Collaborative prevention method for open-pit mine slope spontaneous combustion and instability based on solid waste gunniting and unmanned aerial vehicle inspection

Abstract

This invention relates to the field of open-pit mine safety and disaster prevention technology, and discloses a method for the coordinated prevention and control of spontaneous combustion and instability of open-pit mine slopes based on solid waste spraying and drone inspection. The method involves preparing a solid waste-based fire-resistant and reinforcing slurry with both fireproofing and reinforcement functions using fly ash as the main solid waste. After spraying the slurry across the entire target slope, an initial digital twin benchmark model containing three-dimensional morphology and temperature field is constructed. Data is collected through periodic fully automated drone inspections to identify temperature anomaly zones and newly formed cracks, and a temperature-crack field distribution map is generated. Targeted reinforcement spraying is applied to anomaly areas, and the treatment effect is quantitatively verified. Based on the crack evolution law, high-frequency regeneration areas are identified, a risk index is calculated, and a graded coordinated early warning system for spontaneous combustion and instability is implemented. This method achieves the resource utilization of industrial solid waste, significantly reduces prevention and control costs, and simultaneously enables precise monitoring, targeted treatment, and coordinated early warning of two types of disasters on open-pit mine slopes, improving the systematic and intelligent level of mine safety management.

Description

Technical Field

[0001] This invention relates to the fields of solid waste-based recycling, open-pit mine safety and disaster prevention technology, and in particular to a method for the coordinated prevention and control of spontaneous combustion and instability of open-pit mine slopes based on solid waste spraying and drone inspection. Background Technology

[0002] During open-pit coal mining, large areas of slopes and coal seam outcrops are formed. Affected by blasting and long-term weathering, the coal and rock mass in these areas is fractured, and the coal-oxygen reaction is intense, posing a serious risk of spontaneous combustion. At the same time, the fractured coal and rock mass structure also leads to a significant reduction in slope stability, making it prone to instability and landslide disasters. These two types of disasters influence and promote each other, posing a great threat to mine safety.

[0003] Current open-pit mine slope disaster prevention technologies have many limitations: Regarding fireproofing and reinforcement materials, traditional loess covering methods are ineffective and easily eroded by rainwater; polymer spraying materials are expensive and only provide fire protection without surface reinforcement; conventional cement grouting is costly, fails to achieve resource utilization of large quantities of mine solid waste, and does not meet environmental protection requirements. In terms of monitoring and remediation, disaster hazard investigation relies heavily on manual inspections, which are inefficient, have limited coverage, and pose high operational safety risks. It is difficult to accurately locate hidden ventilation channels and fire sources, resulting in poor targeted remediation and significant material waste.

[0004] At the monitoring and early warning level, existing technologies for fire prevention monitoring and slope stability evaluation are fragmented, failing to achieve data fusion and correlation analysis. The accuracy of coal seam fracture identification is low, and the generation and evolution of new fractures are not linked to coal spontaneous combustion and slope deformation analysis. The lack of real-time and efficient monitoring methods leads to untimely early warnings and an inability to achieve coordinated early warning of coal spontaneous combustion and slope instability, failing to meet the actual needs of mine safety management. Therefore, there is an urgent need to develop an integrated solution that comprehensively utilizes industrial solid waste and automated monitoring methods to simultaneously achieve precise location and control of hidden fire hazards and advanced early warning of slope instability. Summary of the Invention

[0005] This invention aims to provide a collaborative prevention and control method for spontaneous combustion and instability of open-pit mine slopes based on solid waste spraying and drone inspection. This method enables the resource utilization of industrial solid waste, reduces prevention and control costs, and achieves precise monitoring, targeted treatment, and hierarchical collaborative early warning of the two types of disasters through drone inspection and data fusion analysis, thereby improving the intelligence and systematic level of open-pit mine disaster prevention and control.

