Coal mine disaster comprehensive prevention and control method based on deep learning integration

Abstract

The invention belongs to the technical field of artificial intelligence and coal mine safety management, and particularly relates to a coal mine disaster comprehensive prevention and control method based on deep learning integration, a system integrates a sensor data acquisition and processing module, a deep learning integration module and an intelligent early warning and visualization module, and aims to improve coal mine production safety and disaster prevention and control efficiency. The system monitors coal mine environment parameters in real time through a sensor and stores and processes data. And performing data analysis and modeling by using a deep learning integration algorithm, and predicting potential disaster risks. The intelligent early warning and visualization module comprises a real-time monitoring interface, a disaster early warning interface and a post analysis visualization interface. The technical architecture comprises hardware and software technical architectures, and real-time monitoring, data analysis, disaster prediction and intelligent decision support of coal mine environment parameters are achieved. The system has the advantages of being efficient, accurate and intelligent, and the scientificity and timeliness of coal mine disaster prevention and control are remarkably improved.

Description

Technical Field

[0001] The present invention belongs to the technical field of artificial intelligence and coal mine safety management, and specifically relates to a comprehensive prevention and control method for coal mine disasters based on deep learning integration. Background Art

[0002] Safety issues in coal mining have always been the focus of attention of mining departments in various countries. Coal mine disasters, such as gas explosions, coal dust explosions, mine fires, mine collapses and toxic gas leaks, not only threaten the lives of miners, but also cause huge economic losses and environmental damage.

[0003] Traditional means of coal mine disaster prevention and control mainly rely on the experience of miners and simple mechanical equipment. These means include regular testing of gas and coal dust concentrations, ventilation and dust reduction, fire prevention measures, and mine support. However, with the increase in coal mining depth and the expansion of production scale, these traditional methods are unable to cope with the complex and changeable coal mine environment. The response speed and detection accuracy of traditional detection equipment are limited, and the experience judgment of miners is subjective and uncertain, which cannot achieve early and accurate warning of potential disasters. In addition, traditional methods involve more manual intervention and lack a systematic and intelligent disaster prevention and control mechanism, which leads to delayed emergency response and treatment measures after the disaster, and the inability to effectively control the impact of the disaster in a timely manner. Summary of the invention

[0004] In view of the technical problem that the above-mentioned traditional methods involve a lot of manual intervention and lack a systematic and intelligent disaster prevention and control mechanism, resulting in delayed emergency response and treatment measures after a disaster occurs, the present invention provides a comprehensive coal mine disaster prevention and control method based on deep learning integration, aiming to achieve real-time monitoring of coal mine environmental parameters, data analysis, disaster prediction and intelligent decision-making support through multi-level intelligent decision support.

[0005] In order to solve the above technical problems, the technical solution adopted by the present invention is:

[0006] A comprehensive prevention and control method for coal mine disasters based on deep learning integration includes the following steps:

[0007] S1. The sensor data acquisition and processing module includes different types of sensors, which are used to monitor the temperature, humidity, gas concentration, coal dust concentration and air pressure in the coal mine, and are used to monitor the environmental parameters of the coal mine in real time, and store and preliminarily pre-process the collected data;

[0008] S2, the deep learning integrated algorithm module is used to analyze and model the stored and processed data to predict potential disaster risks; the convolutional neural network part is used to process sensor data to extract local environmental features, the recurrent neural network part is used to process time series data, and the graph convolutional network part is used to process the spatial location dependency of sensor locations; the feature vectors output by the convolutional neural network, the recurrent neural network, and the graph convolutional network are concatenated and fused through the fully connected layer to form a comprehensive feature representation; the disaster risk is predicted through the fully connected layer and activation function, and the disaster risk score is output;

[0009] S3, intelligent early warning and data visualization module provides real-time monitoring interface, disaster early warning interface and post-analysis visualization interface, so that users can fully understand the coal mine environment and disaster prevention and control situation;

[0010] S4. The technical architecture includes hardware and software technical architecture. The hardware and software are combined to realize real-time monitoring of coal mine environmental parameters, data analysis, disaster prediction and intelligent decision support.

