3870 results about "Livestock" patented technology

The invention discloses a livestock health state real-time monitoring method and system based on the Internet of Things, and the method comprises the steps: collecting livestock biological data and environment parameters through a biological chip ear tag and an environment sensor, and carrying out the preprocessing, and obtaining a standardized multi-dimensional livestock data flow; in combination with image data provided by a video monitoring system, identifying livestock individuals and analyzing behavior features, and fusing biological data and visual data to generate livestock individualized feature vectors; constructing an individualized health baseline model in combination with historical health records; comparing the deviation degree between the current state and the health baseline through an anomaly detection algorithm, and identifying a potential health problem; early warning is performed according to the abnormity severity level, intervention suggestions are generated, early warning information is pushed to the breeding personnel through multiple channels, and response measures and results are recorded to form closed-loop management. According to the invention, accurate real-time monitoring of the health state of the livestock is realized, the accuracy and timeliness of anomaly detection are improved, the breeding risk is reduced, and the production efficiency of animal husbandry is improved.

The invention discloses a poultry health condition detection method, system and device and a storage medium, and relates to the technical field of poultry health condition detection.The poultry health condition detection method comprises the steps that poultry audio data are collected and preprocessed; based on the time-frequency characteristics of the audio data, constructing an MAE model; and performing pre-training and fine tuning on the MAE model, and evaluating the pre-trained MAE model. Based on a sound spectrum analysis technology, pre-emphasis filtering and Mel-spectrum conversion are combined, the key feature extraction capacity of poultry sound is enhanced, and the method adapts to the complex environment of a livestock farm. An MAE model is adopted, time-frequency characteristics are learned through self-supervised pre-training and a Transform structure, automatic audio acquisition and analysis and real-time health state output are realized, and the labor cost is reduced. The attention visualization technology is introduced, key frequency bands are identified in an auxiliary mode, the model interpretability is enhanced, and the whole scheme has the advantages of automation, high precision and high robustness.

The invention relates to the technical field of livestock and poultry epidemic disease management, and particularly discloses an intelligent livestock and poultry epidemic disease early warning and partitioned prevention and control management system and method, and the method comprises the steps: laying an integrated intelligent sensor network in a livestock and poultry farm, and collecting environment parameters, feeding management data and animal health data in real time through a low-power-consumption Internet of Things technology; transmitting the collected multi-dimensional data to a cloud platform by utilizing an edge computing technology; based on multi-dimensional data, a deep learning and multi-source data fusion analysis technology is adopted, a dynamic disease risk prediction model is constructed, and generalization ability of an adversarial generative network optimization model in a complex environment is introduced; by arranging the integrated intelligent sensor network, environmental parameters, feeding management data and animal health data in the livestock and poultry farm are comprehensively collected in real time, the early monitoring capability of epidemic diseases is remarkably improved, potential epidemic disease risks are found in time, and precious time is won for subsequent prevention and control work.

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 relates to the technical field of image data processing, in particular to an infrared thermal imaging-based body temperature anomaly detection method, which comprises the following steps of: firstly, performing enhancement processing on an initial image according to different block sizes by using a CLAHE algorithm; then, calculating the direction consistency degree of each pixel point in the enhanced image under each block size, and quantifying the noise confidence weight of each pixel point through the information entropy of the pixel points in the neighbor range; then, calculating an average value of products of noise confidence weights and direction consistency degrees of all pixel points in the same enhanced image to obtain a feasible degree of the size of each block; and finally, according to a weight coefficient of feasible degree conversion, carrying out weighted fusion on gray values of the same pixel position in the enhanced images under all block sizes so as to realize abnormal temperature detection based on the generated target enhanced image. According to the invention, the image enhancement process can be optimized, and the abnormal temperature of the livestock and poultry body surface can be accurately detected.

