Full
Outline
Blasting fragmentation is regarded as an important basis for evaluating blasting effects and optimizing blasting parameters, and the subsequent operations such as shoveling, transportation, and crushing and grinding are directly affected. With blasting fragmentation prediction and post-blast fragmentation recognition as the main line, the formation mechanism and main influencing factors of blasting fragments were introduced. Prediction methods, including empirical and semi-empirical models, mechanism analysis and numerical simulation, statistical learning and artificial intelligence, as well as recognition techniques, including traditional detection, 2D image recognition, 3D point cloud, and photogrammetry, were systematically reviewed. The characteristics of different methods in terms of prediction accuracy, mechanism interpretability, computational efficiency, field applicability, and implementation cost were compared. The analysis results show that existing methods have their own advantages and applicable boundaries, and that accurate prediction, stable recognition, and low-cost application are still difficult to achieve simultaneously under complex mine conditions. It is concluded through analysis that current research is mainly constrained by the lack of model interpretability and cross-scene generalization ability, the vulnerability of recognition results to field environments, and the underdevelopment of data standards and engineering application systems. The synergy among mechanistic models, data-driven methods, and multi-source recognition technologies should be strengthened in future research, with emphasis placed on real-time fragmentation prediction based on multi-source data fusion, precise recognition and control in complex environments, and coordinated optimization of intelligent blasting equipment and prediction models, so that support can be provided for establishing a closed-loop control system comprising pre-blast prediction, post-blast recognition, effect evaluation, and parameter feedback.
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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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