89 results about "Behavioral traits" patented technology

The invention relates to the field of intelligent detection, in particular to an induction range setting method and device, equipment and a program product. The method comprises the following steps: collecting behavior characteristics of active personnel in a detection range of a sensor; determining the behavior type of the active person according to the corresponding relation between the value range of the preset behavior characteristics and the behavior type; counting the track distribution of the moving personnel entering the monitoring area according to the behavior category; and setting the induction radius of the monitoring area according to the track distribution, and performing induction monitoring on the monitoring area according to the induction radius. The whole process does not need manual debugging, can automatically adapt to a complex environment, is favorable for improving the sensor deployment efficiency, and reduces the manual debugging cost.

The invention relates to the technical field of language barrier evaluation, and discloses a deep learning-based language barrier executive intervention effect evaluation method. The method comprises the following steps: acquiring real-time voice data and facial expression data of a language barrier patient in intervention training through a multi-modal data acquisition device to form an original behavior feature set; performing acoustic feature hierarchical analysis on the voice data by adopting a time sequence feature extraction network to generate a voice time sequence feature vector; performing micro-expression dynamic capture on the expression data through a three-dimensional convolutional neural network to generate an expression state feature vector; inputting the two types of vectors into a multi-modal feature fusion layer to carry out cross-modal correlation analysis, and generating a comprehensive behavior evaluation matrix; on the basis of the matrix, an intervention effect analysis model driven by an attention mechanism is adopted, a behavior improvement degree index of the current intervention stage is calculated, accurate evaluation of the intervention effect is achieved, and support is provided for dynamic adjustment of language barrier rehabilitation intervention.

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 relates to a self-adaptive excitation method for maintaining healthy behaviors. The method comprises the following steps: acquiring environmental data in a target area and physical signs and behavior data of a person; extracting behavior characteristics of the person according to the physiological indexes and the behavior data; constructing a short-term dynamic behavior and a long-term trend behavior according to the behavior characteristics; fusing the short-term dynamic behavior and the long-term trend behavior by using a cross attention mechanism and a gating fusion formula; establishing a health behavior thinking map; generating personalized incentive content; matching the character features with the excitation content through a multi-dimensional depth similarity algorithm, and generating excitation feedback in a text, voice or video form; and iteratively updating the excitation triggering condition and the content generation strategy according to the character behavior corresponding data. Through the technical scheme of the invention, not only can the probability of behavior change be improved, but also the interpretability of the excitation method can be effectively improved.

The invention relates to the technical field of examination cheating prevention, and discloses an online examination cheating prevention real-time early warning system based on behavior recognition, and the system comprises a real-time monitoring module which employs multi-mode biological feature recognition, carries out the confirmation of the identity and registration information of an examinee through face recognition before an examination, and carries out the real-time monitoring of an examination behavior; the test module is used for identifying examinee information and providing examination questions and an answer interface; the AI algorithm recognition module monitors behavior characteristics of the examinee in the examination process, recognizes abnormal behaviors by analyzing the stability of the behavior characteristics and judges whether the behaviors are cheating behaviors, and a voiceprint recognition module is arranged in the AI algorithm recognition module to monitor environmental noise and record voice. According to the invention, through synchronous acquisition of three paths of videos, a monitoring blind area is further eliminated, multiple paths of videos complement each other, misjudgment of a single view angle is reduced, behaviors of examinees can be automatically identified and analyzed through an AI algorithm, risk prompting and early warning are carried out, and the monitoring efficiency is improved.

The invention discloses a simulation method and system for students with different cognitive levels based on a large language model. According to the method, the cognitive prototype is constructed based on the past learning records of the students, and the mastering degrees of the students on different knowledge concepts are clearly expressed through the knowledge graph. The cognitive prototype can be used for accurately predicting the performance of the student in a new task, including whether the problem can be solved or not and specific errors possibly caused. Meanwhile, a self-evaluation-self-optimization loop process based on beam search is introduced, and a student answer consistent with a prediction behavior is iteratively generated, so that student simulation which is more real, natural and consistent in cognition is realized on the premise that additional training is not needed. Related results show that the staged and cognitive prototype-driven student simulation framework provided by the invention is superior to the existing mainstream method in multiple dimensions, and behavior characteristics of students with different cognitive levels can be reproduced more truly.

