Industrial Computer-Driven Forest Fire Early Warning: A Deep Dive from Sensor Networks to Intelligent Decision-Making

Forest fires, as a global ecological disaster, pose severe threats to ecological conservation, climate regulation, and human life and property safety due to their sudden onset and destructive nature. Traditional forest fire prevention relies on manual patrols and isolated monitoring equipment, suffering from issues such as delayed response, coverage blind spots, and high false alarm rates. With the deep integration of the Internet of Things (IoT), artificial intelligence (AI), and communication technologies, intelligent smoke sensor networks powered by industrial computers are emerging as the core solution for forest fire early warning. This article provides an in-depth analysis of how to construct an efficient and reliable forest fire early warning system from the perspectives of technical architecture, sensor selection, network deployment, and real-world case studies.

1. Technical Architecture: The Central Role of Industrial Computers

The industrial computer serves as the "brain" of the forest fire early warning system, requiring multi-protocol compatibility, edge computing capabilities, and low-power design. Take the industrial-grade industrial computer USR-EG628 as an example. It features a quad-core ARM architecture processor with a clock speed of up to 2.0 GHz and integrates an AI neural network processor (NPU) with 1.0 TOPS computing power, enabling simultaneous operation of edge computing, PLC programming, and local configuration functions. The device supports multiple communication protocols, including 4G/5G, Ethernet, WiFi, and LoRa, and incorporates VPN tunnels and firewall functionality to ensure secure data transmission.

Within the system architecture, the industrial computer undertakes three core tasks:

• Data Acquisition and Protocol Conversion: It connects to temperature and humidity sensors, anemometers, infrared cameras, and smoke sensors via interfaces such as RS485 and CAN, supporting over 100 industrial protocols like Modbus and BACnet to enable unified access to multi-source heterogeneous data.
• Edge Intelligence Analysis: Based on built-in AI models, the system preprocesses data to filter out interference factors such as dust and fog, reducing false alarm rates. For instance, using a convolutional neural network (CNN) algorithm for real-time analysis of camera footage, it can identify smoke columns within a 5-meter diameter with an accuracy rate of 98%.
• Decision-Making and Linked Control: Combining historical fire data with meteorological information, the system dynamically adjusts warning thresholds. When elevated CO concentrations and abnormal temperature rises are detected, it automatically triggers audible and visual alarms, sends alerts to forest rangers' terminals via SMS and app notifications, and initiates drone patrols and firebomb deployment plans.

2. Sensor Selection: From Single Detection to Multimodal Fusion

Traditional smoke sensors (e.g., ionization and photoelectric types) suffer from insufficient sensitivity and susceptibility to environmental interference. In contrast, next-generation multimodal sensors significantly improve fire detection accuracy by integrating multiple detection technologies.

2.1 Aspirating Smoke Detection (ASD)

ASD employs active sampling technology, continuously drawing air samples from forested areas through air ducts and analyzing sub-micron smoke particles via laser scattering. With a sensitivity of 0.001% obs/m—1,000 times higher than traditional photoelectric sensors—it can detect smoldering fires 1–3 hours in advance. For example, while ASDs exhibit a false alarm rate below 0.1% in scenarios like ship engine rooms and telecom data centers, challenges such as duct blockages and animal damage must be addressed in forest environments.

2.2 Infrared Thermal Imagers

Infrared thermal imagers detect concealed fire sources by analyzing surface temperature distributions. The FLIR Vue TZ20 dual-spectrum thermal imager, for instance, simultaneously outputs visible light and thermal imaging footage, pinpointing temperature anomalies within a 5-kilometer range. Combined with deep learning algorithms, the system distinguishes wildlife activity from fire incidents, keeping false alarm rates below 5%.

2.3 Laser Gas Sensors

Laser gas sensors utilize tunable diode laser absorption spectroscopy (TDLAS) technology to monitor concentrations of combustion byproducts such as CO, CO₂, and methane in real time. The Sensirion SCD40 sensor, for example, boasts a response time of under 10 seconds and a detection limit as low as 0.1 ppm, making it suitable for early smoldering fire warnings.

2.4 Multisensor Fusion Solutions

In practical deployments, ASDs, thermal imagers, and gas sensors form a "sensing matrix," with data fusion algorithms enhancing reliability. For example, when an ASD detects smoke particles and a thermal imager confirms temperature anomalies, the system triggers a Level 1 alarm; if gas sensors simultaneously detect elevated CO levels, it escalates to a Level 2 alarm and activates firefighting protocols.

