199 results about "Social platform" patented technology

A social media platform facilitates user interaction through digital environments called “bubbles,” which serve as customizable, collaborative spaces for content sharing and engagement. Users can participate in gamified events such as challenges, races, and raffles within these bubbles. The platform incorporates a blockchain-based reward system that issues digital assets, including tokens and non-fungible tokens (NFTs), based on user participation and performance. Each piece of content shared within a bubble can be assigned its own privacy level, independent of the user's account settings. The system supports role-based access, content moderation, and smart contract integration to ensure secure, transparent reward distribution.

The invention provides a social robot intention recognition system and method based on heterogeneous graph analysis, and the method comprises the steps: carrying out the unified modeling of multi-type nodes and multiple relation edges based on a heterogeneous graph structure, and systematically capturing the complex multi-dimensional behavior association in a social platform; according to the method, technologies such as a large language model and a knowledge graph are fused, fine recognition and classification of multiple types of behavior modes are supported, and meanwhile, hierarchical evaluation for different organized risks is realized by combining account behavior aggregation and emotional polarity analysis, so that a decision basis is provided for supervision and governance; according to the method, automatic collection and preprocessing of multi-source heterogeneous data and heterogeneous graph construction processes are integrated, manual intervention and repeated work are reduced to the maximum extent, the overall data processing efficiency and response speed are improved, and the real-time analysis requirement in a complex dynamic social environment is met; transparent analysis of robot control sites and behavior strategies is supported, and the interpretability of the system, the user credibility and the executive force of policy supervision are greatly improved.

The invention relates to the technical field of online sales, and discloses a healthy retail supply chain optimization method and device and computer device.The method comprises the steps that multi-level semantic analysis is conducted on obtained text data through a natural language processing technology, and potential demand keywords of a user are extracted; constructing a user portrait based on the user potential demand keyword and the user behavior data; grouping the users based on a preset grouping standard and the user portraits in the health scene, screening corresponding required health products for different user groups, and optimizing and adjusting the required health products in combination with market trend data in the user behavior data to obtain an adjusted product selection pool; and calculating a matching degree between the user and a preset candidate social platform based on the user portrait and the product selection pool, and generating a social platform recommendation list based on the matching degree. According to the method, multi-source heterogeneous data are integrated, potential demands of users are mined by using a large model, a product selection pool is optimized, and accurate recommendation and personalized marketing are realized in combination with user portrait analysis.

A method and system for new property acquisition process for utilities. The process uses preprogrammed virtual reality headsets to provide property owners with a virtual reality (VR) social platform, such as a metaverse-enabled presentation, showing projects, answering questions, and delivering individualized property rights acquisition proposals. The invention enhances the property owner's presentation and enables significant scaling without significant headcount growth.

The invention discloses a multi-modal social network public opinion hidden danger troubleshooting method and system, and the method comprises the steps: collecting the multi-modal data of a social platform in real time through a distributed edge node, and the multi-modal data comprise text, image and audio data; dividing a plurality of social network communities based on user interaction relationships, wherein the user interaction relationships include but are not limited to topic circles, friend relationships, comment interaction and forwarding likes; community propagation dynamic features, group topological features and content features are extracted from the multiple social network communities one by one based on the multi-modal data; screening out a plurality of suspected hidden danger communities from the plurality of social network communities based on the community propagation dynamic characteristics, the group topological characteristics, the content characteristics and a preset screening strategy; and after carrying out hidden danger detection and hidden danger classification on the plurality of suspected hidden danger communities based on the multi-modal data, outputting a plurality of hidden danger classifications corresponding to the plurality of suspected hidden danger communities. According to the method and the system, the comprehensiveness and the accuracy of public opinion hidden danger recognition are remarkably improved.

