4659results about "Other apparatus" patented technology

The invention discloses a livestock breeding house environment detection method and system, and relates to the technical field of livestock environment intelligent perception and regulation, and the method comprises the steps: collecting original thermal image data, original audio signals and original gas data, and generating a space-time alignment multi-modal data set; performing space-time gridding processing on the multi-modal data set, extracting temperature gradient, voiceprint MFCC characteristics and gas concentration space distribution, and constructing a temperature-voiceprint-gas three-dimensional incidence matrix; based on the temperature-voiceprint-gas three-dimensional incidence matrix, dynamically compensating the body surface temperature of the animal in combination with the temperature and humidity index, inputting the gas concentration as a weight factor into the LSTM time sequence prediction model, and outputting a stress risk level and a pollution risk level; and according to the stress risk level and the pollution risk level, generating and executing a corresponding environment regulation and control strategy. According to the invention, high-precision monitoring and dynamic regulation and control of the animal house environment state can be realized, the animal health guarantee level is improved, and the breeding risk and the operation and maintenance cost are reduced.

The invention discloses a pig breeding intelligent management and control system based on multi-modal data monitoring, and belongs to the technical field of pig breeding management, and the system comprises a data collection and individual recognition module which comprises an RFID ear tag, an inspection robot, an environment sensor and an intelligent feeder, and is used for determining the identity information of a pig individual based on the data collected by the RFID ear tag and the inspection robot, associating the collected multi-modal data of the live pigs in the pig house, and uploading the multi-modal data to edge computing equipment through an MQTT protocol for preprocessing; the feature extraction module extracts physiological and behavior features of live pigs by using a multi-modal deep learning fusion model; the key breeding index analysis module constructs a live pig health index and an environment quality index; the abnormity early warning module adopts a CUSUM control chart to accumulate index deviation and respond to abnormity; the management regulation and control model constructs a Markov decision process by minimizing energy consumption and maximizing health indexes, intelligently adjusts the rotating speed of a fan and a feeding strategy, and realizes precise environmental control and feeding optimization.

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

A detection and deterrent method and system for detecting and deterring a pet from attacking another animal including a collar configured to be affixed to a pet, the collar having a motion sensor. The collar is configured to determine when a pet is about to attack another animal and to deter the pet from attacking the animal.

The invention discloses a feed regulation and control method and system for beef cattle breeding, and relates to the technical field of feed regulation and control, and the method comprises the steps: carrying out grouping management according to individual differences of beef cattle, and collecting feed component data, growth stage data and climate environment data of beef cattle breeding in each group in real time; on the basis of the feeding and defecation amount of the beef cattle, the nutrient substance digestion and metabolism capacity of the beef cattle is evaluated, a dynamic feeding model driven by multi-modal data is constructed, and a feeding mode and an optimal feed ratio are matched; the optimal feed proportion is converted into a control instruction, and equipment is driven to execute feed blending and feeding; beef cattle growth condition data are monitored in real time, and the feed ratio is optimized according to actual deviation feedback. According to the method, the cattle in different physiological states are finely distinguished, multi-dimensional data such as environmental parameters and metabolic capability evaluation are combined, a dynamic feeding model is constructed, the feed conversion efficiency and the stability of growth performance are remarkably improved, and healthy growth of the beef cattle is guaranteed through effective feeding management.

The present invention introduces a universal AI-powered pet management platform that establishes an entirely new category of technology, transcending conventional pet training systems. This comprehensive system integrates a modular wearable pet device with interchangeable sensors, sophisticated AI processing capabilities, and diverse output modules to create a unified ecosystem for holistic pet care. Unlike traditional training devices focused solely on behavior modification, this platform simultaneously manages multiple domains including real-time health monitoring, environmental safety assessment, emotional well-being analysis, autonomous training, emergency response, and seamless integration with external systems. The platform's universal architecture enables dynamic adaptation across diverse applications from companion animals to service animals, wildlife monitoring, and specialized deployments. By leveraging advanced artificial intelligence models, multimodal communication pathways, and a universal API for third-party integration, the system creates an interconnected technological framework that fundamentally transforms the relationship between pets, technology, and human interaction, rendering isolated pet devices obsolete.

A system for tracking the performance of airborne objects and a method of assessing at least one performance metric of the airborne object. For example, the system is used to improve performance of falcons in completing one or more tasks. The system includes an electronic device that monitors key physiological parameters, such as heart rate, body temperature, and activity levels, to provide real-time feedback to the falconer. The device also includes an accelerometer and gyroscope to track the bird's movements and provide information about its orientation and direction of flight. The system utilizes machine learning algorithms to analyze the collected data and provide recommendations for optimizing the bird's performance, such as adjusting its diet or training regimen. By utilizing this technology, falconers can gain a deeper understanding of their birds' performance and make informed decisions to improve their success in completing tasks, such as hunting and aerial pursuit.

