366 results about "Animal behavior" patented technology

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 provides a field panda behavior monitoring method and system based on artificial intelligence, and relates to the technical field of animal behavior monitoring, and the method comprises the steps: obtaining an original data set of a target panda; performing feature extraction processing according to the video information and the environment information, and analyzing feature change trends in different time periods and different scenes by extracting motion, posture and micro expression information of a target panda and combining environment conditions to obtain a feature set; carrying out numeralization processing according to the feature set to obtain virtual individual numerical value representation; modeling processing is carried out according to the virtual individual numerical value representation, and a panda motor function model is constructed; performing behavior trajectory deduction according to the panda motor function model and the geographic position information to obtain a behavior trajectory deduction result; and generating a behavior monitoring report of the target panda based on the behavior track deduction result. According to the method, the behavior pattern of the panda can be analyzed more comprehensively and accurately, and the monitoring efficiency and the protection effect are improved.

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.

An animal tracking system is provided which include various features that are enabled using satellite-based positioning systems, but further are enabled by fine accuracy position detection, such as may be made available through use of realtime kinematic (RTK) positioning or post-processing kinematic (PPK) positioning technologies. These include, for example, various usage techniques in terms of set up and use of virtual boundaries to define containment areas and or exclusion zones, and to both see historical animal positioning within a containment zone and determine likely animal behaviors based on such positioning. Additionally, fine-grained indoor location via radio frequency technologies, switchover among locating technologies, and power management techniques are described, as well as uses thereof.

The invention belongs to the technical field of animal behavior detection, and particularly relates to an animal behavior recognition enhancement method and system based on self-supervised pre-training. The method comprises the following steps: S1, constructing a reference model based on a PatchTST model, partitioning a time sequence, and introducing channel independence processing and Transform architecture to complete local feature extraction and long-range dependence modeling of time sequence data; s2, adopting a self-supervised pre-training framework based on time-frequency consistency TF-C, carrying out time-frequency consistency contrast learning on the large-scale cross-species unlabeled data set, and enabling the reference model to capture time-frequency domain invariant features through time domain and frequency domain enhancement and contrast loss and consistency loss optimization; and S3, migrating the pre-trained encoder weight to a PatchTST model, performing initialization and fine tuning on a specific species annotation data set, and combining channel attention and feature recombination convolution optimization to complete cross-species knowledge migration and target task adaptation.

This disclosure provides a method, apparatus, electronic device, and storage medium for animal language conversion, relating to the field of artificial intelligence technology, specifically machine learning, deep learning, and natural language processing. The specific implementation involves: acquiring multimodal data related to the animal, including animal vocal data, animal behavioral data, and animal physical characteristics data; preprocessing the multimodal data to obtain fused multimodal data; identifying the animal's current emotion based on the fused multimodal data to obtain an emotion recognition result; and performing semantic mapping and language translation on the emotion recognition result to convert the animal language into human language, obtaining a language conversion result. This disclosure can accurately identify the animal's current emotional state and convert it into human language, thereby achieving deeper emotional communication and understanding between animals and humans, and improving the accuracy and efficiency of cross-species communication.

The invention provides an animal abnormal behavior detection method based on a skeleton sequence, and belongs to animal behavior detection. Firstly, collected animal behavior videos are preprocessed, animal targets are detected, bounding box positions of the animal targets are obtained, then skeleton key points are estimated, and an animal skeleton sequence is generated. On the basis, spatial-temporal feature modeling is carried out on the skeleton sequence, animal behavior modes are analyzed, and abnormal behaviors (such as falling, attacking and the like) are accurately recognized. According to the method, the abnormal behavior detection result can be output in real time, and model optimization is performed according to feedback. The method is suitable for the scenes of animal behavior monitoring, health state evaluation, abnormal behavior early warning and the like, has high efficiency, expandability and practical application value, and solves the problems of low accuracy and poor robustness of animal behavior anomaly detection in a complex environment.

The invention relates to the technical field of information retrieval, in particular to an animal behavior management system based on an intelligent dog training device, and the animal behavior management system based on the intelligent dog training device comprises a behavior data collection module, a high-dimensional offset calculation module, a behavior boundary recognition module, an abnormal influence degree analysis module and a behavior index updating module. According to the method, the comprehensiveness and accuracy of behavior monitoring are improved by integrating environmental factors and multi-dimensional behavior data, abnormal behaviors are accurately identified and predicted by utilizing time sequence analysis and comprehensive processing of motion data, the accuracy of behavior pattern identification is enhanced, and the accuracy of behavior pattern identification is improved by analyzing the change of motion rhythms and the transfer of behavior paths. The abnormal behaviors are stably recognized, the coping complexity of the system is improved, the behavior index is dynamically updated, the matching degree is optimized, the system can adapt to behavior evolution, and the reasonability and effect of an intervention strategy are improved.

