Sticky fly strip intelligent monitoring and killing system based on internet of things

Abstract

The application discloses a fly sticky tape intelligent monitoring and killing system based on Internet of Things, and relates to the technical fields of pest control and Internet of Things intelligent control, and comprises the following: an intelligent fly sticky tape terminal, which is integrated with a quality sensor, a capacitive sensor and an environmental sensor, and is used for collecting fly adhesion data and environmental parameters in real time; a regional fly risk analysis module, which is used for evaluating a risk level through a fly activity intensity index and a cumulative risk index; an early warning and decision support module, which is used for dynamically optimizing a threshold value by using reinforcement learning and pushing early warning through multiple channels; a linkage killing module, which supports the cooperative operation of fly killing lamps, automatic pesticide spraying and automatic tape replacement; and an intelligent control and management platform, which provides GIS visual monitoring and equipment management. The fly sticky tape intelligent monitoring and killing system based on Internet of Things is adopted, and the Internet of Things technology is systematically applied to the fly sticky tape for the first time, so that real-time monitoring, accurate early warning and intelligent killing of flies are realized, and the operation cost can be reduced, and the system is suitable for various scenes.

Description

Technical Field

[0001] This invention relates to the field of pest control and IoT intelligent control technology, and in particular to an IoT-based intelligent monitoring and extermination system for fly sticks. Background Technology

[0002] Flies are significant medical and hygiene pests, carrying bacteria such as Shigella, Vibrio cholerae, Salmonella typhi, Mycobacterium tuberculosis, as well as various enteroviruses and parasite eggs, posing a serious threat to public health. Simultaneously, in agricultural production, flies directly or indirectly harm livestock health and crop quality, leading to economic losses. Therefore, the scientific control of flies has significant social and economic value.

[0003] Fly sticks are widely used in various fly control scenarios due to their low cost, ease of use, lack of chemical pollution, and absence of pesticide resistance. However, traditional fly sticks have the following technical drawbacks: First, monitoring is delayed. Traditional fly sticks rely on manual visual inspection, and managers cannot know the sticks' trapping status in real time. Often, the sticks are completely covered but not replaced in time, resulting in subsequent flies not being captured; conversely, they may be replaced prematurely before they are fully covered, leading to resource waste. Second, there is a lack of data analysis capabilities. Traditional methods cannot quantify fly activity intensity, density trends, and spatial distribution characteristics, making it difficult to scientifically determine the risk level of different areas, resulting in a lack of targeted allocation of control resources. Third, there is no coordinated extermination mechanism. Fly sticks are only used as passive physical fly-catching tools; when fly density is high, they cannot actively coordinate with other extermination equipment to form a closed loop of "monitoring-early warning-extermination." Fourth, management efficiency is low. In large-scale deployment scenarios, manual inspections involve long routes, low frequency, high costs, and fragmented information, making it difficult to achieve refined regional management and data-driven decision optimization. Summary of the Invention

[0004] The purpose of this invention is to provide an intelligent monitoring and extermination system for fly strips based on the Internet of Things, so as to solve the technical problems in the prior art that fly strips cannot be monitored in real time, cannot be automatically warned, and cannot be used for coordinated extermination.

[0005] To achieve the above objectives, this invention provides an intelligent monitoring and extermination system for flypaper based on the Internet of Things, comprising: The intelligent fly-stick terminal is deployed at various monitoring points to collect fly adhesion data and environmental parameters in real time. The regional fly risk analysis module is used to receive and process data uploaded by the smart fly sticky terminal, construct a dynamic assessment model of fly activity, and calculate the regional fly risk index. The early warning and decision support module is used to generate tiered early warning information based on the regional fly risk index and provide prevention and control decision suggestions. The linkage and extermination module is used to automatically or remotely trigger one or more extermination devices to perform prevention and control operations based on early warning instructions; The intelligent control and management platform is used to centrally manage all terminal devices, display monitoring data, analyze historical trends, configure system parameters, and manage user permissions.

[0006] Preferably, the intelligent fly sticky terminal includes a box and a fly-killing lamp set on the top of the box. The top of the box has an opening, an environmental parameter acquisition unit is set near the opening, a scroll device is set below the environmental parameter acquisition unit, a partition is set below the scroll device, and a microcontroller unit, a power supply unit and an automatic spraying device are set below the partition. The reel unit includes a first take-up reel, a second take-up reel, and a feed reel located below the side of the second take-up reel. A double-layered paper roll is detachably mounted on the feed reel; one layer is the fly-stick body, and the other is a tough paper. The fly-stick body is driven to the first take-up reel, and the tough paper is driven to the second take-up reel. A microcontroller unit controls the simultaneous rotation of the first and second take-up reels. The part above the partition corresponding to the fly stick body is set as a hollow structure. An ultra-thin polyester film is set on the top of the hollow structure and is attached to the fly stick body. A mass sensing unit is set on the side of the ultra-thin polyester film away from the fly stick body. Capacitive proximity sensing units are installed below the environmental parameter acquisition unit and on one side of the first take-up reel and the unload reel. The automatic spraying device includes a liquid medicine storage tank, a micro diaphragm pump, and a delivery pipe installed above the micro diaphragm pump. The end of the delivery pipe away from the liquid medicine storage tank is connected to a rotating nozzle, which is located on the top of the tank.