The invention discloses a pig individual identification and health monitoring system based on biological characteristics, and relates to the technical field of medical treatment, and the system comprises a multi-mode biological characteristic collection module which is used for obtaining pig 3D facial characteristics, ear blood vessel distribution characteristics, gait dynamic characteristics and physiological data; the biological feature fusion recognition module is used for fusing multi-modal features based on a Transform architecture and realizing individual unique identification and feature template dynamic updating; and the digital twinborn health monitoring module is used for constructing a pig full-life-cycle digital twinborn model and realizing health state evaluation and disease early warning in combination with an LSTM neural network. According to the pig individual recognition and health monitoring system based on the biological characteristics, 1024-dimensional standardized feature vectors are constructed through cooperation of multiple devices such as an RGB-D camera, a near-infrared camera and a binocular vision sensor, modal weights are dynamically distributed in combination with a Transform network, and high-precision recognition in a complex scene is achieved.

The invention relates to the technical field of environment monitoring, solves the problem that a monitoring system and method cannot meet the requirements for accuracy and real-time performance of animal behavior research and climate environment research in a plateau area at the same time, and particularly discloses a monitoring system and method integrating environment monitoring and animal monitoring recognition. Comprising a sensing system, a data transmission system and a data processing system, the sensing system is used for collecting environment and animal data of a monitoring site, and the data processing system is used for receiving and processing the data and analyzing the change trend and the correlation influence degree of the environment data and the animal data; the monitoring method comprises the following steps: acquiring environment and animal data through the environment monitoring network and the animal behavior sensing network; data is transmitted to the data processing system in real time through the data transmission system; the data processing system analyzes the dynamic change trend of the environment data and the animal data; and generating early warning information and triggering a protection measure execution instruction. The method can be used for monitoring the plateau environment and animals, and is high in accuracy and high in real-time performance.

The invention relates to the technical field of modern agriculture, and discloses an intelligent yak breeding monitoring system which comprises a sensor network module, a data processing center module, an intelligent analysis module and a remote monitoring terminal module. The sensor network module integrates various sensors and is used for monitoring physical signs, behaviors and breeding environment parameters of yaks in real time; the data processing center module is responsible for receiving, processing and storing original data transmitted by the sensor network; the intelligent analysis module establishes a health assessment model and an environment optimization algorithm based on the processed data, and realizes real-time assessment of the health condition of the yaks and intelligent optimization of the breeding environment; the remote monitoring terminal module provides a user interface and supports remote control and early warning notification functions. The intelligent yak breeding monitoring system aims at solving the problems that an existing yak breeding environment monitoring technology is single in data, insufficient in intelligent degree and difficult to meet the requirements of precise breeding and scientific management.

The invention relates to the technical field of target detection, and discloses a pet target detection method and device and a camera, and the method comprises the steps: carrying out the motion triggering collection and image enhancement preprocessing of a front end region of a feeder, and obtaining an enhanced image frame sequence; performing feature extraction of dynamic receptive field adjustment on the enhanced image frame sequence to obtain pet feature descriptors and position information; behavior time sequence feature analysis is executed, and pet behavior sequence feature vectors are obtained; constructing a state transition diagram according to the pet behavior sequence feature vector, and performing time sequence consistency analysis to obtain a pet state judgment result; power management and decision execution are carried out on the feeder based on the pet state judgment result, feeding control under the low-power-consumption condition is achieved, behavior misjudgment caused by posture fluctuation is effectively avoided, the behavior recognition accuracy is improved, the accurate feeding control problem in a multi-pet family is solved, and the user experience is improved.

The invention discloses a pet pacifying method, and relates to the technical field of emotion recognition, and the pet pacifying method comprises the following steps: obtaining pet behavior data, and inputting the pet behavior data into a pet emotion recognition model to recognize the current emotion type of a pet; according to the current emotion type of the pet, determining a pacifying strategy corresponding to the current emotion type; and pacifying the pet based on the pacifying strategy. According to the method, the emotion of the pet is recognized from multiple dimensions, the accuracy of pet emotion recognition is improved, whether the pet is in an anger, happy or calm state is judged, corresponding strategies are selected for pacifying according to different emotional states of the pet, and the anxiety, tension and other emotions of the pet can be relieved more effectively.