The invention discloses a behavior data analysis method and system based on intelligent perception. The method and system are used for improving the reliability of child behavior analysis. The method comprises the following steps: acquiring multi-dimensional monitoring data of a plurality of children in a monitoring environment, wherein the multi-dimensional monitoring data comprises visual information, auditory information and physiological information of each child; performing multi-source fusion on the visual information, the auditory information and the physiological information of each child to generate a behavior feature set; inputting the behavior feature set into a preset behavior and emotion classification model for classification to obtain a behavior mode and an emotion state of each child; on the basis of individual information of different children, the behavior modes and the emotional states in combination with scene information of the monitoring environment, knowledge graph construction on a time sequence is carried out, a children behavior graph is obtained, and the children behavior graph is used for displaying behavior events of the children in a preset time period.

The embodiment of the invention belongs to the technical field of psychological state detection, and discloses a psychological state assessment interaction system and device, and the corresponding system comprises an AI interaction module which is used for interacting with a user and obtaining feedback information of the user in the interaction process, and the feedback information comprises voice reply data, text reply data and physiological data of a question; the deep learning model is used for extracting feature data of the feedback information; the support vector machine model is used for evaluating the psychological state of the user according to the feedback information; input data of a kernel function of the support vector machine model is feature data; and the VR video generation module is used for generating a corresponding VR video according to the psychological state and pushing the VR video to the user, and the VR video has an interaction function with the user so as to further determine a lower state of the psychological state of the user and autonomously modify the VR video according to the lower state. The emotion, cognition and behavior characteristics can be extracted from the dialogue of the user, and the psychological state is quantified.

The invention discloses a dolphin behavior analysis method, device and equipment based on multi-modal characteristics and a medium, and the method comprises the steps: carrying out the framing of an acoustic waveform signal of a dolphin, so as to construct a three-dimensional acoustic tensor; performing feature coding on the original video slice of the dolphin to obtain a global behavior feature vector, and performing behavior clustering on the global behavior feature vector through a preset clustering algorithm to obtain a behavior center vector; the method comprises the following steps: obtaining an audio embedding vector of a preset dimension through a preset time sequence modeling network, and obtaining a video embedding vector of the same dimension as the audio embedding vector through a preset multi-layer perceptron; generating a plurality of groups of sound-line embedding pairs according to the time alignment index table and a preset dynamic time warping algorithm; and through a preset graph convolutional network, carrying out propagation and aggregation on the sound line graph structure constructed according to the plurality of groups of sound-line embedding pairs to obtain a dolphin behavior analysis result. The dolphin behavior analysis method can improve the accuracy of dolphin behavior analysis results.

The invention relates to the technical field of intelligent pet behavior correction based on image recognition, and particularly discloses a pet dog behavior correction method based on training image feedback recognition, which comprises the following steps: capturing activity image data of a pet dog by using a camera, and extracting behavior characteristics such as social interaction distance and energy release level; analyzing the time sequence data of the social interaction distance by using a multi-scale convolutional neural network, and calculating an abnormal feature value of the social interaction distance; performing dimension reduction processing and clustering analysis on the energy release level through a manifold learning algorithm, calculating energy release abnormal feature values, constructing a comprehensive feature vector by the two abnormal feature values, inputting the comprehensive feature vector into a random forest model to predict an abnormal behavior score of the pet dog, and when the score exceeds a preset threshold value, judging that the pet dog shows an abnormal behavior. Based on the behavior correction value calculated by the fuzzy logic system, the intensity of the correction measure is dynamically adjusted.

The invention provides an explanation strategy adjusting method and device, and the method comprises the steps: obtaining the behavior characteristics of a user and a current subject, adjusting the explanation strategy of the current subject based on the behavior characteristics of the user, determining the learning effect, efficiency and cognitive load of the user under the explanation strategy, and obtaining the learning effect, efficiency and cognitive load of the user under the explanation strategy. And obtaining a score according to the learning effect, efficiency and cognitive load of the user under the explanation strategy, and if the score is higher than a preset score, adjusting the explanation strategy of the current subject based on a knowledge graph. By comprehensively capturing the learning behavior and the emotional state of the user, the explanation strategy is adjusted, so that a more personalized explanation strategy is provided for the user.

The embodiment of the invention discloses a background Alpha rhythm-based audio-visual perception ability evaluation method, which comprises the following steps of: acquiring an individual Alpha peak frequency of each subject, and setting various background rhythm stimuli for each subject; enabling each subject to execute various audio-visual perception tasks while the various background rhythms are stimulated, so as to collect electroencephalogram data and behavior data of each subject when the various audio-visual perception tasks are executed under the stimulation of the various background rhythms; performing feature extraction on the electroencephalogram data of each subject to obtain electroencephalogram features; performing feature extraction on the behavior data of each subject to obtain behavior features; screening electroencephalogram features related to improvement of the behavior features from the electroencephalogram features to obtain effective electroencephalogram features; and based on the effective electroencephalogram characteristics of each subject under the individual Alpha rhythm stimulation and the effective electroencephalogram characteristics of each subject under each contrast rhythm stimulation, calculating the modulation index of each subject so as to quantitatively evaluate the adjustment effect of the individual Alpha rhythm stimulation on the audiovisual perception ability of each subject.

