102 results about "Feeding behavior" patented technology

The invention provides a fish feeding control method and system, electronic equipment and a storage medium, and the method comprises the steps: collecting a target fish school video in a target fish school feeding state; according to the target fish school video, determining a dynamic feature and a static feature of a fish school ingestion behavior; wherein the dynamic feature is the confusion degree of the overall movement direction of the target fish school; the static feature is a texture feature of the target fish school ingestion video image; according to the dynamic characteristics and the static characteristics, determining a fish shoal feeding behavior state; wherein the ingestion behavior state of the fish school comprises a satiation state and a starvation state; if it is determined that the target fish school is in the satiation state, feeding is stopped. The fish feeding requirement can be accurately determined, real-time on-demand feeding of fishes is scientifically achieved, the bait utilization rate is effectively increased, and the culture benefits are increased.

The invention discloses a full-automatic feeding control method and system for multiple areas of cattle and sheep, and the method comprises the steps: obtaining basic information and ingestion behavior data of cattle and sheep in each area, carrying out the preprocessing, and carrying out the training through a support vector machine algorithm, and obtaining a cattle and sheep ingestion speed prediction model; a relation model of the feed physical parameters and the feeding speed is constructed, and the initial feeding speed of each area is obtained based on the relation model and the feed physical parameters of each area; acquiring real-time feeding behavior data and real-time satiation degree data of the cattle and the sheep in each area, and judging whether the average feeding speed of the cattle and the sheep meets the requirement or not; if the requirements are not met, based on the change condition of the feeding speed of the cattle and the sheep, a fuzzy control algorithm is adopted to generate a feeding speed regulation and control instruction; and obtaining feeding demand data of cattle and sheep in each region and current feed supply data, and calculating an optimal feed supply speed. Accurate feeding is achieved, the feeding efficiency is improved, and the problems that in traditional feeding, supply and demand are not matched, and the efficiency is low are effectively solved.

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 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 provides a fish school feeding method and device, computer equipment and a storage medium, and belongs to the technical field of aquaculture. The method comprises the following steps: constructing a fish school feeding behavior training sample; constructing a fish school feeding prediction model; acquiring a behavior image of the target fish school; inputting the behavior image into the fish school feeding prediction model to obtain predicted feeding intensity of a target fish school; and feeding a target fish school according to the predicted feeding intensity. Therefore, the behaviors of the fish schools are predicted according to the feeding model of the fish schools, the feeding intensity of the fish schools is obtained, the feeding machine is adjusted in real time according to the feeding intensity of the fish schools to feed the fish schools, the feeding behaviors of the fish schools can be monitored and analyzed in real time according to the intelligent feeding method, and the feeding strategy is adjusted in real time; the accuracy and efficiency of bait casting are improved, and different breeding scenes and requirements can be met. In addition, bait casting cost and labor cost can be reduced, and breeding benefits and economic benefits are improved.

The invention relates to a vibration signal-based fish school ingestion behavior grading identification method, which comprises the steps of controlling a plurality of vibration sensors to synchronously collect vibration signals caused by fish school ingestion, carrying out synthesis preprocessing on the vibration signals to obtain synthesis signals, sequentially carrying out filtering, framing and wavelet transform processing on the synthesis signals to carry out feature extraction, and obtaining the fish school ingestion behavior grading identification result. The method comprises the following steps: obtaining a first feature matrix, carrying out dimension reduction processing on the first feature matrix to obtain a second feature matrix, inputting the second feature matrix into a pre-trained deep learning model to obtain a fish shoal feeding behavior vibration signal grading identification signal, realizing accurate feeding with low cost, and being capable of being widely popularized. The breeding efficiency is improved.

The invention provides a livestock and poultry individual feed intake monitoring method, device and equipment, a storage medium and a product, and relates to the technical field of animal husbandry, the method comprises the following steps: when a UWB positioning tag of a first livestock and poultry monitors that the first livestock and poultry enter a first feeding area, obtaining a first depth image collected by an RGB-D camera at the first feeding area; under the condition that it is monitored that the first livestock and poultry have the abnormal ingestion behavior, an object tracking algorithm is applied to track a feed moving track corresponding to the abnormal ingestion behavior, and a second depth image corresponding to the abnormal ingestion behavior is collected; when it is monitored that the first livestock leaves the first feeding area based on the UWB positioning tag of the first livestock, a second depth image collected by an RGB-D camera at the first feeding area is obtained; and based on the first depth image, the second depth image and the third depth image, obtaining the feed intake of the first livestock and poultry. According to the invention, through combination of the RGB-D camera and the UWB positioning tag, accurate monitoring of the eating behavior of the individual livestock and poultry in the livestock and poultry farm is realized.

