A machine dog-based dynamic detection method for rock burst in underground coal mine

Abstract

The application discloses a kind of coal mine underground rock burst dynamic detection methods based on machine dog, with machine dog as mobile detection carrier, multiple detection sensors are integrated, and through the rigid coupling of openable occlusion mechanism and roadway anchor cable end, high-quality vibration signal acquisition is realized;Multiple machine dogs are configured to form a mobile microseismic monitoring network, and the rock breaking signal of mining equipment is used as a calibration source, and the network layout is dynamically optimized based on the minimum positioning error criterion with mining and excavation;Through multi-parameter weighted fusion calculation, the comprehensive risk index is calculated, and after triggering the early warning, the machine dog is automatically dispatched for intensive detection and personnel active evacuation warning, and cross-regional machine dog dynamic replacement is supported.The application breaks through the range, spacing and time limit of traditional detection methods, without a large number of fixed sensors and cable laying, and can realize unmanned, fine and full-time rock burst dynamic monitoring, significantly improving the intelligent level and safety of coal mine rock burst prevention and control.

Description

Technical Field

[0001] This invention relates to the fields of mine dynamic disaster monitoring technology, coal mine robot technology and mine intelligent detection technology, specifically to a dynamic detection method for rockburst in underground coal mines during mining, and more particularly to an intelligent rockburst monitoring method that uses multiple robot dogs as mobile detection carriers, dynamically optimizes the microseismic monitoring network as the mining face advances, and integrates multiple parameter detection methods. Background Technology

[0002] Rockbursts are frequent dynamic disasters during deep coal mining, characterized by their sudden onset and high destructive intensity, seriously threatening the safety of underground personnel and production order. Currently, rockburst detection in coal mines mainly employs methods such as microseismic monitoring, ground acoustic monitoring, infrared thermal imaging monitoring, electromagnetic radiation monitoring, and roadway deformation monitoring. Engineering applications are primarily divided into two modes: The first type is manual portable testing, where testing personnel carry equipment to the mining face periodically for measurements. The testing cycle is usually once a day, and the testing area is limited to the area 200m behind the tunneling face and 300m ahead of the coal mining face, with a measuring point spacing of no less than 20m. This method has obvious drawbacks: First, the testing workload is large and the efficiency is low, making it difficult to guarantee personnel allocation under the mine's staffing policy; second, testing can only be carried out during shutdown maintenance shifts, failing to cover high-risk periods of mining production; and third, after an anomaly is discovered, personnel must enter the dangerous area for verification, posing a significant safety hazard.

[0003] The second type is fixed online monitoring, which involves deploying a large number of sensors at intervals of 20-30 meters within the tunnel, transmitting signals to the ground monitoring room via cables or wireless base stations. This method requires laying thousands of meters of transmission cables, resulting in high equipment and engineering costs. Furthermore, the sensor locations are fixed, making it impossible to flexibly adjust the density of monitoring points for risk areas. For large-scale dynamic risk areas such as tunnels several kilometers long, deploying sensors across the entire area is extremely uneconomical.

[0004] Both of the above methods suffer from limitations such as limited detection range, large spacing between measuring points, and inability to perform precise detection as needed. Furthermore, rockbursts often occur outside the coverage area of ​​traditional monitoring, resulting in insufficient timeliness and accuracy of early warnings.

[0005] In addition, manual inspection is time-consuming and labor-intensive. Even with fixed online monitoring equipment, sensors need to be moved frequently by people. Due to limitations in cost and manpower, the detection range is small and the accuracy is low. Dangerous areas require underground workers to risk entering for inspection. Unmanned inspection platforms have advantages such as unmanned inspection, high inspection efficiency, avoiding the danger to underground workers, large detection range, and high detection frequency and accuracy. In recent years, they have been gradually promoted in underground coal mines. However, existing unmanned inspection platforms are mainly used for gas environment monitoring and equipment appearance inspection. There is no systematic solution that deeply integrates mobile robots or robot dog platforms with multi-parameter monitoring of rockburst and realizes dynamic optimization of the monitoring network with mining. Therefore, it cannot specifically solve the industry's technical problems in rockburst monitoring. Summary of the Invention

[0006] To address the shortcomings of existing rockburst detection technologies, the present invention aims to provide a dynamic detection method for rockburst in coal mines based on a robot dog. The robot dog serves as a mobile detection carrier, enabling unmanned, large-scale, and dynamically adjustable multi-parameter monitoring of rockburst, thus overcoming the limitations of traditional detection methods in terms of range, spacing, timeliness, and safety.

