Selection and Trend Prediction Method of Time Series Data of Electromagnetic Radiation Intensity in Coal Mine

Abstract

The invention discloses a method for selecting time-series data of electromagnetic radiation intensity underground in a coal mine and predicting a trend, comprising the following steps: sampling the time-series data of electromagnetic radiation intensity underground in a coal mine to obtain a sample data set; Obtain multiple data subsets; determine the importance of sample points in each data subset based on the Hausdorff distance, and remove or retain each sample point according to the importance to obtain a forecast data set; predict the coal mine according to the forecast data set The change trend of downhole electromagnetic radiation intensity is predicted. According to the selection and trend prediction method of time-series data of electromagnetic radiation intensity in underground coal mines of the present invention, the computational complexity of trend prediction is reduced, and the robustness of prediction results 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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