The invention discloses a multi-modal animal abnormal data behavior classification method and device which are applied to the technical field of data processing, and the method comprises the steps: obtaining abnormal data of a target animal in different development stages; the abnormal data comprises behavior video data and intestinal flora data of the same animal; performing feature extraction on the behavior video data to obtain behavior features representing animal action modes; analyzing the intestinal flora data to obtain intestinal flora characteristics; performing multi-modal data fusion on the behavior characteristics and the intestinal flora characteristics to generate fusion characteristics; inputting the fusion features into a pre-trained data model, classifying animal behaviors through the data model, and outputting a classification result; through the behavior-flora multi-modal fusion strategy, the classification accuracy is improved, and clues are provided for early intervention.

An animal tracking system is provided which include various features that are enabled using satellite-based positioning systems, but further are enabled by fine accuracy position detection, such as may be made available through use of realtime kinematic (RTK) positioning or post-processing kinematic (PPK) positioning technologies. These include, for example, various usage techniques in terms of set up and use of virtual boundaries to define containment areas and or exclusion zones, and to both see historical animal positioning within a containment zone and determine likely animal behaviors based on such positioning. Additionally, fine-grained indoor location via radio frequency technologies, switchover among locating technologies, and power management techniques are described, as well as uses thereof.

The invention relates to the technical field of management systems, in particular to a pig behavior and pigsty environment integrated management system based on digital twinning, which comprises a digital twinning system, and the digital twinning system comprises an Internet of Things module used for data acquisition and transmission between a physical pig farm and a digital twinning model; the environmental parameter acquisition module is used for acquiring environmental data in the pig farm in real time by installing various sensors in the pig farm; the animal behavior data acquisition module is used for monitoring and identifying pig behaviors, including motion behaviors, feeding behaviors, land laying behaviors and climbing attack behaviors. In the invention, aiming at the problem of weak capabilities of capturing, analyzing and predicting pig behaviors in the prior art, the animal behavior data acquisition module is used for comprehensively monitoring the behaviors such as movement, feeding, land laying, climbing attack and the like of the pigs, so that the dynamic change and interaction relationship between the environment and the behaviors can be effectively captured, and the accuracy of a prediction result is improved.

The invention relates to the technical field of zoo management, in particular to a method and system for revealing animal behaviors in a zoo. According to the animal behavior identification method and system, the relationship with emotions and behaviors is established through video data, audio data, physiological data and environment data, multi-modal fusion is carried out through video features, audio features, physiological features and environment data, and animal behaviors are identified based on multi-modal fusion features by adopting a multi-modal Transform model; according to the invention, analysis of behaviors of animals in the zoo is realized, and a decision basis is provided for animal management of the zoo.

The invention belongs to the technical field of farm environment regulation and control, and discloses a farm environment regulation and control system and method, and the system comprises a multi-source sensing module, a data analysis module, an intelligent decision module, and a regulation and control execution module. According to the method, the dynamic environment coupling model is constructed, the environment state is comprehensively evaluated in combination with the multi-source sensing data, and the internal conflicts and cooperative relationships among the regulation and control parameters are accurately identified, so that the cooperative regulation and control instruction set considering multiple targets is formulated. Therefore, the problem of linkage environment imbalance caused by single regulation and control in traditional control is avoided, and the overall stability and comprehensive comfort of the breeding environment are remarkably improved. According to the invention, animal behavior perception is taken as a core feedback and feedforward signal, and conversion of a regulation strategy from passive response to active prediction is realized. The system can pre-judge the change of a local environment in advance according to the moving trend of animals and perform fine adjustment, so that stress events such as ammonia peak value or violent fluctuation of temperature and humidity are effectively avoided.

