High-precision ground disaster monitoring and early warning system and method, unmanned aerial vehicle and 5G communication equipment

Abstract

The invention discloses a high-precision ground disaster monitoring and early warning system and method, an unmanned aerial vehicle and 5G communication equipment in the field of geological disaster monitoring, the system adopts a double-layer architecture of'aerial stereo scanning + ground fixed-point monitoring ', a multi-source sensor is carried through an unmanned aerial vehicle cluster to collect data, and calibration is carried out through a Kalman filtering-ICP fusion algorithm; low-delay data transmission is realized based on a 5G high-speed transmission module (integrating an FBMC technology and D2D communication); deploying a dynamic weight double-branch deep learning model at the cloud end, and accurately extracting topographic features and a time sequence change trend; the grading early warning module generates I-IV grade risk signals, an emergency response mechanism is linked, and the terminal interaction module supports visual management and control and instruction issuing. Experiments show that the complex terrain monitoring coverage rate reaches 95%, the data processing time delay is smaller than or equal to 1.5 min, the transmission delay is smaller than or equal to 0.2 s, the early warning accuracy is larger than or equal to 93%, and the comprehensiveness, real-time performance and reliability of geological disaster monitoring and early warning are remarkably improved.

Description

Technical Field

[0001] This invention relates to a wide range of fields, specifically high-precision geological disaster monitoring and early warning systems, methods, drones, and 5G communication equipment. Background Technology

[0002] Geological disasters (such as landslides, collapses, and debris flows) are characterized by their suddenness, destructive power, and wide-ranging impact, posing a serious threat to the lives and property of residents, transportation infrastructure, and the ecological environment in mountainous areas, earthquake zones, and other complex terrain regions. Real-time and accurate monitoring and efficient early warning are the core means to reduce losses from geological disasters and a key goal that has been pursued in the field of geological disaster prevention and control for a long time.

[0003] The current industry-standard technical solution is based on a core architecture of "ground-based fixed monitoring + satellite remote sensing assistance," but it faces significant technical bottlenecks in practical applications across complex terrains.

[0004] 1) Insufficient monitoring coverage and accuracy: Ground sensors are limited by terrain, making it difficult to deploy in areas such as steep canyons and dense forests, resulting in extensive monitoring blind spots; satellite remote sensing has low spatial resolution (above the meter level), long revisit cycles (from several days to tens of days), cannot identify minute deformations at the centimeter level, and is easily affected by weather.

[0005] 2) Low data processing efficiency: The amount of data collected by multi-source sensors is growing exponentially (a single device can collect tens of GB per day). Traditional local computing nodes and simple algorithms are unable to quickly extract key disaster features, and the data processing delay often exceeds several hours, which cannot meet the needs of real-time analysis.

[0006] 3) Poor transmission reliability and timeliness: 4G signal coverage is poor and attenuation is severe in complex terrain areas, and data transmission delay exceeds 2 seconds; wired communication is difficult to lay and is susceptible to disaster damage, resulting in delays or interruptions in the transmission of critical data and early warning information.

[0007] 4) Weak accuracy and coordination in early warning: Traditional models can only extract static terrain features and cannot predict temporal changes, resulting in a high false alarm rate; lack of a hierarchical emergency response mechanism makes it difficult to quickly trigger rescue and evacuation operations.

[0008] Existing technologies are insufficient to meet the demands of modern geological disaster prevention and control for "early identification, accurate assessment, and rapid response." Therefore, this invention proposes a high-precision geological disaster monitoring and early warning system, method, drone, and 5G communication equipment to solve the aforementioned technical problems. Summary of the Invention

[0009] The purpose of this invention is to provide a high-precision geological disaster monitoring and early warning system, method, drone, and 5G communication equipment to solve the problems mentioned in the background art.

[0010] To achieve the above objectives, the present invention provides the following technical solution:

[0011] A high-precision geological disaster monitoring and early warning system includes a multi-source data acquisition module, a cloud-based intelligent processing module, a 5G high-speed transmission module, a tiered early warning module, and a terminal interaction module; the multi-source data acquisition module is communicatively connected to the 5G high-speed transmission module, and the 5G high-speed transmission module is communicatively connected to the cloud-based intelligent processing module, the tiered early warning module, and the terminal interaction module, respectively.

