650 results about "Content analytics" patented technology

The invention discloses a video content intelligent management method and system based on multi-modal analysis, and belongs to the field of video content management.The method comprises the steps that multi-modal features of video content are extracted; performing fine structure segmentation on the video content based on the multi-modal features, and segmenting the video into fine segments related to semantics; the fragments obtained after fine structure segmentation are subjected to tagging, and metadata is generated for each fragment; constructing a multi-modal semantic index of the video content based on the generated metadata; according to the method, a semantic retrieval function is provided by utilizing multi-modal semantic indexing, fine fragments required by a user are accurately recalled in combination with text label matching, measurement numerical value scoring, cross-modal vector retrieval and filtering based on a shot language and a shooting technology label, and the user queries and positions a specific time point or the fine fragments in a video through a natural language. According to the invention, the accuracy of video content analysis is improved, the user experience is optimized, and the operation complexity is reduced.

The invention belongs to the field of natural language processing crossing, and particularly relates to a visual-text collaborative abstract generation method and system based on multi-modal learning, and the system comprises a multi-modal data receiving module which is used for receiving multi-modal input data in parallel, and the multi-modal input data comprises text data and visual data; the visual semantic understanding module is used for extracting high-level semantic features of the image by adopting a visual semantic understanding model and generating text description; the semantic fusion module is used for carrying out semantic fusion on the original text and the generated image description by adopting a vision-text semantic fusion method based on direction consistency and self-adaptive semantic completion; and the abstract optimization module is used for carrying out semantic reconstruction on the fusion features by adopting a multilayer deep fusion hybrid neural network architecture, and generating an abstract text conforming to context semantics and accurate expression. The method has the advantages that visual and text information is accurately aligned, high-quality abstracts are generated, and the method is particularly suitable for news reports, conference records, video content analysis and other scenes needing cross-modal information fusion.

The invention discloses an automatic labeling and acquisition standardization method and system based on video content, and the method comprises the steps: decoding a video file into a processable video frame sequence, carrying out the standardization processing, unifying the format and the coding standard, and adjusting the resolution and the frame rate; performing content analysis on the standardized video frame sequence, extracting key frames, identifying objects, scenes and actions, and generating a video analysis result and a preliminary label; according to a video content analysis result, separating an audio stream from the video to carry out noise reduction processing and voice-to-text analysis, and combining the processed text with the video content analysis result to generate multi-modal comprehensive annotation information; based on the comprehensive labeling information, taking feedback of the user in video retrieval, label clicking and watching behaviors as an adjustment basis of label weights, and updating weight distribution of labels by analyzing user behavior data; labeling information and weight distribution are integrated into an index database, and indexes are updated in real time according to user behaviors and content changes.

The invention discloses an archive management system and method based on data analysis, and particularly relates to the technical field of archive management, and the method comprises the following steps: obtaining document attribute data of archives, generating document attribute vectors, carrying out clustering analysis, finding a similar group set containing similar attributes with a current archive from all archives, and carrying out clustering analysis on the similar group set; then whether an information exposure risk investigation mechanism and a transverse association risk level are triggered or not is judged; respectively carrying out content analysis and access analysis on the current archive, then generating a dynamic desensitization level of the current archive in combination with a transverse association risk level judgment result, and applying the dynamic desensitization level to the current archive in real time; according to the method, the behavior entropy access risk index is obtained by calculating the content sensitivity, dynamically evaluating the file security level and calculating the confusion degree of the user access behavior, the risk perception capability is improved, and the desensitization level of the file is automatically adjusted according to the content sensitivity, the access risk and the overall risk level, so that the data protection is more accurate.

The invention discloses an emotional understanding and feedback method for a full-duplex interactive question-answering digital person, and the method comprises the steps: S1, arousing interaction: enabling a digital person of a digital person system to enter an interaction state in a human body detection arousing or voice instruction arousing manner by a user; s2, state analysis: the digital human system judges the state of the user in real time by collecting image data and audio data of the user, and identifies and analyzes the direction, emotion and speech content of the user in real time; and S3, feedback output: the digital human system drives the digital human in voice, expression and body movement through a screen in real time according to the analysis result of the direction, emotion and speech content of the user, and feeds back the result to the user in a manner of adding auxiliary characters, images and videos. The method has the advantages that the emotional state of the user is accurately judged through an advanced algorithm, digital human expressions, body actions and voice feedback are driven in real time according to the emotion and speech content of the user, response is made in time, and a real-person-like real-time interaction scene is achieved.

