Large beast dynamic monitoring and early warning system and method based on cooperation of unmanned aerial vehicle and ground

Abstract

The application discloses a large-scale ferocious beast dynamic monitoring and early warning system and method cooperating with the ground by a UAV, belongs to the technical field of intelligent wild animal monitoring, and is directed to tigers, leopards or bears. The system comprises a data layer, a service layer, an application layer, an intelligent terminal and a user layer which are sequentially connected. The data layer stores full data and is provided with a storage device and an uninterruptible power supply. The service layer provides core services such as AI species identification. The application layer integrates functional modules such as infrared camera intelligent monitoring. The intelligent terminal comprises an infrared trigger camera, a UAV provided with an AI identification confirmation unit and an audible and visual driving device. The user layer accesses the system according to permissions. The method collects data through the infrared trigger camera, triggers hierarchical early warning after AI identification and distance calculation, and links the UAV for patrol and tracking. When necessary, non-injurious driving is started, and the safety of human activities and the protection of large ferocious beasts are considered.

Description

Technical Field

[0001] This invention relates to the field of intelligent wildlife monitoring technology, and in particular to a dynamic monitoring and early warning system and method for large predators that combines unmanned aerial vehicles (UAVs) with ground-based systems. Background Technology

[0002] As endangered species, the monitoring of the survival status and activity patterns of large predators such as tigers, leopards, and bears is crucial for species conservation. Currently, the following technical methods are mainly used for monitoring large predators:

[0003] Infrared camera traps: These traps passively capture images of large predators by deploying infrared cameras at fixed locations. However, this method has significant drawbacks. First, the monitoring range is limited to the camera deployment locations, resulting in numerous blind spots. Second, data needs to be manually retrieved and transmitted periodically, leading to data transmission delays and making it impossible to monitor the movements of large predators in real time. Third, the traps are easily obstructed by forest vegetation, and the image quality is greatly affected by the environment.

[0004] Satellite remote sensing: Satellites are used to remotely image the monitored area, but satellite remote sensing has low resolution (≥1 meter), making it impossible to clearly identify large predators or track moving targets in real time; at the same time, the dense vegetation in the forest area limits the penetration ability of satellite signals, resulting in poor monitoring capabilities for large predators within the forest area.

[0005] Manual patrols: Relying on staff to patrol and observe within the monitoring area is not only inefficient and has a limited coverage, but the activities of staff may also interfere with the normal life of large predators and increase the risk of human-wildlife conflict.

[0006] Existing technologies also have the following common problems: First, they cannot achieve real-time dynamic tracking and proactive early warning of large predators. When large predators suddenly migrate or there is a risk of human-wildlife conflict, the response is delayed, making it difficult to take timely protective or preventive measures. Second, the data from a single monitoring method exists independently with low integration, making it prone to false alarms. Third, existing drone monitoring is mostly conducted independently with limited endurance and no collaborative mechanism has been established with ground-based sensing networks, resulting in large monitoring blind spots and the inability to form full-area, continuous monitoring coverage. Summary of the Invention

[0007] The purpose of this invention is to provide a dynamic monitoring and early warning system and method for large predators that combines UAVs and ground-based systems, thereby solving the problems of poor real-time performance, high false alarm rate, and discontinuous coverage in existing monitoring technologies.

[0008] To achieve the above objectives, the present invention provides a large predator dynamic monitoring and early warning system that coordinates UAVs and ground systems, comprising a data layer, a service layer, an application layer, a smart terminal, and a user layer connected in sequence to form a data transmission and control link;

[0009] The data layer stores all the data supporting the operation of the system, including basic geographic data, forestry-specific data, large predator monitoring data, and smart terminal operation data, and is equipped with storage devices and an uninterruptible power supply.

[0010] The service layer connects the data layer and the application layer, providing core services to realize system functions, including: AI species identification service for identifying large predators from image data transmitted from smart terminals; GIS general service with geographic information processing function; device collaboration service to realize linkage control between smart terminals; data sharing service to support cross-level data flow; and security authentication service to verify the legitimacy of users and devices.

[0011] The application layer integrates functional modules for monitoring, early warning, and management of large predators, including: a visualization module that displays the real-time status of the monitoring area; an infrared camera intelligent monitoring module that enables infrared triggering and AI recognition of large predators; a drone intelligent control module that controls drones to perform patrol and tracking tasks; an intelligent expulsion management module that plans expulsion routes and controls expulsion equipment; a key species early warning module that issues different levels of early warning information based on the recognition results; an early warning information management module that sends early warning information to mobile devices; and an animal resource management module that manages and analyzes large predator population data.

