438 results about "Target capture" patented technology

The invention provides an unmanned aerial vehicle identification method and system for low-altitude security and protection. An optical compensation parameter set for unmanned aerial vehicle imaging optimization is generated in real time through a joint optimization model of an ambient light intensity change rate and a background motion vector field, and an original optical sequence in a target capture window is processed by using the parameter set. And constructing a time domain deconvolution kernel in combination with the motion characteristics of the unmanned aerial vehicle to generate an enhanced optical image resistant to motion blur. Non-linear weighted fusion is carried out through a disturbance intensity evaluation function, and a confrontation disturbance feature mask is formed. The enhanced optical image and the confrontation disturbance feature mask are subjected to airspace superposition operation, a multi-scale residual network is adopted to carry out target confidence estimation on the superposed image, an unmanned aerial vehicle recognition result is generated, and the unmanned aerial vehicle recognition accuracy and the anti-interference capacity in the low-altitude complex environment are remarkably improved through the technical scheme provided by the invention.

The invention provides a moving target capturing method and system based on AI multi-view vision, and relates to the technical field of data processing.The method comprises the steps that camera parameters in all vision systems are calibrated, and the camera parameters comprise a camera internal parameter matrix and a camera external parameter matrix; extracting 2D feature points of the moving target through each calibrated visual system; reconstructing each 2D feature point by combining a multi-view geometric reconstruction algorithm and a least square method according to the calibrated camera parameters to obtain 3D feature points; constraints are added to the 3D feature points, and the constraints comprise spatial constraints, direction vector constraints and time sequence constraints; reconstructing the 3D feature points added with the constraints through a multi-view geometric analysis algorithm to obtain three-dimensional motion information of the moving target; and outputting the three-dimensional motion information of the moving target to complete the capture of the moving target. And non-contact high-precision capture of the moving target can be realized.

The invention discloses an unmanned aerial vehicle cluster collaborative target hunting method based on brain-like calculation, and belongs to the technical field of autonomous navigation robot and multi-robot coordination control, and the method comprises the steps: obtaining a multi-view image under a camera view, and obtaining the local pose and covariance of each unmanned aerial vehicle; obtaining a global consistent relative pose of the unmanned aerial vehicle cluster according to the local pose and the covariance of each unmanned aerial vehicle; performing state estimation on the target by adopting an unscented Kalman filtering algorithm to obtain target state information under a unified coordinate system; using a Bezier curve generation algorithm to obtain a target motion trail in a section of historical state; the method comprises the following steps: expanding an unmanned aerial vehicle cluster through a centroid extension method to obtain a surrounding queue of target surrounding, solving the minimum surrounding cost by adopting a Gaussian Newton method, and generating a surrounding position of each unmanned aerial vehicle; the motion trajectory of the unmanned aerial vehicle is generated by adopting mixed A * search, and a final surrounding trajectory sequence is generated, so that the accuracy of motion state estimation is remarkably improved.

The invention discloses a pumped storage power station safety construction monitoring method based on image recognition, and relates to the technical field of pumped storage power station safety construction monitoring, and the method comprises the steps: carrying out the intelligent monitoring of a construction site through a high-definition camera, an unmanned plane, and a multispectral imaging device, and optimizing the target capturing capability through an environment adaptive adjustment strategy. And in combination with a deep learning and behavior analysis model, identifying construction personnel, equipment and environment states, detecting behaviors such as no safety helmet wearing, illegal retention, equipment abnormity and the like, and performing real-time tracking and updating. Based on a risk level calculation model, a multi-level alarm mechanism is constructed, acousto-optic early warning, remote alarm and task scheduling are triggered, and construction safety is optimized in combination with cloud collaborative management. And self-supervised learning and cross-scene transfer learning are adopted, so that the recognition precision and the system adaptability in a complex environment are improved. According to the method, better effects are achieved in the aspects of real-time performance and accuracy.

The invention relates to the technical field of artificial intelligence, can be applied to business scenes such as agent autonomous decision making, financial science and technology and medical health, and discloses a task processing method and device based on visual attention enhancement, equipment and a medium. Visual hierarchical features are extracted, a double fovea attention module processes and fuses high-level visual features, a side suppression network obtains enhanced visual features, and a cross-modal fusion module generates fusion features by taking the enhanced visual features as query vectors and taking language components and action components as key and value vectors; and fusing the feature input decision network to generate target category and position information, generating feedback information based on actual label difference, and updating module parameters to complete a target task. According to the invention, through combination of a bionic vision mechanism and multi-modal attention fusion, the visual feature extraction and background suppression capability is improved, and the target capture efficiency and recognition precision in a complex scene can be improved.

