Building termite intelligent monitoring and repelling method and system based on Internet of Things

Abstract

The invention discloses a building termite intelligent monitoring and repelling method and system based on the Internet of Things, and belongs to the technical field of termite detection. Secondly, a timestamp is added, and feature recognition is carried out to obtain sound wave vibration pulse feature data; calculating the arrival time difference of the same pulse signal between each pair of Internet of Things network nodes according to the sound wave vibration pulse characteristic data, and calculating the center coordinate value of the termite nest; generating a spatial-temporal distribution visualization result for the CO2 concentration signal, calculating a nest diameter range of the termite nest, and generating a termite nest distribution map; and finally, according to the termite nest distribution map, issuing a termite positioning and repelling instruction through the Internet of Things. Sensing signals are acquired and processed in real time by utilizing the intervention of the internet of things, so that the intelligence and the dynamic monitoring of the termites in the building are realized; cO2 concentration recognition is introduced as a cooperative condition for termite nest positioning and distribution diagram generation, and the accuracy of termite nest positioning is improved.

Description

Technical Field

[0001] This invention belongs to the technical fields of Internet of Things (IoT) and termite detection, specifically relating to an IoT-based intelligent monitoring and repelling method for termites in buildings. Background Technology

[0002] Termite infestation remains a persistent challenge in the construction industry. Termites, with their unique survival strategies and powerful gnawing abilities, pose a serious threat to the structural safety of buildings, including both wooden and concrete structures. However, current termite detection methods have numerous limitations, creating a significant bottleneck in termite detection efforts.

[0003] Currently, there are four main methods for termite detection. The first is manual detection, which is the most traditional method and requires staff to rely on experience to conduct a meticulous search inside the building. However, this method is extremely inefficient and consumes a lot of manpower and time. Furthermore, due to the concealed nature of termite activity, manual detection is difficult to achieve comprehensive coverage, leading to significant uncertainty in the detection results. Secondly, there is the resistivity detection method, which detects termites based on changes in soil resistivity. While this method can detect termites to some extent, it cannot achieve continuous detection. Since termite activity is a dynamic process, their distribution and numbers change over time. Therefore, the resistivity method can only detect at specific times and locations, making it impossible to keep track of the latest termite activity. Moreover, this method has low detection efficiency and a small detection scale, making it difficult to achieve comprehensive and accurate detection in large-area buildings. Thirdly, there is the ground-penetrating radar method, which uses the propagation characteristics of electromagnetic waves in underground media to detect termites. However, building environments are complex and diverse, with numerous metal components, pipes, and other interfering factors. These factors severely affect the propagation of electromagnetic waves, causing signal distortion and making it impossible to accurately determine the location and distribution of termites. In some structurally complex buildings, the ground-penetrating radar method is almost ineffective.

[0004] The fourth method is the acoustic vibration method. This method detects termites by detecting the acoustic vibrations generated by their activity. However, acoustic waves are affected by various factors during propagation, such as the structure and material of the building and the noise of the surrounding environment, which can lead to propagation uncertainty errors. These errors can significantly reduce the accuracy of the detection results, making it difficult for staff to accurately determine the specific location and activity of the termites.

[0005] As mentioned above, the intelligent detection of termites in buildings is currently facing certain difficulties. There has been little progress in the intelligent detection and repelling of termites in buildings, and the accuracy of termite detection results cannot be guaranteed.

[0006] Therefore, given the aforementioned shortcomings, how to provide an IoT-based intelligent monitoring and repelling method and system for building termites that can automatically and continuously detect and repel termites in buildings, and provide more accurate detection results, has become an urgent problem to be solved. Summary of the Invention

[0007] The purpose of this invention is to provide an Internet of Things-based intelligent monitoring and repellency method for termites in buildings, in order to solve the aforementioned problems existing in the prior art.

[0008] To achieve the above objectives, the present invention adopts the following technical solution: In a first aspect, the present invention provides an intelligent monitoring and repelling method for building termites based on the Internet of Things, comprising: The raw termite activity signal is acquired and preprocessed to obtain the termite activity signal, wherein the termite activity signal includes an acoustic vibration signal and a CO2 concentration signal. A timestamp is added to the acoustic vibration signal to form an acoustic vibration time sequence signal. Feature recognition is performed on the acoustic vibration time sequence signal to obtain acoustic vibration pulse feature data. Based on the Internet of Things (IoT) network, the arrival time difference of the same pulse signal between each pair of IoT network nodes is calculated from the acoustic vibration pulse characteristic data, and the center coordinates of the termite nest are calculated using the hyperbolic positioning method. Based on the Internet of Things network, a visualization result of the spatiotemporal distribution of CO2 concentration is generated from the CO2 concentration signal, and the diameter range of the termite nest is calculated based on the visualization result of the spatiotemporal distribution of CO2 concentration. The center coordinates of the termite nest and the diameter range of the termite nest are transmitted to the Internet of Things network to generate a termite nest distribution map. Based on the termite nest distribution map, a termite location and avoidance command is issued via the Internet of Things network.

