33352results about "Selective content distribution" patented technology

A method is provided by which a subscriber accesses an SDV channel using a set top terminal. The method begins when the set top terminal receives a user request to tune to a first SDV channel. An active services list is also received over an access network. The active services list includes an entry for each currently available SDV program and a time-to-live (TTL) associated therewith. Tuning information is identified for the first SDV channel from its entry in the active services list. The set top terminal tunes to the first SDV channel using the identified tuning information. The channel change information associated with the user request is locally stored in set top terminal for transmission over the access network at a later time.

The invention discloses an intelligent multi-modal virtual digital human interaction system based on an AI language large model. The system comprises a high-authenticity face generation module; the high-authenticity face generation module uses an AdaAN network, based on adaptive feature fusion and voice driving and time sequence modeling of voice features, feature information related to voice is extracted, the extracted voice features are processed through a deep neural network, it is ensured that the voice and facial expressions are highly aligned in time and space, and the face recognition accuracy is improved. Collecting a bio-electricity signal, mapping the signal to facial muscle movement, generating a final facial expression, and interacting with a user; the system further comprises an intelligent interaction module, a training optimization and efficient generation module, an efficient integration module, a multi-modal data acquisition module, an AI large model core processing module, a digital human image generation and driving module, an interaction scene adaptation module and a feedback optimization module. The invention further discloses a multi-mode digital human interaction method which has wide application value.

The invention relates to the technical field of resource allocation, in particular to a multi-channel video stream cooperative transmission method based on dynamic priority, which comprises the following steps of: acquiring task context characteristic parameters, system resource states, user behavior responses and computing node load data in real time, and constructing a priority allocation model to carry out pattern recognition to generate real-time task priority. And establishing a task scheduling strategy generation model, and performing resource allocation by adopting a priority weighting efficiency evaluation function, a memory-computing unit occupancy rate prediction matrix and a gradient optimization target under an adjustment resource constraint condition to form an initial scheduling strategy. And performing multi-dimensional parameter fusion analysis on the data through an adaptive optimization decision model, generating a resource redistribution correction vector, dynamically adjusting an initial scheduling strategy by adopting an online iterative optimization mechanism, and outputting a final real-time optimization task scheduling strategy. The task priority dynamic evaluation and the closed-loop optimization of the resource allocation are realized, and the efficient cooperative execution of the multi-channel video processing task is ensured.

A video streaming system including a video cache system having a cache database and a video storage device, and including a video server configured to communicate with a gateway system configured to store multiple video streams, communicate with a cloud backup system configured to store video in a file system, and communicate with a cloud server to receive a stream request, the stream request indicating requested video that comprises multiple video segments. The video server is further configured to receive the stream request and determine a playlist that indicates where on the video cache system, the cloud backup system, and the gateway system the video segments that are needed to fulfill the stream request are stored, and to communicate needed video segments to the video cache system in preparation for providing streaming video to fulfill the stream request.

The invention provides an AI-based marketing video auditing method, and relates to the technical field of AI marketing video auditing, and the method comprises the steps: obtaining a multi-modal data original structure set, and extracting image semantic features, voice expression features, text semantic features and scene label information, and obtaining an image semantic feature set, a visual rhythm feature set, a voice expression feature set, a voice and picture synchronous association vector structure, a text semantic feature set and a subtitle semantic and image main body linkage relation graph. By constructing an image semantic feature set, a voice expression feature set, a text semantic feature set and a visual rhythm feature set and fusing the image semantic feature set, the voice expression feature set, the text semantic feature set and the visual rhythm feature set into a multi-modal content fusion feature tensor, unified modeling of an AI marketing video at visual, auditory and semantic levels can be realized; and subsequent microscopic consistency detection, compliance knowledge graph and emotion semantic conflict identification are effectively performed, so that full-link risk perception and accurate auditing of video contents are realized.

The invention provides a trajectory control video generation method and device based on depth information and time-frequency optimization, and relates to the technical field of image processing, and the method comprises the steps: optimizing a 3D trajectory through multi-entity segmentation, depth estimation and time-frequency decomposition in combination with a user instruction, and generating a control signal through a multi-scale fusion network; finally, the signals and original images are input into an improved Stable Video Diffusion model to generate a video potential representation sequence, the problems that an existing video generation method is insufficient in dynamic entity motion control precision and poor in cross-frame consistency are solved, and through 3D trajectory modeling guided by depth information and a time-frequency joint optimization mechanism, the video potential representation sequence is generated. And the motion smoothness, the space authenticity and the time-frequency stability of the generated video are obviously improved.

