Coal mine gas early warning method based on class label weighted extreme learning machine

An extreme learning machine and coal mine gas technology, applied in the field of gas concentration prediction, can solve problems such as category imbalance

Active Publication Date: 2021-04-16
江苏中矿安华科技发展有限公司
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  • Application Information

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Problems solved by technology

[0003] Purpose of the invention: In order to overcome the deficiencies in the prior art, the present invention provides a coal mine gas early warning method based on class-marked weighted extreme learning machine. This method can effectively solve the problem of category imbalan

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  • Coal mine gas early warning method based on class label weighted extreme learning machine
  • Coal mine gas early warning method based on class label weighted extreme learning machine
  • Coal mine gas early warning method based on class label weighted extreme learning machine

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Embodiment Construction

[0051] Below in conjunction with accompanying drawing and specific embodiment, further illustrate the present invention, should be understood that these examples are only for illustrating the present invention and are not intended to limit the scope of the present invention, after having read the present invention, those skilled in the art will understand various aspects of the present invention All modifications of the valence form fall within the scope defined by the appended claims of the present application.

[0052] A coal mine gas early warning method based on class label weighted extreme learning machine, such as Figure 1-3 shown, including the following steps:

[0053] A. Extract the historical gas concentration monitoring data of a coal mine monitoring point collected by the sensor, calculate the average value at equal intervals of 10 minutes, obtain standardized gas concentration monitoring data, and analyze whether there is any missing monitoring data. If there is ...

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Abstract

The invention discloses a coal mine gas early warning method based on a class label weighted extreme learning machine, which comprises the steps of acquiring historical gas concentration monitoring data, preprocessing the data and filling missing data, generating a sample set, setting a gas concentration early warning threshold value, and determining a sample imbalance ratio by adopting the early warning threshold value mu; and performing weighting operation on the class label matrix of the sample according to the feedback value of the imbalance ratio, determining the optimal hidden layer node number of the extreme learning machine by utilizing five-fold cross validation, and training and generating an extreme learning machine model by taking a Sigmoid function as an activation function and taking the weighted class label matrix as an expected output matrix of the model. Whether the gas concentration of a certain single monitoring point exceeds the warning line or not in the next three hours is predicted, the potential production safety risk is early warned, the influence of class imbalance is considered, and the method is easy to implement, high in detection rate and low in false alarm rate.

Description

technical field [0001] The invention relates to a coal mine gas early warning method, which belongs to the technical field of gas concentration prediction. Background technique [0002] In my country's energy industry, coal accounts for about 70% of my country's primary energy production and consumption structure. For a long period of time in the future, coal will still be the main energy source in my country, but at present, the safety situation of coal mine production is very serious. Severe, safety accidents still occur from time to time, and the monitoring systems of many coal mines in my country lack the function of disaster prediction. Mine gas is one of the main factors affecting the safety of coal mines. If the gas concentration is too high, it may cause a series of safety accidents. Therefore, it is of great significance to the safe production of coal mines to accurately predict whether the gas concentration in the next few hours will exceed the warning line, and to...

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Application Information

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IPC IPC(8): G06Q10/04G06Q50/02G06Q50/26G06N20/00
CPCY02P90/30
Inventor 虎东成王超曹文敬许冉黄永正赵青青
Owner 江苏中矿安华科技发展有限公司
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