[0009] In one possible design, raw termite activity signals are acquired, and these raw termite activity signals are preprocessed to obtain termite activity signals, including: A preset potential termite activity area is obtained, and multiple Internet of Things (IoT) sensor nodes are arranged non-collinearly within the potential termite activity area. Each IoT sensor node is then connected to an IoT network to serve as an IoT network node. Based on each of the IoT network nodes, the original acoustic vibration signal and the original CO2 concentration signal are acquired, and the original acoustic vibration signal and the original CO2 concentration signal are used as the original termite activity signal. By using bandpass filtering, outlier removal, de-meaning, and noise reduction are performed on the original termite activity signal to enhance the signal and form a termite activity signal.

[0010] In one possible design, a timestamp is added to the acoustic vibration signal to form an acoustic vibration time-series signal, including: Based on the Internet of Things network, the cross-correlation function between each pair of acoustic vibration signals is calculated, and the time offset corresponding to the maximum correlation is obtained. From each pair of IoT network nodes, a reference node is selected, and the signal time axis of each pair of IoT network nodes is adjusted using the time offset corresponding to the maximum correlation, so as to complete the time alignment between each pair of IoT network nodes. The Internet of Things (IoT) network time protocol is used to add timestamps to each pair of time-aligned acoustic vibration signals to form an acoustic vibration timing signal. Accordingly, feature recognition is performed on the acoustic vibration time-series signal to obtain acoustic vibration pulse feature data, which includes: A preset standard pulse amplitude threshold is obtained, and the acoustic vibration timing signal is filtered according to the preset standard pulse amplitude threshold to obtain a standard amplitude acoustic vibration pulse signal. A threshold for the interval between acoustic vibration pulses is obtained. An interval analysis is performed on the standard amplitude acoustic vibration pulse signals that are lower than the threshold to obtain effective acoustic vibration pulse signals. The effective acoustic vibration pulse signals are then used as acoustic vibration pulse feature data.

[0011] In one possible design, based on an Internet of Things (IoT) network, the arrival time difference of the same pulse signal between each pair of IoT network nodes is calculated from the acoustic vibration pulse characteristic data, and the center coordinates of the termite nest are calculated using the hyperbolic positioning method, including: Based on the Internet of Things (IoT) network, the timestamps of the signal reception times of each pair of IoT network nodes under the same pulse signal are obtained from the acoustic vibration pulse characteristic data. Based on the timestamps of the signal reception times of each pair of IoT network nodes under the same pulse signal, the arrival time difference of the same pulse signal between each pair of IoT network nodes is calculated. Based on the Internet of Things (IoT) network, the node coordinates of each pair of IoT network nodes are obtained, and a hyperbolic equation system for ant hole positioning is established according to the node coordinates of each pair of IoT network nodes and the arrival time difference of the same pulse signal between each pair of IoT network nodes. The hyperbolic equations for ant nest location are solved iteratively to obtain the center coordinates of the termite nest.

[0012] In one possible design, based on an Internet of Things (IoT) network, a visualization of the spatiotemporal distribution of CO2 concentration is generated from the CO2 concentration signal. Based on this visualization, the diameter range of the termite nest is calculated, including: The CO2 concentration signal is timestamped using the network time protocol of the Internet of Things (IoT) network to plot a CO2 concentration time series curve, and the peak value is marked in the CO2 concentration time series curve. Based on the Internet of Things (IoT) network, the node coordinates of each pair of IoT network nodes are obtained. Based on the node coordinates of each pair of IoT network nodes and the CO2 concentration signal, a two-dimensional CO2 concentration contour map is drawn, and the area with the maximum concentration gradient is marked on the two-dimensional CO2 concentration contour map. The CO2 concentration time series curve and the two-dimensional CO2 concentration contour map are used as the visualization results of the spatiotemporal distribution of CO2 concentration. Based on the visualization results of the spatiotemporal distribution of CO2 concentration, a CO2 diffusion model is constructed using a steady-state diffusion model. Based on the CO2 diffusion model, the estimated diameter of the termite nest was calculated. Uncertainty processing is applied to the estimated diameter of termite nests to obtain the range of termite nest diameters.

[0013] In one possible design, the center coordinates and diameter of the termite nest are transmitted to an Internet of Things (IoT) network to generate a termite nest distribution map, including: Based on the Internet of Things network, the termite nest area is simulated as a spherical nest area; The maximum value is selected from the range of termite nest diameters as the termite nest diameter, and the center coordinates of the termite nest are used as the center of the termite nest to generate a termite nest area image. Based on the Internet of Things (IoT) network, a termite nest distribution map is generated from the image of the termite nest area.

