Oil temperature is an important index to reflect the state of transformer and predict the fault and remaining useful life of transformer. However, the oil temperature of the transformer is affected by various factors, such as weather conditions, load changes, holidays, seasons, and so on. In order to predict oil temperature more accurately, this study proposes a VCEEMDAN data decomposition algorithm to extract the characteristics of oil temperature data. And we proposed a hybrid long-term series forecasting model of oil temperature based on VCEEMDAN, particle swarm optimization (PSO) and Informer. At the same time, the digital twins system of the manipulator is configured to assist personnel in maintenance when the oil temperature is abnormal. And the digital twins system of an oil-immersed power transformer to monitoring of transformer status. Finally, the above research is integrated into a digital twins system, including transformer condition monitoring, oil temperature prediction, and intelligent maintenance based on Internet of Things (IOT). The proposed prediction method was compared with the six existing models on three datasets. From the experimental results, the best index improvement is 77.97% higher than the second-best model. And the simulation system can provide a good reference for the practical application of the factory. This article presents a hybrid framework that achieves a accurate transformer oil temperature prediction.

With the popularization of the metaverse, virtual reality mapping technology based on digital twins has generated a large amount of spatial data. These data are multidimensional, multi-scale, mobile, and distributed. In order to fully utilize these data, we propose a non mutation multidimensional hash geocoding that can organize and store data with geographic features, and achieve data mapping at different scales from macro to micro. The mapping between them can achieve joint utilization of data of various scales. On this basis, we propose a block network secure storage mapping model for spatial digital twins, which can securely and reliably organize and map spatial data. This article also looks forward to the possible emergence of digital twins of different dimensions and scales in the future metaverse, and proposes an adaptive 3D reconstruction method based on this to adapt to digital twins models of different scales in the metaverse. On the basis of our work, we will further promote the development of the spatial digital twin metaverse.

Currently, people are actively seeking new and clean energy sources to replace traditional fossil fuels. As a widely distributed, abundant, and easily accessible renewable energy source, wind energy has multiple applications both domestically and internationally, and has a promising future. In wind power forecasting, this paper proposes a new algorithm LFTformer that combines the Transformer model with linear fuzzy information granulation (LFIG).The model uses the improved LFIG algorithm to extract the semantic information of time series, and divides the original time series into multiple information granules, which are then used as inputs to the Transformer model. Through comparative experiments for wind power prediction over 72 hours, 120 hours, and 168 hours, this research demonstrates that the LFTformer can improve the accuracy of wind power prediction. Finally, a Digital Twins system for wind power prediction is constructed using three-dimensional visualization techniques, displaying the prediction results and key data indicators, providing reliable data support for wind power systems.