71 results about "Ecological indicator" patented technology

The invention discloses a forest land ecosystem health assessment system, and belongs to the technical field of environmental protection. Comprising the following modules: an intelligent sensing module for realizing omnibearing and high-resolution monitoring of the forest land ecological environment; the feature extraction module automatically extracts, standardizes and dynamically corrects the multi-dimensional features of the ecological system, and constructs a unified ecological index mapping model; the coupling analysis module is used for accurately identifying structural evolution characteristics and key conversion nodes of the ecological system; the health assessment module is used for constructing a dynamic self-adaptive ecological health assessment model, assessing the overall health state of the system and simulating the vulnerability and potential risk of the ecological system under different climate change scenes; the decision and visualization module is used for realizing efficient intelligent interaction and multi-scene decision analysis of ecological big data; and the restoration and regulation and control module intelligently generates a restoration path and an intervention strategy based on ecological risk assessment so as to improve the restoration capability and toughness of the ecological system.

The invention provides a mining area ecological restoration guidance system based on ecological big data. Relates to the technical field of environmental engineering and big data application, and comprises a data acquisition and fusion module used for acquiring remote sensing images, unmanned aerial vehicle aerial photography, ground sensor and historical monitoring data according to a unified space-time coordinate system and outputting fusion data; the prediction and risk quantification module is used for constructing a space-time deep learning model based on the fused data and outputting an ecological index prediction value and a corresponding risk probability; and the strategy optimization module is used for generating a restoration scheme according to the ecological index prediction value and the corresponding risk probability through a multi-target reinforcement learning model. According to the mining area ecological restoration guidance system based on ecological big data, ecological benefits, economic cost and residual risks can be considered synchronously, and a quantifiable and comparable optimal parameter combination is provided for a mining area restoration scheme.

The invention discloses an inland river basin intelligent ecological scheduling rehearsal method fusing a scheduling process and an ecological process. The inland river basin intelligent ecological scheduling rehearsal method comprises the steps of multi-source data acquisition and digital twinborn construction; carrying out reservoir intelligent scheduling reinforcement learning modeling; ecological process lag response modeling is carried out; spatial diffusion modeling of ecological influence; performing cross attention guided ecological response interpolation; and carrying out rehearsal and visual display on the ecological scheduling scheme. According to the method, a hydrological-ecological response modeling mechanism is introduced, and a time-space response relationship between scheduling behaviors such as water level and water volume and ecological indexes such as vegetation indexes and habitat indexes is combined, so that lagging response characteristics of an ecological process to the scheduling behaviors can be quantitatively described, and the defect that a traditional scheduling model is insufficient in ecological expression capability is overcome. A reinforcement learning algorithm is utilized to fuse multi-source data for state perception and strategy iteration, and the scheduling strategy can be dynamically adjusted according to the current hydrological situation and ecological feedback result of the watershed. Compared with static rule type scheduling, the regulation and control efficiency and ecological adaptability are remarkably improved.

The invention relates to the technical field of intelligent mine and environmental protection crossing, and discloses a mine development ecological data fusion and causal mining method based on a double-lineage knowledge graph. The method comprises the steps of constructing a double-pedigree domain ontology model comprising a production activity pedigree and an ecological response pedigree, carrying out multi-modal data space-time alignment and standardization, constructing a dynamic map reflecting the real-time state of a mine, predicting future ecological indexes by using a time sequence diagram neural network introduced with physical constraints, and carrying out real-time monitoring on the real-time state of the mine. And calculating the contribution degree of each production link to abnormity by utilizing anti-fact reasoning so as to lock a disaster-causing source, and generating a production regulation and control instruction according to a causal analysis result, and feeding back and executing the production regulation and control instruction. According to the method and the system, full-process traceability and accurate regulation and control of mine environment problems such as surface deformation and water and soil pollution are realized, so that the problems that data multi-source heterogeneous is difficult to fuse and the causal relationship is difficult to confirm in mine ecological environment monitoring are solved.

