An ecological environment dynamic evaluation and management method based on space-time data analysis

Abstract

The application provides an ecological environment dynamic evaluation and management method based on space-time data analysis, relates to the technical field of ecological environment monitoring, and collects multiple source ecological environment data and carries out cleaning to obtain cleaned data; data fusion is carried out by adopting spatial interpolation and time series analysis, the ecological environment quality index is calculated, and the dynamic change trend is identified. Combined with the future change result and the management target, an optimization strategy is formulated, and a multi-objective optimization algorithm is used to generate an optimal management scheme. The method improves the accuracy of ecological environment monitoring and evaluation, provides scientific decision support for managers, and helps sustainable development.

Description

Technical Field

[0001] The present invention relates to the technical field of ecological environment monitoring, and in particular to a method for dynamic evaluation and management of ecological environment based on spatiotemporal data analysis. Background Art

[0002] With global climate change and intensified human activities, dynamic changes in the ecological environment have profound impacts on socioeconomic development and ecosystem stability. Traditional ecological and environmental assessment methods often rely on static data or monitoring data from a single point in time, which makes it difficult to fully reflect the dynamic characteristics of the ecological environment. Furthermore, existing methods lack data integration, spatiotemporal analysis, and dynamic prediction, making them unable to effectively support scientific ecological and environmental management and decision-making.

[0003] In recent years, the rapid development of remote sensing technology, geographic information systems (GIS), and big data analysis has made dynamic ecological and environmental assessments based on spatiotemporal data possible. However, the efficient integration, analysis, and modeling of multi-source spatiotemporal data, and the application of these analysis results in ecological and environmental management, remain pressing technical challenges. Summary of the Invention

[0004] To overcome the shortcomings of existing technologies, the present invention aims to provide a dynamic ecological and environmental assessment and management method based on spatiotemporal data analysis. By integrating multi-source data, dynamic assessment models, and optimization algorithms, this method provides a comprehensive, dynamic, scientific, and operational ecological and environmental assessment and management approach. Its beneficial effects are not only reflected in the precise monitoring and assessment of the ecological environment, but also provide managers with a scientific decision-making support tool, significantly improving the efficiency and effectiveness of ecological and environmental protection, and contributing to the sustainable development of the ecological environment.

[0005] To achieve the above object, the present invention provides the following solutions:

[0006] A method for dynamic ecological environment assessment and management based on spatiotemporal data analysis, including:

[0007] Collect multi-source ecological and environmental data;

[0008] Cleaning the multi-source ecological environment data to obtain cleaned data;

[0009] Using a spatial interpolation method and a time series analysis method to perform spatiotemporal data fusion on the cleaned data to obtain a fused spatiotemporal data set;

[0010] Inputting the fused spatiotemporal dataset into the trained dynamic assessment model to obtain the results of future ecological and environmental changes;

[0011] Calculating an ecological environment quality index based on the fused spatiotemporal dataset, and using a spatiotemporal statistical analysis method to identify dynamic change trends of the ecological environment on the fused spatiotemporal dataset;

[0012] Based on the future ecological environment change results, the ecological environment quality index and the dynamic change trend, combined with the ecological environment management goals, an optimization strategy is formulated, and a multi-objective optimization algorithm is used to optimize the optimization strategy to generate the optimal management plan.

[0013] Preferably, the multi-source ecological environment data includes remote sensing image data, meteorological data, ground monitoring data and socio-economic data.

[0014] Preferably, cleaning the multi-source ecological environment data to obtain cleaned data includes:

[0015] Grouping the multi-source ecological environment data according to a preset collection period to obtain multiple data groups;

[0016] Calculate the difference coefficient between the current data group and the previous data group in sequence;

[0017] Determine whether the value of the coefficient of difference is within a preset range;

[0018] If the value of the coefficient of difference is not within the preset range, the corresponding data group will be removed;

[0019] If the value of the difference coefficient is within a preset range, the corresponding data group is retained until all data groups are traversed to obtain the cleaned data.

[0020] Preferably, the coefficient of difference calculation formula is:

[0021]

[0022] Among them, p X,Y is the coefficient of difference, cov(X,Y) represents the covariance between the current data set X and the previous data set Y, α X Represents the mean of the current data set X, β Y Represents the mean of the previous data set Y.

[0023] Preferably, the cleaned data is subjected to spatiotemporal data fusion using a spatial interpolation method and a time series analysis method to obtain a fused spatiotemporal data set, including:

[0024] Unifying the cleaned data into a standard spatiotemporal grid format, and adding a timestamp and spatial coordinates to each data point in the spatiotemporal grid;

[0025] Based on the Kriging interpolation method, spatial interpolation is performed on the data at each time point in the space-time grid to generate complete spatial distribution data;

[0026] Performing time series analysis on the data of each spatial location in the spatial distribution data to fill in missing values ​​in time and smooth the time series to obtain time analysis data;

[0027] Performing spatiotemporal data fusion on the spatial distribution data and the temporal analysis data to obtain the fused spatiotemporal data set.

