197 results about "Social behavior" patented technology

The invention relates to the technical field of multi-target tracking, in particular to a dynamic multi-target tracking and trajectory prediction method. Comprising the following steps: acquiring a color image and a depth image, and preprocessing; constructing a dynamic threshold double-flow neural network, extracting features from the preprocessed color image and depth image to obtain a visual feature set and a depth feature set, fusing the visual feature set and the depth feature set through an adaptive attention mechanism to obtain a fused feature set, and combining a dynamic threshold to obtain an observation information list of a target; updating the observation information list through a self-motion decoupling mechanism to obtain a real information list; establishing space association and time association between the target and the track based on the real information list to obtain a newest track set; and analyzing target characteristics, environmental constraints, social behaviors and historical trajectories, generating a prediction trajectory of each target and a corresponding multi-factor score, generating a time-varying influence graph, and performing planning and decision making by adopting a double-layer decision-making mechanism.

The invention provides a communication risk identification method and system based on multi-modal behavior fusion, and relates to the field of communication risk identification, and the method comprises the steps: fusing a network environment vector, an authority feature vector, a multimedia vector and a social behavior vector of a target terminal in a communication process within a preset time period, and obtaining a multi-dimensional feature vector; inputting the multi-dimensional feature vector to a preset causal graph model to obtain a fusion vector; inputting the fusion vector to a TCN-Transform hybrid model to obtain a target risk probability value; and generating a target defense instruction according to the target risk probability value and a preset dynamic defense threshold. The method can dynamically adapt to archive security level changes and semantic association scenes, and the real-time performance and accuracy of prediction are improved. According to the method, the behavior characteristics of the target terminal in the communication process can be reflected more comprehensively, so that the potential communication risk can be identified more accurately, real-time monitoring and early warning of the communication risk are realized, the false alarm rate and the missing report rate are effectively reduced, and the reliability of risk identification is improved.

The invention relates to the technical field of psychological health management, and comprises an AI-based college psychological health comprehensive service system and method, and the system comprises a psychological behavior scoring module, an interactive behavior analysis module, a psychological health assessment module, a social intervention matching module, and a dynamic adaptation optimization module. According to the method, based on behavior data of learning, work and rest, social contact and the like, the time matching degree of behavior categories is calculated, the comprehensiveness of individual psychological health scoring is improved, social interaction data is combined, the social behavior resonance degree is analyzed, the psychological integrating degree between individuals is measured, semantic emotion polarity change is combined, and psychological state fluctuation is quantified; based on psychological health state change analysis, individuals with high social mode similarity are screened, pertinence of intervention matching is enhanced, fluctuation trends of individual social states are synchronously identified, psychological intervention strategies are adjusted according to changes of psychological health scores, personalized adaptability is improved, and fine adjustment of psychological health management of college students can be achieved.

The invention relates to the technical field of artificial intelligence, and discloses a multi-agent social network simulation method and system based on a cognitive inference chain, and the method comprises the steps: initializing a multi-agent system comprising a social environment engine, a user portrait engine and a cognitive inference engine, executing a multi-agent social network simulation cycle of a preset round of iteration, in each iteration round, the social environment engine pushes social information to the intelligent agent as external stimulation and activates the intelligent agent to execute an independent decision, the cognitive state of each dimension in the cognitive reasoning chain is updated through large language model reasoning, corresponding social behaviors are generated, and the social behaviors and corresponding cognitive state tracks are recorded; and periodically analyzing historical records to optimize influence coefficients among all cognitive dimensions of the cognitive inference chain, and adjusting an inference strategy of a preset large language model. The simulation of the cognitive process of the intelligent agent is a transparent and traceable evolutionary process, and the complete and understandable simulation of the'observation-cognition-behavior 'cycle is realized.

