CN116975775B - Deep learning method for coal mine gas outburst prediction based on multi-mode data fusion - Google Patents

Deep learning method for coal mine gas outburst prediction based on multi-mode data fusion Download PDF

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CN116975775B
CN116975775B CN202310790338.0A CN202310790338A CN116975775B CN 116975775 B CN116975775 B CN 116975775B CN 202310790338 A CN202310790338 A CN 202310790338A CN 116975775 B CN116975775 B CN 116975775B
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gas outburst
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coal mine
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裴文良
谢海峰
张旭华
郭永涛
张越超
张连源
尚昱昊
李军伟
郑子东
马心刚
许鑫
孙海龙
王宇轩
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CITIC HIC Kaicheng Intelligence Equipment Co Ltd
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Abstract

The invention discloses a coal mine gas outburst prediction deep learning method based on multi-mode data fusion, which comprises the following steps of S1: different sensors acquire a plurality of gas outburst data under different gas states; the gas outburst data comprise acoustic emission data, gas pressure data and coal resistivity data; step S2: carrying out data preprocessing on the gas outburst data; step S3: a depth prediction network for coal mine gas outburst prediction is constructed, and gas outburst data after data preprocessing is input; step S4: training iteration of a depth prediction network for coal mine gas outburst prediction is executed to form a depth prediction model; step S5: and predicting the probability of occurrence of coal mine gas outburst by using the depth prediction model to obtain a gas outburst prediction result.

Description

Deep learning method for coal mine gas outburst prediction based on multi-mode data fusion
Technical Field
The invention relates to a gas outburst prediction method, in particular to a coal mine gas outburst prediction deep learning method based on multi-mode data fusion.
Background
When the underground gas outburst of the coal mine occurs, a large amount of gas and crushed coal are sprayed out of the coal seam in a very short time, so that holes are formed in the coal seam, a ventilation system or tunnel facilities of the coal mine are easily damaged, a large amount of miners are buried due to the fact that the coal mine collapses more seriously, and the safety of coal mining is seriously threatened. Therefore, if the intensity of the gas outburst can be rapidly and accurately predicted before the gas outburst accident occurs, the accident can be prevented from occurring, and damage and loss to coal production can be prevented. For gas outburst prediction, researchers have proposed various methods such as: patent CN112183901a proposes a "method for predicting the intensity of coal and gas protrusion based on deep learning", and patent CN109711632a proposes a "method for predicting the intensity of coal and gas protrusion based on abnormal sensitivity index of gas emission". The method based on deep learning is adopted to improve the prediction accuracy, but the gas outburst prediction is limited due to single acquired information; the gas outburst is predicted by three indexes of abnormal average fluctuation amplitude change of gas emission, abnormal average change trend of gas emission and abnormal frequency of large amplitude change of gas emission, and the risk of gas outburst can be predicted in real time, but the accuracy is slightly poor.
Disclosure of Invention
In order to solve the problems, the invention aims to provide a coal mine gas outburst prediction deep learning method based on multi-mode data fusion, namely multi-branch and attention mechanisms are adopted in a deep convolution network to fully utilize multi-source data information, so that the robustness of the method and the accuracy of gas outburst prediction are improved.
The invention discloses a coal mine gas outburst prediction deep learning method based on multi-mode data fusion, which comprises the following steps of S1: different sensors acquire a plurality of gas outburst data under different gas states; the gas outburst data comprise acoustic emission data, gas pressure data and coal resistivity data;
step S2: carrying out data preprocessing on the gas outburst data;
step S3: a depth prediction network for coal mine gas outburst prediction is constructed, and gas outburst data after data preprocessing is input;
step S4: training iteration of a depth prediction network for coal mine gas outburst prediction is executed to form a depth prediction model;
step S5: and predicting the probability of occurrence of coal mine gas outburst by using the depth prediction model to obtain a gas outburst prediction result.
Further, step S2 includes the steps of:
step S21: respectively carrying out missing value processing on acoustic emission data, gas pressure data and coal resistivity data of the gas outburst data;
step S22: and respectively carrying out normalization processing on the acoustic emission data, the gas pressure data and the coal resistivity data of the gas outburst data subjected to the missing value processing.