The invention provides a social robot detection method and system, computer equipment and a storage medium, and belongs to the technical field of social robot detection.The method comprises the steps that user multi-source data on a social platform and the social relation between users are collected; a hybrid encoder is adopted to capture local dependence and global time sequence dynamic states of behaviors, and user behavior characteristics are generated; aggregating structure attention features between the initial node features of the target account and the initial node features of the social relation account to obtain multi-modal relation aggregated structure features; introducing a multi-modal adversarial training strategy, adaptively adjusting the disturbance intensity of each modal based on gradient sensitivity, and aligning the characterization of the clean sample and the adversarial sample in combination with a content discriminator and a behavior discriminator; and outputting a target user classification result through the multi-task target function optimization model. According to the method, the problems of insufficient modal fusion and insufficient adversarial robustness of an existing method can be effectively solved, and the accuracy and stability of social robot detection in a complex and adversarial scene are improved.

The invention discloses a psychological health assessment system and method based on big data, and relates to the technical field of physical and psychological health assessment, the system comprises a data acquisition module, a data processing module, a psychological mechanism analysis module, an ability quality modeling module, a multi-modal detection module and an assessment result generation module; the technical key points are as follows: in the implementation process of the technical scheme, a data acquisition module synchronously acquires static, dynamic, self-assessment and real-time emotion data of an assessed person in multiple scenes of campus, security and protection and the like; with a campus scene as an example, static information such as homework documents and social platform records of students is collected, dynamic behavior data such as classroom interaction and extracurricular activities are collected through an intelligent camera and voice equipment, and meanwhile, real-time emotion data are obtained by means of a face video stream collection technology to form a multi-dimensional data source; the heterogeneous data are processed into standardized coded data in a unified mode, and the limitation that traditional evaluation depends on a single questionnaire or simple observation is broken through.

Aspects of the present disclosure are directed to generating media element(s) using a multimodal scene graph. A scene manager can process visual information, such as video, images, and / or a recorded artificial relay scene, and generate a multimodal scene graph that comprises components and metadata generated via the processing. The scene manager can utilize the multimodal scene graph to generate social media elements, such as images, video, and / or artificial reality scenes. For example, a video of a user can be converted to a multimodal scene graph, which can be used to generate one or more images (e.g., memes, animated images, stickers, etc.), such as an image that represents the user via an avatar of the user. This generated media can be shared with other social platform users, and the stored multimodal scene graph can be accessed by the others to generate variations of the media.

The invention discloses a hidden group relationship mining method and device based on a social platform and a medium, and relates to the technical field of big data. The method comprises the steps of collecting to-be-mined tweet data description information; respectively calculating weights corresponding to the current tweet release time, the current tweet source, the current tweet forwarding information, the current comment information and the current mention user information through a multi-dimensional weight calculation method, and generating a target multi-dimensional fusion weight in combination with a multi-dimensional weight fusion calculation method; obtaining a target weighted graph network through a graph network construction method according to the target multi-dimensional fusion weight; and performing community division processing on the target weighted graph network, and determining a hidden group relationship mining result in combination with the current tweet content. The problems that static data analysis is relied on, multi-dimensional user behavior characteristics cannot be fully considered, and hidden relations or coordination behaviors among users cannot be revealed are solved, and the accuracy and interpretability of hidden group relation mining results are improved.

The invention discloses a social group discovery system based on fused semantic information. The system comprises a presentation layer, a presentation layer, a service layer and a data layer. The presentation layer supports checking of a group division result; the display layer displays a user structure network, group dynamic division and group characteristic indexes through a visual tool; the service layer is composed of a semantic tag generation module, a user structure analysis module, a social group division module and a social group analysis module, semantic and structural features are fused through a graph attention network, and accurate group division is realized in combination with an adaptive embedded graph aggregation algorithm; and the data layer stores the original data set of the social platform and the processed data. The system inputs user texts into a large language model to generate semantic tags, node embedding is generated by calculating node association weights, group division is completed by combining an adaptive embedding graph aggregation algorithm, and finally group characteristics are quantitatively analyzed from the aspects of cohesion, conductivity and the like. The system is suitable for scenes such as vertical domain community mining.

