Multimedia classroom teaching equipment state fault analysis and early warning method
By constructing a teaching semantic intensity field model and using multi-source data fusion technology, and dynamically adjusting the prediction threshold, the problem of rigid fault warning strategies in the intelligent operation and maintenance system of multimedia classrooms is solved. This enables real-time response to teaching activities and accurate prediction of equipment status, thereby improving the continuity of teaching and the efficiency of resource support.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- GUANGZHOU TIANZHISHUN INFORMATION TECHNOLOGY CO LTD
- Filing Date
- 2026-04-16
- Publication Date
- 2026-07-17
AI Technical Summary
The existing intelligent operation and maintenance system for multimedia classrooms cannot capture changes in cognitive load and teacher-student interaction density in real time during teaching activities. This results in rigid fault warning strategies that cannot adapt to real-time changes in teaching activities and sudden cognitive peaks, affecting the continuity of teaching and the efficiency of resource support.
A teaching semantic intensity field model is constructed. By fusing multi-source heterogeneous data and using a dual-channel attention mechanism, the characteristics of equipment degradation trend and semantic intensity modulation effect are extracted. The resulting device state representation vector with fused semantic perception is generated and encoded using a multi-head self-attention mechanism and a feedforward neural network. The prediction threshold is dynamically adjusted and emergency warning response instructions are generated.
It significantly improves the scenario sensitivity and business relevance of fault prediction, enabling early identification of potential failure trends and initiation of differentiated responses, ensuring teaching continuity and equipment availability, and reducing operation and maintenance complexity.
Smart Images
Figure CN122413293A_ABST