[0012] The intelligent terminal is a data acquisition and command execution carrier, including: an infrared trigger camera deployed in the wild with infrared sensing shooting and data transmission functions, a drone and its matching nest that supports automatic flight and tracking, and an audio-visual deterrent device for non-harmful removal of large predators.

[0013] The user layer is divided into different user groups according to permissions. Users with different permissions can obtain different early warning information and system function access permissions, and support information reporting and command issuance operations.

[0014] Preferably, the large predator includes a tiger, leopard, or bear.

[0015] Preferably, the infrared trigger camera of the smart terminal is deployed in areas where large predators have a history of frequent activity, including forest trails, village intersections, farmland and forest edges, and is located within 5km of the periphery of the human-wildlife conflict area, and has 4G / 5G wireless data transmission and solar charging functions.

[0016] Preferably, the drone of the intelligent terminal is equipped with a high-definition visible light camera and an infrared thermal imaging camera, and pre-stores elevation data within a 5km radius of the monitoring area; the drone is equipped with an AI recognition and confirmation unit for secondary confirmation of the recognition results of the infrared camera intelligent monitoring module, and continuously tracks the activity trajectory of large predators; the drone nest has an automatic battery swapping function, and the drone can quickly complete the battery swapping after returning to base and return to the mission point to continue working.

[0017] Preferably, the key species early warning module of the application layer sets three levels of early warning based on the distance between large predators and villages: an early warning is triggered every 500 meters when the distance is 3-5km, an early warning is triggered every 300 meters when the distance is 2-3km, and an early warning is triggered every 100 meters when the distance is 1-2km.

[0018] Preferably, users in the user layer access the system through a web version and a mobile application. The web version is used by the management team and expert team and supports equipment management and data analysis functions; the mobile application is used by forest rangers and villagers and supports early warning reception and information feedback operations.

[0019] Preferably, the information displayed by the visualization module of the application layer includes: the distribution location of infrared trigger cameras and drones, the battery level and signal strength of infrared trigger cameras and drones, and the movement path and activity range of large predators.

[0020] A method for dynamic monitoring and early warning of large predators using a drone-ground collaborative system, employing the aforementioned drone-ground collaborative dynamic monitoring and early warning system for large predators, includes the following steps:

[0021] S1. Data Acquisition: When the infrared trigger camera of the smart terminal senses the activity of the target in the wild, it automatically triggers the shooting to generate image data. The image data is transmitted to the data layer in real time through the data return function. The data layer storage device completes the storage. The uninterruptible power supply synchronously ensures the security of the data storage process and avoids the risk of power failure.

[0022] S2. Data Processing: After the service layer initiates the security authentication service to verify the legality of the data, the infrared camera intelligent monitoring module of the application layer calls the species identification service of the service layer to identify large predators from the image data stored in the data layer. At the same time, the geographic coordinates of the infrared trigger camera are parsed through the GIS general service to calculate the distance between the large predator and the human activity area. The data sharing service synchronously completes the preparation for the cross-level flow of identification results and distance data.

[0023] S3. Warning Trigger: The key species warning module at the application layer generates warning information of corresponding level based on the recognition results and distance data output by the infrared camera intelligent monitoring module. The warning information management module pushes the warning information to users with corresponding permissions at the user layer. The visualization module simultaneously displays the real-time status of the monitoring area and the warning information.

[0024] S4. Drone Collaboration: Based on the identification results, the device collaboration service in the service layer sends instructions to the drone management module in the application layer. After receiving the instructions, the drone management module controls the drone to take off from the nest, and uses the location coordinates of the large predator image information captured by the infrared trigger camera as the target to go to the target coordinate area to perform patrol and tracking tasks. The AI ​​recognition and confirmation unit on the drone confirms that the tracked target animal is a large predator through the captured images, and sends the real-time captured image data back to the data layer for storage.

[0025] S5. Removal Execution: The key species early warning module in the application layer continuously monitors the distance between large predators and human activity areas. When the distance reaches the removal threshold, the sound and light removal device on the smart terminal is triggered. The sound and light removal device guides the large predators away in a non-harmful way, and at the same time, it transmits the process data such as removal time, location and the large predator's reaction back to the data layer, which is then synchronized to the relevant modules in the application layer by the data sharing service.

[0026] S6. Data Feedback: Users receive early warning information and task instructions through the Web version of the system or the mobile application according to their permissions, and report and provide feedback on information such as on-site situation records and task execution results. The feedback data is transmitted to the data layer for storage via the service layer data sharing service, providing a basis for subsequent data analysis and population management of the infrared camera intelligent monitoring module and the key species early warning module in the application layer.

