288 results about "Species identification" patented technology

The invention discloses a device and a method for intelligently investigating types and quantity of fishes. The device comprises a self-adaptive sonar detection module, a three-dimensional image acquisition module, an image preprocessing module, a fish detection module, a main body segmentation module and an identification and classification module. The sonar module is integrated with a three-frequency-band transducer, can dynamically switch frequency according to fish school depth, and realizes target tracking counting and quantity estimation in combination with an algorithm; the image acquisition module constructs a fish school three-dimensional image model by combining a depth camera with a light attenuation compensation algorithm; the preprocessing module fuses acoustic and optical features and expands a data set; the detection and segmentation module is used for accurately positioning fishes and removing impurities; the identification and classification module realizes type identification based on transfer learning and supports incremental learning of new fingerlings. According to the method, through cooperation of multiple modules, the problems of insufficient detection precision, poor image quality and the like in traditional investigation are solved, efficient and accurate investigation of fish species and quantity is realized, and technical support is provided for fishery resource management and ecological protection.

The invention discloses an aquatic organism diversity rapid evaluation system and method based on GIS and eDNA technologies, and the method comprises the steps: selecting a plurality of sampling points in a target region to collect water samples, and integrating and processing the geographic information data of the sampling points and surrounding regions by adopting a GIS system; extracting eDNA from the collected water sample, performing amplification and sequencing on the eDNA by adopting a high-throughput sequencing technology, obtaining species and relative abundance of the species existing at the sampling point, and forming a species list; importing species distribution information corresponding to the species list into a GIS system, and obtaining a species distribution area and species types; according to the method, a species distribution prediction model is established, aquatic organism diversity change trends of the hot spot area and the potential ecological threat area are monitored in real time, an evaluation report and protection suggestions are formed, and protection measures are updated in time, so that the data acquisition and processing efficiency is improved, and the accuracy of species identification and the comprehensiveness of diversity evaluation are enhanced; and a real-time monitoring and early warning mechanism is provided.

The invention provides a wild animal and habitat remote sensing monitoring identification method and system, and belongs to the technical field of wild animal monitoring and habitat evaluation, and the method comprises the steps: obtaining a satellite remote sensing image based on the habitat range of a target research species, and delimiting the potential habitat range of the target research species; obtaining unmanned aerial vehicle remote sensing data in the range to obtain a first image feature vector identification activity trace, and determining a target research area of the target research species; processing species monitoring data monitored by an infrared camera in the target research area to obtain a second image feature vector; inputting the second image feature vector into a preset species identification model to obtain a species identification result; performing fusion analysis on the species identification result, the satellite remote sensing image and the unmanned aerial vehicle remote sensing data, and extracting multi-dimensional habitat variables; and inputting the multi-dimensional habitat variables and the species identification result into a preset habitat quality evaluation model to obtain a habitat quality evaluation result of the target research area.

The invention relates to the technical field of sound monitoring, in particular to a wild animal sound detection system, method and device based on voiceprint recognition, and the system comprises a multi-mode sound collection module, a voiceprint feature extraction engine, an acoustic fingerprint database, a species recognition decision module and an ecological behavior analysis unit, the multi-mode sound acquisition module integrates an array microphone and an infrasonic wave sensor, and supports full-band environmental acoustic signal acquisition; the voiceprint feature extraction engine extracts animal voiceprint biological features by adopting a self-adaptive frequency band segmentation algorithm; the acoustic fingerprint database stores a multi-species acoustic feature template and an environment interference noise sample; the species identification decision module constructs a voiceprint classification model based on a deep residual network; and the ecological behavior analysis unit generates a wild animal activity mode report through the acoustic event space-time distribution map. Therefore, the problems of serious environmental noise interference, low individual feature discrimination, difficulty and insufficiency in small sample species identification and the like in the prior art are solved.

The invention provides a wild animal and plant species identification method, which comprises the following steps: preprocessing collected biological data and environmental data associated with a target species to obtain to-be-analyzed data, the biological data comprising an image, an audio and positioning data associated with the target species, the environment data comprises meteorological and physical environment data associated with the living environment of the target species, and the target species are wild animals or wild plants; performing feature extraction on the to-be-analyzed data, and aligning the extracted multi-modal features based on time; based on the dynamically adjusted weight of each modal feature, performing feature fusion on the aligned multi-modal features to generate a comprehensive feature vector of the target species; and processing the comprehensive feature vector based on the target large model, obtaining at least one species name of the target species and the confidence of each species name, and determining a final name based on the confidence. According to the method, environment feature processing and dynamic weight adjustment mechanisms are introduced, so that species identification can be intelligently and accurately carried out.

