27 results about "Diversity index" patented technology

The invention relates to the technical field of marine ecological environment monitoring and data analysis, and particularly discloses a sea area phytoplankton biodiversity index prediction method and system based on multi-model integration and feature engineering. After preprocessing, constructing four groups of nonlinear interaction characteristics of a temperature-salt relationship, oxygen-salt balance, chlorophyll chemical oxygen demand coupling and a nitrogen-phosphorus ratio based on environmental factors, combining station characteristics with basic environment and interaction characteristics, carrying out variance threshold screening, inputting a characteristic set into a multi-model integration framework containing models such as linear regression and gradient lifting, and carrying out multi-model integration; training and tuning according to a time sequence segmentation strategy, selecting model output according to a decision coefficient, using a result if the decision coefficient of the support vector regression model is within a preset range, and otherwise, taking a gradient lifting and extreme gradient lifting tree model to predict a mean value. And the prediction accuracy and the model generalization, stability and reliability are improved.

The invention relates to a flora transplantation clinical pathway standardization management method, device and equipment and a medium. The method comprises the following steps: on the basis of clinical data of a patient and screening data of a donor, executing flora suitability evaluation by using a deep learning model and selecting an adaptive donor; obtaining a flora sample of an adaptive donor, and carrying out high-throughput sequencing; generating a donor flora quality evaluation score according to a preset flora composition diversity index based on a sequencing result; screening the flora samples of which the quality evaluation scores are higher than a preset quality threshold value to prepare a flora preparation; detecting activity parameters and diversity parameters of the flora preparation, and performing colonization potential classification prediction by combining clinical data of the patient and adopting a support vector machine algorithm so as to output a potential colonization success rate; and when the potential colonization success rate exceeds a preset success rate threshold, outputting a clinical transplantation scheme through a preset transplantation scheme generation algorithm based on the flora preparation and the clinical data of the patient. According to the method, through multi-stage intelligent decision making, the safety and the effectiveness are improved.

The invention relates to the technical field of molecular biology, in particular to a betel nut InDel marker and application thereof in provenance detection. According to the invention, on the basis of the results of re-sequencing of one betel nut germplasm resource and comparative analysis of a reference genome sequence, InDel sites are excavated to develop molecular markers, 46 pairs of InDel molecular marker primer pairs with high polymorphism are obtained, the variation range of Shannon's diversity index is 0.361-1.713, and the variation range of polymorphism information content is 0.201-0.752. The clustering analysis result shows that the genetic similarity coefficient is 0.36, 211 parts of areca-nut materials are divided into four groups, the InDel marker pair provided by the invention can be effectively used for detecting and analyzing the genetic background of areca-nut germplasm resources, the genetic relationship between the tested areca-nut materials is accurately identified, and the method has the advantages of high specificity, high accuracy and high accuracy. And a foundation is laid for genetic diversity analysis of betel nut germplasm resources and detection of seed fruit and seedling sources.

The invention relates to an agricultural environment biodiversity index evaluation method based on field biodiversity investigation. Comprising the following steps: acquiring agricultural environment biodiversity basic data through field investigation; according to the collected data, an agricultural environment biodiversity index (ABI) is used for calculation, and the farm biodiversity level is evaluated; grading the farm biodiversity management by using landscape survey data, agricultural cultivation technology and the like; through correlation analysis between the ABI indexes and the ecological landscape management indexes, the accuracy of ABI index scores is checked; and generating a report according to an evaluation result, indicating the biodiversity condition of the farmland, and providing related biodiversity management suggestions. The method has good sensitivity, effectiveness and objectivity on the biological diversity of the farm, is suitable for quickly, comprehensively and scientifically evaluating the biological diversity state of an agricultural system, helps farm managers to enhance the protection consciousness on the biological diversity, and guides sustainable management of ecological agriculture. Meanwhile, the method is also applied to different agricultural systems, and helps the user to carry out farm biodiversity self-evaluation and farm management improvement.

