14 results about "Diversity index" patented technology

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.

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 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 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 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 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 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.