Intelligent monitoring system and monitoring method for activity of termitarium formosana nests and separate flight prediction method of termitarium formosana nests

Abstract

The invention discloses an intelligent monitoring system and monitoring method for activity of a termitarium formosana nest body and a flying prediction method. The intelligent monitoring system comprises a main nest intelligent monitoring termite box and a winged termite grouping hole intelligent monitoring box, a self-cleaning underground gas passive monitoring device driven by a micro screw rod, a high-definition camera and a multi-parameter sensor group are arranged in the main nest intelligent monitoring ant box, and are used for collecting the environment and activity data of the main nest for a long time; the winged ant grouping hole intelligent monitoring box is deployed in a grouping period and is used for collecting grouping hole data; all the devices are powered by solar energy, and data preprocessing and wireless transmission are carried out through the monitoring terminal. According to the method, main nest long-term monitoring and grouping period special monitoring are combined, and accurate prediction of the flight time is realized based on multi-sensor information fusion and Wiener process random modeling, so that the technical problems of low efficiency, discontinuous data and incapability of early warning of grouping behaviors in a traditional method are solved; and real-time online monitoring and accurate decision support are provided for dam termite prevention and control.

Description

TECHNICAL FIELD

[0001] The present application belongs to the technical field of monitoring of black-winged termites, and particularly relates to a black-winged termite nest activity intelligent monitoring system, a monitoring method and a swarming prediction method. BACKGROUND

[0002] Black-winged termites seriously endanger the safety of water conservancy projects and garden trees, and their activities are hidden and the damage range is wide. They have a great impact on the safety of reservoir dams. Black-winged termite nest systems can cause damage to dam structures, induce piping and seepage, accelerate dam aging, and damage seepage prevention facilities. In order to control the damage of black-winged termites to the environment, more ecological characteristics of black-winged termites need to be obtained and analyzed. The swarming behavior of black-winged termites is a core strategy for their reproduction and population expansion. Traditional black-winged termite monitoring methods mainly rely on manual inspection and simple trapping devices, which have low monitoring efficiency, incomplete data, poor real-time performance and other problems. Therefore, there is an urgent need for a black-winged termite monitoring device that is comprehensive, stable and intelligent. Monitoring and analyzing the behavior of black-winged termites can help determine the best killing time and is of great significance to the prevention and control of black-winged termites.

[0003] The existing black-winged termite monitoring system has the following problems:

[0004] Firstly, it mainly relies on manual inspection and simple trapping devices, which have low monitoring efficiency, incomplete data, poor real-time performance and other problems. It is difficult to detect early termite damage, and often the damage is not detected until it is obvious, missing the best prevention and control opportunity.

[0005] Secondly, although ordinary ant boxes have certain monitoring functions, they generally have defects such as insufficient sensor reliability, single function, and incomplete data collection.

[0006] In addition, long-term buried sampling pipelines are easily blocked by mud or soil crusts built by termites, resulting in high maintenance costs and discontinuous data.

[0007] Thirdly, black-winged termite swarming is the main way of their spread, but existing technologies lack special swarming monitoring means, and cannot warn and record this key biological process.

[0008] Fourthly, the environment of reservoir dams is complex and variable, and existing monitoring equipment cannot meet the requirements of long-term stable work in terms of waterproofing, power supply, data transmission and other aspects.

[0009] Fifth, swarming prediction methods are outdated. Traditional termite swarming predictions are mostly based on external meteorological data, neglecting the termites' own swarming evolutionary mechanisms. Although some studies use multiple linear regression, this is essentially a reverse inference, resulting in poor interpretability and low accuracy. For multivariate coupled data, there is a lack of effective information fusion and evolutionary modeling mechanisms, making it difficult to achieve accurate predictions of initiation time. Summary of the Invention

[0010] To address the aforementioned technical problems, this invention proposes an intelligent monitoring system, monitoring method, and swarming prediction method for black-winged subterranean termite nests, thereby resolving the issues present in the prior art.

[0011] To achieve the above objectives, the present invention provides an intelligent monitoring system for the activity of black-winged subterranean termite nests, comprising:

[0012] Intelligent monitoring box for main nest and intelligent monitoring box for winged ant swarming holes;

[0013] The main nest intelligent monitoring ant box is deployed in the termite main nest area to collect internal environmental parameters and termite activity data.

[0014] The intelligent monitoring box for winged ant swarming holes is deployed in the distribution area of ​​swarming holes during the swarming period of black-winged subterranean termites to collect environmental parameters and winged ant activity data during the swarming period.

[0015] Both the main nest intelligent monitoring ant box and the winged ant swarming hole intelligent monitoring box are equipped with monitoring terminals, which are used to preprocess and wirelessly transmit the collected data, and predict the swarming time of black-winged subterranean termites based on multi-sensor information fusion and random evolution modeling, so as to realize intelligent monitoring and early warning of black-winged subterranean termite nest activities.

[0016] Optionally, the main nest intelligent monitoring ant box includes:

[0017] The box is made of waterproof and moisture-proof materials. It has light-transmitting panels and ventilation openings on the sides, and an entrance and exit channel at the bottom for termites to enter and exit.

[0018] A multi-parameter sensor group includes a soil three-parameter sensor, a sound sensor, a gas pressure sensor, a carbon dioxide concentration sensor, a methane concentration sensor, and a pH sensor; the soil three-parameter sensor includes a temperature and humidity sensor and a pH sensor.

[0019] A self-cleaning passive underground gas monitoring device driven by a miniature screw and powered by a solar power system.

[0020] Optionally, the micro-screw-driven self-cleaning passive underground gas monitoring device includes a micro linear drive unit, a gas diffusion chamber, a soil-penetrating conduit, and a cleaning probe.