The invention specifically discloses a long-time-sequence high-frequency ecological environment quality space-time differentiation and driving analysis method, and relates to the technical field of remote sensing and ecological environment. The method comprises the following steps: determining an evaluation area, and constructing a macroscopic ecological safety risk evaluation framework; ecological indexes of greenness, humidity, temperature and dryness are calculated, and a time sequence data set is constructed; reconstructing a time sequence data set and constructing a remote sensing ecological index model based on the reconstructed time sequence data set; verifying the precision of the model, and evaluating the reconstruction precision by taking the screened high-quality pixels as true values; analyzing the spatial and temporal change trend and significance output by the model by using slope estimation and trend test, outputting future change continuity by using a Hurst index analysis model, and measuring spatial autocorrelation output by the model by using a Moran index; remote sensing ecological index evolution factors are researched by means of an optimal parameter geographic detector. According to the invention, the precision and timeliness of ecological assessment are improved, and decision support is provided for ecological management.

The invention relates to the technical field of environment monitoring, in particular to a seawall ecologicalization real-time monitoring method based on multi-source data fusion. The method comprises the following steps: acquiring structure monitoring data, ecological monitoring data and water quality monitoring data of a seawall through an Internet of Things sensor, and acquiring remote sensing data of a seawall area in combination with satellite remote sensing and an unmanned aerial vehicle image; performing space-time registration and fusion on different data sources to generate a unified multi-source fusion data set; identifying a red tide abnormal event based on the fused data set, and constructing a red tide influence conduction model for predicting the damage degree of the red tide to the ecological system and the influence of the red tide to the stability of the embankment body; establishing a coupling evaluation model of ecological indexes and structural indexes, calculating a coupling coefficient by using a fuzzy comprehensive evaluation method, dynamically adjusting ecological and structural weights according to a model output result, and calculating a seawall comprehensive health index; graded early warning information is automatically generated according to a preset threshold value, and collaborative monitoring and intelligent early warning of seawall ecology and structure safety are achieved.

The invention relates to the technical field of ecological environment monitoring and resource scheduling, and discloses an ecological scheduling monitoring method and system based on multi-source remote sensing fusion. The method comprises the following steps: performing time-space consistency correction and fusion on multi-source data, and extracting initial ecological parameters; a feature enhancement parameter set and adaptive inversion parameters are obtained through optimization of a convolutional neural network and a genetic algorithm, key ecological indexes are inverted, and real-time ecological state description is generated; based on real-time ecological state adjustment model input, predicting an ecological change trend, and calculating to obtain an optimization decision variable; a water resource distribution scheme is generated, the distribution balance degree is evaluated, and final scheduling output is formed through iterative optimization; a final scheduling result is fed back to a data integration link, and updating and loop optimization of the preliminarily aligned data set are achieved. According to the invention, the integration of ecological monitoring and water resource scheduling under multi-source data fusion can be realized, and the balance of water resource distribution and the real-time performance of ecological system regulation and control are improved.

The invention discloses a water quality probability forecasting method based on Bayesian multi-time-sequence deep learning. The method comprises the following steps: S1, determining a forecasted water environment water ecological index, a driving index and a forecasting day number; s2, collecting time sequence data monitored by the forecasting indexes and the driving indexes, and after data preprocessing, constructing a data set required by model construction; s3, carrying out data division on the time sequence data, constructing a driving index forecasting model by adopting a multi-time sequence deep learning method, and carrying out parameter learning by selecting a Bayesian random discarding method; s4, performing effect evaluation on the accuracy and precision of the model, and adopting a hyper-parameter optimization method to improve the simulation forecast effect; s5, carrying out model training by adopting all data without segmenting the training set and the test set, carrying out water quality probability forecasting by utilizing the trained model, and outputting a forecasting mean value and a confidence interval; according to the method, the confidence interval is output while high-precision prediction is provided, and the scientificity and stability of prediction are improved.

