Mine underground adaptive fire grading early-warning method and system

A fire early warning and mine underground technology, applied in the field of fire grading early warning system, can solve the problems of inaccurate accuracy and danger, and achieve the effect of improving accuracy, retaining judgment ability, and realizing self-adaptation of fire grading early warning

Active Publication Date: 2019-02-19
SHENZHEN YIRI TECH
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Problems solved by technology

Therefore, if the model is fitted only by adjusting the threshold, the accuracy is often not accurate enough
If we want to get a network model suitable for downhol

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  • Mine underground adaptive fire grading early-warning method and system
  • Mine underground adaptive fire grading early-warning method and system
  • Mine underground adaptive fire grading early-warning method and system

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Embodiment Construction

[0027] The preferred embodiments of the present invention will be further described in detail below in conjunction with the accompanying drawings.

[0028] Such as Figure 1 to Figure 3 As shown, the present invention provides a kind of self-adaptive fire grading early warning method under mine, comprising the following steps:

[0029] Step S1, selecting sample data with labels for BP neural network training;

[0030] Step S2, optimize the BP neural network model by transfer learning strategy, obtain the output layer parameters after fine-tuning training;

[0031] Step S3, input the collected parameter value, and output the fire warning level corresponding to the parameter value through the optimized BP neural network model.

[0032]BP neural network is a multilayer feed-forward network trained by error backpropagation algorithm, and it is one of the most widely used neural network models at present. The BP network can learn and store a large number of "input → output" mode...

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Abstract

The invention provides a mine underground adaptive fire grading early-warning method and system. The mine underground adaptive fire grading early-warning method comprises the following steps that S1,marked sample data are selected for BP neural network training; S2, a BP neural network model is optimized through a migration learning strategy, and an output layer parameter subjected to fine-adjusting training is obtained; and S3, the collected parameter value is input, and the fire early-warning grade corresponding to the parameter value is output through the optimized BP neural network model.In the step S2, the BP neural network model is optimized through the migration learning strategy, then the judgment capability during a fire can be preserved, the non-fire situation in a mine can also be distinguished, thus the accuracy rate of fire grading early-warning of the mine is increased, the situations of false report and report missing are reduced, and the adaptive function of fire grading early-warning in complex environments such as the mine and a construction tunnel is effectively achieved.

Description

technical field [0001] The present invention relates to a fire early warning method, in particular to an underground adaptive fire grading early warning method, and a fire grading early warning system adopting the underground adaptive fire grading early warning method. Background technique [0002] Using BP neural network for fire early warning, firstly, it is necessary to carry out parameter training on the built BP neural network model, so that the model can finally output the probability of the current fire occurrence according to the three data input by the sensor. In the field of fire early warning, there are two public data sets of Chinese standard test fire and European standard test fire. We usually use this kind of data as the training set to train the network model. [0003] However, such data is usually measured in a laboratory or in a specific environment, and the network model trained by it is often not suitable for complex environments. For example, under the ...

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Application Information

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IPC IPC(8): E21F17/18G06F17/50
CPCE21F17/18G06F30/20
Inventor 黄山松张飞翔翁凯利文智力
Owner SHENZHEN YIRI TECH
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