[0028] Preferably, performing time series analysis on the data of each spatial location in the spatial distribution data to fill in missing values ​​in time and smooth the time series to obtain time analysis data includes:

[0029] Extract time series from data at each spatial location;

[0030] The time series is predicted and interpolated according to the LSTM long short-term memory network, missing values ​​in the time interval are filled, and time series fluctuations are smoothed to obtain the time analysis data.

[0031] Preferably, the dynamic evaluation model is a CNN-LSTM combination model.

[0032] Preferably, the calculation formula of the ecological environment quality index is:

[0033] EQI=w1·NDVI+w2·AQI+w3·WQI+w4·LUI+w5·SEI

[0034] Among them: EQI is the ecological environment quality index, ranging from 0 to 1, and the higher the value, the better the ecological environment quality; NDVI is the normalized difference vegetation index, which reflects the vegetation coverage; AQI is the air quality index, which reflects the degree of air pollution; WQI is the water quality index, which reflects the degree of water pollution; LUI is the land use intensity index, which reflects the intensity of human activities on land resources; SEI is the socioeconomic index, which reflects the impact of socioeconomic development on the ecological environment; w1, w2, w3, w4 and w5 are weight coefficients, which indicate the relative importance of each indicator to the ecological environment quality, satisfying w1+w2+w3+w4+w5=1.

[0035] Preferably, the spatiotemporal statistical analysis method is based on the Mann-Kendall trend test method.

[0036] Preferably, the multi-objective optimization algorithm is a particle swarm optimization algorithm.

[0037] According to the specific embodiments provided by the present invention, the present invention discloses the following technical effects:

[0038] The present invention provides a dynamic ecological environment assessment and management method based on spatiotemporal data analysis, comprising: collecting multi-source ecological environment data; cleaning the multi-source ecological environment data to obtain cleaned data; fusing the cleaned data with spatiotemporal data using spatial interpolation and time series analysis methods to obtain a fused spatiotemporal dataset; inputting the fused spatiotemporal dataset into a trained dynamic assessment model to obtain future ecological environment change results; calculating an ecological environment quality index based on the fused spatiotemporal dataset, and identifying dynamic ecological environment change trends in the fused spatiotemporal dataset using spatiotemporal statistical analysis methods; formulating an optimization strategy based on the future ecological environment change results, the ecological environment quality index, and the dynamic change trends, in combination with ecological environment management objectives; and optimizing the optimization strategy using a multi-objective optimization algorithm to generate an optimal management plan. By integrating multi-source data, a dynamic assessment model, and an optimization algorithm, the present invention provides a comprehensive, dynamic, scientific, and operational ecological environment assessment and management method. Its beneficial effects are not only reflected in the precise monitoring and assessment of the ecological environment, but also provide managers with a scientific decision-making support tool that can significantly improve the efficiency and effectiveness of ecological environment protection and contribute to the sustainable development of the ecological environment. BRIEF DESCRIPTION OF THE DRAWINGS

[0039] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0040] Figure 1 A flow chart of a method provided in an embodiment of the present invention. DETAILED DESCRIPTION

[0041] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.

[0042] The purpose of this invention is to provide a dynamic ecological and environmental assessment and management method based on spatiotemporal data analysis. By integrating multi-source data, dynamic assessment models, and optimization algorithms, this method provides a comprehensive, dynamic, scientific, and operational ecological and environmental assessment and management approach. Its beneficial effects are not only reflected in the precise monitoring and assessment of the ecological environment, but also provide managers with a scientific decision-making support tool, which can significantly improve the efficiency and effectiveness of ecological and environmental protection and contribute to the sustainable development of the ecological environment.

[0043] In order to make the above-mentioned objects, features and advantages of the present invention more obvious and easy to understand, the present invention is further described in detail below with reference to the accompanying drawings and specific embodiments.

[0044] Figure 1 A flow chart of the method provided in the embodiment of the present invention is shown in FIG. Figure 1 As shown, the present invention provides a method for dynamic assessment and management of ecological environment based on spatiotemporal data analysis, including:

[0045] Step 100: Collect multi-source ecological environment data;

[0046] Step 200: Cleaning multi-source ecological environment data to obtain cleaned data;

[0047] Step 300: Using spatial interpolation method and time series analysis method to perform spatiotemporal data fusion on the cleaned data to obtain a fused spatiotemporal dataset;

[0048] Step 400: Input the fused spatiotemporal dataset into the trained dynamic assessment model to obtain the results of future ecological environment changes;

[0049] Step 500: Calculate the ecological environment quality index based on the fused spatiotemporal dataset, and use spatiotemporal statistical analysis methods to identify the dynamic change trend of the ecological environment on the fused spatiotemporal dataset;

[0050] Step 600: Based on the future ecological environment change results, ecological environment quality index and dynamic change trends, combined with the ecological environment management goals, an optimization strategy is formulated, and a multi-objective optimization algorithm is used to optimize the optimization strategy to generate an optimal management plan.