The invention discloses a method for evaluating enhancement and release seedling resources based on environmental DNA polymerization analysis. The method comprises the following steps: carrying out gridding partition on a target water area, collecting a water sample through a designed sampling scheme, and carrying out DNA extraction and high-throughput sequencing to obtain species sequence information of each sampling point. Sequencing data is subjected to species identification by using a bioinformatics method, released species are identified, a spatial abundance model is established, and a preliminary distribution map is generated. And establishing a DNA degradation kinetic model in combination with water area environmental parameters, and carrying out reverse correction on abundance distribution. And through a resource inversion model coupled with hydrodynamics, analyzing biomass distribution characteristics and migration laws of the release group, and obtaining a resource evaluation result. And finally, a species environment preference model is constructed based on migration path analysis, an optimal release area is matched in a target water area, a scientific scheme including release point locations, opportunities and quantity is generated, and a whole-process technical support and a decision basis are provided for enhancement and release.

The invention provides a terrestrial ecosystem service function monitoring and evaluation method, and the method comprises the steps: generating a biological feature vector and an environment feature vector according to biological data and environment data collected in an ecological protection region; performing species identification on the biological feature vector based on a biological large model, performing environment analysis on the environment feature vector based on an environment large model, and obtaining animal and plant information and environment quality evaluation conditions in the ecological protection area; and based on an ecological system evaluation model, carrying out comprehensive reasoning analysis on the organisms, the environment, the comprehensive feature vectors, animal and plant information in the ecological protection area and environment quality evaluation conditions, and evaluating an ecological system service function of the ecological protection area. Wherein the comprehensive feature vector is determined based on the fusion of the biological feature vector, the environment feature vector and the dynamically adjusted weight. According to the method, the overall ecological condition of the ecological protection area can be more comprehensively understood by adopting a large model technology, and more comprehensive, accurate, dynamic and scientific ecological system service function evaluation is provided.

The invention discloses a remote monitoring system based on bird species identification and population number estimation, which comprises a front-end monitoring subsystem, a data transmission subsystem, a data analysis and processing subsystem and a user interaction subsystem, and is characterized in that the front-end monitoring subsystem is composed of a plurality of monitoring nodes; each monitoring node is provided with a high-definition camera, a sound sensor, a temperature and humidity sensor and an illumination sensor, the devices work cooperatively, and data of birds and the environment where the birds are located are collected in an omnibearing mode. According to the remote monitoring system based on bird type identification and population number estimation, manual intervention is greatly reduced through an automatic monitoring and analysis process, the monitoring efficiency is improved, the accuracy of bird type identification and population number estimation is remarkably improved based on an algorithm of deep learning and multi-modal data fusion, and the accuracy of bird type identification and population number estimation is improved. And real-time transmission and processing of data are realized through a remote monitoring system.

The invention relates to the technical field of bird flu host species identification, in particular to a new bird flu high-risk host species identification method based on virus sharing entropy. According to the method, on the basis of spatial distribution prediction of bird flu host species, the bird flu virus sharing entropy of the bird flu host species is calculated species by species according to the overlapping areas of the target host species and all other host species by extracting the distribution area overlapping area of the target host species and any other host species, and the bird flu virus sharing entropy of the bird flu host species is obtained. And quantitatively analyzing the possibility that the target host species and other host species are subjected to avian influenza cross-species transmission infection. By comparing and analyzing the entropy value of the shared entropy of the avian influenza virus, the avian influenza host species with high virus reassortment risk are identified, and a scientific basis is provided for prevention and control of epidemic diseases.

The invention belongs to the technical field of species identification, and particularly relates to an InDel molecular marker for identifying a Yunnan acer truncatum population and application, three InDel molecular markers MLP, LZ and WS for identifying the Yunnan acer truncatum population are provided for the first time, the molecular marker MLP is used for identifying a Yunnan acer truncatum chestnut slope population, the molecular marker LZ is used for identifying a Yunnan acer truncatum old Zika population, and the WS is used for identifying a Yunnan acer truncatum population. The molecular marker WS is used for identifying Wenshan population of acer truncatum in Yunnan; then, a primer group for amplifying molecular markers MLP, LZ and WS is designed, and a Yunnan acer truncatum population identification method is developed, so that plant materials from the Yunnan acer truncatum can be accurately and effectively distinguished from related species materials of the Yunnan acer truncatum, and geographical population types of to-be-detected Yunnan acer truncatum samples can be simply, conveniently and efficiently identified; and distinguishing the Chinese chestnut slope population, the old Zihai population and the Wenshan population of the Yunnan acer truncatum.

