Full
Outline
The implementation and application of AI management system module algorithms in coal mines is the core issues in the field of smart mine construction. Promoting integrated mine management and control can help improve the intelligent monitoring capabilities of mines. A three-level and five-layer mining AI management system architecture was constructed based on mining intelligent recognition technology, and mining AI management system algorithms including comprehensive mining module, excavation module, transportation module and general module were developed, forming a software+server intelligent visual application mode. The comprehensive mining module adopts CNN, YOLO Fastest V2 and YOLOv3 models to achieve coal mining equipment recognition, area target and support posture detection. The excavation module uses the YOLO model to identify the front beam support and human related detection. The transportation module uses the YOLACT model to identify the operation status of the belt, large coal blocks and foreign objects. The general module implements safety helmets, personnel behavior and environmental detection through OpenCV, YOLO and YOLOv4 models. Among them, the mean average precision of YOLACT model training prediction boxes is 86.79%, and the mean average precision of mask is 83.58%, both of which are at a high level. Real-time display of equipment, personnel, environment and other status results in the fully mechanized mining face. The excavation working face can realize the identification and alarm of personnel intrusion in hazardous areas and abnormal operation processes. The transportation system can realize monitoring of coal quantity of belt, large blocks, foreign objects, etc., real-time tracking and monitoring of the target and early warning of abnormal behavior. The general scenario timely alarms the unsafe behavior in the key underground areas to achieve comprehensive supervision of the entire mining area. The research results can promote the deep integration of AI technology and coal mine safety production, realize the intelligent application scenarios of reducing manpower and improving efficiency in coal mines, which is of great significance for the intelligent mining of coal mines in China.
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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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