Coal mine gas early warning and monitoring management system

Abstract

The invention discloses a coal mine gas early warning and monitoring management system, which comprises an Internet of Things architecture formed by a sensing layer, a transmission layer, a platform layer and an application layer, and is characterized in that the sensing layer performs full-time and multi-dimensional coal mine state data acquisition on each monitoring area in a coal mine through a sensing subsystem, and transmits the data to the platform layer through the transmission layer; the transmission layer is used for transmitting the coal mine state data acquired by the sensing layer in real time to the platform layer, and the platform layer carries out message queue, parallel computing, protocol processing, operation and maintenance management and real-time alarm according to the coal mine state data transmitted by the transmission layer and serves as sensing data of the sensing layer and service connection of the application layer; the application layer is used for processing and analyzing the coal mine state data of the sensing layer according to the platform layer and acquiring a prediction decision; and basis and data support are provided for making scientific decisions through rich internet-of-things sensing means, and the intelligent decision level of gas management is improved.

Description

[0001] This invention relates to the field of emergency rescue communication technology, and in particular to a coal mine gas early warning and monitoring management system. Background Technology

[0002] Coal mine gas accidents are one of the major threats to coal mine safety. Gas explosions, gas outbursts and other accidents not only cause huge casualties and economic losses, but also have a profound impact on social stability and energy supply.

[0003] However, in existing coal mine early warning systems, there is insufficient real-time collaborative analysis of static data such as geological structure and mining progress with dynamic monitoring data. Early warning models rely heavily on historical data and are slow to respond to sudden anomalies (such as instantaneous gas outbursts). In addition, the sensors have insufficient anti-interference capabilities and are easily affected by the underground environment (such as dust and humidity). Data fusion and multi-source information collaborative analysis still need to be optimized, resulting in a high false alarm rate.

[0004] Therefore, there is an urgent need for a coal mine gas early warning and monitoring management system to solve the above-mentioned technical problems. Summary of the Invention

[0005] The technical problem to be solved by the present invention is to provide a coal mine gas early warning and monitoring management system, which aims to improve the efficiency of mine emergency rescue, in order to address the above-mentioned deficiencies of the prior art.

[0006] The technical solution adopted by the present invention to solve the technical problem is as follows: a coal mine gas early warning and monitoring management system, the system comprising an Internet of Things architecture consisting of a sensing layer, a transmission layer, a platform layer and an application layer, wherein the sensing layer collects coal mine status data in all monitoring areas of the coal mine in a real-time and multi-dimensional manner through a sensing subsystem, and transmits the data to the platform layer through the transmission layer; The transmission layer is used to transmit the coal mine status data acquired in real time by the sensing layer to the platform layer. The network transmission methods of the transmission layer include wired network, wireless network and narrowband Internet of Things. The platform layer, based on the coal mine status data transmitted from the transmission layer, performs message queuing, parallel computing, protocol processing, operation and maintenance management, and real-time alarms. It provides basic data, data organization, resource catalog, data sharing, data association, and analysis management, serving as the perception data of the perception layer and the business connection of the application layer. The application layer obtains predictive decisions based on the coal mine status data of the perception layer processed and analyzed by the platform layer.

[0007] The coal mine gas early warning and monitoring management system includes a transmission layer with network transmission methods including wired network, wireless network and narrowband Internet of Things.

[0008] The coal mine gas early warning and monitoring management system includes a sensing subsystem comprising a gas concentration detection subsystem, a temperature detection subsystem, a wind speed detection subsystem, a gas pressure detection subsystem, and an automatic gas alarm subsystem; the gas concentration detection subsystem, the temperature detection subsystem, the wind speed detection subsystem, the gas pressure detection subsystem, and the gas alarm subsystem are all connected to the same local area network.

[0009] The coal mine gas early warning and monitoring management system includes a gas automatic alarm subsystem comprising a gas alarm controller and an information transmission device. The information transmission device is electrically connected to the gas alarm controller and accesses the local area network via 4G or a wired network.

[0010] The coal mine gas early warning and monitoring management system includes a gas concentration detection subsystem comprising multiple NB-IoT gas concentration detectors and an IoT alarm gateway. The multiple NB-IoT gas concentration detectors are connected to the IoT alarm gateway, and the IoT alarm gateway is connected to the local area network via a 2G / 4G / wired network. The multiple NB-IoT gas concentration detectors are connected to the local area network via NB-IoT.