A low-density liquid-phase chip for Gossypium hirsutum L. based on targeted capture sequencing and use thereof provided. The low-density chip includes 908 SNP loci, where among the 908 SNP loci, 329 loci are significantly related to important agronomic traits, such as fiber quality, yield, and disease resistance of Gossypium hirsutum L., 14 loci are common plant transgenic detection loci, and 565 loci are other loci. This chip is adapted for use of detection of transgenic components in Gossypium hirsutum L. varieties, resource evaluation and lineage identification, seed purity identification, and genetic improvement of major agronomic traits of Gossypium hirsutum L. varieties.

The invention relates to the technical field of crop genetic breeding and plant molecular design breeding, in particular to a pakchoi whole genome liquid phase chip and application. The pakchoi breeding gene chip disclosed by the invention comprises a target capture probe which is designed by taking 1000 core SNP (Single Nucleotide Polymorphism) molecular markers positioned on 40KSNP sites on a pakchoi reference genome version V4.0 as templates. The pakchoi whole genome liquid phase chip provided by the invention comprises a 40K site targeted capture probe mixed solution and a hybridization capture reagent. The pakchoi 40KSNP targeted capture probe can rapidly and effectively track genetic materials of pakchoi, and is suitable for different application scenes such as accurate identification of pakchoi germplasm resources, genetic relationship analysis between materials, variety authenticity identification, QTL gene positioning and mining, molecular marker-assisted breeding, variety improvement and whole genome selective breeding application. Therefore, the method has good application prospect and important social value.

The embodiment of the invention provides a target grabbing point data generation method, device and equipment and a humanoid robot, and belongs to the technical field of target detection. The method comprises the following steps: acquiring a color image and a depth image of a target object shot by shooting equipment in real time; performing alignment correction processing according to the color image and the depth image to obtain a pseudo-color depth map; inputting the pseudo-color depth map into a pre-training deep learning segmentation model for detection segmentation processing to obtain a target segmentation mask map of the target object; performing centroid extraction processing according to the target segmentation mask pattern, and determining centroid coordinates of a target object; and performing depth correction processing according to the target object mass center coordinate, the pseudo-color depth map and a preset internal reference matrix to obtain a target three-dimensional coordinate, and determining the target three-dimensional coordinate as target grabbing point data. The method is used for achieving the effect of improving the accuracy of the captured point data obtained in a complex dynamic scene.

The image processing device 1X mainly includes a three-dimensional reconstruction means 31X, a matching means 32X, a complement means 33X, and a display control means 34X. The three-dimensional reconstruction means generates, based on endoscopic images of an examination target captured by a photographing unit provided in an endoscope, reconstruction data obtained by three-dimensionally reconstructing the examination target. The matching means performs matching between a three-dimensional model of the examination target and the reconstruction data. The complement means generates, based on a result of the matching, complemented reconstruction data which is the reconstruction data complemented with the three-dimensional model. The display control means displays the complemented reconstruction data on a display device.

The invention relates to the technical field of target recognition and capture, in particular to an unmanned aerial vehicle for power transmission line inspection and a target capture method thereof, and the method comprises the steps: determining a second interference degree through analyzing the dispersion degree and energy distribution of the change trend of pixel values in pixel point neighborhoods in different regions in an inspection graph, so as to obtain a stable frame; on the basis of the difference between the discrete degrees of pixel values of all pixel points in neighborhoods of different edge points in each stable frame and the curvature mean value of all the edge points on each edge contour, and in combination with the difference between the optical flow vectors of all the edge points in each stable frame, determining a characteristic value so as to obtain a positioning map; and the foreign matters on the power transmission line are grabbed. The invention aims to solve the interference of the vibration of the unmanned aerial vehicle on the visual feature extraction of the foreign matters on the power transmission line, reduce the false detection and missing detection risks of vegetation tree shielding on the foreign matters on the power transmission line, and improve the positioning precision in the target grabbing process.