The invention provides a fish group feeding behavior identification method and device and a storage medium. The fish group feeding behavior identification method comprises the following steps: acquiring to-be-processed fish behavior data; inputting the to-be-processed fish behavior data into a fish ingestion behavior recognition model to obtain an ingestion behavior recognition result of a target fish school; wherein the fish ingestion behavior recognition model is trained by the following steps: acquiring an original video shot at a culture site, and acquiring a fish ingestion behavior data set according to the original video; based on the fish feeding behavior data set, obtaining a feeding behavior recognition result by using a coordinate attention mechanism and a depth separable convolution; and training the fish feeding behavior recognition model based on the feeding behavior recognition result. The recognition model with higher learning ability and adaptive ability is obtained through training, and the accuracy of fish feeding behavior recognition is improved.

The invention discloses a quality control and filling method and system for boar growth performance data, and relates to the field of boar data process.The method comprises the steps that the boar growth performance data are collected and preprocessed, and the boar growth performance data comprise weight data and feed intake data; performing quality control on the processed weight data by using a regression analysis method in combination with a Tukey weighting function and a robust regression method; filling the processed weight data by adopting a linear interpolation method and a fitting binomial regression method according to the type of the missing data; the method comprises the following steps: processing food intake data, identifying a plurality of abnormal food intake behavior types on the basis of the processed food intake data, setting food intake with abnormal types as vacancy, then correcting non-abnormal data by using a linear hybrid model, and filling missing food intake data through a Loess local weighted regression method on the basis of a field, body weight, daily gain and abnormal type frequency.

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 relates to the technical field of intelligent management, in particular to an intelligent precise pig feeding system and method based on internet-of-things management and control and mutual feeding of ingestion. The multi-modal data acquisition unit is used for acquiring environmental parameters, individual characteristics, behavior videos and food consumption; the ingestion mutual feedback analysis unit is used for processing and analyzing ingestion behavior videos in real time, acquiring real-time data of the live pigs in combination with data fed back by the weighing trough, and establishing an ingestion efficiency evaluation model of the individual live pigs by utilizing the real-time data of the live pigs; and the feeding parameter generation unit is used for acquiring the optimal feeding period and feeding amount according to the ingestion efficiency index of each live pig. According to the system and method, the actual feeding state of each pig can be evaluated more accurately, the accuracy of feeding behavior analysis is improved, misjudgment caused by neglecting individual differences and environmental factors is avoided, the problem of high-frequency noise interference caused by head posture changes can be effectively solved, and the accuracy of feeding behavior analysis is improved.

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.

An automatic feeding device (100) for animals in animal care facilities, comprising: a housing (102), characterized in that the housing (102) comprises a feeding chamber (103a) containing feed; and a tax chamber (103b) which includes: a mechanical user interface (110) comprising one or more controls (110a-110h) configured to allow an animal keeper to set a feeding parameter to provide food at random times following a feeding break; and an electromechanical unit (111) comprising a control unit (136) configured to receive an input signal corresponding to the feeding parameter specified by the animal keeper from the mechanical user interface (110) and to determine a feeding time based on the input signal for the provision of the feed at a specific feeding time, where the specific feeding time for the animals is unpredictable and changes for each feeding event, thereby promoting species-appropriate feeding behavior in the animals, including hunting, stalking and patrolling.

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 invention belongs to the technical field of data processing, and provides an auxiliary monitoring method and system for automatic feeding of a hog house, and the method specifically comprises the steps: firstly, arranging an RFID tag system in the hog house, recognizing a feeding unit in real time, reading a tag distance, and judging a feeding state through the tag distance; the method comprises the following steps: firstly, obtaining a pig feeding state, then obtaining a total feeding duration according to the pig feeding state and calculating a feeding deviation, obtaining a feeding frequency according to the pig feeding state, then calculating a strategy adaptation index according to the feeding deviation and the feeding frequency of each pig in the same feeding unit, and judging the adaptability of the pigs to an automatic feeding strategy according to the strategy adaptation index. Finally, the pigs with over-excitation feeding group adaptation or over-weak feeding group adaptation are called out of the original feeding group, the rejection degree of different pigs in the same pigsty in the feeding behavior after the automatic feeding system is put into the pigsty for use is effectively quantified, growth and development of each pig are more uniform, and therefore the effect of improving the overall breeding quality is achieved.

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.