Termite trapping and monitoring device and termite nest detecting and trapping method

Abstract

The invention relates to a termite trapping monitoring device and a termite nest detection trapping method, the monitoring device comprises a shell, a trapping killing module, a multi-sensor detection module, an image verification module, a main control module and a communication module, the multi-sensor detection module comprises a surface wave sensor and a voiceprint recognition sensor; the image verification module comprises a macro camera and a plurality of light supplementing units surrounding the macro camera. The monitoring method comprises the following steps: the surface wave sensor transmits Rayleigh waves, receives reflected signals, calculates and compares strain characteristics with a preset normal soil body model; the measured value is compared with a preset normal soil body baseline model, whether the area is a suspicious nest area or not is judged, a voiceprint recognition sensor is started, 30-second vibration signals are collected, features are extracted, and the confidence coefficient is calculated; and if the voiceprint confidence is greater than 90% and the surface wave is abnormal and continuous, determining that the nest is a termite nest. According to the device and the method, termite nest positioning, activity monitoring and intelligent prevention and control can be realized.

Description

Technical Field

[0001] This invention belongs to the technical field of pest control, and specifically relates to an intelligent termite trapping and monitoring device based on multi-sensor fusion. Background Technology

[0002] Existing termite control devices are usually independent of each other and have their own limitations. Some devices focus on physical trapping, which can attract termites with bait, but lack effective real-time sensing and early warning capabilities. They still rely on regular manual inspections, which is inefficient and can easily interfere with termite activity.

[0003] Another type of electronic monitoring device relies on a single detection mechanism, such as judging the on / off state based on bite signals or triggering thresholds based on environmental changes. This type of method is easily affected by external environmental interference, has a high false alarm rate, and cannot provide intuitive visual evidence in the early stages of termite infestation, resulting in a lack of accurate basis for prevention and control decisions and making it difficult to achieve early and precise intervention. Summary of the Invention

[0004] This invention provides an intelligent termite trapping and monitoring device based on multi-sensor fusion to solve the problem of low efficiency in termite control devices.

[0005] To solve the above-mentioned technical problems, the technical solution adopted by the present invention is as follows: On the one hand, this application provides a termite trapping and monitoring device, comprising: The casing has an internal accommodating space. The trapping and killing module is set within the containment space, including a fixing slot and a dosing module set at the bottom of the containment space; The multi-sensor detection module includes a surface wave sensor for detecting changes in the physical properties of soil, and an acoustic signature sensor for collecting vibration signals generated by termite activity. The image verification module includes a macro camera and multiple supplementary lighting units surrounding the macro camera; The main control module is electrically connected to the multi-sensor detection module and the image verification module. It is used to process surface wave and acoustic signature signals in real time, and activates the image verification module after the surface wave sensor and acoustic signature recognition sensor jointly determine that the signal has passed. The communication module is used to package and upload alarm information, sensor data, and images to the cloud platform.

[0006] Furthermore, the surface wave sensor is specifically a MEMS differential surface acoustic wave strain sensor, using Y+34° tangential quartz as the substrate; The voiceprint recognition sensor is specifically a high-sensitivity MEMS accelerometer.

[0007] On the other hand, this application provides a method for detecting and trapping termite nests, utilizing the aforementioned intelligent termite trapping and monitoring device based on multi-sensor fusion, comprising the following steps: Step 1: After deployment, the device enters sleep mode and is woken up at intervals. Step 2: After wake-up, the surface wave sensor emits Rayleigh waves in frequency sweep mode, receives the reflected signals, and calculates... , And strain characteristics, compared with the preset normal soil model; Step 3: Compare the measured values ​​with the preset normal soil baseline model. If the surface wave characteristics deviate from the baseline by more than 5%, it is determined to be a suspicious nesting area. Activate the voiceprint recognition sensor, collect 30 seconds of vibration signal, extract features and calculate confidence level. Step 4: If the voiceprint confidence score is >90% and the surface wave anomaly persists, it is determined to be a termite nest. The image verification module is activated to capture the image. The image data, sensor data, timestamp, and voiceprint confidence score are packaged and uploaded through the communication module. Step 5: The user remotely triggers drug administration or arranges on-site treatment. After treatment, the device automatically updates the baseline data and returns to sleep mode.

