2801 results about "Livestock" patented technology

The invention relates to the technical field of food safety intelligent tracing, and discloses a livestock and poultry product epidemic disease and tracing information quality tracing system based on artificial intelligence. The system comprises a raw material file construction module, an abnormal active prediction module, a multi-modal information fusion module, a tracing knowledge graph construction module and an intelligent tracing decision module. The system generates a raw material archive by collecting livestock and poultry raw material information, and analyzes production monitoring data in real time by using a time sequence model to actively predict abnormal nodes of epidemic diseases. After the abnormity is recognized, multi-link heterogeneous traceability information is synchronously called and fused, an abnormal event panorama is generated, and then a traceability knowledge graph is dynamically constructed in combination with an external knowledge base. And finally, automatically generating an optimized traceability query path according to the knowledge graph, and driving a precise traceability process. According to the invention, the conversion from passive recording to active early warning is realized, and the accuracy and efficiency of epidemic disease tracing are improved through deep fusion and intelligent reasoning of multi-source information.

The invention belongs to the technical field of livestock and poultry breeding environment intelligent control, and particularly relates to a meat duck breeding environment temperature regulation and control method and system integrating quantile regression prediction and reinforcement learning. The method comprises the following steps: collecting multi-dimensional data of a breeding environment and operation state information of environment regulation and control equipment, and performing feature construction to obtain a feature vector; using the multi-source data set to predict in-house temperature based on quantile regression to obtain predicted temperatures under different quantiles; modeling a breeding environment temperature regulation problem into a Markov decision process, and dynamically adjusting fan power and a wet curtain equipment state; and setting an online updating model step threshold, and performing actual deployment and application on the reinforcement learning decision model obtained by training and fusing quantile regression information. According to the method, the problems that the meat duck breeding environment temperature regulation and control technology still faces insufficient predictability, regulation and control lag, control strategy static performance, multi-target optimization deficiency and the like are solved.

The invention relates to an agricultural robot, and discloses an animal husbandry pushing robot autonomous navigation method based on a laser radar, which comprises a microsecond-level multi-sensor time synchronization architecture based on an FPGA (Field Programmable Gate Array); in a navigation layer, seamless switching between the GNSS and the laser SLAM is realized by constructing a virtual fusion observation value by utilizing extended Kalman filtering and a self-adaptive dynamic weight algorithm; in the sensing layer, an RGB-I multi-mode model fused with laser reflectivity and a biological micro-motion detection technology are adopted, and feed and biological obstacles are accurately distinguished; and in the execution layer, flexible self-adaptive material pushing is achieved in combination with material groove edge fitting and force-position hybrid control, and the navigation precision, the operation quality and the safety of the material pushing robot in the all-weather and unstructured pasture environment are effectively improved.

The invention provides an uncertainty analysis method and device for a livestock target detection model. The method comprises the following steps: acquiring an RGB image of a livestock target; constructing an uncertainty analysis model of the livestock target detection model, and calling the uncertainty analysis model to detect the livestock target in the RGB image to obtain the category of the livestock target and a detection frame coordinate; and respectively calculating a cognitive uncertainty index and a random uncertainty index of the livestock target detection model according to the category and the coordinates of the detection frame. The method is used for solving the problems that in the prior art, an uncertainty analysis method is not comprehensive enough, uncertainty result attribution is lacked, and the uncertainty evaluation effect is poor.

The invention provides a planting and breeding circulation system-oriented soil health assessment method, and relates to the technical field of soil health, and the method comprises the steps: collecting breeding data and planting data in a target planting and breeding circulation region, dividing a planting and breeding circulation mode, and analyzing the influence degree on soil to obtain circulation soil data. Waste supply energy is determined according to the breeding data, and the soil resource utilization efficiency is obtained by analyzing management measures of the planting data. The influence of waste supply energy on soil is analyzed to obtain waste influence data, and a soil health assessment system is constructed in combination with soil resource utilization efficiency, soil physical indexes and ecological function indexes. And calculating a soil health index based on the soil health assessment system and the circulating soil data, and classifying health grades of the soil. According to the method, the accuracy, scientificity and practicability of soil health assessment are improved, and powerful support is provided for optimal management of a planting and breeding circulation system, precise regulation and control of soil quality and agricultural sustainable development.

