Research on User Interaction Optimization System of Mobile Energy Storage Equipment Based on Natural Language Processing

This paper proposes a user interaction optimization system for mobile energy storage devices based on natural language processing. By combining intelligent speech recognition, intent recognition and sentiment analysis technology, the interaction efficiency and experience between users and devices are improved. Specifically, this paper designs an innovative algorithm framework that combines intent recognition in natural language processing with sentiment analysis technology. According to the user's voice input or text input, it accurately identifies the user's needs and emotional state, and dynamically adjusts the device's response strategy. Through real-time processing of user voice interaction and emotional changes, the system can provide personalized feedback to users in different situations, thereby improving user operation satisfaction. To verify the validity of the system, a series of experiments are carried out, and the experimental data are obtained from the real user interaction records. The experiment results indicate that the NLP interactive optimization system is superior in many respects: the user satisfaction is 18%, the interaction efficiency is 22%, and the system response time is 25% shorter than the traditional method. In particular, the system can accurately recognize the emotion fluctuation of the user, and adjust the interactive way automatically, so that the user can interact with the equipment more naturally. In addition, this paper also demonstrates the superiority of the proposed method in multiple scenarios by comparing it with the traditional command-based interaction method, proving the practical application value of NLP technology in smart devices.