## 2 Related Work  
For edge intelligence, AI provides technologies and methods for edge computing, and edge computing provides scenarios and platforms for AI. \"AI on edge\" focuses on how to build an AI model on the edge computing platform, mainly including model training and inference; \"AI for edge\" focuses on providing better solutions to key problems in edge computing with the help of advanced AI technology, mainly including task unloading and edge caching [15]. From the dimension of edge computing-enabled AI, an on-demand acceleration framework based on edge-end collaboration is proposed, aimed at the deployment of deep learning model on the edge of the network. From the dimension of edge computing empowered by AI, an adaptive edge service placement mechanism based on online learning and an edge service migration method based on the factor graph model are proposed, aimed at the placement of edge computing services [16]. [PERSON] [17] regards the deep neural network as a directed graph and optimizes it from two aspects of segmentation and simplification for a deep learning model. The optimization strategy is a trade-off between the model inference speed and accuracy. Edgeke [18] balances resource consumption and inference performance on resource-constrained edge devices. The neural network is compressed by knowledge distillation to reduce the demand for computing resources, and the early exit technology is used to provide a flexible computing method for the neural network; through the EdgeMI algorithm, the distributed computing of a deep neural network is realized on heterogeneous edge clusters, and the final performance acceleration ratio reaches 1.84x-3.57x. Software-defined computing gives birth to cloud computing, which allows the flexible allocation of computing resources. There are software-defined radios, software-defined networks, software-defined data centers, and so on. SDEC [19] involves abstracting (the logical relationship is abstracted into rules, and inferencing is carried out according to the real-time state), virtualizing (the device model is virtualized to realize the decoupling and separation of software, hardware, and services) and pooling (unified management, sharing, reuse, and cooperation) the physical equipment resources on the edge by using semantic description modeling, knowledge graphs, and other technologies. When the edge computing gateway software [20] processes a variety of IoT communication protocol messages (MQTT, OPC-UA, etc. ), it forwards them to local computing for data collection, analysis, and AI processing, according to the message routing rule engine, and responds quickly. SDN and virtualization technology optimize the edge computing architecture by defining the management and monitoring of computing, storage, data center, security, and other resources through software, to realize resource integration and management collaboration within multiple networks. The collaboration capability of SDN-based cloud edge network framework [21] is reflected in 1 EC-IaaS resource collaboration; 2 EC-PaaS management collaboration; 3 and EC-SaaS application service collaboration, in other words, the application layer, control layer, and infrastructure layer of SDN correspond to the three fields of application services, platform management, and resource facilities in the cloud-edge collaboration. In SDIoT architecture [22], combining SDN and IoT, the resource-limited applications can be efficiently managed at the edge, collecting sensor data from terminal nodes through SBI (southbound interface) and providing connectivity to the cloud service through the NBI (northbound interface). The embedded devices in the IIoT constitute the edge computing layer in the industrial internet. Heterogeneity is their most significant characteristic, low latency is the main motivation for industrial scenarios, and security is another driving force for the application of edge computing in industries.