[0051] Preferably, the multi-source ecological environment data includes remote sensing image data, meteorological data, ground monitoring data and socio-economic data.

[0052] Specifically, step 100 of this embodiment includes:

[0053] Step 101: Remote Sensing Image Data Collection

[0054] Remote sensing imagery data is acquired through satellite remote sensing platforms (such as Landsat, Sentinel, and MODIS) or drones. It primarily includes multispectral imagery (such as red and near-infrared bands) reflecting surface characteristics, as well as information such as surface temperature and vegetation cover. Furthermore, high- or medium-resolution imagery data can be selected based on the specific needs of the study area to ensure data coverage of the entire target area, and imagery data can be downloaded within a specific timeframe to capture dynamic changes in time and space.

[0055] Step 102: Meteorological Data Collection

[0056] Meteorological data is obtained from weather monitoring stations or public meteorological databases (such as NOAA, ECMWF, and the National Weather Service). It mainly includes meteorological parameters such as temperature, precipitation, humidity, wind speed, and air pressure. To ensure the temporal and spatial consistency of the data, meteorological data should be selected that matches the temporal range of the remote sensing image data, and a corresponding meteorological data subset should be extracted based on the geographic scope of the target area.

[0057] Step 103: Ground monitoring data collection

[0058] Ground-based monitoring data is collected from monitoring stations within the target area or from environmental monitoring data provided by relevant agencies. It primarily includes information on air quality (such as PM2.5, PM10, and SO2 concentrations), water quality parameters (such as dissolved oxygen, chemical oxygen demand, and total phosphorus concentrations), and soil quality. During data collection, attention should be paid to the spatial distribution of the data to ensure that the coverage of the monitoring stations reflects the environmental conditions of the entire target area.

[0059] Step 104: Socioeconomic Data Collection

[0060] Socioeconomic data are obtained through government statistical yearbooks, public databases (such as the World Bank and UNDP), or regional economic surveys. They primarily include information such as population density, GDP, land use type, and the intensity of industrial and agricultural activities. When collecting data, ensure that the data has a high spatial resolution (e.g., county or township level), and select key indicators that reflect the impact of socioeconomic activities on the ecological environment to support subsequent dynamic ecological and environmental assessments.

[0061] Through the collection of the above four types of data, a basic data set of multi-source ecological and environmental data was constructed, providing comprehensive and accurate input for subsequent data cleaning, spatiotemporal fusion and dynamic evaluation.

[0062] Preferably, cleaning the multi-source ecological environment data to obtain cleaned data includes:

[0063] Grouping the multi-source ecological environment data according to a preset collection period to obtain multiple data groups;

[0064] Calculate the difference coefficient between the current data group and the previous data group in sequence;

[0065] Determine whether the value of the coefficient of difference is within a preset range;

[0066] If the value of the coefficient of difference is not within the preset range, the corresponding data group will be removed;

[0067] If the value of the difference coefficient is within a preset range, the corresponding data group is retained until all data groups are traversed to obtain the cleaned data.

[0068] Preferably, the coefficient of difference calculation formula is:

[0069]

[0070] Among them, p X,Y is the coefficient of difference, cov(X,Y) represents the covariance between the current data set X and the previous data set Y, α X Represents the mean of the current data set X, β Y Represents the mean of the previous data set Y.

[0071] Specifically, step 200 of this embodiment includes:

[0072] Step 201: Data Grouping

[0073] First, multi-source ecological and environmental data are grouped according to a pre-set collection cycle (e.g., daily, weekly, or monthly), dividing the data into multiple data groups in chronological order. Each data group contains all data points collected during that period, such as remote sensing imagery, meteorological data, ground monitoring data, and socioeconomic data for a particular day. The purpose of grouping is to facilitate consistency analysis of the data over time series and provide a basis for subsequent calculation of coefficients of variation.

[0074] Step 202: Calculate the coefficient of variation

[0075] For each data set, the coefficient of difference (CDI) is calculated between the current and previous data sets. The CDI measures the similarity between two data sets by analyzing the degree of covariance between them. Specifically, the CDI calculation considers the means of the two data sets and their covariance. The covariance reflects the correlation between the two data sets, while the mean normalizes the overall level of the data sets. In this way, the degree of difference between the current and previous data sets can be quantified.

