124 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 discloses an intensive care data acquisition and early warning system, and relates to the technical field of medical early warning, abnormal points in data can be effectively identified through a data processing module, an abnormal data set is generated, and the method is beneficial to screening out possible instantaneous abnormal conditions. And on the basis of the anomaly sets, corresponding monitoring time periods are identified, the relevance between the behavior state and the physiological parameters of the patient is further analyzed, influence factors are calculated, and the relevance-based analysis method effectively solves the problem of false positive anomaly which is difficult to distinguish in a traditional intensive care system. On the basis, the hierarchical response module constructs a screening model by using a convolutional neural network technology, fits and outputs a screening index, distinguishes abnormal types according to the numerical value of the index and sends out a corresponding early warning instruction, and meanwhile, ensures that dynamic adjustment and switching of the early warning instruction are more intelligent and accurate through a preset time window mechanism.

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 learning process-oriented ternary second-order coupling dynamic cognitive diagnosis method, which comprises the following steps of: dividing learning units in a learning process into'knowledge, time sequence and behavior 'triples; knowledge-time sequence and behavior-time sequence first-order coupling analysis knowledge state and behavior state changes are obtained through a neural network model and a behavior feature extraction technology, meanwhile, a guess gate, a proficiency gate and a learning gate are set by adopting a GLU gating technology, complex modeling of cognitive state changes of learners in the learning process is achieved, and the learning efficiency is improved. Obtaining a potential ability state of the learner; finally, through multi-task prediction of behavior characteristics and reaction results, diagnosis of the cognitive state of the learner and prediction of future performance of the learner are achieved. According to the invention, the learning condition of the learner can be scientifically and comprehensively predicted, and the purpose of assisting the teacher in precise teaching is achieved.

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.

A device includes one or more microphones, an output component, and a network interface. The device receives, from the one or more microphones, audio data associated with sound captured within an environment, and determines that the sound is associated with the user. Based on the sound being associated with the user, the device determines one or more biomarkers associated with the sound. A threshold associated with the user having a pathological or behavioral state is determined and when the one or more biomarkers satisfy the threshold, the sound is determined to be indicative of the user having the pathological or behavioral state. The device outputs a first notification on the output component and sends data associated with a second notification to be output on an electronic device associated with a caregiver of the user.

The invention discloses an intelligent bird repelling device and method based on multi-modal recognition, and relates to the technical field of power grid operation, a microphone array is arranged in a target monitoring area, and sound signals of birds are monitored in real time; setting a camera in the target monitoring area, and acquiring an image of the bird in the target monitoring area; according to the images of the birds in the target monitoring area and the sound signals of the birds, the initial emission state of a laser and the initial sound production state of an audio generator are determined, and the birds in the target monitoring area are repelled; acquiring behavior state information that the birds are driven, and adjusting the emission state of the laser and the sound production state of the audio generator according to the behavior state information; in this way, birds can be repelled in various modes of sound and images, behavior characteristics of different birds and actual requirements of a power grid can be flexibly met, the adaptability in different environments is good, the effective bird repelling effect is ensured, and safe operation of the power grid is guaranteed.

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 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 provides an autistic child intervention treatment auxiliary system and method based on intelligent interaction, relates to the field of data intelligent analysis, and adopts an AI-based data processing technology to extract audio features from audio data of an autistic child object and extract facial features from a facial expression image of the autistic child object, so that the autistic child intervention treatment auxiliary system is obtained. And intelligently judging whether the behavior state of the autistic child object is trying to communicate or not based on the multi-modal fine-grained joint representation between the audio feature coding feature and the face image feature coding feature. Thus, through continuous data acquisition, extremely fine changes of autistic children can be captured more accurately, and it is ensured that no instant behavior is missed. Moreover, by combining facial expression analysis and audio feature extraction, the judgment on the behavior state of the autistic child can be more accurate, and then more targeted personalized intervention feedback is generated.

