45 results about "Hot topics" patented technology

The invention provides an AI-based multi-user data storage docking and secure transmission method, which comprises the following steps of: acquiring a propagation path of a hot topic according to a multi-modal content association graph, performing fragmentation storage on the propagation path by adopting a clustering algorithm, and generating a fragmentation storage index table; when the relevance of different modal contents is higher than a preset threshold value, determining that the sensitivity grading result is consistent with the multi-modal relevance, performing dynamic rule adjustment by adopting a deep learning model, generating a dynamic auditing rule set, and obtaining boundary judgment parameters of the rule set; and distributing the dynamic auditing rule set and the decision transparency index to each service line by adopting a cross-platform data synchronization protocol according to the auditing decision log, generating a cross-platform consistency auditing standard, and obtaining a synchronization log of standard execution.

The invention relates to a hot topic chatting method, system and device based on an intelligent agent and a storage medium. The method comprises the steps of extracting key intention information through a large language model according to current input data and historical dialogue data; constructing a topic vector database and automatically updating; retrieval is performed based on cosine similarity, and hot topics are screened out; the hot topics are integrated into a Prompt template, an auxiliary tool is called in real time to obtain auxiliary information, and a large model is used for generating reply content based on the Prompt template according to the current input data and the auxiliary information; and storing the current input data and reply contents, and updating the historical dialogue data. Personalized emotional appeals of a user are analyzed according to data input by the user through a large language model, key intention information is extracted, a topic vector database is constructed, topics most related to current user input are screened out, and by means of auxiliary tools, the real-time performance of hot topics can be guaranteed, and emotional resonance with the user can be enhanced.

The embodiment of the invention provides a marketing short video generation method and device based on a large language model, and the method comprises the steps: carrying out the matching degree analysis of a hot title and product data based on the large language model, and outputting a hot topic which is most matched with a product according to the sequence of the matching degree from high to low; searching a hot video related to the most matched hot topic in a video platform, and extracting an oral copy in the hot video based on an automatic voice recognition technology; generating a creation copywriting by using a large language model based on the oral copywriting, the hot topics and the product data; and performing similarity matching on the embedded vector corresponding to the created copywriting and the semantic vector of the video slice in the database, and sorting and generating a mixed video meeting the duration requirement according to the similarity.

The invention discloses a hot topic cluster generation method and device, equipment, a medium and a product. The method comprises the steps that a news data set meeting collection conditions is acquired; performing topic modeling clustering on the news data in the news data set to obtain a topic cluster set to which each piece of news data belongs; splitting and combining the topic cluster set, and determining candidate topic clusters; and determining a final hot topic cluster based on the large model and the candidate topic cluster. According to the method, splitting processing is carried out before the topic clusters are combined, high consistency of news in each topic cluster is ensured, the situation that irrelevant news or only slightly-relevant news is wrongly classified into the same topic cluster is avoided, and therefore the accuracy and purity of topics in the final hot topic cluster are improved, the title of the final hot topic cluster is determined on the basis of a large model, and the accuracy and purity of the topic are improved. And the quality of the title is improved, so that the quality of the final hot topic cluster is improved.

The invention discloses a hot topic monitoring method and system based on an improved HDBSCAN clustering algorithm, and the method comprises the steps: capturing news and comment data of a social media platform through an incremental crawler technology, recording a page updating state, and dynamically maintaining the data timeliness; performing word segmentation, stop word filtering and entity recognition preprocessing on the text, and constructing a triple data set; the SBERT model is finely adjusted based on a triple loss function, and the clustering center supervision capability represented by semantic vectors is optimized; dimensionality reduction is performed on the high-dimensional semantic vector, and global and local semantic structures of the text are reserved; combining cosine similarity and a time decay factor to improve HDBSCAN distance measurement, and dynamically adjusting a text similarity weight to realize separation of new and old topics; a popularity analysis model is constructed based on user interaction behaviors and a time window, and topic propagation trend and influence evolution are tracked in real time. According to the method, through semantic enhancement and a dynamic clustering mechanism, the accuracy and real-time performance of hot topic recognition are effectively improved.

The invention relates to the technical field of natural language processing, in particular to a network public opinion hot topic recognition method, device and equipment, and the network public opinion hot topic recognition method comprises the steps: embedding social media text data through a Chinese pre-training language model to obtain corresponding high-dimensional vector representation; performing dimension reduction on the high-dimensional vector representation by adopting a dimension reduction algorithm based on manifold learning to obtain dimension-reduced text data; and performing density clustering on the text data subjected to dimension reduction, and extracting keywords from a clustering result as recognized topics. According to the method, public opinion topics with high attention and representativeness can be accurately recognized and summarized from massive social media data, the automation level of public opinion hotspot recognition is improved, and more accurate and timely information support is provided for public opinion analysis and decision making.

