Manufacturing text often exists as unlabeled data; the entity is fine-grained and the extraction is difficult. The above problems mean that the manufacturing industry knowledge utilization rate is low. This paper proposes a novel Chinese fine-grained NER (named entity recognition) method based on symmetry lightweight deep multinetwork collaboration (ALBERT-AttBiLSTM-CRF) and model transfer considering active learning (MTAL) to research fine-grained named entity recognition of a few labeled Chinese textual data types. The method is divided into two stages. In the first stage, the ALBERT-AttBiLSTM-CRF was applied for verification in the CLUENER2020 dataset (Public dataset) to get a pretrained model; the experiments show that the model obtains an F1 score of 0.8962, which is better than the best baseline algorithm, an improvement of 9.2%. In the second stage, the pretrained model was transferred into the Manufacturing-NER dataset (our dataset), and we used the active learning strategy to optimize the model effect. The final F1 result of Manufacturing-NER was 0.8931 after the model transfer (it was higher than 0.8576 before the model transfer); so, this method represents an improvement of 3.55%. Our method effectively transfers the existing knowledge from public source data to scientific target data, solving the problem of named entity recognition with scarce labeled domain data, and proves its effectiveness.

Due to the difference in distance cognition between virtual and real symmetric space, it is difficult for users to accurately interact with the target in the Digital Twin system. In order to study the cross-effects of interaction task, target size and target location on the accuracy of egocentric peripersonal distance cognition, a 2 × 5 × 9 × 5 asymmetric experiment was designed and carried out. There were two kinds of interaction tasks, five kinds of interaction target widths and nine kinds of spatial locations set to estimate the five egocentric peripersonal distances. Based on the experimental data, with interaction task, target width and the actual spatial location as independent variables and virtual interaction location as a dependent variable, the mapping model between the actual physical location and virtual interaction location of different interaction targets was constructed and evaluated by multiple linear regression method. The results showed that the prediction model constructed by stepwise regression method was simple and less computationally intensive, but it had better stability and prediction ability. The correlation coefficients R² on xp, yp and zp were 0.994, 0.999 and 0.998, RMSE values were 2.583 cm, 1.0774 cm and 1.3155 cm, rRMSE values were 26.57%, 12.60% and 1.15%, respectively. The research of relevant experiments and the construction of models are helpful to solve the layout optimization problem of virtual interactive space in the Digital Twin system.