[0008] Furthermore, in step two, Rayleigh wave speed With attenuation coefficient Detectable changes have occurred: ; ; in, For soil density, Poisson's ratio, Shear modulus Rayleigh complex wavenumber, and Represent the real part and the imaginary part, respectively; By comparing the measured values ​​with the baseline values ​​of normal soil, the area is identified as a termite nest area if the following conditions are met: ;and ; In the formula, The measured wave velocity of the Rayleigh wave. The Rayleigh baseline wave velocity for normal soil. The baseline value is the reciprocal of the Rayleigh wave quality factor for normal soil. This is the measured value of the reciprocal of the Rayleigh wave quality factor.

[0009] Furthermore, in step two, wave speed With attenuation coefficient The measurements are based on the following governing equations and boundary conditions: Mass balance equation: ; Among them, For each phase, For each phase displacement vector component, the subscript is... These represent the solid phase, liquid phase, and gas phase, respectively. Momentum balance equation: ; in , Let Lame constant be the soil mass. For Biot coefficient, Liquid phase saturation, , These are pore pressure and gas pressure; Rayleigh wave dispersion equation: ; in Rayleigh complex wavenumber, Angular frequency, Porosity Let be the bulk modulus of each phase.

[0010] Furthermore, in step three, after the voiceprint recognition sensor is activated to collect signals, the collected signals are processed according to the following procedure: Signal normalization: The amplitude of the acquired raw acoustic vibration signal is adjusted to bring it into normal range. The scope of standardization; Background noise filtering: Setting an amplitude threshold for the normalized signal Perform filtering; Voiceprint feature extraction and model matching: The continuous voiceprint signal is segmented into short time frames of fixed length with a 50% overlap rate to capture the quasi-stationary characteristics of the signal; key features are extracted in the time and frequency domains.

[0011] Furthermore, in step three, during the "signal normalization" process, the calculation formula is as follows: ; in, These are the original voiceprint signal sample values. and These are the maximum and minimum values ​​within the signal segment, respectively.

[0012] Furthermore, in step three, "extracting key features in the time domain" specifically includes: Calculate the time-domain energy of each frame of signal A high energy value indicates the presence of strong vibrations; calculate the temporal entropy of each frame of the signal. A high entropy value reflects the irregularity of the vibration signal, and the calculation formula is as follows: ; ; in, This is the normalized voiceprint signal of the m-th frame. For frame size, This is the ratio of subframe energy to total energy. Furthermore, in step three, "extracting key features in the frequency domain" specifically includes: Perform a Fast Fourier Transform on each frame of the signal to convert it to the frequency domain. And calculate the spectral centroid With spectral entropy Features such as the location of the centroid of the frequency spectrum can distinguish termite vibration from environmental noise, and the entropy value can reflect the uniformity of the frequency distribution. The calculation formula is as follows: ; ; in, For frequency points, Let k be the index of the k-th frequency point. For the first Energy percentage of each frequency band Let m be the energy or amplitude of the m-th signal at the k-th frequency point. For the first f The energy percentage of each frequency band.

[0013] Furthermore, in step three, in rainy or high-humidity environments, the surface wave data employs a humidity-wave velocity joint correction model: ; in, The corrected wave velocity, , For correction factor, For real-time saturation, For temperature, , This serves as the calibration reference value.

[0014] The present invention can achieve the following beneficial effects: 1. The termite trapping and monitoring device of this application is equipped with a shell, a trapping and killing module, a multi-sensor detection module, an image verification module, a main control module, a communication module and a power supply module. It adopts a multi-level triggering mechanism of surface wave detection, voiceprint recognition and image verification and a low-power sleep design to achieve all-weather unattended monitoring, effectively reduce false alarms caused by environmental interference, eliminate the need for frequent on-site inspections, greatly improve monitoring efficiency and reduce labor costs, and is suitable for termite nest location, activity monitoring and intelligent prevention and control.

