46 results about "Species distribution" patented technology

The invention discloses a coastal zone planning method and system based on MaXent and hotspot analysis, and relates to the technical field of coastal zone planning. Comprising the following steps: S1, collecting environment variable data, species distribution data and development intensity data, constructing a unified space-time grid system, and realizing time sequence expression of the data through data preprocessing; s2, constructing a quarterly time sequence maximum entropy model, and performing time sequence evaluation on each grid habitat state to form a habitat evolution type; s3, identifying time sequence significant hot spots and hot spot persistence and intensity features, constructing ecological development relationship comprehensive representation, and identifying ecological development conflict types; and S4, spatially superposing the habitat evolution type and the ecological development conflict type to form a self-adaptive access rule, and generating differentiated planning suggestions according to regions. The problem that planning lacks dynamic adaptability due to the fact that coastal zone habitat suitability and development intensity depend on static time point data for a long time and a time sequence evolution rule cannot be recognized is solved.

The invention relates to the technical field of species ecological niche modeling, and provides a coastal wetland waterfowl ecological corridor construction method and system, and the method comprises the steps: collecting target waterfowl species distribution point data, and obtaining a waterfowl species distribution data set; performing habitat suitability modeling on environmental factor data and the waterfowl species distribution data set through a species distribution model to obtain a coastal wetland ecological source land; habitat suitability prediction modeling is carried out on the environment variables through a random forest algorithm, resistance value assignment is carried out on suitability indexes, and a coastal wetland comprehensive resistance surface is obtained; and carrying out multi-scene corridor path identification on the coastal wetland comprehensive resistance surface and the coastal wetland ecological source land to obtain the coastal wetland waterfowl ecological corridor. According to the method, the functionality of the ecological corridor under different ecological backgrounds can be comprehensively reflected, so that the ecological suitability of the constructed corridor is improved.

The invention discloses an intelligent forest wild animal monitoring method and system based on multi-source data fusion, and relates to the field of wild animal monitoring and ecological protection, and the method constructs an integrated monitoring system by arranging a ground infrared camera network and an unmanned aerial vehicle multi-sensor inspection platform and accessing satellite remote sensing data. And the system analyzes infrared data in real time by using the edge computing node and triggers emergency response of the unmanned aerial vehicle, so that quick tracking of key events is realized. Pixel-level space-time alignment and cross-modal feature fusion are performed on multi-source image data, and a target detection model is input, so that species and behaviors can be accurately identified. Meanwhile, after the multispectral data and the geographical environment data are subjected to gridding alignment, the data are input into a habitat analysis model, and a habitat suitability probability graph is generated. And automatically generating protection decision support information through comprehensive correlation analysis of species distribution and habitat quality. According to the invention, all-weather, full-space and intelligent wild animal monitoring and protection response are realized.

The application discloses a coastal zone planning method and system based on MaXent and hotspot analysis, and relates to the technical field of coastal zone planning. The method comprises the following steps: S1, collecting environmental variable data, species distribution data and development intensity data, constructing a unified space-time grid system, and realizing time series expression of the data through data preprocessing; S2, constructing a quarterly time series maximum entropy model, time series evaluating habitat states of each grid, and forming habitat evolution types; S3, identifying time series significant hotspots and hotspot persistence and intensity characteristics, constructing a comprehensive representation of ecological development relationship, and identifying ecological development conflict types; and S4, spatially superimposing the habitat evolution types and the ecological development conflict types, forming adaptive access rules, and generating differentiated planning suggestions according to regions. The problems that the long-term dependence of coastal zone habitat suitability and development intensity on static time point data and the inability to identify time series evolution rules result in the lack of dynamic adaptability of planning are solved.

This invention provides a method and system for assessing the impact of construction disturbance on biodiversity in nature reserves, relating to the field of computer technology. The method includes: acquiring species distribution data, construction disturbance data, and environmental data within the nature reserve; constructing an ecological association network based on the species distribution data to obtain a species interaction network; extracting network structure features from the species interaction network to obtain a set of key species; modeling the disturbance propagation dynamics based on the set of key species and the construction disturbance data to obtain an impact propagation path map; predicting biodiversity responses based on the impact propagation path map and environmental data to obtain trends in changes in biological community composition; and conducting a comprehensive impact assessment based on these trends, outputting an impact level index by comparing the degree of deviation from historical baseline states. This invention effectively improves the overall accuracy and predictive precision of the assessment of the impact of construction disturbance on biodiversity in nature reserves.

