1100 results about "Social media" patented technology

The invention discloses an intelligent financial risk early warning method and system based on a management decision, and relates to the technical field of data intelligence, and the method comprises a multi-source heterogeneous data collection module which obtains enterprise financial data, supply chain data, market public opinion data and industry reference data in real time through an API interface, risk keywords are extracted from news, social media and policy documents through a natural language processing technology according to the market public opinion data; the streaming data processing engine is constructed based on an Apache Flink framework, performs windowing processing on the real-time data stream, calculates the dynamic fluctuation ratio of financial indexes by sliding a time window, and compares the dynamic fluctuation ratio with a preset industry risk threshold value; and a risk decision fusion model, a dynamic threshold adaptive module and a man-machine collaborative early warning terminal. According to the method, millisecond-level financial index fluctuation monitoring is realized through a streaming computing framework, a knowledge graph and a natural language processing technology are fused, a risk entity and a causal chain are extracted from unstructured data, and a multi-dimensional risk portrait is constructed.

The provided are a price elasticity analysis and prediction method and model based on deep learning. The model consists of a CNN layer and an RNN layer. The method comprises the following steps: S1, collecting historical data and merging the historical data into a multi-dimensional time series dataset; S2, extracting sentiment data and trend data from market news and social media; S3, inputting the data obtained into CNN for data preprocessing and feature extraction; S4, inputting the feature extracted by CNN into RNN for time series analysis; S5, training and optimizing model: using Adam algorithm to adjust the learning rate adaptively, and combining the momentum method and RMSProp algorithm to improve the generalization ability and prediction accuracy of the model. The provided combines the advantages of CNN and RNN, which can understand and predict the complex relationship between price and market behavior more comprehensively and accurately.

The invention provides a multi-feature fusion rumor detection method, system and device based on knowledge distillation, and mainly solves the problems that an existing model is high in calculation overhead, insufficient in feature fusion and insufficient in emotion utilization. The method comprises the steps of firstly obtaining multi-dimensional data such as social media original texts and comments; extracting deep semantic representation by using a pre-training model, and analyzing comment emotion features in combination with a hybrid neural network; then, features such as semantics, emotions, emoticons and populations are input into a hierarchical gating interactive fusion network (GIFN), and weights are dynamically adjusted to achieve effective fusion of multi-granularity features; in order to reduce complexity, a knowledge distillation framework is designed: a deep GIFN is used as a teacher network to generate a soft label, and a lightweight student network (LSTM) is guided to perform training. According to the trained student model, the parameter quantity is remarkably reduced, meanwhile, good detection performance is kept, the student model can be conveniently deployed in an actual content auditing system or edge equipment, and social content rumors can be efficiently recognized and judged.

The invention discloses an urban safety risk assessment method and system based on big data, and relates to the technical field of big data, and the method comprises the steps: constructing a multi-source data collection network, and obtaining data from a government department database, Internet of Things equipment, a social media platform and a traffic monitoring system in real time; preprocessing the collected multi-source data, wherein the preprocessing comprises data cleaning, format standardization, unstructured data semantic analysis and sentiment analysis; a cross-department data security sharing mechanism is established, and the traceability and security of data exchange are ensured through a block chain technology; constructing a dynamic risk assessment model, analyzing multi-source data relevance based on a deep learning algorithm, and dynamically adjusting the weight of each risk factor; and generating a visual risk assessment report, and pushing the visual risk assessment report to related departments in real time through an early warning system. According to the invention, through multi-technology fusion and a dynamic optimization mechanism, the accuracy, real-time performance and cooperation efficiency of urban safety risk assessment are significantly improved.

