A device and method for identifying a coal-rock interface during coal mining

Abstract

The application discloses a device and method for identifying a coal-rock interface during mining, and relates to the technical field of intelligent detection of coal mines. The device comprises a hydraulic support baffle coupling sensor module, a seismic signal acquisition module during mining, a data transmission module, a full-automatic processing and identification module, and a visual output module connected in sequence. The seismic signal acquisition module during mining obtains seismic characteristic data during mining based on the original seismic signal collected by the hydraulic support baffle coupling sensor module. The full-automatic processing and identification module adopts a cross-correlation algorithm of channel wave detection technology, and realizes real-time identification of the coal-rock interface during the mining process in combination with the coal seam thickness data of the cut working face. The identification result of the coal-rock interface is visually displayed by the visual output module. The application can solve the technical problems in the existing seismic detection field during mining, and further promote the transformation of unmanned mining in coal mines.

Description

Technical Field

[0001] This application relates to the field of intelligent coal mine detection technology, and in particular to a device and method for identifying the rock interface during coal mining. Background Technology

[0002] Seismic exploration during mining, as a core technology for real-time geological exploration in coal mines, can simultaneously capture seismic wave signals generated by coal and rock fractures during mining, providing accurate data support for coal-rock interface identification. It is a core technological means to achieve synchronous exploration during mining. Hydraulic supports, as the core support equipment for fully mechanized mining faces, are in close contact with coal and rock and cover the entire length of the working face, possessing the inherent advantage of synchronous operation during mining, making them an ideal sensing carrier for seismic signal acquisition during mining. Accurate and fully automatic identification of the coal-rock interface is a core prerequisite for achieving automatic height adjustment of the mining machine, adaptive support of hydraulic supports, and unmanned mining during mining. Accurate acquisition and efficient processing of seismic signals during mining are the key to achieving this prerequisite, directly affecting coal mining efficiency, resource recovery rate, and safety production level.

[0003] Existing seismic detection technologies, coal-rock interface identification technologies, and related devices used in coal mining face numerous technical bottlenecks, making it difficult to meet the actual needs of intelligent and unmanned seismic mining. The core pain points are mainly concentrated in the field of seismic detection during mining, such as: First, existing seismic detection devices are mostly independently deployed and disconnected from hydraulic supports, failing to achieve synchronous full-coverage detection during mining. The devices are cumbersome to install, occupy a large space, and are susceptible to support vibration and coal mining interference during mining, resulting in poor stability of seismic signal acquisition and an inability to accurately capture seismic wave signals generated by coal-rock fracturing during mining. Second, existing hydraulic support-related sensing devices are mostly used for monitoring support posture and working resistance, without specific design for seismic signal acquisition during mining. The coupling stability between sensors and hydraulic supports is poor, and the rigid coupling method is easily affected by strong impacts and vibrations during mining, leading to inefficient seismic signal transmission, low signal-to-noise ratio, and an inability to accurately acquire effective seismic wave components during mining. Third, the underground mining environment is complex, with harsh conditions such as dust, humidity, and interference from multiple devices. The protection level of existing detection devices is insufficient, and the anti-interference ability of the mining seismic signal processing algorithm is weak. It is difficult to extract effective coal and rock features from the non-stationary and multi-interference raw mining seismic signals, and the identification accuracy is difficult to guarantee. Fourth, the existing coal and rock interface identification mostly relies on manual assistance, which cannot achieve fully automatic identification in the mining process. The identification efficiency is low and the lag is strong, which is out of touch with the requirements of unmanned mining. Moreover, the existing identification algorithm is not adapted to the dynamic change characteristics of mining seismic signals, has poor generalization ability, and cannot adapt to the mining seismic detection requirements of different geological conditions and mining conditions. Fifth, the existing devices are mostly single-function modules, which have not achieved the full-process integration of mining seismic signal acquisition, processing, identification, and control. The coordination with the hydraulic support electro-hydraulic control system and the coal mining machine control system is poor, and it is unable to output direct mining collaborative decision control commands, which does not meet the requirements of unmanned mining.

