and land use conditions.

[0151] The interactions between intelligent agents are simulated by setting differentiated objective functions and behavioral rules. For example, farmers aim to maximize farmland yields, while developers pursue maximum economic profits. Governments influence land users' decisions by formulating land policies. The interactions between agents are realized by simulating decision-making and resource allocation processes, which can be described using game theory or agent models.

[0152] In multi-agent simulation, the decision-making and interaction behaviors of multiple stakeholders are simulated to generate multi-agent interaction simulation results. This provides a practical case for formulating land management policies and plans, enabling decision-makers to better understand the conflicts and cooperative relationships among different stakeholders.

[0153] The land use prediction submodule, based on the results of multi-agent interactive simulations, employs time series analysis and trend prediction to forecast future land use changes. First, by analyzing historical land use data, it identifies trends and cyclical patterns in land use change. This can be accomplished through statistical methods, time series analysis, or machine learning algorithms, such as Support Vector Machines (SVM) or Long Short-Term Memory Networks (LSTM).

[0154] After identifying land use change trends, these models are applied to predict future scenarios. By building models to predict land use at different points in time or under different conditions, future land use trend forecasts can be obtained. This provides policymakers with information on future land use development, helping to formulate reasonable land planning and management policies.

[0155] The dynamic change assessment submodule assesses land use change and its environmental impacts based on land use trend forecasts and employs environmental assessment methods. Ecological footprint and ecosystem service value assessment methods are used to quantify the ecological and socio-economic impacts of land use change.

[0156] Ecological footprint assessment involves analyzing the impacts of land use change on environmental factors such as biodiversity, water resources, and carbon storage. Ecosystem service value assessment considers the potential impacts of land use change on socioeconomic conditions. These assessment methods establish mathematical models to link land use change with environmental factors and socioeconomic indicators to achieve quantitative assessment.

[0157] Environmental impact assessments of land use dynamics can help us understand the extent to which land use change affects ecosystems and socio-economic conditions, providing policymakers with a scientific basis for decision-making. This helps to balance economic development and ecological protection, and to formulate sustainable land use policies and plans.

[0158] For a specific implementation example, land use data for a certain region was collected over the past ten years, including changes in the area of ​​different land types, population growth, and economic activity levels. A system of difference equations describing these changes was established using a system dynamics model, and numerical solutions were obtained to generate the changes in various land types over the ten years.

[0159] Based on the system dynamics simulation results, an agent model was established to simulate the interactions between farmers, developers, and the government in the region. They made decisions based on different objectives and strategies, simulating the behavior of land users. Through multi-agent simulation, simulation results of interactions between different stakeholders were generated.

[0160] Time series analysis and trend prediction were conducted using historical data and multi-agent simulation results. A support vector machine algorithm was employed to identify land use change trends, which were then applied to predict future land use scenarios, yielding predicted results.

[0161] Finally, an environmental impact assessment was conducted based on the prediction results. Using ecological footprint and ecosystem service value assessment methods, the ecological and socio-economic impacts of land use change were quantitatively analyzed, providing a scientific reference for future land use decisions in the region.

[0162] Please see Figure 7 The urban heat island effect analysis module includes a climate simulation submodule, a spatial analysis submodule, a first risk assessment submodule, and a response strategy submodule.

[0163] The climate simulation submodule uses numerical weather prediction models to simulate the climate conditions of urban areas based on land dynamic change simulation maps and meteorological data. By calculating the physical state of the atmosphere, it simulates the distribution of climate elements, including temperature, humidity, and wind speed, and generates an urban climate simulation map.

[0164] The spatial analysis submodule is based on the urban climate simulation map and uses geospatial analysis technology to depict the spatial distribution of the heat island effect. Through GIS technology including spatial interpolation and hotspot analysis, the climate simulation data is transformed into a spatial layer in the geographic information system to draw the intensity and range of the urban heat island effect and generate a heat island spatial distribution map.

[0165] The first risk assessment submodule is based on the spatial distribution map of the heat island. It uses environmental risk assessment methods to evaluate the impact and risk level of the heat island effect, analyzes the population density, infrastructure sensitivity and ecological vulnerability of the heat island area, identifies risk areas and potential impacts, and generates heat island risk assessment results.

[0166] The response strategy submodule, based on the heat island risk assessment results, uses urban planning and environmental management methods to formulate strategies to mitigate the heat island effect, analyzes the impact of urban layout, green coverage, and building materials, and proposes adjustment plans, including increasing urban green space, improving building design, and formulating heat island effect mitigation strategies.