[0021] The soil-inserting guide pipe is a hollow tube that is vertically inserted into the ground;

[0022] The cleaning probe is coaxially inserted inside the soil-penetrating guide tube, with its upper end connected to the micro linear drive unit and its lower end being a pointed cone shape.

[0023] The gas diffusion chamber is located between the miniature linear drive unit and the soil-inserting conduit, and integrates the gas pressure sensor, the carbon dioxide concentration sensor, the methane concentration sensor and the sound sensor inside;

[0024] The miniature linear drive unit drives the cleaning probe to reciprocate axially within the soil-inserting guide tube to perform operations such as puncturing blockages, constructing channels, and repositioning blockages.

[0025] Optionally, the side wall of the gas diffusion chamber is provided with a sensor mounting position, and the top of the chamber is provided with a miniature convection exhaust hole; the sound sensor uses the soil-inserting conduit as a sound waveguide to collect underground sound frequency signals.

[0026] Optionally, the winged ant swarming hole intelligent monitoring box includes a lightweight housing, a camera module, a simplified sensor group, and a power supply module;

[0027] The simplified sensor group includes a temperature and humidity sensor, a barometric pressure sensor, a sound sensor, and an infrared counting sensor;

[0028] The power supply module includes a solar panel for powering the camera, a solar panel for powering the monitoring terminal, and a 6000mAh lithium battery.

[0029] Optionally, the probe of the soil three-parameter sensor is independently inserted into the main nest to collect soil temperature, humidity, and pH data.

[0030] Optionally, the solar power supply system includes solar panels, an intelligent charge and discharge controller, a lithium battery pack, and a power management module.

[0031] Optionally, the monitoring terminal includes a data receiving module, a cluster prediction module, and an early warning module;

[0032] The swarm prediction module is used to construct a composite evolution index based on multi-sensor data through data fusion, and to establish a stochastic evolution model of termite swarming using the Wiener process, and to estimate the model parameters.

[0033] The early warning module is used to output cluster early warning information based on the probability distribution of separation time calculated by the stochastic evolution model.

[0034] This invention provides an intelligent monitoring method for the activity of black-winged subterranean termite nests, employing the aforementioned monitoring system and including the following steps:

[0035] The main nest intelligent monitoring ant box is deployed at the predetermined location to continuously collect activity data and environmental parameters of black-winged subterranean termites in the main nest; through the self-cleaning underground gas passive monitoring device driven by the micro screw, the soil breaking and ventilation, channel construction, passive diffusion sampling and sealing and resetting operations are performed in a cycle to continuously collect gas, sound and soil environmental parameters inside the main nest;

[0036] The monitoring terminal analyzes the collected data to predict the swarming period of black-winged subterranean termites.

[0037] Before the predicted swarming period, the winged ant swarming hole intelligent monitoring box is deployed in the swarming hole distribution area to collect swarming period data;

[0038] The intelligent monitoring box for the winged ant colony burrows was retrieved after the swarming period ended.

[0039] The extent of damage caused by black-winged subterranean termites was assessed by combining monitoring data from the main nest and data from the swarming period.

[0040] This invention provides a method for predicting the swarming of black-winged subterranean termites, which is based on the swarm prediction module of the above-mentioned system and includes the following steps:

[0041] Acquire time-series monitoring data collected by various sensors in the main nest intelligent monitoring ant box;

[0042] Based on a preset fusion coefficient vector, the time series monitoring data is weighted and fused to generate a one-dimensional composite evolution index.

[0043] A stochastic evolutionary model of termite swarming was established using the Wiener process, and the model parameters were estimated based on the composite evolutionary index.

[0044] An optimization objective function with minimizing the prediction mean square error as its core is constructed. A strategy combining particle swarm optimization and grid search is adopted to perform reverse iterative optimization on the fusion coefficient vector and the separation critical threshold until the optimal solution is obtained.

[0045] Based on the optimal solution and the stochastic evolution model, the probability distribution of the composite evolution index reaching the separation threshold for the first time is calculated, and the predicted start time is output.

[0046] Compared with the prior art, the present invention has the following advantages and technical effects:

[0047] This invention utilizes multi-sensor fusion technology to achieve comprehensive real-time monitoring of environmental parameters (temperature, humidity, air pressure, gas concentration, etc.) and activity status of black-winged subterranean termite nests. It employs a dual power supply mode of solar energy and an external adapter, reducing energy consumption and making it suitable for long-term field deployment. Furthermore, the upgraded sensor layout and power supply method significantly improve the device's stability and environmental adaptability. Remote data viewing and export are supported, allowing users to monitor black-winged subterranean termite activity in real time and formulate control strategies.

[0048] The integrated micro screw drive device of this invention adopts an extremely quiet design, using a micro geared motor in conjunction with the screw, resulting in low operating noise and avoiding disturbance to termites; it adopts passive diffusion sampling technology, which greatly extends the endurance in the field; the unique "breaking soil-retracting-sealing" working logic realizes the self-cleaning and anti-clogging function of the sampling channel, solving the problem of easy failure of traditional air intake pipelines. Attached Figure Description

[0049] The accompanying drawings, which form part of this application, are used to provide a further understanding of this application. The illustrative embodiments and descriptions of this application are used to explain this application and do not constitute an undue limitation of this application. In the drawings:

[0050] Figure 1 This is a schematic diagram of the module structure of the intelligent monitoring system for black-winged subterranean termite nest activity according to an embodiment of the present invention;

[0051] Figure 2 This is an overall block diagram of the intelligent monitoring system for black-winged subterranean termite nest activity according to an embodiment of the present invention;

[0052] Figure 3 This is a schematic diagram of the main control circuit of the intelligent monitoring ant box in an embodiment of the present invention;

[0053] Figure 4 This is a schematic diagram of the sensor acquisition circuit of the self-cleaning passive underground gas monitoring device according to an embodiment of the present invention;

[0054] Figure 5 This is a circuit diagram of the inlet / outlet sensor according to an embodiment of the present invention;

[0055] Figure 6 This is a diagram of the cloud server framework for the first-generation main nest intelligent monitoring ant box according to an embodiment of the present invention.