The invention relates to an AI intelligent short video generation method and system based on multi-agent collaboration. The method comprises the following steps: S1, input analysis and sub-shot script generation; s2, obtaining materials; s3, sub-shot video generation; step S4: editing and synthesizing; wherein the plurality of sub-shot video clips are edited and synthesized into a complete video according to a script sequence, and style fusion processing is performed on the whole video by using a picture style unification algorithm; step S5, background music matching; and S6, outputting the slices. Through the structured sub-shot script generation, intelligent material completion, multi-shot style fusion and music synchronization technology, a professional short video of multi-scene coherent narrative can be generated, the structured sub-shot script is automatically generated, and intelligent material completion, picture style unification and music rhythm and emotion expression synchronization are realized.

The invention discloses a video editing method and related equipment, and the method comprises the steps: receiving an editing intention of a user, carrying out the semantic analysis of an intention analysis model, and generating an editing copywriting, a style and a structured instruction; the edited copywriting is split according to the edited copywriting; performing cross-modal analysis on video materials by using a video understanding model trained based on an open-source multi-modal model, and accurately positioning sub-lens segments; secondly, editing the sub-lens segments through an editing model to generate a preliminary sheet, performing quantitative scoring through a video scoring model, and obtaining a scoring result according to a multi-dimensional index; and finally, the result is fed back to the editing model, and iterative adjustment is carried out until a final slice is produced. According to the method, the editing efficiency is improved, the manual operation time is shortened, the material adaptability is enhanced, the personalized requirement is met, the editing effect consistency is improved, the video content understanding is deepened, the editing effect is optimized, the technical threshold is reduced, the existing technical problems are solved comprehensively, and an efficient, intelligent and personalized editing scheme is provided.

Some embodiments of a method may include: obtaining Avatar JSON Interchange File (AJIF) data; decoding the AJIF data to generate avatar data; obtaining animation parameters from the avatar data; obtaining user movement data corresponding to a movement of a user; generating updated avatar data based on the user movement data; and sending the updated avatar data to a client device. For some embodiments, a method may include: generating Avatar JSON Interchange File (AJIF) data corresponding to an avatar; sending, to an application server, the AJIF data; obtaining user movement data corresponding to a movement of a user; sending, to the application server, the user movement data corresponding to the movement of the user; receiving, from the application server, scene update data, wherein the scene update data includes updated avatar data corresponding to the movement of the user; and rendering a scene update based on the scene update data.

Some embodiments of a method may include: obtaining information for a three-dimensional (3D) Gaussian model corresponding to a 3D scene, wherein the information comprises a set of attributes of the 3D Gaussian model; parsing the information for a first attribute of the set of attributes, wherein the first attribute corresponds to a position of the 3D Gaussian model; parsing the information for a second attribute of the set of attributes, wherein the second attribute corresponds to a color of the 3D Gaussian model; parsing the information for a third attribute of the set of attributes, wherein the third attribute corresponds to a covariance of the 3D Gaussian model; and rendering the 3D scene using the parsed attributes of the 3D Gaussian model.

Some embodiments of a method may include: obtaining information for a three-dimensional (3D) Gaussian model corresponding to a 3D scene, wherein the information comprises a set of attributes of the 3D Gaussian model; parsing the information for a first attribute of the set of attributes, wherein the first attribute corresponds to a position of the 3D Gaussian model; parsing the information for a second attribute of the set of attributes, wherein the second attribute corresponds to a covariance of the 3D Gaussian model; parsing the information for third, fourth, and fifth attributes, wherein the third, fourth, and fifth attributes correspond to first, second, and third sets of spherical harmonics coefficients associated with the 3D Gaussian model; parsing the information for a sixth attribute of the set of attributes, wherein the sixth attribute is an alpha coefficient for the 3D Gaussian model; and rendering the 3D scene using the parsed attributes.

Embodiments include methods for generating advertisement videos for insertion into a video stream to promote a product, service, or brand in a manner that is consistent with the context and storyline of the video stream before and at the time of ad insertion. Methods may include capturing an image from the video stream and generating caption text using an image-to-text description model. A product, service, or brand that is consistent with the context and storyline of the captured image is selected and ad video sequence description text is generated that includes descriptions and a storyline blending descriptions of the selected product, service, or brand with the context and storyline of the primary video stream. The ad video sequence description text is used to prompt a text-to-video generation model that generates a new advertisement video clip, which is inserted into the primary video stream before distribution to video content rendering devices.

