Intelligent identification and management method of ecological environmental damage based on multi-source data fusion

Abstract

The present invention relates to the technical field of ecological environment protection, and specifically to an intelligent identification and management method for ecological environment damage based on multi-source data fusion, including data collection and preprocessing, data fusion and feature extraction, ecological environment damage assessment model construction, damage dynamic analysis and trend prediction, restoration suggestion generation and visualization display steps, through the sensor network deployed in ecologically sensitive areas, real-time water quality, atmosphere, biological and socio-economic data are collected, after preprocessing the data, multimodal data fusion technology and deep learning model are used to extract key features, an ecological damage assessment model is constructed, and the changing law of ecological damage and potential high-risk areas are predicted based on the time series analysis method, and the optimal ecological restoration plan is generated in combination with the particle swarm optimization algorithm. The present invention displays the assessment results and restoration suggestions through a visual interface, provides a scientific basis for decision makers, and helps to improve the efficiency and effect of ecological environment restoration work.

Description

Technical Field

[0001] The present invention relates to the technical field of ecological environment protection, and in particular to an intelligent identification and management method for ecological environment damage based on multi-source data fusion. Background Art

[0002] The protection and restoration of the ecological environment is an important issue facing the world today. With the intensification of environmental pollution and the gradual degradation of ecosystems, how to scientifically assess ecological environmental damage, timely identify potential risk areas, and take effective restoration measures has become an urgent problem to be solved. Especially in ecologically sensitive areas, accurate monitoring and scientific assessment of the extent and trend of environmental damage are crucial for formulating scientific restoration strategies.

[0003] At present, in the process of collecting and analyzing multi-source ecological and environmental data, there are problems such as huge data volume, diverse data types and inconsistency. These multi-source data include data in the fields of water quality, atmosphere, biology and social economy, involving different time and space scales and different collection methods. At present, many existing technologies have failed to effectively integrate these multi-dimensional and multi-modal data, and lack accurate dynamic analysis capabilities. Traditional damage assessment methods often rely on simple static models and cannot comprehensively and accurately identify the spatiotemporal dynamic changes of the ecological environment and potential risk areas, resulting in the inability to propose effective restoration measures in a timely manner. In addition, existing methods for generating ecological restoration recommendations usually ignore the optimal allocation of resources and the maximization of restoration benefits, resulting in waste of resources or poor restoration effects. Summary of the invention

[0004] Based on the above objectives, the present invention provides an intelligent identification and management method for ecological environmental damage based on multi-source data fusion.

[0005] The intelligent identification and management method of ecological environmental damage based on multi-source data fusion includes the following steps:

[0006] S1, multi-source data collection and preprocessing: multi-source data are collected in real time through sensor networks deployed in ecologically sensitive areas. Multi-source data include water quality data, atmospheric data, biological data and socio-economic data. The collected multi-source data are preprocessed, including spatiotemporal alignment, format conversion and noise removal;

[0007] S2, multi-source data fusion and feature extraction: Based on multimodal data fusion technology, the collected multi-source data is fused and features are extracted from the fused data;

[0008] S3, Ecological environment damage assessment model construction: Based on the extracted features, the ecological environment damage assessment model is constructed using machine learning algorithms;

[0009] S4, Dynamic analysis and trend prediction of ecological environmental damage: Based on the ecological environmental damage assessment model and historical multi-source data, the temporal and spatial dynamics of ecological environmental damage are analyzed using time series analysis methods to identify the changing patterns of environmental damage. Through trend prediction algorithms, potential high-risk areas are analyzed and future trend prediction maps of ecological environmental damage are generated to provide reference for the formulation of restoration strategies;

[0010] S5, generation of ecological damage restoration suggestions: Based on the damage assessment results and trend prediction information, an ecological restoration plan is generated in combination with a multi-objective optimization algorithm. The restoration suggestions include water management, vegetation restoration, soil improvement and pollution control. At the same time, the priority restoration areas and the optimal allocation plan of restoration resources are determined to maximize the restoration benefits;

[0011] S6, Visualization Display: Provides a visualization interface for displaying ecological environmental damage assessment results, dynamic trend forecast graphs and restoration suggestions.

[0012] Optionally, the S1 includes:

[0013] S11, sensor network deployment and data collection: Deploy sensor networks in ecologically sensitive areas to collect multi-source data in real time, including water quality data, atmospheric data, biological data, and socio-economic data;

[0014] S12, data spatiotemporal alignment: perform spatiotemporal alignment processing on the collected multi-source data;

[0015] S13, data format conversion and standardization: convert the format of the collected multi-source data to ensure that different data sources use a unified data format;

[0016] S14, noise removal and outlier processing: The noise generated during data collection is removed by the mean filtering method, and the standard deviation method is used to detect and eliminate outliers.

[0017] Optionally, S2 includes:

[0018] S21, multi-source data fusion method: the collected multi-source data are fused by weighted average method;

[0019] S22, feature extraction and dimensionality reduction: extract features from the fused data and use principal component analysis to reduce dimensionality;

[0020] S24, constructing a feature matrix: constructing a feature matrix according to time or space points based on the extracted and dimensionally reduced features;

[0021] S25, feature optimization and selection: In order to improve the efficiency of subsequent analysis and modeling, feature selection methods are used to optimize the feature matrix after dimensionality reduction.