The invention relates to an intelligent security and protection method and an early warning system based on behavior detection, and the method comprises the steps: obtaining video data in a monitoring region through a camera; performing real-time analysis on the video data, and extracting human body behavior characteristics; performing abnormal behavior detection based on the behavior characteristics; when an abnormal behavior is detected, an early warning mechanism is triggered, and alarm information is sent; according to the invention, by analyzing the video data in the monitoring area, the human body behavior characteristics are extracted, and abnormal behavior detection is carried out based on the behavior characteristics, so that intelligent safety monitoring and early warning are realized.

This technology provides ways to diversify the behavioral characteristics of non-player characters. [Solution] In the acquisition step, situation information indicating the game status of a non-player character is acquired. In the output step, control information for controlling the actions of the non-player character is output based on the situation information and predetermined reference information. The non-player character is associated with a first character or other non-player character with whom it collaborates in a virtual space. The reference information is configured to allow referencing action information about the actions of the first character or other non-player character, and situation information at the timing when the control corresponding to the action information is executed. The action information is acquired sequentially via input from the first player operating the first character, or sequentially in response to actions taken by the first character or other non-player characters, and managed in an information processing system.

This invention discloses a method and system for early warning of abnormal behavior among college students based on multi-source data fusion, relating to the technical field of image processing. The method involves real-time acquisition of video streams of individuals within a target area, extraction of behavioral features from the video streams for classification, and a determination list based on these behavioral features. Multi-source data is collected, and anomaly coefficients are calculated based on the multi-source data. If the anomaly coefficient exceeds a threshold, the target individual is identified as exhibiting abnormal behavior. Anomaly warnings are then issued based on the behavioral features corresponding to the abnormal behavior. By calculating the anomaly coefficient through multi-source data fusion, a preliminary screening of hidden risks is achieved, reducing the detection delay and computational redundancy caused by traditional full-process monitoring analysis. Furthermore, real-time video stream acquisition and cloud-based behavioral feature extraction are initiated for high-risk individuals, avoiding the limitations of audio noise interference and single visual modalities, and improving the robustness of identification and the real-time performance of warnings in complex monitoring scenarios.

This application provides a method for assessing the training ability of a target audience, applicable to the field of artificial intelligence. The method includes: obtaining a first score for the target audience based on their answers in a business training system and the knowledge points in the knowledge graph at the current moment; analyzing the behavioral characteristics of the target audience during the answering process using a time-series analysis model to obtain a second score and a guessing coefficient, where the guessing coefficient characterizes the degree of guessing in the target audience's answers; correcting the second score based on the guessing coefficient to obtain a corrected second score; obtaining a comprehensive score using a gating fusion mechanism based on the first score, the guessing coefficient, and the corrected second score; and obtaining the training ability level corresponding to the comprehensive score from a preset training ability level table, using the training ability level as the assessment result of the target audience's training ability. This application also provides a device, equipment, storage medium, and program product for assessing the training ability of a target audience.

An emotion recognition method includes the following steps: acquiring video data of a human-object interaction behavior process; performing data labeling on the positions of a person and an object and the interaction behaviors and emotions expressed by the person; constructing a feature extraction model based on deep learning, extracting features of interaction between the person and the object in a time-space dimension, and detecting the position and category of the human-object interaction behavior; mapping the detected interaction behavior category into a vector form through a word vector model; and finally, constructing a fusion model based on deep learning, fusing the interaction behavior vector and the time-space interaction behavior features, and identifying the emotion expressed by the interaction person.

The invention discloses a comment emotion recognition method, device and equipment based on multi-modal fusion, and the method comprises the steps: obtaining all comment data associated with any comment target, and carrying out the preprocessing of the data, and obtaining the preprocessing data; respectively extracting image features and text features from the preprocessed data; extracting initial behavior characteristics matched with the plurality of behavior statistical items from the comment data; performing dynamic weighted fusion on each initial behavior feature to generate a target behavior feature; splicing the image features, the text features and the target behavior features to generate spliced features; and performing emotion recognition on the splicing features, and determining a comment emotion category corresponding to the comment target. Therefore, comment emotion category evaluation is carried out in combination with multi-modal data such as texts, images and behavior data, and the accuracy and comprehensiveness of emotion classification are effectively improved by carrying out dynamic weighted fusion on behavior characteristics.

