Research on intelligent prediction and control algorithm design for embedded systems in nonlinear Internet of Things environment

Abstract

With the rapid development of IoT technology, the nonlinear IoT environment has increasing requirements for the intelligence and resource efficiency of embedded systems. Based on the current background of Internet of Things technology, this study focuses on the design and optimization strategy of intelligent prediction and control algorithms for embedded systems under resource constraints. The proposed algorithm innovatively integrates the long short-term memory network (LSTM) of deep learning and the proximal policy optimization algorithm (PPO) of reinforcement learning, LSTM processes long-term dependencies in IoT time series data, accurately captures the dynamic changes of the system, and the PPO helps the agent to optimize the control strategy in real time according to the environmental feedback. Through theoretical analysis and experimental verification, a comprehensive optimization framework combining computing efficiency improvement, storage optimization and energy management is proposed to achieve the best matching of algorithm performance and hardware resources. Experimental data shows that the optimized algorithm performs well in handling nonlinear data prediction tasks, reducing the average computation time by 37%, reducing storage requirements by more than 60%, and reducing energy consumption by 45%. Especially in the nonlinear temperature prediction experiment, the optimization algorithm maintains the same prediction accuracy as the original version, and the response speed is faster and the anti-interference ability is stronger in the face of data peaks, which fully demonstrates the advantages of the algorithm optimization. Compared with the existing methods, the new algorithm overcomes the difficulty of traditional algorithms in dealing with the nonlinear and high-dimensional problems of IoT data in terms of prediction accuracy, and greatly reduces the error. In terms of control performance, it breaks through the limitations of fixed strategies and maintains the efficient and stable operation of the system. It is also highly adaptable and robust. This research fills the gap in the design of intelligent algorithms for embedded systems in the nonlinear Internet of Things environment, and provides strong technical support for enterprises and research institutions to develop the next generation of Internet of Things applications.