[0006] Therefore, the technical solution adopted in this invention is a method for the coordinated prevention and control of spontaneous combustion and instability of open-pit mine slopes based on solid waste spraying and drone inspection, comprising the following steps:

[0007] S1: Prepare solid waste-based fire-retardant and reinforcing slurry; by dry weight ratio: fly ash 60-80 parts, cementitious material 10-20 parts, fire retardant 5-18 parts, water-retaining agent 0.5-1.5 parts, dispersant 0.2-1.0 parts; water to dry material mass ratio is 0.6:1;

[0008] S2: The target slope is sprayed with the first full-area spraying, and thermal infrared images and lidar point cloud data of the slope are collected by drone equipped with lidar and thermal infrared camera. A three-dimensional real scene and temperature distribution point cloud map are generated in WGS-84 coordinate system as the original reference map.

[0009] S3: Conduct periodic drone inspections, collect thermal infrared images and lidar point cloud data of the slope, and generate temperature field-fracture field distribution maps in the WGS-84 coordinate system.

[0010] S4: By comparing the original baseline map, identify temperature anomaly areas and newly formed fractures. Based on the frequency and evolution trend of fractures in each region in the temperature field-fracture field distribution map, calculate the risk index of each region and conduct graded early warning.

[0011] The graded early warning standards and response measures are as follows: Blue indicates normal, the system only records and does not issue an active warning; Orange indicates an early warning, the system marks the area and controls drones to conduct targeted reinforcement spraying on the target area; Red indicates an emergency, the system simultaneously triggers red alarms for coal spontaneous combustion and slope instability, pushes them to the control platform and notifies manual verification, and the manuals first inject grouting material and then spray the surface to cover it, forming a three-dimensional sealing structure with internal grouting and external sealing.

[0012] As a preferred embodiment of the above scheme, the cementing material in step S1 is sulfoaluminate cement or alkali-activated cementing material, the fire retardant is one or more of chloride salts, phosphates, and magnesium hydroxide, or a composite of magnesium chloride, ammonium polyphosphate, and organobentonite, the water-retaining agent is cellulose ether or polyacrylate, and the dispersant is polycarboxylate superplasticizer.

[0013] More preferably, the periodic UAV inspection in step S3 is carried out automatically through a UAV inspection platform, including automatic UAV takeoff, flight along a preset route to collect data, automatic return, and automatic data upload to the processing server. The inspection cycle is once a week. The periodic UAV inspection platform integrates a GNSS RTK module and an inertial measurement unit. The thermal infrared camera and lidar are jointly calibrated to obtain a relative pose transformation matrix. The relative pose transformation matrix is ​​used to establish a precise spatial correspondence between the thermal infrared camera and the lidar, thereby accurately mapping the temperature information captured by the camera onto the three-dimensional map generated by the radar, achieving precise fusion of temperature and spatial position.

[0014] Further preferably, the specific method for generating the temperature field - fracture field distribution map in step S3 is as follows: ① Using the internal and external parameters of the thermal infrared camera, project the 3D point cloud data (X, Y, Z) of the lidar onto the 2D thermal infrared image plane to obtain the corresponding pixel coordinates (u, v) and the temperature value T of this point, thereby endowing each 3D space point with a temperature attribute; ② Use the iterative closest point algorithm to accurately register the point cloud data of different inspection cycles to eliminate spatial errors; ③ Form a temperature field - fracture field distribution map in the WGS - 84 coordinate system.

[0015] Further preferably, the method for identifying newly - formed fractures in step S4 is as follows: ① Preliminary two - dimensional fracture segmentation: Use the U - Net deep learning model to perform semantic segmentation on the inspection visible - light images. Taking the RGB image of the gray slope background after spraying as the input, output a binary mask map of fracture pixels, identify linear features with a width greater than two millimeters, and preliminarily determine newly - formed fractures; ② Three - dimensional geometric feature verification and quantification: Map the two - dimensional fracture mask to the lidar point cloud, extract the 3D point set within the preliminarily identified area and calculate the normal vector and local curvature. When the included angle θ th between the average normal vector of the continuous point set within the area and the overall normal vector of the slope is greater than 15°, and the local elevation mutation Δh > 5 cm, it is determined as a real coal - body fracture.