[0007] To achieve the above objectives, the technical solution adopted by this invention is: a dynamic detection method for rockburst in coal mines during mining, which relies on a mobile detection platform with a robot dog and a ground monitoring system. The robot dog is equipped with a walking mechanism, a wireless communication unit, and an underground positioning unit. The core steps include: S1. Integration of carrier and detection unit: Five types of detection sensors, namely micro-vibration, ground sound, infrared thermal imaging, electromagnetic radiation, and tunnel laser deformation, are integrated into the body of the intrinsically safe mining robot dog. At the same time, an openable biting mechanism is set up. The biting mechanism is rigidly connected to the micro-vibration and ground sound sensors for coupling with the end of the tunnel anchor cable. The robot dog is connected to the underground ring network through wireless transmission for monitoring data feedback and command transmission to the ground monitoring system.

[0008] S2. Rigid Vibration Coupling Detection: During microseismic / ground sound monitoring, the robot dog autonomously identifies the position of the anchor cable end and controls the clamping mechanism to clamp the end, enabling the sensor to form a rigid vibration coupling with the deep coal and rock mass through the anchor cable body, collecting low-frequency microseismic signals or high-frequency ground sound signals. The clamping mechanism is a bionic jaw located on the robot dog's head, or an independently set retractable clamping arm. The front end of the clamping mechanism is equipped with an openable upper jaw plate and a lower jaw plate, and the clamping surfaces of the upper jaw plate and the lower jaw plate are equipped with V-shaped clamping teeth to adapt to anchor cable ends of different diameters and achieve self-centering clamping. S3. Dynamic Microseismic Network Optimization During Mining: A mobile microseismic network is composed of at least four robotic dogs, using the rock-breaking vibrations of the tunneling machine / coal cutter as the known calibration source; the robotic dogs are controlled to move sequentially along the roadway, and the vibration positioning error of the network after each movement is calculated. The position with the smallest error is selected as the optimal network; the optimization is repeated periodically as the mining face advances to ensure that the network is always in the best monitoring position. Specifically: When applied to the tunneling face: The first and second robotic dogs are initially positioned on both sides of the roadway at different distances from the tunneling face. The remaining robotic dogs are then positioned sequentially at intervals away from the tunneling face. Each robotic dog moves sequentially away from the tunneling face with a fixed step size, and the total movement range does not exceed 100m. The network optimization is performed again every 5m to 20m of tunneling. When applied to the longwall face: The first and second robotic dogs are positioned in the intake and return air roadways at different distances from the coal cutting face, respectively. The remaining robotic dogs are then positioned sequentially at intervals away from the longwall face. Each robotic dog moves sequentially away from the coal cutting face with a fixed step size, and the total movement range does not exceed 500m. The network optimization is performed again every 10m to 40m of longwall mining.

[0009] S4. Multi-parameter on-the-go detection: Select at least one robot dog equipped with sensors for ground sound, infrared thermal imaging, electromagnetic radiation, and tunnel laser deformation detection. The robot dog will move along the detection route, performing corresponding parameter detections point by point at preset intervals. It will move along the tunnel as needed, performing ground sound, infrared thermal imaging, electromagnetic radiation, and tunnel deformation detections at preset intervals. For ground sound monitoring, the acquisition frequency should be no less than 2000Hz, and the acquisition time for a single measurement point should be no less than 1 minute. For electromagnetic radiation monitoring, the probe should be 0.05m~0.3m away from the coal surface, and the measurement time for a single measurement point should be no less than 1 minute. During roadway deformation monitoring, the robot dog acquires the three-dimensional point cloud of the roadway cross-section in real time through laser scanning, and calculates the convergence deformation, roof subsidence, and floor heave by comparing it with historical cross-sections. During non-production shifts, the robot dog can walk along the scraper conveyor of the working face to complete the detection work inside the working face. Since the robot dog can simultaneously complete five types of detection at the same measuring point, namely micro-vibration, ground sound, electromagnetic radiation, infrared thermal imaging, and roadway deformation, the detection data are completely aligned in space and time, providing data homogeneity conditions that traditional fixed sensor solutions cannot achieve for subsequent multi-parameter weighted fusion.