[0056] Figure 7 This is a structural diagram of the first-generation main nest intelligent monitoring ant box monitoring system according to an embodiment of the present invention;

[0057] Figure 8 This is a schematic diagram of an intelligent monitoring ant box for the main nest of black-winged subterranean termites, according to an embodiment of the present invention.

[0058] Figure 9 This is a cross-sectional view of the intelligent monitoring ant box for the main nest of black-winged subterranean termites according to an embodiment of the present invention.

[0059] Figure 10 This is a schematic diagram of the intelligent monitoring box for winged ant colony formation holes according to an embodiment of the present invention;

[0060] Figure 11 This is a cross-sectional view of the intelligent monitoring box for winged ant colonies according to an embodiment of the present invention.

[0061] Figure 12 This is a flowchart of the stochastic evolution modeling of termite swarming in an embodiment of the present invention;

[0062] Figure 13 This is a schematic diagram of a self-cleaning passive underground gas monitoring device driven by a miniature screw according to an embodiment of the present invention.

[0063] Figure 14 This is a cross-sectional view of a self-cleaning passive underground gas monitoring device driven by a miniature screw, according to an embodiment of the present invention.

[0064] Figure label:

[0065] 1. Housing; 2. Ventilation opening; 3. Camera; 4. Wireless communication antenna; 5. Light transmission hole; 6. Solar power supply module; 7. Fixing bracket; 8. Terminal. Detailed Implementation

[0066] It should be noted that, unless otherwise specified, the embodiments and features described in this application can be combined with each other. This application will now be described in detail with reference to the accompanying drawings and embodiments.

[0067] It should be noted that the steps shown in the flowchart in the accompanying drawings can be executed in a computer system such as a set of computer-executable instructions, and although a logical order is shown in the flowchart, in some cases the steps shown or described may be executed in a different order than that shown here.

[0068] Example 1

[0069] This embodiment provides an intelligent monitoring system for black-winged termite nest activities. Physically, it consists of a main nest intelligent monitoring ant box located near the dam, an intelligent monitoring box for winged termite swarming holes, and a monitoring terminal equipped with monitoring system software. The main nest monitoring system is integrated and encapsulated inside the main nest intelligent monitoring ant box for comprehensive perception of the main nest environment. The swarming hole monitoring system, encompassing air pressure, sound, temperature, humidity, entry / exit counting sensors, and cameras, is integrated and encapsulated inside the intelligent monitoring box for specific monitoring during the swarming period. Both types of front-end monitoring devices have built-in low-power power management modules for optimized operation and long battery life, and interact with the backend via wireless signal transmission.

[0070] The modular structure of the intelligent monitoring system for black-winged subterranean termite nest activity is as follows: Figure 1 As shown, it includes a main nest monitoring system, a swarm hole monitoring system, a data management module, and a low-power power management module. It can collect various data on the activities of black-winged subterranean termite nests in real time according to preset requirements and send the data to the monitoring system terminal.

[0071] The main nest monitoring system is located near the main nest and consists of an ant box containing sensor modules, an external monitoring module, an image acquisition module, and a power supply module. The sensor modules collect data on nest temperature and humidity, sound, air pressure, carbon dioxide concentration, methane concentration, pH, and other relevant information. The external monitoring module, consisting of temperature and humidity sensors and entry / exit counting sensors installed on the top cover of the ant box, monitors external weather conditions (temperature and humidity) and counts the activity frequency of black-winged subterranean termites. The sensor entry / exit holes have an inner diameter of 16mm and six entry / exit channels. The image acquisition module primarily captures images of winged termites swarming; the image data does not need to be uploaded to a server and is only used for local recording.

[0072] The swarming hole monitoring system includes multiple intelligent monitoring boxes for winged termites swarming holes, equipped with temperature and humidity sensors, sound sensors, barometric pressure sensors, entry / exit counting sensors, cameras, and a power supply module. It is used to monitor external weather conditions (temperature and humidity) and the swarming activity of black-winged termites. This system only operates near and during the swarming period of black-winged termites and is significantly smaller in size than the main nest monitoring system.

[0073] The power supply module includes a solar panel for powering the camera, a solar panel for powering the monitoring terminal, and a 6000mAh lithium battery. It also supports both solar charging and external adapter power supply to ensure continuous operation of the device in complex environments.

[0074] The data management module is primarily used for data reception, storage, and display, and supports information interaction with the monitoring terminal server. Users can view graphical data through a simplified data display interface and export data based on the database.

[0075] The low-power power management module optimizes the sensor interface driver and data acquisition process, and combines 4G network communication technology to achieve low-power operation of the device.

[0076] All ant hives in this system are solar-powered, feature a waterproof and moisture-proof structure, and undergo data preprocessing and transmission via a monitoring terminal. The main hive's intelligent monitoring ant hive incorporates a high-definition camera and multi-parameter sensors. It has access channels and ventilation openings on all four sides, and light-transmitting panels on both sides to achieve all-weather intelligent monitoring of black-winged termite behavior. The main hive innovatively integrates a micro-screw-driven self-cleaning passive underground gas monitoring device and a soil three-parameter sensor: the micro-screw-driven device uses a micro-gear motor to drive a cleaning probe in the soil entry conduit, performing a reciprocating motion of "breaking soil for ventilation - channel construction - sealing and resetting," which, combined with pressure, carbon dioxide, methane, and sound sensors in the gas diffusion chamber, achieves high-fidelity passive sampling of the underground environment while preventing blockage; the soil three-parameter sensor includes a temperature and humidity sensor and a pH sensor; the probe of the soil three-parameter sensor is independently inserted into the main hive to simultaneously monitor the temperature, humidity, and pH data inside the main hive. The intelligent monitoring box for winged termite swarming holes adopts a lightweight design and is deployed in the swarming hole distribution area during the swarming period of black-winged subterranean termites.