[0012] The multi-source data acquisition module is used to acquire three-dimensional point clouds of terrain, high-definition images and environmental parameters, forming a two-layer acquisition architecture of "aerial stereo scanning + ground fixed-point monitoring";

[0013] The cloud-based intelligent processing module is used for multi-source data feature extraction and risk level assessment, and outputs risk results of levels I-IV.

[0014] The 5G high-speed transmission module is used to achieve low-latency and high-reliability transmission of data and instructions;

[0015] The graded early warning module is used to generate disaster risk graded signals and trigger corresponding emergency response operations.

[0016] The terminal interaction module is used by decision-makers to receive early warning information, visualize and manage data, and issue control commands.

[0017] As a further aspect of the present invention: the multi-source data acquisition module includes an unmanned aerial vehicle platform, a sensor group, and a data calibration unit;

[0018] The drone swarm consists of 3-5 drones and adopts a "master-slave cooperative cruise" mode. The master drone plans the basic route and the slave drones dynamically fill in the blind spots. The cooperative response latency is ≤100ms.

[0019] The sensor group includes a LiDAR device, a high-resolution image sensor, a temperature and humidity sensor, a soil moisture sensor, and a vibration sensor, all integrated into the UAV platform. The ground-based supplementary sensing network deploys 3-5 fixed sensor points per 1 km², with a sampling frequency of 1 Hz.

[0020] The data calibration unit employs a Kalman filter-ICP fusion algorithm to perform spatiotemporal synchronization calibration of LiDAR and image sensor data. The calibration formula satisfies:

[0021]

[0022] in, Here, A is the calibrated state variable, H is the observation matrix, K(k) is the Kalman gain, and Q and R are the covariances of process noise and observation noise, respectively. Here, d(k) represents LiDAR point cloud data, and d(k) represents the matching bias calculated by the ICP algorithm.

[0023] As a further embodiment of the present invention: the cloud-based intelligent processing module includes an edge preprocessing unit, a dual-branch deep learning model, and a risk assessment unit;

[0024] The edge preprocessing unit employs Gaussian filtering for noise reduction (kernel size 3×3), PCA dimensionality reduction (retaining 95% of feature information), and 3σ criterion outlier removal, with a processing latency of ≤10s.

[0025] The dual-branch deep learning model includes a terrain feature branch (based on ResNet50) and a temporal variation branch (based on an improved LSTM), and the loss function is:

[0026]

[0027] in The loss for terrain feature extraction is cross-entropy loss. The loss is the time-varying prediction loss (mean squared error loss), and α and β are the dynamic weights. When the risk level is ≥ III, α = 0.3 and β = 0.7; when the risk level is low, α = 0.7 and β = 0.3.

[0028] The risk assessment unit generates risk levels I-IV based on the model output and the fuzzy comprehensive evaluation model, and the thresholds are dynamically updated quarterly based on newly added disaster data.

[0029] As a further embodiment of the present invention: the 5G high-speed transmission module includes a filter bank multicarrier (FBMC) unit, a D2D communication unit, and a QoS guarantee unit;

[0030] The spectral efficiency formula for the multi-carrier unit of the filter bank is:

[0031]

[0032] Where γ=0.05 is the guard interval ratio, B=100MHz is the bandwidth, and SNR is the signal-to-noise ratio;

[0033] The D2D communication unit supports direct transmission between devices and automatically switches when the 5G signal strength is <-100dBm, with a direct connection delay of ≤50ms.

[0034] The QoS guarantee unit adopts a weighted fair queue algorithm, with data priorities from high to low as follows: risk level ≥ III warning information, UAV real-time data, historical data and logs, with a scheduling weight ratio of 5:3:2.

[0035] As a further embodiment of the present invention: the graded early warning module includes a threshold configuration unit, a signal generation unit, and an emergency response unit;

[0036] The threshold configuration unit constructs a dataset based on 100,000+ terrain samples and 50,000+ time-series change samples, and dynamically updates the risk threshold.

[0037] The emergency response unit triggers differentiated operations based on the risk level: Level I only updates the database; Level II pushes a notification and encrypts patrols; Level III activates audible and visual warnings, activates ground sensors, and synchronizes with the emergency platform; Level IV additionally coordinates with the rescue dispatch and resident evacuation system.

