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location:Home>News>Industry dynamics>Hardware configuration of forest fire risk factor monitoring stations

Hardware configuration of forest fire risk factor monitoring stations

Article source:Weather station   time:2024-08-06 09:00:44    viewed:23times

Forest fire is a kind of fire that spreads rapidly in forests, which not only destroys trees, but also harms animals, damages soil, affects water sources, and even destroys the environmental balance, posing a threat to people's lives and property.


The Forest Fire Risk Factor Monitoring Station is a set of equipment specially designed to collect forest fire risk information. It consists of multiple sensors that can monitor the temperature, humidity and other key factors in the forest in real time and send the data to the center for analysis. Through intelligent analysis, the system can quickly identify fire risks and help us take measures in advance to prevent fires.


Hardware Configuration


Control unit: integrated with data collection and transmission functions.


Display: Equipped with a camera with intrusion detection, voice warning and automatic video recording functions.


Sensing Unit: Contains a variety of environmental monitoring sensors, soil detectors, etc.


Connectivity Options: Supports 4G, 5G, NB-IoT or Beidou satellite communication.


Power supply: DC 12V, AC 220V power supply options are provided, solar power supply solution is also supported.


The Forest Fire Monitoring Station is a complete surveillance system specifically designed to continuously monitor weather conditions in the forest. It utilizes 4G network technology to upload real-time collected weather data to a central monitoring system. The system is able to analyze this data to determine the level of fire risk and is able to transmit panoramic video of the scene, enabling monitors to observe forest conditions remotely. This feature not only helps to quickly identify possible sources of fire, but also provides important scientific data for the development of effective fire prevention measures.

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