Research on optimization algorithm in grid engineering survey data acquisition and processing based on cloud computing

With the increasing scale of power grids and the growing demand for intelligence, efficient and accurate collection and processing of grid engineering survey data has become critical. This study aims to explore the optimization application of cloud computing technology in grid engineering data collection and processing. We design and implement an optimization algorithm based on cloud computing, which integrates several technologies such as distributed computing, big data storage and management, cluster monitoring and security management. Through comparative experiments, our algorithm significantly outperforms traditional methods in terms of data processing speed, achieving at least a four-fold improvement; in terms of data accuracy, the error rate is controlled at a lower level of 0.2%-0.3%. In addition, our method demonstrates good scalability and cost-effectiveness. This study not only provides an efficient data processing solution for power grid engineering but also lays a solid foundation for the intelligent and digital transformation of future power grids.