340 results about "Ecological risk" patented technology

The invention provides a mine ecological risk prediction method and system based on artificial intelligence, and the method comprises the steps: collecting multi-source ecological monitoring data through a distributed sensor network disposed in a mine region, carrying out the time-space alignment processing to generate a time-space correlation feature set, calling a pre-trained ecological risk prediction model to predict the features, and carrying out the prediction of the features. Generating a risk conduction mode set including a risk propagation path, an initial node, a propagation direction and strength, constructing an ecological risk evolution network based on the risk conduction mode set, and displaying a topological structure and a time sequence dependency relationship of risk nodes, and according to the ecological risk evolution network, generating a risk early warning instruction set containing a risk grade division result and an ecological restoration strategy, and sending the risk early warning instruction set to an ecological management platform, thereby comprehensively and accurately predicting the ecological risk of the mine, providing an effective risk early warning and ecological restoration strategy, and ensuring the ecological safety of the mine.

The invention relates to the technical field of ecological environment protection, in particular to an ecological protection red line division method applied to ecological basic investigation, which comprises the following steps: evaluating the service function and stability of an ecological unit through ecological basic data collection and processing in combination with an ecological model, and further delimiting an ecological protection red line area. The boundary of the red line area is optimized through space overlay analysis and conflict analysis of the current land utilization situation, and real-time adjustment is carried out based on ecological risk assessment and a dynamic monitoring mechanism. In combination with deep learning and reinforcement learning algorithms, dynamic monitoring and flexible adjustment of the red line area are realized, continuity and safety of an ecological system are guaranteed, intelligent management of the ecological protection red line is realized through a GIS technology and an early warning mechanism, scientificity and effectiveness of the ecological protection red line area are ensured, and the ecological protection red line area management method and system are suitable for popularization and application. And a reliable guarantee is provided for long-term stable development of an ecological system.

The invention provides a mine ecological restoration dynamic optimization method based on multi-modal data fusion, and the method comprises the steps: obtaining multi-modal data of a to-be-restored mining area, and carrying out the preprocessing and space-time alignment of the multi-modal data; a dynamic knowledge graph of the to-be-restored mining area is constructed, and the dynamic knowledge graph takes ecological elements and engineering entities as nodes, takes incidence relations between the nodes as edges, and takes the multi-modal data as dynamic attributes of the nodes and the edges; based on the dynamic knowledge graph and the fused multi-modal data, ecological risk diagnosis and stability prediction are carried out, and a diagnosis and prediction result is generated; based on the diagnosis and prediction result, performing restoration scheme simulation deduction in the digital twin environment by using an optimization algorithm, and outputting a dynamically optimized restoration scheme; and iteratively updating the dynamic knowledge graph according to the repair scheme execution effect. The method has the technical effect of improving the early warning timeliness and reliability.

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 discloses a coastal wetland vegetation degradation dynamic monitoring method based on multi-modal transfer learning, and belongs to the technical field of ecological environment monitoring, and the method comprises the steps: data acquisition: collecting coastal wetland region data through a plurality of sensors, feature extraction and fusion: preprocessing multi-source data, extracting space, time series and physical parameter features, and carrying out feature fusion; and fusing the data features of different modalities. According to the method, all-around and high-precision perception of the coastal wetland ecological environment is realized, the spatial-temporal resolution of data acquisition is improved, the response capability to complex environmental factors is enhanced, the generalization capability and prediction precision of the model are effectively improved, key environmental factors such as soil salinity can be incorporated into an evaluation system, and the evaluation accuracy is improved. The scientificity and pertinence of ecological risk identification are further improved, the efficiency and response speed of coastal wetland ecological management are improved, and powerful support is provided for formulating scientific and reasonable ecological protection and restoration measures.