The invention discloses a dynamic prediction method for the yield of breeding manure, and relates to the technical field of agricultural information.The method comprises the steps that farm real-time data, manure returning historical data and external auxiliary data are synchronously obtained through multi-source data collection and preprocessing, and feature vectors are formed through cleaning and feature engineering; a dynamic prediction model is constructed based on an LSTM neural network and an attention mechanism, the LSTM captures time sequence dependence through a gating mechanism, and the attention mechanism weights and focuses key features; every set period, calculating a prediction error by utilizing newly acquired data, updating model parameters by adopting an incremental gradient descent method and correcting a prediction result; and engineering deployment is realized through edge calculation, data security transmission and performance monitoring, a complete closed loop from data acquisition, model prediction to result correction is formed, and prospective decision support is provided for resource utilization of planting and breeding feces.

The invention provides an ewe oestrus detection method and device based on multi-modal feature fusion, and relates to the technical field of livestock behavior monitoring, the method comprises the following steps: acquiring behavior data and audio data of an ewe to obtain a first behavior data set and a first audio data set; inputting the first behavior data set into an ewe behavior recognition model to obtain a second behavior data set, and inputting the first audio data set into an ewe sound detection model to obtain a second audio data set; performing feature extraction on the second behavior data set and the second audio data set to obtain a multi-modal feature fusion data set; and inputting the multi-modal feature fusion data set into an ewe oestrus discrimination model to obtain an ewe oestrus detection result. According to the ewe oestrus detection method based on multi-modal feature fusion, on the basis of fully learning the multi-modal features of the ewe, whether the ewe is oestrus or not is intelligently judged through the ewe oestrus judgment model, and the accuracy and efficiency of ewe oestrus detection are improved.

The invention discloses intelligent pasture livestock health monitoring equipment. The equipment comprises a vital sign monitoring mechanism worn on the legs of livestock, a behavior monitoring mechanism worn on the neck of the livestock and an environment monitoring mechanism vertically extending and arranged on the pasture ground; the vital sign monitoring mechanism comprises a vital sign monitoring fixing ring, a vital sign monitoring supporting shell with an upward opening is connected to the vital sign monitoring fixing ring, and a plurality of body temperature monitoring sensors are connected into the vital sign monitoring supporting shell; a pulse monitoring through hole penetrating in the radial direction of the physical sign monitoring fixing ring is formed in the side face of the physical sign monitoring fixing ring, and a pulse monitor is arranged at the pulse monitoring through hole; the behavior monitoring mechanism comprises a conventional monitoring camera, a high-speed monitoring camera and an infrared monitoring camera; the equipment can accurately collect various physiological data of livestock, such as body temperature, heart rate, respiratory rate and the like, as well as behavior data, such as activity amount, ingestion time, rumination times and the like, so that the accuracy and reliability of the data are ensured, and an accurate basis is provided for evaluating the health condition of the livestock.

The invention discloses a ventilation method and device based on precise monitoring of the environment in a farm and a storage medium, relates to the technical field of analysis control, and constructs a three-dimensional pollution concentration field model by collecting data such as the pollution gas concentration, suspended particles, temperature and humidity, airflow velocity and the like of a manure ditch in real time and combining the spatial layout of a breeding house. On the basis of the model and ventilation constraints, a virtual ventilation strategy composed of multiple sets of air supply speeds, air directions and air opening degrees is automatically generated, simulation is conducted, disturbance scores are calculated, and the optimal strategy is selected to be applied to ventilation control. Environmental parameters are continuously monitored in the ventilation process, if it is detected that pollutants rise or temperature and humidity are abnormal, the strategy generation and screening process is automatically restarted, and dynamic closed-loop optimization adjustment of a ventilation scheme is achieved; dynamic ventilation control can be carried out according to the actual manure environment, the influence of gas backflow and pollution disturbance is reduced, and the influence on the farm environment is reduced; the influence on the growth health of livestock and poultry is reduced.

The invention discloses a livestock and poultry house environment parameter self-optimization regulation and control method based on swarm intelligence, and the method comprises the steps: collecting multi-dimensional environment parameters, such as temperature and humidity, through arranging a sensor array in a livestock and poultry house, and constructing an environment parameter sensing matrix; and converting the matrix into hypergraph structure data, inputting the hypergraph structure data into an optimized hypergraph neural network, mining high-order correlation and dynamic change characteristics among parameters, and clustering environment states through a swarm intelligence optimization strategy. A multi-dimensional environmental data regulation and control model is constructed based on the information, a parameter regulation and control target interval is set in combination with livestock and poultry growth requirements, a hypergraph network search strategy is regulated and controlled after deviation is calculated, equipment is driven to adjust environmental parameters, and closed-loop feedback is formed. According to the method, the hypergraph neural network and the swarm intelligence technology are utilized, the complex relation of the environmental parameters is accurately analyzed, the growth requirements of livestock and poultry are dynamically met, efficient self-optimization regulation and control of the environmental parameters of the livestock and poultry house are achieved, and the breeding environment quality and the production benefits are improved.