The invention relates to the technical field of social network analysis and generative artificial intelligence, and discloses a multi-agent-based complex social behavior simulation and public opinion deduction method, which comprises the following steps of: dividing social groups, and dividing users in a social network into key opinion leader communities and common user communities; respectively constructing a key opinion leader agent and a common user agent which are driven by the large language model; constructing a dynamic weighted directed network, taking each key opinion leader agent model as a node, calculating a network node neighborhood through opinion index and node influence weighting, calculating an emotion score based on a text emotion index, dynamically adjusting the network node neighborhood based on the emotion score, and performing loop iteration for several times to complete deduction and simulation of network public opinions; the problem that public opinion propagation modeling is difficult under the cross-domain dynamic background is solved, the difference of public opinion individuals and the complex social interaction relation can be simulated more accurately, and the method is feasible.

The invention discloses a user social behavior-based public opinion propagation prediction method and system. The method comprises the following steps of S1, perfecting and dividing data set categories according to data set forms and contents; s2, performing sentiment analysis according to the content of the comments forwarded by the user, and performing preliminary clustering on themes of the tweets forwarded by the user; s3, fusing with user theme features, and performing feature construction; s4, constructing a feature module of the user node, and pruning the whole social network according to the propagation condition; s5, calculating the credibility degree and the susceptibility degree of the user according to the historical forwarding data to construct a mask matrix, and performing similarity evaluation on the propagation network of the specified event; and S6, constructing a propagation prediction result evaluation index. According to the method, the dependency and independence of the user are combined, the user topic features are combined, the user node features with more practical significance are obtained, and compared with traditional public opinion analysis and prediction, the method has higher accuracy.

The invention discloses a student user portrait construction method based on multi-modal data fusion and a terminal. The method comprises the following steps: acquiring learning behavior data, social behavior data and hobby and interest data of a user, and respectively extracting corresponding feature information; calculating the fusion weight of the feature information of each mode and generating a comprehensive feature vector; and on the basis of the comprehensive feature vector, performing student user portrait construction by using a clustering algorithm, an analytic hierarchy process and knowledge graph analysis. According to the method, data features of learning behaviors, social behaviors and hobbies and interests are extracted, modal information is synthesized by using a fusion weight mechanism, and finally a multi-dimensional and fine-grained student portrait is formed. Compared with a traditional single data source modeling method, the method has the advantages that the comprehensiveness, the accuracy and the personalized adaptive capacity of portraits are remarkably improved, and various application scenes such as personalized teaching recommendation, academic early warning and interest expansion activity matching can be effectively supported.

The invention provides an intelligent message pushing method and system, and the method comprises the steps: collecting the social behavior data and personal attribute data of a user in a social network; dividing the users into a plurality of groups by using the social behavior data and the personal attribute data based on an ant colony algorithm; analyzing a social relation and an interaction mode among users in the group; collecting message resources in the social network, and classifying and labeling messages; performing collaborative filtering processing on the to-be-pushed message, determining a target user group, and generating a message recommendation list; and pushing the recommended message to users in the target user group according to a preset pushing strategy. The user social behavior data and the personal attribute data are converted into the ant feature vectors based on the ant colony algorithm, similar feature vector ants are gathered through pheromone updating and path selection mechanisms, different user groups are formed, the user group division accuracy is improved, and user requirements are more accurately grasped.

The invention discloses a method applied to infant health education, which comprises the following steps of: 1, data collection and preprocessing: body development data: collecting physiological data, such as heart rate, body temperature, activity amount, height and weight, of a child in real time by wearing health monitoring equipment; psychological development data: collecting data such as emotional states, attention concentration conditions and cognitive competence of children through intelligent questionnaires, behavior analysis and emotion recognition technologies; social adaptation data: collecting data such as social behaviors, cooperative capabilities and language expressions of children by observing and recording expressions of the children in social interaction; and data cleaning and preprocessing: cleaning the collected data, removing abnormal values and noise data, and performing standardization processing to ensure the quality and consistency of the data. By wearing the health monitoring device, the intelligent questionnaire, behavior analysis and emotion recognition technology, the physical, psychological and social data of children are comprehensively collected, and the diversity and comprehensiveness of the data are ensured.