[0004] Existing related technologies also have significant limitations, the core deficiency being the lack of effective integration of on-the-fly seismic technology and hydraulic support sensing technology. For example, Chinese invention patent application CN110424964A uses multiple sensors mounted on the coal mining machine drum to collect signals and uses a BP neural network to identify the coal-rock interface. However, its core flaw is that it does not combine on-the-fly seismic detection technology, making it unable to achieve synchronous full-coverage detection during mining, and it suffers from poor signal stability due to interference from the cutting vibration of the coal mining machine. Chinese invention patent application CN117967278A uses borehole detection to identify the coal-rock interface, but it does not involve the integration of on-the-fly seismic detection and hydraulic support coupled sensing, resulting in low detection efficiency and the inability to achieve real-time dynamic detection. Current seismic detection technologies for hydraulic supports suffer from poor adaptability and are unsuitable for complex geological conditions such as thin coal seams and fractured, soft coal seams. Some hydraulic support-related sensing technologies focus only on monitoring the support's own condition, neglecting seismic signal acquisition and coal-rock interface identification, or employing antenna-type detection structures, resulting in low accuracy and weak anti-interference capabilities, failing to adapt to complex underground mining conditions. Furthermore, existing seismic technologies for mining lack stable coupling with hydraulic supports, leading to a disconnect between seismic signal acquisition and the mining process, failing to meet the requirements for fully automated intelligent identification and failing to leverage the core role of seismic technology in mining detection. In addition, existing hydraulic support sensors are mostly deployed in a decentralized manner, lacking a standardized integrated structure, resulting in poor adaptability and incompatibility with different types of hydraulic supports during mining operations. Moreover, the sensors are largely imported, leading to high costs and hindering industrial-scale promotion. According to relevant technical research, the coupling state between the hydraulic support and the surrounding rock directly affects the acquisition accuracy of seismic sensing signals during mining, and the acquisition accuracy of seismic signals during mining directly determines the accuracy of coal-rock interface identification. However, existing technologies have not optimized the sensor coupling structure for seismic signal acquisition during mining, and cannot achieve accurate and continuous acquisition of effective seismic wave signals during mining, which restricts the application of seismic technology during mining in coal-rock interface detection.

[0005] Therefore, developing a coal-rock interface detection and identification technology and device based on hydraulic support baffle coupled with seismic sensors, deeply integrating on-the-fly seismic technology, and possessing high stability, high precision, and full-process automation is an urgent need to address the technical pain points in the existing on-the-fly seismic detection field and promote the transformation of coal mines to unmanned on-the-fly mining. Summary of the Invention

[0006] The purpose of this application is to provide a device and method for identifying coal-rock interfaces during mining, in order to overcome the technical pain points in the field of seismic detection during mining.

[0007] To achieve the above objectives, this application provides the following solution: In a first aspect, this application provides a coal-rock interface identification device, comprising: The hydraulic support baffle is coupled with a sensor module for real-time acquisition of raw seismic signals during mining. The seismic signal acquisition module during mining is connected to the hydraulic support baffle coupled sensor module and is used to obtain seismic characteristic data during mining based on the raw seismic signal during mining. A data transmission module, connected to the seismic signal acquisition module during mining, is used to transmit the seismic characteristic data during mining. A fully automatic processing and identification module, connected to the data transmission module, is used to implement real-time identification of the coal-rock interface during mining based on the seismic feature data and the coal seam thickness data of the cut working face using the cross-correlation algorithm of the channel wave detection technology, and to obtain the coal-rock interface identification result. The visualization output module is connected to the fully automatic processing and recognition module and is used to visualize the coal-rock interface recognition results.

[0008] Secondly, this application provides a method for identifying the coal-rock interface during mining, including: Real-time acquisition of raw seismic signals during mining; Based on the raw seismic signals obtained during mining, seismic characteristic data during mining are obtained; The cross-correlation algorithm of the channel wave detection technology is used to realize the real-time identification of the coal-rock interface during mining based on the seismic feature data and the coal seam thickness data of the cut working face, and the coal-rock interface identification result is obtained. The results of coal-rock interface identification are visualized.

[0009] According to the specific embodiments provided in this application, this application has the following technical effects: This application provides a coal-rock interface identification device and method for underground mining. By employing a hydraulic support baffle coupled with a sensor module, the sensor can be coupled with the coal face as the core sensing carrier, closely addressing the core needs of underground seismic detection. By integrating an underground seismic signal acquisition module, a data transmission module, a fully automated processing and identification module, and a visualization output module, a highly stable, high-precision, and fully automated coal-rock interface detection and identification technology is formed. This enables synchronous, fully automated, and continuous acquisition of seismic wave signals from coal-rock fractures during underground mining, solving the problem of the disconnect between underground seismic signal acquisition and mining conditions. By employing a cross-correlation algorithm based on channel wave detection technology, fully automated intelligent identification of the coal-rock interface is achieved based on underground seismic characteristic data. This facilitates adaptation to complex underground mining conditions and different types of hydraulic supports, effectively improving mining efficiency and resource recovery rate, reducing safety risks, and thus making it suitable for intelligent and unmanned coal mining scenarios. Furthermore, this application achieves deep integration of underground seismic technology and the hydraulic support baffle coupled with a sensor module, overcoming existing technical pain points in the field of underground seismic detection, and possessing significant engineering application value and industrialization prospects. Attached Figure Description

[0010] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0011] Figure 1 A schematic diagram of a coal-rock interface identification device provided in an embodiment of this application; Figure 2 A schematic diagram of the installation position of a hydraulic support baffle coupled to a seismic sensor according to an embodiment of this application; Figure 3 This is a schematic diagram of an elastic damping coupling structure provided in an embodiment of this application; Figure 4 A schematic diagram of the combination of a seismic signal acquisition module and a data transmission module provided in an embodiment of this application; Figure 5 A schematic flowchart of a method for identifying the coal-rock interface during mining, provided in an embodiment of this application; Figure 6 This is a schematic diagram illustrating the implementation process of a method for identifying the coal-rock interface during mining, provided in an embodiment of this application.

