Unmanned aerial vehicle-mechanical dog cooperative multi-frequency sound wave mine heap cavity monitoring system and method

Abstract

The invention discloses an unmanned aerial vehicle-mechanical dog cooperative multi-frequency sound wave mine pile cavity monitoring system and method. The system is characterized in that a plurality of receiving sensors are uniformly arranged on the surface of a mine pile in a stoping space; the unmanned aerial vehicle platform and the mechanical dog are arranged in the stoping space; the multi-sensor integration unit comprises a sound wave emission module, an RTK real-time dynamic positioning module, a laser radar module and a visual SLAM module, and the calculation analysis terminal is connected with the mechanical dog, the multi-sensor integration unit and the multiple receiving sensors. The method comprises the following steps: after finishing each ore drawing operation, arranging a plurality of receiving sensors on the surface of an ore heap at the bottom of a stoping space in a rhombic grid manner by adopting a mechanical dog; and the unmanned aerial vehicle platform is controlled to sequentially traverse the arrangement point positions of the receiving sensors, sound wave signals are transmitted, the head wave arrival time, the amplitude attenuation rate and the frequency spectrum gravity center offset are obtained through the receiving sensors, and third-level early warning is carried out. According to the system and the method, the cavity condition in the ore heap can be efficiently and accurately monitored.

Description

Technical Field

[0001] The present invention belongs to the field of intelligent mining technology, and specifically provides a drone-mechanical dog collaborative multi-frequency acoustic wave mine pile cavity monitoring system and method. Background Art

[0002] As the core technology of traditional open-stop mining, the shallow-hole ore retention method has been widely used in my country's metal and non-metal mines. During the shallow-hole ore retention process, two-thirds of the mined ore must be temporarily retained in the mine chamber as a temporary workbench. However, due to uneven ore size and the articulation of gravity, hidden cavities can easily form within the ore pile. Such cavities can lead to local collapse of the working surface, posing a significant safety hazard to personnel falling or falling into the ore hopper during the ore discharge process. Traditional monitoring methods typically rely on manual percussion or drilling exploration to detect cavities. These methods suffer from low detection efficiency, limited coverage, and the inability to perceive in real time. Furthermore, the risk of manual deep-drilling exploration in the ore pile is extremely high. While existing technologies have introduced drones or robots for mine inspections, they still suffer from the following shortcomings: The limited functionality of individual devices makes it difficult for drones to accurately deploy sensors on complex ore pile surfaces, and mechanical devices lack efficient mobility and obstacle avoidance capabilities. Acoustic wave detection technologies often rely on single-frequency signals, failing to balance ore penetration depth with surface crack resolution. Data processing algorithms are static and fail to dynamically adjust thresholds based on ore pile height, resulting in high false alarm rates. Furthermore, existing systems lack a graded response mechanism, preventing them from triggering differentiated emergency measures based on void risk levels. While recent research has attempted to integrate multi-sensor technology, key technical challenges remain, such as insufficient sensor alignment, multi-frequency collaborative analysis of acoustic signals, and adaptive construction of dynamic benchmark models. For example, fixed sensor networks struggle to adapt to dynamic changes in the ore pile surface, while single-frequency acoustic wave technology lacks the complementary mechanism of low-frequency penetration of the ore body and high-frequency detection of surface cracks to improve detection accuracy. Therefore, there is an urgent need to develop an intelligent ore pile void monitoring system that integrates drone-robot collaborative operation, multi-frequency acoustic wave joint scanning, and dynamic threshold adaptive calculation to enhance the inherent safety of shallow hole ore retention methods. Summary of the Invention

[0003] In response to the problems existing in the above-mentioned prior art, the present invention provides a drone-mechanical dog collaborative multi-frequency acoustic wave ore pile cavity monitoring system and method. The system has a high degree of intelligence and can accurately and efficiently detect the cavity conditions inside the ore pile through the collaborative operation of drones and mechanical dogs, which is beneficial to improving the safety factor of shallow hole ore-leaving operations; the method can efficiently and accurately monitor the cavity conditions inside the ore pile, and can effectively solve the defects of low efficiency, poor real-time performance and high risk of missed detection of ore pile voids in the prior art.

[0004] To achieve the above objectives, the present invention provides a drone-robot dog collaborative multi-frequency acoustic wave ore pile cavity monitoring system, comprising a mine room, a mining space located in the mine room, pillars distributed on both sides of the mine room, a top pillar located at the top of the mine room, a bottom pillar located at the bottom of the mine room, a skylight opened in the pillars, a connecting road connecting the skylight and the mine room, a vein transport tunnel located at the bottom of the skylight and extending laterally along the mine room, a ore drawing funnel opened in the mine room chassis and connected to the vein transport tunnel, a receiving sensor, a drone platform, a robot dog, a multi-sensor integrated unit, and a computing and analysis terminal;

[0005] A plurality of receiving sensors are evenly arranged on the surface of the ore pile in the mining space;

[0006] The UAV platform is arranged in the mining space;

[0007] The mechanical dog is arranged in the mining space;