[0014] In one possible design, based on the termite nest distribution map, a termite location and repellency command is issued via an Internet of Things (IoT) network, including: Based on the Internet of Things network, a termite location and avoidance command is generated from the termite nest distribution map. According to the termite location and repellency command, the termite repellency device corresponding to the termite nest area in the termite nest distribution map is activated to repel termites.

[0015] Secondly, the present invention provides an Internet of Things-based intelligent termite monitoring and repellency system for buildings, comprising: The data acquisition module is used to acquire raw termite activity signals and perform signal preprocessing on the raw termite activity signals to obtain termite activity signals, wherein the termite activity signals include acoustic vibration signals and CO2 concentration signals. The feature recognition module is used to add a timestamp to the acoustic vibration signal to form an acoustic vibration time sequence signal, and to perform feature recognition on the acoustic vibration time sequence signal to obtain acoustic vibration pulse feature data. The coordinate calculation module is used to calculate the arrival time difference of the same pulse signal between each pair of IoT network nodes based on the acoustic vibration pulse characteristic data of the Internet of Things network, and to calculate the center coordinate value of the termite nest using the hyperbolic positioning method. The diameter calculation module is used to generate a visualization result of the spatiotemporal distribution of CO2 concentration based on the IoT network, and to calculate the range of the termite nest diameter based on the visualization result of the spatiotemporal distribution of CO2 concentration. An image generation module is used to transmit the center coordinates of the termite nest and the diameter range of the termite nest to the Internet of Things network to generate a termite nest distribution map. The instruction issuing module is used to issue termite location and avoidance instructions via the Internet of Things network based on the termite nest distribution map.

[0016] Thirdly, the present invention provides an electronic device comprising a memory, a processor, and a transceiver connected in sequence, wherein the memory is used to store a computer program, the transceiver is used to send and receive messages, and the processor is used to read the computer program and execute the Internet of Things-based intelligent monitoring and repelling method for building termites as described in the first aspect or any possible design of the first aspect.

[0017] Fourthly, the present invention provides a computer-readable storage medium storing instructions that, when executed on a computer, perform the IoT-based intelligent monitoring and repellency method for building termites as described in the first aspect or any possible design of the first aspect.

[0018] Fifthly, the present invention provides a computer program product containing instructions that, when executed on a computer, cause the computer to perform the Internet of Things-based intelligent monitoring and repellency method for building termites as described in the first aspect or any possible design of the first aspect.

[0019] Beneficial Effects: This invention provides an intelligent termite monitoring and repellency method and system for buildings based on the Internet of Things (IoT), comprising: firstly, acquiring raw termite activity signals and preprocessing the raw termite activity signals to obtain termite activity signals, wherein the termite activity signals include acoustic vibration signals and CO2 concentration signals; secondly, adding timestamps to the acoustic vibration signals to form acoustic vibration time-series signals, and performing feature recognition on the acoustic vibration time-series signals to obtain acoustic vibration pulse feature data; and then, based on the IoT network, calculating the same... The arrival time difference of a pulse signal between pairs of IoT network nodes is used, and the center coordinates of the termite nest are calculated using the hyperbolic positioning method. Then, based on the IoT network, a spatial-temporal distribution visualization of CO2 concentration is generated from the CO2 concentration signal, and the diameter range of the termite nest is calculated based on this visualization. The center coordinates and diameter range of the termite nest are then transmitted to the IoT network to generate a termite nest distribution map. Finally, based on the termite nest distribution map, a termite location and avoidance command is issued through the IoT network. By utilizing the IoT, acoustic vibration sensing signals can be acquired and processed in real time, and the center coordinates of the termite nest can be generated accordingly, thus achieving intelligent and dynamic termite monitoring of buildings. Furthermore, the introduction of CO2 concentration identification as a co-condition for termite nest location and distribution map generation avoids the error paths of the acoustic vibration method, greatly improving the accuracy of actual termite nest distribution location. Attached Figure Description

[0020] Figure 1 A flowchart illustrating the IoT-based intelligent monitoring and repellency method for building termites provided in this embodiment of the invention; Figure 2 A functional structure diagram of an IoT-based intelligent termite monitoring and repellency system for buildings provided in an embodiment of the present invention; Figure 3 This is a schematic diagram of the structure of an electronic device provided in an embodiment of the present invention. Detailed Implementation

[0021] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the present invention will be briefly introduced below in conjunction with the accompanying drawings and descriptions of the embodiments or the prior art. Obviously, the following description of the structure of the accompanying drawings is only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort. It should be noted that the description of these embodiments is for the purpose of helping to understand the present invention, but does not constitute a limitation of the present invention.

