270 results about "Ecological risk" patented technology

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 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.

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 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 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 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 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.

The invention relates to the technical field of ecological monitoring, in particular to a resident-free island resource monitoring method based on a spatial three-dimensional model, and the method comprises the following steps: S1, building a remote sensing image interpretation information base; s2, based on the remote sensing image interpretation information base, generating an island space three-dimensional model containing island land terrain and shoreline distribution; s3, generating a total element three-dimensional dynamic map; s4, the interpretation result and the total element three-dimensional dynamic map in the S3 are superposed, and a reef area comprehensive distribution map is generated; s5, calculating the island resource development intensity and the ecological risk level, and outputting an island ecological assessment report; and S6, generating an island resource monitoring report. According to the invention, unmanned aerial vehicle aerial survey, GNSS measurement, depth sounding technology, image interpretation and ecological assessment are combined, a high-precision total-factor three-dimensional dynamic monitoring system is constructed, accurate analysis and visual expression of the current island resource utilization situation are realized, and scientific decision support is provided for island management and ecological protection.

The invention provides a small hydropower station safety and ecological multi-source risk dynamic assessment method and system, and belongs to the technical field of hydropower station management. Constructing a multi-dimensional risk feature tensor, including preprocessing multi-source data to obtain standardized data, extracting features from the standardized data, organizing the features according to risk types, time, space and feature four-dimensional organizations to form a four-order tensor, and performing decomposition to extract a core risk mode; constructing a conditional risk probability field, including conditional probability density estimation, risk probability field dynamic evolution model establishment and multi-source information entropy weight fusion; constructing a risk degree assessment model, including risk influence degree matrix construction, matrix spectrum analysis and risk measurement generation; according to the method, the defects of single dimension, static evaluation, high subjectivity and the like in the prior art are overcome, and accurate quantification and dynamic evaluation of the safety and ecological risk of the small hydropower station are realized.

The invention relates to the technical field of water environment monitoring and ecological risk assessment, solves the technical problem that the existing endogenous pollutant release risk assessment technology has multiple deficiencies, and particularly relates to a sediment aggregate pollutant release risk assessment system based on three-dimensional microstructure inversion prediction. Comprising a high-resolution three-dimensional structure acquisition module, a multi-physics field disturbance-diffusion simulation module, a structure-function parameter inversion module, an in-situ verification and online correction module and a risk index generation and visualization module. According to the method, the critical disturbance intensity of pollutant release is obtained according to the flux-disturbance relation, and function mapping is established with the structural characteristic parameters to obtain a function relation; the model is automatically calibrated in combination with field monitoring; and converting the ratio of the environmental disturbance intensity to the critical intensity into a risk space distribution diagram of the geographic information system. According to the system, the structure can be quantified, the threshold value can be unified, the model can be corrected online, and the system is suitable for accurate evaluation and visual display of different water areas and scenes.

The invention relates to the technical field of risk early warning, and discloses a drainage basin ecological environment risk early warning method, which comprises the following steps of: obtaining meteorological hydrology, water quality soil and biological behavior high-frequency time sequence data in a target drainage basin; determining key risk indexes and weights based on the ecological characteristics of the target drainage basin; according to the time delay embedding theorem, the key risk indexes are embedded into the ecological phase space, the distribution distance between the ecological state of the target drainage basin and historical equilibrium state attractors is calculated, and a critical distance index is determined; and based on the critical distance index, determining the early warning level of the ecological environment of the target drainage basin in combination with a phase change grading threshold, and visualizing the real-time early warning map and coping decision of the target drainage basin. The drainage basin ecological environment risk early warning method overcomes the defects of an existing drainage basin ecological environment risk early warning method in the aspects of data utilization, model adaptability, index scientificity, space accuracy, emergency response and the like, and full-chain optimization of ecological risks from identification to disposal is achieved.

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 relates to the technical field of wind power hoisting, and discloses a wind power hoisting risk prediction and sudden stop control system based on edge AI, and the system comprises a multi-source fusion sensing module which is used for obtaining the environment of a hoisting site, and the multi-dimensional physical state data of hoisting equipment and fan parts in real time; the edge AI calculation and fusion early warning module is used for receiving and fusing the multi-dimensional physical state data in real time and outputting a comprehensive risk level; and the digital twinning and intelligent control module is used for executing multi-stage visual early warning or issuing an emergency stop instruction to a hoisting equipment control system according to a preset strategy. According to the invention, a high-precision integrated three-dimensional digital base map is generated through a terrain and forest region integrated scanning unit, and a forest growth model is coupled to carry out future clearance conflict prediction; digitization and predictable management of a complex construction environment are achieved, path interference and ecological damage caused by unclear environment are avoided from the source, and operation and maintenance cost and ecological risks are directly reduced.