[0016] Further preferably, in step S4, the calculation method of the risk index for each region: Divide the grid cells by 5 m×10 m and calculate the risk index R, and conduct hierarchical early warning: When R ≤ 1.0, it is blue - normal; when 1.0 < R ≤ 2.5, it is orange - warning; when R > 2.5, it is red - emergency; R is composed of the product of the fracture occurrence frequency coefficient F, the temperature anomaly coefficient T, and the fracture expansion rate coefficient G, that is, R = F×T×G;

[0017] ① Frequency coefficient F:

[0018]

[0019] Among them, is the number of newly - formed fractures identified within this grid cell during the current monitoring cycle; is the set high - frequency occurrence threshold constant, with a value of 3;

[0020] ② Temperature anomaly coefficient T:

[0021]

[0022] Among them, is the highest temperature in all fracture - related point clouds within this grid cell, is the average background temperature of the intact shotcrete layer area within this cell, is the critical temperature difference threshold, with a value of 15 °C;

[0023] ③ Spread rate coefficient G:

[0024]

[0025] in, and These represent the total area of ​​cracks within the unit in the current cycle and the previous cycle, respectively; Δt is the inspection cycle in days; V base The baseline value for volume change is taken as 0.1m. 3 / sky.

[0026] The beneficial effects of this invention are as follows: using fly ash as the main raw material to prepare solid waste-based fireproofing and reinforcement slurry, achieving "waste treatment with waste" and significantly reducing the overall material cost; the slurry solidified solid has a compressive strength of 4.8~8.2MPa after 28 days, which can effectively seal air leakage channels, inhibit coal-oxygen reaction, anchor the broken coal and rock mass on the surface of the slope, improve the stability of the shallow surface layer of the slope, and achieve the dual functions of fireproofing and reinforcement.

[0027] By employing fully automated periodic inspections using drones, combined with lidar, thermal infrared cameras, and GNSS RTK positioning technology, high-precision dynamic monitoring of the entire slope area is achieved. A crack identification method using two-dimensional image segmentation and three-dimensional point cloud distribution map verification improves crack identification accuracy. Simultaneously, comparative analysis of the temperature field-crack field distribution map with the original baseline map accurately locates hidden fire hazards and structurally weak areas. Targeted reinforcement spraying is applied to early warning areas, significantly improving material utilization and disaster management efficiency while avoiding material waste.

[0028] Using the generation of fissures as a common criterion for determining two types of disasters (slope instability and coal spontaneous combustion) breaks the current situation where fire prevention monitoring and slope stability evaluation are separated. By constructing a temperature field-fissure field distribution map, the monitoring data can be integrated and correlated. At the same time, a risk index is introduced to realize graded early warning. Differentiated response measures are taken according to different risk levels to achieve "one monitoring with multiple effects". The risk of slope instability can be identified in advance from fire prevention monitoring data, providing technical support for the early prevention and control of mine disasters. Attached Figure Description

[0029] Figure 1 This is a flowchart of the present invention;

[0030] Figure 2 (a) is a schematic diagram of the working status of the UAV monitoring the target area (the UAV is equipped with a lidar and a thermal infrared camera).

[0031] (b) Schematic diagram of the working status of the UAV spraying reinforcement target area (UAV loading spraying module);

[0032] Figure 3 (a) is a map showing the fracture field distribution in the target area;

[0033] (b) is a temperature field-fracture field distribution map of the target area. Detailed Implementation

[0034] The present invention will now be further described with reference to the accompanying drawings and embodiments.