The invention discloses a mining area ecological index intelligent evaluation platform, and relates to the technical field of ecological evaluation, and the mining area ecological index intelligent evaluation platform comprises the following steps: obtaining multi-source remote sensing data, and obtaining an initial ecological index; performing ecological factor fusion according to the initial ecological indexes to obtain comprehensive ecological factors; performing principal component dimensionality reduction according to the comprehensive ecological factors to obtain principal component features; performing cross-cycle stability calculation according to the principal component characteristics to obtain a stability index; performing spatial overlay analysis according to the stability index to obtain an ecological distribution pattern; and performing multi-scale coupling analysis according to the ecological distribution pattern to obtain a mining area ecological index. According to the method, the cross-period stability index is calculated based on the principal component characteristics, the change trend of the mining area ecosystem in multiple time periods is comprehensively reflected, the accuracy of dynamic monitoring is improved, the scientificity and interpretability of ecological assessment are improved, and the systematicness and comprehensiveness of ecological assessment are enhanced.

The invention discloses an intelligent water storage irrigation system suitable for ecological restoration of an abandoned mine, and relates to the technical field of ecological restoration, and the system comprises a multi-source water quality regulation and control module which is used for providing an irrigation water source adaptive to plant growth and soil improvement requirements in different restoration subareas; the ecological perception decision module is used for monitoring ecological indexes in real time; generating an irrigation decision executed according to the global scheduling plan through cloud data fusion analysis; the geology and hydrology safety module is used for constructing a digital twinborn model of a mine area and simulating restoration scenes under different irrigation decisions; the distributed irrigation module is used for building a distributed irrigation pipe network; and an energy circulation module. According to the method, fundamental transformation from single water supply to water-soil-plant synergistic remediation is achieved, the integrity of ecological remediation is improved, the vicious circle that traditional irrigation aggravates soil hardening or pollution leaching is effectively broken through, and a stable foundation is built for survival and growth of vegetation in the severe mine environment.

The invention relates to the technical field of marine ecological environment monitoring, in particular to a marine ecological environment assessment early warning system based on big data and AI, which comprises a multi-source data fusion module, a dynamic feature extraction module, a meteorological disturbance coefficient calculation module, an ecological index generation module and a coupling early warning generation module. Wherein the multi-source data fusion module is used for collecting multi-source data in real time; the dynamic feature extraction module is used for extracting 72-hour change gradients of the three types of core parameters; the meteorological disturbance coefficient calculation module is used for calculating and outputting a meteorological disturbance coefficient; the ecological index generation module is used for generating a water quality deterioration index and a biological stress index; and the coupling early warning generation module is used for outputting an ecological risk level signal. According to the method, meteorological interference is reduced in real time, water quality deterioration and biological stress indexes are subjected to weighted fusion, the marine ecological environment risk is accurately evaluated, and scientific support is provided for ecological protection and pollution early warning.

The invention discloses a mangrove forest protection effect intelligent evaluation and scene prediction system, and relates to the field of ecological environment information intelligence, and the system comprises a key driving factor recognition module which is used for determining a core variable influencing the protection effect and a dynamic response interval of the core variable based on a statistical analysis and nonlinear fitting method; the protection effect quantitative evaluation module is used for calculating a net improvement effect of protection intervention by comparing ecological index differences inside and outside the protection area; the causal effect analysis module is used for separating independent contributions of natural factors and artificial protection by adopting a dual machine learning framework; the space-time dynamic modeling module is used for performing uncertainty modeling on the space-time evolution of the ecological indexes by using a Bayesian hierarchical structure; and the multi-scene prediction module is used for realizing ecological response prediction under multi-factor driving and supporting input and simulation of user-defined scenes. According to the scheme, multi-dimensional and high-precision evaluation and future trend reliable prediction of mangrove forest protection effects can be realized.

