A risk assessment and prediction method and system applicable to county resources and environment

Abstract

The present invention proposes a risk assessment and prediction method and system applicable to county resources and environment. It belongs to the field of resource and environmental risk assessment technology; the method includes: obtaining multi-source data within the county, the multi-source data including meteorological data, water quality data, soil data, remote sensing images and demographic data, and pre-processing the obtained multi-source data; fusing the pre-processed multi-source data through a data fusion algorithm based on machine learning, constructing a comprehensive database of county resources and environment, and extracting key features from the comprehensive database through feature engineering. By integrating multi-source data from different fields and fusing the data through a machine learning algorithm, it is possible to fully consider multi-dimensional factors and make full use of information from different data sources, thereby improving the accuracy and comprehensiveness of risk assessment.

Description

Technical Field

[0001] The present invention proposes a risk assessment and prediction method and system applicable to county resources and environment, belonging to the technical field of resource and environmental risk assessment. Background Art

[0002] As the fundamental unit of national economic and social development, the resource and environmental conditions of counties directly impact regional sustainable development and the quality of life of their people. Traditional risk assessment methods, often based on single data sources and simple models, fail to fully and accurately reflect the complexity and dynamic nature of county-level resources and environments. Therefore, developing a resource and environmental risk assessment and prediction method applicable to the county scale is crucial. Summary of the Invention

[0003] The present invention provides a risk assessment and prediction method and system applicable to county-level resource and environmental risks, which is used to solve the problems mentioned in the above background technology:

[0004] The present invention proposes a risk assessment and prediction method applicable to county resources and environment, the method comprising:

[0005] S1. Acquire multi-source data within the county, including meteorological data, water quality data, soil data, remote sensing images, and demographic data, and pre-process the acquired multi-source data;

[0006] S2. Use a machine learning-based data fusion algorithm to fuse pre-processed multi-source data, build a county-level resource and environmental comprehensive database, and extract key features from the comprehensive database through feature engineering;

[0007] S3. Based on the deep learning framework, build a convolutional neural network model and train it using historical data.

[0008] S4. Using GIS technology, the county is divided into several grid cells. The trained model is applied to each grid cell to calculate its resource and environmental risk score. Based on time series analysis, the county's resource and environmental risks in the future are predicted.

[0009] S5. Use ArcGIS tools to display risk assessment and prediction results in map form and generate risk assessment and prediction reports.

[0010] The present invention proposes a risk assessment and prediction system applicable to county resources and environment, the system comprising:

[0011] Data acquisition module: acquires multi-source data within the county, including meteorological data, water quality data, soil data, remote sensing images, and demographic data, and pre-processes the acquired multi-source data;

[0012] Data fusion module: This module uses a machine learning-based data fusion algorithm to fuse pre-processed multi-source data, build a comprehensive database of county resources and environment, and extract key features from the comprehensive database through feature engineering.

[0013] Model training module: Based on the deep learning framework, build a convolutional neural network model and train the convolutional neural network model using historical data;

[0014] Model application module: Using GIS technology, the county is divided into several grid cells. The trained model is applied to each grid cell to calculate its resource and environmental risk score. Based on time series analysis, the county's resource and environmental risks in the future are predicted.

[0015] Report generation module: Using ArcGIS tools, risk assessment and prediction results are displayed in map form, and risk assessment and prediction reports are generated.

[0016] The present invention has the following beneficial effects: By integrating multi-source data from different fields (such as meteorological, water quality, soil, remote sensing imagery, and demographic data) and fusing the data through machine learning algorithms, it is possible to comprehensively consider multi-dimensional factors and fully utilize information from different data sources, thereby improving the accuracy and comprehensiveness of risk assessments. Using the convolutional neural network (CNN) model in deep learning for training, it is possible to deeply explore the potential patterns in historical data and conduct effective risk assessment and prediction based on the spatiotemporal characteristics of county-level resources and environment. By optimizing the model's structure and parameters, the model's prediction accuracy can be improved, thereby improving the reliability of risk assessments. Combining GIS technology with time series analysis, it is possible to conduct a detailed risk analysis of the spatial distribution and temporal dynamics within the county. By combining space and time, it can accurately reflect the changing trends of resource and environmental risks in different regions and time periods, helping policymakers to take targeted measures. Using ArcGIS tools, the assessment results are displayed in the form of a map, and color coding and icon annotation are used to intuitively display the spatial distribution and dynamic changes of risks, enhancing the visualization effect and the clarity of information communication. At the same time, automatically generated risk assessment and forecast reports can provide a scientific basis for decision-makers, facilitating subsequent response and management. Future risk forecasting through time series analysis models can provide early warning of potential resource and environmental risks. This predictive capability can provide forward-looking information support for county-level resource management, environmental protection, and disaster prevention, enhancing risk response capabilities. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] Figure 1 A diagram showing the steps of the method of the present invention;

[0018] Figure 2 This is a system module diagram of the present invention. DETAILED DESCRIPTION

[0019] The preferred embodiments of the present invention are described below with reference to the accompanying drawings. It should be understood that the preferred embodiments described herein are only used to illustrate and explain the present invention, and are not used to limit the present invention.

[0020] One embodiment of the present invention, as Figure 1 As shown, a risk assessment and prediction method applicable to county resources and environment, the method includes:

[0021] S1. Acquire multi-source data within the county, including meteorological data, water quality data, soil data, remote sensing images, and demographic data, and pre-process the acquired multi-source data;

[0022] S2. Using a machine learning-based data fusion algorithm, the preprocessed multi-source data is integrated to construct a comprehensive county resource and environmental database. Key features are extracted from the comprehensive database through feature engineering; these key features include environmentally sensitive areas, pollution source distribution, and socioeconomic pressure indicators.

[0023] S3. Based on the deep learning framework, a convolutional neural network model is constructed to capture the spatiotemporal characteristics of county-level resource and environmental risks, and the convolutional neural network model is trained using historical data.

[0024] S4. Using GIS technology, the county is divided into several grid cells. Each grid cell serves as the basic unit for risk assessment. The trained model is applied to each grid cell to calculate its resource and environmental risk score. Based on time series analysis, the county's resource and environmental risks in the future are predicted.

[0025] S5. Use ArcGIS tools to display risk assessment and prediction results in map form and generate risk assessment and prediction reports.

[0026] The working principle of the above technical solution is as follows: first, relevant data within the county are collected from multiple sources (such as sensors, weather stations, and information disclosure websites), including meteorological data (such as temperature and precipitation), water quality data (such as dissolved oxygen and pH value), soil data (such as heavy metal content and fertility), remote sensing images (used to monitor changes in surface cover and vegetation health), and demographic data (such as population density and distribution of economic activities); the collected data are preprocessed; and the preprocessed multi-source data are integrated using a data fusion algorithm based on machine learning to form a database that comprehensively reflects the resource and environmental conditions of the county. From the comprehensive database, feature engineering techniques were used to extract key features directly related to resource and environmental risks, such as environmentally sensitive areas (nature reserves, water sources, etc.) and the distribution of pollution sources (industrial emissions, agricultural non-point source pollution, etc.). A model was constructed based on a deep learning framework to capture the spatiotemporal characteristics of county-level resource and environmental risks. Convolutional neural networks (CNNs) excel at processing image data, but are also effective in processing time series and multidimensional data, making them suitable for analyzing complex patterns of resource and environmental risks. The model was trained using historical data, and performance was optimized by adjusting model parameters (such as the learning rate, which controls the speed of model updates, and the batch size, which affects the amount of data processed per iteration) and optimizers (such as the Adam optimizer, which adaptively adjusts the learning rate, and the SGD stochastic gradient descent, which is simple and direct). Cross-validation was used to evaluate the model's performance on different datasets, and grid search techniques were used to identify the optimal parameter combination, ensuring both high accuracy and generalizability to new data. GIS technology was used to divide the county into multiple grid cells, each serving as the basic unit of risk assessment. The trained CNN model is applied to these grid cells to calculate the resource and environmental risk score of each cell; based on the time series data of historical risk scores, time series analysis techniques (such as ARIMA, LSTM, etc.) are used to predict risk trends in the future; through professional GIS tools such as ArcGIS, the risk assessment and prediction results are intuitively displayed in the form of maps, including risk level distribution maps and high-risk area markers; a risk assessment and prediction report is compiled, covering the risk overview (overall risk status), major risk points (high-risk areas or specific issues), and recommended measures based on the analysis results, providing a scientific basis for county-level resource and environmental management.