The invention discloses a method for fertilizing dry land white slurry soil by coupling liquid fermentation cow dung and straw to return to field. The Baijiang soil fertilizing method provided by the invention comprises the following steps of: throwing crop straws and stubbles into the field in a full amount, then sowing liquid fermented cow dung, and turning over and burying soil; the liquid fermented cow dung dirt is a product obtained by anaerobic fermentation of cow dung, urine and colony house flushing sewage. According to the method, the liquid fermented cow dung and the straws are fully crushed and returned to the field to jointly fertilize the Baijiang soil, so that the cultivated land quality of the Baijiang soil can be remarkably improved, the straw decomposition and nutrient release rate can be accelerated, planting and breeding circulation and livestock and poultry manure resource utilization can be promoted, and the method has important significance on guaranteeing the national food safety.

The invention relates to the technical field of ecological monitoring, and discloses a grassland ecological assessment early warning monitoring system based on artificial intelligence. Collecting vegetation indexes, soil parameters, livestock tracks and topographic data in real time through satellite remote sensing, an unmanned aerial vehicle and Internet of Things equipment; an intelligent algorithm is adopted to process missing values and standardized data, and vegetation phenological characteristics are extracted; the weight is dynamically calculated by fusing the index volatility, the correlation and the ecological coordination rule, and the net influence of the grazing activity on the ecology is accurately quantified based on the contrast area with strict geographical constraints, so that a degradation risk score is generated; high, medium and low risk areas are divided according to scores, and a livestock capacity optimization scheme and targeted measures of a matching policy library are dynamically output. Natural interference and artificial grazing influence are accurately separated, the problem that the risk level is misjudged in a traditional method is solved, and the evaluation adaptability is improved through a dynamic weight model.

The invention discloses a livestock identity recognition method and system based on an unmanned aerial vehicle, and relates to the technical field of livestock identity recognition, and the method comprises the steps: constructing an identity information database which comprises the electronic ear tag IDs of all target livestock individuals in a pasture, a visible light feature image, an infrared thermal imaging contour template and a historical motion behavior sequence in a cloud server; initialization of an unmanned aerial vehicle system carrying a visible light camera, an infrared thermal imager, an RFID reader and an edge calculation module is completed, and communication connection between the unmanned aerial vehicle system and a ground control station and between the unmanned aerial vehicle system and a cloud server is established; controlling the unmanned aerial vehicle to enter an inspection area, monitoring a ground target through a visible light camera, synchronously acquiring a visible light image, an infrared thermal image and a radio frequency response signal of a target individual when the livestock is detected to enter an effective identification range, and performing timestamp alignment on the three types of data; a first visual feature vector is extracted from the visible light image and a visual confidence score is generated.

The invention relates to the technical field of pollution treatment of livestock and poultry breeding, in particular to a pollutant emission prediction and management system for a livestock and poultry farm, which comprises a data acquisition and storage unit, a data processing unit, a data processing unit and a data processing unit, and is characterized in that the data acquisition and storage unit is used for acquiring multi-source data of the farm in real time; the influence factor calculation unit obtains key influence factors influencing water quality change and malodorous gas based on production data and facility operation data of the farm; the model prediction analysis unit generates a predicted value of pollutant emission by using a dynamic multi-dimensional pollutant emission prediction model, and key influence factors are introduced in the prediction process for correction; and the management decision-making unit judges the early warning level and generates a control instruction according to the early warning level. According to the system, accurate prediction of emission of malodorous gas and water pollutants is realized by constructing a dynamic multi-dimensional pollutant emission prediction model, and meanwhile, the system not only prevents excessive emission of pollutants in advance, but also is deeply integrated with aquaculture production management to guide actual aquaculture operation.

