142 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 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 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 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 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 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 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 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.

The invention belongs to the field of artificial intelligence, and discloses a social robot detection method and system based on multi-type social behavior heterogeneous graph modeling, and the method comprises the steps: inputting social user information, and constructing a metadata feature channel and a text feature channel; the method comprises the following steps: constructing a network topology structure by using social behaviors of social users, and forming a plurality of different types of HINs by adopting a graph neural network; fusing the obtained metadata and text features to form comprehensive user features; performing supervised contrast learning training by using the fused user features to obtain contrast loss; fusing network structure information in a social behavior scene into user features, and performing message aggregation and feature updating; and inputting the user feature vector into the classifier to complete the detection of the social robot account. According to the method, the problem of social robot detection in a multi-social behavior scene can be effectively solved, the detection performance is ensured, the robustness and adaptive capacity of the model are ensured, and subsequent detection tasks are facilitated.

The invention discloses a hand contact state detection method and system, and relates to the technical field of computer vision and artificial intelligence. The method comprises the following steps: inputting a to-be-detected image, and extracting bounding boxes and initial features of hands, objects and people in the to-be-detected image through a backbone network and an auxiliary detector; analyzing a spatial and semantic relationship between a hand and a detected object through a hand-object relationship module, and outputting a confidence score related to hand-object interaction; modeling an interaction relationship between the hand and the detected person through a contrast space refining module, improving the interpersonal contact recognition capability in a complex scene by the contrast space refining module based on contrast learning and spatial relationship enhancement, and outputting a confidence score related to hand-person interaction; and predicting the contact state of each detected hand in combination with the confidence score related to the hand-object interaction and the confidence score related to the hand-human interaction. The method can be applied to human-computer interaction, video monitoring, social behavior analysis, virtual reality and the like.

The invention discloses a generative agent-based Ebola virus social simulation system and a generative agent-based Ebola virus social simulation method, which are used for dynamically simulating the interactive influence of social behaviors and virus propagation by integrating an SEIR infectious disease model and a generative agent framework. The system comprises a social factor interaction module, an experimental scene configuration module and a dynamic analysis module, supports high-fidelity simulation of scenes such as management scheme deviation and resource allocation optimization, and can quantitatively evaluate social chain reactions (for example, the infection rate is reduced by 30%-40%) of prevention and control measures. According to the method, the defect that a traditional model ignores social complexity is overcome, a prospective experimental platform is provided for policy making of social sensitive infectious diseases such as Ebola, and the method is adapted and expanded to other infectious disease research.

The invention relates to the technical field of data processing, and discloses a multi-dimensional data-based lonely risk prediction method, device and equipment and a medium, and the scheme provides accurate and comprehensive data support for social risk assessment by collecting multi-dimensional social behavior data of a target object. By calculating the social network features, a quantitative basis is provided for lonely risk assessment. And analyzing the voice data through an emotion analysis model, and capturing voice emotion features of the target object in social interaction. According to the method, social network features and voice emotion features are comprehensively analyzed based on an autism risk assessment model, an autism risk prediction value is generated, the possibility that a target object is in an autism state is visually reflected, an autism intervention strategy is formulated according to an autism risk level, intervention can be effectively performed according to the actual autism risk condition of the target object, and the risk assessment efficiency is improved. And the intervention accuracy and timeliness of the social health of the target object in the home-based care service in the medical field are obviously improved.

The present application belongs to the technical field of unmanned aerial vehicle cluster control, and particularly relates to a multi-unmanned aerial vehicle cooperative target tracking path planning method based on a spider wasp optimization algorithm. The method comprises the following steps: (1) establishing a mathematical constraint model of a flight path; (2) constructing an evaluation function according to the mathematical constraint model, including a fitness function and a total distance length; (3) based on the spider peak optimization algorithm, solving the evaluation function, and constantly iterating, calculating and comparing to obtain an optimal solution or an approximate optimal solution, i.e. an optimal path; and (4) distributing the optimal path to each unmanned aerial vehicle for execution. The present application simulates the predatory behavior of spiders and the social behavior of bees, realizes efficient path planning of multi-unmanned aerial vehicles in cooperative target tracking in a complex three-dimensional environment, improves the flight efficiency and safety of unmanned aerial vehicle clusters, and enhances the adaptability of unmanned aerial vehicle clusters in complex environments.

