Aiming at the difficulty in modelling the diving process of spherical shells, a data-driven algorithm for diving process modelling and anomaly detection of deep-sea pressurized spherical shells was proposed in this paper. Firstly, the spherical shell structures and historical diving data of manned capsules were analyzed. Then, the diving process modelling algorithm was established based on the long short-term memory network (LSTM), taking the diving depth as the input and the key hot spot strain as the output. The deduction results were analyzed and compared with the DNN model and BP model, the derivation error was reduced by 35.89% and 63.80%, respectively. Finally, based on the LSTM model, a data anomaly detection algorithm was proposed. The proposed algorithm can diagnose and correct abnormal data when a sensor fails.
| 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 |