Abstract
The current digital twin system is extremely dependent on the integrity of the data when performing data analysis, and in the process of data collection and transmission, the phenomenon of data loss is very easy to occur. In this paper, the types of missing data are classified according to the size of missing data. For long-term data missing, it is processed in segments. For short-term data missing, the training of random forest algorithm model and the prediction of missing values are carried out according to different missing data sizes, And filled in the prediction using missing values from the wind power data twin system, which shows a good filling effect and helps to solve the problem of missing data filling in the digital twin system.
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Acknowledgements
The paper was funded by Shanghai Industrial Development Innovation Project “Digital Twin Health Prediction System of Space Docking Mechanism Driven by Hybrid Mathematics and Physics” (Grant No. XTCX-KJ-2022-03), Key Research Project on Military Civilian Integration "Digital Twin Predictive Control System Driven by Hybrid Data and Mechanism" (2022YFF1400303). And it was supported by Science and Technology Department of Xinjiang Uygur Autonomous Region under Grant (2021B01003-2).
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Zhou, B. et al. (2024). Processing Method for Missing Data in Digital Twin System. In: Wang, Y., Yu, T., Wang, K. (eds) Advanced Manufacturing and Automation XIII. IWAMA 2023. Lecture Notes in Electrical Engineering, vol 1154. Springer, Singapore. https://doi.org/10.1007/978-981-97-0665-5_18
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DOI: https://doi.org/10.1007/978-981-97-0665-5_18
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