53 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.

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 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.

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.

The invention discloses a fish school feeding behavior automatic detection method under a complex background and a feeding control system, and belongs to the technical field of intelligent control. The method comprises the following steps: acquiring a fish school feeding image in real time, analyzing the fish school feeding image in real time by using a fish school feeding behavior automatic analysis algorithm, and determining the number of fed fish schools at the current time point as an analysis result; analysis results at different time points are continuously obtained, the change trend of the number of the ingestion fish schools is determined according to the data of the number of the ingestion fish schools at the different time points, and the feeding device is regulated and controlled based on the change trend. According to the method, the working state of the fish feeding machine is intelligently controlled through a method of automatically analyzing the feeding behavior of a fish school, manual control over the fish feeding machine through manual squatting is replaced, and implementation of a fishery precise feeding technology in a pond culture environment is accelerated.

The invention discloses a fish feeding state recognition method, device and equipment and a medium, and relates to the technical field of intelligent fishery monitoring and signal recognizing.The method comprises the steps that firstly, main modal components are subjected to VMD decomposition and self-adaptive screening according to energy and correlation, then STFT is conducted on the main modal components, and the main modal components are obtained; the VMD and STFT double-layer feature construction strategy can fully excavate the energy change rule in the disturbance signal by using the two-layer feature construction strategy of the VMD and the STFT to keep the energy distribution features of the signal in time and frequency at the same time; secondly, importance weights of different feature channels are learned through channel attention, feature channel responses sensitive to feeding behaviors are enhanced, spatial weight distribution of a spatial attention learning feature map is enhanced so as to accurately position a key area, the design focuses on discriminative features most related to feeding behaviors from the two aspects of time and space, and the accuracy of positioning of the key area is improved. The method focuses on characteristics representing disturbance signals and feeding behavior energy changes, and finally optimal feeding is carried out on the fishes based on the identified feeding state.

The present disclosure relates generally to keeping animals (10) and in particular to a method for investigating eating behavior of animals (10) in a livestock area (30). The present disclosure also relates to a control device (100) and to a computer program for performing the method. According to a first aspect, the present disclosure relates to a method for investigating eating behavior of animals (10) in a livestock area (30). The method comprises monitoring animal data for a period of time after feed has been dispensed at a feeding station (31) in the livestock area (30), the animal data being collected using tags carried by the animals (10) and being indicative of activity of the animals (10) and / or position of the animals (10) relative to the feeding station (31). The method further comprises performing an action when the monitored animal data fails to meet one or more eating criteria, the one or more eating criteria defining normal eating behavior of the animals (10) in terms of activity of the animals (10) and / or position of the animals (10) relative to the feeding station (31). The present disclosure also relates to a control device (100) and to a computer program for performing the method.

The present application relates to a kind of quantitative detection method of insect cross-feeding ability.The present application changes the active search cross-feeding between the existing insects that contribute food and the insects that receive food into direct cross-feeding, skips the search stage, saves the detection time, improves the detection efficiency, and at the same time, the present application can extract the cylinder for batch testing, and in short-time testing, the cross-feeding behavior of several insects can be tested, further improving the detection efficiency;And after the insects are fixed, the problem of inaccurate data caused by food being transferred is avoided;At the same time, the present application uses infrared high-definition wide-angle camera to record and shoot, analyzes the image, and confirms the average cross-feeding weight, times and duration of each insect in dark environment.

This invention relates to the field of poultry farming technology, and more particularly to a method for detecting abnormal feed intake in poultry and a feed additive for poultry farming. The method includes: acquiring continuous images of a target poultry flock within a monitoring period and preprocessing the continuous images; identifying the feeding behavior of individual target poultry based on the continuous images of the target poultry flock, and then constructing the basic behavioral characteristics of the target individuals; extracting the physiological state characteristics of the target individuals based on the continuous images of the target poultry flock; acquiring environmental parameters in real time and constructing an environmental anomaly index based on the environmental parameters, and then establishing a feeding anomaly judgment model for the target individuals by combining the basic behavioral characteristics and physiological state characteristics of the target individuals, and outputting the feeding judgment result; constructing a regional anomaly index for the target poultry flock based on the feeding status of the target individuals, and then issuing an anomaly alarm to the user. This invention effectively improves the efficiency and accuracy of detecting abnormal feed intake in poultry.

The invention discloses a livestock health data intelligent management method and system, and relates to the technical field of image recognition and edge calculation. The livestock health data intelligent management method comprises the following steps: S1, acquiring an individual behavior image sequence in a free feeding environment, constructing a behavior image basic data set, and preprocessing the behavior image basic data set; s2, identifying visible states of a head and a slot position based on the behavior image basic data set, extracting a corresponding shielding section and evaluating a shielding coverage degree; s3, identifying continuity characteristics of behaviors before and after the shielding section, and dividing continuation levels based on head posture and position change; and S4, calling an ingestion behavior correction sequence generated after behavior connection as a judgment basis, and judging the interference intensity level of the interpolation section on the ingestion behavior structure. The problem that the feeding time is severely underestimated due to interruption of a feeding behavior section caused by the fact that a head-slot position contact structure in an image in a sheltered scene cannot be continuously recognized is solved.