[0076] Step 203: Determine the range of the coefficient of difference

[0077] After calculating the coefficient of variation, it is compared to a preset range. This range is typically determined based on the actual application scenario and data characteristics, such as reasonable upper and lower limits determined through historical data analysis. If the coefficient of variation value exceeds the preset range, it indicates that there is an abnormal difference between the current data set and the previous data set, which may be caused by data collection errors, noise interference, or other abnormal conditions. In this case, the data set needs to be marked as abnormal data.

[0078] Step 204: Data set screening and cleaning

[0079] Data sets are screened based on the range of the coefficient of variation. If the coefficient of variation is within the preset range, the current data set is retained and deemed to meet the quality requirements. If the coefficient of variation is outside the preset range, the current data set is removed to prevent the impact of abnormal data on subsequent analysis. By traversing all data sets and gradually selecting those that meet the quality requirements, cleaned multi-source ecological and environmental data are ultimately obtained, providing high-quality input data for subsequent spatiotemporal data fusion and dynamic assessment.

[0080] Specifically, after data cleaning, this embodiment performs denoising on the retained data set to further improve the quality and reliability of the data. For remote sensing image data, median filtering or wavelet transform methods can be used to remove random noise in the image; for meteorological data and ground monitoring data, sliding average method or Kalman filter can be used to smooth the time series data to eliminate abnormal fluctuations; for socioeconomic data, statistical methods (such as box plot analysis) can be used to identify and eliminate outliers. During the denoising process, the appropriate denoising algorithm should be selected according to different data types to ensure the authenticity and integrity of the data.

[0081] After denoising, this embodiment also converts multi-source data into a standardized format for subsequent spatiotemporal data fusion and dynamic evaluation. Remote sensing image data can be converted to GeoTIFF format and the image can be corrected for coordinate projection. Meteorological data and ground monitoring data can be stored in a time series format (such as CSV or NetCDF) with timestamps and spatial coordinate information. Socioeconomic data can be converted to a structured table format (such as Excel or a database table) and the data fields can be named in a standardized manner. This format conversion ensures that all data have a consistent temporal and spatial reference system, providing standardized data input for subsequent analysis.

[0082] Preferably, the cleaned data is subjected to spatiotemporal data fusion using a spatial interpolation method and a time series analysis method to obtain a fused spatiotemporal data set, including:

[0083] Unifying the cleaned data into a standard spatiotemporal grid format, and adding a timestamp and spatial coordinates to each data point in the spatiotemporal grid;

[0084] Based on the Kriging interpolation method, spatial interpolation is performed on the data at each time point in the space-time grid to generate complete spatial distribution data;

[0085] Performing time series analysis on the data of each spatial location in the spatial distribution data to fill in missing values ​​in time and smooth the time series to obtain time analysis data;

[0086] Performing spatiotemporal data fusion on the spatial distribution data and the temporal analysis data to obtain the fused spatiotemporal data set.

[0087] Preferably, performing time series analysis on the data of each spatial location in the spatial distribution data to fill in missing values ​​in time and smooth the time series to obtain time analysis data includes:

[0088] Extract time series from data at each spatial location;

[0089] The time series is predicted and interpolated according to the LSTM long short-term memory network, missing values ​​in the time interval are filled, and time series fluctuations are smoothed to obtain the time analysis data.

[0090] Preferably, the dynamic evaluation model is a CNN-LSTM combination model.

[0091] Specifically, step 300 of this embodiment includes:

[0092] Step 301: Standardization and spatiotemporal gridding of cleaned data

[0093] First, the cleaned multi-source ecological and environmental data are uniformly converted into a standard spatiotemporal grid format for spatiotemporal data fusion. Specifically, the spatial grid is divided according to the geographical scope of the study area (such as 1km×1km grid cells), and spatial coordinates (latitude and longitude or grid number) are added to each grid cell. At the same time, the data is sorted according to the time dimension, and a timestamp (such as YYYY-MM-DD) is added to each data point to ensure that the data has clear spatial and temporal attributes. Through this standardized processing, an initial data grid containing spatiotemporal coordinates is generated, providing a basis for subsequent spatial interpolation and time series analysis.

[0094] Step 302: Spatial interpolation based on Kriging interpolation

[0095] For the data at each time point in the space-time grid, the Kriging interpolation method is used for spatial interpolation to generate complete spatial distribution data. Kriging interpolation is a spatial interpolation method based on geostatistics that can generate high-precision interpolation results based on the spatial correlation of the data. In the specific implementation process, the semivariogram of the data is first calculated, a spatial correlation model (such as a spherical model or a Gaussian model) is fitted, and then the target grid cell is interpolated. Through Kriging interpolation, spatial data gaps can be filled and complete spatial distribution data can be generated at each time point, providing support for subsequent time series analysis.

