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Conventional dam displacement monitoring methods are often associated with large errors and low efficiency. Manual monitoring methods cannot provide continuous real-time monitoring, while automated monitoring methods, such as total station robot and GNSS, are affected by weather and have limited accuracy for vertical displacement. To overcome these shortcomings, this study proposes a new intelligent monitoring method for dam displacement based on machine vision. The method utilizes the internet of things and intelligent disaster recognition algorithm to convert picture data into deformation data, enabling ultra-high precision non-contact real-time measurement of the dam. The monitoring system was tested at Lianghui Reservoir, the results demonstrate that the operation of monitoring system is stable, and the horizontal and vertical monitoring accuracy are both 1.5 mm. The proposed method has the potential to be widely applied in other water conservancy projects for surface displacement monitoring.
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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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