[0027] The effects of the above technical solution are: through the data fusion algorithm based on machine learning, multi-source data such as meteorology, water quality, soil, remote sensing images and demographics in the county are effectively integrated, avoiding the phenomenon of information islands and improving the comprehensive utilization efficiency of data; the application of feature engineering technology enables the extraction of key features directly related to environmental risks from the fused comprehensive database, such as environmentally sensitive areas, pollution source distribution and socio-economic pressure indicators, providing accurate data support for subsequent risk assessment; the convolutional neural network model built based on the deep learning framework can capture the spatiotemporal characteristics of county resource and environmental risks, and improve the model's prediction through training with historical data. Accuracy; the model is verified and tuned through cross-validation, grid search and other technologies to ensure the stability and generalization ability of the model, making the risk assessment results more reliable; based on time series analysis, the county's resource and environmental risks in the future are predicted, providing decision makers with forward-looking risk warning information; the county is divided into several grid units through GIS technology, realizing the spatialization of risk assessment, and the risk score of each grid unit intuitively shows the spatial distribution characteristics of the risk within the county; ArcGIS tools are used to display the risk assessment and prediction results in the form of a map, including risk level distribution and high-risk area markings, so that decision makers can quickly understand the risk situation. At the same time, the generated risk assessment and prediction report provides a risk overview, main risk points and recommended measures, providing a scientific basis for the formulation of risk management strategies; this technical solution provides scientific and systematic methodological support for county resource and environmental risk assessment, ensuring the objectivity and accuracy of risk assessment results; based on the risk assessment results, targeted risk management policies can be formulated, such as strengthening the protection of environmentally sensitive areas and optimizing the distribution of pollution sources, providing strong guarantees for the sustainable development of the county.

[0028] In one embodiment of the present invention, the S2 includes:

[0029] S21. Analyze various data sources, including data sources, nature, accuracy, and potential relevance, and construct a fusion framework using a machine learning algorithm based on the data source analysis results.

[0030] S22. Before fusion, align various data, including timestamp calibration and spatial coordinate conversion, and perform normalization on data with different dimensions;

[0031] S23. Based on the use of machine learning algorithms to fuse the pre-processed data, after data fusion, key features related to county resource and environmental risk assessment are screened out through correlation analysis;

[0032] S24. Further extract key features through feature extraction technology, and optimize the extracted features. The optimization includes removing redundant features and improving the independence between features.

[0033] The working principle of the above technical solution is to conduct in-depth analysis of various data sources, including understanding the reliability of the data sources, the nature of the data (e.g., continuous, discrete, time series), the accuracy of the data (e.g., measurement error, missing data), and potential correlations between the data. Based on the results of the data source analysis, a data fusion framework is constructed using machine learning algorithms (e.g., cluster analysis and principal component analysis). The various data types are then aligned to ensure temporal and spatial consistency. Timestamp alignment involves aligning time records from different data sources to a common time base for time series analysis. Spatial coordinate transformation unifies the spatial location information of different data sources into a common coordinate system to facilitate spatial analysis. For data with different dimensions (e.g., temperature, humidity, water quality indicators), normalization is performed to eliminate the impact of dimensional differences on data analysis. Normalization typically involves scaling the data to a specific range (e.g., 0-1) to enable comparison and fusion of data of different dimensions on the same scale. The preprocessed data is then fused using machine learning algorithms (e.g., support vector machines and neural networks). The fused data is then analyzed through correlation analysis to identify key features directly relevant to county-level resource and environmental risk assessments. These key features are typically variables significantly associated with resource and environmental risks, reflecting their status and development trends. Feature extraction techniques (such as principal component analysis and linear discriminant analysis) are then used to further extract these key features to obtain more representative feature vectors. These feature vectors serve as input to subsequent deep learning models and more accurately reflect the essential characteristics of resource and environmental risks. After feature extraction, these features are optimized, including removing redundant features (i.e., those that are highly correlated with other features or have low information content) and increasing the independence of features (i.e., reducing their correlation).

[0034] The effects of the above technical solutions are as follows: through in-depth analysis of various data sources, we can accurately understand the source, nature, accuracy and potential correlation of the data, provide a basis for building an efficient data fusion framework, and ensure that the fusion process can fully consider the characteristics and needs of the data to avoid information loss or redundancy; the fusion framework built based on the data source analysis results can guide the data fusion process and optimize the fusion strategy, thereby improving the fusion efficiency and accuracy; timestamp calibration and spatial coordinate transformation ensure the consistency of data in time and space, providing a reliable basis for subsequent data analysis and fusion; normalization processing eliminates the differences between data of different dimensions, so that data can be analyzed on the same scale. Comparison and analysis improve the accuracy and effectiveness of data fusion; data fusion through machine learning algorithms can integrate information from different data sources, reveal the potential relationships and patterns between data, and provide more comprehensive data support for county resource and environmental risk assessment; correlation analysis can screen out key features directly related to county resource and environmental risk assessment, and obtain more representative feature vectors through feature extraction technology, which can reduce the input dimension of the deep learning model and reduce the model complexity, thereby improving the model's training efficiency and prediction performance; removing redundant features and improving the independence between features can further optimize feature quality, reduce the risk of model overfitting, and improve the model's generalization ability.

[0035] In one embodiment of the present invention, the step S21 includes:

[0036] The acquired data sources are evaluated based on their timeliness, completeness, accuracy, and accessibility. Statistical methods (such as missing value ratio and outlier detection) and technical indicators (such as data update frequency) are used to quantify their scores. The following formula is used to evaluate the nature of the data source:

[0037]

[0038] in, represents the property evaluation score of the rth data source; represents a positive constant used to adjust the scale of the final score; A mathematical quantity (in this case, 4: timeliness, completeness, accuracy, and accessibility); Represents the weight of j attributes; Represents the calculation function of the rth data source on the jth attribute; A positive constant representing the jth attribute, used for normalization The value of Represents the exponent of the jth attribute, which is used to adjust the influence of the logarithmic function; represents the base of the logarithmic function; Represents a very small positive number; For all data sources The j-th attribute of takes the maximum value;

[0039] Based on the results of the nature assessment, data sources are divided into three categories: basic data, auxiliary data, and supplementary data, and their priorities are set according to their importance to the county resource and environmental risk assessment;

[0040] Through cross-validation, the accuracy of key indicators in the data source is verified, low-precision data is identified and marked, and through association rule mining, potential correlations between different data sources are obtained to identify data combinations or patterns that may have a significant impact on county resource and environmental risk assessments;

[0041] Based on the data source analysis results, a multi-source data fusion framework is designed, including data access layer, preprocessing layer, fusion layer, analysis layer and application layer.