The present invention relates to a computer-implemented method and system for generating a video editing project using artificial intelligence (AI) and machine learning (ML) techniques. The method includes processing a collection of video clips to generate text-based metadata, receiving selection criteria to identify relevant video clips, and generating a natural language prompt based on the selection criteria. The prompt, comprising instructions and context, is provided to a large language model (LLM), which processes the input and outputs data for constructing a video project data model. The project data model includes timing data for salient snippets within the selected video clips. A dynamic and interactive web-based user interface is rendered to visually represent the project data model, offering a timeline view and editing tools for refining the video project. This system streamlines the video editing process by integrating AI-driven content analysis with user-directed editing, resulting in a tailored video project that aligns with user-defined thematic elements.

A platform for dynamically generating application experiences. The platform comprises a design management system, an agent orchestration system, an analytics system, a model management system, a user management system, and databases for storing design elements and templates. The design management system provides a portal for application owners / designers to create UX / UI designs, allowing them to select design elements from a set of categories or templates. The platform gathers existing websites / applications to identify common design patterns, stored in a design catalogue database, and suggests historical interfaces for design exploration. It enables the generation of templated applications that integrate with legacy systems. The agent orchestration system parses user specifications, selects generative AI systems, and generates UX / UI content based on the specifications. The analytics system collects and analyzes data to provide insights for improving UX / UI design and optimizing website performance. The model management system trains and maintains generative AI models used for content generation.

The invention discloses a method and device for automatically converting a cross-format document into a Markdown document and a form enhancement method and system. The Markdown document conversion method comprises the following steps: firstly, obtaining a document image; mining hidden structure information from the document image, wherein the hidden structure information comprises target elements and hierarchical relationships among the target elements; performing semantic content analysis on the document image based on the structure information to obtain a semantic content analysis result; extracting key information in the document image according to an analysis result; the key information is converted into a Markdown format, preliminary formatting is carried out in a form or list mode, and a preliminary Markdown form is obtained; and finally, optimizing the preliminary Markdown form to form a target Markdown form, so that the cross-format document is automatically converted into the Markdown. According to the method, a complex nested structure and a form with irregular layout in the document can be effectively identified and converted, and the integrity and readability of form information when the form is converted into the Markdown format are ensured.

The invention is suitable for the technical field of database management, and provides a distributed database management method and system based on artificial intelligence. The system comprises a database content analysis module, a storage layout adjustment module, an anomaly detection module, an unmanned aerial vehicle storage communication module, an access request guide module and an unmanned aerial vehicle node recovery module. The scheme that an unmanned aerial vehicle is adopted as a storage and communication node brings remarkable innovation and multiple advantages to a distributed database management method. Firstly, the mobility of the unmanned aerial vehicle enables the unmanned aerial vehicle to quickly take over a data storage task when a network is interrupted or a node fails, so that the integrity and availability of data are ensured, and the response capability in a traditional architecture is relatively limited. In complex scenes such as disaster recovery or remote area deployment, the unmanned aerial vehicle can break through network limitation through off-line transmission, and data safety and storage continuity are effectively guaranteed.

HyperText Markup Language (HTML) content analysis (HCA) using machine learning is described. A feature vector schema may be generated based on domain names corresponding to HTML webpages and corresponding indications of a status of the HTML webpage. The schema may map each position in a feature vector of a given HTML webpage to a resource identifier. Information may be processed using the schema to generate respective feature vectors. The feature vectors may be used to train a model to generate risk indicators for HTML webpages. A potentially parked domain webpage or a potentially malicious domain webpage may be received. A feature vector for the webpage may be generated and inputted to the model. The model may generate a risk indicator for the webpage. The risk indicator may be output and may cause responsive actions. The model may be updated based on a determination indicating whether the webpage was a parked domain webpage or a malicious domain webpage.