The invention discloses a multi-modal fusion rumor detection method and system based on a dynamic graph convolutional neural network. According to the method, a dynamic feature graph of a language propagation path is constructed, and potential features in the language propagation process are extracted and analyzed by utilizing time sequence changes and key node relations between nodes in a propagation graph. A neural network is adopted to extract and enhance image data, text semantic features are extracted in combination with a text feature modeling network, text feature vectorization expression is achieved based on a BERT model, and rich semantic information is obtained. And a gating mechanism is introduced to dynamically adjust fusion weights of different modal features, and an information fusion strategy is optimized. A collaborative attention mechanism is further adopted for deep fusion, interactive learning of text, image and propagation path features is enhanced, and the relevance of cross-modal and time series data is improved. And finally, inputting the fused feature vectors into a classifier for accurate classification, thereby realizing accurate detection of the social media rumors. According to the method, the multi-modal features are effectively integrated, and the false information identification efficiency is remarkably improved.

The invention discloses a false information detection method based on cross-modal confrontation and progressive training, and belongs to the technical field of artificial intelligence and information security. Mainly aiming at a false information detection task in a social media image-text contradiction form, a progressive adversarial training framework with multi-modal consistency constraint is provided. The core content of the method comprises: multi-modal data acquisition and processing: acquiring aligned social media multi-modal data (text, image / video); multi-modal adversarial sample generation: generating an adversarial sample based on text semantic disturbance and a visual contradiction scene; performing cross-modal progressive training, and optimizing model robustness by combining cross-modal cross attention fusion and a progressive three-stage dynamic training strategy; and generating an interpretability analysis result, and outputting an interpretability thermodynamic diagram to position cross-modal logic conflicts. According to the method, the accuracy and robustness of false information detection of the model in a multi-modal scene can be improved.

The invention discloses a dynamic vehicle scheduling intelligent decision-making system based on big data, and relates to the technical field of intelligent traffic and logistics scheduling, and the system comprises a data collection module, an event analysis module, a knowledge graph construction module, a hypergraph modeling module, a constraint processing engine, an optimization decision-making module, a strategy verification module and an output interaction module. The system has the advantages that multi-source data such as government announcement texts and social media information are acquired in real time through the data acquisition module, event key information is extracted through the event analysis module by utilizing a natural language processing technology, and an event knowledge graph of an incidence relation is constructed through the knowledge graph construction module; the strategy verification module simulates and verifies the strategy effect through the digital twinning technology and iteratively optimizes the strategy effect, the whole process does not need manual intervention to adjust rules, the limitation that a traditional system depends on manual processing is broken through, and the problems that dynamic strategy adjustment is time-consuming, labor-consuming and error-prone in an extreme scene are solved.

An inclusive prompt recommendation system for generative AI utilizes an inclusive prompt recommendation model to provide recommendations of inclusive language to include in a prompt in order to promote inclusivity and diversity of generated content. The inclusive prompt recommendation model is trained to analyze input text to identify situations, such as gaming, storytelling, social media, projects or presentations for work / school, and like, where the user's intent is to generate an image or description of a person. The model is trained to identify patterns associated with ways users have historically incorporated inclusive terminology intext. The system can include an ethical filtering mechanism for ensuring that prompt recommendations do not have language that directly or indirectly promotes bias and / or stereotypes.

The invention relates to the technical field of big data, in particular to an e-commerce marketing propaganda system based on behavior analysis, which comprises a global user portrait module, an intelligent recommendation engine, a marketing fatigue management module, a supply chain collaboration module, a lightweight terminal module and a budget and value management module. In the prior art, user portraits are constructed only depending on single channel data such as online clicking or purchase records, so that user interest modeling is incomplete and lagged; according to the method, all-channel behavior data such as APP, Web, offline POS and social media are integrated, a user-commodity-scene heterogeneous graph is constructed by using a graph neural network, and an interest attenuation period (for example, the weight is reduced by 50% after the interest of mother and infant users lasts for 18 months) is dynamically captured through an LSTM model; for example, after a user tries on a certain style of clothes offline, the system associates online behaviors in real time and recommends commodities of the same style, the cross-scene conversion rate is improved by 32%, and the user portrait coverage degree is improved by 60%.