[0035] like Figures 1-3 As shown, a method for the coordinated prevention and control of spontaneous combustion and instability of open-pit mine slopes based on solid waste spraying and drone inspection includes the following steps:

[0036] S1: Prepare solid waste-based fire-retardant and reinforcing slurry; by dry weight ratio: fly ash 60-80 parts, cementitious material 10-20 parts, fire retardant 5-18 parts, water-retaining agent 0.5-1.5 parts, dispersant 0.2-1.0 parts; water to dry material mass ratio is 0.6:1;

[0037] In step S1, the cementing material is sulfoaluminate cement or alkali-activated cementing material, the fire retardant is one or more of chloride salts, phosphates, and magnesium hydroxide, or a composite of magnesium chloride, ammonium polyphosphate, and organobentonite, the water-retaining agent is cellulose ether or polyacrylate, and the dispersant is polycarboxylate superplasticizer.

[0038] S2: The target slope is sprayed with the first full-area spraying, and thermal infrared images and lidar point cloud data of the slope are collected by drone equipped with lidar and thermal infrared camera. A three-dimensional real scene and temperature distribution point cloud map are generated in WGS-84 coordinate system as the original reference map.

[0039] In step S2, a continuous gray protective layer is formed by the first full-area spraying. The protective layer has a significant color difference from the underlying coal body, which is used to improve the accuracy of vision-based crack identification in step S3.

[0040] S3: Conduct periodic drone inspections, collect thermal infrared images and lidar point cloud data of the slope, and generate temperature field-fracture field distribution maps in the WGS-84 coordinate system.

[0041] In step S3, the periodic UAV inspection is carried out automatically through the UAV inspection platform. This includes automatic UAV takeoff, data collection along a preset flight path, automatic return to home, and automatic data upload to the processing server. The inspection cycle is once a week. The UAV inspection platform integrates a GNSS RTK module and a high-precision inertial measurement unit. The thermal infrared camera and lidar are jointly calibrated to obtain a relative pose transformation matrix. The relative pose transformation matrix is ​​used to establish a precise spatial correspondence between the thermal infrared camera and lidar, thereby accurately mapping the temperature information captured by the camera onto the 3D map generated by the lidar, achieving precise fusion of temperature and spatial position.

[0042] The specific method for generating the temperature field - fracture field distribution map in step S3 is as follows: ① Using the internal and external parameters of the thermal infrared camera, project the lidar three-dimensional point cloud data (X, Y, Z) onto the two-dimensional thermal infrared image plane to obtain the corresponding pixel coordinates (u, v) and the temperature value T of this point, thereby endowing each three-dimensional space point with temperature attributes and generating a temperature cloud map; ② Use the iterative closest point algorithm to accurately register the point cloud data of different inspection cycles and eliminate spatial errors; ③ Overlay and fuse the temperature cloud map with the three-dimensional fracture space distribution map under the WGS-84 coordinate system to generate the temperature field - fracture field distribution map.

[0043] S4: By comparing with the original reference map, identify the temperature anomaly areas and newly generated fractures. According to the frequency and evolution trend of fractures in each area in the temperature field - fracture field distribution map, calculate the risk index of each area and conduct graded early warning;

[0044] The graded early warning criteria and response measures are as follows: Blue indicates normal, the system only makes records without active early warning; Orange indicates early warning, the system marks the area, and controls the drone to spray and reinforce the target area at a fixed point; Red indicates emergency, the system synchronously triggers the red alarms for coal spontaneous combustion and slope instability, pushes them to the control platform and notifies manual verification. Manually inject grouting materials first, and then perform surface spraying and covering to form a three-dimensional plugging structure with internal grouting and external sealing.