[0096] Step 303: Time series analysis based on LSTM

[0097] Time series data is extracted for each spatial location (i.e., each grid cell) in the spatially distributed data. Long short-term memory (LSTM) networks are then used to predict and interpolate the time series, filling in missing values ​​and smoothing out fluctuations. LSTM is a deep learning model suitable for processing time series data, capable of capturing both short-term changes and long-term trends. In the implementation, each time series is first normalized. The time series is then fed into a pre-trained LSTM model, which predicts and interpolates missing values ​​within the time interval, smoothing out any abnormal fluctuations in the data and generating temporal analysis data. The use of LSTM can effectively improve the accuracy and robustness of time series analysis.

[0098] Step 304: Application of spatiotemporal data fusion and dynamic assessment model

[0099] The spatial distribution data generated by kriging interpolation is fused with the temporal analysis data obtained through LSTM time series analysis. During the fusion process, the time series results in the temporal analysis data are matched with the spatial distribution data based on timestamps and spatial coordinates, and the overlapping portions are weightedly fused (such as simple averaging or weighted averaging). This ultimately generates a complete fused spatiotemporal dataset, containing ecological and environmental attribute values ​​for each time point and spatial location. This fused spatiotemporal dataset is then input into a CNN-LSTM combined model for dynamic evaluation. The CNN extracts spatial features, while the LSTM captures time series features, resulting in predictions of future ecological and environmental changes, providing a scientific basis for subsequent optimization and management strategies.

[0100] Furthermore, the time series analysis based on LSTM in this embodiment is as follows:

[0101] Extract a time series from each spatial location (i.e., each grid cell) in the spatially distributed data according to the timestamp to form a data series with time as the dimension. For example, for a given grid cell, extract its ecological and environmental attribute values ​​(such as NDVI, AQI, etc.) at different time points (such as daily and monthly) to form a corresponding time series.

[0102] The extracted time series data is fed into an LSTM model for prediction and interpolation. LSTM uses its memory cells to capture both short-term dynamics and long-term trends in a time series, enabling prediction of missing values ​​within a time interval. Specifically, during the training phase, the LSTM model learns the patterns of the time series using complete historical data. During the interpolation phase, it uses incomplete time series input to predict missing values. Furthermore, the LSTM model can smooth out abnormal fluctuations in the time series, generating more stable temporal analysis data.

[0103] Furthermore, the CNN-LSTM combination model of the dynamic evaluation model is as follows:

[0104] Convolutional neural networks (CNNs) are used to extract spatial features from fused spatiotemporal datasets. Specifically, the spatial distribution data within the spatiotemporal dataset (e.g., grid data at each time point) is fed into the CNN model, and convolution operations are used to extract spatial features, such as the spatial distribution patterns of ecological environments and hotspots. The convolution kernels of CNNs capture local spatial features, while the pooling layer reduces the dimensionality of these features, preserving key spatial information.

[0105] LSTM is used to capture time series features in spatiotemporal data. Spatial features extracted by CNN are fed into the LSTM model in chronological order. Through its memory cells, LSTM learns the dynamic patterns of time series changes, thereby predicting future ecological and environmental trends. The combination of CNN and LSTM can simultaneously capture spatial and temporal features, improving the accuracy and predictive power of dynamic assessment models.

[0106] Through the above steps, this embodiment finally realizes the spatiotemporal data fusion of the cleaned data, and generates future ecological environment change results through the CNN-LSTM dynamic evaluation model, providing scientific support for subsequent optimization management.

[0107] Preferably, the calculation formula of the ecological environment quality index is:

[0108] EQI=w1·NDVI+w2·AQI+w3·WQI+w4·LUI+w5·SEI

[0109] Among them: EQI is the ecological environment quality index, ranging from 0 to 1, and the higher the value, the better the ecological environment quality; NDVI is the normalized difference vegetation index, which reflects the vegetation coverage; AQI is the air quality index, which reflects the degree of air pollution; WQI is the water quality index, which reflects the degree of water pollution; LUI is the land use intensity index, which reflects the intensity of human activities on land resources; SEI is the socioeconomic index, which reflects the impact of socioeconomic development on the ecological environment; w1, w2, w3, w4 and w5 are weight coefficients, which indicate the relative importance of each indicator to the ecological environment quality, satisfying w1+w2+w3+w4+w5=1.

