System of environmental data collection and analytics for operational detection of extreme values using electroinduction sensors

by

S.S. Kolmogorova1,2

1St. Petersburg State Electrotechnical University “LETI” named after V.I. Ulyanov (Lenin),

 RF, St. Petersburg, Prof. Popov St., 5, literature F

2St. Petersburg State Forest Engineering University named after S.M. Kirov,

 RF, St. Petersburg, Institutskiy per. 5, literature U

 E-mail: ss.kolmogorova@mail.ru

DOI: 10.33075/2220-5861-2025-3-75-83

UDC  621.317.328                              

EDN: https://elibrary.ru/vgmhcw

Abstract:

The research work is concerned with the development of a system for environmental data collection and analytics for operational detection of extreme values using electroinduction sensors. Integration with cloud platforms and third-party meteorological services extends the functionality of the system and increases the accuracy of analysis. Microservice architecture provides high fault tolerance and scalability, which is critical for modern environmental monitoring systems. Application of Apache Kafka for real-time processing of data streams allows timely response to changes in the ecosystem. Networked electromagnetic sensor systems integrated with machine learning algorithms efficiently collect and analyze environmental data, detecting climate anomalies in real time. Special attention is given to the robustness of electroinductive sensors and their ability to operate under extreme conditions. The paper describes the architecture of hardware and software subsystems, methods of processing and classification of big data, as well as the possibilities of visualization and monitoring of the results. The presented system contributes to the efficiency of environmental monitoring and provides timely detection and response to critical changes in the environment, which is especially relevant in the context of growing environmental service.

Keywords: electroinduction sensors, electrometric measurements, monitoring system, data processing, extreme values, data collection

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