[0012] Figure label: 1-Hydraulic support baffle coupled sensor module; 1.1-Hydraulic support; 1.2-Hydraulic support baffle; 1.3-Hydraulic support baffle coupled seismic sensor; 1.3.1-Outer sleeve; 1.3.2-Inner sleeve; 1.3.3-Elastic damping layer; 2-Seismic signal acquisition module during mining; 3-Data transmission module; 4-Fully automatic identification module; 5-Visual output module; 6-Power supply and network cable; 7-Signal transmission line. Detailed Implementation

[0013] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0014] To make the above-mentioned objectives, features and advantages of this application more apparent and understandable, the application will be further described in detail below with reference to the accompanying drawings and specific embodiments.

[0015] In one exemplary embodiment, to address the technical pain points of existing seismic detection technology during mining being disconnected from hydraulic supports, and the coal-rock interface identification relying on manual labor, lacking accuracy, and unable to achieve fully automated adaptation to mining conditions, this application provides a coal-rock interface identification device during mining, such as... Figure 1 As shown, the device includes a hydraulic support baffle coupling sensor module 1, a seismic signal acquisition module 2, a data transmission module 3, a fully automatic processing and identification module 4, and a visualization output module 5, connected in sequence. These modules work together, with the core design revolving around the entire process of seismic signal acquisition, transmission, processing, identification, and decision control during mining operations. This enables fully automatic detection of the coal-rock interface under mining conditions, and the technical features of each module are adapted to the core requirements of seismic detection during mining.

[0016] The hydraulic support baffle coupled sensor module 1 acquires raw seismic signals in real time during mining operations. The hydraulic support baffle coupled sensor module 1 is a hydraulic support baffle coupled seismic sensor 1.3. It is detachably connected to the hydraulic support baffle 1.2 via an elastic damping coupling structure to provide key points for synchronous acquisition during mining operations, achieving stable coupling between the sensor and the hydraulic support 1.1, facilitating synchronous movement with the support. The installation position of the hydraulic support baffle coupled seismic sensor 1.3 is as follows... Figure 2 As shown.

[0017] Furthermore, such as Figure 3 As shown, the elastic damping coupling structure includes: an outer sleeve 1.3.1, an inner sleeve 1.3.2, and an elastic damping layer 1.3.3. The elastic damping layer 1.3.3 is disposed between the outer sleeve 1.3.1 and the inner sleeve 1.3.2. The outer sleeve 1.3.1 is detachably connected to the hydraulic support baffle 1.2. The hydraulic support baffle coupled seismic sensor 1.3 is disposed in the inner sleeve 1.3.2. The matching arrangement of the outer sleeve 1.3.1 and the inner sleeve 1.3.2 allows the inner sleeve 1.3.2 to freely expand and contract within the outer sleeve 1.3.1, thereby driving the hydraulic support baffle coupled seismic sensor 1.3 to contact the coal face through the hydraulic support baffle 1.2. This facilitates the synchronous and continuous acquisition of seismic wave signals generated by coal and rock fracturing during mining, providing core data support for coal-rock interface identification.

[0018] In practical applications, the hydraulic support baffle coupled seismic sensor 1.3 used in this application has a sensitivity greater than or equal to 200V / (m / s) and a protection level of IP68, which can ensure the stability and accuracy of seismic wave signal acquisition under mining conditions.

[0019] Furthermore, the elastic damping layer 1.3.3 adopts a composite structure of nitrile rubber and metal skeleton or a damping spring (such as...). Figure 3(As shown). The thickness of the nitrile rubber and metal skeleton composite structure can be 8-12mm. Both the outer sleeve 1.3.1 and the inner sleeve 1.3.2 are made of stainless steel with an anti-corrosion coating. The outer sleeve 1.3.1 is coupled to the hydraulic support baffle 1.2 via vibration-damping rubber. Sealing gaskets are provided between the outer sleeve 1.3.1 and the inner sleeve 1.3.2, and between the inner sleeve 1.3.2 and the hydraulic support baffle and the seismic sensor 1.3, with a sealing rating of IP68, to adapt to the vibration and lifting dynamic conditions of the hydraulic support 1.1 during mining, ensure coupling stability, avoid interference from mining vibration on the transmission and acquisition of mining seismic wave signals, and ensure the acquisition accuracy of mining seismic signals.