The invention discloses a livestock feed proportioning method based on livestock growth, and relates to the technical field of feed proportioning, and the livestock feed proportioning method comprises the following steps: collecting the body temperature and motion track data and environmental temperature and humidity data of livestock individuals, monitoring the concentration of amino acids in digestive tracts, preprocessing the collected data, and generating a standardized biological data set; analyzing the compensation amount of vitamins and trace elements according to the metabolic fingerprint spectrum, and constructing a multi-objective optimization function in combination with an individual growth curve; solving the multi-objective optimization function through a quantum annealing algorithm, and matching local raw material inventory data of block chain evidence storage to generate an optimization strategy; and executing the optimization strategy and detecting the mixing uniformity, and when the mixing uniformity index exceeds a preset mixing uniformity threshold, starting the compensation device to adjust the optimization strategy. According to the invention, through a cooperation mechanism of dynamic nutritional requirement identification and multi-constraint optimization, the precise regulation and control capability of livestock breeding is significantly improved.

The invention relates to the technical field of artificial intelligence, and discloses a method and a system for dynamically predicting the risk of harm of feed mycotoxin to livestock and poultry. The method comprises the following steps: constructing a metabolic digital twin model based on a multi-organ coupling dynamics mechanism, connecting a gastrointestinal tract, a liver and a kidney through blood flow parameters to form a closed loop topology network, establishing a toxin migration rate equation, and verifying bioavailability; collecting feed toxin concentration and livestock and poultry physiological data in real time; after distributing the initial toxin load, iteratively generating target organ dynamic concentration distribution by adopting a Runge-Kutta method; an organ exposure index is calculated based on an organ specific hazard threshold, key organs are positioned, and low / high risk levels and toxin accumulation paths are output. The method solves the problems that a traditional static model is low in efficiency and poor in organ difference adaptability when being used for dynamically predicting the risk of harm to livestock and poultry caused by feed mycotoxin.

The invention discloses a microbial compound feed additive, which is prepared from the following ingredients in parts by weight: 35 to 45 parts of traditional Chinese medicine residues, 25 to 35 parts of soybean meal, 15 to 25 parts of bran, 8 to 12 parts of corn flour, 0.3 to 0.7 part of selenium-enriched yeast, 8 to 12 parts of aspergillus niger spore powder, 10 to 15 parts of bacillus subtilis, 6 to 9 parts of lactobacillus plantarum and 5 to 8 parts of candida utilis. The invention relates to the technical field of feed additives, through the synergistic effect of the aspergillus niger, the bacillus subtilis, the lactobacillus plantarum and the candida utilis, the aspergillus niger can generate various enzymes, and decomposition of raw materials and release of nutritional ingredients are facilitated; the bacillus subtilis can enhance the immunity of livestock and poultry and inhibit the growth of harmful bacteria; lactobacillus plantarum can regulate intestinal microecological balance and promote digestion and absorption; candida utilis is rich in protein and vitamins and provides nutrition for livestock and poultry, the four components cooperate with each other, the function limitation of a single strain is broken through, and the health level and production performance of livestock and poultry are comprehensively improved.

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 relates to a livestock and poultry health real-time monitoring method and device based on machine learning, a storage medium and a system, and belongs to the technical field of machine learning, and the method comprises the steps: collecting image data, acoustic data, environment data and physiological metabolism data in real time through a sensor system, and forming multi-modal data together; performing preprocessing and feature extraction on the multi-modal data to generate multiple groups of heterogeneous features, and fusing the multiple groups of heterogeneous features into a fusion feature vector representing physiological and behavior states of livestock and poultry through a multi-modal data fusion network; inputting the fusion feature vector into a self-adaptive integrated learning model, and calculating the fusion feature vector by the self-adaptive integrated learning model to output an evaluation result representing the health state of the livestock and poultry; when the evaluation result meets a preset risk condition, an intelligent intervention instruction is generated and executed, and the intelligent intervention instruction is used for controlling environment control equipment and / or feeding equipment in the livestock and poultry farm.