[0010] S5. Multi-source fusion risk warning: Normalize various types of detection data and calculate the comprehensive risk index through weighted fusion; when the comprehensive risk index exceeds the preset threshold, trigger the rockburst hazard warning and issue encrypted detection instructions to the robot dog.

[0011] S6. Encrypted Detection of Dangerous Areas: Upon receiving the encrypted detection command, the robot dog enters the warning area, narrows the detection interval for refined detection, and covers the outer edge of the warning area for at least 20m until the range and risk level of the dangerous area are confirmed. The detection interval of the encrypted detection is 1 / 2 to 1 / 4 of the conventional detection interval (generally 3 to 50m). A single robot dog is used for unidirectional detection from one end of the warning area to the other, or two robot dogs are used for bidirectional detection from the center of the warning area to both sides.

[0012] S7. Active Early Warning and Cluster Scheduling: After an early warning is triggered, the robot dog will actively go to the location of the personnel in the surrounding area based on the personnel's location information underground, and guide them to evacuate through sound, light and voice; a cross-regional supplementary scheduling module is configured to schedule nearby idle robot dogs to supplement detection capabilities based on a comprehensive cost function.

[0013] Furthermore, the vibration positioning error is calculated using the following formula:

[0014] In the formula, The positioning error of the mobile microseismic monitoring network composed of robot dogs after the k-th shift; To determine the spatial coordinates of the earthquake source, the tunneling machine / coal cutter uses its onboard positioning device to determine the known spatial coordinates of the earthquake source during rock breaking operations; The time when the sensor of the i-th robot dog receives the vibration signal after the k-th shift; To determine the timing of the earthquake source, the vibration sensor on the tunneling machine / coal cutter's onboard system can be used to synchronously trigger the signal. Let be the wave velocity of the seismic wave after the k-th displacement, which propagates from the source to the i-th robot dog. The value is assigned based on the wave velocity test results in the early stage of the mine, or it is corrected in real time by the foot contact signal during the robot dog's movement; n is the number of robot dogs in the network.

[0015] Furthermore, the formula for calculating the comprehensive risk index is as follows:

[0016] In the formula, R is the comprehensive risk index; M is the normalized index for microseismic monitoring, A is the normalized index for ground sound monitoring, E is the normalized index for electromagnetic radiation monitoring, T is the normalized index for infrared thermal imaging monitoring, and D is the normalized index for roadway deformation monitoring; α, β, γ, δ, and ε are the weight coefficients of the corresponding indicators, and satisfy α+β+γ+δ+ε=1. The weight coefficients are dynamically adjusted according to the geological conditions of the mine. All the above normalized indicators are obtained by normalizing industry-known data.

[0017] Furthermore, the scheduling module selects the optimal replacement robot dog through a replacement comprehensive cost function, which is:

[0018] In the formula, The total cost of replacing the m-th robot dog with the k-th working face; Let m be the distance between the current position of the m-th robot dog and the k-th working surface; The preset maximum fill distance; Estimated walking energy consumption for replacing the m-th robot dog; Let m be the remaining battery power of the m-th robot dog; Let m be the matching degree of the detection capability of the m-th robot dog to the k-th working face; This represents the remaining time for the m-th robot dog to perform its current task. Let m be the maximum acceptable task interruption time for the m-th robot dog; Let be the weight coefficient, and satisfy... Select the robot dog with the lowest overall cost as the replacement robot dog and issue the replacement command.