[0011] The method of collecting sensor data in S1 is:

[0012] The sensor data acquisition and processing module includes different types of sensors for monitoring key environmental parameters in coal mines: temperature sensor (T i ), humidity sensor (H i ), gas concentration sensor (G i ), coal dust concentration sensor (D i ), air pressure sensor (P i ); The reading recorded by each sensor at time t is represented by X i (t), where X i Indicates the corresponding sensor type; the overall state of the sensor data at time t is expressed in vector form:

[0013] X(t)=[T i (t), H i (t), G i (t), D i (t), P i (t)]

[0014] These sensors monitor the coal mine environmental parameters in real time and send the data to the data processing unit.

[0015] The method for performing preliminary preprocessing on the collected data in S1 is:

[0016] The data processing unit is responsible for storing and preliminarily processing the collected data; the data is normalized to eliminate the differences in the data ranges of different sensors. The normalization formula is as follows:

[0017]

[0018] Among them, X′ i (t) is the normalized sensor data, min(X i ) and max(X i ) represent sensor X i The minimum and maximum values ​​of the normalized data are used for denoising, and the sliding average method is used as follows:

[0019]

[0020] in, represents the denoised data, and N is the size of the sliding window; the processed data will be stored in the database for subsequent analysis and modeling.

[0021] The method for processing sensor data to extract local environmental features in S2 is:

[0022] The convolutional neural network part processes sensor data and extracts local environmental features. The input sensor data is X(t), and the operation of the convolutional layer is expressed as:

[0023] F l =f(W l *X+b l )

[0024] Among them, F l represents the feature map after the lth layer of convolution, W l and b l are convolution kernel and bias respectively, * represents convolution operation, and f is activation function. Through convolution layer and pooling layer, CNN extracts local features of sensor data to form feature vector F. CNN .

[0025] The method for processing time series data in S2 is:

[0026] The time series data of the input of the recurrent neural network is X(tk), X(t-k+1), ..., X(t), which can be expressed as:

[0027] h t =g(W h X t +U h h t-1 +b h )

[0028] Among them, h t represents the hidden state at the current moment, W h , U h and b hare weight matrices and biases, respectively, and g is the activation function; through the recursive operation of time steps, RNN generates the time series feature vector F RNN .

[0029] The method for processing the spatial position dependency of the sensor position in S2 is:

[0030] The graph convolutional network is used to process the spatial dependency of sensor positions. The graph structure of sensor positions is: in and ε represent nodes and edges respectively, and the GCN operation is expressed as:

[0031]

[0032] Among them, H (l) represents the node features of layer l, is the normalized adjacency matrix of the graph, W (l) is the weight matrix, σ is the activation function; after GCN operation, the spatial feature vector F is obtained GCN .

[0033] The method for forming comprehensive characteristics and disaster risk prediction in S2 is:

[0034] The feature vectors output by the convolutional neural network, recurrent neural network, and graph convolutional network are concatenated to form a comprehensive feature representation:

[0035] F concat =[F CNN , F RNN , F GCN ]

[0036] Then disaster risk prediction is performed through the fully connected layer and activation function:

[0037] y=σ(W out F concat +b out )

[0038] Among them, W out and b out is the weight and bias of the fully connected layer, σ is the activation function, and y is the output disaster risk score. Through training and optimization, the model can accurately predict the potential disaster risks in the coal mine environment.

[0039] The intelligent early warning and data visualization module in S3 includes: the real-time monitoring interface displays real-time data of parameters such as temperature, humidity, gas concentration, coal dust concentration and air pressure collected by sensors, ensuring that users can instantly grasp changes in the coal mine environment; when a potential risk is detected, the disaster early warning interface will notify users of specific risk information and response suggestions through visual charts and alarms, helping users to take protective measures in time; the post-analysis visualization interface summarizes and displays historical data and disaster events, and helps users analyze the causes and patterns of disasters through charts and reports, providing a scientific basis and decision-making support for future disaster prevention and control work.

[0040] The hardware technology architecture of S4 is composed of a variety of sensors, a data processing unit, a network communication device and a display terminal. The various sensors are composed of a temperature sensor, a humidity sensor, a gas concentration sensor, a coal dust concentration sensor and an air pressure sensor. The data processing unit is composed of an edge computing device and a server. The network communication device is composed of a wireless transmission module and a wired network interface. The hardware components work together to realize real-time monitoring and data transmission of coal mine environmental parameters.