The invention discloses a cloud platform auditing method based on multi-modal data processing, and relates to the technical field of cloud platform auditing. Independent features of text, image, audio, video and log data are extracted, each piece of modal information can be subjected to refined analysis in a corresponding feature space, and in a cross-modal feature fusion stage, a multi-modal feature fusion model is obtained. The correlation between different modal data is calculated by using a cross-modal consistency comparison network CMCN, and feature alignment is optimized through an attention mechanism, so that multi-modal information can be mutually verified, and misjudgment and missed judgment caused by independent auditing are reduced; in the auditing decision-making link, an auditing model based on deep learning is constructed, and end-to-end illegal content judgment is realized in combination with anomaly detection and a self-adaptive risk control threshold value, so that the intelligent level of an auditing system is improved; in addition, the suspicious areas of the image and the video are visually marked, and an auditing conclusion is generated by utilizing a natural language to generate an NLG model, so that the auditing result is more transparent.

The invention relates to the field of natural language processing. The invention discloses a cross-modal knowledge optimization system for improving localization adaptability of a large language model, and the system comprises a modal data collection module which collects cross-modal localization data of characters, audios and videos; the data preprocessing module is connected with the data acquisition module and preprocesses acquired data; the cross-modal knowledge fusion module is connected with the preprocessing module, fuses data and large language model general knowledge, and performs mining association by means of a cross-modal learning algorithm to form a localized cross-modal knowledge graph; the model fine-tuning module is connected with the fusion module and uses the map to finely tune the large language model; the evaluation feedback module is connected with the fine tuning module and evaluates the localization performance of the model. Through multi-modal data integration, dynamic preprocessing, deep knowledge fusion, efficient fine adjustment and intelligent feedback, various problems in the large language model localization process are systematically solved, and the expression of the model in dialect understanding, cultural questions and answers and cross-modal tasks is remarkably improved.

The invention discloses a video stream dynamic fragmentation encryption and block chain evidence storage method, and relates to the technical field of video content security, and the method comprises the steps: calculating a color histogram difference value and an optical flow vector change rate between adjacent frames of an input video, marking the difference value as a scene switching point when the difference value exceeds a preset threshold value, and storing the scene switching point; the method comprises the following steps: preliminarily dividing a video into a plurality of scene segments according to scene switching points, performing content complexity evaluation on the scene segments, calculating gray level co-occurrence matrix characteristics of each frame of image through texture density analysis, calculating edge complexity to extract the number and distribution of Canny edges, and performing motion vector statistics to analyze the size and direction of inter-frame object displacement. The change rate between adjacent pixels in the color space is measured according to the color change gradient; the video stream dynamic fragment encryption and block chain evidence storage method is suitable for video contents of different types and complexities, and has relatively high detection accuracy and robustness.

The invention relates to the technical field of live broadcast, and discloses a live broadcast scene perception recommendation system, which is characterized in that a data acquisition module is used for acquiring multi-source data in a live broadcast process in real time; the scene analysis module is connected with the data acquisition module and is used for receiving and integrating the acquired data and analyzing the live broadcast scene; the user portrait construction module is used for tracking a behavior track of a user on a live broadcast platform in real time, and constructing a dynamic user portrait in combination with historical watching data, a collection record, a consumption record and an interactive behavior in current live broadcast; a recommendation strategy generation module generates a personalized recommendation strategy according to the result of the scene analysis module and the user portrait constructed by the user portrait construction module; the recommendation content pushing module pushes the recommendation content generated by the recommendation strategy generation module to the user; according to the method, personalized and precise recommendation is realized, and the watching experience and participation degree of the user are effectively improved.

The invention overcomes the constraints imposed by conventional wireless communication standards by introducing a physical-layer optimized architecture that decouples transmission control from any single protocol. Rather than being confined by the predefined behaviors of LTE, 5G NR, Wi-Fi, or NB-IoT, the system implements a unified control system that dynamically manages radio parameters—such as modulation, coding, and power—based on real-time link quality and network context. This cross-standard, multimode capability effectively supersedes traditional standard-driven implementations, enabling adaptive, low-latency, and spectrum-efficient communication in complex heterogeneous environments.The system dynamically controls radio access network (RAN) and physical layer parameters, including carrier aggregation, dynamic spectrum allocation, modulation and coding scheme (MCS) adaptation, beamforming configuration, channel coding, transmit power control, and frequency selection. The system architecture includes multi-mode base stations, relay nodes, and edge access points that support inter-RAT handover, fast radio link recovery, and seamless mobility across diverse wireless technologies.

