110 results about "Social behavior" patented technology

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 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 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.

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 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 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.

The embodiment of the invention discloses a response information generation method, device and equipment for a game assistant, and the method comprises the steps: obtaining social behavior data, attack behavior data, defense behavior data and player dialogue information in real time, carrying out the semantic analysis of the player dialogue information, and determining basic response information corresponding to a semantic analysis result; performing social range analysis and social content analysis on the social behavior data, obtaining a corresponding first activeness value and a corresponding second activeness value according to an analysis result, and determining an activeness level of the game role according to the first activeness value, the second activeness value and a preset level parameter; the attack behavior data and the defense behavior data are analyzed to obtain the proportion of the attack operation duration and the defense operation duration, response tone data associated with the game style of the player is determined based on the proportion, the basic response information is adjusted according to the activeness level and the response tone data, and game assistant response information is generated. And the flexibility and diversity of response information of the game assistant are improved.

The invention belongs to the technical field of computers, and discloses an electronic coupon recommendation obtaining method and system based on social network behaviors, and the method comprises the following steps: S1, obtaining multi-dimensional behavior data of a target user and a social associated user from a social network platform, the behavior data comprises consumption preference data, social interaction data and content generation data of the user; s2, performing cleaning and structured processing on the multi-dimensional behavior data, and extracting user social behavior feature vectors; according to the method, consumption preference, social interaction, original content and other multi-source data are fused, the constructed dynamic interest preference graph can deeply and prospectively insight into user interests, the problems of data sparseness and cold start are effectively relieved, the recommendation result is more accurate, and surprise can be brought; by quantifying the social influence index, key opinion leaders or users with high propagation potential can be identified, and coupons which are more attractive or have sharing incentive attributes can be recommended for the key opinion leaders or users with high propagation potential.

The present disclosure provides a multi-energy IDR scheduling method and system based on energy element digital twins, which belongs to the field of energy optimization scheduling technology. The scheme designs digital twin intelligent entities corresponding to typical energy elements of physical entity systems through model mapping and data mapping between digital environment and physical entity system, so as to establish flexible resource twin mapping; and on this basis, conducts subject behavior simulation design including value orientation and social behavior of energy elements in the digital environment, constructs digital twins that can simulate the behavioral characteristics of massive flexible resources, and then completes IDR scheduling, so that the scheduling method can effectively meet user needs, and at the same time, meet low-carbon goals to a certain extent, and reduce scheduling costs.

This invention discloses the application of Compound C108 in the preparation of drugs for treating autism spectrum disorder, belonging to the biomedical field. Experimental studies have demonstrated that SGs (sigma cells) are key factors involved in the formation of autism caused by genetic mutations and environmental factors. Compound C108 can inhibit SG levels by intervening in G3BP2 protein expression, ultimately alleviating autistic social behaviors. Therefore, Compound C108 can serve as a novel compound for preparing drugs to treat autistic social disorders.

Embodiments of the methods of the disclosure and compositions related to managing the microbiota in an individual having at least one social behavior deficit. In particular embodiments, an individual has at least one social behavior deficit and was born to a mother who was obese, overweight, or consumed a high-fat diet during pregnancy or carries mutations associated with neurodevelopmental behaviors. In particular embodiments, an effective amount of Lactobacillus reuteri is provided to the individual having at least one social behavior deficit to improve at least one symptom of a social behavior deficit.

The application discloses a data mining method and system for a social robot. The method comprises the following steps: determining an interactive object currently communicating with the social robot and collecting social media data; performing word segmentation and vectorization processing on the text through a natural language processing algorithm, and extracting an emotional tendency value through an emotional analysis model, and dividing the data into multiple emotional grades according to a preset critical value; extracting keywords and topic fields based on word frequency statistics and topic clustering, and deleting the data if the number of keywords is lower than a threshold value; constructing time series data according to social behaviors and time stamps, predicting an active period and a behavior trend by using an LSTM model, and adjusting the model through a sliding window mechanism; and combining multi-dimensional feature vectors, and assigning personalized labels to the interactive object through clustering results. The application realizes accurate analysis of behavior and emotional characteristics of the interactive object, and provides an effective personalized interaction strategy for the social robot.

The invention discloses a method for improving autism behaviors through Npas4 expression and overexpression in a cat chat syndrome model, and belongs to the technical field of animal model construction. Selecting SD wild rats and CdCS model rats, and normally feeding and breeding the rats; the method comprises the following steps: measuring the protein concentration by adopting a BCA method, then carrying out Western Blot, then carrying out brain stereotactic injection, and finally carrying out behavioral testing. According to the invention, a CdCS rat model is established by using a CRISPR-Cas9 technology, and the model shows cognitive and social behavior disorders and medial prefrontal cortex (mPFC) neuron dendritic abnormality; in addition, an abnormally expressed gene is found in the model mouse, the research focuses on Npas4, and a new insight is possibly provided for disease treatment.

The application provides a social behavior prediction method, device and equipment and medium, and relates to the technical field of artificial intelligence. The method comprises the following steps: obtaining social behavior initial data; wherein the social behavior initial data at least comprises a character portrait and a social behavior prediction task; inputting the character portrait and the social behavior prediction task into a trained social behavior prediction model to output an initial social behavior prediction result; the social behavior prediction model is obtained by supervising and fine-tuning a pre-trained large language model based on a social behavior reasoning data sample set. By using the prediction model fine-tuned based on the social behavior reasoning data, the explainable reasoning process and the corresponding behavior selection can be output simultaneously when the social behavior prediction is performed, so that the explainability of the decision-making process and the reliability of the behavior result are ensured simultaneously in a single prediction framework.

The invention provides a social disorder animal model construction method based on a basal lateral amygdaloid nucleus-screen nucleus neural circuit and application thereof, and belongs to the technical field of animal model construction. The method comprises the following steps: injecting a viral vector carrying a photosensitive protein gene or a photosuppressive protein gene into amygdaloid nucleus on the outer side of an animal basal to express neurons of cholecystokinin or neurons of CaMKII alpha; embedding an optical fiber above the screen-shaped core; first light stimulation is applied through an optical fiber to activate nerve projection from amygdaloid nucleus on the outer side of the substrate to the screen nucleus, and a social disorder animal model is obtained; or applying a second photostimulus to inhibit the projection, resulting in a social disorder relief animal model. According to the invention, specific photogenetic regulation and control of the BLACCK +-CLA neural circuit is realized for the first time, social behaviors can be accurately regulated and controlled without interfering with movement and anxiety levels, and a reliable experimental tool is provided for mechanism research and drug screening of social obstacles.