[0020] Furthermore, the hydraulic support baffle coupled seismic sensor 1.3 includes at least two sensors. The sensors are connected to the mining-as-you-go seismic signal acquisition module 2 via signal transmission line 7. The sensors can be any one of the following: one-dimensional sensor, two-dimensional sensor, three-dimensional sensor, moving-coil velocity sensor, and acceleration sensor. For example, when using a three-dimensional seismic sensor, its frequency response range is 1-1000Hz, which can simultaneously capture mining-as-you-go seismic wave signals of different intensities and frequencies, accurately covering the frequency range of seismic waves generated by coal and rock fracturing, ensuring the comprehensiveness and accuracy of mining-as-you-go seismic signal acquisition.

[0021] The seismic signal acquisition module 2, based on the raw seismic signals acquired during mining operations, obtains seismic characteristic data to adapt to the dynamic changes in seismic wave signals during mining. In practical applications, this module includes a signal conditioning circuit and an FPGA acceleration chip. The signal conditioning circuit is connected to the hydraulic support baffle coupled sensor module 1. The FPGA acceleration chip is connected to both the signal conditioning circuit and the data transmission module 3. The signal conditioning circuit performs preprocessing such as filtering, amplification, and analog-to-digital conversion on the raw seismic signals acquired by the hydraulic support baffle coupled seismic sensor 1.3 to specifically adapt to the non-stationary and multi-interference characteristics of the raw seismic signals acquired during mining. The FPGA acceleration chip enables high-speed computation of relevant algorithms, ensuring an effective seismic signal extraction rate of greater than or equal to 95% and a signal-to-noise ratio improvement of more than 40%, thereby adapting to the real-time processing requirements of seismic signals acquired during mining and ensuring subsequent identification accuracy.

[0022] The data transmission module 3 adopts industrial Ethernet converged transmission technology. Its core function is to achieve real-time transmission of seismic signals, coal-rock interface identification results, and equipment operating status data between modules. This ensures zero delay throughout the entire process of seismic signal acquisition, processing, identification, and decision-making, with a transmission latency of less than 5ms and a transmission success rate of greater than or equal to 99.9%, guaranteeing the real-time performance and collaborative nature of seismic detection during mining. Furthermore, the data transmission module 3 can electrically connect all seismic signal acquisition modules 2 end-to-end via network cables. Depending on the network transmission distance, network repeaters can be added between the seismic signal acquisition modules 2 to improve network data transmission capacity. The data transmission module 3 can drive the transmission of data from 5 to 500 seismic signal acquisition modules 2, and its core function is to achieve real-time transmission of seismic signals and equipment operating status data between modules. The combined connection method of the seismic signal acquisition module 2 and the data transmission module 3 is as follows: Figure 4 As shown. Figure 4 In the middle, the host computer and the main control unit are connected to the network cable 6 via power supply.

[0023] The fully automatic processing and identification module 4 is electrically connected to the seismic signal acquisition module 2 during mining via a network. Employing a cross-correlation algorithm based on channel wave detection technology, it achieves real-time identification of the coal-rock interface during mining based on seismic characteristic data (such as the frequency, amplitude, and wave velocity corresponding to the coal-rock interface) and the thickness data of the coal seam in the cut working face. This yields the coal-rock interface identification results, enabling fully automatic identification of the coal-rock interface. Furthermore, it adapts to the dynamic changes in seismic signals during mining, achieving real-time identification of the coal-rock interface without manual intervention.

[0024] The visualization output module 5 visualizes the coal-rock interface identification results and displays the coal-rock interface identification results and the operating status of the hydraulic support baffle coupled seismic sensor 1.3 (acquired by the seismic signal acquisition module 2) during the mining process in real time. It focuses on displaying the seismic signal waveform during mining (generated by the fully automatic processing and identification module 4) and seismic characteristic data during mining, so that staff can intuitively monitor the entire process of seismic detection during mining and keep abreast of the dynamic changes of the coal-rock interface.

[0025] The visualization output module 5 includes a display screen (such as an intrinsically safe underground display screen) and a back-end monitoring terminal. The display screen is used to display in real time the coal-rock interface identification results, the operating status data of the hydraulic support baffle coupling sensor module 1, the seismic signal waveform during mining, and the seismic characteristic data during mining. The back-end monitoring terminal is used for querying and tracing historical data related to seismic activity during mining. It can achieve seamless integration with the electro-hydraulic control system of the hydraulic support 1.1 and the coal mining machine control system, outputting collaborative control commands based on the seismic detection results during mining to achieve mining-exploration collaboration.

[0026] As an optional implementation of this application, the hydraulic support baffle coupling sensor module 1, the seismic signal acquisition module 2, and the data transmission module 3 used in this application can all be powered by the explosion-proof intrinsically safe DC power supply of the coal mining equipment in the longwall face. Seismic data acquisition and transmission can be carried out as soon as the coal mining machine starts working.

[0027] As another optional implementation of this application, the number of hydraulic support baffle coupling sensor modules 1 and mining-in-process seismic signal acquisition modules 2 installed is generally one mining-in-process seismic signal acquisition module 2 every 1, 2 or 3 hydraulic supports 1.1. One mining-in-process seismic signal acquisition module 2 can be installed in parallel with two hydraulic support baffle coupling sensor modules 1 on the baffle of one hydraulic support 1.1.