The invention discloses an AI-based game material comment analysis method, system and device and a medium, and the method specifically comprises the steps: carrying out the multi-dimensional sentiment analysis of current material comment data based on a special game sentiment dictionary, and outputting a sentiment tendency analysis result; constructing a player clustering model according to the emotional tendency analysis result and the game behavior data, and establishing a social behavior and comment association model according to the emotional tendency analysis result and the social behavior data; extracting positive and negative feedback information for different player groups according to the player clustering model and the association model; and calculating an optimization priority of each material dimension according to the positive and negative feedback information and a preset player value evaluation model, associating the negative feedback information of the high-value player group with abnormal points in the game behavior data, and generating a material iteration suggestion. According to the method, accurate game material comment analysis is realized, customized iteration suggestions are generated, and the game material optimization efficiency and quality are improved.

The embodiment of the invention provides an information interaction range adjusting method and device, electronic equipment, a medium and a product, and relates to the technical field of computers.In the embodiment of the invention, a first control is displayed in response to a trigger display operation of the first control, the first control is used for adjusting an information interaction range corresponding to the target virtual object in the virtual scene, then responding to an adjustment operation for the first control, displaying the adjusted information interaction range, and then displaying interaction information among the virtual objects in the information interaction range. According to the information interaction range adjusting method and device, the electronic equipment, the medium and the product provided by the embodiment of the invention, the interference of information in a game can be further adjusted, and the immersion of social behaviors in the game can be further improved.

The application discloses a robot social behavior control method based on comprehensive evaluation and optimal control principles, and the method comprises the following steps: collecting external multi-modal information, making a comprehensive decision on a social object, modeling a robot eye-head actuator, solving a motion trajectory with minimum nerve conduction noise, and performing optimal control by using an MPC controller to realize parallel interaction between the robot and multiple social objects. The optimal control is performed on the robot to generate human-like motion and gaze effects.

The invention relates to the technical field of telecommunication, in particular to an eSIM card security encryption data transmission system which comprises a social simulation management module, a social behavior learning and strategy evolution module, a self-adaptive encryption strategy module, a trust collaborative verification module and an anomaly detection and self-healing module. According to the method, historical experience is precipitated through a digital twin memory bank, an optimization strategy is generated through reinforcement learning training, and a closed-loop mechanism of real-time calling and dynamic adjustment is adopted, so that autonomous collaborative optimization of the concealment and the transmission efficiency in a specific social situation is realized, and comprehensive risk quantitative evaluation based on multi-dimensional situation awareness and dynamic weighting is realized. And the dynamic matching is adaptive to encryption algorithms of different risk levels, so that the accurate balance among the security strength, the communication efficiency and the equipment resource consumption is realized.

The application provides a trust feature completion and trust relationship prediction method and system for missing trust information, comprising: constructing a social relationship graph and a trust relationship graph to obtain 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, taking the compressed features of the user as input, and outputting a prediction label; taking the converted compressed social attribute features as input, and outputting a prediction label; taking a real label as supervision information, combining a reconstruction loss and an information bottleneck loss, training the model, and outputting a trust relationship. The application constructs a social behavior graph and a trust relationship graph, and learns the social attribute features and the trust attribute features of the user respectively. This multi-view learning method can more comprehensively capture the feature information of the user, and improve the accuracy of trust relationship prediction.

The invention relates to a method for recording and identifying social behaviors of drosophila melanogaster, and belongs to the technical field of neuroscience. The method comprises the steps of building a shooting platform, training an adaptive model through an existing machine learning model, recognizing postures of fruit flies in a video through the adaptive model, and putting a machine learning coordinate file into a patent program to obtain social behavior statistics. Through collaborative design of an adaptive machine learning model and a structured data processing program, full-automatic identification and index quantification of the social behaviors of the drosophila melanogaster are realized, the technical blank that the social behaviors cannot be identified by a drosophila melanogaster mating identification method is filled up, key data support is provided for research of a neural circuit mechanism, gene-behavior association and the like, and the social behaviors of the drosophila melanogaster mating identification method are optimized. Compared with the prior art, the method is obviously improved.

This invention relates to the field of artificial intelligence technology and discloses a multi-agent social network simulation method and system based on a cognitive reasoning chain. The method includes: initializing a multi-agent system comprising a social environment engine, a user profile engine, and a cognitive reasoning engine; executing a multi-agent social network simulation loop with a preset number of iterations; in each iteration, the social environment engine pushes social information to the agents as external stimuli and activates the agents to make independent decisions; updating the cognitive states of each dimension in the cognitive reasoning chain and generating corresponding social behaviors through reasoning using a large-scale language model; recording the social behaviors and their corresponding cognitive state trajectories; periodically analyzing historical records to optimize the influence coefficients between the cognitive dimensions of the cognitive reasoning chain; and adjusting the reasoning strategy of the preset large-scale language model. This makes the simulation of the agent's cognitive process a transparent and traceable evolutionary process, achieving a complete and understandable simulation of the "observation-cognition-behavior" cycle.