The invention discloses a humanoid robot situational social behavior expression method, which comprises the following steps of: sensing user behaviors by adopting multiple sensors, constructing a multi-layer interactive situation sensing structure, and establishing association between a human-computer interaction situation and the user behaviors; self-adaptive layered isolated word recognition and voice emotion recognition based on an optimized neural network are adopted, user voice features are fully utilized, a matching relation between user emotion and robot situational social action emotion is constructed, and an emotion-fused robot situational social behavior expression decision is provided. Therefore, the multi-modal social behavior expression of the humanoid robot, which can express emotions while implementing an interaction task, is realized. According to the method, the relationship between the user behavior and the interaction situation and the relationship between the interaction situation and the social behavior of the humanoid robot are constructed, the problem that in the prior art, the emotion interaction is less when the humanoid robot executes the social task is solved, and the environment situation and the user emotion are fully considered; and a multi-situation social expression realization path of the humanoid robot is provided.

The invention relates to a player negative behavior detection method and system, electronic equipment and a storage medium, and the method comprises the steps: comprehensively collecting behavior data such as player operation times, chat content, combat data, position information and cooperation request response conditions in a single game; multi-dimensional detection data such as game behaviors, game performance and social behaviors are obtained by processing and analyzing data such as calculation of operation frequency scores and single behavior scores, construction of a position thermodynamic diagram, statistics of the number of negative words, calculation of cooperation request response times and the like, and the comprehensive operation frequency scores and single behavior scores of players are compared with expected scores. Comparing with scores of other players of the team to judge negative behaviors, and outputting prompt information. According to the scheme, the problems that an existing reporting system depends on active reporting of players, and the detection efficiency is low are effectively solved, negative behaviors can be detected more comprehensively and accurately, game fairness and a healthy environment are maintained, and the game experience of the players is improved.

A system, method, and computer-readable medium are disclosed for performing entity interaction risk analysis operation. The entity interaction risk analysis operation includes: monitoring an entity, the monitoring observing an electronically-observable data source; identifying an interaction between the entity and another entity based upon the monitoring; analyzing the interaction between the entity and the another entity; and, performing a security operation in response to the analyzing the interaction.

The invention provides an ai-based teaching interaction method and system. The method comprises the steps of collecting multi-modal data of students; constructing an individual absorption model to calculate the knowledge absorption rate of the student and predict the knowledge absorption rate sequence trend of the student; constructing feature mapping to embed the knowledge absorptivity and the predicted knowledge absorptivity sequence, and constructing a dynamic adaptive content generator according to the feature mapping; and obtaining a teaching content feature vector generated by the adaptive content generator, and mapping the teaching content feature vector to the social behavior features of the students to obtain a social feature vector. Dynamically adjusting interactive recommendation and group distribution among the students; building an adaptive learning curve model according to the second dynamic social weight updating model, designing a feedback mechanism, and updating interactive recommendation and group distribution among the students; and constructing data output by the block chain network updating model to encrypt and upload, and constructing a learning feedback smart contract. According to the invention, a more intelligent and more efficient solution is provided for modern education.

The invention discloses a multi-modal data management method and system for cognitive function risk assessment, and relates to the technical field of health monitoring and data management. Calculating a personal basic physiological index baseline value and a social behavior index baseline value, calculating a physiological risk score and a social risk score based on real-time basic physiological data and social behavior data, constructing a dynamic coupling coefficient matrix, and combining the physiological risk score and the social risk score to obtain an initial risk score; and through the trained random forest model, outputting a correction coefficient to obtain a comprehensive risk score, and according to the comprehensive risk score, dividing risk levels and executing corresponding processing measures. The early warning efficiency of the cognitive function of the old people is improved.

This disclosure herein describes the use of cannabidiol (CBD) with tetramethylpyrazine (TMP) to improve the bioavailability and efficacy of CBD. Behavioral studies indicate that the oral administration of CBD with TMP has a strong anxiolytic effect, increases social behavior in stressed rats, and alleviates anhedonia-like symptoms in stressed rats. These effects were significantly greater than observed with CBD alone, most likely due to increased CBD exposure with TMP administration and / or a metabolism effect of the co-administration of CBD and TMP.

