Studies on Deep Reinforcement Learning-based Recognition Techniques and Models for Global Information Management

Studies on Deep Reinforcement Learning-based Recognition Techniques and Models for Global Information Management

연구 분야: Safety

학회: 2024 International Conference on Interactive Intelligent Systems and Techniques (IIST)

This research is dedicated to achieving real-time collection of global security information by adopting an innovative methodology for generating time-rich data through real-time recognition of sounds that may appear at any time. This technical solution not only provides an efficient means for global security monitoring, but also achieves real-time monitoring of a wide and diverse range of security events without relying on traditional data sources with the help of sound recognition technology. In the process of real-time collection of global security information, the sound recognition technology used gives full play to its superiority. Through real-time recognition of sounds that may occur at any time, the system is able to quickly capture potential security event clues, which not only strengthens the response speed to emergencies, but also effectively reduces the reliance on traditional data sources. This approach is remarkably practical and highly adaptable when dealing with global security issues. By incorporating voice recognition technology, the technical solution proposed in this study ushers in an innovative development in the field of global security monitoring. Not only does it enable timely identification of and response to a wide range of security events, but it also provides a more flexible and efficient method for realising the monitoring of a wide range of security issues. The results of this research provide a new technological path for real-time monitoring and response in the field of global security, which has important theoretical and practical value.