An AI robot self-training system based on a traditional opera action library

By constructing a library of opera movements and an AI robot self-training system, and utilizing high-precision motion capture and deep learning algorithms, the problems of intelligent and personalized opera teaching have been solved, improving learning efficiency and cultural dissemination effectiveness.

CN122154746APending Publication Date: 2026-06-05SHANGHAI FEILAIFEIQU NEW MEDIA EXHIBITION DESIGN CO LTD

Patent Information

Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
SHANGHAI FEILAIFEIQU NEW MEDIA EXHIBITION DESIGN CO LTD
Filing Date
2025-11-17
Publication Date
2026-06-05

AI Technical Summary

Technical Problem

Existing opera teaching aids lack intelligence and personalization, and cannot provide customized teaching based on learners' characteristics and needs. AI systems lack self-training mechanisms and movement data recording and management in opera movement learning, which is not conducive to data sharing and utilization.

Method used

An AI robot self-training system based on a traditional Chinese opera motion library is constructed, including modules for motion capture, data preprocessing, classification and labeling, database management, AI robot learning, and self-training optimization. High-precision motion capture equipment, deep learning, and reinforcement learning algorithms are used to achieve accurate recording, learning, and optimization of motion data.

Benefits of technology

It improves the accuracy and fluency of learning opera movements, reduces the cost of inheriting traditional opera, promotes the dissemination and internationalization of opera culture, breaks the limitations of time and space, and provides personalized teaching solutions.

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Abstract

The application discloses an AI robot self-training system based on a traditional opera action library, and aims to solve many problems existing in traditional opera inheritance and teaching. The system mainly comprises a traditional opera action library construction module, an AI robot learning module and a self-training optimization module. By using deep learning and reinforcement learning algorithms, the AI robot can autonomously learn and optimize actions, greatly improving the learning efficiency. Compared with traditional traditional opera teaching methods, the AI robot can tirelessly learn and train, and can master a large number of traditional opera actions in a short time. The system can also be applied to the traditional opera training market, provide a new teaching tool for training institutions, reduce training costs and improve training quality.
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