[0042] The working principle of the above technical solution is as follows: First, focus on the four key dimensions of data: timeliness, completeness, accuracy, and accessibility. Timeliness assesses the freshness of the data, namely, whether the data reflects the current situation; completeness checks whether the data is comprehensive and complete; accuracy measures the authenticity and reliability of the data; and accessibility considers the difficulty and cost of obtaining the data. Statistical methods, such as calculating the proportion of missing values, are used to assess data completeness, and outlier detection is used to identify errors or unreasonable values ​​in the data to assess accuracy. Furthermore, technical indicators, such as data update frequency, are used to assess data timeliness and accessibility. The assessment results are presented as quantitative scores, providing a basis for subsequent data classification and prioritization. Based on the results of the quality assessment, the technical solution categorizes data sources into three categories: basic data, auxiliary data, and supplementary data. Basic data are the core data necessary for assessing county-level resource and environmental risks and are highly timely and accurate. Auxiliary data provide additional information to enhance the comprehensiveness and depth of the assessment; and supplementary data are used to fill data gaps or provide background information. Different priorities are set based on the importance of data sources to the county-level resource and environmental risk assessment. Basic data has the highest priority, ensuring that it is given priority during the data fusion and analysis process; auxiliary data is second and is used to enrich the assessment content; supplementary data is handled flexibly according to specific circumstances. The accuracy of key indicators in the data source is verified through cross-validation. Cross-validation is a statistical method that evaluates the accuracy of the data by dividing the data into training and test sets, training the model with the training set, and then verifying the model performance with the test set. Low-precision data will be identified and marked for subsequent data cleaning and correction. At the same time, association rule mining technology is used to explore potential correlations between different data sources. Association rule mining is a machine learning technology that discovers interesting relationships between items in a data set. Through this technology, data combinations or patterns that may have a significant impact on county-level resource and environmental risk assessments can be identified, providing valuable clues for data fusion. Based on the results of the data source analysis, the technical solution designed a multi-source data fusion framework, including a data access layer, a preprocessing layer, a fusion layer, an analysis layer, and an application layer.

[0043] The data access layer is responsible for receiving data from different data sources and ensuring smooth data access.

[0044] The preprocessing layer cleans, transforms and normalizes the data to eliminate errors, inconsistencies and redundancies in the data, providing high-quality data for subsequent data fusion and analysis.

[0045] The fusion layer adopts appropriate fusion strategies and methods according to the nature and importance of the data source to organically integrate data from different data sources and form a unified data view.

[0046] The analysis layer uses machine learning algorithms and models to conduct in-depth analysis of the integrated data, extracting key information and features to provide a scientific basis for county-level resource and environmental risk assessments. The application layer presents the analysis results to users in the form of visual reports and early warning systems to support decision-making and risk management.

[0047] The effects of the above technical solutions are: by comprehensively evaluating the timeliness, completeness, accuracy and accessibility of data, and using statistical methods and technical indicators for quantitative scoring, it can objectively and accurately reflect the quality status of data sources, identify high-quality data sources, and provide a reliable basis for subsequent data processing and analysis; divide data sources into three categories: basic data, auxiliary data, and supplementary data, and set priorities according to their importance to county resource and environmental risk assessment, which helps to achieve hierarchical management and effective use of data; priority setting can ensure that in the process of data processing and analysis, priority attention and use are given to data sources that have an important impact on the assessment results, thereby improving the accuracy and efficiency of the assessment; cross-validation is used to verify the accuracy of key indicators in the data source, which can identify and mark low-precision data, thereby avoiding the negative impact of these data on the assessment results; the results of accuracy verification can also be used to guide data cleaning The data is then cleaned and corrected to further improve its accuracy and reliability. Association rule mining technology can be used to discover potential correlations between different data sources and identify data combinations or patterns that may have a significant impact on county-level resource and environmental risk assessments. The designed data access layer can conveniently receive data from different data sources and achieve unified data access and management. The fusion layer uses appropriate fusion strategies and methods to organically integrate data from different data sources to form a unified data view. The analysis layer uses machine learning algorithms and models to conduct in-depth analysis of the fused data, extracting key information and features to provide a scientific basis for county-level resource and environmental risk assessments. The application layer can present the analysis results to users in the form of visual reports, early warning systems, etc., to support decision-making and risk management. Diverse output methods help users more intuitively understand the assessment results and take appropriate measures to address county-level resource and environmental risks. The above formula comprehensively evaluates the quality of data sources by comprehensively considering the timeliness, completeness, accuracy, and accessibility of data. The multi-dimensional evaluation method helps to more accurately identify the advantages and disadvantages of data sources, thereby improving the quality of data-driven services; by introducing maximum value normalization processing, the evaluation scores between different data sources can be compared on a relative scale, enhancing the comparability between data sources; by evaluating the quality of data sources, quality marks can be made when the data is pushed to the cloud or stored in the database, similar to store credit.In this way, when query results need to be returned quickly, high-quality data sources can be read directly, saving a lot of resources; since high-quality data sources can be read directly, the need to traverse and calculate all data is reduced, thus saving time and computing resources; in applications that need to extract data from sensors instantly, after understanding the quality of the data source, low-quality data sources can be directly ignored to avoid poor-quality data contaminating the query results; after knowing the quality of the data source, data requests for low-quality data sources can be reduced, saving resources such as network bandwidth; even sensors with only high-quality data sources can be required to return data, thereby saving energy.

[0048] In one embodiment of the present invention, the step S22 includes:

[0049] Determine a unified time base (such as UTC) and convert timestamps from different data sources to this base time using a timestamp calibration algorithm. Evaluate the effectiveness of timestamp calibration based on timestamp quality evaluation metrics, such as timestamp accuracy and timestamp continuity.

[0050] The time series data is interpolated by linear interpolation to fill the missing time points. At the same time, based on the time series synchronization algorithm, the data with different frequencies are converted to the same sampling interval;

[0051] According to the needs of county-level resource and environmental risk assessment, set a time window (such as daily, weekly, monthly, etc.) and perform aggregation processing on time series data, such as calculating the average, sum, and maximum value;

[0052] Through geographic coordinate systems (such as WGS-84, UTM, etc.), and based on coordinate system conversion algorithms, coordinates in different coordinate systems are converted to coordinates in the same coordinate system;

[0053] Image pyramids are used to match and fuse data of different spatial resolutions. Based on the needs of county-level resource and environmental risk assessment, a unified spatial range is set, and data outside the range is clipped based on a spatial clipping algorithm.

[0054] The data are standardized and normalized through the minimum-maximum normalization algorithm, and data of different physical dimensions are converted into data of the same physical unit.

[0055] The working principle of the above technical solution is as follows: when processing multi-source data, first determine a globally common time base, such as Coordinated Universal Time (UTC); UTC is an accurate time standard that can ensure the uniformity and comparability of time data around the world; use the timestamp calibration algorithm to convert timestamps from different data sources into UTC time; evaluate the calibrated timestamps through timestamp quality assessment indicators, such as timestamp accuracy and continuity; timestamp accuracy assesses the accuracy of timestamps, while continuity assesses the integrity and continuity of timestamps; for missing time points in time series data, linear interpolation algorithm is used to fill in the gaps; linear interpolation is based on the linear relationship between adjacent data points and fills in missing data by calculating the values ​​of interpolation points; use the time series synchronization algorithm to convert data of different frequencies into the same sampling interval; the algorithm will take into account the sampling frequency and periodicity of the data, and achieve alignment of data in the time dimension through methods such as resampling or aggregation; set a reasonable time window (such as day, week, month, etc.) according to the needs of county-level resource and environmental risk assessment; Aggregate time series data, such as calculating the average, sum, maximum, etc., to extract key time features; use geographic coordinate systems (such as WGS-84, UTM, etc.) as a unified spatial reference system; use coordinate system conversion algorithms to convert coordinates in different coordinate systems into coordinates in the same coordinate system; use image pyramid and other algorithms to match and fuse data with different spatial resolutions; the algorithm will consider the spatial distribution characteristics and correlation of the data, and improve the spatial resolution and integrity of the data through multi-scale analysis and fusion; set a unified spatial range based on the needs of county resource and environmental risk assessment; use spatial clipping algorithms to clip data that exceeds the range; the clipped data will be strictly limited to the set spatial range to facilitate subsequent analysis and processing; use the minimum-maximum normalization algorithm to standardize and normalize the data; the algorithm will scale the data to a specific range (such as 0-1) to eliminate the impact of different dimensions on data comparison and analysis; convert data of different physical dimensions into data under the same physical unit.