[0110] For example, based on historical ecological and environmental data, this embodiment uses the principal component analysis (PCA) method to determine the weight coefficient. Specifically, after the multi-source ecological and environmental data (NDVI, AQI, WQI, LUI, SEI) are standardized, the covariance matrix of each indicator is calculated, and the principal components are extracted. By analyzing the eigenvalues ​​and eigenvectors of each principal component, the contribution rate of each indicator in the principal component is determined and normalized into a weight coefficient. The PCA method can objectively extract the importance of each indicator from the data and avoid the influence of human intervention on the weight distribution.

[0111] Specifically, the future ecological and environmental change results of this embodiment include the ecological and environmental quality change trends and spatial distribution characteristics of the target area within a specific future time frame (such as the next 1, 5, or 10 years). Specific content includes the predicted value of the Ecological and Environmental Quality Index (EQI), the changing trends of key indicators (such as NDVI, AQI, WQI, LUI, SEI), the dynamic changes in ecological and environmental hotspots and cold spots, and possible environmental risks (such as areas of increased pollution or ecological degradation) and improvement areas (such as areas of vegetation restoration or water quality improvement). These results provide a scientific basis and forward-looking guidance for ecological and environmental protection and management.

[0112] Preferably, the spatiotemporal statistical analysis method is based on the Mann-Kendall trend test method.

[0113] Specifically, step 500 of this embodiment includes:

[0114] Step 501: Calculation of Ecological Environment Quality Index (EQI)

[0115] First, based on the fused spatiotemporal dataset, key indicator data for each grid cell at different time points were extracted, including NDVI, AQI, WQI, LUI, and SEI. These indicators were normalized, converting their values ​​to the same range (e.g., 0 to 1) for unified calculation. Then, based on predetermined weight coefficients, each indicator was weighted and summed according to its relative importance to ecological and environmental quality to calculate the ecological and environmental quality index (EQI) for each grid cell at different time points. The calculated EQI can reflect the spatial and temporal distribution of ecological and environmental quality in the target area.

[0116] Step 502: Preparation for Mann-Kendall trend test

[0117] After calculating the EQI for each grid cell, its time series data is extracted to form a time series of EQI for each grid cell. The Mann-Kendall trend test is a nonparametric statistical method suitable for analyzing the changing trends of time series data, thus eliminating the need to assume the distribution characteristics of the data. To perform this test, the EQI time series for each grid cell must be arranged in chronological order and the statistical parameters used for the test (such as time series length and data values) must be prepared.

[0118] Step 503: Implementation of Mann-Kendall trend test

[0119] A Mann-Kendall trend test is performed on the EQI time series for each grid cell to determine whether the trend is significantly increasing, significantly decreasing, or no significant change. Specifically, the test method compares the size of the data points in the time series, calculates the trend statistic, and determines the significance of the trend based on a significance level (such as 0.05 or 0.01). The test results can be labeled as "increasing trend," "declining trend," or "no significant trend," and this trend information is associated with the spatial coordinates of the grid cell.

[0120] Step 504: Spatial visualization and analysis of dynamic change trends

[0121] The results of the Mann-Kendall test are mapped onto the spatial grid of the target area to generate a spatial distribution map of ecological and environmental quality trends. For example, different colors can be used to represent areas with increasing trends, decreasing trends, and no significant trends, thereby visually demonstrating the dynamic changes in ecological and environmental quality. Furthermore, hotspot analysis can be combined to identify key areas of ecological and environmental change (such as areas of significant improvement or degradation), providing a scientific basis and decision-making support for ecological and environmental protection and management.

[0122] Preferably, the multi-objective optimization algorithm is a particle swarm optimization algorithm.

[0123] Specifically, step 600 of this embodiment includes:

[0124] Step 601: Formulate optimization objectives and constraints

[0125] Based on future ecological and environmental changes, the Ecological and Environmental Quality Index (EQI), and dynamic trends, combined with ecological and environmental management objectives, the optimization objective function and constraints are clearly defined. Optimization objectives can include improving the Ecological and Environmental Quality Index (e.g., maximizing the EQI), reducing pollution (e.g., minimizing the AQI), improving water quality (e.g., maximizing the WQI), and reducing land use intensity (e.g., minimizing the LUI). Constraints are set based on actual circumstances, such as budget constraints, land use planning requirements, and socioeconomic development goals. Clearly defined objectives and constraints provide a clear direction for subsequent optimization calculations.

[0126] Step 602: Particle swarm initialization and parameter setting

[0127] The particle swarm optimization (PSO) algorithm is used for optimization. The particle swarm is first initialized. Each particle in the swarm represents a possible ecological and environmental management strategy, and its position indicates the specific parameter values ​​of the strategy (such as pollution control investment and vegetation restoration area). During initialization, the position and velocity of the particle swarm are randomly generated, and key PSO parameters are set, including the particle swarm size, maximum number of iterations, inertia weight, and acceleration factor. Proper settings of these parameters can influence the algorithm's convergence speed and optimization effectiveness.