The application discloses a kind of urban bird species distribution prediction method and system based on deep learning, it is related to the technical field of ecology and landscape architecture, by realizing the unified modeling of environmental characteristics, species co-occurrence relationship and spatial proximity information;Through environmental characteristic intelligent weighting and enhancement mechanism and multi-task joint learning strategy, the expression ability of model to ecological correlation structure is improved;Combined with species richness stratified evaluation and calibration mechanism, the reliability of the prediction result of uninvestigated area is enhanced;Form the automation technical process from data processing, model training to regional species richness output, can provide efficient, systematic technical support for urban biodiversity assessment and ecological planning.

The invention relates to the technical field of natural resource protection and ecological system management, and discloses a method and a system for constructing a three-dimensional dynamic model of forest grass wet and waste areas based on digital twinning, and the method comprises the steps: obtaining multispectral image data and environment readings of forest, grassland, wetland and desert areas; performing feature data primary processing to obtain vegetation coverage information and environment variable data; performing species distribution boundary labeling to obtain a species distribution boundary; performing pattern correlation analysis to obtain a preliminary relation matrix; if the association strength of the water level parameter and the desertification parameter in the preliminary relation matrix is higher than a preset strength threshold value, calculating a correction coefficient of the water level parameter and the desertification parameter; carrying out weight balance to obtain a balance relation matrix; time sequence deviation detection and stability verification are carried out, and an ecological change simulation framework is determined. According to the method, comprehensive analysis of complex ecological systems such as forests, grasslands, wetlands and deserts can be realized, and dynamic monitoring and protection are carried out.

The invention relates to a method for constructing a marine organism diversity distribution model, which is used for constructing a marine species distribution prediction model framework with ecological adaptability, prediction accuracy and management guidance by relying on technical paths such as multi-source data fusion, sample optimization generation, adaptive clustering recognition, multi-model fusion prediction, mechanism interpretable analysis and the like. And quantitative tool support is provided for marine ecological red line demarcation, flagship species protection area planning, ecological restoration priority area identification and the like.

The application discloses a natural ecosystem authenticity determination method and device, relates to the technical field of ecological evaluation, and comprises the following steps: acquiring land use data of a research area, and calculating an ecological structure authenticity index; identifying a natural reference area based on ecological environment zoning and the ecological structure authenticity index; acquiring ecosystem service data, and calculating an ecological process authenticity index and an ecological function authenticity index based on the natural reference area; acquiring species distribution data, and calculating a biological and indicator species authenticity index; acquiring human activity data, and calculating a human disturbance intensity index; and finally, normalizing and weightedly fusing the indexes to obtain a natural ecosystem authenticity comprehensive index. The application solves the problems of single dimension, neglect of regional natural benchmark differences and lack of a multi-factor comprehensive framework of the existing evaluation method, and significantly improves the scientific nature of natural reserve management.

The invention relates to the technical field of plant ecological monitoring and computer vision, and discloses a grassland dominant species identification method based on unmanned aerial vehicle remote sensing and deep learning, and the method comprises the steps: carrying out the fusion processing of a to-be-identified unmanned aerial vehicle remote sensing image of a target grassland region, and obtaining a to-be-identified fusion image; inputting the fusion image to be identified into a trained species identification model to obtain a species classification result of the target grassland area; combining the species classification result with the space coordinates of the target grassland area to generate a species distribution diagram of the target grassland area; according to the method, the problem of insufficient species identification precision in a complex grassland environment can be solved, manual investigation cost and subjective deviation are reduced, grassland dominant species identification accuracy and spatial analysis capability are improved, and refined data support is provided for grassland ecological protection.