The invention provides a construction method based on multi-marshalling hinged vehicle BEV environment perception. The method specifically comprises the following steps that 1, a multi-view camera is installed at the joint of multiple marshalling vehicles; 2, camera pose calibration in marshalling and between marshalling is completed; 3, angle pose transformation and association of cameras between marshalling are achieved; step 4, detecting a target captured by the multi-view camera by using a deep learning target detection network; step 5, obtaining a projection matrix calibrated by the multi-marshalling camera; step 6, projecting the detected target position information to the BEV sensing map, and constructing the BEV sensing map of the single-marshalling articulated vehicles; and step 7, according to the hinging angle information and the single-marshalling BEV perception map, splicing to generate a global BEV perception map of the multi-marshalling hinging vehicles. According to the method, efficient association of target sensing positions of multi-marshalling hinged vehicles and accurate splicing of a global BEV sensing map are realized.

The invention discloses an SNP (Single Nucleotide Polymorphism) and InDel site combination for identifying variety of Inner Mongolia cattle, a 5K targeted capture gene chip and application of the 5K targeted capture gene chip. The SNP and InDel site combination for identifying the variety of the Inner Mongolia cattle is firstly disclosed and comprises 5000 SNP and InDel sites, and the physical position information of a cattle reference genome with the version number of AR-UCD1.2 is shown in a table 1. The invention further discloses a 5K targeting capture gene chip comprising the probe targeting the SNP and InDel site combination as well as a synthesis method and application of the 5K targeting capture gene chip. The 5K targeted capture gene chip completes genetic typing through a targeted capture probe by means of a high-throughput sequencing technology, has the characteristics of low cost, high efficiency and high accuracy, and can realize the variety identification function of Mongolian cattle, Sanhue cattle and Sunitt cattle by utilizing genotype data; important technical support and specific means are provided for identification and evaluation of Inner Mongolia cattle germplasm resources.

The invention relates to the field of industrial automation, in particular to a material grabbing method, device and equipment and a program product. The method comprises the following steps: collecting a first image comprising a material through a top camera; determining a target capture area of the first image through a preset first visual model; the mechanical arm is controlled to move to the target grabbing area, and a second image including the target grabbing area is collected through the wrist camera; determining an action sequence and a grabbing pose of the mechanical arm for grabbing the material in the second image through a preset second visual model; and the materials are grabbed according to the action sequence and the grabbing poses. According to the method, the reliability in a disordered stacking scene can be improved, even if the materials move in the grabbing process, the output action sequence can be adjusted in time through timely updated observation of the wrist camera, the grabbing success probability can be improved, and the method can effectively adapt to grabbing operation in a dynamic change scene.

The invention discloses a corn whole genome SNP (Single Nucleotide Polymorphism) site combination, a probe, a liquid chip and application thereof, the corn 10K whole genome liquid chip is developed based on a genome of a corn inbred line T32, SNP sites targeted by the corn 10K whole genome liquid chip uniformly cover a chromosome group, and key functional molecular marker SNP site markers of most important characters are more comprehensively included; compared with the prior art, the method has the advantages that more sites are provided, the detection range is more comprehensive, the detection rate is higher, and the repeatability is better. The target capture probe involved based on 10k SNP site information can rapidly and effectively track genetic materials of corn, the corn breeding process is accelerated, and the new variety breeding efficiency is improved.

The invention discloses a humanoid robot falling prediction method, device and equipment and a storage medium, and relates to the technical field of robot control, and the method comprises the steps: obtaining target state information and a target capture point position corresponding to a target humanoid robot in real time in a task execution process of the target humanoid robot; whether the target humanoid robot can reach the target capture point position or not is judged based on the target state information, if yes, the predicted falling time of the target humanoid robot is determined, and the target motion time of the target humanoid robot reaching the target capture point position is determined; and if the predicted fall-down time is greater than or equal to the target motion time, generating a ZMP sequence of the target humanoid robot through a model prediction control algorithm, and predicting whether the target humanoid robot falls down or not based on the ZMP sequence and the target capture point position so as to perform fall-down protection on the target humanoid robot. According to the method, the dynamicity and timeliness of fall prediction of the humanoid robot can be improved, and safety protection is carried out during fall.

