289 results about "Social platform" patented technology

This invention relates to a system and method that enhances user interaction and marketing effectiveness through advanced data integration and personalized algorithms. The system and method collects detailed user data through body scanning and questionnaires, creating comprehensive profiles using machine learning. These profiles facilitate precise matching of influencers with businesses based on physical attributes, audience demographics, and content preferences. The system generates AI-driven marketing personas tailored to specific demographics and enables live shopping integration with social platforms. A novel double-blind review mechanism ensures transparent feedback between businesses and influencers. The platform supports group profiles that aggregate multiple users' data, enabling family-based shopping with privacy controls and preference weighting. The system continuously refines its algorithms using campaign performance data and engagement metrics, optimizing future matches and marketing strategies. This innovative approach significantly improves marketing ROI, user engagement, and personalization for digital social interaction and targeted marketing.

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.

The invention provides an online value evaluation system based on real estate, and belongs to the technical field of online evaluation, and the system comprises an online data obtaining module which is used for obtaining a basic feature vector and multi-modal evaluation data of a target real estate online; the feature extraction and mining module is used for carrying out space mapping on the explicit features and the defect features and carrying out potential association mining on the implicit features, and obtaining high-association implicit description in combination with the basic feature vector; the evaluation processing module is used for identifying real evaluation, false evaluation and fuzzy evaluation, and correcting the fuzzy evaluation after preliminary analysis according to a spatial mapping result and high-correlation implicit description in combination with social platform subject discussion data; and the value evaluation module is used for obtaining a comprehensive value according to the basic value reference line, the real evaluation and the correction evaluation, and outputting a value evaluation report. The problems that real estate evaluation is difficult to distinguish virtuality and reality and is fuzzy and difficult to solve are effectively solved, and the evaluation result can be comprehensive and accurate.

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 provides a network storm group division-oriented graph neural network community discovery method and system, and the method comprises the steps: constructing a feature extraction model, training the feature extraction model, and obtaining a trained feature extraction model; obtaining a topological structure of the social network and social network user attributes, and extracting user features by using the trained feature extraction model to obtain social network node representation; the social network node representation obtains a community division result through clustering processing; the feature extraction model is an improved graph neural network model fusing DNN and GCN, learning attribute information of a user through the DNN, and fusing the attribute information with total graph information learned by the GCN to realize node representation of a social network in an online social platform; pre-training the model through a graph comparison learning method to obtain node embedding; dimensionality reduction is carried out on node embedding through a multi-layer perceptron; and performing joint training on the model through the self-expression matrix and modularity to realize end-to-end accurate division of the network storm group.

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 relates to an intelligent employee performance evaluation method, which comprises the following steps of: capturing various information of work through an integrated enterprise internal information system comprising ERP (Enterprise Resource Planning) and HRM (High Rate Management), an external social platform and a work log multi-dimensional data source of employees, and updating the information in real time; a natural language processing NLP technology is applied to intelligently screen, clean and standardize various data formats; integrating the data through a unified evaluation framework, calculating and summarizing multi-dimensional indexes in real time, and forming a preliminary performance evaluation model; analyzing historical data of a company by using a machine learning algorithm, and identifying influence factors of different posts and different service periods on performance evaluation; the weights of evaluation indexes are adjusted in combination with business requirements and work contents of employees, and the fairness and dynamic adaptability of evaluation are enhanced; by analyzing interaction records between the employees and colleagues, leaders and clients, emotion information is extracted, and the emotion states of the employees and the influence on work are recognized.

The invention belongs to the field of information security, and relates to a robust video steganography method and system based on neighbor block difference cost, which can resist video transcoding of a social platform and is used for covert communication of a lossy channel. According to the method, a cost distribution method based on neighbor block difference is designed to evaluate the robustness of an embedded unit, a global robust modulation mode is selected by using a minimization distortion frame, and the embedded unit is modulated by adopting quantization index modulation according to the global robust modulation mode to embed secret information. The robust video steganography method and system provided by the invention integrate strategies such as adjacent block difference cost, an optimal robust modulation mode and a minimization distortion frame, and can effectively support robust video steganography communication under a social network lossy channel.

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.