[0128] Step 603: Design and calculation of fitness function

[0129] A fitness function is designed for each particle to evaluate the effectiveness of its corresponding management strategy. This function is designed based on the optimization objective and constraints, such as the magnitude of EQI improvement, pollutant reduction, and economic cost control. For each particle, its position (i.e., management strategy parameters) is substituted into the fitness function to calculate its fitness value. A higher fitness value indicates that the management strategy associated with the particle is closer to the optimization objective.

[0130] Step 604: Iterative optimization of particle swarm

[0131] In each iteration, the velocity and position of each particle are updated based on the swarm's fitness. Particle position updates adhere to the core mechanism of PSO, which adjusts its direction based on the global and individual optimal solutions, while also incorporating inertia weights to control the particle's exploration and exploitation capabilities. Through multiple iterations, the particle swarm gradually converges to the global optimal solution, finding the optimal management strategy that satisfies the optimization objective and constraints.

[0132] Step 605: Generate optimal management plan

[0133] When the particle swarm reaches the maximum number of iterations or meets the convergence criteria, the algorithm stops and outputs the global optimal solution. The particle position corresponding to this optimal solution is the parameter value of the optimal management plan, such as specific pollution control measures, area allocation for vegetation restoration, and optimized land use planning. Combining these parameter values, a specific ecological and environmental management plan is generated, and the optimization results are displayed through visualization tools, providing decision makers with scientific evidence and implementation guidance. The final management plan can meet ecological and environmental management goals while balancing multiple economic, social, and environmental needs, achieving sustainable ecological and environmental development.

[0134] The beneficial effects of the present invention are as follows:

[0135] (1) The present invention integrates multi-source ecological and environmental data (remote sensing image data, meteorological data, ground monitoring data, and socio-economic data). Through data cleaning, denoising, and format conversion, the quality and consistency of the data are ensured, and the multi-dimensional characteristics of the ecological environment are fully reflected. Through the calculation of the Ecological Environment Quality Index (EQI), a variety of key factors such as vegetation, air, water quality, land use, and socio-economics are comprehensively considered, and the quality of the ecological environment can be comprehensively evaluated.

[0136] (2) The present invention uses spatial interpolation methods and time series analysis methods to perform spatiotemporal data fusion on the cleaned data, which can dynamically capture the spatiotemporal change characteristics of the ecological environment and make up for the shortcomings of static evaluation in traditional methods; through spatiotemporal statistical analysis methods (such as spatiotemporal hotspot analysis and trend analysis), it can identify the dynamic change trend of the ecological environment, discover the hot spots, cold spots and potential problem areas of the ecological environment, and provide a scientific basis for management.

[0137] (3) The present invention uses a trained dynamic assessment model to analyze the fused spatiotemporal dataset, which can predict future trends in ecological and environmental changes, identify possible environmental risks or improvement areas in advance, and support forward-looking decision-making; through the prediction results, it can provide more accurate time windows and spatial guidance for ecological protection and restoration.

[0138] (4) The present invention combines the future ecological environment change results, ecological environment quality index and dynamic change trends to formulate optimization strategies, and generates optimal management plans through multi-objective optimization algorithms to ensure the scientific nature and efficiency of ecological environment management; the multi-objective optimization algorithm can find a balance between ecological protection, resource utilization and social and economic development to achieve sustainable management of the ecological environment.

[0139] (5) The final results of the present invention include optimal management plans and visual displays, which facilitate decision makers to intuitively understand the ecological environment status and its changing trends, and support scientific decision-making; by delineating ecological restoration priority areas, optimizing resource allocation plans, and providing targeted policy recommendations, the operability and implementation efficiency of management measures are improved.

[0140] (6) The framework of the present invention has strong adaptability and can adjust data sources, weight coefficients and optimization targets according to the ecological and environmental characteristics and management objectives of different regions. It is applicable to a variety of ecological and environmental assessment and management scenarios. The dynamic assessment model and optimization algorithm can be updated and expanded according to actual needs to adapt to complex and changing ecological and environmental problems.

[0141] (7) The present invention can help managers quickly identify problem areas in the ecological environment (such as pollution hotspots and degraded areas), prioritize targeted restoration measures, and improve the accuracy and efficiency of ecological protection; through dynamic monitoring and prediction, it can provide a scientific basis for the formulation of ecological and environmental protection policies and help achieve sustainable development of the ecological environment.

[0142] (8) By optimizing resource allocation and formulating scientific management strategies, the present invention can reduce resource waste and improve the input-output ratio of ecological restoration and environmental protection; the multi-objective optimization algorithm can maximize benefits under limited resource conditions and provide an economical and efficient solution for ecological environment management.