The invention discloses a microplastic transport simulation and risk identification method based on multi-factor coupling, which comprises the following steps: by coupling a hydrodynamic model and a microplastic transport model, comprehensively considering multiple environmental factors such as tide, water level, runoff, wind speed, wind direction and the like, constructing a wave flow coupling microplastic migration and diffusion model suitable for complex hydrodynamic conditions of a Pearl River estuary; the model is calibrated and verified through measured data, and the precision of the model is evaluated by using indexes such as a Nash efficiency coefficient and a root-mean-square error; in combination with the simulation result and the spatial distribution characteristics of the typical sensitive area, representative sections and stations are selected, and annual-scale micro-plastic concentration change analysis is carried out; and further introducing an ecological risk index to carry out regional ecological risk grade identification, and determining a micro-plastic high-risk area and influence main control factors. The method has high regional adaptability and expansibility, and scientific support and technical reference can be provided for prevention and control of microplastic pollution of estuary and coastal water.

The invention belongs to the technical field of ecological risk prediction, and relates to an extreme weather event and ecological risk prediction method using a generative adversarial network, through a meteorological generation branch, spatial-temporal characteristics of a meteorological field are synchronously extracted by using a 3D residual network and Transform and a future meteorological state is predicted, and an ecological generation branch is based on a meteorological prediction result and topographic data. An ecological risk map is dynamically generated through hole convolution U-Net, and mismatch of resolution and dimension caused by cross-model interpolation is avoided; a discriminator introduces a physical conservation verification module and an ecological association module, physical rationality loss and ecological logic consistency loss of a generator are jointly optimized in adversarial training, in addition, a dynamic feedback mechanism corrects and generates errors through real-time observation data, and spectrum normalization is utilized to constrain model parameters, so that the reliability of the model is improved. The accuracy of extreme event and ecological risk coupling prediction is further improved; therefore, seamless space-time coupling of the weather and the ecological field is realized, and the generated result has both physical conservation and ecological relevance.

The invention provides a marine ecological environment influence tracking and monitoring method based on an offshore wind power project, and relates to the technical field of marine ecological environment, and the method comprises the steps: carrying out the ecological risk index calculation based on an obtained multi-modal feature vector, and obtaining ecological risk data, and configuring a space-time sequence based on the preprocessed comprehensive data source and the ecological risk data, and predicting a pollution diffusion path and migration blocking based on the preprocessed comprehensive data source. By introducing a dynamic evaluation model of an ecological risk index and combining time sequence modeling and spatial distribution modeling, dynamic analysis and future trend prediction of the ecological risk are realized, and by generating a risk thermodynamic diagram in real time, identifying a high-risk area and simulating a pollutant diffusion path and a biological migration blocking condition, the risk of the ecological risk can be predicted. According to the invention, the monitoring strategy can be dynamically adjusted according to the real-time monitoring data, a scientific basis is provided for construction optimization and ecological restoration, and the accuracy and timeliness of monitoring and evaluation are improved.

The invention provides an urban ecological risk prediction system based on a cellular automaton. The system integrates and obtains remote sensing images, meteorological data and human activity data through a data acquisition module, and extracts green land coverage, land utilization types and species distribution information of cities. Thirdly, a space grid is divided through a space grid division module by means of a dynamic quadtree algorithm, ecological units are generated, and therefore microcosmic space heterogeneity is captured; then, the system utilizes a cellular automaton model to optimize a state transition rule through deep reinforcement learning, and couples a soil erosion model and a hydrological model to simulate the dynamic change of urban ecology. And finally, quantizing parameter uncertainty through a risk assessment module by using a Bayesian belief network and Monte Carlo simulation. According to the system, urban ecological risks can be effectively simulated and predicted, the spatial precision and scientificity of prediction are improved, and the coupling influence of multiple ecological factors is considered at the same time.