[0056] The effects of the above technical solutions are as follows: by determining a unified time base and performing timestamp calibration, the time differences between different data sources are eliminated, and the accuracy of the data in the time dimension is improved; the application of linear interpolation and time series synchronization algorithms effectively fills in the missing time points and converts data of different frequencies into the same sampling interval, further enhancing the accuracy and comparability of the data; the coordinate system conversion algorithm ensures that coordinates in different coordinate systems can be converted into coordinates in the same coordinate system, thereby achieving consistency of data in the spatial dimension; spatial resolution matching and fusion processing, as well as the application of spatial clipping algorithms, further enhance the consistency and integrity of the data in the spatial dimension; through The minimum-maximum standardization algorithm standardizes and normalizes the data, eliminating the impact of different physical dimensions on data comparison and analysis, making the data comparable in terms of dimension; the setting of time windows and the aggregation of time series data extract key time features, providing a more intuitive and comparable data basis for data analysis and risk assessment; at the same time, through automated and intelligent data processing processes, human intervention and errors are reduced, and the accuracy and reliability of data processing are improved; by integrating multi-source data and performing alignment and preprocessing, comprehensive, accurate and comparable data support is provided for county-level resource and environmental risk assessment, which is conducive to the formulation of scientific and reasonable environmental protection and resource management policies.

[0057] In one embodiment of the present invention, S3 includes:

[0058] S31. Based on the deep learning framework and the spatiotemporal characteristics of county-level resource and environmental risks, a convolutional neural network model was constructed, including input layer, convolution layer, pooling layer, fully connected layer, and output layer.

[0059] S32. Use the extracted key features as input data and historical risk events as label data to construct training and validation datasets.

[0060] S33. Train the convolutional neural network model using the training data set, and improve the prediction accuracy of the model by adjusting the model parameters and optimizer;

[0061] S34. Verify and tune the model through cross-validation. At the same time, monitor the model training process through visualization tools.

[0062] The working principle of the above technical solution is to build a convolutional neural network (CNN) model suitable for county-level resource and environmental risk assessment based on deep learning frameworks (such as TensorFlow and PyTorch). This model typically consists of an input layer, a convolutional layer, a pooling layer, a fully connected layer, and an output layer.

[0063] Input layer: Receives key feature vectors after feature extraction and optimization as input data.

[0064] Convolution layer: extracts local features from the input data through convolution operations, and the local features can reflect the spatiotemporal characteristics of county resource and environmental risks.

[0065] Pooling layer: Downsamples the features output by the convolutional layer to reduce the dimension and computational complexity of the data while retaining important features.

[0066] Fully connected layer: maps the features output by the pooling layer to the result space of risk assessment and performs linear transformation through weight and bias parameters.

[0067] Output layer: Outputs the prediction results of county resource and environmental risks, usually one or more continuous values ​​or classification labels.

[0068] Using the extracted key features as input data and historical risk events (such as pollution incidents and natural disasters) as labeled data, a training dataset and a validation dataset are constructed. The training dataset is used to train the CNN model, enabling it to learn the mapping between input data and risk events. The validation dataset is used to evaluate the model's performance and ensure that the model demonstrates good predictive ability even on unseen data. The CNN model is trained using the training dataset. During training, the model output is calculated using forward propagation, and the model weights and bias parameters are adjusted using the backpropagation algorithm to minimize the error between the output and the true label. This involves adjusting hyperparameters such as the learning rate and batch size. The learning rate determines the magnitude of the model weight update in each iteration, and the batch size determines the amount of data used for training in each iteration. By adjusting these parameters, the model's training speed and prediction accuracy can be optimized. An optimizer (such as Adam or SGD) is selected to update the model weights. The model is validated and tuned through cross-validation. Cross-validation divides the dataset into multiple parts, rotating one part as the validation set and the other part as the training set to evaluate the model's performance on different datasets. The cross-validation results are used to assess the model's stability and generalization ability. If the model performs poorly on the validation set, you may need to adjust the model structure or hyperparameters. Based on the cross-validation results, fine-tune the model, including adjusting the number of convolutional layers, kernel size, and pooling methods. Also, monitor the model training process using visualization tools (such as TensorBoard) to promptly identify and resolve issues such as vanishing gradients and overfitting.

[0069] The effects of the above technical solutions are as follows: the convolutional neural network model constructed according to the spatiotemporal characteristics of county resource and environmental risks can efficiently capture and utilize these characteristics to improve the accuracy and timeliness of risk assessment; the design of the model structure (including input layer, convolution layer, pooling layer, fully connected layer and output layer) fully considers the complexity and diversity of county resource and environmental risks, so that the model can adapt to the risk assessment needs of different regions and different types; by extracting key features as input data and using historical risk events as label data, high-quality training data sets and verification data sets are constructed, providing reliable data support for model training; the construction of training data sets and verification data sets helps to evaluate the generalization ability of the model and ensure that the model is applicable to unseen data. It can also show good predictive performance; by training the convolutional neural network model with the training data set and adjusting the model parameters and optimizer, the prediction accuracy of the model can be significantly improved, so that it can more accurately reflect the actual situation of resource and environmental risks in the county; reasonable parameter adjustment and optimizer selection can accelerate the model training process, reduce training time, and improve training efficiency; by verifying and tuning the model through cross-validation, the performance of the model on different data sets can be evaluated, potential problems can be discovered and improved, thereby enhancing the stability and reliability of the model; by using visualization tools to monitor the model training process, the training status of the model can be understood in real time, and problems such as gradient disappearance and overfitting can be discovered and solved in a timely manner to ensure the successful implementation of the model training.

[0070] In one embodiment of the present invention, the S4 includes:

[0071] S41. Based on GIS technology, the county is divided into several grid cells, and each grid cell is used as the basic unit for risk assessment;

[0072] S42. Apply the trained convolutional neural network model to each grid cell to calculate its resource and environmental risk score; at the same time, combine the spatiotemporal feature analysis to perform spatiotemporal distribution analysis on the risk score; the resource and environmental risk score is calculated using the following formula:

[0073]

[0074] in, Indicates resource and environmental risk score; represents the risk probability output by the convolutional neural network model of the i-th grid cell; represents the environmental sensitivity of the i-th grid cell; α and β represent the parameters for adjusting the weights of temporal and spatial features; and Respectively represent the temporal feature weight and spatial feature weight of the i-th grid unit; and Represent the maximum feature weight and maximum spatial feature weight in all grid cells respectively; represents the socioeconomic impact factor of the i-th grid cell; represents the effect of risk mitigation measures in the i-th grid cell; represents the rate of change (derivative) of the risk probability of the i-th grid cell over time; N represents the total number of grid cells;

[0075] S43. Based on the time series analysis algorithm, a time series prediction model is constructed to combine historical risk data and current environmental conditions to predict county resource and environmental risks in the future.

[0076] The working principle of the above technical solution is as follows: Utilizing Geographic Information System (GIS) technology, the county is divided into several grid cells. These grid cells serve as the basic units for risk assessment, enabling refined management and assessment of county-level resource and environmental risks. The size and shape of each grid cell can be flexibly adjusted based on the county's actual situation and assessment needs, ensuring the accuracy and reliability of the assessment results. A trained convolutional neural network model is applied to each grid cell, and its resource and environmental risk score is calculated by inputting key feature data within the grid cell. Simultaneously, spatiotemporal analysis is combined to analyze the spatiotemporal distribution of the risk score. This spatiotemporal analysis reveals how risk scores vary over time and space, helping to identify high-risk areas and time periods, providing an important basis for subsequent risk management and forecasting. A time series prediction model is constructed based on a time series analysis algorithm. The time series analysis algorithm utilizes historical risk data and current environmental conditions to reveal patterns and trends in risk over time. By inputting historical risk data and current environmental conditions, the time series prediction model can predict county-level resource and environmental risks over future periods. The prediction results can be used to guide the management and response of county-level resource and environmental risks, help decision makers formulate targeted risk management strategies and policy measures, and reduce the possibility and impact of risks.

