3020 results about "Crop livestock" patented technology

The invention provides a livestock breeding risk intelligent assessment method and system based on multi-source data fusion, and the method comprises the steps: collecting livestock individual vital sign data, breeding environment parameters, management behavior data and risk-related historical data through Internet of Things equipment, and carrying out the data preprocessing to form a standardized multi-source data set; extracting risk features of individual, group and environment levels based on the data set, and fusing the risk features to form a multi-dimensional risk feature library; utilizing machine learning to construct a differentiated risk assessment model; analyzing the incidence relation between the risk factor and the actual event through the Bayesian network to calibrate the model; realizing livestock risk grade dynamic division and early warning based on the calibrated risk scoring system; and finally, generating intervention suggestions for risk quantitative evaluation, risk prevention and control decision and loss evaluation. According to the method, accurate evaluation of livestock breeding risks is realized, decision support is provided for breeding safety management, and the method has relatively high application value.

The invention discloses an intelligent livestock breeding traceability method and system, and the method comprises the steps: building an overall framework of the intelligent livestock breeding traceability system based on a micro-service architecture, and employing Spring Cloud Al ibaaba as a micro-service technology stack; livestock physiological data and environmental parameters are collected through Internet of Things equipment, and a livestock electronic file is established; an AI intelligent analysis engine is constructed, and livestock health state evaluation, oestrus prediction and abnormal behavior recognition are achieved; the whole life cycle data of livestock from breeding to selling is recorded based on the block chain technology, and a full-chain traceability system is constructed. The method further comprises the functions of integrating a financial service interface, designing a multi-terminal collaborative application platform, constructing a matchmaking transaction platform and the like. According to the invention, the problems of data islanding, incomplete traceability system, low intelligent level, insufficient system expandability and the like in the prior art are solved, and the accuracy, traceability and system stability of livestock breeding are improved.

The invention discloses a livestock epidemic disease early warning method and system based on big data analysis, and the method comprises the steps: generating a preliminary epidemic situation diffusion path through employing the farm distribution and traffic network data of a geographic information system in combination with a space-time propagation model, and carrying out the dynamic adjustment according to the real-time updated data; predicting a high-risk area by adopting a multi-source data fusion early warning model; the strain mutation risk is judged by analyzing the relevance between pathogen genomes and environmental parameters; deploying a deep learning model at an edge computing node, analyzing animal husbandry behaviors and voiceprint data in real time, and evaluating the animal husbandry health state; and integrating mutation risk analysis and animal husbandry health assessment results to generate a comprehensive epidemic situation early warning report. Through multi-dimensional data analysis and edge intelligent calculation, accurate early warning of animal epidemic situations is realized, and a scientific basis is provided for epidemic situation prevention and control.

The invention relates to the technical field of livestock breeding, in particular to a cow manure automatic scoring system and method.The cow manure automatic scoring system comprises an image acquisition unit, a physical property sensor, a chemical component detection module and a data processing unit.The image acquisition unit is used for capturing the form, color and fiber residue of manure; the physical property sensor is used for measuring the water content of excrement and evaluating the viscosity and hardness of the excrement; the chemical component detection module comprises a near infrared spectrum analysis unit and an electrochemical sensor unit; and the data processing unit is used for integrating an AI algorithm, realizing multi-source data fusion and scoring, and uploading the data to the cloud management platform through the communication module. Therefore, the problems that in the prior art, manual scoring is subjective, low in efficiency and difficult to quantify, diseases are difficult to find early due to the lack of rapid component detection means, and accurate nutrition regulation and control are difficult to achieve due to the fact that scoring data are difficult to integrate into a breeding system are solved.

The invention relates to the cross technical field of computers and livestock breeding, and discloses an intelligent nutrition mode management method and system based on HAP multi-modal data, and the method comprises the steps: obtaining the multi-source heterogeneous HAP data of livestock, and carrying out the feature extraction and feature fusion of the multi-source heterogeneous HAP data; utilizing a controllable generative adversarial network to generate enhanced HAP feature data and an HAP feature data sequence simulating a physiological process; early abnormal signals are detected through an abnormal detection algorithm, and multi-modal attribution analysis is carried out; continuously optimizing the nutrition demand prediction model and the feeding strategy optimization model; according to the method, by effectively utilizing the neglected unstructured information in the past and combining the multi-modal fusion analysis, the early-stage discovery capability of the weak signal for indicating the physiological abnormality or nutritional imbalance of the livestock and poultry is improved.

