Risk assessment prediction method and system suitable for county resource environment

Abstract

The invention provides a risk assessment prediction method and system suitable for a county resource environment. Belongs to the technical field of resource environment risk assessment. The method comprises the steps that multi-source data in a county area are acquired, the multi-source data comprise meteorological data, water quality data, soil data, remote sensing images and demographic data, and the acquired multi-source data are preprocessed; and fusing the preprocessed multi-source data through a data fusion algorithm based on machine learning, constructing a county resource environment comprehensive database, and extracting key features from the comprehensive database through feature engineering. By integrating multi-source data from different fields and performing data fusion through a machine learning algorithm, multi-dimensional factors can be comprehensively considered, and information of different data sources is fully utilized, so that the accuracy and comprehensiveness of risk assessment are improved.

Description

Technical Field

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

[0002] As the basic unit of national economic and social development, the resource and environmental conditions of counties directly affect regional sustainable development and people's quality of life. Traditional risk assessment methods are often based on a single data source and a simple model, which is difficult to fully and accurately reflect the complexity and dynamics of county resources and environment. Therefore, it is particularly important to develop a resource and environmental risk assessment and prediction method suitable for the county scale. Summary of the invention

[0003] The present invention provides a risk assessment and prediction method and system applicable to county resources and environment, 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 in the county, wherein the multi-source data includes meteorological data, water quality data, soil data, remote sensing images and demographic data, and pre-process the acquired multi-source data;

[0006] S2. Through the data fusion algorithm based on machine learning, the pre-processed multi-source data are fused to build a comprehensive database of county resources and environment, and key features are extracted from the comprehensive database through feature engineering;

[0007] S3. Based on the deep learning framework, a convolutional neural network model is constructed and trained using historical data.

[0008] S4. Using GIS technology, the county is divided into several grid units, and the trained model is applied to each grid unit to calculate its resource and environmental risk score. Based on time series analysis, the county 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: Through the data fusion algorithm based on machine learning, the pre-processed multi-source data is fused to build a comprehensive database of county resources and environment, and key features are extracted 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 through historical data;

[0014] Model application module: Using GIS technology, the county is divided into several grid units, and the trained model is applied to each grid unit 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: Through ArcGIS tools, the risk assessment and prediction results are displayed in the form of maps, and risk assessment and prediction reports are generated.

[0016] The invention has the following beneficial effects: by integrating multi-source data from different fields (such as meteorology, water quality, soil, remote sensing images and demographic data), and performing data fusion through machine learning algorithms, it is possible to comprehensively consider multi-dimensional factors and make full use of information from different data sources, thereby improving the accuracy and comprehensiveness of risk assessment; by using the convolutional neural network (CNN) model in deep learning for training, it is possible to deeply explore the potential laws in historical data, and to conduct effective risk assessment and prediction based on the spatiotemporal characteristics of county resources and environment. By optimizing the structure and parameters of the model, the prediction accuracy of the model can be improved, thereby improving the reliability of risk assessment; by combining GIS technology and time series analysis, a detailed risk analysis of the spatial distribution and temporal dynamics within the county can be performed, and through the combination of space and time, the changing trends of resource and environmental risks in different regions and time periods can be accurately reflected, helping policy makers to take targeted measures; by using ArcGIS tools to display the assessment results in the form of a map, the spatial distribution and dynamic changes of risks can be intuitively displayed by using color coding and icon annotation, thereby enhancing the visualization effect and the clarity of information communication. At the same time, the automatically generated risk assessment and forecast reports can provide scientific basis for decision makers, facilitating subsequent response and management; through the time series analysis model to predict future risks, early warning of potential resource and environmental risks can be achieved. This forecasting capability can provide forward-looking information support for resource management, environmental protection and disaster prevention in the county, and enhance the ability to respond to risks. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] Figure 1 It is a step diagram 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 in conjunction with 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 comprising:

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

[0022] S2. The pre-processed multi-source data are fused through a data fusion algorithm based on machine learning to build a county-level resource and environmental comprehensive database, and key features are extracted from the comprehensive database through feature engineering; the key features include environmentally sensitive areas, pollution source distribution, and socio-economic pressure indicators;