In step S21, the missing value processing is to delete the data with more missing values of the gas projection, and the data with less missing values of the gas projection is interpolated by KNN algorithm;
in step S22, the normalization process is to restrict the missing value interpolated gas projection data to the range of [0,1] by normalization.
Further, the depth prediction network comprises a feature extraction module, a block attention module and a regression network module which are sequentially connected; the characteristic extraction module comprises three branches, and the three branches respectively correspond to acoustic emission data, gas pressure data and coal resistivity data for processing the gas outburst data; each branch comprises a depth gating convolutional network module and a channel attention module; the block attention module fuses the features output by the three branches of the feature extraction module.
Further, in step S3, the data preprocessing process of the gas highlighting data is as follows:
step S31: respectively inputting acoustic emission data, gas pressure data and coal resistivity data of the gas outburst data after data preprocessing into a depth gating convolution network module and a channel attention module of corresponding branches to perform feature extraction;
step S32: fusing the characteristics output in the step S31 through a block attention module;
step S33: and inputting the characteristics output by the block attention module into a regression network module to obtain a gas outburst prediction result.
Further, in step S31, the depth-gating convolution network module is formed by stacking multiple layers of gating convolutions, where the gating convolutions operate as follows:
in the formula, h i Conv, the output feature of the gating convolution for layer i 1 And Conv 2 Sigma is two convolution operations 1 To activate a function Sigmoid, sigma 2 For the Selu activation function,is Hadamard product;
the channel attention module is calculated as follows:
in the formula, h ca For the output characteristics of the channel attention module, h f For the output characteristics of the depth gating convolution module, MP is the maximum pooling operation, AP is the average pooling operation, and w ca Is the weight of the full connection layer, sigma ca The function is activated for Softmax.
Further, the calculation process in step S32 is as follows:
in the formula, h b As a feature of the block attention module output,for the feature of the ith branch output, concat is a join operation, w b Is the weight of the full connection layer, sigma b The function is activated for Softmax.
Further, the calculation process in step S33 is as follows:
in the method, in the process of the invention,for the prediction result of the network, w y Is all thatWeights, sigma, of the connection layer y The function is activated for Sigmoid.
Further, in step S4, a loss function and Adam algorithm are adopted to perform network training, where the loss function is a mean square error loss function.
Further, in step S5, a threshold value for gas outburst is set, and the gas outburst data after data preprocessing is input into a depth prediction model to predict the occurrence probability of gas outburst;
if the probability of the occurrence of the predicted gas outburst is greater than the threshold value, the occurrence of the gas outburst is indicated; otherwise, it indicates that no gas outburst occurred.
The invention has the beneficial effects that:
according to the deep learning method, the gas salient data collected by the three sensors are used as the input of a deep prediction network, and the multi-branch deep gating convolution network module is used as the feature extraction module, so that the hidden features of multi-source historical data are fully mined. The extracted depth features are screened and fused by adopting different attention mechanisms, so that the complexity of the features can be reduced, the data features can be deeply mined, and the prediction accuracy can be improved.
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In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings that are needed in the description of the embodiments or the prior art will be briefly described, and it is obvious that the drawings in the description below are some embodiments of the present invention, and other drawings can be obtained according to the drawings without inventive effort for a person skilled in the art.
FIG. 1 is a schematic illustration of the process of the present invention;
FIG. 2 is a schematic diagram of a depth-gated convolutional network module of the present invention;
FIG. 3 is a schematic diagram of a channel attention module of the present invention;
FIG. 4 is a block attention module schematic of the present invention;
fig. 5 is a schematic diagram of a regression network module of the present invention.
Detailed Description
The following description of the technical solution in the embodiments of the present invention is clear and complete. In the following description, numerous specific details are set forth in order to provide a thorough understanding of the present invention, but the present invention may be practiced in other ways other than those described herein, and persons skilled in the art will readily appreciate that the present invention is not limited to the specific embodiments disclosed below.