[0167] In the climate simulation submodule, high-precision simulations of urban climate conditions are conducted using numerical weather prediction models based on land dynamics simulation maps and meteorological data. First, land dynamics data, including land use, cover type, and topography, are collected for the urban area, and meteorological data such as temperature, humidity, and wind speed are integrated. These data form the input for the climate simulation.

[0168] Numerical weather prediction models are employed, including commonly used atmospheric circulation models such as the WRF (Weather Research and Forecasting Model). These models are based on physical equations, solved discretized using finite difference methods, and consider the thermodynamic and dynamic processes of the atmosphere to accurately simulate the climate conditions of urban areas. Specifically, the model's boundary conditions and initial fields are adjusted to meet the meteorological simulation requirements at the urban scale.

[0169] The simulation focuses on the physical state of the atmosphere, emphasizing the distribution of climate elements such as temperature, humidity, and wind speed. This involves adjusting parameters in the numerical model, including surface parameters and urban layout parameters, to better reflect the unique meteorological characteristics of urban areas. The simulation output is presented as a high-resolution urban climate simulation map, visually displaying the meteorological conditions at various locations within the urban area.

[0170] In the spatial analysis submodule, geospatial analysis techniques are used to depict the spatial distribution of the urban heat island effect based on urban climate simulation maps. Using GIS technology and spatial interpolation methods, such as Kriging interpolation, the distribution of meteorological elements in the city is interpolated to obtain a continuous spatial scene. Simultaneously, hotspot analysis methods are used to identify areas in the city with significantly increased temperatures, thereby revealing the intensity and extent of the heat island effect.

[0171] In the first risk assessment submodule, based on the spatial distribution map of the heat island, environmental risk assessment methods are used to comprehensively evaluate the impact of the heat island effect. Factors such as population density, infrastructure sensitivity, and ecological vulnerability in the heat island region are analyzed to identify potential risk areas and impacts. Specifically, vulnerability and exposure indicators are used for risk assessment to calculate the risk level of each region. Finally, the heat island risk assessment results are generated, clearly indicating which areas have a higher risk of heat island effect.

[0172] In the sub-module of response strategies, strategies for mitigating the urban heat island effect are formulated based on the results of the heat island risk assessment, using urban planning and environmental management methods. By analyzing the impact of factors such as urban layout, green coverage, and building materials, specific adjustment schemes are proposed. For example, increasing urban green space to improve heat dissipation capacity and improving building design to reduce heat absorption. These strategies form a comprehensive heat island mitigation scheme to reduce the adverse effects of the urban heat island effect.

[0173] In the implementation of the climate simulation submodule, taking a specific city as an example, land use data and meteorological observation data from the past five years were used. This includes land type, land cover area, topographic information, and meteorological data such as temperature, humidity, and wind speed. The simulated values ​​for these data items were obtained through analysis and processing of past observation data.

[0174] First, the WRF numerical weather prediction model was used in the climate simulation. The model's parameters, such as horizontal resolution, time step, and surface parameters, were adjusted to ensure that the simulation results more accurately reflect the climate conditions of the target city. By inputting simulation maps of land dynamics and meteorological data, the model calculated the temperature, humidity, and wind speed distributions at various points within the urban area.

[0175] The simulation results are presented as high-resolution urban climate simulation maps, which include climate change over a period of time at different locations. For example, a dynamic map of temperature changes throughout the day is available, showing the temperature differences between day and night in different areas of the city.

[0176] In the spatial analysis submodule, GIS technology is used to analyze the urban heat island effect. By processing the urban climate simulation map, a more continuous spatial temperature distribution map is obtained using methods such as Kriging interpolation. Simultaneously, hotspot analysis is used to identify areas in the city significantly affected by the heat island effect.

[0177] In the first risk assessment submodule, environmental risk assessment is conducted based on the spatial distribution map of the urban heat island. Taking a certain indicator system as an example, data such as population density, infrastructure sensitivity, and ecological vulnerability are combined with the degree of the heat island effect. Through certain mathematical models and assessment methods, heat island risk assessment results are generated, identifying areas in the city that are significantly affected.

[0178] In the sub-module of response strategies, strategies for mitigating the heat island effect are developed based on the results of the heat island risk assessment, using urban planning and environmental management methods. For example, specific solutions such as increasing green spaces and improving building design are proposed to reduce the impact of the heat island effect. These strategies form a comprehensive heat island mitigation plan.

[0179] In practical applications, by operating the aforementioned modules, detailed information about the city's climate and heat island effect was obtained. This provides urban decision-makers with a scientific basis, helping them to better understand the city's climate characteristics and take corresponding management measures to improve the city's climate adaptability and sustainability.

