Аннотации:
© 2020 IEEE. Currently, when solving a number of geophysical and cartographic tasks, one uses corporate graphic stations (CGS) that have particular software packages and digital databases. CGS are used due to the presence of licensed software and authors developments whose installation on several personal computers is not economically and strategically viable. At the same time, CGS may represent a limited access server. Obviously, widening of CGS functions leads to the rise in the number of users. Correspondingly, the increase in the number of CGS users leads to the worsening of software resources usage. To optimize the work, it is necessary to investigate the traffic dynamics (TD) for CGS. The traffic dynamics analysis may be performed using robust methods. For this purpose, one constructs mathematical models of TD for CGS. The aim of this paper is to analyze TD for CGS using the adaptive regression modeling and to find efficient prediction parameters for CGS work. To solve this task, we used adaptive regression multi-parameter (ARMP) modeling. Within ARMP approach, several multi-parameter iterations for assessing the data on time series (DTS) of CGS activity are performed. During the iterations, one finds the most efficient structure DTS, determines the efficiency of adapting the observed values to model ones (?), and assesses the prediction parameters (?). At harmonic analysis of DTS, 2 main harmonics with periods of 1 day and 6 months were selected. At 1-day period, CGS workload gradient starts increasing at 8 a.m. and achieves maximum at noon decreasing by 10 p.m. The study of the main and other harmonic terms when analyzing DTS will allow increasing the efficiency of using CGS and developing a progressive system of TD.
