85 results about "Behavioral state" patented technology

A multimodal intelligent agent system for dynamic environmental monitoring and user-centered support, consisting of: a multimodal sensor module configured to continuously acquire environmental and behavioral data from multiple input modalities, including at least one visual sensor, at least one acoustic sensor, at least one environmental conditions sensor, and at least one proximity or motion detection sensor, each generating modality-specific data streams representing visual images, audio waveforms, physical environmental parameters, and motion signatures within a monitored environment; a data preprocessing and fusion subsystem that is operationally coupled with the multimodal sensor module and configured to normalize, temporally align, and transform the modality-specific data streams into high-dimensional feature embeddings using a variety of encoders, wherein the visual encoder uses convolutional or vision transformer architectures, the audio encoder uses a spectral-temporal feature extractor, and the sensor encoder transforms raw analog data into context vectors suitable for multimodal alignment; a multimodal processing unit consisting of a transformer-based large language model (LLM) trained on paired multimodal datasets and configured to perform semantic fusion, context abstraction, and inference across the aforementioned aligned multimodal feature embeddings to generate a contextual understanding of environmental and behavioral states; an adaptive agent controller coupled to the multimodal inference processing unit and configured to instantiate, manage, and terminate a variety of task-specific intelligent agents, each agent being a software unit configured to perform a specialized function selected from meeting summarization, behavioral analysis, misplaced object detection, or environmental anomaly identification, with the agents dynamically interacting with the inference engine to retrieve contextually relevant multimodal embeddings for task execution; a personalization and adaptive learning subsystem consisting of a user preference database and a neural memory structure configured to update and refine model parameters based on user-specific interaction history, thereby enabling personalized output generation, prioritization of recommendations, and long-term behavioral adaptation; and An output generation interface is operationally connected to the adaptive agent controller and configured to produce multimodal output in textual, visual, and auditory form. The interface is capable of displaying human-readable summaries, notifications, and visual reconstructions of identified entities or environmental states.

The invention relates to the field of breeding industry, and discloses a pig behavior-based pig health condition analysis method and system, and the method comprises the steps: carrying out the comprehensive monitoring of pig behaviors, capturing the gait, feeding mode, excretion behavior and activity range of a pig in real time based on a behavior feature extraction algorithm, and obtaining a behavior state video stream sequence; multi-dimensional time sequence correlation analysis is carried out on the behavior state video stream sequence, and historical behavior data, pig weight changes and physiological parameters are combined; based on the dynamic time sequence feature vector, dynamically identifying a change track of pig behaviors by applying a self-adaptive behavior identification algorithm; performing correlation analysis on the detected abnormal behavior pattern and the potential health risk of the pig, fusing the environmental factors, group behavior data and health history of the pig, and identifying a potential health problem; and based on a risk early warning result, automatically adjusting environmental parameters and feeding management strategies, and providing intervention measure suggestions. The pig health management system has the advantage of improving the efficiency and accuracy of pig health management.

The invention discloses a multi-modal data fusion-based household aging transformation demand evaluation and scheme generation system. According to the system, technologies such as attitude sensing and image acquisition are comprehensively applied, acceleration and angular velocity data of a user are acquired by means of an attitude sensing module, and behavior image data are acquired by an image acquisition module. Fusion analysis is carried out on the multi-mode signals through edge AI processing equipment, accurate recognition of the behavior state of the old people is achieved, and alarm is triggered when an abnormal posture is detected. Meanwhile, the system utilizes the environment sensing module to assist in behavior judgment and path planning, has the advantages of non-intrusive type, high real-time performance, high accuracy and the like, can effectively improve the life safety and behavior monitoring intelligence level of the old people under an unattended condition, is suitable for a safety monitoring scene in a family old people nursing environment, and has a wide application prospect. Good popularization and application values are realized.

