14 results about "Feeding behavior" patented technology

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.

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.

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 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 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.

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 present application relates to the field of intelligent aquaculture and computer vision technology, in particular to a kind of abalone feed intake monitoring system based on image recognition, comprising: image acquisition module, for collecting high frame rate video stream data containing biological activity and feed distribution;Physical reference reconstruction module, based on initial feed characteristics, combined with fluid dynamics and dissolution parameters, deduce the theoretical evolution reference map of the current time;Behavior probability generation module, identify biological mouth key points and motion vectors, generate dynamic predation probability field;Double rail difference module, difference operation is carried out to real image and theoretical evolution reference map, and non-physical disappearance residual area is generated;Coupling verification module, residual area is mapped into probability field, and effective feeding behavior is determined based on spatial overlap degree;Data output module, according to the determination result, the accurate intake data is output cumulatively;The present application solves the counting problem caused by shielding, and significantly improves the monitoring robustness.

This application discloses an intelligent matching method and system for lamb feeding programs based on growth prediction, relating to the field of lamb feeding technology. The method includes: acquiring a set of growth parameters and a set of feeding behavior parameters for the target lamb; predicting growth status and acquiring feeding proficiency parameters and rumen development parameters; acquiring developmental synergy parameters to determine the lamb's developmental pattern; and intelligently matching a lamb feeding program based on the feeding proficiency parameters, rumen development parameters, and the lamb's developmental pattern. This solves the technical problems of traditional lamb feeding programs relying on experience-based judgment, which leads to strong subjectivity and significant lag, and the inability of standardized feeding patterns to meet the individualized needs of lambs.

This invention relates to the field of feeding management technology, and in particular to a method and system for automatically adjusting feeding amount based on fish feeding behavior. The method involves acquiring historical feeding status parameters of a target aquaculture pond, including historical environmental data, historical feeding data, historical fish feeding behavior data, and historical feed residue rate. Based on these historical feeding status parameters, a target feeding behavior characteristic for the target aquaculture pond is constructed. The similarity of feeding behavior between the target aquaculture pond and each reference aquaculture pond is determined based on this characteristic. Reference aquaculture ponds with a feeding behavior similarity greater than a preset threshold are marked as candidate aquaculture ponds. The target feeding amount for the target aquaculture pond is determined based on the historical feeding amount and feeding behavior similarity of the candidate aquaculture ponds. This invention enables precise, adaptive, and cross-scenario intelligent decision-making regarding feeding amount by analyzing the historical feeding patterns of fish and matching empirical data from similar aquaculture scenarios.

This invention discloses a multi-architecture hybrid method for recognizing fish feeding behavior, belonging to the field of aquaculture image processing technology. The method includes: acquiring video data of fish feeding processes; preprocessing the video data to obtain a spatiotemporal input tensor; using a multi-architecture hybrid backbone network to extract spatiotemporal features from the spatiotemporal input tensor to obtain an augmented tensor; mapping the augmented tensor to a classifier, outputting the probability distribution of each feeding behavior category, and using the category index corresponding to the highest probability as the recognition result. This invention achieves a unified expression of local burst features and long-range temporal dependencies in fish feeding behavior, reducing computational load while improving the ability to eliminate channel redundancy and model semantic relevance; it also balances computational overhead, enabling high-efficiency and high-precision recognition of fish feeding behavior.

The present application relates to the technical field of agricultural intelligence and computer vision, and discloses a pig feeding amount estimation method and system based on computer vision recognition, which comprises the following steps: synchronously collecting image sequences and audio signals; extracting live pig head posture features and chewing / swallowing acoustic features; fusing audio-visual features through a cross-modal attention mechanism and discriminating real feeding behaviors; and combining a single swallowing amount model to cumulatively estimate the feeding amount. The system comprises multi-source data collection, visual feature processing, acoustic feature processing, cross-modal fusion and discrimination, and feeding amount quantitative estimation modules. Through audio-visual cross-modal fusion and adaptive weight adjustment, the present application significantly improves the feeding behavior recognition accuracy and system robustness, and realizes individualized accurate feeding amount estimation.

The application discloses an industrial Internet of Things platform data intelligent processing and analysis system based on artificial intelligence and belongs to the technical field of platform data processing and analysis. The system implements monitoring analysis and judgment classification on feeding time, feeding food type and corresponding feeding food amount, and feeding and water feeding of pet feeding by staff based on an Internet of Things platform, integrates and calculates feeding behavior monitoring and processing data of different aspects of different staff on the same day to analyze and classify the overall feeding state of the staff on the same day, so that subsequent targeted management and training can be implemented on different staff and the overall work team, and more comprehensive and various data monitoring and data expansion and utilization effects can be implemented. The application is used for solving the technical problems that the remote pet feeding management supervision mode is single and feeding management cannot be optimized and improved in a targeted manner in the prior art.