82 results about "Feeding behavior" patented technology

The embodiment of the invention discloses a feeding method and device capable of accurately regulating and controlling the feed intake of sows. The method comprises the following steps: identifying an individual identity through an electronic ear tag, and collecting backfat thickness, body condition score and weight data by using an inspection robot; dynamically calling a standard feeding curve based on the fetus number and the body condition score, and finely adjusting the target feed intake in combination with the body weight change trend; a quantitative or interactive feeding mode is adopted in the gestation period, feeding is carried out with different meals of wet mixed feed in the lactation period, and the feed-water ratio is dynamically adjusted; meanwhile, a data closed loop is constructed, a feeding strategy is optimized through big data analysis, and abnormal individuals are visualized. According to the scheme, precise self-adaptive regulation and control of the feeding behavior of the sow in the whole breeding cycle are achieved, and the feed efficiency and the breeding performance are remarkably improved.

The invention belongs to the technical field of artificial intelligence crossing, and provides a method for intelligently identifying abnormal foraging behaviors of chickens under environmental stress, which comprises the following steps of: acquiring data and analyzing an abnormal foraging behavior identification model of the chickens; the training process of the model comprises the following steps: defining key feature points and a behavior recognition range, acquiring a continuous three-dimensional point cloud sequence based on a time window, acquiring multi-parameter environmental data in a poultry house, marking, dividing a data set, constructing a chicken abnormal feeding behavior recognition model and iteratively training the chicken abnormal feeding behavior recognition model; according to the invention, the multi-layer feature extraction module, the point sequence transformation sensing module, the resolution collaborative enhancement module, the dynamic feedback control module and the behavior recognition module are constructed, and environmental stress variables and deep visual data are fused, so that the three-dimensional structural features and dynamic behavior information of chickens can be accurately captured in a complex breeding environment; the automatic identification capability of abnormal foraging behaviors of chickens is improved, and the method has good generalization performance, interpretability and practical deployment feasibility.

The invention provides a meat duck ingestion and drinking behavior recognition algorithm based on MOT and instance segmentation, and the method comprises the steps: collecting and marking an image of the ingestion and drinking behavior of a meat duck, and building a caged meat duck ingestion and drinking behavior detection data set through image data preprocessing; meat duck group multi-target segmentation and tracking based on target perception TAM; constructing and training a meat duck ingestion and drinking behavior recognition and detection network based on a Mask R-CNN algorithm; and classifying and judging the behaviors of the meat ducks, and counting the total frame number of the water drinking behaviors and the total frame number of the feeding behaviors of the target meat ducks in the video detection period to obtain the water drinking duration and the feeding duration. According to the invention, high-precision identification and time duration statistics of meat duck ingestion and water drinking behaviors can be realized in a stacked cage culture environment, the meat duck health state perception and fine management level of a farm can be improved, abnormal behaviors can be early warned in time, scientific feeding and risk control can be assisted, and the method has good practicability and popularization value.

The invention relates to the technical field of fish culture feeding, in particular to a vision-based fish self-adaptive feeding method and device, and the method comprises the following steps: sequentially carrying out attraction feeding operation and formal feeding operation on fishes in a culture area; after the formal feeding operation, obtaining a feeding video of the fish; according to the ingestion video, obtaining an average ingestion behavior quantitative index of the fish; and obtaining a feeding judgment result according to the average feeding behavior quantitative index, and performing adaptive feeding on the fish according to the feeding judgment result. According to the method, the ingestion desire of the fish school can be represented on the basis of the quantized fish school ingestion behavior, and accurate feeding of the fishes in the breeding area is achieved.

The invention belongs to the technical field of molecular biotechnology and molecular markers, and particularly relates to an SNP molecular marker located on a pig RBFOX1 gene locus and related to ingestion behavior characters and application of the SNP molecular marker. An SNP site of the SNP molecular marker corresponds to the 35094559 Ggt on a chromosome 3 of a reference sequence of an international pig reference genome version 11.1; t mutation. By optimizing the dominant alleles of the molecular marker, the frequency of the dominant alleles can be increased generation by generation, the average daily ingestion times of pigs can be reduced, the average ingestion time of each time can be increased, and the feed conversion efficiency of the pigs can be improved. The breeding of the excellent boars with the characters is beneficial to accelerating the progress of genetic improvement of the boars and improving the economic benefits of pig breeding.