[0028] Based on the above description, in practical applications, the application process of the device provided in this application includes: S1: The seismic sensor 1.3 is coupled to the hydraulic support baffle 1.2 via an elastic damping coupling structure. The outer sleeve 1.3.1 is coupled to the hydraulic support baffle 1.2 via vibration-damping rubber. The coupling gap is adjusted to ensure that the inner sleeve 1.3.2 can contact the coal wall when the hydraulic support baffle 1.2 is open to protect the coal face. This adapts to the dynamic movement of the support during mining, ensuring the stability of seismic signal acquisition. The installation, debugging, and power-on of each module are completed to ensure the normal operation of the entire process of seismic signal acquisition, processing, and transmission during mining.

[0029] S2: During the mining process, the hydraulic support baffle is coupled with the seismic sensor 1.3 to synchronously and in real time sense the seismic wave signal during mining. The sensed raw seismic signal during mining is transmitted to the seismic signal acquisition module 2 during mining through the signal transmission line 7, ensuring that the acquisition of seismic signals during mining is synchronized with the mining action without any lag.

[0030] S3: The seismic signal acquisition module 2 during mining uses a signal conditioning circuit to filter, amplify, and perform analog-to-digital conversion on the raw seismic signal during mining. It accurately separates the effective seismic wave signal during mining from interference signals such as those from underground water pumps, fans, and support vibrations. It focuses on retaining the seismic signal components corresponding to the coal-rock interface features during mining and outputs the purified effective seismic signal during mining (i.e., seismic feature data during mining), providing high-quality data support for subsequent identification.

[0031] S4, the data transmission module 3 connects all the seismic signal acquisition modules 2 along the mine through network cables and makes an electrical connection with the underground industrial ring network, transmitting the data of all the seismic signal acquisition modules 2 along the mine to the fully automatic processing and identification module 4.

[0032] S5: The fully automatic processing and identification module 4 receives the valid seismic signal from the seismic signal acquisition module 2 during mining. It extracts the characteristic parameters of the coal-rock interface in the seismic signal through the cross-correlation algorithm of the channel wave detection technology. Then, it combines the coal seam thickness data of the cut working face to complete the fully automatic identification of the coal-rock interface based on the seismic signal during mining and outputs the identification result (i.e., the coal-rock interface identification result).

[0033] The processing flow of the cross-correlation algorithm for slot wave detection technology is as follows: The seismic sources in the collected seismic data do not have a definite excitation time, and the sources are continuous and random. It is assumed that there are seismic sources on the working face. n Two sensors are mounted on a hydraulic support 1.1. Cross-correlation is performed on the two data channels from the sensor-equipped hydraulic support 1.1 to extract the travel time difference of the effective signal. The waveforms of effective signals excited by the same source and received by different sensors exhibit similarity. The cross-correlation function between the first and second channels is: (1) In the formula: x 1( k )and x 2( k The numbers () represent the seismic data collected from the two sensors attached to each hydraulic support. The cross-correlation time acquisition time only considers the location and magnitude corresponding to the maximum value of the cross-correlation function, assuming the cross-correlation function... c 1,2 ( k The maximum value of ) is c 1,2 max Then the time difference between the two signals corresponding to the maximum value is Δ. t 1,2 .

[0034] Based on the distance between the two sensors L The coal seam velocity between the two sensors can be calculated. V = L / Δt 1,2 The frequency of the vibration signal following sampling was extracted by performing spectral analysis on the two signals. f The thickness of the coal seam can be calculated according to the above formula (1). H Due to the thickness of the coal seam H To identify the interface between the coal seam and the rock strata.

[0035] (2) In the formula, α This is the coal seam thickness correction factor, obtained by correcting for the actual exposed coal seam thickness.

[0036] Furthermore, the fully automatic processing and identification module 4 receives the valid seismic signals output by the seismic signal acquisition module 2 during mining. Through full-waveform seismic inversion analysis, it can perform inversion imaging of the geological structure in front of the mining face and the geological structure inside the surrounding rock of the coal seam roof and floor, generating a three-dimensional transparent geological model of the mining face.

[0037] S6: The visualization output module 5 displays in real time the dynamic changes of the coal-rock interface during mining, the operating status of the hydraulic support baffle coupled seismic sensor 1.3, the seismic signal waveform during mining, and the seismic characteristic data during mining. It can realize the query and historical traceability of seismic data during mining, and achieve seamless connection with the electro-hydraulic control system of hydraulic support 1.1 and the coal mining machine control system, thereby realizing the precise cutting of coal seams.