[0022] It should be understood that although the terms first, second, etc., may be used herein to describe various units, these units should not be limited by these terms. These terms are only used to distinguish one unit from another. For example, the first unit may be referred to as the second unit, and similarly, the second unit may be referred to as the first unit, without departing from the exemplary embodiments of the invention.

[0023] It should be understood that the term "and / or" that may appear in this document is merely a description of the relationship between related objects, indicating that three relationships can exist. For example, A and / or B can mean: A exists alone, B exists alone, and A and B exist simultaneously. The term " / and" that may appear in this document describes another relationship between related objects, indicating that two relationships can exist. For example, A / and B can mean: A exists alone, and A and B exist alone. In addition, the character " / " that may appear in this document generally indicates that the related objects before and after it are in an "or" relationship.

[0024] Example: like Figure 1 As shown, this embodiment provides an IoT-based intelligent monitoring and repellency method for building termites, including: S1. Acquire raw termite activity signals and perform signal preprocessing on the raw termite activity signals to obtain termite activity signals, wherein the termite activity signals include acoustic vibration signals and CO2 concentration signals; In one possible implementation, step S1 involves acquiring raw termite activity signals and performing signal preprocessing on these signals to obtain termite activity signals. This process can be broken down into, but is not limited to, the following steps S11-S13, including: S11. Obtain a preset potential termite activity area, arrange multiple Internet of Things (IoT) sensor nodes non-collinearly within the potential termite activity area, and connect each IoT sensor node to the IoT network so that each IoT sensor node serves as an IoT network node. S12. Based on each of the IoT network nodes, acquire the original acoustic vibration signal and the original CO2 concentration signal, and use the original acoustic vibration signal and the original CO2 concentration signal as the original termite activity signal; S13. Using bandpass filtering, the original termite activity signal is subjected to outlier removal, de-meaning, and filtering noise reduction to enhance the signal and form a termite activity signal.

[0025] It should be noted that a fourth-order Butterworth bandpass filter can be used preferentially for zero-phase filtering (bidirectional filtering) to avoid phase distortion. The filtering frequency band for noise reduction is preferentially set to 0.1kHz to 1.5kHz under normal detection conditions. However, the frequency band can be dynamically adjusted according to the termite species to be detected (for example, when detecting yellow-winged termites in buildings, the filtering frequency band can be adjusted to 0.5kHz to 1.0kHz; when detecting black-winged subterranean termites in buildings, the filtering frequency band can be adjusted to 0.8kHz to 1.5kHz). Furthermore, the spectral characteristics of the signal can be extracted through Fourier transform to identify pulse signals with a main frequency of 0.1kHz to 1.5kHz.

[0026] Furthermore, each of the IoT sensing nodes includes an accelerometer (for acquiring acoustic vibration signals), a CO2 concentration detector (for acquiring CO2 concentration signals), a signal collector, and a data transmission unit. In order to simplify the complexity of the propagation medium, the accelerometer can be installed close to the surface of the building to collect acoustic vibration signals emitted by termite nests.

[0027] S2. Add a timestamp to the acoustic vibration signal to form an acoustic vibration time sequence signal, and perform feature recognition on the acoustic vibration time sequence signal to obtain acoustic vibration pulse feature data; In one possible implementation, step S2, adding a timestamp to the acoustic vibration signal to form an acoustic vibration time sequence signal, can be decomposed into, but is not limited to, the following steps S21-S25, including: S21. Based on the Internet of Things network, calculate the cross-correlation function between each pair of acoustic vibration signals, and obtain the time offset corresponding to the maximum correlation. S22. Select a reference node from each pair of IoT network nodes, and adjust the signal time axis of each pair of IoT network nodes using the time offset corresponding to the maximum correlation, so as to complete the time alignment between each pair of IoT network nodes. S23. Using the network time protocol of the Internet of Things network, add timestamps to each pair of time-aligned acoustic vibration signals to form an acoustic vibration timing signal; Accordingly, feature recognition is performed on the acoustic vibration time-series signal to obtain acoustic vibration pulse feature data, which includes: S24. Obtain a preset standard pulse amplitude threshold, and perform signal filtering on the acoustic vibration timing signal according to the preset standard pulse amplitude threshold to obtain a standard amplitude acoustic vibration pulse signal; S25. Obtain the acoustic vibration pulse interval threshold, perform interval analysis on the standard amplitude acoustic vibration pulse signal below the acoustic vibration pulse interval threshold to obtain the effective acoustic vibration pulse signal, and use the effective acoustic vibration pulse signal as acoustic vibration pulse feature data.

[0028] It should be noted that, under normal detection conditions, the preset standard pulse amplitude threshold is preferably set to ≥0.01. Furthermore, the preset standard pulse amplitude threshold can be dynamically adjusted according to the termite species to be detected; the acoustic vibration pulse interval threshold is preferably set to 0.05 seconds; through the above method, effective pulse signals can be screened out.

