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In response to the issue of inadequate monitoring and management of the environment of ship field painting, a three-dimensional matrix data representation method considering time, monitoring stations, and environmental factors has been constructed. Taking into account the impact of various factors such as paint types and actual usage, meteorological factors, the CNN-LSTM model was used for monitoring data training and model prediction, achieving the concentration prediction of atmospheric pollution for a certain period in the future. The predicted results are more objective, which can guide workers in scientific operations and help promote the green development of shipbuilding.

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Research on Air Pollution Prediction of Ship Field Painting Based on CNN-LSTM

  • 会议论文

  • 发表日期：

    2025年

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  • Caiyun Liu,
  • Dapeng Zhu

摘要

In response to the issue of inadequate monitoring and management of the environment of ship field painting, a three-dimensional matrix data representation method considering time, monitoring stations, and environmental factors has been constructed. Taking into account the impact of various factors such as paint types and actual usage, meteorological factors, the CNN-LSTM model was used for monitoring data training and model prediction, achieving the concentration prediction of atmospheric pollution for a certain period in the future. The predicted results are more objective, which can guide workers in scientific operations and help promote the green development of shipbuilding.