[0077] The effect of the above technical solution is: by dividing the county into several grid units, each grid unit is used as the basic unit of risk assessment, and a refined assessment of the resource and environmental risks in the county is achieved. This division method can more accurately reflect the risk differences between different areas in the county, and provide decision makers with more detailed risk information; the application of GIS technology allows the risk assessment results to be displayed intuitively on the map, which is convenient for decision makers to quickly identify high-risk areas and provide a spatial reference for risk management and response; the trained convolutional neural network model can quickly and accurately calculate the resource and environmental risk score of each grid unit, greatly improving the efficiency of risk assessment; combined with spatiotemporal feature analysis, it can reveal the change pattern of risk scores in time and space, help identify the development trend of risks and high-risk periods, and provide a scientific basis for risk warning and response; the results of spatiotemporal distribution analysis can provide decision makers with information about risk distribution The intuitive information on the risk and changing trends can help to formulate targeted risk management strategies and policy measures; the time series prediction model constructed based on the time series analysis algorithm can combine historical risk data and current environmental conditions to accurately predict the county's resource and environmental risks in the future, which can help decision makers take measures in advance to reduce the possibility and impact of risks; the time series prediction model can dynamically adjust the prediction results according to changes in the current environmental conditions, so that the prediction is closer to the actual situation and improve the pertinence and effectiveness of decision-making; through the prediction of future risks, it can provide important reference for the county's long-term planning and sustainable development, and help decision makers formulate more scientific and reasonable risk management strategies and policy measures.The above formula effectively improves the accuracy, rationality and actual matching of resource and environmental risk assessment by adding dynamic adjustment parameters, improving accuracy, reducing dependence on expert experience and preventing unreasonable weight setting. At the same time, it prevents the one-sidedness caused by single factor assessment, reduces the blindness of risk management and improves the reliability of prediction. At the same time, the above formula introduces dynamically adjusted parameters α and β, which can automatically adjust the weights of time features and spatial features according to changes in different environments and time, thereby improving the adaptability and flexibility of the model. Combining the advantages of automatic feature extraction of convolutional neural network model, it reduces the intervention of human factors, realizes the unification of feature processing and model training process, and thus improves the accuracy of risk assessment. Traditional risk assessment methods often rely on the experience of experts for weight allocation, while the above formula uses numbers to automatically adjust the weights of time features and spatial features. The weights are determined in a data-driven manner, which reduces dependence on expert experience and makes the assessment results more objective. By combining GIS technology and time series analysis algorithms, the formula can more accurately reflect the spatiotemporal distribution of county resource and environmental risks, improving the matching of assessment results with actual conditions. The formula integrates multiple factors such as risk probability, environmental sensitivity, temporal and spatial characteristics, and socioeconomic influencing factors, preventing the one-sidedness caused by relying on a single factor for assessment, making the assessment results more comprehensive. The time series prediction model is combined with historical risk data and current environmental conditions to predict resource and environmental risks in the future, improving the reliability and foresight of the prediction. Through refined grid unit division and risk assessment, the blindness of risk management is reduced, risk prevention and control measures are more targeted, and the efficiency of risk management is improved.

[0078] In one embodiment of the present invention, the S5 includes:

[0079] S51. Using ArcGIS tools, risk assessment and prediction results are displayed in map form, including risk level distribution, high-risk area markings, and risk change trends. Color coding and icon annotation are used to visually display the spatial distribution and dynamic changes of county-level resource and environmental risks.

[0080] S52. Automatically generate a risk assessment and prediction report based on the risk assessment and prediction results; the report content includes a risk overview (such as risk type and distribution characteristics), major risk points (such as high-risk areas and potential pollution sources), and recommended measures (such as risk prevention and control, emergency response), etc.

[0081] The technical solution works as follows: Data required for risk assessment and prediction is collected, including risk level distribution, identification of high-risk areas, and risk trends. ArcGIS's mapping capabilities are then used to create a county-wide resource and environmental risk map based on the collected data. The map uses color coding and icon annotation to visually display the spatial distribution and dynamic changes of risk. For example, different colors can be used to represent different risk levels, specific icons can be used to mark high-risk areas, and arrows or color gradients can be used to indicate risk trends. Finally, the resulting map is presented using ArcGIS's display capabilities. By viewing the map, decision makers can quickly understand the overall resource and environmental risk situation within the county, as well as the specific locations of high-risk areas and potential risk points. The data generated during the risk assessment and prediction process is integrated, including key information such as risk level, high-risk areas, and potential pollution sources. A risk assessment and prediction report is compiled based on the integrated data. The report primarily includes a risk overview (e.g., risk type and distribution characteristics), key risk points (e.g., high-risk areas and potential pollution sources), and recommended measures (e.g., risk prevention and control, emergency response). Utilize automated tools or software to automatically format and generate reports according to pre-defined formats and templates. The generated reports contain detailed data and analysis results, along with a clear structure and easy-to-read format, making them easy for decision makers to review and understand. Generated reports are reviewed to ensure data accuracy and completeness. Once approved, they are distributed to relevant decision makers or departments for reference and decision-making support.

[0082] The above technical solution achieves the following: Using ArcGIS tools, risk assessment and forecast results are displayed in a map format, making the spatial distribution and dynamic changes of county-level resource and environmental risks intuitive and clear. Decision makers can clearly see the distribution of risk levels, high-risk areas, and risk trends, making it easier to understand the actual risk situation. The visualization allows decision makers to quickly capture key information, reducing the time spent interpreting large amounts of data and improving decision-making efficiency. Decision makers can more quickly identify high-risk areas and potential risk points, allowing them to take timely measures for risk prevention and control and emergency response. Through color coding and icon annotation, the visualization makes risk information easier to understand and disseminate, helping to reduce communication costs with relevant departments, stakeholders, and the public, and improving information transparency and credibility. The automatically generated risk assessment and forecast reports, based on detailed data analysis and scientific methodology, ensure accuracy and objectivity. The reports cover a risk overview, key risk points, and recommended measures, providing comprehensive support for decision makers. The automated report generation process reduces manual intervention and duplication of effort, improving work efficiency. Decision-makers can obtain the reports they need faster, enabling them to make timely decisions. The automated report generation process follows pre-defined formats and templates, ensuring standardized and consistent reporting. This helps decision-makers compare and analyze risks at different time points and in different scenarios, improving the consistency and scientific nature of their decisions.

[0083] One embodiment of the present invention, as Figure 2 As shown, a risk assessment and prediction system applicable to county resources and environment, the system includes:

[0084] Data acquisition module: acquires multi-source data within the county and pre-processes the acquired multi-source data;

[0085] Data fusion module: This module uses a machine learning-based data fusion algorithm to fuse pre-processed multi-source data, build a comprehensive database of county resources and environment, and extract key features from the comprehensive database through feature engineering.

[0086] Model training module: Based on the deep learning framework, build a convolutional neural network model and train the convolutional neural network model using historical data;

[0087] Model application module: Using GIS technology, the county is divided into several grid cells. The trained model is applied to each grid cell to calculate its resource and environmental risk score. Based on time series analysis, the county's resource and environmental risks in the future are predicted.

[0088] Report generation module: Using ArcGIS tools, risk assessment and prediction results are displayed in map form, and risk assessment and prediction reports are generated.

[0089] The working principle of the above technical solution is as follows: first, relevant data within the county are collected from multiple sources (such as sensors, weather stations, and information disclosure websites), including meteorological data (such as temperature and precipitation), water quality data (such as dissolved oxygen and pH value), soil data (such as heavy metal content and fertility), remote sensing images (used to monitor changes in surface cover and vegetation health), and demographic data (such as population density and distribution of economic activities); the collected data are preprocessed; and the preprocessed multi-source data are integrated using a data fusion algorithm based on machine learning to form a database that comprehensively reflects the resource and environmental conditions of the county. From the comprehensive database, feature engineering techniques were used to extract key features directly related to resource and environmental risks, such as environmentally sensitive areas (nature reserves, water sources, etc.) and the distribution of pollution sources (industrial emissions, agricultural non-point source pollution, etc.). A model was constructed based on a deep learning framework to capture the spatiotemporal characteristics of county-level resource and environmental risks. Convolutional neural networks (CNNs) excel at processing image data, but are also effective in processing time series and multidimensional data, making them suitable for analyzing complex patterns of resource and environmental risks. The model was trained using historical data, and performance was optimized by adjusting model parameters (such as the learning rate, which controls the speed of model updates, and the batch size, which affects the amount of data processed per iteration) and optimizers (such as the Adam optimizer, which adaptively adjusts the learning rate, and the SGD stochastic gradient descent, which is simple and direct). Cross-validation was used to evaluate the model's performance on different datasets, and grid search techniques were used to identify the optimal parameter combination, ensuring both high accuracy and generalizability to new data. GIS technology was used to divide the county into multiple grid cells, each serving as the basic unit of risk assessment. The trained CNN model is applied to these grid cells to calculate the resource and environmental risk score of each cell; based on the time series data of historical risk scores, time series analysis techniques (such as ARIMA, LSTM, etc.) are used to predict risk trends in the future; through professional GIS tools such as ArcGIS, the risk assessment and prediction results are intuitively displayed in the form of maps, including risk level distribution maps and high-risk area markers; a risk assessment and prediction report is compiled, covering the risk overview (overall risk status), major risk points (high-risk areas or specific issues), and recommended measures based on the analysis results, providing a scientific basis for county-level resource and environmental management.