[0029] S3. Based on the Internet of Things (IoT) network, the arrival time difference of the same pulse signal between each pair of IoT network nodes is calculated from the acoustic vibration pulse characteristic data, and the center coordinates of the termite nest are calculated using the hyperbolic positioning method. In one possible implementation, in step S3, based on the Internet of Things (IoT) network, the arrival time difference of the same pulse signal between each pair of IoT network nodes is calculated from the acoustic vibration pulse characteristic data, and the center coordinates of the termite nest are calculated using the hyperbolic positioning method. This can be decomposed into, but is not limited to, the following steps S31-S34, including: S31. Based on the Internet of Things (IoT) network, obtain the timestamps of the signal reception times of each pair of IoT network nodes under the same pulse signal in the acoustic vibration pulse characteristic data; S32. Based on the timestamps of the signal reception times of each pair of IoT network nodes under the same pulse signal, calculate the arrival time difference of the same pulse signal between each pair of IoT network nodes; S33. Based on the Internet of Things (IoT) network, obtain the node coordinates of each pair of IoT network nodes, and establish a hyperbolic equation system for ant hole positioning based on the node coordinates of each pair of IoT network nodes and the arrival time difference of the same pulse signal between each pair of IoT network nodes. S34. Iteratively solve the hyperbolic equation system for ant nest location to obtain the center coordinates of the termite nest.

[0030] It should be noted that the propagation speed of sound waves in a building can be determined in advance, and combined with the coordinate values ​​of IoT network nodes, a hyperbolic equation system for ant nest location can be constructed. Then, the least squares method is used to iteratively solve the hyperbolic equation system for ant nest location to obtain the preliminary coordinate values ​​of the termite nest. Finally, the wave speed error is corrected through an iterative optimization algorithm to obtain the center coordinate value of the termite nest.

[0031] S4. Based on the Internet of Things network, generate a visualization result of the spatiotemporal distribution of CO2 concentration from the CO2 concentration signal, and calculate the range of the nest diameter of the termite nest based on the visualization result of the spatiotemporal distribution of CO2 concentration. In one possible implementation, step S4, based on the Internet of Things (IoT) network, generates a visualization result of the spatiotemporal distribution of CO2 concentration from the CO2 concentration signal, and calculates the range of the termite nest diameter based on the visualization result of the spatiotemporal distribution of CO2 concentration. This can be broken down into, but is not limited to, the following steps S41-S45, including: S41. Add a timestamp to the CO2 concentration signal using the network time protocol of the Internet of Things network to plot a CO2 concentration time series curve and mark the peak value in the CO2 concentration time series curve; S42. Based on the Internet of Things (IoT) network, obtain the node coordinates of each pair of IoT network nodes, and draw a two-dimensional CO2 concentration contour map according to the node coordinates of each pair of IoT network nodes and the CO2 concentration signal, and mark the area with the maximum concentration gradient in the two-dimensional CO2 concentration contour map. S43. The CO2 concentration time series curve and the two-dimensional CO2 concentration contour map are used as the visualization results of the spatiotemporal distribution of CO2 concentration. Based on the visualization results of the spatiotemporal distribution of CO2 concentration, a CO2 diffusion model is constructed using a steady-state diffusion model. S44. Based on the CO2 diffusion model, calculate the estimated diameter of the termite nest; S45. Perform uncertainty processing on the estimated diameter of termite nests to obtain the range of termite nest diameters.

[0032] It should be noted that termite activity produces CO2, especially inside the nest. Due to the respiration of termites and the metabolism of fungal gardens, the CO2 concentration is significantly higher than that of the surrounding environment. Therefore, CO2 concentration can serve as an important indicator of the existence of a termite nest. Furthermore, CO2 in a termite nest diffuses from the center outwards, and the concentration gradient is related to the size of the nest. Larger nests will produce higher CO2 concentrations and diffuse over a wider range. Therefore, by obtaining the spatiotemporal distribution visualization results of CO2 concentration and constructing a CO2 diffusion model, the CO2 concentration gradient distribution of termite nests can be obtained intuitively.

[0033] To determine the range of termite nest diameter, it is necessary to consider the uncertainty of the shape of the termite nest in the building and the uncertainty of the building structure. The estimated value of the termite nest diameter needs to be processed for uncertainty. This processing can be done by obtaining the error range from the existing experimental data, and the range of termite nest diameter can be obtained based on the error range.