[0022] Optionally, the S3 includes:

[0023] S31, Model construction: Construct an ecological environment damage assessment model based on support vector regression algorithm;

[0024] S32, kernel function selection: selecting a radial basis kernel function as the kernel function of the ecological environmental damage assessment model, and optimizing the hyperparameters of the ecological environmental damage assessment model to improve the performance of the model;

[0025] S33, data preparation: According to the actual situation of ecological environmental damage, a labeled data set is constructed, and the feature matrix and the labeled data set are divided into a training set and a test set for model training and evaluation;

[0026] S34, model training and cross-validation: Use the training set data to train the ecological environment damage assessment model, and optimize the model's hyperparameters through cross-validation;

[0027] S35, model evaluation and performance verification: Use the test set data to evaluate the trained ecological environmental damage assessment model;

[0028] S36, model application and damage assessment output: Use the trained ecological environmental damage assessment model to conduct damage assessment on new multi-source data and output the damage assessment value.

[0029] Optionally, the S4 includes:

[0030] S41, Data Integration: Integrate and process historical multi-source data to combine with the ecological environmental damage assessment model and provide high-quality data support for spatiotemporal dynamic analysis;

[0031] S42, spatiotemporal dynamic analysis: Use spatiotemporal dynamic analysis methods to identify the changing characteristics of ecological environmental damage in different regions and time periods, and analyze the spatiotemporal clustering and change trends of historical multi-source data to discover the regular changes in ecological environmental damage;

[0032] S43, Environmental damage change patterns and anomaly detection: By analyzing historical multi-source data on ecological environmental damage, the normal change patterns and abnormal changes can be identified.

[0033] Optionally, S4 further includes:

[0034] S44, trend prediction model establishment: based on historical multi-source data and the established ecological environmental damage assessment model, a trend prediction algorithm is used to predict the trend of future ecological environmental damage;

[0035] S45, Identification of high-risk areas and generation of trend forecast maps: Based on the trend forecast results, further identify high-risk areas and generate future trend forecast maps to show the changing trend of future ecological environmental damage.

[0036] Optionally, the S5 includes:

[0037] S51, Ecological Damage Restoration Needs Analysis: Comprehensively analyze the ecological damage assessment results and trend forecast information to identify specific restoration needs and provide a basis for the formulation of restoration plans;

[0038] S52, Restoration measures selection and effect evaluation: Select restoration measures according to different restoration requirements, and evaluate the effect of each restoration measure to provide a basis for multi-objective optimization;

[0039] S53, application of multi-objective optimization algorithm: adopt multi-objective optimization algorithm, comprehensively consider the effects of different repair measures and resource constraints, and generate the optimal repair plan.

[0040] Optionally, S5 further includes:

[0041] S54, Determination of priority repair areas: Determine priority repair areas based on damage assessment results, repair effect assessment and trend forecast information. Determine the areas that need repair most urgently through comprehensive assessment of repair needs, damage intensity and repair effect;

[0042] S55, generation of optimal allocation plan for restoration resources: using multi-objective optimization method, combined with effect evaluation and cost analysis of restoration measures, to generate the optimal allocation plan for restoration resources to ensure maximum restoration benefits;

[0043] S56, repair strategy implementation path optimization: optimize the implementation path of the repair strategy according to the repair resource allocation result.

[0044] Optionally, the S6 includes:

[0045] S61, display of ecological environmental damage assessment results: display the output results of the ecological environmental damage assessment model through a visual interface;

[0046] S62, display of spatiotemporal dynamic analysis results: using heat maps and time series diagrams to display the spatiotemporal dynamic changes of ecological environmental damage;

[0047] S63, trend forecast chart display: the future trend forecast results of ecological environment damage are displayed in the form of charts;

[0048] S64, Ecological Restoration Proposal Presentation: Present restoration proposals generated based on damage assessment results and trend forecast information;

[0049] S65, Interactive function: The visualization interface supports user interaction, allowing users to customize the viewing range, time period and data level.

[0050] Beneficial effects of the present invention:

[0051] The present invention collects multi-source data such as water quality, atmosphere, biology and socio-economics in real time by deploying sensor networks in ecologically sensitive areas, and effectively pre-processes and fuses the data. Through multimodal data fusion technology, the timeliness, accuracy and consistency of the data are ensured, providing accurate basic data support for subsequent ecological environmental damage assessment, spatiotemporal dynamic analysis and restoration plans. This process not only improves data quality, but also realizes the effective integration of different types of data, making the formulation of ecological environmental assessment and restoration plans more comprehensive and scientific.

[0052] The present invention combines the support vector regression algorithm in the construction of the ecological environment damage assessment model to effectively quantify the ecological environment damage. Through time series analysis and trend prediction, the spatiotemporal dynamic change law of environmental damage can be identified, and potential high-risk areas can be predicted, providing a scientific basis for future ecological environment restoration work. In addition, the use of time series analysis to regress historical data can accurately capture the trend of damage development, help decision makers take targeted measures in advance, and reduce the risk of environmental damage.

[0053] The present invention combines a multi-objective optimization algorithm to ensure the optimality and feasibility of the restoration plan by setting goals such as maximizing restoration benefits and minimizing restoration costs during the generation of ecological damage restoration plans. At the same time, based on the optimization results, the optimal allocation of resources is achieved and the priority restoration areas are determined, thereby maximizing the overall benefits of ecological restoration. The restoration plan generation process effectively improves the efficiency of the use of restoration resources, optimizes the restoration path and measures, ensures that the restoration work is reasonably arranged in terms of time and resources, and maximizes the restoration effect. BRIEF DESCRIPTION OF THE DRAWINGS

[0054] In order to more clearly illustrate the technical solutions in the present invention or the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings in the following description are only for the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0055] Figure 1 A schematic diagram of a method flow of an embodiment of the present invention;

[0056] Figure 2 Schematic diagram of S3 process of an embodiment of the present invention. DETAILED DESCRIPTION

[0057] The present invention is described in detail below in conjunction with the accompanying drawings and specific embodiments. At the same time, it is explained here that in order to make the embodiments more detailed, the following embodiments are the best and preferred embodiments, and those skilled in the art may also adopt other alternatives to implement some known technologies; and the accompanying drawings are only for more specific description of the embodiments, and are not intended to specifically limit the present invention.