The invention provides a soil heavy metal pollution ecological risk assessment method based on machine learning and application, and relates to the field of ecological risk assessment. According to the method, a multi-index data set is constructed through a constructed metal data set and corresponding pollution load indexes, Neimero indexes, potential ecological risk indexes, geological accumulation indexes and improved biomarker indexes, coupling weight determination and dimension reduction processing are achieved by means of PCA, machine learning modeling is combined, and a multi-index data set is constructed. And constructing a soil heavy metal multi-classification ecological risk assessment model so as to carry out heavy metal pollution ecological risk assessment on the target soil. The method combines the advantages of multiple biological and non-biological indexes, solves the limitation of a single index in coping with complex pollution conditions, and has high accuracy. When the method is applied to grading planning of the soil heavy metal pollution area, the soil heavy metal pollution area can be reasonably controlled and delimited efficiently and accurately; the method is high in applicability and suitable for large-area popularization and application.

The invention relates to the technical field of data integration, and particularly discloses a perfluoro / polyfluoroalkyl substance multi-source exposure data integration risk assessment system, which is characterized in that environmental exposure data of perfluoro / polyfluoroalkyl substances is acquired through a multi-source environmental sensor array, and a PFAS multi-mode exposure feature database is established; carrying out pollution source isotope fingerprint analysis, and obtaining source contribution rate distribution maps of three pollution sources of industrial emission, surface runoff and atmospheric settlement through a nonlinear source analysis algorithm; constructing a three-dimensional geographic information dynamic migration model according to the source contribution rate distribution map, and generating a multi-medium dynamic migration flux matrix; a composite risk assessment model is established based on the multi-medium dynamic migration flux matrix, probability risk assessment is executed in combination with an ecological toxicity threshold database, and a space gridding risk grade map is output; the method not only fills the blank of the prior art in the aspects of multi-medium dynamic modeling and nonlinear source analysis, but also provides powerful technical support for environmental pollution control and ecological risk prevention and control.

A method of determining ecological risk with spatial aggregation and regional imbalance level applicable for use in a river basin which involves geographic information, ecology, environmental engineering and computer technology includes the steps of: dividing a river basin into a plurality of fishnets; obtaining a basin landscape disturbance index and a basin landscape vulnerability index; obtaining a basin ecological risk index based on the basin landscape disturbance index and the basin landscape vulnerability index; and determining and evaluating the basin ecological risk in spatial aggregation and the regional imbalance with reference to a spatial autocorrelation value and a basin ecological risk imbalance value obtained by using the basin ecological risk index. The ecological risk in spatial aggregation and the regional imbalance is output and evaluate so that the ecological risk in river basin can be assessed comprehensively with high evaluation accuracy.

The invention discloses an ecological environment monitoring method and system based on a geographic information technology, and relates to the technical field of ecological environment monitoring, and the method comprises the steps: carrying out the multi-source heterogeneous data fusion of a multispectral image and point-like time series data of a water ecological protection target water area, and obtaining a three-dimensional water quality dynamic situation field; future trajectory prediction and water ecological health analysis are carried out on the three-dimensional water quality dynamic situation field to obtain a global water ecological health index thermodynamic cloud picture, pollution diffusion is carried out on the global water ecological health index thermodynamic cloud picture to obtain a pollutant trajectory matrix, ecological risk prediction is carried out based on the pollutant trajectory matrix, and the ecological risk prediction result is obtained. According to the method, the spatial-temporal dynamic risk early warning grade map is obtained, the restoration scheme is optimized based on the spatial-temporal dynamic risk early warning grade map, the restoration scheme optimal solution set is obtained, the timeliness and accuracy of ecological risk early warning are improved, and the economy of restoration decision is enhanced.

The invention relates to a habitat restoration-oriented sediment elution critical threshold evaluation method. The method comprises the following steps: introducing form classification including an exchange state, a carbonate binding state, an iron and manganese oxide binding state, an organic binding state and a residue state; a dynamic disturbance response mechanism is fused, and the elutable proportion is re-evaluated under the conditions of flow velocity, pH, oxidation reduction potential Eh and change; calculating a potential active flux index PAFI to quantify a real releasable part of the pollutants in the bottom mud; establishing pollution flux and biological response time sequence data; depicting ecological response sensitive inflection points by using nonlinear fitting; extracting an ecological function critical damage point EBCP as an ecological constraint upper limit of the elution threshold; constructing a habitat bearing factor set comprising a water body volume ratio, submerged vegetation density and benthic organism biomass; introducing a bearing feedback coefficient CFC, and dynamically correcting a pollutant critical flux value; and outputting a loadable elution flux interval for judging whether the ecological risk red line is touched or not.

