Bus real-time geographic information data cleaning method and system

Abstract

The invention relates to the technical field of intelligent buses and provides a bus real-time geographic information data cleaning method and system. The method includes: under an initial state, building a basic data table according to real-time data; correcting data records arriving continuously in real time according to the basic data table; dynamically updating the basic data table; wherein the basic data table saves the historical information of various data records in real-time data. By the method, reliable and real-time cleaning of the real-time bus geographic information data is achieved, and foundation is laid on bus data real-time calculation.

Description

【Technical field】 [0001] The invention relates to the technical field of intelligent public transportation, in particular to a method and system for cleaning real-time geographical information data of public transportation. 【Background technique】 [0002] In recent years, the number of urban motor vehicles has increased rapidly, which has caused many problems such as traffic jams, parking difficulties, and difficulty in taking taxis, which seriously affect the quality of ordinary people's travel. At the same time, the urban traffic network is becoming more and more complex, and the requirements for a perfect management system are getting higher and higher. In the blueprint of building a smart city in the future, smart transportation is the top priority. [0003] The arrival of the era of big data provides an opportunity for the establishment of intelligent transportation, in which the use value of bus data is huge. Since the bus is one of the mainstream means of transporta...

AI Technical Summary

Problems solved by technology

[0005] (1) The amount of data is large. Due to the large number of buses, the amount of data generated per second is huge, so it is required to be able to clean these data correctly and quickly;

[0006] (2) The abnormal data is complex, and there are many types of Global Positioning System (GPS) equipment, which are affected by various objective and unpredictable factors such as the accuracy of satellite positioning, the limitation of positioning equipment, and network signals, resulting in a large number of abnormalities in the data. Unpredictable data, there are many types of abnormal data, so it is required to correctly identify and correct these abnormalities

Since the existing technology uses batch processing to process the data received in real time, it has the following defects: 1. It cannot make full use of the real-time computing power, resulting in a huge amount of calculation in the later stage; 2. It cannot use dynamic information to combine into prior knowledge to intelligently correct the data

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