The invention discloses a cattle manure automatic cleaning device and method for a cattle farm, and relates to the technical field of livestock breeding intelligent equipment.The method comprises the steps that a hexagonal monitoring grid is established based on excretion coordinates, a manure accumulation thermodynamic diagram is established through a three-dimensional space point cloud reconstruction method, and according to thermodynamic value intensity distribution in the manure accumulation thermodynamic diagram, the manure accumulation thermodynamic diagram is obtained; marking a to-be-cleaned area; according to the to-be-cleaned area, dynamically analyzing faeces conductivity characteristics by adopting an electromagnetic eddy current detection method, and generating a cattle faeces viscosity index and a cleaning difficulty coefficient through a multi-physics coupling algorithm in combination with environmental parameters; and according to the excrement viscosity index and the cleaning difficulty coefficient, a control instruction set of the cleaning device is generated through a dynamic adjustment algorithm. According to the invention, by constructing the hexagonal monitoring grid and the excrement accumulation thermodynamic diagram, spatial modeling of excrement distribution and intelligent marking of the cleaning area are realized.

The invention belongs to the technical field of environment regulation and control, and discloses a livestock breeding environment dynamic prediction and regulation and control method based on machine learning. Comprising the following six key steps: firstly, preprocessing multi-source data, and constructing a standardized feature data set; secondly, performing time sequence feature extraction and environment-biological response correlation analysis to obtain a multi-factor interaction network; then, constructing a dynamic coupling model of the environmental parameters and the production efficiency; a multi-target reinforcement learning regulation and control model is constructed based on the animal husbandry production efficiency prediction space; predictive environmental parameter regulation and risk assessment are carried out in combination with real-time monitoring data; and finally, intelligent environment intervention is executed, and a regulation and control strategy is iteratively optimized through production index feedback. According to the method, the breeding environment is converted from passive response to active prediction, complex correlation understanding between environmental factors and biological indexes is established, and multi-target balance optimization of production efficiency, energy consumption and animal welfare is achieved.

The invention relates to the field of livestock breeding, in particular to a cattle body size performance measurement system based on a binocular camera, which comprises a binocular camera acquisition module, a posture recognition and key point extraction module, a point cloud processing module, a posture correction module and a data processing and optimization module. According to the invention, the binocular camera is combined with an advanced algorithm, so that the measurement efficiency in the cattle breeding process is greatly improved; through cooperative work of multiple modules, reliable data support is provided for cattle breeding; manual measurement is not needed in the whole process, manual operation errors are avoided, the labor intensity of workers is reduced, meanwhile, stress reactions of cattle caused by manual fixing and measurement are reduced, and healthy growth of the cattle and scientization and humanization of breeding management are facilitated; the system can adapt to different light conditions, cattle body types and motion states, can stably and accurately complete body size performance measurement tasks no matter in an indoor breeding environment or in an outdoor pasture environment, and has a wide application prospect.

The invention relates to the technical field of livestock breeding, in particular to an intelligent feed optimal proportioning method based on pig herd nutrition analysis, which comprises the following steps: S1, collecting stress index data of a pig herd; s2, analyzing the stress level and the stress source of the swinery; s3, dynamically adjusting nutritional requirement parameters according to the stress level and the stress source, and determining nutrients needing to be adjusted under different stress conditions; s4, making corresponding stress mitigation measures based on the stress source and the stress intensity; s5, generating an optimal feed formula meeting the nutritional requirements of the swinery under the stress condition; and S6, converting the optimal feed formula into a production instruction, and sending the production instruction to feed production equipment. By dynamically adjusting the nutritional requirements of the swinery under different stress conditions and combining intelligent feed production equipment, precise feed formula optimization and efficient production are achieved, the feed utilization rate and the swinery health level are improved, and meanwhile the breeding cost is reduced.

The invention discloses a poultry multi-stage feed net energy in-vitro prediction method and device based on factorial screening, and relates to the technical field of animal husbandry. The method comprises the following steps: collecting and screening poultry feed raw material samples; analyzing the factors of the raw material components influencing the net energy value by adopting a factorization method to obtain a key component set; based on poultry feed raw material samples, determining the content of key components of each feed raw material and net energy values of the poultry feed raw material samples in different growth stages of poultry; based on the measured content of the key components of each feed raw material and the net energy values of the poultry feed raw material samples in different growth stages of poultry, performing principal component analysis to obtain predictive factors; constructing target net energy prediction equations of different growth stages of the poultry by using a linear regression analysis method based on the prediction factors; and performing net energy prediction on the poultry feed raw materials to be detected based on the target net energy prediction models of different growth stages of the poultry to obtain predicted net energy values of different growth stages of the poultry.

