KEYWORDS: Computer architecture, Software development, Space operations, Data processing, Data communications, Operating systems, System integration, Real-time computing, Embedded systems
According to the main problems of spacecraft embedded software design, the universal real-time architecture of spacecraft embedded software without operating system is proposed, and the data flow, control flow, time-sequence design techniques and methods under this architecture are given. The architecture aims at layering and decoupling, and integrates object-oriented and process-oriented programming ideas with task description word as the starting point. The main technical features are : the task modules run independently according to their respective task description word information, and do not affect each other ; task module controlled by the state machine, step by step execution, efficient operation ; inter-layer communication using queues, messages, notifications and other ways, data flow fluent. Finally, an embedded software design example of the universal real-time architecture is given. Compared with the traditional software development model, it has significant improvements in reducing software complexity, improving development efficiency and saving costs.
Aerospace equipment is developing in the direction of high speed, accuracy, stability and long life. However, the reliability and life expectancy of some equipment systems are still far from the world advanced level. The main reason is that the data volume is not complete enough, and the data format is single. In order to ensure the healthy operation of these equipment systems, it is necessary to use a large amount of data to diagnose and predict faults. Therefore, the research from simple data collection to the whole process analysis of big data is of great significance for equipment life assessment and reliability analysis. Based on the analysis of big data, this paper proposes a processing method for equipment fault diagnosis and prediction, which is carried out from the aspects of aerospace big data characteristics, data acquisition, data storage, algorithm model and prediction, from complex equipment operation. The fault information is discovered and analyzed to ensure the stable and safe operation of the entire system. Finally, we use the cloud service platform method to simulate the life of the equipment. Compared with the traditional method, the long-term memory network model has been added to predict the full life cycle of the equipment by more than 10%.
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