176 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 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 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 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 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 provides an ecological risk evaluation and prediction evaluation method based on a multi-source evaluation framework, and relates to the technical field of ecological risk evaluation, and the method specifically comprises the steps: dividing a target region into grid units, and collecting landscape pattern and environmental social economic data; analyzing landscape data through a sliding window method, and calculating a landscape ecological risk value of each unit; constructing a regional ecological risk assessment index system based on social economic data, determining an index weight by adopting Bayesian BWM and CRITIC methods, and performing weighted calculation to obtain a regional ecological risk value; integrating the two types of risk values to form a spatial distribution diagram, and superposing to obtain a unit comprehensive ecological risk value; and performing risk grading by adopting a natural breakpoint method, generating a grading graph, predicting risk early warning time of each unit based on a historical trend, and realizing ecological risk dynamic evaluation and early warning.

The invention provides a soil ecological environment monitoring and management method based on big data, and relates to the technical field of soil ecological environment monitoring and management. According to the method, multi-source data including soil and pasture are collected and integrated to construct a central database through field stratified sampling, classified deployment of sensors and butt joint with a third-party mechanism; space-time pattern analysis, pollution source analysis and ecological risk evaluation are carried out based on the central database, and a pollution traceability report is generated; the method comprises the following steps: constructing a multi-dimensional risk evaluation system, dividing a region into four classes and implementing differentiation management, constructing a governance technology database, screening an optimal scheme based on multiple criteria, performing demonstration verification in a typical region, comparing monitoring data, judging a governance effect and generating a demonstration application summary report; the problems that the soil monitoring and treatment accuracy of the heavy metal mining area is insufficient and the scheme adaptability is poor are effectively solved, and the monitoring and management efficiency and scientificity are remarkably improved.

The invention discloses a confocal laser microscope detection method for micro-plastic surface biofilm components and biomass, and belongs to the field of micro-plastic pollution monitoring. The method comprises the following steps: carrying out stratified sampling and polymer component identification on micro-plastic in an environmental water body; the method comprises the following steps: marking algae, blue-green algae, bacteria and extracellular polymeric substances in a biological membrane through specific fluorescent staining, carrying out high-resolution three-dimensional imaging by utilizing a confocal laser microscope, and realizing batch quantitative analysis of each component of the biological membrane by adopting ImageJ software. The technical problems that an existing detection method is insufficient in parameter dimension, evaluation result deviation is caused only based on partial component quantification, and the influence of the overall structure of the biological membrane on the micro-plastic migration behavior cannot be truly reflected are solved. According to the method, the biomass and spatial distribution of each component of the biological membrane can be comprehensively and accurately quantified, the accuracy of analyzing the vertical migration mechanism of the micro-plastic is improved, and multi-dimensional data support is provided for ecological risk assessment.

The invention proposes a lake comprehensive ecological risk assessment method and system based on multi-source data fusion, and relates to the technical field of ecological risk assessment, and the method comprises the steps: carrying out the sampling of a preset assessment space range of a target lake, and obtaining the multi-source ecological data of the target lake; obtaining a water environment risk value according to historical water quality monitoring data in the multi-source information database; acquiring ecological water level demand data of the target lake according to the historical water level monitoring data, and acquiring a hydrological situation risk value of the target lake; based on remote sensing image data and historical survey monitoring data of the target lake in the multi-source information database, evaluating lakeside vegetation condition, wetland area, blue-green algae coverage area and benthonic animal monitoring data respectively to obtain a water ecology risk value; and performing weight distribution operation on the water environment risk value, the hydrological situation risk value and the water ecology risk value to obtain a comprehensive ecology risk assessment index corresponding to the target lake. The method is beneficial to the accuracy and systematicness of ecological risk assessment.

The invention discloses a method for evaluating the ecological risk of a perfluorinated compound based on the chlorophyll concentration of microalgae, which comprises the following steps: collecting a water body of a region to be evaluated, detecting the concentration of the perfluorinated compound in the water body, and obtaining a localized microalgae culture system at the same time; adding perfluorinated compound solutions with different concentrations into the localized microalgae culture system to form an experimental group, and setting a blank group without perfluorinated compounds; sampling and detecting the chlorophyll concentration of the microalgae in the experimental group and the blank group, and calculating the chlorophyll inhibition ratio of the microalgae; calculating the half effect concentration according to the function relationship between the concentration of the perfluorinated compound and the chlorophyll inhibition ratio of the microalgae; according to the concentration and the half effect concentration of the perfluorinated compounds in the water body, calculating to obtain a risk quotient for judging and evaluating the ecological risk of the perfluorinated compounds in the water body. According to the method, the half effect concentration, the mixed exposure effect and the risk quotient value are integrated to serve as ecological risk composite criteria, and the method has the advantages of being high in environmental correlation, high in sensitivity and high in indication performance.