[0058] It should be noted that the references to "one embodiment", "embodiment", "exemplary embodiments", "some embodiments" and the like in the specification indicate that the embodiments described may include specific features, structures or characteristics, but not every embodiment may include the specific features, structures or characteristics. In addition, when a specific feature, structure or characteristic is described in conjunction with an embodiment, it should be within the knowledge of a person skilled in the art to implement such feature, structure or characteristic in conjunction with other embodiments (whether or not explicitly described).

[0059] In general, a term can be understood, at least in part, from its use in context. For example, depending, at least in part, on the context, the term "one or more" as used herein can be used to describe any feature, structure, or characteristic in the singular sense, or can be used to describe a combination of features, structures, or characteristics in the plural sense. Additionally, the term "based on" can be understood as not necessarily intended to convey an exclusive set of factors, but can instead, depending, at least in part, on the context, allow for the presence of other factors that are not necessarily explicitly described.

[0060] like Figure 1-Figure 2 As shown, the intelligent identification and management method of ecological environment damage based on multi-source data fusion includes the following steps:

[0061] S1, Multi-source data collection and preprocessing: Multi-source data is collected in real time through sensor networks deployed in ecologically sensitive areas. Multi-source data includes water quality data, atmospheric data, biological data and socio-economic data. The collected multi-source data is preprocessed, including spatiotemporal alignment, format conversion and noise removal, to ensure the timeliness and accuracy of the data. Ecologically sensitive areas refer to areas in natural ecosystems that are highly sensitive to changes in the external environment due to their special geographical location, climatic conditions, biodiversity or environmental characteristics. These areas usually include the following types:

[0062] Water source protection areas: such as lakes and river source areas, which directly affect downstream water quality and water resource security;

[0063] Wetlands: Wetlands have high biodiversity and important ecological functions, but are extremely sensitive to pollution and climate change;

[0064] Coastal areas and coral reefs: These areas are particularly affected by human development and climate change (such as sea level rise);

[0065] S2, multi-source data fusion and feature extraction: Based on multimodal data fusion technology, the collected multi-source data is fused and features are extracted from the fused data;

[0066] S3, Ecological environment damage assessment model construction: Based on the extracted features, the ecological environment damage assessment model is constructed using machine learning algorithms;

[0067] S4, Dynamic analysis and trend prediction of ecological environmental damage: Based on the ecological environmental damage assessment model and historical multi-source data, the temporal and spatial dynamics of ecological environmental damage are analyzed using time series analysis methods to identify the changing patterns of environmental damage. Through trend prediction algorithms, potential high-risk areas are analyzed and future trend prediction maps of ecological environmental damage are generated to provide reference for the formulation of restoration strategies;

[0068] S5, generation of ecological damage restoration suggestions: Based on the damage assessment results and trend prediction information, an ecological restoration plan is generated in combination with a multi-objective optimization algorithm. The restoration suggestions include water management, vegetation restoration, soil improvement and pollution control. At the same time, the priority restoration areas and the optimal allocation plan of restoration resources are determined to maximize the restoration benefits;

[0069] S6, Visualization Display: Provides a visualization interface for displaying ecological environmental damage assessment results, dynamic trend forecast graphs and restoration suggestions.

[0070] S1 includes:

[0071] S11, sensor network deployment and data collection: Deploy sensor networks in ecologically sensitive areas to collect multi-source data in real time. The multi-source data includes water quality data, atmospheric data, biological data, and socio-economic data, among which;

[0072] Water quality data include dissolved oxygen concentrations and nitrogen and phosphorus levels;

[0073] Dissolved oxygen concentration: Real-time monitoring of dissolved oxygen concentration in water using electrochemical oxygen sensors. Electrochemical sensors measure dissolved oxygen through oxygen reduction reactions;

[0074] Nitrogen and phosphorus content: Use water quality monitoring equipment (such as ion selective electrodes, ultraviolet spectrophotometers, and automated water quality analyzers) to detect the nitrogen (such as ammonia nitrogen and nitrate nitrogen) and phosphorus (such as total phosphorus and orthophosphorus) content in water bodies in real time;

[0075] Atmospheric data include particulate matter concentrations and carbon dioxide levels;

[0076] Particle concentration: The light scattering method is used to measure fine particles in the air (such as PM2.5 and PM10). The light scattering method estimates the concentration of particles by measuring the scattering intensity of the laser beam.

[0077] Carbon dioxide content: Use non-dispersive infrared sensor NDIR to measure the carbon dioxide concentration in the air in real time. The sensor measures the absorption of infrared light of different wavelengths to estimate the concentration of carbon dioxide in the air. It has high accuracy and strong environmental adaptability.

