56 results about "Ecological index" patented technology

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 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 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 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 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.

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.

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 a marine disaster key hidden danger identification method for marine space planning monitoring and early warning, and relates to the technical field of marine space hidden danger identification, and the method comprises the following steps: obtaining ecological indexes and disaster parameters of a fishery resource area; analyzing the incidence relation between the ecological indexes and different disaster parameters to obtain influence parameters; analyzing historical influence parameters to obtain a comprehensive influence graph of the influence parameters on the ecological indexes; predicting the trend of the influence parameter to obtain a prediction parameter, and predicting the change of the ecological index based on the comprehensive influence graph and the prediction parameter to obtain an ecological prediction index; judging whether the fishery resource area has hidden dangers or not according to the ecological prediction index; the method is used for solving the problems that when an existing marine space hidden danger recognition technology is applied to a fishery resource area, the hidden danger recognition mode is not reasonable, influence factors are not comprehensively considered, and fishery resources are reduced.

The application provides a kind of improved remote sensing ecological index calculation method based on space-time data fusion, the method comprises the following steps: collecting remote sensing data of the study area;The collected remote sensing data is pretreated;The water in the processed remote sensing data is masked and treated;The remote sensing data after pretreatment is spatiotemporally fused;According to the remote sensing data after fusion processing, the index data for representing the improved remote sensing ecological index is calculated respectively;Get aerosol optical depth to represent air quality and nighttime light data to represent human activity intensity on the gee platform, and resample the two index data;The index data is parameter standardized, and the standardized data is obtained;Principal component analysis is used, and principal component extraction is carried out according to the standardized data, to obtain the evaluation index result.The application can improve the spatiotemporal resolution of data through space-time data fusion technology, and solve the problem of lack of comprehensiveness in index selection of remote sensing ecological index.

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.

This invention belongs to the field of remote sensing technology and discloses a method for optimizing habitat quality parameters in the InVEST model based on Remote Sensing Ecological Indices (RSEI). This invention overcomes the technical bottleneck of traditional InVEST models, which rely on manual experience to set parameters and are difficult to update dynamically over time. By constructing a multi-source remote sensing data-driven RSEI, and utilizing its statistical characteristics and spatial distribution patterns across different land use types, it achieves objective quantification and dynamic updating of four key parameters: habitat suitability, threat weight, threat attenuation distance, and habitat sensitivity. Without requiring data from ecological monitoring stations, it directly incorporates continuous changes in ecological environment quality into the InVEST parameter system, resulting in higher objectivity, temporal consistency, and spatial autocorrelation in habitat quality calculations. This provides a novel technical approach for the automated and time-series assessment of regional-scale habitat quality.

The application provides a toxic element pollution ecological risk evaluation method based on a soil nematode ecological index, and belongs to the technical field of toxic element pollution ecological risk evaluation; a technical problem to be solved is to provide a toxic element pollution ecological risk evaluation method based on a soil nematode ecological index; a technical scheme is that sampling is carried out on soils in a region polluted by toxic elements in different degrees in multiple time periods and multiple sites, and the sampled soils are respectively used for soil potential toxic element content detection, soil physical and chemical property detection and soil nematode community structure analysis; characteristic indexes of potential toxic element pollution in the soils are respectively calculated, including a pollution factor, a Nemerow index, a pollution load index and a potential ecological risk index; soil nematode community structure is analyzed, and evaluation indexes of the soil nematode community structure are respectively calculated; and the application is applied to toxic element pollution ecological risk evaluation.

The application relates to a remote sensing ecological index monitoring method and device suitable for long time sequences. The method and device perform data synthesis on acquired long time sequence Landsat remote sensing data; calculate time sequence remote sensing indexes of the Landsat remote sensing data; detect invariable regions of the time sequence remote sensing indexes of the Landsat remote sensing data; normalize the time sequence remote sensing indexes in the probability space of the invariable regions; uniformly perform principal component analysis on normalized indexes of multiple time phases to acquire unified principal component load coefficients. The application is based on medium spatial resolution Landsat remote sensing images, utilizes time sequence data synthesis, time sequence invariable region detection, time sequence principal component analysis and other methods to design a remote sensing ecological index suitable for long time sequence dramatic change regions, and finally accurately reflects the distribution and evolution conditions of ecological environment conditions in a large area range and a long time sequence.

