Synchronous early warning and sensing device for coal seam gas and hard roof rock burst

Abstract

The invention relates to the technical field of coal mine safety production monitoring, in particular to a coal seam gas and hard roof rock burst synchronous early warning sensing device which comprises a composite resonant cavity sensor unit, a multi-mode signal collecting and processing module and a collaborative early warning mechanism module. The composite resonant cavity sensor unit is arranged at the junction of a roadway roof and a coal seam and used for sensing stress waves generated by coal seam gas abnormity and roof rock burst and generating characteristic resonance signals. According to the method, the traditional mode that a single sensor independently monitors gas or roof pressure is changed, synchronous monitoring of coal seam gas and hard roof rock burst is achieved through the composite resonant cavity sensor unit and the multi-modal signal collecting and processing technology, multi-modal signals are subjected to fusion analysis through the convolutional neural network, and the real-time monitoring of the coal seam gas and the hard roof rock burst is achieved. The accuracy and timeliness of early warning are remarkably improved, the false alarm rate is effectively reduced, and the defects that traditional monitoring is low in sensitivity and lagged in response are overcome.

Description

Technical Field

[0001] This invention relates to the field of coal mine safety production monitoring technology, and in particular to a synchronous early warning and sensing device for coal seam gas and hard roof rockburst. Background Technology

[0002] In coal mining operations, coal seam gas outbursts and hard roof rockbursts are two major hazards that seriously threaten safe production. During a coal seam gas outburst, the gas concentration rises sharply, which can easily lead to major accidents such as explosions and personnel asphyxiation; while hard roof rockbursts can cause roadway collapses and equipment damage, seriously threatening the lives of underground workers.

[0003] Currently, most disaster monitoring methods in coal mines rely on single sensors to independently monitor gas or roof pressure. This method cannot provide simultaneous early warning of coal seam gas and roof rockbursts, and suffers from low sensitivity, high false alarm rates, and delayed response. Furthermore, the harsh underground environment of coal mines, characterized by high humidity, strong electromagnetic interference, and high dust levels, renders existing monitoring devices unstable and unreliable, failing to meet the actual needs of safe coal mine production. Therefore, developing a device capable of simultaneously monitoring coal seam gas and hard roof rockbursts, and providing accurate early warnings, is urgently needed. Summary of the Invention

[0004] The purpose of this invention is to provide a synchronous early warning and sensing device for coal seam gas and hard roof rockburst, which solves the problems of traditional coal mine disaster monitoring methods that cannot provide synchronous early warning of coal seam gas and roof rockburst, have low monitoring sensitivity, high false alarm rate, and slow response, as well as the poor stability and reliability of existing monitoring devices in harsh underground environments, and the lack of intelligent maintenance and efficient hierarchical prevention and control mechanisms.

[0005] To achieve the above objectives, the present invention adopts the following technical solution: A synchronous early warning and sensing device for coal seam gas and hard roof rockburst includes a composite resonant cavity sensor unit, a multimodal signal acquisition and processing module, and a collaborative early warning mechanism module. The composite resonant cavity sensor unit is installed at the interface between the roadway roof and the coal seam to sense abnormal coal seam gas and stress waves generated by roof rockburst and to generate characteristic resonance signals. The multimodal signal acquisition and processing module is connected to the composite resonant cavity sensor unit via a shielded cable to acquire and analyze the characteristic resonance signals. The collaborative early warning mechanism module is electrically connected to the multimodal signal acquisition and processing module and is used to issue early warnings based on the analysis results.

[0006] Preferably, the composite resonant cavity sensor unit comprises an outer high-strength wear-resistant alloy steel layer, a middle damping adjustment layer, and an inner resonant sensitive layer. The outer high-strength wear-resistant alloy steel layer has a cylindrical structure and wraps around the entire composite resonant cavity sensor unit to ensure structural stability in the harsh environment of underground coal mines. The middle damping adjustment layer is composed of rubber material with adjustable hardness and is arranged in a ring between the outer high-strength wear-resistant alloy steel layer and the inner resonant sensitive layer to adjust the reflection and absorption characteristics of stress waves according to different geological conditions. The inner resonant sensitive layer is made of a special piezoelectric ceramic material, is cylindrical, and is located on the innermost side of the composite resonant cavity sensor unit to generate a strong resonant response to stress waves of a specific frequency. Multiple composite resonant cavity units are arranged in a matrix along the roadway direction and vertical direction, and each composite resonant cavity unit corresponds to a different frequency response range, covering the frequency range of stress waves generated by coal seam gas anomalies and roof rock bursts.