[0015] 2. This application uses a multi-sensor detection module to collaboratively acquire soil characteristic data and termite vibration signals, and combines them with an image verification module to provide intuitive visual evidence, providing an accurate basis for prevention and control decisions, enabling early and precise intervention in termite activity, and improving the targeting and effectiveness of termite control.

[0016] 3. The baiting and killing module of this application adopts a replaceable bait design, the power module supports solar-assisted charging and wide-temperature operation, the device shell has a sealed protection and self-draining structure, and is equipped with an environmental parameter compensation algorithm, which can adapt to a variety of complex outdoor scenarios such as dams, building foundations, and landscaping, ensuring long-term stable operation of the device and convenient maintenance. Attached Figure Description

[0017] The present invention will be further described below with reference to the accompanying drawings and embodiments: Figure 1 This is an exploded view of the overall structure of a termite trapping and monitoring device according to the present invention; Figure 2 This is a flowchart of the working method of the present invention; Figure 3 This is an example screenshot of the alarm information received by the device of the present invention on the cloud platform.

[0018] The attached diagram lists the components represented by each number as follows: 1. Shell; 11. Termite intrusion channel; 2. Trapping and extermination module; 3. Surface wave sensor; 4. Voiceprint recognition sensor; 5. Image verification module; 6. Main control module; 7. Communication module; 8. Power supply module. Detailed Implementation

[0019] To facilitate understanding of this application, a more complete description will be provided below with reference to the accompanying drawings, which illustrate embodiments of the present application. However, the present application can be implemented in many different forms and is not limited to the embodiments described herein. Rather, these embodiments are provided so that the disclosure of this application will be thorough and complete.

[0020] A termite trapping and monitoring device, referring to Figure 1It includes a shell 1, a trapping and killing module 2, a multi-sensor detection module, an image verification module 5, a main control module 6, a communication module 7, and a power supply module 8.

[0021] The shell 1 is a cylindrical sealed structure with an internal accommodating space containing a termite intrusion channel 11. The attracting and killing module 2 is located inside the accommodating space and includes a fixing slot and a dosing module located at the bottom of the accommodating space. The fixing slot is filled with pine bait to attract termites. The pine bait can be replaced with other types of wood bait.

[0022] The multi-sensor detection module includes a surface wave sensor 3 and an acoustic signature sensor 4. The surface wave sensor 3 is specifically a MEMS differential acoustic surface wave strain sensor, based on Rayleigh wave propagation theory. It emits frequency-tunable surface acoustic waves into the soil and receives the reflected signals. By analyzing wave velocity, attenuation coefficient, strain sensitivity, and dispersion curves, it determines whether the soil is a termite nest area. The acoustic signature sensor 4 is specifically a high-sensitivity MEMS accelerometer with a frequency response range of 1kHz to 15kHz, used to collect vibration signals generated by termite activities such as gnawing, crawling, and chewing inside the device.

[0023] The image verification module 5 includes a macro camera and multiple infrared / white LED supplementary lighting units arranged around it. The macro camera's field of view is aligned with the trapping and killing module 2. The main control module 6 uses a low-power microcontroller and is electrically connected to the multi-sensor detection module and the image verification module 5. It is used to process surface wave and acoustic signature signals in real time, and activates the image verification module 5 after the surface wave sensor 3 and the acoustic signature recognition sensor 4 jointly determine that the signal has passed. Specifically, the main control module 6 can be an ultra-low-power microcontroller based on the STM32 series.

[0024] Communication module 7 is an NB-IoT / 4G / 5G IoT transmission module, supporting low-power wide-area network communication. It is used to package and upload alarm information, sensor data, and images to the cloud platform. Communication module 7 supports both timed reporting and event-triggered modes. Data includes device ID, timestamp, surface wave feature vector, acoustic signature confidence, image compression data, and GPS location information. Power module 8 includes a lithium iron phosphate battery and a solar charging interface, supporting long-term field deployment.

[0025] In practice, the device is woken up every 12 hours. After waking up, the main control module 6 reads the data from the surface wave sensor 3 and compares it with the normal soil baseline to see if the deviation is greater than 5%. If the deviation is found, the environment is determined to be abnormal, and the voiceprint recognition sensor 4 is activated. The voiceprint recognition sensor 4 collects 30 seconds of vibration data. After FFT transformation and algorithm analysis, if the confidence level exceeds 90%, it is determined to be termite activity.

