A Real-time Query Method for Graph Structure Data in Passenger Flow Data of Rail Transit Network

Abstract

The invention discloses a real-time query method for graph structure data in passenger flow data of a rail transit network. The data graph with attributes and labels is divided based on hops, and a relevant index is established; the query graph is input, and the radius of the query graph is calculated. and select the query starting point; generate the query sequence of the query graph; filter the query starting point of the data graph that meets the conditions according to the characteristics of the query starting point of the query graph in each partition; perform a cascaded graph query process; collect all partitions The query result is completed, and the distributed graph query process is completed. The invention transmits the query graph instead of the data graph in the cross-partition query process, thereby reducing the amount of data transmission; the cascaded query process is based on the starting point and is not performed within the entire data graph range, thereby greatly reducing the query range; asynchronous concurrency is adopted. It can maximize the query efficiency and meet the real-time query processing requirements of the graph structure data in the passenger flow data of the rail transit network.

Description

technical field [0001] The invention belongs to the technical field of data processing, and in particular relates to a real-time query method for graph structure data in rail transit network passenger flow data. Background technique [0002] Rail transit construction is one of the important infrastructure constructions. Currently, the construction of rail transit-related facilities is proceeding rapidly, accompanied by a sharp increase in data size and complexity. For the construction of new rail transit, it is necessary to meet the real-time processing requirements of big data in its life cycle, that is, to meet the requirements of high real-time and low hysteresis when querying. [0003] Network passenger flow data is an extremely important data type in the field of rail transit data. Passenger flow is the most basic and important factor in urban rail transit. The distribution characteristics and changing laws of passenger flow are systematically analyzed to grasp the curr...

AI Technical Summary

Problems solved by technology

Based on the graph query statement decomposition method, the query results of simple sub-query statements need to be combined step by step. For more complex graph query statements with fewer bound nodes, an excessively large intermediate result set will be generated, which will greatly increase the query efficiency. At the same time, this method needs to query in the entire data graph, which also leads to low query efficiency; the iterative query method based on BSP (Block Synchronous Parallel Computing Model) has a high degree of concurrency, but it will generate many unnecessary calculation, because not all nodes need to participate in the iteration in each iteration

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