The invention provides a penaeus vannamei behavior state acoustic intelligent identification method and a model building method, and belongs to the technical field of aquaculture acoustic monitoring. Comprising the following steps: acquiring audio data of penaeus vannamei in different behavior states; filtering and framing, threshold detection and feature extraction are carried out on the audio, and a standardized audio feature data set is constructed; a deep learning model ASP-SEResNet fusing attention statistical pooling and a channel attention mechanism is built, the deep learning model comprises a feature channel optimization branch based on a compression-excitation module and a time sequence feature expression branch adopting attention weighted statistic aggregation, and joint representation of acoustic features is achieved through a double-path feature fusion mechanism; training and optimizing the model to obtain a final model; experimental results show that the method shows excellent classification performance on a prawn audio feature data set, and high-precision identification of five behavior states of the penaeus vannamei can be realized.

The invention discloses an interaction method and device, electronic equipment and a readable storage medium, and belongs to the technical field of electronic equipment, and the method comprises the steps: obtaining a video stream of a user; determining a behavior state of the user according to the video stream; wherein the behavior state comprises at least one of the following items: an emotional state, an attention state, a facial expression, a body posture and an action; and outputting first interaction content according to the behavior state of the user.

The invention relates to an online self-study system and method fusing user behavior analysis and immersive interaction, equipment and a medium. The system comprises a multi-source data acquisition module for acquiring behavior data and physiological data of a user in real time; the user behavior analysis module is used for analyzing the behavior data and the physiological data by utilizing a pre-trained user learning state model and generating learning state information containing fatigue characteristics and cognitive load characteristics; the scene parameter generation module is used for generating immersive learning scene parameters according to a preset environment generation rule on the basis of the learning state information; and the immersive presentation module constructs and presents an immersive learning scene dynamically adaptive to the user state according to the parameters. The technical problem that a traditional online self-study environment lacks personalized adaptation ability is solved, and immersive interaction experience of automatically optimizing the learning environment based on the real-time physiological and behavior state of the user is achieved.

The invention relates to the technical field of intelligent health monitoring, and particularly discloses a multi-modal AI-fused senior non-inductive assessment method and system and electronic device.The method comprises the following steps that in the daily living environment of the senior, original multi-modal data reflecting the physiological and behavior states of the senior are collected in a non-inductive mode through at least two non-contact sensors; and preprocessing the original multi-modal data, and extracting a single-modal health feature vector from the data of each modal by using a preset feature extraction model. According to the invention, non-inductive data acquisition is realized by deploying the non-contact multi-mode sensor in the daily environment of the elderly, the elderly does not need to cooperate deliberately, and the use convenience and acceptability are effectively improved; through preprocessing and feature extraction of multi-modal data, multi-dimensional health features can be mined from visual, audio and radar data, the limitation of single data dimension in the traditional technology is broken through, and comprehensive representation of physiological and behavior states of old people is realized.

The invention discloses a brain wave monitoring and intelligent feedback combined modularized head ring glasses device, and relates to the technical field of brain wave sensing and adjusting equipment.According to the device, brain electrical activities, eye movement behaviors, eye fatigue states, wearing fitness of the head ring glasses device, environmental noise interference and use situations of a user are monitored in real time; the brain wave fitting quality index is calculated through the pressure variance, the EEG signal-to-noise ratio and the historical template fitting degree, and self-adaptive compensation adjustment is carried out when the brain wave fitting quality index is unqualified; combining electroencephalogram and eye movement characteristics to calculate a brain-eye collaborative concentration index, and generating a comprehensive adjustment strategy when the brain-eye collaborative concentration index does not reach the standard; and calculating an emotional scene switching index according to the brain wave emotional characteristics, the situation parameters and the historical trend, and triggering personalized emotional regulation when the emotional scene switching index is not matched with the situation parameters. The device can quantify the brain wave and behavior state of the user in real time, realizes intelligent concentration and emotion management, and improves the reliability and individual suitability of electroencephalogram monitoring.

The invention discloses a system security access control method based on deep learning and dynamic risk awareness, which comprises the following steps of: generating a behavior state vector by collecting user identity, operation, equipment state and environment information, constructing an access behavior map, and establishing an access behavior map through a Hebbian learning rule; the method comprises the following steps: dynamically updating connection strength between behaviors in combination with historical behavior data, after receiving an access request, selecting an association path from a map, calculating an activation potential value, selecting an optimal path as an access decision basis by applying a Winner-Take-All selection mechanism, executing an access control operation, generating a risk mark and a behavior evolution mark, and establishing an access control strategy; the method is used for adjusting a selection threshold and an atlas weight. And finally, compressing and coding the related information, and recording the information in an access log. According to the method, intelligent judgment on the access behavior is realized by using deep learning and a dynamic risk perception mechanism.