[0078] Biological data include vegetation cover and species diversity index;

[0079] Vegetation coverage: Vegetation coverage is measured by combining remote sensing technology and ground monitoring. Remote sensing data comes from satellite images or drone image analysis, and vegetation indices (such as NDVI) are used for data analysis to estimate the coverage ratio of ground vegetation. On the ground, ground remote sensing sensors (such as vegetation reflectance sensors) are used in conjunction with ground surveys for auxiliary verification;

[0080] Species diversity index: Species diversity index is calculated through ecological sensor networks and field surveys. Sensors can capture environmental variables (such as temperature and humidity) and physical characteristics of species (such as bird song frequency and insect activity), and cooperate with field biologists to record species population density and biodiversity to calculate the diversity index;

[0081] Socioeconomic data include industrial emissions and population density;

[0082] Industrial emissions: Data on sulfur dioxide, nitrogen oxides, and particulate matter emitted by factories are collected through emission monitoring equipment (such as gas analyzers, smoke sensors, and chemical composition monitoring instruments) installed in industrial areas;

[0083] Population density: Obtain population density data through geographic information systems and big data analysis technology, and dynamically estimate the population distribution of the target area using multi-source information such as demographics, mobile communication data or traffic flow data;

[0084] S12, data spatiotemporal alignment: perform spatiotemporal alignment on the collected multi-source data. Spatiotemporal alignment means unifying the time data collected by different sensors to ensure that various types of data in the same time window can be compared and associated with each other. The sampling frequencies of different sensors may be different. Linear interpolation is used to fill in missing values ​​to ensure the consistency of time series data.

[0085] S13, data format conversion and standardization: Convert the format of the collected multi-source data to ensure that different data sources use a unified data format, and standardize water quality data, atmospheric data, biological data, and socioeconomic data to have consistent units and dimensions. For example, water quality parameters are in mg / L, and atmospheric parameters are in µg / m³. This step also includes data preprocessing and formatting, converting all data into a standard format that can be used for subsequent analysis and fusion, such as CSV or JSON format;

[0086] S14, noise removal and outlier processing: The mean filtering method is used to remove the noise generated during the data collection process, and the standard deviation method is used to detect and eliminate outliers to avoid the impact of data anomalies on subsequent analysis and modeling.

[0087] S2 includes:

[0088] S21, multi-source data fusion method: The collected multi-source data are fused by weighted average method. The specific implementation steps of the weighted average method are as follows:

[0089] (1) Determine the weight: According to the quality, reliability and importance of each data source to the ecological and environmental damage assessment, set the weight of each data source. The specific steps are as follows:

[0090] (1.1) Evaluate the quality of each data source, including the timeliness, accuracy, and completeness of the data;

[0091] (1.2) Standardize each data source and convert its dimension into a unified scale (such as a range from 0 to 1);

[0092] (1.3) Assign a weight to each data source based on the quality assessment results, and the sum of the weight values ​​is 1. Data sources with higher weight values ​​have a greater proportion in the fusion process;

[0093] (2) Execute the weighted average method: For each data point (such as the measurement value at a certain time or in a certain spatial area), calculate its weighted average value according to the weight. The calculation formula is:

[0094] ;

[0095] in, is the fused data, For the The weight of the data source, For the The original data of each data source;

[0096] This step can effectively integrate different types of data (such as water quality data, atmospheric data, biological data, etc.) into a unified and comprehensive ecological environment data;

[0097] S22, feature extraction and dimensionality reduction: extract features from the fused data and use principal component analysis to reduce the dimensionality of the data to improve computational efficiency. The specific steps are as follows:

[0098] (1) Feature extraction: For different types of data (such as numerical data, water quality, atmosphere, etc.), the following key features are extracted:

[0099] (1.1) For numerical data (such as dissolved oxygen concentration, nitrogen and phosphorus content, etc.), extract statistical features such as mean, standard deviation, maximum value, minimum value, and variance;

[0100] (1.2) For time series data (such as atmospheric data), extract time series trends, volatility, periodicity and other characteristics;

[0101] (1.3) For spatial data (such as vegetation coverage, species diversity index, etc.), spatial statistical methods (such as spatial autocorrelation, spatial clustering) are used to extract spatial features;

[0102] (2) Dimensionality reduction by principal component analysis: Apply principal component analysis to the fused feature data to reduce the dimension, reduce the number of features, and improve data processing efficiency. The specific steps are:

[0103] (2.1) Calculate the covariance matrix of the fused data features to represent the correlation between the features;

[0104] (2.2) Perform eigenvalue decomposition on the covariance matrix to obtain the eigenvalues ​​and corresponding eigenvectors;

[0105] (2.3) Select the first few principal components according to the size of the eigenvalues, usually selecting the principal components that explain more than 80% of the variance;

[0106] (2.4) Mapping the original data to the selected principal component space to obtain a reduced-dimensional data set;

[0107] S24, construct feature matrix: construct feature matrix according to time or space points for the extracted and reduced features, with each row representing a data point (such as a certain time point or space area) and each column representing a feature. The feature matrix provides efficient data representation for subsequent ecological environmental damage assessment;

[0108] S25, feature optimization and selection: In order to improve the efficiency of subsequent analysis and modeling, the feature selection method is used to optimize the feature matrix after dimensionality reduction. The specific steps are as follows:

[0109] Screening the most representative features: By calculating the importance scores of the features, the features that contribute most to the ecological environmental damage assessment and prediction are selected;

[0110] Remove redundant features: According to the correlation between features, remove redundant and highly correlated features to reduce data noise;

[0111] Optimize feature set: Through model validation and cross-validation, optimize the feature set to improve the performance and accuracy of the dataset.

