Multi-Modal Sensor Network for Early Environmental Threat Detection
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Solution Overview
Problem
Current environmental monitors rely on limited sensors that fail to provide early threat detection and situational awareness, often only responding after a threat has occurred, such as fires starting with smoke or burglaries after a breach.
Innovation Solution
A network of multi-modality sensors, including LiDAR, multi-spectral, and acoustic sensors, connected to a central processor with artificial intelligence, capable of distinguishing between normal and abnormal events, providing early threat detection and communication with emergency services.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If limited traditional sensors are used for environmental monitoring, then device complexity is reduced, but threat detection precision and early warning capability deteriorate
Solution Approach 1:
The patent combines multiple different sensor types (acoustic sensors, multi-spectral sensors including LiDAR, visible light cameras, infrared sensors) into a unified monitoring network. This merging of diverse sensing modalities enables comprehensive threat detection with high precision by capturing environmental data from multiple wavelengths and measurement principles simultaneously, resolving the contradiction between detection precision and device complexity through integrated multi-modal sensing.
Solution Approach 2:
The monitoring system is designed with multi-functional sensors that can detect various threat types (fire, intruders, falls, gas leaks) using the same sensor array. The acoustic sensors detect both smoke inhalation events and intruder presence, while multi-spectral sensors identify both thermal anomalies from fires and chemical signatures from gas leaks. This universal sensing approach achieves high detection precision across multiple threat scenarios without proportionally increasing device complexity.
2Loss of information
If multiple multi-modality sensors are deployed for early threat detection, then situational awareness is enhanced, but device complexity increases
Solution Approach 1:
The patent segments the monitoring environment into distinct spectral and spatial zones, with different sensor types optimized for detecting specific threat modalities in different regions. Acoustic sensors are positioned for breath detection in respiratory zones, while multi-spectral sensors monitor thermal and chemical signatures in environmental zones. This segmentation of monitoring responsibilities across sensor types and spatial regions enhances situational awareness while managing network complexity through specialized, distributed sensing functions.
Solution Approach 2:
The system employs artificial intelligence algorithms as intermediary processing layers that integrate and correlate data from multiple sensor modalities. The AI intermediary synthesizes acoustic, optical, thermal, and chemical sensor inputs to create a unified situational awareness model, reducing the complexity burden of processing raw multi-modal sensor data while maximizing the information value for early threat detection and response.
3Speed
If traditional single-modality sensors are used, then ease of manufacture is improved, but response time to threats deteriorates
Solution Approach 1:
The patent implements preliminary detection capabilities by deploying multi-modality sensors that can identify threat precursors before full-scale events occur. Acoustic sensors detect early signs of smoke inhalation through breath analysis, multi-spectral sensors identify initial thermal anomalies from developing fires, and gas sensors detect early chemical signatures from potential leaks. This preliminary detection across multiple modalities accelerates threat response speed by providing early warnings, while the standardized sensor platforms maintain reasonable ease of manufacture and deployment.
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
Enhances threat prevention and detection by providing early warnings and situational awareness, differentiating between humans and animals, tracking motion, and integrating with smart home devices for comprehensive environmental monitoring.
Implementation Method 1
The networks also comprise: (b) the computing device that comprises programmable media, instructions, data storage, a video monitor, and an input keyboard
Implementation Method 2
A network of multi-modality sensors, including LiDAR, multi-spectral, and acoustic sensors
Implementation Method 3
A network of multi-modality sensors, including LiDAR, multi-spectral, and acoustic sensors
Data Source
AI summary
Environmental monitors, networks and methods of monitoring discrete environments are provided that include a plurality of sensors connected to a computer device that uses trained and trainable artificial intelligence, voice recognition, and sensor fusion capabilities to discern threats and dangers from non-threatening and non-dangerous occurrences in order to determine when to issue an alarm and other communications to users and others.