[0090] The effects of the above technical solution are: through the data fusion algorithm based on machine learning, multi-source data such as meteorology, water quality, soil, remote sensing images and demographics in the county are effectively integrated, avoiding the phenomenon of information islands and improving the comprehensive utilization efficiency of data; the application of feature engineering technology enables the extraction of key features directly related to environmental risks from the fused comprehensive database, such as environmentally sensitive areas, pollution source distribution and socio-economic pressure indicators, providing accurate data support for subsequent risk assessment; the convolutional neural network model built based on the deep learning framework can capture the spatiotemporal characteristics of county resource and environmental risks, and improve the model's prediction through training with historical data. Accuracy; the model is verified and tuned through cross-validation, grid search and other technologies to ensure the stability and generalization ability of the model, making the risk assessment results more reliable; based on time series analysis, the county's resource and environmental risks in the future are predicted, providing decision makers with forward-looking risk warning information; the county is divided into several grid units through GIS technology, realizing the spatialization of risk assessment, and the risk score of each grid unit intuitively shows the spatial distribution characteristics of the risk within the county; ArcGIS tools are used to display the risk assessment and prediction results in the form of a map, including risk level distribution and high-risk area markings, so that decision makers can quickly understand the risk situation. At the same time, the generated risk assessment and prediction report provides a risk overview, main risk points and recommended measures, providing a scientific basis for the formulation of risk management strategies; this technical solution provides scientific and systematic methodological support for county resource and environmental risk assessment, ensuring the objectivity and accuracy of risk assessment results; based on the risk assessment results, targeted risk management policies can be formulated, such as strengthening the protection of environmentally sensitive areas and optimizing the distribution of pollution sources, providing strong guarantees for the sustainable development of the county.

[0091] In one embodiment of the present invention, the data fusion module includes:

[0092] Data analysis module: Analyzes various data sources and builds a fusion framework based on the analysis results using machine learning algorithms;

[0093] Data alignment module: Before fusion, various types of data are aligned, including timestamp calibration and spatial coordinate conversion, and data with different dimensions are normalized;

[0094] Data fusion module: Based on the use of machine learning algorithms to fuse the pre-processed data, after data fusion, the key features related to the county resource and environmental risk assessment are screened out through correlation analysis;

[0095] Optimization processing module: further extracts key features through feature extraction technology and optimizes the extracted features, which includes removing redundant features and improving the independence between features.

[0096] The working principle of the above technical solution is to conduct in-depth analysis of various data sources, including understanding the reliability of the data sources, the nature of the data (e.g., continuous, discrete, time series), the accuracy of the data (e.g., measurement error, missing data), and potential correlations between the data. Based on the results of the data source analysis, a data fusion framework is constructed using machine learning algorithms (e.g., cluster analysis and principal component analysis). The various data types are then aligned to ensure temporal and spatial consistency. Timestamp alignment involves aligning time records from different data sources to a common time base for time series analysis. Spatial coordinate transformation unifies the spatial location information of different data sources into a common coordinate system to facilitate spatial analysis. For data with different dimensions (e.g., temperature, humidity, water quality indicators), normalization is performed to eliminate the impact of dimensional differences on data analysis. Normalization typically involves scaling the data to a specific range (e.g., 0-1) to enable comparison and fusion of data of different dimensions on the same scale. The preprocessed data is then fused using machine learning algorithms (e.g., support vector machines and neural networks). The fused data is then analyzed through correlation analysis to identify key features directly relevant to county-level resource and environmental risk assessments. These key features are typically variables significantly associated with resource and environmental risks, reflecting their status and development trends. Feature extraction techniques (such as principal component analysis and linear discriminant analysis) are then used to further extract these key features to obtain more representative feature vectors. These feature vectors serve as input to subsequent deep learning models and more accurately reflect the essential characteristics of resource and environmental risks. After feature extraction, these features are optimized, including removing redundant features (i.e., those that are highly correlated with other features or have low information content) and increasing the independence of features (i.e., reducing their correlation).

[0097] The effects of the above technical solutions are as follows: through in-depth analysis of various data sources, we can accurately understand the source, nature, accuracy and potential correlation of the data, provide a basis for building an efficient data fusion framework, and ensure that the fusion process can fully consider the characteristics and needs of the data to avoid information loss or redundancy; the fusion framework built based on the data source analysis results can guide the data fusion process and optimize the fusion strategy, thereby improving the fusion efficiency and accuracy; timestamp calibration and spatial coordinate transformation ensure the consistency of data in time and space, providing a reliable basis for subsequent data analysis and fusion; normalization processing eliminates the differences between data of different dimensions, so that data can be analyzed on the same scale. Comparison and analysis improve the accuracy and effectiveness of data fusion; data fusion through machine learning algorithms can integrate information from different data sources, reveal the potential relationships and patterns between data, and provide more comprehensive data support for county resource and environmental risk assessment; correlation analysis can screen out key features directly related to county resource and environmental risk assessment, and obtain more representative feature vectors through feature extraction technology, which can reduce the input dimension of the deep learning model and reduce the model complexity, thereby improving the model's training efficiency and prediction performance; removing redundant features and improving the independence between features can further optimize feature quality, reduce the risk of model overfitting, and improve the model's generalization ability.

[0098] In one embodiment of the present invention, the model training module includes:

[0099] Model building module: Based on the deep learning framework and the spatiotemporal characteristics of county-level resource and environmental risks, a convolutional neural network model is constructed, including input layer, convolution layer, pooling layer, fully connected layer, and output layer;

[0100] Dataset construction module: Use the extracted key features as input data and historical risk events as label data to construct training and validation datasets;

[0101] Accuracy improvement module: trains the convolutional neural network model through the training data set, and improves the prediction accuracy of the model by adjusting the model parameters (and optimizer);

[0102] Process monitoring module: Validates and tunes the model through cross-validation, and monitors the model training process through visualization tools.

[0103] The working principle of the above technical solution is to build a convolutional neural network (CNN) model suitable for county-level resource and environmental risk assessment based on deep learning frameworks (such as TensorFlow and PyTorch). This model typically consists of an input layer, a convolutional layer, a pooling layer, a fully connected layer, and an output layer.

[0104] Input layer: Receives key feature vectors after feature extraction and optimization as input data.

[0105] Convolution layer: extracts local features from the input data through convolution operations, and the local features can reflect the spatiotemporal characteristics of county resource and environmental risks.

[0106] Pooling layer: Downsamples the features output by the convolutional layer to reduce the dimension and computational complexity of the data while retaining important features.

[0107] Fully connected layer: maps the features output by the pooling layer to the result space of risk assessment and performs linear transformation through weight and bias parameters.

[0108] Output layer: Outputs the prediction results of county resource and environmental risks, usually one or more continuous values ​​or classification labels.