The invention provides a technology trend prediction method and device, a computer system and a medium, and aims to excavate key technology evolution trends in various technical fields and identify future hot topics. The method comprises the following steps: performing document matching on generalized topics of various technical fields based on a highly cited document database, and extracting documents related to the generalized topics; performing text preprocessing on the literature under the generalized theme, wherein the text preprocessing comprises field proper noun standardization, word segmentation, stop word processing and word form merging and text vectorization; adopting an LDA topic clustering model to perform detailed topic clustering on the pre-processed literature, generating a keyword set and keyword contribution degrees, and condensing the keyword set and the keyword contribution degrees into detailed topics; and training and predicting the popularity data of the detailed theme over the years based on a Chronos time sequence model, and identifying the detailed theme with high popularity in the future.

The invention discloses a social media hot topic prediction method, system and device, and a storage medium. The method comprises the following steps: obtaining a query post, a target user, a historical post set related to the target user, and a recent post set published by a followers of the target user; generating interest representation of the target user based on the historical post set; on the basis of the recent post set and in combination with the query post, a social exposure representation of the target user is generated by applying a self-attention shielding mechanism, and the social exposure representation reflects hot topic information related to the query post in the recent post set; fusing the user interest representation and the social contact exposure representation into a comprehensive representation; and predicting a possibility index of forwarding the query post by the target user based on the comprehensive representation. According to the invention, more accurate prediction of the user forwarding behavior is realized, and the sensitivity and prediction efficiency of hot topic identification are significantly improved.

The invention relates to the technical field of computers and provides an automatic content generation method, computer equipment, a medium and a system. The method comprises the following steps: acquiring a hotspot theme through a hotspot screening module; inputting the hotspot topics into a plurality of generative artificial intelligence models in a hotspot analysis module so as to obtain contents associated with the hotspot topics generated by the plurality of generative artificial intelligence models respectively; transmitting contents associated with the hot topics respectively generated by the plurality of generative artificial intelligence models to a style conversion module; and through the style conversion module, based on a system cue word at least comprising a style cue word, generating stylized content for the hot topic by using the content associated with the hot topic generated by each of the plurality of generative artificial intelligence models. In this way, the overall efficiency is improved, and individual requirements are met.

The invention provides an enterprise brand reputation monitoring method and device based on a large model, a medium and equipment. The method comprises the following steps: capturing text data of an enterprise brand from a plurality of data sources; the text data is preprocessed; analyzing emotional tendency information and identifying hot topics from the preprocessed text data by using a large model; extracting keywords from the preprocessed text data; according to the keyword, determining a reputation index of the enterprise brand; continuously monitoring the reputation index by using a time sequence analysis method so as to determine the change trend of the reputation index; and carrying out visual display on the emotional tendency information, the hot topic and the change trend. The embodiment of the invention provides an efficient and intelligent brand reputation monitoring scheme.

This invention relates to a big data topic analysis method based on an embedding model. First, the Sentence-BERT model is used to perform sentence embedding representation on preprocessed Chinese text data. Then, the UMAP projection dimensionality reduction algorithm is used to reduce the dimensionality of the embedded vectors. Next, the HDBSCAN clustering algorithm is used to cluster the dimensionality-reduced vectors. Based on the assignment of each Chinese text in the target Chinese dataset to a corresponding topic class, the Chinese words with the highest c-TF-IDF scores are selected to represent each topic class. Finally, the DSG model is used to perform word embedding representation on the topic words, calculating the similarity between different topic words and between different topic classes, thereby detecting the volatility of newly emerging topic classes. The entire scheme design has higher topic consistency and topic diversity, and can detect new hot topics in a timely and accurate manner, providing early warnings.

The present invention discloses a method for extracting topic tags and related devices, which can split the pre-obtained dialogue text data to obtain multiple text segments; perform word segmentation on each of the text segments to obtain multiple single-word phrases; calculate the frequency of occurrence of each of the single-word phrases in each of the text segments; determine the hot topic group corresponding to the dialogue text data according to the frequency of occurrence of each of the single-word phrases, wherein the hot topic group includes multiple of the single-word phrases; for any one of the text segments, determine whether at least one of the single-word phrases in the hot topic group is included in the text segment; if included, determine the single-word phrases in the hot topic group included in the text segment as the first-level topic tags corresponding to the respective text segments. It can be seen from this that the present invention can process dialogue text data to automatically determine topic tags without manual participation and has high efficiency.