[0026] The main control module 6 then activates the image verification module 5, turns on the LED supplementary lighting unit, and captures three photos using a macro camera. The main control module 6 packages the photos, device ID, timestamp, temperature and humidity data, and voiceprint confidence score. It then initiates the NB-IoT / 4G / 5G IoT communication module 7, sending the data packet to the cloud platform. After parsing the data on the cloud platform, an advanced alert containing an image is pushed to the maintenance personnel's mobile app. Maintenance personnel can then choose whether to apply medication based on the actual situation.

[0027] To ensure reliable operation of the device under special climatic conditions such as rain, high humidity, and low temperature: the surface wave sensor 3 adopts a temperature and humidity compensation algorithm to correct wave velocity drift caused by changes in water content; the housing 1 has an IP67 protection rating, and the sensor probe is sealed with stainless steel; the power module 8 includes a high-capacity lithium iron phosphate battery and can be equipped with a solar panel for auxiliary charging, supports wide temperature range of -20℃ to +60℃, and has a self-draining structure for rainwater.

[0028] A method for detecting and trapping termite nests, referring to Figure 2 and Figure 3 The above-mentioned termite trapping and monitoring device includes the following steps: Step 1: After deployment, the device enters a low-power sleep mode and wakes up every 12 hours. Step 2: After wake-up, the surface wave sensor emits Rayleigh waves in frequency sweep mode, receives the reflected signals, and calculates... , And strain characteristics, compared with the preset normal soil model.

[0029] Specifically, the MEMS differential surface acoustic wave strain sensor uses Y+34° tangential quartz as a substrate and contains three resonators, with a strain sensitivity of [missing information]. The calculation formula is: ; In the formula, This is the frequency offset. The initial resonant frequency, For soil strain.

[0030] Strain sensitivity It is a key indicator of surface acoustic wave resonators, used to measure the sensitivity of soil strain to resonator frequency changes. The larger the value, the more significant the frequency shift in the resonator, indicating that even minute soil strain can trigger a noticeable change in soil deformation, and the stronger the sensor's ability to detect soil deformation. Termite activity alters soil structure and generates minute strains, which can be calculated... This can clarify whether the resonator can accurately capture such minute strains, that is, determine whether this soil wave sensor can perform this task, and provide a core basis for sensor selection and detection accuracy verification.

[0031] Termites alter the physical properties of soil during nest building by: biological cementation, where saliva and fecal secretions containing glycoproteins and calcium carbonate solidify soil particles and increase the soil's elastic modulus. With shear modulus Pore ​​reconstruction leads to the formation of complex channels and chambers within the nest, increasing porosity. Changes include: distorted pore size distribution; increased density due to the transport and compaction of fine particles, resulting in a 10%–30% higher density in the nest wall compared to the surrounding soil; and abnormal moisture content, maintaining a high and stable humidity environment inside the nest, with saturation... Uneven distribution. Consequently, the elastic modulus of the soil in the termite nest is... shear modulus Compared with Poisson The nest soil differs significantly from the surrounding normal soil in acoustic impedance. Wave speed With attenuation coefficient Measurable differences are generated above: The propagation of Rayleigh waves in unsaturated soil satisfies the following dispersion equation: ; In the formula, These are the elements of the boundary condition matrix. For complex wave number, Let be the angular frequency. Solving the above equation yields: ; Experiments have shown that the soil in the nest... It is 5% to 15% higher than the surrounding soil. It is 20% to 40% lower, and the dispersion curve shows a characteristic inflection point in the range of 100Hz to 1kHz.

[0032] Furthermore, the MEMS differential surface acoustic wave strain sensor is based on the Rayleigh wave propagation theory in three-phase unsaturated porous media, and its wave velocity... With attenuation coefficient The measurements are based on the following governing equations and boundary conditions: Mass balance equation: ; Among them, For each phase, For each phase displacement vector component, the subscript is... These represent the solid phase, liquid phase, and gas phase, respectively.