[0023] S3. Based on the deep learning framework, a convolutional neural network model is constructed to capture the spatiotemporal characteristics of county 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 units. Each grid unit is used as the basic unit for risk assessment. The trained model is applied to each grid unit to calculate its resource and environmental risk score. Based on time series analysis, the county 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, collect relevant data within the county from multiple sources (such as sensors, weather stations, and information disclosure websites), including meteorological data (such as temperature, precipitation), water quality data (such as dissolved oxygen, pH value), soil data (such as heavy metal content, fertility), remote sensing images (used to monitor surface cover changes, vegetation health, etc.) and demographic data (such as population density, distribution of economic activities); pre-process the collected data; use a data fusion algorithm based on machine learning to integrate the pre-processed multi-source data to form a database that comprehensively reflects the resource and environmental conditions of the county. In the comprehensive database, key features are extracted through feature engineering technology, where the key features are 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.); based on the deep learning framework, a model that can capture the spatiotemporal characteristics of county resource and environmental risks is constructed; CNN is good at processing image data, but it can also effectively process time series and multidimensional data, which is suitable for analyzing complex patterns of resource and environmental risks; historical data is used to train the model, and the model parameters (such as learning rate controls the model update speed, batch size affects the amount of data processed in each iteration) and optimizers (such as Adam optimizer adaptively adjusts the learning rate, SGD stochastic gradient descent is simple and direct) are adjusted to optimize the model performance. The performance of the model on different data sets is evaluated through cross-validation, and the best parameter combination is found in combination with grid search technology to ensure that the model has both high accuracy and can be generalized to new data; GIS technology is used to divide the county into multiple grid cells, each of which is used 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, risk assessment and prediction results are intuitively displayed in the form of maps, including risk level distribution maps and high-risk area markers; risk assessment and prediction reports are compiled, covering 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 resource and environmental management.

[0027] The effects of the above technical solutions are as follows: 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, distribution of pollution sources 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 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 risks in the county; ArcGIS tools are used to display the risk assessment and prediction results in the form of maps, 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, optimizing the distribution of pollution sources, etc., to provide 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, properties, accuracy, and potential relevance, and build a fusion framework through a machine learning algorithm based on the data source analysis results;

[0030] S22, before fusion, aligning various types of data, wherein the alignment includes timestamp calibration and spatial coordinate conversion, and normalizing data with different dimensions;

[0031] 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;

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

[0033] The working principle of the above technical solution is: conduct in-depth analysis of various data sources, including understanding the reliability of data sources, the nature of data (such as continuous, discrete, time series, etc.), the accuracy of data (such as measurement error, data missing, etc.) and the potential correlation between data. Based on the results of data source analysis, a data fusion framework is constructed using machine learning algorithms (such as cluster analysis, principal component analysis, etc.). Then, various types of data are aligned to ensure the consistency of data in time and space. Among them, timestamp calibration refers to adjusting the time records of different data sources to the same time base for time series analysis. Spatial coordinate transformation is to unify the spatial location information of different data sources into the same coordinate system for spatial analysis; for data with different dimensions (such as temperature, humidity, water quality indicators, etc.), normalization is performed to eliminate the impact of dimension differences on data analysis. Normalization usually scales the data to a specific range (such as 0-1) so that data of different dimensions can be compared and fused on the same scale; and the pre-processed data is fused using machine learning algorithms (such as support vector machines, neural networks, etc.). The fused data is screened out through correlation analysis to identify key features directly related to the county resource and environmental risk assessment. The key features are usually those variables that are significantly associated with resource and environmental risks, which can reflect the status and development trend of resource and environmental risks; the key features are further extracted through feature extraction techniques (such as principal component analysis, linear discriminant analysis, etc.) to obtain more representative feature vectors. These feature vectors are the input of subsequent deep learning models, and they can more accurately reflect the essential characteristics of resource and environmental risks; after feature extraction, the extracted features need to be optimized, including removing redundant features (i.e., those that are highly correlated with other features or have low information content) and improving the independence between features (i.e., reducing the correlation between features).

[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 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 training efficiency and prediction performance of the model; removing redundant features and improving the independence between features can further optimize feature quality, reduce the risk of model overfitting, and improve the generalization ability of the model.

