Digital twin target field data processing system based on super computer

Abstract

The invention relates to a digital twinning target field data processing system based on a super computer, comprising a super computer which comprises an initial digital twinning model library, a database, a processor and a memory in which a computer program is stored, the digital twinborn model library is used for storing a model id and a corresponding initial digital twinborn model; the database is used for storing digital twinborn model parameter records, and the digital twinborn model parameter records comprise a model id, a fixed parameter list, a variable parameter list, a value range of each variable parameter and an adjustment step length field. According to the method, the virtual digital twinborn model is revised based on the actually acquired measuring point data and the virtual prediction field data to obtain the field data, so that the accuracy and the efficiency of obtaining the field data are improved, and the calculation amount is reduced.

Description

technical field [0001] The invention relates to the technical field of data processing, in particular to a supercomputer-based digital twin target field data processing system. Background technique [0002] The digital twin model is a virtual physical mapping that can construct a real device or equipment, which can display the operating status of the physical device in real time and reveal the original simulation model of the internal operation rules of the physical device. Building a digital twin model to obtain field data is of great significance for the development and finalization of physical devices in the early stage, virtual process adjustment during operation, and preventive maintenance in the later stage. However, due to the strong coupling between the digital twin model itself and ultra-large-scale computing, a huge amount of calculation is required, and because the full life cycle of many physical devices is long, the management and control of the full life cycle ...

AI Technical Summary

Problems solved by technology

However, if the digital twin model is continuously updated as the life cycle of the physical device changes, it will take a lot of calculations, and in the face of massive data, some data may be lost, so it cannot be guaranteed to generate an updated digital twin model accuracy, making it impossible to obtain accurate field data

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