[0019] Compared with the prior art, the present invention has the following advantages: 1. The robot dog of this invention can enter restricted areas to carry out detection during mining and production periods without stopping production, solving the existing technical problem of mismatch between manual detection and production periods. Moreover, it eliminates the safety risks of verification operations by eliminating the need for personnel to enter dangerous areas. Furthermore, compared with the limitation of existing fixed sensor networks that cannot be moved once installed, this invention utilizes the mobility of the robot dog to enable the micro-seismic network to dynamically migrate and optimize as the mining face advances, always maintaining the best monitoring position. This fundamentally solves the technical problem of the positioning accuracy of fixed networks decreasing as the seismic source moves away.

[0020] 2. The spacing between single measurement points in this invention can be set as needed, with a minimum of meter level. Key areas can be dynamically densified for detection. Compared with the traditional fixed spacing of 20-30m, the level of detection precision is significantly improved. The detection range can cover the entire tunnel and main tunnel area, without being limited by the traditional monitoring boundaries.

[0021] 3. This invention ensures the quality of vibration signal acquisition through rigid coupling of anchor cables, and at the same time, it optimizes the position of the monitoring network based on the self-calibration of rock breaking signals to ensure the accuracy of micro-seismic positioning. Compared with fixed monitoring schemes, it does not require a large number of sensors and transmission cables, which greatly reduces equipment and engineering costs, and can be repeatedly deployed on multiple working faces.

[0022] 4. This invention realizes intelligent control of the entire process of "daily inspection - risk identification - encrypted verification - personnel early warning - cluster scheduling". The fusion of multiple parameters improves the accuracy of early warning, and the dynamic supplementation mechanism ensures the reliability of the system, adapting to the development trend of intelligent mining in coal mines. Attached Figure Description

[0023] Figure 1 This is a schematic diagram of the overall system architecture according to an embodiment of the present invention.

[0024] Figure 2 This is a schematic diagram of the layout and dynamic optimization of the microseismic monitoring network at the tunneling face of the present invention.

[0025] Figure 3 This is a schematic diagram of the layout and dynamic optimization of the microseismic monitoring network in the longwall mining face according to the present invention.

[0026] Figure 4 This is a schematic diagram showing the deployment of different monitoring methods in this invention.

[0027] Figure 5 This is a schematic diagram of the robotic dog with a bionic biting mouth part in this invention.

[0028] Figure 6 This is a schematic diagram of the mechanical dog structure with a retractable gripping arm in this invention. Detailed Implementation

[0029] The present invention will be further described below.

[0030] The execution subject of this embodiment is an intrinsically safe quadruped robot dog for mining and a ground monitoring system. The robot dog meets the requirements for explosion-proof, dust-proof, and waterproof in underground coal mines. It is equipped with lidar and depth camera for environmental perception and autonomous navigation. It connects to the industrial ring network through an underground wireless base station and communicates with the ground monitoring room in real time.

[0031] Example 1: Dynamic Layout and Optimization of Microseismic Network at Tunneling Face This embodiment describes the dynamic optimization process of a mobile microseismic monitoring network in a tunneling face scenario. A coal mine tunneling face is advancing along a coal seam with a strong tendency to impact. Four robotic dogs equipped with microseismic sensors and a meshing mechanism are deployed to perform monitoring tasks.

[0032] S1. Initial Robotic Network Layout: The ground monitoring system issues task commands to four robotic dogs. The first and second robotic dogs move to the sides of the tunnel at distances of L1=5m and L2=15m from the tunnel face, respectively. The third and fourth robotic dogs move to positions ΔL=3m from the first and second robotic dogs (i.e., 8m and 18m from the tunnel face), respectively. The four robotic dogs form a diamond-shaped network in space. After each robotic dog reaches its designated position, it identifies the anchor bolt end on the tunnel side using a depth camera and controls the bionic biting mechanism of its head to close. The upper and lower jaw plates close, clamping the anchor bolt end with V-shaped teeth, creating a rigid coupling between the micro-vibration sensor and the anchor bolt body. The clamping pressure is controlled at 0.3~0.5MPa to ensure efficient transmission of vibration signals.

