Predictive measurement model of connectivity strength between vehicle nodes based on spatio-temporal data analysis of vehicle networking in urban scenarios

A vehicle node and connectivity strength technology, applied in the field of Internet of Vehicles, can solve problems such as low efficiency

Active Publication Date: 2019-01-29
TONGJI UNIV
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  • Abstract
  • Description
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AI Technical Summary

Problems solved by technology

Therefore, it is imminent to explore the general data processing method for the big data research of the Internet of Vehicles to clean the data and establish the prediction of the connectivity strength between vehicle nodes, rather than the low-efficiency mode of case analysis.

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  • Predictive measurement model of connectivity strength between vehicle nodes based on spatio-temporal data analysis of vehicle networking in urban scenarios
  • Predictive measurement model of connectivity strength between vehicle nodes based on spatio-temporal data analysis of vehicle networking in urban scenarios
  • Predictive measurement model of connectivity strength between vehicle nodes based on spatio-temporal data analysis of vehicle networking in urban scenarios

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Embodiment Construction

[0038] The concrete implementation process of the present invention is as Figure 7 As shown, including the following two aspects:

[0039] ① Definition of relevant properties

[0040] ②Measurement of connectivity strength

[0041] ③ Neural network model construction of tensor factor aggregation

[0042] ④ Target tuning function selection

[0043] ⑤Establishment of connectivity strength prediction model between vehicle nodes

[0044] ①

[0045] Related property definition

[0046] The nodes in the Internet of Vehicles are different vehicles, and the connectivity between vehicles determines whether there is an edge between the nodes. Nodes and edges constitute the entire Internet of Vehicles. The research on the accessibility of the vehicle network is essential to study the connectivity between vehicles, which can be recorded as (vehicle 1, vehicle 2, connectivity strength) if it is represented by a triple similar to the knowledge base. In many network models, such as ...

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Abstract

Disclsoed is a predictive measurement model of connectivity strength between vehicle nodes based on spatio-temporal data analysis of vehicle networking in urban scenarios. Aiming at the connectivity problems caused by spatio-temporal data heterogeneity and topology frequent changes in vehicle networking, a neural network model based on tensor factor aggregation is constructed to predict the connectivity strength between vehicle nodes, thus providing model support for the connectivity research of vehicle networking without infrastructure and with infrastructure.

Description

technical field [0001] The invention relates to the field of the Internet of Vehicles, in particular to a prediction model of connection strength between vehicle nodes for analysis of spatio-temporal data of the Internet of Vehicles in urban scenes. Background technique [0002] In the urban scene, the vehicle itself has the characteristics of fast moving speed and high degree of freedom, which leads to a high frequency of changes in the topology of the entire network, resulting in unstable network connections. Moreover, the distribution of vehicles is also affected by road conditions and urban planning. It is very complex and brings great challenges to network connectivity. Therefore, analyzing and solving the connectivity of the Internet of Vehicles in urban road scenes is an urgent problem in the current research on Internet of Vehicles. [0003] The connectivity problem of the Internet of Vehicles is mainly caused by the frequent changes of the topology and the volatile ...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06Q10/04G06Q50/30G06N3/04G06N3/08
CPCG06N3/08G06Q10/04G06Q50/30G06N3/045
Inventor 程久军王从军马骐
Owner TONGJI UNIV
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