Data Processing Method for Power Marketing System Based on Wavelet Theory

For power marketing data, there are a variety of noise interference, which is not conducive to accurate data analysis.An effective denoising method for power marketing data is proposed,Firstly, the basic theories of wavelet denoising method are introduced.Secondly,for the classical Particle Swarm Secondly, to solve the problem that the classical particle swarm optimization algorithm is easy to fall into local optimal, an adaptive inertial weight coefficient varying with the number of iterations is designed to enhance the algorithm. Thirdly, a wavelet threshold function containing two form factors is constructed to solve the vibration and deviation phenomena that often occur in signal reconstruction in the process of classical wavelet threshold denoising. Then, an adaptive wavelet threshold denoising method based on the augmented particle swarm optimization algorithm is developed.finally, the power marketing data signal is simulated in MATLAB,and the method proposed in this paper is verified based on this.