Systems and methods are described for identifying content on a social media platform and reactions thereto. The system may input, to a first machine learning model, data indicating the reactions, and receive, as output, sentiment data for the reactions. The system may determine, based on the sentiment data, a reaction having a negative sentiment. The system may identify, as a portion of the content to be modified, a portion of the content corresponding to a portion of the reaction having the negative sentiment, and input, to a second machine learning model, data indicating at least a portion of the content and data indicating the identified portion of the content. The system may receive, as output, a regenerated version of the content, and cause the content on the social media platform to be modified based on, or supplemented with the regenerated version of the content.

The invention relates to the technical field of finance, in particular to a real-time investment decision-making system and method based on multi-modal fusion, and the method comprises the steps: integrating social media emotion data, public opinion event classification data and market structured data through a multi-modal emotion information fusion module, generating a comprehensive emotion representation vector, the market state judgment module judges market states based on historical market data and generates time-weighted market state representations, the reinforcement learning combination management module generates asset allocation decisions according to the market state representations and the time-weighted market state representations, and the multi-strategy collaborative decision module integrates reinforcement learning, decision trees and regression analysis strategies and generates collaborative decision signals. The risk self-adaptive control module predicts the market fluctuation rate, assesses the investment risk and adjusts decision signal execution parameters, the decision signal generation module generates a final investment decision signal and outputs the final investment decision signal to the transaction execution system, and the transaction execution system performs multi-modal information fusion and a time-sensitive attention mechanism. And the comprehensive understanding capability and the time sequence change adaptive capability of the market are improved.

The invention belongs to the technical field of precision marketing and intelligent recommendation, and discloses a B2B customer deep insight and precision reaching method based on a multi-modal large model, and the method comprises the specific steps: S1, carrying out the fusion and deep analysis of multi-modal data; s2, performing customer multi-dimensional preference modeling; s3, an intention and emotion analysis engine; s4, generating a personalized marketing strategy; s5, recommendation execution and verbal skill optimization; s6, feedback collection and reward function drive optimization; and S7, applying a cold start solution and transfer learning. According to the invention, through integration of multi-mode data of client official websites, news information, social media dynamics, mail communication, product images and conference recording, panoramic insight of clients from a business level to behavior details is realized; in combination with deep analysis of a pre-trained large model on texts, images and audios, explicit demands can be obtained, and industry features, cultural characteristics and potential concerns of customers can be understood.

Systems and methods for reducing the search space by processing media content to refine search parameters. A computing device may obtain the media content in response to receiving a request for inclusion of the media content in a media content knowledge repository, extract an audio component, a video component, and a text component of the media content, and determine attributes within the extracted components. The computing device may determine segment attributes based on a result of correlating the determined audio, video, and text attributes, integrate the segment attributes into the media content knowledge repository, and / or perform any of a variety of responsive actions.

The invention relates to the technical field of food and beverage, in particular to a food and beverage network sales trend prediction model construction system and method based on multi-source data fusion and deep learning. Comprising a data acquisition unit; a data processing unit; the model construction unit is used for constructing a deep learning prediction model, and an improved LSTM-Transform fusion algorithm is adopted to realize nonlinear mapping modeling of the food and beverage sales trend by integrating time sequence feature modeling and a global dependency relationship analysis technology; a model training verification unit; and a prediction output unit. According to the method, multi-source data such as network sales platform data, social media emotion texts, weather information and industry information are integrated, cross-correlation features such as time dimension features, text emotion features and price elasticity-weather influence are extracted in combination with a feature engineering technology, influence factors of food and beverage sales are comprehensively covered, and the sales quality is improved. The problem that a traditional scheme is single in data dimension is solved.