The invention relates to the technical field of biodiversity research, and discloses a mountain tree species diversity remote sensing estimation method, which comprises the following steps: acquiring mountain tree species distribution data, acquiring a mountain remote sensing image, preprocessing the data, and calculating a tree species diversity index according to tree species distribution. Calculating the correlation between the spectral heterogeneity index and the tree species diversity index, constructing a mountain tree species diversity remote sensing mapping model according to the influence factor calculated by the spectral heterogeneity index, extracting the optimal parameter of mountain tree species diversity remote sensing estimation, and making a tree species diversity index spatial distribution map according to the spectral heterogeneity index to realize tree species diversity estimation. A terrain correction mechanism is introduced into the spectral heterogeneity index constructed by the method, the stability and reliability of the spectral heterogeneity index are improved, spectral response of the model to different tree species is improved after a weighted distance construction mechanism between pixels is combined, and the method is suitable for mountain forest areas with obvious topographic relief and has good application prospects. And multi-source input of multi-sensor remote sensing images is supported.

The invention discloses a data-driven biodiversity monitoring management method based on multi-source fusion, and particularly relates to the technical field of ecological environment monitoring. Multi-source heterogeneous ecological data of remote sensing images, species records, environmental sensors, community science and the like are obtained and subjected to standardization processing to generate a unified data matrix; based on a space grid model and a sliding time window mechanism, space-time sequence features of all grid points are extracted; generating an embedded vector by using a pre-trained multi-modal ecological representation learning model, and predicting a species diversity index of each region in combination with a species inhabiting adaptability model; a spatio-temporal evolution graph is further constructed, an abnormal change area is identified, and unmanned aerial vehicle cruising and high-frequency monitoring tasks are dynamically scheduled; according to the method, the intelligence, the real-time performance and the precision of ecological monitoring can be improved, and the method is suitable for various application scenes such as natural reserves and biodiversity hot spots.

The invention relates to the field of water ecological environment monitoring, in particular to a fish diversity monitoring method and system based on visual identification, and the method comprises the steps: obtaining the video data of fishes in a target water area in real time, and monitoring the water quality data in real time; processing the video data, and constructing a target detection model to obtain the position and category label of each fish; the occurrence probability and number distribution of the fishes are counted according to the position and category labels, and diversity index scores are calculated; acquiring historical monitoring data, and constructing a time sequence model to predict a target index score; and finally, adjusting parameters of the monitoring model according to the error between the diversity index score and the target index score and the water quality data. According to the invention, continuous optimization and upgrading of the monitoring model are realized, and high efficiency, accuracy and real-time performance of fish diversity monitoring are ensured.

This invention discloses a method for a bird acoustic diversity index with low sensitivity to noise. The method first obtains the time-frequency power spectrum of field bird call monitoring data during the analysis period using short-time Fourier transform. Then, based on narrowband noise level estimation, it calculates candidate floating detection thresholds for each frequency point that meet local signal-to-noise ratio requirements. The full-scale relative level threshold is used as the lower limit to obtain the frequency-varying detection threshold after binarization of the time-spectrum. Finally, the Shannon index is calculated by counting the number of time-frequency points in each sub-band where the power is higher than the detection threshold, resulting in the acoustic diversity index based on frequency-varying threshold detection. This invention effectively addresses the fundamental limitation of ADI (Ambient Audio Diversity Index) being highly sensitive to noise, while remaining fully compatible with the ecological interpretations and findings of existing ADI studies. It provides a new tool for achieving stable and reliable rapid assessment of bird species diversity, and is of great significance for ecological and environmental acoustic monitoring.