[0077] The intelligent monitoring system for black-winged subterranean termite nest activity of this invention achieves comprehensive monitoring and intelligent analysis of black-winged termite activity through a highly integrated software architecture. The software system consists of three parts: a terminal embedded program, a cloud server platform, and a user interactive application, forming a complete closed loop of data acquisition, transmission, processing, and application.

[0078] The embedded terminal program runs on the main controller within the main nest intelligent monitoring ant box and the winged ant swarming hole intelligent monitoring box. It employs a low-power design and is responsible for coordinating the operation of each sensor module. The overall block diagram of the black-winged subterranean termite nest activity intelligent monitoring system is shown below. Figure 2 As shown, the program collects environmental parameters such as temperature, humidity, gas concentration, and air pressure in real time through an optimized driver interface, and controls a 5-megapixel high-definition camera to record images of black-winged subterranean termites' activity. After local preprocessing, the collected data is transmitted to the cloud server via a 4G network, and can be temporarily stored in local Flash memory in case of network failure. The terminal program also integrates intelligent power management functions, which can automatically adjust the working mode according to the solar power supply status to ensure long-term stable operation in the field.

[0079] The main control circuit of the main hive intelligent monitoring ant box is the core of the entire system, and its principle block diagram is as follows: Figure 3As shown in the diagram, this circuit uses a low-power microcontroller (MCU) STM32L431RCT6 as its control core, responsible for data collection and processing. For power management, the circuit board incorporates a TP4056 charging management chip to enable intelligent solar charging of the lithium battery, and works with an HT7533 voltage regulator chip and an AP2007 power converter chip to convert the power to the 3.3V required by the system modules and the core voltage. For communication, an A7670C4G communication module is integrated, connecting to the MCU via a UART interface to remotely transmit monitoring data to a cloud server. Regarding sensors and expansion interfaces, the main control circuit connects to an AHT20 temperature and humidity sensor and an HP203B barometric pressure sensor via an I2C bus for real-time monitoring of the nest environment; simultaneously, the circuit integrates an audio acquisition module, using a MAX9813 microphone amplifier chip to amplify and acquire weak underground audio signals. In addition, the circuit board integrates a SIT3485 chip to support RS485 bus communication for connecting underground data acquisition devices and other expansion equipment, and has reserved multiple infrared counting interfaces for connecting to inlet and outlet sensors.

[0080] The miniature screw-driven self-cleaning passive underground gas monitoring device employs an independent data acquisition circuit module, the principle block diagram of which is shown below. Figure 4 As shown, this circuit uses the low-power microcontroller STM32L431RCT6 as the slave control core, focusing on the acquisition and preprocessing of multi-dimensional underground environmental data. For communication, the circuit integrates a SIT3485 transceiver chip, connecting to the MCU via a UART interface. This module is responsible for uploading the acquired sensor data to the main controller of the intelligent monitoring ant box in real time via a high-interference-resistance RS485 bus. In terms of environmental sensing, the circuit board carries multiple high-precision sensors to achieve comprehensive monitoring of the underground microenvironment:

[0081] 1. Audio Acquisition: The MAX9813 low-noise microphone amplifier, in conjunction with a high-sensitivity microphone, is used to preamplify the weak audio signals of underground termite activity and output analog signals to the MCU's ADC interface.

[0082] 2. Air pressure monitoring: The onboard HP203B high-precision air pressure sensor communicates directly with the MCU via the I2C bus to capture subtle fluctuations in underground air pressure in real time;

[0083] 3. Gas monitoring: The circuit has a standard sensor interface for connecting carbon dioxide and methane sensors to read gas concentration data.

[0084] In addition, the circuit is equipped with an HT7533 voltage regulator chip to convert the bus power supply into a stable 3.3V voltage to power the microcontroller and various sensors.

[0085] The inlet and outlet sensor circuit adopts the infrared photoelectric detection principle, and its principle block diagram is as follows: Figure 5 As shown, this circuit mainly consists of a power supply regulator unit, an infrared transceiver unit, and a signal shaping unit. The power supply section uses a C112031 voltage regulator chip to convert the input power into a stable operating voltage, ensuring the stability of infrared transmission and reception. The signal detection and processing core consists of a C2944066 signal processing chip and its peripheral circuitry. During operation, the infrared transmitter emits an infrared beam; when termites pass through the monitoring channel and block the beam, the signal at the infrared receiver changes. This changed signal is input to the C2944066 chip for comparison and shaping, and finally outputs a clear digital pulse signal from the chip's OUT pin. This digital pulse signal is directly connected to the input of the counting MCU. The MCU triggers an interrupt by detecting the edge of the pulse, thereby achieving accurate accumulation of the number of termites entering and leaving the MCU.

[0086] The front-end data acquisition function consists of a smart ant box for main nests that is deployed long-term in the main termite nest area, a smart monitoring box for winged ant swarming holes that is deployed only in the swarming hole distribution area during the swarming period, and a set of high-precision sensors.

[0087] Both the main nest intelligent monitoring ant box and the winged ant swarming hole intelligent monitoring box use a low-power microcontroller as the main control core, specifically the STM32L431RCT6 microcontroller, which is responsible for the collection, protocol conversion, and preliminary processing of multi-channel sensor data. For communication, an A7670C4G communication module is integrated, connecting to the microcontroller via a UART interface to achieve remote wireless transmission of monitoring data to a cloud server. For power supply, an HT7533 low-dropout linear regulator is used for voltage conversion, along with a 2W monocrystalline silicon solar panel and a 6000mAh lithium iron phosphate battery pack to form a dual power supply system, ensuring long-term stable operation of the equipment in outdoor environments without mains power.