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 relates to an acousto-optic fusion repelling system and method based on bird habits. The system comprises a bird behavior recognition module, an environment sensing module, a bird repelling strategy generation module, an acousto-optic control module and a self-adaptive learning optimization module. The method comprises the following steps: acquiring bird images, sound and environmental parameters in real time; bird types are identified through ResNet + MFCC feature extraction, and a behavior state is analyzed in combination with a CNN-LSTM model; generating an acousto-optic fusion stimulation sequence according to species characteristics, behavior states and environmental parameters; and performing expelling according to a multi-modal sequence and the like. According to the method for generating the acousto-optic bird repelling sequence in combination with the bird behavior characteristics and the environment state, the bird repelling efficiency is improved and the occurrence frequency of bird damage is reduced through a feedback-based adaptive learning optimization algorithm and a dynamic acousto-optic bird repelling strategy matching and execution mechanism; the problems of high adaptability and high maintenance cost of a traditional bird repelling mode are solved; the intelligent, dynamic and sustainable bird repelling strategy is realized.

In various examples, multi-sensor subject tracking for supervised environments is provided for real-time and near-real-time systems and applications. A positioning system performs multi-subject tracking using streaming data from multiple sensors. Subject tracking can be based on individual anchors and behavioral states initialized for individual subjects using representations (e.g., behavioral embeddings) derived from the streaming data. Clustering can be used to generate behavioral clusters that individually represent a trackable subject. Behavioral states for live anchors can be identified and tracked based on trajectory continuity by iteratively propagating their behavioral states forward over time.Clusters lacking trajectory continuity can be used to initialize new anchors or matched with inactive anchors, which can be reclassified and propagated as live anchors. Propagated behavioral states can be updated using behavioral data represented by the behavioral embeddings.

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.

This invention provides an intelligent delivery room care system integrating multimodal environmental control based on sound, light, and temperature, belonging to the field of medical care equipment technology. By integrating multimodal environmental sensing and control capabilities based on sound, light, and temperature, combined with real-time analysis of the mother's physiological and behavioral states, this invention achieves intelligent, personalized, and contextualized management of the delivery room environment. Its main advantages include: significantly reducing the mother's anxiety and pain perception during childbirth, increasing the success rate of natural childbirth; reducing the burden of environmental control operations on medical staff, improving nursing efficiency; supporting adaptive environmental switching across multiple stages and scenarios, meeting the needs of the entire cycle from labor to postpartum recovery; ensuring the dignity of the mother and data security through non-invasive monitoring and privacy protection design; and a modular system design that facilitates integration with existing hospital information systems (HIS) and intelligent ward platforms.

In various examples, multi-sensor body tracking of monitored environments for real-time and near-real-time systems and applications is provided. A positioning system performs multi-subject tracking using streaming data from a plurality of sensors. The subject tracking may be based on individual anchors and behavioral states initialized for individual subjects using representations derived from the streaming data (e.g., behavioral embedding). Clusters may be used to generate behavioral clusters that individually represent trackable subjects. The behavioral state of the active anchor may be identified based on continuity of the trajectory and tracked by iteratively propagating forward its behavioral state over time. Clusters lacking trajectory continuity may be used to initialize new anchors, or match dormant anchors that may be re-classified as active anchors and propagated. The propagated behavioral state may be updated using behavioral data represented by the behavioral embedding.

The invention discloses a pet emotion recognition method, device and equipment and a storage medium, and relates to the technical field of intelligent vehicles, and the method comprises the steps: obtaining the local state information and behavior state information of a pet leaving a vehicle; constructing a local feature vector according to the local state information and the local weight matrix, and constructing a behavior amplification coefficient according to the behavior state information and the amplification coefficient matrix; constructing a state analysis quantity based on the local feature vector and the behavior amplification coefficient; and determining whether the emotion of the car-leaving pet is in an abnormal state according to the state analysis quantity. According to the method and the device, whether the current emotion of the car-leaving pet is in the abnormal state or not can be determined by comprehensively identifying the body local state and the behavior state of the car-leaving pet, and the user is notified to pay attention in time when the current emotion is in the abnormal state, so that the user only needs to pay attention to the corresponding notification without worrying about anxiety all the time.