The invention belongs to the technical field of data processing and animal behavior recognition, and discloses an animal abnormal state intelligent monitoring method and system based on biological behavioristics, and the method comprises the following steps: S1, constructing and training an animal abnormal state monitoring model; s2, constructing an animal abnormal state monitoring network system; and S3, carrying out intelligent monitoring on the abnormal state of the animal. According to the method and the system provided by the invention, multi-model fusion is adopted, the animal behavior data is efficiently processed in real time, and the accuracy and the robustness of animal abnormal state monitoring are improved through more comprehensive and abundant feature representation data and twice judgment; the system can realize real-time monitoring of animal individual and group behaviors, timely discover abnormal states and perform early warning or intervention, can be widely applied to zoos, farms and the like, meets industrial requirements of large-scale and large-quantity animal synchronous monitoring, and effectively protects health and safety of animals.

The invention discloses a pet emotion recognition system and method, and relates to the technical field of pet emotion recognition, and the system comprises a data acquisition module, a data analysis module, an environment analysis module, an emotion analysis module, an emotion comparison module, and an abnormality processing module. The data acquisition module is used for acquiring classified pet historical emotion data, pet emotion data acquired in real time and environment data acquired in real time, and then sorting the classified pet historical emotion data; the technical key points are as follows: analyzing and processing historical emotion data of the pet, identifying the emotion of the pet from two perspectives of sound and physiology, knowing the relationship between the emotion of the pet and the sound and physiology, and then identifying the emotion of the pet in real time based on the relationship between the emotion of the pet and the sound and physiology. The external environment and the state of the pet are analyzed in the pet emotion recognition process, the pet emotion recognition accuracy can be improved, and the good use prospect is achieved.

A system for intelligently selecting sensors and their associated operating parameters includes one or more source sensors that gather animal data from at least one targeted individual and a collecting computing device in electrical communication with the one or more source sensors. The collecting computing device is operable to utilize one or more Artificial Intelligence techniques to: (1) intelligently gather the animal data from the one or more source sensors; (2) create, modify, or access one or more commands; and (3) and intelligently transmit the one or more commands either directly or indirectly to the one or more source sensors to create or modify one or more sensor operating parameters.

The invention relates to the Internet of Things technology, and discloses an animal health monitoring system which comprises wearable equipment and a cloud server. The wearable equipment comprises a processor, and an acceleration sensor, a body temperature sensor and a wireless transceiver which are connected with the processor; the processor is configured to determine the posture of the animal according to the acceleration data detected by the acceleration sensor, and obtain the body temperature of the animal from the body temperature sensor; the posture-body temperature data pair is sent to a cloud server through a wireless transceiver; the cloud server is configured to input a plurality of groups of posture-body temperature data pairs which are sequenced according to the time sequence and come from the wearable device into a pre-trained first machine learning model, and the animal health state output by the first machine learning model is obtained. According to the system, the transmission energy consumption is reduced, the endurance time is prolonged, and the accuracy of animal health state judgment is improved.

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

A computer-implemented system and method operate an automatic and adaptive animal behavioral system. The system receives user configuration and feedback, and generates a training set based on online animal behavior patterns and received user feedback. The system trains one or multiple models based on the training set, if the training set is sufficiently large. It then validates the models, and provides system generated classifications and action types and levels for animal's behavior patterns, if the models' accuracy rate is above a threshold.

The invention relates to the technical field of livestock breeding, in particular to a cow manure automatic scoring system and method.The cow manure automatic scoring system comprises an image acquisition unit, a physical property sensor, a chemical component detection module and a data processing unit.The image acquisition unit is used for capturing the form, color and fiber residue of manure; the physical property sensor is used for measuring the water content of excrement and evaluating the viscosity and hardness of the excrement; the chemical component detection module comprises a near infrared spectrum analysis unit and an electrochemical sensor unit; and the data processing unit is used for integrating an AI algorithm, realizing multi-source data fusion and scoring, and uploading the data to the cloud management platform through the communication module. Therefore, the problems that in the prior art, manual scoring is subjective, low in efficiency and difficult to quantify, diseases are difficult to find early due to the lack of rapid component detection means, and accurate nutrition regulation and control are difficult to achieve due to the fact that scoring data are difficult to integrate into a breeding system are solved.

A livestock management system includes a first type of livestock tag, a second type of livestock tag, and a computing device. The first type of livestock tag includes both a first long-range wireless transceiver and a first short-range wireless transceiver and is attached to a first livestock animal. The second type of livestock tag, which is attached to a second livestock animal, includes second short-range range transceiver that transmits a unique tag identifier associated with the second type of livestock tag to the first short-range wireless transceiver; the second type of livestock tag is incapable of transmitting a long-range wireless transmission. The computing device receives the unique tag identifier of the second type of livestock tag from the first type of livestock tag via long-range wireless transmission from the first long-range wireless transceiver.

