19 results about "Internet television" patented technology

The invention relates to the technical field of artificial intelligence, and discloses an internet television regulation and control method and device, a storage medium and electronic device.The method comprises the steps that network topology data, real-time performance parameters, user behavior data and environment data of nodes of an internet television are collected in real time through a space-time hypergraph convolutional network, a dynamic hypergraph model is constructed, and the network topology data of the nodes of the internet television, the real-time performance parameters of the nodes of the internet television and the environment data of the nodes of the internet television are obtained; extracting a spatial feature matrix and a time feature matrix of the nodes of the Internet television; performing feature fusion on the spatial feature matrix and the time feature matrix to generate a congestion probability prediction result of each node of the Internet television; and based on a congestion probability prediction result, combining historical flow data, generating a resource allocation optimal strategy by using multi-target Bayesian optimization, and training and updating a congestion prediction model by using a lightweight federated incremental learning framework. The Internet television regulation and control method provided by the invention is further improved in the aspects of network congestion prediction precision, real-time performance, energy consumption and the like.

The invention relates to the technical field of Internet television services, in particular to an OTT visual feature extraction system and method based on multi-modal agent driving, and the method comprises the steps: capturing a screen real-time video stream of equipment; processing the target advertisement image and the real-time video stream, and extracting double-flow heterogeneous visual features, including global content perception features and local geometric structure features, through a multi-modal visual perception model; executing a hierarchical matching algorithm, calculating and screening out candidate frames by using global content perception features, matching in the candidate frames by using local geometric structure features to establish a corresponding relation set containing all matched initial key points, performing spatial clustering on the set to separate out advertisement instances, and obtaining bounding boxes of the instances through geometric transformation calculation; and according to the bounding box, performing highlight display on the area where the target advertisement is located on the original video frame to generate a visual broadcast monitoring result. According to the invention, through multi-mode intelligent body driving, OTT advertisement visual feature extraction and broadcast monitoring are realized.

The invention relates to the technical field of poor quality positioning, and provides an Internet television program source poor quality positioning method and device. The method comprises the following steps: carrying out service dial testing on a program source data stream, and generating a program source potential quality abnormity alarm; carrying out soft probe acquisition on the program source quality data to generate a program source quality excellent rate; performing time sequence correlation on the program source potential quality abnormity alarm and the program source quality excellent rate, and identifying a program source poor quality event; performing simulation dial testing on the program source poor-quality event on the program source data stream to generate a program source poor-quality simulation result; and generating a program source poor quality positioning result based on the program source potential quality abnormity alarm, the program source quality excellent rate and the program source poor quality simulation result. According to the method and the device, the efficiency and the accuracy of poor quality positioning can be effectively improved, serious influence on user experience is avoided, and subsequent countermeasures can be abandoned when it is accurately determined that the user does not perceive poor quality of the program source actually, so that unnecessary resource waste is avoided.

The invention relates to an off-network user prediction method and device, computer equipment and a storage medium. Comprising the following steps: in a process that a target user uses an Internet television service by means of an Internet television receiving terminal, obtaining target operation data corresponding to the Internet television receiving terminal; determining target watching behavior data corresponding to the target user based on the target operation data; and inputting the target watching behavior data into a pre-trained off-network behavior prediction model to obtain an off-network probability corresponding to the target user, and when the off-network probability is greater than or equal to a preset threshold, determining the target user as an off-network user. On the basis, the method and the device have the advantages of advanced technologies such as machine learning, the influence of multiple factors on the user off-network behavior can be comprehensively considered, and the accuracy and the reliability of predicting the user off-network behavior are further improved.

1. The name of the design product: wisdom in the screen (TB-CE007). 2. The use of the design product: the product is used for wisdom in the screen, and the main functions are audio and video interaction, Internet television playing, smart home control, home security, office video conference, home network learning, etc. 3. The design points of the design product: in shape. 4. The picture or photo that best indicates the design points: perspective view 1.

The application discloses a method, device and equipment for detecting quality difference of a bearing network and a computer storage medium. The method comprises the following steps: determining the degrees of nodes in the bearing network, determining key performance indicators of the bearing network which affect Internet TV services, determining objective weights corresponding to the key performance indicators, performing secondary weighting correction on the objective weights according to preset subjective estimation weights to obtain modified weights, determining overall deviation degrees of the key performance indicators corresponding to a current node in each node, determining a target quality evaluation value of the current node according to the modified weights and the overall deviation degrees, and determining a comprehensive evaluation value of the current node on the quality difference of the Internet TV services according to the degrees of the nodes in the bearing network and the target quality evaluation value. The application can effectively evaluate the influence of the bearing network quality on the Internet TV services while saving costs.