The invention discloses a social platform post popularity prediction method and system, and the method comprises the steps: data obtaining and cleaning: continuously capturing user information and post comment data on social media in a preset time period through an API in an assembly line manner, and carrying out the data cleaning and time alignment, so as to guarantee the validity and consistency of the data; respectively extracting a statistical participation feature vector, a semantic emotion feature vector and a topological interaction feature vector; then, training the model, integrating the multi-level feature vectors and the labels into a training data set, and performing training by using a deep learning model; semantic analysis is conducted on Prompt input by a user, after the intention of the user is recognized, return results of all the modules are collected, and a popularity judgment result and a probability value are assembled into a natural language answer to be returned to the front end; and selecting a test post to carry out a prediction experiment, and displaying the model performance through visual effects such as an accuracy comparison graph and the like. According to the method, a large language model technology and a multi-level feature extraction method are fused, high-precision heat prediction is achieved, natural interaction with a user can be achieved, analysis reports and decision suggestions can be provided, and the problems that hot events are lagged in discovery, information is not pushed in time and guidance is difficult are effectively solved.

Embodiments provide a social networking platform offering various services, such as, facilitating aggregation and management of a user's interaction on one or more social networking platforms, offering enhanced control over the level of privacy associated with the flow of user data, offering tools to customize the user's exposure to advertisement-related content on the social networking platform(s), integrating features to control aspects of how data / content is presented to and visualized by the user, empowering the user to multicast direct messages to other users without the other users having to meet certain constraints, empowering the user to create and / or join a group based on messaging threads, and the like. One or more of these enhanced services / features are associated with a powerful framework of authentication / permission model for access control.

The invention belongs to the technical field of network space security and social platform governance, and particularly provides an agent-driven active interactive social robot detection method, electronic equipment and a storage medium, and the method comprises the steps: obtaining original data of a to-be-detected account, and forming a structured data packet; constructing a preliminary screening evaluation model, and outputting a detection task state based on the structured data packet; generating an initial detection state; obtaining an interaction record; carrying out evidence extraction on the interaction record to obtain an evidence vector of the round; and performing sequential updating on the initial risk score and the uncertainty based on the evidence vector, and outputting a detection result when a stop condition is met. According to the method, an active interactive evidence obtaining mechanism of a detection agent is introduced, and on the basis of passive preliminary screening, probe interaction is automatically selected and initiated to obtain incremental evidence, so that the recognition capability of a high-simulation social robot is improved, and an auditing report containing key evidence and an interactive link is output.

The invention discloses a social relation chain value evaluation method and system based on multi-dimensional data modeling, and the method comprises the steps: integrating a social platform API, a user behavior log, geographic position data and equipment information through a data collection module, carrying out the cleaning and standardization of a preprocessing module, dividing a geographic region through a space-time modeling module based on K-means clustering, and carrying out the calculation of the value of a social relation chain. An exponential decay function is introduced to dynamically update an interaction weight, an emotion analysis module adopts a BERT model to carry out emotion classification on comments, a role classification module marks community roles through a rule engine, and finally a value calculation module fuses feature parameters of data of all dimensions to construct a propagation value evaluation model. According to the method and the system, the propagation value evaluation accuracy is remarkably improved, the misjudgment rate is greatly reduced, the social e-commerce propagator screening efficiency, the local life service precise popularization coverage rate and the brand propagation positive word-of-mouth recognition accuracy are improved, and scientific decision support is provided for social fission marketing.

The invention discloses a social network malicious propagation node identification method and apparatus, a medium, a device and a product. The method comprises the steps of obtaining propagation data of a target topic in a target social platform; constructing a social network propagation graph of the target topic and generating a node embedding vector by adopting a graph attention network; extracting a node embedding vector and a historical text abstract of the target node, and fusing to obtain a structured natural language cue word; taking a structured natural language cue word as input, and adopting a function fusing classification loss and interpretation generation loss to supervise and finely adjust the initial large language model; evaluating the output quality of the fine-tuned model by using a reward model, and optimizing the parameters of the fine-tuned model through reinforcement learning by taking an evaluation result as a reward signal to obtain an optimized model; and deploying the optimized model to carry out malicious propagation node identification on the social network of the real-time propagation event. According to the invention, malicious propagation nodes in the social network can be accurately identified and decision explanation can be provided.