The invention provides an intelligent data analysis method for a pension information service system, relates to the technical field of physiological data processing, and aims to judge behavior stages according to the daily moving speed of the elderly and the continuity of the speed in time, select stable effective behavior stages through deterministic scores, and improve the accuracy of the behavior stages. Judging a corresponding behavior state according to the change characteristic of the heart rate in the effective behavior stage, identifying the body function expression of the old person in the corresponding state according to the change rate characteristic of the central rate of each behavior state, and judging the reaction intensity in the first behavior state and the flat intensity in the second behavior state; and a heart function change index is constructed by combining the long-term comprehensive expression of the reaction intensity, the flat intensity and the heart rate in the third behavior state, so that the overall body function change characteristics are reflected and quantified, and a guardian can conveniently know the overall body condition change of the old.

This application relates to a device control method, apparatus, device, and system based on behavior monitoring. The device control method based on behavior monitoring includes: extracting features from behavior monitoring data of a target object to obtain behavior features; performing a first behavior evaluation process on the behavior features to obtain a first evaluation result; performing a second behavior evaluation process on the behavior features to obtain a second evaluation result; identifying the target behavior state of the target object based on the first and second evaluation results; and adjusting the device in the space where the target object is located based on the target behavior state. This solution can improve the intelligence level of the device control process.

This invention discloses an antenna reverse compensation method and system using digital twin virtual commissioning. The method acquires continuous behavioral data streams of the antenna system, reconstructs the state using a pre-associated physical characteristic computational model of the digital twin, and performs temporal evolution prediction using a data-driven fitting model to generate a behavioral state stream. Within the digital twin, a multi-dimensional deviation evolution analysis is performed between the behavioral state stream and a healthy baseline behavioral template to extract early covert mode feature sets. Based on this, a strategy self-exploration domain is defined in the virtual commissioning space. A perturbation action generator generates a perturbation action self-combination sequence and injects it into the digital twin, monitoring the repair state stream to construct a repair state mapping table. Finally, based on the repair state mapping table and the healthy baseline behavioral template, effective trial strategy clusters are selected, and the optimal reverse compensation strategy is selected based on the strategy stability metric, generating physical antenna reverse compensation commands.

Software storable on and executable by an electronic computing resource (10), the software being designed, when executed, to configure the electronic computing resource (10) to detect and / or predict in real time a behavioral state and / or transition among one or more of a subject's wakefulness (W), drowsiness (D), and sleep (S) phases. The software, when executed, is designed to configure the electronic computing resource (10) to: receive (20) a subject's biometric signal, process (21-29) the received biometric signal to classify the received biometric signal into one of different classes associated with the behavioral state and / or transition among one or more of the subject's wakefulness (W), drowsiness (D), and sleep (S) phases, and detect and / or predict (30) the subject's behavioral state and / or transition among the wakefulness (W), drowsiness (D), and sleep (S) phases based on the classified biometric signal. The software, when executed, is designed to configure the electronic computing resource (10) to: calculate (24) at least a first one composite quantity for and based on the received biometric signal; calculate (29) at least one threshold value for the received biometric signal based on the at least first one composite quantity calculated therefor; and classify (29) the biometric signal into one of different classes associated with behavioral states and / or transitions among one or more of wakefulness (W), drowsiness (D) and sleep (S) phases based on the calculated threshold value.

The invention discloses a state monitoring method and device for elderly people living alone based on active following interaction, and relates to the field of health monitoring, and the method comprises the steps: determining the action type of the elderly people living alone based on action data through employing a hidden Markov model, and carrying out the abnormal behavior monitoring of the elderly people living alone according to an abnormal behavior quantitative standard; according to the current position, through autonomous positioning navigation, following the elderly living alone at a preset following distance, and initiating active interaction to obtain an interaction record; determining the current emotion of the elderly living alone based on the interaction record; based on the interaction record, the abnormal behavior monitoring result and the current emotion, the behavior state of the elderly living alone is evaluated through a data analysis algorithm, a behavior state evaluation result is obtained and input into a behavior state database for comparison, intervention suggestions are generated, and meanwhile the state database is updated. According to the invention, active following interaction of the elderly people living alone is realized, the interaction efficiency and the health state monitoring level of the elderly people living alone are improved, and the timeliness of medical intervention is enhanced.