The invention, which relates to the technical field of artificial intelligence, discloses an AI-driven student behavior analysis and early warning system comprising a behavior data acquisition module, a behavior feature extraction module, a psychological analysis module, a psychological pre-diagnosis module, a psychological health management module and a map drawing module. The behavior data acquisition module acquires physiological and spatial trajectory data of students, the behavior feature extraction module extracts physiological rhythm and behavior pattern features, the psychological analysis module analyzes psychological stress indexes and social behavior abnormality, and the psychological pre-diagnosis module generates psychological health risk indexes. The psychological health management module implements graded early warning and closed-loop feedback, and the map drawing module performs multi-dimensional visual monitoring. Through the multi-modal data fusion and deep learning technology, real-time accurate analysis of student behaviors is realized, the early warning threshold is dynamically optimized, multi-dimensional visual support is provided, the psychological health problem recognition accuracy is remarkably improved, education managers are assisted in timely intervention, and physical and psychological health development of students is promoted.

The invention provides a psychological health abnormity reason tracing method and system based on a social behavior complex network, and the method comprises the following steps: obtaining user social network data, and carrying out the preprocessing of the user social network data; constructing a user social behavior complex network based on the interaction relationship between the users; evaluating the current mental health state of the user based on the social network data of the user; performing anomaly detection according to an evaluation result, and performing reason tracing through the user social behavior complex network; and providing mental health intervention suggestions according to the abnormal detection result and the traceability result. According to the method, information can be effectively extracted from massive, complex and multi-dimensional social data, so that accurate evaluation of individual mental health states and tracing of abnormal reasons are realized.

The invention provides a trust information missing trust feature complementation and trust relationship prediction method and system, and the method comprises the steps: constructing a social relation graph and a trust relation graph, and obtaining social attribute features and trust attribute features respectively; compressing the social attribute features and the trust attribute features, and constructing a feature converter to convert the compressed social attribute features; constructing a user trust relationship prediction model, and outputting a prediction label by taking the compression characteristics of the user as input; taking the converted and compressed social attribute features as input, and outputting a prediction tag; and taking a real label as supervision information, combining reconstruction loss and information bottleneck loss, training the model, and outputting a trust relationship. According to the method, the social behavior graph and the trust relation graph are constructed, and the social attribute characteristics and the trust attribute characteristics of the user are learned respectively. According to the multi-view learning mode, the feature information of the user can be more comprehensively captured, and the accuracy of trust relationship prediction is improved.

The invention belongs to the field of nutrient substance research, and particularly relates to a nutritional composition beneficial to improvement of neural behaviors and cognition. The nutritional composition provided by the invention comprises necessary active ingredients shown in the following (i) and (ii): (i) lactose-N-neotetraose, (ii) 1, 2-diacyl-sn-glycerol-3-phosphorylcholine, and (iii) 1, 2-diacyl-sn-glycerol-3-phosphorylcholine. The nutritional composition comprises lactose-N-neotetraose and 1, 2-diacyl-sn-glycerol-3-phosphorylcholine, and in the nutritional composition, the mass ratio of the lactose-N-neotetraose to the 1, 2-diacyl-sn-glycerol-3-phosphorylcholine is 1: (0.010-2.0). The lactose-N-neotetraose and the 1, 2-diacyl-sn-glycerol-3-phosphorylcholine can achieve a synergistic effect to improve filial generation repeated engraving behaviors caused by maternal immune activation, decrease of social behavior ability and decrease of anxiety state and memory and recognition ability.

Systems and computer-implemented methods are disclosed for providing social entertainment experiences in a moving vehicle via an apparatus that simulates human social behavior relevant to a journey undertaken by the vehicle, for displaying human-perceivable exterior communication on the moving vehicle to neighboring vehicles and / or pedestrians, and for providing a modular travel experience.