The invention discloses an intelligent dialogue method, device and equipment based on private data and a storage medium, and belongs to the technical field of artificial intelligence. The method comprises the following steps: collecting and cleaning private data containing user dialogue records and behavior data; a LoRA technology is adopted to carry out fine tuning on the preset large language model, and a miniature large language model is generated; inputting the private data into the miniature large language model for training, and generating a personalized miniature large language model; deploying the personalized miniature large language model to downstream ports of a social platform, a media platform, a communication tool and the like; a personalized dialogue is generated based on input information received by the downstream port. The method further comprises the steps of packaging the model and the dependency environment into a preset container, deploying a uniform interface and an adaptation layer, extracting and converting input information, initializing a dialogue context and generating a personalized dialogue according to platform characteristics. The personalized intelligent dialogue service can be provided based on the private data of the user, and the adaptability and the user experience of the dialogue system are improved.

The invention provides a doctor influence comprehensive evaluation method based on multi-modal data fusion, and the method comprises the steps: collecting media reports, clinical information, social platforms and other multi-source heterogeneous data, introducing timestamp marks, and achieving the time sequence semantic vector representation of a doctor entity through a medical field pre-training model and sine function embedding; in combination with BiLSTM-CRF and an attention mechanism, doctor attributes and key events are identified, and an LSTM and a dynamic clustering algorithm are adopted to extract and divide doctor attribute evolution trajectories in stages; an influence score is calculated through an exponential decay function, stage knowledge graph nodes are generated, and incremental updating and node merging and splitting are supported; a graph convolutional network and a graph attention mechanism are adopted, a doctor influence evolution chain is constructed, information dynamic association and trend prediction are achieved, and the time sequence precision and data comprehensiveness of doctor influence evaluation and the dynamic evolution ability of a knowledge graph are improved.

Systems and methods translate and govern directives across heterogeneous platforms. In some embodiments, directives are normalized into a common intermediate representation (CIR) for validation, conflict handling, and deployment. In other embodiments, a direct semantic mapping engine converts source directives directly into target-native, machine-executable directives without a CIR and without normalization, consulting a platform capability database and synthesizing target-compatible scripts, profiles, workflows, or rules where native primitives are absent. Ephemeral triggers (e.g., time window, incident, risk, posture, geofence) cause generation and atomic activation of temporary directives that supersede baseline directives for a defined scope and interval, with withdrawal on expiry to restore the baseline. Adapters actuate across families including cloud control planes, operating systems (Windows / macOS / Linux), IoT / OT, Kubernetes, identity, email / DLP, browser, and SaaS / social platforms. Both approaches share validation, simulation, and governance / deployment engines supporting approvals, audit logging, staged rollout, rollback, and continuous feedback.

The invention discloses a social media key user identification system based on information non-uniform propagation characteristics, and relates to the technical field of key user identification, and the system comprises a dynamic module which is used for calling an information propagation model through a weighted directed graph of N time windows, simulating information propagation, obtaining initial influence scores of a user in the N time windows, and obtaining initial influence scores of the user in the N time windows; and a cross-layer module which constructs a multi-layer network, quantifies the influence of the user in different layers in the multi-layer network based on the dynamic influence score, obtains a cross-layer influence score, and performs weighted average on the initial influence score of the user in N time windows to obtain a dynamic influence score. The recognition module is used for calculating comprehensive scores according to the initial influence scores, the dynamic influence scores and the cross-layer influence scores of the users, and the comprehensive scores are arranged in a descending order to recognize key users; through the key user identification method, the social platform can automatically identify key users really having transmission force and guiding force.

The invention discloses a multi-modal content generation method based on a diffusion model and multi-dimensional feature fusion, which comprises the following steps: S1, collecting image-text data from a plurality of social platforms, converting the image-text data into structured data, and performing multi-dimensional attribute labeling on the basis of a pre-training language model; s2, carrying out feature coding on an input text and a label as well as an image and a label thereof by adopting a CLIP encoder and an Embedding and MLP module respectively, and carrying out multi-modal fusion on all feature codes; s3, constructing a pseudo Prompt embedding vector; on the basis of a diffusion generation model SDXL and in combination with a LoRA fine tuning technology, personalized fine tuning of generated contents in a social scene is realized, and a high-quality image generation model is constructed; s4, based on the trained student model and a sampling acceleration strategy, finally quickly generating multiple groups of images with consistent semantics and different styles; and S5, establishing a multi-dimensional automatic image generation quality evaluation system, and performing reliability verification on the automatic image generation quality evaluation system based on expert review. According to the invention, high-quality rapid generation of the multi-modal content is realized.