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 present application relates to the technical field of water environment treatment, solves the technical problems that the existing buffer zone design generally has the problems of width / structure determined by experience, lack of multi-scenario adaptation and quantifiable ecological target constraint, and particularly relates to a method for constructing a composite buffer zone at a lake inlet based on a hydrology-water ecology response model, a multi-scenario fingerprint library is constructed based on multi-source data of a target lake inlet area, a hydrology-water ecology response model is established for quantifying ecological indexes such as transparency, algal risk and submerged plant adaptability probability into an ecological response vector, a functional unit library is constructed and structure gene coding is used to describe the topology and parameters of units such as dissipation allocation, sedimentation dredging, reaction bed fixation, vegetation rhizosphere reaction and bottom protection and motion suppression; multi-objective inversion optimization and robustness evaluation are carried out under multi-scenarios, and a composite buffer zone spatial layout, size parameters and operation and maintenance trigger threshold values that meet flood discharge and stability constraints are output, so that parameterization and deliverable design of the lake inlet buffer zone are realized.

The invention discloses an ecological environment quality prediction method, and relates to the technical field of ecological environment, and the method comprises the steps: obtaining remote sensing images of a research region in different periods, and calculating a vegetation normalization index NDVI, a humidity index WET, a heat index LST, a dryness index NDBSI, a PM2.5 concentration difference index DI and a comprehensive salinity index CSI of the research region in different periods; constructing a novel remote sensing ecological index RSEInew by using a principal component analysis method, and dividing the novel remote sensing ecological index RSEInew into five ecological environment quality levels; calculating novel remote sensing ecological indexes RSEInew of the monthly scale, the annual scale and the different seasonal scales of the research area, and determining ecological environment quality levels of the monthly scale, the annual scale and the different seasonal scales of the research area; a CA-Markov model is constructed, the novel remote sensing ecological indexes RSEInew of the monthly scale, the annual scale and different seasonal scales of the research area and the ecological environment quality level are used as data sources, and the data sources are input into the CA-Markov model to predict the ecological environment quality; in conclusion, the method can accurately evaluate and predict the environment quality.

The present application relates to the technical field of plant ecological configuration, and particularly relates to an aquatic plant ecological configuration parameterization design method and system. The design method can provide quantitative basis for the design and optimization of the scheme based on the dual targets of aquatic plant ecological function and landscape benefit by constructing a parameterization standard module including ecological index standards and landscape index standards, realize precise design of the aquatic plant configuration scheme, and reduce the dependence on artificial experience. Meanwhile, by constructing a multi-dimensional database and adopting a multi-database linkage data fusion mechanism, the design recommendation and optimization can be provided with data support, and the quantitative linkage of plant variety-planting amount-purification effect-landscape score-cost can be realized. Meanwhile, by automatically matching plant varieties through an algorithm design process, an optimized combination structure can be obtained, the aquatic plant configuration scheme considering ecological efficiency and landscape value can be quickly obtained, and the precise design of aquatic plant configuration from 'experience-driven' to 'data-driven' is realized.

The invention discloses an urban and rural landscape sustainable geographic design method based on random optimization and deep learning. According to the method, land utilization type data, geospatial data, environmental data and social economic data of a to-be-optimized area are utilized, four ecological system services including grain supply amount, carbon reserve, water conservation amount and culture service value are taken as optimization indexes, and calculation mode information of the indexes is input into an urban and rural landscape sustainable geographic framework; and predicting a high-probability change region through an artificial neural network model, performing iterative optimization on a current land utilization scheme by taking landscape sustainability as a target guide through a simulated annealing algorithm, and driving generation of an urban and rural landscape sustainability global optimal land utilization scheme. According to the method, a land utilization scheme with remarkable and better ecological indexes can be generated, multi-party benefit conflicts are efficiently coordinated, a flexible framework supports multi-target extension, and support can be provided for complex planning decision making.