3. Network Deployment: From Device Interconnection to Full Coverage

Forest environments demand sensor networks that balance coverage, transmission stability, and power efficiency. Below are typical deployment strategies:

3.1 Hierarchical Architecture Design

• Terminal Layer: Smoke sensor clusters are deployed at critical forest nodes (e.g., road intersections, water sources, historical fire sites) with 500–1,000-meter spacing to form a foundational monitoring network.
• Transmission Layer: A "LoRa + 4G/5G" dual-link backup scheme is adopted. LoRa handles short-range (1–5 km) low-power data transmission, with USR-EG628 gateways aggregating data for 4G/5G cloud backhaul.
• Platform Layer: A GIS-based digital twin system provides real-time visualization of sensor status, fire locations, and spread trends, supporting drone patrols and firefighting resource allocation.

3.2 Key Equipment Selection

• Industrial Router USR-G816: Supporting dual-mode 5G/4G full Netcom, it features an IP65 rating and operates in temperatures ranging from -40°C to 75°C, adapting to extreme environments. Its built-in VPN tunnels and firewalls ensure secure data transmission.
• Solar Power Systems: Monocrystalline solar panels (200 W power) and lithium iron phosphate batteries (200 Ah capacity) sustain sensor operation for seven consecutive days.
• Mesh Networking Modules: In areas without public network coverage, Mesh self-organizing network devices enable data backhaul via multi-hop relay, with single-hop distances up to 3 km.

Case Study: A State-Owned Forest Farm Renovation Project

A 120-square-kilometer state-owned forest farm, predominantly mountainous, previously had 30% monitoring blind spots. The post-renovation deployment included:

• Sensor Layout: 150 ASD detectors, 30 thermal imagers, and 200 gas sensors were installed, prioritizing human activity zones and natural fire-prone areas.
• Network Optimization: A 4G private network was established using USR-EG628 controllers, achieving an average transmission delay below 200 ms and a packet loss rate below 0.5%.
• Performance Evaluation: Over six months, the system successfully warned of three fires, reducing average response time from 47 minutes to 8 minutes and cutting fire losses by 65%.

4. Intelligent Algorithms: From Rule-Based to Autonomous Decision-Making

Traditional warning systems rely on fixed thresholds, whereas AI algorithms dynamically adapt to environmental changes. Two typical applications are:

4.1 Time Series Forecasting Models

Using LSTM neural networks, the system analyzes historical temperature, humidity, wind speed, and fire data to predict 24-hour fire risk levels. For example, when relative humidity remains below 30% and wind speeds exceed Level 5 for three consecutive days, the model automatically escalates the warning level.

4.2 Computer Vision Recognition

The YOLOv8 object detection algorithm enables real-time analysis of camera footage to identify smoke columns, flames, and combustion traces. In a pilot project, the algorithm achieved 98% accuracy in detecting early-stage fires, improving patrol efficiency 20-fold compared to manual methods.

5. Challenges and Future Trends

Despite technological advancements, forest fire early warning systems face three key challenges:

• Power Efficiency: Sensors in remote areas rely on solar power, necessitating low-power designs (e.g., event-driven sampling).
• Data Security: Protecting against cyberattacks and data tampering requires blockchain technology for immutable monitoring data storage.
• Cross-System Collaboration: Breaking down data silos across forestry, meteorology, and emergency departments is essential for unified warning platforms.

Looking ahead, the integration of 6G communication, satellite IoT, and digital twin technologies will drive forest fire early warning toward "omnipresent sensing-intelligent decision-making-autonomous response" systems. For instance, low-Earth-orbit satellites could enable global second-level fire monitoring, while drone swarms equipped with firebombs could reach incident sites within 10 minutes, forming an "air-space-ground integrated" prevention and control network.

The fusion of industrial computers and multimodal sensors is redefining the boundaries of forest fire early warning. From hardware-level device interconnection to algorithm-level autonomous decision-making, technological iterations not only enhance warning efficiency but also construct a digital barrier for ecological protection. As costs decline, intelligent warning systems are poised to cover more small and medium-sized forest farms, offering a Chinese solution for global forest resource conservation.