[0008] The multi-sensor integrated unit includes an installation platform, an acoustic wave transmitting module, an RTK real-time dynamic positioning module, a laser radar module, a visual SLAM module, a wireless communication module and a controller, and the controller is respectively connected to the acoustic wave transmitting module, the RTK real-time dynamic positioning module, the laser radar module, the visual SLAM module and the wireless communication module; the installation platform is fixedly installed at the bottom of the UAV platform, and the acoustic wave transmitting module, the RTK real-time dynamic positioning module, the laser radar module and the visual SLAM module are installed on the installation platform at intervals; the wireless communication module and the controller are installed on the UAV platform; the controller is respectively connected to the acoustic wave transmitting module, the RTK real-time dynamic positioning module, the laser radar module, the visual SLAM module, the UAV platform and the wireless communication module;

[0009] The computing and analyzing terminal is connected to the mechanical dog, the multi-sensor integration unit and the plurality of receiving sensors respectively.

[0010] As a preference, the computing and analysis terminal is an industrial computer.

[0011] As a preference, the controller is a PLC controller.

[0012] In the present invention, by equipping a drone platform with a multi-sensor integrated unit, it is possible to facilitate the use of the drone platform to enter the mining area for scanning and detection operations. This eliminates the need for human intervention during the scanning and detection phase, effectively saving labor costs and reducing the workload, while also improving the safety factor of the operators. By providing an acoustic wave transmitting module within the multi-sensor integrated unit, acoustic wave signals can be easily transmitted to the ore pile below. In this way, the receiving sensors arranged on the surface of the ore pile can receive the echo signals, and the internal conditions of the ore pile can be detected based on the echo conditions, which facilitates the rapid detection of cavities and surface cracks within the ore pile. By providing an RTK real-time dynamic positioning module within the multi-sensor integrated unit, the drone platform's position information within the mining area can be easily obtained in real time, thereby facilitating the convenient and efficient management and control of the drone platform by relevant management personnel. By providing a visual SLAM module within the multi-sensor integrated unit, the drone platform can obtain surrounding image data in real time during flight. In this way, relevant management personnel can use the image data to achieve full monitoring of the operation process and intuitively obtain the environmental conditions of each part of the mining area. Furthermore, the combination of an RTK real-time dynamic positioning module and a visual SLAM module significantly enhances the drone's autonomous navigation capabilities, enabling high-precision, highly reliable autonomous flight in complex scenarios. The inclusion of a lidar module within the multi-sensor integrated unit facilitates obstacle detection around the drone platform, facilitating autonomous obstacle avoidance path planning during flight. The inclusion of a wireless communication module facilitates convenient communication between the multi-sensor integrated unit and the computing and analysis terminal. Furthermore, using a drone platform as the carrier for the multi-sensor integrated unit and autonomously controlling its movements through a controller allows for flexible adjustment of detection methods. This allows for unmanned collection of waveform data from each receiving sensor within the mining area, significantly enhancing the intelligence of the detection process and freeing up manpower, effectively reducing labor costs. This improves operator safety and significantly enhances detection efficiency. Connecting the computing and analysis terminal to multiple receiving sensors and the multi-sensor integrated unit allows the terminal to receive waveform data from multiple receiving sensors in real time. This allows for the precise identification of cavities within the ore pile and cracks on the surface of the ore pile through a combined analysis of acoustic wave time and frequency domain characteristics. The robotic dog allows for the automated placement and recovery of several modular receiving sensors on the surface of the ore pile, eliminating the need for human intervention during both placement and recovery, significantly improving the safety of the operation.

[0013] The system is highly intelligent and can accurately and efficiently detect the voids inside the ore pile through the collaborative operation of inorganic humans and mechanical dogs, which is conducive to improving the safety factor of shallow hole ore retention operations.

[0014] The present invention also provides a method for monitoring ore pile voids using a drone-robot dog collaborative multi-frequency acoustic wave system, which includes the following steps:

[0015] Step 1: When using the shallow hole ore-retention method for mining operations in metal and non-metal mines, ore is discharged through a hopper, releasing 1 / 3 of the ore each time, leaving 2 / 3 of the remaining ore as a temporary workbench for the next rock drilling and blasting operation, and forming a mining space above the temporary workbench;

[0016] Step 2: After completing each ore drawing operation, the robot dog is placed in the mining space. The robot dog is used to arrange several receiving sensors in a diamond grid on the surface of the ore pile at the bottom of the mining space.

[0017] Step 3: Control the UAV platform to traverse the layout points of each receiving sensor in turn, and hover above each layout point for an initial detection cycle. In each initial detection cycle, the acoustic wave transmission module transmits a 20kHz standard acoustic wave pulse downward, and uses the receiving sensor below to receive the 20kHz acoustic wave signal in real time. The signal is sent to the calculation and analysis terminal via wireless communication. The calculation and analysis terminal records the arrival time and amplitude attenuation rate of the first wave of each receiving sensor, and generates an initial acoustic wave propagation parameter matrix based on the accumulation height of the remaining ore.