The invention discloses a public opinion event multi-mode semantic fusion modeling and abstract generation method and system, and relates to the field of natural language processing and social network analysis. Through the multi-mode semantic fusion technology, the short text understanding ability is improved, and the problems of semantic fuzziness and network language diversification are solved. Meanwhile, through a cross-window event cluster matching technology, an event evolution path with time continuity is constructed, and comprehensive capture of event dynamic characteristics is realized. Besides, the structured event abstract is automatically generated by utilizing the generative model, so that the consistency and the information density of the abstract are improved, and the actual application requirements are met. Through the innovations, the defects in the aspects of semantic comprehension, dynamic modeling and abstract generation in the prior art can be effectively overcome, a more efficient and accurate solution is provided for monitoring and analysis of public opinion events, and the method has wide application prospects in the fields of public opinion monitoring, emergency early warning, social media data analysis and the like.

The invention discloses a road risk grading early warning method and system based on a Beidou satellite system, and the method comprises the steps: firstly carrying out the real-time positioning of a vehicle through Beidou dual-frequency signals, inertial navigation data, road side unit differential data and vehicle-mounted sensor data, and obtaining the precise position information; fusing the real-time position of the vehicle with meteorological data, vehicle-mounted OBD parameters and social media public opinion data to generate a dynamic risk factor matrix; a fuzzy rule base is constructed based on expert experience, the fuzzy weight of each risk factor in a matrix is obtained, meanwhile, the time sequence weight of each factor is predicted by means of an LSTM model, and a final road risk score is obtained through dynamic weighting. And performing graded early warning on the road risk in combination with the driver portrait, and feeding back and updating the fusion parameter, the fuzzy rule base or the LSTM model parameter according to the early warning effect to form a closed-loop optimization mechanism. Dynamic coupling analysis of multi-dimensional risk factors is realized, and the real-time performance and accuracy of road risk early warning are improved.

The invention relates to the technical field of data analysis and processing, in particular to an identity data analysis method based on a spatial frequency sensing fusion network, and the method specifically comprises the following steps: collecting identity data to be detected, and marking real identity information; preprocessing the collected identity data, and dividing the collected identity data into a training set and a test set in proportion; constructing a spatial frequency sensing multi-scale network for real-time deep identity analysis, and training the network; inputting the identity data in the test set into the trained network for forgery detection and prediction, and outputting a probability score that each identity is true or false so as to obtain an identity analysis result. By constructing a lightweight deep neural network structure which combines space and frequency feature perception and has a multi-scale fusion capability, the method can be used for efficiently detecting identity information in actual scenes such as video conferences and social media.

Certain aspects of the disclosure provide artificial intelligence (AI) methods and systems for generating personalized social media content with trend integration. A method generally includes retrieving data from data sources that includes customer interactions with a business, and inventory data of the business, determining trending-product pairs that increase engagement of the customers with products recorded in the inventory data of the business based on the retrieved data. A generative artificial intelligence (AI) model is used to generate one or more of a caption, a hashtag, and a promotional image that are personalized to each of the customers in response to receiving prompts that contain information about the customers, information about trending-product pairs, and social media platforms of the customers. The method sends one or more of the captions, the hashtags, and the promotional images that are personalized to the customers to social media platforms of the customers.

The invention discloses a data-enhanced fine-grained multi-mode false information detection method and system, and aims to improve the accuracy of image-text false news detection. The method comprises the following steps: acquiring a news text and associated pictures thereof, extracting a text core entity semantic sequence and a visual entity semantic sequence by respectively utilizing a pre-training language model and a visual entity recognition model, and performing knowledge enhancement on original word-level text representation and low-level visual features through an attention mechanism; calculating a correlation matrix between the enhanced text and the visual features, dividing consistent and inconsistent regions, and respectively extracting consistent and inconsistent features; and finally, fusing the two types of features and global text representation to generate classification features so as to judge the authenticity of the news. Through double-channel knowledge enhancement and fine-grained cross-modal consistency analysis, the detection performance is remarkably improved, and the method is suitable for scenes such as social media and news platforms.