This application discloses a method, apparatus, device, medium, and program product for identifying student psychological states, aiming to address the problems in existing technologies such as the difficulty in real-time, comprehensive, and dynamic identification of students' psychological states, and the lack of effective early detection and warning capabilities. The method includes: acquiring a multi-dimensional behavioral feature sequence of the target student, which includes at least an expression feature sequence, a behavioral trajectory feature sequence, and a voice emotion feature sequence; inputting the multi-dimensional behavioral feature sequence into a trained psychological state identification model to output psychological state representation information for characterizing the target student's psychological state; and identifying the target student's psychological state based on the psychological state representation information.

The invention discloses a student abnormal behavior analysis and early warning method based on deep learning, and particularly relates to the technical field of data analysis. The method comprises the steps of collecting multi-source behavior data related to student safety in real time, performing standardization processing, inputting a deep learning model to extract high-level behavior characteristics, further calculating a behavior fluctuation degree and a situation behavior inconsistency index, and generating a normalized risk score; in combination with a classification model output result, a suspected pseudo normal sample which is high in risk but is misjudged as normal is identified, and targeted optimization is performed on a model classification boundary through a local weighted increment support vector machine, so that the identification capability and early warning accuracy of a system on hidden abnormal behaviors are remarkably improved, and the system reliability is improved. And the robustness and practicability of the model in a complex campus behavior scene are enhanced.

To provide feedback information on the behaviors performed by the person being evaluated, taking into account their behavioral characteristics. [Solution] The information processing device comprises: a first acquisition unit that acquires evaluator information relating to evaluators who evaluate the actions of the person being evaluated; a second acquisition unit that acquires behavioral information relating to the actions of the person being evaluated; a first analysis unit that analyzes methods for changing the evaluator's behavior based on the evaluator information; a second analysis unit that analyzes methods for changing the person being evaluated based on the behavioral information; a feedback generation unit that generates feedback information for the person being evaluated based on the analysis results from the first and second analysis units; and an output unit that outputs the generated feedback information.

The present application relates to the field of animal behavior monitoring technology, and specifically to a method for tracking and recording animal behavior during an experiment. The method comprises: obtaining an experimental video of the animal during the experiment; segmenting the experimental video based on the animal's behavioral characteristics, predicting the animal's position in the segmented video, and determining the animal's initiative based on the difference between the predicted position and the actual position; and tracking and recording the animal's behavior based on the animal's initiative in all segmented videos of the experimental video and the curve of the animal's center of gravity in all segmented videos. The present application aims to track and monitor animal behavior using high-precision motion capture technology, fully identifying the animal's initiative and motion state trajectory during the experiment.

The invention discloses a training management method based on new media operation, which belongs to the technical field of training management and comprises the following steps: acquiring behavior characteristic data of a user on a new media platform; generating a behavior characteristic expression value according to the behavior characteristic data of the user on the new media platform; generating a behavior characteristic expression signal according to the behavior characteristic expression value; acquiring training test data of the user, and generating a training test performance value; generating a training result state signal according to the training test performance value; analyzing the behavior characteristic expression signal and the training result state signal of the user, and judging whether the training content of the user is reasonable or not; if the training content is unreasonable, adjusting the training content; according to the method, the training effect of the user can be comprehensively and objectively evaluated, the training content can be adjusted according to the evaluation result of the training effect of the user, the pertinence and effectiveness of the training content are ensured, and the training content better meets the actual requirements and learning characteristics of the user.

The application discloses a radar human behavior recognition method based on multi-domain feature fusion, and is applied to the problem that the existing human behavior recognition method based on a radar sensor and a deep learning technology only adopts the features of one domain or only adopts one time-frequency analysis method in a time-frequency domain, thereby causing insufficient expression of human behavior features; three time-frequency analysis methods with different time-frequency resolutions, namely, a short-time Fourier transform, an adaptive optimal kernel time-frequency representation method and a Hann kernel reduction cross-term interference distribution, are selected; three types of frequency spectrum diagrams are obtained; then, the three types of time-frequency spectrum diagrams are combined and used by using a SE Net and a 3DCNN network in the time-frequency domain, so that human behavior features are more fully expressed; in a distance domain, a key feature is extracted by using the SE Net; and the features of two domains are combined, the mutual relationship is found, and the recognition accuracy of human behaviors is effectively improved.