[0018] Step 4: Control the UAV platform again to traverse the layout points of each receiving sensor in turn, and hover above each layout point for a formal detection cycle. In each formal detection cycle, the acoustic wave transmission module transmits a 20kHz main frequency detection signal and a 50kHz auxiliary frequency detection signal downward in sequence. At the same time, the receiving sensor below synchronously receives the 20kHz and 50kHz acoustic wave signals and sends them to the calculation and analysis terminal via wireless communication. The calculation and analysis terminal records the waveform data of each receiving sensor.

[0019] Step 5: The calculation and analysis terminal calculates the V of each detection point based on the 20kHz waveform data received by each receiving sensor. p,实测 , where V p,实测 = (transmitting distance - receiving distance) / first wave arrival time, calculate the α of each detection point 实测 , where α 实测=20×log10(reference amplitude / measured amplitude) / propagation distance; at the same time, based on the frequency spectrum center change value of the 50kHz waveform data received by each receiving sensor, f is obtained. c , 实测 ;

[0020] Step 6: The calculation and analysis terminal calculates the longitudinal wave velocity drop rate ΔV based on the 20kHz waveform data received by each receiving sensor according to formula (1) p , calculate the attenuation coefficient rise rate Δα according to formula (2); at the same time, based on the 50kHz waveform data received by each receiving sensor, calculate the spectrum center of gravity offset Δf according to formula (3) c , calculate the crack density ρ according to formula (4);

[0021]

[0022] Δf c =f c,实测 -f c,基准 (3);

[0023] ρ=k·|Δf c | (4);

[0024] Where k is the calibration coefficient; V p,基准 , α 基准 and f c,基准 Obtained for benchmark model implementation based on dense and crack-free ore pile;

[0025] Step 7: When any parameter at any detection point exceeds the standard, an area with a radius of 0.8m is demarcated with the detection point as the center as the key monitoring area. Based on the degree of deviation of the parameter from the baseline value, the risk level of the ore pile is determined, and graded warning actions are taken;

[0026] When 5%≤ΔV p <10% and 20%≤Δα<50%, or when ρ≥0.1mm -1 When the risk level is determined to be level 1, a level 1 warning will be issued;

[0027] When 10%≤ΔV p <15% and 50%≤Δα<80%, or when ρ≥0.3mm -1 When the second level risk occurs, it is determined that a second level risk has occurred and a second level warning is issued;

[0028] When 15%<ΔV p And 80% < Δα, or when ρ ≥ 0.5 mm -1 When the risk level is 3, it is determined that there is a level 3 risk and a level 3 warning is issued;

[0029] Step 8: After eliminating all safety risks through correct emergency measures, the robot dog is deployed in the mining space again to retrieve several receiving sensors on the surface of the ore pile one by one;

[0030] Step 9: Carry out the next rock drilling and blasting operation.

[0031] As a preference, in step 1, the height of the mining space formed is 2 to 3 meters.

[0032] As a preference, in step 2, the receiving sensor is a piezoelectric receiving sensor.

[0033] As a preference, in step 2, a receiving sensor is arranged at each of the four corners, the inner center and the midpoint of each side of each rhombus area.

[0034] As a preferred embodiment, in step three, while the UAV platform is flying in the mining space, the RTK real-time dynamic positioning module is used to perceive the position information of the UAV platform in real time and send it to the controller, which then sends it to the computing and analysis terminal through the wireless communication module. At the same time, the visual SLAM module is used to collect image data around the UAV platform in real time and send it to the controller, which then sends it to the computing and analysis terminal through the wireless communication module. At the same time, the controller uses the lidar module to obtain the obstacle conditions around the UAV platform in real time and dynamically adjust the flight path according to the surrounding obstacle conditions.

[0035] The present invention provides a drone-robot dog collaborative multi-frequency acoustic wave ore pile void monitoring method, which uses a robotic dog to actively deploy modular piezoelectric sensors in a diamond grid on the surface of the ore pile and form a receiving sensor array. The drone is then used to transmit a composite acoustic wave signal with a main frequency of 20kHz and an auxiliary frequency of 50kHz along a preset path, and the waveform data of the receiving sensor is used to obtain the first wave arrival time, amplitude attenuation rate, and spectrum center of gravity offset. Thus, through the coordination technology of directional acoustic wave emission and receiving echo, and adopting multi-frequency composite acoustic wave scanning technology (main frequency 20kHz, auxiliary frequency 50kHz), low-frequency signals are used to penetrate the ore body to find internal voids and high-frequency signals are used to capture surface cracks. Then, combined with the joint analysis of acoustic wave time domain / frequency domain characteristics, based on, the longitudinal wave velocity and attenuation coefficient are calculated in real time, and the internal voids and surface cracks of the ore pile are accurately identified. Finally, through a three-level early warning mechanism, different levels of risk warnings are triggered respectively, so that corresponding emergency measures can be taken according to different levels of warnings.

[0036] This invention utilizes multi-frequency acoustic wave collaborative scanning technology, leveraging the complementary mechanism of low-frequency penetration of ore bodies and high-frequency capture of surface cracks. This overcomes the inability of traditional single-frequency acoustic waves to achieve both depth and resolution. By integrating drone-robot collaborative operations, multi-source data fusion, and intelligent response, it overcomes the limitations of manual exploration, such as low efficiency and poor accuracy. It enables real-time dynamic monitoring of ore pile voids and risk-based management, demonstrating high precision, high safety, and strong engineering practicality.