The invention discloses a multi-modal label fusion method based on stability screening and conflict mediation. The method comprises the following steps: (1) preprocessing a social media text; (2) encoding a text, calculating the similarity between the text and a label semantic vector, and screening candidate labels; (3) generating an adversarial sample on the basis of Fast Grading Sign Method, and removing tags with excessive confidence coefficient fluctuation to obtain a stable text tag set; (4) encoding the image information, and performing step-by-step matching in combination with a label hierarchical structure to obtain an image label set; (5) calculating modal scores of the text and the image; (6) performing softmax normalization on the modal score to obtain a fusion weight; (7) fusing labels and extracting conflict features, and inputting the conflict features into a neural network for mediation; and (8) outputting a final fusion label set. The stability and accuracy of label fusion are improved, and the method is suitable for scenes such as tourist perception modeling and personalized recommendation.

The invention discloses an all-media operation intelligent analysis platform based on big data, and relates to the technical field of computers, the platform comprises a data acquisition module, the data acquisition module applies a distributed crawler technology, compiles customized scripts, and combines social media official API calling and data interface docking or pushing of a cooperation platform to obtain a data acquisition module; all-media data including texts, images, audios and videos are collected from web pages, social media and news clients in real time; through a distributed crawler technology, social media official API calling and cooperation platform data interface docking, all-media data including texts, images, audios and videos are collected in real time, and cross-modal data are converted into feature vectors in a unified format for fusion by using a dynamic weight fusion algorithm. The problem that in a traditional analysis method, single-modal data information is limited is solved, and a cross-modal dynamic fusion formula is adopted.

The present invention comprises a novel social media video debating web and mobile application. The platform will provide a space for users to debate uninterrupted by both the audience and the opponent whereby each participant is given a set time to express their thoughts on a subject matter. The online debate platform provides a controlled setting for the participants to have their debates viewed, voted on and subsequently ranked by the other users of the platform. The online debate platform is also monitored by a unique AI system that updates debate “winners,” flags offensive content, and moderates each debate on the platform in real time. The disclosed platform and following figures will provide a space for individuals to debate subjects in a uniformed structure and have real-time results from active user viewership. The online debate platform aims to provide an established place for constructive debating.

The invention provides an automatic consumption tag analysis system and method based on multi-agent collaboration, and the system comprises a data processing agent which is used for collecting data from a social media platform and carrying out the data preprocessing; the label identification intelligent agent is used for extracting consumption labels of different dimensions from the preprocessed data; the sentiment analysis agent is used for carrying out context modeling and sentiment tendency recognition and binding the recognized sentiment tendency to the corresponding consumption label; the label normalization agent is used for performing clustering and normalization processing on all consumption labels bound with emotional tendencies to generate a structured multi-layer label atlas; the central scheduling agent is used for scheduling other agents, generating a label analysis result by using the multi-layer label atlas and sending the label analysis result to the user; according to the invention, based on a multi-agent architecture, structured analysis is carried out on user tags, behavior attributes and consumption intentions in social media contents, so that high-precision and high-efficiency intelligent consumption insight is realized.

The invention discloses a passenger flow monitoring and analysis system, and relates to the technical field of scenic spot management, and the system comprises the following components: a data collection module which is used for deploying a plurality of Internet of Things devices at key points of a scenic spot, and is combined with a communication operator, social media and a scenic spot internal system; collecting and covering tourist behaviors, moving tracks, social feedback and consumption ticket buying data; according to the method, the multi-source heterogeneous data acquisition system is constructed, the scenic spot passenger flow related information is accurately and comprehensively acquired, the time-space sequence analysis and graph neural network technology is combined, the time and space correlation characteristics of the scenic spot passenger flow are fully mined, the constructed multi-scenic spot passenger flow dynamic correlation prediction model can realize accurate cross-scenic spot passenger flow prediction, and the scenic spot passenger flow dynamic correlation prediction method is high in practicability. Therefore, a scenic spot manager can grasp the visitor flow rate condition of each scenic spot in different time periods in the future in advance and prepare for coping in advance, the capacity of coping with the passenger flow peak of the scenic spot is effectively improved, safety accidents caused by passenger flow congestion are avoided, and the touring safety of tourists is guaranteed.