The present invention provides a reseeding and restoration method and system for the Mao Us Sandy Land, belonging to the field of sandy land restoration technology. The method comprises: measuring various characterizing indicators of reseeding plants before and after reseeding and restoration of the Mao Us Sandy Land, including changes in plant community composition and stoichiometric characteristics, changes in soil physical and chemical properties and carbon, nitrogen, and phosphorus stoichiometric characteristics, and changes in soil microbial carbon, nitrogen, and phosphorus community characteristics; using the Alpha diversity index to determine the species richness and diversity of the soil microbial community and obtain the changes in richness and diversity; analyzing the microbial species composition in the soil using a stacked bar chart to obtain information on the composition and abundance changes of dominant species in the soil microorganisms, and using the changes in richness and diversity, as well as the composition and abundance changes of dominant species, as characteristics of changes in soil microbial community structure; using the Pearson correlation analysis method to obtain correlations between the various characterizing indicators; and selecting corresponding reseeding plants based on the correlations between the various characterizing indicators to reseed and restore degraded grassland vegetation in the Mao Us Sandy Land. This method can effectively restore the Mao Us Sandy Land.

The invention relates to a rectification system multi-objective optimization method based on dynamic NSGA-II, and belongs to the technical field of chemical process system optimization. The method comprises: determining a decision variable and a target function according to a rectification system model; a diversity index is obtained by calculating an invalid solution proportion of each generation of population and diversity of a target and decision space; and the crossover probability, the mutation probability and the population size are dynamically adjusted, a dynamic NSGA-II algorithm is constructed, the rectification system is optimized, and an optimal solution is obtained. According to the method, dynamic parameter adjustment is achieved by introducing the invalid solution proportion and the population diversity index, the number of non-convergent solutions can be effectively reduced, the population diversity is enhanced, premature convergence is avoided, the calculation cost is reduced while the optimization efficiency is improved, and an efficient and practical solution is provided for multi-objective optimization of a complex chemical system.

This invention proposes a comprehensive calculation method and system for marine biodiversity based on multiple indices, belonging to the field of marine biodiversity assessment technology. The method acquires species abundance data and environmental parameter data for phytoplankton, zooplankton, and fish in a target sea area; it correlates species occurrence frequency with the numerical range of environmental factors to generate the niche width of each species for each environmental factor, then multiplies this value by relative abundance, sums the results, and compares the sum with the theoretical maximum niche width to generate the overall niche occupancy intensity of the community; based on this intensity, it assigns weights to multiple diversity indices using preset baseline weights to generate dynamic weights; and based on these dynamic weights, it performs a weighted summation of the diversity indices to generate a comprehensive biodiversity index. This invention achieves adaptive weight allocation, improving the accuracy and ecological relevance of the assessment.

The invention relates to an ecological system service loss assessment method and system suitable for arid and semi-arid regions, and the method comprises the steps: employing an arthropod Shannon diversity index, a vegetation average height and the soil organic matter content as key indexes, employing a minimum-maximum standardization method to carry out the normalization processing of each index in the key indexes, introducing an entropy weight method to weight each index so as to calculate a weight value of each index; and then calculating ecological integrity indexes of the contrast region and the fragmentation region, further calculating an ecological integrity loss rate, and calculating an economic loss amount of the ecosystem in combination with a region area and a unit area ecosystem service value, thereby quantitatively evaluating an ecological service value loss condition caused by human activities. The method can comprehensively reflect the degradation path and economic consequences of the ecological system under agricultural interference, provides a scientific basis for ecological compensation, recovery priority judgment and environmental policy making, and realizes quantitative evaluation of the health condition of the ecological system.

The invention provides a recommendation method and device for a next interest point based on a large language model. The method comprises the steps that interest point information, user long-term preference information and user recent track information are obtained; obtaining a candidate sequence according to the user long-term preference information and the user recent trajectory information; determining geographical distance information according to the interest point information; determining a category diversity index according to the user long-term preference information and the user recent trajectory information; determining a track length index according to the recent track information of the user; according to the track length index and the category diversity index, weights corresponding to the user long-term preference information, the user recent track information and the geographic distance information are determined respectively; and inputting the geographic distance information, the user long-term preference information, the user recent trajectory information, the weights of the three information and the candidate sequence into a preset large language model to obtain a recommendation result of the next interest point. According to the method, the problems existing when a big language model executes a recommendation method can be solved.

