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To address the complexity of fire environment and the difficulty in predicting the temperature field in modern commercial buildings, a fire temperature field prediction model was constructed by combining CNN with SVM. Firstly, FDS was used to construct a commercial building fire model, and the sequence data received by the temperature measurement points were obtained. The temperature, position coordinates, and fire duration were used as input parameters to build the dataset. Secondly, the Rime Optimization Algorithm (RIME) was introduced to optimize the number of hidden layer nodes, regularization coefficient, and learning rate in the CNN-SVM, and then the prediction model was established. Finally, experiments were conducted based on the established dataset and prediction model, and the anti-interference ability of the model under different sensor failure rates was discussed. The results show that the model performs optimally in the prediction of the temperature field plane, with an average absolute percentage error of 5.6% and a maximum relative temperature error not exceeding 25%. The anti-interference performance is the best under three working conditions, and the maximum error does not exceed 15% under extreme conditions.
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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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