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Large data cluster prediction method of extreme wind speed along a high-speed railway line

A technology for wind speed forecasting and high-speed railways, applied in forecasting, data processing applications, electrical digital data processing, etc., can solve the problems that restrict the speed-up of railway passenger and freight transportation and economic development, so as to improve the prediction accuracy, reduce the number of iterations, and avoid forecasting Effect

Active Publication Date: 2018-12-18
CENT SOUTH UNIV
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Problems solved by technology

[0003] The bad influence of strong wind becomes more and more obvious with the increase of train speed, which seriously restricts the speed-up of railway passenger freight and economic development

Method used

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  • Large data cluster prediction method of extreme wind speed along a high-speed railway line
  • Large data cluster prediction method of extreme wind speed along a high-speed railway line
  • Large data cluster prediction method of extreme wind speed along a high-speed railway line

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Embodiment Construction

[0092] The present invention will be further described below in conjunction with the accompanying drawings and embodiments.

[0093] Such as figure 1 and figure 2 As shown, a big data clustering prediction method of extreme wind speed along the high-speed railway, including the following steps:

[0094] Step 1: Set up wind measuring stations at railway target wind measuring points, including target wind measuring stations and time-shift wind measuring stations;

[0095] The target wind measuring station is 100 meters away from the railway target wind measuring point, and the time-shifting wind measuring station includes at least 3, and the connection line between the railway target wind measuring point and the target wind measuring station is set, and the first time-shifting wind measuring station The wind station is 500 meters away from the railway target wind measurement point, and the distance between adjacent time-shift wind measurement stations is 500 meters;

[0096]...

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Abstract

A method for cluster prediction of extreme wind speed includes constructing target wind measuring station and time-lapse wind measuring station at target wind measuring point according to near-term wind speed condition, de-noising data of wind measuring station, clustering wind speed samples, utilizing LS-SVM trains the denoised wind speed clustering sample data, and constructing the wind speed prediction model of each anemometer station under various step sizes, choosing each model to carry on the best combination of many kinds of steps, realizing the multi-step iterative forecast, improvingthe forecast precision, reducing the random error disturbance. The wind speed prediction along the railway line can be realized, and the wind speed environment in the accident-prone area can be knownin advance, which can guide the train operation timely and effectively, and ensure the safety of train operation.

Description

technical field [0001] The invention belongs to the field of railway wind speed prediction, in particular to a large data clustering prediction method of extreme wind speed along a high-speed railway. Background technique [0002] The rapid development of my country's high-speed railway has become a representative business card of China. As far as the current development status in the world is concerned, China's high-speed railway has achieved brilliant results. But with the improvement of the speed of high-speed railway, it faces new problems and challenges. Strong wind is one of the main obstacles restricting the development of high-speed railway, which seriously threatens the safety of operation. On the one hand, it is essential to optimize the body design and body materials. On the other hand, how to quickly and accurately predict the time and speed of strong winds is also of great significance. [0003] The adverse effect of strong wind becomes more and more obvious ...

Claims

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Application Information

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IPC IPC(8): G06Q10/04G06Q50/30G06F17/30
CPCG06Q10/04G06Q50/40
Inventor 刘辉吴海平段铸尹恒鑫
Owner CENT SOUTH UNIV
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