The invention relates to the technical field of artificial intelligence and social media public opinion monitoring, in particular to an image recognition method and system based on machine learning, and the method comprises the steps: carrying out the OCR text extraction and regional visual feature extraction of an input image, generating a text emotion embedding vector in combination with a preset brand emotion dictionary, and carrying out the recognition of the emotion embedding vector; and visual and text features are fused through a cross-modal adversarial encoder to obtain joint semantic embedding representation, and whether hidden negative public opinions are formed or not is judged through a brand deviation degree discriminator. According to the method, semantic antagonism image contents including derogatory characters, competitive product comparison or sensitive symbols and the like can be effectively identified, the detection rate of recessive negative public opinions is improved, and the method is suitable for real-time monitoring of a large-scale social platform.

The application discloses a kind of important node identification methods of multi-view based graph neural network, obtain user information from social platform;According to user information, all users are represented as nodes, the relationship between users is represented as edge, and the relevant attributes of users are represented as features, to establish network graph structure;Extension is multi-view, data is described from different angles, and the embedding of nodes is obtained using the method of graph neural network on different views;The relationship between nodes is represented using high-order topological structure matrix, and the final representation of nodes is obtained by using attention method to fuse multi-view information, to predict the importance of nodes, to obtain end-to-end training model, to identify important nodes;The application not only can improve the expression ability of model, but also can be generalized to data without obvious graph structure, extract new relationship, establish graph structure, use graph neural network model, and improve the performance of important node identification in network.

Systems and methods for providing opportunities for interactions and communication between vehicles and passengers. In particular, a vehicle socialization platform is provided for facilitating a wide range of social and utilitarian exchanges between vehicles and their occupants, thus providing a community of interconnected vehicles. Vehicle interactions can include passive communication, including vehicles from a particular fleet acknowledging other fleet vehicles without intervention from a passenger or user. Vehicle interactions can include active communication, in which passengers are engaged with and have some control over vehicle communications through dedicated experiences provided in a ridehail application or on an in-vehicle tablet. In particular, some experiences can become dynamically available based on the fleet vehicle's proximity to another fleet vehicle. The vehicle socialization platform is a smart socialization platform that enables two-way communication between vehicles and passengers, facilitating a wide range of social and utilitarian exchanges between vehicles and vehicle occupants.

The invention discloses a communication method, electronic equipment, a storage medium and a computer program product, and the method comprises the steps that a first terminal responds to a triggering operation of a first user on an interaction entrance in a call interface, and sends a first data packet to first network equipment in a cellular network, sending, by the first network device, a first request to a first social platform in the Internet based on the first data packet; wherein the call interface represents a call interface for cellular network call between the first user and a second user; first information used for describing a first intention is packaged in the first data packet; the first intention represents that the first user intention initiates interaction of a first interaction type to the second user in the first social platform; the first request is used for requesting the first social platform to realize the first intention.

The application discloses a multi-round multi-modal toxicity detection method and device, and relates to the technical field of natural language processing and computer vision. The method comprises the following steps: obtaining multi-round dialogue data of a Chinese social platform, constructing a multi-modal dialogue data set containing text and images and performing preprocessing to obtain a processed multi-modal data set; designing a multi-dimensional labeling system, performing multi-dimensional toxicity type labeling on each dialogue, and calculating a labeled real cumulative toxicity score; constructing a systematic evaluation framework, and setting a toxicity content evaluation task; based on a test sample data set and the set toxicity content evaluation task, performing toxicity detection on the test sample data set by using a pre-trained visual language model, outputting a cumulative toxicity score predicted by the pre-trained visual language model, and calculating a cumulative toxicity detection accuracy; and based on the cumulative toxicity detection accuracy, performing evaluation, and outputting a model evaluation result. The application can improve the evaluation of original visual content understanding ability.