The invention provides a coastal wetland mud flat ecological environment monitoring method, system and device and a medium, and belongs to the field of remote sensing image processing.The method comprises the steps that a double-branch network sharing an OfficientNet network backbone parameter is rebuilt, the improved network is connected with a connection terminal attention mechanism to form an OfficientNet model, the model is trained, and the OfficientNet model is constructed; enabling the model to predict a double-time-phase characteristic graph of the target coastal wetland mud flat; performing multi-level differential feature fusion on the dual-time-phase feature map, inputting the dual-time-phase feature map into a decoder, and generating a change prediction map with clear boundaries; obtaining a historical data set, an environmental factor and an ecological index; optimizing the ecological degradation rate and the ecological change correlation factors through normalization and cross validation, and generating a degradation trend prediction model; the method is high in data prediction precision and high in accuracy, can output degradation tendency charts and degradation risk level quantitative reports of the target area in the next few years, and provides powerful support for ecological protection.

The invention relates to the technical field of intelligent environmental protection monitoring, in particular to a method for intelligently monitoring and analyzing land utilization change in an ecological protection red line, which comprises the following steps of: acquiring a historical remote sensing image, extracting pixel-level vegetation and artificial indexes, constructing an ecological index time sequence data set, and constructing an ecological index time sequence data set based on the data set; a dynamic baseline prediction model fusing a long-term trend term, a seasonal periodic term and a standard deviation is established for each pixel, personalized prediction of a theoretical state of the pixel is realized, abnormality diagnosis is performed through comparison with a confidence interval output by the model, and an abnormal pixel is marked as a collaborative abnormal pixel, so that comprehensive risk assessment is carried out, and the risk assessment efficiency is improved. The method comprises the steps of calculating risk scores of all indexes and carrying out weighted fusion to obtain a comprehensive risk index, then dividing risk levels, outputting an anomaly diagnosis report, finally, carrying out reverse calibration on model parameters based on confirmed anomaly cases, and mining risk modes from case characteristics through unsupervised clustering.

The invention provides a rain and snow enhancement ecological benefit dynamic evaluation method, device and equipment and a storage medium, and relates to the field of agricultural planting, and the method comprises the steps: extracting a plurality of key ecological index data based on obtained multi-source data, and constructing a coupling model; the coupling model obtains a simulation data set based on the key ecological index data corresponding to the data before operation, corrects the simulation data set based on the multi-source data to obtain analysis state data, and generates an optimal ecological benefit index based on the analysis state data and the simulation data set; a comprehensive ecological benefit index is obtained through calculation based on a continuous time sequence generated by the multi-source data within a preset time step length, and the optimal ecological benefit index and the comprehensive ecological benefit index form an ecological benefit evaluation result. According to the technical scheme of the embodiment of the invention, the optimal ecological benefit index is generated from multiple dimensions, the ecological benefit of the artificial rain and snow enhancement operation on the ecological system is comprehensively evaluated, the simulation data set is corrected, and the precision of the optimal ecological benefit index is improved.

The invention discloses an ecological product value analysis method and system based on multi-source data fusion, and relates to the technical field of ecological product value analysis. Original ecological data are divided into two-dimensional space grid units according to a fixed scale through a space grid construction and index unit; according to the method, automatic mapping from an actual geographic coordinate system to an analysis grid unit is realized, so that multi-source data has a unified projection basis in space, the problems that multiple data source space references are inconsistent and results are difficult to transversely compare in original ecological evaluation are solved, and a strict space organization structure is provided for subsequent ecological supply modeling. In the process of mapping the ecological indexes to the grid units, the system designs a neighborhood interpolation-based mechanism for averaging and filling multiple observation points and invalid units, so that the problem of local deficiency caused by non-uniform distribution of the observation points or influence of shielding on remote sensing data in field sampling is effectively relieved, and the integrity and analysis reliability of a spatial expression matrix are improved.

This invention discloses a comprehensive assessment method and system for wetland ecological quality based on multi-indicator fusion, belonging to the field of wetland ecological protection technology. The method includes acquiring water environment index data, biodiversity index data, carbon storage index data, and remote sensing images of wetland edges; establishing a multi-indicator fusion model based on the water environment index data, biodiversity index data, and carbon storage index data to generate a basic wetland ecological quality index; generating a predicted value of the basic wetland ecological quality index based on the basic wetland ecological quality index; and generating a comprehensive wetland ecological quality index based on the remote sensing images of wetland edges and the predicted value of the basic wetland ecological quality index, and assessing the wetland ecological quality. This invention can integrate multi-dimensional ecological indicators such as water environment, biodiversity, and carbon sink function, achieving dynamic prediction of wetland ecological quality and quantitative assessment of the impact of external pressures, thus improving the comprehensiveness and foresight of the assessment results.