[0037] Compared with the prior art, the present invention has the following advantages:

[0038] 1) Efficient collaborative detection. This invention uses a robotic dog to actively deploy piezoelectric sensors in a diamond-shaped grid, combined with a drone to transmit multi-frequency composite acoustic signals, enabling simultaneous dynamic scanning of the surface and internal structure of the ore pile. Compared to traditional manual percussion or drilling exploration, the system can complete full-area monitoring within a 30-second cycle, improving detection efficiency by over 80% and eliminating the risks of manual exploration into the ore pile.

[0039] 2) Precise multi-frequency acoustic identification. Using a collaborative scanning technology combining a primary frequency of 20kHz (low frequency to penetrate the ore body) and a secondary frequency of 50kHz (high frequency to capture surface cracks), combined with combined analysis of acoustic time and frequency domain characteristics, this technology can simultaneously detect internal cavities and surface cracks in the ore pile, significantly improving detection resolution. By calculating the center of gravity offset of the spectrum, it effectively distinguishes between abnormal ore pile density and external interference signals, reducing the rate of false positives.

[0040] 3) Hierarchical Intelligent Response. The system integrates drone-robot collaborative operations with multi-source data fusion to provide a three-level early warning system, providing high-precision, real-time safety assurance for shallow hole ore retention methods. This significantly reduces the risk of personnel falls and offers strong engineering practicality.

[0041] This method can monitor the void conditions inside the ore pile efficiently and accurately, and can effectively solve the defects of low efficiency, poor real-time performance and high risk of missed detection in the existing technology. It is particularly suitable for the field of shallow hole ore mining in metal and non-metal mines. It can realize dynamic identification and graded early warning of voids inside the ore pile, effectively ensuring the safety of mining workers. BRIEF DESCRIPTION OF THE DRAWINGS

[0042] Figure 1 It is a structural diagram of the shallow hole ore retention method monitoring system of the present invention;

[0043] Figure 2 It is a schematic diagram of the route of the diamond grid in the present invention;

[0044] Figure 3 This is a schematic diagram of the receiving sensor arrangement completed in the present invention;

[0045] Figure 4It is a partial schematic diagram of the receiving sensor in the diamond grid of the present invention;

[0046] Figure 5 is a top view of the UAV platform of the present invention;

[0047] Figure 6 is a front view of the UAV platform of the present invention;

[0048] Figure 7 It is a structural diagram of the multi-sensor integrated unit in the present invention.

[0049] Figure 8 It is a principle block diagram of the control part in the present invention.

[0050] In the figure: 1. Return air tunnel, 2. Top pillar, 3. Skylight, 4. Connecting road, 5. Intermediate pillar, 6. Ore storage, 7. Bottom pillar, 8. Ore hopper, 9. Vein transport tunnel, 10. Unmined ore, 11. Mining space, 12. Diamond grid, 13. Receiving sensor, 14. UAV platform, 15. Multi-sensor integrated unit, 15-1. Acoustic wave emission module, 15-2. RTK real-time dynamic positioning module, 15-3. LiDAR module, 15-4. Visual SLAM module, 15-5. Installation platform, 16. Mine room, 17. Robot dog. DETAILED DESCRIPTION

[0051] The present invention will be further described below with reference to the accompanying drawings.

[0052] like Figures 1 to 8 As shown, the present invention provides a drone-robot dog collaborative multi-frequency acoustic wave ore pile cavity monitoring system, including a mine room 16, a recovery space 11 located in the mine room 16, pillars 5 distributed on both sides of the mine room 16, a top pillar 2 located at the top of the mine room 16, a bottom pillar 7 located at the bottom of the mine room 16, a skylight 3 opened in the pillar 5, a connecting road 4 connecting the skylight 3 and the mine room 16, a vein transport tunnel 9 located at the bottom of the skylight 3 and extending laterally along the mine room 16, a ore drawing funnel 8 opened in the chassis of the mine room 16 and connected to the vein transport tunnel 9, a receiving sensor 13, a drone platform 14, a robot dog 17, a multi-sensor integration unit 15 and a computing and analysis terminal;

[0053] The return air tunnel 1 is located above the top pillar 2, the unmined ore 10 is located above the mining space 11, and the remaining ore 6 is located between the mining space 11 and the ore draw hopper 8.

[0054] A plurality of receiving sensors 13 are evenly arranged on the surface of the ore pile in the mining space 11 ; preferably, the sampling frequency of the receiving sensors 13 is 1 MHz.