[0033] Momentum balance equation: ; in , Let Lame constant be the soil mass. For Biot coefficient, Liquid phase saturation, , These are pore pressure and gas pressure.

[0034] Rayleigh wave dispersion equation: ; in Rayleigh complex wavenumber, Angular frequency, Porosity Let be the bulk modulus of each phase.

[0035] Formulas for calculating wave velocity and attenuation coefficient: ; ; in, For soil density, Rayleigh complex wavenumber, and These represent the real part and the imaginary part, respectively.

[0036] Termite nest soil differs significantly from surrounding normal soil due to factors such as biocementation, pore structure reconstruction, increased density, abnormal moisture content, and the influence of cementing substances. These changes in physical properties lead to changes in the equivalent elastic parameters of the nest soil. , , This significantly alters the propagation characteristics of Rayleigh waves, thus affecting their propagation properties. This is achieved through measurements... , The dispersion curve is compared with the preset normal soil baseline model. If the following conditions are met, it is determined to be a termite nest area: and ; In the formula, The measured wave velocity of the Rayleigh wave. The Rayleigh baseline wave velocity for normal soil. The baseline value is the reciprocal of the Rayleigh wave quality factor for normal soil. This is the measured value of the reciprocal of the Rayleigh wave quality factor.

[0037] Step 3: Compare the measured values ​​with the preset normal soil baseline model. If the surface wave characteristics deviate from the baseline by more than 5%, it is determined to be a suspicious nest or burrow area. Activate the voiceprint recognition sensor, collect 30 seconds of vibration signal, extract features, and calculate the confidence level.

[0038] After confirming that the environmental conditions are suitable, the voiceprint recognition sensor is activated to collect signals, and the signal-to-noise ratio is improved through the following process: Signal normalization: The amplitude of the collected raw acoustic vibration signal (reflecting the vibrations generated by termite feeding and activity) is adjusted to be within the range of normal values. The standardized range is calculated using the following formula: ; in, These are the original voiceprint signal sample values. and These are the maximum and minimum values ​​within the signal segment, respectively.

[0039] Background noise filtering: Setting an amplitude threshold for the normalized signal Filtering is performed. If the signal value is within the threshold, it is determined to be background noise (such as soil vibration or airflow interference) and set to zero; otherwise, it is retained as a valid termite activity signal.

[0040] Voiceprint Feature Extraction and Model Matching: The preprocessed voiceprint signal enters the feature extraction stage. First, the continuous voiceprint signal is segmented into short frames of fixed length (e.g., 4 seconds per frame) with a 50% overlap rate to capture the quasi-stationary characteristics of the signal. Then, key features are extracted in the time and frequency domains: Temporal characteristics: Calculate the temporal energy of each frame of the signal. High energy values ​​indicate strong vibrations, such as concentrated termite feeding; calculate the temporal entropy of each frame of the signal. A high entropy value reflects the irregularity of the vibration signal, which matches the random vibration characteristics of termite activity.

[0041] The formula is as follows: ; ; in, This is the normalized voiceprint signal of the m-th frame. For frame size, It is the ratio of subframe energy to total energy.

[0042] Frequency domain characteristics: Perform a Fast Fourier Transform on each frame of signal to convert it to the frequency domain. And calculate the spectral centroid With spectral entropy Features such as the centroid location of the spectrum can distinguish termite vibrations (mainly concentrated in the 1kHz~15kHz range) from environmental noise (mostly low-frequency or high-frequency interference). Entropy values ​​can reflect the uniformity of frequency distribution, and the spectral entropy of termite activity signals has significant characteristics.

[0043] The formula is as follows: ; ; in, For frequency points, Let k be the index of the k-th frequency point. For the first Energy percentage of each frequency band Let m be the energy or amplitude of the m-th signal at the k-th frequency point. For the first f The energy percentage of each frequency band. The number of frequency bands, the total number of frequency bands in the spectrum, and the sum of the energy percentages of all frequency bands is 1.

[0044] A pre-trained Support Vector Machine (SVM) classifier is used, with the input feature vector [ , Output confidence level: ; The confidence level is expressed as a percentage; the higher the value, the more reliable the match. The number of successfully matched samples / data points; The total number of samples / data points involved in the matching.