The invention provides a key species population spatial distribution quantification method and system in a complex ecological system, and the method comprises the steps: carrying out the feature extraction of biological and environmental data collected in a target ecological region, and carrying out the fusion of the extracted multi-modal features, and generating a comprehensive feature vector; based on the target large model, analyzing the comprehensive feature vector, identifying species in the target ecological region, and obtaining species identification information of each species; based on the species identification information and the comprehensive feature vector corresponding to the target ecological region, quantifying a species population space distribution condition, and generating species population quantification information at least comprising a population distribution diagram, a population density diagram and a population dynamic change diagram; and based on the species population quantitative information and the collected original data, generating and displaying a quantitative report. According to the method, the species can be intelligently, efficiently and accurately identified based on the large model, the distribution condition and dynamic change of the species can be visually displayed, a quantitative report is provided, and a powerful basis is provided for ecological protection and management.

An intelligent bird feeding method and apparatus. The method comprises: continuously acquiring images of a bird feeding area by means of a camera; extracting image features by means of a backbone network of a pre-trained bird identification model to form an original feature vector; performing species identification on the original feature vector by means of a detection branch of the pre-trained bird identification model, and mapping identified species data into a continuous vector of a specified dimension by means of an embedding matrix, the dimension of the continuous vector being the same as the dimension of the original feature vector; superposing the continuous vector and the original feature vector to form a new feature vector, and inputting the new feature vector into a re-identification branch of the pre-trained bird identification model to obtain an individual identity of a bird; and when the identified animal species is bird, recording the individual identity and feeding data of the bird, and when the identified animal species is not bird, issuing an expelling signal.

The invention discloses a forest stand tree species identification method and system based on multi-source remote sensing data fusion, and the method comprises the following steps: obtaining data which comprises forest land remote sensing image data and ground data; carrying out preprocessing on the obtained data; a dual-path CBAM-UNet network model is constructed; inputting the preprocessed data into the double-path CBAM-UNet network model to carry out tree species classification prediction and obtain a tree species classification chart; performing image splicing on the tree species classification map to obtain a tree species classification grid map; the system comprises a data acquisition module, a data preprocessing module, a prediction module and an image processing module. According to the forest stand tree species identification method and system based on multi-source remote sensing data fusion provided by the invention, multi-scale feature capture and feature fusion of multi-source heterogeneous data can be realized through the constructed dual-path CBAM-UNet network model, high-precision identification can be performed on forest stand tree species under complex links, and high-quality data can be provided for forest management.

The invention provides a six-axis inertial data behavior identification universal platform method for cattle, pigs, sheep and other livestock, and is suitable for behavior monitoring and management scenes of various livestock and poultry animals. The method comprises the steps of animal identity and species recognition, corresponding behavior recognition template loading, six-axis data feature extraction, behavior judgment and state recognition, recognition result output, platform interface butt joint and the like. Uniform adaptation and efficient processing of different animal types and sampling strategies (including fixed frequency, intermittent sampling, event triggering and the like) are achieved through a modular structure and a parameterized configuration mode. The platform supports continuous expansion of newly added species and behavior templates, can be deployed on edge equipment or a cloud server, has low power consumption, high compatibility and cross-scene applicability, and is suitable for intelligent management requirements of health monitoring, oestrus recognition, abnormity early warning and the like of livestock such as dairy cattle, beef cattle, live pigs, sheep, goats and the like in a farm.

The invention relates to the technical field of biodiversity monitoring, and discloses a multi-region-oriented biodiversity intelligent monitoring system, which comprises a data acquisition module, a data transmission module, a data processing module, a data analysis module and a rule analysis module, infrared cameras are widely deployed in multiple areas, comprehensive biological activity data containing timestamps and area numbers are collected according to fixed intervals, and then the low-power-consumption LoRa technology is used for compression transmission, so that data richness is guaranteed, transmission efficiency is improved, and energy consumption is reduced; in the aspects of feature extraction and species identification, key features are scientifically and accurately extracted and species are identified by comprehensively applying a form and behavior feature extraction method and combining a constructed species feature database and an Euclidean distance algorithm, and the database is perfected by means of expert judgment on the condition that the species cannot be identified; and on the aspect of activity law analysis, not only can the active region and time of the same species be determined, but also the immigration and emigration periods of seasonal migration species can be accurately judged.