[0055] The UAV platform 14 is arranged in the mining space 11;

[0056] The mechanical dog 17 is arranged in the mining space 11;

[0057] The multi-sensor integrated unit 15 includes an installation platform 15-5, an acoustic wave transmitting module 15-1, an RTK real-time dynamic positioning module 15-2, a laser radar module 15-3, a visual SLAM module 15-4, a wireless communication module and a controller, and the controller is respectively connected to the acoustic wave transmitting module 15-1, the RTK real-time dynamic positioning module 15-2, the laser radar module 15-3, the visual SLAM module 15-4 and the wireless communication module; the installation platform 15-5 is fixedly mounted on the bottom of the UAV platform 14, and the acoustic wave transmitting module 15-1, the RTK real-time dynamic positioning module 15-2, the laser radar module 15-3 and the visual SLAM module 15-4 are installed on the installation platform 15-5 at intervals from each other; the wireless communication module and the controller are installed on the UAV platform 14; the controller is respectively connected to the acoustic wave transmitting module 15-1, the RTK real-time dynamic positioning module 15-2, the laser radar module 15-3, the visual SLAM module 15-4, the UAV platform 14 and the wireless communication module;

[0058] As a preferred embodiment, the frequency range of the acoustic wave transmitting module 15-1 is 10-60kHz, and the power is 50W. It can transmit acoustic wave signals with a main frequency of 20kHz and an auxiliary frequency of 50kHz; among them, the RTK real-time dynamic positioning module 15-2 and the laser radar module 15-3 form a dual redundant navigation system, which can facilitate dynamic obstacle avoidance path planning. At the same time, it can further facilitate the dynamic adjustment of flight altitude and path by constructing a three-dimensional point cloud model of the ore pile.

[0059] The computing and analyzing terminal is connected to the multi-sensor integration unit 15 and the plurality of receiving sensors 13 respectively.

[0060] The mechanical dog 17 is used to arrange the receiving sensors 13 on the surface of the ore pile in a set layout, and is also used to collect the receiving sensors 13 located on the surface of the ore pile; as a preferred embodiment, the mechanical dog 17 has a built-in pressure feedback sensing unit to ensure that the placed receiving sensors 13 can fully fit the surface of the ore pile. At the same time, it supports a torque adaptive algorithm and can overcome obstacles of 30 cm.

[0061] As a preference, it further includes an alarm, which is connected to the computing and analysis terminal and is used to execute an alarm action according to the control of the computing and analysis terminal.

[0062] As a preferred embodiment, a UWB base station can be arranged in the mining space 11, so that the receiving sensor 13 and the UAV platform 14 can establish a more reliable wireless communication connection with the computing and analysis terminal through the UWB base station.

[0063] As a preference, the computing and analysis terminal is an industrial computer.

[0064] As a preference, the controller is a PLC controller.

[0065] In the present invention, by equipping a drone platform with a multi-sensor integrated unit, it is possible to facilitate the use of the drone platform to enter the mining area for scanning and detection operations. This eliminates the need for human intervention during the scanning and detection phase, effectively saving labor costs and reducing the workload, while also improving the safety factor of the operators. By providing an acoustic wave transmitting module within the multi-sensor integrated unit, acoustic wave signals can be easily transmitted to the ore pile below. In this way, the receiving sensors arranged on the surface of the ore pile can receive the echo signals, and the internal conditions of the ore pile can be detected based on the echo conditions, which facilitates the rapid detection of cavities and surface cracks within the ore pile. By providing an RTK real-time dynamic positioning module within the multi-sensor integrated unit, the drone platform's position information within the mining area can be easily obtained in real time, thereby facilitating the convenient and efficient management and control of the drone platform by relevant management personnel. By providing a visual SLAM module within the multi-sensor integrated unit, the drone platform can obtain surrounding image data in real time during flight. In this way, relevant management personnel can use the image data to achieve full monitoring of the operation process and intuitively obtain the environmental conditions of each part of the mining area. Furthermore, the combination of an RTK real-time dynamic positioning module and a visual SLAM module significantly enhances the drone's autonomous navigation capabilities, enabling high-precision, highly reliable autonomous flight in complex scenarios. The inclusion of a lidar module within the multi-sensor integrated unit facilitates obstacle detection around the drone platform, facilitating autonomous obstacle avoidance path planning during flight. The inclusion of a wireless communication module facilitates convenient communication between the multi-sensor integrated unit and the computing and analysis terminal. Furthermore, using a drone platform as the carrier for the multi-sensor integrated unit and autonomously controlling its movements through a controller allows for flexible adjustment of detection methods. This allows for unmanned collection of waveform data from each receiving sensor within the mining area, significantly enhancing the intelligence of the detection process and freeing up manpower, effectively reducing labor costs. This improves operator safety and significantly enhances detection efficiency. Connecting the computing and analysis terminal to multiple receiving sensors and the multi-sensor integrated unit allows the terminal to receive waveform data from multiple receiving sensors in real time. This allows for the precise identification of cavities within the ore pile and cracks on the surface of the ore pile through a combined analysis of acoustic wave time and frequency domain characteristics. The robotic dog allows for the automated placement and recovery of several modular receiving sensors on the surface of the ore pile, eliminating the need for human intervention during both placement and recovery, significantly improving the safety of the operation.

[0066] The system is highly intelligent and can accurately and efficiently detect the voids inside the ore pile through the collaborative operation of inorganic humans and mechanical dogs, which is conducive to improving the safety factor of shallow hole ore retention operations.