[0027] Preferably, in S4, when the drone performs a patrol mission, it initially takes the coordinates of the infrared trigger camera that captured the large predator as the center and conducts a patrol with a radius of 5km; if no large predator is found, the patrol is narrowed to a core area of ​​2km and continues; after a large predator is found, the target is locked and continuous aerial tracking is carried out within a range of 1km.

[0028] According to specific embodiments provided by the present invention, the present invention discloses the following technical effects:

[0029] This system relies on a collaborative architecture of unmanned aerial vehicle (UAV) subsystems, ground sensor networks, a central processing unit (CPU), and early warning terminals to address the core pain points of existing monitoring technologies. To address real-time tracking and early warning delays, the system rapidly wakes up the UAV after detecting characteristic signals from ground sensors. Combined with real-time edge computing identification and data fusion from the CPU, it achieves a highly efficient response from target discovery to early warning delivery. To address the high false alarm rate of single-monitor systems, it improves identification reliability through cross-validation of multimodal data including thermal imaging, acoustic signatures, vibration, and odor. To address discontinuous coverage, it constructs a three-dimensional "ground + air" monitoring network using a ground sensor grid and dynamic UAV patrols, eliminating monitoring blind spots.

[0030] In terms of performance, the system optimizes operation and maintenance through low-power design: ground sensors default to low-power standby and are only activated when triggered, with low-power communication technology extending battery life; the drones adopt a trigger-wake mode, which can autonomously return to a preset charging base station to recharge, reducing manual intervention and meeting the needs of long-term field monitoring. At the same time, the drones and ground equipment work together deeply, and the data can be fused in real time to generate animal population activity heat maps and migration path predictions, providing comprehensive support for monitoring decisions.

[0031] At the application level, the system's hierarchical early warning mechanism accurately prevents and controls human-wildlife conflict, with different response measures corresponding to different risk levels, ensuring human safety while reducing casualties among large predators. The core architecture is highly versatile, and parameters can be adjusted to adapt to the monitoring of other endangered large mammals. Its collaborative monitoring and intelligent analysis model also provides a reusable technological paradigm for the intelligent wildlife monitoring industry, promoting the industry's transformation towards technology-driven development.

[0032] 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

[0033] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0034] Figure 1 This is a system architecture diagram of an embodiment of the UAV-ground collaborative dynamic monitoring and early warning system for large predators according to the present invention;

[0035] Figure 2 This is a schematic diagram of the drone identification and tracking process according to an embodiment of the present invention;

[0036] Figure 3 This is a flowchart illustrating the operation of the infrared trigger camera according to an embodiment of the present invention.

[0037] Figure 4 This is a schematic diagram of the interface of the infrared camera intelligent monitoring module in an embodiment of the present invention. Detailed Implementation

[0038] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative effort are within the scope of protection of the present invention.

[0039] To make the above-mentioned objects, features and advantages of the present invention more apparent and understandable, the present invention will be further described in detail below with reference to the accompanying drawings and specific embodiments.

[0040] Example

[0041] This embodiment uses a national nature reserve in Northeast China and surrounding villages, farmland, and other human-inhabited areas within a 5km radius as the application scenario, with the monitored object being the Siberian tiger. For example... Figure 1 As shown below, a detailed explanation of the dynamic monitoring and early warning system and methods for Siberian tigers, specifically developed based on their morphological characteristics and physiological habits, will be provided.

[0042] (a) Data layer configuration

[0043] The data layer uses a 2U rack-mount AI and application combo server as the storage device, equipped with two Intel Xeon Gold 6338 processors, 64GB of memory, an RTX 4080 24G graphics card, and 40TB of storage. It supports 12 3.5-inch or 8 2.5-inch hard drives, supports multiple data backup methods, and has two gigabit network cards, dual power supplies, and four hot-swappable counter-rotating fans.

[0044] To prevent unexpected power outages from causing server malfunctions, an uninterruptible power supply (UPS) needs to be installed in the server room. It should have a capacity of 6000W or higher and feature surge protection, short-circuit protection, voltage instability protection, electronic interference protection, over-temperature protection, overcharge protection, and overload protection.

[0045] The basic geographic data stored in the data layer includes 1:10,000 topographic vector map of the protected area, elevation DEM data (5m accuracy), and 3D modeling data; forestry-specific data covers the protected area's small-compartment zoning map, management station locations, patrol routes, etc.; the Siberian tiger monitoring data has a reserved interface that can receive infrared trigger camera images, drone tracking videos, etc. in real time; and the smart terminal operation data collects real-time status information such as device power and signal strength.

