Intelligent analysis and processing large heterogeneous data for parrying threats in complex distributed systems

The paper proposes a method of intelligent analysis and processing of large heterogeneous data for predicting threats in complex distributed systems. The method is based on the results of automatic monitoring of changes in water level in water bodies and air temperature at the measurement point. Such monitoring makes it possible to increase the efficiency of planning and implementing measures to fend off such and similar threats. The method is based on general approaches and mathematical models previously used by the authors to develop adaptive algorithms for controlling gas turbine engines. It is particularly relevant in the context of the increasingly widespread introduction of software and hardware systems for monitoring the state of complex distributed systems and the exponential growth in the number of data used to support decision-making. The choice of the future value of the water level at the measurement point is based on the results of processing the data accumulated over all previous flood periods on the compliance of the water level and its changes per day with the values of air temperature and its changes over the same day. The analyzed data are the values of air temperature and water level measured at equidistant points in time, computational values of changes in the water level and air temperature, as well as forecast values (according to the official data of the hydrometeorological service) of changes in air temperature. Based on the calculation of the retrospective frequency of changes in this temperature and the water level at the corresponding point, it is proposed to choose as the predicted the value that corresponds to the maximum frequency of occurrence of such a combination of measured parameters. The paper presents the results of an experimental assessment of the accuracy of forecasting the water level in the water bodies of the Republic of Bashkortostan in the flood period of 2021 are. They confirm the applicability of the proposed forecasting method to support decision making to fend off threats in complex distributed systems from a sharp rise in water, even with the current insufficiently automated observation system. With a wider change in highly automated software and hardware com-plexes for monitoring the flood situation, the amount of data analyzed and processed by software significantly increases, which will complicate the application of traditional methods of data use, and, on the other hand, will increase the efficiency and relevance of the method proposed in this paper.