## 6 Future Research Directions  
(1) Combination of big data and intelligent algorithms  
With the continuous progress of hydrodynamic noise measurement technology, advanced sensors and data acquisition equipment are able to perform large-scale data acquisition under complex flow conditions. This provides a broad prospect for using big data to drive intelligent algorithms, especially in realizing real-time detection and the adaptive control of noise. Large amounts of high-quality data can not only provide the basis for the construction of noise prediction models, but also provide critical support for noise source identification and localization. By applying machine learning and deep learning algorithms, researchers are able to extract key features of noise signals from historical data and further construct efficient noise prediction models to predict noise levels under different operating conditions in real time. * Research Direction:  
Efficient Data Processing and Analysis: research on how to efficiently process and analyze large-scale data to overcome challenges such as high dimensionality and noise interference. Improve the response speed and accuracy of noise monitoring systems by optimizing the data acquisition and processing processes. Real-time data analysis and adaptive control: explore how to combine real-time data analysis with adaptive control to significantly improve the effectiveness of noise management. In dynamically changing environments, this approach can quickly respond and adjust control strategies to promote real-time positioning and dynamic optimal control of noise sources. Data-driven noise prediction model: develop a noise prediction model based on big data, using machine learning and deep learning algorithms to extract key features from historical data and construct an efficient noise prediction model to predict noise levels under different operating conditions in real time. * Application Prospects:  
Through the combination of big data and intelligent algorithms, researchers will be able to significantly improve the response speed and accuracy of a noise monitoring system to achieve real-time noise detection and adaptive control. This will promote real-time positioning and optimal dynamic control of noise sources, thus enhancing the comprehensive effectiveness of hydrodynamic noise management and promoting the development of noise management technology. (2) Interpretability of intelligent algorithms  
Although intelligent algorithms perform well in hydrodynamic noise optimization, their 'black box' nature makes their decision-making process difficult to understand. Future research can focus on how to improve the interpretability of intelligent algorithms, so that researchers and engineers can better understand the internal working mechanism of the algorithms. This will not only help in the optimization and improvement of the algorithms, but also enhance the users' trust in the algorithm results and promote the application of intelligent algorithms in practical engineering. * Research Direction:  
Developing Interpretable Intelligent Algorithm Models: research on how to design and develop interpretable intelligent algorithm models that make the decision-makingprocess of the algorithms more transparent and easier to understand. This can be achieved by introducing interpretability constraints, optimizing algorithm structures, and adding interpretable components to the model. Application of interpretable techniques: explore how existing interpretable techniques, such as feature importance analysis, locally interpretable model-independent interpretation (LIME), and SHAP values, can be applied to intelligent algorithms in hydrodynamic noise optimization. These techniques can help researchers understand how algorithms use input data to make decisions. User-friendly visualization tools: develop user-friendly visualization tools to present the decision-making process and results of intelligent algorithms to the user in an intuitive way.