With the construction and development of modern and smart cities, people’s lives are becoming more intelligent and diversified. Surveillance systems increasingly play an active role in target tracking, vehicle identification, traffic management, etc. In the 6G network environment, facing the massive and large-scale data information in the monitoring system, it is difficult for the ordinary processing platform to meet this computing demand. This paper provides a data governance solution based on a 6G environment. The shortcomings of critical technologies in wireless sensor networks are addressed through ZigBee energy optimization to address the shortage of energy supply and high energy consumption in the practical application of wireless sensor networks. At the same time, this improved routing algorithm is combined with embedded cloud computing to optimize the monitoring system and achieve efficient data processing. The ZigBee-optimized wireless sensor network consumes less energy in practice and also increases the service life of the network, as proven by research and experiments. This optimized data monitoring system ensures data security and reliability.

Sharing scientific data is an effective means to rationally exploit scientific data and is vital to promote the development of the industrial chain and improve the level of science and technology. In recent years, the popularity of the open data platform has increased, but problems remain, including imperfect system architecture, unsound privacy and security, and non-standardized interaction data. To address these problems, the blockchain’s decentralization, smart contracts, distributed storage, and other features can be used as the core technology for open data systems. This paper addresses the problems of opening, allocation-right confirmation, sharing, and rational use of wild-bird data from Yunnan Province, China. A data storage model is proposed based on the blockchain and interstellar file system and is applied to wild-bird data to overcome the mutual distrust between ornithology institutions in the collaborative processing and data storage of bird data. The model provides secure storage and secure access control of bird data in the cloud, thereby ensuring the decentralized and secure storage of wild-bird data for multiple research institutions.

In recent years, the rise in global warming has significantly increased forest fires, affecting the environment and economy. Predicting forest fire dynamics under climate change is now a crucial research field. To address this need, this study focuses on the impact of climate change on forest fires, with a particular focus on the fire dynamics in Yunnan Province. This study utilizes the RegCM regional climate model and the Canadian Fire Weather Index (FWI) to simulate and analyze forest fire dynamics in Yunnan Province from 2019 to 2033 under three climate scenarios: RCP2.6, RCP4.5, and RCP8.5. Findings indicate climate change will increase temperatures, alter humidity and wind speed, and reduce precipitation in Yunnan, extending the fire danger period, especially under RCP8.5 scenarios. The FWI values rise across Yunnan, particularly in the west under RCP2.6 and RCP8.5. The study concludes that future carbon emissions correlate with these changes, leading to more frequent, longer, and severe forest fires. This research is vital for managing and preventing forest fires in Yunnan, a region prone to such disasters.