面向工业互联网的语义编码传输方法及应用

Semantic coding transmission method and application for industrial internet

通过将语义通信技术引入工业网络,构建了一个面向工业互联网的语义编码传输系统。系统中设计了语义编解码器以提取信源中的语义信息,相对于传统通信系统,基于语义信息的通信有更高的信息压缩效率与更高的符号差错容忍能力。同时引入信源信道联合编解码器,以信源信道联合编码的方式将语义信息转化为信道符号传输,进一步提升系统对工业网络通信资源的利用效率。所有编解码器均构建在深度神经网络架构Transformer上,确保了编解码器对语义信息的理解能力及系统的泛化能力。在工业药品生产场景中,对该系统进行测试,结果显示:相较于传统通信方案,该语义编码传输系统在图像重建质量和传输处理速度方面均有显著提升。且系统对下游任务的性能影响极小,保证了工业生产中如缺陷检测等关键任务的准确性。

The integration of semantic communication technology into industrial networks is proposed and a semantic coding transmission system for the industrial internet is established.Within this system,a semantic codec is developed to extract semantic information from the source.Compared with traditional communication systems,semantic-based communication offers higher information compression efficiency and greater symbol error tolerance.Furthermore,a co-codec for joint source-channel coding have been introduced,which enhances the utilization efficiency of industrial network communication resources through co-coding techniques.All codecs are built upon leading deep neural network architecture“Transformer”,ensuring their ability to comprehend semantic information and maintain network universality.The system has been tested in real-world scenarios within industrial drug production facilities,demonstrating significant improvements in image reconstruction quality and transmission processing speed compared to traditional communication schemes.Additionally,minimal impact on downstream task performance ensures accuracy in critical tasks such as defect detection in industrial production processes.