“倒梯形矢量法”数据压缩的智能家居底层控制系统

Smart House Bottom Control System Based on Data Compression By “Inverted Trapezoidal Vector Method”

中文关键词:  倒梯形矢量法  红外学习  压缩算法  智能家居

英文关键词:inverted trapezoidal vector method  infrared learning  compression algorithm  smart house

中文摘要:

      为了向智能家居控制提供底层支持，得到具有良好的稳定性和可扩展性的底层设备控制模式，加强红外学习的适用范围，优化红外数据存储和传输效率，研究了红外学习、数据压缩和控制通信协议。系统将底层传感器、红外学习复制等进行功能封装，通过自定义的编码协议供上层设备调用。利用波形记录法实现万能红外学习，提出了“倒梯形矢量法”用于数据压缩，可同时保障优化的压缩率和较小的算法复杂度。理论分析证明其红外数据压缩率最优可达约63%，系统具有可靠性强、响应速度快、可扩展性强的特点。

英文摘要:

      In order to provide underlying support to smart home control system and get the underlying device control mode with good stability and scalability,strengthening the scope of infrared learning,optimizing infrared data storage and transmission efficiency,infrared learning,data compression and control communication protocol are investigated.The system functionally encapsulates the underlying sensors and infrared learning copy,which can be invoked by the upper equipment through self-defining coding agreement.The system can achieve universal infrared study through the utilization of waveform recording,and the “inverted-trapezoid vector method” is put forward,which can be used for data compression.At the same time,the system can ensure optimal compression ratio and less complexity.Theoretical analysis shows that the optimal infrared data compression ratio is up to about 6.3%.The system has the characteristics of high reliability,fast response speed and strong scalability.