The invention discloses a wetland ecological risk regulation and control method, system and equipment based on multi-modal early warning, and relates to the related field of water quality parameter abnormity alarm, the method comprises the following steps: collecting physical and chemical parameters of a wetland water body in real time, and generating a dynamic water quality data set; performing detection optimization on the dynamic water quality data set according to the historical abnormal data frequency, and determining a water quality detection sequence; and performing multi-level risk assessment on the target wetland area according to the water quality detection sequence to obtain a plurality of risk scores, triggering a multi-modal early warning mechanism based on the plurality of risk scores, generating a self-adaptive purification scheme, and pushing the self-adaptive purification scheme to a management terminal. The technical problems of insufficient early warning timeliness, monitoring comprehensiveness and data accuracy of the existing wetland water quality monitoring are solved, and the technical effects of improving the early warning timeliness, the monitoring comprehensiveness and the data accuracy of the wetland water quality monitoring are achieved.

An intelligent large-water-surface ecological fishery management system based on multi-source data fusion relates to the technical field of intelligent fishery, and comprises the following steps: dividing a large-water-surface aquaculture area into a plurality of sub-areas, setting an environment monitoring point and a biological monitoring point in each sub-area, collecting multi-source data, and constructing a multi-source data integrated visual graph; outputting real-time threshold intervals corresponding to the water quality monitoring indexes, the meteorological monitoring indexes and the biological characteristic indexes in the current collection period; performing real-time monitoring on each sub-region in the multi-source data integrated visual graph, and generating a sudden abnormal alarm or performing hidden trend analysis according to a real-time monitoring result; performing intelligent decision support operation on the sub-regions according to the hidden trend analysis result or marking the sub-regions as ecological risk regions; based on the multiple groups of time data of the sub-regions, the optimal fishing opportunity is obtained through a dual-drive fishing algorithm, and comprehensive monitoring, accurate sensing and risk prevention and control of ecological fishery management are achieved.

The invention relates to the technical field of regional ecological risk assessment and space management and control, in particular to a regional ecological risk assessment and partitioning method based on a multi-system spatio-temporal dynamic relationship, which comprises the following steps: collecting and preprocessing multi-source spatio-temporal data; a natural ecological risk index and a human social risk index are fused, and a comprehensive ecological risk assessment model based on a social-ecological system is constructed; divariate spatial autocorrelation analysis and cold and hot spot analysis are adopted, and a region is divided into three-level subareas including a management and control area, a monitoring area and a prevention area and six types of subareas; and analyzing the partition dominant driving factor by using the optimal parameter geographic detector, and generating a differentiated control strategy. The method realizes quantitative coupling of natural ecology and human social system risk spatio-temporal dynamic relationships, solves the problems of strong subjectivity, insufficient system comprehensiveness and lack of dynamic association mechanisms of a traditional method through multi-dimensional index fusion and spatial analysis technologies, and provides systematic technical support for accurate management of regional ecological risks.

The invention discloses a hydrological data model fusion analysis method based on water resource scheduling decision support, and relates to the field of water resource scheduling, and the method comprises the steps: loading an HMM-BP noise detection model, obtaining a multi-source data fusion model, and outputting a space-time grid data cube; constructing a water consumption behavior prediction unit through a TPB decision tree model, adding a water right transaction engine, and outputting a water consumption prediction value, a behavior of an intelligent contract protocol and a transaction cooperation model; an incremental method is adopted to generate N combined scene matrixes of rainfall and temperature rise correlation adjustment, an ecological stability evaluator is combined, a climate and ecological coupling model is constructed, and a scene and risk mapping table and a resource re-allocation weight are output; and constructing an intelligent rule base platform to obtain an executable decision logic tree. The system has the advantage that full-chain intelligentization of water resource scheduling is realized through modular integration. Therefore, social and market collaborative prediction, climate and ecological risk quantification and adaptive decision support are achieved.

