174 results about "Animal behavior" patented technology

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 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 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 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 utility model relates to an animal aerosol preference behavior detection system, and belongs to the technical field of animal behavior detection. The triggering device, the drinking water reward device and the data recording device are arranged in the box body, the at least two aerosol release devices are arranged outside the box body, the triggering device is used for triggering operation of animals, and corresponding effective operation is set for each aerosol release device. The triggering device controls the corresponding aerosol release device to release the corresponding aerosol into the box body when detecting effective operation of the animal and controls the water drinking award device to carry out water drinking awarding on the animal at the same time, and the effective operation of the animal on each aerosol release device is recorded through the data recording device. Therefore, the animal aerosol preference behavior can be detected. Therefore, the animal aerosol preference behavior detection device can effectively detect the animal aerosol preference behavior, and is simple in structure and easy to implement.

The invention relates to the technical field of automatic identification of animal behaviors, in particular to an automatic identification method for pulmonary respiration behaviors of giant salamanders, and the method comprises the steps: obtaining an initial video image of a target region; preprocessing the initial video image to obtain a standard video image; performing feature enhancement on the standard video image to obtain an enhanced feature image; performing multi-scale respiratory behavior feature extraction on the enhanced feature image to obtain depth feature maps of different scales; performing multi-level feature aggregation on the depth feature maps of different scales, and generating an aggregated feature map; and generating an identification result based on the aggregated feature map. According to the method, the problems that the omission ratio of manual monitoring of the breathing behavior in a night low-contrast scene is high and long-term efficient and objective monitoring is difficult to realize due to night walking and cave habits of the giant salamander are solved.

The invention relates to a pig abnormal behavior recognition method based on a three-axis attitude angle, and belongs to the technical field of intelligent breeding and animal behavior monitoring. The method comprises the following steps: acquiring three-axis attitude angle data of a pig, wherein the three-axis attitude angle data comprises a yaw angle, a pitch angle and a roll angle; performing time sequence processing, frequency domain analysis, statistical feature extraction or state sequence modeling on the attitude angle data; and constructing a behavior recognition model, and judging whether an abnormal behavior exists or not. The behavior recognition model may include weighted scoring functions, spectral energy analysis, Z-score mutation detection, or behavior chain modeling based on state transition diagrams. According to the method, multi-dimensional fusion of identification logic is realized, the method is suitable for monitoring various abnormal behaviors of pigs such as oestrus, parturient, fright, rolling and manic-depressive pigs, has the characteristics of low power consumption, simple equipment, extensible algorithm and the like, and is suitable for being deployed in a wearable intelligent terminal of the pigs.

The invention relates to the technical field of image recognition, in particular to a wild animal behavior recognition method and system based on visual recognition. The method comprises the following steps: acquiring continuous image frames, analyzing gray direction changes of pixel blocks in adjacent frames, extracting low-photosensitive texture features and forming a texture change track; then fusing the texture trajectory with the geometric offset trend of the animal contour edge points to generate a behavior dynamic fusion feature capable of representing the action direction and intensity; and carrying out continuity calculation on the fusion features, and judging whether the action meets a behavior triggering condition based on a change continuity index, thereby realizing automatic identification and output of the wild animal target behavior. According to the method, the texture change track and the contour geometric deformation direction are fused, stable recognition of continuous actions under the low-light condition is achieved, and the wild animal behavior recognition result is more accurate and reliable and has the quantitative judgment capacity.

The invention relates to the technical field of animal behavior analysis, and discloses an animal behavior accurate recording and analyzing system based on multi-mode keyboard coding. A data acquisition and synchronization module of the system acquires original key event data and generates a time sequence behavior data stream with millisecond-level precision; the coding logic engine module is used for converting the physical key sequence into a hierarchical behavior semantic coding sequence through a dynamic mode switching and key mapping technology; the behavior state modeling module is used for constructing an animal behavior state transition probability model, performing state decoding and estimation by adopting a hidden Markov model, and outputting a behavior state sequence with confidence; the behavior analysis and prediction module is used for predicting a behavior evolution trend and identifying an abnormal behavior event; and the threshold value dynamic adjustment module is used for dynamically adjusting an early warning threshold value through interaction between the normal behavior generator and the real-time discriminator aiming at an abnormal behavior event so as to finish accurate recording and risk analysis of animal behaviors.

