Research on Multi-source Data Fusion Technology for Vehicle-Track Integration Testing Based on 5G Communication

Xue Junyi (), Chai Jinchuan () and Wei Guili ()
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Xue Junyi: Beijing Jiaotong University
Chai Jinchuan: National Railway Track Test Center, China Academy of Railway Sciences Corporation Limited (CARS)
Wei Guili: Beijing Jiaotong University

A chapter in IEIS 2023, 2024, pp 57-72 from Springer

Abstract: Abstract 5G, as a new communication technology, has the characteristics of large bandwidth, large connection, and low delay. In railway application scenarios, such as the car integration test scenario, 5G support is needed for multi-time, multi-terminal, and multi-dimensional access. Car integration data needs to maintain space-time synchronization and carry terminal location information. 5G must ensure network security, reliable data transmission, and meet low delay requirements. In this paper, we explore the vehicle integration test multi-source data fusion scheme. We design the monitoring system and positioning synchronization system connection scheme using linear reference and dynamic segmentation technology. Based on space-time database technology support, we use the Lagrange linear interpolation method for linear reference detection data. We reuse dynamic segmentation based on events to establish a railway space-time data model. This study enhances the timeliness of data analysis and mining. The proposed technological solution enables the rapid transmission and efficient interaction of detection data between the train and the ground, providing a basis for accurate diagnosis of railway infrastructure operation, maintenance, and repair conditions.

Keywords: 5G communication technology; Vehicle-ground synchronization control; multi-source data fusion; technical solutions (search for similar items in EconPapers)
Date: 2024
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DOI: 10.1007/978-981-97-4137-3_6

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