The invention discloses a bred animal behavior tracking system and method based on artificial intelligence, and the system comprises six modules which are sequentially connected to achieve data transmission and processing. Video streams, audio streams and infrared thermal imaging data of animals in a breeding scene are obtained through a multi-modal data acquisition device, animal contours are extracted through a dynamic contour anchoring Mamba network processing module, time sequence modeling is carried out, and a behavior dynamic map construction module constructs a behavior dynamic map. The behavior dynamic map deviation evaluation module calculates a characteristic deviation value between real time and a standard map, the intelligent breeding multi-mode space-time engine module fuses space-time information and time sequence characteristics to generate space-time correlation behavior data, and finally the behavior tracking result output module outputs animal real-time behavior categories and movement tracks. According to the invention, the behavior feature extraction precision is improved to reflect the continuous change process of animal behaviors; and the requirements of intelligent breeding on high efficiency, accuracy and deep analysis of animal behavior tracking are met.

The invention relates to the technical field of image recognition, and discloses a breeding environment regulation and control method and system based on machine learning, and the method comprises the steps: collecting environment parameters of a breeding farm; shooting images of animals in the farm by using a camera system installed in the farm; analyzing the image of the animal through a preset convolutional neural network, and extracting a behavior feature vector of the animal; inputting the environment parameters of the farm and the behavior feature vectors of the animals into a preset deep neural network, and outputting comprehensive behavior indexes of the animals; according to the comprehensive behavior indexes of the animals, environment control equipment of the farm is regulated and controlled. The invention provides an intelligent breeding environment regulation and control method by introducing an advanced machine learning technology, especially a convolutional neural network (CNN) and a deep neural network (DNN). According to the method, the farm environment parameters and animal behavior characteristics can be accurately monitored and analyzed, and the environment control equipment is dynamically adjusted according to the analysis result.

The invention relates to the technical field of animal experiment teaching demonstration, in particular to an animal experiment teaching demonstration center management device and a use method thereof, and the method comprises the following steps: I, device initialization: arranging remote monitoring equipment and close sensing equipment, and ensuring that an animal activity area is covered and no blind area exists; defining monitoring modes in the main control system, wherein the monitoring modes comprise a remote monitoring mode, a close monitoring mode and a cooperative monitoring mode; according to the invention, through the dynamic cooperation of remote and close-range monitoring equipment and in combination with an interference evaluation algorithm, the balance between monitoring accuracy and interference minimization is realized, the natural behavior mode of animals is effectively reserved, and meanwhile, an adaptive mechanism is adopted, so that equipment parameters and a monitoring mode can be adjusted in real time, and the monitoring effect is dynamically optimized according to an experimental scene; manual intervention requirements are greatly reduced, the contradiction between accuracy and interference of an existing animal behavior monitoring system is effectively solved, and the animal behavior monitoring system is suitable for various animal experiment teaching and scientific research scenes.

The invention provides an animal behavior training device based on an air floating ball before magnetic resonance imaging, and particularly relates to the technical field of experimental equipment.The animal behavior training device based on the air floating ball before magnetic resonance imaging comprises a supporting mechanism and a floating ball mechanism, an air floating ball base clamping groove is formed in the supporting platform; the floating ball mechanism comprises an air floating ball base arranged in the air floating ball base clamping groove, a semi-circular groove is formed in the air floating ball base, an air floating hole is formed in the bottom of the semi-circular groove, an air floating ball is loaded in the semi-circular groove, a plurality of air inlet pipe openings communicated with the air floating hole are formed in the bottom of the air floating ball base, and the air inlet pipe openings are communicated with the air floating hole. An external air blower is utilized to ventilate air into the air floating holes through the air inlet pipes and drive the air floating balls to suspend, and the four limbs of the small animal can move on the air floating balls in a multi-degree-of-freedom mode and are not limited.

An animal tracking system is provided which include various features that are enabled using satellite-based positioning systems, but further are enabled by fine accuracy position detection, such as may be made available through use of realtime kinematic (RTK) positioning or post-processing kinematic (PPK) positioning technologies. These include, for example, various usage techniques in terms of set up and use of virtual boundaries to define containment areas and or exclusion zones, and to both see historical animal positioning within a containment zone and determine likely animal behaviors based on such positioning. Additionally, fine-grained indoor location via radio frequency technologies, switchover among locating technologies, and power management techniques are described, as well as uses thereof.

The invention discloses an animal husbandry environment monitoring and early warning system based on a big data platform, and relates to the technical field of intelligent animal husbandry environment monitoring and early warning systems, and the system comprises an acquisition module which acquires farm environment data and animal behavior image data and uploads the data to an edge computing node, a preprocessing module, a data processing module and a data processing module, the edge computing node is used for preprocessing the farm environment data and the animal behavior image data, the fusion module is used for fusing the preprocessed farm environment data and animal behavior image data to form a multi-modal data set, and the prediction module is used for establishing a health and risk prediction model. Obtaining an animal health condition assessment score and a potential risk level score according to the multi-modal data set; according to the method, data from different sources are integrated, so that health and risk assessment is more comprehensive and accurate, the spatial relevance of the data is adjusted by using a geographic weighting function, and the representativeness of the data is further improved.