[0007] Preferably, the multimodal signal acquisition and processing module includes a miniature accelerometer and a strain sensor mounted on the surface of the inner piezoelectric ceramic layer, used to acquire vibration acceleration and strain signals induced by stress waves in the resonant cavity; it also includes a filtering unit, a fast Fourier transform unit, and a convolutional neural network analysis unit; the filtering unit, fast Fourier transform unit, and convolutional neural network analysis unit are integrated in an explosion-proof box; the filtering unit is used to filter the acquired signals, the fast Fourier transform unit is used to convert the time-domain signal into a frequency-domain signal and extract characteristic parameters such as frequency, amplitude, and phase of the stress wave, and the convolutional neural network analysis unit is used to perform fusion analysis on the multimodal signals to identify coal seam gas anomalies or roof rockburst states corresponding to different combinations of characteristic parameters.

[0008] Preferably, the collaborative early warning mechanism module includes a feature signal recognition model based on a convolutional neural network. The feature signal recognition model is trained by learning from a large amount of historical data to distinguish stress wave resonance feature signals generated by different disaster types such as abnormal increase in coal seam gas concentration, gas outburst, roof stress concentration, and rock burst. The collaborative early warning mechanism module is set up in the ground monitoring center and communicates with the multimodal signal acquisition and processing module through a wireless network.

[0009] Preferably, the collaborative early warning mechanism module further includes a graded early warning and linkage response unit, which divides the early warning into four levels: Level 1, Level 2, Level 3, and Level 4. When the system identifies an early warning signal, it activates different early warning measures according to the corresponding level. The graded early warning and linkage response unit is electrically connected to the underground sound and light alarm device, ventilation system control device, and power cut-off device in the coal mine.

[0010] Preferably, the system further includes an integrated device housing, which is made of high-strength aluminum alloy with anodized surface and an internal heat insulation layer; the composite resonant cavity sensor unit, the multimodal signal acquisition and processing module, and the power supply module are all integrated into the integrated device housing, which has an IP68 protection rating and is installed on the side wall 0.5-1 meters below the tunnel roof.

[0011] Preferably, the system further includes a self-calibration and maintenance system. This system has a built-in self-calibration program that generates simulated stress wave signals using a built-in standard signal generator to calibrate the sensors and signal processing module. The self-calibration and maintenance system also includes status indicator lights on the surface of the integrated device housing, used to display the system's operating status and fault information in real time. When a sensor fault or abnormal data is detected, the system automatically locates the fault and sends maintenance prompts to maintenance personnel through the monitoring software platform. The self-calibration and maintenance system is integrated with the multimodal signal acquisition and processing module within the same explosion-proof enclosure.

[0012] Preferably, the outer high-strength wear-resistant alloy steel surface of the composite resonant cavity sensor unit is provided with anti-slip texture. The anti-slip texture is evenly distributed along the circumference of the alloy steel to enhance the friction between the composite resonant cavity sensor unit and the roadway roof or coal seam, and to prevent displacement after installation.

[0013] Preferably, the shielded cable in the multimodal signal acquisition and processing module adopts a double-layer shielding structure, with an outer layer of tin-plated copper braided mesh and an inner layer of aluminum foil shielding. An insulating isolation layer is provided between the two shielding layers to effectively shield electromagnetic interference in coal mines and ensure the stability and accuracy of signal transmission.

[0014] Preferably, the integrated device housing is provided with a quick-release interface, which includes a snap-fit ​​connection structure and an electrical quick-connect connector. The snap-fit ​​connection structure is used to quickly fix and disassemble the integrated device housing to the roadway sidewall, and the electrical quick-connect connector is used to quickly connect and disconnect the internal modules from the external wiring, facilitating the installation and maintenance of the system.

