## 4 Methodology
### Graph Convolutional Neural Network Module
This classification system provides a new framework for gantry equipment management, allowing for operations and maintenance personnel to gain a more comprehensive understanding of gantry system problems and develop more targeted maintenance strategies. We also employed a composite model including GCN, context-focused mechanisms, and GRU to comprehensively consider the spatial and temporal characteristics of gantry data. This approach demonstrates excellent performance in experiments, with over 99% accuracy, a 2-20% performance improvement over other traditional anomaly detection algorithms and other graph flow anomaly detection methods, and the fluctuation range of the model's performance metrics is within 10% under different data volumes. This finding provides strong support for further research and its practical applications in the field of dynamic graph anomaly detection. Finally, despite the remarkable results of our research, there are still some limitations. Our method is based on offline data, and thus presents challenges in terms of real-time performance. Future research directions will include the establishment of a simulation system for the dynamic detection of real-time gantry transaction data to meet the practical needs of gantry equipment operation and maintenance. Conceptualization, F.Z. and Y.X. ; methodology, F.Z. ; software, F.Z. ; validation, F.Z., Y.X., Q.R., F.G., Z.Z. and Z.Y., formal analysis, F.Z. and Y.X., investigation, F.Z. and Y.X. ; resources, Q.R. and F.G.; data curation, F.Z., Y.X., Q.R., F.G., Z.Z. and Z.Y. ; writing--original draft preparation, F.Z. ; writing--review and editing, F.Z., Y.X., F.G., Z.Z. and Z.Y. ; visualization, F.Z. ; supervision, Z.Y. ; project administration, F.G. and Z.Z. ; funding acquisition, Y.X. All authors have read and agreed to the published version of the manuscript. This work is partially supported by the Renewable Energy Technology Research institution of Fujan University of Technology Ningde, China (Funding number: KY310338, Funder: F.Z. ), the 2020 Fujan Province \"Belt and Road\" Technology Innovation Platform (Funding number: 2020D002, Funder: F.Z. ), the Provincial Candidates for the Hundred, Thousand and Ten Thousand Talent of Fujian (Funding number: GY-Z19113, Funder: F.Z. ), the Patent Grant project (Funding number: GY-Z18081, GY-Z19099, GY-Z20074, Funder: F.Z. ), Horizontal projects (Funding number: GY-H-20077, Funder: F.Z. ), Municipal level science and technology projects (Funding number: GY-Z-22006, GY-Z-220230, Funder: F.Z. ), Fujian Provincial Department of Science and Technology Foreign

Figure 14: Changes in evaluation indicators at different data volumes. Cooperation Project (Funding number: 2023I0024, Funder: F.Z. ), the Open Fund project (Funding number: KF-X19002, KF-19-22001, Funder: F.Z.). ## Institutional Review Board Statement: Not applicable. **Informed Consent Statement**: Not applicable. **Data Availability Statement**: The ETC transaction data utilized in this study were obtained from Fujian Expressway Information Technology Co., Ltd., Fu Zhou, China. Restrictions apply to the availability of these data, which were used under license for this study and are not publicly available. Data are available from the authors with the permission of Fujian Expressway Information Technology Co., Ltd. All data processing and analyses were conducted in compliance with relevant data protection and privacy laws. No individual or personal data were used in this study. **Conflicts of Interest**: The authors declare no conflict of interest. ## References

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