The invention discloses a queen bee breeding value evaluation and core population selection method based on multi-omics data, and relates to the technical field of bee genetic breeding and multi-omics data application, and the method comprises the steps: obtaining candidate queen bee genome, intestinal microbiome and host transcriptome data; analyzing data and screening disease-resistant related core microbial markers, immune genes and bacterium-gene interaction network characteristics; by taking the characteristics as input, constructing a machine learning model to calculate a disease-resistant genome-microbiome combined breeding value; combining the breeding value, a core group genetic coefficient and an intestinal flora diversity index to construct a comprehensive selection index; according to the method, accurate evaluation of disease-resistant breeding values is achieved through multi-omics fusion and machine learning, genetic relationship and flora diversity optimization selection are combined, the disease resistance and genetic diversity of the core populations are improved, and a scientific basis is provided for queen bee breeding.

The present invention discloses a method and device for calculating vegetation phenological diversity based on phenological camera time series images, which is used to solve the technical problem that most existing vegetation phenological diversity calculation methods adopt manual ground survey methods, resulting in low efficiency in calculating vegetation phenological diversity. The method includes obtaining phenological camera time series images of multiple vegetations, and preprocessing the phenological camera time series images of each vegetation based on the bicubic interpolation method, outputting multiple crown selection area maps of each vegetation; generating a green index annual change curve corresponding to each vegetation based on the multiple crown selection area maps of each vegetation; extracting data from the green index annual change curve corresponding to each vegetation, and determining the vegetation physical quantity corresponding to each vegetation; using a preset phenological diversity index function to perform calculations based on the vegetation physical quantity corresponding to each vegetation, and determining the vegetation phenological diversity index.

The invention provides a sample screening method and device, a storage medium and a program product. The sample screening method comprises the following steps: for each unlabeled sample in an unlabeled sample set, calculating a prediction uncertainty index value according to a prediction result of a current model on the unlabeled sample, and calculating a diversity index value according to a distance from the unlabeled sample to each preset cluster; according to the training progress and performance of the current model, determining a first weight value aiming at the prediction uncertainty index value and a second weight value aiming at the diversity index value; for each unlabeled sample, performing weighted summation on the prediction uncertainty index value and the diversity index value according to the first weight value and the second weight value to obtain an evaluation score of the unlabeled sample; and based on the evaluation score of each unlabeled sample, screening out a to-be-labeled sample from the unlabeled sample set. The sample screening strategy can meet the requirements of different stages of model training.

The invention relates to the technical field of remote sensing ecology, in particular to a fish diversity charting method and device based on hyperspectral data and eDNA, and the method comprises the steps: obtaining an eDNA sample and a hyperspectral remote sensing image of a target water area, calculating a fish diversity index of the target water area according to the eDNA sample, and calculating the fish diversity index of the target water area according to the eDNA sample; extracting spectral band information corresponding to the eDNA sample from the hyperspectral remote sensing image; a spectral feature library of fish diversity is constructed according to spectral band information corresponding to the eDNA samples, a fish diversity prediction model is constructed based on the spectral feature library and a machine learning regression model, and the fish diversity prediction model comprises a mapping relation between spectral features and fish diversity established based on the machine learning regression model; utilizing the fish diversity prediction model to predict the fish diversity of any water area, and generating a fish diversity spatial distribution diagram of any water area according to the fish diversity. Therefore, the problems of limited sampling points, poor characterization effect of the fish habitat and the like in related technologies are solved.

The invention discloses a method for evaluating the influence of gene editing wheat on non-target organism symbiotic microorganisms, and relates to the technical field of safety evaluation of gene editing plants on non-target organisms. The method comprises the following steps: selecting non-target organisms growing on a gene editing wheat material and a control material at the same time, analyzing diversity index difference through DNA extraction, PCR amplification and high-throughput sequencing, carrying out inter-group difference analysis by utilizing a difference detection method, and carrying out quantitative analysis and statistical analysis by adopting a method of combining qualitative analysis and statistical analysis. The effect on non-target biosymbiotic microorganisms is evaluated. For example, the diversity of non-target organism symbiotic microbial communities on the gene edited wheat and the receptor wheat has no significant difference, and the gene edited wheat has no significant influence on the diversity of microorganisms in non-target organisms. The scientific evaluation method is established, the influence of the gene editing crops on the non-target biosymbiotic microorganisms can be more comprehensively and accurately analyzed, and the method has important significance for improving environmental safety evaluation of the gene editing plants.