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 vehicle control method, electronic equipment, a vehicle, a computer readable storage medium and a computer program product. The method comprises the steps that a vehicle is controlled according to psychological state detection information of a main driving user in a vehicle cabin space, and the psychological state detection information is determined according to behavior state information of each user in the vehicle cabin space. Therefore, the main driving psychological state detection information can be determined according to the behavior state information of each user in the cabin space of the vehicle, the vehicle is controlled, such as the related parameters of the vehicle are adjusted, and the vehicle control effect can reach the expectation to a certain extent. The main driving psychological state detection information is determined according to the behavior state information of each user in the vehicle cabin space, so that the main driving psychological state detection information can be associated with the behavior state information of the passengers in the vehicle cabin space, and the effectiveness and reliability of the main driving psychological state detection information can be guaranteed; the situation that the reliability degree of the psychological state detection result of the main driving user is low can be improved to a certain extent.

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.

Various systems and methods are provided herein for performing health status detection. In one embodiment, ECG data, acceleration data, and audio data is received via a sensor unit. One or more parameters are extracted from the ECG data. An activity associated with the acceleration data is determined. At least one of an emotional state, a behavioral state, or an infant-caregiving interaction associated with the audio data is detected. One or more physiological and behavioral indicators are determined based on the one or more parameters, the activity, and the emotion state, behavioral state, or the infant-caregiver interaction. A health status corresponding to the physiological and behavioral indicators is outputted.

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.

The invention belongs to the technical field of data mining, and discloses a user behavior dynamic data deep mining analysis method. Comprising the steps of obtaining behavior data and environment data; the behavior data comprises motion data and physiological data; calculating exercise intensity and stride frequency data through the total acceleration data; the pressure data acquisition mode in the physiological data is to predict the pressure data by using the trained support vector regression model; preprocessing the obtained behavior data and environment data to obtain a preprocessed data set, and processing an abnormal value by using a local abnormal factor algorithm; based on the preprocessed data set, an automatic encoder is used for fusion, and a comprehensive behavior vector is output; judging the current behavior state of the user based on the comprehensive behavior vector; according to the method, the accuracy of behavior data analysis is improved, real-time and intelligent user behavior state judgment can be achieved, and the method has high practical value and innovativeness and is particularly suitable for the fields of health management, personalized services and the like.

The invention relates to an error detection method when operating a control program, which includes controlling communication between a number of control components (100-1, 100-2, 100-n) and a common control communication instance (100), and generates diagnostic signals for monitoring the control using state values ​​transmitted during the communication of a behavior criterion common to the number of control components, which respectively represent current behavior states of the number of control components. Using these behavioral states, a behavioral characteristic of each control component is determined, and these determined behavioral characteristics are then checked as a whole to determine whether they indicate an abnormal communication characteristic. If they indicate an abnormal communication characteristic, an error detection signal is generated as a diagnostic signal. For this purpose, at least one standard communication characteristic is defined based on a large number of different behavioral characteristics and / or artificial intelligence is used and, based on a large number of different behavioral characteristics, is trained on at least one communication characteristic that is valid as a model and / or is taught during operation to check the determined behavioral characteristics as a whole for a deviating and therefore abnormal communication characteristic.

The invention discloses a personalized virtual-real interaction cognitive game system based on AI adaptive brain cognitive enhancement, comprising: a cognitive game design module for designing a plurality of cognitive tasks corresponding to attention, memory, execution function and perception function based on a dynamic difficulty adjustment algorithm according to user characteristics; the dynamic difficulty adjustment algorithm is used for dynamically adjusting the difficulty of a specific level task according to the behavior state and the psychological state of the user; the cognitive task guiding module interacts with the user through a multi-mode guiding mechanism to guide the user to complete a cognitive task; the data tracking analysis module is used for recording the performance and growth trajectory of the user in the completion process of each cognitive task; the instant feedback module provides feedback based on an instant feedback generation algorithm; the cognitive competence evaluation report module is used for generating a corresponding cognitive competence evaluation report according to the performance and data tracking analysis algorithm; and the data storage module is used for storing behavior data, emotion records and cognitive competence evaluation reports of the user.

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.