[0041] The software technology architecture of S4 includes data acquisition software, data processing and storage software, deep learning integrated model, disaster warning and emergency response software and data visualization software; the data visualization software displays monitoring data, prediction results and emergency response information to users in an intuitive graphical interface to achieve intelligent decision support.

[0042] Compared with the prior art, the present invention has the following beneficial effects:

[0043] The present invention integrates a variety of highly sensitive sensors to monitor key parameters such as temperature, humidity, gas concentration, coal dust concentration and air pressure in the coal mine environment in real time. Through deep learning integrated algorithm modules, including convolutional neural network (CNN), recurrent neural network (RNN) and graph convolutional network (GCN), the collected data is analyzed and modeled, which can accurately predict potential disaster risks, achieve early warning, and significantly improve the timeliness and accuracy of disaster prevention and control.

[0044] The intelligent early warning subsystem of the present invention can issue disaster warnings in a timely manner based on the prediction results of the deep learning model, and automatically trigger emergency response measures to guide coal mine managers to take quick actions to reduce losses caused by disasters. Compared with traditional systems that rely on manual judgment and response, the present invention greatly improves the intelligence level and response speed of early warning and emergency response, and enhances the safety assurance capability of coal mine production.

[0045] The data visualization subsystem of the present invention provides a variety of interfaces, including a real-time monitoring interface, a disaster warning interface, and a post-analysis visualization interface, to help users fully understand the environmental conditions and disaster prevention and control of coal mines. Through intuitive graphics and charts, users can quickly obtain key information and make scientific decisions. Compared with the prior art, the data visualization capability of the present invention is stronger, the information presentation is more intuitive, and the scientificity and effectiveness of coal mine management are greatly improved. BRIEF DESCRIPTION OF THE DRAWINGS

[0046] In order to more clearly illustrate the implementation methods of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for the implementation methods or the description of the prior art. Obviously, the drawings in the following description are only exemplary, and for ordinary technicians in this field, other implementation drawings can be derived from the provided drawings without creative work.

[0047] The structures, proportions, sizes, etc. illustrated in this specification are only used to match the contents disclosed in the specification so as to facilitate understanding and reading by persons familiar with the technology. They are not used to limit the conditions under which the present invention can be implemented, and therefore have no substantial technical significance. Any structural modification, change in proportion or adjustment of size shall still fall within the scope of the technical contents disclosed in the present invention without affecting the effects and purposes that can be achieved by the present invention.

[0048] Figure 1 is a flow chart of an embodiment of the present invention.

[0049] Figure 2 It is the overall framework diagram of the present invention. DETAILED DESCRIPTION

[0050] In order to make the purpose, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below. Obviously, the described embodiments are only part of the embodiments of the present application, rather than all the embodiments. These descriptions are only to further illustrate the features and advantages of the present invention, rather than to limit the claims of the present invention. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of this application.

[0051] The specific implementation of the present invention is further described in detail below in conjunction with the accompanying drawings and examples. The following examples are used to illustrate the present invention, but are not intended to limit the scope of the present invention.

[0052] like Figure 1 As shown, the framework of the present invention is mainly divided into the following five steps, which are connected layer by layer and finally integrated. The process mainly includes the following steps:

[0053] Step 1, a coal mine disaster comprehensive prevention and control system and technical architecture based on deep learning integration, characterized in that it includes: a sensor data acquisition and processing module uses sensors to collect coal mine environmental parameters, and stores and preprocesses the collected data; a deep learning integration module analyzes and models the collected data to predict potential disaster risks; an intelligent early warning and visualization module connects the deep learning integration model and issues early warning signals according to the prediction results; the technical architecture includes hardware and software technologies to realize intelligent decision support;

[0054] The overall framework of the cross-domain sample transfer aspect-level sentiment classification method based on enhanced transfer learning proposed in this invention is as follows: Figure 2 The method proposed in the present invention comprises three main parts: a data processing and shared encoder module, an aspect-level sentiment classification module and a sample selection module.