[0180] Please see Figure 8 The decision optimization module includes a strategy simulation submodule, a multi-objective optimization submodule, a decision analysis submodule, and a strategy evaluation submodule.

[0181] The strategy simulation submodule uses system simulation technology to simulate strategies based on the heat island effect analysis results and land dynamic change simulation map. This includes creating rule-based models to simulate the implementation effects of multiple land planning and management strategies, and generating strategy simulation analysis maps by referring to economic, social and environmental factors.

[0182] The multi-objective optimization submodule is based on the strategy simulation analysis graph. It uses multi-objective optimization algorithms to formulate the optimal strategy, sets the objective functions of multiple strategies, including maximizing economic benefits, minimizing environmental impact and optimizing social benefits, and uses genetic algorithms or particle swarm optimization to balance the objective functions and select the optimal strategy scheme.

[0183] The decision analysis submodule is based on optimized strategy options. It uses a decision support system to analyze the feasibility and impact of multiple strategy options. By integrating historical data, simulation results and expert opinions, it compares and analyzes the advantages and disadvantages of differentiated strategy options, as well as the impact of multiple options on the economy, society and environment, and generates decision analysis results.

[0184] Based on the decision analysis results, the strategy evaluation submodule evaluates the established land planning strategies using effectiveness evaluation methods, compares the changes in economic, social and environmental indicators before and after strategy implementation, and uses cost-benefit analysis and life cycle assessment to quantify the actual impact and lasting effects of the strategies, generating strategy effectiveness evaluation results.

[0185] In the strategy simulation submodule, strategy simulations were conducted using system simulation technology combined with heat island effect analysis results and land dynamic change simulation maps. A rule-based model was created to simulate the implementation effects of various land planning and management strategies. Using system simulation technology, the impacts of these strategies on the urban environment, economy, and society were simulated, and a strategy simulation analysis map was generated based on economic, social, and environmental factors. This map vividly illustrates the potential impacts of different strategies on urban development, providing a basis for further decision-making.

[0186] In the multi-objective optimization submodule, a multi-objective optimization algorithm was employed based on the policy simulation analysis graph. Multiple objective functions were set for different policies, including maximizing economic benefits, minimizing environmental impact, and optimizing social benefits. These objective functions were balanced using algorithms such as genetic algorithms or particle swarm optimization, ultimately determining the optimal policy scheme. This process, based on precise calculations using mathematical models and algorithms, ensured the multifaceted optimization effect of the final policy.

[0187] In the decision analysis submodule, an in-depth analysis was conducted using a decision support system based on optimized strategic options. By integrating historical data, simulation results, and expert opinions, the advantages and disadvantages of differentiated strategic options were compared and analyzed, and the economic, social, and environmental impacts of various options were assessed. This analysis generated decision analysis results, providing decision-makers with important references regarding feasibility and impact.

[0188] In the strategy evaluation submodule, the formulated land planning strategy was evaluated using effectiveness evaluation methods based on the decision analysis results. Changes in economic, social, and environmental indicators before and after strategy implementation were compared, and cost-benefit analysis and life cycle assessment methods were used to quantify the actual impact and lasting effects of the strategy. This process generated strategy effectiveness evaluation results, providing a scientific basis for assessing the comprehensiveness and long-term effectiveness of the strategy.

[0189] For example, taking a certain city as an example, land use data, meteorological data, and urban planning schemes from the past ten years were used as inputs. Through system simulation and multi-objective optimization, simulation results of different land planning strategies were obtained, including changes in various indicators, economic impacts, and social benefits. These data formed a strategy simulation analysis diagram, clearly demonstrating the potential effects of each strategy. Decision analysis and evaluation further compared and analyzed the advantages and disadvantages of different strategies and quantified their impact on urban development, providing important reference for decision-makers.

[0190] Please see Figure 9 The information interpretation module includes a deep learning interpretation submodule, a multi-source data fusion submodule, a prediction trend analysis submodule, and a feedback learning adjustment submodule.

[0191] The deep learning interpretation submodule is based on an optimized decision support scheme. It uses deep learning algorithms to extract features, captures local features through convolutional layers, processes sequence data through recurrent layers to analyze temporal dynamics, processes non-Euclidean structure data through layers, performs in-depth analysis of data structure and correlation, performs pattern recognition, and generates data feature interpretation results.