A nervous system emulator engine includes working computational models of the vertebrate nervous system to generate lifelike animal behavior in a robot. These models include functions representing several anatomical features of the vertebrate nervous system, such as spinal cord, brainstem, basal ganglia, thalamus, and cortex. The emulator engine includes a hierarchy of controllers in which controllers at higher levels accomplish goals by continuously specifying desired goals for lower-level controllers. The lowest levels of the hierarchy reflect spinal cord circuits that control muscle tension and length. Moving up the hierarchy into the brainstem and midbrain / cortex, progressively more abstract perceptual variables are controlled. The nervous system emulator engine may be used to build a robot that generates the majority of animal behavior, including human behavior. The nervous system emulator engine may also be used to build working models of nervous system functions for clinical experimentation.

The invention relates to the technical field of smart breeding and big data analysis, and discloses a smart breeding monitoring method and system based on big data analysis. The method comprises the following steps: periodically acquiring original data of a farm sensing network, and constructing a multi-dimensional state image through time-space synchronization and normalization processing; inputting the image into a pre-trained behavior-environment decoupling analysis network, and stripping an independent behavior feature tensor and an independent environment background field tensor in parallel; and based on the two, calculating a coupling degree index through a cross impact evaluation matrix and generating a comprehensive anomaly scoring graph. According to the graph, a strategy generation engine dynamically synthesizes a regulation and control instruction set containing directional airflow guide, refined feeding and temperature and humidity coordinated regulation. According to the method, deep separation and correlation analysis of animal behaviors and breeding environment factors are realized, an abnormal root can be accurately traced, and early warning and systematic precise regulation and control are realized.

The invention discloses a safe area management method and system for a zoo, and relates to the field of animal positioning. The safe area management method for the zoo comprises the following steps: monitoring an image and multiple environmental parameters of an animal activity area in real time; identifying the position and the moving track of the animal in the image; when it is detected that the position and the moving track of the animal are abnormal, early warning is given out; performing three-dimensional modeling on the animal activity area; identifying a dynamic obstacle in the animal activity area; and analyzing a movable area and a movable path of the animal according to the dynamic obstacle. According to the invention, recognition and prediction of complex animal behaviors are realized, and an early warning mechanism of animal abnormal behaviors is perfected.

The invention discloses an animal abnormal behavior phenotype rapid evaluation method and device, and is applied to the technical field of animal behavior analysis, and the method comprises the steps: obtaining video data of animals of different weeks old in spontaneous behavior experiments; tracking animal body key points in the video data to obtain animal behavior characteristics; performing classification extraction on the basis of the behavior characteristics to obtain target behavior parameters, and calculating the target behavior parameters to obtain indexes for evaluating animal behavior states; analyzing the difference change of the indexes, and evaluating and classifying the behavior state of the animal; by analyzing the dynamic balance relationship between the exploration behavior and the engraving behavior, the accurate classification of the animal abnormal behavior state is improved.

The invention discloses a configurable low-power-consumption six-axis inertial sensor sampling method suitable for livestock and poultry, and belongs to the technical field of animal behavior recognition and intelligent breeding. According to the method, by setting an intermittent sampling strategy and combining low-frequency period awakening and a short-time high-frequency data sampling window, behavior recognition data collection of the six-axis inertial sensor under the low-power-consumption condition is achieved. Meanwhile, a sampling template mechanism based on a behavior target is introduced, various sampling parameters can be configured according to the growth stage of the animal, the monitoring requirement and the variety difference, and multi-scene switching application such as oestrus recognition, parturient monitoring and night energy-saving guarding is supported. In addition, the system has an edge recognition capability, and can automatically trigger temporary recollection when detecting that the attitude angle is abnormal, so that the integrity of key behavior data is guaranteed. The method has the advantages of low power consumption, accurate recognition, flexible configuration, high adaptability and the like, and is widely applied to behavior monitoring scenes of bred animals such as dairy cows, pigs, sheep, chickens, ducks, geese and the like.

