An Intelligent Optimization Method for Key Data in the Entire Cost Process of Dual Carbon Transmission and Transformation Projects

Against the backdrop of the dual carbon goals of promoting green energy solutions and reducing carbon emissions globally, improving the quality and efficiency of data management throughout the entire process of power transmission and transformation projects is more crucial than ever before. This paper proposes a key data processing method for the entire process of power transmission and transformation based on hybrid intelligent optimization algorithm. This method focuses on cost data management and designs an improved random neighborhood embedding algorithm to achieve data dimensionality reduction. Then, the adaptive whale optimization algorithm and particle swarm optimization algorithm were introduced. Under the cross strategy framework, the two are combined to obtain a whale particle swarm hybrid optimization algorithm. The experimental results show that this method has better processing effect on key data throughout the entire process of power transmission and transformation engineering. Compared with other methods, its accuracy and efficiency also have significant advantages, which can improve the level of data management.