The present invention discloses a method and a marking system for scenic text annotation of bloggers' live video AR glasses. The method specifically includes: dynamically adjusting the knowledge graph retrieval strategy of the tourism live broadcast scene through the hot topic set and the sentiment classification result to obtain a dynamic retrieval result; obtaining the blogger's head pose data based on the IMU unit of the AR glasses, combining the geographical positioning information, and constructing a 3D spatial topology map of the tourism live broadcast scene through the SLAM algorithm; performing spatial registration on the dynamic retrieval result and the 3D spatial topology map, and combining to generate a scene enhancement annotation layer through the NeRF algorithm; superimposing and displaying the video stream and the scene enhancement annotation layer, and dynamically adjusting the annotation visibility by using an adaptive transparency algorithm. The present invention realizes the dynamic annotation of the scenic text content of the AR glasses by collecting the video stream and the interactive data stream in real time, can timely respond to the changes in the live broadcast scene and the interactive needs of the audience, and improves the real-time performance and accuracy of the live broadcast annotation.

The present application relates to a kind of brand influence dynamic monitoring method based on big data driving, comprising the following steps: collecting the webpage data of various network media and various forums by network crawler and carrying out pretreatment to webpage data;Media public opinion sentiment classification model is built, and the public opinion information in the webpage data of various network media after pretreatment is classified by media public opinion sentiment classification model;Hot topic mining model is built, and hot topic in the webpage data of various forums after pretreatment is mined by hot topic mining model, meanwhile, hot topic tracking model is built, and hot topic is tracked by hot topic tracking model;Brand influence trend prediction model is built, and the influence trend of the brand is predicted by the public opinion information related to the brand corresponding to the model and relevant hot topic, which provides early warning capability for brand party.

The opinion report generation method based on search enhancement generation relates to the technical field of artificial intelligence, and aims to solve the problem of poor timeliness of existing methods in generating opinion reports. The present application can be applied to analyze the public opinion of recent hot topics in social media platforms. A large amount of news and comment data is collected through a crawler or an API provided by each website. A large language model is used to summarize the collected news and detect the position of the comments. Then, a large language model is used to generate an opinion report. The opinion report generated by the present application has strong timeliness.

The invention discloses a public culture data processing method and system, and belongs to the technical field of travel propaganda, and the method comprises the following steps: collecting all available public culture resources in a region through big data search, comprising basic data of cultural relics and historic sites, museums, non-abandoned projects, local literatures, historical stories, characteristic blocks, old sizes and natural landscapes, and a structured cultural asset map is formed; the public culture data processing method and system are invented by automatically capturing hot topics of social media in a local area, Gao-like comments and user-generated contents everyday and identifying potential travel hot spots through data cleaning and aggregation, and through combination of multi-dimensional data acquisition, intelligent analysis and hierarchical planning, manual dependence can be eliminated, travel demands can be accurately captured, and the user experience is improved. The propaganda dimension is widened, high efficiency, precision and continuity of text travel propaganda are achieved, and the exposure and attraction of local text travel resources are improved.

Embodiments of the present application disclose an intent recognition method and device, and equipment. The method comprises: obtaining to-be-recognized text; inputting the to-be-recognized text and hot feature corresponding to the to-be-recognized text into an intent recognizer, and outputting a recognition result, the intent recognizer being a neural network for recognizing multiple intents, the recognition result being used for indicating an intent of the to-be-recognized text, and the hot feature being a feature extracted from target hot text, the target hot text including at least one hot text similar to the to-be-recognized text. It can be known that the intent recognition method considers that hot topics change over time, and the same to-be-recognized text can correspond to different intents. Therefore, in the process of intent recognition, the to-be-recognized text is combined with hot topics, and hot features are extracted from hot text related to the to-be-recognized text, so that the timeliness of the intent can be considered, and the accuracy of intent recognition is improved.

The present invention relates to the field of artificial intelligence and discloses a rumor detection method, device, electronic device, and storage medium that can be used by insurance business personnel in the financial industry to judge current hot topics. The method includes: extracting a text set and corresponding timestamps from an event to be detected; inputting the text set into the upper network of a rumor detection model in the order of the corresponding timestamps to perform rumor detection, obtaining a first rumor detection result corresponding to each text; and simultaneously inputting the timestamp corresponding to each text into the lower network of the rumor detection model to perform rumor detection, obtaining a second rumor detection result corresponding to each timestamp; when the second rumor detection result meets a preset stop condition, stopping rumor detection in the upper network and obtaining the last rumor detection result output by the upper network as the rumor judgment result for the event to be detected. The present invention can improve the accuracy and efficiency of rumor event detection in the financial industry.