The invention provides a contrast analysis-based rain and snow enhancement benefit evaluation method, system and device, and a medium. The method comprises the steps of obtaining a first data set and a second data set of an influence area, obtaining a third data set of a contrast area, and obtaining a meteorological data set; determining an index mean difference based on the first data set and the second data set; determining a standardized effect quantity based on the second data set and the third data set; after a covariable effect value is determined through covariable analysis, the standardized effect quantity is updated to be a target effect quantity, a comprehensive contribution index is determined based on the index mean value difference of all the ecological indexes and the target effect quantity, and an ecological benefit evaluation result is determined. According to the technical scheme of the embodiment of the invention, multi-dimensional analysis is realized through a plurality of ecological indexes, a longitudinal analysis result of rain and snow enhancement is represented through the index mean value difference, a transverse analysis result is represented through the target effect quantity, and covariable analysis is introduced, so that the comprehensive contribution index can eliminate climate fluctuation, and the evaluation accuracy of ecological benefits is improved.

The invention relates to a global natural reserve biodiversity protection effect evaluation method and device, and the method comprises the steps: obtaining multi-source remote sensing and statistical data, constructing a multi-dimensional ecological index system covering forest changes, vegetation vitality, human interference and a landscape structure, carrying out the standardization processing and weight setting, and carrying out the evaluation of the biodiversity protection effect of the global natural reserve. Generating a plurality of standardized index data elements; constructing an equal-area buffer area as a contrast area on the basis of the boundary of each protected area, performing internal and external pairing analysis, and identifying a net ecological effect brought by a protection behavior through methods such as an index difference value, a ratio, a time trend and spatial autocorrelation; and performing weighted synthesis on the normalized and weighted indexes, constructing a comprehensive scoring model, dividing effect levels according to scores, and visually outputting effect distribution results through a GIS (Geographic Information System). Therefore, the problem that the biodiversity protection effect of the global natural reserve cannot be systematically and quantitatively evaluated in a multi-dimensional and multi-level mode in the prior art is solved.

The invention discloses a mine ecological environment monitoring method and system fusing soil heavy metal ecological indexes, and the method comprises the steps: calculating a corresponding potential ecological risk index RI based on the soil heavy metal content of a sample point of a mine sample region, and carrying out the fitting of remote sensing data of the sample point and the RI, and obtaining an RI inversion model; performing wave band operation on the multispectral remote sensing data of the mine sample area, and screening and constructing an optimal wave band combination; using the optimal wave band of the sample region and RI-ML regression to obtain an RRI inversion model; using the RRI inversion model and according to the optimal wave band of the mine monitoring area, performing inversion on the mine monitoring area to obtain a remote sensing ecological risk index RRI; the method comprises the following steps: calculating NDVI, WET and NDBSI by using multispectral remote sensing data of a mine monitoring area, then calculating a first principal component as a pollution enhanced remote sensing ecological index PE-RSEI, and carrying out space-time comparison on the ecological environment of the mine monitoring area; according to the method, simple and feasible soil heavy metal pollution fusion indexes are constructed and fused into a mining area ecological monitoring system, the current situation of mining area ecological environment quality can be visually reflected, and reliable reference is provided for mining area ecological monitoring and protection work.

The invention relates to the technical field of water environment treatment, solves the technical problem that the existing buffer zone design generally has the defects of width determination / structure determination according to experience and lack of multi-scene adaptation and quantifiable ecological target constraint, and particularly relates to a lake inlet composite buffer zone construction method based on a hydrological-water ecological response model. The method comprises the following steps: constructing a multi-scene fingerprint database based on multi-source data of a target lake entrance area, and establishing a hydrological-water ecological response model for quantifying ecological indexes such as transparency, algae risk and submerged plant survival probability into ecological response vectors; constructing a functional unit library, and describing topologies and parameters of units such as dissipation distribution, sedimentation dredging, reaction bed fixation, vegetation rhizosphere reaction and bottom protection movement inhibition by adopting structural gene coding; and carrying out multi-target inversion optimization and robustness evaluation under multiple scenes, and outputting a spatial layout, a size parameter and an operation and maintenance trigger threshold value of the composite buffer zone meeting flood discharge and stability constraints, so that parameterization and deliverable design of the lake entrance buffer zone are realized.