[0067] The present invention also provides a method for monitoring ore pile voids using a drone-robot dog collaborative multi-frequency acoustic wave system, which includes the following steps:

[0068] Step 1: When using the shallow hole ore retention method for mining in metal and non-metal mines, the ore is discharged through the ore discharge funnel 8, and 1 / 3 of the ore is discharged each time, leaving 2 / 3 of the remaining ore 6 as a temporary workbench for the next rock drilling and blasting work, and forming a recovery space 11 above the temporary workbench;

[0069] Step 2: After completing each ore drawing operation, the robot dog 17 is placed in the mining space 11. The robot dog 17 is used to arrange a plurality of receiving sensors 13 in a diamond grid on the surface of the ore pile at the bottom of the mining space 11;

[0070] Step 3: Control the UAV platform 14 to traverse the layout points of each receiving sensor 13 in turn, and hover above each layout point for an initial detection cycle. In each initial detection cycle, a standard sound wave pulse of 20kHz is emitted downward through the sound wave transmitting module 15-1, and the receiving sensor 13 below is used to receive the 20kHz sound wave signal in real time, and send it to the calculation and analysis terminal through wireless communication. The calculation and analysis terminal records the time of arrival of the first wave and the amplitude attenuation rate of each receiving sensor 13, and generates an initial sound wave propagation parameter matrix in combination with the accumulation height of the remaining ore 6;

[0071] As a preferred embodiment, the initial detection period is 10ms. The standard acoustic wave pulse is a sine wave.

[0072] Step 4: Control the UAV platform 14 again to traverse the layout points of each receiving sensor 13 in sequence, and hover above each layout point for a formal detection cycle. In each formal detection cycle, the acoustic wave transmitting module 15-1 transmits a 20kHz main frequency detection signal and a 50kHz auxiliary frequency detection signal downward in sequence. At the same time, the receiving sensor 13 below synchronously receives the 20kHz acoustic wave signal and the 50kHz acoustic wave signal, and sends them to the calculation and analysis terminal via wireless communication. The calculation and analysis terminal records the waveform data of each receiving sensor 13;

[0073] As a preferred embodiment, the formal detection period is 10ms.

[0074] Step 5: The calculation and analysis terminal calculates the V of each detection point based on the 20kHz waveform data received by each receiving sensor 13. p,实测 , where V p,实测 = (transmitting distance - receiving distance) / first wave arrival time, calculate the α of each detection point 实测 , where α 实测 =20×log10(reference amplitude / measured amplitude) / propagation distance; at the same time, based on the frequency spectrum center change value of the 50kHz waveform data received by each receiving sensor 13, f is obtained. c,实测 ;

[0075] Step 6: The calculation and analysis terminal calculates the longitudinal wave velocity drop rate ΔV based on the 20kHz waveform data received by each receiving sensor 13 according to formula (1): p , calculate the attenuation coefficient rise rate Δα according to formula (2); at the same time, based on the 50kHz waveform data received by each receiving sensor 13, calculate the spectrum center of gravity offset Δf according to formula (3) c , calculate the crack density ρ according to formula (4);

[0076]

[0077] Δf c =f c,实测 -f c,基准 (3);

[0078] ρ=k·|Δf c | (4);

[0079] Where k is the calibration coefficient, which can be calibrated by experiment and can be taken as 0.005mm -1 ; V p,基准 , α 基准 and f c,基准 Obtained for detection experiments based on a dense and crack-free ore pile benchmark model;

[0080] Step 7: When any parameter at any detection point exceeds the standard, an area with a radius of 0.8m is demarcated with the detection point as the center as the key monitoring area. Based on the degree of deviation of the parameter from the baseline value, the risk level of the ore pile is determined, and graded warning actions are taken;

[0081] When 5%≤ΔV p <10% and 20%≤Δα<50%, or when ρ≥0.1mm -1 (minor cracks), it is determined that a level one risk has occurred and a level one warning is issued; when a level one warning occurs, the alarm is triggered to emit an audible and visual alarm signal to notify relevant operating personnel to further check the actual situation of the monitored area;

[0082] When 10%≤ΔV p <15% and 50%≤Δα<80%, or when ρ≥0.3mm -1 (moderate cracks), a second-level risk is determined and a second-level warning is issued; when a second-level warning occurs, relevant operators spray quick-setting materials on the monitored area to solidify the surface of the ore pile in the risk area and have the set bearing capacity;

[0083] When 15%<ΔV p And 80% < Δα, or when ρ ≥ 0.5 mm -1 1 (severe cracks), it is determined that a level 3 risk has occurred and a level 3 warning is issued; when a level 3 warning occurs, the relevant workers are guided to evacuate along a safe path and emergency support measures are initiated.

[0084] Step 8: After all safety risks have been eliminated through correct emergency measures, the robot dog 17 is deployed again in the mining space 11 to retrieve the multiple receiving sensors 13 on the surface of the ore pile one by one;

[0085] Step 9: Carry out the next rock drilling and blasting operation.