The application provides a next point of interest recommendation method and device based on a large language model, the method comprising: obtaining point of interest information, user long-term preference information and user recent trajectory information; obtaining a candidate sequence according to the user long-term preference information and the user recent trajectory information; determining geographical distance information according to the point of interest information; determining a category diversity index according to the user long-term preference information and the user recent trajectory information; determining a trajectory length index according to the user recent trajectory information; determining weights corresponding to the user long-term preference information, the user recent trajectory information and the geographical distance information respectively according to the trajectory length index and the category diversity index; inputting the geographical distance information, the user long-term preference information, the user recent trajectory information, the weights of the three, and the candidate sequence into a preset large language model to obtain a next point of interest recommendation result. The application can solve the problems existing when the large language model executes the recommendation method.

This invention discloses a method for estimating grassland plant beta diversity based on airborne hyperspectral remote sensing technology. Several 1x1m quadrats are uniformly distributed within the survey area, their location information is recorded, and the beta diversity index is calculated. Hyperspectral remote sensing images of the survey area are acquired, with an image resolution of 0.25m, and each quadrat covers 16 pixels. The 16 pixels covered by each quadrat are sorted according to the normalized difference vegetation index. Multi-pixel spectral angles and multi-pixel spectral distances between quadrats are calculated. The image is resampled to the quadrat size. The single-pixel spectral distances between quadrats are calculated. A multiple regression model is constructed with multi-pixel spectral angles, multi-pixel spectral distances, and single-pixel spectral distances as independent variables and beta diversity as the dependent variable. Applying this model to the entire remote sensing imagery allows for the acquisition of vegetation beta diversity at any location within the survey area, making it suitable for estimating plant beta diversity over large areas in grassland regions.

The invention relates to the technical field of environmental ecology, in particular to application of a plant-litter-soil continuum in a terrestrial ecosystem. The method comprises the following steps: S1, setting a plurality of standard sample plots in a research area; s2, litter samples and soil samples with different depths in the sample plot are collected; s3, calculating an important value, a community diversity index and a stability index of the plant; s4, evaluating a response mechanism of the plant to soil elements through the homeostatic state model; and S5, disclosing a migration path and a driving mechanism of elements in the plant-litter-soil continuum by utilizing statistical analysis and a structural equation model. According to the application of the plant-litter-soil continuum in the terrestrial ecosystem, provided by the invention, the complex relationship among plant-litter-soil and microbial biomass stoichiometric characteristics is disclosed; the plants, the litters and the soil are closely associated and jointly promote nutrient circulation of the system.

The invention relates to the technical field of optimal solution search, in particular to a genetic model-based artificial intelligence optimal solution search method, which comprises the following steps of: generating an initial population by utilizing a Latin hypercube sampling method according to a constraint condition, uniformly distributing individuals in a solution space, calculating individual fitness values, constructing a fitness distribution histogram, and performing optimal solution search. Genotype and phenotype diversity indexes are monitored in real time, when the diversity indexes reach a specific threshold value or meet early warning conditions, gradient descent local search is performed on the optimal individual, a dynamic step length adjustment strategy is adopted to optimize the search process, validity verification is performed on an optimization vector, and when local search is not triggered, the optimal individual is subjected to gradient descent local search. According to the method, the hierarchical selection, single-point crossover and Gaussian mutation operations are performed on the population, and the boundary of a variation individual is repaired, so that the problems of insufficient population diversity control and easy local optimum in a complex solution space in a traditional method are solved, and the search efficiency and the result quality are improved.

