Shared traffic tool fault prediction method and system

A fault prediction and vehicle technology, applied in prediction, instrument, character and pattern recognition, etc., can solve the problems of low prediction accuracy, low efficiency, complex calculation, etc., and achieve independence and simplicity, high efficiency, and learning efficiency high effect

Pending Publication Date: 2021-06-18
GUANGDONG UNIV OF TECH
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Therefore, this patent needs to train multiple models, the calculation is complex, the efficiency is low, and the pre

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  • Shared traffic tool fault prediction method and system
  • Shared traffic tool fault prediction method and system
  • Shared traffic tool fault prediction method and system

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[0050] The specific implementation manners of the present invention will be further described in detail below in conjunction with the accompanying drawings and embodiments. The following examples are used to illustrate the present invention, but are not intended to limit the scope of the present invention.

[0051] Such as figure 1 Shown, a kind of shared vehicle failure prediction method of the preferred embodiment of the present invention, comprises the following steps:

[0052] S1. Obtain vehicle condition fault data; the vehicle condition fault data obtained in this embodiment is the vehicle condition fault data retained by operation and maintenance personnel and users in the local server log file, and read the log file data of the local server by Apache Flume, Upload to HDFS file system in Hadoop.

[0053] Using Apache Flume to read the log files of the local server into HDFS improves the speed of data reading to a certain extent, acts as an intermediary between data pr...

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Abstract

The invention relates to the technical field of traffic tool fault prediction, and discloses a shared traffic tool fault prediction method and system. The method comprises the steps of collecting vehicle condition fault data which are uploaded by operation and maintenance personnel and users and are reserved in a local server log file, reading the log file data through Apache Flume, uploading the log file data to an HDFS file system in Hadoop, then cleaning data stored in an HDFS, collecting the cleaned data in a MongoDB, screening out a new feature index set from the initial feature index set by using Spark and adopting a random forest model for the data collected in the MongoDB, establishing a naive Bayesian prediction model of vehicle faults on the basis of the new feature index set, and writing the prediction result back to the data document of each vehicle in the MongoDB to realize the prediction function of the vehicle fault, and feeding back information to the user and the maintenance department subsequently, so that the next user using the vehicle is warned, the working efficiency of the maintenance department is improved, and an improvement direction is provided for the technical department.

Description

technical field [0001] The invention relates to the technical field of vehicle fault prediction, in particular to a shared vehicle fault prediction method and system. Background technique [0002] In the context of building a powerful country in transportation, with the advancement of technology and the improvement of national consumption concepts in our country, in order to alleviate the problems of urban traffic congestion, safety, energy consumption, and exhaust emissions, shared transportation, which plays an important role in urban transportation, is developed in my country. Major cities have been gradually promoted and applied. Over time, there have been problems such as missing parts of shared vehicles, high scrapping rates, privately added car locks, and "difficult maintenance and high costs". As an important way for national green travel, shared transportation has promoted the healthy development of my country's sharing economy and new energy industry, and has becom...

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Application Information

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IPC IPC(8): G06Q10/04G06Q10/00G06K9/62
CPCG06Q10/04G06Q10/20G06F18/2113G06F18/24155G06F18/10
Inventor 任鸿儒卢桦茵鲁仁全吴卓儒张辰
Owner GUANGDONG UNIV OF TECH
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