This invention discloses a method, device, electronic device, and storage medium for generating medical science popularization content. The method includes: parsing medical research content to be popularized to obtain structured content data; identifying medical terms in the structured content data to obtain target category labels for multiple medical terms; acquiring hot topic public opinion data and style content based on the target category labels; and inputting the structured content data, hot topic public opinion data, and style content into a pre-trained science popularization content generation model to obtain medical science popularization content. This invention can guide the science popularization content generation model based on hot topic public opinion and the desired style, prompting it to generate medical science popularization content with a hot topic public opinion orientation and a specified style based on the structured content data. The generated medical science popularization content is more accessible, approachable, and humanized, improving the reading experience and better catering to readers' interests, thus enhancing the efficiency and quality of medical science popularization.

The invention provides a method for automatically generating and distributing network guidance content based on a large model, which comprises the following steps of: 1, data monitoring: collecting related data of a monitoring target in the Internet; 2, analyzing a hot topic, processing the related data collected in the step 1, and obtaining the topic of the hot event and the corresponding data content; step 3, guide content generation: setting a caliber requirement of the guide content according to the theme of the hot event obtained in the step 2, and generating the guide content through a large language model; and 4, task distribution management: selecting a distribution method and a distribution strategy according to the guide content obtained in the step 3, and carrying out guide content distribution.

A video processing method, apparatus, device and storage device is provided. The method comprises: displaying, on a play page of a target video, a hot topic panel entrance; and in response to receiving a trigger operation for the hot topic panel entrance, displaying a hot topic panel on the play page of the target video, wherein the hot topic panel displays hot topic contents for a first set of videos to which the target video belongs.

The present application relates to a method, apparatus, computer equipment and storage medium for determining an advertising delivery strategy. The method comprises: receiving an advertising delivery strategy determination message, wherein the advertising delivery strategy determination message carries data of products to be delivered; obtaining a target topic and characteristic data of the target topic based on the data of the products to be delivered; constructing a feature vector based on the characteristic data of the target topic; inputting the characteristic vector into a trained topic trend prediction model to obtain a trend prediction result of the target topic; determining an advertising delivery strategy based on the topic trend prediction result of the target topic; wherein the topic trend prediction model is trained based on historical topic data and historical topic characteristic data carrying topic classification labels, and the historical topic data corresponds to the same product field as the data of the products to be delivered. The use of this method can get rid of the practice of relying on human experience to predict hot topics, ensure the accuracy of topic trend prediction, and improve the effect of advertising delivery.

The application relates to the field of data processing, in particular to an information data processing method and device and electronic equipment. The method comprises the following steps: obtaining information data to be analyzed; using a hot topic detection model to identify hot topics in the information data in real time, tracking the change trend of the hot topics in a social network dynamic graph, and obtaining an information analysis result of the hot topics; generating a real-time interaction strategy corresponding to the hot topics based on the information analysis result and the types of various social media platforms; executing the real-time interaction strategy through a real-time interaction model to simulate the behavior characteristics of users in various social media platforms, to obtain matched information to be published and / or behavior operation instructions, and to push the information to the users for confirmation of whether to execute. The method can flexibly adjust the strategy according to different platform characteristics and dynamic information, enhance the adaptability to complex information environment and emergencies, realize intelligent information processing, simplify the information processing procedure, and improve the processing efficiency and quality.

This invention discloses a video generation method and system based on artificial intelligence and hot topic monitoring, relating to the field of video generation technology. It includes a hot topic monitoring module, a compliance processing module, a video generation module, and a compliance verification module. By capturing and filtering hot topic information, and combining dissemination frequency and timeliness indices, it accurately identifies high-value content, achieving efficient topic selection for hot topic video creation. Through multi-layered compliance processing including false information identification, value orientation assessment, and portrait rights infringement detection, it ensures that the content is authentic, positive, and legal. By leveraging artificial intelligence to extract script elements and intelligently match or generate materials, it significantly improves video production efficiency and creative richness. Simultaneously, through multi-dimensional compliance verification and correction of the initial video, it ensures that the final video is fully compliant in terms of information authenticity, value orientation, and portrait rights protection. This provides users with high-quality video content that keeps up with hot topics and helps platforms efficiently produce compliant and high-quality works.