[0112] S3 includes:

[0113] S31, Model construction: Construct an ecological environment damage assessment model based on support vector regression algorithm;

[0114] S32, kernel function selection: select the radial basis kernel function as the kernel function of the ecological environment damage assessment model, and optimize the hyperparameters of the ecological environment damage assessment model to improve the performance of the model. The steps are as follows:

[0115] Optimize hyperparameters: Optimize the hyperparameters (such as C parameter, ε parameter and γ parameter) of the ecological environment damage assessment model. The steps of hyperparameter tuning are as follows:

[0116] C parameter: controls the complexity of the model and the degree of fit to the training data. A larger C value makes the model fit the training data more strictly, while a smaller C value allows more error;

[0117] ε parameter: controls the error range allowed in the training data and is usually adjusted during the optimization process;

[0118] γ parameter: controls the influence range of the radial basis kernel function. A larger γ value will make the influence range of each training sample larger, while a smaller γ value will make the model smoother.

[0119] Hyperparameter optimization: Cross-validate multiple hyperparameter combinations using grid search or random search methods to select the optimal hyperparameter combination to ensure maximum model performance;

[0120] S33, data preparation: Based on the actual situation of ecological environmental damage, a labeled data set is constructed, and the feature matrix and labeled data set are divided into a training set and a test set for model training and evaluation, including:

[0121] Construct feature matrix: Use the optimized feature matrix as the input data of the ecological environment damage assessment model, where each row represents a sample (such as ecological data at a certain time point or region), and each column represents an optimized feature;

[0122] Constructing a labeled data set: Based on the actual situation of ecological environmental damage, a labeled data set is constructed. The labeled data set is a damage value, which indicates the degree of damage to the ecological environment (such as pollution concentration, damage index, etc.);

[0123] Data partitioning: Divide the feature matrix and label data set into training set and test set. The common partition ratio is 80% training set and 20% test set.

[0124] S34, model training and cross-validation: Use the training set data to train the ecological environment damage assessment model, and optimize the model's hyperparameters through cross-validation. The steps are as follows:

[0125] Training model: Use training set data to input the ecological environmental damage assessment model for training, and learn the mapping relationship between input features and ecological environmental damage values.

[0126] Cross-validation: The training process is evaluated through k-fold cross-validation to ensure that the model has strong generalization ability. The steps of cross-validation are as follows:

[0127] The training set data is divided into k subsets, and each subset is used alternately as a validation set and the remaining subsets are used as training sets.

[0128] Calculate the model performance of each fold and take the average as the final evaluation result.

[0129] S35, model evaluation and performance verification: Use the test set data to evaluate the trained ecological environmental damage assessment model. The steps are as follows:

[0130] Model evaluation: Evaluate the performance of the ecological environmental damage assessment model. Commonly used evaluation indicators include mean square error and mean absolute error. The calculation formula is as follows:

[0131] The mean square error (MSE) is calculated as:

[0132] ;

[0133] in, is the predicted value, is the true value, is the number of test set samples;

[0134] The mean absolute error (MAE) is calculated as:

[0135] ;

[0136] in, is the predicted value, is the true value, is the number of test set samples;

[0137] Evaluation index analysis: By calculating indicators such as MSE and MAE, the accuracy of the model in predicting the ecological environmental damage value is evaluated.

[0138] S36, model application and damage assessment output: Use the trained ecological environmental damage assessment model to conduct damage assessment on new multi-source data and output the damage assessment value. The steps are as follows:

[0139] Input new data: Input new multi-source data (such as new water quality, air data, etc.) into the trained ecological environmental damage assessment model for prediction.

[0140] Output assessment results: The model outputs damage assessment results, including the degree of damage to the ecological environment (such as pollution concentration, damage index, etc.), and can be further visualized in space or time.

[0141] S4 includes:

[0142] S41, Data Integration: Integrate and process historical multi-source data to combine with the ecological environmental damage assessment model and provide high-quality data support for spatiotemporal dynamic analysis. The specific steps include:

[0143] Historical data collection and integration: Collect historical data from different data sources (such as water quality, atmosphere, soil, vegetation, and socio-economics) and integrate these data into the database in a unified format and standard, ensuring that all data contain unified timestamps, spatial coordinates, and corresponding damage assessment values;

[0144] Spatiotemporal synchronization and calibration: Align data from different sources through a unified time and space coordinate system to ensure that all types of data have consistent time spans and spatial locations. Interpolation methods are used to handle missing data in time, and data is standardized according to the spatial coordinate system;

[0145] S42, Spatiotemporal dynamic analysis: Spatiotemporal dynamic analysis methods are used to identify the changing characteristics of ecological environmental damage in different regions and time periods. By analyzing the spatiotemporal clustering and changing trends of historical multi-source data, regular changes in ecological environmental damage are found, including:

[0146] (1) Cluster analysis of spatiotemporal data: The damage data in each spatiotemporal segment is clustered using the K-means clustering algorithm to identify the damage characteristics in different time and space regions. Based on the clustering results, the regions with large and small damage changes are divided.

[0147] (2) Time series analysis: Time series analysis is performed on the damage data in each spatiotemporal clustering area. ARIMA (autoregressive integrated moving average model) is used for modeling to study the seasonality, trend and periodicity characteristics of the damage data. The formula of the ARIMA model is:

[0148] ;

[0149] in, is the actual value of the time series (in this invention, it is the ecological environmental damage assessment value), is a constant term, is the autoregression coefficient, is the white noise error term, which represents the prediction error, and are the orders of the autoregressive part and the moving average part respectively;

[0150] (3) Identification of change patterns: By analyzing the change trends and characteristics of spatiotemporal data, we can identify the long-term change patterns, seasonal fluctuations, and abnormal fluctuations of ecological environmental damage in each region;

[0151] S43, Environmental damage change patterns and anomaly detection: By analyzing historical multi-source data on ecological environmental damage, the normal change patterns and abnormal changes are identified, including:

[0152] (1) Analysis of normal change patterns: Based on the results of time series analysis, identify the normal change patterns of environmental damage, including seasonal changes and long-term trends, and present these change patterns through charts and data models to provide a basis for subsequent trend prediction;

[0153] (2) Anomaly detection and outlier identification: Use statistical methods (such as control chart-based methods, Z-score method, etc.) to detect anomalies in historical multi-source data, identify abnormal fluctuations in damage assessment values, and analyze possible sources of pollution or other sudden factors.

