Shield tunneling position and pose intelligent prediction method and system based on hybrid depth learning
A deep learning and intelligent prediction technology, applied in earth-moving drilling, mining equipment, tunnels, etc., can solve the problems of unavoidable shield serpentine motion, poor shield posture control effect, and inability to completely eliminate it, so as to solve the problem of snakes. It can solve the problem of shape movement, improve the control level, improve the accuracy and the effect of timeliness
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[0043] In order to make the object, technical solution and advantages of the present invention clearer, the present invention will be further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described here are only used to explain the present invention, not to limit the present invention. In addition, the technical features involved in the various embodiments of the present invention described below can be combined with each other as long as they do not constitute a conflict with each other.
[0044] Such as Figure 5 As shown, a shield excavation intelligent prediction method based on hybrid deep learning in a preferred embodiment of the present invention includes a model training stage and a pose prediction stage, wherein:
[0045] The model pre-training stage is based on the hybrid deep learning model WCNN-LSTM, including:
[0046] Step 1: Data determination and collection:
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