The invention relates to the technical field of environment monitoring, in particular to a sediment heavy metal resuspension pollution evaluation system based on multi-dimensional data, which comprises a parameter acquisition module, a coupling resuspension simulation module, a heavy metal form migration calculation module and an ecological risk entropy evaluation module. The parameter acquisition module is used for acquiring hydrodynamic parameters, sediment heavy metal occurrence forms and biological disturbance intensity indexes; the coupling resuspension simulation module fuses hydrodynamic shear stress and biological irrigation flux and simulates resuspension concentration distribution; the heavy metal form migration calculation module combines the redox sensitive factor field and the exchangeable heavy metal concentration to calculate the dynamic release flux of the dissolved heavy metal; the ecological risk entropy evaluation module introduces an acute and chronic toxicity dual evaluation mechanism, constructs a pollution risk entropy matrix and generates a three-dimensional pollution risk entropy cloud picture. The method is suitable for dynamic evaluation and early warning of resuspension pollution risks in water bodies such as offshore water bodies, river mouths and lakes.

The invention discloses a rapid positioning and early warning system for ambrosia trifida in forest planting, and the system comprises a climate and growth prediction module which analyzes historical climate data and the growth law of ambrosia trifida, combines with a climate model, and predicts the rapid positioning and early warning of ambrosia trifida under different climate change conditions. The growth mode, the expansion trend and the possible distribution area of ambrosia trifida are determined; the real-time monitoring and data acquisition module is used for collecting environmental data and the growth state of ambrosia trifida in real time; the data fusion and analysis module is used for comprehensively processing the multi-source data collected by the climate and growth prediction module and the real-time monitoring module, evaluating the growth trend, spatial distribution and ecological risk of ambrosia trifida and providing accurate data support for subsequent early warning and decision making; and the early warning and decision support module automatically generates early warning information of different risk levels according to the growth trend and the risk assessment result generated by the data fusion and analysis module. The method has the advantages of high positioning precision, strong adaptive adjustment capability, accurate grading early warning mechanism and the like.

The invention discloses a trend prediction and early warning method applied to health evaluation of a bay ecological system, and relates to the technical field of bay ecological early warning, and the method comprises the following steps: collecting monitoring data of bay ecology, and dividing the monitoring data into historical data and real-time data, wherein the monitoring data comprises water quality data, geological data and hydro meteorological data, the collected monitoring data is preprocessed, bay ecosystem impact factors are screened, the bay ecosystem impact factors comprise physical impact factors and chemical impact factors, and the preprocessed historical data and the bay ecosystem impact factors are utilized. According to the method, trend prediction is carried out on the health condition of an ecological system in a period of time in the future by constructing the gulf ecological prediction model based on historical data and real-time monitoring information, so that related departments and the public can take actions before problems occur, and potential ecological risks are effectively prevented.

The invention discloses an environmental risk step-by-step assessment method in a multi-medium and multi-scale complex scene, and belongs to the technical field of environmental risk assessment. The method comprises the following steps: obtaining the heavy metal concentration of the waste residue through a leaching experiment, and determining the toxicity grade by using a single pollution index method and a potential ecological risk index method; dividing volume grades based on quantiles, dividing local diffusion risk grades by using an inverse distance weighted interpolation method and the like, dividing remote receptor sensitivity grades according to a surrounding land and water source construction model, and generating priority control grades by integrating the grades; and finally, fusing multi-source data to construct a spatio-temporal conjoint analysis model verification result. Therefore, by integrating pollution source toxicity, volume, diffusion risk and receptor sensitivity to carry out risk step-by-step assessment, a multi-source data fusion and spatio-temporal conjoint analysis method is proposed for the first time, and the problems of single assessment dimension, insufficient data fusion, lack of dynamic verification and the like of an existing assessment method in a multi-medium and multi-scale scene are effectively solved.