The invention discloses a monitoring and regulation system for restoration of a caragana microphylla artificial forest in a high and cold sand area, and relates to the technical field of artificial forest monitoring, the system comprises a monitoring module, a control analysis module and an execution module; the monitoring module is used for acquiring a plurality of underground ecological indexes of a measured area of the caragana microphylla artificial forest; the control analysis module is used for judging a system state based on the plurality of underground ecological indexes and a plurality of preset recovery stage thresholds, and generating a regulation and control instruction based on the system state; the execution module is used for executing the regulation and control instruction. According to the invention, in-situ, real-time and continuous monitoring of key physicochemical and biological indexes of forest underground soil can be realized.

The invention discloses a digital environmental protection information interaction display system, which comprises a data acquisition and preprocessing layer, an ecological calculation engine layer, a knowledge graph narrative generation layer and an interaction rendering and feedback layer, and is characterized in that the data acquisition and preprocessing layer is used for acquiring multi-source heterogeneous ecological data and user interaction data; the ecological calculation engine layer calculates a regional ecological health index through a constructed multi-dimensional index fusion model, integrates a geographic information system (GIS) to carry out space-time prediction, and generates an ecological index and future trend early warning data; the knowledge graph narrative generation layer is used for generating an RAG framework by utilizing retrieval enhancement based on an environmental protection knowledge graph, combining the received ecological index with future trend early warning data and user behavior data, and generating controllable narrative content with emotional design; and the interactive rendering and feedback layer is used for visually displaying the controllable narrative content and the real-time ecological data through a dynamic level of detail (LOD) rendering pipeline.

A northern arid region ecological system key node identification and evaluation method comprises the following steps: calling long-time-sequence remote sensing data, and extracting ecological indexes from the called remote sensing data to construct ecological risk indexes; based on an ecological space network analysis method, degree centrality (DC) is adopted as a node importance evaluation index, quantitative evaluation is carried out on the network node connection capability, and nodes with the DC located at the top 10% are screened out to serve as main key ecological nodes; according to the direct flow matrix and the comprehensive flow matrix between the nodes, risk flow intensity is graded, a main control path, a risk diffusion path and a key connection path in the network are extracted, and a key ecological path is obtained; and constructing an ecological comprehensive evaluation system based on the ecological risks, the key nodes and the key paths, identifying ecological partitions, and realizing dynamic closed-loop evaluation of the ecological system. According to the method, the key ecological path can be obtained while the key nodes of the ecological system in the arid region are quickly and accurately recognized, and the recognition and dynamic closed-loop updating of the restoration region are realized.

The application discloses a kind of forest ecological environment monitoring systems based on data analysis, comprising: data processing module, for collecting environmental parameters, and pre-processing;Heterogeneous modeling module, for building heterogeneous graph structure, and using Gaussian kernel function to calculate edge weight value;Convolution residual module, for performing multilayer graph convolution operation, and residual output is applied in next layer graph convolution;Pooling mapping module, for executing global pooling and multilayer perceptron mapping to node feature representation, generates state feature vector;Ecological clustering module, for performing clustering analysis using DENCLUE clustering algorithm, and calculating the ecological index value of each region;Monitoring analysis module, for comparing and analyzing the clustering result with historical environmental state data, and generating forest ecological risk monitoring result.The application realizes the structured modeling and dynamic risk intelligent identification of forest ecological environment, improves the precision of ecological monitoring and the timeliness of early warning.

The invention relates to the technical field of environmental science and engineering, in particular to an ecological health system evaluation method and device for a travel lake and computer equipment, and aims to introduce tourist perception text subjective information on the basis of objective ecological indexes to construct an evaluation system and objectively fix the weight. And the cloud model is introduced to characterize uncertainty and fuzziness and calculate the comprehensive health index of the lake, and the ecological health system evaluation is performed based on the obtained comprehensive health index of the lake, so that the reliability of the ecological health evaluation result of the Zhongguo lake is improved.