The invention relates to a high-barrier antibacterial polylactic acid packaging material and a preparation method thereof, and belongs to the technical field of biodegradable packaging materials. The technical problems that a traditional polylactic acid material is insufficient in barrier property, poor in antibacterial performance and difficult to meet the packaging requirements of livestock and poultry meat, food and medicine are solved. A graphene oxide lamellar material is subjected to copper ion functionalization treatment and is bridged with tannic acid molecules through copper ions to form a nano filler, and then the filler is blended with a plasticized polylactic acid solution to prepare the membrane. The preparation method comprises the following steps: preparing a copper ion functionalized dispersion liquid from graphene oxide and copper nitrate, self-assembling the copper ion functionalized dispersion liquid and tannic acid under the condition that the pH is 8-10 to generate a nanofiller, uniformly dispersing the nanofiller, mixing the nanofiller with polylactic acid, polyethylene glycol and glycerol according to a specific mass ratio, coating to form a film, and controlling solvent volatilization conditions to obtain the high-performance packaging material. According to the method, the oxygen barrier property and the antibacterial activity of the material are remarkably improved through uniform dispersion of the nanofiller and reinforcement of a coordination network. The obtained material is suitable for packaging foods such as livestock and poultry meat.

The invention relates to the technical field of agricultural wastes and microorganisms, in particular to a complex microbial inoculant for high-temperature composting and rapid decomposition of livestock and poultry manure and straw cellulose and a preparation method of the complex microbial inoculant. The compound microorganism bacterial liquid comprises streptomyces thermocarbonic oxide, bacillus stearothermophilus, bacillus subtilis, bacillus licheniformis, phanerochaete chrysosporium, trichoderma viride and aspergillus niger, and the bacterial liquid carrier is obtained by compounding modified charcoal and modified sepiolite through amidation reaction. According to the complex microbial inoculant provided by the invention, through the synergistic effect of the seven functional complementary microbial strains and the special three-dimensional reticulate carrier, rapid decomposition of high-temperature compost of livestock and poultry manure and straw cellulose is realized.

The invention relates to a livestock health early warning and intelligent driving mechanical auxiliary system based on bio-electricity signal monitoring, and belongs to the technical field of animal husbandry intelligent management. Aiming at the problems of single parameter, strong driving irritability and low efficiency of a traditional monitoring method, the system is used for generating a health score in real time by adopting a CNN-LSTM hybrid model through data fusion of a bio-electricity signal acquisition module (electrocardio, myoelectricity and respiratory frequency) and an environment sensor, and driving an intelligent driving machine in combination with a deep reinforcement learning algorithm, so that the driving effect is improved. The abnormal livestock is guided to be isolated in a non-intrusive mode through ultrasonic directional wave beams and low-intensity laser. The monitoring accuracy is larger than or equal to 95%, the driving efficiency is improved by 60%, all-weather stable operation (availability is larger than or equal to 98%) is achieved, the epidemic disease risk and animal stress are effectively reduced, and an efficient intelligent solution is provided for large-scale breeding.

The invention discloses a feed additive for relieving animal mycotoxin poisoning and a preparation method thereof. The feed additive comprises the following components in parts by weight: 25-30 parts of attapulgite, 25-30 parts of montmorillonite, 25-30 parts of yeast cell walls, 2-5 parts of vitamin E, 5-10 parts of taurine and 5-10 parts of lipoic acid. The feed additive disclosed by the invention can be used for remarkably improving the harm problem of mycotoxin in the livestock and poultry breeding production process and reducing the economic loss of livestock and poultry farmers caused by the mycotoxin pollution of the feed.

The invention belongs to the technical field of molecular markers, and particularly relates to an SNP (Single Nucleotide Polymorphism) molecular marker related to a goat body height character and application of the SNP molecular marker. Stable SNP sites SNP01, SNP02, SNP03, SNP04 and SNP05 related to the goat body height character are screened out through whole genome association analysis, the dominant genotype of the SNP01 is GG, the dominant genotype of the SNP02 is TT, the dominant genotype of the SNP03 is TT and TC, the dominant genotype of the SNP04 is AA, and the dominant genotype of the SNP05 is CC. By utilizing the SNP molecular marker provided by the invention, the body height character of the goat can be predicted, a scientific basis is provided for molecular breeding of the goat, the efficiency of genetic improvement of the goat can also be improved, and a new molecular marker resource is also provided for genetic breeding of other livestock.