[0015] This invention has at least the following beneficial effects: This method changes the traditional approach of using a single sensor to independently monitor gas or roof pressure. By employing a composite resonant cavity sensor unit and multimodal signal acquisition and processing technology, it achieves simultaneous monitoring of coal seam gas and rockburst from a hard roof. Furthermore, it utilizes a convolutional neural network to fuse and analyze the multimodal signals, significantly improving the accuracy and timeliness of early warnings, effectively reducing false alarm rates, and compensating for the shortcomings of traditional monitoring methods, such as low sensitivity and delayed response.

[0016] The integrated device housing adopts a high-strength, high-protection design. The composite resonant cavity sensor unit has wear-resistant and anti-interference characteristics. The shielded cable of the multi-mode signal acquisition and processing module effectively shields electromagnetic interference, enabling the device to operate stably and reliably in the harsh environment of underground coal mines.

[0017] The self-calibration and maintenance system enables automatic calibration and fault diagnosis of the device, reducing manual maintenance costs. The integrated housing features quick-release interfaces, facilitating installation, disassembly, and maintenance, thus improving work efficiency. Furthermore, integrating relevant modules within the housing further simplifies the installation process.

[0018] The collaborative early warning mechanism module's tiered early warning and linkage response unit activates different levels of early warning measures based on the severity of the disaster, and coordinates the control of relevant underground equipment to achieve effective disaster prevention and control, and ensure safe production in coal mines. Attached Figure Description

[0019] To more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings used in the following description of the embodiments will be briefly introduced. Obviously, the drawings described below are some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0020] Figure 1 This is a schematic diagram of the integrated device housing structure of the present invention; Figure 2 This is a schematic diagram of the internal structure of the explosion-proof box of the present invention; Figure 3 This is a schematic diagram of the composite resonant cavity sensor unit structure of the present invention; Figure 4 This is a schematic diagram of the anti-slip texture structure of the present invention; Figure 5 This is a schematic diagram of the overall system architecture of the present invention; Figure 6 This is a schematic diagram of the signal acquisition and processing flow of the present invention; Figure 7 This is a schematic diagram of the collaborative early warning and linkage response process of the present invention; Figure 8 This is a block diagram of the multimodal signal acquisition and processing module of the present invention.

[0021] In the diagram: 1. Integrated device housing; 2. Composite resonant cavity sensor unit; 21. High-strength wear-resistant alloy steel; 22. Damping adjustment layer; 23. Resonance sensitive layer; 24. Anti-slip texture; 3. Multimodal signal acquisition and processing module; 31. Fast Fourier Transform unit; 32. Filtering unit; 33. Convolutional neural network analysis unit; 34. Miniature accelerometer; 35. Strain sensor; 4. Explosion-proof box; 5. Power supply module; 6. Status indicator light. Detailed Implementation

[0022] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. The components of the embodiments of the present invention described and shown in the accompanying drawings can generally be arranged and designed in various different configurations.

[0023] Therefore, the following detailed description of the embodiments of the invention provided in the accompanying drawings is not intended to limit the scope of the claimed invention, but merely to illustrate selected embodiments of the invention. All other embodiments obtained by those skilled in the art based on the embodiments of the invention without inventive effort are within the scope of protection of the invention.

[0024] Reference Figure 1-8 A synchronous early warning and sensing device for coal seam gas and hard roof rockburst is disclosed. It mainly consists of an integrated device housing 1, a composite resonant cavity sensor unit 2, a multimodal signal acquisition and processing module 3, a collaborative early warning mechanism module, a power supply module 5, and a self-calibration and maintenance system. The integrated device housing 1 is made of high-strength aluminum alloy with an anodized surface and an internal heat insulation layer, achieving an IP68 protection rating, enabling it to adapt to the harsh environmental conditions of underground coal mines. The housing is installed on the side wall 0.5-1 meters below the roadway roof and is equipped with quick-disassembly interfaces, including snap-fit ​​connections and electrical quick-connect fittings, greatly facilitating the installation, disassembly, and maintenance of the device.