[0088] Sensor selection and configuration: The system integrates multi-dimensional sensor groups to obtain accurate biological and environmental characteristic data. Specific selections are as follows:

[0089] Temperature and humidity monitoring: The AHT20 temperature and humidity sensor is used to collect real-time temperature and humidity data of the inside of the ant box and the soil environment.

[0090] Barometric pressure monitoring: The HP203B high-precision barometric pressure sensor is used to monitor micro-barometric pressure changes related to termite nesting activities.

[0091] Sound monitoring: The MIC52 sound sensor is used in conjunction with a waveguide structure to collect sound signals of underground termite gnawing and activity.

[0092] Entry and exit counting monitoring: The system uses a CAP200 entry and exit sensor and a counting circuit module based on the infrared beam / reflection principle. The counting circuit includes an infrared transmitter and a receiver, which is used to accurately count the flow of worker ants and the number of winged ants during the swarming period.

[0093] Gas monitoring: Carbon dioxide concentration sensors and methane concentration sensors supporting RS485 communication protocol are used to monitor gas indicators produced by the respiration and metabolism of ant nests.

[0094] To address the issues of traditional underground gas sampling pipelines being easily clogged by mud or failing due to moisture, a self-cleaning passive underground gas monitoring device driven by a miniature lead screw is specially integrated at the front end. This device includes a GA12-N20 miniature geared motor, a lead screw, an insertion guide tube, and a cleaning probe.

[0095] Its workflow is controlled by instructions from the main controller and consists of four stages:

[0096] Breaking and aerating: The motor drives the cleaning probe to move downwards and extend to the bottom of the soil-penetrating guide tube, physically breaking through the soil compaction layer or the mud blanket built by termites.

[0097] Channel construction: The probe is retracted upwards to above the gas diffusion chamber to construct a completely hollow gas passage connecting to the underground;

[0098] Passive diffusion sampling: Maintain stillness for a preset time (e.g., 60 seconds), and utilize the natural diffusion effect caused by the temperature or concentration difference between the underground gas and the outside to allow the underground gas to enter the diffusion chamber equipped with sensors for detection;

[0099] Blocking and resetting: After sampling, the probe moves downward to block the duct opening, preventing biological invasion and water backflow.

[0100] The cloud server platform adopts a distributed architecture and includes four core modules: data reception, storage, analysis, and early warning. Figure 6 This is the initial generation of intelligent ant colony monitoring cloud server framework. The backend data processing and early warning functions are deployed on a cloud server, and its core lies in the use of a termite swarm prediction method based on "data-model linkage". This method uses a machine learning model to identify the spatiotemporal patterns of black-winged subterranean termite activity and achieves accurate prediction of swarming periods based on multi-parameter fusion analysis.

[0101] The specific algorithm flow and mathematical model are as follows:

[0102] Step S1: Multi-source data fusion and construction of composite evolutionary indicators. Considering that single sensor data cannot accurately reflect the complex evolutionary process of termite swarming, the system employs an information fusion method to map multi-dimensional data into one-dimensional indicators. Let... For the first The first ant box One sensor in The normalized evolutionary indices extracted at each moment are weighted and fused to obtain composite evolutionary indices. The calculation formula is as follows:

[0103]

[0104] in, The total number of sensors, The fusion coefficient of each sensor measures its weight in the data fusion process. This is the fusion coefficient vector.

[0105] Step S2: Establish a stochastic evolutionary model based on the Wiener process. Given that the evolution of nest environmental parameters before termite swarming satisfies infinite divisibility and exhibits non-monotonic fluctuations, this invention utilizes the Wiener process to describe this stochastic evolutionary process. The stochastic differential equations are established as follows:

[0106] ;

[0107] in, The index value at the initial time. This is the drift coefficient (characterizing the evolutionary trend). The diffusion coefficient (characterizing the intensity of random fluctuations) is the diffusion coefficient. This represents standard Brownian motion. Based on the monitored discrete data sequence, the model parameters are estimated using the maximum likelihood estimation (MLE) method. Perform real-time estimation.

[0108] Step S3: Digital-Model Linkage Optimization and Objective Function Construction to Solve the Fusion Coefficient Vector and separation threshold For a defined isolated problem, the system constructs an optimization objective function with minimizing the mean square error of prediction as its core. This achieves a closed-loop feedback mechanism for evolutionary feature extraction and stochastic evolution modeling. The objective function is defined as:

[0109]

[0110] in, For the sample size, The remaining time before takeoff is predicted based on the current fusion coefficient and threshold. This is the actual remaining time. The system's goal is to find the optimal solution. This minimizes the objective function.

[0111] Step S4: Two-layer iterative optimization solution. For solving the objective function, this system employs a strategy combining particle swarm optimization (PSO) and grid search for reverse iterative optimization: First, a heuristic PSO is used for a large-step global search to quickly locate the approximate range of the global optimum, avoiding getting trapped in local optima; then, within the range determined by PSO, a small-step fine search is performed using grid search to determine the final optimal parameters. and .

[0112] Step S5: Separation time prediction output. Based on the optimized model parameters and thresholds, calculate the composite evolution index when it first reaches the separation threshold. The system calculates the probability distribution of the first arrival time and outputs the predicted takeoff time. As the predicted time approaches, the early warning module sends clustered alerts to administrators via a visual interface and push notifications.