The invention provides a method and a system for predicting a potential suitable growth area of alien invasive species in the technical field of risk management of invasive species. The method comprises the following steps: S1, acquiring a large amount of environment variable data and species distribution sample data; s2, downscaling the environment variable data through a spatial regression model to construct a high-resolution environment data set, performing spatial refinement on the species distribution sample data through a Moran index to construct a sample data subset, and constructing a data set based on the high-resolution environment data set and the sample data subset; s3, creating a species distribution model based on a maximum entropy model, a random forest, a support vector machine, a generalized linear model, an XGBOOST or a deep learning model, and training the species distribution model through the data set; and S4, predicting the suitable growth area of the alien invasive species through the trained species distribution model. The method has the advantages that the prediction precision and generalization ability of the potential suitable growth area of the alien invasive species are greatly improved.

The present application relates to the technical field of ecological environment and carbon emission management, and discloses a method, device and equipment for evaluating carbon sink potential of ecological restoration in an open-pit mine area, which comprises the following steps: obtaining basic data of the mine area, and constructing a multi-dimensional remote sensing data cube, an environmental factor database and a species distribution sample database based on the basic data of the mine area; generating a closed pit spatiotemporal distribution database based on the multi-dimensional remote sensing data cube; modeling the habitat suitability probability of species based on the environmental factor database, the species distribution sample database and the closed pit spatiotemporal distribution database, and generating an ecological restoration suitability spatial zoning database; generating a carbon sink potential spatiotemporal distribution map based on the closed pit spatiotemporal distribution database and the ecological restoration suitability spatial zoning database, and evaluating the carbon sink potential of the mine area based on the carbon sink potential spatiotemporal distribution map; the present application can finely depict the spatiotemporal evolution law of the restoration of the mine area, dynamically quantify the carbon fixation potential of different restoration measures and species types, and obtain a scientific carbon sink evaluation method.

The application discloses a biodiversity investigation and monitoring method based on satellite remote sensing and environmental DNA sequencing, comprising the following steps: S1, obtaining habitat remote sensing data and environmental DNA data; S2, preprocessing the obtained data and comparing with a public gene database to obtain species distribution data; S3, aligning and fusing the habitat remote sensing data and the environmental DNA data in time and space scales to generate a biodiversity feature data set; and S4, constructing a correlation model and a three-dimensional model between the habitat and the species distribution, and visually displaying the dynamic change of biodiversity. The application improves the comprehensiveness, accuracy and timeliness of investigation and monitoring, and provides a scientific basis for biodiversity protection.

The invention provides a karst rocky desertification side slope ecological restoration method which comprises the following steps: selecting a rocky desertification side slope region, generating a spherical region range based on geographic coordinates, and recording center coordinates of a target side slope; the method comprises the following steps: acquiring vegetation coverage, height and type distribution data based on remote sensing data and field sampling, and acquiring wind speed, wind direction, soil element distribution, precipitation, runoff, vegetation seed bank quantity, seed bank soil humidity, seed particle size distribution and type distribution data at the same time; generating a wind power field function according to the wind speed and wind direction data, analyzing the soil element content, calculating the total rainwater amount and the total runoff amount, and generating the number of effective vegetation seeds; integrating the parameters to construct a carbon sequestration and slow release model, and outputting a carbon cycle model. According to the method, the carbon sequestration capability of the karst rocky desertification slope ecosystem can be accurately evaluated, the dynamic monitoring and optimization of the ecological restoration effect are realized, and the ecological restoration efficiency and the regional ecological stability are improved.

This invention discloses a method for delineating restoration areas based on biodiversity and habitat stress in the field of natural resource management technology. The method includes acquiring species distribution records and environmental variable data for each species in a target spatial area, and then filtering the environmental variable data to obtain filtered environmental variable data. This invention processes the input data using multiple models in an integrated species distribution model to calculate species richness, functional diversity, and phylogenetic diversity as representative indicators. This overcomes the limitations of previous methods that only focused on a single dimension of biodiversity information, providing a more comprehensive representation of biodiversity levels. By constructing a comprehensive biodiversity index and a comprehensive habitat stress index to comprehensively identify priority restoration areas, this method avoids the limitations and shortcomings of previous methods that only focused on a single dimension of biodiversity, and fully considers the dynamic coupling relationship between biodiversity and habitat conditions.