[0086] As a preference, in step 1, the height of the mining space 11 formed is 2 to 3 meters.

[0087] As a preference, in step 2, the receiving sensor 13 is a piezoelectric receiving sensor.

[0088] As a preference, in step 2, a receiving sensor 13 is arranged at each of the four corners, the inner center and the midpoint of each side of each rhombus area.

[0089] As a preferred embodiment, in step three, while the UAV platform 14 is flying in the mining space 11, the RTK real-time dynamic positioning module 15-2 is used to perceive the position information of the UAV platform 14 in real time and send it to the controller, which then sends it to the computing and analysis terminal through the wireless communication module. At the same time, the visual SLAM module 15-4 is used to collect image data around the UAV platform 14 in real time and send it to the controller, which then sends it to the computing and analysis terminal through the wireless communication module. At the same time, the controller uses the lidar module 15-3 to obtain the obstacle conditions around the UAV platform 14 in real time and dynamically adjust the flight path according to the surrounding obstacle conditions.

[0090] This invention utilizes multi-frequency acoustic wave collaborative scanning technology, leveraging the complementary mechanism of low-frequency penetration of ore bodies and high-frequency capture of surface cracks. This overcomes the inability of traditional single-frequency acoustic waves to achieve both depth and resolution. By integrating drone-robot collaborative operations, multi-source data fusion, and intelligent response, it overcomes the limitations of manual exploration, such as low efficiency and poor accuracy. It enables real-time dynamic monitoring of ore pile voids and risk-based management, demonstrating high precision, high safety, and strong engineering practicality.

[0091] This method can monitor the void conditions inside the ore pile efficiently and accurately, and can effectively solve the defects of low efficiency, poor real-time performance and high risk of missed detection in the existing technology. It is particularly suitable for the field of shallow hole ore mining in metal and non-metal mines. It can realize dynamic identification and graded early warning of voids inside the ore pile, effectively ensuring the safety of mining workers.

Claims

1. A drone-robot dog collaborative multi-frequency acoustic wave ore pile cavity monitoring system, comprising a mine room (16), a mining space (11) located in the mine room (16), pillars (5) distributed on both sides of the mine room (16), a top pillar (2) located at the top of the mine room (16), a bottom pillar (7) located at the bottom of the mine room (16), a skylight (3) opened in the pillars (5), a connecting road (4) connecting the skylight (3) and the mine room (16), a vein transport tunnel (9) located at the bottom of the skylight (3) and extending laterally along the mine room (16), and a ore draw funnel (8) opened in the bottom of the mine room (16) and connected to the vein transport tunnel (9), characterized in that: It also includes a receiving sensor (13), a drone platform (14), a mechanical dog (17), a multi-sensor integration unit (15) and a computing and analysis terminal; A plurality of receiving sensors (13) are evenly arranged on the surface of the ore pile in the mining space (11); The drone platform (14) is arranged in the mining space (11); The mechanical dog (17) is arranged in the mining space (11); The multi-sensor integrated unit (15) comprises an installation platform (15-5), an acoustic wave emission module (15-1), an RTK real-time dynamic positioning module (15-2), a laser radar module (15-3), a visual SLAM module (15-4), a wireless communication module and a controller, wherein the controller is connected to the acoustic wave emission module (15-1), the RTK real-time dynamic positioning module (15-2), the laser radar module (15-3), the visual SLAM module (15-4) and the wireless communication module respectively; the installation platform (15-5) is fixedly installed on the unmanned aerial vehicle platform (14 ), the acoustic wave emission module (15-1), the RTK real-time dynamic positioning module (15-2), the laser radar module (15-3), and the visual SLAM module (15-4) are installed on the installation platform (15-5) at intervals; the wireless communication module and the controller are installed on the unmanned aerial vehicle platform (14); the controller is respectively connected to the acoustic wave emission module (15-1), the RTK real-time dynamic positioning module (15-2), the laser radar module (15-3), the visual SLAM module (15-4), the unmanned aerial vehicle platform (14), and the wireless communication module; The computing and analyzing terminal is respectively connected to the mechanical dog (17), the multi-sensor integrated unit (15) and the plurality of receiving sensors (13).

2. The drone-robot dog collaborative multi-frequency acoustic wave mine cavity monitoring system according to claim 1 is characterized in that: The computing and analyzing terminal is an industrial computer.

3. The drone-robot dog collaborative multi-frequency acoustic wave mine cavity monitoring system according to claim 2 is characterized in that: The controller is a PLC controller.