[0192] The multi-source data fusion submodule integrates data based on the data feature interpretation results and uses data fusion algorithms. It combines data features from multiple sources through feature-level fusion technology and integrates decision results from multiple decision items through decision-level fusion technology to generate a comprehensive data profile.

[0193] The trend prediction analysis submodule is based on comprehensive data profiling. It uses time series analysis and machine learning algorithms to predict trends. It captures the time dependence of data through an autoregressive model, identifies and utilizes the statistical regularity of historical data through a moving average model, analyzes and predicts data change trends, performs trend analysis, and generates trend prediction analysis results.

[0194] The feedback learning adjustment submodule optimizes the model based on the trend prediction analysis results using a feedback learning strategy. It updates the model by receiving new data points through online learning, incrementally adjusts the model to match data changes, adjusts the parameters and structure of the prediction model, performs continuous optimization and adaptive adjustment, and generates the optimization adjustment interpretation results.

[0195] In the deep learning interpretation submodule, based on an optimized decision support scheme, deep learning algorithms are employed for feature extraction and data interpretation. Convolutional Neural Networks (CNNs) are used to capture local features in the data, Recurrent Neural Networks (RNNs) process sequential data to analyze temporal dynamics, and Graph Neural Networks (GNNs) are used to process non-Euclidean structured data, thereby performing in-depth analysis of data structure and relationships. In practice, various deep learning frameworks (such as TensorFlow, PyTorch, etc.) are used, different layers of the network (convolutional layers, recurrent layers, and multilayer layers) are configured, and appropriate hyperparameters (convolutional kernel size, number of nodes in recurrent layers, etc.) are set.

[0196] The specific execution process includes data preprocessing, building a deep learning model, model training, and feature extraction. First, the data preprocessing stage involves steps such as data cleaning and normalization to ensure the data is suitable for input to the deep learning model. Then, the deep learning model is built, setting convolutional layers, recurrent layers, and multilayers to extract and interpret features from the data. During model training, a large amount of data is used to train the model, optimizing its parameters to improve its feature extraction capabilities. Finally, the trained model is used to extract and interpret features from the data, generating data feature interpretation results. These results can be representations of various features, such as time series trends or the identification of local features.

[0197] In the multi-source data fusion submodule, data integration is performed using data fusion algorithms based on the data feature interpretation results. This includes feature-level fusion technology and decision-level fusion technology. Feature-level fusion technology integrates data features from multiple sources to create a comprehensive data feature profile. Decision-level fusion technology integrates decision results from multiple decision items to form a comprehensive decision data profile. In practice, we may use data fusion algorithms, such as feature-level fusion which may involve concatenating feature vectors or weighted summation, while decision-level fusion may employ methods such as voting or weighted decision-making.

[0198] In the trend prediction and analysis submodule, trend prediction is performed using time series analysis and machine learning algorithms based on comprehensive data profiling. An autoregressive model is used to capture the time dependence of the data, and a moving average model is used to identify and utilize statistical patterns from historical data. These methods can analyze the trends in data changes, generate trend prediction analysis results, and demonstrate the future development trends of the data. These results can be used to guide future decision-making and planning, providing important references for business operations.

[0199] In the feedback learning and adjustment submodule, a feedback learning strategy is used to optimize the model based on the trend prediction analysis results. New data points are received through online learning, allowing for incremental learning and gradual adjustment of the model to match data changes. This includes adjusting the parameters and structure of the prediction model, achieving continuous optimization and adaptive adjustment. This process enables the model to more closely reflect reality, improves prediction accuracy, generates optimized adjustment interpretation results, and provides more precise support for decision-making.

[0200] For example, consider environmental, economic, and social data from a city over the past five years as input. A deep learning algorithm is used to build a deep neural network model. In the feature extraction stage, convolutional layers, recurrent layers, and multilayers are used for data interpretation and feature extraction. Subsequently, a data fusion algorithm is employed to integrate data features from different sources. In the trend prediction analysis stage, time series analysis and machine learning algorithms are used to predict future trends in the data. Finally, based on the prediction results, a feedback learning strategy is used to optimize the model, adapting it to data changes and generating optimized and adjusted interpretation results to provide reliable support for decision-making.

[0201] Please see Figure 10 The early warning response module includes an early warning signal issuance submodule, a second risk assessment submodule, an emergency response plan submodule, and a response effect evaluation submodule.

[0202] The early warning signal issuing submodule, based on comprehensive interpretation results and dynamic change assessment, uses Bayesian network algorithm to identify potential risks, calculates the conditional probabilities between multiple risk factors, comprehensively analyzes and forms a risk map, formulates early warning signal rules, executes risk level classification and early warning signal initiation, and generates potential risk early warning signals.