[0033] S2. Signal Acquisition and Positioning Error Calculation: During rock breaking operations at the tunnel boring machine face, a known seismic source signal is generated. The micro-vibration sensors of each robot dog synchronously acquire low-frequency vibration signals (sampling frequency 1000Hz) and record the arrival time of the P-wave. The waveform data and timestamps are uploaded to the ground monitoring system in real time.

[0034] Ground system combined with tunneling machine for real-time positioning coordinates The positioning error of the current mobile microseismic monitoring network composed of robot dogs is calculated using the following formula:

[0035] In the formula, The moment of vibration during rock breaking by the tunnel boring machine is synchronously collected by the tunnel boring machine's onboard system; Let i be the spatial coordinates of the i-th robot dog; The wave velocity of the seismic wave along this path is assigned based on the wave velocity test results from the mine's earlier stages.

[0036] S3. Successive Shifting and Optimization: After calculating the error of the initial position, the four robotic dogs synchronously move ΔL=3m away from the head-on direction, repeating the engagement, data acquisition, and error calculation process at the new position; this cyclical shifting continues until the robotic dog's movement range reaches the 100m upper limit. The ground system compares the positioning errors of all shifted positions. The location corresponding to the minimum error is selected as the current optimal microseismic network layout scheme.

[0037] S4. Periodic dynamic update: As the tunneling face advances, the above-mentioned network optimization process is repeated every 10m of advance to update the robot dog deployment position, so that the network always maintains the best monitoring accuracy.

[0038] Example 2: Dynamic Layout and Optimization of Microseismic Network in Longwall Mining Face This embodiment describes a differentiated implementation method for network optimization in a longwall mining face scenario. A certain longwall mining face is equipped with four micro-vibration detection robots, deployed in the intake and return airways respectively.

[0039] S1. Initial Monitoring Network Layout: The first robotic dog is positioned in the intake airway, 80m from the coal cutting face (D1); the second is positioned in the return airway, 120m from the coal cutting face (D2); the third and fourth are positioned in the intake and return airways respectively, ΔD=30m from the first and second robotic dogs, forming a diamond-shaped monitoring network spanning the two airways. Each robotic dog is rigidly coupled to the end of the anchor bolt via a meshing mechanism.

[0040] S2. Optimization and Dynamic Update: Using the rock-breaking vibration of the coal cutter in the middle of the working face as the calibration source, each robot dog synchronously collects signals and calculates the positioning error. Then, the four robot dogs synchronously move ΔD=30m away from the working face, repeating the test. The total displacement range does not exceed 500m, and the position with the smallest positioning error is selected as the optimal network. Every 20m of mining advance, the network optimization is re-executed to adapt to the working face advance rhythm. When the working face roadway length is short, some robot dogs can be deployed in nearby main roadways and connecting roadways to ensure the spatial span of the network.

[0041] Example 3: Implementation details of multi-parameter on-the-go inspection and occlusal mechanism This embodiment illustrates the detection implementation methods of various sensors and the specific structure of the biting mechanism.

[0042] S1. Structure and operation of the biting mechanism: The biting mechanism adopts an electrically driven telescopic clamping arm, which is installed at the front of the robot dog body, with a telescopic stroke of 0~300mm; the front end of the clamping arm is equipped with an upper jaw plate and a lower jaw plate, and the inner side of the jaw plate is inlaid with hard alloy V-shaped biting teeth with a V-angle of 90°, which can be adapted to anchor rods and anchor cable ends of different specifications of φ16~φ24mm, and automatically centering during biting.

[0043] After the robot dog locates the end of the anchor rod through visual recognition, the clamping arm extends and adjusts its posture to allow the end rod to enter between the jaw plates, driving the jaw plates to close and clamp, thus completing rigid coupling; after the detection is completed, the jaw plates open, the clamping arm retracts, and the robot dog continues to move.

[0044] S2, various sensor detection methods Ground sound monitoring: It shares the same interlocking coupling mechanism with microseismic monitoring, with a sampling frequency of 2000Hz, continuous sampling at a single measuring point for 1 minute, and extraction of characteristic parameters such as frequency and energy of ground sound events, with a signal-to-noise ratio of not less than 30dB.