[0038] High-precision geological disaster monitoring and early warning methods include:

[0039] S1. Multi-source data acquisition: The UAV swarm cruises in a "master-slave collaborative" mode, collecting terrain and environmental data, supplemented by ground sensors for monitoring, and outputting fused data after calibration by Kalman filter-ICP algorithm;

[0040] The S2.5G high-speed transmission module uses FBMC technology and sliding window overlay coding to transmit fused data. The coding efficiency formula is:

[0041]

[0042] Where: N is the number of windows, and L is the data volume per window. This is redundant data.

[0043] S3. Intelligent data processing: The edge preprocessing unit compresses the data, and the cloud-based dual-branch deep learning model extracts features and predicts the risk level. The model uses the Adam optimizer (learning rate 1e-4) and is iteratively trained until L≤0.05.

[0044] S4. Graded Early Warning Generation: The graded early warning module generates early warning signals based on risk levels and transmits them to the terminal interaction module via 5G;

[0045] S5. Closed-loop management: Decision-makers can view data visually through terminals, issue instructions such as route adjustments and sensor parameter modifications, and the system records the response results.

[0046] As a further aspect of the present invention, it also includes a fault adaptive adjustment step:

[0047] When the sensor data drops by 50%, the data acquisition mode will be automatically switched and a replacement reminder will be sent.

[0048] If 5G transmission is interrupted for more than 30 seconds, the drone will activate local storage (built-in ≥2TB SSD) and resume transmission after the signal is restored.

[0049] When the drone's battery level is less than 20%, a backup drone is triggered to take over the patrol, with a switching latency of ≤200ms.

[0050] A drone includes a fuselage, a power system, a navigation module, a sensor mounting bay, a 5G communication module, an obstacle avoidance module, and a flight endurance optimization module;

[0051] The navigation module uses GPS + Beidou dual-mode positioning with a positioning accuracy of ≤0.5m;

[0052] The sensor mount is compatible with LiDAR (range 0.1-300m, point cloud density 100-200 points / ㎡) and high-resolution image sensors (≥64 million pixels), and supports autofocus and exposure adjustment;

[0053] The 5G communication module integrates FBMC technology and D2D communication protocol, with a transmission rate of ≥1Gbps;

[0054] The obstacle avoidance module uses millimeter-wave radar with a detection range of ≥50m;

[0055] The flight endurance optimization module dynamically adjusts the flight speed (8-15m / s) based on wind speed and load, with a flight endurance of ≥40min.

[0056] A 5G communication device includes a radio frequency unit, a baseband processing unit, and an interface unit;

[0057] The radio frequency unit supports the 3.5GHz band, with a bandwidth of 100MHz and a maximum transmission rate of 1.6Gbps;

[0058] The baseband processing unit integrates the D2D communication protocol and the sliding window coding module, and supports QoS priority scheduling.

[0059] The interface unit is compatible with USB 3.0 and Ethernet, and can connect to sensors and drones. It has a protection rating of ≥IP67.

[0060] As a further improvement of the present invention: the system adopts a modular design, which can be expanded and adapted to different disaster scenarios such as volcanoes and mudslides, and reserves a 6G communication interface and a federated learning interface to support smooth technology upgrades; all hardware adopts industrial-grade protection design, with an operating temperature range of -20℃ to 60℃, and is suitable for complex terrain environments.

[0061] Compared with the prior art, the beneficial effects of the present invention are:

[0062] 1) Comprehensive and accurate monitoring coverage: The dual-layer "air + ground" acquisition architecture and UAV collaboration fill blind spots, achieving a 95% monitoring coverage rate in complex terrain; Kalman filter-ICP calibration technology reduces data matching error to ≤0.1m, providing a high-precision data source for subsequent analysis.

[0063] 2) Real-time and efficient data processing: The edge-cloud collaborative architecture reduces the transmission pressure on the cloud, and the dynamic weight dual-branch model accurately extracts static and dynamic features. The data processing latency is ≤1.5min, which is 94.6% higher than traditional technologies.