The invention provides a dense target detection method based on comparative learning characterization and reinforcement learning decision, which is suitable for dense animal individual detection in a livestock breeding scene. The method comprises the following steps: constructing a dense target detection model comprising a feature encoder, a reinforcement learning decision module and a detection head; the training process is divided into two stages: in the contrast learning characterization stage, an anchor sample, a positive sample and a negative sample are constructed, a first loss function is utilized to optimize a feature encoder, and an optimal feature encoder is obtained; in the reinforcement learning decision-making stage, the feature vector is used as an input state, a decision-making module dynamically selects a threshold lowering, maintaining or improving action, a detection head outputs a detection result in combination with the feature and the action, and parameters of the decision-making module are optimized through a reward function. During detection, after features of a to-be-detected image are extracted through the feature encoder, the optimized decision module selects the optimal action, and finally a detection result is output. The method has relatively high robustness and accuracy.

The invention discloses a full life cycle supervision method for stockbreeding machinery, relates to the related technical field of control systems, and generates a regional discharge target sequence by determining a plurality of regional discharge targets according to a transportation path of an automatic discharge vehicle. And selecting a first discharging target from the regional discharging target sequence, and carrying out discharging speed optimization analysis by taking the minimum deviation from the first discharging target as an expectation to obtain a first optimized discharging speed. Discharging angle optimization analysis is carried out, a first optimized discharging angle is output, and a first optimized discharging parameter is generated in combination with the discharging speed. And continuously carrying out discharging parameter optimization analysis on other discharging targets to generate an optimized discharging parameter sequence, and carrying out discharging error correction on the optimized discharging parameter sequence to obtain an optimal discharging scheme for carrying out automatic discharging control on the cow farm. The technical problems that in the prior art, livestock machinery lacks a dynamic full-life-cycle supervision mechanism, the discharging deviation in the automatic discharging process is large, and the feeding effect is affected are solved.

The invention discloses a livestock and poultry health state intelligent evaluation method based on multi-sensor fusion and an AI prediction model, and belongs to the technical field of livestock breeding, and the method comprises the following steps: setting multiple types of sensors in a livestock and poultry breeding area, collecting multi-source data such as temperature, humidity, carbon dioxide concentration, ammonia gas concentration, illumination intensity, weight and current, and carrying out the analysis of the data; wherein the weight data is obtained through the weighing platform, and the current data is used for sensing the operation state of drinking, ingestion, ventilation or lighting equipment; various types of sensors such as temperature and humidity sensors, carbon dioxide sensors, ammonia gas sensors, illumination sensors, weight sensors and current sensors are deployed in livestock and poultry breeding areas, all-directional data acquisition of environments and behavior states is realized, and dynamic modeling and feature extraction of key behavior nodes are realized by introducing a deep neural network structure of a gating circulation unit and an attention mechanism.

The utility model relates to the technical field of isolation cages, in particular to an isolation cage for animal husbandry and veterinary medicine. Comprising a supporting part and an isolation cage body, the supporting part is detachably connected with the isolation cage body, the middle of the isolation cage body is connected with an operation part, the operation part is slidably connected with the isolation cage body, and a limiting assembly is arranged on the outer side of the isolation cage body and used for limiting the operation part; the supporting part comprises a limiting base and a supporting disc, the supporting disc is rotationally connected with the limiting base, and the supporting disc is detachably connected with the isolation cage body. When livestock enter the isolation cage body, the isolation cage body can be driven to rotate through the supporting part, the livestock rotates along with the isolation cage body, and the illness state of the livestock can be conveniently detected; when the livestock is treated, the operation part can be fixed, the posture of the livestock is adjusted, and examination and treatment are facilitated.

Disclosed is an Artificial Intelligence (AI)-based livestock management system and a livestock management method therefor. According to the inventive concept, it is possible to determine whether the animal has abnormal symptoms by learning the standard livestock stall management data and analyzing the body temperature information and behavior information of livestock from livestock image data obtained in real time from livestock such as cattle, thus giving the manager confidence.