[0045] The method for identifying newly generated fractures in step S4 is as follows: ① Preliminary two-dimensional fracture segmentation: Use the U-Net deep learning model to perform semantic segmentation (reference plane and fractures) on the inspection visible light images. Taking the RGB image of the gray slope background after spraying as the input, output the binary mask map of fracture pixels, identify linear features with a width greater than two millimeters, and preliminarily determine newly generated fractures; ② Three-dimensional geometric feature verification and quantification: Map the two-dimensional fracture mask to the lidar point cloud, extract the three-dimensional point set in the preliminarily identified area and calculate the normal vector and local curvature. When the included angle θ th between the average normal vector of the continuous point set in the area and the overall normal vector of the slope is greater than 15°, and the local elevation mutation Δh > 5 cm (for any point and its neighborhood point set within a radius of 5 cm), it is determined as a real coal body fracture.

[0046] In step S4, the calculation method of the risk index for each area: Divide the grid cells by 5m × 10m and calculate the risk index R, and conduct graded early warning. When R ≤ 1.0, it is blue and normal; when 1.0 < R ≤ 2.5, it is orange and early warning; when R > 2.5, it is red and emergency; R is composed of the product of the fracture occurrence frequency coefficient F, the temperature anomaly coefficient T, and the fracture expansion rate coefficient G, that is, R = F × T × G;

[0047] ① Frequency coefficient F:

[0048]

[0049] in, This represents the number of newly formed cracks identified within this grid cell during the current monitoring period. This is a set high-frequency occurrence threshold constant, with a value of 3;

[0050] ②Temperature anomaly coefficient T:

[0051]

[0052] in, This represents the highest temperature in the point cloud associated with all cracks within this grid cell. This represents the average background temperature of the intact shotcrete layer area within the unit. The critical temperature difference threshold is set at 15°C.

[0053] ③ Spread rate coefficient G:

[0054]

[0055] in, and These represent the total area of ​​cracks within the unit in the current cycle and the previous cycle, respectively; Δt is the inspection cycle in days; V base The baseline value for volume change is taken as 0.1m. 3 / sky.

[0056] Solid waste-based fireproofing and reinforcement slurry is prepared using fly ash as the main raw material, achieving "waste treatment" and significantly reducing the overall material cost. The slurry solidified solid has a compressive strength of 4.8~8.2MPa after 28 days. It can effectively seal air leakage channels, inhibit coal-oxygen reaction, anchor the broken coal and rock mass on the surface of the slope, improve the stability of the shallow surface layer of the slope, and achieve the dual functions of fireproofing and reinforcement.

[0057] By employing fully automated periodic inspections using drones, combined with lidar, thermal infrared cameras, and GNSS RTK positioning technology, high-precision dynamic monitoring of the entire slope area is achieved. A crack identification method using two-dimensional image segmentation and three-dimensional point cloud distribution map verification improves crack identification accuracy. Simultaneously, comparative analysis of the temperature field-crack field distribution map with a baseline map accurately locates hidden fire hazards and structurally weak areas. Targeted reinforcement spraying is applied to early warning areas, significantly improving material utilization and disaster management efficiency while avoiding material waste.

[0058] Using the generation of fissures as a common criterion for determining two types of disasters (slope instability and coal spontaneous combustion) breaks the current situation where fire prevention monitoring and slope stability evaluation are separated. By constructing a temperature field-fissure field distribution map, the monitoring data can be integrated and correlated. At the same time, a risk index is introduced to realize graded early warning. Differentiated response measures are taken according to different risk levels to achieve "one monitoring with multiple effects". The risk of slope instability can be identified in advance from fire prevention monitoring data, providing technical support for the early prevention and control of mine disasters.

[0059] The mechanical properties of three groups of solid waste-based slurry coagulated solids with different ratios were tested, and the experimental data are shown in Table 1:

[0060] (1) 70% fly ash + 16% cement + 12% fire retardant + 1.5% water retention agent + 0.5% dispersant.

[0061] (2) 60% fly ash + 20% cement + 18% fire retardant + 1.2% water retention agent + 0.8% dispersant.

[0062] (3) 80% fly ash + 10% cement + 8.5% fire retardant + 1.0% water-retaining agent + 0.5% dispersant.

