Information Pushing System Using Real-Time Multimedia Content Generation
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Solution Overview
Problem
Current methods for pushing information using artificial intelligence models are inefficient, as they primarily rely on pre-existing information, leading to one-sided content and requiring users to search extensively for comprehensive real-time information.
Innovation Solution
A method and apparatus for pushing information that involves acquiring real-time multimedia contents for each broadcast subject, generating text information based on these contents, and using a content generation model to integrate this information and create a target broadcast content, which is then pushed to users when specific trigger conditions are met.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If pre-existing information is used for pushing content, then information delivery is simple and fast, but the information is one-sided and users cannot acquire comprehensive real-time information efficiently
Solution Approach 1:
The system performs preliminary actions by acquiring and processing real-time multimedia contents from multiple information sources before user requests, generating text information and training the content generation model in advance. This allows comprehensive information to be ready for immediate delivery when users need it, resolving the contradiction between fast delivery and comprehensive content.
Solution Approach 2:
The system combines multiple different information sources (text, images, videos, audio) into a composite information structure. By integrating diverse multimedia contents from various sources and processing them through the content generation model, the system creates comprehensive broadcast content that maintains both completeness and delivery efficiency.
2Loss of information
If multiple real-time multimedia contents from diverse sources are processed, then comprehensive information is achieved, but processing complexity and time consumption increase
Solution Approach 1:
The system extracts only the essential and relevant information from multiple multimedia sources through structured processing. The content generation model extracts key features and generates condensed text information, removing redundant data while preserving comprehensive content. This reduces processing complexity while maintaining information completeness.
Solution Approach 2:
The content generation model serves as an intermediary that mediates between multiple diverse information sources and the final broadcast content. It standardizes and integrates various multimedia formats into a unified structure, simplifying the processing system while maintaining comprehensive information delivery.
3Loss of information
If real-time multimedia contents are acquired and processed through content generation model, then comprehensive and readable content is produced, but processing time and computational resources increase
Solution Approach 1:
The system performs preliminary processing of real-time multimedia contents, generating text information and training the content generation model in advance. This preparation work is done before actual content delivery needs occur, reducing processing time when users request information while maintaining high content quality and readability.
Solution Approach 2:
The content generation model transforms multiple parameters of input multimedia data (format, structure, detail level) into optimized output parameters that ensure readability and quality. By adjusting these parameters efficiently, the system produces high-quality content with reduced processing time compared to traditional methods.
Data Source
AI summary
A method of pushing information, a computer device and a storage medium are provided. The method includes: acquiring a plurality of real-time multimedia contents corresponding to each broadcast subject of at least one broadcast subject, wherein the multimedia contents correspond to at least one of a plurality of genres; for each broadcast subject, generating text information corresponding to the each broadcast subject based on the multimedia contents corresponding to the each broadcast subject; inputting a constraint condition and the text information corresponding to the each broadcast subject into a content generation model to obtain a target broadcast content corresponding to the each broadcast subject; and in response to satisfying a push trigger condition of a target broadcast subject of the at least one broadcast subject, pushing a target broadcast content under the target broadcast subject to a target user.