Mr. Xiaolei Xu Profile

Xiaolei Xu

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Publications (2)

Proceedings Article | 15 November 2023 Paper

Research on railroad line spatial information management technology

Congxu Li, Xiaolei Xu, Boqing Feng, Maolin Zhang

Proceedings Volume 12815, 128152H (2023) https://doi.org/10.1117/12.3010655

KEYWORDS: Data storage, Data modeling, Databases, 3D modeling, Geographic information systems, Data communications, Remote sensing, 3D image processing, Information technology, Analytical research

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With the completion of railroad line resurvey work, the measurement data are scattered in various units and lack unified storage and management, which brings many challenges and problems to railroad operation and maintenance. To solve this problem, this study proposes a multi-version, multi-temporal spatial data storage technology, as well as a distributed fast storage technology and a spatial service engine by studying the data management work, aiming to realize unified and fast storage, management and sharing of railroad survey data to improve the reliability, consistency and validity of data. First, this study focuses on the multi-version and multi-temporal spatial data storage technology. By adopting this technology, a large amount of spatial data generated by railroad line resurvey can be effectively handled, and the storage and management of different versions and temporal phases of data can be realized. Meanwhile, distributed fast storage technology is introduced to provide high-speed data access and processing capability to cope with the storage and processing demand of massive data. In addition, using the spatial service engine, fast query and analysis of measurement data can be realized. Second, this study also conducts in-depth research on the railroad line measurement data management system. First, the overall architecture of the system is designed to ensure the scalability and flexibility of the system. Then, the data structure of the system was optimized to accommodate the storage and query requirements of multi-version and multi-temporal data. In terms of technical architecture, distributed storage technology is adopted to realize fast storage and retrieval of remote sensing, 2D vector and 3D model data. In addition, the sharing and exchange of measurement data is realized through information sharing and information interface. The innovation of this study is to propose a multi-version and multi-temporal spatial data storage technology, and to realize the fast storage of different types of data by using distributed storage technology. By constructing a railroad line measurement data management system, the problems caused by decentralized data management can be effectively solved and the reliability, consistency and validity of data can be improved. This is of great significance for railroad operation and maintenance.

Proceedings Article | 15 November 2023 Paper

Modeling technology for fusion of massive strip 3D terrain and feature models

Congxu Li, Boqing Feng, Xiaolei Xu, Miao Zhang

Proceedings Volume 12815, 128152G (2023) https://doi.org/10.1117/12.3010343

KEYWORDS: Data modeling, 3D modeling, Data storage, Data fusion, Feature fusion, Modeling, Visual process modeling, Data processing, 3D displays, Geographic information systems

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In order to solve the problem that traditional GIS methods are difficult to handle massive data, complex spatial relationships and accurate geographic phenomenon modeling. By fusing terrain and feature models, geographic phenomena can be efficiently and accurately represented and analyzed to provide a reliable basis for decision making.This paper proposes a fusion modeling technique for massive strip 3D terrain and feature models, aiming to solve the problem of effective fusion and modeling of 3D terrain and feature models for strip data in large-scale spatial data processing. Based on open-source geographic information technology, the traditional method is designed to unify and model 2D vector data, raster data, BIM data and 3D data from different sources, formats and scales to form a standardized spatial data set, in response to the problems of high computational complexity, large storage requirements and data redundancy in processing massive data. For the characteristics of strip data and 3D terrain and feature models, geometric matching and semantic matching methods are used to fuse and process vector data representing the same entity with different attribute information and spatial location information to solve the problem of data redundancy and duplication; it is used to describe the different levels and precision of spatial data, and the appropriate data precision can be flexibly selected in the fusion modeling process, so as to achieve a balance between data quality and calculation The purpose of balancing data quality and computational efficiency. Based on LOD hierarchical detail technology fusion processing of terrain-independent BIM models, terrain-dependent fusion of linear 3D feature models, to achieve the surface fitting of multi-source 3D model data in a specific scene, to improve the 3D scene display effect.In terms of data processing efficiency: traditional GIS methods may face bottlenecks in computation and storage when dealing with massive data. The efficiency of data processing can be improved by adopting fusion modeling technology for massive data. For spatial relationship accuracy: fusion modeling technology can capture the complex spatial relationships between terrain and features more accurately, providing more accurate simulation and analysis results.

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