The Wildlife Cloud Platform is an online platform specially established for the protection and research of wildlife. On the Oonica Optical Wildlife Cloud Platform, users can browse the information of various wild animals sent back by infrared cameras to understand their ecological habits, distribution and protection status. At the same time, users can also upload their own observation records and photos to share their findings and opinions with other users.

my country is one of the countries with the richest wildlife resources in the world. On the basis of the national wildlife resource survey. Establishing and improving my country's wildlife resource monitoring system is of great significance to protecting my country's wildlife and biodiversity.

Wildlife monitoring is part of the overall environmental monitoring. From the beginning, it should be considered to combine with other environmental monitoring projects, including the setting of monitoring sites, hardware configuration, data sharing, etc. However, wildlife monitoring has its own particularity in terms of monitoring objects, monitoring methods, sample plot settings, etc. The relative independence of the wildlife monitoring system should be maintained to ensure the smooth progress of wildlife monitoring in my country.

The Wildlife Cloud Platform also provides a series of protection and research tools, species analysis and data analysis software to help users better understand and protect wildlife.

Through the Wildlife Cloud Platform, people can more conveniently participate in wildlife protection and research work, and promote the harmonious coexistence of man and nature.

The system uses infrared sensors to sense wildlife, uses image sensors to collect images, and transmits images to coordination nodes through low-power wireless sensor networks, and then uploads them to the server using 4G networks. The monitoring system uses deep learning modeling to automatically identify and perform statistical analysis.

The system uses infrared sensors to sense wildlife, uses image sensors to collect images, and transmits images to coordination nodes through low-power wireless sensor networks, and then uploads them to the server using 4G networks. The monitoring system uses deep learning modeling to automatically identify and perform statistical analysis.