The invention discloses a pathogen detection primer group, a kit and application in eye infection pathogen detection, the detection primer group comprises 162 pairs of primers for 54 pathogenic microorganisms, the interference competition among the primers is small, the 54 pathogenic microorganisms can be detected at the same time by randomly selecting one pair of primers for each pathogenic microorganism, and the detection efficiency is high. When all 162 pairs of primers are selected, the sensitivity and specificity of species identification can be improved, the specific and high-sensitivity detection of bacteria, fungi, viruses, parasites and accurately identified species can be met, and the requirements of clinical accurate diagnosis are met.

The present application relates to the technical field of biological sensing, and discloses a composite functional porous metal membrane and detection system for monitoring and identifying airborne microorganisms, and a method for monitoring and identifying airborne microorganisms. The monitoring and identification method comprises: applying an electric field to the composite functional porous metal membrane, causing the membrane to freely switch charge and capture airborne microorganisms to be tested; adding a fluorescent probe to the composite functional porous metal membrane, which binds with the microorganisms to be tested to obtain a fluorescent sample, and collecting imaging of light emitted after irradiation of excitation light, to achieve preliminary determination and addressing of the microorganisms to be tested; performing Raman spectroscopy scanning on individual bacteria, and comparing with a standard Raman spectrum library to achieve species identification of the individual bacteria. Relying on the "composite functional porous metal membrane," an integrated process of "selective enrichment-total bacteria monitoring-species identification" is realized; the present invention takes selective enrichment of the microorganisms to be tested as an entry point, lays the foundation for preliminary judgment accuracy and species identification accuracy, and improves the efficiency and precision of monitoring and identifying airborne microorganisms.

The invention provides a phytophagous insect food web DNA molecular data automatic analysis method and device based on high-pass sequencing and a storage medium, and relates to the field of molecular biological information detection.The method comprises the steps that sequence splicing, screening and species identification are carried out on obtained double-end sequencing data and local and downloaded DNA databases through an automatic system, and a DNA molecular database is obtained; generating an Excel table containing species names and a DNA bar code sequence file; performing comparative analysis on the double-end sequencing data by adopting matching splicing, and generating a contiguous group sequence based on a local DNA database; if the matching splicing cannot generate the effective sequence, generating a new gene file by adopting non-parameter splicing, and performing gene annotation in combination with the downloaded DNA database; all analysis steps are connected in series through standardized parameter input, including gene screening through threshold values and generation of insect recipe identification results. According to the method, the sequencing data can be subjected to full-process automatic analysis through a one-key command, and the efficiency of food web authentication high-throughput sequencing data processing is greatly improved.

The invention provides an ecological system model parameter optimization system and method based on parallel scheduling, and belongs to the technical field of resources and environments. By constructing a species feature library, adopting a clustering method for species identification, and combining various sensitivity analysis algorithms and an automatic parameter optimization strategy, full-process automatic configuration of remote sensing data preprocessing, feature extraction, species identification and PFT mapping is realized, and the workload of manual intervention and manual parameter adjustment is remarkably reduced; an unsupervised / semi-supervised learning method is introduced, so that high-precision automatic identification of main vegetation species in the region is ensured; according to the method, a multi-method fusion sensitivity analysis of Sobol ', Morris, EFAST and the like is adopted, a multi-algorithm collaborative optimization framework is combined, accurate and automatic adjustment of key physiological parameters is achieved, so that the precision and adaptability of model prediction are greatly improved, a Dask parallel computing framework and an SLURM scheduler are utilized, efficient fragmentation and dynamic resource scheduling of tasks are achieved, and the computing efficiency is remarkably improved.

