Full
Outline
Aiming at the requirement of high accuracy of roof deformation monitoring in mine working face under complex environment, a set of foof deformation of working face monitoring system based on LiDAR was designed, and the matching deformation monitoring data algorithm was developed, and the deformation risk was evaluated. The monitoring system employed time-lapse scanning to acquire two phase point cloud data of the mine working face. Following preprocessing steps such as angular transformation and ICP registration, high-precision triaxial tilt sensors was used to real-time calibrate the LiDAR attitude, enabling precise extraction of the point cloud morphology of roof. Subsequently, the Delaunay triangulation mesh division was applied to process the two phase point clouds, and innovatively incorporated a point-to-plane monitoring algorithm to calculate the distance between the central points of the second phase and the triangular facets of the first phase, thereby enhancing the accuracy of deformation monitoring. Furthermore, based on the DBSCAN clustering algorithm, the deformation point cloud density clustering filtering algorithm was proposed to identify and mark the deformation area, and on this basis, the risk level assessment was conducted to realize the intelligent monitoring of roof deformation. Field research results demonstrate that the deformation displacement range of 0−5 mm in April and July 2024 is in the middle risk level, which needs to be paid attention to and corresponding monitoring and management measures should be taken. The deformation displacement range of 5−10 mm is in the low risk level, which needs to be continuously monitored to prevent the risk from escalating. The monitoring system achieve a monitoring accuracy range from 1 mm to 5 mm, which is significantly improved compared to traditional monitoring methods, and can achieve visual and accurate marking of deformation areas. The research results provide strong technical support for mine safety production, further enhancing the safety and reliability of mining operations.
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| 科 Family | 属数 Number of genus | 种数 Number of species | 占总种数比例 Percentage of total species (%) | 属 Genus | 种数 Number of species | 占总种数比例 Percentage of total species (%) |
|---|---|---|---|---|---|---|
| 鹅膏菌科Amanitaceae | 2 | 11 | 5.26 | 鹅膏菌属 Amanita | 10 | 4.78 |
| 小菇科 Mycenaceae | 2 | 12 | 5.74 | 丝盖伞属 Inocybe | 5 | 2.39 |
| 多孔菌科 Polyporaceae | 8 | 14 | 6.70 | 蜡蘑属 Laccaria | 5 | 2.39 |
| 红菇科 Russulaceae | 3 | 23 | 11.00 | 小皮伞属 Marasmius | 6 | 2.87 |
| 小菇属 Mycena | 11 | 5.26 | ||||
| 光柄菇属 Pluteus | 5 | 2.39 | ||||
| 红菇属 Russula | 17 | 8.13 | ||||
| 栓菌属 Trametes | 5 | 2.39 |
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