The invention discloses a wild animal attitude estimation method based on dynamic prompt and semi-supervised multi-mode learning, and relates to the technical field of computer vision and animal behavior analysis, and the method comprises the steps: firstly carrying out the motion state analysis of an input video, so as to extract parameters such as the centroid velocity, the acceleration and the steering angle of a moving material; the method comprises the following steps: generating a dynamic text prompt containing directional semantics, constructing a cross-modal fusion time sequence convolutional network, fusing visual features and text prompt features through a space alignment mask mechanism and a cross attention module, capturing time sequence dependence by using a bidirectional ConvGRU network to realize attitude estimation, and finally, based on a semi-supervised multi-task learning framework, carrying out dynamic text prompt processing on the basis of the semi-supervised multi-task learning framework. A joint loss function including supervision loss, time difference loss, attitude PCA reconstruction loss, multi-modal alignment loss and motion consistency loss is adopted, a loss weight is adaptively adjusted in combination with prediction uncertainty to optimize network parameters, a smooth 2D / 3D animal skeleton is finally output, real-time reasoning can be realized on edge equipment, and the real-time reasoning efficiency is improved. Ecological analysis software is compatible, high precision is still kept under the condition of low-label data, and the performance is remarkably improved especially in an animal sharp turning scene.

The application discloses an experimental animal behavior monitoring system of a feeding room, and relates to the technical field of animal behavior research.The system comprises a behavior information collection module, which uses a monitoring device to collect original data of experimental animal behavior information of the feeding room in real time, and transmits the original data to a data processing and classification module after collection; the data processing and classification module receives the original data transmitted by the behavior information collection module, calculates, classifies, counts and stores the original data by using a processing and classification algorithm, and forms animal behavior characteristic data after processing the original data, and respectively transmits the animal behavior characteristic data to a correlation analysis module and a comparative analysis module; the data processing and classification module, the correlation analysis module and the comparative analysis module are adopted, original data of experimental animal behavior information collected by the behavior information collection module in real time and comprehensively is accurately processed, and the correlation law and the behavior mode between experimental animal behaviors in a large amount of animal behavior data are found.

The application discloses a kind of quick assessment system and method of the influence of flood disaster on wetland ecosystem, and the system includes, animal image acquisition module, for obtaining animal image data;Animal behavior analysis module, for identifying animal behavior based on animal image data, to obtain animal behavior distribution map and animal behavior timetable;Plant remote sensing module, for obtaining wetland plant distribution map based on wetland remote sensing data;Flood data acquisition module, for obtaining water environment parameters when flood disaster occurs;Influence index evaluation module, for evaluating model to determine ecosystem influence index.The application integrates animal image acquisition and analysis, plant remote sensing identification, real-time acquisition of water environment parameters and other multi-source data, and constructs a unified influence index evaluation model, can quickly generate the comprehensive influence index of wetland animals and plants after flood disaster occurs, so that the assessment of disaster influence is timely and effective.

The invention provides a sleep disorder animal model device and an experiment method, the sleep disorder animal model device comprises an experiment box, the experiment box is internally provided with grid division boxes in grid distribution, and the sleep disorder animal model device integrates a liftable auxiliary box body, a cover plate mechanism capable of amplitude modulation and jittering and a sound, light, vibration and tactile stimulation module; complex stimulation close to insomnia inducements of real human beings can be applied in a staged and combined mode, the problem of strong stress caused by a traditional single stimulation model is effectively solved, and the clinical goodness of fit of an animal model is remarkably improved. Meanwhile, based on intelligent management of a central control system, real-time non-invasive monitoring of animal behaviors and sleep states is achieved by fusing camera visual analysis, pressure sensing and wireless physiological telemetry technologies, execution units are automatically and accurately regulated according to the real-time non-invasive monitoring, standardization and repeatability of the experimental process are ensured, and the experimental efficiency is improved. And the construction efficiency and the evaluation reliability of the sleep disorder animal model are improved.

The application discloses a kind of abnormal behavior identification methods of forest musk deer, it is related to animal behavior monitoring technical field, method includes: the core key point of historical forest musk deer image is labeled;Key point detection model is obtained based on historical forest musk deer image, key point detection model is identified to obtain target data to the forest musk deer image to be detected, and abnormal behavior is obtained based on target data;Key point detection model includes successively connected backbone module, neck module and head module, backbone module includes successively connected convolution module and first module, and convolution module includes several CoordConv layers, and CoordConv layer is used to carry out convolution operation and extract feature;Neck module includes successively connected second module, CBAM module and third module, and second module and third module are used to enhance and fuse feature, and CBAM module is used to focus key feature, solve the problem that the detection model of traditional forest musk deer abnormal behavior is susceptible to background interference and leads to large detection deviation, and limb joint positioning precision is poor.