[0038] S7: During the mining process, the coupling status of the hydraulic support baffle coupled seismic sensor 1.3 and the accuracy of the mining seismic signal acquisition are monitored in real time to ensure the continuity and accuracy of the mining seismic signal acquisition. Combined with the downhole mining test data, the signal processing algorithm and recognition algorithm parameters are continuously iterated and optimized, with a focus on optimizing the extraction and recognition accuracy of the mining seismic signal to ensure the accuracy and system stability of the mining seismic coal-rock interface detection during the mining process.

[0039] In summary, the device provided in this application uses a hydraulic support baffle coupled with a seismic sensor 1.3 as the core sensing carrier, deeply integrating on-site seismic detection technology. Through innovative coupling structure and optimized on-site seismic signal processing and fully automatic identification algorithms, it achieves full-process integration, fundamentally solving the key problems of accurate acquisition, efficient processing, and fully automatic identification of on-site seismic signals. It achieves the goal of synchronous, high-precision, and fully automatic detection and identification during mining, adapting to complex underground mining conditions and different models of hydraulic supports 1.1, promoting the implementation of intelligent and unmanned on-site mining in coal mines, while reducing costs and improving industrial feasibility.

[0040] Compared with the prior art, this application has at least the following advantages: 1. In this application, the combination of the coupling structure and the seismic technology during mining solves the pain points of existing seismic detection during mining being disconnected from the hydraulic support 1.1 and resulting in unstable signal acquisition. Specifically, this application employs an elastically damped coupling structure to stably connect the seismic sensor 1.3 to the hydraulic support 1.1 via the hydraulic support baffle. Combined with a composite structure of nitrile rubber and a metal skeleton, this reduces interference from strong downhole impacts and support vibrations during mining, while also achieving efficient and continuous transmission of seismic wave signals, enabling synchronous detection during mining. Furthermore, the detachable design adapts to different models of hydraulic supports 1.1 during mining operations, facilitating installation and commissioning. Unlike existing independent seismic detection devices and rigid coupling sensing structures, this application achieves deep integration of seismic detection during mining and the hydraulic support 1.1, ensuring the stability and accuracy of seismic signal acquisition during mining and overcoming the industry pain point of the disconnect between seismic signal acquisition and mining conditions.

[0041] 2. Strong anti-interference capability and high accuracy in processing and identifying seismic signals during mining operations. This application employs relevant algorithms to extract effective seismic wave features during mining operations, automatically extracts seismic feature data corresponding to the coal-rock interface, and completes fully automatic identification of the coal-rock interface, solving the problems of difficult processing of seismic signals during mining operations, insufficient identification accuracy, and inability to achieve fully automatic identification in complex underground mining environments.

[0042] 3. Achieve full automation and integration of the mining process, adapting to the needs of unmanned mining operations. This application integrates modules such as coupled sensing, mining seismic signal processing, fully automatic identification, data transmission, and visualization output. Taking mining seismic signals as the core clue, it achieves full automation of the mining interface seismic signal acquisition, processing, fully automatic identification, decision-making, and display process without manual intervention. It can seamlessly interface with the hydraulic support 1.1 electro-hydraulic control system and the coal mining machine control system, outputting mining collaborative decision-making commands based on mining seismic detection results, realizing synchronous and collaborative control of mining and exploration. This breaks through the bottleneck of existing technologies where mining and exploration are disconnected and require manual intervention. Referring to the networked intelligent sensing and control architecture of the hydraulic support 1.1, a fully automated mining seismic detection and identification system is constructed.

[0043] 4. Strong adaptability and broad industrialization prospects. The device provided in this application adopts a miniaturized and integrated design. Its installation and fixing structure can be adapted to the mining conditions of different models of hydraulic supports 1.1. The protection level of the seismic sensor 1.3 coupled to the hydraulic support baffle reaches IP68, facilitating adaptation to complex mining conditions such as dust, humidity, and strong impacts. It can stably acquire seismic signals during mining while reducing manufacturing and procurement costs. The device can be widely used in various intelligent coal mines, especially in mining mines with complex geological conditions such as thin coal seams and broken soft coal seams. Its core adaptability meets the seismic detection needs under different working conditions. Referring to the stable operation experience of related hydraulic supports 1.1 in complex geological conditions, the device's engineering adaptability is improved, demonstrating significant engineering application value and promising prospects for industrialization.

[0044] 5. Stable operation and convenient maintenance. The hydraulic support baffle 1.2 and signal acquisition nodes utilize the mining support power supply system, eliminating the need for additional power supply and reducing potential faults caused by other connections. This adapts well to mobile mining operations. Each module of the device adopts a modular design, facilitating maintenance and replacement. An anomaly warning function is also included, enabling real-time monitoring of the coupling status of the hydraulic support baffle coupled to the seismic sensor 1.3 and the accuracy of seismic signal acquisition during mining. This allows for timely detection and handling of faults, reducing maintenance costs and ensuring long-term stable operation throughout the mining process, thus improving the reliability and ease of maintenance of the device.