[0007] Preferably, the regional fly risk analysis module includes: The data preprocessing submodule is used to perform outlier removal, missing value imputation, and signal denoising on the raw sensor data. The adhesion quantity conversion submodule converts the mass change into an estimated adhesion quantity based on the pre-calibrated average mass parameter of a single fly, and calculates the adhesion rate per unit time. The fly activity intensity index calculation submodule uses the following formula to calculate the fly activity intensity index. : ; in, Time interval Change in mass within (unit: mg); Time interval (unit: min); The current temperature (unit: °C). For reference temperature; Current relative humidity (unit: %); Current light intensity (unit: lux) This refers to light saturation and reference values; These are the weighting coefficients, with values ​​ranging from 1 to 10. , , And satisfy ; The cumulative risk index calculation submodule uses the following decay integral formula to calculate the cumulative risk index over a continuous period of time. : ; in The evaluation time window is 1 minute. It is the attenuation factor; The regional risk level determination submodule is based on the cumulative risk index. The risks are divided into four levels: Low risk (green); Medium risk (yellow); High risk (orange); Extremely high risk (red).

[0008] Preferred weighting coefficient The Bayesian online learning algorithm is used for dynamic updates, specifically: ; in The loss function is based on historical adhesion data and the actual fly density verification value. The learning rate has a range of values. This mechanism enables the model to automatically optimize parameters based on fly behavior characteristics in different geographical regions and seasons, without the need for manual parameter tuning.

[0009] Preferably, the early warning and decision support module includes: The tiered early warning generation submodule generates warnings when the cumulative risk index... When the risk level reaches medium or above and the duration exceeds the preset threshold (default 15 minutes), an early warning message will be automatically generated. The early warning message includes: the device's unique identifier, geographical coordinates, current risk level, estimated amount of adhesion, environmental parameter snapshot, and recommended handling measures. The multi-channel push submodule supports pushing alert information to users with different permissions via one or more of the following methods: mobile APP, SMS, WeChat Enterprise Account, DingTalk, or email. The adaptive threshold adjustment submodule, based on historical early warning records and actual eradication effect feedback, dynamically adjusts the threshold boundaries for each risk level using a reinforcement learning Q-learning algorithm. Specifically: ; Among them, state Includes current risk index, time period, season, historical equipment response rate, and actions. Adjust the threshold step size. As a reward signal based on the success rate of epidemic prevention and control; The discount factor is set to 0.9. Through continuous learning, the system can adapt to the changing patterns of different scenarios.

[0010] The decision suggestion generation submodule automatically outputs suggested extermination methods, pesticide types, application dosages, and treatment priorities based on risk level, fly species (with optional image recognition assistance) and scenario type.

[0011] Preferably, the linkage extermination module includes one or more of the following extermination devices and their control units: The electric fly killer lamp control unit controls the power supply of the fly killer lamp through a relay switch. It supports timed working mode (default 30 minutes per session) and pulse working mode (5 minutes of operation followed by a 2-minute pause). It also has a built-in light decay detection sensor that automatically sends a replacement reminder when the UV lamp illuminance is lower than 60% of the initial value. The automatic spraying device control unit includes a pesticide storage tank (capacity 1-5L), a miniature diaphragm pump, a rotatable nozzle, and a controller. Upon receiving a trigger command, it executes the spraying operation according to preset parameters (spray volume 50-200mL, spray angle 60°-120°, effective range 3-10m). The device has a built-in pesticide level sensor and nozzle anti-clogging detection module, which automatically alarms when the level is below 10% or when clogging is detected. The automatic fly strip replacement device control unit adopts a dual-spindle winding structure, including an active spindle (rewinding used fly strips), a driven spindle (releasing new fly strips), a stepper motor, and a torque sensor. When the mass sensor detects that the number of sticky fly strips has reached the saturation threshold (default 300 sticks / strip) or the mass exceeds the preset upper limit (default 30g), it controls the stepper motor to rotate a preset angle (default 360° / time), winding up the saturated section and unfolding a new section. The device has a built-in counting encoder that records the length replaced and estimates the remaining usable days. The control unit of the electric fly killer lamp includes an ultrasonic generator (frequency adjustable from 20-40kHz, sound pressure level 80-110dB) and a high-brightness strobe light (power 10-30W, flashing frequency 2-10Hz), which is suitable for scenarios sensitive to chemical agents and reduces fly density by repelling them.

[0012] Preferably, the coordinated extermination module supports a collaborative operation mode, which automatically combines multiple extermination methods based on the risk level: In medium-risk situations: trigger the electric fly killer lamp alone; In high-risk situations: Simultaneously trigger the fly-killing lamp and the automatic spraying device, and record the coordinated response time; In cases of extremely high risk: the automatic fly strip replacement device is triggered in sequence (first updating the fly trap surface), then the spraying device is triggered (to enhance extermination), and finally the electric fly killer lamp is triggered (to drive away any remaining flies), forming a three-level linkage of "replacement - spraying - repulsion".

