摘要
提出一套基于深度学习的广播电视系统故障预测模型优化策略,通过优化特征提取、改进双向长短时记忆网络(Bidirectional Long Short Term Memory,Bi-LSTM)结构并引入多头注意力机制,实现对广播电视系统设备运行数据的深度建模与精准预测。实验结果表明,优化后的故障预测模型在预测准确性、健壮性及实时性方面显著优于传统模型,可为广播电视系统的智能化故障管理提供有力支持。
This study presents a comprehensive optimization strategy for a fault prediction model tailored to broadcasting and television systems,leveraging advanced deep learning techniques.The proposed approach integrates multiple enhancements,including refined feature extraction processes,structural improvements to the Bidirectional Long Short-Term Memory(Bi-LSTM)network,and the adoption of multi-head attention mechanisms.These enhancements enable the model to deeply analyze and accurately predict faults in operational data from broadcasting and television equipment.By addressing the challenges posed by high-dimensional,dynamic,and noisy data,the optimized model achieves superior performance compared to traditional approaches.Specifically,it demonstrates marked improvements in prediction accuracy,robustness under varying operational conditions,and real-time response capabilities.Experimental validation underscores its practical value and effectiveness,offering a robust solution for the intelligent fault management of broadcasting and television systems.
作者
孙华
王猛
SUN Hua;WANG Meng(Zoucheng Convergent Media Center,Zoucheng 273500,China)
出处
《电视技术》
2025年第7期216-218,共3页
Video Engineering
关键词
深度学习
广播电视系统
故障预测模型
优化策略
双向长短时记忆网络(Bi-LSTM)
deep learning
broadcasting and television system
fault prediction model
optimization strategy
Bidirectional Long Short Term Memory Network(Bi-LSTM)