[0063] Table 1 Compressive strength and application cost of solid waste-based slurry coagulated solids

[0064] Group 3d compressive strength (MPa) 28-day compressive strength (MPa) Comprehensive cost per ton of slurry (RMB) 1 2.82 6.52 120 2 3.50 8.23 145 3 1.97 4.86 95

[0065] Preparation process of solid waste-based fireproofing and reinforcing slurry: The dry powder materials of the solid waste-based slurry solids with the above three different proportions are put into a mixer. The stirring speed is set to 200~300r / min and the dry mixing time is set to 10~15min to ensure that the dispersant is fully mixed, prevent the fly ash and inhibitor from agglomerating, and ensure the uniform dispersion of multiphase materials to prepare solid waste-based fireproofing and reinforcing slurry.

[0066] The uniformly mixed dry materials are pumped into a high-speed slurry mixer. Water is added in two stages: first, 70% of the total water is added, and after stirring for 3 minutes, the remaining 30% is added. The shear stirring speed is set to 500-800 rpm, and stirring is continued for 5-8 minutes. The high-speed shear action can fully activate the early hydration activity of the cementitious materials and fly ash, while the water-retaining agent forms steric hindrance in the hydration network, ensuring the stability of the slurry during pumping and spraying.

[0067] Figure 2 In (a), information on newly formed cracks 2 in the slope area is collected by a drone equipped with a lidar and a thermal infrared camera 1. Then, in (b), a drone equipped with a shotcrete module 3 reinforces the target reinforcement area 4.

[0068] Although embodiments of the invention have been shown and described, those skilled in the art will understand that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the claims and their equivalents.

Claims

1. A method for the coordinated prevention and control of spontaneous combustion and instability of open-pit mine slopes based on solid waste spraying and UAV inspection, characterized in that, Includes the following steps: S1: Prepare solid waste-based fire-retardant and reinforcing slurry; by dry weight ratio: fly ash 60-80 parts, cementitious material 10-20 parts, fire retardant 5-18 parts, water-retaining agent 0.5-1.5 parts, dispersant 0.2-1.0 parts; water to dry material mass ratio is 0.6:1; S2: The target slope is sprayed with the first full-area spraying, and thermal infrared images and lidar point cloud data of the slope are collected by drone equipped with lidar and thermal infrared camera. A three-dimensional real scene and temperature distribution point cloud map are generated in WGS-84 coordinate system as the original reference map. S3: Conduct periodic drone inspections, collect thermal infrared images and lidar point cloud data of the slope, and generate temperature field-fracture field distribution maps in the WGS-84 coordinate system. S4: By comparing the original baseline map, identify temperature anomaly areas and newly formed fractures. Based on the frequency and evolution trend of fractures in each region in the temperature field-fracture field distribution map, calculate the risk index of each region and conduct graded early warning. The tiered early warning standards and response measures are as follows: Blue indicates normal operation, the system only records information, and there is no proactive early warning; Orange indicates an early warning; the system marks the area and controls drones to perform targeted reinforcement spraying on the target area. Red indicates an emergency; the system simultaneously triggers red alarms for coal spontaneous combustion and slope instability, pushes the information to the control platform, and notifies manual verification. Manual personnel first inject grouting material and then spray the surface to cover it, forming a three-dimensional sealing structure with internal grouting and external sealing.

2. The method for coordinated prevention and control of spontaneous combustion and instability of open-pit mine slopes based on solid waste spraying and UAV inspection as described in claim 1, characterized in that: The cementing material mentioned in step S1 is sulfoaluminate cement or alkali-activated cementing material; the fire retardant is one or more of chloride salts, phosphates, and magnesium hydroxide, or a composite of magnesium chloride, ammonium polyphosphate, and organobentonite; the water-retaining agent is cellulose ether or polyacrylate; and the dispersant is polycarboxylate superplasticizer.

