A Generation Method of Routing Metrics Based on Multimodal Data

A multi-modal, data-based technology, applied in relational databases, database models, data processing applications, etc., can solve problems such as inability to provide transmission reliability

Active Publication Date: 2020-11-06
辽宁数联达科技有限公司
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  • Application Information

AI Technical Summary

Problems solved by technology

ETX and its derived routing metrics only monitor the reliability of the link. Although the throughput is increased, it cannot provide better transmission reliability because the routing metrics do not consider other routing factors.

Method used

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  • A Generation Method of Routing Metrics Based on Multimodal Data
  • A Generation Method of Routing Metrics Based on Multimodal Data
  • A Generation Method of Routing Metrics Based on Multimodal Data

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Embodiment Construction

[0026] In order to make the object, technical solution and advantages of the present invention clearer, the present invention will be further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described here are only used to explain the present invention, not to limit the present invention.

[0027] refer to Figure 1-4 , the invention provides a method for generating routing metrics based on multimodal data, comprising the steps of:

[0028] Step 1: Collect multi-modal data samples, including environment, drivers, vehicles, traffic data, etc. The sources of each modal data are as follows: figure 2 As shown, 201-207 is the source of environmental information, 209-215 is the source of driver information, 217-223 is the source of vehicle information, 225-231 is the source of traffic data information, such as: weather, regional level, buildings, other environments, etc. Several character...

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Abstract

The invention belongs to the technical field of wireless networks, especially the technical field of network layer routing search in a vehicular ad-hoc network, and particularly provides a multimodal data-based routing metric generation method. By analyzing multiple types of modal data representing environment, driver, vehicle and traffic information, traffic-data based routing metric (TDR) is established, so that the judgment precision of reliability of an intermediate node is improved; a small amount of labeled data is learned and cooperatively trained by using a semi-supervised multimodal machine learning architecture; the cooperative training process is divided into two iterative processes including positive training and label upgrading; and the routing metric is constructed. A mechanism for generating the routing metric based on a machine learning algorithm is proposed; massive routing constraint points are considered; the influence of related data on routing is analyzed from the data mining perspective; and the routing metric for accurately judging the routing reliability is generated. Massive unlabeled data and a small amount of the labeled data are analyzed by utilizing the semi-supervised multimodal architecture, and a training modeling process is finished with the minimum cost.

Description

technical field [0001] The invention relates to a semi-supervised multimodal machine learning framework, a method for constructing routing metrics through learning and collaborative training on a small amount of labeled data, specifically a method for generating routing metrics based on multimodal data. This analysis includes a variety of modal data representing the environment, drivers, vehicles, and traffic information, and establishes a routing metric TDR (Traffic-Databased Routing Metric) to improve the reliability of intermediate nodes (next hop nodes). Background technique [0002] Since entering the 21st century, the total number of cars in my country has increased from more than 20 million to nearly 190 million in 2016, and will continue to rise at a growth rate of 10%. Along with this, problems such as road congestion and traffic accidents will follow. The Internet of Vehicles can provide services such as vehicle lane change reminders, intersection reminders, accide...

Claims

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): G06K9/62G06N3/08G06Q10/04
CPCG06F16/285G06N3/08G06Q10/047
Inventor 赵亮赵伟莨李照奎拱长青林娜李席广吴昊
Owner 辽宁数联达科技有限公司
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