The invention discloses a hydrological and biological communication-based degraded marsh rapid recovery method, which aims at the problems of water resource shortage and biological isolation in a degraded marsh area, and comprises the following steps of: constructing a controllable hydrological communication channel between the degraded marsh area and an adjacent natural river, accurately opening the controllable hydrological communication channel in a natural river flooding season, and utilizing flood power to realize rapid recovery of the degraded marsh area. And the natural and efficient supply of water resources to the recovered degraded marsh area and the direct input of various aquatic organisms in the natural river are synchronously realized. And according to the life cycle key stage of the target aquatic organism, the opening time, frequency and period of the controllable hydrological communication channel are regulated and controlled, and the biological input effect is maximized. After the controllable hydrological communication channel is closed, the degenerated marsh area is kept stable in the non-flood period, and habitat building and monitoring are assisted. The method is low in cost, high in efficiency and small in ecological risk, can quickly reconstruct aquatic organism communities and ecological network functions, and is particularly suitable for restoration engineering of large-scale returning to wetness and degraded marsh.

The invention discloses a network toughness prediction method, system and device based on digital twinning, and relates to the technical field of ecological monitoring and network toughness modeling. The network toughness prediction method, system and device based on digital twinning comprises the following steps: S1, collecting multiple types of ecological monitoring data through ecological monitoring equipment, and carrying out standardized preprocessing on the ecological monitoring data; s2, the modeling difficulty of each piece of ecological data is comprehensively evaluated, and automatic screening and grading processing of data quality are achieved; s3, performing state synchronous evaluation on the to-be-modeled data, and dynamically updating the operation state of the corresponding ecological entity in the digital twin based on a state evaluation result; and S4, identifying key fragile nodes and risk paths in each ecological entity, and outputting corresponding ecological risk levels and regulation and control suggestions. The problem that in the prior art, static threshold risk judgment is carried out only based on a small amount of observation data, and multi-source isomerism and dynamic evolution characteristics existing in an ecological system cannot be effectively handled is solved.

The invention discloses an ecological risk assessment method for organic pollutants in a natural water body, and belongs to the field of ecological risk assessment of the natural water body, and the method comprises the steps: building a quantitative structure-activity relationship toxicity prediction model of species based on a machine learning and feature screening method; predicting and obtaining biotoxicity data in a to-be-predicted water body by using the quantitative structure-activity relationship model, establishing a species sensitivity distribution model of pollutants in the water body according to the biotoxicity data, and calculating predicted non-effect concentration; on the basis of database retrieval, acute toxicity experiments and an inter-species relationship prediction model, collecting, predicting and supplementing toxicity data of species in the water body, and evaluating the harmfulness of pollutants in the water body by using a risk entropy method and a comprehensive scoring method respectively. According to the method, ecological risk assessment is carried out by utilizing a machine learning method, the toxicity of the persistent organic pollutants in seawater and fresh water can be effectively assessed, and the ecological risk of the organic pollutants in the water body can be effectively assessed.

The invention relates to the technical field of soil pollution monitoring, and discloses a farmland soil heavy metal ecological risk assessment method and system, and the system comprises a multi-source collaborative collection module, a dynamic risk assessment module, a climate-driven correction module and a risk prediction execution module. A multi-dimensional dynamic parameter set is generated by constructing a soil-crop-microorganism collaborative collection mechanism, and systematicness and integrity of pollution data are guaranteed; meanwhile, based on heavy metal spatial distribution modeling, a three-dimensional pollution pattern is analyzed in real time, and a combination mode of deep migration risk and table enrichment characteristics is identified, so that the system can overcome the defect of insufficient sampling depth in the prior art, the accuracy of pollution source identification and risk boundary is improved, and the reliability of an evaluation result is enhanced. A dual-drive model is constructed by fusing historical climate parameters and rotation parameters, and risk early warning and prevention strategy dynamic adjustment in a climate sudden change scene are realized.