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.

A social media platform provides a map-based graphical user interface (GUI) for accessing social media content submitted for public accessibility via the social media platform supported by the map-based GUI. The GUI includes a map providing interactive location-based searching functionality in that selection of a target location by the user in the GUI, such as by tapping or clicking at the target location, triggers a search for social media content having geo-tag data indicating geographic locations within a geographical search area centered on the target location. A search period for which content is returned is dynamically variable based on the duration for which the tap or click is held.

The invention relates to a multi-dimensional user grouping matching method and device, equipment and a medium. The method comprises the following steps: firstly, acquiring real-time behavior data, consumption data, interest data, historical conversion data and social media data of a user group, and integrating to form a user feature data set; performing multi-dimensional data fusion and cross-dimensional correlation analysis on the data set to generate a user group feature set; the value weight of the user group is updated based on the feature set, grouping levels are divided in combination with service accuracy requirements, and a dynamic grouping mechanism is constructed; and finally, receiving bidding requests of the service providers, constructing a dynamic bidding model in combination with a dynamic grouping mechanism to calculate bidding scores of the service providers, sorting the service providers according to the scores, and generating an accurate matching result with the target user group. According to the method, the limitation of single-dimensional grouping is overcome, the timeliness and flexibility of grouping are improved, and the accuracy and efficiency of matching between the service provider and the target user group are effectively improved.

The invention relates to the technical field of tourism information data analysis, and discloses a tourism information data analysis method based on multiple data sources. According to the method, multi-source heterogeneous tourism data such as tourist position tracks, social media comments, scenic spot real-time flow and meteorological environments are obtained. Performing spatial-temporal clustering analysis on the tourist position trajectory data to generate a behavior pattern map; extracting an emotional tendency index from the social media comment data; and associating and matching the scenic spot real-time flow with the meteorological environment data to generate an environment bearing pressure coefficient. And inputting the results into a multi-modal fusion model to obtain an analysis decision vector, constructing a personalized recommendation strategy graph through a collaborative filtering algorithm, and outputting a tourism service optimization scheme. According to the invention, deep analysis of multi-source data can be integrated, scenic spots can be assisted to know behaviors and demands of tourists, operation management is optimized, personalized services are provided, and tourism service quality and scenic spot competitiveness are improved.

The invention discloses a user participation degree prediction method based on distillation multi-modal retrieval enhancement, and belongs to the technical field of social media data mining and user behavior analysis. According to the method, the correlation of the UGC is evaluated by introducing the self-enhanced distillation module, and the top-K related UGC is selected in combination with the selected retriever, so that the interference of irrelevant information on the related UGC is effectively avoided, and the noise caused by irrelevant artifacts can be filtered out when the related UGC is retrieved, thereby keeping the original feature representation of the related UGC. The heterogeneous graph construction module enhances the interactive representation capability between UGCs through multi-relation modeling, and can more accurately optimize a prediction result in user participation prediction. According to the method, information retrieval between the related UGC and the unrelated UGC can be better balanced, excessive diffusion of unrelated information is avoided, and the performance of the model in user participation degree prediction is improved. Particularly, when complex multi-modal data containing a large number of irrelevant UGCs is processed, the mechanism can effectively enhance the distinction degree of the relevant UGCs, and the prediction accuracy is improved.

The invention discloses a big language model-based sales plan generation method and device, and the method comprises the steps: constructing a marketing knowledge base which comprises a commodity basic information database, a social media database, a competition pattern database and a high-quality sales plan reference database; performing fine tuning training on a large language model according to the high-quality sales plan reference database to obtain a sales plan generation model; and generating a target sales plan according to the marketing knowledge base and the sales plan generation model. According to the method, the marketing plan can be generated by finely adjusting the large language model and utilizing a plurality of databases, so that the efficiency and the applicability are improved, and the cost is reduced. The method can be widely applied to the technical field of artificial intelligence.