[0045] Electromagnetic radiation and infrared thermal imaging monitoring: After the robot dog moves to the measuring point, it adjusts its body posture to align the probe with the coal body on the roadway side. The electromagnetic radiation probe is 0.1~0.2m away from the coal body and measures continuously for 1 minute. Then, it switches to the infrared thermal imaging lens to collect thermal images of the roadway side and collects continuously for 30 seconds to obtain the surface temperature distribution of the coal body.

[0046] Roadway deformation monitoring: During the robot dog's movement, it continuously scans the roadway cross-section at a frequency of 20Hz using an airborne lidar to acquire three-dimensional point cloud data; it then aligns and fits the real-time cross-section with the reference cross-section to calculate the convergence of the roadway sides, the subsidence of the roof, and the heave of the floor, thus achieving continuous deformation monitoring.

[0047] The standard detection spacing can be set from 3 to 50 meters according to requirements, and the spacing of different sensors can be set independently. During non-production shifts, the robot dog can climb the scraper conveyor of the working face through the walking mechanism and set up measuring points at intervals along the working face to complete the internal detection of the working face.

[0048] Example 4: Complete Process of Integrated Early Warning and Cluster Scheduling This embodiment uses the complete monitoring process of a tunneling face as an example to illustrate the entire process of risk warning, intensified detection, personnel warning, and dynamic replacement. A certain mine's tunneling face is equipped with 8 detection robot dogs, of which 4 are used for microseismic network monitoring, 2 for multi-parameter inspection, and 2 as backup mobile units.

[0049] S1. Routine Monitoring and Risk Assessment: Each robot dog conducts routine monitoring according to a pre-set plan, and the data is uploaded to the ground system in real time. The system normalizes indicators such as microseismic energy, geophonic frequency, electromagnetic radiation intensity, thermal imaging temperature, and deformation rate from 0 to 1, and calculates the comprehensive risk index using the following formula:

[0050] In the formula, R is the comprehensive risk index; M is the normalized index for microseismic monitoring, A is the normalized index for ground sound monitoring, E is the normalized index for electromagnetic radiation monitoring, T is the normalized index for infrared thermal imaging monitoring, and D is the normalized index for roadway deformation monitoring; α, β, γ, δ, and ε are the weighting coefficients of the corresponding indicators, and satisfy α+β+γ+δ+ε=1. The weighting coefficients are dynamically adjusted according to the geological conditions of the mine. M, A, E, T, and D are calculated using the following formula:

[0051] In the formula, , These are the historical maximum and minimum values ​​of microseismic energy, respectively. , These are the historical maximum and minimum values ​​of the ground frequency order, respectively. , These are the historical maximum and minimum values ​​of electromagnetic radiation intensity, respectively. , These are the historical maximum and minimum values ​​of thermal imaging temperature, respectively. , These represent the historical maximum and minimum values ​​of the roadway deformation rate, respectively. , , , , These are the real-time detection values ​​for microseismic energy, ground audio frequency, electromagnetic radiation intensity, thermal imaging temperature, and tunnel deformation rate, respectively.

[0052] In this embodiment, the results are calculated based on monitoring data and the above formula. Based on the geological conditions of the mine, weighting coefficients were set as α=0.3, β=0.25, γ=0.25, δ=0.15, and ε=0.05. The comprehensive risk index R was calculated as follows:

[0053] The calculated value of R is 0.41. The method for determining the danger of an early warning impact is as follows:

[0054] When the value exceeds the preset threshold of 0.25, the system will automatically trigger a yellow rockburst warning.

[0055] S2. Encrypted Detection and Verification: The ground system issues an encrypted detection command to the inspection robot dogs, requiring them to conduct bidirectional encrypted detection of the warning area at 5m intervals (1 / 4 of the conventional 20m interval). Two robot dogs start from the center of the warning area and move towards the face and the outside of the roadway respectively for detection. Finally, the danger range is confirmed to be 20-50m behind the face, and the danger level is consistent with the warning.