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

[0036] According to the timeliness, completeness, accuracy and accessibility of the data, the acquired data sources are evaluated for their properties, and quantitative scores are given using statistical methods (such as missing value ratio, outlier detection) and technical indicators (such as data update frequency); the properties of the data sources are evaluated using the following formula:

[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 index 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 property assessment, data sources are divided into three categories: basic data, auxiliary data, and supplementary data, and 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, the potential correlation between different data sources is obtained to identify data combinations or patterns that may have a significant impact on the county's resource and environmental risk assessment;

[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 evaluates the freshness of data, that is, whether the data reflects the current situation; completeness checks whether the data is comprehensive and without omissions; accuracy measures the authenticity and reliability of data; accessibility considers the difficulty and cost of obtaining data. Statistical methods are used, such as calculating the proportion of missing values ​​to evaluate the completeness of data, and outlier detection is used to identify errors or unreasonable values ​​in the data to evaluate accuracy. At the same time, technical indicators, such as the frequency of data updates, are used to evaluate the timeliness and accessibility of data. The evaluation results are presented in the form of quantitative scores to provide a basis for subsequent data classification and priority setting. Based on the results of the property evaluation, the technical solution divides data sources into three categories: basic data, auxiliary data and supplementary data. Basic data is the core data necessary for assessing county resource and environmental risks, and has a high degree of timeliness and accuracy; auxiliary data provides additional information for the assessment, enhances the comprehensiveness and depth of the assessment; supplementary data is used to fill data gaps or provide background information. Different priorities are set according to the importance of data sources to county resource and environmental risk assessment. Basic data has the highest priority to ensure that it is given priority in the data fusion and analysis process; auxiliary data is second and is used to enrich the evaluation 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 data by dividing the data into training sets 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 the potential correlation 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 an important impact on county resource and environmental risk assessment can be identified, providing valuable clues for data fusion. Based on the results of data source analysis, the technical solution designs a multi-source data fusion framework, including data access layer, preprocessing layer, fusion layer, analysis layer and 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 the data from different data sources to form a unified data view.

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

[0047] The effects of the above technical scheme are: by comprehensively evaluating the timeliness, completeness, accuracy and accessibility of the data, and using statistical methods and technical indicators for quantitative scoring, it can objectively and accurately reflect the quality status of the data source, identify high-quality data sources, and provide a reliable basis for subsequent data processing and analysis; divide the data sources into three categories: basic data, auxiliary data, and supplementary data, and set priorities according to their importance to the county resource and environmental risk assessment, which is conducive to the hierarchical management and effective use of data; priority setting can ensure that in the process of data processing and analysis, priority attention is paid to and used for data sources that have an important impact on the assessment results, thereby improving the accuracy and efficiency of the assessment; through cross-validation, the accuracy of key indicators in the data source is verified, and low-precision data can be identified and marked, thereby avoiding these data from having a negative impact on the assessment results; the results of accuracy verification can also be used to guide data cleaning The data is cleaned and corrected to further improve the accuracy and reliability of the data; the association rule mining technology can be used to discover the potential correlation between different data sources and identify the data combination or model that may have an important impact on the county resource and environmental risk assessment; the designed data access layer can easily receive data from different data sources and realize unified access and management of data; the fusion layer adopts 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, extract key information and features, and provide a scientific basis for the county resource and environmental risk assessment; 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; and the diversified output methods help users understand the assessment results more intuitively and take corresponding measures to deal with county resource and environmental risks. The above formula can comprehensively evaluate 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 pros and cons of data sources, thereby improving the quality of data-driven services; by introducing maximum value normalization, 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 marking can be made when data is pushed to the cloud or stored in a 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, thereby 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 S22 includes:

[0049] Determine a unified time base (such as UTC) and convert timestamps from different data sources to the base time through a timestamp calibration algorithm; and evaluate the effect of timestamp calibration based on timestamp quality evaluation indicators, 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 resource and environmental risk assessment, set the time window (such as day, week, month, etc.) and aggregate the 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 with different spatial resolutions. A unified spatial range is set according to the needs of county resource and environmental risk assessment, and data outside the range is clipped based on a spatial clipping algorithm.

[0054] The data are standardized and normalized through the minimum-maximum standardization algorithm, and the 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 ensures that time data from all over the world are unified and comparable; 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, use linear interpolation algorithms to fill in; 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 data alignment 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 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 algorithms such as image pyramids 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 of the same physical unit.