[0025] Furthermore, the composite resonant cavity sensor unit 2 is located at the interface between the roadway roof and the coal seam. It is the core component for disaster monitoring, used to sense abnormal coal seam gas and stress waves generated by roof rockburst, and convert them into characteristic resonant signals. This sensor unit comprises an outer layer of high-strength wear-resistant alloy steel 21, a middle layer of damping adjustment layer 22, and an inner layer of resonant sensitive layer 23. The outer layer of high-strength wear-resistant alloy steel 21 is cylindrical, wrapping around the entire sensor unit. Its surface is designed with uniformly distributed anti-slip textures 24 along the circumference, which enhances the friction between the sensor unit and the roadway roof or coal seam, preventing displacement after installation and ensuring structural stability in harsh environments.

[0026] Furthermore, the intermediate damping adjustment layer 22 is composed of a rubber material with adjustable hardness. This rubber material uses ethylene-propylene-diene rubber (EPDM) as a matrix and incorporates shape memory polymer particles and conductive carbon nanotubes. By changing the electric field strength applied to the rubber material, the conductive carbon nanotubes generate Joule heating, causing a phase transition in the shape memory polymer particles, thereby adjusting the hardness of the rubber material to adapt to the stress wave reflection and absorption requirements under different geological conditions.

[0027] Furthermore, the inner resonant sensitive layer 23 employs a special lead zirconate titanate (PZT)-based composite material, which is doped with 5% by mass of potassium sodium niobate (KNN) and 3% by mass of titanium dioxide (TiO2) nanoparticles. This material exhibits a wider frequency response range and a higher electromechanical coupling coefficient, enabling it to generate a strong resonant response to stress waves in the 10–1000 Hz frequency band generated by coal seam gas anomalies and roof rockbursts, converting these into electrical signals. Multiple composite resonant cavity units are arranged in a matrix along the roadway direction and vertically, each unit corresponding to a different frequency response range, collectively covering the frequency range of stress waves generated by coal seam gas anomalies and roof rockbursts.

[0028] Furthermore, the multimodal signal acquisition and processing module 3 is connected to the composite resonant cavity sensor unit 2 via a shielded cable, and is responsible for acquiring and analyzing characteristic resonant signals. This module includes a miniature accelerometer 34 and a strain sensor 35 mounted on the surface of the inner piezoelectric ceramic layer, used to acquire vibration acceleration and strain signals induced by stress waves within the resonant cavity; it also includes a filtering unit 32, a fast Fourier transform unit 31, and a convolutional neural network analysis unit 33. The filtering unit 32, fast Fourier transform unit 31, and convolutional neural network analysis unit 33 are integrated with the self-calibration and maintenance system within the same explosion-proof box 4. This explosion-proof box 4 is installed inside the integrated device housing 1 and works closely with the composite resonant cavity sensor unit 2, effectively shortening the signal transmission distance and reducing signal attenuation and interference. The system includes a filtering unit 32 for filtering the acquired signals to remove noise interference; a fast Fourier transform unit 31 for converting the time-domain signal into a frequency-domain signal and extracting characteristic parameters such as frequency, amplitude, and phase of the stress wave; and a convolutional neural network analysis unit 33 for fusion analysis of multimodal signals to identify coal seam gas anomalies or roof rockburst states corresponding to different combinations of characteristic parameters. The shielded cable employs a double-layer shielding structure: an outer layer of tin-plated copper braided mesh and an inner layer of aluminum foil shielding. An insulating layer separates the two shielding layers, effectively shielding against electromagnetic interference in the coal mine and ensuring the stability and accuracy of signal transmission.

[0029] Furthermore, the collaborative early warning mechanism module is electrically connected to the multimodal signal acquisition and processing module 3, and issues early warnings based on the analysis results. This module includes a feature signal recognition model based on a convolutional neural network. Through learning and training on a large amount of historical data, it can accurately distinguish the stress wave resonance characteristic signals generated by different disaster types such as abnormal increases in coal seam gas concentration, gas outbursts, roof stress concentration, and rockbursts. The collaborative early warning mechanism module is located in the ground monitoring center and communicates with the multimodal signal acquisition and processing module 3 via a wireless network. In addition, this module also has a graded early warning and linkage response unit, which divides the early warning into four levels: Level 1, Level 2, Level 3, and Level 4. When the system identifies an early warning signal, it activates different early warning measures according to the corresponding level. The graded early warning and linkage response unit is electrically connected to the underground audible and visual alarm devices, ventilation system control devices, and power cut-off devices to realize graded handling and linkage control of disasters.