The invention discloses a disease auxiliary diagnosis system and method based on multi-modal structured causal reasoning, and belongs to the technical field of intelligent auxiliary diagnosis. The system comprises a behavior feature layer which obtains behavior features representing behavior changes of a target object by synchronously collecting audio data and facial image video data of the target object; the scale entry layer is used for mapping the behavior characteristics into entry vectors of a psychological assessment clinical scale by utilizing a regression model; the symptom latent variable layer is used for aggregating entry vectors into symptom latent variables reflecting different clinical symptom dimensions according to the corresponding relation between scale entries and the clinical symptom dimensions; a disease category layer: obtaining a disease category influence probability used for analyzing the influence of symptoms on the disease risk by constructing a structured causal relationship graph; and the result interpretation layer is used for outputting a readable causal interpretation path and a feature contribution degree based on the causal structure diagram and the disease type prediction result. According to the invention, objective, structured and interpretable auxiliary decision-making of mental disease diagnosis can be realized.

This invention discloses an abnormal user analysis system based on big data, belonging to the field of big data technology. Its key technical solution includes a segmentation module: acquiring behavioral characteristic data of game platform users; classifying game platform users according to the degree of abnormality in the behavioral characteristic data to obtain a user category segmentation result set; collecting behavioral data from the second category of users in the user category segmentation result set according to different monitoring dimensions to obtain a first behavior dataset, a second behavior dataset, and a third behavior dataset; extracting the abnormal deviation between the user behavior and normal behavior of the second category of users under different monitoring dimensions to obtain a first risk dataset, a second risk dataset, and a third risk dataset, respectively; the effect is that by effectively identifying and handling abnormal users, the fairness and normal order of the game are ensured.

The application discloses a kind of venue Internet of Things multimedia monitoring management systems, it is related to multimedia monitoring technical field, behavior sensing module, the behavior characteristic data of target crowd is collected, the behavior characteristic data at least includes voice interaction data, mobile trajectory data, wherein language interaction data includes preparation word and execution word;Data analysis module, the behavior characteristic data collected is analyzed and handled, extracts the key feature information that can reflect the acceptance degree of target crowd to interpretation model, the voice interaction and mobile trajectory data of customer are collected in real time by the application, the demand of different crowd can be accurately analyzed, generates the personalized interpretation model in line with current crowd, so that interpretation service is more and more accurate, is suitable for different types of crowd.

The invention relates to the field of child mental evaluation, and discloses a child mental development self-service evaluation pavilion and an evaluation method thereof.The child mental development self-service evaluation pavilion comprises a pavilion body, the front end surface of the pavilion body is rotationally connected with an opening and closing door, the front end surface of the pavilion body is electrically connected with a coded lock module, and the coded lock module is electrically connected with a display panel; a folding chair is arranged on the right side of the inner side wall of the pavilion body. Behavior characteristics such as infant touch actions, facial expressions and the like are recorded through the video recording module with the resolution of 1080P and the field angle of 120 degrees, and the pickup module collects sound signals; after the AI algorithm preprocesses the data, quantitative parameters such as the effective number of times of touching red dots, the emotion similarity and the crying duration are extracted and compared with a built-in norm database of 1-3 years old, scores are calculated through a Z-score standardization and weighted summation formula, and subjective deviation is eliminated. The age correction coefficient further ensures the comparability of evaluation results of children of different month ages, so that the evaluation accuracy is improved by more than 40% compared with the traditional manual observation.

A behavior recognition system for analyzing an audio-video signal to detect challenging behaviors in autism via behavioral features. The system includes a processor configured to segment the audio-video signal into clips of audio data and video data, each of said clips having a predefined duration and annotated with interaction styles. The processor samples and preprocesses the audio data and video data of said clips to provide square video patches and square audio patches. The processor tokenizes the square video patches to embed video positional information and video modality information, and tokenize the square audio patches to embed audio positional information and audio modality information. And, the processor predicts behaviors based on the tokenized square video patches and the tokenized square audio patches.

The application belongs to the technical field of intelligent transportation, and discloses a driver workload prediction method based on multiple physiological signals under multiple auditory disturbances, which is created by combining three music rhythms (none, slow and fast), two navigation modes (high frequency and low frequency) and three traffic scenes (regular road, school road and construction site road), collects multi-modal data including skin electricity signal EDA, electrocardiogram signal ECG, electroencephalogram signal EEG, driving behavior characteristics and subjective workload evaluation, and the data is used to build a workload prediction model, and a stacking model is adopted, and the stacking model integrates multiple machine learning models to improve the prediction performance.