## 2 Hierarchical Dispatch Carrying Mechanism
### Neural Network-Based Hierarchical Scheduling Carrying Mechanism  
The application of network slicing is greatly significant to the construction of 5G private networks in power scenarios. In the intelligent processing of the power access network, the edge access room can achieve intelligent sensing and prediction by automatically identifying different types of access terminal services, such as power distribution automation and video monitoring; by automatically adapting them to the appropriate communication lines and virtual network slices according to the different communication needs of the services; and, lastly, by achieving intelligent classification. Service traffic, flow direction and network congestion are continuously monitored and analysed, the network is dispatched based on predicted future conditions, and traffic adjustment and routing optimisation of network slices are carried out. Terminal data from different regions enter the edge data centre, and for power slicing management needs, as well as for power service needs, the power intelligent gateway is used as the edge computing node mainly responsible for receiving and forwarding data. At the same time, the characteristics of the electric power services are analysed and classified according to rules or data models, providing basic slice configuration parameters for network slice carrying planning under different conditions and generating various types of customized slices. Under the condition of considering the soft isolation of base station equipment, the mode of slice priority scheduling is proposed to ensure the resource occupation of high-priority users through the hierarchical classification of power services characteristics. Then, combined with service traffic prediction, it actively generates dynamic adjustment to improve the transmission efficiency of the whole network [27].  
This paper implements feature extraction of service data based on 1D CNN and carries out service classification matching to satisfy the classification of differentiated power 5G services according to different service features and mapping deployment with power 5G slices. At the same time, the dynamic prediction of service traffic of network slices based on LSTM can better satisfy network slices according to different service conditions and service requirements for different units of differentiated power traffic network slicing services to meet the carrying of power 5G traffic network slices. At this edge computing node, the deployment of power 5G slicing service mapping for the access network is completed. Afterward, by developing a slicing network management system, network resource management and monitoring of the wireless network, bearer network, and core network for 5G slicing are all realized. In order to realize the docking of the bearer network and 5G slicing within the enterprise, a bearer network slicing device is developed in order to realize the end-to-end slicing establishment between the traffic terminal, the 5G network, the enterprise intranet and the traffic system, and the overall structure is shown in Figure 1, where the classifications and traffic forecasts mentioned are marked in red.  
The 5G network slicing service carrying is the process of deploying power service to 5G slices. In the process of deployment and implementation, the demand described in the hierarchical classification of the grid service is transformed into the service level indicator of network slices in the 5G network. The network slice Service Level Agreement (SLA) is part of the service agreement signed between the operator and the network slice customer, and the network slice SLA contains the requirements of the network slice customer for the services and network provided by the operator. On the basis of the service level classification description, in order for the control equipment of the 5G network to understand the power service requirements, it is also necessary to translate the power service statute into the corresponding SLA quantitative model.