The invention provides a multi-sensor cooperative stress-free three-dimensional body size measurement method and system for sheep, and belongs to the technical field of livestock breeding, and the method comprises the following steps: collecting the point cloud data of the sheep from multiple perspectives, and carrying out the preprocessing of the collected point cloud data; performing registration and three-dimensional reconstruction according to the preprocessed point cloud data to obtain a sheep three-dimensional point cloud model; performing point cloud repair and attitude correction on the three-dimensional point cloud model of the sheep to obtain a final three-dimensional point cloud model of the sheep; and inputting the three-dimensional point cloud model of the sheep into the trained deep learning model for semantic segmentation and key point detection, and measuring body size parameters of the sheep. According to the multi-sensor cooperative stress-free three-dimensional body size measurement method and system for the sheep, full-life-cycle automatic accurate measurement of body size parameters of the sheep is realized, so that manual intervention is reduced, the stress risk of the sheep is reduced, the measurement precision and efficiency are improved, and technical support is provided for intelligent breeding.

The invention provides a cattle behavior recognition method based on a three-axis attitude angle, and belongs to the technical field of intelligent animal husbandry management. According to the method, three-axis attitude angle signals such as the yaw angle (Yaw), the pitch angle (Pitch) and the roll angle (Roll) of the head of a cattle are collected in real time through a gyroscope sensor arranged in wearable equipment such as a cattle ear tag. The method does not depend on images, accelerometers, microphones or other modalities, only based on attitude angle signals, multiple algorithms such as a time sequence sliding window, frequency domain analysis, weighted scoring, sudden change detection and state transition modeling are utilized, and multiple typical behaviors such as ingestion, rumination, water drinking, bed lying, limping, licking, fright jumping and low activity are recognized. The method is suitable for multiple cattle breeds such as dairy cattle, beef cattle and buffalo, has the advantages of low power consumption, lightweight calculation, low deployment cost, accurate identification and the like, and is convenient for realizing behavior monitoring and health management of large-scale cattle individuals. The algorithm path can be flexibly selected, but a core input signal is only limited to attitude angle data, so that the technical boundary of the patent protection of the method is formed.

The invention belongs to the technical field of environment monitoring, and discloses a livestock production environment monitoring method and system based on the Internet of Things, and the method comprises the steps: deploying sensor modules in different regions of a farm, collecting temperature, humidity, illumination and harmful gas concentration data, and carrying out the data preprocessing; performing primary processing on the preprocessed data by adopting an edge computing technology, extracting key features, and transmitting the key features to a cloud platform; a regional differentiation monitoring model is established in the cloud platform, key feature data are monitored in real time, and if abnormal fluctuation of temperature, humidity or harmful gas concentration is found, an early warning mechanism is triggered, and management personnel are notified to handle the abnormal fluctuation in time; and deploying a data fusion algorithm in the cloud platform, integrating multi-region sensor data, and generating a global environment parameter distribution diagram. According to the invention, the problems of data acquisition, transmission and analysis in a complex environment of a farm are effectively solved, refined environment management is realized, and the breeding efficiency and safety are improved.

The invention provides a GIS-based intelligent livestock breeding full-chain visualization method and system, and the method comprises the steps: collecting the basic information of a farm, livestock information, production process data and other multi-source data through a standardized data interface, and carrying out the correlation mapping of business data and GIS spatial data based on a geographic coordinate system; constructing a three-dimensional data model containing spatial information, attribute information and relation information; three-dimensional scene rendering is realized by utilizing a WebGL technology, a macroscopic visual engine, a mesoscopic visual engine and a microcosmic visual engine are constructed, and multi-scale visual display is realized; constructing a full-life-cycle data model of the livestock products, and realizing visual display of full-chain traceability of the livestock products; based on a visual data analysis method, a decision support system for industrial layout analysis, breeding scale prediction, epidemic disease risk assessment and market supply and demand analysis is constructed. Visual management of the whole industry chain of livestock breeding is achieved, the industry management efficiency is improved, the epidemic disease risk prevention and control capacity is enhanced, and data support is provided for decision making of the livestock industry.

The invention discloses an intelligent animal husbandry and veterinary diagnosis system based on machine learning, and particularly relates to the technical field of intelligent animal husbandry diagnosis. The livestock body surface temperature, the livestock chewing data and the livestock movement data are processed to obtain the real body temperature, the rumination time, the movement state and the movement speed of the livestock, and the livestock heart rate, the respiratory rate, the real body temperature, the rumination time and the movement speed are input into a livestock sign abnormity recognition model for abnormity recognition. The behavior data of the livestock is obtained through the multi-angle camera, the abnormal image data set is marked after a series of processing, the received livestock sign abnormal data set and the received livestock behavior abnormal image data set are input into the livestock health intelligent diagnosis model for health diagnosis, and the livestock illness state diagnosis efficiency and accuracy can be effectively improved.