Provide a system. 【Solution means】 Means for recording the voice of an animal, Means for taking a picture of an animal, Means for inputting information regarding the behavior of the animal, Means for collecting and transmitting the recorded voice, the taken picture, and the input information in one data packet, Means for preprocessing the voice data and the picture data, Means for analyzing the preprocessed data using a deep learning model and predicting the emotion of the animal, Means for outputting the predicted emotion result, A system including the above.

The application discloses a kind of based on deep learning's lizard mating behavior intelligent monitoring identification method and system, belong to animal behavior monitoring and deep learning technical field.Method includes: data acquisition and pre-processing, collect lizard behavior video and environmental parameter;Sample data set construction, to effective frame is labeled, division and data enhancement;Improved deep learning identification model construction, with YOLOv8 as backbone, join lizard behavior feature enhancement module (LBFE) and time sequence attention module (TAM), using improved hybrid loss function;Model training and optimization, by training, verification, test and light weight optimization;Real-time monitoring and identification, deployment model realizes real-time identification and abnormal early warning;Identification result correction and anti.This application has high identification precision, good real-time performance, easy to deploy, can realize the all-weather accurate monitoring of lizard mating behavior, provides data support for lizard behavior research and species protection.

The present application relates to a kind of based on gyroscope three-axis angular velocity signal's cattle posture recognition method, belong to the technical field of the cross of animal behavior recognition, sensor signal processing and artificial intelligence application.The method includes the following steps:1) data acquisition and feature construction;2) unsupervised clustering label generation;3) supervised signal processing and classifier construction;4) posture discrimination.This method is by collecting the three-axis angular velocity data generated by gyroscope sensor in cattle body, and is combined with feature extraction, unsupervised clustering and supervised classification etc., realizes the automatic identification to cattle posture state.The data relied on by the present application all come from in-vivo sensor signal, is almost not disturbed by temperature change, topography fluctuation and breeding environment etc.external factors, therefore has data acquisition stable, low computing overhead, deployment flexible etc.significant advantages, can be widely applied to captive, grazing etc.various breeding scenarios, can effectively improve the practicability and adaptability of cattle behavior monitoring system.

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 discloses an animal behavior record collecting device which comprises a three-dimensional supporting frame, a transparent experiment platform is arranged on the three-dimensional supporting frame, a circle of baffles are arranged on the transparent experiment platform, and at least one experiment target is placed in an area defined by the baffles. The dual-camera module comprises a first camera and a second camera, and the first camera and the second camera respectively collect behavior data of the experimental target from the upper part and the lower part of the baffle plate; and the video acquisition and processing unit is electrically connected with the first camera and the second camera respectively. According to the device, through high-precision three-dimensional behavior analysis, the limitation of a traditional device is broken through, three-dimensional behaviors which cannot be captured by the traditional device are quantified, and through a high-frame-rate look-up camera and a wide-angle look-up camera, a macroscopic track is captured from the look-up angle, and microscopic details are recorded from the look-up angle.

This invention discloses a remote discrimination method for giant panda behavior based on collar sensors, specifically relating to the field of animal behavior monitoring using collar sensors. The method includes collecting behavior monitoring data output by the collar during continuous monitoring, arranging the data chronologically according to sampling times, and calculating the corresponding activity and displacement values ​​at each sampling time to obtain a monitoring sequence. Based on the direction of activity and displacement changes at adjacent sampling times in the monitoring sequence, the connection point where the direction of activity change changes and the displacement change state simultaneously change is determined as the behavior transition point. This invention solves the problems in existing technologies where the correlation between behavior transitions is difficult to retain, and the correspondence and stability between remote discrimination results and actual behavior states are insufficient. It extracts continuous segments around the behavior transition point before and after the transition and determines the behavior state based on the corresponding changes in these segments.