[0011] The coal mine gas early warning and monitoring management system, wherein the application layer includes a feature analysis module, which is used to obtain state features from the coal mine state data and construct a feature training set; A gas over-limit early warning model construction module is used to construct a gas over-limit early warning model based on an MLP neural network. The module takes the feature training set as input and optimizes the parameters of the MLP neural network using a stochastic gradient descent method to obtain the gas over-limit early warning model. The decision generation module is used to obtain gas exceedance assessment decisions based on real-time coal mine status data.

[0012] The coal mine gas early warning and monitoring management system further includes a display layer; the display layer is used to display the coal mine status data processed and analyzed from the platform layer, and includes a BS client, a mobile APP, and a video wall.

[0013] The coal mine gas early warning and monitoring management system, wherein the wireless network is a 2G / 4G / 5G wireless network.

[0014] The coal mine gas early warning and monitoring management system, wherein the narrowband Internet of Things is NB-IoT / LoRa.

[0015] A method for early warning and monitoring management of coal mine gas includes: collecting coal mine status data in all monitoring areas of the coal mine in real time and in multiple dimensions through a sensing subsystem in the sensing layer, and transmitting the data to the platform layer through the transmission layer; the coal mine status data includes gas concentration, temperature, wind speed, and air pressure; transmitting the coal mine status data acquired in real time by the sensing layer to the platform layer, wherein the network transmission method of the transmission layer includes wired network, wireless network, and narrowband Internet of Things; based on the coal mine status data transmitted from the transmission layer, performing message queuing, parallel computing, protocol processing, operation and maintenance management, and real-time alarms in the platform layer, providing basic data, data organization, resource catalog, data sharing, data association, and analysis management, serving as the sensing data of the sensing layer and the business connection of the application layer; and obtaining predictive decisions based on the processing and analysis of the coal mine status data of the sensing layer by the platform layer.

[0016] Beneficial effects: 1. The coal mine gas early warning and monitoring management system of the present invention combines an Internet of Things architecture. The perception layer deploys multiple perception subsystems according to different application scenarios and network environments to achieve full coverage and thus accurately collect real-time coal mine status data.

[0017] 2. The coal mine gas early warning and monitoring management system of this invention achieves data sharing and business linkage through rich IoT sensing methods, thereby improving the efficiency of handling gas leakage incidents; it provides a basis and data support for coal mine management to make scientific decisions, and improves the level of intelligent decision-making in gas management. Attached Figure Description

[0018] Figure 1 This is a schematic diagram of the structure of the coal mine gas early warning and monitoring management system described in this invention; Figure 2 This is a schematic diagram of the structure of the coal mine gas early warning and monitoring management method described in this invention. Detailed Implementation

[0019] To make the objectives, technical solutions, and advantages of this invention clearer and more explicit, the invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention.

[0020] This invention provides a coal mine gas early warning and monitoring management system, such as... Figure 1 As shown, the coal mine gas early warning and monitoring management system includes an Internet of Things (IoT) architecture consisting of a perception layer, a transmission layer, a platform layer, and an application layer. The sensing layer collects coal mine status data in all monitoring areas of the coal mine around the clock and in multiple dimensions through the sensing subsystem, and transmits it to the platform layer through the transmission layer; the coal mine status data includes gas concentration, temperature, wind speed and air pressure. The transport layer is used to transmit the coal mine status data acquired in real time by the perception layer to the platform layer. The network transmission methods of the transport layer include wired network, wireless network and narrowband Internet of Things. The platform layer performs message queuing, parallel computing, protocol processing, operation and maintenance management, and real-time alarms based on the coal mine status data transmitted from the transmission layer. It provides basic data, data organization, resource catalog, data sharing, data association, and analysis management, serving as the perception data of the perception layer and the business connection of the application layer. The application layer processes and analyzes the coal mine status data from the perception layer based on the platform layer, and then obtains predictive decisions. Presentation layer: The presentation of coal mine status data processed and analyzed from the platform layer. The presentation layer includes a BS client, a mobile APP, and a video wall.

[0021] The network transmission methods in the transmission layer include wired networks, wireless networks, and narrowband IoT; the wireless network is a 2G / 4G / 5G wireless network, and the narrowband IoT is NB-IoT / LoRa.