[0045] In rainy or high-humidity environments, surface wave data are corrected using a humidity-wave velocity joint correction model. ; in, The corrected wave velocity, , For correction factor, For real-time saturation, For temperature, , To calibrate the reference value, The correction factor is 0.015. The correction factor is 0.003. =25°C, =50% is the calibration benchmark.

[0046] Step 4: If the voiceprint confidence score is >90% and the surface wave anomaly persists, it is determined to be a termite nest, and termites are already present inside the device. This triggers a Level 1 alarm, activates the image verification module, and the macro camera captures images under infrared LED illumination to obtain direct visual evidence of termite activity. Finally, the image data, sensor data, timestamp, and voiceprint confidence score are packaged and uploaded to the cloud platform via the NB-IoT / 4G / 5G IoT communication module, and an alarm is pushed to the mobile app.

[0047] Step 5: The user remotely triggers drug administration or arranges on-site treatment. After treatment, the device automatically updates the baseline data and returns to sleep mode.

[0048] Termite activity significantly alters the physical and mechanical properties of the soil surrounding the nest, such as pore structure, saturation, and elastic modulus. These changes can be characterized by acoustic wave propagation characteristics. In recent years, MEMS surface acoustic wave sensors have been widely used for measuring parameters such as strain, temperature, and humidity due to their advantages of high sensitivity, small size, and low power consumption. Studies have shown that the propagation velocity and attenuation coefficient of Rayleigh waves in unsaturated soil are extremely sensitive to the soil's saturation, pore structure, and elastic parameters. This invention innovatively integrates a MEMS surface acoustic wave sensor and a voiceprint recognition sensor into a termite trapping and monitoring device and termite nest detection and trapping method. By actively transmitting and receiving Rayleigh surface waves, it analyzes the differences in wave velocity, attenuation, and dispersion characteristics between the termite nest soil and the surrounding normal soil, achieving non-invasive nest location detection. Through a multi-level judgment mechanism combining surface wave detection, voiceprint recognition, and image verification, it achieves accurate termite nest location, early activity warning, and a closed-loop visual evidence chain. This invention is applicable to various scenarios that require early warning, precise location and visual monitoring of termite activity, and can significantly improve the initiative, accuracy and management efficiency of termite control.

[0049] The above description is only a preferred embodiment of this application and is not intended to limit this application. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of this application should be included within the protection scope of this application.

Claims

1. A termite trapping and monitoring device, characterized in that, include: The housing (1) has an internal accommodating space; The trapping and killing module (2) is set in the containment space, including a fixing slot and a dosing module set at the bottom of the containment space; The multi-sensor detection module includes a surface wave sensor (3) for detecting changes in the physical properties of the soil, and an acoustic signature sensor (4) for collecting vibration signals generated by termite activity. The image verification module (5) includes a macro camera and multiple supplementary lighting units surrounding the macro camera; The main control module (6) is electrically connected to the multi-sensor detection module and the image verification module (5) to process surface wave and acoustic signature signals in real time, and activates the image verification module (5) after the surface wave sensor (3) and the acoustic signature sensor (4) jointly determine that the signal has passed. The communication module (7) is used to package and upload alarm information, sensor data and images to the cloud platform.

2. The termite nest detection and trapping method according to claim 1, characterized in that: The surface wave sensor (3) is specifically a MEMS differential acoustic surface wave strain sensor, using Y+34° tangential quartz as the substrate; The voiceprint recognition sensor (4) is specifically a high-sensitivity MEMS accelerometer.

3. A method for detecting and trapping termite nests, utilizing an intelligent termite trapping and monitoring device based on multi-sensor fusion as described in claim 1 or 2, characterized in that, Includes the following steps: Step 1: After deployment, the device enters sleep mode and is woken up at intervals. Step 2: After wake-up, the surface wave sensor emits Rayleigh waves in frequency sweep mode, receives the reflected signals, and calculates... , And strain characteristics, compared with the preset normal soil model; Step 3: Compare the measured values ​​with the preset normal soil baseline model. If the surface wave characteristics deviate from the baseline by more than 5%, it is determined to be a suspicious nesting area. Activate the voiceprint recognition sensor, collect 30 seconds of vibration signal, extract features and calculate confidence level. Step 4: If the voiceprint confidence score is >90% and the surface wave anomaly persists, it is determined to be a termite nest. The image verification module is activated to capture the image. The image data, sensor data, timestamp, and voiceprint confidence score are packaged and uploaded through the communication module. Step 5: The user remotely triggers drug administration or arranges on-site treatment. After treatment, the device automatically updates the baseline data and returns to sleep mode.

