Optimizing Multi-Dimensional SCADA Report Generation Using LSO-GAN for Web-Based Applications

This paper addresses the challenges of custom-generating multi-dimensional data SCADA (Supervisory Control And Data Acquisition) reports using web technologies. To improve efficiency, reduce maintenance costs, and enhance scalability, the paper proposes a custom generation method based on the LSO-GAN (Light Spectrum Optimizer - Generative Adversarial Network) model. The study begins by analyzing the requirements for multi-dimensional SCADA reports and proposes a web-based design scheme. The LSO algorithm is employed to optimize the GAN model, enabling efficient generation of customizable SCADA reports. The proposed LSO-GAN model was validated using relevant SCADA data, with experimental results showing that the method outperformed other models in terms of accuracy and generation efficiency. Specifically, the LSO-GAN model achieved an RMSE of 14.98 and a MAPE of 0.93, surpassing traditional models such as Conv-LSTM and FC-LSTM. The custom report generation method based on LSO-GAN significantly improves the customization and generation of multi-dimensional data SCADA reports, demonstrating superior performance in both accuracy and operational efficiency.

@article{Fang2025,
  title     = {Optimizing Multi-Dimensional SCADA Report Generation Using LSO-GAN for Web-Based Applications},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {16},
  number    = {1},
  year      = {2025},
  publisher = {The Science and Information Organization},
  author    = {Fanxiu Fang and Guocheng Qi and Haijun Cao and He Huang and Lingyi Sun and Jingli Yang and Yan Sui and Yun Liu and Dongqing You and Wenyu Pei},
  doi       = {10.14569/IJACSA.2025.0160124},
  url       = {https://doi.org/10.14569/IJACSA.2025.0160124}
}