[0045] Based on the same inventive concept, this application also provides a method for identifying the coal-rock interface during mining, which is used to implement the aforementioned coal-rock interface identification device. The solution provided by this method is similar to the implementation scheme described in the aforementioned device. Therefore, the specific limitations in one or more embodiments of the coal-rock interface identification method provided below can be found in the limitations of the coal-rock interface identification method described above, and will not be repeated here.

[0046] In one exemplary embodiment, such as Figure 5 As shown, a method for identifying the rock interface during coal mining is provided, including: Step 100: Acquire raw seismic signals in real time.

[0047] Step 101: Obtain seismic characteristic data based on the raw seismic signals obtained during seismic mining.

[0048] Step 102: Using the cross-correlation algorithm of the channel wave detection technology, based on the seismic characteristic data of mining and the coal seam thickness data of the cut working face, the coal-rock interface is identified in real time during mining, and the coal-rock interface identification result is obtained.

[0049] Step 103: Visualize the coal-rock interface identification results.

[0050] As an optional implementation method, the seismic-assisted coal-rock interface identification method integrates seismic exploration, signal processing, and deep learning technologies to achieve dynamic and high-precision identification of the coal seam-rock interface. The method provided in this application uses seismic data from mining operations as a foundation, combining real-time data acquisition and intelligent algorithms to automate the entire process from raw signal acquisition to interface identification. Based on this, in practical applications, the actual implementation process of the aforementioned seismic-assisted coal-rock interface identification method provided in this application can be as follows: Figure 6 As shown, it includes: (1) Data acquisition and preprocessing.

[0051] During the operation of the coal mining machine, seismic waves are emitted using a seismic system that penetrates the coal seam and receives reflected signals from the coal-rock interface. The raw seismic signals acquired during mining contain noise and interference, requiring preprocessing such as denoising, filtering, and gain adjustment to improve the signal-to-noise ratio and resolution.

[0052] (2) Seismic signal analysis and feature extraction.

[0053] Based on the propagation characteristics of seismic waves at the interface between the top and bottom of the coal seam, the frequency, time difference (phase), and waveform of the seismic waves are extracted using a cross-correlation algorithm to establish the relationship between these characteristics and the coal seam thickness, and to calculate the coal seam thickness.

[0054] (3) Construction of an intelligent identification model for the coal-rock interface.

[0055] Deep learning algorithms can be used to train a coal-rock interface recognition model. The input consists of preprocessed seismic profiles and extracted features (i.e., seismic feature data from mining operations), and the output is the predicted location of the coal-rock interface. The model can be trained online by combining historical data and real-time observations to improve recognition accuracy.

[0056] (4) Dynamic tracking and 3D modeling.

[0057] The identification results are fused with the inertial navigation system and spatial pose data of the coal mining machine to establish the transformation relationship between the geodetic coordinate system, the coal mining machine coordinate system, and the sensor coordinate system, enabling dynamic updates of the spatial position of the coal-rock interface. Combined with multi-point detection data, a three-dimensional contour model of the coal seam is constructed and transmitted to the coal mining machine control system in real time.

[0058] (5) Closed-loop control and autonomous truncation.

[0059] The identified coal-rock interface information is used as a feedback signal to drive the adaptive adjustment of the rolling height of the coal mining machine, achieving contour-following cutting, reducing the amount of rock cut, and improving coal mining efficiency and safety. The system supports multi-time-segment fusion detection—pre-mining perspective, in-mining monitoring, and post-mining correction—forming a closed-loop intelligent control system.

[0060] In one exemplary embodiment, a computer device is provided, including a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to implement the steps in the above-described method embodiments.

[0061] In one exemplary embodiment, a computer-readable storage medium is provided storing a computer program that, when executed by a processor, implements the steps in the above-described method embodiments.

[0062] In one exemplary embodiment, a computer program product is provided, including a computer program that, when executed by a processor, implements the steps in the above-described method embodiments.

[0063] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, data stored, data displayed, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties, and the collection, use and processing of the relevant data must comply with relevant regulations.

[0064] Those skilled in the art will understand that all or part of the processes in the above embodiments can be implemented by a computer program instructing related hardware. The computer program can be stored in a non-volatile computer-readable storage medium, and when executed, it can include the processes of the embodiments described above. Any references to memory, databases, or other media used in the embodiments provided in this application can include at least one of non-volatile and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (RRAM), magnetic random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. By way of illustration and not limitation, RAM can take many forms, such as Static Random Access Memory (SRAM) or Dynamic Random Access Memory (DRAM).

[0065] The databases involved in the embodiments provided in this application may include at least one type of relational database and non-relational database. Non-relational databases may include, but are not limited to, blockchain-based distributed databases. The processors involved in the embodiments provided in this application may be general-purpose processors, central processing units, graphics processing units, digital signal processors, programmable logic devices, quantum computing-based data processing logic devices, etc., and are not limited to these.