This application relates to a method for identifying avian influenza spillover risk areas at the waterfowl-seabird interface, comprising: acquiring indicator data and environmental data of avian influenza waterfowl-seabird hosts; wherein, the indicator data includes species distribution point data of avian influenza waterfowl-seabird hosts and avian influenza occurrence point data in seabirds; screening species distribution point data monthly; determining whether the species distribution point data is sufficient according to preset conditions; if all species distribution point data is sufficient, calculating the waterfowl-seabird spillover risk index based on activity intensity; otherwise, calculating the waterfowl-seabird spillover risk index based on species richness, thereby identifying avian influenza spillover risk areas between waterfowl and seabirds. By integrating avian influenza waterfowl-seabird host indicator data and environmental data, and using different methods to calculate the waterfowl-seabird spillover risk index pixel by pixel according to different species distribution point distribution situations, the method can accurately identify avian influenza spillover risk areas between waterfowl and seabirds.

The present invention provides a species distribution data aggregation method and system, and a storage medium. The method comprises: obtaining original species distribution data; determining a map grid scale displaying the species distribution, and acquiring the original species distribution data within a range of each grid; and taking each grid as a central grid, and performing data enhancement on the central grid by using original species distribution data of multiple other grids within a set range around each grid, thereby obtaining a species distribution data aggregation result of each grid. The species distribution data aggregation method provided by the invention can enhance the display of the species distribution data, thereby improving the data display effects.

The invention discloses an endangered plant phoebe hainanensis identification and systematic monitoring method based on multi-source data fusion, and belongs to the technical field of endangered plant protection. According to the method, a potential distribution area is delineated through a species distribution model, individual accurate identification and filing are achieved through a multi-branch feature fusion model, multi-scale growth and microenvironment data are collected through Internet of Things nodes, and finally a'discovery-identification-monitoring-protection 'whole-process closed loop is formed through a unified data management platform. According to the method, phenological feature extraction, transfer learning modeling, man-machine collaborative verification and low-power-consumption Internet of Things monitoring technologies are adopted, and the problems that a traditional method is low in efficiency, insufficient in recognition precision and fragmented in monitoring are solved. According to the method, efficient discovery, accurate identification and long-term dynamic monitoring of the wild population of the phoebe hainanensis are achieved, scientific support is provided for protection decision making, and the method is convenient to operate and high in practicability and has protection value and application prospects.

The application discloses an ecological evolution simulation method based on rainfall runoff assimilation, and comprises the following steps: estimating environmental effects such as topographic erosion, water resource distribution change and light and heat resource change by using rainfall runoff assimilation results output by a rainfall runoff assimilation model; and calculating resource supply conditions and species distribution conditions of each layer of a food chain to simulate changes of a target environment ecological circle on the basis. The application simulates rainfall runoff processes by using the rainfall runoff assimilation model, adds short-term influences of rainfall runoff in ecological environment evolution simulation, improves granularity of the ecological evolution simulation method, and promotes ecological coupling rainfall runoff assimilation to be more accurate.

The application discloses a physical guidance-based spatio-temporal hypergraph condition denoising diffusion probability model construction method, and relates to the field of ecological environment modeling and artificial intelligence prediction. The application innovatively combines the physical heat diffusion process and the spatio-temporal modeling of multi-modal environmental factors by introducing a temperature driving module based on heat conduction and a spatio-temporal perception fusion module. In addition, a lightweight convolution block attention module and a hierarchical hypergraph attention mechanism are embedded in the diffusion denoising network to realize high-order correlation modeling and dynamic fusion of multi-scale spatial features. The method can effectively capture the spatio-temporal evolution characteristics and ecological dependence of species distribution while maintaining reasonable computational complexity, thereby significantly improving the accuracy of distribution prediction. The experimental results on different types of data sets prove the superiority and robustness of the proposed PSTH-CDPM.

The invention provides a method for evaluating the pollution risk grade of micro-plastics in soil, and belongs to the technical field of ecological safety evaluation.The method comprises the steps that a soil sample of a target area is collected in a layered mode, the micro-plastics are extracted, and characteristic parameters of the micro-plastics in the soil sample are obtained, the micro-plastic characteristic parameters at least comprise micro-plastic abundance, micro-plastic type distribution, adsorbed pollutant concentration, soil organic matter content, soil porosity and annual average rainfall, and micro-plastic shape, color and material composition, processing the characteristic parameters to generate a plurality of risk assessment values, and performing risk grading on the soil sample according to the risk assessment values, according to the evaluation method for the micro-plastic pollution risk grade in the soil, the micro-plastic soil pollution grade is subdivided, so that agricultural cultivation activities are guided more accurately.