The invention relates to the technical field of artificial intelligence monitoring, in particular to a continuous monitoring method and system for multi-modal physiological parameter fusion, and the method comprises the steps: carrying out the continuous collection of physiological electric signals of a target individual in a plurality of behavior states; performing period segmentation on the multi-lead electric signal according to the heartbeat rhythm, and standardizing the multi-lead electric signal into characteristic fragments with the same time length and channel number; performing two-stage weighting processing on each feature fragment to obtain high-weight feature representation; performing feature alignment on the high-weight feature representation according to the corresponding behavior state, and executing fusion operation; and inputting the fusion feature representation into a convolutional neural network, performing individual physiological state recognition, and periodically judging whether the target individual has a physiological abnormality risk at present. According to the method and the device, the problem of signal distortion caused by neglecting multi-behavior state switching in the prior art is effectively solved, the feature adaptability and the cross-state recognition robustness in a dynamic scene are remarkably improved, and continuous and accurate physiological anomaly risk tracking is realized.

The invention relates to the technical field of biomedicine, in particular to a microcapsule probiotic mixture for treating attention deficit hyperactivity disorder and application. The microcapsule probiotic mixture disclosed by the invention is used for relieving related symptoms of attention deficit hyperactivity disorder, and is mainly used for regulating endocrine dysfunction through an intestine-brain axis, so that an abnormal psychological behavior state related to ADHD is relieved; animal experiments prove that the microcapsule bacterial mixture can improve ADHD hyperactivity and impulse behaviors; according to the present invention, the neuroendocrine system gene expression level can be improved, the neuroendocrine system Nr3c1, Nr3c2 and Crh gene expression level in the brain tissue can be improved, the neuroendocrine system GR, MR and CRH related protein expression level in the brain tissue can be improved so as to improve the neuroendocrine system dysfunction, and relieve ADHD hyperactivity and impulse behaviors; a theoretical basis is provided for the application prospect of relieving the ADHD symptom, and certain social significance and potential market value are achieved.

This invention provides a functional architecture and method for an intelligent human-machine adaptive collaborative system in an aircraft cockpit. It features advantages such as hierarchy, modularity, and reusability, supporting detailed design of the physical architecture of the intelligent human-machine system in the aircraft cockpit and providing a technical means to achieve efficient human-machine matching and dynamic natural interaction in the aircraft cockpit. The intelligent human-machine adaptive collaborative method provided by this invention, by adjusting the human-machine interaction level, the autonomous dimension of the intelligent agent, the information processing dimension, and the task allocation dimension, and based on the decision steps of the information flow graph, can obtain the optimal intelligent human-machine adaptive collaborative solution for the aircraft cockpit. The system functional architecture and method provided by this invention can realize intelligent human-machine interaction that adapts to the pilot's physiological, psychological, behavioral state and task requirements. It can provide technical support for solving problems such as pilot cognitive load imbalance, loss of situational awareness, and frequent human errors, enhancing the pilot's situational judgment and decision-making capabilities, and optimizing the overall effectiveness of the cockpit human-machine system.

The invention relates to the technical field of computers, and discloses a digital twin model management method and system based on behavior analysis. The method comprises the following steps: acquiring a historical behavior sequence and real-time sensing data of a target object, and respectively encoding the historical behavior sequence and the real-time sensing data into a first behavior representation vector and a second behavior representation vector; a behavior driving model composed of a behavior state observer, an environment disturbance compensator and an intention inference engine is constructed, and behavior state probability output and intention inference are achieved; generating a regulation and control instruction set of minimum intervention and risk avoidance and issuing and executing the regulation and control instruction set; feedback data increment is collected to update model parameters, and closed-loop optimization is achieved. According to the method, through multi-dimensional behavior feature fusion and an environment adaptive compensation mechanism, the prediction precision, the decision-making efficiency and the sustainable evolution ability of the digital twin model under complex working conditions are significantly improved.