According to the coal mine gas outburst prediction deep learning method based on multi-mode data fusion, namely, multi-branch and attention mechanisms are adopted in a deep convolution network, multi-source data information is fully utilized, and the robustness of the method and the accuracy of gas outburst prediction are improved.
The invention aims to provide a coal mine gas outburst prediction deep learning method based on multi-mode data fusion, which comprises the steps of acquiring acoustic emission data, gas pressure data and coal resistivity data by using a sensor; interpolation is carried out on missing values existing in the data by utilizing a KNN algorithm, and then normalization processing is carried out on the data; constructing a multi-branch depth gating convolution network based on an attention mechanism; adopting the mean square error as a loss function of the network, and optimizing network parameters by utilizing Adam; and predicting the probability of occurrence of gas outburst by using the trained network model to obtain a final result.
In connection with fig. 1, a schematic diagram of the deep learning method of the present invention: the invention provides a coal mine gas outburst prediction deep learning method based on multi-mode data fusion, which comprises the following specific steps:
step S1: acquiring gas outburst data under a plurality of different gas states by using different sensors; the gas outburst data comprise acoustic emission data X 1 Data X of gas pressure 2 And coal resistivity data X 3 The three data are three modality data, which are obtained by 3 different sensors.
The three types of data related to the gas acquired by the three sensors at the same time are called gas outburst data, and the three types of data have relevance in time space. Acoustic emission data X 1 The method is characterized in thatIncluding amplitude, energy, signal bandwidth, peak frequency, and peak frequency amplitude; gas pressure data X 2 The method is characterized by comprising a change amplitude and a climbing event; coal resistivity data X 3 The characteristics include kurtosis coefficient, skewness coefficient and inflection point resistivity.
Step S2: carrying out data preprocessing on the gas outburst data;
the data preprocessing is to transmit acoustic emission data X of the gas outburst data 1 Data X of gas pressure 2 And coal resistivity data X 3 The missing value processing and the normalization processing are performed, respectively, as shown in fig. 1.
The missing value processing is to delete the data with more missing values directly, and the data with less missing values are subjected to missing value interpolation through a KNN algorithm. The missing value interpolation is not limited to the KNN algorithm, and other algorithms that can realize the missing value interpolation may be used.
The core idea of the KNN algorithm is to determine K samples that are spatially similar or close to the training set by distance measurement, where the training set refers to a series of data collected by a sensor and the samples refer to a data under the training set. The K samples are then used to estimate the value of the missing data. For example c x Obtaining K points { c } adjacent to a missing value of a certain point in data through Euclidean distance 1 ,c 2 ,Λ,c K Then the estimate for cx is:
wherein d i Is the distance between the missing value and its i-th neighbor.
And respectively carrying out normalization processing on the gas projection data after the missing value interpolation, as shown in fig. 1. The normalization processing is to restrict the gas salient data after the interpolation of the missing values to the range of 0,1 by adopting normalization,
wherein x is min For data minimum in training set, x max For the maximum value of the data in the training set,for the normalized values, x is the raw data value (the gas projection data before pretreatment).
Step S3: and (3) constructing a depth prediction network for coal mine gas outburst prediction, and inputting gas outburst data after the data pretreatment in the step (S2).
The depth prediction network comprises a feature extraction module, a block attention module and a regression network module which are connected in sequence; the feature extraction module includes three branches, each including a depth-gated convolutional network module and a channel attention module. Acoustic emission data X of the gas outburst data are processed correspondingly by the three branches respectively 1 Data X of gas pressure 2 And coal resistivity data X 3 . The acoustic emission data X after normalization processing in the step S2 1 Data X of gas pressure 2 And coal resistivity data X 3 The characteristics are respectively input into the constructed depth prediction network and are respectively extracted by the characteristic extraction module, the block attention module is used for fusing the characteristics of the three branches of the characteristic extraction module, and the regression network module obtains the gas outburst prediction result.