The invention belongs to the technical field of bioengineering, and particularly relates to a zearalenone toxin degrading enzyme variant and application thereof. The amino acid sequence of the zearalenone toxin degrading enzyme is as shown in SEQ ID NO.2, or the zearalenone toxin degrading enzyme with the amino acid sequence as shown in SEQ ID NO.2 is obtained through amino acid mutation. The zearalenone toxin degrading enzyme mutant is obtained through mutation screening, and compared with a wild type, the zearalenone toxin degrading enzyme mutant has the advantages that the zearalenone degrading capability is obviously improved; besides, the zearalenone toxin degrading enzyme is expressed by using alfalfa, the zearalenone toxin degrading enzyme with biological activity is easy to obtain, and the zearalenone toxin degrading enzyme has certain application potential in prevention and treatment of animal poisoning caused by zearalenone toxin pollution in agriculture and animal husbandry production.

The invention relates to the technical field of video monitoring, in particular to a livestock farm safety management method and system based on video monitoring, and the method comprises the steps: collecting a monitoring video of a livestock farm, and equally dividing each frame of video image into each CTU block; marking pixel points corresponding to the target object in each frame of video image as salient pixel points, and determining the static saliency of each CTU block; obtaining the motion pixel aggregation degree of each coordinate point in each frame of video image; determining the center-of-mass coordinate and the motion displacement of the target object in each frame of video image, and obtaining the dynamic saliency of each CTU block; and combining the static saliency and the dynamic saliency, determining a saliency weight of each CTU block, distributing a code rate for each CTU block, and carrying out coding compression on the video image for livestock farm safety management. Therefore, the monitoring video quality of the livestock farm is improved, and the safety management effect of the livestock farm is enhanced.

The invention provides a microbial agent type bio-organic fertilizer capable of improving continuous cropping tobacco planting soil hardening and a preparation method of the microbial agent type bio-organic fertilizer, and belongs to the technical field of organic fertilizers. The microbial agent type bio-organic fertilizer capable of improving continuous cropping tobacco planting soil hardening comprises the following raw materials: an organic material, a microbial agent, an inorganic nutritional ingredient, vinasse and an additive, wherein the organic material is composed of livestock and poultry manure, crop straw, tobacco waste and a fermentation inoculant; the additive is prepared from humic acid, seaweed extract and amino acid. The organic fertilizer contains rich organic materials and humic acid, the content of organic matters in soil can be increased, the granular structure of the soil can be improved, the growth of soil-borne pathogenic bacteria can be inhibited by the added multiple beneficial microorganisms, the micro-ecological environment of the soil can be improved by the photosynthetic bacteria, and the incidence rate of diseases such as tobacco black shank and bacterial wilt can be reduced.

The invention discloses an ecological planting method for remarkably improving the amino acid content of citrus, which comprises the following steps: (1) mixing and fermenting pruned citrus branches, weed straws, livestock and poultry manure and indigenous probiotic ferment powder to prepare a basic microbial fertilizer, and adding a calcium magnesium phosphate fertilizer and monopotassium phosphate to prepare a special microbial fertilizer for citrus, applying as a base fertilizer in winter; (2) after citrus pruning, in key phenological periods such as a flower bud period, a young fruit period and a fruit swelling period, an indigenous probiotic enzyme diluent with a specific dilution ratio is used for systematic foliage spraying on a tree body. The indigenous probiotic enzyme contains bacillus amyloliquefaciens, saccharomycetes, active enzymes and a plurality of nutrient elements. The method can significantly increase the total amino acid content of citrus fruits, greatly increase the yield, improve the taste of the fruits, enhance the tree body resistance, reduce the use of chemical pesticides, improve soil, and realize quality improvement, yield increase and green sustainable development of citrus planting.

The invention relates to the technical field of material conveying equipment, in particular to a livestock slaughtering automatic feeding device and a feeding method thereof.The livestock slaughtering automatic feeding device comprises a feeding table, the feeding table is provided with a conveying belt with a cutting gap, side plates and limiting frames are arranged on the two sides of the feeding table, and a cutting knife is arranged between the limiting frames; gravity detection assemblies (including pressure detection tables, detection supporting plates and the like) are symmetrically arranged in the feeding table, limiting assemblies (including clamping frames, driving shafts, auxiliary driving belts and the like) are arranged on the inner sides of the side plates, and driving assemblies (including driving equipment, pressure sensors and the like) electrically connected with the gravity detection assemblies are arranged on the outer sides of the side plates; a flow dividing box with a spray head and a cleaning rod is arranged below the feeding table, cleaning of the conveying belt, intermittent discharging of materials to the conveying belt, triggering and limiting when the materials are conveyed to a detection area, starting of weight detection after a pressure sensor reaches the standard, adjustment of the positions of the materials through a driving assembly to guarantee equal-weight cutting, and resetting of a system after completion are achieved. Accurate slitting is achieved, weight deviation is reduced, and production efficiency and product standardization are improved.