The invention discloses pediococcus acidilactici for improving offspring social behaviors of a parent and application thereof, and belongs to the technical field of microorganisms. According to the pediococcus acidilactici CCFM1344 provided by the invention, by intervening neurodysplasia caused by high-fat diet of a parent body, the improvement of hyperactivity and social behaviors of the offspring is specifically shown as follows: (1) hyperactivity disorder, induced by high-fat diet of the parent body, of the offspring in juvenile and adult periods is recovered; (2) restoring parent high-fat diet induced social contact disorders of offspring juveniles and adult periods; (3) reducing the gene expression of the small glial cell M1 type differentiation marker CD86 in the offspring hippocampus; and (4) reducing the gene expression of the macrophage activation marker F4 / 80 in the offspring hippocampus. Through the probiotic preparation containing the pediococcus acidilactici CCFM1344, a new thought and method can be provided for early intervention of the autism spectrum disorder.

The present invention provides special medical use formulae useful for enhancing and / or improving the social behavior of an individual. The formula food for the special medical purpose comprises DHA (docosahexaenoic acid), ARA (arachidonic acid) and breast milk oligosaccharide 6-SL and / or LNnT, and the mass ratio of the DHA to the ARA to the breast milk oligosaccharide 6-SL and / or LNnT is (70-210): (60-420): (10-400). The special medical use formulae of the present invention contribute to enhancing and / or improving the social behavior of an individual.

The invention discloses a public space social behavior and environment influence identification method based on a sensing equipment network. The method comprises the following steps of public space area selection, measuring point layout and sensing equipment distribution, public space physical environment monitoring, public space social behavior identification, behavior environment index influence analysis and influence rating and output. Based on the network layout of the sensing equipment, the physical environment of the public space is monitored through the environment sensing equipment, the social behaviors of the public space are identified through the behavior sensing equipment, the influence relation between the environment indexes and the behavior indexes is comprehensively analyzed, and the influence degrees are sequenced and output. The method can be used for identifying the behavior environment influence relationship in the urban intelligent sensing field, and is beneficial to building a more efficient and comfortable urban public space.

The invention discloses an insect movement and social behavior research and analysis system. The system is characterized by comprising a camera for video acquisition, a bearing device for insect movement and a data processing module for analysis and research, the camera directly faces the bearing device to obtain a field background and an insect behavior video, and the data analysis module calculates and outputs insect behavioral characteristics based on insect position data obtained after the field background in the video is removed by software such as Ctrax and UMATracker on the basis of the recorded insect behavior video. According to the method, insect behavior characteristics are established according to insect position data obtained by means of digital camera shooting and the like, so that comprehensive interpretation of insect movement and social behaviors is provided, and support is provided for scientific research personnel to research the relationship between animal behaviors and experimental processing.

The invention discloses a machine learning-based binary social contact mode and an implementation method, and relates to the technical field of social networks, the mode comprises an on-campus social contact domain module, an off-campus self-media domain module, a hybrid processing architecture and cross-domain information fusion module, and a social contact enhancement recommendation engine. Through the on-campus social domain module and the off-campus self-media domain module, on-campus and off-campus information can be comprehensively utilized, more comprehensive social portraits are further provided, and the fusion of the double-domain information is beneficial to more accurate understanding of social behaviors and interest preferences of users. Differentiated modeling of an intra-school strong relationship and an extra-school weak relationship is realized through a domain separation architecture, a cross-domain attention layer effectively captures feature interaction of different social scenes by adopting a domain specific parameter matrix, and semantic-level feature fusion is realized while the independence of a dual-domain topology is kept in cooperation with a feature migration mechanism of a bridging matrix. And the node characterization simultaneously comprises stable campus social features and dynamic cross-school interest features.