The present application relates to the technical field of marine ecological environment monitoring, and particularly relates to a marine ecological environment assessment and early warning system based on big data and AI, comprising a multi-source data fusion module, a dynamic feature extraction module, a meteorological disturbance coefficient calculation module, an ecological index generation module and a coupling early warning generation module; wherein: the multi-source data fusion module is used for real-time collection of multi-source data; the dynamic feature extraction module is used for extraction of 72-hour change gradients of three types of core parameters; the meteorological disturbance coefficient calculation module is used for calculation and output of meteorological disturbance coefficients; the ecological index generation module is used for generation of water quality deterioration indexes and biological stress indexes; and the coupling early warning generation module is used for output of ecological risk grade signals. Through real-time reduction of meteorological interference and weighted fusion of water quality deterioration and biological stress indexes, the present application can accurately assess marine ecological environment risks and provide scientific support for ecological protection and pollution early warning.

This invention discloses a method for assessing the impact of photovoltaic (PV) installations on grassland ecosystems based on multi-source remote sensing data. The method includes the following steps: constructing a multi-source remote sensing dataset of the PV installation area and surrounding grasslands; acquiring time series data of multi-dimensional ecological indicators such as vegetation, water, and energy; establishing multi-ring buffer zones based on the PV boundary, and extracting the variation characteristics of each ecological indicator within and outside the PV area and at different distances; and quantitatively assessing the intensity and spatial decay characteristics of the impact of PV installations on the grassland ecosystem through spatial difference analysis and comprehensive ecological impact indicators. This method enables long-term dynamic monitoring of the ecological effects of PV construction without ground-based observation, and has the advantages of high automation, strong scalability, and wide application range, providing scientific support for the ecological assessment and management of grassland renewable energy development.

The present application relates to the field of ecological monitoring and augmented reality technology, and more particularly to an ecological footprint AR tracking calculation method and system, which obtains target user behavior event sequence data through an augmented display device, identifies the causal correlation between behavior event types and ecological impact factors through semantic analysis and converts it into ecological impact values, constructs an ecological load distribution matrix by registering a spatial coordinate system, calls an augmented reality display engine to render a virtual ecological indicator layer superimposed on a real scene, collects interactive feedback signals to analyze and identify behavior improvement modes to adaptively calibrate the causal correlation and regenerate the distribution matrix to drive the dynamic refreshing of the virtual layer, and performs time dimension accumulation on the matrix after dynamic refreshing to form ecological footprint time sequence change data and render it on a visual interface, which realizes real-time visual tracking and dynamic adaptive calibration of ecological footprint.

The application relates to the technical field of ecological safety optimization, and discloses an ecological safety pattern intelligent simulation optimization system based on reinforcement learning. An experience pool forming module of the system divides historical optimization strategies into multiple experience layers to form a multilayer experience pool based on the gap between ecological indexes and strategy confidence of the historical optimization strategies. A strategy determining module determines intelligent optimization strategies at each stage based on a set ecological safety target state, and screens the multilayer experience pool to obtain experience optimization strategies in combination with strategy confidence and target matching degree. A strategy adjusting module adjusts the two strategies by using a dynamic mechanism to cope with real-time ecological changes, and determines a target optimization strategy. A simulation optimization module simulates and optimizes the ecological safety pattern based on the target optimization strategy. A state acquisition module acquires an actual state. An experience pool updating module updates the content of the experience layers based on the difference factors between the actual state and the target state, and perfects the multilayer experience pool data. The system can improve the dynamics and adaptability of ecological safety pattern optimization.