[0046] (ii) Service layer configuration

[0047] AI-powered species identification service: This model, based on infrared-triggered camera data, utilizes artificial intelligence technology to build a deep learning-based species image monitoring and identification model. The system can automatically process images captured by infrared-triggered cameras, accurately identifying the species type, quantity, and time of occurrence within the images. For Siberian tiger identification, the model achieves a species identification accuracy of no less than 85%, and a real-time video stream tagging accuracy of no less than 95%. This enables conservationists to quickly obtain information on Siberian tiger activity and take timely protective measures.

[0048] General GIS services: Developed based on ArcGIS Server, it supports WGS84 coordinate system analysis, can calculate the straight-line distance between Siberian tigers and villages in real time (error ≤10m), and convert Siberian tiger activity latitude and longitude data into dynamic trajectories on the map.

[0049] Device Collaboration Service: Using the MQTT IoT communication protocol, a device linkage control module is built, which can send take-off and target location commands to the drone within 10 seconds after the infrared trigger camera identifies the Siberian tiger.

[0050] Data sharing service: Role-based access control (RBAC) mechanism, setting three levels of data access permissions for administrators, forest rangers, and villagers. Only administrators can view the full monitoring data, forest rangers can view the data of their assigned area, and villagers can only receive early warning information.

[0051] Security authentication service: It adopts a dual authentication method of account password + dynamic verification code. Users need to verify via mobile phone SMS verification code to log in to the system. When a smart terminal accesses the system, it needs to submit a unique device code, which can only be connected to the data transmission link after being verified by the service layer.

[0052] (III) Implementation of Application Layer Functions

[0053] Visualization Module: The visualization module is the core display platform of the Northeast Tiger Intelligent Monitoring and Early Warning System. It intuitively presents the real-time status of the entire monitoring area in map form, providing users with comprehensive and dynamic monitoring information. Users can clearly view the distribution locations of infrared trigger cameras and drones on the map, as well as the detailed status of these devices, including key parameters such as battery level and signal strength. This real-time monitoring function ensures that users can keep track of the equipment's operating status at all times and promptly detect and handle equipment malfunctions or abnormal situations.

[0054] Furthermore, this module displays the activity tracks of Siberian tigers. Through different colors and markers, users can intuitively understand the tigers' movement paths and activity ranges. Combined with village location information, the system can quickly assess the distance between Siberian tigers and human settlements, providing crucial data for early warning and response measures. Simultaneously, the visualization module also supports viewing historical data, such as the frequency of Siberian tiger sightings and their activity range, helping users analyze tiger behavior patterns and habitat change trends. Through these rich data display and analysis functions, users can gain a more comprehensive understanding of the overall situation in the monitored area, providing strong support for conservation efforts.

[0055] Infrared camera intelligent monitoring module: This module is a key component of the Siberian tiger intelligent monitoring and early warning system. Its core function is to utilize advanced infrared-triggered camera equipment and artificial intelligence technology to achieve automatic identification and monitoring of Siberian tigers. Figure 4As shown, when the infrared-triggered camera captures an image of a wild animal, the system will automatically activate its intelligent recognition program to accurately determine whether it is a Siberian tiger. Once confirmed, the system will quickly store the image file on the platform and promptly notify relevant personnel through various channels, such as an app, WeChat mini-program, or SMS, ensuring rapid information dissemination. The process is as follows: Figure 3 As shown.

[0056] The UAV intelligent control module is a crucial component of the Siberian tiger intelligent monitoring and early warning system, designed to enable efficient remote control and real-time monitoring of UAVs. Upon receiving the location coordinates of the Siberian tiger image from the infrared trigger camera, this module automatically plans the UAV's flight path, ensuring the UAV can quickly and accurately reach the target area for patrol.

[0057] Users can view high-definition footage captured by drones in real time through the system, remotely control the drone's flight and shooting operations, and flexibly adjust the drone's flight altitude, speed, and shooting angle to obtain the best monitoring results. The drone's image data will be automatically stored in the system platform for subsequent analysis and use, providing important data support for the protection of Siberian tigers.

[0058] Intelligent Repelling Management Module: This module is a key safety component of the Siberian Tiger Intelligent Monitoring and Early Warning System, designed for effective tiger removal when a Siberian tiger approaches a village. When the system detects that the tiger is within a preset warning range, the module activates rapidly. Staff immediately drive to the designated removal location, and after taking appropriate protective measures, the high-intensity acoustic and laser deterrence system is activated, quickly entering deterrence mode to guide the tiger away from the village in a non-harmful manner.