4. A drone-robot dog collaborative multi-frequency acoustic wave ore pile void monitoring method, using the drone-robot dog collaborative multi-frequency acoustic wave ore pile void monitoring system as claimed in claim 1, characterized in that: The following steps are involved: Step 1: When using the shallow hole ore-retention method for mining in metal and non-metal mines, the ore-retention operation is carried out through the ore-retention funnel (8), and 1 / 3 of the ore mined each time is released, leaving 2 / 3 of the remaining ore (6) as a temporary workbench for the next rock drilling and blasting work, and forming a mining space (11) above the temporary workbench; Step 2: After completing each ore drawing operation, the mechanical dog (17) is arranged in the mining space (11), and the mechanical dog (17) is used to arrange a plurality of receiving sensors (13) in a diamond grid on the surface of the ore pile at the bottom of the mining space (11); Step 3: Control the UAV platform (14) to traverse the layout points of each receiving sensor (13) in turn, and hover above each layout point for an initial detection cycle. In each initial detection cycle, a standard sound wave pulse of 20kHz is emitted downward through the sound wave transmitting module (15-1), and the receiving sensor (13) below is used to receive the 20kHz sound wave signal in real time, and the sound wave signal is sent to the calculation and analysis terminal through wireless communication. The calculation and analysis terminal records the time of arrival of the first wave of each receiving sensor (13) and the amplitude attenuation rate, and generates an initial sound wave propagation parameter matrix in combination with the accumulation height of the remaining ore (6); Step 4: Control the UAV platform (14) again to traverse the layout points of each receiving sensor (13) in sequence, and hover above each layout point for a formal detection cycle. In each formal detection cycle, the sound wave transmitting module (15-1) transmits a 20kHz main frequency detection signal and a 50kHz auxiliary frequency detection signal downward in sequence. At the same time, the receiving sensor (13) below is used to synchronously receive the 20kHz sound wave signal and the 50kHz sound wave signal, and sends them to the calculation and analysis terminal through wireless communication. The calculation and analysis terminal records the waveform data of each receiving sensor (13); Step 5: The calculation and analysis terminal calculates the V of each detection point based on the 20kHz waveform data received by each receiving sensor (13). p,实测 , where V p,实测 = (transmitting distance - receiving distance) / first wave arrival time, calculate the α of each detection point 实测 , where α 实测 =20×log10(reference amplitude / measured amplitude) / propagation distance; at the same time, based on the frequency spectrum center of gravity change value of the 50kHz waveform data received by each receiving sensor (13), f is obtained. c,实测 ; Step 6: The calculation and analysis terminal calculates the longitudinal wave velocity drop rate ΔV based on the 20kHz waveform data received by each receiving sensor (13) according to formula (1) p , the attenuation coefficient rise rate Δα is calculated according to formula (2); at the same time, based on the 50kHz waveform data received by each receiving sensor (13), the spectrum center of gravity offset Δf is calculated according to formula (3) c , calculate the crack density ρ according to formula (4); Δf c =f c,实测 -f c,基准 (3); ρ=k·|Δf c | (4); Where k is the calibration coefficient; V p,基准 , α 基准 and f c,基准 Obtained for benchmark model implementation based on dense and crack-free ore pile; Step 7: When any parameter at any detection point exceeds the standard, an area with a radius of 0.8m is demarcated with the detection point as the center as the key monitoring area. Based on the degree of deviation of the parameter from the baseline value, the risk level of the ore pile is determined, and graded warning actions are taken; When 5%≤ΔV p <10% and 20%≤Δα<50%, or when ρ≥0.1mm -1 When the risk level is determined to be level 1, a level 1 warning will be issued; When 10%≤ΔV p <15% and 50%≤Δα<80%, or when ρ≥0.3mm -1 When the second level risk occurs, it is determined that a second level risk has occurred and a second level warning is issued; When 15%<ΔV p And 80% < Δα, or when ρ ≥ 0.5 mm -1 When the risk level is 3, it is determined that there is a level 3 risk and a level 3 warning is issued; Step 8: After all safety risks are eliminated through correct emergency measures, the mechanical dog (17) is placed in the mining space (11) again to recover several receiving sensors (13) on the surface of the ore pile one by one; Step 9: Carry out the next rock drilling and blasting operation.

5. The laser-weighing dual-parameter closed-loop precise ore drawing control method for shallow hole ore retention method according to claim 4 is characterized in that: In step 1, the height of the mining space (11) formed is 2 to 3 meters.

6. The method for monitoring ore pile voids using a drone and a mechanical dog in collaboration with multi-frequency acoustic waves according to claim 5 is characterized in that: In step 2, the receiving sensor (13) is a piezoelectric receiving sensor.

7. The method for monitoring ore pile voids using a drone and a mechanical dog in collaboration with multi-frequency acoustic waves according to claim 5 is characterized in that: In step 2, a receiving sensor (13) is arranged at each of the four corners, the inner center and the midpoint of each side of each rhombus area.

8. The method for monitoring ore pile voids using a drone and a mechanical dog in collaboration with multi-frequency acoustic waves according to claim 5 is characterized in that: In step three, while the UAV platform (14) is flying in the mining space (11), the RTK real-time dynamic positioning module (15-2) is used to perceive the position information of the UAV platform (14) in real time and send it to the controller, which then sends it to the computing and analysis terminal via the wireless communication module. At the same time, the visual SLAM module (15-4) is used to collect image data around the UAV platform (14) in real time and send it to the controller, which then sends it to the computing and analysis terminal via the wireless communication module. At the same time, the controller uses the laser radar module (15-3) to obtain the obstacle conditions around the UAV platform (14) in real time and dynamically adjust the flight path according to the surrounding obstacle conditions.

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