The invention discloses a system and a method for detecting attack behaviors and ingestion behaviors of crabs, and belongs to the technical field of animal behavior detection. A plurality of experimental crabs are placed in an experimental box, a semi-natural environment is provided, and under the group level, on the basis that carapace of the experimental crabs is coated with marks with different colors, the carapace of the experimental crabs is marked with marks with different colors; crab behavior videos are shot in real time through a high-definition camera shooting observation system arranged above the experiment box, and the activity or movement of each crab in a group is recognized and tracked in real time by using a computer animal behavior tracking and analysis unit and adopting a dynamic subtraction method and a color marking auxiliary technology. The behavior phenotype data, the movement track and the heat map of each crab are automatically identified and analyzed, and the behavior change of the experimental crab group is tracked, analyzed and observed in a semi-natural environment. According to the method, researchers can be helped to quantify attack behaviors and ingestion behaviors of crabs in a group better, so that the experimental result is good in stability, high in repeatability, low in labor cost and convenient and rapid to operate.

The invention relates to the technical field of agricultural biology, in particular to dsRNA of a pinus massoniana gene11 gene and application of the dsRNA, and the dsRNA is composed of a sense RNA chain shown in SEQ ID NO: 1 and an antisense RNA chain reversely complementary with the sense RNA chain. According to the dsRNA of the pine caterpillars gene11 gene and the preparation method and application thereof, the dsRNA is injected into pine caterpillars, target genes of the pine caterpillars are efficiently silenced, the death rate of the pine caterpillars can be increased, feeding behavior taxis is reduced, food digestion is affected, and finally the prevention and treatment effect is achieved; and a new effective way is provided for preventing and treating the pine caterpillars.

The invention provides a behavioral analysis method based on an echelette grating principle, which comprises the following steps: putting a cage with an experimental animal on an activity monitoring device, arranging a sensor assembly in the cage, and arranging an echelette grating assembly on the activity monitoring device; the weight of a trough in the cage is measured, and whether the experimental animal has a feeding behavior or not is analyzed; measuring the weight of a water tank in the cage, and analyzing whether the experimental animal has a water drinking behavior or not; measuring the movement of the experimental animal, and analyzing whether the experimental animal has a sleep behavior; measuring the movement of the experimental animal, and analyzing whether the experimental animal has a rest behavior or not according to the measured movement data; and measuring the movement of the experimental animal, and analyzing whether the experimental animal has a movement behavior or not according to the measured movement data. Through cooperation of the echelette grating assembly and the sensor assembly, monitoring of five core behaviors of eating, water drinking, sleep, rest and exercise is achieved at the same time, and the one-sidedness of a single index is avoided.

An adjustable bird feeder designed to regulate access to food based on beak length and feeding behavior of different bird species. The feeder includes at least one food cage positioned within a frame and at least one movable face located adjacent to the food cage. A user-operated adjustment mechanism, such as a threaded rod and control knob, moves the face toward or away from the food cage. In a spring-assisted configuration, compression springs provide controlled retraction, while in a single-cage configuration, the face is adjusted without springs using guide rails for stability. The food cage may be aligned or offset with the front grill to adjust aperture size. Crumb collection trays and aluminum fittings enhance durability and prevent food waste. The feeder accommodates suet, seeds, and nuts by modifying cage design and deters squirrels through controlled spacing. In some embodiments, the spacing may alternatively be fixed at a predetermined distance.

This invention discloses a deep learning-based image recognition system and method for chicken disease symptoms, relating to the field of image recognition technology. The system includes: acquiring a set of spatial location images and a set of feeding images of target chickens within a preset time period; extracting a dataset of spatial location relationships between the target chickens and feed troughs, feeding frequency data, and a social network graph based on the spatial location image set and the feeding image set; correcting the feeding frequency data based on the spatial location relationship dataset to obtain corrected feeding behavior data; inputting the feeding behavior data and the social network graph into a preset deep learning model to generate a probability of behavioral abnormality; if the probability of abnormality exceeds a preset judgment threshold, the target chicken is judged to have suspected disease behavior. Its beneficial effects include: it can integrate spatial location relationship correction and social network graph analysis, effectively eliminating the interference of chicken house layout on behavioral characteristics, and has the advantages of improving the accuracy of disease identification and reducing misjudgments due to environmental factors.

The invention discloses a mode recognition-based precise feeding method of a feeding robot, which comprises the following steps of: acquiring behavior observation data of a breeding object to obtain an effective behavior sequence; constructing a behavior time sequence characterization module to obtain a behavior potential characterization sequence; organizing and forming a behavior unit representation sequence, and constructing a coupling relationship between behavior phase variables; inputting an improved Kuramoto model to obtain a synchronization degree index and a synchronization degree evolution sequence; performing mode judgment on the group feeding behavior, and generating a feeding trigger signal; a feeding control parameter set is generated, and the feeding robot is controlled to execute feeding operation. According to the invention, by introducing the comparative predictive codes and combining with the improved Kuramoto model, accurate identification of real feeding behaviors and intelligent accurate feeding control of the feeding robot in a group breeding scene are realized.