The embodiment of the invention provides an alert state triggering method and device in a sentry mode and a vehicle, and relates to the technical field of vehicle monitoring, and the method comprises the steps: continuously obtaining various behavior data, at least including distance and hands, of surrounding people and current environment factor information in a monitoring state of a sentry system, wherein the behavior state detection of the personnel is used as a main calculation factor of the occurrence probability of the risk event, the environmental factor considers that the behavior of the personnel is associated with the influence of the environment, and the personnel factor is corrected through the environmental factor in order to reduce the prediction result of the possible risk of damaging the vehicle by misjudgment; therefore, whether the sentry system is triggered to enter the vigilance state or not can be decided according to more accurate risk probability prediction. Compared with an existing sentry state triggering mode, the method can enter the vigilance state more reliably and accurately, and the vigilance mechanism of a sentry system achieves a practical effect while it is guaranteed that meaningless resource consumption is reduced.

The invention belongs to the technical field of state monitoring of elderly people living alone, and particularly relates to a state monitoring and early warning method and system for elderly people living alone. According to the specific technical scheme, the method comprises the following steps of: acquiring multi-source behavior state data when the elderly living alone uses different mobile phone applications in different seasons, different weathers and different postures by utilizing a plurality of sensors built in the smart phone, and acquiring corresponding physiological state data; establishing a personalized physiological state prediction model according to the multi-source behavior state data and the physiological state data; acquiring normal physiological status characteristics of the elderly living alone when using different mobile phone applications in different seasons, different weathers and different postures; acquiring daily mobile phone use record data of the elderly people living alone in different seasons, and generating a daily mobile phone use work and rest table of the elderly people living alone in different seasons; the physiological state of the elderly living alone is monitored by using the work and rest table according to the mobile phone in different seasons. Multi-dimensional data such as mobile phone use habits, environmental perception and physiological states of the elderly living alone are deeply integrated, real-time behavior states and physiological states of the elderly living alone are accurately analyzed, abnormity is timely detected, corresponding rescue processes are rapidly started, and the personalized state monitoring and early warning method for the elderly living alone is provided.

Electronic system (1) designed to analyse and validate of emergency interventions of a vehicle according to a behavioural state of a subject driving the vehicle and comprising: a sensory system (2) configured to output signals indicative of physiological and image data related to the subject; and an electronic processing unit (3) in communication with the sensory system and being configured to receive the signals and process them to output corresponding statistical data related to the subject. The electronic system (1) is designed to process the data outputted by electronic processing unit (3) to determine a behavioural state and / or transitions thereof to determine the sleepiness level, the motion sickness level and the emotional state of the subject and generate a feedback to provide to an end user and provide emergency intervention to the subject based on the sleepiness level, the motion sickness level and the emotional state of the subject.

The invention relates to an SVM-based fatigue driving state detection method and system, and the method comprises the following steps: collecting behavior feature data of a driver, and constructing a multi-modal behavior feature vector; classifying the multi-modal behavior feature vectors through a mixed kernel function SVM model to obtain behavior state classification, wherein the mixed kernel function SVM model comprises a dual-kernel function pool of a radial basis kernel and a polynomial kernel and an online incremental learning framework; the behavior state classification is judged according to a judgment threshold value, a driver fatigue driving state detection result is obtained, and the judgment threshold value is obtained through dynamic adjustment of a dynamic threshold value adjustment module according to the multi-mode behavior characteristic data. Compared with the prior art, the fatigue driving state detection method has the advantages that the conditions of misjudgment and missed judgment are effectively reduced, and the accuracy and reliability of fatigue driving state detection are improved.

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.

The invention relates to an intelligent management system for home safety and health of old people. The method comprises the following steps: carrying out key frame sampling on an obtained behavior state monitoring video in a home safety health management server; performing feature extraction on the time sequence of the behavior state monitoring key frame through a behavior state feature extractor based on a deep neural network; inputting the time sequence of the behavior state semantic feature vector into a behavior state semantic time sequence propagation fusion network to obtain a behavior state time sequence propagation aggregation semantic representation vector as the behavior state time sequence propagation aggregation semantics of the old person object; and determining whether the monitored old person falls down or not, and determining whether to generate an early warning prompt and send the early warning prompt to remote terminal equipment or not. Thus, the behavior pattern and state of the old at home can be monitored in real time, early warning of falling of the old is achieved, falling and accidental injury are prevented, a safe and healthy home living environment is provided for the old, and meanwhile the care burden of family members is relieved.

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.