[0056] S3. Active Personnel Warning: The ground system simultaneously retrieves data from the underground personnel positioning system to obtain the locations of three workers around the danger zone and sends the information to the nearest robotic dog. The robotic dog quickly moves to the vicinity of the personnel, activates the red warning light and buzzer, and simultaneously plays a voice prompt: "Danger of rockburst, please evacuate to a safe area immediately," guiding the personnel to evacuate in an orderly manner.

[0057] S4. Cross-regional dynamic replacement: During encrypted detection, if the remaining battery power of one inspection robot drops to 25%, it needs to return to the charging station for recharging, resulting in a gap in detection capacity. The ground replacement scheduling module is activated, selecting the optimal replacement target from the backup robot dogs in the nearby main street, and calculating the comprehensive replacement cost using the following formula:

[0058] The calculated cost of the backup robot dog is 0.1125, which is lower than the threshold of 0.3. Therefore, the system issues a replacement command to it. The backup robot dog automatically proceeds to the target work surface to take over the detection task, ensuring continuous monitoring.

[0059] S5. Danger Cleared: After 2 hours of continuous monitoring, the comprehensive risk index drops to 0.45, below the warning threshold. The ground system issues a danger clearing command, and each robot dog resumes its daily inspection mode; after the original robot dog returns to its post after charging, the standby robot dog returns to mobile status.

[0060] The above description is only a preferred embodiment of the present invention. It should be noted that for those skilled in the art, several improvements and modifications can be made without departing from the principle of the present invention, and these improvements and modifications should also be considered within the scope of protection of the present invention.

Claims

1. A method for dynamic detection of rockburst in coal mines based on a robot dog, implemented using a robot dog mobile detection platform and a ground monitoring system, characterized in that... Includes the following steps: S1. Sensor and mechanism integration: The robot dog body integrates a micro-vibration sensor, a ground sound sensor, an infrared thermal imaging sensor, an electromagnetic radiation sensor, and a tunnel laser deformation detection sensor; and is equipped with an openable and closable biting mechanism, which is rigidly connected to the micro-vibration sensor and the ground sound sensor. S2. Rigid coupling detection of vibration signals: When performing micro-seismic or ground sound monitoring, the robot dog moves to the designated measuring point and controls the biting mechanism to clamp the end of the anchor cable in the roadway, so that the micro-seismic sensor and the ground sound sensor form a rigid vibration coupling with the coal and rock mass through the anchor cable body to collect vibration signals. S3. Dynamic microseismic monitoring network optimization during mining: Configure at least 4 robot dogs to form a mobile microseismic monitoring network; During the advance of the mining face, use the rock-breaking vibration signal of the mining equipment as a known calibration source, control each robot dog to move along the roadway one by one, calculate the vibration positioning error of the network after each movement, and select the position with the smallest positioning error as the current optimal network layout. The network optimization process is repeated according to a preset cycle as the mining face advances. S4. Multi-parameter walking detection: Select at least one robot dog to walk along the detection route and complete the corresponding parameter detection point by point according to the preset detection interval; S5. Comprehensive Risk Assessment and Early Warning: Normalize various types of detection data and calculate the comprehensive risk index through weighted fusion. When the comprehensive risk index exceeds the preset threshold, a rockburst hazard warning is triggered, and an encrypted detection command is sent to the robot dog. S6. Encrypted Detection of Dangerous Areas: Upon receiving the encrypted detection command, the robot dog enters the warning area, narrows the detection interval, and performs refined detection until the range and risk level of the dangerous area are confirmed.

2. The method according to claim 1, characterized in that, In step S3, when applied to the tunneling face: the first and second robot dogs are initially placed on both sides of the roadway at different distances from the tunneling face, and the remaining robot dogs are arranged in sequence along the direction away from the tunneling face; each robot dog moves in sequence in the direction away from the tunneling face with a fixed step size, and the total range of movement does not exceed 100m; every 5m to 20m of tunneling, the robot network optimization is performed again.

3. The method according to claim 1, characterized in that, In step S3, when applied to the longwall face: the first and second robot dogs are respectively placed in the intake and return airways at different distances from the coal cutting face, and the remaining robot dogs are arranged at intervals along the direction away from the working face; each robot dog moves in sequence in the direction away from the coal cutting face with a fixed step size, and the total movement range does not exceed 500m; every 10m~40m of longwall mining, the network optimization is re-executed.