[0034] S5. Transmit the center coordinates of the termite nest and the diameter range of the termite nest to the Internet of Things network to generate a termite nest distribution map; In one possible implementation, step S5, transmitting the center coordinates and diameter of the termite nest to an Internet of Things (IoT) network to generate a termite nest distribution map, can be, but is not limited to, decomposed into the following steps S51-S53, including: S51. Based on the Internet of Things network, the termite nest area is simulated as a spherical nest area; S52. Select the maximum value from the range of termite nest diameters as the termite nest diameter, and use the center coordinates of the termite nest as the center of the termite nest to generate a termite nest area image. S53. Based on the Internet of Things network, generate a termite nest distribution map according to the termite nest area image.

[0035] It should be noted that, S6. Based on the termite nest distribution map, issue termite location and avoidance commands via the Internet of Things network.

[0036] In one possible implementation, step S6, based on the termite nest distribution map, issues a termite location and repellency command via the Internet of Things network. This can be broken down into, but is not limited to, the following steps S61-S62, including: S61. Based on the Internet of Things network, generate termite location and avoidance commands for the termite nest distribution map; S62. According to the termite location and repellency command, activate the termite repellency device corresponding to the termite nest area in the termite nest distribution map to repel termites.

[0037] like Figure 2 As shown, the second aspect of this embodiment provides a hardware system for implementing the IoT-based intelligent monitoring and repellency method for building termites described in the first aspect of the embodiment, comprising: The data acquisition module is used to acquire raw termite activity signals and perform signal preprocessing on the raw termite activity signals to obtain termite activity signals, wherein the termite activity signals include acoustic vibration signals and CO2 concentration signals. The feature recognition module is used to add a timestamp to the acoustic vibration signal to form an acoustic vibration time sequence signal, and to perform feature recognition on the acoustic vibration time sequence signal to obtain acoustic vibration pulse feature data. The coordinate calculation module is used to calculate the arrival time difference of the same pulse signal between each pair of IoT network nodes based on the acoustic vibration pulse characteristic data of the Internet of Things network, and to calculate the center coordinate value of the termite nest using the hyperbolic positioning method. The diameter calculation module is used to generate a visualization result of the spatiotemporal distribution of CO2 concentration based on the IoT network, and to calculate the range of the termite nest diameter based on the visualization result of the spatiotemporal distribution of CO2 concentration. An image generation module is used to transmit the center coordinates of the termite nest and the diameter range of the termite nest to the Internet of Things network to generate a termite nest distribution map. The instruction issuing module is used to issue termite location and avoidance instructions via the Internet of Things network based on the termite nest distribution map.

[0038] It should be noted that the instruction issuing module corresponds to a termite repellency module. The termite repellency module may include, but is not limited to, a chemical repellent release unit and a physical interference unit. The chemical repellent release unit can release repellent in the form of atomization or microcapsules into the termite nest area in the building. The physical interference unit can interfere with the activity of termites in the building through low-frequency vibration or electromagnetic waves.

[0039] The working process, working details and technical effects of the system provided in this embodiment can be found in the first aspect of the embodiment, and will not be repeated here.

[0040] like Figure 3 As shown, the third aspect of this embodiment provides an electronic device, including: a memory, a processor, and a transceiver that are sequentially and communicatively connected, wherein the memory is used to store a computer program, the transceiver is used to send and receive messages, and the processor is used to read the computer program and execute the Internet of Things-based intelligent monitoring and repelling method for building termites as described in the first aspect of the embodiment.

[0041] For specific examples, the memory may include, but is not limited to, random access memory (RAM), read-only memory (ROM), flash memory, first-in-first-out (FIFO) memory, and / or first-in-last-out (FILO) memory, etc.; specifically, the processor may include one or more processing cores, such as a 4-core processor, an 8-core processor, etc. The processor may be implemented using at least one hardware form of DSP (Digital Signal Processing), FPGA (Field-Programmable Gate Array), PLA (Programmable Logic Array). The processor may also include a main processor and a coprocessor. The main processor, also known as the CPU (Central Processing Unit), is used to process data in the wake-up state; the coprocessor is a low-power processor used to process data in the standby state.

[0042] In some embodiments, the processor may integrate a GPU (Graphics Processing Unit), which is responsible for rendering and drawing the content to be displayed on the screen. For example, the processor may not be limited to microprocessors of the STM32F105 series, reduced instruction set computer (RISC) microprocessors, x86 architecture processors, or processors with integrated neural network processing units (NPUs). The transceiver may be, but is not limited to, a Wi-Fi transceiver, a Bluetooth transceiver, a General Packet Radio Service (GPRS) transceiver, a ZigBee (a low-power LAN protocol based on the IEEE 802.15.4 standard) transceiver, a 3G transceiver, a 4G transceiver, and / or a 5G transceiver. Furthermore, the device may also include, but is not limited to, a power module, a display screen, and other necessary components.

[0043] The working process, working details and technical effects of the electronic device provided in this embodiment can be found in the first aspect of the embodiment, and will not be repeated here.

