LoRa Wireless Temperature and Humidity Monitoring System
In today's fast-developing society, effective sensor production, item management, and warehouse storage have become increasingly critical. Many warehouses store essential substances such as tobacco, textiles, pharmaceutical materials, and food items. Industries like cold chain transportation, pharmaceuticals, laboratories, agriculture, forestry, animal husbandry, and food cold storage require precise temperature and humidity monitoring systems. As technology continues to advance, warehouse management will be further optimized. With the 21st-century prevalence of electronic computers, temperature and humidity monitoring has transitioned into the realm of automation. Presently, communication technologies like RS485, ZigBee, LoRa, and LoRaWAN are widely used in temperature and humidity monitoring systems.
Traditional temperature and humidity monitoring systems face challenges like cable laying, exposed cables, lengthy communication lines, and power-consuming networking. These issues lead to weak anti-interference abilities and developmental difficulties, making them inadequate for meeting various industries' environmental temperature and humidity needs. However, LoRa technology offers significant advantages over other wireless methods, covering longer distances with lower power consumption. It achieves a perfect balance between low power usage and long-range communication, making it an ideal choice for temperature and humidity monitoring.
The temperature and humidity monitoring and early warning system deploy HKT LoRa collectors and LoRa wireless gateways for complete coverage in the monitoring area. Additionally, HKT temperature and humidity sensors are installed at each data acquisition node in the storage area, enabling real-time data monitoring. The collected temperature and humidity data is transmitted to the cloud platform for analysis and optimization. The system generates accurate reports and provides valuable insights through the WEB service system.
1. Real-time Data Collection:
Collects data from each monitoring point and transmits it to the cloud platform for analysis using big data.
2. Enhanced Early Warning and Intelligent Analysis:
Real-time data is compared to set threshold values, enabling early warning to eliminate potential safety hazards promptly.
3. Historical Data Statistics:
Records historical data of each monitoring point in separate files, enabling quick access to historical curve points. Statistical classification and storage ensure smooth data browsing, without the need for recalculation during annual report viewing, improving display accuracy and reliability.