The present application relates to the technical fields of animal feed preparation, and discloses a livestock feed preparation device, in order to solve the problem of clogging when the filter screen filters for a long time, the sieve cylinder is driven to rotate by the driving shaft, in this process, the relative movement between the driving wheel and the inside of the shell is utilized to force the driving wheel to move in the opposite direction between the driving sieve roller and the driven sieve roller by the transmission gear set. Since the filter part is located between the distance between the driving sieve roller and the driven sieve roller, the feed meeting the distance can be screened out, and the large stone or the granular particles are retained in the inside of the sieve cylinder. By the continuous rotation of the driving sieve roller and the driven sieve roller, the filter material clogged between the two is driven to have a tendency to move away from the filter part, thereby preventing the filter part from accumulating, and finally realizing the effect that the filter part is not easy to be clogged.

The invention discloses a method for promoting skeletal muscle development and application, and belongs to the technical field of biology. The invention provides a method for promoting skeletal muscle development, an AMPK / p38MAPK pathway is regulated and controlled by activating PPARalpha, the way of activating the PPARalpha is to give a PPARalpha agonist GW7647, and the regulation and control of the AMPK / p38MAPK pathway is shown as reduction of AMPK and P38 phosphorylation levels. Experiments show that the expression of MyoD, MyoG and Pax7 in skeletal muscle can be remarkably improved by activating PPARalpha, the phosphorylation level of AMPK and P38 is inhibited, and therefore muscle fiber proliferation and myoblast proliferation are promoted. The technology can be applied to livestock breeding to improve meat yield or develop drugs for treating skeletal muscle atrophy, and has remarkable economic and clinical values.

The invention belongs to the technical field of livestock feeding management, and particularly relates to a livestock breeding feeding precise management method and system based on an AI technology. A fixed feeding mode based on a growth stage and a self-adaptive dynamic correction algorithm based on environment deviation and activity fluctuation are fused; a multi-dimensional data-driven feeding amount decision model is constructed, dependence of a single parameter is broken through, accurate feeding can be performed according to real-time monitoring data by adopting a self-adaptive feeding mode when the environment or activity amount fluctuates and deviates, the nutritional requirements of livestock are better met, and the feeding efficiency is improved. When data acquisition is abnormal or the environment and activity amount are stable and appropriate, a fixed feeding mode is adopted, and the fixed feeding mode can be periodically updated according to a self-adaptive feeding mode, so that the feeding accuracy and efficiency are improved, the feed waste is reduced, the stability of feed feeding is guaranteed, and the feeding efficiency is improved. And the feeding effect can be brought into an optimal state.

The invention relates to the technical field of livestock breeding environment monitoring, regulation and control, and discloses a livestock breeding environment gas dynamic monitoring system. The system comprises a data acquisition module for acquiring data by using a multi-source gas sensor; the gas component analysis module is used for analyzing the data based on a mixed spectrum decomposition algorithm to generate a gas component characteristic matrix; the environment regulation and control decision module inputs the matrix into a multi-mode collaborative decision model to generate a regulation and control instruction; the optimization model construction module is used for constructing a multi-constraint dynamic optimization model to optimize ventilation equipment parameters; and the hierarchical control execution module is used for executing the regulation and control instruction through a hierarchical architecture. The system can comprehensively monitor breeding environment gas, accurately regulate and control ventilation equipment, achieve gas concentration equalization and energy consumption minimization, improve the breeding environment quality, reduce the breeding cost and push intelligent development of the livestock breeding industry.

The invention belongs to the technical field of protein engineering modification, and particularly relates to a method for improving thermal stability of fumonisins degrading enzyme by forming disulfide bonds. The thermal stability of the fumonisins degrading enzyme FumDPS mutants T194V, T194V-P40C / Y173C, T194V-N43C / D83C and T194V-F71C / S117C related to the invention is obviously improved compared with that of the wild type fumonisins degrading enzyme, and the thermal stability of the fumonisins degrading enzyme FumDPS mutants is obviously improved. The fumonisins enzyme is mainly applied to the industries of livestock feed processing, food production and the like so as to reduce the harm of fumonisins to the health of animals and human beings. The obtained fumonisins degrading enzyme mutant with improved thermal stability is convenient to transport and store, so that the investment of industrial cost can be reduced.

