Multi-level deep fusion mining method for multi-mode cross-boundary big data of commercial vehicles

Abstract

The invention discloses a multi-level deep fusion mining method for multi-mode cross-boundary big data of commercial vehicles. The method comprises the following steps: S1, collecting an original dataset of multi-mode cross-boundary big data of a vehicle; s2, performing data preprocessing on the collected original data set; s3, performing data mining on the preprocessed data by using a WEKA algorithm to extract feature keywords; s4, calculating the weights of the feature keywords and the similarity between the different feature keywords through a TF-IDF technology, and constructing a weight and similarity matrix; and S5, constructing a regression model based on the sample. According to the invention, via t-SNE dimension reduction, WEKA algorithm feature extraction and a TF-IDF algorithm,an analysis strategy of dimension reduction and feature extraction in sequence is adopted for high-dimensional data, effective fusion of multi-level deep fusion mining of cross-boundary big data is achieved, and the problems that due to a high-dimensional data set with complex data types and numerous data features, the fusion efficiency is low, and the working efficiency is not remarkably improvedare solved.

Description

technical field [0001] The invention relates to the field of big data technology, in particular to a multi-level deep fusion mining method for multi-mode cross-border big data of commercial vehicles. Background technique [0002] With the advent of the era of big data and the rapid development of the national economy, the rapid growth of motor vehicles makes it necessary to timely grasp the road condition information when motor vehicles are driving in order to determine the smoothness of urban traffic. [0003] Although the basic application system has reached a relatively high level of technology and application, there are still some problems and deficiencies: each application system is only for the data processing of the system, and is limited to simple statistics, and statistical information has its limitations. The phenomenon of "information islands" cannot achieve data fusion and information sharing, so that the work efficiency has not been significantly improved in the...

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Problems solved by technology

[0005] The purpose of the present invention is to solve the shortcomings in the prior art, and propose a multi-level deep fusion mining method for multi-mode cross-border big data of commercial vehicles, which uses t-SNE dimensionality reduction, WEKA algorithm feature extraction and TF -IDF algorithm, which adopts the analysis strategy of reducing dimension first and then extracting features for high-dimensional data, not only realizes the effective integration of multi-level deep fusion mining of cross-border big data, but also solves the problem of high-dimensional data sets with complex data types and numerous data features The problems caused by low integration efficiency and no significant improvement in work efficiency

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