[0055] Step 2, the sensor data acquisition and processing module includes different types of sensors, which are used to monitor the temperature, humidity, gas concentration, coal dust concentration and air pressure in the coal mine, and are used to monitor the coal mine environmental parameters in real time, and store and preliminarily preprocess the collected data;

[0056] The data processing operations such as target data labeling, source domain data segmentation, and transformer-based shared encoder in step 2 further include:

[0057] Step 21, the sensor data acquisition and processing module includes different types of sensors for monitoring key environmental parameters in the coal mine: temperature sensor (T i ), humidity sensor (H i ), gas concentration sensor (G i ), coal dust concentration sensor (D i ), air pressure sensor (P i ); The reading recorded by each sensor at time t can be expressed as X i (t), where X i Indicates the corresponding sensor type (such as temperature, humidity, etc.); the overall state of the sensor data at time t can be expressed in vector form:

[0058] X(t)=[T i (t), H i (t), G i (t), D i (t), P i (t)]

[0059] These sensors monitor coal mine environmental parameters in real time and send the data to the data processing unit;

[0060] Step 22, the data processing unit is responsible for storing and preliminarily processing the collected data; the data is normalized to eliminate the difference in the data range of different sensors. The normalization formula is as follows:

[0061]

[0062] Among them, X′ i (t) is the normalized sensor data, min(X i ) and max(X i ) represent sensor X i The minimum and maximum values ​​of the normalized data can be de-noised by using the sliding average method:

[0063]

[0064] in, represents the denoised data, and N is the size of the sliding window; the processed data will be stored in the database for subsequent analysis and modeling.

[0065] Step 3: The deep learning integrated algorithm module is used to analyze and model the stored and processed data to predict potential disaster risks; the convolutional neural network part is used to process sensor data to extract local environmental features, the recurrent neural network part is used to process time series data, and the graph convolutional network part is used to process the spatial location dependencies of sensor locations; the feature vectors output by the convolutional neural network, the recurrent neural network, and the graph convolutional network are concatenated and fused through the fully connected layer to form a comprehensive feature representation; the disaster risk is predicted through the fully connected layer and activation function, and the disaster risk score is output;

[0066] The document-level sentiment classification task on the source domain and the aspect-level sentiment classification task on the target domain in step 3 further include:

[0067] Step 31, the deep learning integrated algorithm module is used to analyze and model the stored and processed data to predict potential disaster risks; the convolutional neural network part processes the sensor data and extracts local environmental features; the input sensor data is X(t), and the operation of the convolutional layer is expressed as:

[0068] F l =f(W l *X+b l )

[0069] Among them, F l represents the feature map after the lth layer of convolution, W l and b lare convolution kernel and bias respectively, * represents convolution operation, and f is activation function; through convolution layer and pooling layer, CNN extracts local features of sensor data to form feature vector F CNN ;

[0070] The time series data of the input of the recurrent neural network is X(tk), X(t-k+1), ..., X(t), which can be expressed as:

[0071] h t =g(W h X t +U h h t-1 +b h )

[0072] Among them, h t represents the hidden state at the current moment, W h , U h and b h are weight matrices and biases, respectively, and g is the activation function; through the recursive operation of time steps, RNN generates the time series feature vector F RNN ;

[0073] The graph convolutional network is used to process the spatial dependency of sensor positions. The graph structure of sensor positions is: in and ε represent nodes and edges respectively, and the GCN operation is expressed as:

[0074]

[0075] Among them, H (l) represents the node features of layer l, is the normalized adjacency matrix of the graph, W (l) is the weight matrix, σ is the activation function; after GCN operation, the spatial feature vector F is obtained GCN .

[0076] Step 32, concatenate the feature vectors output by the convolutional neural network, the recurrent neural network, and the graph convolutional network to form a comprehensive feature representation:

[0077] F concat =[F CNN , F RNN ,F GCN ]

[0078] Then disaster risk prediction is performed through the fully connected layer and activation function:

[0079] y=σ(W out F concat +b out )

[0080] Among them, W out and b out is the weight and bias of the fully connected layer, σ is the activation function, and y is the output disaster risk score. Through training and optimization, the model can accurately predict the potential disaster risks in the coal mine environment.