The specific input process is as follows:
step S31: acoustic emission data X of normalized gas outburst data 1 Data X of gas pressure 2 And coal resistivity data X 3 And respectively inputting the depth gating convolutional network module and the channel attention module of the corresponding branch to extract the characteristics.
The depth-gated convolutional network module is formed by stacking multiple layers of gated convolutions, as shown in FIG. 2, the structure operates faster than LSTM and GRU and has stronger fitting capacity than a structure using only normal convolutions. The depth-gated convolutional network module operates as follows:
in the formula, h i Conv, the output feature of the gating convolution for layer i 1 And Conv 2 Sigma is two convolution operations 1 To activate a function Sigmoid, sigma 2 For the Selu activation function,is Hadamard product; in the gated convolution, selu is used as the activation function.
After the depth-gating convolutional network module extracts the features, the channel attention module continues to extract the features, as shown in fig. 3, and the specific calculation process is as follows:
the channel attention module for each branch is calculated as follows:
in the formula, h ca For the output characteristics of the channel attention module, h f For the output characteristics of the depth gating convolution module, MP is the maximum pooling operation, AP is the average pooling operation, and w ca Is the weight of the full connection layer, sigma ca The function is activated for Softmax.
The channel attention module specifically operates as follows: will last layer of features h f Respectively carrying out maximum pooling operation MP and average pooling operation AP to obtain a feature 1 and a feature 2, adding corresponding positions of the feature 1 and the feature 2 to obtain a feature 3, and sequentially passing the feature 3 through a full connection layer w ca And Softmax activation function sigma ca Get and input feature h f Vector with the same dimension of the channel, and finally multiplying the vector with the channel corresponding to the input characteristic to obtain the output characteristic h ca 。
After completion, the channel attention module of each branch outputs a characteristic respectively;
step S32: the features output by the channel attention modules of the three branches are all input into the block attention module for feature fusion, as shown in fig. 4, and the specific calculation process is as follows:
in the formula, h b As a feature of the block attention module output,for the feature of the ith branch output, concat is a join operation, w b Is the weight of the full connection layer, sigma b Activating a function for Softmax; MP is the max pooling operation and AP is the average pooling operation.
Step S33: the characteristics output by the block attention module are input to the regression network module. The regression network module can obtain the gas outburst prediction result. The regression network module is obtained by connecting the full connection layer and the Sigmoid activation function in series. As shown in fig. 5, the specific calculation process is as follows:
in the method, in the process of the invention,as the gas outburst prediction result, w y Is the weight of the full connection layer, sigma y The function is activated for Sigmoid.
Step S4: training iteration of a depth prediction network for coal mine gas outburst prediction is executed, and a depth prediction model is obtained after training is completed;
and performing network training by adopting a loss function and an Adam algorithm, wherein the loss function is a mean square error loss function, and the method comprises the following steps of:
wherein M is the number of samples;is a model predictive value; y is the gas highlighting real label.
In the training process, the gas salient real labels are input into the loss function to perform back propagation training on the depth prediction network. The mean square error loss function is used as the loss of the depth prediction network, and the Adam algorithm is utilized to iteratively update the network parameters of the depth prediction network. And after the deep prediction network training is completed, obtaining a deep prediction model. The gas highlighting real label means whether gas actually occurs or not.
Step S5: and predicting the probability of occurrence of coal mine gas outburst by using the depth prediction model, and obtaining a gas outburst prediction result. The gas outburst prediction result refers to gas outburst occurrence or gas outburst non-occurrence. In the step, a threshold value of gas outburst is set, and the gas outburst data after data preprocessing is input into a depth prediction model to predict the probability of gas outburst occurrence so as to obtain a gas outburst prediction result.
If the probability of the occurrence of the predicted gas outburst is greater than the threshold value, the occurrence of the gas outburst is indicated; otherwise, it indicates that no gas outburst occurred.
It will be apparent that the described embodiments are only some, but not all, embodiments of the invention. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.