4. The method according to claim 1, characterized in that, In step S3, the vibration positioning error is calculated using the following formula: In the formula, The positioning error of the mobile microseismic monitoring network composed of robot dogs after the k-th shift; To determine the spatial coordinates of the earthquake source; The time when the sensor of the i-th robot dog receives the vibration signal after the k-th shift; To determine the time of origin of the earthquake source; denoted as , where is the wave velocity of the seismic wave propagating from the source to the i-th robot dog after the k-th shift; n is the number of robot dogs in the network.

5. The method according to claim 1, characterized in that, In step S1, the biting mechanism is a bionic biting mouth set on the head of the robot dog, or an independently set telescopic clamping arm; the front end of the biting mechanism is provided with an openable upper jaw plate and a lower jaw plate, and the clamping surfaces of the upper jaw plate and the lower jaw plate are provided with V-shaped biting teeth, which are used to adapt to anchor cable ends of different diameters and achieve self-centering clamping.

6. The method according to claim 1, characterized in that, The formula for calculating the comprehensive risk index is as follows: In the formula, R is the comprehensive risk index; M is the normalized index for microseismic monitoring, A is the normalized index for ground sound monitoring, E is the normalized index for electromagnetic radiation monitoring, T is the normalized index for infrared thermal imaging monitoring, and D is the normalized index for roadway deformation monitoring; α, β, γ, δ, and ε are the weight coefficients of the corresponding indicators, and satisfy α+β+γ+δ+ε=1. The weight coefficients are dynamically adjusted according to the geological conditions of the mine.

7. The method according to claim 1, characterized in that, In step S6, the detection interval of the encryption detection is 1 / 2 to 1 / 4 of the regular detection interval; a single robot dog is used to detect unidirectionally from one end of the warning area to the other, or two robot dogs are used to detect bidirectionally from the center of the warning area to both sides.

8. The method according to claim 1, characterized in that, It also includes a proactive warning process for personnel: when a rockburst hazard warning is triggered, the ground monitoring system obtains the location information of personnel underground and sends it to the robot dog; the robot dog moves to the location of nearby personnel and guides them to evacuate the danger zone through audible and visual alarms and voice prompts.

9. The method according to claim 1, characterized in that, It also includes a cross-regional dynamic replacement scheduling step: the ground monitoring system is configured with a cross-regional dynamic replacement scheduling module. When the robot dog on duty at the work site has insufficient power, malfunctions, or insufficient detection capability due to the execution of encryption tasks, the scheduling module will retrieve an idle robot dog from the neighboring area to replace it. The scheduling module selects the optimal replacement robot dog by using a replacement comprehensive cost function, which is: In the formula, The total cost of replacing the m-th robot dog with the k-th working face; Let m be the distance between the current position of the m-th robot dog and the k-th working surface; The preset maximum fill distance; The estimated walking energy consumption for replacing the m-th robot dog; Let m be the remaining battery power of the m-th robot dog; Let m be the matching degree of the detection capability of the m-th robot dog to the k-th working face; The remaining time for the m-th robot dog to perform its current task; Let m be the maximum acceptable task interruption time for the m-th robot dog; Let be the weight coefficient, and satisfy... Select the robot dog with the lowest overall cost as the replacement robot dog and issue the replacement command.

10. The method according to claim 1, characterized in that, When monitoring ground sound, the sampling frequency should be no less than 2000Hz, and the sampling time for a single measuring point should be no less than 1 minute; when monitoring electromagnetic radiation, the probe should be 0.05m~0.3m away from the coal surface, and the measurement time for a single measuring point should be no less than 1 minute; when monitoring roadway deformation, the three-dimensional point cloud of the roadway cross-section is obtained in real time through laser scanning, and the convergence deformation, roof subsidence and floor heave are calculated by comparing with historical cross-sections; during non-production shifts, the robot dog can walk along the scraper conveyor of the working face to complete the detection work inside the working face.

Images