[0059] During the removal process, the system records detailed information such as the time, location, and the Siberian tiger's reaction, providing data support for subsequent analysis and strategy adjustments. Furthermore, the system automatically generates a report detailing the execution of the removal operation, the tiger's reaction, and the final removal effect. This facilitates management's evaluation and optimization of the removal strategy, further enhancing the system's security and reliability.

[0060] Key Species Early Warning Module: This module connects with the nature reserve management bureau's SMS platform, APP, and WeChat mini-program for villagers. When a Siberian tiger is within 3-5km of a village, an early warning SMS is sent to the forest ranger's APP every 500 meters; when it is 2-3km away, an early warning is sent to both the forest ranger's APP and the village broadcast system every 300 meters; and when it is 1-2km away, an early warning is sent to both the forest ranger's APP and the village broadcast system every 100 meters, and village officials are contacted to remind and organize villagers to evacuate.

[0061] Early Warning Information Management Module: The early warning information management module is linked to WeChat mini-programs, mobile SMS, and APP. The system provides different permissions and functions to management, administrators, expert teams, forest rangers, and villagers. Based on different responsibilities and user permissions, the early warning information management module will send different early warning information and levels, and provide a rapid decision-making and response interface to promptly report information or issue instructions.

[0062] Animal Resource Management Module: This module is the core data management unit of the Siberian Tiger Intelligent Monitoring and Early Warning System. It focuses on managing resource data for all animals within the protected area, particularly the monitoring and protection of the critical species, the Siberian tiger. This module covers detailed information on animal species, numbers, distribution, and activity range, providing comprehensive data support for animal resource management within the protected area. By regularly updating this data, the system ensures users can promptly understand the changing trends and survival status of animal populations, especially the population dynamics of Siberian tigers.

[0063] Regarding the management of Siberian tigers, the system meticulously records key data such as individual tiger identification information, activity areas, and behavioral habits. Through in-depth analysis of this data, users can better understand the survival needs and conservation status of Siberian tigers, thereby developing more scientific and targeted conservation measures. The system also supports dynamic monitoring of Siberian tiger populations, promptly identifying trends in population changes and providing a basis for adjusting and optimizing conservation efforts.

[0064] Furthermore, the animal resource management module possesses powerful data analysis capabilities, capable of generating various statistical reports and analytical results based on user needs. These reports and results can provide a scientific basis for the management decisions of protected areas, ensuring the effective protection of wildlife habitats. Through the comprehensive application of this module, protected area managers can better fulfill their conservation responsibilities, promote the in-depth development of biodiversity conservation efforts, and especially protect the rare Siberian tiger.

[0065] In addition, the application layer also includes a system configuration management module and reserved platform extension interfaces.

[0066] The System Configuration Management module is the central management unit of the Northeast Tiger Intelligent Monitoring and Early Warning System, responsible for comprehensive control of all system configurations to ensure efficient and stable operation. This module encompasses key functions such as user access management, device information management, log recording, and auditing. Administrators can easily add or delete user accounts and assign different permission levels according to work needs, ensuring system security and controllability. Simultaneously, the system automatically records all operation logs, meticulously tracking every user login, operation, and data change, providing detailed evidence for system security audits. Administrators can also fine-tune system parameters through the System Configuration Management module, including device operating parameters, data update frequency, and early warning thresholds, ensuring the system is always in optimal operating condition. Regarding device information management, the module records detailed information such as the model, installation location, and maintenance records of monitoring devices such as infrared trigger cameras and drones, facilitating device maintenance and management. Through these comprehensive configuration management functions, the System Configuration Management module provides a solid guarantee for the stable operation and efficient management of the Northeast Tiger Intelligent Monitoring and Early Warning System.

[0067] Key features designed to ensure the platform's future sustainability and technological adaptability. By reserving standardized data exchange and function integration interfaces within the current system architecture, the platform offers flexibility for future upgrades and functional expansions. These interfaces will allow the system to easily integrate new monitoring technologies, analysis tools, and communication protocols in the future without requiring large-scale modifications to the existing system. This design considers technological advancements and changing user needs, ensuring the platform's long-term effectiveness and forward-looking nature, while also reducing the cost and complexity of future maintenance and upgrades. The reserved expansion interfaces are a crucial component in building a reliable, scalable, and future-adaptable intelligent monitoring and early warning system.