This application relates to the fields of artificial intelligence and human-computer interaction technology, and discloses an internet TV voice interaction method, device, and related products. The method includes: recognizing user-input voice data to generate text data; processing the text data using a pre-trained intent recognition model to generate user intent; filtering multiple business processing agents based on the intent type and agent feature data of multiple business processing agents to obtain a target business processing agent; processing the text data using the target business processing agent to generate processing result data; and outputting the processing result data in voice, image / text, or video format. This application can significantly improve the semantic understanding ability of TV voice assistants, and by filtering multiple business processing agents according to intent type, accurately matching processing units to handle diverse tasks, ultimately achieving a more stable and efficient internet TV voice interaction effect.

The media player includes speaker hardware including communication devices and plays audio obtained from audio sources such as the Internet, television, and radio.

The invention relates to the technical field of internet television services, in particular to an OTT advertisement optimization system based on a pointing behavior of a remote controller, which comprises a data acquisition module for distinguishing a cruising state, a staring state and a shaking state and extracting a user intention track by adopting a track processing algorithm based on speed and spatial-temporal clustering; the semantic analysis module is used for identifying advertisement creative elements by means of first frame analysis tracking and the like and generating advertisement semantic element map data; and the instruction generation module is used for performing space-time correlation mapping on the user intention track and a semantic map, calculating element attention scores including high-cost activation times and staring micro-motion amplitudes, finally generating a creative optimization instruction, and automatically adjusting the layout of advertisement creative elements. According to the method, refined, quantifiable and automatic OTT advertisement closed-loop optimization is realized.

The invention discloses an IPTV (Internet Protocol Television) similar recommendation method based on media asset medium attributes and high-frequency weights, which comprises the following steps: classifying hot media asset data according to a preset first attribute parameter to obtain classified data, the hot media asset data being obtained from an external data source, and the hot media asset data being obtained from the external data source; and performing weight calculation on the classified data according to the list data of the external data source and the behavior data of the user to obtain a data weight, extracting hot media asset data required to be recommended from the classified data according to the data weight to obtain target media asset data, and returning the target media asset data to a display area of the network television in a paging manner. According to the invention, media asset resources can be intelligently recommended, manpower resources are reduced, and operation cost is reduced.

The application relates to the technical field of Internet TV service, in particular to an OTT advertisement optimization system based on a remote controller pointing behavior, which comprises a data acquisition module, a trajectory processing algorithm based on speed and space-time clustering is adopted, cruise state, gaze state and jitter state are distinguished, and user intention trajectory is extracted; a semantic analysis module identifies advertisement creative elements through first frame analysis tracking and other means, and generates advertisement semantic element map data; an instruction generation module performs space-time correlation mapping on the user intention trajectory and the semantic map, calculates element attention score containing high-cost activation frequency and gaze micro-motion amplitude, finally generates creative optimization instructions, and automatically adjusts the layout of advertisement creative elements. The application realizes fine, quantifiable and automatic OTT advertisement closed-loop optimization.

The media player includes speaker hardware including communication devices and plays audio obtained from audio sources such as the Internet, television, and radio.

This invention relates to the field of Internet television service technology, specifically to an OTT visual feature extraction system and method driven by a multimodal intelligent agent. The system includes: capturing a real-time video stream from a device's screen; processing the target advertisement image and the real-time video stream, extracting heterogeneous visual features from the dual streams using a multimodal visual perception model, including global content-aware features and local geometric structure features; executing a hierarchical matching algorithm, using the global content-aware features to calculate and filter candidate frames, establishing an initial keypoint correspondence set containing all matches within the candidate frames using local geometric structure features, spatially clustering the set to separate advertisement instances, and calculating the bounding boxes of the instances through geometric transformations; and highlighting the area where the target advertisement is located on the original video frame based on the bounding boxes to generate a visual monitoring result. This invention achieves OTT advertisement visual feature extraction and monitoring through multimodal intelligent agent-driven processing.

The media player includes speaker hardware including communication devices and plays audio obtained from audio sources such as the Internet, television, and radio.

The media player includes speaker hardware including communication devices and plays audio obtained from audio sources such as the Internet, television, and radio.

1. The name of the design product: wisdom in the screen (E8-R). 2. The use of the design product: the product is used for wisdom in the screen, and the main functions are audio and video interaction, Internet television playing, smart home control, home security, office video conference, home network learning, etc. 3. The design points of the design product: in shape. 4. The picture or photo that best indicates the design points: perspective view 1.

The application provides a user loss early warning method and device, equipment and a storage medium, and relates to the technical field of user operation. The method comprises: obtaining behavior data and device attribute data of a target user at multiple historical times for an Internet TV system; obtaining user time sequence data of the target user according to the behavior data and the device attribute data at the multiple historical times; and predicting, by using a loss early warning model, a loss probability and a loss reason of the target user according to the user time sequence data. The application can accurately depict the gradual process of user behavior from active to recession, solve the problem that a static model cannot capture dynamic changes, realize early identification and accurate attribution of loss risk, and thus intervene in advance.