ABSTRACT
ABSTRACT: Changes have taken place in the multi-variable prediction structure of cloud computing TaskScheduler algorithm applied to coal mine underground ventilation capacity. This paper discusses the use of cloud computing TaskScheduler algorithm to detect the underground power supply capacity quality, the use of FPGA to sample 16-way AC signals and perform 64 harmonic analysis fusion, as well as a cloud computing information nonlinear aggregation technique and four-dimensional nonlinear indexing for newly developed intelligent ventilation networks. With options for dynamic modifications of R+tree, the minimum TaskScheduler algorithm prediction error is used to determine the optimal embedding dimension, and prediction customer error is used to predict wind waveform flicker and power supply diagonal imbalance.