[0068] (iv) Deployment and configuration of smart terminals

[0069] Infrared trigger cameras: As the core monitoring device of the system, infrared trigger cameras are the primary initiators of Siberian tiger monitoring. These cameras are mainly deployed within a 5km radius of areas affected by human-wildlife conflict, in the following locations: areas historically frequented by Siberian tigers, forest trails, village intersections, culverts under bridges, and the edges of farmland and forests. Utilizing their high-sensitivity infrared sensing technology and high-definition imaging capabilities, they can automatically capture images of Siberian tigers at night or in low-light conditions. These cameras record the behavior and activity trajectories of Siberian tigers in real time, uploading relevant information to the system platform. The intelligent monitoring module of the infrared cameras then quickly determines whether it is a Siberian tiger. When an infrared trigger camera identifies a Siberian tiger and it is close to a village, the system automatically triggers an early warning mechanism, notifying relevant personnel through multiple channels and sending the tiger's location information to the nearest drone, initiating a coordinated patrol mission. The high-resolution imaging, low power consumption, easy installation, solar charging, and strong environmental adaptability of the infrared trigger cameras ensure their long-term stable operation in complex wild environments, providing reliable data support for the monitoring and protection of Siberian tigers.

[0070] Drones and their associated nesting systems: The drones are equipped with advanced AI recognition and verification units, enabling them to automatically identify and track Siberian tigers during flight, capturing their behavior and movements in real time. This model is a high-precision Siberian tiger identification system specifically developed for drone tracking applications. It utilizes deep learning training based on high-definition visible light and infrared thermal imaging data of Siberian tigers collected by the drones during daytime and nighttime flights. Through continuous algorithm optimization and extensive field data collection, the model can quickly and accurately identify Siberian tiger characteristics in complex natural environments, achieving real-time monitoring of their behavior and location. Designed specifically for drone platforms, the model is lightweight and features efficient processing, ensuring rapid response with limited computing resources. Furthermore, the model supports dynamic updates and optimization, continuously adjusting its recognition accuracy based on new data, providing ongoing technical support for Siberian tiger conservation and research. Its patrol process is as follows: Figure 2 As shown.

[0071] The drone nest has an automatic battery swapping function, which can complete the battery swap within 150 seconds after the drone returns to base. After the battery swap, the drone can immediately return to the mission point to continue working. A total of 3 drone nests are deployed, located at the protection station of the reserve, the edge of the core area and near villages with concentrated human activities, to ensure that the drone response radius is ≤5km.

[0072] Sound and light deterrent device: Deployed in the woods 200m outside the village, it receives application layer commands through the 4G network and supports both remote and manual on-site activation modes to non-harmfully guide Siberian tigers away from human activity areas.

[0073] (v) User layer configuration

[0074] Management level: Accessed via the web-based system (deployed on the protected area's intranet server), with permissions for device management (modifying infrared camera sensor sensitivity, drone flight parameters, etc.), data analysis (viewing monitoring data of Siberian tigers across the entire area and generating population reports), and early warning rule configuration (adjusting early warning distance thresholds and push channels).

[0075] Expert Team: Accessible via the web-based system, with permissions for data analysis and population dynamics assessment, and able to view historical monitoring data for scientific research.

[0076] Forest rangers: Accessible via APP, they have the authority to view real-time Siberian tiger warning information in their area of ​​responsibility, share their own location (accuracy 10m), receive expulsion commands and report execution results.

[0077] Villagers: We receive early warning notifications through a WeChat mini-program and can upload photos of the scene to report the situation.

[0078] The method for dynamic monitoring and early warning of Siberian tigers using a combination of UAVs and ground-based systems, employing the aforementioned UAV-ground-based dynamic monitoring and early warning system, comprises the following steps:

[0079] S1. Data Acquisition: When the infrared trigger camera of the smart terminal senses the activity of the target in the wild, it automatically triggers the shooting to generate image data. The image data is transmitted to the data layer in real time through the data return function, and the storage device of the data layer completes the storage. The uninterruptible power supply synchronously ensures the security of the data storage process.

[0080] S2. Data Processing: After the service layer initiates the security authentication service to verify the legality of the data, the infrared camera intelligent monitoring module of the application layer calls the species identification service of the service layer to identify Siberian tigers from the image data stored in the data layer. At the same time, the geographic coordinates of the infrared trigger camera are parsed through the GIS general service to calculate the distance between the Siberian tiger and the human activity area. The data sharing service synchronously completes the preparation for the cross-level transfer of identification results and distance data.

[0081] S3. Warning Trigger: The key species warning module at the application layer generates warning information of corresponding level based on the recognition results and distance data output by the infrared camera intelligent monitoring module. The warning information management module pushes the warning information to users with corresponding permissions at the user layer. The visualization module simultaneously displays the real-time status of the monitoring area and the warning information.