This invention provides a postoperative patient monitoring system for neurointerventional therapy, relating to the fields of medical big data and artificial intelligence. It includes a multimodal data acquisition center, an interference feature decoupling unit, a trust capital quantification unit, a game strategy arbitration unit, and a monitoring execution unit. The multimodal data acquisition center is configured to retrieve time-series monitoring data of the monitored subjects. The trust capital quantification unit performs trust loss assessment and analysis on historical interaction feedback data. The game strategy arbitration unit is configured to compare and analyze the current clinical trust capital index with a preset trust threshold. This invention's system performs time-frequency domain matching between residual sequences and behavioral state labeling data, ensuring that the system only predicts the risk evolution of neurogenic hemodynamic changes. This significantly reduces the false alarm rate caused by external interference in complex postoperative monitoring environments and extracts pure pathological feature components.

This application discloses a music recommendation method, device, and vehicle. The music recommendation method includes: acquiring passenger images, with the number of passengers being no less than two; identifying the behavioral state characteristics of each passenger based on the acquired images, including the passenger's facial expression, whether the passenger is asleep, and the passenger's body posture; and analyzing the human body state data of each passenger based on the behavioral state characteristics, whereby the human body state data can characterize the user's cognitive and physiological state; recommending music that matches the human body state data of the same category when the number of passengers corresponding to the same category of human body state data reaches a first threshold; and recommending music that matches the human body state data of the category with the highest priority based on the priority of each category of human body state data when the number of passengers corresponding to the same category of human body state data does not reach the first threshold, thereby improving the user experience.

The present application provides an artificial intelligence education and learning method, system, device and storage medium, which relates to the field of artificial intelligence technology. The method includes: obtaining the teacher's location information and the teacher's first behavior state information; combining the location information and the first behavior state information to generate the student's standard behavior state information; obtaining the target student's second behavior state information, comparing the standard behavior state information and the second behavior state information, generating a comparison result, and generating the target student's concentration score based on the comparison result; obtaining the third behavior state information of other students within a preset range of the target student, generating an adjustment index based on the third behavior state information; adjusting the concentration score by the adjustment index to generate a target concentration score, and generating a reminder message when the target concentration score is lower than the preset score. The technical effect of the present application is to improve the accuracy of the concentration assessment results.

The invention relates to the technical field of physical and psychological health early warning, and discloses a multi-modal physiological perception physical and psychological state grading early warning system for a child watch, and the system comprises a multi-modal perception analysis module which is configured to analyze physiological data and behavior state data, determining an initial physical and mental state evaluation value of the to-be-monitored child according to the analysis result; the judgment module is configured to judge whether the initial physical and mental state evaluation value is adjusted or not according to the historical physiological behavior data; the adjusting module is configured to adjust the initial physical and mental state evaluation value based on the current environment data to obtain a final physical and mental state evaluation value; and the grading early warning module is configured to divide the physical and mental state of the to-be-monitored child into a plurality of early warning grades according to the final physical and mental state evaluation value. The system can comprehensively and accurately monitor and evaluate the physical and psychological states of children and provide timely early warning information.

This invention discloses an energy-saving control method and device for a bird tracker based on sensor fusion. The method uses an accelerometer to collect triaxial acceleration data to identify the bird's behavioral state, activating the heart rate and blood oxygenation sensors only when the bird is stationary and deactivating them during movement. It combines illumination data, location information, and the device's real-time time to determine signal obstruction environments and dynamically adjusts the operating strategies of the positioning and communication modules. Before positioning calculation, it detects the number of satellites in a low-power mode, performing full positioning only when the target is met. Power management independently controls the on / off operation of the positioning, communication, and storage modules, with standby power supplied only to the real-time clock module. The corresponding device includes a main control unit, a sensor data acquisition and tracking unit, and an energy-saving control unit, integrating multiple types of sensors and a power management module. This invention significantly reduces the tracker's ineffective power consumption and extends its battery life in the field through multi-sensor fusion and dynamic energy-saving scheduling, adapting to the long-term monitoring needs of birds.