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A prediction method based on moving Markov model under spatiotemporal big data

A forecasting method and big data technology, applied in database models, forecasting, relational databases, etc., can solve problems such as unsatisfactory forecasting accuracy and precision, large data storage and processing capacity, etc., to reduce storage capacity, improve forecasting speed, and improve The effect of precision and accuracy

Active Publication Date: 2021-08-24
HENAN UNIV OF URBAN CONSTR
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Problems solved by technology

[0007] In order to overcome the deficiencies of the prior art, the present invention provides a prediction method based on the moving Markov model under the spatio-temporal big data environment to solve the problem of large amount of data storage and processing in the spatio-temporal big data environment and the prediction accuracy. and precision are not ideal, improve the precision and accuracy of mobile user location prediction

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  • A prediction method based on moving Markov model under spatiotemporal big data
  • A prediction method based on moving Markov model under spatiotemporal big data
  • A prediction method based on moving Markov model under spatiotemporal big data

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[0024] The present invention will be further described in detail below in conjunction with the accompanying drawings and specific embodiments, but the protection scope of the present invention is not limited thereto.

[0025] Such as figure 1 As shown, a prediction method based on moving Markov model under spatiotemporal big data includes the following steps:

[0026] Step 1: Data cleaning. First denoise the collected historical location data, filter out the dynamic movement track, and keep the static movement track.

[0027] Due to changes in the speed of moving objects, the accuracy of positioning equipment is not high, and the collected mobile user trajectory data does not fully conform to the real situation; in addition, due to equipment stability issues, the collected data often contains certain noise. Since the trajectory of mobile users is a continuous signal of time, and the mobile Markov chain is a discrete random process, it is necessary to discretize the collected...

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Abstract

The present invention relates to the technical fields of intelligent transportation and spatio-temporal big data, and in particular to a prediction method based on a moving Markov model under spatio-temporal big data. The method comprises: performing denoising processing, clustering processing, and Obtain clusters through the joint density clustering algorithm, establish interest points for the clusters, denoise the interest points to retain the real interest points, and establish a mobile Markov model; after the mobile user data is collected, the above processing Finally, the interest points of mobile users are extracted, and the next position of mobile users is predicted according to the mobile Markov model. The invention solves the problem of large amount of data storage and processing in the spatio-temporal big data environment and unsatisfactory prediction accuracy and accuracy, and improves the accuracy and accuracy of mobile user position prediction.

Description

technical field [0001] The invention relates to the technical fields of intelligent transportation and spatiotemporal big data, in particular to a prediction method based on a moving Markov model under spatiotemporal big data. Background technique [0002] Due to the inherent characteristics of spatial entities and spatial phenomena in the space in which spatiotemporal data reside in three aspects: time, space, and attributes, spatiotemporal big data presents the complexity of multidimensional, semantic, and spatiotemporal dynamic associations. Spatiotemporal big data includes three-dimensional information of time, space, and thematic attributes, and has the comprehensive characteristics of multi-source, massive, and fast update. Spatio-temporal data has become a key element of smart city resources. By studying the formal expression of multi-dimensional correlation description of spatio-temporal big data, dynamic modeling of correlation and analysis of multi-scale correlatio...

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): G06Q10/04G06Q50/30G06F16/29G06F16/2458G06F16/28
CPCG06F16/2462G06F16/285G06F16/29G06Q10/04G06Q50/40
Inventor 郭力争闫涛王春丽李蓓柳运昌董国忠赵军民何宗耀
Owner HENAN UNIV OF URBAN CONSTR