The invention belongs to the technical field of wastewater treatment and recycling, and relates to a low-pressure ultrafiltration sewage treatment method and device based on bioelectrochemical enhancement and application. The treatment device consists of a water inlet tank, a constant water level water tank, a cathode, a resistor, a membrane assembly, a biological filter cake layer, a pre-filtering layer-anode, an ultrafiltration membrane, a water collecting device, a data acquisition module and a computer. The treatment device is used for treating effluent of a secondary sedimentation tank of a sewage plant and tail water of livestock and poultry breeding wastewater; on the basis of retaining nitrogen and phosphorus resources, deep removal of heavy metals and antibiotics is realized, propagation of resistance genes is inhibited, and safe reuse of nitrogen and phosphorus resources in effluent of sewage plants and tail water of livestock and poultry breeding wastewater can be realized. In the invention, low-pressure ultrafiltration adopts transmembrane pressure difference generated by gravitational potential energy to realize continuous flow filtration, coupled bioelectrochemistry utilizes electric energy generated by biodegradation of organic matters to realize reduction of heavy metals and deep removal of reinforced antibiotics, and the method has the advantages of greenness, low carbon and sustainability.

The invention relates to the technical field of livestock and poultry intelligent breeding, and discloses a laying hen body type and weight evaluation method and system based on image recognition, and the method comprises the steps: obtaining laying hen behavior video data and environment parameter data; processing the video data, identifying the behavior mode of the laying hens, analyzing the relevance between the environmental parameter data and behaviors, and establishing state representation; constructing a strategy network, realizing stress state detection and optimal evaluation opportunity automatic selection, and extracting body shape features in a stable attitude; constructing a body weight evaluation model by using the body shape characteristics at the selected time and combining the identified behavior state information; learning feature distribution of a normal evaluation process of the body weight evaluation model, and detecting an abnormal evaluation result through double indexes of reconstruction error and probability deviation; through fusion of behavior analysis, environment perception and quality control, high-precision and high-reliability evaluation of the body type and weight of the laying hen is realized.

The present invention provides a method and a system for monitoring the mobility levels of individual farm animals and accordingly determining their corresponding mobility score. The mobility score may be indicative of the health and / or welfare status of the animals. The present invention processes a 2D video recording obtained from an imaging device to detect the movement of individual animals through a space. The video recording is segmented over a set of individual frames and in each frame the individual instances of the animal appearing in the vide frame are detected. The detected instances of each animal over a number of frames are grouped together. From each detected instance of an individual animal a set of reference points are extracted. The reference points are associated with location on the animal body. The present invention determines the mobility score of each animal by monitoring the relative position between reference points in each frame and the relative position of each reference point across the set of individual frames associated with an animal.

The invention discloses a livestock and poultry product full-chain traceability and slaughter supervision system based on a block chain, and belongs to the technical field of food safety traceability, and the system comprises a multi-source data fusion collection module, a block chain intelligent evidence storage module, an intelligent contract supervision module and a traceability credibility verification module. The multi-source data fusion acquisition module integrates RFID, IoT and video AI technologies to acquire full-dimensional traceability data and evaluate data quality, and the block chain intelligent evidence storage module adopts an alliance chain and an improved Hash algorithm to realize data credible evidence storage and dynamically adjust consensus weight. The intelligent contract supervision module automatically executes compliance check and generates a supervision adjustment instruction to form closed-loop feedback, the traceability credibility verification module adopts deep learning to perform authenticity verification and calculate a comprehensive risk score, and the four modules realize mutual promotion and superimposed synergy through a deep coupling closed-loop cooperation mechanism. And the efficiency and reliability of food safety supervision are obviously improved.