[0056] The effects of the above technical solution are as follows: by determining a unified time base and performing timestamp calibration, the time difference between different data sources is 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 the coordinates in different coordinate systems can be converted into the coordinates in the same coordinate system, thereby achieving the consistency of the data in the spatial dimension; the spatial resolution matching and fusion processing, as well as the application of the spatial clipping algorithm, 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 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, it reduces human intervention and errors, and improves the accuracy and reliability of data processing; by integrating multi-source data and performing alignment and preprocessing, it provides comprehensive, accurate and comparable data support for county resource and environmental risk assessment, which helps to formulate 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 resource and environmental risks, a convolutional neural network model is 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 the historical risk events as label data to construct a training data set and a validation data set;

[0060] S33, training the convolutional neural network model using the training data set, and improving the prediction accuracy of the model by adjusting the model parameters and the 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 resource and environmental risk assessment based on a deep learning framework (such as TensorFlow, PyTorch, etc.). The model usually consists of an input layer, a convolution layer, a pooling layer, a fully connected layer, and an output layer.

[0063] Input layer: receives the key feature vector 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 amount of computation 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] The extracted key features are used as input data, and historical risk events (such as pollution events, natural disasters, etc.) are used as label data to construct training data sets and validation data sets. The training data set is used to train the CNN model so that it can learn the mapping relationship between input data and risk events; the validation data set is used to evaluate the performance of the model to ensure that the model can also show good predictive ability on unseen data; the CNN model is trained using the training data set. During the training process, the output of the model is calculated through forward propagation, and the weights and bias parameters of the model are adjusted through the back propagation algorithm to minimize the error between the output and the true label; including the adjustment of hyperparameters such as 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 training speed and prediction accuracy of the model can be optimized; select an optimizer (such as Adam, SGD, etc.) to update the weights of the model. The model is verified and tuned through cross-validation. Cross-validation divides the data set into multiple parts, and trains one part as the validation set and the rest as the training set in turn to evaluate the performance of the model on different data sets; the stability and generalization ability of the model are evaluated through the results of cross-validation. If the model performs poorly on the validation set, you may need to adjust the model structure or hyperparameters; tune the model based on the results of cross-validation, including adjusting the number of convolutional layers, the size of the convolution kernel, the pooling method, etc. At the same time, monitor the model training process through visualization tools (such as TensorBoard, etc.) to promptly identify and solve problems such as gradient disappearance 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 prediction performance; by training the convolutional neural network model with training data sets and adjusting the model parameters and optimizer, the prediction accuracy of the model can be significantly improved, making it more accurately reflect the actual situation of resource and environmental risks in the county; reasonable parameter adjustment and optimizer selection can accelerate the training process of the model, 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 training process of the model, 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 units, each of which is used as the basic unit for risk assessment;

[0072] S42, applying the trained convolutional neural network model to each grid unit to calculate its resource and environmental risk score; at the same time, combining the spatiotemporal feature analysis, performing spatiotemporal distribution analysis on the risk score; the resource and environmental risk score is calculated by 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 unit; represents the environmental sensitivity of the i-th grid unit; 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 Respectively represent the maximum feature weight and the maximum spatial feature weight in all grid cells; represents the socioeconomic impact factor of the ith grid cell; represents the effect of risk mitigation measures in the ith grid cell; represents the rate of change (derivative) of the risk probability of the ith grid unit over time; N represents the total number of grid units;

[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: using geographic information system (GIS) technology, the county is divided into several grid units. The grid units are the basic units of risk assessment, which can realize the refined management and assessment of county resource and environmental risks. The size and shape of each grid unit can be flexibly adjusted according to the actual situation and assessment needs of the county to ensure the accuracy and reliability of the assessment results; the trained convolutional neural network model is applied to each grid unit, and its resource and environmental risk score is calculated by inputting the key feature data in the grid unit; at the same time, combined with spatiotemporal feature analysis, the spatiotemporal distribution analysis of the risk score is performed. Spatiotemporal feature analysis can reveal the changing rules of risk scores in time and space, help identify high-risk areas and time periods, and provide an important basis for subsequent risk management and prediction; based on the time series analysis algorithm, a time series prediction model is constructed. The time series analysis algorithm can use historical risk data and current environmental conditions to reveal the rules and trends of risk changes over time; by inputting historical risk data and current environmental conditions data, the time series prediction model can predict the county resource and environmental risks in the future. The prediction results can be used to guide the management and response of county 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 intuitively displayed 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 changing patterns of risk scores in time and space, help identify risk development trends 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, making the prediction closer to the actual situation and improving 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. It also 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, by introducing dynamically adjusted parameters α and β, the above formula can automatically adjust the weights of temporal 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 and realizes the unification of feature processing and model training process, thereby improving 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 calculate the weights of spatial and temporal features. The weights are determined in a data-driven manner, which reduces the reliance 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 socio-economic influencing factors, preventing the one-sidedness caused by relying solely 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, making risk prevention and control measures more targeted and improving the efficiency of risk management.