[0203] The second risk assessment submodule, based on potential risk warning signals, uses neural network algorithms to conduct in-depth risk analysis, analyzes the characteristics and patterns of risk data, performs a comprehensive assessment of the likelihood and impact of risks, and generates a comprehensive risk assessment result.

[0204] The emergency response plan submodule, based on the comprehensive risk assessment results, uses fault tree analysis to plan emergency measures. By identifying and assessing potential failure points and their interrelationships, it plans resource allocation and action steps based on risk type and urgency, generating an emergency response strategy plan.

[0205] The response effectiveness evaluation submodule is based on emergency response strategy planning. It uses utility analysis methods to monitor and evaluate the effectiveness. By quantitatively analyzing the utility and value of response measures, it performs an overall efficiency and adaptability assessment of the response and generates a comprehensive response effectiveness evaluation.

[0206] In the early warning signal issuance submodule, a Bayesian network algorithm is used to identify potential risks based on comprehensive interpretation results and dynamic change assessment. This process includes calculating the conditional probabilities between multiple risk factors, comprehensively analyzing to form a risk map, formulating early warning signal rules, executing risk level classification and early warning signal initiation, and finally generating potential risk early warning signals. Specifically, a Bayesian network model is used, combined with conditional probability calculation, to identify risks and generate risk maps. The formulation of early warning signal rules is based on threshold settings or comprehensive evaluation of multiple indicators to classify risk levels and initiate early warning signals.

[0207] In the second risk assessment submodule, a neural network algorithm is used for in-depth risk analysis based on potential risk warning signals. The neural network analyzes the characteristics and patterns of risk data, performs a comprehensive assessment of the likelihood and impact of risks, and ultimately generates a comprehensive risk assessment result. In practice, various neural network architectures, including deep neural networks (DNNs), convolutional neural networks (CNNs), and recurrent neural networks (RNNs), are used to perform deep learning and analysis on risk data to obtain the comprehensive risk assessment result.

[0208] The emergency response planning submodule, based on comprehensive risk assessment results, employs fault tree analysis to plan emergency measures. By identifying and assessing potential failure points and their interrelationships, it plans resource allocation and action steps based on risk type and urgency, generating an emergency response strategy plan. In practice, fault tree analysis involves decomposing system failures and modeling logical relationships, as well as developing corresponding emergency response plans.

[0209] In the response effectiveness evaluation submodule, based on emergency response strategy planning, utility analysis methods are used to monitor and evaluate the effectiveness. By quantitatively analyzing the utility and value of response measures, the efficiency and adaptability of the overall response are assessed, ultimately generating a comprehensive evaluation of the response effectiveness. In practice, a utility analysis model is used to quantitatively evaluate the effectiveness of various response measures, and combined with comprehensive indicators to obtain a complete assessment of the emergency response.

[0210] For example, consider a city's transportation system as input data, including traffic flow, road conditions, and weather information. In the early warning signal issuance submodule, a Bayesian network algorithm is used to identify potential risks in the transportation system and generate potential risk early warning signals. In the second risk assessment submodule, a neural network algorithm is used for deep analysis of these risk signals to assess their probability and impact, generating a comprehensive risk assessment result. Then, in the emergency response planning submodule, fault tree analysis is used to plan emergency measures and resource allocation schemes to address the risks. Finally, in the response effectiveness evaluation submodule, utility analysis is used to quantitatively evaluate the actual effectiveness of the emergency response measures, forming a comprehensive evaluation result of the response effectiveness, providing important reference for future decision-making.

[0211] The above are merely preferred embodiments of the present invention and are not intended to limit the present invention in any other way. Any person skilled in the art may make changes or modifications to the above-disclosed technical content to create equivalent embodiments that can be applied to other fields. However, any simple modifications, equivalent changes, and modifications made to the above embodiments based on the technical essence of the present invention without departing from the scope of the present invention shall still fall within the protection scope of the present invention.

