The construction of electric power internet of things is an important link of the transformation to the energy internet

and it is important to ensure the stability of the operation of the transmission line monitoring system in remote areas to build a new type of power system. Transmission lines in remote areas have such characteristics as weak mobile network coverage

long distance from the access point of power backbone network and difficulty in taking energy from equipment. Considering the complex computing tasks such as real-time video analysis in this network

the traditional ‘cloud-end’ mode cannot meet the existing time delay and energy consumption requirements. Considering the characteristics of remote areas in this paper

the ‘solar energy collection + battery’ mode is used to supply energy to the equipment

and the multi-access edge computing (MEC) technology is used in order to further reduce the energy consumption due to transmission data. Furthermore

the transmission line monitoring system network and each node entity in the network are modeled

and a MEC service deployment scheme based on K-means and weights is proposed to provide computational offloading services for nodes under the constraints of computational task size and energy consumption based on the information of nodes' location and task volume and energy. The simulation results show that the deployment scheme reduces system latency and energy consumption while ensuring load balancing of MEC nodes.