The invention discloses a dialogue type enterprise knowledge question answering method and device, a storage medium and electronic equipment, and relates to the technical field of data analysis, and the method comprises the steps: obtaining user request information; obtaining a corresponding channel; reconstructing the question content to obtain the reconstructed question content; performing vectorization processing on the reconstructed question content to obtain vectorization information of the reconstructed question content; obtaining a plurality of first FAQ knowledge and second FAQ knowledge; obtaining a plurality of pieces of first document block knowledge and second document block knowledge, and obtaining documents corresponding to a plurality of document blocks; obtaining a corresponding prompt word template based on the dialogue intention and the channel; and constructing a large language model, and sorting to obtain corresponding answer information. Based on the question content of the user, the dialogue intention is obtained through analysis, the knowledge base is matched, the answer information is obtained through arrangement, the user question is automatically answered, and the method has the advantages of being simple, efficient and high in real-time performance.

The invention discloses a multi-agent automatic image retouching system based on content analysis, and the method comprises the steps: 1, receiving the input of an original image, calling a finely-adjusted multi-mode large model to carry out the combined semantic-visual analysis of the image, extracting key semantic elements in the image, and carrying out the recognition of the key semantic elements; comprising but not limited to background coordination degree, illumination condition, figure hair style definition, weather quality and composition layout rationality content information. Based on these information, the system performs comprehensive image quality scoring on the image, which combines image style, aesthetic level, definition and subjective quality multi-dimensional evaluation criteria. Meanwhile, based on a multi-dimensional image quality scoring result and understanding of image semantic content, the system automatically generates a repair and optimization strategy set for specific defects so as to guide a subsequent image quality enhancement process. The invention relates to the field of multi-modal model and multi-agent cooperation, and can meet the increasing requirements of rapid optimization and high-quality propagation of image contents.

The invention discloses a multi-modal learning automatic complaint content analysis and classification method and system, and relates to the technical field of semantic analysis, and the method comprises the steps: collecting multi-modal complaint data, and carrying out the preprocessing of the collected multi-modal complaint data; extracting features of the preprocessed multi-modal complaint data, and fusing the features of different modal data; constructing a complaint content classification model by using the fused features, carrying out model training, and evaluating and optimizing the trained model; and deploying the optimized model for real-time classification. According to the multi-modal learning automatic complaint content analysis and classification method provided by the invention, the complaint content is quickly and accurately classified, and a targeted processing strategy is provided according to the main category, the emotional tendency and the specific theme of the complaint. And an emergency response mechanism is automatically triggered to ensure that problems are processed in real time and effective utilization of resources is ensured. Decision rules are continuously optimized from historical data learning, and the intelligent level of a processing strategy and the customer satisfaction degree are improved.

The invention discloses a public information publishing platform and method, and relates to the technical field of information public facilities, and the method comprises the following steps: obtaining to-be-evaluated information and publisher data, and analyzing to generate a credibility score PA of an information source; performing sentiment analysis and keyword inspection on the to-be-evaluated information, identifying sentiment deviation and semantic contradiction in the to-be-evaluated information, and analyzing a credibility score PB of the generated content; and acquiring an external authoritative data source, comparing the external authoritative data source with a time consistency check algorithm by using a text similarity algorithm, judging the consistency of the to-be-evaluated information and the external authoritative data source, and outputting a consistency reliability index Pc. According to the method, the problems of low false information identification efficiency and poor accuracy in the prior art are effectively solved by combining multi-dimensional data processing such as information source credibility evaluation, content analysis, external authoritative data source comparison, propagation path analysis and the like.

The invention relates to the technical field of display, and particularly discloses an intelligent self-adaptive eye protection display system which sequentially comprises a content analysis module, an ambient light detection module, an eye protection parameter calculation module, a display parameter adjustment module and a user feedback module. The system identifies the screen content type and the dynamic degree in real time, synchronously detects the ambient light intensity and the color temperature, obtains the optimal combination of the blue light proportion, the brightness, the contrast ratio, the color temperature and the sharpness through a multi-target optimization model, and smoothly adjusts the display through driving; a user can score, finely adjust and mark a scene, and feedback data enters a self-learning engine to iteratively update a parameter weight so as to form a content-environment-user closed loop. The system can significantly reduce blue light harm and improve visual comfort without external hardware.