[0143] The various embodiments in this specification are described in a progressive manner, and each embodiment focuses on the differences from other embodiments. The same or similar parts between the various embodiments can be referenced to each other.

[0144] This document uses specific examples to illustrate the principles and implementation methods of the present invention. The above examples are only intended to help understand the method and core concept of the present invention. At the same time, those skilled in the art will find that the specific implementation methods and application scopes may vary based on the concept of the present invention. In summary, the contents of this specification should not be construed as limiting the present invention.

Claims

1. A method for dynamic assessment and management of ecological environment based on spatiotemporal data analysis, characterized in that: include: Collect multi-source ecological and environmental data; Cleaning the multi-source ecological environment data to obtain cleaned data; The cleaned data are subjected to spatiotemporal data fusion using a spatial interpolation method and a time series analysis method to obtain a fused spatiotemporal data set, specifically comprising: unifying the cleaned data into a standard spatiotemporal grid format, and adding a timestamp and spatial coordinates to each data point in the spatiotemporal grid; performing spatial interpolation on the data at each time point in the spatiotemporal grid based on a Kriging interpolation method to generate complete spatial distribution data; performing time series analysis on the data at each spatial position in the spatial distribution data to fill in missing values ​​in time and smooth the time series to obtain time analysis data; performing spatiotemporal data fusion on the spatial distribution data and the time analysis data to obtain the fused spatiotemporal data set; The performing time series analysis on the data of each spatial location in the spatially distributed data to fill missing values ​​in time and smooth the time series to obtain time analysis data includes: extracting a time series from the data of each spatial location; predicting and interpolating the time series according to an LSTM long short-term memory network to fill missing values ​​in a time interval and smooth time series fluctuations to obtain the time analysis data; Inputting the fused spatiotemporal dataset into the trained dynamic assessment model to obtain the results of future ecological and environmental changes; Calculating an ecological environment quality index based on the fused spatiotemporal dataset, and using a spatiotemporal statistical analysis method to identify dynamic change trends of the ecological environment on the fused spatiotemporal dataset; Based on the future ecological environment change results, the ecological environment quality index and the dynamic change trend, combined with the ecological environment management goals, an optimization strategy is formulated, and a multi-objective optimization algorithm is used to optimize the optimization strategy to generate the optimal management plan.

2. The method for dynamic assessment and management of ecological environment based on spatiotemporal data analysis according to claim 1 is characterized in that: The multi-source ecological and environmental data include remote sensing image data, meteorological data, ground monitoring data and socio-economic data.

3. The method for dynamic assessment and management of ecological environment based on spatiotemporal data analysis according to claim 1, characterized in that: Cleaning the multi-source ecological environment data to obtain cleaned data includes: Grouping the multi-source ecological environment data according to a preset collection period to obtain multiple data groups; Calculate the difference coefficient between the current data group and the previous data group in sequence; Determine whether the value of the coefficient of difference is within a preset range; If the value of the coefficient of difference is not within the preset range, the corresponding data group will be removed; If the value of the difference coefficient is within a preset range, the corresponding data group is retained until all data groups are traversed to obtain the cleaned data.

4. The method for dynamic assessment and management of ecological environment based on spatiotemporal data analysis according to claim 3 is characterized in that: The coefficient of difference calculation formula is: ； in, is the coefficient of variation, Indicates the current data group X With the previous data group Y The covariance between Indicates the current data group X The mean of Indicates the previous data group Y The mean of .

5. The method for dynamic assessment and management of ecological environment based on spatiotemporal data analysis according to claim 1, characterized in that: The dynamic evaluation model is a CNN-LSTM combination model.

6. The method for dynamic assessment and management of ecological environment based on spatiotemporal data analysis according to claim 1, characterized in that: The calculation formula of the ecological environment quality index is: ； Among them: EQI is the ecological environment quality index, ranging from 0 to 1, and the higher the value, the better the ecological environment quality; NDVI is the normalized difference vegetation index, which reflects the vegetation cover; AQI is the air quality index, which reflects the degree of air pollution; WQI is the water quality index, which reflects the degree of water pollution; LUI is the land use intensity index, which reflects the intensity of human activities on land resources; SEI is the socioeconomic index, which reflects the impact of socioeconomic development on the ecological environment; 、 、 、 and is the weight coefficient, which indicates the relative importance of each indicator to the ecological environment quality. .

7. The method for dynamic assessment and management of ecological environment based on spatiotemporal data analysis according to claim 1 is characterized in that: The spatiotemporal statistical analysis method is based on the Mann-Kendall trend test method.

8. The method for dynamic assessment and management of ecological environment based on spatiotemporal data analysis according to claim 1 is characterized in that: The multi-objective optimization algorithm is a particle swarm optimization algorithm.

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