The invention relates to an ecological protection priority area identification method and system based on multi-source data and space optimization, and the method comprises the steps: collecting and preprocessing multi-source space data in a target area, and generating a theme space data set; quantifying the ecosystem service in the target area, and obtaining a standardized magnitude spatial distribution map; combining the species distribution point location in the target area with the theme space data set, and simulating to generate a suitability space distribution diagram; fusing the standardized magnitude spatial distribution map and the suitability spatial distribution map to form a regional comprehensive protection value curved surface; and carrying out iterative calculation on the regional comprehensive protection value curved surface by adopting a space optimization algorithm to obtain an ecological protection space layout scheme, and delimiting different protection level priority ecological protection regions of the target region. According to the method, the accuracy, scientificity and operability of an ecological protection priority area identification delimiting process are greatly improved, and an efficient technical approach is provided for cooperative protection of biodiversity and ecological system service.

The invention relates to an avian influenza overflow risk area identification method based on a waterfowl-sea bird interface. The method comprises the following steps: obtaining index data and environment data of an avian influenza waterfowl-sea bird host; wherein the index data comprises species distribution point data of bird flu waterfowl-sea bird hosts and sea bird flu occurrence point data; screening species distribution point data month by month; judging whether the species distribution point data is sufficient or not according to a preset condition; if all species distribution point data are sufficient, calculating a waterfowl-sea bird overflow risk index based on an activity intensity method; otherwise, calculating a waterfowl-sea bird overflow risk index based on a species richness method, further identifying an avian influenza overflow risk area between waterfowls and sea birds, and identifying an avian influenza overflow risk area between waterfowls and sea birds by integrating the avian influenza waterfowl-sea bird host index data and the environmental data according to different distribution conditions of species distribution points. And respectively calculating waterfowl-sea bird overflow risk indexes pixel by pixel by adopting different methods so as to accurately identify an avian influenza overflow risk area between waterfowls and sea birds.

The present application belongs to the technical field of smart city, and particularly relates to a smart city environment management method and system based on digital twinning. The method not only realizes high-precision collection of species distribution, vegetation coverage, ground temperature and wind speed data in the urban area, but also successfully simulates the dynamic changes of ground thermal radiation absorption and release caused by the increase and decrease of different species by constructing a dynamic spatial model containing species-environment interaction. Further, by calculating the hourly influence coefficient and iteratively deriving the microclimate evolution trend in a ten-year period, a priority ranking scheme for species protection or regulation is finally generated. This method greatly improves the understanding depth and response speed of urban managers on ecological environment changes, makes real-time calibration based on the latest monitoring data possible, and ensures the scientificity and foresight of urban management decisions.

The invention provides a method and system for evaluating the influence of construction disturbance on the biodiversity of a natural reserve, and relates to the technical field of computers, and the method comprises the steps: obtaining species distribution data, construction disturbance data and environment data in the natural reserve; constructing an ecological association network according to the species distribution data to obtain a species interaction network; performing network structure feature extraction according to the species interaction network to obtain a key species set; performing disturbance propagation dynamics modeling according to the key species set and the construction disturbance data to obtain an influence propagation path diagram; performing biodiversity response prediction according to the influence propagation path diagram and the environmental data to obtain a biocommunity composition change trend; and performing comprehensive influence evaluation according to the change trend of the biocenosis composition, and outputting an influence grade index by comparing the deviation degree of the historical baseline state. According to the method, the integrality and predictive evaluation precision of the construction disturbance influence on the biodiversity of the natural reserve is effectively improved.