An electronic device is provided. The electronic device includes a memory, and at least one processor electrically connected to the memory, wherein the at least one processor is configured to obtain a video including an image and an audio, obtain information on at least one object included in the image from the image, obtain a visual feature of the at least one object, based on the image and the information on the at least one object, obtain a spectrogram of the audio, obtain an audio feature of the at least one object from the spectrogram of the audio, combine the visual feature and the audio feature, obtain, based on the combined visual feature and audio feature, information on a position of the at least one object the information indicating the position of the at least one object in the image, obtain an audio part corresponding to the at least one object in the audio, based on the combined visual feature and audio feature, and store, in the memory, the information on the position of the at least one object and the audio part corresponding to the at least one object.

The invention provides a video plot generation and scene synthesis method and system based on natural language processing, and relates to the technical field of video generation, and the method comprises the steps: receiving a script text to construct a multilayer scene map, extracting keywords to calculate semantic relevancy, executing feature decomposition and reconstruction to obtain a scene synthesis vector, and generating a video scene based on distance measurement. And extracting spatio-temporal features by using the feature pyramid, executing adaptive feature fusion, segmenting the video by applying a self-attention mechanism, and after a transition effect is inserted, performing style migration to output a finished video.

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 the technical field of big data, and discloses an audio and video monitoring and early warning method and system based on multi-modal model driving, and the method comprises the steps: S1, collecting audio and video signals in a monitoring scene, and extracting the energy distribution characteristics of the audio and video signals, wherein the energy distribution characteristics at least comprise the frequency energy distribution of the audio signal and the brightness and color change rate of the video signal; s2, multi-modal feature extraction of energy distribution features is carried out based on audio and video signals, and environment background feature vectors are constructed in combination with environment background information; s3, dynamically adjusting anomaly detection thresholds of the audio signal and the video signal based on the environmental background feature vector; s4, comparing the audio and video signal energy distribution characteristics extracted in real time with an anomaly detection threshold value, and when the audio and video signal energy distribution characteristics exceed the range of the anomaly detection threshold value, determining an abnormal event and obtaining early warning information; and S5, sending the early warning information from the edge equipment to a monitoring center through a low-bandwidth communication protocol.

The invention discloses an e-commerce live broadcast real-time interaction quality evaluation system based on edge calculation, and relates to the technical field of e-commerce live broadcast, and the system comprises a multi-modal interaction data collection module which collects multi-modal interaction data of a live broadcast stream in real time through a distributed edge calculation node cluster, and constructs a multi-dimensional quality feature vector, the multi-modal interaction data comprises a video coding parameter, an audio quality index, user interaction behavior data and network transmission state data; according to the invention, the distributed edge computing node cluster collects the multi-modal interaction data of the live stream in real time, the lightweight space-time attention neural network model carries out data fusion processing, the deep reinforcement learning network generates a quality optimization scheme, and the adaptive fuzzy inference system corrects the optimization scheme in real time. And dynamic parameter adjustment is carried out in combination with network bandwidth fluctuation and a terminal device resource state, so that the effect of accurately and comprehensively evaluating the e-commerce live broadcast interaction quality in real time is achieved.

The invention relates to the technical field of user interaction, in particular to a live broadcast bullet screen real-time feedback method and system based on interaction semantic matching, and the method comprises the steps: receiving and caching user bullet screens in real time, carrying out the density analysis and priority evaluation of bullet screen flows through the combination of filtering and resource optimization rules, dynamically adjusting the response frequency, and screening key bullet screens; a distillation processing mechanism intention unit is introduced, a user intention label in a key bullet screen is extracted, a vector is generated, historical behavior data of a sending user is obtained at the same time, the intention label, the vector and current real-time scene data of a live broadcast room are fused, and a bullet screen vector is constructed; performing similarity calculation in combination with the live broadcast content and user historical behaviors to obtain first feedback content; based on a reinforcement learning sentiment analysis strategy, combining user preference memory to generate second feedback content; and embedding the second feedback content into the current live broadcast interface through the client. According to the invention, real-time forward feedback and intelligent guidance of the live broadcast bullet screen are realized through interactive semantic matching.