[0081] Step 4: The intelligent early warning and data visualization module provides a real-time monitoring interface, a disaster early warning interface, and a post-analysis visualization interface, so that users can fully understand the coal mine environment and disaster prevention and control situation;

[0082] The reinforcement learning-based sample selection module in step 4 further includes:

[0083] Step 41, the real-time monitoring interface displays real-time data of parameters such as temperature, humidity, gas concentration, coal dust concentration and air pressure collected by sensors, ensuring that users can instantly grasp changes in the coal mine environment; when a potential risk is detected, the disaster warning interface will notify users of specific risk information and response suggestions through visual charts and alarms, helping users to take protective measures in time; the post-analysis visualization interface summarizes and displays historical data and disaster events, and helps users analyze the causes and patterns of disasters through charts and reports, providing a scientific basis and decision-making support for future disaster prevention and control work.

[0084] Step 5, the technical architecture includes hardware and software technical architecture. The hardware and software are combined to achieve real-time monitoring of coal mine environmental parameters, data analysis, disaster prediction and intelligent decision support.

[0085] Step 51, the hardware architecture is mainly composed of multiple sensors (temperature sensor, humidity sensor, gas concentration sensor, coal dust concentration sensor and air pressure sensor), data processing unit (edge ​​computing device and server), network communication equipment (wireless transmission module and wired network interface) and display terminal; the hardware components work together to realize real-time monitoring and data transmission of coal mine environmental parameters;

[0086] Step 52, the software technology architecture includes data acquisition software, data processing and storage software, deep learning integrated model, disaster warning and emergency response software and data visualization software; the data visualization software displays monitoring data, prediction results and emergency response information to users in an intuitive graphical interface to achieve intelligent decision support.

[0087] Only the preferred embodiments of the present invention are described in detail above, but the present invention is not limited to the above embodiments. Various changes can be made within the knowledge scope of ordinary technicians in this field without departing from the purpose of the present invention, and various changes should be included in the protection scope of the present invention.

Claims

1. A comprehensive prevention and control method for coal mine disasters based on deep learning integration, characterized in that: The following steps are involved: S1. The sensor data acquisition and processing module includes different types of sensors, which are used to monitor the temperature, humidity, gas concentration, coal dust concentration and air pressure in the coal mine, and are used to monitor the environmental parameters of the coal mine in real time, and store and preliminarily pre-process the collected data; S2, the deep learning integrated algorithm module is used to analyze and model the stored and processed data to predict potential disaster risks; the convolutional neural network part is used to process sensor data to extract local environmental features, the recurrent neural network part is used to process time series data, and the graph convolutional network part is used to process the spatial location dependency of sensor locations; the feature vectors output by the convolutional neural network, the recurrent neural network, and the graph convolutional network are concatenated and fused through the fully connected layer to form a comprehensive feature representation; the disaster risk is predicted through the fully connected layer and activation function, and the disaster risk score is output; S3, intelligent early warning and data visualization module provides real-time monitoring interface, disaster early warning interface and post-analysis visualization interface, so that users can fully understand the coal mine environment and disaster prevention and control situation; S4. The technical architecture includes hardware and software technical architecture. The hardware and software are combined to realize real-time monitoring of coal mine environmental parameters, data analysis, disaster prediction and intelligent decision support.

2. A method for comprehensive prevention and control of coal mine disasters based on deep learning integration according to claim 1, characterized in that: The method of collecting sensor data in S1 is: The sensor data acquisition and processing module includes different types of sensors for monitoring key environmental parameters in coal mines: temperature sensor (T i ), humidity sensor (H i ), gas concentration sensor (G i ), coal dust concentration sensor (D i ), air pressure sensor (P i ); The reading recorded by each sensor at time t is represented by X i (t), where X i Indicates the corresponding sensor type; the overall state of the sensor data at time t is expressed in vector form: X(t)=[T i (t),H i (t),G i (t),D i (t),P i (t)] These sensors monitor the coal mine environmental parameters in real time and send the data to the data processing unit.

3. A method for comprehensive prevention and control of coal mine disasters based on deep learning integration according to claim 1, characterized in that: The method for performing preliminary preprocessing on the collected data in S1 is: The data processing unit is responsible for storing and preliminarily processing the collected data; the data is normalized to eliminate the differences in the data ranges of different sensors. The normalization formula is as follows: Among them, X′ i (t) is the normalized sensor data, min(X i ) and max(X i ) represent sensor X i The minimum and maximum values ​​of the normalized data are denoised using the sliding average method: in, represents the denoised data, and N is the size of the sliding window; the processed data will be stored in the database for subsequent analysis and modeling.