The utility model belongs to the technical field of drilling and riveting machines, and discloses an upper actuator of an automatic drilling and riveting machine. The device comprises an AC swing head unit, the AC swing head unit comprises a C-axis driving motor, the driving end of the C-axis driving motor is connected with a cross beam, the two ends of the cross beam are provided with an A-axis driven end and an A-axis driving end respectively, and a multi-station sliding seat unit is connected between the A-axis driven end and the A-axis driving end; the multi-station sliding seat unit comprises a frame, a sliding seat is slidably connected to the interior of the frame, a drilling spindle mechanism, a gluing mechanism, a riveting mechanism, a target capturing camera, a milling mechanism and a pin inserting mechanism are linearly and sequentially arranged in the sliding seat, and a pressure foot unit is further fixedly connected to the outer side of the frame. The device can control each machining mechanism to move, is high in efficiency and accurate in moving position, has the capability of monitoring the working condition of each station in real time, and can effectively prevent machine collision in the machining process.

The invention discloses a spread spectrum communication multi-target capturing-to-tracking fast channel matching method, device and system, and belongs to the field of wireless communication, and the method comprises the steps: S1, obtaining capturing and tracking parameters, and initializing channel matching parameters; s2, identifying the same target from capturing / tracking parameters based on a code load consistency criterion to perform channel matching; s3, channel allocation is carried out on capturing and tracking parameters of different targets based on the criterion of capturing after tracking; and S4, resolving and correcting the pseudo code phase in the channel matching parameters. The method solves the problem of channel matching between multi-target pseudo code capturing and code load tracking of a receiving terminal of a spread spectrum communication system, has the advantages of capturing and tracking dynamic decoupling, high code load matching precision and good channel processing timeliness, and can realize fast channel matching processing of multi-target pseudo code capturing and code load tracking of same-frequency same-code spread spectrum signals.

The present application provides a photographing control method and a related apparatus. The embodiments of the present application can be applied to the technical field of computers. The method is applied to an image acquisition device, and the image acquisition device comprises a camera and a lamp. The method comprises: in response to an image acquisition instruction, setting an operating mode of the lamp to be an illumination mode; adjusting a photographing mode to be an automatic exposure mode; controlling the camera to carry out image acquisition on the basis of the automatic exposure mode to obtain M captured images; and determining a target captured image from the M captured images, wherein the target captured image is a captured image of which the image quality satisfies a preset image quality requirement. According to the method provided by the present application, when the image acquisition instruction is responded, the lamp is set to be in the illumination mode so as to illuminate a target object, so that a clear image of the target object can be formed under sufficient light, and when the target object is illuminated, the camera is adjusted to be in the automatic exposure mode so as to carry out photographing, so that the clear image of the target object can be acquired, thereby improving the success rate of target object verification.

The invention discloses an introduced-variety pig 10K SNP liquid chip based on targeted capture sequencing and application, and belongs to the technical field of biomolecular breeding. Target SNP sites are collected, selected and filtered, an SNP site set with comprehensive performance is screened out, a probe is designed on the basis of the SNP site set, the introduced variety pig 10K SNP liquid phase chip is further prepared, the SNP sites of the chip have the characteristics of uniformity, polymorphism, compatibility, stability and functionality, pig genomes can be covered to the maximum extent, and the detection sensitivity is high. Meanwhile, the selection of pig genomes can be greatly reduced, especially the cost of early selection of variety pigs is reduced, in addition, the selection efficiency and filling accuracy of chip sites filled to more than 50K can be ensured, and the requirements of subsequent variety identification, lineage analysis, genetic relationship identification, genome selective breeding and whole genome association analysis are met.

Techniques for optical lens characterization and calibration are described herein. In an example, a computer system receives a first image of a target captured by an image acquisition system having a camera and a lens and using first setting values in a setting space. The computer system inputs the first setting values and a first decode performance associated with image acquisition system having the first setting values for the barcode sets in the first image into a machine learning model and determines a representation of the setting space. The computer system inputs the machine learning model and one or more conditions into an algorithm and receives an output indicating second setting values in the setting space for the image acquisition system. The computer system sends the second setting values to the controller, which is configured to set the settings for the image acquisition system based on the second setting values.