[0030] Furthermore, the power supply module 5 is installed inside the integrated device housing 1, and through a reasonable circuit layout, it provides a stable power supply to each component of the device, ensuring the normal operation of the entire device.

[0031] Furthermore, the self-calibration and maintenance system incorporates a self-calibration program that uses a built-in standard signal generator to generate simulated stress wave signals to calibrate the sensor and signal processing module. The self-calibration program employs a least-squares calibration algorithm. Specifically, the standard signal generator generates a series of simulated stress wave signals with different frequencies (covering the frequency range of coal seam gas and roof rockburst stress waves) and amplitudes, which are then input into the composite resonant cavity sensor unit 2. The sensor unit converts the simulated stress waves into electrical signals, which are then acquired by the multi-modal signal acquisition and processing module 3. By comparing the acquired signal with the original input signal from the standard signal generator, the least-squares method is used to calculate the error parameters of the sensor and signal processing module at different frequencies and amplitudes. Finally, based on the calculated error parameters, the sensitivity coefficient of the sensor and the filtering parameters and amplification factor of the signal processing module are adjusted to complete the calibration process. The self-calibration and maintenance system also includes status indicator lights 6 on the surface of the integrated device housing 1, used to display the system's operating status and fault information in real time. When a sensor fault or abnormal data is detected, the system automatically locates the fault and sends maintenance prompts to maintenance personnel through the monitoring software platform.

[0032] In summary, the integrated device housing 1 is fixed to the side wall 0.5-1 meters below the tunnel roof using bolts, and external wiring is connected via electrical quick-connect connectors to complete the overall installation. Since the multi-modal signal acquisition and processing module 3 is integrated within the housing, the location of the explosion-proof box 4 does not need to be considered during installation, simplifying the process.

[0033] At the junction of the roadway roof and the coal seam, composite resonant cavity sensor units 2 are installed in a matrix arrangement as designed to ensure that the installation position of each sensor unit is accurate and that the anti-slip texture 24 is in close contact with the roadway roof or coal seam to prevent displacement.

[0034] When coal seam gas is abnormal or a rockburst occurs in the roof, the stress wave generated propagates to the composite resonant cavity sensor unit 2. The piezoelectric ceramic material of the inner resonant sensitive layer 23 resonates with the stress wave at a specific frequency, causing vibration acceleration and strain changes.

[0035] Miniature accelerometers 34 and strain sensors 35, mounted on the inner piezoelectric ceramic surface, collect vibration acceleration and strain signals, which are then transmitted to the multimodal signal acquisition and processing module 3 via a shielded cable with a double-layer shielding structure. Because the module is integrated into the housing 1 of the integrated device, the signal transmission path is short, enabling fast and accurate transmission.

[0036] The signal first enters the filtering unit 32 for filtering to remove noise interference generated by the underground coal mine environment; then the fast Fourier transform unit 31 converts the time domain signal into a frequency domain signal and extracts the frequency, amplitude, phase and other characteristic parameters of the stress wave; finally, the convolutional neural network analysis unit 33 performs fusion analysis on the multimodal signal to identify the coal seam gas anomaly or roof rockburst state corresponding to different combinations of characteristic parameters.

[0037] The multimodal signal acquisition and processing module 3 transmits the analysis results to the collaborative early warning mechanism module of the ground monitoring center via a wireless network.

[0038] The feature signal recognition model based on convolutional neural networks in the collaborative early warning mechanism module analyzes the characteristics of received signals to distinguish different disaster types such as abnormal increase in coal seam gas concentration, gas outburst, roof stress concentration, and rock burst.

[0039] Based on the severity of the disaster, the graded early warning and linkage response unit divides the early warning into four levels: Level 1 Early Warning: When a slight abnormal signal is detected, indicating a potential disaster risk, the audible and visual alarm device is activated to issue an early warning to the underground workers, reminding them to strengthen monitoring. Level 2 Early Warning: If the abnormal signal continues to intensify, indicating an increased disaster risk, in addition to activating the audible and visual alarm, the ventilation system control device is activated to increase ventilation volume and reduce gas concentration, while relevant personnel are notified to conduct on-site investigation.