The invention relates to the technical field of forestry ecological management, and discloses a forestry jurisdiction ecological management system based on risk prediction. The system comprises an ecological environment monitoring module, a risk prediction analysis module, an emergency regulation and control module, a risk early warning output module and a background management end. The ecological environment monitoring module collects ecological environment parameters in real time through multiple types of sensors and sends the ecological environment parameters to the risk prediction analysis module and the background management end, and the background dynamically displays the parameters. The risk prediction analysis module determines ecological risk factors in combination with the real-time parameters and a historical ecological risk database, calculates regulation and control instructions through a risk prediction model and sends the regulation and control instructions to the emergency regulation and control module. And the emergency regulation and control module executes intervention operation to adjust the ecological state. The risk early warning output module monitors the intervention process, evaluates the operation state of the emergency regulation and control module, generates an intervention normal signal or a risk early warning signal and sends the signal to the background, and the background triggers a prompt when receiving the early warning signal. The system realizes real-time, scientific and closed-loop forestry ecological management.

The invention discloses a water-soil-vegetation monitoring method and system for mine ecological restoration, and the method comprises the steps: collecting related index data of water quality, soil and vegetation in a mine region, and enabling the indexes to respectively and correspondingly reflect the water condition, the soil attribute and the vegetation growth state; carrying out abnormal value elimination on the collected data to eliminate invalid data, fusing data from different sources into a unified coordinate system, and standardizing the data to a preset interval; carrying out dimension compression on the preprocessed standard data, and screening and extracting core indexes capable of representing a water-soil-vegetation key state; constructing a model based on historical and real-time data, performing health level evaluation on the ecological condition, and predicting a core index change trend; and analyzing an interaction relationship among water, soil and vegetation, calculating a system coupling coordination degree, generating a targeted ecological restoration scheme and realizing ecological risk early warning.

The invention discloses a water level and water quality dynamic monitoring management system and a water level and water quality dynamic monitoring management method, and particularly relates to the technical field of water level and water quality monitoring. And selecting an LSTM neural network, an RANSAC or a hybrid compensation algorithm thereof through a decision mechanism to carry out data correction. Meanwhile, an anomaly detection model is deployed at an edge node, and the model threshold is dynamically adjusted to improve the mutation recognition accuracy. And the cloud end constructs a hydrodynamic force-water quality coupling prediction model through multi-source data fusion to realize early warning of ecological risk events. The system is matched with solar energy-hydraulic power dual-mode energy supply and a LoRaWAN + 5G hybrid communication network, and is suitable for long-period automatic operation in an area without commercial power. The method has the advantages of intelligent data processing, adaptive compensation algorithm, flexible system deployment and the like, and is suitable for continuous monitoring and dynamic management of reservoirs.

The invention belongs to the technical field of water ecology risk early warning, and provides an algal bloom risk remote sensing intelligent identification method and system, and the method comprises the steps: obtaining remote sensing image data, meteorological data and water quality data of a target region, and carrying out the preprocessing; performing frequency domain feature extraction to generate a three-dimensional frequency domain feature vector; fusing the global feature representation obtained by modeling and the generated three-dimensional frequency domain feature vector by using a multi-task deep learning network to obtain a feature map; obtaining an algae bloom binary segmentation probability graph and a continuous value distribution graph of chlorophyll a concentration based on the characteristic graph; generating an image semantic embedding vector by utilizing a semantic embedding head, and performing semantic alignment on the image semantic embedding vector by adopting a pre-trained knowledge graph to generate a research and judgment information text; and performing cross validation on the study and judgment information text, the algae bloom binary segmentation probability graph and the continuous value distribution graph of the chlorophyll a concentration to obtain a final algae bloom risk judgment result. According to the invention, the algal bloom risk remote sensing intelligent identification is realized.