The utility model relates to the technical field of livestock and poultry manure fermentation, in particular to a fermentation device for quickly decomposing livestock and poultry manure, which comprises a fermentation tank mounted on a bottom plate, and a pressure relief mechanism for automatically adjusting the pressure in the fermentation tank is arranged on the fermentation tank. A fermentation mechanism for mixing livestock and poultry manure to promote fermentation is arranged in the fermentation tank. The telescopic spring is impacted by high-pressure gas to extend, so that the rubber pad is pushed to slide to the position below the pressure relief hole formed in the pressure relief pipe, the pressure is discharged through the pressure relief hole, and the phenomenon that the tank body is broken due to the fact that the pressure in the fermentation tank is too high is avoided; according to the gas purification device, harmful substances, dissolved in water, in gas are purified, peculiar smell can be removed to a certain degree, the peculiar smell in the gas is adsorbed and removed again in cooperation with the activated carbon plate, and therefore the situation that the peculiar smell of the fermentation gas is dispersed in a fermentation place and pollutes the environment is avoided.

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 relates to the field of molecular markers, in particular to application of an SNP (Single Nucleotide Polymorphism) molecular marker in identification or auxiliary identification of goat chest circumference. The invention provides an SNP (Single Nucleotide Polymorphism) molecular marker related to a goat chest circumference character. The SNP molecular marker comprises SNP01-SNP06; wherein the SNP01, the SNP02 and the SNP04 are all beneficial mutations, and the remarkable dominant genotypes of the SNP01, the SNP02 and the SNP04 are CC, GG and CC respectively; sNP03 and SNP05 are both harmful mutations, and the obvious dominant genotypes are TT and AA respectively; sNP06 is beneficial mutation, and the significant dominant genotype is GG. The SNP molecular marker provided by the invention is related to the chest circumference character of the goat, and the chest circumference character of the goat can be more accurately predicted, so that effective selection is carried out in the breeding process, the breeding efficiency of the goat with larger chest circumference is improved, and a new molecular marker resource is also provided for genetic breeding of other livestock.

The present disclosure generally relates to a monitoring device (100) for remotely monitoring health of a young livestock animal (10), the device (100) comprising: an elongated body (110) within predefined dimensional limits; and a set of electronic components housed within the elongated body (110), the electronic components comprising: a temperature sensor for measuring body temperature of the young livestock animal (10); a computer processor for processing the measured temperature into temperature data; and a wireless communications component for transmitting the temperature data to a remote computer device (210) for processing to monitor the health of the young livestock animal (10), wherein the predefined dimensional limits of the elongated body (110) enable the monitoring device (100) to be orally administrable into the young livestock animal (10) and to remain inside its stomach (12) without being excreted.

The invention discloses an intelligent livestock necklace system with a self-adaptive adjustment function and a control method. The system comprises a behavior perception control module, a network perception transmission module, a group cooperation module, a strategy optimization module and an energy management module.

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.

A method for improving health conditions of mammals or livestock by feeding, to the mammals or livestock, a licorice extract including: (A) glycyrrhizic acid, a glycyrrhizic acid derivative, glycyrrhetinic acid, and / or a glycyrrhetinic acid derivative; (B) licorice saponins other than (A); and (C) licorice flavonoids; (B) including at least a licorice saponin H2, a licorice saponin G2, and macedonoside A, (C) including at least liquiritin apioside, isoliquiritin apioside, and isoliquiritin, where feeding is of 0.017 g / day / head or more of the licorice saponin H2, 0.002 g / day / head or more of the licorice saponin G2, and 0.002 g / day / head or more of the macedonoside A, 0.001 g / day / head or more of the liquiritin apioside, 0.001 g / day / head or more of the isoliquiritin apioside, and 0.001 g / day / head or more of the isoliquiritin.