[0109] Using the extracted key features as input data and historical risk events (such as pollution incidents and natural disasters) as labeled data, a training dataset and a validation dataset are constructed. The training dataset is used to train the CNN model, enabling it to learn the mapping between input data and risk events. The validation dataset is used to evaluate the model's performance and ensure that the model demonstrates good predictive ability even on unseen data. The CNN model is trained using the training dataset. During training, the model output is calculated using forward propagation, and the model weights and bias parameters are adjusted using the backpropagation algorithm to minimize the error between the output and the true label. This involves adjusting hyperparameters such as the learning rate and batch size. The learning rate determines the magnitude of the model weight update in each iteration, and the batch size determines the amount of data used for training in each iteration. By adjusting these parameters, the model's training speed and prediction accuracy can be optimized. An optimizer (such as Adam or SGD) is selected to update the model weights. The model is validated and tuned through cross-validation. Cross-validation divides the dataset into multiple parts, rotating one part as the validation set and the other part as the training set to evaluate the model's performance on different datasets. The cross-validation results are used to assess the model's stability and generalization ability. If the model performs poorly on the validation set, you may need to adjust the model structure or hyperparameters. Based on the cross-validation results, fine-tune the model, including adjusting the number of convolutional layers, kernel size, and pooling methods. Also, monitor the model training process using visualization tools (such as TensorBoard) to promptly identify and resolve issues such as vanishing gradients and overfitting.

[0110] The effects of the above technical solutions are as follows: the convolutional neural network model constructed according to the spatiotemporal characteristics of county resource and environmental risks can efficiently capture and utilize these characteristics to improve the accuracy and timeliness of risk assessment; the design of the model structure (including input layer, convolution layer, pooling layer, fully connected layer and output layer) fully considers the complexity and diversity of county resource and environmental risks, so that the model can adapt to the risk assessment needs of different regions and different types; by extracting key features as input data and using historical risk events as label data, high-quality training data sets and verification data sets are constructed, providing reliable data support for model training; the construction of training data sets and verification data sets helps to evaluate the generalization ability of the model and ensure that the model is applicable to unseen data. It can also show good predictive performance; by training the convolutional neural network model with the training data set and adjusting the model parameters and optimizer, the prediction accuracy of the model can be significantly improved, so that it can more accurately reflect the actual situation of resource and environmental risks in the county; reasonable parameter adjustment and optimizer selection can accelerate the model training process, reduce training time, and improve training efficiency; by verifying and tuning the model through cross-validation, the performance of the model on different data sets can be evaluated, potential problems can be discovered and improved, thereby enhancing the stability and reliability of the model; by using visualization tools to monitor the model training process, the training status of the model can be understood in real time, and problems such as gradient disappearance and overfitting can be discovered and solved in a timely manner to ensure the successful implementation of the model training.

[0111] In one embodiment of the present invention, the model application module includes:

[0112] Unit division module: Based on GIS technology, the county is divided into several grid units, each of which serves as the basic unit for risk assessment;

[0113] Distribution Analysis Module: This module applies the trained convolutional neural network model to each grid cell to calculate its resource and environmental risk score. It also analyzes the spatiotemporal distribution of the risk score in combination with spatiotemporal feature analysis.

[0114] Risk prediction module: Based on the time series analysis algorithm, a time series prediction model is constructed to combine historical risk data and current environmental conditions to predict county resource and environmental risks in the future.

[0115] The working principle of the above technical solution is as follows: Utilizing Geographic Information System (GIS) technology, the county is divided into several grid cells. These grid cells serve as the basic units for risk assessment, enabling refined management and assessment of county-level resource and environmental risks. The size and shape of each grid cell can be flexibly adjusted based on the county's actual situation and assessment needs, ensuring the accuracy and reliability of the assessment results. A trained convolutional neural network model is applied to each grid cell, and its resource and environmental risk score is calculated by inputting key feature data within the grid cell. Simultaneously, spatiotemporal analysis is combined to analyze the spatiotemporal distribution of the risk score. This spatiotemporal analysis reveals how risk scores vary over time and space, helping to identify high-risk areas and time periods, providing an important basis for subsequent risk management and forecasting. A time series prediction model is constructed based on a time series analysis algorithm. The time series analysis algorithm utilizes historical risk data and current environmental conditions to reveal patterns and trends in risk over time. By inputting historical risk data and current environmental conditions, the time series prediction model can predict county-level resource and environmental risks over future periods. The prediction results can be used to guide the management and response of county-level resource and environmental risks, help decision makers formulate targeted risk management strategies and policy measures, and reduce the possibility and impact of risks.

[0116] The effect of the above technical solution is: by dividing the county into several grid units, each grid unit is used as the basic unit of risk assessment, and a refined assessment of the resource and environmental risks in the county is achieved. This division method can more accurately reflect the risk differences between different areas in the county, and provide decision makers with more detailed risk information; the application of GIS technology allows the risk assessment results to be displayed intuitively on the map, which is convenient for decision makers to quickly identify high-risk areas and provide a spatial reference for risk management and response; the trained convolutional neural network model can quickly and accurately calculate the resource and environmental risk score of each grid unit, greatly improving the efficiency of risk assessment; combined with spatiotemporal feature analysis, it can reveal the change pattern of risk scores in time and space, help identify the development trend of risks and high-risk periods, and provide a scientific basis for risk warning and response; the results of spatiotemporal distribution analysis can provide decision makers with information about risk distribution The intuitive information on the risk and changing trends can help to formulate targeted risk management strategies and policy measures; the time series prediction model constructed based on the time series analysis algorithm can combine historical risk data and current environmental conditions to accurately predict the county's resource and environmental risks in the future, which can help decision makers take measures in advance to reduce the possibility and impact of risks; the time series prediction model can dynamically adjust the prediction results according to changes in the current environmental conditions, so that the prediction is closer to the actual situation and improve the pertinence and effectiveness of decision-making; through the prediction of future risks, it can provide important reference for the county's long-term planning and sustainable development, and help decision makers formulate more scientific and reasonable risk management strategies and policy measures.

[0117] In one embodiment of the present invention, the report generation module includes:

[0118] Map display module: Using ArcGIS tools, risk assessment and prediction results are displayed in the form of maps. Through color coding and icon annotation, the spatial distribution and dynamic changes of county resource and environmental risks are intuitively displayed;

[0119] Automatic generation module: automatically generates risk assessment and prediction reports based on risk assessment and prediction results.

[0120] The technical solution works as follows: Data required for risk assessment and prediction is collected, including risk level distribution, identification of high-risk areas, and risk trends. ArcGIS's mapping capabilities are then used to create a county-wide resource and environmental risk map based on the collected data. The map uses color coding and icon annotation to visually display the spatial distribution and dynamic changes of risk. For example, different colors can be used to represent different risk levels, specific icons can be used to mark high-risk areas, and arrows or color gradients can be used to indicate risk trends. Finally, the resulting map is presented using ArcGIS's display capabilities. By viewing the map, decision makers can quickly understand the overall resource and environmental risk situation within the county, as well as the specific locations of high-risk areas and potential risk points. The data generated during the risk assessment and prediction process is integrated, including key information such as risk level, high-risk areas, and potential pollution sources. A risk assessment and prediction report is compiled based on the integrated data. The report primarily includes a risk overview (e.g., risk type and distribution characteristics), key risk points (e.g., high-risk areas and potential pollution sources), and recommended measures (e.g., risk prevention and control, emergency response). Utilize automated tools or software to automatically format and generate reports according to pre-defined formats and templates. The generated reports contain detailed data and analysis results, along with a clear structure and easy-to-read format, making them easy for decision makers to review and understand. Generated reports are reviewed to ensure data accuracy and completeness. Once approved, they are distributed to relevant decision makers or departments for reference and decision-making support.

[0121] The above technical solution achieves the following: Using ArcGIS tools, risk assessment and forecast results are displayed in a map format, making the spatial distribution and dynamic changes of county-level resource and environmental risks intuitive and clear. Decision makers can clearly see the distribution of risk levels, high-risk areas, and risk trends, making it easier to understand the actual risk situation. The visualization allows decision makers to quickly capture key information, reducing the time spent interpreting large amounts of data and improving decision-making efficiency. Decision makers can more quickly identify high-risk areas and potential risk points, allowing them to take timely measures for risk prevention and control and emergency response. Through color coding and icon annotation, the visualization makes risk information easier to understand and disseminate, helping to reduce communication costs with relevant departments, stakeholders, and the public, and improving information transparency and credibility. The automatically generated risk assessment and forecast reports, based on detailed data analysis and scientific methodology, ensure accuracy and objectivity. The reports cover a risk overview, key risk points, and recommended measures, providing comprehensive support for decision makers. The automated report generation process reduces manual intervention and duplication of effort, improving work efficiency. Decision-makers can obtain the reports they need faster, enabling them to make timely decisions. The automated report generation process follows pre-defined formats and templates, ensuring standardized and consistent reporting. This helps decision-makers compare and analyze risks at different time points and in different scenarios, improving the consistency and scientific nature of their decisions.