The invention discloses an ecological environment quality evaluation method, system and device, and relates to the technical field of ecological protection, and the method comprises the steps: obtaining Sentinel-2A remote sensing data, landscape pattern indexes and various remote sensing ecological indexes of a to-be-detected region; classifying the Sentinel-2A remote sensing data by adopting a random forest supervised classification method, and determining a classification result of land utilization; based on the classification result of land utilization, constructing a land utilization transfer matrix, and obtaining land utilization change data through the land utilization transfer matrix; and according to the multiple types of remote sensing ecological indexes, constructing an ecological environment quality index RESI model, obtaining an initial ecological environment quality index RESI, adjusting the initial ecological environment quality index RESI through the land utilization change data and the landscape pattern index, and obtaining an ecological environment quality evaluation result of the to-be-detected area.

The invention belongs to the technical field of big data analysis and processing, and particularly relates to a geothermal development full-life-cycle carbon emission reduction and ecological cooperative gain accounting method. The method comprises the following steps of: 1, constructing a graph containing life cycle stage unit process nodes, space grid or plot nodes and ecological index nodes, and setting a process influence edge, a space adjacent edge and a space observation edge in the graph; 2, constructing a graph neural network model adopting a combination of a graph attention network layer and a graph convolutional network layer on the graph; and step 3, writing operation information of the target geothermal development scheme into attribute fields of process nodes of the life cycle stage unit and space grids or plot nodes to obtain a geothermal development full life cycle carbon emission reduction and ecological cooperative gain accounting result. According to the method, the phased influence of engineering activities, the spatial propagation characteristics and the dynamic response of an ecological system can be considered at the same time, so that the carbon emission reduction effect and the ecological cooperative benefit are presented in a spatialization mode on the full life cycle scale.

The invention relates to the technical field of man-machine interaction and augmented reality, and discloses a garden landscape dynamic planning method and system based on augmented reality. The method comprises the steps of obtaining a garden scene calling instruction input by a user based on augmented reality equipment, extracting corresponding data from a BIM model database and a plant growth database, and performing fusion to generate garden scene interaction data; based on a user simulation request, calling the generative adversarial network to generate a plant community dynamic evolution model; performing three-dimensional projection and rendering based on the display instruction to generate a garden simulation scene; dynamically adjusting the plant community evolution model based on user interaction input, generating an adjusted model and rendering in real time; and based on the optimization request, calling an AI analysis module, evaluating garden scene ecological indexes according to the ecological balance function, generating optimization suggestions and feeding back the optimization suggestions. According to the method, the interactive planning capability of plant community dynamic simulation, spatial layout and ecological optimization in garden landscape design is improved, and the user participation degree and the design scientificity are enhanced.

The invention belongs to the technical field of environment monitoring and intelligent analysis, and discloses an intelligent microscopic imaging method and system for water ecology investigation, and the method comprises the steps: carrying out the preprocessing of the microscopic image data of a water ecology sample, and obtaining the microscopic image feature data of the water ecology sample; positioning and classifying targets in the microscopic image feature data of the water ecological sample by using a dual-stage recognition mechanism to obtain key features of each target; and performing ecological health analysis on the key features of each target, evaluating ecological indexes of the target, generating an identification report in real time, uploading the identification report to a cloud for data storage and sharing, constructing a regional species database, and assisting ecological early warning. According to the method, through a two-stage identification mechanism combining the YOLO algorithm and the visual converter, rapid and accurate target positioning and classification are realized in microscopic image analysis of the water ecological sample, so that the problem of individual separation in a high-density sample is effectively solved, and accurate classification of aquatic species is ensured.

The invention relates to the technical field of water conservancy projects, and discloses a high seismic intensity area and reservoir area bridge scheme comparison and selection method and system, and the method comprises the steps: building a multi-field coupling model based on the earthquake, landslide and large water level rise and fall, and obtaining a safety index, a function index and an ecological index in SPI values of a comprehensive performance index SPI based on the multi-field coupling model; establishing a dynamic full-life-cycle dynamic LCC model; an improved NSGA-III algorithm is adopted, optimization is carried out to generate a plurality of initial candidate schemes by taking minimization of the LCC value of the dynamic full-life-cycle dynamic LCC model, maximization of the comprehensive performance index SPI and minimization of carbon emission as multiple objectives, and an optimal candidate scheme is screened from the initial candidate schemes in combination with TOPSIS multi-attribute decision, the comprehensive performance index SPI comprises a safety index, a functional index, an ecological index and a cultural index. According to the method, the slope instability probability is reduced by arranging the multi-field coupling model based on earthquake, landslide and great water level rising and falling.