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 discloses a feed for improving earthworm breeding yield and a preparation method thereof, and belongs to the technical field of earthworm breeding, the feed comprises the following components: 30-50 parts of fermented livestock and poultry manure, 20-30 parts of crop straw, 10-20 parts of soybean curb residue, 10-15 parts of corn flour, 5-10 parts of wheat bran, 3-5 parts of bone meal, 2-4 parts of shell powder, 0.5-1 part of a microbial agent, 1-2 parts of brown sugar, and a proper amount of water. The fermented livestock and poultry manure is rich in various mineral elements and organic nutrients such as nitrogen, phosphorus and potassium, and is an important source of nutrients required by earthworm growth; the crop straws are rich in cellulose and can promote intestinal peristalsis of the earthworms and help digestion; the soybean curb residues, the corn flour and the wheat bran are rich in nutritional ingredients such as protein and carbohydrate, and sufficient energy is provided for the earthworms; the bone meal and the shell powder contain a large amount of mineral substances such as calcium and phosphorus, so that the development of bones and bodies of the earthworms is facilitated; the microbial agent can regulate microbial flora in the feed and promote the absorption and utilization of the earthworms to nutrient substances.

The invention belongs to the technical field of livestock and poultry genetic markers and animal genetic breeding, and particularly relates to a structural variation molecular marker related to chicken secondary average feed intake and application of the structural variation molecular marker. The nucleotide sequence of the structural variation molecular marker provided by the invention is as shown in SEQ ID NO: 1, the structural variation molecular marker is located at 14th, 881, 990-14th, 882 and 295bp of a chicken chromosome 12, and the structural variation molecular marker is a deletion marker. The key genetic marker related to the chicken feeding behavior is provided, important information is provided for genetic breeding of the broiler chicken, meanwhile, broiler chicken varieties with different feeding behavior habits can be bred according to different markets and feeding environments, refined feeding management measures are formulated, and therefore the production efficiency and the feed utilization rate are improved, and the economic benefit is increased. And the economic benefit of the broiler industry is improved.

The invention provides a microecological preparation with an immunological enhancement function and application of the microecological preparation in antibiotic-free breeding of livestock and poultry, and relates to the technical field of agricultural biology. The microecological preparation with the immunological enhancement function comprises a probiotic component, a prebiotics component and an immunological enhancement component, and the probiotic component, the prebiotics component and the immunological enhancement component are respectively composed of lactic acid bacteria, bacillus, clostridium butyricum, fructo-oligosaccharide, yeast beta-glucan, astragalus polysaccharide and chitosan oligosaccharide. Wherein the lactic acid bacteria accounts for 30%-40%, the bacillus accounts for 20%-30%, the clostridium butyricum accounts for 10%-20%, the fructo-oligosaccharide accounts for 5%-10%, the yeast beta-glucan accounts for 3%-8%, the astragalus polysaccharide accounts for 2%-5%, and the chitosan The probiotics can enhance immunity and regulate intestinal tracts at the same time, the effect is improved by 40% or above compared with common probiotics, animals suffer less illness and grow fast, flora can resist high temperature and gastric acid through a special protection technology, the loss of viable bacteria is less than 5% after the probiotics are stored for 3 months, and the probiotics are stable by two times compared with conventional products.

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.

A method for predicting body mass of herd animal that comprises the steps of: collecting data from the livestock environment; detect and select the animal; determining geometric characteristics of each RGB-D depth image; and obtaining the prediction of herd animal body mass. Furthermore, the present invention relates to a system for predicting body mass of herd, comprising: at least one RGB-D capture set including at least one RGB-D imaging sensor; at least one storage module; and at least one remote monitoring module; wherein the storage module stores a set of instructions that, when executed by a processor, carries out the method for predicting body mass of herd animal.

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.