[0013] Preferably, the intelligent control and management platform includes: Real-time monitoring dashboard, based on GIS map, displays the location, online status, current cumulative risk index, battery level, and most recent warning time of all terminal devices; supports clicking on a device to pop up a details window; The historical data analysis engine supports querying adhesion quantity curves, risk index change trends, and environmental parameter correlation analysis by hour, day, week, month, year, and custom time range; it also supports exporting data to CSV, Excel, or JSON formats. The fly activity heatmap module generates a regional fly activity heatmap based on the cumulative risk index of each point using the Gaussian kernel density estimation method. The darker the color, the higher the risk. It supports sliding the time axis to view dynamic changes. The device management system supports batch addition, import, and deletion of devices; remote configuration of sampling frequency, communication parameters, and early warning thresholds; viewing of device firmware versions and supporting OTA remote upgrades (supporting breakpoint resume and version rollback). The user access control system supports multi-tenant isolation, and roles can be set under each tenant. Super Administrator: Full privileges; Area Administrator: Responsible for device management and alert confirmation within a specific area; Inspector: Check data, report on-site maintenance records, and scan QR codes to confirm equipment status; Auditor: Read-only access to system logs and operation records; Visitors: Only view publicly available statistics; Prevention and control strategy library: Pre-set recommended prevention and control plans for different scenarios (breeding farms, canteens, garbage stations, food factories), different fly species, and different seasons, including combinations of extermination methods, pesticide formulations, and application frequencies; supports user-defined strategies and saving them as templates; The log auditing system records all user logins, parameter modifications, alert triggers, kill operations, device logins and logouts, etc. The logs include timestamps, user IDs, operation types, operation objects, operation results, and IP addresses. The logs are retained for no less than 180 days and support searching and exporting by conditions.

[0014] Therefore, the present invention employs the above-mentioned IoT-based intelligent monitoring and extermination system for fly strips, which has the following beneficial effects: (1) The adhesion status is continuously monitored by mass sensor and capacitance sensor, replacing the traditional manual visual inspection. The response time is shortened from the day level to the minute level, avoiding the waste of resources such as "not replacing when full" or "replacing when not full".

[0015] (2) A fly activity intensity index that integrates three factors, namely temperature, humidity and light, and a cumulative risk index with decay memory were constructed to realize the quantitative assessment of fly activity and the four-color risk level classification, which is more objective and accurate than experience judgment.

[0016] (3) It integrates three extermination methods: electric shock, spraying and automatic bar replacement, and supports "cooperative operation mode" (single start for medium risk, double linkage for high risk, and triple linkage for extremely high risk) and "learning automatic mode" to achieve fully automated operation.

[0017] (4) Bayesian online learning is used to achieve adaptive model parameters. Inefficient strategies are automatically marked and included in the training set based on the prevention and control effect score, forming a positive cycle of "monitoring → evaluation → optimization".

[0018] The technical solution of the present invention will be further described in detail below with reference to the accompanying drawings and embodiments. Attached Figure Description

[0019] Figure 1 This is a schematic diagram of the overall architecture of an embodiment of the IoT-based intelligent monitoring and extermination system for fly strips according to the present invention. Figure 2 The fly activity intensity index in this embodiment of the invention. and cumulative risk index Calculation flowchart; Figure 3 This is a flowchart of the collaborative combat mode of the linkage extermination module in an embodiment of the present invention; Figure 4 This is a schematic diagram of the intelligent fly-stick terminal structure according to an embodiment of the present invention; Figure Labels 1. Housing; 2. Environmental parameter acquisition unit; 3. Capacitive proximity sensor unit; 4. First take-up reel; 5. Second take-up reel; 6. Discharge reel; 7. Mass sensor unit; 8. Microcontroller unit; 9. Power supply unit; 10. Fly stick body; 11. Rotating nozzle; 12. Liquid storage tank; 13. Fly killer lamp; 14. Miniature diaphragm pump. Detailed Implementation

[0020] The technical solution of the present invention will be further described below with reference to the accompanying drawings and embodiments.

[0021] Unless otherwise defined, the technical or scientific terms used in this invention shall have the ordinary meaning understood by one of ordinary skill in the art to which this invention pertains. The terms "first," "second," and similar terms used in this invention do not indicate any order, quantity, or importance, but are merely used to distinguish different components. Terms such as "comprising" or "including" mean that the element or object preceding the word encompasses the elements or objects listed following the word and their equivalents, without excluding other elements or objects. Terms such as "connected" or "linked" are not limited to physical or mechanical connections, but can include electrical connections, whether direct or indirect. Terms such as "upper," "lower," "left," and "right" are used only to indicate relative positional relationships; when the absolute position of the described object changes, the relative positional relationship may also change accordingly.