[0066] The technical features of the above embodiments can be combined in any way. For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0067] This document uses specific examples to illustrate the principles and implementation methods of this application. The descriptions of the above embodiments are only for the purpose of helping to understand the methods and core ideas of this application. Furthermore, those skilled in the art will recognize that, based on the ideas of this application, there will be changes in the specific implementation methods and application scope. Therefore, the content of this specification should not be construed as a limitation of this application.

Claims

1. A device for identifying the coal-rock interface during mining, characterized in that, include: The hydraulic support baffle is coupled with a sensor module for real-time acquisition of raw seismic signals during mining. The seismic signal acquisition module during mining is connected to the hydraulic support baffle coupled sensor module and is used to obtain seismic characteristic data during mining based on the raw seismic signal during mining. A data transmission module, connected to the seismic signal acquisition module during mining, is used to transmit the seismic characteristic data during mining. A fully automatic processing and identification module, connected to the data transmission module, is used to implement real-time identification of the coal-rock interface during mining based on the cross-correlation algorithm of the channel wave detection technology and the coal seam thickness data of the cut working face, and to obtain the coal-rock interface identification result. A visualization output module, connected to the fully automatic processing and recognition module, is used to visualize the coal-rock interface recognition results. The hydraulic support baffle coupling sensor module is a hydraulic support baffle coupling seismic sensor; the hydraulic support baffle coupling seismic sensor is detachably connected to the hydraulic support baffle through an elastic damping coupling structure. The elastic damping coupling structure includes: an outer sleeve, an inner sleeve, and an elastic damping layer; The elastic damping layer is disposed between the outer sleeve and the inner sleeve; the outer sleeve and the inner sleeve are matched to allow the inner sleeve to freely expand and contract within the outer sleeve; the outer sleeve is detachably connected to the hydraulic support baffle; the seismic sensor coupled to the hydraulic support baffle is disposed within the inner sleeve.

2. The coal-rock interface identification device according to claim 1, characterized in that, The elastic damping layer adopts a composite structure of nitrile rubber and metal skeleton or a damping spring.

3. The coal-rock interface identification device according to claim 2, characterized in that, The thickness of the composite structure of nitrile rubber and metal skeleton is 8mm-12mm.

4. The coal-rock interface identification device according to claim 1, characterized in that, Both the outer sleeve and the inner sleeve are made of stainless steel; the outer sleeve and the hydraulic support baffle are coupled by vibration damping rubber; sealing gaskets are provided between the outer sleeve and the inner sleeve, and between the inner sleeve and the hydraulic support baffle and the seismic sensor; the sealing gaskets have a sealing rating of IP68.

5. The coal-rock interface identification device according to claim 1, characterized in that, The hydraulic support baffle coupled seismic sensor includes at least two sensors; The sensor is connected to the seismic signal acquisition module via a signal transmission line.

6. The coal-rock interface identification device according to claim 5, characterized in that, The sensor can be any one of a one-dimensional sensor, a two-dimensional sensor, a three-dimensional sensor, a moving-coil velocity sensor, and an acceleration sensor.

7. The coal-rock interface identification device according to claim 1, characterized in that, The seismic signal acquisition module during mining includes: The signal conditioning circuit is connected to the sensor module coupled to the hydraulic support baffle and is used to preprocess the raw seismic signal during mining. An FPGA acceleration chip is connected to the signal conditioning circuit and the data transmission module, respectively, and is used to obtain the seismic feature data during mining based on the preprocessed raw seismic signal during mining, so as to adapt to the dynamic change characteristics of the seismic wave signal during mining.

8. The coal-rock interface identification device according to claim 1, characterized in that, The seismic signal acquisition module is also used to acquire the operating status data of the hydraulic support baffle coupling sensor module; The fully automatic processing and identification module is also used to generate seismic signal waveforms during mining. The visualization output module is also used to visualize the operating status data of the hydraulic support baffle coupling sensor module, the seismic signal waveform during mining, and the seismic characteristic data during mining.

9. The coal-rock interface identification device according to claim 8, characterized in that, The visualization output module includes: The display screen is used to display the coal-rock interface identification results, the operating status data of the hydraulic support baffle coupling sensor module, the seismic signal waveform during mining, and the seismic characteristic data during mining. The background monitoring terminal interfaces with the electro-hydraulic control system of the hydraulic support and the control system of the coal mining machine. It is used to query and trace historical seismic data during mining, and to output collaborative control commands for mining based on the seismic detection results, so as to realize the collaboration between mining and exploration.

10. A method for identifying the rock interface during coal mining, characterized in that, Applied to the coal-rock interface identification device as described in any one of claims 1-9; The method for identifying the coal-rock interface during mining includes: Real-time acquisition of raw seismic signals during mining; Based on the raw seismic signals obtained during mining, seismic characteristic data during mining are obtained; The cross-correlation algorithm of the channel wave detection technology is used to realize the real-time identification of the coal-rock interface during mining based on the seismic feature data and the coal seam thickness data of the cut working face, and the coal-rock interface identification result is obtained. The results of coal-rock interface identification are visualized.

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