With the rapid development of technology, traditional building material testing methods face many challenges, such as low efficiency, high cost, and insufficient accuracy. Therefore, introducing intelligent technologies such as artificial intelligence, machine learning, and the Internet of Things has become a key way to improve the efficiency and accuracy of building material detection. Artificial intelligence algorithms can achieve intelligent recognition and evaluation of building materials through a large amount of data learning and training, thereby improving the accuracy and speed of detection. At the same time, the application of Internet of Things technology makes the process of building material testing more intelligent and automated, achieving real-time monitoring and data transmission. In addition, the application of machine learning algorithms has also provided new ideas and methods for building material detection, further improving the accuracy and reliability of detection through deep data analysis and pattern recognition. Therefore, applying intelligent technology to material testing in construction projects is expected to bring revolutionary changes to the construction industry.
| 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 |