[0040] Level 3 warning: When the probability of a disaster is high, in addition to the above measures, activate the power cut-off device to cut off the non-essential power supply to the dangerous area to prevent accidents such as gas explosions, and organize the orderly evacuation of personnel.

[0041] Level 4 Warning: If it is confirmed that a disaster is about to occur or has already occurred, immediately activate all linkage devices to do everything possible to ensure the safety of personnel and reduce property losses.

[0042] The self-calibration and maintenance system periodically, or upon device startup, generates simulated stress wave signals using a built-in standard signal generator to calibrate the composite resonant cavity sensor unit 2 and the multimodal signal acquisition and processing module 3, ensuring the device's monitoring accuracy. Due to module integration, the calibration process is more efficient, signal transmission is more stable, and calibration results are more accurate.

[0043] Status indicator 6 displays the system's operating status and fault information in real time. When a sensor fault or abnormal data is detected, the self-calibration and maintenance system automatically locates the fault and sends maintenance prompts to maintenance personnel through the monitoring software platform, facilitating timely troubleshooting and equipment maintenance.

[0044] The foregoing has shown and described the basic principles, main features, and advantages of the present invention. Those skilled in the art should understand that the present invention is not limited to the above embodiments. The embodiments and descriptions in the specification are merely principles of the invention. Various changes and modifications can be made to the invention without departing from its spirit and scope, and all such changes and modifications fall within the scope of the claimed invention. The scope of protection claimed by the appended claims and their equivalents is defined.

Claims

1. A synchronous early warning and sensing device for coal seam gas and hard roof rockburst, characterized in that, The system includes a composite resonant cavity sensor unit (2), a multimodal signal acquisition and processing module (3), and a collaborative early warning mechanism module. The composite resonant cavity sensor unit (2) is located at the junction of the roadway roof and the coal seam to sense coal seam gas anomalies and stress waves generated by roof rock pressure and generate characteristic resonance signals. The multimodal signal acquisition and processing module (3) is connected to the composite resonant cavity sensor unit (2) via a shielded cable to acquire and analyze the characteristic resonance signals. The collaborative early warning mechanism module is electrically connected to the multimodal signal acquisition and processing module (3) to issue early warnings based on the analysis results.

2. The synchronous early warning and sensing device for coal seam gas and hard roof rockburst according to claim 1, characterized in that, The composite resonant cavity sensor unit (2) includes an outer high-strength wear-resistant alloy steel (21), a middle damping adjustment layer (22), and an inner resonant sensitive layer (23). The outer high-strength wear-resistant alloy steel (21) has a cylindrical structure and is wrapped around the outside of the entire composite resonant cavity sensor unit (2) to ensure structural stability in the harsh environment of underground coal mines. The middle damping adjustment layer (22) is composed of rubber material with adjustable hardness and is arranged in a ring between the outer high-strength wear-resistant alloy steel (21) and the inner resonant sensitive layer (23). 3) Between, used to adjust the reflection and absorption characteristics of stress waves according to different geological conditions; the inner resonance sensitive layer (23) adopts special piezoelectric ceramic material, is cylindrical and located at the innermost side of the composite resonance cavity sensor unit (2), used to generate a strong resonance response to stress waves of a specific frequency; multiple composite resonance cavity units are arranged in a matrix along the roadway direction and vertical direction, and each composite resonance cavity unit corresponds to a different frequency response range, covering the frequency range of stress waves generated by coal seam gas anomalies and roof rock pressure.

