Intelligent mine helmet control method based on human body information and environment information fusion

Abstract

The invention discloses an intelligent mine helmet control method based on human body information and environment information fusion. The method includes acquisition of original brain wave signals; preprocessing of original brain waves; wavelet decomposition and reconstruction of the original brain wave signals; extraction of brain wave signal characteristics; optimization of a BP neural network through an artificial fish swarm algorithm to identify fatigue characteristics; identification of miner fatigue, monitoring of miner environmental information and alarm operation for dangerous situations. According to method, the energy of four sub-bands, ApEn approximate entropy, KC complexity and C0 complexity in the high-precision brain wave signals obtained by preprocessing (filtering and denoising) of the original brain wave signals are extracted as an artificial fish swarm algorithm to optimize an input layer of the BP neural network. The artificial fish swarm algorithm is trained to optimize the BP neural network to identify the miner fatigue, and the identification accuracy is high. An intelligent wearable bracelet worn on the wrist of a miner is adopted to give triple alarm of sound, light and vibration on the safety states of different dangerous levels to improve the underground operation safety coefficient.

Description

technical field [0001] The invention relates to the technical field of miner fatigue recognition, in particular to an intelligent mining helmet control method based on fusion of human body information and environmental information. Background technique [0002] my country is the largest coal-producing country in the world. On the basis of the technical background of existing mines, traditional mining helmets can no longer meet market demand, so it is necessary to combine digital mine monitoring systems to intelligently upgrade mining helmets. The study of mining smart helmets can complete the monitoring and early warning of complex underground environments, and at the same time provide mining tool body information for the monitoring room on the well, and provide reliable data for accident elimination, rescue and accident cause analysis. The existing mining helmet monitoring system is as follows: [0003] 1. Most of the mine alarm methods are installed in the roadway for so...

AI Technical Summary

Problems solved by technology

[0003] 1. Most of the mine alarm methods are installed in the roadway for sound and light alarm. For those who deviate from the alarm source (such as in the environment of ultra-noise and dust in the excavation line), there are situations such as not receiving the alarm information in time, that is, The timeliness of information cannot be guaranteed

[0004] 2. Underground sensors in coal mines often monitor environmental information at a fixed position. When personnel walk through this position, they cannot understand the information of their own environment, etc., and there are potential safety hazards

[0005] 3. Most of the existing mining helmets only complete the monitoring of the environment where the miners live, voice calls, etc., and basically do not monitor the miners themselves, let alone the detection and evaluation of the fatigue of the miners.

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