[0113] User-interactive applications, including web and mobile versions, provide users with an intuitive data visualization interface. Figure 7 This is the structure of the first-generation main nest intelligent monitoring ant box monitoring system. The application adopts a responsive design, displaying real-time environmental parameter change curves, black-winged subterranean termite activity heatmaps, and high-definition monitoring images at each monitoring point. Users can query historical data via a timeline, and the system automatically generates analysis reports containing key indicators. The mobile application supports push notifications to ensure users receive timely alerts. Furthermore, the application provides remote control functionality, allowing users to adjust parameters such as sensor sampling frequency for flexible management of the monitoring process.

[0114] The entire software system employs multiple security mechanisms to ensure data reliability, including transport layer encryption, database field encryption, and off-site disaster recovery backup. The system supports horizontal scaling, easily connecting tens of thousands of monitoring nodes, providing strong technical support for the control of black-winged subterranean termites in large dam complexes. Through coordinated optimization of software and hardware, this invention achieves a leapfrog development in black-winged subterranean termite monitoring from manual inspection to intelligent analysis, providing an innovative solution for the safety management of water conservancy facilities.

[0115] like Figure 8 and Figure 9 As shown, the main nest intelligent monitoring ant box is placed near the main nest of the dam. The main nest intelligent monitoring ant box includes a box body 1, a ventilation opening 2, a camera 3, a multi-parameter sensor, a terminal 8, a wireless communication antenna 4, a light-transmitting hole 5, and a solar power supply module 6.

[0116] Ventilation openings are located on both sides of the ant box to reduce the temperature inside the ant box;

[0117] The camera system includes a 5-megapixel high-definition camera and an infrared supplementary light. The camera is mounted on top of the main hive's intelligent monitoring ant box and its shooting angle is adjusted via a universal bracket to ensure complete coverage of the monitoring area. The camera's external rectangular casing is glued to the ant box for further waterproofing, facing the termite entry and exit channels. The bottom of the ant box has six entry and exit channels equipped with inlet and outlet sensors. The camera can cover more than four of these channels. When black-winged subterranean termites enter or exit, the inlet and outlet sensors collect data from the termites' entrances and exits, and the camera collects and records the termite activity inside the ant box. The camera module supports the following operating modes: Active Period Mode (peak black-winged subterranean termite activity): continuous shooting at full resolution; Quiet Period Mode: reduced resolution and frame rate to save energy; Night Mode: supplementary light is activated to maintain continuous monitoring.

[0118] The light-transmitting hole 5 is located at the top of the ant box to ensure that the camera can clearly monitor the activities of the black-winged subterranean termites;

[0119] The sensors include temperature and humidity sensors, sound sensors, air pressure sensors, carbon dioxide concentration sensors, methane concentration sensors, and pH sensors.

[0120] Each sensor is placed near the nest and covered with soil. When black-winged subterranean termites are active near the nest, each sensor will collect data on the internal and external environment of the ant box and use this data in the background system to analyze various data of the black-winged subterranean termites.

[0121] Terminal 8 is located inside the ant box and is wired to each of the sensor modules. It transmits the data collected by each sensor to the server, which is used for data collection, processing, and data transmission within the ant box. The terminal connects to the sensor module via a connector at the end of the wiring harness. The ant box has pathways inside for arranging the wiring harness.

[0122] The wireless communication antenna is installed on the top of the enclosure. Data is transmitted through the 4G wireless communication and monitoring terminal platform, and the collected ant colony activity data is transmitted to the database in real time.

[0123] Solar power module 6 employs a dual power supply method, utilizing both solar charging and an external adapter, to ensure continuous operation of the equipment. The power supply module includes: a monocrystalline silicon solar panel, an intelligent charge / discharge controller, and a lithium iron phosphate battery pack.

[0124] A self-cleaning passive underground gas monitoring device driven by a miniature screw, its structure is as follows: Figure 13 As shown, a cross-sectional view of the micro-screw-driven self-cleaning passive underground gas monitoring device is shown below. Figure 14As shown. The main body is supported by a ground base unit, with the soil insertion guide vertically positioned below the base. It is a hollow 304 stainless steel tube with an inner diameter of 8mm, inserted into the soil. A miniature linear drive unit is installed at the top of the device, using a GA12-N20 miniature gear reducer motor, whose output shaft is connected to an M4 lead screw. The cleaning probe is coaxially inserted into the soil insertion guide, using a rigid rod with a diameter of 4mm. Three V-shaped air guide grooves are axially formed on its surface to enhance airflow. The upper end of the probe is connected to the lead screw, and the lower end is ground into a pointed cone shape. The gas diffusion chamber is located between the drive unit and the soil insertion guide. Gas sensors such as carbon dioxide are installed on the side walls. The probe is wrapped with a breathable and waterproof ePTFE membrane, and the top has a 1mm diameter miniature convection exhaust hole to create a "chimney effect" airflow outlet during sampling.

[0125] The control logic of this device is as follows:

[0126] 1. Wake-up and Breaking: After the system wakes up at a set time, the MCU controls the motor to rotate forward, driving the cleaning probe to move downward, so that its tip extends 15mm out of the conduit opening and pierces any termite mud or blockages that may be present.

[0127] 2. Retraction to construct the channel: The motor reverses, retracting the probe completely above the diffusion chamber. At this point, an unobstructed hollow air path is formed inside the soil-inserting guide tube.

[0128] 3. Passive diffusion monitoring: The system remains stationary for 60 seconds. Warm, humid gas inside the nest rises naturally along the duct due to density differences, filling the diffusion chamber. Exhaust gas is discharged from the top convection hole, and the sensor continuously reads data.

[0129] 4. Blocking and Dormant: After sampling is completed, the motor is restarted to push the probe into the conduit, so that its tip is flush with the conduit opening, which acts as a physical plug to prevent external insects from entering or water from flowing back in. The system then enters dormancy.