A generation device (10) includes: a 3D reconstruction unit (12) that reconstructs an original three-dimensional image based on a plurality of images and a plurality of depth images, and acquires information indicating a position, a posture, a shape, and an appearance of a mapping target object that is a target of mapping to a digital space, and position information and posture information of an imaging device that has captured the image and the depth image; a labeling unit (13) that acquires a plurality of two-dimensional images in which labels or categories are associated with all pixels in an image based on the plurality of images; an estimation unit (14) that estimates a material and mass of the mapping target object based on the plurality of two-dimensional images and the position information and the posture information of the imaging device; and a generation unit (16) that integrates the information indicating a position, a posture, a shape, and an appearance of the mapping target object, and the information indicating the material and mass of the mapping target object, and generates digital twin data including position information, posture information, shape information, appearance information, material information, and mass information of the mapping target object.

The invention belongs to the technical field of spacecraft ground simulation testing, and discloses a high-precision space GNC full-physical simulation test platform which is composed of a three-dimensional motion execution system, a distributed real-time control system, a capture interaction simulation system, a safety protection system and a three-dimensional motion simulation and path planning system. By integrating a three-dimensional track type large-range motion execution system and a double-grating ruler closed-loop detection structure, the simulation motion range is remarkably expanded, the technical bottleneck that an existing platform is limited by a single axis, a two-dimensional sliding rail and limited motion amplitude is broken through, high-precision cooperation of a motion control system and a track system is achieved through the platform, and high-precision cooperation of the motion control system and the track system is achieved. The space high-dynamic motion simulation of a large-size and high-inertia complex target can be met, autonomous tracking of a target flight path, flexible capture of a flying claw and small multi-degree-of-freedom control verification are supported through integrated design of a capture interactive simulation system, and complex tasks such as space rendezvous and docking, target capture and micro-force control can be effectively simulated.

The embodiment of the invention discloses a cluster robot self-organization control method based on environment gradient and local interaction. According to the method, individual autonomous motion control is achieved through distributed sensing and behavior decision, a light intensity gradient field is constructed through environment interaction to achieve implicit communication, and by combining state analysis, cluster synchronization and firmware maintenance functions, a robot can self-organize to emerge macroscopic intelligent behaviors such as feedback adjustment and target capture under the condition of no centralized control. The corresponding system can comprise a robot individual, an external state analysis device, a synchronous beacon and an updating server, through cooperative work of all the modules, the problems that a traditional system is complex in communication and poor in expandability are effectively solved while individual simplicity is kept, and low-cost and high-robustness cluster cooperative control is achieved.

An electronic includes a communication device that receives object information of a target captured by a camera and recognized based on a deep learning model. The electronic device also includes a memory storing calibration information of the camera and physical information of the target. The electronic device additionally includes a processor configured to determine movement information of the target based on the object information. The processor is also configured to determine a first physical quantity to the target based on the movement information and the calibration information. The processor is further configured to determine a second physical quantity to the target based on the movement information and the physical information. The processor is additionally configured to compare the first physical quantity with the second physical quantity to determine whether to adjust the calibration information.

The invention discloses a dairy cow 40K medium-low density SNP chip based on targeted capture sequencing and application thereof. The invention provides a chip for detecting genotypes of dairy cows. The chip comprises a reagent for specifically detecting SNP (Single Nucleotide Polymorphism) sites; the SNP loci are 44373 SNP loci in a table 2; the position information of the 44373 SNP loci in the table 2 is determined by comparison based on a dairy cow genome reference sequence, and the dairy cow genome reference sequence is in the version of Bosula UMD3.1. Compared with the existing 30K, 126K and 150K chips, the 40K chip disclosed by the invention has consistent detection performance in the aspects of SNP (Single Nucleotide Polymorphism) detection rate, polymorphism detection, individual genotype detection rate, genetic evaluation accuracy and the like. The chip disclosed by the invention can be applied to cow whole genome selection and genetic defect identification, and has important significance on early breeding of replacement cows, cow core group establishment, seed selection and matching, acceleration of genetic progress and improvement of feed management efficiency.

The invention relates to a composite self-adaptive compliant control method for synchronously capturing a target by a space double-arm robot. The method comprises the following steps: firstly, establishing a dynamic model of the space double-arm robot, a target to be captured and a contact process; in order to ensure that the tail ends of the two arms simultaneously reach the surface of the to-be-captured target, a tail end path planning algorithm based on a B spline curve is designed; then, in order to reduce impact borne by the double-arm robot system in the capturing process, an impedance control strategy is designed according to contact force borne by the tail ends of the double mechanical arms; secondly, the resultant force borne by the target is decomposed into internal force and external force through the kinematics and geometrical relationship; and finally, designing a self-adaptive internal force tracking impedance control strategy in order to prevent capture failure or target damage caused by simultaneous target capture by two arms. Aiming at a task scene in which the space double-arm robot synchronously captures the target, the problem that the space double-arm robot system is subjected to too large rigid impact and too large / too small clamping force, so that the target is damaged or clamping fails can be solved.