The present application relates to the technical field of climate change warning, and discloses a mangrove blue carbon ecological risk identification and warning method and system for climate change, comprising the following steps: acquiring time series of global land surface characteristic satellite leaf area index data and environmental factors, performing detrending, deseasonalization and standardization processing to extract abnormal sequences; determining climate oscillation windows and neutral periods according to multivariate El Nino-Southern Oscillation index and dipole mode index, and calculating the mean value of the environmental factors in the neutral period as a reference; inputting the environmental factors into a long short-term memory network to extract attention weights, calculating the factor difference between the mean value of the climate oscillation window and the reference, mapping the factor difference by using a nonlinear saturation kernel function, combining the weights to obtain contribution amplitude, and selecting a dominant risk factor by comparing the direction; processing the absolute value of the leaf area index anomaly by using an S function to output risk intensity, and predicting future leaf area index anomaly based on the dominant risk factor to generate a warning evaluation.

The invention relates to a soil heavy metal risk assessment method for a fuzzy region, fully considers spatial heterogeneity of soil heavy metals and a complex relationship between the soil heavy metals and agricultural products, and provides a fuzzy region averaging method, an improved potential ecological risk index method and an agricultural product risk index method. Therefore, soil heavy metal risk assessment of the fuzzy region is realized, and technical reference is provided for regional soil heavy metal risk assessment.

The present application belongs to the technical field of water ecological risk early warning, and provides an algal bloom risk remote sensing intelligent identification method and system, remote sensing image data, meteorological data and water quality data of a target area are acquired and preprocessed; frequency domain feature extraction is performed to generate a three-dimensional frequency domain feature vector; a multi-task deep learning network is used to fuse the global feature representation obtained by modeling and the generated three-dimensional frequency domain feature vector to obtain a feature map; based on the feature map, an algal bloom binary segmentation probability map and a continuous value distribution map of chlorophyll a concentration are obtained; a semantic embedding head is used to generate an image semantic embedding vector, a pre-trained knowledge graph is used to perform semantic alignment on the image semantic embedding vector to generate research and judgment information text; the research and judgment information text, the algal bloom binary segmentation probability map and the continuous value distribution map of chlorophyll a concentration are cross-validated to obtain a final algal bloom risk judgment result. The present application realizes algal bloom risk remote sensing intelligent identification.

The application discloses a network resilience prediction method, system and device based on digital twinning, and relates to the technical field of ecological monitoring and network resilience modeling. The network resilience prediction method, system and device based on digital twinning comprise the following steps: S1, collecting multiple types of ecological monitoring data through an ecological monitoring device, and performing standardized preprocessing on the ecological monitoring data; S2, comprehensively evaluating the modeling difficulty of each ecological data, and realizing automatic filtering and hierarchical processing of the data quality; S3, performing state synchronization evaluation on the data to be modeled, and dynamically updating the running state of the corresponding ecological entity in the digital twinning based on the state evaluation result; and S4, identifying key fragile nodes and risk paths in each ecological entity, and outputting corresponding ecological risk grades and control suggestions. The problems that in the prior art, only a small amount of observation data is used for static threshold risk judgment, and the problems that the dynamic evolution characteristics and the multi-source heterogeneity existing in the ecological system cannot be effectively coped with are solved.

The invention discloses a Yellow River basin multi-source coal-based solid waste environmental risk comprehensive assessment method, which comprises the following steps: collecting a Yellow River basin coal-based solid waste sample, analyzing physicochemical properties, and determining the leaching concentration and toxicity content of heavy metals and harmful elements in the sample under different environmental conditions according to the physicochemical property analysis result; based on a potential ecological risk index method and by introducing a toxicity response coefficient, calculating a single-factor potential ecological risk index of each pollutant; calculating a comprehensive potential ecological risk index of the sample according to the single-factor potential ecological risk index, and determining relative toxicity weights of different heavy metals under the ecological background of the Yellow River basin; and according to the comprehensive potential ecological risk index and the relative toxicity weight, carrying out quantitative evaluation and grade judgment on the environmental risk of the coal-based solid waste according to a preset risk grade division standard, and identifying a key risk factor and a spatial differentiation rule thereof. The method can quantitatively evaluate the environmental risk level.