3. The synchronous early warning and sensing device for coal seam gas and hard roof rockburst according to claim 2, characterized in that, The multimodal signal acquisition and processing module (3) includes a miniature accelerometer (34) and a strain sensor (35) installed on the surface of the inner piezoelectric ceramic, used to acquire vibration acceleration signals and strain signals induced by stress waves in the resonant cavity; it also includes a filtering unit (32), a fast Fourier transform unit (31) and a convolutional neural network analysis unit (33); the filtering unit (32), the fast Fourier transform unit (31) and the convolutional neural network analysis unit (33) are integrated in an explosion-proof box (4); the filtering unit (32) is used to filter the acquired signals, the fast Fourier transform unit (31) is used to convert the time domain signal into a frequency domain signal and extract the frequency, amplitude, phase and other characteristic parameters of the stress wave, and the convolutional neural network analysis unit (33) is used to perform fusion analysis on the multimodal signals and identify the coal seam gas anomaly or roof rockburst state corresponding to different combinations of characteristic parameters.

4. The synchronous early warning and sensing device for coal seam gas and hard roof rockburst according to claim 3, characterized in that, The collaborative early warning mechanism module includes a feature signal recognition model based on a convolutional neural network. The feature signal recognition model is trained by learning from a large amount of historical data to distinguish stress wave resonance feature signals generated by different disaster types such as abnormal increase in coal seam gas concentration, gas outburst, roof stress concentration, and rock burst. The collaborative early warning mechanism module is set up in the ground monitoring center and communicates with the multimodal signal acquisition and processing module (3) through a wireless network.

5. The synchronous early warning and sensing device for coal seam gas and hard roof rockburst according to claim 4, characterized in that, The collaborative early warning mechanism module also includes a graded early warning and linkage response unit, which divides the early warning into four levels: Level 1, Level 2, Level 3, and Level 4. When the system identifies an early warning signal, it activates different early warning measures according to the corresponding level. The graded early warning and linkage response unit is electrically connected to the audible and visual alarm device, ventilation system control device, and power cut-off device in the coal mine.

6. The synchronous early warning and sensing device for coal seam gas and hard roof rockburst according to claim 5, characterized in that, The system also includes an integrated device housing (1), which is made of high-strength aluminum alloy, with anodized surface and heat insulation layer inside; the composite resonant cavity sensor unit (2), multimodal signal acquisition and processing module (3) and power supply module (5) are all integrated in the integrated device housing (1), which has an IP68 protection rating and is installed on the side wall 0.5-1 meters below the tunnel roof.

7. The synchronous early warning and sensing device for coal seam gas and hard roof rockburst according to claim 6, characterized in that, The system also includes a self-calibration and maintenance system, which has a built-in self-calibration program that generates simulated stress wave signals through a built-in standard signal generator to calibrate the sensors and signal processing modules. The self-calibration and maintenance system also includes a status indicator (6) set on the surface of the integrated device housing (1) to display the system's operating status and fault information in real time. When a sensor fault or abnormal data is detected, the fault location is automatically located, and a maintenance prompt is sent to the maintenance personnel through the monitoring software platform. The self-calibration and maintenance system and the multimodal signal acquisition and processing module (3) are integrated in the same explosion-proof box (4).

8. The synchronous early warning and sensing device for coal seam gas and hard roof rockburst according to claim 1, characterized in that, The outer high-strength wear-resistant alloy steel (21) of the composite resonant cavity sensor unit (2) is provided with anti-slip texture (24). The anti-slip texture (24) is evenly distributed along the circumferential direction of the alloy steel to enhance the friction between the composite resonant cavity sensor unit (2) and the roadway roof or coal seam, and to prevent displacement after installation.

9. The synchronous early warning and sensing device for coal seam gas and hard roof rockburst according to claim 3, characterized in that, The shielded cable in the multimodal signal acquisition and processing module (3) adopts a double-layer shielding structure. The outer layer is a tin-plated copper braided mesh, and the inner layer is an aluminum foil shielding layer. An insulating isolation layer is provided between the two shielding layers to effectively shield electromagnetic interference in the coal mine and ensure the stability and accuracy of signal transmission.

10. A synchronous early warning and sensing device for coal seam gas and hard roof rockburst according to claim 6, characterized in that, The integrated device housing (1) is provided with a quick-release interface, which includes a snap-fit ​​connection structure and an electrical quick-connect connector. The snap-fit ​​connection structure is used to quickly fix and disassemble the integrated device housing (1) with the roadway sidewall, and the electrical quick-connect connector is used to quickly connect and disconnect the internal module and the external line, which facilitates the installation and maintenance of the system.

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