The application discloses an animal tracking and posture estimation method and device, electronic equipment and a storage medium, the method comprising: acquiring an animal ethology video and preprocessing to obtain a plurality of original sampling images; the images are sequentially input into a pre-trained instance segmentation model and a posture estimation model respectively to obtain instance segmentation masks and posture data of each frame of image, and then the original trajectory is created based on the correlation of the instance segmentation masks of adjacent frames; the instance segmentation masks of each frame of image are superimposed into the corresponding original image, and cutting and rotating are performed to obtain instance cutting images; the original trajectory is updated based on the instance cutting images, a preset first trajectory mutual exclusion condition and a preset trajectory matching condition to obtain an animal trajectory; the animal trajectory is combined with the corresponding posture data to generate a visual animal behavior space-time graph. The application solves the problem that current multi-experimental animal tracking and posture estimation cannot be better performed.

The application discloses a kind of big animal approach avoidance behavior detection method and system, it is related to animal behavior detection technical field, including, 5 or more than 5 standard unit cage is connected in series or parallel to form combined transparent cage, according to the detection paradigm of pre-set, install grid partition, entity partition or entity partition with loose-leaf small door between adjacent unit cage;Using the overhead camera and side video recorder installed in unit cage top synchronously collects the behavior video of big animal in the combined transparent cage;After big animal is introduced into the combined transparent cage and is trained, by changing the state of partition and introducing external stimulant, trigger the approach or avoidance behavior of big animal, and use the behavior video to carry out index analysis.The application solves the problem that the existing detection device is rigid due to structure, which leads to rigid experimental paradigm and difficulty in comprehensive evaluation of complex approach avoidance behavior of animals.In addition, the prior art relies on manual observation and recording, and the analysis efficiency is low and subjective, and high-throughput automatic extraction of animal instantaneous action and fine behavior index cannot be realized.

The application relates to the technical field of animal activity behavior monitoring, and discloses an animal behavior activity monitoring device, which comprises a supporting seat, supporting columns are symmetrically arranged at the bottom end of the supporting seat, a supporting frame is fixedly connected to the top end of the supporting seat, a monitoring body is arranged at the top end of the supporting frame, a receiving cavity is arranged in the supporting seat, a discharging pipe, which is in communication with the receiving cavity, is arranged at the bottom end of the supporting seat, an annular track is arranged at the top end of the supporting seat, an opening is arranged at the bottom end of the annular track, and a supporting and discharging assembly is arranged in the opening; the two feeding frames can be driven to move in the annular track, the feeding frames can push the mice to move to achieve sleep deprivation of the mice, the problem that, when a traditional feeding frame with a cuboid structure is fixedly arranged, the mice are driven to move only by a deprivation rod and the sleep deprivation of the mice is interrupted due to a gap caused by the deprivation rod is solved, and the sleep deprivation of the mice can be realized continuously.

The invention relates to the technical field of geological disaster early warning, in particular to a geological disaster collaborative early warning system and method. The defects that traditional animal behavior observation is high in subjectivity, cannot be quantized and is difficult to standardize are overcome. Through a standardized multi-mode biosensing terminal, an objective baseline modeling algorithm and an automatic anomaly recognition process, the concept that a fuzzy animal can predict disasters is converted into a set of engineering technical scheme which is driven by data, clear in process and reproducible in result. Through a reverse verification mechanism of a physical signal to a biological signal, the technical bottleneck of high false alarm rate of a traditional biological observation method is fundamentally solved. The whole process from data acquisition to early warning release of the whole system is automatic, continuous manual intervention is not needed, the monitoring efficiency is greatly improved, and the system has practical feasibility of deployment and popularization in a large-scale geological disaster high-incidence area.

The present disclosure provides systems and methods for animal health monitoring. Load data can be obtained from a plurality of load sensors associated with a platform carrying contained litter thereabove, wherein individual load sensors of the plurality of load sensors are separated from one another and receive pressure input from the platform independent of one another. If the load data is determined or not to be from an animal interaction with the contained litter, an animal behavior property associated with an animal is recognized if a determination is made based on load data that the interaction with the contained litter was due to the animal interaction. The animal behavior property is classified into an animal classified event using a machine learning classifier. A change in the animal classified event is identified as compared to a previously recorded event associated with the animal.

