为解决工控行业中大量非标准协议带来的传统协议识别方法不适用性问题，通过基于边缘分布式深度学习的方法研究了工控协议识别技术。提出了一种基于卷积神经网络技术的工控协议识别方法：获取网络中真实工控协议数据并进行预处理，根据协议特性选择合适的卷积神经网络模型，隐式提取协议本质特征，实现对7种工控协议的识别分类，准确率可达99.92%。此外，将工控协议识别模型部署至网络边缘，采用数据并行分布式策略，在边缘服务器计算集群中开展协同训练，模型训练效率提高1.87 ~ 2.81倍，同时保持高准确率。结果表明：该方法有效提高了工控协议识别准确率；显著提升了模型训练效率；适合部署于边缘计算环境。可见，该方法在工控协议识别性能优化方面具有重要意义。

To address the limitations of traditional protocol recognition methods caused by the presence of numerous non-standard protocols in IC (industrial control) sector, a method based on edge-distributed deep learning was studied to enhance IC protocol recognition technology. A recognition method based on CNN (convolutional neural networks) was proposed: real IC protocol data from the network was collected and preprocessed, and an appropriate CNN model was selected according to protocol characteristics to implicitly extract the essential features of the protocols. This achieved classification and recognition of seven types of IC protocols with an accuracy of up to 99.92%. Furthermore, the IC protocol recognition model was deployed at the network edge, leveraging a data-parallel distributed strategy for collaborative training within an edge server computing cluster. This improved the training efficiency of the model by 1.87 ~ 2.81 times while maintaining high accuracy. The results show that this method significantly improves the accuracy of IC protocol recognition, greatly enhances model training efficiency, and is well-suited for deployment in edge computing environments. It is evident that this method has significant value in optimizing IC protocol recognition performance.