[0022] Example Please see Figures 1-4 This invention provides an IoT-based intelligent monitoring and extermination system for flypaper, comprising: Intelligent fly-stick terminals are deployed at various monitoring points to collect real-time data on fly adhesion and environmental parameters. These include: Fly Stick Body 10: Made of yellow or white waterproof substrate, coated with fly-attracting adhesive containing pheromones or bait ingredients. The adhesive has good weather resistance and maintains its stickiness for no less than 30 days at room temperature.

[0023] Mass sensing unit 7: Employs a miniature resistance strain gauge sensor or piezoelectric sensor, attached to the back of the fly-stick strip, to monitor changes in the strip's mass in real time. The measurement range is 0-100g, with an accuracy of at least ±0.01g. The sampling frequency is adjustable (default once per minute, configurable remotely via the platform). When a fly is adhered, the total mass of the fly-stick strip increases, and the sensor outputs an electrical signal proportional to the mass, which is then converted from analog to digital and sent to the microcontroller.

[0024] Capacitive proximity sensing unit 3: Employing a flexible interdigitated electrode structure, it is positioned around or on the back of the fly sticky strip to detect changes in dielectric constant caused by fly approach or contact, thereby aiding in determining fly density. This sensing unit is particularly suitable for small flies (such as fruit flies) with insignificant mass changes or for rapid response during the initial stages of adhesion. The capacitance detection range is 1-100 pF, and the sensitivity is adjustable.

[0025] Environmental parameter acquisition unit 2 integrates a temperature sensor, a relative humidity sensor, and a light intensity sensor. Temperature measurement range: -40-85℃, accuracy ±0.3℃; relative humidity measurement range: 0-100%RH, accuracy ±3%RH; light intensity measurement range: 0-65535 lux, accuracy ±10 lux. This unit is used to collect local microenvironment data, providing crucial input for fly activity prediction models.

[0026] Microcontroller Unit 8: Employs a low-power chip (such as ESP32-S3, STM32L4 series, or nRF52840) that supports deep sleep mode, responsible for sensor data acquisition, preliminary digital filtering (such as median filtering to remove outliers), data protocol packaging, and communication control. Standby power consumption is less than 10μA, and operating power consumption is dynamically adjusted according to the sampling frequency.

[0027] IoT communication unit: Supports one or more of the following communication protocols: NB-IoT, LoRa, Wi-Fi, or 4G Cat.1, allowing for flexible selection based on communication coverage and power consumption requirements of the application scenario. The communication unit uploads packaged data (including fields such as device ID, timestamp, quality value, capacitance value, temperature and humidity, light intensity, and battery level) to the cloud platform or edge gateway via encrypted transmission (TLS / DTLS).

[0028] It may also include an identification unit: using RFID electronic tags (frequency 13.56MHz or 860-960MHz) or printed QR codes (compliant with ISO / IEC 18004 standards) for unique identification of the equipment and binding it to its geographical location. Inspection personnel can quickly obtain equipment information, historical data, and maintenance records by scanning the QR code with a handheld terminal.

[0029] Power Supply Unit 9: Employs a rechargeable lithium battery pack (nominal voltage 3.7V, capacity 2000-5000mAh), capable of continuous operation for 3-6 months at typical sampling frequencies. Optional features include a solar charging panel (5V / 1-2W) for outdoor areas without power, or a USB power interface (5V / 1A) for continuous indoor power supply. Power Supply Unit 9 incorporates a power management chip, supporting charging status monitoring, low battery warnings, and overcharge / over-discharge protection.

[0030] The regional fly risk analysis module receives and processes data uploaded by smart fly-stick terminals, constructs a dynamic assessment model of fly activity, and calculates the regional fly risk index. It includes: The data preprocessing submodule is used to perform outlier removal, missing value imputation, and signal denoising on the raw sensor data. Outlier removal (e.g., using the three-standard-deviation method), missing value imputation (linear interpolation or nearest-neighbor filling), and signal denoising (moving average filtering or low-pass filtering) are performed on the raw sensor data to ensure the accuracy and stability of subsequent analysis.

[0031] The adhesion quantity conversion submodule converts the mass change into an estimated adhesion quantity based on pre-calibrated average mass parameters of a single fly, and calculates the adhesion rate per unit time. The module uses pre-calibrated average mass parameters of a single fly (obtainable through laboratory testing or automatic platform calibration; default values ​​are approximately 12 mg for houseflies, 25 mg for blowflies, and 3 mg for fruit flies) to convert the mass change into an estimated adhesion quantity and calculate the adhesion rate per unit time.

[0032] The fly activity intensity index calculation submodule uses the following formula to calculate the fly activity intensity index. : ; in, Time interval Change in mass within (unit: mg); Time interval (unit: min); The current temperature (unit: °C). For reference temperature; Current relative humidity (unit: %); Current light intensity (unit: lux) This refers to light saturation and reference values; These are the weighting coefficients, with values ​​ranging from 1 to 10. , , And satisfy .

