Optimizing Unmanned Vessel Vision Systems for Biological Outbreak Prediction in Nuclear Power Plant Waters Using Machine Learning

As the importance of nuclear power plants as a source of clean energy continues to grow, the problem of biological outbreaks in the sea area of nuclear power plants poses a serious threat to their normal operation, especially as the proliferation of plankton may lead to the blockage of the cooling water system, thus affecting the safe operation of nuclear power plants. Traditional methods for predicting biological outbreaks often rely on manual observation and empirical judgement, which are difficult to meet the demand for efficient and real-time monitoring. For this reason, this study proposes an unmanned vessel vision system based on machine learning optimisation for the prediction of biological outbreaks in the sea area of nuclear power plants. By combining various machine learning algorithms such as Convolutional Neural Network (CNN), Support Vector Machine (SVM), Random Forest, XGBoost and LightGBM, the system achieves efficient processing and fusion of multimodal data. The experimental results show that the integrated model performs well in terms of accuracy and advance warning time, which is significantly better than a single model, and is able to effectively predict biological outbreaks in complex marine environments. The successful application of the system demonstrates the strong potential of machine learning in environmental monitoring, while revealing its limitations in terms of cost and adaptability. Future research will focus on the further optimisation and wide application of the system to promote the development of intelligent environmental monitoring technologies.