[0154] S4 also includes:

[0155] S44, trend prediction model establishment: Based on historical multi-source data and the established ecological environmental damage assessment model, a trend prediction algorithm is used to predict the trend of future ecological environmental damage, including:

[0156] Model selection and construction: According to the characteristics and objectives of the data, the long short-term memory network is selected to build the trend prediction model. The long short-term memory network has good memory and prediction capabilities when processing time series data, and is suitable for predicting complex ecological environment change trends. The basic formulas of the long short-term memory network include:

[0157] ;

[0158] ;

[0159] ;

[0160] ;

[0161] ;

[0162] in, is the hidden state at the current moment, For the Gate of Forgetfulness, is the input gate, is the output gate, is the cell state, , , , is the weight matrix, , , , is bias;

[0163] Model training and optimization: Use historical data to train trend prediction models, use loss functions (such as mean square error) to optimize models, and adjust model parameters to make the prediction results as close to the actual values ​​as possible;

[0164] Model evaluation and verification: Use independent test set data to evaluate the trained model, calculate the error between the predicted result and the actual value, and ensure that the prediction accuracy of the model meets the expected requirements;

[0165] S45, Identification of high-risk areas and generation of trend forecast maps: Based on the trend forecast results, further identify high-risk areas and generate future trend forecast maps to show the changing trend of future ecological environmental damage, including:

[0166] Identification of high-risk areas: Based on the output of the trend prediction model, identify areas where significant damage may occur in the future. By setting thresholds or using regional demarcation algorithms, identify high-risk areas where damage values ​​exceed the set thresholds. These areas may include areas with concentrated pollution sources, ecologically sensitive areas, etc.

[0167] Generation of future trend prediction map: Based on the time series prediction results, a future trend prediction map is generated to show the ecological environment damage trend of different regions in a certain period of time in the future. Using map visualization tools, combined with spatial coordinates and time series data, a damage intensity heat map or time series map is drawn to predict the future evolution of damage.

[0168] Restoration strategy reference generation: Based on the prediction map and the identification results of high-risk areas, restoration strategy recommendations are formulated to provide decision makers with effective restoration paths.

[0169] S5 includes:

[0170] S51, Ecological Damage Restoration Needs Analysis: Comprehensively analyze the ecological damage assessment results and trend forecast information to identify specific restoration needs and provide a basis for the formulation of restoration plans. The specific steps include:

[0171] Fusion of damage assessment and trend forecast information: Combine the damage assessment results in step S4 with the future trend forecast map to identify the intensity of restoration needs in each region. The assessment results include the severity of ecological damage in different regions, while the trend forecast information provides the trend of future damage changes;

[0172] Classification of restoration needs: Classify the restoration needs of different areas according to different categories of damage assessment (such as water pollution, soil pollution, vegetation degradation, etc.). Under each category, prioritize the areas according to the degree of damage;

[0173] Restoration goal setting: Determine the specific restoration goals based on restoration needs, such as restoring water quality, increasing vegetation coverage, improving soil fertility, etc. Ensure that the restoration goals are clear and quantifiable to facilitate the formulation of subsequent optimization plans;

[0174] S52, Restoration Measures Selection and Effect Evaluation: Select restoration measures according to different restoration requirements and evaluate the effects of each restoration measure to provide a basis for multi-objective optimization. The specific steps include:

[0175] Selection of repair measures: According to the classification of repair requirements, select the corresponding repair measures, including:

[0176] Water treatment: Choose measures such as water purification, wetland restoration, and sediment management.

[0177] Vegetation restoration: Select appropriate vegetation restoration measures, including afforestation, grassland restoration, etc.

[0178] Soil improvement: Choose soil improvement measures such as soil acidity and alkalinity adjustment and organic matter addition.

[0179] Pollution control: Select control measures such as pollution source control and emission reduction technologies.

[0180] Restoration effect evaluation: Use quantitative methods to evaluate the effects of each restoration measure, and use the ecological restoration index to quantify the restoration effect of each measure. For different restoration plans, estimate their long-term restoration benefits. The ecological restoration index (ERI) formula is expressed as:

[0181] ;

[0182] in, is the water quality treatment effect, with a value range of 0 to 1. It is the soil improvement effect, ranging from 0 to 1. It is the vegetation restoration effect, and its value range is from 0 to 1. They are the weights of the three restoration targets of water quality, soil, and vegetation;

[0183] S53, application of multi-objective optimization algorithm: using a multi-objective optimization algorithm, comprehensively considering the effects of different repair measures and resource constraints, to generate the optimal repair plan. The specific steps include:

[0184] Objective function definition: Set the objective function of multi-objective optimization. The objective function should include multiple dimensions such as maximizing the restoration benefit and minimizing the restoration cost. The objective function can be expressed as:

[0185] ;

[0186] in, is the repair effect of the i-th repair measure, is the benefit weight of the i-th restoration measure, is the cost of the jth repair measure, is the cost weight of the jth repair measure;

[0187] Optimization algorithm: Use particle swarm optimization algorithm to find the optimal repair solution;

[0188] Result output: The optimization algorithm outputs the optimal restoration plan, including the specific allocation of restoration measures, allocation of resources, and determination of priority restoration areas.

