Through incremental integration and independent research and development, build a method library of big data quality control, automatic modeling and analysis, data mining and interactive visualization, form a tool library with high reliability, high scalability, high efficiency and high fault tolerance, realize the integration and sharing of collaborative analysis methods of multi-source heterogeneous, multi-granularity, multi-phase, long-time series big data in three pole environment, as well as high Efficient and online big data analysis and processing.
By solving the frequency domain variational optimization problem, each signal component is estimated. Assuming that all components are narrowband signals concentrated near their respective central frequencies, VMD establishes a constrained optimization problem according to the component narrowband conditions, so as to estimate the central frequencies of signal components and reconstruct the corresponding components.
The principle of EWT is to divide the Fourier spectrum of the signal into continuous intervals, then construct wavelet filter banks on each interval for filtering, and finally obtain a group of AM and FM components through signal reconstruction. This method can identify the position of the feature information in the Fourier spectrum of the signal by using the wavelet filter bank with tight support characteristics, and adaptively extract the different frequency components of the signal.