Research on intelligent line selection and optimization scheme of transmission lines based on big data analysis and artificial intelligence

As the wave of data-driven informatization sweeps the world, society is moving from the information age (IT) to the data age (DT). GIS technology has become an important tool for complex regional development and prediction. However, the existing power grid design methods cannot meet the needs of improving production efficiency and controlling engineering costs. This paper proposes an intelligent line selection and optimization scheme for transmission lines based on big data analysis and artificial intelligence. The map model is designed using GIS multi-band raster maps, and data mining technology is used to maximize the application value of multi-factor geographic information data. The reverse dynamic programming algorithm is used for intelligent line selection, and the particle swarm optimization-convolutional neural network (PSO-CNN) model is used for engineering quantity prediction to improve the prediction accuracy and obtain more accurate optimization scheme ranking results. The scheme aims to reduce the design workload, reduce the industry design cost, improve the intelligence and standardization level of transmission and transformation engineering design, and provide strong support for the construction of smart grids.