The invention discloses a goaf ecological restoration method and system based on artificial intelligence, and the method comprises the steps: obtaining multi-source remote sensing data and geological survey data of a goaf, and generating a three-dimensional geological damage model of the goaf through a space-time fusion technology; performing multi-scale feature extraction on the three-dimensional geological damage model by using a deep convolutional neural network to obtain a multi-dimensional ecological feature data set, identifying an ecologically fragile region and an instability risk region, and outputting an ecological risk level map; performing parallel processing on the multi-dimensional ecological characteristic data set to generate an ecological assessment report containing health degree scores of each ecological subsystem of the goaf; and based on the ecological risk grade map and the ecological assessment report, a multi-objective optimization algorithm is adopted to generate a vegetation-soil / rock mass-water body collaborative restoration strategy. By means of the goaf ecological restoration method and device, accurate assessment and collaborative strategy generation of goaf ecological restoration can be achieved, and scientificity, efficiency and overall ecological benefits of restoration engineering are improved.

The invention belongs to the technical field of environmental monitoring and ecological risk assessment, and particularly relates to a Yangtze River Delta basin water ecological risk prediction method and system based on a Bayesian network model. The method comprises the following steps: firstly, constructing a water ecology risk assessment system, and preprocessing sample data; sub-basins are divided based on DEM data, and the sub-basins which are adjacent in space and similar in data feature are aggregated into non-overlapping data blocks; constructing a dynamic Bayesian network model by adopting a random variational inference method, and inputting meteorological prediction data to obtain probability distribution of risk levels of each sub-basin; constructing a risk space correlation model according to sub-basin division, and simulating conduction and superposition processes of risks in a basin network; and finally, evaluating the comprehensive risk state of each sub-basin in combination with a network topology index. By constructing the dynamic Bayesian network model and the risk space correlation network, the spatial-temporal dynamic assessment of the watershed water ecological risk is realized, and the propagation path and the cumulative effect of the risk in the watershed can be predicted.

The invention discloses a Hakanson-LCA-based shield muck ecological influence assessment method, and relates to the field of shield muck ecological influence assessment. A Hakanson ecological risk assessment method and an LCA method are combined, and meanwhile, the potential ecological risk of heavy metal elements in shield muck and the carbon emission influence generated in the resource utilization and landfill disposal process are assessed; through the dual evaluation method, comprehensive analysis can be carried out on the muck from two dimensions of environmental pollution and climate change, more comprehensive ecological influence evaluation is provided, and a scientific basis is provided for environmental protection and waste management.

The invention discloses a method for evaluating soil pollution hazards based on ecological risk indexes in the technical field of ecological protection, which comprises the following steps: sampling soil in a target area to obtain sampled soil; the method comprises the following steps: collecting the concentration value of pollutants in sampled soil, and determining the basic toxicity coefficient of the pollutants by utilizing a toxicological experiment; constructing a basic toxicity coefficient dynamic correction model to obtain a dynamic toxicity response coefficient of the pollutant concentration value; and constructing an environment factor interaction model. According to the method, the multi-source data is composed of the concentration value and the pH value of the inorganic pollutant and the concentration value of the organic matter, and through fusion and nonlinear modeling of the multi-source data, the problems of weight solidification and neglect of spatial heterogeneity in a traditional method can be solved; the evaluation scientificity, accuracy and operability of the soil pollution risk in the target area can be improved, and the influence of the toxicity of the pollutants on the soil pollution hazard can be effectively evaluated by fusing the synergistic and antagonistic effects among the pollutants.

The invention discloses a fish habitat real-time monitoring system based on Internet of Things. Comprising a data acquisition module used for acquiring fish habitat data, a communication networking module used for transmitting the acquired data, an analysis early warning module used for analyzing the data, a comprehensive management module used for assisting device operation and a propaganda education module used for propagandizing habitat education. Water quality parameters, hydrological parameters, fish activity data and human activity data of a fish habitat are collected, analysis data are generated in an analysis and early warning module, an ecological health index of the fish habitat is calculated, an early warning signal is generated, ecological risks of a fish living environment are predicted, and an ecological health index thermodynamic diagram is generated in a comprehensive management module. And real-time monitoring is completed under cooperation of multiple modules. The fish ecological resource environment is subjected to multi-source collection and analysis through the multi-source sensing technology, the correlation between fish habitat data and fish population distribution is deeply excavated, information of the habitat is accurately grasped, the monitoring accuracy is high, and the risk pre-judgment capability is high.