The application provides a trust evaluation method of a multi-layer relationship-aware graph neural network, belongs to the technical field of social network data processing and graph learning, and aims to solve the problems of mixed multi-source trust information, insufficient modeling of nonlinear interaction between nodes, and insufficient utilization of multi-layer graph convolution features of existing trust evaluation methods in sparse social networks. The method constructs a directed trust graph, uses a relationship-aware encoder to model the node's own features and neighborhood features, combines an adaptive propagation module to represent the directional and conditional dependence relationship between nodes, and obtains the final node pair representation through cross-layer attention fusion to realize trust evaluation between target node pairs. The method can be applied to social platform risk control, recommendation system credibility analysis, transaction network reputation evaluation and other scenes.

The invention provides a Chinese intention recognition method based on a cue word template, and relates to the field of network security in computer science and technology. The method comprises the following steps: segmenting a to-be-recognized text to obtain a token sequence; replacing attitude word tokens in the token sequence with special tokens [MASK] through a cue word template, and respectively adding special tokens [CLS] and [SEP] at the left end and the right end to obtain an input token sequence; inputting the input token sequence into a multi-layer Transform encoder to obtain a text token vector, a [MASK] special token vector and a [CLS] special token vector; averagely pooling the text token vector and the [MASK] special token vector to obtain a text pooling vector and a [MASK] pooling vector; sequentially splicing the [CLS] special token vector, the text pooling vector and the [MASK] pooling vector to obtain a final representation vector of the to-be-recognized text; and inputting the final representation vector into the MLP, and identifying the Chinese intention of the to-be-identified text. According to the method, accurate recognition of the Chinese text intention in the social platform is realized.

A computer-implemented improved system for producing studio quality e-commerce product videos displaying a 3D rendering created from a 3D model of a product that includes generally the steps of uploading a 3D model of the consumer product onto a computer or an online platform: selecting product features for highlighting in the video, rotating the 3D model to identify selected features, providing text to highlight portions of the video and the selected features: selecting and providing musical accompaniment for the video; and, creating a timeline to establish the sequence of individual portions of the video where-upon the 3D rendered video is then automatically generated for the vendor's e-commerce product page and various social platforms.

The invention relates to a group speech viewpoint tracking method and device, computer equipment and a storage medium. The method comprises the following steps: acquiring comment data about a target object of each user in a group at a sampling time point and character attribute information of each user from a social platform; performing part-of-speech tagging on the comment data, and constructing and analyzing a dependency syntax tree for a part-of-speech tagging result to obtain viewpoint features; performing weighted summation according to the viewpoint polarity score and the polarity weight value corresponding to each viewpoint feature to obtain a personal speech viewpoint polarity score, and determining a personal speech viewpoint according to the personal speech viewpoint polarity score; calculating a viewpoint weight score according to the personal speech viewpoint polarity score of each user and the user weight value, and determining a group speech viewpoint according to the viewpoint weight score; and the group speech viewpoints corresponding to different sampling time points are connected in series according to a time sequence to obtain group speech viewpoint change veins. By adopting the method, the opinion opinions of the group can be dynamically tracked, and efficient and accurate support is provided for public opinion analysis.

The invention discloses a self-service guide explanation system and method based on mobile phone NFC, and belongs to the technical field of intelligent services of museums and exhibition venues. The system is composed of an NFC tag array, a WeChat applet client, a cloud management server and a content distribution network. A user holds a mobile phone to touch an NFC label close to an exhibit, so that a WeChat applet can be automatically opened, 'e touch-to-play 'can be achieved, label identification, content pulling and multimedia playing can be completed in a zero-configuration mode, and offline and online mixed display of audios and videos, pictures and texts and AR / VR is supported; meanwhile, the system records user behaviors and generates a personalized visiting report. The method solves the problems that in the prior art, special equipment needs to be rented, operation is complex, interaction is single, and fusion with a social platform cannot be achieved, and has the advantages of being low in cost, fast in deployment, zero in learning cost, rich in experience, easy to maintain and the like.