ABSTRACT
ABSTRACT: Coal mine accidents are generally associated with the ventilation system used. Cloud computing k-unth algorithmhas brought aboutmajor changes in themulti-variable prediction structure of coal mine underground ventilation capacity. This paper discusses the use of cloud computing k-unth algorithm for the detection of coal mine underground ventilation capacity and a spatial data fusion approach as well as a cloud computing information aggregation technique and four-dimensional nonlinear indexing for newly developed intelligent ventilation networks are discussed. With options for dynamic modifications of R+tree, the minimum k-unth algorithm prediction error is used to determine the optimal embedding dimension. Prediction customer error is used to predict wind waveform flicker and ventilation diagonal branch unbalance.