[0022] In this embodiment of the application, combined with the Internet of Things architecture, the perception layer deploys multiple perception subsystems according to different application scenarios and network environments to achieve full coverage and thus accurately collect real-time coal mine status data.

[0023] In one embodiment, the sensing subsystem includes a gas concentration detection subsystem, a temperature detection subsystem, a wind speed detection subsystem, an air pressure detection subsystem, and a gas automatic alarm subsystem; the gas concentration detection subsystem, temperature detection subsystem, wind speed detection subsystem, air pressure detection subsystem, and gas alarm subsystem are all connected to the same local area network; The automatic gas alarm subsystem includes a gas alarm controller and an information transmission device. The information transmission device is electrically connected to the gas alarm controller and accesses the local area network (LAN) via 4G or a wired network. The gas concentration detection subsystem includes multiple NB-IoT gas concentration detectors and an IoT alarm gateway. All of the multiple NB-IoT gas concentration detectors are connected to the IoT alarm gateway, which accesses the LAN via 2G / 4G / wired network. The multiple NB-IoT gas concentration detectors access the LAN via NB-IoT.

[0024] It should be noted that the information transmission device connects to the gas alarm controller via RS232 / RS485 / CAN to promptly obtain alarm information and operating status information of the automatic gas alarm subsystem of the networked unit, and transmits it to the platform layer in real time through the transmission layer.

[0025] In this embodiment, the sensing layer uses a gas concentration detection subsystem, a temperature detection subsystem, a wind speed detection subsystem, a gas pressure detection subsystem, and a gas automatic alarm subsystem. The sensing subsystems are arranged according to different application scenarios and network environments to achieve full coverage and thus accurately collect real-time coal mine status data.

[0026] In one embodiment of the application, the application layer includes: The feature analysis module is used to extract state features from coal mine state data and construct a feature training set. The gas over-limit early warning model construction module is used to construct a gas over-limit early warning model based on an MLP neural network. It takes the feature training set as input and optimizes the parameters of the MLP neural network through the stochastic gradient descent method to obtain the gas over-limit early warning model. The decision generation module is used to obtain gas exceedance assessment decisions based on real-time coal mine status data.

[0027] In the application layer, prediction decisions are obtained based on the processing and analysis of coal mine condition data. The steps for obtaining prediction decisions include: extracting condition features from the coal mine condition data and constructing a feature training set; building a gas exceedance early warning model based on an MLP neural network, and optimizing the parameters of the MLP neural network using the feature training set as input through stochastic gradient descent to obtain the gas exceedance early warning model; and obtaining a gas exceedance assessment decision using real-time coal mine condition data as input.

[0028] In this embodiment, a gas exceedance early warning model is used to fuse multi-source data, including data on gas concentration, temperature, wind speed, and air pressure in the target coal mine, thereby obtaining more accurate gas exceedance assessment decisions; achieving data sharing and business linkage, and improving the efficiency of handling gas leakage incidents.

[0029] In one embodiment of the application, such as Figure 2 As shown, a method for early warning and monitoring management of coal mine gas is proposed, the method comprising: S201 collects coal mine status data in all time periods and in multiple dimensions from the sensing subsystem in the sensing layer, and transmits it to the platform layer through the transmission layer. S202 transmits the coal mine status data acquired in real time by the perception layer to the platform layer. The network transmission methods of the transmission layer include wired network, wireless network and narrowband Internet of Things. S203, based on the coal mine status data transmitted from the transmission layer, performs message queuing, parallel computing, protocol processing, operation and maintenance management, and real-time alarms in the platform layer, providing basic data, data organization, resource catalog, data sharing, data association, and analysis management, serving as the perception data of the perception layer and the business connection of the application layer; S204, Based on the processing and analysis of coal mine status data from the perception layer by the platform layer, predictive decisions are obtained.

[0030] This application provides a method for early warning and monitoring management of coal mine gas. Combining an Internet of Things (IoT) architecture, the perception layer deploys multiple perception subsystems according to different application scenarios and network environments to achieve full coverage and accurately collect real-time coal mine status data. By enriching IoT sensing methods, it achieves data sharing and business linkage, improving the efficiency of handling gas leakage incidents. It provides a basis and data support for coal mine management to make scientific decisions, and enhances the level of intelligent decision-making in gas management for building an intelligent urban safety guarantee system.

