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Path planning distributed computation method based on iterative computations in big data environment

A technology of distributed computing and path planning, applied to road network navigators, navigation, instruments, etc., to achieve the effect of speeding up the processing speed

Inactive Publication Date: 2018-10-02
HOHAI UNIV CHANGZHOU
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  • Summary
  • Abstract
  • Description
  • Claims
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AI Technical Summary

Problems solved by technology

However, the current common distributed computing framework cannot meet the needs of the above iterative algorithms, and it is necessary to design a corresponding distributed computing framework for this path planning algorithm

Method used

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  • Path planning distributed computation method based on iterative computations in big data environment
  • Path planning distributed computation method based on iterative computations in big data environment
  • Path planning distributed computation method based on iterative computations in big data environment

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Experimental program
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Embodiment

[0061] Taking Dijkstra's path planning algorithm as an example to illustrate the serial method, this algorithm uses breadth-first search to solve the single-source shortest path problem of weighted directed or undirected graphs, and finally obtains a shortest path tree. The specific method is:

[0062] (1) Initialization: declare an array dist to save the shortest distance from the source point to each vertex, and declare a set T to save the vertices that have found the shortest path. Initially, the path weight of the origin s is assigned 0 (dis[s]=0). If there is a directly reachable edge (s,m) for vertex s, then set dis[m] to w(s,m), and set the path lengths of all other vertices (not directly reachable by s) to infinity, Initially, the set T has only vertex s.

[0063] (2) Select the minimum value from the dis array, then this value is the shortest path from the source point s to the vertex corresponding to this value, and add this point to T, and complete a vertex at thi...

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Abstract

The invention discloses a path planning distributed computation method based on iterative computations in a big data environment. The path planning distributed computation method comprises the following steps: (1) firstly, defining the correlation of a path planning request; (2) secondly, dividing clusters for the path planning request according to the correlation; (3) finally designing a distributed computing framework, performing parallel processing on request clusters with small correlation, and performing serial processing on requests with high correlation in one cluster. According to thecomputation method disclosed by the invention, the superiority of the algorithm in a load balancing aspect is maintained. Moreover, distributed computation is realized, the system processing speed isaccelerated, and traffic response requirements in the big data environment are met.

Description

technical field [0001] The present invention relates to the computer field, in particular to the distributed field, and especially designs a path planning distributed computing method based on iterative computing in a big data environment. Background technique [0002] With the development of transportation, the number of social motor vehicles has increased rapidly. However, traffic problems in large and medium-sized cities have become more and more prominent, manifested in serious congestion and frequent traffic accidents, especially during rush hour. It will also cause waste of resources, pollute the environment, and affect the improvement of urban residents' living standards and the development of urban economy. [0003] The current navigation system usually recommends the same optimal path to a large number of users, but considering the load balance of the road network, an iterative calculation path planning method is proposed, which incorporates the current recommended ...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G01C21/34
CPCG01C21/3446G01C21/3492
Inventor 孙宁石慧珠王彬苗红霞
Owner HOHAI UNIV CHANGZHOU
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