Digital twin model correction method based on deep neural network

Abstract

The invention relates to a digital twin model correction method based on a deep neural network, and the method comprises the steps: S1, arranging a sensor, and obtaining the data collected by the sensor; s2, constructing a physical data space based on the data collected by the sensor; s3, constructing a virtual data space through simulation modeling based on the data collected by the sensor as a boundary condition; s4, comparing the virtual data space with the physical data space, judging whether the error exceeds a threshold value or not, if so, performing error learning correction by utilizing a deep neural network and executing the step S5, and otherwise, executing the step S5; and S5, extracting feature data of the physical data space and the virtual data space to correct the digital twin model. Compared with the prior art, the method has the advantages of high model consistency of the virtual space and the physical space and the like.

Description

technical field [0001] The invention relates to the field of digital twin models, in particular to a method for correcting digital twin models based on a deep neural network. Background technique [0002] With the continuous development and deepening of applications of big data, machine learning, artificial intelligence and other technologies, product design and development are developing towards the direction of deep integration of digitization, informatization, and intelligence. The digital twin technology based on multi-source data fusion is the direction The important supporting theories and technologies have received more attention and recognition. The basis of digital twin model construction is the data validity of physical space and virtual space. Physical space data can be obtained through precise sensor collection, data preprocessing and calibration process, but the validity of virtual space data has been widely questioned by the industry. Spatial and physical spat...

AI Technical Summary

Problems solved by technology

However, there are three major defects in this method of applying deep neural network modeling. First, the universality of the model is not strong. Second, the model cannot be interpreted. Third, it requires high computing power.

The above defects are not conducive to the dynamic deduction and reasoning of the digital twin model, so it is difficult to realize the construction of the digital twin model

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