[0031] It should be understood that the application of the present invention is not limited to the examples above. Those skilled in the art can make improvements or modifications based on the above description, and all such improvements and modifications should fall within the protection scope of the appended claims.

Claims

1. A coal mine gas early warning and monitoring management system, characterized in that, The system comprises an Internet of Things architecture consisting of a perception layer, a transmission layer, a platform layer, and an application layer. The perception layer collects coal mine status data in all monitoring areas of the coal mine in real time and in multiple dimensions through a perception subsystem, and transmits the data to the platform layer through the transmission layer. The transmission layer is used to transmit the coal mine status data acquired in real time by the sensing layer to the platform layer. The network transmission methods of the transmission layer include wired network, wireless network and narrowband Internet of Things. The platform layer, based on the coal mine status data transmitted from the transmission layer, performs message queuing, parallel computing, protocol processing, operation and maintenance management, and real-time alarms. It provides basic data, data organization, resource catalog, data sharing, data association, and analysis management, serving as the perception data of the perception layer and the business connection of the application layer. The application layer obtains predictive decisions based on the coal mine status data of the perception layer processed and analyzed by the platform layer.

2. The coal mine gas early warning and monitoring management system according to claim 1, characterized in that, The network transmission methods of the transmission layer include wired networks, wireless networks, and narrowband Internet of Things (IoT).

3. The coal mine gas early warning and monitoring management system according to claim 2, characterized in that, The sensing subsystem includes a gas concentration detection subsystem, a temperature detection subsystem, a wind speed detection subsystem, an air pressure detection subsystem, and a gas automatic alarm subsystem; the gas concentration detection subsystem, the temperature detection subsystem, the wind speed detection subsystem, the air pressure detection subsystem, and the gas alarm subsystem are all connected to the same local area network.

4. The coal mine gas early warning and monitoring management system according to claim 2, characterized in that, The automatic gas alarm subsystem includes a gas alarm controller and an information transmission device. The information transmission device is electrically connected to the gas alarm controller and accesses the local area network via 4G or a wired network.

5. The coal mine gas early warning and monitoring management system according to claim 2, characterized in that, The gas concentration detection subsystem includes multiple NB-IoT gas concentration detectors and an IoT alarm gateway. The multiple NB-IoT gas concentration detectors are all connected to the IoT alarm gateway, and the IoT alarm gateway is connected to the local area network via 2G / 4G / wired network; the multiple NB-IoT gas concentration detectors are connected to the local area network via NB-IoT.

6. The coal mine gas early warning and monitoring management system according to claim 1, characterized in that, The application layer includes a feature analysis module, which is used to obtain state features from the coal mine state data and construct a feature training set; A gas over-limit early warning model construction module is used to construct a gas over-limit early warning model based on an MLP neural network. The module takes the feature training set as input and optimizes the parameters of the MLP neural network using a stochastic gradient descent method to obtain the gas over-limit early warning model. The decision generation module is used to obtain gas exceedance assessment decisions based on real-time coal mine status data.

7. The coal mine gas early warning and monitoring management system according to claim 1, characterized in that, The system also includes a presentation layer; the presentation layer is used to display the coal mine status data processed and analyzed from the platform layer, and the presentation layer includes a BS client, a mobile APP, and a video wall.

8. The coal mine gas early warning and monitoring management system according to claim 2, characterized in that, The wireless network is a 2G / 4G / 5G wireless network.

9. The coal mine gas early warning and monitoring management system according to claim 2, characterized in that, The narrowband IoT refers to NB-IoT / LoRa.

10. A method for early warning and monitoring management of coal mine gas, characterized in that, The method includes: collecting coal mine status data in all monitoring areas of the coal mine in real time and in multiple dimensions through the sensing subsystem in the sensing layer, and transmitting the data to the platform layer through the transmission layer; the coal mine status data includes gas concentration, temperature, wind speed and air pressure; The coal mine status data acquired in real time by the perception layer is transmitted to the platform layer. The network transmission methods of the transmission layer include wired network, wireless network and narrowband Internet of Things. Based on the coal mine status data transmitted from the transmission layer, message queuing, parallel computing, protocol processing, operation and maintenance management, and real-time alarms are performed in the platform layer. It provides basic data, data organization, resource catalog, data sharing, data association, and analysis management, serving as the perception data of the perception layer and the business connection of the application layer. Based on the processing and analysis of coal mine status data from the perception layer by the platform layer, predictive decisions are obtained.

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