[0064] 3) Reliable and low-latency transmission: FBMC technology and D2D communication are adapted to complex terrains, with a transmission latency of ≤0.2s and a signal interruption rate of <1%; the QoS scheduling mechanism ensures that high-risk information is transmitted first, with a packet loss rate of <0.1%.

[0065] 4) Precise early warning and strong linkage: The fuzzy comprehensive evaluation model and dynamic threshold update ensure an early warning accuracy rate of ≥93% and a false alarm rate of <1%; the hierarchical emergency linkage mechanism realizes a closed loop of "early warning-response-feedback", shortening the emergency response time from 30 minutes to 4 minutes.

[0066] 5) Good adaptability and scalability: The industrial-grade protective design is suitable for environments ranging from -20℃ to 60℃, the modular design can be expanded to various disaster scenarios, and the reserved upgrade interface ensures long-term adaptability to technological development. Attached Figure Description

[0067] Figure 1 This is a schematic diagram of the overall system architecture;

[0068] Figure 2 This is a schematic diagram of the architecture of the multi-source data acquisition module;

[0069] Figure 3 This is a schematic diagram of a dual-branch deep learning model structure;

[0070] Figure 4 This is a schematic diagram of the operation of a 5G high-speed transmission module;

[0071] Figure 5 This is a schematic diagram of the hierarchical early warning linkage logic. Detailed Implementation

[0072] 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 skilled in the art without creative effort are within the scope of protection of the present invention.

[0073] Please see Figures 1-5 In this embodiment of the invention, the high-precision geological disaster monitoring and early warning system, method, drone, and 5G communication equipment are based on the core logic of "multi-source acquisition and calibration - intelligent dynamic processing - high-speed and reliable transmission - hierarchical closed-loop early warning". The modules are deeply collaborative and have scalable adaptability, as detailed below:

[0074] Multi-source data acquisition module: full terrain coverage and high-precision data calibration

[0075] The dual-layer data acquisition architecture consists of a drone swarm and a ground-based supplementary sensing network. The drone swarm adopts a "master-slave collaborative patrol" mode, with 3-5 drones working together. The master drone plans the basic flight path, while the slave drones dynamically fill in the gaps. The swarm communication is achieved through a 5G D2D module. The ground-based supplementary sensing network is deployed in key areas, with 3-5 points deployed every 1 km² to verify the long-term stability of the drone data.

[0076] Composite sensor suite: The UAV integrates LiDAR (to acquire 3D terrain point clouds), high-resolution image sensor (to acquire high-definition texture images), and environmental sensing kit (to monitor temperature, humidity, soil moisture content, etc.); the ground sensor has a sampling frequency of 1Hz to supplement fixed-point monitoring data.

[0077] Core technology: Kalman filter-ICP fusion calibration: First, the sensor acquisition timestamp deviation is corrected by IMU (≤10ms). Then, based on LiDAR point cloud, the initial coordinates are predicted by Kalman filter and iterative optimization by ICP algorithm to reduce the matching error to ≤0.1m, thus solving the problem of data spatiotemporal misalignment.

[0078] Cloud-based intelligent processing module: Dynamic weighted deep learning and real-time risk assessment

[0079] Edge-cloud collaborative architecture: The edge preprocessing layer is deployed on the ground station to complete data denoising, dimensionality reduction and outlier removal, with a compression ratio of 1:5 and a processing latency of ≤10s; the cloud deep analysis layer is deployed on the GPU cluster to run a dual-branch model and risk assessment engine.

[0080] The dynamic weighted dual-branch model is as follows: the terrain feature branch extracts static features such as crack length and slope based on ResNet50, while the temporal change branch predicts the trend of change in the next 7 days based on an improved LSTM; the dynamic weights α and β are adjusted according to the risk level, strengthening the prediction of temporal change when the risk is high and focusing on terrain feature extraction when the risk is low.

[0081] Fuzzy comprehensive evaluation model: Based on four indicators, namely “model risk value (weight 0.5), 24-hour rainfall (0.2), soil moisture content (0.2), and historical disaster frequency (0.1),” risk levels I-IV are generated. The thresholds are dynamically updated every quarter, and the goodness of fit is ≥0.92.