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

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

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

[0081] The working principle of the above technical solution is as follows: collect various data required for risk assessment and prediction, including risk level distribution, high-risk area marking, and risk change trend; use ArcGIS's map making function to make a map of county resource and environmental risks based on the collected data. On the map, the spatial distribution and dynamic changes of risks are intuitively displayed through color coding and icon marking. For example, different colors can be used to represent different risk levels, high-risk areas can be marked with specific icons, and arrows or color gradients can be used to represent the risk change trend; finally, the prepared map is presented through ArcGIS's display function. Decision makers can quickly understand the overall situation of resource and environmental risks in the county, as well as the specific locations of high-risk areas and potential risk points by observing the map. Integrate various data generated during risk assessment and prediction, including key information such as risk level, high-risk area, and potential pollution source. Write a risk assessment and prediction report based on the integrated data. The report mainly includes risk overview (such as risk type, distribution characteristics), main risk points (such as high-risk areas, potential pollution sources), and recommended measures (such as risk prevention and control, emergency response), etc. Utilize automated tools or software to automatically typeset and generate report content according to a predetermined format and template. The generated report contains detailed data and analysis results, and has a clear structure and easy-to-read format, making it easy for decision makers to review and understand. The generated report is reviewed to ensure the accuracy of the data and the completeness of the report. After the review is passed, the report is released to relevant decision makers or departments for reference and decision support.

[0082] The effect of the above technical solution is: the results of risk assessment and prediction are displayed in the form of maps through ArcGIS tools, making the spatial distribution and dynamic changes of county resource and environmental risks intuitive and clear. Decision makers can see the risk level distribution, high-risk areas and risk change trends at a glance, so that they can more easily understand the actual situation of the risk; the visual display enables decision makers to quickly capture key information, reduce the time for interpreting large amounts of data, and improve decision-making efficiency. Decision makers can identify high-risk areas and potential risk points more quickly, so as to take timely measures for risk prevention and control and emergency response; through color coding and icon marking, the visual display makes risk information easier to understand and disseminate, which helps to reduce the communication costs with relevant departments, stakeholders and the public, and improve the transparency and credibility of information; the automatically generated risk assessment and prediction report is based on detailed data analysis and scientific methodology to ensure the accuracy and objectivity of the report. The report content covers aspects such as risk overview, main risk points and recommended measures, providing comprehensive decision support for decision makers; the process of automatically generating reports reduces manual intervention and repetitive work, and improves work efficiency. Decision makers can get the required reports faster so that they can make decisions in a timely manner; the process of automatically generating reports follows the predetermined format and template, ensuring the standardization and consistency of reports. This helps decision makers compare and analyze risks at different time points and in different situations, and improves the consistency and scientificity of decision making.

[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 comprises:

[0084] Data acquisition module: obtain multi-source data within the county and pre-process the obtained multi-source data;

[0085] Data fusion module: Through the data fusion algorithm based on machine learning, the pre-processed multi-source data is fused to build a comprehensive database of county resources and environment, and key features are extracted 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 through historical data;

[0087] Model application module: Using GIS technology, the county is divided into several grid units, and the trained model is applied to each grid unit 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: Through ArcGIS tools, the risk assessment and prediction results are displayed in the form of maps, and risk assessment and prediction reports are generated.