Claims

1. A dynamic monitoring system for land resources, characterized in that: The system includes a resource perception module, a feature extraction module, a land cover classification module, a dynamic simulation module, a heat island effect analysis module, a decision optimization module, an information interpretation module, and an early warning response module. The resource perception module is based on remote sensing technology and ground monitoring network. It uses time series analysis and data synchronization technology to collect and synchronize land, climate and ecological data in real time, and conduct preliminary quality assessment to generate environmental perception data. The feature extraction module, based on environmental perception data, uses principal component analysis and random forest methods to perform feature screening and transformation, extract and identify key features affecting land cover and land use change, and evaluate the importance of features to generate a key feature set. The land cover classification module uses a convolutional neural network for deep learning classification based on a key feature set to identify and classify land cover types. It also uses cross-validation to analyze the accuracy and stability of the classification and generate a land classification map. The dynamic simulation module is based on a land classification map and uses multi-agent simulation and system dynamics model to simulate the dynamic process of land use change, analyze the land resource use efficiency and change trend, assess environmental impact, and generate a land dynamic change simulation map. The urban heat island effect analysis module, based on land dynamic change simulation maps and meteorological data, uses urban climate models and geospatial analysis methods to analyze the spatial distribution and temporal variation of the urban heat island effect, identify influencing factors, assess risk areas, propose mitigation strategies, and generate heat island effect analysis results. The decision optimization module, based on the heat island effect analysis results and land dynamic change simulation map, adopts a multi-objective optimization algorithm and decision support system, and takes into account economic, social and environmental factors to formulate the optimal strategy for land planning and management, and conducts effect simulation and evaluation to generate an optimized decision support scheme. The information interpretation module is based on an optimized decision support scheme and uses artificial intelligence interpretation technology. It integrates convolutional neural networks, recurrent neural networks and graph neural networks to interpret data, analyze trends and predict future changes, and optimize and adjust the results to generate comprehensive interpretation results. The early warning and response module, based on comprehensive interpretation results and dynamic change assessment, adopts early warning models and emergency management strategies to assess and classify potential risks, design and implement early warning and response measures, and generate early warning signals and emergency response strategies.

2. The land resource dynamic monitoring system according to claim 1, characterized in that: The environmental perception data includes land type data, climate data, ecological data, and human activity data. The key feature set includes land type indicators, climate variability indicators, ecosystem service indicators, and population distribution indicators. The land classification map includes the distribution area and attribute information of multiple land cover types. The land dynamic change simulation map includes land use status change prediction and environmental impact assessment results. The heat island effect analysis results include heat island intensity distribution map, key influencing factor list, and risk area map. The optimization decision support scheme includes land use optimization planning, resource allocation strategies, and environmental protection measures. The comprehensive interpretation results include data interpretation results, trend prediction analysis, and strategy adjustment schemes.

3. The land resource dynamic monitoring system according to claim 1, characterized in that: The resource perception module includes a remote sensing submodule, a ground monitoring submodule, a climate perception submodule, and an ecological perception submodule; The remote sensing submodule is based on satellite images and uses spectral analysis algorithms to preprocess the satellite images, including radiometric correction and atmospheric correction. Then, it uses support vector machine classification and decision tree classification to extract and classify land cover features. It uses maximum likelihood classification to label multiple types of land features and generate a land type remote sensing map. The ground monitoring submodule is based on land type remote sensing map, uses geographic information system, and uses the spatial analysis function of geographic information system to draw spatial distribution map of soil moisture. At the same time, it deploys wireless sensor network to monitor soil moisture in real time, collects land quality data and merges it with remote sensing map to generate land quality monitoring data. The climate perception submodule is based on land quality monitoring data, uses meteorological models, numerical weather prediction models and climate statistics models to analyze regional climate change, and uses autoregressive moving average and seasonal decomposition trend and seasonal analysis to perform time series climate analysis, extract temperature, precipitation and wind speed information, and generate regional climate information maps. The ecological perception submodule is based on regional climate information maps, uses ecological footprint analysis and biodiversity index calculation, calculates the regional ecological footprint using the global ecological footprint network method, and then assesses biodiversity through the Simpson diversity index and Shannon diversity index. It comprehensively assesses the health status and change trend of the ecosystem and generates ecosystem status results.

4. The land resource dynamic monitoring system according to claim 1, characterized in that: The feature extraction module includes a land feature extraction submodule, a climate feature extraction submodule, an ecological feature extraction submodule, and a socio-economic feature extraction submodule; The land feature extraction submodule is based on the land type remote sensing map in the environmental perception data. It uses principal component analysis to extract spectral features from the remote sensing image. It reduces the data dimensionality by calculating the covariance matrix of the dataset and extracting key components to generate a land feature dataset. The climate feature extraction submodule is based on the regional climate information map in the environmental perception data. It uses time series analysis to extract key climate change indicators, including average temperature, precipitation and wind speed, by analyzing the long-term trends and periodic patterns of historical climate data, and generates a climate feature dataset. The ecological feature extraction submodule, based on the ecosystem status results in the environmental perception data, uses ecological model analysis methods to extract ecological features associated with land cover and use by evaluating the ecosystem's productivity, diversity, and stability indicators, and generates an ecological feature dataset. The socioeconomic feature extraction submodule, based on human activity information in environmental perception data, uses statistical analysis and socioeconomic models to analyze population distribution, economic activities, and land policy data to extract key socioeconomic features reflecting the impact of human activities on land cover and use, including land use type, economic development level, and policies and regulations, and generates a socioeconomic feature dataset.