This invention discloses a method for constructing background values ​​of river and lake sediments based on curvature extrema and dynamic zoning, relating to the field of environmental benchmarking and ecological risk assessment technology. The method includes the following steps: zonal sampling and multi-element determination; construction of a background sediment database; construction of zonal background values; calculation of the entire lake's background value; and three-way verification of the background value. This invention verifies the natural background values ​​of heavy metals in river and lake sediments through dynamic zonal sampling, geometric feature analysis, and multi-source verification. It provides a dynamic zonal sampling strategy that can truly reflect the spatial heterogeneity of river and lake sediments, establishes a background sediment identification system that balances cost and accuracy, and includes both precise approaches based on sophisticated instruments and practical approaches based on empirical knowledge. This enhances the applicability of the method under different conditions. An objective background value calculation model based on data geometric features is introduced to eliminate the biases caused by subjective threshold settings and outlier sensitivity in traditional statistical methods.

The invention relates to the technical field of marine ecology monitoring, and discloses a marine ecology-based monitoring and early warning system, which comprises a monitoring terminal, a data transmission module for transmitting monitoring data acquired by the monitoring terminal to a data analysis and processing module, and an early warning module for performing early warning according to an ecological risk assessment result of the analysis and processing module, the early warning module is used for issuing early warning information in a grading manner; the monitoring terminal comprises a hydro-meteorological monitoring module and a water body environment monitoring module; the water body environment monitoring module comprises a water body monitoring unit, a sediment monitoring unit and an acquisition unit. According to the monitoring and early warning system based on marine ecology, the hydrometeorological monitoring module and the water body environment monitoring module are integrated, so that multi-dimensional monitoring data can be synchronously acquired, and comprehensive evaluation of marine ecological conditions is realized; and a box-type mud sampler for collecting seabed sediments and a water body collector for collecting water bodies with different depths are jointly arranged on a steel rope through the collecting unit, so that an efficient and synergistic collecting combination is formed.

The invention provides an ecological risk evaluation method and system based on new pollutants, and the method comprises the steps: obtaining mass spectrum data of sample data, determining a normal mass spectrum fingerprint corresponding to a geographic position and time, marking an ion peak as an unknown peak when the ion peak in the mass spectrum data deviates from a preset range of the normal mass spectrum fingerprint, and determining that the ion peak is an unknown peak; the method comprises the following steps: acquiring a fragmented spectrum of an unknown peak based on secondary mass spectrometry, inferring a possible molecular structure of the unknown peak through a graph neural network model in combination with a preset chemical database, and constructing a quantitative structure-activity relationship of possible molecular structure analogues of the unknown peak based on a preset structural similarity network. The method comprises the following steps: acquiring predicted toxicity and signal channels influenced by the predicted toxicity, performing ecological risk assessment in a preset range of a petrochemical plant based on the predicted toxicity and the signal channels influenced by the predicted toxicity, and acquiring potential ecological risks of new pollutants. And a basic basis is provided for environment management and decision making.

The invention provides a method, system and electronic equipment for judging the mechanization-suitable ecological risk of a mountain farmland, and relates to the technical field of agriculture, and the method comprises the steps: calculating the ecological safety level and the weight of each index through employing a trained radial basis function neural network based on risk index data generated by a constructed multi-dimensional index system; further dynamically fusing the weight into a PLUS model, and performing risk weighted correction on the conversion resistance and the expansion probability to generate a dynamic control parameter; and simulating a land utilization pattern under multiple scenes based on the parameter driving model, feeding back a result to the RBF network for re-evaluation until a convergence condition is met, realizing iterative optimization of the control parameters, and finally generating a comprehensive evaluation result based on an optimal pattern and an ecological level. Therefore, the technical problems of incomplete evaluation dimensionality and insufficient accuracy of existing mountainous region farmland mechanization-suitable ecological risk evaluation are solved, and the comprehensiveness and accuracy of mountainous region farmland mechanization-suitable ecological risk judgment are improved.

The invention relates to the technical field of ecological risk evaluation test strategies, in particular to a method for predicting the content of a biological enrichment factor by utilizing QICAR modeling, which comprises the following steps of: by taking known BCF, metal physicochemical properties and soil physicochemical properties of a corresponding area as data, obtaining a predicted value of unknown heavy metal BCF of a target area by utilizing training of a QICAR coupled machine learning model; the method comprises the following steps of: selecting a plurality of rounds of cross validation to screen test set data, evaluating the performance of a model through a decision coefficient R2 and a mean absolute error MAE to obtain an optimal model, and explaining an evaluation result through feature analysis and Shapley weighting to obtain an accurate and scientific predicted value, so that effective support is provided for predicting the heavy metal enrichment capability of crops. And guarantee is provided for food safety.