[0089] The working principle of the above technical solution is as follows: first, collect relevant data within the county from multiple sources (such as sensors, weather stations, and information disclosure websites), including meteorological data (such as temperature, precipitation), water quality data (such as dissolved oxygen, pH value), soil data (such as heavy metal content, fertility), remote sensing images (used to monitor surface cover changes, vegetation health, etc.) and demographic data (such as population density, distribution of economic activities); pre-process the collected data; use a data fusion algorithm based on machine learning to integrate the pre-processed multi-source data to form a database that comprehensively reflects the resource and environmental conditions of the county. In the comprehensive database, key features are extracted through feature engineering technology, where the key features are 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.); based on the deep learning framework, a model that can capture the spatiotemporal characteristics of county resource and environmental risks is constructed; CNN is good at processing image data, but it can also effectively process time series and multidimensional data, which is suitable for analyzing complex patterns of resource and environmental risks; historical data is used to train the model, and the model parameters (such as learning rate controls the model update speed, batch size affects the amount of data processed in each iteration) and optimizers (such as Adam optimizer adaptively adjusts the learning rate, SGD stochastic gradient descent is simple and direct) are adjusted to optimize the model performance. The performance of the model on different data sets is evaluated through cross-validation, and the best parameter combination is found in combination with grid search technology to ensure that the model has both high accuracy and can be generalized to new data; GIS technology is used to divide the county into multiple grid cells, each of which is used 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, risk assessment and prediction results are intuitively displayed in the form of maps, including risk level distribution maps and high-risk area markers; risk assessment and prediction reports are compiled, covering 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 resource and environmental management.

[0090] The effects of the above technical solutions are as follows: 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, distribution of pollution sources 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 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 risks in the county; ArcGIS tools are used to display the risk assessment and prediction results in the form of maps, 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, optimizing the distribution of pollution sources, etc., to provide 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 through machine learning algorithms based on the data source analysis results;

[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 extract key features through feature extraction technology, and optimize the extracted features, which includes removing redundant features and improving the independence between features.

[0096] The working principle of the above technical solution is: conduct in-depth analysis of various data sources, including understanding the reliability of data sources, the nature of data (such as continuous, discrete, time series, etc.), the accuracy of data (such as measurement error, data missing, etc.) and the potential correlation between data. Based on the results of data source analysis, a data fusion framework is constructed using machine learning algorithms (such as cluster analysis, principal component analysis, etc.). Then, various types of data are aligned to ensure the consistency of data in time and space. Among them, timestamp calibration refers to adjusting the time records of different data sources to the same time base for time series analysis. Spatial coordinate transformation is to unify the spatial location information of different data sources into the same coordinate system for spatial analysis; for data with different dimensions (such as temperature, humidity, water quality indicators, etc.), normalization is performed to eliminate the impact of dimension differences on data analysis. Normalization usually scales the data to a specific range (such as 0-1) so that data of different dimensions can be compared and fused on the same scale; and the pre-processed data is fused using machine learning algorithms (such as support vector machines, neural networks, etc.). The fused data is screened out through correlation analysis to identify key features directly related to the county resource and environmental risk assessment. The key features are usually those variables that are significantly associated with resource and environmental risks, which can reflect the status and development trend of resource and environmental risks; the key features are further extracted through feature extraction techniques (such as principal component analysis, linear discriminant analysis, etc.) to obtain more representative feature vectors. These feature vectors are the input of subsequent deep learning models, and they can more accurately reflect the essential characteristics of resource and environmental risks; after feature extraction, the extracted features need to be optimized, including removing redundant features (i.e., those that are highly correlated with other features or have low information content) and improving the independence between features (i.e., reducing the correlation between features).

[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 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 training efficiency and prediction performance of the model; removing redundant features and improving the independence between features can further optimize feature quality, reduce the risk of model overfitting, and improve the generalization ability of the model.

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

[0099] Model building module: Based on the deep learning framework, a convolutional neural network model is constructed according to the spatiotemporal characteristics of county resource and environmental risks, 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 datasets and validation datasets;

[0101] Precision 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: The model is verified and tuned through cross-validation, and the model training process is monitored through visualization tools.

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

[0104] Input layer: receives the key feature vector 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 amount of computation 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] The extracted key features are used as input data, and historical risk events (such as pollution events, natural disasters, etc.) are used as label data to construct training data sets and validation data sets. The training data set is used to train the CNN model so that it can learn the mapping relationship between input data and risk events; the validation data set is used to evaluate the performance of the model to ensure that the model can also show good predictive ability on unseen data; the CNN model is trained using the training data set. During the training process, the output of the model is calculated through forward propagation, and the weights and bias parameters of the model are adjusted through the back propagation algorithm to minimize the error between the output and the true label; including the adjustment of hyperparameters such as 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 training speed and prediction accuracy of the model can be optimized; select an optimizer (such as Adam, SGD, etc.) to update the weights of the model. The model is verified and tuned through cross-validation. Cross-validation divides the data set into multiple parts, and trains one part as the validation set and the rest as the training set in turn to evaluate the performance of the model on different data sets; the stability and generalization ability of the model are evaluated through the results of cross-validation. If the model performs poorly on the validation set, you may need to adjust the model structure or hyperparameters; tune the model based on the results of cross-validation, including adjusting the number of convolutional layers, the size of the convolution kernel, the pooling method, etc. At the same time, monitor the model training process through visualization tools (such as TensorBoard, etc.) to promptly identify and solve problems such as gradient disappearance 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 prediction performance; by training the convolutional neural network model with training data sets and adjusting the model parameters and optimizer, the prediction accuracy of the model can be significantly improved, making it more accurately reflect the actual situation of resource and environmental risks in the county; reasonable parameter adjustment and optimizer selection can accelerate the training process of the model, 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 training process of the model, 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: Apply the trained convolutional neural network model to each grid unit 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;

