A data following relationship analysis method and analysis server

Abstract

The invention discloses a data follow-up relationship analysis method, which is applied to an analysis server in a data analysis network. The data analysis network also includes a plurality of data acquisition devices, and all data acquisition devices in the data analysis network are bound in pairs. And generate a unique binding relationship code, obtain the binding relationship code corresponding to the data to be analyzed according to the data type and space following conditions of the data to be analyzed, group the data to be analyzed according to the binding relationship code corresponding to the data to be analyzed, and judge Whether the data to be analyzed in the group corresponding to each binding relationship code has time following, and output the judgment result of the data to be analyzed, which reduces the amount of calculation when analyzing the data following relationship, and improves the efficiency of data analysis.

Description

technical field [0001] The present invention relates to the field, in particular to a data following relationship analysis method, and in particular to an analysis server. Background technique [0002] With the vigorous development of the transportation industry, more and more passing data are generated every minute. Traditional data analysis strategies, in the case of big data, are increasingly unable to meet people's requirements for real-time and accuracy. For example, in the case of a large amount of data (more than 100 million), analyze the follow-up suspicion relationship between the MAC of the passing vehicle record and the passing vehicle data, using the previous method based on relational databases such as PG and Oracle, or using a similar follow-up analysis algorithm, etc. Follow-up analysis to solve the same type of data has obviously been unable to meet the needs of mining follow-up relationships among a large number of multi-dimensional data. [0003] Taking t...

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Problems solved by technology

And as Figure 2a As shown in the schematic diagram of the data time following relationship, the collected data B7, B8 and the collected data A have no basis in time

[0004] To sum up, only the data that satisfies the following relationship in time and space at the same time can be considered as following correlation between data. Therefore, when judging the following relationship between data, it is necessary to judge whether time and space follow at the same time, and for space following In the judgment process, each piece of data needs to be calculated for latitude and longitude, and the amount of calculation is huge, which affects the efficiency of analysis

[0005] On the other hand, due to the characteristics of spatial coordinates, Spark analysis operations cannot be partitioned according to the recorded latitude and longitude information, but can only be partitioned according to the recorded time conditions. As a result, the data within the following time range of two partitions may be different from the records of other partitions. There is also a following relationship, so it is necessary to broadcast this part of the data, and then perform space-time calculations with the time in other partitions, which will result in a large number of repeated calculations

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