[0082] S4, Drone Collaboration: Based on the identification results, the device collaboration service in the service layer sends instructions to the drone management module in the application layer. After receiving the instructions, the drone management module controls the drone to take off from its nest, using the location coordinates of the Siberian tiger image information captured by the infrared trigger camera as the target, and proceeds to the target coordinate area to perform patrol and tracking tasks. The AI ​​recognition and confirmation unit on the drone confirms that the tracked target animal is a Siberian tiger through the captured images and sends the real-time captured image data back to the data layer for storage. In S4, when the drone performs the patrol task, it initially uses the location coordinates of the infrared trigger camera that captured the Siberian tiger as the center and conducts a patrol within a 5km radius. If the Siberian tiger is not found, the patrol narrows to a 2km core area. After the Siberian tiger is found, the target is locked, and continuous aerial tracking is carried out within a 1km range.

[0083] S5. Removal Execution: The key species early warning module in the application layer continuously monitors the distance between the Siberian tiger and the human activity area. When the distance reaches the removal threshold, the sound and light removal device on the smart terminal is triggered. The sound and light removal device guides the Siberian tiger away in a non-harmful way, and at the same time, it transmits the process data such as removal time, location and Siberian tiger reaction back to the data layer, which is then synchronized to the relevant modules in the application layer by the data sharing service.

[0084] S6. Data Feedback: Users receive early warning information and task instructions through the Web version of the system or the mobile application according to their permissions, and report and provide feedback on information such as on-site situation records and task execution results. The feedback data is transmitted to the data layer for storage via the service layer data sharing service, providing a basis for subsequent data analysis and population management of the infrared camera intelligent monitoring module and the key species early warning module in the application layer.

[0085] The remaining technical features in the above embodiments can be flexibly selected by those skilled in the art to meet different specific practical needs according to actual circumstances. Modifications and variations made by those skilled in the art that do not depart from the spirit and scope of the present invention should be within the protection scope of the appended claims. In the above description, numerous specific details have been set forth to provide a thorough understanding of the present invention. However, it will be apparent to those skilled in the art that these specific details are not necessary to implement the present invention. In other instances, to avoid obscuring the present invention, well-known techniques, such as specific construction details, operating conditions, and other technical conditions, have not been specifically described.

[0086] This document uses specific examples to illustrate the principles and implementation methods of the present invention. The descriptions of the above embodiments are only for the purpose of helping to understand the method and core ideas of the present invention. Furthermore, those skilled in the art will recognize that, based on the ideas of the present invention, there will be changes in the specific implementation methods and application scope. Therefore, the content of this specification should not be construed as a limitation of the present invention.

Claims

1. A large predator dynamic monitoring and early warning system that combines UAVs and ground-based systems, characterized in that: It includes the data layer, service layer, application layer, smart terminal, and user layer, which are connected in sequence to form a data transmission and control link; The data layer stores all the data supporting the operation of the system, including basic geographic data, forestry-specific data, large predator monitoring data, and smart terminal operation data, and is equipped with storage devices and an uninterruptible power supply. The service layer connects the data layer and the application layer, providing core services to realize system functions, including: AI species identification service for identifying large predators from image data transmitted from smart terminals; GIS general service with geographic information processing function; device collaboration service to realize linkage control between smart terminals; data sharing service to support cross-level data flow; and security authentication service to verify the legitimacy of users and devices. The application layer integrates functional modules for monitoring, early warning, and management of large predators, including: a visualization module that displays the real-time status of the monitoring area; an infrared camera intelligent monitoring module that enables infrared triggering and AI recognition of large predators; a drone intelligent control module that controls drones to perform patrol and tracking tasks; an intelligent expulsion management module that plans expulsion routes and controls expulsion equipment; a key species early warning module that issues different levels of early warning information based on the recognition results; an early warning information management module that sends early warning information to mobile devices; and an animal resource management module that manages and analyzes large predator population data. The intelligent terminal is a data acquisition and command execution carrier, including: an infrared trigger camera deployed in the wild with infrared sensing shooting and data transmission functions, a drone and its matching nest that supports automatic flight and tracking, and an audio-visual deterrent device for non-harmful removal of large predators. The user layer is divided into different user groups according to permissions. Users with different permissions can obtain different early warning information and system function access permissions, and support information reporting and command issuance operations.

2. The large predator dynamic monitoring and early warning system in collaboration between UAVs and ground systems according to claim 1, characterized in that: The large predators mentioned include tigers, leopards, or bears.

3. The large predator dynamic monitoring and early warning system in collaboration between UAVs and ground systems according to claim 2, characterized in that: The infrared trigger camera of the smart terminal is deployed in areas where large predators have a history of frequent activity, including forest trails, village intersections, farmland and forest edges, and is located within 5km of the periphery of human-wildlife conflict areas. It has 4G / 5G wireless data transmission and solar charging capabilities.