The invention belongs to the technical field of data mining, and provides a work order data processing method and system based on a topic model, a medium and equipment, and the method comprises the steps: extracting a flow type work order data set through employing a trained representation extraction model, and obtaining a second corpus; constructing a second topic model based on the second corpus, and calculating a topic corresponding to the process type work order; counting the quantity of the flow-type work orders associated with each theme, and selecting the theme with the quantity of the associated flow-type work orders ranked as the first sixth preset rank as a second work order hotspot theme; obtaining an original process entry data set, and preprocessing the original process entry data set to obtain a third corpus and a third entry sequence; and based on the third entry sequence, utilizing the second topic model to calculate and obtain a topic corresponding to each process entry. The topic modeling is carried out on the corpus based on the topic model, the hot topic of the work order is intelligently extracted, and the topic is associated with the process entry data, so that management personnel at all levels can carry out optimization work in a targeted manner.

The present invention discloses a progressive opinion extraction method and system for hot topics. The method is as follows: providing prior knowledge; constructing a seed event structure diagram based on the prior knowledge, where the diagram contains opinion information nodes and edges representing the relationships between opinion elements; combining the event structure diagram and data in the current stage to train and predict an opinion extraction model, and giving the prediction results of the data in the current stage after the training ends; removing the opinions that already exist in the event structure diagram, and submitting the new opinions to experts for confirmation; screening the confirmation results returned by the experts, removing the unqualified opinions, and adding the qualified opinions to the event structure diagram; returning to the opinion extraction step again, and repeating this cycle until the opinion extraction model converges. The present invention uses historical information for opinion extraction from new texts under the same topic, can effectively mitigate the impact of unbalanced hot topic datasets on neural models, and can obtain high-quality opinion information with a small amount of labeled data.

The invention provides an advertisement image generation method and device, equipment and a storage medium, which can be applied to various scenes such as questions and answers. The processing method comprises the steps of obtaining first information and an original advertisement image of a question and answer application, wherein the first information comprises a target hot topic name; generating N questions based on the first information; reasoning and answering each question in the N questions to generate response content of each question; and based on the generated N questions and the generated response content of each question, modifying contents in a question box and a response box in the original advertisement image, and generating N new advertisement images of the question and answer application. According to the method, the labor cost of generating the advertisement image of the question and answer application is reduced, the generation efficiency of the advertisement image is improved, and the conversion rate of the advertisement image can be improved.

The invention relates to the technical field of intelligent marketing, in particular to a multimodal intelligent generation IP deformation method and system based on global marketing. The method comprises the following steps: acquiring topic information and issuing account information; analyzing the published account information, and determining an account type; determining associated hotspot information according to the account type; and determining script generation logic according to the associated hotspot information, the account type and the topic information. The risk that the content is not matched with account positioning is reduced, and the pertinence and efficiency are improved. According to the account type, the associated hotspot information is determined, the utilization efficiency of hotspot resources is optimized, the relevance and timeliness of contents are enhanced, and resource waste or user participation reduction caused by misuse of hotspots is avoided. The script generation logic is determined according to the associated hotspot information, the account type and the topic information, so that the integration and the performability of the script are improved, and insufficient coverage of stiff contents or user pain points caused by logic dispersion is avoided.

The utility model discloses a live broadcast box with an analog output camera, which comprises an equipment body, the equipment body comprises an equipment interface module, a picture processing module and a man-machine interaction module, and the equipment interface module is arranged on the outer side of the equipment body. According to the utility model, through the picture processing module, the focal length of the live broadcast picture can be intelligently identified and automatically adjusted, an anchor is ensured to be always in a clear focus, the watching experience of audiences is improved, a real-time background replacement function is carried out on the live broadcast picture, a diversified live broadcast environment can be created without complex setting, and the interestingness and speciality of live broadcast are increased; screen character information can be quickly recognized through the man-machine interaction module, live broadcast content is intelligently analyzed based on an advanced algorithm, valuable data support such as audience behavior feedback and hot topic prediction is provided, finally recognized and input characters are directly converted into subtitles in a live broadcast picture, the information transmission effect is enhanced, and the user experience is improved. And feedback is brought to the user watching the live broadcast.

The application provides a hot topic selection method and system for energy power field science and technology periodicals, extracts keywords from papers of domestic and foreign science and technology periodicals in the energy power field, merges and groups the keywords, counts the word frequencies, forms a hot keyword library, displays the keywords in a set number of rankings according to different proportions according to the different appearance frequencies of the keywords in the hot keyword library, and selects hot topics according to the statistical results of the keywords. The application can accurately grasp the front hot information of the energy internet.