Real-time task scheduling and network device security for complex embedded systems based on deep learning networks

As a hotspot of machine learning research, deep learning is applied in many fields. Embedded systems are becoming more and more complex and networked, so the real-time performance of embedded systems and the security of network embedded devices face severe challenges. Based on this, this paper studies the real-time task scheduling problem for complex embedded systems and the security of embedded network devices. For real-time, this paper proposes a comprehensive task scheduling algorithm. Based on the task classification in the embedded system, different scheduling methods are adopted for different tasks, and the scheduling mode is flexibly changed as the system load changes. A dynamic integrity measurement model is established based on the star trust chain structure, and the hardware implementation mechanism of constructing dynamic trust chain in embedded system is studied. The dynamic reconfigurable hardware design method based on FPGA is applied to the construction of dynamic trust chain, and a verification system is designed to verify the dynamic measurement mechanism. This can solve the security problem of deep network embedded devices to a certain extent.