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 invention discloses a plateau mountain area ecological protection red line monitoring and evaluating method, and relates to the technical field of ecological risk evaluation. The method mainly comprises the following steps: monitoring four dimensional indexes of ecological protection red line area and structure, land utilization type, ecological system quality and management capability; an evaluation index system is constructed in a way that the area is not reduced, the property is not changed, the function is not reduced, the quality is not reduced, the value is improved and the supervision and management are more strict; and carrying out protection effect evaluation on six dimension indexes of ecological protection red line area, land utilization type change, ecological system quality change, ecological system function change, ecological system service value change and management capability construction.

The invention provides a drainage basin ecological risk evolution method based on state variation prediction, which comprises the following steps: collecting multi-source data of each time period in a historical period to construct a training set, the multi-source data comprising an ecological system loss degree D, a congenital susceptibility V and a time period pressure load P; constructing a state variation prediction model by taking the ecological system loss degree variation delta D as an output variable and the ecological system loss degree D, the congenital susceptibility V and the time period pressure load P as input variables, and training the state variation prediction model by using the training set to obtain a trained state variation prediction model; collecting multi-source data of a current time period, inputting the multi-source data into the trained state variable quantity prediction model for dynamic prediction, obtaining a current ecological system loss degree variable quantity delta Dpred, and updating a current ecological system loss degree D; the steps are repeated, and drainage basin ecological risk evolution is completed. According to the method, the problem that historical dependence is ignored in the prior art is solved, and ecological risk simulation dynamic evolution is realized.

The utility model belongs to the technical field of data acquisition units, and particularly relates to an ecological risk assessment handheld data acquisition unit which comprises an acquisition unit body, a supporting tripod is arranged below the acquisition unit body, an auxiliary rotating block is fixedly installed on the acquisition unit body, a rear handle is installed on the auxiliary rotating block in a rotating mode, and the rear handle is connected with the supporting tripod. The front hand support is fixedly installed below the collector body, the shoulder strap hanging rings are fixedly installed on the front side and the rear side of the collector body, the rear handle and the front hand support are installed on the collector body of the data collector, a worker can use the data collector through cooperation of the two hands, and the shoulder strap hanging rings can be connected with shoulder straps. A worker can carry the shoulder strap, so that the condition that arms are sore and swollen when the data collector is used for a long time is avoided, meanwhile, the data collector is prevented from falling off, when data needs to be collected for a long time in a place, the data collector can be placed on the camera tripod, and the workload of the worker is greatly reduced.

The invention relates to the field of plant ecological risk assessment, and discloses a foreign mangrove species ecological influence assessment and risk prediction method, which comprises the following steps: performing biological characteristic information investigation and analysis on target foreign mangrove species to construct a multi-dimensional assessment index system, and performing risk judgment on the target foreign mangrove species according to different index score values, given by the system, of the target foreign mangrove species. And evaluating a risk evolution process after the risk judgment, and outputting a management scheme for the risk. According to the method, the comprehensive influence of external mangrove species can be comprehensively and comprehensively evaluated, key risk points are not missed, meanwhile, the management efficiency is improved, and the decision randomness and uncertainty are reduced.

The application provides a comprehensive decision-making and evaluation method for halogenated hydrocarbon contaminated soil and groundwater remediation strategy, comprising: establishing a site baseline and conducting technical preliminary screening based on health and ecological risk assessment (HERA) and a three-level evidence system; conducting rapid resource efficiency assessment based on environmental footprint analysis (EFA); conducting global long-term assessment based on life cycle assessment (LCA) and cost-benefit analysis (CBA); and dynamic evaluation and closed-loop control based on digital twinning and multi-criteria decision analysis (MCDA) coupling. In the dynamic evaluation stage, an adaptive hybrid dynamic weighting mechanism is constructed, the subjective benchmark weight and the objective fluctuation weight calculated based on real-time data information entropy are integrated, and the comprehensive utility score is calculated; at the same time, the apparent degradation rate constant of the pollutant is solved in real time, and when it is lower than the target rate and the strategy is replaced, the intervention instruction is automatically output. The application realizes the deep integration of multi-dimensional indicators, dynamic adaptive evaluation and closed-loop intelligent regulation and control, and solves the problems of single, static and lack of feedback of the existing evaluation method.