The invention discloses an AI-based classroom content analysis method, system and device, and a storage medium. The method comprises the following steps: acquiring classroom video and voice data in real time; identifying and judging a current classroom scene by using the first multi-modal fusion and scene identification model; according to a preset classroom scene-interaction object mapping rule, determining an interaction object correspondingly mapped by the current classroom scene; utilizing a second multi-mode fusion and effective interaction identification model to extract behavior characteristics of the student, and judging whether the current student behavior is effective interaction; counting the ratio of the effective interaction duration to the total detection duration, and calculating the classroom concentration of the students; based on the interaction quality evaluation model, performing weighted calculation to obtain an interaction quality index of the student; and automatically generating an analysis report about the class concentration level and the interaction quality level according to a preset level evaluation standard. According to the method, accurate quantitative analysis of the concentration degree and the interaction quality of the students in the classroom can be realized, and the classroom analysis report is automatically generated.

The invention discloses a courseware content analysis method and device, equipment and a storage medium. The method comprises the following steps: acquiring a courseware picture; detecting each frame of courseware picture, and detecting a title and a text; inputting the detected title and text of each frame into a pre-trained character recognition model, and recognizing a title keyword set and a text keyword set of each frame of courseware picture; summarizing the title keyword set and the text keyword set of all frames of courseware pictures to obtain a courseware keyword set; obtaining a knowledge point set of a course corresponding to the courseware; obtaining a courseware integrity analysis result according to the matching-union ratio; and obtaining a courseware content analysis result according to the courseware integrity analysis result. According to the method, the teaching courseware content is analyzed and evaluated according to an objective specification standard, so that a scientific and objective evaluation conclusion is obtained.

A trained model is retrained for video quality assessment and used to identify sets of adaptive compression parameters for transcoding user generated video content. Using transfer learning, the model, which is initially trained for image object detection, is retrained for technical content assessment and then again retrained for video quality assessment. The model is then deployed into a transcoding pipeline and used for transcoding an input video stream of user generated content. The transcoding pipeline may be structured in one of several ways. In one example, a secondary pathway for video content analysis using the model is introduced into the pipeline, which does not interfere with the ultimate output of the transcoding should there be a network or other issue. In another example, the model is introduced as a library within the existing pipeline, which would maintain a single pathway, but ultimately is not expected to introduce significant latency.

The invention relates to the technical field of content recognition, in particular to an Al-based video content analysis method and system, and the method comprises the following steps: carrying out the image segmentation learning based on a video sequence through employing a U-Net convolutional neural network, analyzing scenes and elements in a video frame, recognizing and isolating key visual elements in a video through network learning, and carrying out the recognition of the key visual elements in the video. Comprising objects and figures. According to the method, image segmentation learning is carried out by adopting the U-Net convolutional neural network, key visual elements in the video can be identified and isolated more accurately, a clearer basis is provided for follow-up scene change and key event tracking, video content analysis is carried out by applying the graph neural network, the identification capability of dynamic scenes and events is enhanced, and the identification efficiency is improved. Deeper structured understanding is provided for video content indexing, the video quality is analyzed through a structural similarity index evaluation method, and the reason of visual quality reduction can be accurately recognized and improved.

The invention relates to the technical field of training data auditing, in particular to a semantic perception black box large language model training data auditing method and system.The method comprises the following steps that content is returned based on a text generation interface, lexical elements and candidate content are analyzed for multi-round sampling distribution, and a convergence index is determined; and calculating semantic paths and weights of the real content and the candidate content, and analyzing differences between the real content and the candidate content and the reference content to obtain an affiliation adaptation judgment result. According to the method, through multi-round sampling behavior fluctuation tracking, section feature dynamic extraction, stability change judgment and sequence-level weight aggregation, fine separation of training data attribution signals is achieved, a multi-level judgment system is constructed for complex expression and diversified output, judgment accuracy is optimized through a tension comparison signal group and an attribution adaptation judgment mechanism, and the judgment accuracy is improved. And a sensitive and adaptive training data member auditing strategy is formed, so that the risk hidden danger omission is effectively prevented, and the model data security boundary control is enhanced.