[0082] 5G High-Speed ​​Transmission Module: Interference-resistant Transmission and QoS Priority Scheduling

[0083] Core Technology: FBMC - Sliding Window Coding Collaboration: FBMC technology reduces subcarrier interference through a prototype filter, improving spectral efficiency by 20% compared to OFDM and reducing the bit error rate to 10⁻5 Sliding window overlay coding divides data into multiple windows for overlay transmission, with a coding efficiency of ≥80% and a data retransmission rate of <2%.

[0084] D2D Communication and Fault Adaptation: Supports direct transmission between devices, automatically switches when signal strength is <-100dBm, and direct connection delay is ≤50ms; when the signal is interrupted for more than 30s, the drone starts local storage and resumes transmission after the signal is restored.

[0085] QoS scheduling mechanism: Data priority is divided according to risk level, high-risk early warning information is transmitted first, transmission delay is ≤0.2s, and packet loss rate is <0.1%.

[0086] Tiered early warning and terminal interaction module: closed-loop linkage and visualized control

[0087] Tiered early warning and linkage: Level I (no risk) only updates the database; Level II (low risk) pushes attention notifications and encrypts patrols; Level III (high risk) activates audible and visual warnings, activates ground sensors, and synchronizes with the emergency platform; Level IV (extremely high risk) additionally links rescue dispatch and resident evacuation systems.

[0088] Visualization and control functions: Supports web and mobile APP, displaying cruise trajectory, terrain model and risk distribution in 3D map, supporting zoom, rotation and detailed query; decision-makers can remotely adjust the route, modify sensor parameters, and view the equipment operation status in real time.

[0089] Fault Adaptation and Extension Mechanism

[0090] End-to-end fault self-healing: Automatically switches acquisition mode when sensor fails, starts local storage when transmission is interrupted, and automatically takes over when drone fails, ensuring stable system operation.

[0091] Scenario expansion and adaptation: expandable gas sensors (volcano monitoring), enhanced soil moisture monitoring (debris flow monitoring), reserved 6G and federated learning interface, supporting technology upgrades.

[0092] Example 1: Landslide monitoring scenario in mountainous area (a landslide-prone area in Sichuan Province)

[0093] System deployment: The monitoring area is 50km², with 3 DJI Matrice350RTK drones (equipped with Velodyne VLP-16LiDAR and Sony IMX686 sensors) deployed, supplemented by 15 ground-based sensors; the 5G module uses Huawei MH5000 module, and the cloud is deployed with Alibaba Cloud ECSg7 instance (8×NVIDIA A100 GPU).

[0094] Implementation process:

[0095] S1. The UAV cruises at an altitude of 100m and a speed of 12m / s, collecting data every 5 minutes. The data is then fused by Kalman filtering and ICP calibration.

[0096] The S2.5G module uses sliding window encoding (N=10, L=2GB, O=0.4GB) to transmit data, with a transmission latency of 0.15s.

[0097] S3. Cloud-based model processes data (denoising 10s + inference 45s) and identifies a certain area as having a Level III landslide risk;

[0098] S4. The graded early warning module activates audible and visual warnings, pushes information to the emergency management APP, and dispatches drones to conduct intensive patrols every 2 hours.

[0099] S5. Decision-makers adjust flight paths via terminals, focusing on high-risk areas and acquiring high-precision point cloud data of 200 points / ㎡.

[0100] Experimental results: 95% monitoring coverage, 1.5-minute data processing delay, 93% early warning accuracy, and emergency response time reduced from 30 minutes to 4 minutes, representing a significant improvement over existing technologies.

[0101] Example 2: Monitoring scenario of secondary disasters after an earthquake (an earthquake-stricken area in Yunnan)

[0102] Special configuration: The drone is equipped with a millimeter-wave radar obstacle avoidance module, and the 5G module enables D2D communication to connect to temporary rescue points; the model weights are adjusted to α=0.3 and β=0.7 to enhance the prediction of time series changes.

[0103] Implementation results: 100 km² monitoring was completed within 24 hours, identifying 12 potential landslide points (compared to only 5 points with existing technology); after a Level IV risk point warning was issued, threatened residents were evacuated 2 hours in advance; the transmission success rate in areas with weak signal (SNR=5dB) was 99%, which is better than the 72% of existing technology.