The invention relates to the technical field of aquaculture, in particular to a net cage culture fish ingestion behavior recognition and accurate feeding system and a method thereof.The system comprises a multi-modal data acquisition module arranged around a net cage and used for acquiring video data and biological density information of fish behaviors, a data preprocessing module used for coding and compressing the video data, and a data processing module used for processing the coded and compressed video data; the biological density information is packaged, the 3D space-time attention analysis module receives the processed data, space-time features are extracted through a 3D deep learning model, and fish feeding behaviors are recognized. The multi-modal feature fusion module adaptively fuses the spatial-temporal features of the video data and the biological density information to generate ingestion intensity data, and the feeding control module calculates the vibration frequency according to the ingestion intensity data and controls the opening state of a netting door in a net cage, so that precise feeding is realized. The problem that a single data source is low in recognition accuracy in a complex underwater environment is solved, the recognition accuracy of the system under various water quality conditions is improved, and accurate feeding of fishes is achieved.

The invention provides a fish shoal feeding behavior identification method and device, and relates to the technical field of artificial intelligence, and the method comprises the steps: obtaining synchronously collected fish shoal feeding video data and water surface fluctuation signal data, and a pre-trained fish shoal feeding behavior identification model; wherein the water surface fluctuation signal data is a multi-axis motion sensing signal acquired by a multi-axis sensor; and inputting the fish school ingestion video data and the water surface fluctuation signal data into the pre-trained fish school ingestion behavior recognition model to obtain a fish school ingestion behavior recognition result. Through multi-modal feature fusion, behavior features in a video and physical features in a fluctuation signal are comprehensively analyzed, so that the ingestion intensity is more accurately judged. A multi-task learning strategy is used in the training process of the model, the synergistic effect of visual features and fluctuation signal features is optimized, subtle differences of ingestion intensity can be captured, and misjudgment caused by insufficient single-mode data is avoided.

The invention discloses a livestock and poultry feeding behavior feature extraction and anomaly recognition method based on visual guidance, and the method comprises the steps: synchronously collecting livestock and poultry feeding region images through a multi-view visual imaging device, and carrying out the image preprocessing through space registration and adaptive threshold segmentation; extracting features by using a deep learning network comprising a convolutional layer, a pooling layer and an attention mechanism, processing a feature sequence by means of dynamic time warping and a sliding window, and realizing behavior classification through a model constructed by a full connection layer and a Softmax activation function; and determining a normal behavior range based on historical data clustering, and completing anomaly recognition. The method overcomes the problems that traditional monitoring is low in efficiency, single in data, high in misjudgment rate and the like, can accurately obtain the foraging behavior characteristics, timely identifies the abnormity, effectively improves the refinement and intelligence level of livestock and poultry breeding management, reduces the breeding risk, and has important application value.

The invention discloses a cowshed ingestion channel management method based on AI behavior partition, and the method comprises the following steps: S1, collecting and preprocessing cattle multi-source behavior data, and constructing a behavior time series data set; s2, establishing a recurrent neural network model, and predicting a future behavior trend; s3, constructing a behavior distribution graph and generating a dynamic behavior partition graph; s4, initializing a dragonfly optimization algorithm population, and searching an optimal ingestion channel configuration scheme; s5, the optimal channel configuration parameters are issued to a guide control device, and channel guide and shunting are executed; and S6, collecting feedback data, and updating the recurrent neural network model and the optimization algorithm. According to the method, intelligent prediction, dynamic partition and channel optimization guidance of the cattle shed feeding behavior are realized, so that the feeding efficiency is improved, and congestion and conflicts are reduced.

The invention discloses an intelligent feed proportioning system based on deep learning, and the system comprises a data collection module which generates synchronous preprocessing data; the conditional diffusion semantic segmentation module is used for outputting semantic segmentation mask data in an anti-diffusion iteration process; the behavior recognition module is input into a poultry behavior recognition network constructed based on an improved ResNeXt architecture to obtain ingestion behavior recognition result data; the individual track association module is used for outputting ingestion behavior time sequence data; the ingestion-feed prediction module is used for respectively generating ingestion rate prediction data and feed surplus prediction data; the dynamic feeding optimization decision-making module is used for obtaining target feeding amount data and nutrition proportion adjustment coefficient data; and the execution control module is used for adjusting the intelligent proportion of the feed in real time. According to the method, the target feeding amount and the nutrition proportion of protein, energy and microelements are adjusted in real time in stages, whole-process closed-loop regulation and control are achieved, and the feeding precision and economical efficiency are both superior to those of an existing automatic feeding scheme.

