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Track quality prediction method and system based on improved grey combination model

A track quality and combined model technology, applied in the field of rail transit data analysis and prediction, can solve the problems of not taking into account the different degrees of model influence, not considering the impact, and the accuracy of background value calculation needs to be improved.

Pending Publication Date: 2020-06-19
BEIJING UNION UNIVERSITY
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Problems solved by technology

The shortcoming of this article is that it does not consider the impact of the detection time interval on the first-order accumulation generation process, the model parameter solution process does not take into account the different degrees of influence of the detection time on the model, and the accuracy of the background value calculation needs to be improved

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  • Track quality prediction method and system based on improved grey combination model
  • Track quality prediction method and system based on improved grey combination model
  • Track quality prediction method and system based on improved grey combination model

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Embodiment 1

[0079] Aiming at the problems and shortcomings of existing track quality prediction methods, the present invention proposes an improved gray combination model track quality prediction method, which can well reflect the development trend of TQI sequence. Improvements are made on the basis of traditional gray forecasting modeling. The weight distribution coefficient is introduced into the cumulative generation process to optimize the weight distribution of the time interval. At the same time, the weight matrix is ​​introduced to solve the model parameters. The closer the prediction time is, the greater the weight is given. The prediction accuracy has been greatly improved; the use of the integral area to optimize the solution of the background value of the difference equation has solved the problem of large errors caused by the calculation of the trapezoidal area in the past; the PSO-Elman residual correction model can reduce data fluctuations and reduce Improved unequal time int...

Embodiment 2

[0099] The invention provides a track quality prediction method based on the improved gray combination model, which has the following obvious advantages and beneficial effects compared with the existing technology:

[0100] (1) Improve on the basis of traditional gray prediction modeling, introduce the weight distribution coefficient into the cumulative generation process, optimize the weight distribution of time intervals, and introduce the weight matrix to solve the model parameters, and the closer the prediction time is, the greater the value given to the data weight, the prediction accuracy has been greatly improved.

[0101] (2) Using the integral area to optimize the solution of the background value of the difference equation solves the problem of large errors caused by the calculation of the trapezoidal area in the past, reduces the prediction error of the model, and enhances the reliability of the prediction results.

[0102] (3) The TQI sequence belongs to the time se...

Embodiment 3

[0165] like Figure 5 As shown, although the single gray model has a good linear change trend, the error between it and the actual measured value is relatively large. If the track maintenance department arranges maintenance tasks with reference to its predicted value, it will not be able to carry out targeted track maintenance operations. Using the PSO-Elman model for residual correction, it can be seen from the figure that the error of the final prediction result is lower than that of the preliminary prediction model.

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Abstract

The invention provides a track quality prediction method and system based on an improved gray combination model, and the method comprises the steps: preprocessing original data, and also comprises thefollowing steps: constructing an improved non-equal-time-interval gray IGM prediction model; optimizing the weight and the threshold of the Elman network by using a PSO algorithm, and constructing aPSO-Elman neural network correction model; and constructing an IGM-PSO-Elman model by integrating the advantages of the improved non-equitime-interval grey IGM model, the particle swarm optimization PSO algorithm and the Elman neural network. The invention provides the orbit quality prediction method and system based on the improved grey combination model; improvement is carried out on the basis of a traditional gray model, a weight coefficient optimization first-order accumulation generation process is introduced, weight matrix optimization model parameter solving is added, calculation of a difference equation background value is optimized by utilizing an integral area, and a PSO-Elman residual error correction model can reduce data fluctuation and reduce an improved non-equal-time-interval gray prediction residual error.

Description

technical field [0001] The invention relates to the technical field of rail transit data analysis and prediction, in particular to a rail quality prediction method and system based on an improved gray combination model. Background technique [0002] The shortening of train running intervals and the increase of traffic density have greatly increased the daily load of the track and the difficulty of maintenance work by the public works department. The railway track is the carrier and support of the train operation, an important factor affecting the smooth and safe operation of the train and the comfort of the passengers, and it is also the focus of the daily maintenance of the railway maintenance department. Therefore, it is of great significance to study the prediction of track status in the future to promote the level of track safety early warning management and the development of digital and intelligent operation and maintenance. [0003] The current track quality predicti...

Claims

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Application Information

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IPC IPC(8): G06Q10/04G06Q10/06G06Q50/30G06N3/00G06N3/04
CPCG06Q10/04G06Q10/067G06N3/006G06N3/044G06Q50/40
Inventor 饶志强赵玉林
Owner BEIJING UNION UNIVERSITY
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