The invention discloses a dairy cow 10K low-density SNP (Single Nucleotide Polymorphism) chip based on targeted capture sequencing and application thereof. The invention provides a 10K low-density chip for detecting genotypes of dairy cows. Comprising 11352 SNP loci and 6 INDEL loci (mainly comprising three types of loci related to dairy cow importance characters, paternity test and genetic defect loci and loci selected from an existing commercial dairy cow genome chip) on a dairy cow genome. Compared with an existing high-density chip, the 10K low-density genome chip provided by the invention is low in detection cost, and compared with the existing high-density chip, the 10K chip provided by the invention is consistent in detection performance in the aspects of SNP (Single Nucleotide Polymorphism) detection rate, polymorphism detection, individual genotype detection rate, genetic evaluation accuracy and the like. The chip disclosed by the invention can be applied to cow whole genome selection and genetic defect identification, and has important significance on early breeding of replacement cows, establishment of cow core groups, seed selection and matching, acceleration of genetic progress and improvement of feed management efficiency.

The invention discloses a shooting method and wearable equipment. The shooting method is applied to the wearable equipment with a camera. The method comprises the steps that a preview frame corresponding to a shooting target is obtained, and the preview frame is single-frame image data in a target preview flow collected by a camera and is not displayed in the collection process; fusing the preset visual features of the preview frame to obtain a decision score of the preview frame; the preset visual features comprise at least two items of a target length-width ratio, an edge density ratio and an image scene category; and analyzing the decision score based on a dynamic threshold strategy, and adjusting the shooting direction of the shooting target. In a scene that a user cannot preview a shot picture, multi-dimensional visual feature analysis is carried out on a preview frame in a real-time preview stream shot by the wearable device, after visual features of all dimensions are fused, a decision score is obtained, then the decision score is analyzed through a dynamic threshold strategy, and in a scene that the shot picture cannot be previewed, the preview frame in the real-time preview stream shot by the wearable device is subjected to multi-dimensional visual feature analysis. And the shooting direction of the shooting target is adjusted.

The application provides a variable configuration spacecraft system and method for non-cooperative target capture, and belongs to the field of deep space exploration. The method comprises the following steps: 1) a rigid combination flies to the vicinity of the target; 2) the combination is spun to be expanded into a ring belt; 3) the ring belt envelopes the target; and 4) the ring belt encloses the target. Large space debris and small celestial bodies both belong to non-cooperative targets, and cannot provide special interfaces for assisting capture. It is extremely difficult to develop general control mechanisms and methods to realize reliable capture and stable connection of various targets. In order to solve the problem, the application utilizes the inter-satellite connection to expand a plurality of controllable satellites into a ring-shaped multi-body tether satellite formation system, and carries out ring belt enveloping and then enclosing the tether to realize capture of a non-cooperative target with large scale, complex geometric shape, great uncertainty of motion state and inertia information according to a predetermined trajectory. The application has the advantages of high applicability, flexibility, high intelligence, reusability and small size.

The invention discloses an SNP (Single Nucleotide Polymorphism) molecular marker combination for predicting the shape of Chinese rose petals and application of the SNP molecular marker combination. Based on whole genome re-sequencing data of 358 Chinese rose germplasm resources, significant SNP sites with the length (2882), the width (2360) and the length-width ratio (2581) of the petals are screened out through multi-model GWAS analysis, and a high-precision prediction model (BRR model R2gt; 0.651) of the optical fiber. A liquid phase probe is synthesized on the basis of an Agilent SureDesign platform, a targeted capture chip is integrated, the dynamic development trajectory of petals can be predicted through genotyping in the seedling stage, and the longitudinal growth potential and the transverse expansion trend are quantified. According to the method, the technical bottleneck of traditional static measurement is broken through, target strains are accurately screened within 6-8 months after sowing, the breeding period is shortened to 2-3 years, and meanwhile the field planting and manual screening cost is reduced.