Embedded System Based on Task Interval Data Interaction Model

Abstract

In the Internet of Things era, more intelligent systems can communicate with each other. Embedded system combined with network communication applications has become the basis for Internet of Things research. The programmable logic unit designed by ARM architecture has great advantages in running speed, power control and so on. In the paper, to solve the problems of common embedded MCU control resources occupying a large amount of memory and the slow speed of building engineering simulation model, it is necessary to use the queuing connection algorithm model to directly input the timing physical characteristics of the code stream in the embedded system to calculate and match the timing physical characteristics of the output code stream. The optimization algorithm of DTF-MARTE to detect the probability of timing deviation is used in the paper. It is to detect the problem of inaccurate timing information in the demand. We compare the expected physical characteristics of the timing sequence and obtain the timing deviation probability of the output data stream. The model developed in this paper has the characteristic of dynamic reconfiguration of the task interval. The design of monotonically decreasing data tasks can be realized, and the reconfigured task modules are used for interacting the data buffer area and dynamically reconstructing the instruction overhead and transmission. We analyze the performance comparison between the proposed model and the traditional communication connection model. It proves that the proposed model can further improve the priority queue and guide the data flow. According to that method, the problem of asynchronous spatial data interaction by controlling and combining different communication modes in a large scene can be solved. Data interaction can be triggered at a fixed time, and mutual interference of randomly triggered wireless communication and data acquisition modules can be avoided. It can solve the problem of insufficient computing power when future embedded devices need massive data encryption in the Internet of Things era, and provide a new way of thinking for fast, safe and efficient implementation.