[0130] like Figure 10 and Figure 11 As shown, the intelligent monitoring box for winged ant colonies includes a housing, ventilation openings, light-transmitting holes, a camera, sensors, a wireless communication antenna, a terminal, and a solar power module. Its size is significantly reduced compared to the main nest monitoring system.

[0131] The intelligent monitoring box for winged termite swarming holes needs to be placed on the swarming holes of black-winged subterranean termites before the swarming behavior occurs. It is used to monitor the external weather, temperature, humidity, and swarming activity of black-winged subterranean termites. The swarming hole monitoring system does not need to operate year-round, but only during and before the swarming period to monitor the swarming behavior of black-winged subterranean termites.

[0132] It should be noted that the ventilation openings of the winged ant swarming hole intelligent monitoring box are different in size from those of the main nest monitoring ant box, resulting in a more significant ventilation and heat dissipation effect.

[0133] The light-transmitting holes are located on both sides of the ant box, with a trapezoidal design that covers almost the entire side. Because the intelligent monitoring box for winged ant swarming holes is small in size, it ensures that the camera can clearly monitor the swarming activities of black-winged subterranean termites.

[0134] The sensors include temperature and humidity sensors and entry / exit counting sensors. The temperature and humidity sensors monitor the external weather temperature and humidity. When black-winged subterranean termites enter or exit, the entry / exit sensors are used to collect data on the entry and exit of black-winged subterranean termites at the burrow entrance.

[0135] The working principle of the camera, wireless communication cable, terminal, and solar power module of the winged ant colony intelligent monitoring box is no different from that of the main nest intelligent monitoring ant box.

[0136] When the main nest intelligent monitoring ant box and the winged ant swarming hole intelligent monitoring box detect activities related to black-winged subterranean termites, they will transmit the signal to the monitoring terminal through the antenna on the ant box cover. The low-power power management module is responsible for optimizing the sensor drive and data acquisition process to ensure long-term efficient operation of the equipment.

[0137] The specific usage method of this intelligent monitoring system for black-winged subterranean termite nest activity is as follows:

[0138] 1. System Preparation: Start the system, connect all modules, and check if each part is working properly. Ensure that sensors, cameras, and other equipment are in optimal working condition.

[0139] 2. Install the enclosure: Select an ant nest near the dam and install the ant box nearby. Secure the four corners of the enclosure with patches and bolts to ensure stability and prevent tipping or tilting. Also, ensure all sensors are covered with soil.

[0140] 3. Data Acquisition: Data will be collected during the activity of black-winged subterranean termites, simultaneously recording various sensor data and high-definition video to ensure accurate data capture. During the measurement process, the experimental environment should be kept stable to avoid external interference affecting the measurement results.

[0141] 4. Data Analysis: After measurement, the collected data is transmitted to the detection terminal. Data processing software is then used to analyze the data, extract valid data, and calculate relevant parameters. This facilitates subsequent analysis of the habits and patterns of black-winged subterranean termites and the development of control strategies.

[0142] This invention runs a flight prediction algorithm based on statistical data in a central processing platform. The specific technical approach is as follows: Figure 12 As shown. This method adopts the "digital-model linkage" approach, that is, evolutionary feature extraction and stochastic evolution modeling mutually reinforce each other. The specific steps are as follows:

[0143] Step 1: Multi-source data fusion and indicator construction. Considering that a single feature cannot accurately reflect the evolutionary process, this system employs an information fusion method. Let... It is the first The first ant box One sensor in Evolutionary indicators extracted at various times (such as temperature, carbon dioxide concentration, and the number of people entering and leaving the environment). Based on multi-source sensor data, a weighted method is used to obtain a composite evolutionary index, calculated as follows: in, It is the first The fusion coefficient of a sensor measures the proportion of that sensor in data fusion.

[0144] Step Two: Stochastic Evolution Modeling Based on Wiener Processes. This embodiment utilizes Wiener processes to describe the evolution of termite swarming. Since termite swarming evolution satisfies infinite divisibility and exhibits non-monotonic fluctuations, it is suitable for modeling using Wiener processes. By solving the probability distribution of the time when the stochastic evolution process first reaches the swarming threshold, the start-of-swarming time can be predicted.

[0145] Step 3: Digital Modeling and Adaptive Optimization. To address the issues of unclear physical meaning of composite indices and isolated threshold determination, this embodiment constructs a feedback loop for constructing composite evolutionary indices and stochastic evolutionary modeling. Specifically, based on the composite evolutionary index sets of each ant box, point estimates of the remaining prediction time are obtained, and the following optimization objective function is constructed:

[0146] ;

[0147] in, This is the fusion coefficient vector corresponding to the sensor. It is the separation threshold. To predict the mean square error.

[0148] Step 4: Two-layer optimization solution. To obtain the optimal fusion coefficient... and separation threshold That is, the solution to the formula: This system employs an optimization method combining grid search and particle swarm optimization (PSO):

[0149] First, a heuristic particle swarm optimization algorithm is used to perform a large-step search to quickly locate the approximate range of the global optimal solution;

[0150] Then, a grid search is used to perform a fine-grained search with small steps to determine the final optimal parameters.

[0151] Through the above process, the system can adaptively adjust the weights of different sensors (such as temperature, gas, and air pressure) to provide a high-precision early warning time before termites swarm.

[0152] The above are merely preferred embodiments of this application, but the scope of protection of this application is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in this application should be included within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.