[0033] An exponential function was used to characterize the accelerating effect of temperature on fly metabolism and activity; a sine function was used to characterize the nonlinear effect of humidity on fly survival suitability (50%-80% is the optimal range); and an inverse proportional function was used to characterize the inhibitory effect of light on fly activity (flies prefer low-light environments). The weighted sum of these three parameters was multiplied by the rate of change in mass to achieve deep integration of environmental and behavioral data.

[0034] The cumulative risk index calculation submodule uses the following decay integral formula to calculate the cumulative risk index over a continuous period of time. : ; in The evaluation time window is 1 minute. The integral form is a decay factor; it gives greater weight to recent activities, while the contributions of older activities decay exponentially, thus maintaining both stability and sensitivity.

[0035] The regional risk level determination submodule is based on the cumulative risk index. The risks are divided into four levels: Low risk (green); Medium risk (yellow); High risk (orange); Extremely high risk (red).

[0036] Attenuation factor It can also adapt to seasonal changes: ; That is, summer (June-August). Smaller (approximately 0.03-0.04), with slow risk decay, reflecting the sustained activity of flies during the high-temperature season; winter (December-February). The value is relatively large (approximately 0.05-0.06), and the risk decays rapidly, reflecting the large fluctuations in fly activity during the low-temperature season, with a rapid decline after a brief period of activity.

[0037] The early warning and decision support module is used to generate tiered early warning information based on the regional fly risk index and provide prevention and control decision suggestions.

[0038] The coordinated pest control module is used to automatically or remotely trigger one or more pest control devices to perform prevention and control operations based on early warning commands. This includes: Electric fly killer lamp control unit: Controls the power supply to and from the fly killer lamp (recommended wavelength 365nm, power 10-40W) via a relay switch. Supports timed operation mode (default 30 minutes per cycle, adjustable from 1-120 minutes) and pulse operation mode (5 minutes of operation followed by a 2-minute pause, repeating cyclically). The unit has a built-in light decay detection sensor (photodiode), which automatically sends a replacement reminder to the platform when the UV lamp illuminance falls below 60% of the initial value.

[0039] The automatic pesticide spraying device control unit includes a pesticide storage tank (capacity 1-5L, food-grade PE material), a miniature diaphragm pump (working pressure 0.3-0.8MPa, flow rate 200-1000mL / min), a rotatable nozzle (horizontal adjustable 0-180°, pitch adjustable 0-90°), and a controller (based on STM32 or ESP32). Upon receiving a trigger command, the controller executes the spraying operation according to preset parameters (powder volume 50-200mL, spray angle 60°-120°, effective range 3-10m). The device has a built-in pesticide level sensor (ultrasonic or float type) and a nozzle anti-clogging detection module (pressure feedback), which automatically alarms when the level is below 10% or when a blockage is detected.

[0040] The automatic flypaper replacement device control unit adopts a dual-spindle winding structure, including an active spindle (rewinding used flypaper), a driven spindle (releasing new flypaper), a stepper motor (torque 2-5 N·m), and a torque sensor (monitoring winding resistance). When the mass sensor detects that the number of adhered flypapers has reached the saturation threshold (default 300 flypapers / spindle, configurable 200-500 flypapers) or the mass exceeds the preset upper limit (default 30g, configurable 20-50g), the controller drives the stepper motor to rotate a preset angle (default 360° / rotation, corresponding to approximately 30 replacements for a 10m roll of flypaper). The device has a built-in counting encoder (Hall sensor or grating encoder) that records the length replaced and estimates the remaining usable days, and pushes replacement reminders via the platform.

[0041] The control unit of the electric fly killer lamp includes an ultrasonic generator (frequency adjustable from 20-40kHz, sound pressure level 80-110dB, employing a sweeping frequency mode to prevent flies from adapting) and a high-brightness strobe light (power 10-30W, flashing frequency adjustable from 2-10Hz, LED light source). This unit is suitable for scenarios sensitive to chemical agents (such as hospital wards and food processing areas), reducing fly density through physical repulsion without leaving any residual pollution.

[0042] Collaborative Operation Mode: The system supports automatically combining multiple elimination methods based on risk level to form a tiered response strategy. In medium-risk situations: trigger electric fly killer lamps individually, with low-intensity intervention as the main approach; In high-risk situations: Simultaneously trigger the fly-killing lamp and the automatic spraying device, and record the coordinated response time to form an enhanced extermination strategy; In cases of extremely high risk: the automatic fly-stick replacement device is triggered sequentially (first updating the fly-catching surface to ensure capture capacity), then the spraying device is triggered (to enhance chemical extermination), and finally the electric fly-killing lamp is triggered (to drive away any remaining flies), forming a three-level linkage of "replacement - spraying - repulsion" to achieve comprehensive management.

[0043] The intelligent control and management platform is used to centrally manage all terminal devices, display monitoring data, analyze historical trends, configure system parameters, and manage user permissions. It includes: Real-time monitoring dashboard: Displays the location, online status, and current cumulative risk index of all terminal devices based on a GIS map. Displays battery level and recent warning time. Users can click on the device icon on the map to bring up a details window to view real-time data and historical curves. The dashboard supports filtering by region, risk level, and device type.

