AI Acoustic Sensor Nodes for Tunneling and Pest Detection
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Solution Overview
Problem
Existing technologies face challenges in detecting underground tunneling activities and pest infestations in wood-based structures, as these activities are often concealed and difficult to monitor effectively.
Innovation Solution
The implementation of sensor nodes attached to wood-based structures, equipped with microphones, temperature sensors, humidity sensors, and ohmmeters, which use artificial intelligence to analyze audio and vibration data to detect pest activity and underground tunneling.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If visual inspection methods are used to detect tunneling activities, then the detection system is simple, but the detection capability is insufficient because tunnels are concealed below ground and not visible
Solution Approach 1:
The patent replaces visual inspection (mechanical/optical system) with acoustic sensing (acoustic system). Microphones mounted on fence posts detect sounds transmitted through the ground from tunneling activities, enabling detection of concealed underground operations without requiring direct visual access.
Solution Approach 2:
The patent uses the ground and fence posts as intermediary elements to transmit acoustic signals from underground tunneling activities to the microphones. The ground acts as a medium that conducts vibrations from tunneling tools and human activity up to the surface-mounted sensors.
2Measurement precision
If multiple sensor types are deployed to detect both pest activity and tunneling, then the detection accuracy improves, but the device complexity and cost increase
Solution Approach 1:
The patent implements multi-functional sensor nodes that perform multiple detection tasks using the same hardware platform. The microphones detect both pest-related sounds (chewing, movement) and tunneling sounds (digging, tool use), while environmental sensors monitor conditions relevant to both pest activity and ground stability, allowing one system to serve multiple protective functions.
Solution Approach 2:
The patent combines previously separate detection systems (pest monitoring and tunnel detection) into a unified sensor network. By mounting microphones and environmental sensors on existing fence posts, the system merges structural monitoring with biological and intrusion detection functions.
3Loss of time
If continuous monitoring is implemented to detect underground activities in real-time, then the response time improves, but the energy consumption increases
Solution Approach 1:
The patent implements periodic sampling of acoustic data rather than continuous recording. The system captures audio at intervals and analyzes the data, allowing real-time detection capability while reducing processor load and energy consumption compared to continuous analysis of uninterrupted audio streams.
Solution Approach 2:
The system uses AI-based analysis to provide feedback on detected patterns, allowing the monitoring system to adjust its behavior. When unusual acoustic patterns are detected, the system can increase monitoring intensity; during normal conditions, it reduces activity to conserve energy while maintaining detection readiness.
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
This solution enables early detection of pest infestations and underground tunneling activities, allowing for timely intervention and reducing damage to wood-based structures.
Implementation Method 1
The sensor node records, at a first time, first audio data, using the microphone... determines a difference in decibels between a first volume of the first filtered audio and a second volume of the second filtered audio
Data Source
AI summary
In some examples, a sensor node is mounted to a structure and includes a microphone that is coupled to the structure. The sensor node records, at a first time, first audio data, using the microphone, filters the first audio data to create first filtered audio, records, at a second time that occurs after the first time, second audio data using the microphone, filters the second audio data to create second filtered audio, and determines a difference in decibels between a first volume of the first filtered audio and a second volume of the second filtered audio. If the sensor node determines that the difference in decibels is greater than a predetermined threshold, then the sensor node sends a notification of intrusion activity that includes the difference in decibels.