Selection and trend prediction method of underground coal mine electromagnetic radiation intensity time series data

Abstract

The invention discloses a selection and trend prediction method of underground coal mine electromagnetic radiation intensity time series data. The method comprises the following steps: sampling time series data of underground coal mine electromagnetic radiation intensity to obtain a sample data set; performing clustering granulation processing on the sample data set to obtain multiple data subsets; determining the significance of sample points in each data subset based on Hausdorff distance, and removing or reserving each sample point according to the significance so as to obtain a predictiondata set; and predicting the change trend of the underground coal main electromagnetic radiation intensity according to the prediction data set. According to the selection and trend prediction methodof underground coal mine electromagnetic radiation intensity time series data disclosed by the invention, the computation complexity of the trend prediction is reduced, and the robustness of the prediction result is enhanced.

Description

technical field [0001] The invention relates to the technical field of coal-rock dynamic disaster prediction, in particular to a method for selecting time-series data of electromagnetic radiation intensity underground in a coal mine and predicting a trend. Background technique [0002] In the coal industry, the safety of mines is the top priority. It is not only related to the safety of people's lives and property, but also to whether the coal industry can develop sustainably and healthily. However, judging from the characteristics of my country's coal mines, 95% of mines are mined underground, and most of the coal deposits are complicated. Many small and medium-sized mines have backward production technology and equipment, and inadequate implementation of safety protection measures, resulting in frequent disasters and accidents. Although my country's mine safety situation is improving year by year, it is still not optimistic, and accidents in some large state-owned coal min...

AI Technical Summary

Problems solved by technology

However, at present, the prediction of time series data often encounters large data sets, which contain a large amount of redundant and noisy data. The low quality of data leads to complex data calculation, high complexity of model tuning, difficult prediction and poor robustness of prediction results.

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