[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: using geographic information system (GIS) technology, the county is divided into several grid units. The grid units are the basic units of risk assessment, which can realize the refined management and assessment of county resource and environmental risks. The size and shape of each grid unit can be flexibly adjusted according to the actual situation and assessment needs of the county to ensure the accuracy and reliability of the assessment results; the trained convolutional neural network model is applied to each grid unit, and its resource and environmental risk score is calculated by inputting the key feature data in the grid unit; at the same time, combined with spatiotemporal feature analysis, the spatiotemporal distribution analysis of the risk score is performed. Spatiotemporal feature analysis can reveal the changing rules of risk scores in time and space, help identify high-risk areas and time periods, and provide an important basis for subsequent risk management and prediction; based on the time series analysis algorithm, a time series prediction model is constructed. The time series analysis algorithm can use historical risk data and current environmental conditions to reveal the rules and trends of risk changes over time; by inputting historical risk data and current environmental conditions data, the time series prediction model can predict the county resource and environmental risks in the future. The prediction results can be used to guide the management and response of county 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 intuitively displayed 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 changing patterns of risk scores in time and space, help identify risk development trends 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, making the prediction closer to the actual situation and improving 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, the risk assessment and prediction results are displayed in the form of maps, and the spatial distribution and dynamic changes of county resource and environmental risks are intuitively displayed through color coding and icon annotation;

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

[0120] The working principle of the above technical solution is as follows: collect various data required for risk assessment and prediction, including risk level distribution, high-risk area marking, and risk change trend; use ArcGIS's map making function to make a map of county resource and environmental risks based on the collected data. On the map, the spatial distribution and dynamic changes of risks are intuitively displayed through color coding and icon marking. For example, different colors can be used to represent different risk levels, high-risk areas can be marked with specific icons, and arrows or color gradients can be used to represent the risk change trend; finally, the prepared map is presented through ArcGIS's display function. Decision makers can quickly understand the overall situation of resource and environmental risks in the county, as well as the specific locations of high-risk areas and potential risk points by observing the map. Integrate various data generated during risk assessment and prediction, including key information such as risk level, high-risk area, and potential pollution source. Write a risk assessment and prediction report based on the integrated data. The report mainly includes risk overview (such as risk type, distribution characteristics), main risk points (such as high-risk areas, potential pollution sources), and recommended measures (such as risk prevention and control, emergency response), etc. Utilize automated tools or software to automatically typeset and generate report content according to a predetermined format and template. The generated report contains detailed data and analysis results, and has a clear structure and easy-to-read format, making it easy for decision makers to review and understand. The generated report is reviewed to ensure the accuracy of the data and the completeness of the report. After the review is passed, the report is released to relevant decision makers or departments for reference and decision support.

[0121] The effect of the above technical solution is: the results of risk assessment and prediction are displayed in the form of maps through ArcGIS tools, making the spatial distribution and dynamic changes of county resource and environmental risks intuitive and clear. Decision makers can see the risk level distribution, high-risk areas and risk change trends at a glance, so that they can more easily understand the actual situation of the risk; the visual display enables decision makers to quickly capture key information, reduce the time for interpreting large amounts of data, and improve decision-making efficiency. Decision makers can identify high-risk areas and potential risk points more quickly, so as to take timely measures for risk prevention and control and emergency response; through color coding and icon marking, the visual display makes risk information easier to understand and disseminate, which helps to reduce the communication costs with relevant departments, stakeholders and the public, and improve the transparency and credibility of information; the automatically generated risk assessment and prediction report is based on detailed data analysis and scientific methodology to ensure the accuracy and objectivity of the report. The report content covers aspects such as risk overview, main risk points and recommended measures, providing comprehensive decision support for decision makers; the process of automatically generating reports reduces manual intervention and repetitive work, and improves work efficiency. Decision makers can get the required reports faster so that they can make decisions in a timely manner; the process of automatically generating reports follows the predetermined format and template, ensuring the standardization and consistency of reports. This helps decision makers compare and analyze risks at different time points and in different situations, and improves the consistency and scientificity of decision making.