[0189] The S5 also includes:

[0190] S54, Determination of priority repair areas: Determine priority repair areas based on damage assessment results, repair effect assessment, and trend forecast information. Through a comprehensive assessment of repair needs, damage intensity, and repair effects, determine the areas that are most in need of repair, including:

[0191] Repair demand analysis: Analyze the repair demand of each area, and give priority to the areas with the most serious damage. Combined with the damage assessment results, set a threshold, and areas below this threshold will not be considered for repair;

[0192] Regional priority assessment: Based on factors such as restoration needs, ecological restoration potential and restoration costs, the restoration priority of each region is scored using a weighted scoring method or a hierarchical analysis process (AHP). The region with the highest score is the priority restoration area;

[0193] Prioritization of repair solutions: Prioritize repair solutions in all areas to ensure that repair resources are allocated to the areas most in need;

[0194] S55, Generate the optimal allocation plan for restoration resources: Use multi-objective optimization methods, combined with the effect evaluation and cost analysis of restoration measures, to generate the optimal allocation plan for restoration resources to ensure the maximization of restoration benefits. The specific steps include:

[0195] Determination of resource constraints: Determine the available restoration resources, including the total amount of funds, technology, manpower and other resources, as well as the resource consumption of each restoration measure;

[0196] Resource allocation optimization: Based on the repair priority, repair effect and resource consumption, the resource allocation optimization algorithm (such as linear programming, integer programming, etc.) is used to generate the optimal repair resource allocation plan to ensure that each repair measure can be fully supported by resources;

[0197] Repair plan adjustment and verification: According to the optimization results, adjust the repair plan to ensure the maximum repair benefit, and verify the plan to ensure that the repair effect meets expectations;

[0198] S56, Optimize the implementation path of the repair strategy: According to the repair resource allocation results, optimize the implementation path of the repair strategy to ensure the efficiency of the repair process, including:

[0199] Determine the implementation path: Determine the implementation order and priority of each repair measure based on the repair resource allocation plan. Use project management methods (such as Gantt charts) to plan the implementation path;

[0200] Monitoring and adjustment of the repair process: During the repair process, real-time monitoring of the progress and effects of the repair is carried out. If any deviation occurs, timely adjustments to the repair strategy and resource allocation are made to ensure the achievement of the repair goals.

[0201] S6 includes:

[0202] S61, Ecological Environment Damage Assessment Results Display: Display the output results of the ecological environment damage assessment model through a visual interface, including the weight of each assessment factor, the degree of damage, and the assessment conclusion. Use different colors and icons to mark the degree of damage, for example:

[0203] Green, yellow, and red are used to represent mild, moderate, and severe damage, respectively.

[0204] Use bar charts, heat maps, and other forms to visually display the extent and distribution of damage.

[0205] S62, display of spatiotemporal dynamic analysis results: use heat maps and time series diagrams to display the spatiotemporal dynamic changes of ecological environmental damage, and use interactive maps to display historical damage change trends. Users can select different time periods to view and identify key areas and time nodes of damage changes. For high-risk areas, they can be highlighted through color gradients, heat maps, etc.;

[0206] S63, Trend forecast chart display: The future trend forecast results of ecological environmental damage are displayed in the form of charts, using trend charts, line charts or dynamic animations to show the future forecast development trend of damage. The forecast results can be displayed on a timeline, including possible changes in environmental damage and changes in potential high-risk areas in the future;

[0207] S64, ecological restoration proposal display: Display the restoration plan generated based on the damage assessment results and trend forecast information, and use flowcharts, pie charts, bar charts, etc. to display the specific content of the restoration measures, including water treatment, vegetation restoration, soil improvement and pollution control, etc. The spatial distribution of the restoration area and the implementation priority of each restoration measure are indicated on the map.

[0208] S65, Interactive function: The visual interface supports user interaction, allowing users to customize the viewing range, time period and data level. For example, users can select a specific area to view the historical damage data, prediction results and recommended repair plans for that area. At the same time, users can also export data reports and charts as needed.

[0209] The present invention covers any substitution, modification, equivalent method and scheme made on the essence and scope of the present invention. In order to make the public have a thorough understanding of the present invention, specific details are described in detail in the following preferred embodiments of the present invention, but those skilled in the art can fully understand the present invention without the description of these details. In addition, in order to avoid unnecessary confusion about the essence of the present invention, well-known methods, processes, procedures, components and circuits are not described in detail.

[0210] The above is only a preferred embodiment of the present invention. It should be pointed out that for ordinary technicians in this technical field, several improvements and modifications can be made without departing from the principle of the present invention. These improvements and modifications should also be regarded as the scope of protection of the present invention.

