Abstract
The article presents a study devoted to improving the developed Internet of Things system based on open APIs and geolocation, which is aimed at analyzing data about the state of the environment using an expert approach and data visualization for possible prevention of human health disorders. Based on the developed Internet of Things system, open APIs, geolocation using intelligent gadgets, and the Meteorological Geographic Information System, the study generates a message about the danger to human health associated with meteorological factors. Accordingly, a person is informed promptly about potential risks and threats, particularly about the presence of pollen in the air, indicating the level of its concentration in the air, and about problems with air quality. What is the “anthropo-geo-sensory-digital” prerequisite for making effective real-time decisions to prevent human health disorders? New features were added to the developed system to analyze data about potential risks and threats that could lead to human health disorders, in particular, about the presence of temperature problems, under the condition that this indicator goes beyond the normative and optimal zone; the presence of relative humidity problems, under the condition that this indicator go beyond the normative and optimal zone; the presence of wind speed problems, if the air wind speed exceeds the permissible standards. Effective decision-making based on providing timely information about potential risks and threats to human health, in addition to preventive, has significant methodological and technological potential that can be used to improve the effectiveness of health care, both in extreme conditions and in conditions of sustainable existence. The system developed and improved by us can also be considered as one of the ways of introducing innovations in health care, the IT field, the educational process in institutions of higher education and conducting further research in this field, in particular, in the direction of data processing in health care systems based on machine learning.
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