This article connects mobile terminals through improved network protocols, and connects monitoring devices through improved network protocols in the local area network. At the same time, it can convert mobile terminal requests into requests that comply with improved network protocols and forward them to monitoring devices. Method of three-dimensional feature extraction based on dynamic data. First, normalize the coordinates and scales of the three-dimensional calculus model, and use two kinds of data (voxel representation and pixel representation) to deal with video object recognition. Then construct a convolutional neural network as an input to the network, extract visual information and geometric information, and use deep learning to improve the recognition ability of single-modal features. Based on the convolutional neural network, the geometric descriptor and the view descriptor of the three-dimensional calculus model are extracted separately, and the two feature descriptors are fused in a network to find the association between the patterns. After forming the fused feature descriptor, it is applied to micro Classification and retrieval of integral 3D features. The multi-feature fusion layer can not only learn more distinguishing features of the two descriptors, but also supplement relevant information between the two descriptors. Compared with using these two representations alone, this method produces a much better classifier and improves retrieval efficiency.

本文通过改进的网络协议连接移动终端，并通过局域网中的改进的网络协议连接监视设备。同时，它可以将移动终端请求转换为符合改进的网络协议的请求，并将其转发给监视设备。基于动态数据的三维特征提取方法。首先，规范三维演算模型的坐标和比例，并使用两种数据（体素表示和像素表示）处理视频对象识别。然后构建卷积神经网络作为该网络的输入，提取视觉信息和几何信息，并使用深度学习提高单模态特征的识别能力。基于卷积神经网络，分别提取三维演算模型的几何描述符和视图描述符，并将两个特征描述符融合到网络中以找到模式之间的关联。形成融合的特征描述符后，将其应用于微观分类和整体3D特征的检索。多特征融合层不仅可以学习两个描述符的更多区别特征，而且可以补充两个描述符之间的相关信息。与仅使用这两种表示形式相比，此方法产生了更好的分类器并提高了检索效率。形成融合的特征描述符后，将其应用于微观分类和整体3D特征的检索。多特征融合层不仅可以学习两个描述符的更多区别特征，而且可以补充两个描述符之间的相关信息。与仅使用这两种表示形式相比，此方法产生了更好的分类器并提高了检索效率。形成融合的特征描述符后，将其应用于微观分类和整体3D特征的检索。多特征融合层不仅可以学习两个描述符的更多区别特征，而且可以补充两个描述符之间的相关信息。与仅使用这两种表示形式相比，此方法产生了更好的分类器并提高了检索效率。