The invention discloses a polygonatum sibiricum medicinal material species identification method based on a polygonatum sibiricum characteristic compound and application of the polygonatum sibiricum medicinal material species identification method. The invention discloses a polygonatum sibiricum medicinal material species identification method based on a polygonatum sibiricum characteristic compound. The method comprises the following steps: 1) preparing a polygonatum sibiricum rhizome sample solution; 2) analyzing different sealwort rhizome metabolites by liquid chromatography-mass spectrometry; and 3) discriminating the species of the rhizoma polygonati medicinal material, namely discriminating the species of the rhizoma polygonati medicinal material if the rhizoma polygonati rhizome metabolite contains a corresponding characteristic compound. The polygonatum sibiricum variety is polygonatum kingianum, polygonatum sibiricum, polygonatum cyrtonema and polygonatum odoratum. According to the application of the polygonatum kingianum characteristic compounds in polygonatum kingianum medicinal material species identification, each polygonatum kingianum species selects three representative characteristic compounds which are relatively high in content, have functional specificity representatives and are common in the market, the species of the polygonatum kingianum medicinal material can be identified as long as the three representative characteristic compounds are contained at the same time, the accuracy is high, the number of components to be detected is small, and the accuracy is high. The detection period is short.

The invention relates to a wildlife intelligent monitoring device and method based on multi-modal fusion and an ad hoc network, and relates to the technical field of wildlife monitoring. Through fusion of binocular infrared vision and microphone array hearing, more comprehensive and more accurate wild animal activity information can be obtained, and the limitation of a single sensor is overcome; intelligent false trigger screening and species identification are realized through a WS-YOLO algorithm of an equipment end, manual intervention is reduced, and data processing efficiency is improved; through the ad hoc network technology, the monitoring system can cover a wider network-free area, and equipment deployment is more flexible and is not limited by network infrastructures; by reducing false triggering, local intelligent processing and efficient data transmission, the cost of data storage, transmission and later manual analysis is reduced. The method provides a more powerful technical means for dynamic monitoring of wild animal populations, behavioral ecology research, biodiversity protection and the like, and has important ecological protection and scientific research values.

The invention provides a species detection and identification method based on a hybrid scaling strategy and a cascade architecture, which belongs to the technical field of image identification, integrates multiple technologies such as image preprocessing, neural network model construction and expansion, model transfer learning and fine tuning, optimizes the network structure by means of the hybrid scaling strategy, and improves the identification accuracy. A detection network and a recognition network are organically combined, species recognition tasks are concentrated in a region where detection is meaningful, fine-grained detection and accurate recognition of species are achieved, and the accuracy and reliability of species recognition are greatly improved. The hybrid scaling strategy realizes feature extraction optimization of targets with different scales by dynamically adjusting the depth, width and input resolution of the network; the cascade architecture reduces the influence of background interference on a classification task through progressive processing of positioning and then classification. In addition, an adaptive learning rate adjustment mechanism is introduced, and a verification set early stop strategy is combined, so that the convergence efficiency and generalization performance of the model are further improved.

The invention provides a cynaroides cynaroides chloroplast genome and application thereof, and belongs to the technical field of genes. The nucleotide sequence of the cynaros cynaroides chloroplast genome is shown as SEQ.ID.NO.1. The invention provides application of the cynaros cynaroides chloroplast genome in identification of cynaros cynaroides varieties and reconstruction of cynaros phylogenetic trees, and provides technical support for protection of cynaros cynaroides germplasm resources and species identification. According to the invention, the chloroplast genome of the cynaroides is used as a super DNA bar code for identifying the variety of the cynaroides, so that technical support is provided for protection of germplasm resources and species identification, and the application of the chloroplast genome of the cynaroides in variety identification and reconstruction of phylogenetic trees of the cynaroides is realized. The method is of great significance to plant breeding and genetic diversity evaluation, protection and utilization.

Methods and systems for species identification are disclosed. The methods and systems include: obtaining first, second, and third trained artificial intelligence (AI) models; obtaining one or more runtime images including a subject; determining a first confidence level of a morphological group of the subject based on the first AI model; determining a second confidence level of a species of the subject based on the morphological group and the second AI model; in response to the second confidence level being lower than a predetermined confidence level; performing a genomic test for the subject based on the determined morphological group or the determined species of the subject; and identifying the species of the subject based on a test result of the genomic test based on the third AI model. Other aspects, embodiments, and features are also claimed and described.