Claims

1. An intelligent identification and management method for ecological environmental damage based on multi-source data fusion, characterized in that: The following steps are involved: S1, multi-source data collection and preprocessing: multi-source data are collected in real time through sensor networks deployed in ecologically sensitive areas. Multi-source data include water quality data, atmospheric data, biological data and socio-economic data. The collected multi-source data are preprocessed, including spatiotemporal alignment, format conversion and noise removal; S2, multi-source data fusion and feature extraction: Based on multimodal data fusion technology, the collected multi-source data is fused and features are extracted from the fused data; S3, Ecological Environment Damage Assessment Model Construction: Based on the extracted features, the ecological environment damage assessment model is constructed using machine learning algorithms; S4, Dynamic analysis and trend prediction of ecological environmental damage: Based on the ecological environmental damage assessment model and historical multi-source data, the temporal and spatial dynamics of ecological environmental damage are analyzed using time series analysis methods to identify the changing patterns of environmental damage. Through trend prediction algorithms, potential high-risk areas are analyzed and future trend prediction maps of ecological environmental damage are generated to provide reference for the formulation of restoration strategies; The S4 includes: S41, Data Integration: Integrate and process historical multi-source data to combine with the ecological environmental damage assessment model and provide data support for spatiotemporal dynamic analysis; S42, spatiotemporal dynamic analysis: Use spatiotemporal dynamic analysis methods to identify the changing characteristics of ecological environmental damage in different regions and time periods, and analyze the spatiotemporal clustering and change trends of historical multi-source data to discover the regular changes in ecological environmental damage; S43, Environmental damage change patterns and anomaly detection: By analyzing historical multi-source data on ecological environmental damage, the normal change patterns and abnormal changes are identified; The S4 further comprises: S44, trend prediction model establishment: based on historical multi-source data and the established ecological environmental damage assessment model, a trend prediction algorithm is used to predict the trend of future ecological environmental damage; S45, high-risk area identification and trend forecast map generation: Based on the trend forecast results, high-risk areas are further identified and future trend forecast maps are generated to show the changing trend of future ecological environmental damage; S5, generation of ecological damage restoration suggestions: Based on the damage assessment results and trend prediction information, an ecological restoration plan is generated in combination with a multi-objective optimization algorithm. The restoration suggestions include water management, vegetation restoration, soil improvement and pollution control. At the same time, the priority restoration areas and the optimal allocation plan of restoration resources are determined to maximize the restoration benefits; The S5 includes: S51, Ecological Damage Restoration Demand Analysis: Comprehensively analyze the ecological damage assessment results and trend forecast information to identify restoration needs and provide a basis for the formulation of restoration plans; S52, Restoration measures selection and effect evaluation: Select restoration measures according to different restoration needs, and evaluate the effect of each restoration measure to provide a basis for multi-objective optimization; S53, application of multi-objective optimization algorithm: using multi-objective optimization algorithm, comprehensively considering the effects of different restoration measures and resource constraints, to generate the optimal restoration plan; The S5 further includes: S54, determination of priority repair areas: Determine priority repair areas based on damage assessment results, repair effect assessment and trend forecast information, and determine the areas most in need of repair through a comprehensive assessment of repair needs, damage intensity and repair effect; S55, generation of optimal allocation plan for restoration resources: using multi-objective optimization method, combined with effect evaluation and cost analysis of restoration measures, to generate the optimal allocation plan for restoration resources to ensure maximum restoration benefits; S56, optimization of the implementation path of the repair strategy: optimizing the implementation path of the repair strategy according to the repair resource allocation result; S6, Visualization Display: Provides a visualization interface for displaying ecological environmental damage assessment results, dynamic trend forecast graphs and restoration suggestions.

2. The method for intelligent identification and management of ecological environmental damage based on multi-source data fusion according to claim 1 is characterized in that: The S1 includes: S11, sensor network deployment and data collection: Deploy sensor networks in ecologically sensitive areas to collect multi-source data in real time, including water quality data, atmospheric data, biological data, and socio-economic data; S12, data spatiotemporal alignment: perform spatiotemporal alignment processing on the collected multi-source data; S13, data format conversion and standardization: convert the format of the collected multi-source data to ensure that different data sources use a unified data format; S14, noise removal and outlier processing: The noise generated during data collection is removed by the mean filtering method, and the standard deviation method is used to detect and eliminate outliers.

3. The method for intelligent identification and management of ecological environmental damage based on multi-source data fusion according to claim 2 is characterized in that: The S2 includes: S21, multi-source data fusion method: the collected multi-source data are fused by weighted average method; S22, feature extraction and dimensionality reduction: extract features from the fused data and use principal component analysis to reduce dimensionality; S24, constructing a feature matrix: constructing a feature matrix according to time or space points based on the extracted and dimensionally reduced features; S25, feature optimization and selection: Use feature selection method to optimize the feature matrix after dimensionality reduction.

4. The method for intelligent identification and management of ecological environmental damage based on multi-source data fusion according to claim 3 is characterized in that: The S3 includes: S31, Model construction: Construct an ecological environment damage assessment model based on support vector regression algorithm; S32, kernel function selection: selecting a radial basis kernel function as the kernel function of the ecological environmental damage assessment model, and optimizing the hyperparameters of the ecological environmental damage assessment model to improve the performance of the model; S33, data preparation: According to the actual situation of ecological environmental damage, a labeled data set is constructed, and the feature matrix and the labeled data set are divided into a training set and a test set for model training and evaluation; S34, model training and cross-validation: Use the training set data to train the ecological environment damage assessment model, and optimize the model's hyperparameters through cross-validation; S35, model evaluation and performance verification: Use the test set data to evaluate the trained ecological environmental damage assessment model; S36, model application and damage assessment output: Use the trained ecological environmental damage assessment model to conduct damage assessment on new multi-source data and output the damage assessment value.

5. The method for intelligent identification and management of ecological environmental damage based on multi-source data fusion according to claim 4 is characterized in that: The S6 includes: S61, display of ecological environmental damage assessment results: display the output results of the ecological environmental damage assessment model through a visual interface; S62, display of spatiotemporal dynamic analysis results: using heat maps and time series diagrams to display the spatiotemporal dynamic changes of ecological environmental damage; S63, trend forecast chart display: the future trend forecast results of ecological environment damage are displayed in the form of charts; S64, Ecological Restoration Proposal Presentation: Present restoration proposals generated based on damage assessment results and trend forecast information; S65, Interactive function: The visualization interface supports user interaction, allowing users to customize the viewing range, time period and data level.

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