The invention relates to a method for selecting a radio station, in particular for a vehicle, exhibiting: - Capture of content (IA, IB) from multiple radio stations (RA, RB), - Analyzing the content (IA, IB) of several radio stations (RA, RB), in particular by means of a computer-aided language model, preferably by means of a large language model (LLM), - Capturing at least one input from a user, in particular active or passive, conscious or unconscious (B), - Analyzing the user's input (B), in particular by the computer-aided language model, preferably by the large language model (LLM), - Suggesting a specific radio station (RA, RB), depending on the analysis of the content (IA, IB) of the multiple radio stations (RA, RB) and depending on the analysis of the user's input (B).

The invention relates to the technical field of computer vision, in particular to an AI-based video content analysis system, which comprises a video stream acquisition and detection module, a background interference analysis and filtering module, a target movement track calculation module, a visual angle conversion and track compensation module and an abnormal behavior analysis module. According to the method, the pixel brightness values of continuous frames are extracted, the foreground region recognition accuracy is improved, the brightness change trend is screened to accurately lock a dynamic target, the background change rate is analyzed, similar regions are removed, interference is reduced, and the target edge feature and inter-frame position change are extracted to perform fine-grained trajectory tracking. And track prediction is optimized in combination with the view angle switching time, track changes and speed changes are recorded, the stability and accuracy of the moving target under multiple view angles are improved, abnormal change modes are classified and analyzed, abnormal events are effectively captured, background interference is reduced, and the target detection precision and the abnormal behavior analysis response speed are improved.

The invention relates to the technical field of video coding, in particular to an intelligent video coding optimization system and method based on computer vision, and the system comprises a computer vision video content analysis module; identifying an object, a behavior and a face in the video; the video classification module based on the neural network is in communication connection with the computer video content analysis module; based on the video feature data, classifying videos by using a deep neural network; the video coding parameter optimization module is in communication connection with the video classification module based on the neural network; adaptively optimizing video coding parameters based on the video classification result and the video feature data; the video coding quality evaluation module is in communication connection with the video coding parameter optimization module; evaluating the video quality of coding based on the optimized coding parameters; and the evaluation result is fed back to the video coding parameter optimization module for further optimizing the coding parameter, so that the understanding depth of the video content is greatly improved.

The present disclosure relates to the technical field of computers. Disclosed are a video processing method and apparatus, and electronic device, and a storage medium. The method provided by the present disclosure comprises: obtaining a current video frame and a current bandwidth corresponding to the current video frame; performing bit rate prediction on the basis of video frame information and a target quantization parameter to obtain a predicted bit rate of the current video frame, wherein the video frame information comprises a content analysis result of the current video frame and / or frame information of a previous output frame, and the previous output frame is an output frame corresponding to a previous video frame; determining a target bit rate of the current video frame on the basis of the predicted bit rate and the current bandwidth; and encoding the current video frame on the basis of the target bit rate, and determining a current output frame. The method realizes dynamic adjustment of a bit rate of an encoder, and avoids bandwidth resource waste when a definition objective is met.

The invention relates to the technical field of voice signal processing, and discloses a voice segmentation intelligent editing system based on deep learning. The system comprises a voice feature extraction module, a segmentation boundary detection module, a semantic content analysis module, an editing strategy generation module and a real-time quality evaluation module. The voice feature extraction module collects multi-dimensional voice features and timestamp information, and verifies feature integrity and timeliness; the segmentation boundary detection module identifies voice pause intervals and semantic turning nodes and divides segmentation units and boundary types; a semantic content analysis module extracts text content and emotion features of each segment, and analyzes semantic topic relevance and information density; an editing strategy generation module formulates a segmentation retention rule and a sequence adjustment scheme, and matches user preferences and scene demands; the real-time quality evaluation module monitors voice fluency and information integrity in the editing process and analyzes splicing errors and user feedback. According to the system, intelligent processing of the whole voice editing process is realized.