The invention discloses a primer composition and a kit for identifying testudinate species and application of the primer composition and the kit. The primer composition comprises primers (SEQ ID NO.1-6) for targeted amplification of specific marker genes 16S, COI and Cytb of testudinate mitochondrial species and an enrichment universal primer (SEQ ID NO.7). According to the invention, full lengths of three mitochondrial genes 16S, COI and Cytb are taken as targets, a specific primer group added with an enrichment tag and an enrichment primer are designed, and a targeted nanopore sequencing method capable of realizing testudinate species identification is established and optimized, and the method can simultaneously capture testudinate mitochondrial species specific marker gene information of a family species level, and can be used for identifying testudinate species. The problem that the existing DNA bar code is difficult to accurately identify the testudinate species due to the lack of database information is effectively solved; and each marker gene can obtain a complete coding sequence, so that genetic information is richer, and the method can be used for geographical traceability analysis of testudinate species.

The invention discloses a species identification kit for animal mitochondrial bar code multiplex PCR amplification and nanopore sequencing, and relates to the technical field of species identification. The species identification kit comprises optimized primer pairs aiming at four hyper-variable genes (Cytb, COI, 16S rRNA and 12S rRNA) of mitochondria, by introducing R, Y, D and other degenerate bases at key sites, the binding capacity of the primers and template DNA of different species is remarkably improved, the problems that an existing universal primer is prone to mismatch, low in efficiency and even failed in amplification during cross-species amplification are solved, and the species identification kit has the advantages of being high in specificity, high in specificity and high in specificity. The method can be used for species identification of a mixed DNA sample containing at least two different animal species sources. The species identification primer combination is applied to preparation of an animal species identification kit, and the detection coverage range of the kit is expanded. According to the animal species identification method provided by the invention, a multiple PCR amplification system and a nanopore length reading sequencing technology are combined, congeneric sibling species with highly similar sequences can be distinguished, and the method has good accuracy.

The invention relates to a floating algae detection method, system and device. The method comprises the following steps: preprocessing a multi-vision original image sequence to obtain a multi-vision preprocessed image sequence; performing image splicing on the multi-vision preprocessed image sequence according to the boundary features to obtain a panoramic image; carrying out step-by-step edge identification and labeling on the panoramic image by adopting a multi-operator and region growing method to obtain a labeled image; and performing type identification and counting on the floating algae in the labeled image based on a stacked integrated learning method to obtain floating algae biological information. According to the method, image splicing is performed firstly, then edge recognition is performed, and multi-operator step-by-step edge recognition from low precision to high precision is performed on the panoramic image, so that the detection efficiency can be improved; in addition, the types of the floating algae in the annotated image are identified and counted based on the stacked integrated learning method, and the identification accuracy is higher than that of an existing single model.

The invention relates to an SNP (Single Nucleotide Polymorphism) marker for identifying silver pheasant species and application thereof, and relates to the technical field of biology. The SNP marker can effectively solve the current identification problem caused by the problem that the silver pheasants are hybridized, nestlings and female pheasants are difficult to distinguish, and provides solid technical support for silver pheasant protection, population monitoring and related scientific research.

The embodiment of the invention discloses a bird feeder-based driving and feeding method, device and equipment and a readable medium. A specific embodiment of the method comprises the steps of collecting multi-modal monitoring data of a corresponding feeding area; on the basis of the multi-modal monitoring data, species identification processing is carried out on the feeding area, and species identification information is obtained; in response to determining that the identified species information represents that the identification result is a bird identifier and a confidence coefficient value represented by the confidence coefficient information is smaller than a preset value, performing auxiliary identification processing based on preset species appearing and disappearing information corresponding to geographic position information of an area where the preset feeder is located to obtain auxiliary identified species information; based on the auxiliary identification species information, a preset bird feeder is controlled to execute driving and feeding treatment; and in response to determining that a confidence coefficient value represented by the confidence coefficient information is greater than or equal to a preset value, controlling a preset bird feeder to execute driving and feeding treatment based on the identified species information. According to the embodiment, the driving and feeding accuracy of the bird feeder is improved.

The application discloses a biological individual species identification method based on a deep neural network, and relates to the technical field of zoology image recognition. Different direction image data of biological individual skull samples are collected as a data set, and the data set is divided into a "test set" and a "training set" according to a ratio of 8:2 based on a non-segmentation individual picture benchmark (the pictures of the same individual only exist in the same data set). Meanwhile, the training set images are subjected to data enhancement by using methods including but not limited to rotation, mirroring, changing brightness, blurring, random deletion and the like; the weight and value of the sample angle image data in a specific task are obtained through a corresponding algorithm, and the species identification result of the individual sample is obtained through a data fusion method.