The present application relates to the technical field of plant phenotype analysis, and particularly relates to a plant phenotype digital diversity index analysis method. The method effectively solves the problems of single data dimension, discretization limitation, insufficient dynamics and lack of system maintenance scheme in the existing plant phenotype analysis method. The method collects environment-phenotype dynamic data by constructing a heterogeneous monitoring network, converts discrete phenotype data into continuous probability space by combining a three-dimensional ellipsoid domain model, realizes accurate evaluation and dynamic maintenance of plant phenotype diversity through sub-domain decomposition, dynamic correction, heat map generation, deep tracking and inverse mapping analysis. The present application can effectively capture genotype-environment interaction characteristics, accurately locate phenotype response risk sites and lag units, and provide a systematic solution for plant variety-environment adaptation and diversity maintenance, thereby improving the dynamics, accuracy and practicality of plant phenotype analysis.

The present application relates to the technical field of water environment treatment and biodiversity protection technology, and particularly relates to a lakeside zone landscape configuration method based on aquatic animal diversity. The present application takes the actual response of aquatic animal diversity to water quality as the core, solves the concentration threshold of each water quality index when the diversity index reaches the balanced optimum through curve fitting and multi-objective optimization, and takes the concentration threshold as the water quality target range. The water quality target determination method based on ecological response realizes the collaborative improvement of water quality improvement and biodiversity, and is more scientific and economical. The method breaks through the traditional idea of setting water quality targets according to the environmental function and protection target of surface water area, makes the establishment of water quality targets more in line with the actual state of the ecological system, effectively reduces the subjective deviation of human experience determination, improves the scientificity and economy of target setting, and at the same time, maximizes the protection of the structural and functional integrity of the water ecological system.

The present application belongs to the technical field of ecology and surveying and mapping, and more particularly relates to a plant community investigation method and device based on multi-modal three-dimensional point cloud. The method collects multi-source data through a four-in-one sensor pod carried by a UAV, combines adaptive quadrat division, wind-induced swing artifact removal, self-supervised graph neural network hierarchical segmentation, spectrum-structure coupled biomass calculation and diversity index analysis, and finally outputs a three-dimensional visual report. The present application solves the problems of low efficiency, strong subjectivity and great destructiveness of traditional investigation, and the problems of insufficient data precision, asynchronous multi-modal data and the like in existing three-dimensional scanning technology, and has the advantages of high efficiency, high precision, zero destruction, strong real-time performance and the like, and is suitable for plant community investigation in complex terrain areas.

The invention discloses an alpine grassland ecosystem multifunctionality evaluation method based on multidimensional indexes. Acquiring overground and underground biomass, species richness, diversity index and soil physical and chemical indexes of plants through field sampling; a comprehensive index system covering key ecological functions such as productivity, plant diversity and soil nutrient circulation is constructed, and the ecological system multifunctional index is calculated after dimensional difference is eliminated through normalization processing. And through variance analysis and correlation analysis in combination with a tradeoff and collaboration mechanism between identification functions, a management scheme for multi-function collaboration improvement is evaluated. According to the method, an integrated and quantitative multi-dimensional ecological function is taken as a core, the problems of single evaluation index dimension, lack of index correlation analysis, weak comprehensive decision support capability and the like in the prior art are solved, and a quantifiable and systematic ecological system state evaluation framework is established. Scientific cognition on the overall function state of the alpine grassland ecosystem is improved, and technical support is provided for sustainable management and protection of grassland resources in alpine regions.

The invention provides a vegetation community spatio-temporal evolution monitoring method and device, and is applied to the technical field of ecological environment monitoring. The method comprises the following steps: acquiring field survey data and remote sensing data of vegetation in a vegetation sample area; determining a vegetation diversity index according to the field survey data, and determining a vegetation coverage according to the remote sensing data; a vegetation community spatio-temporal evolution monitoring model is constructed based on the vegetation diversity index and the vegetation coverage, and the vegetation community spatio-temporal evolution monitoring model is used for integrating the vegetation diversity index and the vegetation coverage into a unified monitoring index so as to quantify spatio-temporal characteristics of the vegetation community; and based on the vegetation community spatio-temporal evolution monitoring model and the field survey data and remote sensing data at different time periods, analyzing spatio-temporal evolution characteristics of the vegetation communities in the vegetation sample area.