Claims (7)

1. The deep learning method for coal mine gas outburst prediction based on multi-mode data fusion is characterized by comprising the following steps:
step S1: different sensors acquire a plurality of gas outburst data under different gas states; the gas outburst data comprise acoustic emission data, gas pressure data and coal resistivity data;
step S2: carrying out data preprocessing on the gas outburst data;
step S3: a depth prediction network for coal mine gas outburst prediction is constructed, and gas outburst data after data preprocessing is input;
step S4: training iteration of a depth prediction network for coal mine gas outburst prediction is executed to form a depth prediction model;
step S5: predicting the probability of occurrence of coal mine gas outburst by using a depth prediction model to obtain a gas outburst prediction result;
step S2 includes the steps of:
step S21: respectively carrying out missing value processing on acoustic emission data, gas pressure data and coal resistivity data of the gas outburst data;
step S22: respectively carrying out normalization processing on the acoustic emission data, the gas pressure data and the coal resistivity data of the gas outburst data subjected to the missing value processing;
in the step S21, the missing value processing is to delete the gas outburst data with more missing values, and the gas outburst data with less missing values is subjected to missing value interpolation through a KNN algorithm;
in the step S22, the normalization processing is to restrict the gas salient data after the missing value interpolation to the range of [0,1] by adopting normalization;
the depth prediction network comprises a feature extraction module, a block attention module and a regression network module which are connected in sequence; the characteristic extraction module comprises three branches, and the three branches respectively correspond to acoustic emission data, gas pressure data and coal resistivity data for processing the gas outburst data; each branch comprises a depth gating convolutional network module and a channel attention module; the block attention module fuses the features output by the three branches of the feature extraction module.
2. The deep learning method of coal mine gas outburst prediction based on multi-mode data fusion according to claim 1, wherein in step S3, the data preprocessing post-gas outburst data input process is as follows:
step S31: respectively inputting acoustic emission data, gas pressure data and coal resistivity data of the gas outburst data after data preprocessing into a depth gating convolution network module and a channel attention module of corresponding branches to perform feature extraction;
step S32: fusing the characteristics output in the step S31 through a block attention module;
step S33: and inputting the characteristics output by the block attention module into a regression network module to obtain a gas outburst prediction result.
3. The method for deep learning of coal mine gas outburst prediction based on multi-modal data fusion according to claim 2, wherein in step S31, the deep gating convolution network module is formed by stacking multi-layer gating convolutions, and the gating convolution operation is as follows:
in the formula, h i Conv, the output feature of the gating convolution for layer i 1 And Conv 2 Sigma is two convolution operations 1 To activate a function Sigmoid, sigma 2 For the Selu activation function,is Hadamard product;
the channel attention module is calculated as follows:
in the formula, h ca For the output characteristics of the channel attention module, h f For the output characteristics of the depth gating convolution module, MP is the maximum pooling operation, AP is the average pooling operation, and w ca Is the weight of the full connection layer, sigma ca The function is activated for Softmax.
4. The deep learning method of coal mine gas outburst prediction based on multi-modal data fusion according to claim 3, wherein the calculation process in step S32 is as follows:
in the formula, h b As a feature of the block attention module output,for the feature of the ith branch output, concat is a join operation, w b Is the weight of the full connection layer, sigma b The function is activated for Softmax.
5. The deep learning method of coal mine gas outburst prediction based on multi-modal data fusion according to claim 4, wherein the calculation process in step S33 is as follows:
in the method, in the process of the invention,as the gas outburst prediction result, w y Is the weight of the full connection layer, sigma y The function is activated for Sigmoid.
6. The multi-modal data fusion-based coal mine gas outburst prediction deep learning method according to claim 5, characterized in that in step S4, network training is performed by adopting a loss function and Adam algorithm, wherein the loss function is a mean square error loss function.
7. The method for deep learning coal mine gas outburst prediction based on multi-mode data fusion according to any one of claims 1 to 6, wherein in step S5, a threshold value for gas outburst is set, and the data-preprocessed gas outburst data is input into a deep prediction model to predict the occurrence probability of gas outburst;
if the probability of the occurrence of the predicted gas outburst is greater than the threshold value, the occurrence of the gas outburst is indicated; otherwise, it indicates that no gas outburst occurred.
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