Method and system for radio and television public opinion analysis based on deep learning technology

A technology of deep learning, broadcasting and television, applied in the direction of electrical components, electrical digital data processing, selective content distribution, etc., can solve the problems of lack of full automation and heavy manual workload

Inactive Publication Date: 2016-11-09
CHENGDU SANLING KAITIAN COMM IND
View PDF8 Cites 24 Cited by
  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

Traditional radio and television supervision is mainly limited to signal quality monitoring and broadcast content comparison and verification. Since the supervision has not been fully automated, the manual workload is relatively large

Method used

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
View more

Image

Smart Image Click on the blue labels to locate them in the text.
Viewing Examples
Smart Image
  • Method and system for radio and television public opinion analysis based on deep learning technology
  • Method and system for radio and television public opinion analysis based on deep learning technology

Examples

Experimental program
Comparison scheme
Effect test

Embodiment Construction

[0027] Describe technical scheme of the present invention in further detail below in conjunction with accompanying drawing: as figure 1 As shown, a radio and television public opinion analysis method based on deep learning technology, which includes deep learning analysis steps and visual technology analysis steps;

[0028] The described deep learning analysis step comprises the following sub-steps:

[0029] S11: Establish a public opinion data model: On parallel computing equipment, use deep learning technology to train and learn a large number of picture materials and video materials including special scenes, corporate icons, specific landmarks, objects, and sensitive people, and finally form a special Scenes, corporate icons, specific landmarks, objects, and sensitive figures based on image, voice and text classification data models; the deep learning technology includes deep learning-based voice recognition and deep learning-based ORC optical character recognition; the des...

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
Login to view more

PUM

No PUM Login to view more

Abstract

The invention discloses a method and system for radio and television public opinion analysis based on a deep learning technology. The method comprises the process of extraction and identification of public opinion information data based on the deep learning technology: first a data model is generated by repeated learning and training of plenty of material data (including images, videos, voices and optical character recognition (OCR) optical characters), and an image feature library is generated in combination with a computer vision technology (local feature grabbing and matching). To-be-monitored data (including audios, videos and pictures) is obtained from communication media such as cable digital television, wired radio television, satellite television and wireless radio television to serve as input of a deep learning data model, and the established data model is utilized to identify the to-be-monitored data. The plenty of material data forms a characteristic fingerprint database in combination with a computer vision technology, characteristic values of the to-be-monitored data are compared with data in the characteristic fingerprint database, and if the comparison is successful, it is thought that the to-be-monitored data contains public opinion information.

Description

technical field [0001] The invention relates to the field of public opinion monitoring, in particular to a radio and television public opinion analysis method and system based on deep learning technology. Background technique [0002] With the rapid development of network technology and the diversification of radio and television transmission means, radio and television has formed a transmission and coverage network that runs through wireless broadcasting, satellite coverage, cable transmission, and network connectivity simultaneously across the country. The supervision of radio and television has been paid more and more attention by the relevant state departments. Traditional radio and television supervision is mainly limited to signal quality monitoring and broadcast content comparison and verification. Since the supervision has not been fully automated, the manual workload is relatively large. With the development of information technology and the deepening of supervisio...

Claims

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
Login to view more

Application Information

Patent Timeline
no application Login to view more
Patent Type & Authority Applications(China)
IPC IPC(8): G06F17/30G06K9/62H04N21/234H04N21/235
CPCH04N21/23418H04N21/235G06F16/951G06F18/24
Inventor 曾兵贾宇郭先会何海诣董文杰
Owner CHENGDU SANLING KAITIAN COMM IND
Who we serve
  • R&D Engineer
  • R&D Manager
  • IP Professional
Why Eureka
  • Industry Leading Data Capabilities
  • Powerful AI technology
  • Patent DNA Extraction
Social media
Try Eureka
PatSnap group products

Browse by: Latest US Patents, China's latest patents, Technical Efficacy Thesaurus, Application Domain, Technology Topic.

© 2024 PatSnap. All rights reserved.Legal|Privacy policy|Modern Slavery Act Transparency Statement|Sitemap