Optimization of Distribution Network Data Management Mode Based on BP Neural Network Algorithm and Atlas Technology

Distribution network is the link between power system and customers, and it is an important municipal infrastructure. Its safe and stable operation is closely related to economic development, public safety, production and life, and plays an important role in the whole power system. Distribution equipment is at the bottom of the power system, which is the part closest to users. Whether it can run safely, stably and reliably directly affects the quality of power supply for users. With the rapid growth of smart grid construction, the number of real-time data analysis in power grid is also increasing. It is a very arduous task to analyze, process, allocate, manage and comprehensively control a large number of electric energy resources and ensure the safe and efficient implementation of electric power production. In this paper, a method of distribution network data collection and management based on back propagation neural network (BPNN) algorithm and map technology is proposed, which provides theoretical and data support for the optimization of distribution network data management mode. The experimental results show that, compared with ID3 algorithm, BPNN has obvious advantages in the later stage of operation, and the error is reduced by 30.78%. By comparing the evaluation results of this model with ID3 algorithm, it is proved that this model is effective and feasible.