The invention relates to the field of reinforcement learning, in particular to a system and method for monitoring and analyzing the feeding state of bred chickens based on machine vision. The system comprises a feeding device module, a visual perception module, a weight perception module, an image processing module and a behavior recognition module, and is used for judging whether feeding behaviors exist in each foodstuff unit through a reinforcement learning model, and the reinforcement learning model learns based on image feature-weight change data input in a training stage. In the reasoning stage, identification is performed only based on image data. According to the invention, efficient and non-contact ingestion state intelligent identification without wearing labels by chickens can be realized, and the method has the advantages of high precision, strong generalization ability and simple deployment, and is suitable for intelligent monitoring scenes of large-scale poultry breeding.

This invention discloses a method for monitoring dairy cow feeding behavior, belonging to the field of dairy farming technology. Based on historical factors influencing feeding and historical feed intake, a dairy cow feed intake prediction model is trained, enabling the model to predict feed intake and providing a reference for feed distribution. After feed is distributed, feed intake data can be collected to monitor feeding behavior, effectively improving feed intake and allowing staff to manage dairy cows based on feeding behavior, thus increasing management efficiency.

The invention discloses a feed feeding control method, system and equipment for opsariichthys bidens culture and a storage medium, and relates to the technical field of aquaculture automatic control. Comprising the following steps: step 1) collecting a water surface vibration spectrum and dissolved oxygen space gradient data through a non-intrusive sensor arranged at the edge of a culture pond, inputting an environmental factor-ingestion behavior mapping model, and outputting a fish school aggregation probability thermodynamic diagram; according to the feed feeding control method, system and equipment for opsariichthys bidens culture and the storage medium, underwater monitoring is replaced with non-intrusive environment perception, and the risk of disturbance of physical disturbance of sensors to opsariichthys bidens groups is eliminated; a two-channel verification mechanism is utilized, subthreshold sound pulses are converted into a fish school sensitivity quantification tool, and accurate grading judgment of the disturbance degree is achieved based on Doppler frequency shift signals; based on the triple cooperation mechanism, the problems of monitoring distortion, feeding misjudgment and feed waste are systematically solved.

The application discloses a fruit fly feeding behavior analysis method and device, and relates to the technical field of insect behavior analysis, and comprises the following steps: setting different environment parameter groups, performing a fruit fly feeding monitoring test, collecting the motion video of a single fruit fly at a feeding device, and dividing the video frames into continuous characteristic segments. By analyzing the characteristic segments, the starting point and the ending point of each round of suspected feeding process are determined, and the food surface images before and after the suspected feeding are obtained. The structural feature changes of the surface images before and after the suspected feeding are compared to determine whether the feeding behavior occurs, if the feeding behavior occurs, the round of process is defined as the feeding process, and the feeding amount is represented based on the surface feature changes. The feeding times, the average feeding interval and the average feeding amount of the fruit fly under different environment parameters are analyzed, and the influence of the environment parameters on the fruit fly feeding behavior is output. The scheme has significant advantages in time resolution and result accuracy, and can especially analyze the short-time feeding behavior of the fruit fly.

The invention discloses a method for measuring the feeding behavior of adult zebrafish, which comprises the following steps: S1, experimental fish preparation: selecting healthy female TU zebrafish at the age of six months, and dividing the female TU zebrafish into a control group and an exposure group after the female TU zebrafish adapts to a system water environment; s2, long-term drug exposure: using system water in a control group, using system water containing a target drug in an exposure group, continuously exposing for 28 days, replacing exposure liquid every day and maintaining a constant environmental condition; s3, food intake behavior determination: transferring the single zebra fish to an independent water tank, putting quantitative feed after adaptation, recording the food intake process through a behavior analysis system, and analyzing the first food intake time, the incomplete food intake proportion and the total food intake duration; and S4, performing data statistics: performing difference significance analysis by adopting SPSS software, and judging the influence of the medicine on the ingestion behavior. The invention aims to detect the potential influence of long-term exposure of environment-related concentration drugs on the ingestion function of fishes, and is suitable for evaluating the interference effect of long-term exposure of low-concentration drugs on the ingestion function of aquatic organisms.