This utility model discloses an animal behavior observation cage. The upper cover includes a side cover with an enclosing structure and a cover body disposed on the upper end of the side cover. At least the upper part of the side cover and the entire cover body are made of tempered glass. A cavity is formed between the side cover and the cover body. A fixed plate is disposed at the lower end of the side cover and covers one side of the cavity. One end of a movable plate is hinged to the side cover. After rotation, the movable plate abuts against the lower end of the side cover and covers the other side of the cavity, forming a gap band connecting the cavity between the movable plate and the fixed plate. Support members are provided at the lower ends of both the fixed plate and the movable plate. Locking members are disposed on the side cover and the movable plate for fixing the two together. Most or all of the upper cover of this utility model is made of tempered glass with good light transmission, which allows for more comprehensive and unobstructed observation of animal behavior. Moreover, because tempered glass is hard and smooth, it is not easy to leave scratches. Taking pictures through tempered glass can be clearer and can more effectively capture subtle movements.

This invention relates to an animal behavior detection device and method based on an infrared laser light sheet. The detection device includes a test platform module, an optical path module, an imaging module, and a control module. The test platform module includes a through-hole and a light-transmitting aperture for placing the animal to be tested. The optical path module employs a Powell prism structure, with a one-dimensional laser line generated by the optical path module aligned with the light-transmitting aperture. When the animal behavior detection device is in use, the one-dimensional laser line generated by the optical path module fills the light-transmitting aperture. The imaging module's first camera records changes in reflected light signals, while a second camera uses a beam splitter to record animal behavior. The control module is connected to the first and second cameras, sending trigger signals to them and recording the image data transmitted back by the first and second cameras for subsequent experimental analysis. This invention features a simple and flexible device structure. Parameters such as the light source wavelength and the height of the padding can be adjusted according to the characteristics of the experimental animals.

This invention relates to the fields of animal behavior, animal welfare, and mental health conditioning, specifically a music conditioning system and method for pets and laboratory animals. It is a pure PC-based software system, including an audio editing and playback control module, a music library management module, and a music playback module. The output of the audio editing and playback control module is connected to the input of the music playback module, and the music library management module and the audio editing and playback control module have a bidirectional data connection. This invention precisely matches different animals through three parameters: pitch shifting, speed adjustment, and volume adjustment, significantly improving audio compatibility. The effects of emotional stabilization, anxiety relief, and sleep promotion are significantly better than general music. One-click templates are suitable for quick home use. Customizable visual editing is suitable for pet hospitals, laboratory animal centers, and research institutions to establish standardized programs. Waveform graphs, spectrograms, and acoustic spectrograms are displayed synchronously in real time, allowing users to intuitively judge the audio structure. Adjustment accuracy is improved by more than 80%, requiring no professional audio knowledge.

The invention relates to a cattle posture recognition method based on triaxial angular velocity signals of a gyroscope, and belongs to the technical field of crossing of animal behavior recognition, sensor signal processing and artificial intelligence application. The method comprises the following steps: 1) data acquisition and feature construction; 2) generating an unsupervised clustering label; 3) supervising signal processing and classifier construction; and 4) attitude discrimination. According to the method, the automatic identification of the posture state of the cattle is realized by collecting the three-axis angular velocity data generated by the gyroscope sensor in the cattle and combining the means of feature extraction, unsupervised clustering, supervised classification and the like. Data dependent on the method are all from in-vivo sensor signals, and are hardly interfered by external factors such as temperature change, terrain fluctuation and breeding environment, so that the method has the remarkable advantages of stable data acquisition, low calculation overhead, flexible deployment and the like, can be widely applied to various breeding scenes such as captivity and grazing, and has wide application prospects. And the practicability and adaptability of the cattle behavior monitoring system can be effectively improved.

The invention discloses an animal olfactory behavior training experiment device, relates to the technical field of animal behavioristics experiment devices, can be cooperatively used with an external nerve recording system and a behavior detection analysis system, and aims to explore a correlation mechanism among smell stimulation, behavior response and nerve activity. The device adopts a radial single-starting-point and multi-terminal-point space structure, and is combined with a multi-odor channel system with physical isolation, so that approaching or avoiding behaviors of animals can be accurately induced. The system is integrated with an intelligent closed-loop control module based on an STM32 single-chip microcomputer, and a graded gas path cleaning mechanism is designed and comprises the functions of countercurrent purging, ultraviolet photolysis, electronic nose cleaning verification and the like, so that the purity of odor stimulation and the reliability of the experimental process are ensured. Meanwhile, the device supports TTL synchronization signal output with millisecond-level precision, can be seamlessly connected with commercially available electrophysiology and behavioristics equipment, and is suitable for multi-mode animal behavior training, recording and regulation scenes.