[0044] Historical data analysis engine: Supports querying adhesion quantity curves, risk index trends, and environmental parameter correlation analysis by hour, day, week, month, year, and custom time ranges. Provides comparative analysis functionality, capable of simultaneously displaying data curves from multiple devices or regions. Supports data export to CSV, Excel, or JSON formats for easy external statistics or report generation.

[0045] Fly activity heatmap module: based on cumulative risk index at each location A Gaussian kernel density estimation method is used to generate a heatmap of fly activity in the region. The color gradient ranges from light green to dark red, with darker colors indicating higher risk. The heatmap supports a timeline slider, allowing users to drag the timeline to view the dynamic changes in risk distribution at different times and intuitively identify high-risk clusters.

[0046] The device management system supports batch device addition (via Excel import or QR code scanning), template download, batch deletion, and batch configuration modification. Users can remotely configure parameters such as terminal sampling frequency (selectable from 10 seconds to 10 minutes), communication reporting cycle, and early warning thresholds. The system automatically tracks device firmware versions, supports OTA remote upgrades, and supports resume download and version rollback during the upgrade process to ensure stable device operation.

[0047] User Access Control System: Supports multi-tenant isolation, allowing each tenant (such as a livestock group or a district / county CDC) to independently manage its own devices and data. The following roles can be configured within a tenant: Super Administrator: Has full permissions for this tenant, including device management, user management, system configuration, etc. Area Administrator: Responsible for equipment management and early warning confirmation within a specific geographical area (such as a chicken coop or a workshop); Inspector: Check data, report on-site maintenance records, scan QR codes to confirm equipment status, and manually replace flypaper strips; Auditor: Read-only access to system logs and operation records; no configuration modifications allowed. Visitors can only view publicly available statistical data (such as the overall risk level of the region) and do not have permission to view specific location information.

[0048] The prevention and control strategy library includes pre-set recommended prevention and control plans for different scenarios (farms, canteens, garbage stations, food factories, hospitals), different fly species, and different seasons. These plans include combinations of extermination methods (e.g., "fly trap + spraying"), pesticide formulations (e.g., 0.5% high-efficiency cyhalothrin suspension), and application frequency (1-4 times daily). Users can personalize these pre-set strategies and save them as custom templates. The strategy library supports version management, allowing users to trace the history of strategy changes.

[0049] Log auditing system: Records all user logins, parameter modifications, alert triggers, kill operations, device login / logout, firmware upgrades, and other operations. Each log entry includes a timestamp (accurate to milliseconds), user ID (or system identifier), operation type, operation object (e.g., device ID), operation result (success / failure), and source IP address. Logs are retained for at least 180 days and support searching by time range, operation type, user ID, and other combined criteria. Logs can also be exported for auditing purposes.

[0050] For scenarios requiring category identification or verification, the system can be optionally equipped with an image-assisted recognition module, deployed at key locations. The module includes: Miniature camera: resolution no less than 1920×1080, supports infrared night vision illumination, field of view 60°-120°, installed directly opposite the flypaper; Embedded AI Recognition Unit: Employing a lightweight convolutional neural network model (such as MobileNetV3-SSD or YOLOv5-Nano), deployed at the edge gateway or camera (e.g., using K210 or RV1126 chips). This pre-trained model possesses the following capabilities: Identify at least five common sanitary pests, including houseflies, blowflies, fruit flies, flesh flies, and stable flies. Estimate the percentage of fly-covered area on the surface of fly strips (regression output, error ≤10%). Verify the amount of adhesion estimated by the sensor (consistent if the error does not exceed ±15%, otherwise trigger the verification mechanism). Image data is transmitted to the cloud platform only when upload trigger conditions are met, in order to save bandwidth and storage space. Trigger conditions include: recognition confidence level below 80%, sensor and image estimation deviation exceeding 20%, user actively requesting capture, or risk level reaching high risk or above.

[0051] The intelligent fly control method based on the above system includes the following steps: S1: Data Acquisition The intelligent fly stick terminal collects real-time data on quality changes. ,temperature ,humidity ,illumination It is then encrypted and uploaded to the regional fly risk analysis module (or edge gateway) via the IoT communication unit.

[0052] S2: Risk Assessment The regional fly risk analysis module preprocesses the data and calculates the fly activity intensity index according to the formula. and cumulative risk index .

[0053] S3: Warning Generation The early warning and decision support module will It is compared with a dynamic threshold. When the medium-risk threshold (≥0.3) is reached or exceeded and the duration exceeds the preset duration (default 15 minutes), a graded warning message is generated and relevant users are notified according to the preset push strategy.

[0054] S4: Decision-making and Execution After the user confirms the alert (or the system is in automatic mode), a prevention and control command is issued to the linkage and extermination module. The system supports two modes: manual and automatic. In manual mode, the user needs to click to confirm; in automatic mode, the system makes decisions autonomously based on the "learning automatic mode" strategy.