4. A method for comprehensive prevention and control of coal mine disasters based on deep learning integration according to claim 1, characterized in that: The method for processing sensor data to extract local environmental features in S2 is: The convolutional neural network part processes sensor data and extracts local environmental features. The input sensor data is X(t), and the operation of the convolutional layer is expressed as: F l =f(W l *X+b l ) Among them, F l represents the feature map after the lth layer of convolution, W l and b l are convolution kernel and bias respectively, * represents convolution operation, and f is activation function; through convolution layer and pooling layer, CNN extracts local features of sensor data to form feature vector F CNN .

5. The method for comprehensive prevention and control of coal mine disasters based on deep learning integration according to claim 1, characterized in that: The method for processing time series data in S2 is: The time series data of the input of the recurrent neural network is X(tk),X(t-k+1),...,X(t), which can be expressed as: h t =g(W h X t +U h h t-1 +b h ) Among them, h t represents the hidden state at the current moment, W h , U h and b h are weight matrices and biases, respectively, and g is the activation function; through the recursive operation of time steps, RNN generates the time series feature vector F RNN .

6. A method for comprehensive prevention and control of coal mine disasters based on deep learning integration according to claim 1, characterized in that: The method for processing the spatial position dependency of the sensor position in S2 is: The graph convolutional network is used to process the spatial dependency of sensor positions. The graph structure of sensor positions is: in and ε represent nodes and edges respectively, and the GCN operation is expressed as: Among them, H (l) represents the node features of layer l, is the normalized adjacency matrix of the graph, W (l) is the weight matrix, σ is the activation function; after GCN operation, the spatial feature vector F is obtained GCN .

7. A method for comprehensive prevention and control of coal mine disasters based on deep learning integration according to claim 1, characterized in that: The method for forming comprehensive characteristics and disaster risk prediction in S2 is: The feature vectors output by the convolutional neural network, recurrent neural network, and graph convolutional network are concatenated to form a comprehensive feature representation: F concat =[F CNN ,F RNN ,F GCN ] Then disaster risk prediction is performed through the fully connected layer and activation function: y=σ(W out F concat +b out ) Among them, W out and b out is the weight and bias of the fully connected layer, σ is the activation function, and y is the output disaster risk score. Through training and optimization, the model can accurately predict the potential disaster risks in the coal mine environment.

8. The method for comprehensive prevention and control of coal mine disasters based on deep learning integration according to claim 1, characterized in that: The intelligent early warning and data visualization module in S3 includes: the real-time monitoring interface displays real-time data of parameters such as temperature, humidity, gas concentration, coal dust concentration and air pressure collected by sensors, ensuring that users can instantly grasp changes in the coal mine environment; when a potential risk is detected, the disaster early warning interface will notify users of specific risk information and response suggestions through visual charts and alarms, helping users to take protective measures in time; the post-analysis visualization interface summarizes and displays historical data and disaster events, and helps users analyze the causes and patterns of disasters through charts and reports, providing a scientific basis and decision-making support for future disaster prevention and control work.

9. A method for comprehensive prevention and control of coal mine disasters based on deep learning integration according to claim 1, characterized in that: The hardware technology architecture of S4 is composed of a variety of sensors, a data processing unit, a network communication device and a display terminal. The various sensors are composed of a temperature sensor, a humidity sensor, a gas concentration sensor, a coal dust concentration sensor and an air pressure sensor. The data processing unit is composed of an edge computing device and a server. The network communication device is composed of a wireless transmission module and a wired network interface. The hardware components work together to realize real-time monitoring and data transmission of coal mine environmental parameters.

10. A method for comprehensive prevention and control of coal mine disasters based on deep learning integration according to claim 1, characterized in that: The software technology architecture of S4 includes data acquisition software, data processing and storage software, deep learning integrated model, disaster warning and emergency response software and data visualization software; the data visualization software displays monitoring data, prediction results and emergency response information to users in an intuitive graphical interface to achieve intelligent decision support.

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