[0044] The fourth aspect of this embodiment provides a storage medium that stores instructions containing the IoT-based intelligent monitoring and repellency method for building termites as described in the first aspect of the embodiment. That is, the storage medium stores instructions that, when executed on a computer, perform the IoT-based intelligent monitoring and repellency method for building termites as described in the first aspect of the embodiment.

[0045] The storage medium refers to a carrier for storing data, which may include, but is not limited to, floppy disks, optical disks, hard disks, flash memory, USB flash drives, and / or memory sticks. The computer may be a general-purpose computer, a special-purpose computer, a computer network, or other programmable devices.

[0046] The working process, working details and technical effects of the storage medium provided in this embodiment can be found in the first aspect of the embodiment, and will not be repeated here.

[0047] The fifth aspect of this embodiment provides a computer program product containing instructions that, when executed on a computer, cause the computer to perform the Internet of Things-based intelligent monitoring and repellency method for building termites as described in the first aspect of this embodiment, wherein the computer may be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device.

[0048] Finally, it should be noted that the above description is merely a preferred embodiment of the present invention and is not intended to limit the protection of the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A smart termite monitoring and repellency method for buildings based on the Internet of Things, characterized in that, include: The raw termite activity signal is acquired and preprocessed to obtain the termite activity signal, wherein the termite activity signal includes an acoustic vibration signal and a CO2 concentration signal. A timestamp is added to the acoustic vibration signal to form an acoustic vibration time sequence signal. Feature recognition is performed on the acoustic vibration time sequence signal to obtain acoustic vibration pulse feature data. Based on the Internet of Things (IoT) network, the arrival time difference of the same pulse signal between each pair of IoT network nodes is calculated from the acoustic vibration pulse characteristic data, and the center coordinates of the termite nest are calculated using the hyperbolic positioning method. Based on the Internet of Things network, a visualization result of the spatiotemporal distribution of CO2 concentration is generated from the CO2 concentration signal, and the diameter range of the termite nest is calculated based on the visualization result of the spatiotemporal distribution of CO2 concentration. The center coordinates of the termite nest and the diameter range of the termite nest are transmitted to the Internet of Things network to generate a termite nest distribution map. Based on the termite nest distribution map, a termite location and avoidance command is issued via the Internet of Things network.

2. The IoT-based intelligent monitoring and repellency method for building termites according to claim 1, characterized in that, Acquire raw termite activity signals and perform signal preprocessing on the raw termite activity signals to obtain termite activity signals, including: A preset potential termite activity area is obtained, and multiple Internet of Things (IoT) sensor nodes are arranged non-collinearly within the potential termite activity area. Each IoT sensor node is then connected to an IoT network to serve as an IoT network node. Based on each of the IoT network nodes, the original acoustic vibration signal and the original CO2 concentration signal are acquired, and the original acoustic vibration signal and the original CO2 concentration signal are used as the original termite activity signal. By using bandpass filtering, outlier removal, de-meaning, and noise reduction are performed on the original termite activity signal to enhance the signal and form a termite activity signal.

3. The IoT-based intelligent monitoring and repellency method for building termites according to claim 1, characterized in that, Adding a timestamp to the acoustic vibration signal to form an acoustic vibration time sequence signal includes: Based on the Internet of Things network, the cross-correlation function between each pair of acoustic vibration signals is calculated, and the time offset corresponding to the maximum correlation is obtained. From each pair of IoT network nodes, a reference node is selected, and the signal time axis of each pair of IoT network nodes is adjusted using the time offset corresponding to the maximum correlation, so as to complete the time alignment between each pair of IoT network nodes. The Internet of Things (IoT) network time protocol is used to add timestamps to each pair of time-aligned acoustic vibration signals to form an acoustic vibration timing signal. Accordingly, feature recognition is performed on the acoustic vibration time-series signal to obtain acoustic vibration pulse feature data, which includes: A preset standard pulse amplitude threshold is obtained, and the acoustic vibration timing signal is filtered according to the preset standard pulse amplitude threshold to obtain a standard amplitude acoustic vibration pulse signal. A threshold for the interval between acoustic vibration pulses is obtained. An interval analysis is performed on the standard amplitude acoustic vibration pulse signals that are lower than the threshold to obtain effective acoustic vibration pulse signals. The effective acoustic vibration pulse signals are then used as acoustic vibration pulse feature data.