The invention discloses a social network trust evaluation method and system based on a multi-view graph neural network, and the method comprises the steps: obtaining social network behavior information, constructing social behavior network graphs of different views, and constructing a user trust relationship network graph according to the trust relationship between users; based on social behavior network diagrams of different perspectives, a multi-head attention mechanism is built to extract influences of different types of neighbor nodes on target user node features and fuse the influences, and social attribute features are output; on the basis of the user trust relationship network diagram, utilizing an attention mechanism to fuse neighbor information of the nodes to form trust features of the user nodes; and in combination with the social attribute features and the trust features of the user nodes, performing multi-view feature fusion, and outputting a predicted trust relationship. The problems that a traditional trust evaluation method depends on a predefined model and is poor in universality, and an existing trust relation prediction scheme based on a graph neural network is difficult to obtain a trust relation graph and is poor in feature fusion effect are solved.

The invention discloses an SCRM customer intelligent portraying system and method based on multi-source data, and the system comprises a multi-source data collection and integration module which is used for collecting and cleaning the internal and external multi-source data of an enterprise; the client dynamic label engine is used for generating labels based on business rules and calculating dynamic weights based on data sources and recency; the client intelligent portrait generation and updating module generates a unified portrait through a multi-dimensional portrait model and a portrait fusion device and tracks version evolution; the interactive portrait application and feedback module provides visual display, intelligent early warning and strategy reaching functions and optimizes a label rule through a feedback closed loop; according to the method, internal and external multi-source data of an enterprise are integrated and uniformly coded, so that a data island phenomenon is broken, a multi-dimensional portrait model covering basic identities, transaction values, social behaviors and dynamic intentions is constructed, and a uniform customer panoramic view is formed.

The invention relates to the technical field of advertisement putting, in particular to an advertisement putting optimization system and method for commodity promotion, and the system comprises a data alignment unit which is used for obtaining the multi-source interaction data of a user, the multi-source interaction data comprises click stream data, transaction data and social behavior data, and the data alignment unit is used for obtaining the multi-source interaction data of the user; performing heterogeneous data space-time alignment on the multi-source interaction data to obtain aligned data corresponding to the multi-source interaction data; and the feature updating unit is used for carrying out cross-modal attention fusion on the alignment data so as to obtain attention features. According to the method, space-time alignment is performed through the multi-source interaction data, and the user characteristics are updated by using a cross-modal attention fusion technology, so that the multi-dimensional interest change and behavior pattern of the user can be accurately captured, the user demand is accurately matched in the advertisement putting process, and the advertisement effect is improved.

The invention provides a waterlogging prediction method based on a behavior thermodynamic diagram and infiltration dynamic modeling, and the method comprises the steps: firstly dividing a space region based on topographic data, extracting a surface type, a soil attribute and vegetation information, and constructing an infiltration capacity model which can be dynamically adjusted along with rainfall and time; then combining the surface temperature and meteorological data to establish an evaporation model considering the urban heat island effect; further generating a behavior thermodynamic diagram by using the crowd trajectory data, and extracting a behavior feedback factor reflecting an abnormal movement mode; according to the method, rainfall, infiltration, evaporation, drainage capacity, behavior feedback factors and the like are fused, a machine learning model used for predicting the waterlogging probability of each region is constructed, a loss function is optimized by introducing a weight mechanism of the behavior feedback factors, and the response sensitivity of the model to a risk region is improved. The method gives consideration to the physical mechanism and social behavior characteristics, and improves the accuracy and timeliness of waterlogging prediction in a complex urban environment.

The invention provides a travel preference modeling method and system based on user social behavior mining, and relates to the technical field of travel preference modeling, and the method comprises the steps: executing the cross-domain social behavior data collection of a target user; performing multi-modal feature extraction on the cross-domain social behavior data, establishing a text emotion topic vector, an image scene recognition vector, a time sequence behavior feature vector and a friend influence weight matrix, and executing joint embedding analysis; constructing a preference state machine by using the initial embedded vector set; constructing a target user interest diffusion network, and executing relation-driven reinforcement of the state transition probability of the preference state machine by using the target user interest diffusion network; and generating the tourism preference of the target user according to the enhanced preference state machine, and visually presenting the tourism preference through a preference radar map. According to the method and the device, the technical problem that user travel preference modeling is not accurate enough in the prior art can be solved, and the technical effect of improving travel preference modeling accuracy is achieved.