The invention discloses a full-process AI image creation method and system based on diffusion type image generation, and relates to the field of AI image creation, and the method comprises the steps: obtaining a natural language creation instruction inputted by a user, and analyzing and extracting a main body element data set and a visual style label data set; generating a sequential task sequence including script generation, split mirror design, picture generation and video synthesis; generating a script frame and a split script data set through the large model; calculating the semantic similarity between the split content and the main body element, and performing supplementary optimization; generating a diffusion model cue word and obtaining rendering parameters to generate a rendered picture; the video is synthesized after the visual coverage is verified, and final output is achieved through feedback adjustment; according to the method, a complete control link from creation guiding to generation output can be constructed, and the customized generation requirement in a complex creation scene is met.

The invention discloses a video stream processing method for dynamic Gaussian compression and adaptive code rate regulation, which is suitable for scenes such as virtual reality, augmented reality and three-dimensional video, and comprises the following steps: S1, Gaussian attribute modeling and initialization; s2, constructing a binary hash grid; s3, constructing a deformation prediction network; s4, designing a mask pruning mechanism; s5, entropy modeling and arithmetic coding and decoding module design; s6, model training; and S7, video stream transmission under multiple code rates. According to the method, a unified scheme combining Gaussian volume cloud coding and adaptive video transmission is proposed for the first time, the video data storage and transmission cost is remarkably reduced, and the comprehensive performance superior to that of an existing method is obtained on multiple real and synthetic data sets.

Some embodiments of a method may include: obtaining a MPEG Avatar Representation Format (MARF)-based file; parsing the MARF-based file into a data structure; selecting an asset from the data structure; verifying the asset comprises a controller set property; obtaining controller set data corresponding to the controller set property; obtaining container data using the controller set data; determining a mime type using the controller set data; decoding the container data using the mime type; obtaining a mesh and a skeleton using the container data; and animating an avatar corresponding to the mesh and the skeleton.

A system comprising security devices and a computing device. The security devices may each be configured to detect an event in response to video frames and audio and communicate the video frames, the audio and a notification of the event. The computing device may be configured to execute one or more steps, the steps comprising performing a classification of the notifications of the events into a plurality of event levels and displaying a list of the notifications of the events according to the classification. Each of the plurality of event levels determined to be high priority events are added to a top of the list as the high priority events are detected. Each of the plurality of event levels determined to be a lower priority than the high priority events are added to the list below the high priority events as the events with the lower priority are detected.

The invention relates to the technical field of audio and video control, and discloses an audio and video player control method based on a voice instruction. The method comprises the steps that an original voice instruction stream of a user is collected, the instruction stream comprises a time domain audio signal sequence, an environment noise spectrum and user pronunciation characteristic parameters, and voice information can be comprehensively captured; multi-modal instruction analysis processing is carried out on the original voice instruction stream, a structured control instruction set containing acoustic control intention identification, semantic operation object description and context correlation parameters is generated, and the analysis precision is improved; then executing player state adaptation based on the set, generating a dynamic control response sequence containing an equipment state adjustment command, a media content positioning parameter and an interface interaction logic identifier, driving a player to execute a multi-dimensional control operation and generating real-time play control effect feedback data; and finally, multi-modal analysis parameters are optimized according to feedback data, a self-adaptive instruction analysis strategy is generated, and the control experience of a user on the audio and video player is optimized.

The invention relates to the technical field of equipment operation and maintenance, can be applied to business scenes of financial science and technology, medical health and the like, and discloses a multi-terminal scene synchronization control method, which comprises the following steps: a control terminal receives current virtual scene position and current virtual scene view angle information sent when a user terminal starts a virtual scene, and generates a synchronization preview picture based on the information; the control terminal triggers an anti-control mode and sends an operation authority forbidding instruction to the user terminal, and the user terminal forbids an operation input signal acquisition function after receiving the instruction; and the control terminal sets a target virtual scene position and a target virtual scene view angle parameter of the user terminal through the interactive interface, generates a rendering execution instruction based on the target parameter, and controls the user terminal to load the instruction to complete virtual scene rendering. The target virtual scene position and the view angle parameter of the user terminal are set at the control end, dynamic guidance and picture control of the control end on the user terminal are achieved, and view angle conflicts are avoided in cooperation with an operation authority forbidding mechanism.