The invention discloses a pigsty environment regulation and control optimization method and system, and belongs to the technical field of breeding environment management. The system comprises an intelligent sensing module, an environment analysis module, a parameter optimization module and a control management module. The intelligent sensing module is used for collecting pig house environment parameters and live pig physiological parameters through a multi-mode sensor; the environment analysis module is used for constructing a behavior recognition model, analyzing behavior characteristics based on physiological parameters and dividing abnormal regions; the parameter optimization module is used for establishing a physiological environment control model and setting an optimal optimization scheme for each abnormal region; and the control management module is used for generating an environment control instruction according to the optimal optimization scheme and controlling equipment execution. The pig house environment and the pig physiological parameters are collected and intelligently analyzed through the multi-mode sensor, automatic and precise environment optimization management is achieved, and the comfort level and the health level of the pigs are improved.

The invention relates to the technical field of image processing, and discloses an intelligent weight monitoring method, system and device for bred pigs and a storage medium. The method comprises the following steps: acquiring an original data set through multi-angle image acquisition; carrying out image processing by utilizing adaptive histogram equalization and Gaussian filtering; obtaining body shape parameters by adopting contour-skeleton feature extraction and curvature analysis; performing multi-source fusion processing in combination with the data of the Internet of Things; body weight modeling is carried out based on a hierarchy-increment prediction model and an attention mechanism; and generating a feeding scheme through reinforcement learning. According to the invention, the technical problems of insufficient data fusion and low prediction precision in the existing breeding pig weight monitoring method are solved.

The invention discloses a cattle manure automatic cleaning device and method for a cattle farm, and relates to the technical field of livestock breeding intelligent equipment.The method comprises the steps that a hexagonal monitoring grid is established based on excretion coordinates, a manure accumulation thermodynamic diagram is established through a three-dimensional space point cloud reconstruction method, and according to thermodynamic value intensity distribution in the manure accumulation thermodynamic diagram, the manure accumulation thermodynamic diagram is obtained; marking a to-be-cleaned area; according to the to-be-cleaned area, dynamically analyzing faeces conductivity characteristics by adopting an electromagnetic eddy current detection method, and generating a cattle faeces viscosity index and a cleaning difficulty coefficient through a multi-physics coupling algorithm in combination with environmental parameters; and according to the excrement viscosity index and the cleaning difficulty coefficient, a control instruction set of the cleaning device is generated through a dynamic adjustment algorithm. According to the invention, by constructing the hexagonal monitoring grid and the excrement accumulation thermodynamic diagram, spatial modeling of excrement distribution and intelligent marking of the cleaning area are realized.

The invention discloses a goat oestrus monitoring system. The goat oestrus monitoring system comprises a multi-mode infrared thermal imaging data acquisition sensing unit and a multi-dimensional environment parameter collaborative real-time sensing unit. A goat body surface temperature field and a multispectral image are acquired through an uncooled vanadium oxide focal plane array infrared detector and a multiband spectral imaging module, environmental parameters are acquired in combination with a distributed sensing network, and a motion track is recorded through depth vision and inertia measurement. The heterogeneous multi-core data processing central unit is used for preprocessing, storing and preliminarily analyzing multi-source data, the feature extraction unit is used for mining multi-dimensional features, and the intelligent decision judgment unit is used for judging an estrus state by combining a deep neural network and dynamic threshold adjustment. The system integrates multiple technologies, can accurately monitor the oestrus state of the goats in real time, provides data support for scientific breeding, and improves breeding benefits.

The invention discloses an aquaculture organism health state monitoring system based on multi-dimensional feature association, and belongs to the technical field of aquaculture, and the system comprises a visual feature collection layer which is composed of an image collection module, an image preprocessing module, a target analysis module and a feature extraction module; the molecular biology verification layer is composed of an immune gene monitoring module and a metabolic gene monitoring module; the data association prediction layer is composed of a standardization module, a feature screening module, a deep learning module and an evaluation output module; a self-adaptive optimization mechanism, a fault-tolerant mechanism and an environment adaptability adjustment mechanism. According to the aquaculture organism health state monitoring system based on multi-dimensional feature association, intelligent monitoring of the health state of aquaculture organisms is achieved by integrating a computer vision technology and a molecular biology detection method, and a scientific basis is provided for aquaculture production management.

