Optimisation research and analysis of aeronautical satellite navigation based on the background of big data algorithms

Big data is a data processing and application model based on cloud computing, with the ability to integrate and share data, cross reuse and form intellectual resources and knowledge services, etc. Big data has four characteristic quantities: massive (Volume), diverse (Variety), real (Value) and rapid (Velocity). With the traditional data mining using random analysis (sampling) is different, big data methods have not simply increase the amount of analytical data, but a systematic and structured analysis of massive data. Only through systematic analysis can we obtain enough intelligent, in-depth and valuable information. The basic process of analysing data using big data processing can be summarised in four steps, namely acquisition, pre-processing, statistics/analysis, and mining. Big data analysis of time series mainly involves five major aspects, namely visual analysis, data mining algorithm analysis, predictive analysis, semantic engine, data quality and data management. Among them, data mining algorithm analysis and predictive analytics are the main research focus of time series big data analytics, i.e., through the advantage of massive data, the use of algorithms to deeply analyse the attributes of the sequence, including the number, feature diversity, complexity, etc., and then according to the attribute characteristics of the sequence, mining analysis processing and other operations. Although big data is a newly emerging technology, its application in time series has achieved good results. The use of big data methods to analyse time series, on the one hand, solves the shortcomings brought by the traditional method of analysing only a small amount of data, on the other hand, big data technology goes deep into the data, and is able to dig out the useful information hidden behind the huge amount of data, so as to make the prediction and analysis more accurate and reliable.