Wildfire Detection Using Heuristic Prediction Model
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Solution Overview
Problem
Existing wildfire detection systems based on image data and environmental data suffer from inaccuracies and inefficiencies, particularly in topographically challenging areas and windy conditions, leading to delayed detection and insufficient location precision.
Innovation Solution
A system utilizing a dense network of sensors that monitor environmental parameters, combining heuristic and machine learning algorithms to analyze data from multiple sensors, and a central unit for real-time fire prediction and localization, using a hybrid prediction model to enhance accuracy and efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If camera-based systems are used for wildfire detection, then detection coverage can be achieved, but detection speed is delayed due to smoke formation time
Solution Approach 1:
The system performs preliminary detection by monitoring environmental parameters (temperature, humidity, gas composition) before actual fire ignition occurs. This early warning capability allows the system to detect fire risk conditions before visible smoke or flames are present, thereby resolving the time delay inherent in camera-based systems that wait for smoke formation.
Solution Approach 2:
The patent replaces optical/mechanical detection systems (cameras) with sensor-based environmental monitoring systems. By substituting camera-based visual detection with electronic sensors that measure temperature, humidity, and gas composition, the system achieves faster detection response times without sacrificing coverage area.
2Measurement precision
If sensor networks are deployed to improve detection accuracy, then location precision is enhanced, but device complexity increases
Solution Approach 1:
The system divides the monitoring area into multiple zones with distributed sensor nodes. Each node independently monitors its local environment and transmits data to a central processing system. This segmentation allows precise location identification through triangulation of sensor readings while keeping individual node complexity low.
Solution Approach 2:
The sensor nodes are designed as universal, multi-functional units that can detect multiple parameters (temperature, humidity, gas composition) and perform multiple functions (local processing, wireless communication, power management). This universality reduces overall system complexity by using standardized components rather than specialized devices for each function.
3Reliability
If environmental parameters are monitored continuously to improve detection accuracy, then false alarms are reduced, but energy consumption increases
Solution Approach 1:
The system dynamically adjusts its monitoring strategy based on detected conditions. During periods of low fire risk, sensors operate at reduced sampling rates to conserve energy. When anomaly detection algorithms identify suspicious patterns or when environmental conditions approach critical thresholds, the system automatically increases monitoring frequency, thereby maintaining high detection accuracy while minimizing overall energy consumption.
Solution Approach 2:
The system changes operational parameters (sampling frequency, transmission power, processing intensity) based on detected environmental conditions and risk levels. This adaptive parameter adjustment allows the system to maintain high detection accuracy when needed while reducing energy consumption during normal conditions, resolving the contradiction between continuous monitoring and energy efficiency.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
Enables ultra-early wildfire detection with precise location and spread forecasting, reducing false alarms and improving firefighting operations through real-time data monitoring and intelligent power management.
Implementation Method 1
The heuristic prediction model is configured to calculate, for at least two detection devices, the maximum value of an alarm function, the alarm function taking as an input at least a concentration of volatile compounds
Data Source
Figure 1
Figure 2A~2B
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AI summary
The subject of the invention is a system and method for detecting and predicting fires, in particular wildfires. The system for detecting and predicting fires comprises a set of local detection devices (100) and a central unit (200), the central unit (200) being configured to receive and analyse environmental parameter measurements from the set of local detection devices (100). The system is characterized in that the central unit (200) is further configured to receive an alarm signal indicative of fire detection risk from at least one detection device (100), and to verify the fire detection risk by checking the presence of predefined number of detection devices (100) sending the alarm signal within a common analysis time window, and if the fire detection risk is confirmed then to predict the fire probability and fire localization with the use of an heuristic prediction model (201b).