Attitude detection system, method, computer equipment and storage medium

Abstract

The invention discloses a suspension electromagnet posture detection system, method, computer equipment and storage medium of a maglev train. The detection data can be obtained from the same installation position, so that each detection data comes from the same position, and there is no need to approximately convert the detection data from different positions to the same position, avoiding the error caused by approximate conversion, and the detection accuracy is high; all components in the signal acquisition component start at the same time, And it is transmitted through the same channel, which ensures that all the detection data are on the same time axis, avoiding the problem of large phase error in dynamic analysis caused by different channels to obtain data, and improving the dynamic analysis. Time accuracy; and the signal conditioning and transmission module is used for data transmission instead of the vehicle CAN network, which improves the sampling frequency and sampling accuracy.

Description

technical field [0001] The invention belongs to the technical field of medium and low-speed maglev trains, and in particular relates to a system, a method, a computer device and a storage medium for detecting the attitude of a levitation electromagnet of a maglev train. Background technique [0002] Maglev trains use electromagnetic force to support and guide, and are equipped with linear motors to drive. There is no mechanical contact between the vehicle and the track, so it has high running speed, low noise, low vibration, fast starting and braking speed, small turning radius, and easy climbing. Strong capacity, safety and comfort, less maintenance and other significant advantages are one of the development directions of high-speed and green transportation in the future. [0003] In order to simplify the system structure and reduce the cost of the whole vehicle, the suspension system of the existing low- and medium-speed maglev trains only applies the active closed-loop co...

AI Technical Summary

Problems solved by technology

[0006] The shortcomings of the traditional method are: (1) Due to the large number of connected devices on the vehicle CAN network, the large amount of data, and the low sampling frequency (currently only 10Hz), it cannot meet the analysis requirements; (2) Due to structural limitations, the lateral displacement detection It is not at the same position as the vertical displacement detection. During comprehensive analysis, the data needs to be approximately converted to the same position on the suspension electromagnet, so the error is relatively large; (3) Due to different channels for obtaining data, they are not on the same time axis. When there is a large error in each data phase

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