Multi-Sensor Pest Trap Detection Using Gas and Non-Gas Signals

The patent combines gas sensors (electronic noses) with non-gas sensors (acoustic, optical, mechanical sensors) into an integrated pest detection system. This merging allows the system to use multiple sensing modalities, where non-gas sensors can detect pest presence through alternative mechanisms (sound, light, movement), thereby maintaining high detection capability while reducing dependence on complex gas sensor arrays alone.

The trap system is designed with multi-functional capabilities: gas sensors detect VOCs, non-gas sensors detect acoustic and optical signals, and the same trap structure serves both as a capture device and a monitoring platform. This universality allows a single system to perform multiple detection functions, reducing the need for separate specialized devices and lowering overall system complexity.

The patent uses acoustic signals (sound waves) and optical signals as alternative 'copies' or proxies for chemical VOC detection. Instead of relying solely on complex electronic nose systems that require extensive training data and development, the system captures pest-related acoustic emissions and optical reflections, which can be processed with simpler algorithms and lower R&D investment while maintaining identification accuracy.

The system transitions from detecting chemical parameters (VOCs requiring complex spectrometry) to detecting physical parameters (acoustic frequency, optical intensity) that are easier and cheaper to measure. By changing the detection parameter from chemical composition to physical signals, the system achieves pest identification with reduced R&D costs and simpler manufacturing.

The patent segments the detection system into modular components: gas sensing module, acoustic sensing module, optical sensing module, and data processing module. Each module can be independently configured and calibrated for local conditions. This segmentation allows flexible adaptation to different regional pest species and environmental conditions without requiring complete system re-tuning, thereby maintaining reliability while reducing fine-tuning complexity.

The system employs dynamic adaptability where sensors and processing algorithms can adjust to local conditions in real-time. Acoustic and optical parameters can be dynamically calibrated based on environmental factors (temperature, humidity, background noise) without requiring complex pre-programming of VOC profiles for each location. This dynamic approach maintains detection reliability across different regions while simplifying the deployment process.