KEYWORDS: Data integration, Data acquisition, System integration, Data storage, Data processing, Human-machine interfaces, Aerospace engineering, Data modeling, Data analysis
With the rapid development of modern aerospace technology, traditional aerospace experiment data integration methods have problems such as low automation, poor adaptability, and time-consuming for the increasing application requirements. Aiming at the shortcomings of traditional integration methods in terms of time performance, this paper conducts a comprehensive analysis and locates the crux, analyzes data integration needs, and proposes a multivariate data integration method based on ETL, uses concurrent multivariate data collection technology, unstructured data preprocessing technology and standardized data loading technology, carries out the architecture design of the data integration system, and verifies the practical effect of the data integration method. Engineering practice shows that this method can greatly improve the performance of system data integration and greatly improve the efficiency of experiment data processing.
Aiming at the high importance of software quality work in aerospace field and the requirement of high reliability of software products, we study the software defect prediction system based on deep learning; introduce the process of prediction model generation in the system from three aspects: metric selection, prediction model and evaluation indicator. The main points of design of the software defect prediction system based on TensorFlow Serving and Docker container technology are introduced from three aspects: system requirements, architecture design and model deployment. The software prediction system can be used to discover potential defects in software, improve software development and testing efficiency, and enhance the quality of aerospace software products.
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