[0104] Example 3: Long-term stability verification (30 consecutive days)

[0105] The system runs for an average of 20 hours per day, collecting 500GB of data without any hardware failures; the risk level prediction consistency is 91%, with no false alarms; 5G transmission was interrupted only twice (during extreme rainstorms), and resumed after automatic switching to D2D communication, with an interruption duration of less than 1 minute, verifying the stability and reliability of the system.

[0106] The above description is only a preferred embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any equivalent substitutions or modifications made by those skilled in the art within the scope of the technology disclosed in the present invention, based on the technical solution and inventive concept of the present invention, should be covered within the scope of protection of the present invention.

Claims

1. A high-precision geological disaster monitoring and early warning system, characterized in that: It includes a multi-source data acquisition module, a cloud-based intelligent processing module, a 5G high-speed transmission module, a graded early warning module, and a terminal interaction module; the multi-source data acquisition module is communicatively connected to the 5G high-speed transmission module, and the 5G high-speed transmission module is communicatively connected to the cloud-based intelligent processing module, the graded early warning module, and the terminal interaction module, respectively. The multi-source data acquisition module is used to acquire 3D point clouds of terrain, high-definition images and environmental parameters, forming a dual-layer acquisition architecture of "aerial stereo scanning + ground fixed-point monitoring"; The cloud-based intelligent processing module is used for multi-source data feature extraction and risk level assessment, and outputs risk results of levels I-IV. The 5G high-speed transmission module is used to achieve low-latency and high-reliability transmission of data and instructions; The graded early warning module is used to generate disaster risk graded signals and trigger corresponding emergency response operations. The terminal interaction module is used by decision-makers to receive early warning information, visualize and manage data, and issue control commands.

2. The high-precision geological disaster monitoring and early warning system according to claim 1, characterized in that: The multi-source data acquisition module includes an unmanned aerial vehicle platform, a sensor group, and a data calibration unit; The drone swarm consists of 3-5 drones and adopts a "master-slave cooperative cruise" mode. The master drone plans the basic route and the slave drones dynamically fill in the blind spots. The cooperative response latency is ≤100ms. The sensor group includes a LiDAR device, a high-resolution image sensor, a temperature and humidity sensor, a soil moisture sensor, and a vibration sensor, all integrated into the UAV platform. The ground-based supplementary sensing network deploys 3-5 fixed sensor points per 1 km², with a sampling frequency of 1 Hz. The data calibration unit employs a Kalman filter-ICP fusion algorithm to perform spatiotemporal synchronization calibration of LiDAR and image sensor data. The calibration formula satisfies: ;in, Here, A is the calibrated state variable, H is the observation matrix, K(k) is the Kalman gain, and Q and R are the covariances of process noise and observation noise, respectively. Here, d(k) represents LiDAR point cloud data, and d(k) represents the matching bias calculated by the ICP algorithm.

3. The high-precision geological disaster monitoring and early warning system according to claim 1, characterized in that: The cloud-based intelligent processing module includes an edge preprocessing unit, a dual-branch deep learning model, and a risk assessment unit. The edge preprocessing unit employs Gaussian filtering for noise reduction (kernel size 3×3), PCA dimensionality reduction (retaining 95% of feature information), and 3σ criterion outlier removal, with a processing latency of ≤10s. The dual-branch deep learning model includes a terrain feature branch (based on ResNet50) and a temporal variation branch (based on an improved LSTM), and the loss function is: ;in The loss for terrain feature extraction is cross-entropy loss. The loss is the time-varying prediction loss (mean squared error loss), and α and β are the dynamic weights. When the risk level is ≥ III, α = 0.3 and β = 0.7; when the risk level is low, α = 0.7 and β = 0.

3. The risk assessment unit generates risk levels I-IV based on the model output and the fuzzy comprehensive evaluation model, and the thresholds are dynamically updated quarterly based on newly added disaster data.

4. The high-precision geological disaster monitoring and early warning system according to claim 1, characterized in that: The 5G high-speed transmission module includes a filter bank multicarrier (FBMC) unit, a D2D communication unit, and a QoS guarantee unit. The spectral efficiency formula for the multi-carrier unit of the filter bank is: Where γ=0.05 is the guard interval ratio, B=100MHz is the bandwidth, and SNR is the signal-to-noise ratio; The D2D communication unit supports direct transmission between devices and automatically switches when the 5G signal strength is <-100dBm, with a direct connection delay of ≤50ms. The QoS guarantee unit adopts a weighted fair queue algorithm, with data priorities from high to low as follows: risk level ≥ III warning information, UAV real-time data, historical data and logs, with a scheduling weight ratio of 5:3:

2.