[0122] Obviously, those skilled in the art may make various changes and modifications to the present invention without departing from the spirit and scope of the present invention. Thus, if such changes and modifications fall within the scope of the claims and their equivalents, the present invention is intended to include such changes and modifications.

Claims

1. A risk assessment and prediction method applicable to county resources and environment, characterized by: The method comprises: S1. Acquire multi-source data within the county, including meteorological data, water quality data, soil data, remote sensing images, and demographic data, and pre-process the acquired multi-source data; S2. Use a machine learning-based data fusion algorithm to fuse pre-processed multi-source data, build a county-level resource and environmental comprehensive database, and extract key features from the comprehensive database through feature engineering; S3. Based on the deep learning framework, build a convolutional neural network model and train it using historical data. S4. Using GIS technology, the county is divided into several grid cells. The trained model is applied to each grid cell to calculate its resource and environmental risk score. Based on time series analysis, the county's resource and environmental risks in the future are predicted. S5. Use ArcGIS tools to display risk assessment and prediction results in map form and generate risk assessment and prediction reports; Said S4 comprises: S41. Based on GIS technology, the county is divided into several grid cells, and each grid cell is used as the basic unit for risk assessment; S42. Apply the trained convolutional neural network model to each grid cell to calculate its resource and environmental risk score; at the same time, combine the spatiotemporal feature analysis to perform spatiotemporal distribution analysis on the risk score; the resource and environmental risk score is calculated using the following formula: Among them, RS represents the resource and environmental risk score; represents the risk probability output by the convolutional neural network model of the i-th grid cell; represents the environmental sensitivity of the i-th grid cell; α and β represent the parameters for adjusting the weights of temporal and spatial features; and Respectively represent the temporal feature weight and spatial feature weight of the i-th grid unit; and Represent the maximum feature weight and maximum spatial feature weight in all grid cells respectively; represents the socioeconomic impact factor of the i-th grid cell; represents the effect of risk mitigation measures in the i-th grid cell; represents the rate of change of the risk probability of the i-th grid unit over time; N represents the total number of grid units; S43. Based on the time series analysis algorithm, a time series prediction model is constructed to combine historical risk data and current environmental conditions to predict county resource and environmental risks in the future.

2. A risk assessment and prediction method applicable to county resources and environment according to claim 1, characterized in that: Said S2 comprises: S21. Analyze various data sources and build a fusion framework based on the analysis results using machine learning algorithms. S22. Before fusion, align various data, including timestamp calibration and spatial coordinate conversion, and perform normalization on data with different dimensions; S23. Based on the use of machine learning algorithms to fuse the pre-processed data, after data fusion, the key features related to the county resource and environmental risk assessment are screened out through correlation analysis; S24. Further extract key features through feature extraction technology, and optimize the extracted features. The optimization includes removing redundant features and improving the independence between features.

3. The risk assessment and prediction method applicable to county resources and environment according to claim 1 is characterized in that: Said S3 comprises: S31. Based on the deep learning framework and the spatiotemporal characteristics of county-level resource and environmental risks, a convolutional neural network model was constructed, including input layer, convolution layer, pooling layer, fully connected layer, and output layer. S32. Use the extracted key features as input data and historical risk events as label data to construct training and validation datasets. S33. Train the convolutional neural network model using the training data set, and improve the prediction accuracy of the model by adjusting the model parameters and optimizer; S34. Verify and tune the model through cross-validation. At the same time, monitor the model training process through visualization tools.

4. A risk assessment and prediction method applicable to county resources and environment according to claim 1, characterized in that: Said S5 comprises: S51. Use ArcGIS tools to display risk assessment and prediction results in map form, and use color coding and icon annotation to intuitively display the spatial distribution and dynamic changes of county resource and environmental risks; S52. Automatically generate a risk assessment and prediction report based on the risk assessment and prediction results.

5. A risk assessment and prediction system applicable to county resources and environment, characterized by: The system comprises: Data acquisition module: acquires multi-source data within the county, including meteorological data, water quality data, soil data, remote sensing images, and demographic data, and pre-processes the acquired multi-source data; Data fusion module: This module uses a machine learning-based data fusion algorithm to fuse pre-processed multi-source data, build a comprehensive database of county resources and environment, and extract key features from the comprehensive database through feature engineering. Model training module: Based on the deep learning framework, build a convolutional neural network model and train the convolutional neural network model using historical data; Model application module: Using GIS technology, the county is divided into several grid cells. The trained model is applied to each grid cell to calculate its resource and environmental risk score. Based on time series analysis, the county's resource and environmental risks in the future are predicted. Report generation module: Using ArcGIS tools, risk assessment and prediction results are displayed in map form, and risk assessment and prediction reports are generated; The model application module includes: Unit division module: Based on GIS technology, the county is divided into several grid units, each of which serves as the basic unit for risk assessment; Distribution Analysis Module: Apply the trained convolutional neural network model to each grid cell to calculate its resource and environmental risk score. At the same time, combine the spatiotemporal feature analysis to perform spatiotemporal distribution analysis on the risk score. The resource and environmental risk score is calculated using the following formula: Among them, RS represents the resource and environmental risk score; represents the risk probability output by the convolutional neural network model of the i-th grid cell; represents the environmental sensitivity of the i-th grid cell; α and β represent the parameters for adjusting the weights of temporal and spatial features; and Respectively represent the temporal feature weight and spatial feature weight of the i-th grid unit; and Represent the maximum feature weight and maximum spatial feature weight in all grid cells respectively; represents the socioeconomic impact factor of the i-th grid cell; represents the effect of risk mitigation measures in the i-th grid cell; represents the rate of change of the risk probability of the i-th grid unit over time; N represents the total number of grid units; Risk prediction module: Based on the time series analysis algorithm, a time series prediction model is constructed to combine historical risk data and current environmental conditions to predict county resource and environmental risks in the future.

6. A risk assessment and prediction system for county resources and environment according to claim 5, characterized in that: The data fusion module includes: Data analysis module: Analyzes various data sources and builds a fusion framework based on the analysis results using machine learning algorithms; Data alignment module: Before fusion, various types of data are aligned, including timestamp calibration and spatial coordinate conversion, and data with different dimensions are normalized; Data fusion module: Based on the use of machine learning algorithms to fuse the pre-processed data, after data fusion, the key features related to the county resource and environmental risk assessment are screened out through correlation analysis; Optimization processing module: further extracts key features through feature extraction technology and optimizes the extracted features, which includes removing redundant features and improving the independence between features.

7. A risk assessment and prediction system for county resources and environment according to claim 5, characterized in that: The model training module includes: Model building module: Based on the deep learning framework and the spatiotemporal characteristics of county-level resource and environmental risks, a convolutional neural network model is constructed, including input layer, convolution layer, pooling layer, fully connected layer, and output layer; Dataset construction module: Use the extracted key features as input data and historical risk events as label data to construct training and validation datasets; Precision improvement module: trains the convolutional neural network model using a training data set and improves the model's prediction accuracy by adjusting model parameters and optimizers; Process monitoring module: Validates and tunes the model through cross-validation, and monitors the model training process through visualization tools.

8. A risk assessment and prediction system for county resources and environment according to claim 5, characterized in that: The report generation module includes: Map display module: Using ArcGIS tools, the risk assessment and prediction results are displayed in the form of maps. Through color coding and icon annotation, the spatial distribution and dynamic changes of county resource and environmental risks are intuitively displayed; Automatic generation module: automatically generates risk assessment and prediction reports based on risk assessment and prediction results.

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