3. The method for coordinated prevention and control of spontaneous combustion and instability of open-pit mine slopes based on solid waste spraying and UAV inspection as described in claim 1, characterized in that: The periodic UAV inspection described in step S3 is an automated operation performed by a UAV inspection platform. This includes automatic UAV takeoff, data collection along a preset flight path, automatic return to home, and automatic data upload to the processing server. The inspection cycle is once a week. The periodic UAV inspection platform integrates a GNSS RTK module and an inertial measurement unit. The thermal infrared camera and lidar are jointly calibrated to obtain a relative pose transformation matrix. The relative pose transformation matrix is ​​used to establish a precise spatial correspondence between the thermal infrared camera and the lidar, thereby accurately mapping the temperature information captured by the camera onto the 3D map generated by the lidar, achieving precise fusion of temperature and spatial position.

4. The method for coordinated prevention and control of spontaneous combustion and instability of open-pit mine slopes based on solid waste spraying and UAV inspection as described in claim 1, characterized in that, The specific method for generating the temperature field-fracture field distribution map in step S3 is as follows: ① Using the intrinsic and extrinsic parameters of the thermal infrared camera, the three-dimensional point cloud data (X,Y,Z) of the lidar is projected onto the two-dimensional thermal infrared image plane to obtain the corresponding pixel coordinates (u,v) and the temperature value T of that point, thereby assigning temperature attributes to each three-dimensional spatial point; ② The iterative nearest point algorithm is used to accurately register the point cloud data of different inspection cycles to eliminate spatial errors; ③ The temperature field-fracture field distribution map is formed in the WGS-84 coordinate system.

5. The method for coordinated prevention and control of spontaneous combustion and instability of open-pit mine slopes based on solid waste spraying and UAV inspection as described in claim 4, characterized in that, The method for identifying newly formed cracks in step S4 is as follows: ① Preliminary two-dimensional crack segmentation: The U-Net deep learning model is used to perform semantic segmentation on the inspection visible light image. The RGB image of the gray slope background after spraying is used as input, and the binary mask image of the crack pixels is output. Linear features with a width greater than two millimeters are identified to preliminarily determine the newly formed cracks; ② Three-dimensional geometric feature verification and quantization: The two-dimensional crack mask is mapped onto the lidar point cloud, the three-dimensional point set in the preliminarily identified area is extracted, and the normal vector and local curvature are calculated. When the average normal vector of the continuous point set in the area makes an angle θ with the overall normal vector of the slope, the cracks are identified. th When the angle is greater than 15° and the local elevation change Δh > 5cm, it is determined to be a real coal body fracture.

6. The method for coordinated prevention and control of spontaneous combustion and instability of open-pit mine slopes based on solid waste spraying and UAV inspection as described in claim 1, characterized in that, In step S4, the calculation method of the risk index for each area is as follows: divide the grid cells by 5m×10m, calculate the risk index R, and conduct hierarchical early warning. When R≤1.0, it is a normal blue level; when 1.0<R≤2.5, it is an orange early warning; when R>2.5, it is a red emergency. R is composed of the product of the crack occurrence frequency coefficient F, the temperature anomaly coefficient T, and the crack expansion rate coefficient G, that is, R = F×T×G; ① Frequency coefficient F: in, This represents the number of newly formed cracks identified within this grid cell during the current monitoring period. This is a set high-frequency occurrence threshold constant, with a value of 3; ② Temperature anomaly coefficient T: in, This represents the highest temperature in the point cloud associated with all cracks within this grid cell. This represents the average background temperature of the intact shotcrete layer area within the unit. The critical temperature difference threshold is set at 15°C. ③ Expansion rate coefficient G: in, and These represent the total area of ​​cracks within the unit in the current cycle and the previous cycle, respectively; Δt is the inspection cycle in days; V base The baseline value for volume change is taken as 0.1m. 3 / sky.

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