The invention discloses a grassland biodiversity monitoring method and system based on an intelligent sensor, and the method comprises the steps: obtaining biological monitoring data according to the intelligent sensor, carrying out the preprocessing, carrying out the statistical calculation of the initial species distribution change based on the coordinate point location of ground sampling data and the sampling period, extracting the features of species through the data of an unmanned plane, and carrying out the recognition of the features. Performing direction fitting on the initial species distribution change according to the environmental data to obtain probability characteristics of the coincidence point locations, reducing the species quantity of the coincidence point locations through a Bayesian network according to the probability characteristics and the ecological nodes, and optimizing the reduction process through migration entropy to obtain the coincidence point locations. And based on the reduction data and the monitoring period, extracting species characteristics, species richness, species uniformity and species distribution change in a narrowed range, and obtaining biodiversity monitoring data. According to the method, a species enrichment area is focused through spectrum and space screening of species characteristics, an ecological state time-varying rule and transfer characteristics are captured through time sequence and state modeling, and the precision and interpretability of grassland biodiversity monitoring are improved.

The application discloses a low-disturbance lake biological diversity protection monitoring device, which comprises a driving motor fixedly arranged on the lake bottom, a middle monitoring module capable of moving up and down in the water body and a floating monitoring module on the lake surface, the output shaft of the driving motor is arranged vertically and the end of the output shaft is connected with an extension rod parallel to the lake bottom, and the driving motor drives the extension rod to rotate along the plane parallel to the lake bottom. The low-disturbance monitoring is realized from four dimensions of physical damage, habitat destruction, behavior interference and chemical pollution, the vertical section monitoring in the lake is realized, the horizontal scanning and the vertical lifting constitute a three-dimensional monitoring network, three types of data of organisms, water quality and weather are synchronously collected by the device, and a quantitative relationship model of species distribution and environmental factors can be established.

The application discloses an intelligent prediction method and system for urban green space species and community structure, relates to the cross technical field of urban ecological planning, biodiversity protection and artificial intelligence, and comprises the following steps: performing spatial embedding processing and mapping on multi-dimensional environmental variable data and plant functional trait data respectively to obtain an environmental embedding matrix and a trait embedding matrix; inputting the environmental embedding matrix and the trait embedding matrix into a pre-trained cross-modal contrast learning model to output a grid species similarity matrix; performing threshold determination on the grid species similarity matrix based on a preset optimal existence determination threshold to obtain a plant species distribution prediction result of each grid; identifying a habitat type based on the multi-dimensional environmental variable data to obtain a habitat type of each grid; determining a capacity boundary constraint parameter of each grid in a vegetation layer based on the habitat type of each grid and vegetation structure observation data; and performing probabilistic completion on a missing plant species based on the plant species distribution prediction result of each grid and the capacity boundary constraint parameter of each grid in the vegetation layer to obtain a final plant community composition result of the grid.

The invention discloses a climate connectivity network optimization method based on temperature constraint and a storage medium. The method comprises the following steps: identifying different habitat biodiversity hotspots based on a pre-constructed species distribution model; taking the existing protected area in the research area as an initial node, and combining a comprehensive resistance surface and a diffusion distance threshold which are obtained in advance to construct a basic ecological network; a temperature difference constraint rule is introduced into the basic ecological network, all minimum cost paths of initial nodes meeting the temperature difference constraint rule are calculated, and a current climate connectivity network is formed; for the climate connectivity network, taking different habitat biodiversity hotspots as supplementary nodes to obtain the climate connectivity network after first optimization; and performing screening and data cleaning on the climate connectivity network after the first optimization based on the path weight value to obtain a climate connectivity network after the second optimization. According to the method, the climate connectivity of a protected area ecological system in a multi-climate scene can be improved.

The application discloses a kind of based on big data analysis's mine area subsidence area landscape ecological risk early warning method, comprising: by synthetic aperture radar interferometry obtains and processes subsidence time sequence, extracts cumulative amount and rate parameter and divides subsidence grade area;Based on the area, obtain landscape type and species distribution data, construct hierarchical response model to obtain fragile feature description;Accordingly, extract vegetation coverage and land use conversion law, assess habitat quality trend and generate ecological risk grade;When risk exceeds threshold, trigger early warning and dynamically adjust model parameters, through iterative optimization sensitive grading scheme;Finally, based on time series analysis, determine long-term degradation trend and risk path, form landscape ecological risk assessment file.The application realizes the whole process dynamic early warning from ground deformation to landscape ecological risk, improves the accuracy and timeliness of mine area landscape ecological risk identification, and provides technical basis for ecological protection and sustainable management.