The invention discloses an animal positioning and tracking method and system for a zoo, and relates to the field of animal positioning. The animal positioning and tracking method for the zoo comprises the following steps: monitoring an image and environmental parameters of an animal activity area in real time; identifying a behavior pattern of an animal in the image; when it is detected that the behavior mode of each animal is abnormal, early warning is given out; performing three-dimensional modeling on the animal activity area; identifying a dynamic obstacle in the animal activity area; and analyzing a movable area and a movable path of the animal according to the dynamic obstacle. According to the invention, recognition and prediction of complex animal behaviors are realized, and an early warning mechanism of animal abnormal behaviors is perfected.

The invention discloses a laboratory animal behavior analysis method and system, and belongs to the technical field of animal behavior analys.The laboratory animal behavior analysis method includes the steps that A00, not less than three small animals are placed in a standard animal test box, and each small animal is dyed and distinguished; a01, the AI camera identifies and marks animal actions and postures and transmits marked video segments to the AI server; a02, the AI server carries out video reconstruction on the marked video segments to obtain 3D pictures, and repeated marks are removed; a03, the AI server counts the number of times of actions and postures of each animal. The AI camera carries out real-time edge processing on a shot video, animal postures and behaviors are recognized through an AI model library, and if it is judged that the postures and behaviors conform to actions and postures needing to be counted, relevant marks are synchronously marked in video stream signals; transmitting the marked video to an AI server; and the AI server performs screening statistics on the identification videos and gives a statistical result, so that the contrast experiment efficiency is improved.

A wireless network, systems and methods, wherein each passenger-carrying rideable vehicle deployed in the system includes: a modular construction consisting of a lightweight body driven by an electric-battery driven motor, and specially adapted for receiving a customizable ornamental body having the appearance and personality of a particular or an inanimate object; motorized and / or animated elements disposed beneath the animal character skin covering for enabling animal behaviors and / or expressions that promote a fun and entertaining ride experience; an audio-transducers mounted within the animal character body for playing music from smartphone applications and animated animal character body portions to provide a sense of realism and excitement as the electric entertainment vehicle is ridden through an amusement facility.

The invention provides an animal behavior identification method and device based on millimeter wave perception. A specific embodiment of the method comprises the following steps: acquiring a millimeter wave point cloud sequence of a target animal; and inputting the millimeter wave point cloud sequence into an animal behavior recognition model matched with a target category corresponding to the target animal to obtain an animal behavior of the target animal, the animal behavior recognition model is trained in the following mode: determining initial model parameters of the animal behavior recognition model to be trained based on model parameters of a pre-training model obtained in a pre-training stage, wherein a first sample millimeter wave point cloud sequence used in the pre-training stage comprises millimeter wave point cloud sequences of various types of animals; obtaining a second sample millimeter wave point cloud sequence and a corresponding animal behavior tag; identifying the second sample millimeter wave point cloud sequence by using a to-be-trained animal behavior identification model to obtain a predicted animal behavior; model parameters are adjusted based on differences in predicted animal behavior and animal behavior tags.

The invention discloses a habitat adaptability evaluation method based on a wild animal behavior pattern, and the method comprises the steps: S1, arranging a wild animal monitoring station, and obtaining an animal behavior pattern feature data set of a wild animal; s2, acquiring a habitat environment feature data set of wild animals; s3, constructing an ecological niche behavior-environment hypervolume model, identifying environment dimensions having influences on animal behavior modes, and screening out influence factors; s4, taking the screened influence factors as nodes, and constructing a behavior-environment dynamic complex network; and S5, analyzing an influence path and influence intensity of habitat change on an animal behavior mode through a behavior-environment dynamic complex network, dynamically updating probability distribution of habitat adaptability, outputting an adaptability score, and dividing an adaptation grade area according to the score. According to the invention, the dynamic monitoring of the habitat adaptability of wild animals is realized, and the technical problem that the habitat change influence is difficult to dynamically monitor is solved.

The invention discloses an animal state rapid discrimination method and system based on hierarchical behavior information, and is applied to the technical field of animal behavior analysis, and the method comprises the steps: obtaining an animal spontaneous behavior; performing behavior analysis on the animal spontaneous behavior, and decomposing the animal spontaneous behavior into an action module; and calculating the action conversion frequency of the animal in the preset time and the behavior ratio of the sniffing action and the carding action, and judging the animal behavior state based on the data of the original database. According to the method, the animal does not need to be trained in advance, and the behavior state of the animal can be identified only by recording the spontaneous behavior of the animal and carrying out fine action analysis and calculation.