Intelligent generation algorithm for fusion model based on massive multi-source heterogeneous data

Ting Liu; Mingjiang Wang; Qun Zhang; Huanhuan Gao

doi:10.1117/12.3029381

6 May 2024 Intelligent generation algorithm for fusion model based on massive multi-source heterogeneous data

Ting Liu, Mingjiang Wang, Qun Zhang, Huanhuan Gao

Proceedings Volume 13107, Fourth International Conference on Sensors and Information Technology (ICSI 2024); 131070Y (2024) https://doi.org/10.1117/12.3029381
Event: Fourth International Conference on Sensors and Information Technology (ICSI 2024), 2024, Xiamen, China

Abstract

Traditional engineering safety evaluation methods based on single sensor data fusion often have low reliability and lack the ability to comprehensively consider multiple factors, resulting in incomplete evaluation results. This study innovatively proposes a multi-source fusion model generation method, which combines advanced artificial intelligence algorithms and considers both structured monitoring information and unstructured detection information. Firstly, based on the structural characteristics of the building, a multi-source fusion system is constructed based on the target layer, location layer, and fusion layer. Before data fusion, the preprocessed multi-source data needs to be stored in the same type of database for physical fusion, and then input into the fusion model. Then, feature extractors based on bidirectional long short term memory network (BiLSTM) and coupled BiLSTM with adaptive weighted average method (AWAM) were constructed to achieve text vectorization and feature extraction of multi-source data. Then, by introducing the Bhattacharyya distance to improve the D-S evidence theory, multi-source heterogeneous data fusion is achieved, and the fusion result is the overall safety status of the building. Finally, the accuracy of this algorithm was verified through engineering examples, providing a new algorithm for engineering safety evaluation.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Ting Liu, Mingjiang Wang, Qun Zhang, and Huanhuan Gao "Intelligent generation algorithm for fusion model based on massive multi-source heterogeneous data", Proc. SPIE 13107, Fourth International Conference on Sensors and Information Technology (ICSI 2024), 131070Y (6 May 2024); https://doi.org/10.1117/12.3029381

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KEYWORDS

Data fusion

Inspection

Data modeling

Safety

Feature extraction

Feature fusion

Structural monitoring

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