Claims

1. An intelligent monitoring system for the activity of black-winged subterranean termite nests, characterized in that, include: Intelligent monitoring box for main nest and intelligent monitoring box for winged ant swarming holes; The main nest intelligent monitoring ant box is deployed in the termite main nest area to collect internal environmental parameters and termite activity data. The intelligent monitoring box for winged ant swarming holes is deployed in the distribution area of ​​swarming holes during the swarming period of black-winged subterranean termites to collect environmental parameters and winged ant activity data during the swarming period. Both the main nest intelligent monitoring ant box and the winged ant swarming hole intelligent monitoring box are equipped with monitoring terminals, which are used to preprocess and wirelessly transmit the collected data, and predict the swarming time of black-winged subterranean termites based on multi-sensor information fusion and random evolution modeling, so as to realize intelligent monitoring and early warning of black-winged subterranean termite nest activities.

2. The intelligent monitoring system for black-winged subterranean termite nest activity according to claim 1, characterized in that, The main nest intelligent monitoring ant box includes: The box is made of waterproof and moisture-proof materials. It has light-transmitting panels and ventilation openings on the sides, and an entrance and exit channel at the bottom for termites to enter and exit. The video surveillance module includes a high-definition camera with supplemental lighting; A multi-parameter sensor group includes a soil three-parameter sensor, a sound sensor, a barometric pressure sensor, a carbon dioxide concentration sensor, a methane concentration sensor, and a temperature and humidity sensor; the soil three-parameter sensor includes a temperature and humidity sensor and a pH sensor. A self-cleaning passive underground gas monitoring device driven by a miniature screw and powered by a solar power system.

3. The intelligent monitoring system for black-winged subterranean termite nest activity according to claim 2, characterized in that, The micro-screw driven self-cleaning passive underground gas monitoring device includes a micro linear drive unit, a gas diffusion chamber, a soil-entry conduit, and a cleaning probe. The soil-inserting guide pipe is a hollow tube that is vertically inserted into the ground; The cleaning probe is coaxially inserted inside the soil-penetrating guide tube, with its upper end connected to the micro linear drive unit and its lower end being a pointed cone shape. The gas diffusion chamber is located between the miniature linear drive unit and the soil-inserting conduit, and integrates the gas pressure sensor, the carbon dioxide concentration sensor, the methane concentration sensor and the sound sensor inside; The miniature linear drive unit drives the cleaning probe to reciprocate axially within the soil-inserting guide tube to perform operations such as puncturing blockages, constructing channels, and repositioning blockages.

4. The intelligent monitoring system for black-winged subterranean termite nest activity according to claim 3, characterized in that, The gas diffusion chamber has a sensor mounting position on its side wall and a miniature convection exhaust hole on its top; the sound sensor uses the soil-inserting conduit as a sound waveguide to collect underground sound frequency signals.

5. The intelligent monitoring system for black-winged subterranean termite nest activity according to claim 1, characterized in that, The intelligent monitoring box for winged ant colony holes includes a lightweight housing, a camera module, a simplified sensor group, and a power supply module. The simplified sensor group includes a temperature and humidity sensor, a barometric pressure sensor, a sound sensor, and an infrared counting sensor; The power supply module includes a solar panel for powering the camera, a solar panel for powering the monitoring terminal, and a 6000mAh lithium battery.

6. The intelligent monitoring system for black-winged subterranean termite nest activity according to claim 2, characterized in that, The probe of the soil three-parameter sensor is independently inserted into the main nest to collect temperature, humidity and pH data inside the main nest.

7. The intelligent monitoring system for black-winged subterranean termite nest activity according to claim 2, characterized in that, The solar power supply system includes solar panels, an intelligent charge and discharge controller, a lithium battery pack, and a power management module.

8. The intelligent monitoring system for black-winged subterranean termite nest activity according to claim 1, characterized in that, The monitoring terminal includes a data receiving module, a cluster prediction module, and an early warning module. The swarm prediction module is used to construct a composite evolution index based on multi-sensor data through data fusion, and to establish a stochastic evolution model of termite swarming using the Wiener process, and to estimate the model parameters. The early warning module is used to output cluster early warning information based on the probability distribution of separation time calculated by the stochastic evolution model.

9. A method for intelligent monitoring of black-winged subterranean termite nest activity, employing the monitoring system described in any one of claims 1 to 8, characterized in that, Including the following steps: The main nest intelligent monitoring ant box is deployed at the predetermined location to continuously collect activity data and environmental parameters of black-winged subterranean termites in the main nest; through the self-cleaning underground gas passive monitoring device driven by the micro screw, the soil breaking and ventilation, channel construction, passive diffusion sampling and sealing and resetting operations are performed in a cycle to continuously collect gas, sound and soil environmental parameters inside the main nest; The monitoring terminal analyzes the collected data to predict the swarming period of black-winged subterranean termites. Before the predicted swarming period, the winged ant swarming hole intelligent monitoring box is deployed in the swarming hole distribution area to collect swarming period data; The intelligent monitoring box for the winged ant colony burrows was retrieved after the swarming period ended. The extent of damage caused by black-winged subterranean termites was assessed by combining monitoring data from the main nest and data from the swarming period.

10. A method for predicting the swarming of black-winged subterranean termites, executed based on the swarm prediction module of the system described in claim 8, characterized in that, Including the following steps: Acquire time-series monitoring data collected by various sensors in the main nest intelligent monitoring ant box; Based on a preset fusion coefficient vector, the time series monitoring data is weighted and fused to generate a one-dimensional composite evolution index. A stochastic evolutionary model of termite swarming was established using the Wiener process, and the model parameters were estimated based on the composite evolutionary index. An optimization objective function with minimizing the prediction mean square error as its core is constructed. A strategy combining particle swarm optimization and grid search is adopted to perform reverse iterative optimization on the fusion coefficient vector and the separation critical threshold until the optimal solution is obtained. Based on the optimal solution and the stochastic evolution model, the probability distribution of the composite evolution index reaching the separation threshold for the first time is calculated, and the predicted start time is output.

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