A system (100) for AI-driven real-time optimization of advertising placement in dynamic media streams, comprising: (a) a media stream ingestion module configured to receive and pre-process live or continuously streamed content; (b) a content analysis and context understanding module operatively coupled to the recording module and configured to analyse the media content in real time using natural language processing, computer vision and audio signal analysis; (c) an audience profiling and behaviour tracking module configured to generate dynamic viewer profiles based on demographic, behavioural and contextual data; (d) an AI-based ad matching and ranking engine configured to select and prioritize ads based on content relevance, audience profile match, and predicted engagement; (e) a real-time decision and placement engine configured to insert selected advertisements at contextually appropriate points in the media stream without interrupting the viewer experience; and f) a feedback and analytics module configured to track ad performance and update AI models for continuous optimization of future ad placements.

A social networking system is provided. The system encompasses a robust processing subsystem hosted on a server, comprises various modules including a registration module linking social media accounts, a blockchain-backed bio page module storing audience metrics, a payment gateway with a reward system, and a CRM module offering dashboards for influencers, brands, agencies, and followers. An analysis module integrates natural language processing to assess content tone, engagement, and sentiment. A button module generates emoticon categorization and auto-suggestions based on metadata, leveraging machine learning for predictions. A compliance analysis module ensures adherence to regulations in paid social media content, and a royalty distribution module calculates and allocates royalties based on engagement, location, compliance, and fan type via smart contracts, This comprehensive subsystem integrates functionalities to elevate user interaction, content analysis, and monetization opportunities within the social media platforms.

The invention relates to the technical field of content analysis, and particularly discloses a delivery analysis method and system based on content characteristics and propagation effects, and the method comprises the steps: obtaining a plurality of delivery contents based on a target keyword; multi-dimensional putting effect analysis is carried out, and a putting effect comprehensive score is generated; performing content characteristic analysis on the plurality of delivery contents to generate a content quality score; acquiring propagation data of a plurality of delivery contents, constructing a propagation network based on the propagation data, inputting the propagation network into a pagerank model, and outputting a propagation effect score; and generating a delivery analysis report according to the delivery effect comprehensive score, the content quality score and the propagation effect score. Through analysis of multi-dimensional indexes such as the delivery amount, the category, the time interval distribution and the re-delivery proportion, a delivery effect comprehensive score is generated based on standardization and a weight distribution mechanism, the overall performance of a delivery strategy can be evaluated, delivery effect feedback can be provided, systematic decision support is provided, and the delivery efficiency and the return rate are improved.

The invention relates to an image steganography method. The method comprises the following steps: carrying out content analysis on an original image serving as a carrier; generating a self-adaptive embedding strategy according to an analysis result, and encoding the embedding strategy into a compact binary format; embedding to-be-embedded data into different areas and channels of the original image in a layered manner according to an embedding strategy to obtain an embedded image; the embedded data is extracted from the embedded image by decoding the embedding policy and mapping reconstruction. According to the method, a region self-adaptive embedding strategy is formulated, the embedding capacity is maximized on the premise of keeping the visual quality of the original image, and the hidden data detection risk is reduced.

The invention discloses a video generation method, system and device based on a large language model and a medium, and the method comprises the steps: obtaining product information inputted by a user, the product information comprising the name, description and selling point of a product; preprocessing the product information, and carrying out semantic information split processing on the preprocessed product information through a large language model to obtain split description information corresponding to the product information; the method comprises the following steps: acquiring an original video, and performing video clip segmentation on the original video to generate a plurality of video clips and picture description information corresponding to the video clips; semantic matching processing is carried out on the sub-mirror description information and the picture description information, and a video clip with the highest similarity with each piece of sub-mirror description information is obtained through matching; and splicing the video clips according to the sequence of the sub-mirror description information to generate a complete video. Compared with the prior art, the product video can be automatically and efficiently generated by integrating natural language processing, video content analysis and an intelligent matching algorithm.