5. The high-precision geological disaster monitoring and early warning system according to claim 1, characterized in that: The graded early warning module includes a threshold configuration unit, a signal generation unit, and an emergency response unit. The threshold configuration unit constructs a dataset based on 100,000+ terrain samples and 50,000+ time-series change samples, and dynamically updates the risk threshold. The emergency response unit triggers differentiated operations based on the risk level: Level I only updates the database; Level II pushes a notification and encrypts patrols; Level III activates audible and visual warnings, activates ground sensors, and synchronizes with the emergency platform. Level IV additional linkage rescue dispatch and resident evacuation system.

6. A high-precision geological disaster monitoring and early warning method, applied to the system described in any one of claims 1-5, characterized in that, include: S1. Multi-source data acquisition: The UAV swarm cruises in a "master-slave collaborative" mode, collecting terrain and environmental data, supplemented by ground sensors for monitoring, and outputting fused data after calibration by Kalman filter-ICP algorithm; The S2.5G high-speed transmission module uses FBMC technology and sliding window overlay coding to transmit fused data. The coding efficiency formula is: Where: N is the number of windows, and L is the data volume per window. This is redundant data. S3. Intelligent data processing: The edge preprocessing unit compresses the data, and the cloud-based dual-branch deep learning model extracts features and predicts the risk level. The model uses the Adam optimizer (learning rate 1e-4) and is iteratively trained until L≤0.

05. S4. Graded Early Warning Generation: The graded early warning module generates early warning signals based on risk levels and transmits them to the terminal interaction module via 5G; S5. Closed-loop management: Decision-makers can view data visually through terminals, issue instructions such as route adjustments and sensor parameter modifications, and the system records the response results.

7. The high-precision geological disaster monitoring and early warning method according to claim 6, characterized in that: It also includes fault adaptive adjustment steps: When the sensor data drops by 50%, the data acquisition mode will be automatically switched and a replacement reminder will be sent. If 5G transmission is interrupted for more than 30 seconds, the drone will activate local storage (built-in ≥2TB SSD) and resume transmission after the signal is restored. When the drone's battery level is less than 20%, a backup drone is triggered to take over the patrol, with a switching latency of ≤200ms.

8. A drone for use in the system of claim 1, characterized in that, This includes the fuselage, power system, navigation module, sensor bay, 5G communication module, obstacle avoidance module, and range optimization module; The navigation module uses GPS + Beidou dual-mode positioning with a positioning accuracy of ≤0.5m; The sensor mount is compatible with LiDAR (range 0.1-300m, point cloud density 100-200 points / ㎡) and high-resolution image sensors (≥64 million pixels), and supports autofocus and exposure adjustment; The 5G communication module integrates FBMC technology and D2D communication protocol, with a transmission rate of ≥1Gbps; The obstacle avoidance module uses millimeter-wave radar with a detection range of ≥50m; The flight endurance optimization module dynamically adjusts the flight speed (8-15m / s) based on wind speed and load, with a flight endurance of ≥40min.

9. A 5G communication device for use in the system of claim 1, characterized in that, Includes radio frequency unit, baseband processing unit and interface unit; The radio frequency unit supports the 3.5GHz band, with a bandwidth of 100MHz and a maximum transmission rate of 1.6Gbps; The baseband processing unit integrates the D2D communication protocol and the sliding window coding module, and supports QoS priority scheduling. The interface unit is compatible with USB 3.0 and Ethernet, and can connect to sensors and drones. It has a protection rating of ≥ IP67.

10. The high-precision geological disaster monitoring and early warning system according to claim 1, characterized in that: The system adopts a modular design, which can be expanded and adapted to different disaster scenarios such as volcanoes and mudslides. It reserves a 6G communication interface and a federated learning interface to support smooth technology upgrades. All hardware features industrial-grade protection design, with an operating temperature range of -20℃ to 60℃, adaptable to complex terrain environments.

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