5. The land resource dynamic monitoring system according to claim 1, characterized in that: The land cover classification module includes a deep learning classification submodule, a map generation submodule, a classification accuracy evaluation submodule, and a classification result feedback submodule. The deep learning classification submodule is based on a key feature set and uses a convolutional neural network to extract features from land cover images. Layer convolution operations extract low- to high-level features of the image in sequence, pooling layers reduce feature dimensionality and retain key information, and fully connected layers make classification decisions based on features to generate preliminary land classification results. The map generation submodule uses a geographic information system to draw a land classification map based on the preliminary land classification results. The classification data is rasterized according to geographic coordinates, with each raster representing a land cover type, thus generating a land classification map. The classification accuracy evaluation submodule is based on the land classification map and uses cross-validation and confusion matrix to evaluate the accuracy of the classification results. The stability and reliability of the model are tested by repeatedly training and validating the data in multiple parts. The confusion matrix shows the prediction of each category, evaluates the accuracy and error rate of classification, and generates accuracy evaluation results. The classification result feedback submodule optimizes the CNN model based on the accuracy evaluation results using a machine learning feedback mechanism. It adjusts the layer parameters and learning rate in the CNN according to the accuracy evaluation results, and improves the generalization ability of the model through data augmentation, and regenerates the optimized land classification map.

6. The land resource dynamic monitoring system according to claim 1, characterized in that: The dynamic simulation module includes a system dynamics submodule, a multi-agent simulation submodule, a land use prediction submodule, and a dynamic change assessment submodule. The system dynamics submodule is based on the land classification map and uses system dynamics to simulate land use dynamics. By establishing feedback loops about land resources, population and economic activity factors, it expresses the interaction between variables with difference equations or differential equations, performs continuous time simulation of stock and flow, describes the evolution of land use over time, and generates system dynamic simulation results. The multi-agent simulation submodule simulates the behavior of land users based on the system's dynamic simulation results. The agents interact with each other according to their own goals, strategies and land use conditions, simulating the decision-making and interaction behaviors among multiple stakeholders, including farmers, developers and the government. By setting objective functions and behavioral rules for differentiated agents, the module simulates the dynamic reactions of various types of land users under resource constraints and policy changes, generating multi-agent interaction simulation results. The land use prediction submodule is based on the results of multi-agent interaction simulation. It uses time series analysis and trend prediction to predict future land use changes. By analyzing historical land use data, it identifies trends and periodic patterns of land use changes and applies these patterns to future scenario predictions. By establishing models, it predicts land use at different time points or under different conditions in the future and generates land use trend prediction results. The dynamic change assessment submodule assesses land use change and its environmental impact based on land use trend prediction results. It uses ecological footprint and ecosystem service value assessment methods to quantify the ecological and socio-economic impacts of land use change, analyzes the impact of land change on biodiversity, water resources, carbon storage environmental factors, and the potential impact of change on socio-economic conditions, and generates an environmental impact assessment of land dynamic change.

7. The land resource dynamic monitoring system according to claim 1, characterized in that: The heat island effect analysis module includes a climate simulation submodule, a spatial analysis submodule, a first risk assessment submodule, and a response strategy submodule. The climate simulation submodule uses a numerical weather prediction model to simulate the climate conditions of urban areas based on land dynamic change simulation maps and meteorological data. By calculating the physical state of the atmosphere, it simulates the distribution of climate elements, including temperature, humidity, and wind speed, and generates an urban climate simulation map. The spatial analysis submodule is based on the urban climate simulation map and uses geospatial analysis technology to depict the spatial distribution of the heat island effect. Through GIS technology including spatial interpolation and hotspot analysis, the climate simulation data is transformed into a spatial layer in the geographic information system to draw the intensity and range of the urban heat island effect and generate a heat island spatial distribution map. The first risk assessment submodule is based on the spatial distribution map of the heat island, uses environmental risk assessment methods to assess the impact and risk level of the heat island effect, analyzes the population density, infrastructure sensitivity and ecological vulnerability of the heat island area, identifies risk areas and potential impacts, and generates heat island risk assessment results. The aforementioned response strategy submodule, based on the heat island risk assessment results, uses urban planning and environmental management methods to formulate strategies to mitigate the heat island effect, analyzes the impact of urban layout, green coverage, and building materials, and proposes adjustment plans, including increasing urban green space, improving building design, and formulating heat island effect mitigation strategies.