The present invention belongs to the technical field of livestock and poultry genetic markers and animal genetic breeding, and specifically relates to a SNP molecular marker related to the average feeding time of chickens and its application. The SNP molecular markers are located at 1,035,978bp and 1,035,986bp of chicken chromosome 2, respectively. The candidate genes are <h2 style=";text-align:left;direction:ltr">GIMAP7 Gene. The SNP molecular markers associated with average feeding time per chicken provided by the present invention provide key genetic information for broiler genetic breeding, facilitating the precise selection of populations with excellent average feeding time traits during broiler breeding, improving broiler feeding efficiency and production performance, and enhancing the economic benefits of the broiler industry. The discovery of SNP molecular markers associated with average feeding time per chicken also provides an important research foundation for in-depth studies of the genetic mechanisms of chicken feeding behavior, helping to promote the development of livestock and poultry genetic breeding.

The application discloses a fish feeding behavior state analysis method based on an improved RepVGG network, image acquisition and preprocessing of a breeding pond are performed through an industrial camera and a computer, and water quality information of the breeding pond is acquired through a multi-parameter sensor and monitored in real time; a RepVGG network framework is designed to identify fish group feeding behavior, Identity and residual branch are added in the Block block of the VGG network to ensure the acceleration operation of the accuracy rate, and an ECA module is added to reduce dimension processing, balance speed and accuracy, and a training strategy is adopted to improve the identification accuracy of the model. Through verification on the constructed fish group feeding behavior data set, and comparison with a convolutional neural network (CNN), after 15 epochs of training, the accuracy of the method can reach 0.97, and the test Loss converges at 0.16. The application can be integrated into an aquatic breeding visual system to guide the breeding users to plan the feeding strategy.

The invention discloses a fry feeding behavior monitoring and accurate feeding system based on visual identification, and belongs to the technical field of aquaculture, the system comprises a base plate with buoyancy, a feeding box is arranged above the base plate, a discharging port of the feeding box is formed in the bottom of the base plate, a second frame with a frame structure formed by splicing pipe bodies is connected to the lower portion of the base plate, and the second frame is connected with the bottom of the base plate. The two opposite side faces of the bottom of the second frame are connected with a first frame through connecting rods, a first image collector is arranged on one side of the first frame, an energy storage box is arranged on the first frame on the same side of the first image collector, and the energy storage box is externally connected with light sources in at least two projection directions. According to the system, the problems that an existing device is large in monitoring blind area and image blurring is caused by insufficient light are solved, fodder is accurately put according to monitoring data, and the system is high in practicability and high in practicability. And the fed feed can be uniformly diffused and suspended for a long time, so that the feeding rate of the fries is improved.

The invention relates to the technical field of intelligent breeding of animal husbandry, in particular to a beef cattle feeding behavior intelligent monitoring system based on machine learning. The technical problems that according to the technical scheme, the monitoring precision is low, and the actual requirement of large-scale breeding for precise individual health management is difficult to meet are solved. The system comprises an attitude decoupling module used for decomposing original acceleration data into reference axial vibration strength and reference plane swing strength based on a quasi-static attitude reference vector of a target cattle; the effectiveness auditing module is used for determining the ingestion confidence coefficient based on the difference between the reference axial vibration strength and a pre-generated resting vibration benchmark; the group difference module is used for constructing a group average material sweeping ratio; and the excess sorting deviation determination module is used for determining excess sorting deviation based on the group average feed sweeping ratio, the feed sweeping chewing ratio of the target cattle and the feed intake confidence coefficient. The method is used for the quantitative monitoring scene of the sorting behavior in the cattle feeding process.

The present application relates to the technical field of feeding management, and particularly relates to a method and system for automatically adjusting feeding amount based on fish feeding behavior, historical feeding state parameters of a target breeding pond are acquired, the historical feeding state parameters include historical environment data, historical feeding data, historical fish feeding behavior data and historical residual material rate; target feeding behavior characteristics of the target breeding pond are constructed according to the historical feeding state parameters; feeding behavior similarity between the target breeding pond and each reference breeding pond is determined based on the target feeding behavior characteristics; the reference breeding ponds with feeding behavior similarity greater than a preset threshold are marked as candidate breeding ponds; and the target feeding amount of the target breeding pond is determined based on the historical feeding amount and the feeding behavior similarity of the candidate breeding ponds. The present application can analyze the historical feeding law of the fish population and match the experience data of similar breeding scenes, so as to realize intelligent decision of the feeding amount which is accurate, self-adaptive and cross-scene.