[0055] S5: Extermination Operation The linkage extermination module executes the corresponding extermination operation according to the instruction type (electric shock, spraying, strip replacement or coordinated combination mode), and returns the execution status (success / failure, execution time, remaining liquid, etc.) in real time through the status feedback channel.

[0056] S6: Effectiveness Evaluation The intelligent control and management platform receives pest control feedback data and, combined with subsequent monitoring of fly activity changes, calculates a score for the effectiveness of the pest control efforts. : ; like The system automatically marks the control measure as "inefficient" and adds the sample (including risk index, extermination methods, environmental parameters, etc.) to the reinforcement learning training set for subsequent optimization of thresholds and strategies.

[0057] S7: Closed-loop recording The system records all data throughout the process (collection, analysis, early warning, eradication, and evaluation) in a log system, forming a complete closed-loop management archive that supports subsequent traceability and auditing.

[0058] Among them, the preset strategy supports "learning automatic mode", that is, the system uses Q-learning algorithm to autonomously decide whether to carry out extermination, select which extermination combination and set the execution parameters based on historical control effect records, without human intervention, and achieves fully automated operation.

[0059] like Figure 4 As shown, the intelligent fly sticky strip terminal includes a box 1 and a fly-killing lamp 13 set on the top of the box 1. The top of the box 1 has an opening, and an environmental parameter acquisition unit 2 is set near the opening. A scroll device is set below the environmental parameter acquisition unit 2. A partition is set below the scroll device. A microcontroller unit 8, a power supply unit 9 and an automatic spraying device are set below the partition.

[0060] The reel unit includes a first take-up reel 4, a second take-up reel 5, and a feed reel 6 located below and to the side of the second take-up reel 5. A double-layered paper roll is detachably mounted on the feed reel 6. One layer is the fly-stick body 10, and the other is a tough paper. The fly-stick body 10 is driven to the first take-up reel 4, and the tough paper is driven to the second take-up reel 5. The microcontroller unit 8 can control the simultaneous rotation of the first take-up reel 4 and the second take-up reel 5, allowing the fly-stick body 10 and the tough paper to separate from each other. This ensures that the old fly-stick body 10 can be automatically recycled, avoiding the drawback of requiring manual replacement of fly monitoring instruments after a certain monitoring period.

[0061] The portion above the partition corresponding to the fly stick body 10 is configured as a hollow structure. An ultra-thin polyester film is placed on the top of the hollow structure and is attached to the fly stick body 10. A mass sensing unit 7 is placed on the side of the ultra-thin polyester film away from the fly stick body 10.

[0062] Capacitive proximity sensing units 3 are installed below the environmental parameter acquisition unit 2 and on one side of the first take-up reel 4 and the unload reel 6.

[0063] The automatic spraying device includes a liquid medicine storage tank 12, a micro diaphragm pump 14, and a delivery pipe disposed above the micro diaphragm pump 14. The end of the delivery pipe away from the liquid medicine storage tank 12 is connected to a rotating nozzle 11, which is disposed on the top of the housing 1.

[0064] In addition, the motors, micro diaphragm pumps, and other equipment used in the above structure, as well as the connection methods of each unit, are all existing technologies and will not be elaborated on further here.

[0065] Therefore, this invention adopts the aforementioned IoT-based intelligent monitoring and extermination system for fly sticks, with "low cost, high real-time performance, strong intelligence, and easy scalability" as its core design concept. It transforms traditional fly sticks into IoT intelligent terminals, constructing a complete closed-loop system of "sensing—analysis—early warning—extermination—assessment." This system not only solves the practical pain points in fly control but also provides a new path for the IoT-based implementation of Integrated Pest Management (IPM), demonstrating significant technological advancement, economic benefits, and social value.

[0066] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and not to limit them. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can still be made to the technical solutions of the present invention, and these modifications or equivalent substitutions cannot cause the modified technical solutions to deviate from the spirit and scope of the technical solutions of the present invention.

Claims

1. An IoT-based intelligent monitoring and extermination system for flypaper, characterized in that: include: The intelligent fly-stick terminal is deployed at various monitoring points to collect fly adhesion data and environmental parameters in real time. The regional fly risk analysis module is used to receive and process data uploaded by the smart fly sticky terminal, construct a dynamic assessment model of fly activity, and calculate the regional fly risk index. The early warning and decision support module is used to generate tiered early warning information based on the regional fly risk index and provide prevention and control decision suggestions. The linkage and extermination module is used to automatically or remotely trigger one or more extermination devices to perform prevention and control operations based on early warning instructions; The intelligent control and management platform is used to centrally manage all terminal devices, display monitoring data, analyze historical trends, configure system parameters, and manage user permissions.