The invention relates to the technical field of environmental toxicology and ecological risk assessment, in particular to a system and a method for predicting chronic toxicity of bioaccumulation pollutants, and the system comprises a data acquisition and processing module, a model training and optimization module, a toxicity prediction module and an ecological risk assessment module. According to the method, full-process prediction is realized through data standardization, feature engineering, model training and model prediction; tissue residual toxicity data is obtained and standardized, a multi-dimensional characteristic matrix containing pollutant physicochemical properties, species biological characteristics and experimental conditions is constructed, a toxicity prediction model is optimized by using a machine learning algorithm, and tissue residual toxicity prediction of fishes in different life stages is realized. And calculating a risk quotient based on the predicted value, and generating a risk assessment report. The problems that an existing system depends on water body concentration toxicity data, is lack of biological accumulation pollutant chronic toxicity prediction capacity and does not aim at an endangered characteristic optimization model are solved.

The invention relates to the technical field of environment monitoring and intelligent sensors, and particularly discloses a soil pollutant detection and analysis system based on an intelligent sensor. The system comprises a sensor array module, a data preprocessing and fusion module, an ecological toxicity dynamic evaluation module and a decision output module. According to the invention, the biological response sensing unit capable of directly reflecting the ecological effect is integrated, and the ecological toxicity dynamic evaluation model fusing the concentration, the biological response and the environmental factors is innovatively constructed, so that direct and dynamic quantification from the pollutant content to the ecological risk is realized. According to the method, the problem that an existing assessment method is insufficient in biological correlation is solved, the risk assessment result is closer to the real ecological environment effect, and a reliable basis is provided for precise treatment.

The invention discloses a land stony desertification ecological risk evaluation method based on multi-source aerospace data, and relates to the technical field of ecological risk evaluation. Comprising the following steps of multi-source air and space data acquisition and space-time collaborative registration, multi-dimensional ecological environment factor dynamic extraction, stony desertification ecological risk three-dimensional index system construction, multi-source weight fusion and dynamic risk evaluation, space-time dynamic early warning and risk transfer simulation and differential ecological management adaptive optimization. The method can provide high-precision and multi-dimensional space-time consistent basic data for risk evaluation through multi-source space-time collaborative registration of space-time data and integration of remote sensing, meteorological and ground monitoring data, breaks through the limitation of single-dimensional evaluation through construction of a three-dimensional index system, can systematically reveal risk causes, and improves the risk evaluation efficiency. And subjective and objective combination weighting balances expert experience and data laws, so that the crossing from static evaluation to dynamic early warning is realized, and finally, optimization measures are treated and optimized in a differentiated manner through spatial layout and benefit cost optimization.

The invention discloses a total factor monitoring method for a marine ranch, and belongs to the technical field of ocean engineering and information. The method comprises the following steps: collecting water quality parameters and biological population data in real time; the planktons and pollutants are identified through spectral analysis and chemical sensing, and environmental elements are classified; a sonar and an electronic tag are combined to track a fish trajectory, and dynamic monitoring data are obtained; predicting a red tide probability based on machine learning and outputting risk early warning; continuously updating the model to form multi-element integrated monitoring data; and finally, sorting the key indexes to generate marine ranch key element monitoring framework data. According to the invention, the real-time monitoring capability of the marine ranching environment and the biological state is obviously improved, the ecological risks such as red tide are reduced, and the sustainable development of the marine ranching is ensured.

The invention discloses a soybean producing area soil combined pollution micro-ecological risk assessment method and system, and belongs to the technical field of agricultural environment monitoring. The method comprises the following steps: acquiring soil pollutant data and microbial community data, calculating a risk entropy, constructing a micro-ecological health index, and respectively obtaining a first result and a second result by utilizing a risk assessment neural network and a comprehensive model combined with principal component analysis, a clustering algorithm and a hunter optimization algorithm; and determining a final risk level and high-risk pollutants after key factor matching and weighted correction mechanism verification. According to the method, the problems of single evaluation dimension, insufficient dynamic adaptability and the like in the prior art are solved, accurate diagnosis and risk early warning of soil combined pollution are realized through multi-source data fusion and multi-method cooperation, the evaluation accuracy and decision-making efficiency are improved, and technical support is provided for soybean producing area soil micro-ecological risk management and control and agricultural sustainable development.