4. The termite nest detection and trapping method according to claim 3, characterized in that: In step two, Rayleigh wave speed With attenuation coefficient Detectable changes have occurred: ； ； in, For soil density, Poisson's ratio, Shear modulus Rayleigh complex wavenumber, and Represent the real part and the imaginary part, respectively; By comparing the measured values ​​with the baseline values ​​of normal soil, the area is identified as a termite nest area if the following conditions are met: ;and ; In the formula, The measured wave velocity of the Rayleigh wave. The Rayleigh baseline wave velocity for normal soil. The baseline value is the reciprocal of the Rayleigh wave quality factor for normal soil. This is the measured value of the reciprocal of the Rayleigh wave quality factor.

5. The termite nest detection and trapping method according to claim 4, characterized in that: In step two, wave speed With attenuation coefficient The measurements are based on the following governing equations and boundary conditions: Mass balance equation: ； Among them, For each phase, the apparent density is... For each phase displacement vector component, the subscript is... These represent the solid phase, liquid phase, and gas phase, respectively. Momentum balance equation: ； in , Let Lame constant be the soil mass. For Biot coefficient, Liquid phase saturation, , These are pore pressure and gas pressure; Rayleigh wave dispersion equation: ； in Rayleigh complex wavenumber, Angular frequency, Porosity Let be the bulk modulus of each phase.

6. The termite nest detection and trapping method according to claim 3, characterized in that: In step three, after the voiceprint recognition sensor is activated to collect signals, the collected signals are processed according to the following procedure: Signal normalization: The amplitude of the acquired raw acoustic vibration signal is adjusted to bring it into normal range. The scope of standardization; Background noise filtering: Setting an amplitude threshold for the normalized signal Perform filtering; Voiceprint feature extraction and model matching: The continuous voiceprint signal is segmented into short time frames of fixed length with a 50% overlap rate to capture the quasi-stationary characteristics of the signal; key features are extracted in the time and frequency domains.

7. The termite nest detection and trapping method according to claim 6, characterized in that: In step three, during the "signal normalization" process, the calculation formula is as follows: ； in, These are the original voiceprint signal sample values. and These are the maximum and minimum values ​​within the signal segment, respectively.

8. The termite nest detection and trapping method according to claim 6, characterized in that: In step three, "extracting key features in the time domain" specifically includes: Calculate the time-domain energy of each frame of signal A high energy value indicates the presence of strong vibrations; calculate the temporal entropy of each frame of the signal. A high entropy value reflects the irregularity of the vibration signal, and the calculation formula is as follows: ； ； in, This is the normalized voiceprint signal of the m-th frame. For frame size, It is the ratio of subframe energy to total energy.

9. The termite nest detection and trapping method according to claim 6, characterized in that: In step three, "extracting key features in the frequency domain" specifically includes: Perform a Fast Fourier Transform on each frame of the signal to convert it to the frequency domain. And calculate the spectral centroid With spectral entropy Features such as the location of the centroid of the frequency spectrum can distinguish termite vibration from environmental noise, and the entropy value can reflect the uniformity of the frequency distribution. The calculation formula is as follows: ； ； in, For frequency points, Let k be the index of the k-th frequency point. For the first Energy percentage of each frequency band Let m be the energy or amplitude of the m-th signal at the k-th frequency point. For the first f The energy percentage of each frequency band.

10. The termite nest detection and trapping method according to claim 6, characterized in that: In step three, in rainy or high-humidity environments, the surface wave data are corrected using a humidity-wave velocity joint correction model: ； in, For the corrected wave velocity, , For correction factor, For real-time saturation, For temperature, , This serves as the calibration reference value.

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