## 5 Discussion and Conclusions
### Technical Bottlenecks in Real-Time Applications  
In the domain of real-time hydrodynamic noise monitoring and control, the computational complexity of algorithms and resource requirements have historically posed significant challenges in implementing real-time feedback systems. Real-time applications necessitate algorithms that maintain a high response speed and computational efficiency in dynamic and changing environments. The dynamic nature of the noise signal itself contributes to the algorithmic complexity. This, in turn, poses a significant challenge to the efficacy of conventional algorithms in meeting the demands of real-time processing and feedback within high-speed, dynamic fluid environments. Consequently, researchers have begun to prioritize the development of lightweight algorithms and the optimization of existing algorithms. This is aimed at enhancing the adaptability and response speed of systems operating in dynamic environments. In his analysis, [PERSON] [19] examined the impact of reducing the population size (i.e., search agents) on the performance and efficiency of algorithms. He proposed that, in the design of intelligent algorithms, it is essential to prioritize not only the accuracy of the algorithms but also their computational cost in real-time environments. The author contended that contemporary research frequently disregards the equilibrium between the computational demands of an algorithm and its precision. Consequently, future research endeavors should explore intelligent algorithms that can strike a balance between computational cost and accuracy. This research direction is of critical importance for real-time hydrostatic noise monitoring and control. Real-time feedback systems must process a substantial volume of sensor data and make optimized decisions in a brief timeframe to effectively suppress and manage noise.  
In order to operate efficiently in real-time control systems, many researchers have worked on developing lightweight and fast-response model structures [4]. These structures are typically adapted to the demands of efficient real-time computation in environments with limited resources by simplifying the complexity of the model and reducing the consumption of computational resources. For instance, simplified neural networks or decision tree algorithms are frequently employed in these contexts to guarantee that the model maintains high efficiency despite constrained computational resources by diminishing superfluous computational processes. This lightweight structure not only enhances the algorithm's response speed but also substantially reduces the computational cost in practical applications. Furthermore, the integration of machine learning algorithms with feedback control endows a system with augmented adaptive capabilities. Through real-time monitoring of noise signals and environmental changes, the system can automatically adjust the control parameters to ensure that the best noise control effect can still be achieved under complex and changing working conditions. This real-time adjustment capability enhances a system's responsiveness and effectiveness, ensuring its effective operation in dynamic environments.  
Real-time algorithms in hydrodynamic noise monitoring and control require further optimization to achieve a balance between high accuracy and fast response under conditions that limit available resources. Continued research in this area will promote the advancement of the application of intelligent algorithms in hydrodynamic noise control and provide more efficient and economical solutions for practical applications. The continuous advancement of intelligent algorithms and computing technology will enable hydrodynamic noise monitoring and control systems to exhibit enhanced adaptability and control efficacy in increasingly complex and dynamic environments. Consequently,these systems will provide more reliable technical support for the optimal management of hydrodynamic noise.