The invention provides a sheep body size measuring method and device based on three-dimensional reconstruction and point cloud segmentation, a medium and a program product, and relates to the technical field of computer vision. The method comprises the following steps: acquiring a sheep image of a to-be-detected sheep, and preprocessing the sheep image to obtain a multi-view sheep target point cloud; performing point cloud registration processing on the multi-view sheep target point cloud to obtain a sheep three-dimensional model; performing point cloud segmentation processing based on the body size measurement key points and the sheep three-dimensional model to obtain key point region point clouds; based on the key point region point cloud, determining a region point cloud projection space, performing attitude normalization on the interval point cloud, and determining an attitude normalized key point region point cloud; and calculating the body size data of the to-be-measured sheep based on the point cloud of the key point region subjected to attitude normalization. According to the embodiment of the invention, through three-dimensional reconstruction of the body surface point cloud of the sheep, non-contact measurement of the body size data of the sheep is realized, and the measurement efficiency and accuracy are improved.

The invention relates to the technical field of livestock health management and early warning, and particularly discloses a livestock health management and early warning method and system for animal husbandry based on the Internet of Things, and the method mainly comprises the steps: obtaining the GPS data and monitoring data of livestock, the GPS data comprising a timestamp and a GPS coordinate; calculating an activity distance of the livestock in a specified time period based on the GPS data; judging whether the activity distance belongs to a preset interval or not, and if yes, identifying the livestock from the monitoring data of the specified time period as the livestock to be monitored; and extracting behavior characteristics of the to-be-monitored livestock from the monitoring data of the specified time period, and judging state information of the behavior characteristics. According to the system, the Internet of Things and the data analysis technology are adopted, intelligent monitoring of the livestock activity state is achieved by obtaining the GPS data and the monitoring data of the livestock in real time, the activity and behavior characteristics of the livestock can be effectively monitored, abnormal conditions can be found in time, early warning is made, the burden of manual monitoring is reduced, and the overall efficiency of pasture management is improved.

The invention provides an intelligent feed recommendation and precise feeding system based on multi-mode intelligent perception of live pigs. The system comprises a sensing module, a data processing module, a decision module and an execution module. The sensing module collects visual, sound, behavior and physiological data of live pigs, the data processing module cleans, standardizes and fuses the data, the decision module generates a feed feeding decision through a reinforcement learning algorithm, and the execution module automatically controls feeding equipment according to the decision. Data cleaning adopts different strategies to remove noise, standardization ensures compatibility of different modal data, and data fusion extracts features through a convolutional neural network and LSTM and uses a self-attention mechanism to perform weighted fusion. A reinforcement learning algorithm optimizes decisions, balances the health state, the feed utilization rate and the cost, and dynamically adjusts the feeding strategy. Through automatic whole-process management, the system can significantly improve the feed utilization rate, reduce the feed cost, improve the health condition of live pigs, and maximize the economic benefits of a farm.

The invention provides a pet state analysis method and system based on remote monitoring, and relates to the technical field of pet state evaluation. The method comprises the following steps: collecting multi-source historical health monitoring data about a pet, and determining a plurality of multi-source combination states of the pet; performing physiological behavior joint analysis based on morphological change on the plurality of multi-source combination states of the pet, generating a plurality of physiological behavior joint modes of the pet, and determining a recessive compensation mode in each physiological behavior joint mode; constructing a state association map of the pet, and performing anomaly analysis on the multi-source real-time health monitoring data of the pet to determine a state anomaly event; and determining a target joint mode to which the pet belongs according to the multi-source real-time health monitoring data, performing compensation stability analysis on the pet according to a recessive compensation mode of the target joint mode, and generating a compensation imbalance evaluation result of the pet. According to the invention, accurate evaluation of the health state of the pet in the transportation process is realized.

The invention relates to the technical field of intelligent breeding, and discloses an intelligent hog house monitoring system, which comprises a plurality of agent nodes used for collecting physiological parameters, behavior characteristics and environment data of a hog; the edge calculation module is used for constructing an individual health state vector according to the collected data; the state atlas construction module is used for generating a health state atlas based on a time sequence state vector; the control feedback module is used for driving the environment adjusting equipment when the deviation from the optimal state is judged; the communication and cooperation module is used for exchanging prediction information and state feedback among different agent nodes; the federal learning coordination module carries out aggregation and synchronously updates to the agent node; and the system evolution module is used for adjusting a model structure or control parameters during system-level anomaly detection. A federal learning coordination mechanism is adopted to aggregate and synchronize model parameters uploaded by a plurality of agent nodes, and the technical effects of data local processing and model global optimization are achieved.