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.

Traffic flow prediction is considered to be one of the fundamental technologies in intelligent transportation systems (ITSs) with a tremendous application prospect. Unlike traditional time series analysis tasks, the key challenge in traffic flow prediction lies in effectively modelling the highly complex and dynamic spatiotemporal dependencies within the traffic data. In recent years, researchers have proposed various methods to enhance the accuracy of traffic flow prediction, but certain issues still persist. For instance, some methods rely on specific static assumptions, failing to adequately simulate the dynamic changes in the data, thus limiting their modelling capacity. On the other hand, some approaches inadequately capture the spatiotemporal dependencies, resulting in the omission of crucial information and leading to unsatisfactory prediction outcomes. To address these challenges, this paper proposes a model called the Dynamic Spatial–Temporal Self-Attention Network (DSTSAN). Firstly, this research enhances the interaction between different dimension features in the traffic data through a feature augmentation module, thereby improving the model’s representational capacity. Subsequently, the current investigation introduces two masking matrices: one captures local spatial dependencies and the other captures global spatial dependencies, based on the spatial self-attention module. Finally, the methodology employs a temporal self-attention module to capture and integrate the dynamic temporal dependencies of traffic data. We designed experiments using historical data from the previous hour to predict traffic flow conditions in the hour ahead, and the experiments were extensively compared to the DSTSAN model, with 11 baseline methods using four real-world datasets. The results demonstrate the effectiveness and superiority of the proposed approach.

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.

Poplars are widely distributed in the northern hemisphere and have good adaptability to different living environments. The accumulation of genome and transcriptome data provides a chance to conduct comparative genomics and transcriptomics analyses to elucidate the evolutionary patterns of Populus phylogeny. Transcript sequences of eight Salicaceae species were downloaded from public databases. All of the pairwise orthologues were identified by comparative transcriptome analysis in these species, from which we constructed a phylogenetic tree and estimated the rate of divergence. The divergence times of the phylogenetic clades were mainly estimated during the Middle Miocene Climate Transition (MMCT) to Quaternary Ice Age. We also identified all of the fast-evolving sequences of positive selection and found some resistance genes that were related to environmental factors. Our results suggest that drought-, H₂O₂- and cold-stress genes are involved in positive selection along with the paleoclimate change. These data are useful in elucidating the evolutionary patterns and causes of speciation in the Populus lineage.