Maglev train levitation system debugging method based on digital twinning technology

Abstract

The invention relates to a maglev train levitation system debugging method based on a digital twinning technology, which comprises the following steps: a digital twinning construction step: constructing a digital twinning body of a maglev train levitation system, and carrying out communication connection between the digital twinning body and the maglev train levitation system; a levitation data collection and perception step: acquiring and perceiving levitation data by the levitation system of the maglev train through a sensor, and transmitting the levitation data to the digital twinning bodyin real time; and a suspension system debugging step: observing and debugging the suspension state of the digital twinning body in real time through a visual means so as to react on the maglev train suspension system. Compared with the prior art, the method effectively verifies the effectiveness of parameter debugging when the train operation condition changes significantly, further improves the debugging accuracy based on the digital twinning body virtual model, improves the working environment of debugging personnel, and reduces the labor time cost and economic cost.

Description

technical field [0001] The invention relates to the field of rail transit, in particular to a method for debugging a maglev train suspension system based on digital twin technology. Background technique [0002] At present, the running speed of high-speed wheel-rail trains has reached 350km / h, and if the speed is increased again, the wear of the wheel-rails will increase, which in turn will lead to a double increase in operating costs. The maglev train ensures the non-contact operation between the rails during operation, and can achieve higher speed at a lower cost, thereby realizing short-term commuting between long-distance cities. In addition, due to the avoidance of wheel-rail contact, the mechanical noise during operation is effectively reduced, which is another reason why existing wheel-rail trains cannot match. [0003] The stable suspension of the maglev train is one of the most important prerequisites to ensure its normal operation, and the adjustment of the suspen...

AI Technical Summary

Problems solved by technology

At present, the debugging of the suspension system needs to carry out on-site car-following tests, and the actual data can be observed through two-dimensional curves through CAN signals, but this debugging method requires a lot of manpower, time and economic costs

In addition, the suspension train has multiple car formations, and each car has multiple suspension points. Repeated observations of the two-dimensional curves of multiple suspension single points cannot be visualized, which may lead to poor debugging results and prolong debugging. time

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