The utility model provides a livestock cub incubator which comprises a fan-shaped incubator body, a fixing sleeve, an excrement scraper and a liquid passing hose, two heat preservation lamps are installed at the upper end of the interior of the fan-shaped incubator body, and a positive and negative rotation motor is installed on the left front side of the upper end of the fan-shaped incubator body. A rotating shaft is longitudinally installed on the left front side of the interior of the fan-shaped heat preservation box body through a bearing, the lower end of the exterior of the rotating shaft is fixedly sleeved with a fixing sleeve, a swing plate is longitudinally fixed to the rear end of the fixing sleeve, a plurality of rubber push blocks are installed at the right end of the swing plate, and a lateral groove is formed in the left end of the swing plate. In order to solve the problem that when excrement of a cub is cleaned regularly, a livestock worker needs to move the cub out of a heat preservation box firstly and then manually clean the excrement of the cub, which is relatively inconvenient, the utility model designs a novel swing type scraper structure, and the excrement of the cub can be automatically scraped out; the cubs can be pushed out of the device in advance in the swinging process to be disinfected.

The invention discloses a multi-view three-dimensional reconstruction driven cattle body size online measurement method, and relates to the technical field of animal husbandry three-dimensional measurement, and the method comprises the steps: extracting a ground reference plane, a segment elevation function, a guardrail reference line, a segment guardrail spacing, a periodic pitch and a hoof ground contact event, and generating a calibration opportunity score in combination with a pixel-level quality weight map; in the sliding window factor graph, jointly solving an external parameter rotation matrix, an external parameter translation vector and a global scale by taking the prior as a constraint, and outputting an uncertainty matrix; implementing hierarchical self-recovery according to the comprehensive health degree and the score, and replacing safe writing with double buffer atoms; carrying out weighted splicing under a unified measurement coordinate and carrying out profile robust measurement, forwards transmitting uncertainty to a body size layer and supporting individual multi-time column-passing convergence; the method is suitable for channel visibility interruption and structure variable scenes, and under the condition of uninterrupted release, measurement consistency is kept and auditable results are output.

The invention relates to the technical field of smart agriculture, and discloses a livestock breeding environment real-time monitoring method, which comprises the following steps: an edge computing node deployed in a livestock farm collects a multi-mode sensing signal and executes edge preprocessing; analyzing the preprocessed signal by using a lightweight feature extraction model and constructing a comprehensive health stress index; edge cloud cooperative training based on federal learning is executed, and the cloud server carries out global model aggregation; generating an updated environment regulation and control strategy, and synchronizing the updated environment regulation and control strategy to an edge computing node through an edge network; and the environment regulation and control strategy is combined with the comprehensive health stress index to generate and execute a regulation and control instruction for environment control equipment in the edge Internet of Things. According to the invention, the limitation of one-sided information of a single data source can be broken through, low delay and high reliability of environment regulation and control are guaranteed, and fine management and intelligent decision-making of livestock breeding are realized.

The invention relates to the technical field of intelligent livestock breeding, and discloses an Euler sheep body condition score automatic identification system and method based on machine vision. Comprising an automatic identification system, and the automatic identification system comprises a region division module, an image acquisition module, an image preprocessing module, a feature extraction module, a multi-dimensional body condition scoring model and a data output and decision support module. Through the arrangement of the automatic identification system, non-contact automatic acquisition and analysis of Euler sheep body characteristics can be realized through integration of a computer vision technology, a deep learning algorithm and an image processing technology, and a multi-dimensional body condition scoring model is constructed in combination with physiological characteristics and growth stages of the Euler sheep. The method is used for accurately evaluating the fat condition, the health state and the fattening potential of the Tibetan sheep. The system can be applied to large-scale cultivation management, precise nutrition regulation and control and efficient fattening strategy formulation of Tibetan sheep in plateau pasturing areas, and provides intelligent technical support for improving Tibetan sheep cultivation benefits and meat quality.

The invention relates to the field of animal husbandry industry environment regulation and control system optimization, in particular to a lightweight digital twinborn breeding environment monitoring method and system, and the method comprises the following steps: S1, carrying out the construction of a computational fluid dynamics (CFD) model for a breeding farm environment; s2, environment data are collected; s3, comparing model prediction with real-time data, and monitoring a breeding environment according to a comparison result; and S4, data presentation. According to the invention, through cooperative work of the flow field modeling unit, the environment data acquisition unit, the model prediction and data calibration unit and the visualization unit, light-weight, full-range and high-precision breeding environment monitoring and regulation are realized.