New algorithm to ensure virtual simulation data security based on deep learning using applied innovation design

New algorithm to ensure virtual simulation data security based on deep learning using applied innovation design

The virtual simulation laboratory saves the material resources and equipment investment required for simulating a real experiment environment. This paves the learners to experiment and explore in a virtual environment, reducing resource waste and cost. In addition, the virtual simulation laboratory can also realize the sharing of resources, and academic institutions can share the platform and content of the virtual laboratory to improve the efficiency of resource utilization. But the virtual simulation experiment data can be is easily hacked from the network, hence making it challenging task to study virtual simulation data security. In this paper, we research the virtual simulation data security based on deep learning through applied innovation design and proposed a new algorithm. The minimum violation sequence set in the virtual simulation data set is identified and the suppression mode of the minimum violation sequence is judged. The score table is constructed for the instances in the sequence, and the corresponding instances are selected and suppressed according to the score value. The cross-attention module of Transformer Structure is proposed to aggregate the global and local feature information between left and right graphs and obtain the long-distance dependence relationship between left and right graphs along the polar direction, which can more effectively fuse the global feature information of left and right graphs. The results show that the proposed algorithm can not only ensure the safety of trajectory data but also improve the availability of data.