8. The land resource dynamic monitoring system according to claim 1, characterized in that: The decision optimization module includes a strategy simulation submodule, a multi-objective optimization submodule, a decision analysis submodule, and a strategy evaluation submodule; The strategy simulation submodule uses system simulation technology to simulate strategies based on the heat island effect analysis results and land dynamic change simulation map. This includes creating a rule-based model to simulate the implementation effects of multiple land planning and management strategies, and generating a strategy simulation analysis map by referring to economic, social and environmental factors. The multi-objective optimization submodule is based on the strategy simulation analysis graph. It uses a multi-objective optimization algorithm to formulate the optimal strategy, sets the objective functions of multiple strategies, including maximizing economic benefits, minimizing environmental impact and optimizing social benefits, and uses genetic algorithm or particle swarm optimization to balance the objective functions and select the optimal strategy scheme. The decision analysis submodule is based on optimized strategy schemes. It uses a decision support system to analyze the feasibility and impact of multiple strategy schemes. By integrating historical data, simulation results and expert opinions, it compares and analyzes the advantages and disadvantages of differentiated strategy schemes, as well as the impact of multiple schemes on the economy, society and environment, and generates decision analysis results. The strategy evaluation submodule evaluates the established land planning strategies based on the decision analysis results using effect evaluation methods. It compares the changes in economic, social, and environmental indicators before and after the strategy implementation, and uses cost-benefit analysis and life cycle assessment to quantify the actual impact and lasting effects of the strategies, generating strategy effect evaluation results.

9. The land resource dynamic monitoring system according to claim 1, characterized in that: The information interpretation module includes a deep learning interpretation submodule, a multi-source data fusion submodule, a prediction trend analysis submodule, and a feedback learning adjustment submodule. The deep learning interpretation submodule is based on an optimized decision support scheme and uses deep learning algorithms to extract features. It captures local features through convolutional layers, processes sequence data through recurrent layers to analyze temporal dynamics, processes non-Euclidean structure data through layers, performs in-depth analysis of data structure and correlation, performs pattern recognition, and generates data feature interpretation results. The multi-source data fusion submodule integrates data based on the data feature interpretation results and uses a data fusion algorithm. It combines data features from multiple sources through feature-level fusion technology and integrates decision results from multiple decision items through decision-level fusion technology to generate a comprehensive data profile. The predicted trend analysis submodule is based on comprehensive data profiling, uses time series analysis and machine learning algorithms to predict trends, captures the time dependence of data through an autoregressive model, identifies and utilizes the statistical regularity of historical data through a moving average model, analyzes and predicts data change trends, performs trend analysis, and generates trend prediction analysis results. The feedback learning adjustment submodule optimizes the model based on the trend prediction analysis results using a feedback learning strategy. It updates the model by receiving new data points through online learning, incrementally adjusts the model to match data changes, adjusts the parameters and structure of the prediction model, performs continuous optimization and adaptive adjustment, and generates optimization adjustment interpretation results.

10. The land resource dynamic monitoring system according to claim 1, characterized in that: The early warning response module includes an early warning signal issuance submodule, a second risk assessment submodule, an emergency response plan submodule, and a response effect evaluation submodule. The warning signal issuing submodule, based on comprehensive interpretation results and dynamic change assessment, uses a Bayesian network algorithm to identify potential risks, calculates the conditional probabilities between multiple risk factors, comprehensively analyzes and forms a risk map, formulates warning signal rules, executes risk level classification and warning signal initiation, and generates potential risk warning signals. The second risk assessment submodule, based on potential risk warning signals, uses a neural network algorithm to conduct in-depth risk analysis, analyzes the characteristics and patterns of risk data, performs a comprehensive assessment of the likelihood and impact of risks, and generates a comprehensive risk assessment result. The emergency response plan submodule, based on the comprehensive risk assessment results, uses fault tree analysis to plan emergency measures. By identifying and assessing potential failure points and their interrelationships, it plans resource allocation and action steps based on risk type and urgency, generating an emergency response strategy plan. The response effectiveness evaluation submodule is based on emergency response strategy planning, uses utility analysis methods to monitor and evaluate the effectiveness, quantitatively analyzes the utility and value of response measures, performs an overall response efficiency and adaptability assessment, and generates a comprehensive response effectiveness evaluation.

Images