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

Claims

1. A risk assessment and prediction method applicable to county resources and environment, characterized in that: The method comprises: S1. Acquire multi-source data in the county, wherein the multi-source data includes meteorological data, water quality data, soil data, remote sensing images, and demographic data, and pre-process the acquired multi-source data; S2. Through the data fusion algorithm based on machine learning, the pre-processed multi-source data are fused to build a comprehensive database of county resources and environment, and key features are extracted from the comprehensive database through feature engineering; S3. Based on the deep learning framework, a convolutional neural network model is constructed and trained using historical data. S4. Using GIS technology, the county is divided into several grid units, and the trained model is applied to each grid unit to calculate its resource and environmental risk score. Based on time series analysis, the county 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.

2. A risk assessment and prediction method applicable to county resources and environment according to claim 1, characterized in that: The S2 comprises: S21. Analyze various data sources and build a fusion framework through machine learning algorithms based on the analysis results of the data sources; S22, before fusion, aligning various types of data, wherein the alignment includes timestamp calibration and spatial coordinate conversion, and normalizing 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, wherein the optimization includes removing redundant features and improving the independence between features.

3. A risk assessment and prediction method applicable to county resources and environment according to claim 1, characterized in that: The S3 includes: S31. Based on the deep learning framework and the spatiotemporal characteristics of county resource and environmental risks, a convolutional neural network model is constructed, including input layer, convolution layer, pooling layer, fully connected layer and output layer; S32. Use the extracted key features as input data and the historical risk events as label data to construct a training data set and a validation data set; S33, training the convolutional neural network model using the training data set, and improving the prediction accuracy of the model by adjusting the model parameters and the 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: The S4 comprises: S41. Based on GIS technology, the county is divided into several grid units, each of which is used as the basic unit for risk assessment; S42, applying the trained convolutional neural network model to each grid unit to calculate its resource and environmental risk score; at the same time, combining the spatiotemporal feature analysis, the spatiotemporal distribution analysis of the risk score is performed; 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.

5. A risk assessment and prediction method applicable to county resources and environment according to claim 1, characterized in that: The S5 comprises: S51. Using ArcGIS tools, the risk assessment and prediction results are displayed in the form of maps, and the spatial distribution and dynamic changes of county resource and environmental risks are intuitively displayed through color coding and icon annotation; S52. Automatically generate a risk assessment and prediction report based on the risk assessment and prediction results.

6. A risk assessment and prediction system applicable to county resources and environment, characterized in that: 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: Through the data fusion algorithm based on machine learning, the pre-processed multi-source data is fused to build a comprehensive database of county resources and environment, and key features are extracted 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 through historical data; Model application module: Using GIS technology, the county is divided into several grid units, and the trained model is applied to each grid unit 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: Through ArcGIS tools, the risk assessment and prediction results are displayed in the form of maps, and risk assessment and prediction reports are generated.

7. A risk assessment and prediction system for county resources and environment according to claim 6, characterized in that: The data fusion module comprises: Data analysis module: analyzes various data sources and builds a fusion framework through machine learning algorithms based on the data source analysis results; 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 extract key features through feature extraction technology, and optimize the extracted features, which includes removing redundant features and improving the independence between features.

8. A risk assessment and prediction system for county resources and environment according to claim 6, characterized in that: The model training module includes: Model building module: Based on the deep learning framework, a convolutional neural network model is constructed according to the spatiotemporal characteristics of county resource and environmental risks, 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 datasets and validation datasets; Precision 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; Process monitoring module: The model is verified and tuned through cross-validation, and the model training process is monitored through visualization tools.

9. A risk assessment and prediction system for county resources and environment according to claim 6, characterized in that: 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 unit 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; 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.

10. A risk assessment and prediction system applicable to county resources and environment according to claim 6, characterized in that: The report generation module comprises: 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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