The invention relates to the technical field of tourism industry data analysis, and discloses a tourism resource development suitability analysis method and device based on resource partition. The method comprises the following steps: firstly, obtaining initial partitions, and calculating a tourism evaluation index of each initial partition; and then according to the tourism evaluation indexes corresponding to all the initial sub-regions, the to-be-analyzed region is divided into a plurality of tourism large regions, and tourism development levels corresponding to all the sub-regions in each tourism large region are basically equivalent. According to the method, the to-be-analyzed area is not regarded as a whole to directly analyze the tourism resource development suitability of the to-be-analyzed area, but places close to the tourism industry development level are clustered into a large tourism area by designing a corresponding partition scheme, so that the analysis of suitability indexes is more reasonable; and finally, the ecological index data and the cultural index data are used as analysis indexes, so that the tourism resource development suitability analysis of each tourism region is more reliable and accurate.

The invention belongs to the field of ecological environment protection and treatment, and discloses a water ecological environment deep learning prediction method based on space-time heterogeneity nesting. The method comprises the following steps: S1, preprocessing water ecology simulation prediction data; s2, fitting a water ecology basic model; s3, watershed spatial element information embedding and serialized interpolation are carried out; and S4, constructing a sampling point water ecological index prediction model. On the basis of a deep neural network algorithm coupling gradient lifting method, a nonlinear response relationship between environmental elements and water ecology evaluation indexes is fitted, and the problem of simulation difficulty caused by a complex physical, chemical and biological multi-process interaction mechanism is solved; the spatial distribution data and the time sequence data are nested and coupled, the spatial correlation data are serialized and converted, and the time sequence data are embedded, so that the time resolution of the sampling data is improved, and the accuracy and applicability of model simulation are improved.

The present application provides a kind of coal mining three-dimensional ecological environment protection method. It relates to coal mining and ecological environment protection technical field, including, S1. the ore body occurrence condition investigation is implemented to the mining area to be mined, according to the ore body occurrence condition investigation result, mining area, dumping area and buffer protection zone are divided, and three-dimensional ecological environment database containing space coordinates and ecological index is established;S2. in the mining process, the soil is stripped in the order of humus layer, plough layer and parent material layer, and each layer of soil is transported to the closed functional soil storage for storage. The coal mining three-dimensional ecological environment protection method significantly shortens the ecological restoration period and improves the agricultural and landscape value of the reclaimed land in the mining area, realizing the long-term stability and sustainable development of green mine.

The application discloses a method for monitoring the effect of marine ecological restoration in an estuary and sea area based on multi-period remote sensing, and relates to the technical field of marine ecological restoration, which comprises the following steps: obtaining multi-period satellite and unmanned aerial vehicle remote sensing images before and after the implementation of a restoration project, and constructing a long-time sequence standardized remote sensing data set; interpreting the multi-period remote sensing images to generate multi-period ecological element data; inputting the multi-period ecological element data into an intelligent simulation model for water and sediment transport and topographic evolution, simulating the water and sediment transport and topographic evolution law in different restoration stages, and evaluating the implementation efficiency of the project; constructing an ecological network topology graph with each ecological patch in the restoration area as a node, using a graph neural network to learn the spatio-temporal characteristics and predict the state evolution of the multi-period ecological indicators, and generating a dynamic change curve of a comprehensive index of the restoration effect; and triggering a graded early warning when the comprehensive index deviates from a preset restoration target track. The application realizes quantitative monitoring and dynamic early warning of the effect of marine ecological restoration in an estuary and sea area.