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 a nucleic acid aptamer for specifically recognizing tilmicosin as well as screening and application of the nucleic acid aptamer, and belongs to the technical field of food safety detection and biosensing. Aiming at the defects of the existing method for detecting tilmicosin residues in food, the invention provides a high-affinity and high-specificity nucleic acid aptamer obtained by screening based on a graphene oxide index enrichment ligand systematic evolution technology. The aptamer is obtained by multiple rounds of screening from a random single-stranded DNA library through a GO-SELEX technology, and a core sequence Apt-2-1 with the length of 33 nt is finally obtained through sequence analysis and structure optimization. The aptamer shows excellent binding capacity to tilmicosin, the dissociation constant reaches 9.82 nM, and the aptamer has good specificity. The nucleic acid aptamer provided by the invention can be used for constructing a rapid and sensitive tilmicosin residue detection method, and has important application value in monitoring of livestock and poultry products and food safety.

The present invention relates to a modified stem cell and an ex vivo method for preparing defined mature cells from genetic programming of said modified stem cell which can be carried out in absence of certain growth factors, as well as a kit allowing to transform stem cells, and the different uses of the mature cells, for example for in vitro drug screening and toxicology or as cultivated meat. The genetic programming involves a molecular switch between two mutually exclusive programs of stem cell expansion and differentiation. The invention may be used for stem cells or other progenitors cells of any type, from any eukaryotic organism, but finds particular application in pluripotent stem cells from humans and livestock animals.

The invention provides a livestock number identification method and system combining AI vision and multi-target tracking, and the method comprises the steps: collecting a continuous video stream of a livestock activity region, generating a video frame sequence with a timestamp mark, carrying out the dynamic background modeling operation, extracting a contour feature set of a livestock object in each frame image, generating a multi-dimensional motion representation vector, and carrying out the recognition of the number of livestock. The method comprises the following steps: performing adaptive multi-scale feature enhancement on a livestock cluster region, decoupling pixel-level features of the livestock cluster region, outputting a form-motion joint coding sequence of each independent livestock individual, constructing a space-time incidence matrix based on the form-motion joint coding sequence, and extracting topological features of a livestock motion track through a lightweight feature extraction network. And generating a global ID mapping table containing the unique identifier of the individual, carrying out real-time statistics on the number of the livestock based on the global ID mapping table, and carrying out behavior tracing analysis on the individual livestock corresponding to the abnormal movement track to obtain a number statistics result and an abnormal behavior identification result. The precision of livestock quantity statistics can be improved.

The invention discloses a botanical attractant for thrips hawaiiensis in a banana field and application of the botanical attractant. According to the application, alpha-pinene, caryophyllene, DL-limonene, beta-pinene and D-limonene which have a remarkable attracting effect on thrips hawaiiensis are separated and identified from banana flower buds which are host plants of the thrips hawaiiensis, and effective concentrations of the compounds with the attracting effect on the thrips hawaiiensis are screened out; the five compounds are mixed according to a certain volume ratio to prepare the thrips hawaiiensis luring composition. Indoor and outdoor experiments prove that the trapping composition provided by the invention has remarkable trapping activity on thrips hawaiiensis in a banana field. The attractant provided by the invention is derived from plants, is simple to prepare, is environment-friendly, is non-toxic to people and livestock, and has a good popularization and application prospect.

The invention provides a saline-alkali soil multi-source carbon sequestration synergistic wheat yield-increasing and quality-improving fertilization method, and belongs to the technical field of crop cultivation. The fertilizing method comprises the following steps: applying a base fertilizer to wheat, wherein the base fertilizer comprises decomposed livestock and poultry manure and biochar; the base fertilizer contains 4-6t / hm < 2 > of organic carbon, and the organic carbon input ratio of the decomposed livestock and poultry manure to the biochar is (1-3): (1-3). The soil carbon sequestration capacity is improved through the synergistic effect of the multi-source carbon sequestration materials, the wheat yield and the grain zinc content are remarkably improved by combining precise nutrient regulation and control, meanwhile, the content of organic matter in saline-alkali soil is improved, and the method is suitable for wheat cultivation in saline-alkali soil with the pH being 8-9 and the salt content being smaller than or equal to 0.3%.

The invention provides a local livestock and poultry variety identification method based on an SNP marker, and relates to the field of variety identification, and the method comprises the following steps: S1, genotype data acquisition, S2, genotype data quality control, S3, variety identification standard library establishment, and S4, bloodline content evaluation. According to the method, on the basis of a standardized molecular identification analysis process of genomic SNP marker bloodline content evaluation, genotype information of a core SNP site is analyzed, so that whole analysis process processing from original genotyping data to variety identity judgment is realized; the method can provide standardized, convenient and efficient molecular detection technical support for protection and utilization of genetic resources of local livestock and poultry in China, germplasm innovation and industrial development.