Provided are an intelligent feeding method and system for fish in a recirculating aquaculture system based on repeated games. The method includes: constructing repeated games, determining Nash equilibrium strategies based on the repeated games, determining a payoff relationship between different strategies in a research phase, and quantifying a hunger level and determining a feeding amount based on the payoff relationship as well as an intrinsic connection between hunger levels and energy intake and expenditure in fish group feeding behavior. This application uses a simple and effective repeated game method to determine the feeding amount that promotes cooperative swimming feeding behavior in the fish group, effectively reducing feed waste caused by the existing method of determining the feeding amount based on total fish biomass and farming experience. This application ensures sufficient energy supply for fish growth while promoting cooperative swimming feeding behavior among individual fish in the group.

The present application relates to the technical field of intelligent management, in particular to a pig intelligent precise feeding system and method based on internet of things management and control and feeding reciprocity. It comprises: a multi-modal data acquisition unit for collecting environmental parameters, individual characteristics, behavior videos and feeding amount; a feeding reciprocity analysis unit for real-time processing and analysis of feeding behavior videos, combining the data obtained from the feedback of the weighing type trough to obtain the real-time data of pigs, and using the real-time data of pigs to establish a feeding efficiency evaluation model of individual pigs; a feeding parameter generation unit for obtaining the optimal feeding cycle and feeding amount according to the feeding efficiency index of each pig. The system and method can more accurately evaluate the actual eating state of each pig, not only improving the accuracy of eating behavior analysis, avoiding misjudgment caused by ignoring individual differences and environmental factors, but also effectively dealing with the problem of high-frequency noise interference caused by head posture changes, improving the accuracy of eating behavior analysis.

This invention discloses a method for identifying abalone feeding behavior and assessing feeding intensity based on acoustic signals. The method collects acoustic signals from the aquaculture water, extracts feeding pulses using the Lp-L1 / 2 sparse reconstruction algorithm, calculates the Local Pulse Quantity Feature (LPN), and generates a robust feature LPN- by setting an adaptive threshold based on the statistical distribution of background noise. Then, it statistically analyzes the effective feeding frequencies to obtain the abalone feeding activity frequency (EAF). Combining the mapping model between EAF and actual feeding amount, it achieves a scientific assessment of feeding intensity. This invention significantly improves the accuracy of feeding behavior identification and the ability to quantify intensity, making it suitable for intelligent aquaculture scenarios.

This invention proposes an intelligent feeding method and system based on an AI dynamic formulation engine, comprising: constructing a digital twin model of the animal, mapping the animal's physiological parameters to a unified physiological state vector; inputting the physiological state vector into a quantum-inspired neural network to obtain a real-time nutrient requirement spectrum and a nutrient deficiency risk assessment matrix, and generating an optimal feed formulation based on these two; controlling the feeding equipment to feed the animal according to the optimal feed formulation; monitoring the animal's feeding behavior and uneaten feed, and transmitting the feeding feedback data back to the AI ​​engine to form a closed-loop control system; storing the formulation data, feeding records, and animal response data for each feeding using blockchain technology to construct a complete feeding traceability chain; and quantitatively evaluating the effectiveness of the feeding program by comparing and analyzing historical data with current growth indicators. This invention can improve feed conversion rate and formulation adaptability, reduce feed costs, reduce human intervention, and improve the level of automation in animal husbandry.

The present disclosure provides an automatic feeding device 100 and a method for feeding animals in animal care facilities at unpredictable, irregular times. The automatic feeding device 100 comprises a housing 102 containing a feed chamber 103a for storing feed, as well as a control chamber 103b with a mechanical user interface 110 and an electromechanical unit 111 with a control unit 136. The mechanical user interface 110 allows the setting of a feeding parameter to dispense feed at random times after a feeding break. The control unit 136 is configured to receive an input signal corresponding to the feeding parameter set by the animal caretaker and, based on the input signal, to determine a feeding time for dispensing the feed at a specific time.The specific feeding time is unpredictable for the animals and varies with each feeding, thus promoting species-appropriate feeding behavior, including hunting, stalking and patrolling.

