The balance of experimental resources needs the help of distributed resource scheduling algorithm to integrate the discrete resources logically, and plan the scheduling and application uniformly, so as to ameliorate the utilization efficiency of resources, which provides a broad space for the application of Docker tech. Based on this, this paper first analyses the concept and connotation of Docker container platform, then studies the construction of laboratory cloud platform based on Docker platform, and finally gives the utilization effect of laboratory management cloud platform based on Docker tech.

No abstract

With the rapid development of modern technology, due to the light-weight, small size, and good concealment, unmanned aerial vehicle (UAV) has received much attention from the society and has been vigorously promoted. Photoelectric tracking detection system is an important means in the field of modern detection. The combination of a UAV and photoelectric detection system can effectively play an important role in reconnaissance and exploration, target positioning, communication, and navigation. At present, the relevant personnel have higher and higher requirements for the accuracy of the photoelectric detection function of UAV, and the original POS data relied on by the existing photoelectric detection devices of UAV has systematic errors, which leads to the low accuracy of photoelectric detection control. Therefore, in order to achieve the purpose of improving the high-precision control of the photoelectric detection device of UAV, this paper designed an optimization method for the high-precision control device of photoelectric detection of UAV based on POS data. Firstly, the improved PID control algorithm is applied to the optimization of the UAV control device, and secondly the error correction model is established by analyzing the error source, and the original POS data is corrected by the model. This paper used the designed high-precision control device optimization algorithm and the traditional algorithm to compare the stability control experiments of the UAV platform, respectively. The experimental results showed that the application of the improved UAV photoelectric detection control device optimization method could effectively improve the control device optimization accuracy of UAV photoelectric detection by 8.23%, which was conducive to the efficient implementation of the project.