2. The IoT-based intelligent monitoring and extermination system for flypaper as described in claim 1, characterized in that: The intelligent fly sticky strip terminal includes a box and a fly-killing lamp set on the top of the box. The top of the box has an opening, and an environmental parameter acquisition unit is set near the opening. Below the environmental parameter acquisition unit is a scroll device, below the scroll device is a partition, and below the partition are a microcontroller unit, a power supply unit and an automatic spraying device. The reel unit includes a first take-up reel, a second take-up reel, and a feed reel located below the side of the second take-up reel. A double-layered paper roll is detachably mounted on the feed reel; one layer is the fly-stick body, and the other is a tough paper. The fly-stick body is driven to the first take-up reel, and the tough paper is driven to the second take-up reel. A microcontroller unit controls the simultaneous rotation of the first and second take-up reels. The part above the partition corresponding to the fly stick body is set as a hollow structure. An ultra-thin polyester film is set on the top of the hollow structure and is attached to the fly stick body. A mass sensing unit is set on the side of the ultra-thin polyester film away from the fly stick body. Capacitive proximity sensing units are installed below the environmental parameter acquisition unit and on one side of the first take-up reel and the unload reel. The automatic spraying device includes a liquid medicine storage tank, a micro diaphragm pump, and a delivery pipe installed above the micro diaphragm pump. The end of the delivery pipe away from the liquid medicine storage tank is connected to a rotating nozzle, which is located on the top of the tank.

3. The IoT-based intelligent monitoring and extermination system for flypaper as described in claim 1, characterized in that, The regional fly risk analysis module includes: The data preprocessing submodule is used to perform outlier removal, missing value imputation, and signal denoising on the raw sensor data. The adhesion quantity conversion submodule converts the mass change into an estimated adhesion quantity based on the pre-calibrated average mass parameter of a single fly, and calculates the adhesion rate per unit time. The fly activity intensity index calculation submodule uses the following formula to calculate the fly activity intensity index. : ； in, Time interval The change in mass within; For time intervals; The current temperature. For reference temperature; The current relative humidity; Given the current light intensity, This refers to light saturation and reference values. These are the weighting coefficients, with values ​​ranging from 1 to 10. , , And satisfy ; The cumulative risk index calculation submodule uses the following decay integral formula to calculate the cumulative risk index over a continuous period of time. : ； in The evaluation time window is 1 minute. It is the attenuation factor; The regional risk level determination submodule is based on the cumulative risk index. The risks are divided into four levels: Low risk; Medium risk; High risk; Extremely high risk.

4. The IoT-based intelligent monitoring and extermination system for fly strips according to claim 3, characterized in that, Weighting coefficient The Bayesian online learning algorithm is used for dynamic updates, specifically: ； in The loss function is based on historical adhesion data and actual fly density verification values. The learning rate has a range of values. .

5. The IoT-based intelligent monitoring and extermination system for flypaper as described in claim 4, characterized in that, The early warning and decision support module includes: The tiered early warning generation submodule generates warnings when the cumulative risk index... When the risk level reaches medium or above and the duration exceeds the preset threshold, an early warning message will be automatically generated. The early warning message includes: the device's unique identifier, geographic location coordinates, current risk level, estimated amount of adhesion, environmental parameter snapshot, and recommended handling measures. The multi-channel push submodule is used to push alert information to users with different permissions; The adaptive threshold adjustment submodule, based on historical early warning records and actual eradication effect feedback, dynamically adjusts the threshold boundaries for each risk level using a reinforcement learning Q-learning algorithm. Specifically: ； Among them, state Includes current risk index, time period, season, historical equipment response rate, and actions. Adjust the threshold in long steps. As a reward signal based on the success rate of epidemic prevention and control; The discount factor is set to 0.

9. The decision suggestion generation submodule automatically outputs suggested extermination methods, pesticide types, application dosages, and treatment priorities based on risk level, fly species, and scenario type.

6. The IoT-based intelligent monitoring and extermination system for fly strips according to claim 2, characterized in that, The coordinated eradication module can automatically combine multiple eradication methods based on the risk level: In medium-risk situations: trigger the electric fly killer lamp alone; In high-risk situations: Simultaneously trigger the fly-killing lamp and the automatic spraying device, and record the coordinated response time; In cases of extremely high risk: the automatic fly strip replacement device is triggered in sequence, followed by the spraying device, and finally the electric fly killer lamp, forming a three-level linkage of replacement, spraying, and fly killer lamp.

7. The IoT-based intelligent monitoring and extermination system for fly strips according to claim 6, characterized in that, The intelligent control and management platform includes: Real-time monitoring dashboard, based on GIS map, displays the location, online status, current cumulative risk index, battery level, and most recent warning time of all terminal devices; supports clicking on a device to pop up a details window; The historical data analysis engine can query adhesion quantity curves, risk index change trends, and environmental parameter correlation analysis by hour, day, week, month, year, and custom time range; The fly activity heatmap module generates a regional fly activity heatmap based on the cumulative risk index of each location using the Gaussian kernel density estimation method. The darker the color, the higher the risk. The device management system can add, import, and delete devices in batches; remotely configure sampling frequency, communication parameters, and early warning thresholds; view device firmware versions and support OTA remote upgrades; The user access control system supports multi-tenant isolation, allowing roles to be set for each tenant. The log auditing system records all user logins, parameter modifications, alert triggers, kill operations, and device offline / remove operations. The logs include timestamps, user IDs, operation types, operation objects, operation results, and IP addresses.

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