Utilizing IoT sensors and drone technology can significantly enhance the efficiency of automated carbon emission data collection. Below are the specific methods and steps:
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Application of IoT Sensors
- Real-Time Data Collection: IoT sensors can delve into carbon emission sources for real-time data collection, ensuring every emission point is closely monitored. For example, by installing IoT positioning and fuel consumption monitoring devices on vehicles, data on transport mileage and fuel consumption can be recorded to support the calculation of carbon emissions during transportation.
- Multi-Dimensional Monitoring: IoT sensors can be integrated into various devices, such as carbon sniffing boxes, OCR recognition terminals, and infrared smart terminals. These devices can quickly connect to existing enterprise systems, automatically collect carbon-related data, and upload it to carbon account integrated machines or IoT platforms through self-developed technology.
- Data Transmission and Processing: IoT devices transmit the collected data to data centers or cloud servers via wired or wireless methods. The servers analyze and process the data to monitor the carbon emissions of objects.
- Application of Drone Technology:
- High-Precision Flight and Autonomous Decision-Making: Drones offer higher flight precision and maneuverability, allowing them to hover and autonomously decide flight paths. For instance, the Low-Cost Drone Coordinated Carbon Observation Network (LUCCN) system combines ground monitoring stations and drones to collect data using solar-powered ground stations and quadcopters.
- Fine-Grained Data Collection: Drones equipped with greenhouse gas monitoring instruments collect real-time fine-grained, high-precision, and high-resolution carbon dioxide concentration data over parks. This method combines low-altitude photography with large-area monitoring to achieve comprehensive carbon measurement.
- Three-Dimensional Spatial Monitoring: Based on remote sensing, satellite positioning navigation, and drones, a three-dimensional carbon emission monitoring system uses satellite GPS for fully automated route planning, with precise control of flight paths, altitudes, and orientations.
- Data Integration and Analysis:
- Data Integration: Data collected by drones is integrated with data from ground monitoring points, forming distribution and trend charts of carbon emission data in three-dimensional space by region, airspace, and time domain. This helps address the technical challenges of monitoring carbon emissions at various heights up to 3000 meters.
- Big Data Analysis: By deeply mining real-time data using big data analysis techniques, it can help accurately grasp the patterns and trends of carbon emissions, providing solid data support for subsequent assessments and governance efforts.
- Comprehensive Application and Optimization:
- Multi-Technology Integration: Combining IoT sensor technology and drone technology enables comprehensive, all-time monitoring and analysis of carbon emissions. For instance, in key industries such as thermal power and steel, carbon emission monitoring can be conducted using non-dispersive infrared monitoring technology (NDIR) and cavity ring-down spectroscopy (CRDS).
- Intelligent Management: Utilizing IoT technology to implement a “cloud-management-edge-terminal” architecture effectively collects, processes, and analyzes environmental data, which is crucial for achieving carbon neutrality.
There are many cases of enhancing the efficiency of automated carbon emission data collection using IoT sensors and drone technology. Here are some specific examples:
- Low-Cost Drone Coordinated Carbon Observation Network (LUCCN): The Institute of Atmospheric Physics, Chinese Academy of Sciences, developed a system called LUCCN, which combines ground monitoring stations and drones to collect data using solar-powered ground stations and quadcopters. Drones have higher flight precision and maneuverability, allowing them to hover and autonomously decide flight paths. A three-day test was conducted at a power plant in Shenzhen, Guangdong, successfully measuring emissions from the plume released by the nuclear power plant.
- Drone Low-Altitude Photography Technology: Images are obtained through a drone low-altitude photography system, and convolutional neural networks are used to identify factors affecting carbon emissions in the images, such as road traffic flow, land use types, population data, meteorological conditions, and industrial areas. Carbon emission estimation models are constructed based on these factors to derive carbon dioxide concentration values and create spatiotemporal distribution maps of carbon dioxide concentration.
- IoT Sensor Network: Research shows that IoT sensor technology can delve into carbon emission sources for real-time data collection, ensuring every emission point is closely monitored. These real-time data, through deep mining with big data analysis techniques, can help accurately grasp the patterns and trends of carbon emissions.
- Industrial Park Carbon Emission Monitoring: Drones equipped with greenhouse gas monitoring instruments collect fine-grained, high-precision, and high-resolution carbon dioxide concentration data at different heights above industrial parks, combined with artificial intelligence algorithms to achieve high-precision carbon emission measurement.
- Regional Scale Drone CO2 Flux Observation: Tianjin Feiyan Drone Technology Co., Ltd. developed small drone carbon flux monitoring equipment and systems, conducting regional scale drone CO2 flux observation flights to effectively obtain observation data for carbon balance in regional terrestrial ecosystems.
