Research On Coal Mine Safety Early Warning

With the requirements of the 13 th Five-Year Energy Plan,the national energy development is moving in a clean and clean direction.The coal industry's share of primary energy consumption is decreasing year by year.In 2020,China's coal will account for about 57% of primary energy consumption,but the coal industry is still my country's Basic industries still occupy the main position in my country's energy production.Coal mining at the current stage also has great risks.Coal mine accidents occur every year,with huge numbers of casualties and property losses.Coal mine safety is still an unavoidable and serious safety issue for the country.The country's safety requirements for coal production are increasing year by year,and under this situation,the country is also gradually advancing the development of clean and intelligent coal energy production.In this context,the establishment of a coal mine underground monitoring system combined with the Internet,automatic control technology and other technologies to build a coal mine safety early warning system to achieve real-time detection of coal mine production environmental parameters,while using massive coal mine environment data to build a safety early warning system to discover coal mine production in advance To achieve early warning of environmental safety issues in the mine,and timely take relevant measures to ensure the safety of underground workers and protect coal mine production equipment.It is of great significance for reducing casualties,reducing economic losses and escorting coal production safety.By studying the above-mentioned problems,this paper constructs a coal mine safety early warning model based on an improved T-S fuzzy neural network.The main research contents are:(1)Through the collection of relevant national regulations on coal mines,the classification of coal mine safety grades and other documents,this paper obtains the comparison of relevant underground safety environment parameters and coal mine safety grades;in view of the complexity of coal safety,underground risk factors are more than a single Parameter function is the mutual coupling effect of multiple parameters.Therefore,fuzzy control theory is used to describe its safety level.However,due to the insensitivity of fuzzy control to data,the results are often unsatisfactory when dealing with cases with a large amount of data,so this article adopts The combination of fuzzy theory and neural network,using the improved TS fuzzy neural network to do early warning research on coal mines,to a certain extent improves the accuracy of coal Mine safety early warning,and improves the safe production capacity of underground work.(2)Secondly,based on the STC15 W series chip to build the downhole environment acquisition and control device hardware system,simulate and collect data of underground gas concentration,CO concentration,temperature,coal dust concentration and wind speed in the laboratory,and simulate and control underground gas overrun Control operations such as opening and closing of electricity and blower.(3)The configuration software is used to build a coal mine underground environmental data management platform,and it is combined with the underground hardware system.RS485 and Ethernet are used as the communication method between the hardware device and the management platform to realize data collection,storage,and real-time data.The display,early warning setting and control of the actuator;use the mixed programming function of configuration software and MATLAB to analyze and process the collected underground hazard source data to realize the safety evaluation of the coal mine.The above research content can realize coal mine safety early warning and can effectively improve the safety of coal mine production.The research work of this article has certain practical value and reference significance for coal mine safety production.