This application discloses a method, device, and system for intervening in eating behavior based on an ear-worn device. The method involves: acquiring vibration signals collected by the ear-worn device during a user's eating process in real time; determining a set of candidate chewing segments based on the vibration signals; identifying the event type of each candidate chewing segment in the set of candidate chewing segments, and constructing a chewing event sequence based on the identified multiple chewing events; determining the corresponding swallowing event and its time boundary point based on the time interval between two adjacent chewing events in the chewing event sequence; determining an eating rhythm index based on the swallowing event and its time boundary point; and triggering the ear-worn device to play voice prompts according to a preset intervention strategy based on the eating rhythm index. This method enables the ear-worn device to sense the user's eating rhythm during daily meals and provide timely reminders at appropriate times, helping the user consciously slow down their eating speed.

The present invention discloses a feed intelligent proportioning system based on deep learning, which includes a data acquisition module that generates synchronous preprocessing data; a conditional diffusion semantic segmentation module that outputs semantic segmentation mask data during the back-diffusion iterative process; a behavior recognition module that inputs into a poultry behavior recognition network built based on an improved ResNeXt architecture to obtain feeding behavior recognition result data; an individual trajectory association module that outputs feeding behavior time series data; a feeding-feed prediction module that generates feeding rate prediction data and feed remaining amount prediction data respectively; a dynamic feeding optimization decision module that obtains target feed amount data and nutrient ratio adjustment coefficient data; and an execution control module that adjusts the intelligent feed proportioning in real time. The present invention adjusts the target feed amount and the nutrient ratio of protein, energy, and trace elements in real time in stages, achieving closed-loop control of the entire process. The feeding accuracy and economy are superior to existing automatic feeding solutions.

This invention discloses a fully automated feeding control method and system for cattle and sheep in multiple regions, comprising: acquiring basic information and feeding behavior data of cattle and sheep in each region, preprocessing the data, and then training a feeding speed prediction model using a support vector machine algorithm; constructing a relationship model between feed physical parameters and feeding speed, and obtaining the initial feeding speed for each region based on the relationship model and the feed physical parameters of each region; acquiring real-time feeding behavior data and real-time satiety data of cattle and sheep in each region, and determining whether the average feeding speed of cattle and sheep meets the requirements; if it does not meet the requirements, generating a feeding speed adjustment command based on the changes in the feeding speed of cattle and sheep using a fuzzy control algorithm; acquiring feeding demand data and current feed supply data of cattle and sheep in each region, and calculating the optimal feeding speed. This invention achieves precise feeding, improves feeding efficiency, and effectively solves the problems of supply-demand mismatch and low efficiency in traditional feeding methods.

The application discloses a kind of based on repeated game's circulating water aquaculture fish intelligent feeding method and system, the method includes constructing repeated game, based on the Nash equilibrium strategy of repeated game, determine the income relationship between different strategies in research phase, finally according to the internal connection of the energy intake and consumption in starvation degree and fish feeding behavior, quantification starvation degree and determine feeding amount.The repeated game method of the application is simple and effective to determine the feeding amount to promote fish cooperative swimming feeding behavior, can effectively reduce the feed waste caused by determining the feeding amount by the total amount of fish combined with the experience of aquaculture, can effectively promote the cooperative swimming feeding behavior of fish individuals while fully meeting the energy required for fish growth, avoid deterioration of aquaculture water quality, improve the efficiency of aquaculture, meet the development needs of modern aquaculture.

The application discloses a beef cattle feed efficiency comprehensive determination method based on feeding behavior and growth indexes, and belongs to the field of beef cattle feeding and livestock technology. The method comprises the following steps: collecting individual cattle daily multi-source data packets; inputting video data into a visual recognition model to output a daily feeding behavior parameter set; calculating average daily weight gain and feed conversion rate, and standardizing the daily feeding behavior parameter set, average daily weight gain and feed conversion rate to obtain a daily standardized parameter set; constructing a feeding behavior health index and a traditional feed efficiency benchmark value based on the daily standardized parameter set; calculating a comprehensive feed efficiency value based on the feeding behavior health index and the traditional feed efficiency benchmark value; and performing beef cattle feed efficiency comprehensive determination based on the comprehensive feed efficiency value. The application realizes multi-source data fusion of feeding behavior and growth indexes, and achieves early precise early warning and individualized management of feed efficiency through high-precision model recognition and comprehensive evaluation.