4. The IoT-based intelligent monitoring and repellency method for building termites according to claim 1, characterized in that, Based on the Internet of Things (IoT) network, the arrival time difference of the same pulse signal between each pair of IoT network nodes is calculated from the acoustic vibration pulse characteristic data, and the center coordinates of the termite nest are calculated using the hyperbolic positioning method, including: Based on the Internet of Things (IoT) network, the timestamps of the signal reception times of each pair of IoT network nodes under the same pulse signal are obtained from the acoustic vibration pulse characteristic data. Based on the timestamps of the signal reception times of each pair of IoT network nodes under the same pulse signal, the arrival time difference of the same pulse signal between each pair of IoT network nodes is calculated. Based on the Internet of Things (IoT) network, the node coordinates of each pair of IoT network nodes are obtained, and a hyperbolic equation system for ant hole positioning is established according to the node coordinates of each pair of IoT network nodes and the arrival time difference of the same pulse signal between each pair of IoT network nodes. The hyperbolic equations for ant nest location are solved iteratively to obtain the center coordinates of the termite nest.

5. The IoT-based intelligent monitoring and repellency method for building termites according to claim 1, characterized in that, Based on the Internet of Things (IoT) network, a visualization of the spatiotemporal distribution of CO2 concentration is generated from the CO2 concentration signal. Based on this visualization, the diameter range of the termite nest is calculated, including: The CO2 concentration signal is timestamped using the network time protocol of the Internet of Things (IoT) network to plot a CO2 concentration time series curve, and the peak value is marked in the CO2 concentration time series curve. Based on the Internet of Things (IoT) network, the node coordinates of each pair of IoT network nodes are obtained. Based on the node coordinates of each pair of IoT network nodes and the CO2 concentration signal, a two-dimensional CO2 concentration contour map is drawn, and the area with the maximum concentration gradient is marked on the two-dimensional CO2 concentration contour map. The CO2 concentration time series curve and the two-dimensional CO2 concentration contour map are used as the visualization results of the spatiotemporal distribution of CO2 concentration. Based on the visualization results of the spatiotemporal distribution of CO2 concentration, a CO2 diffusion model is constructed using a steady-state diffusion model. Based on the CO2 diffusion model, the estimated diameter of the termite nest was calculated. Uncertainty processing is applied to the estimated diameter of termite nests to obtain the range of termite nest diameters.

6. The IoT-based intelligent monitoring and repellency method for building termites according to claim 1, characterized in that, The center coordinates and diameter of the termite nest are transmitted to an Internet of Things (IoT) network to generate a termite nest distribution map, including: Based on the Internet of Things network, the termite nest area is simulated as a spherical nest area; The maximum value is selected from the range of termite nest diameters as the termite nest diameter, and the center coordinates of the termite nest are used as the center of the termite nest to generate a termite nest area image. Based on the Internet of Things (IoT) network, a termite nest distribution map is generated from the image of the termite nest area.

7. The IoT-based intelligent monitoring and repellency method for building termites according to claim 1, characterized in that, Based on the termite nest distribution map, a termite location and avoidance command is issued via the Internet of Things (IoT) network, including: Based on the Internet of Things network, a termite location and avoidance command is generated from the termite nest distribution map. According to the termite location and repellency command, the termite repellency device corresponding to the termite nest area in the termite nest distribution map is activated to repel termites.

8. An intelligent termite monitoring and repellency system for buildings based on the Internet of Things, characterized in that, include: The data acquisition module is used to acquire raw termite activity signals and perform signal preprocessing on the raw termite activity signals to obtain termite activity signals, wherein the termite activity signals include acoustic vibration signals and CO2 concentration signals. The feature recognition module is used to add a timestamp to the acoustic vibration signal to form an acoustic vibration time sequence signal, and to perform feature recognition on the acoustic vibration time sequence signal to obtain acoustic vibration pulse feature data. The coordinate calculation module is used to calculate the arrival time difference of the same pulse signal between each pair of IoT network nodes based on the acoustic vibration pulse characteristic data of the Internet of Things network, and to calculate the center coordinate value of the termite nest using the hyperbolic positioning method. The diameter calculation module is used to generate a visualization result of the spatiotemporal distribution of CO2 concentration based on the IoT network, and to calculate the range of the termite nest diameter based on the visualization result of the spatiotemporal distribution of CO2 concentration. An image generation module is used to transmit the center coordinates of the termite nest and the diameter range of the termite nest to the Internet of Things network to generate a termite nest distribution map. The instruction issuing module is used to issue termite location and avoidance instructions via the Internet of Things network based on the termite nest distribution map.

9. An electronic device, characterized in that, The device includes a memory, a processor, and a transceiver that are sequentially and communicatively connected. The memory is used to store a computer program, the transceiver is used to send and receive messages, and the processor is used to read the computer program and execute the Internet of Things-based intelligent monitoring and repelling method for building termites as described in any one of claims 1 to 7.

10. A computer program product, comprising a computer program or instructions, characterized in that, When the computer program or the instructions are executed by the computer, they implement the Internet of Things-based intelligent monitoring and repellency method for building termites as described in any one of claims 1 to 7.

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