4. The large predator dynamic monitoring and early warning system in collaboration between UAVs and ground systems according to claim 3, characterized in that: The drone in the intelligent terminal is equipped with a high-definition visible light camera and an infrared thermal imaging camera, and pre-stores elevation data within a 5km radius of the monitoring area; the drone is equipped with an AI recognition and confirmation unit, which is used to confirm the recognition results of the infrared camera's intelligent monitoring module and continuously track the activity trajectory of large predators; the drone nest has an automatic battery swapping function, and the drone can quickly complete the battery swapping after returning to base and return to the mission point to continue its work.

5. The large predator dynamic monitoring and early warning system in collaboration between UAVs and ground systems according to claim 4, characterized in that: The application layer's key species early warning module sets up three levels of early warning based on the distance between large predators and villages: an early warning is triggered every 500 meters when the distance is 3-5km, every 300 meters when the distance is 2-3km, and every 100 meters when the distance is 1-2km.

6. The large predator dynamic monitoring and early warning system in collaboration between UAVs and ground systems according to claim 1, characterized in that: Users in the user layer access the system through the web version and the mobile application. The web version is used by the management and expert team and supports equipment management and data analysis functions; the mobile application is used by forest rangers and villagers and supports early warning reception and information feedback operations.

7. The large predator dynamic monitoring and early warning system in collaboration between UAVs and ground systems according to claim 5, characterized in that: The information displayed by the visualization module of the application layer includes: the distribution location of infrared trigger cameras and drones, the battery level and signal strength of infrared trigger cameras and drones, and the movement path and activity range of large predators.

8. A method for dynamic monitoring and early warning of large predators using a drone-ground coordinated system, employing the drone-ground coordinated dynamic monitoring and early warning system for large predators as described in any one of claims 1-7, characterized in that... The steps are as follows: S1. Data Acquisition: When the infrared trigger camera of the smart terminal senses the activity of the target in the wild, it automatically triggers the shooting to generate image data. The image data is transmitted to the data layer in real time through the data return function, and the storage device of the data layer completes the storage. The uninterruptible power supply synchronously ensures the security of the data storage process. S2. Data Processing: After the service layer initiates the security authentication service to verify the legality of the data, the infrared camera intelligent monitoring module of the application layer calls the species identification service of the service layer to identify large predators from the image data stored in the data layer. At the same time, the geographic coordinates of the infrared trigger camera are parsed through the GIS general service to calculate the distance between the large predator and the human activity area. The data sharing service synchronously completes the preparation for the cross-level flow of identification results and distance data. S3. Warning Trigger: The key species warning module at the application layer generates warning information of corresponding level based on the recognition results and distance data output by the infrared camera intelligent monitoring module. The warning information management module pushes the warning information to users with corresponding permissions at the user layer. The visualization module simultaneously displays the real-time status of the monitoring area and the warning information. S4. Drone Collaboration: The device collaboration service in the service layer sends instructions to the drone management module in the application layer based on the recognition results. After receiving the instruction, the drone management module controls the drone to take off from the nest, and uses the location coordinates of the large predator image information captured by the infrared trigger camera as the target to go to the target coordinate area to perform patrol and tracking tasks. The AI ​​recognition and confirmation unit on the drone confirms that the tracked target animal is a large predator through the captured images, and sends the real-time captured image data back to the data layer for storage. S5. Removal Execution: The key species early warning module in the application layer continuously monitors the distance between large predators and human activity areas. When the distance reaches the removal threshold, the sound and light removal device on the smart terminal is triggered. The sound and light removal device removes the large predators in a non-harmful manner, and at the same time, it transmits the removal time, location and the large predator's reaction process data back to the data layer, which is then synchronized to the application layer by the data sharing service. S6. Data Feedback: Users receive early warning information and task instructions through the Web version of the system or the mobile application according to their permissions, and complete the on-site situation record and task execution result information reporting and feedback. The feedback data is transmitted to the data layer storage through the service layer data sharing service, providing a basis for subsequent data analysis and population management of the infrared camera intelligent monitoring module and the key species early warning module of the application layer.

9. The method for dynamic monitoring and early warning of large predators using UAV and ground coordination as described in claim 8, characterized in that: In S4, when the drone performs a patrol mission, it initially uses the coordinates of the infrared trigger camera that captured the large predator as the center and conducts a patrol with a radius of 5km. If no large predators are found, the patrol will be narrowed to a core area of ​​2km and continued; if a large predator is found, the target will be locked and continuous aerial tracking will be carried out within a 1km range.

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