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 provides a religious ceremony type virtual reality simulation teaching system, which belongs to the field of teaching, combines a virtual reality technology and an artificial intelligence algorithm, and provides brand-new, interactive and immersive learning experience for users. The system comprises a user interface, a self-adaptive religious ceremony module, a religious knowledge database, an interactive experience module, a voice explanation module and a data analysis module. The user can practice religious ceremonies in the virtual space and obtain real-time guidance and feedback, so that the learning efficiency and effect are greatly improved. In addition, the system also combines learning data and behavior feedback of the user, realizes personalized teaching, and meets personalized learning requirements of different users. In addition, the system further comprises rich religious ceremony information, real-time updating is achieved, the interestingness and the sociality of learning are improved through an article generation module and a social platform, and the learning enthusiasm and the learning effect of the user are greatly improved.

The invention discloses a method and a system for evaluating an anti-fraud index. The method comprises the following steps of: 1, acquiring and processing multi-source heterogeneous data; the multi-source heterogeneous data comprises telecommunication data, a social platform, historical fraud data, a user portrait and a system log; the data processing comprises text cleaning, voice-to-text conversion, image feature extraction and data anonymization; 2, calculating personal risk features, regional risk features and system efficiency features; 3, comprehensively evaluating an anti-fraud index based on the personal risk characteristics, the regional risk characteristics and the system efficiency characteristics; the method is based on a multi-dimensional index system of a personal risk index (PRI), a regional threat index (RTI) and a system efficiency index (SEI), and provides comprehensive support of accurate prevention and control, dynamic response and scientific decision for an anti-fraud system.

The invention relates to a topic page display method and device, a terminal, a storage medium and a program product. The method comprises the following steps: displaying a topic page of a target topic, wherein the topic page comprises an activity area and a content area; wherein the activity area comprises an activity entrance and content information blocks of at least two target published contents, the content area comprises at least one of published contents related to the target topic and live broadcast contents, and the target published contents are published based on the activity entrance; and completing the target activity in response to a selection operation based on the content information blocks of the at least two target published contents, and obtaining a target article which is matched with the target published contents selected by the selection operation. By adopting the method, interaction between the user and the social platform can be enriched, and the popularity of the target topic is improved.

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 a steganographic text detection technology based on text reconstruction and word order semantic features, and belongs to the technical field of information hiding. The method comprises the following steps: selecting an open source English steganography text data set containing three fields of social platform speaking, news manuscripts and film review; words in non-steganographic text sentences in the data set are randomly disorganized and sent to a large language model for reconstruction training, and the training target of reconstruction is an original text; and randomly disorganizing a data set text, and inputting the disorganized data set text into the trained model to generate a reconstructed text. And finally, inputting the original text, the disordered text and the reconstructed text into a steganography detection model, calculating a cosine similarity matrix, extracting a semantic difference feature map through a CNN, flattening, splicing semantic vectors of the original text, and inputting the semantic vectors into a classifier to output a binary detection result. Through continuous training, an error between a classifier output result of the model and a real labeling result is continuously reduced, so that parameters of a feature extractor and a classifier are optimized, and the model steganography detection accuracy is improved. According to the method, semantic information and word order features are utilized for classification, high accuracy, low cost and good interpretability are achieved, steganographic texts can be effectively detected, and information safety is improved.

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.

The invention discloses a commodity recommendation method and system based on big data analysis, and relates to the technical field of intelligent recommendation systems.The method comprises the steps that behavior data of a user on a platform and store data of a region where the user is located are collected; public opinion data of the commodities are collected from the social platform and the comment area, and the behavior data, the store data and the public opinion data are integrated to form a recommendation data set; extracting behavior features from the behavior data, extracting store features from the store data, and extracting public opinion features from the public opinion data; integrating the behavior features, the store features and the public opinion features into a comprehensive feature vector, and generating a user portrait according to the comprehensive feature vector; based on the user portrait, utilizing a machine learning algorithm to construct a commodity recommendation model, taking the user portrait as input, and outputting a commodity recommendation list; and behavior data and real-time data of store activities are collected, a recommendation list is dynamically adjusted, and feedback of the user to recommended commodities is recorded.

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.