Track data processing method, device and system

Abstract

The invention provides a track data processing method, device and system. The method comprises the following steps: acquiring track point data of a target; and merging and storing the two or more pieces of trajectory point data of the target to serve as trajectory fragment data of the target. Through fragment storage of the trajectory data, partial operations of trajectory construction and segmentation are naturally completed in the data storage process, the stored data structure is optimized, the storage space is saved, and trajectory construction after data query is facilitated. Furthermore, storage can comprise caching of the message-oriented middleware and persistence of the database, so that the high concurrency characteristic of the message-oriented middleware is fully utilized, and the burden of the database is reduced.

Description

technical field [0001] The invention relates to database technology, in particular to a trajectory data processing method, device and system. Background technique [0002] Moving target trajectory data is a very important spatio-temporal data, which plays a very important role in logistics distribution, navigation, shared bicycles and other fields. Figures 1A-1B Shown is a display example of the target movement trajectory. specifically, Figure 1A It shows an example of the riding movement trajectory of the shared bicycle displayed by the client user query. Figure 1B It shows an example of logistics distribution displayed through client user query. [0003] Spatio-temporal trajectory data has the characteristics of large data volume, frequent updates, and high real-time requirements, and has high requirements for data storage and management. In the prior art, the track location information is written into the data storage system by a data producer in the form of a point...

AI Technical Summary

Problems solved by technology

[0004] In high-concurrency data read and write scenarios, high-throughput I / O operations will increase the load on the data storage system, resulting in read and write delays

In addition, converting the stored point model into trajectory data will consume a large amount of processing resources, and it is difficult to meet the real-time requirements of trajectory data processing and analysis

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