Space-air-ground integrated avalanche cooperative monitoring system and method
By integrating air, space, and ground avalanche monitoring system with ground sensors and UAV remote sensing data, the system enables automated, accurate assessment and efficient early warning of avalanche risks, solving the problems of passive monitoring, data blind spots, and communication vulnerabilities in existing technologies.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- CHINA INFOMRAITON CONSULTING & DESIGNING INST CO LTD
- Filing Date
- 2026-01-20
- Publication Date
- 2026-06-09
AI Technical Summary
Existing avalanche monitoring systems suffer from problems such as passive monitoring lacking proactive capabilities, single data dimensions leading to monitoring blind spots, fragile communication links, and fragmented data lacking in-depth fusion analysis, resulting in low efficiency and insufficient reliability.
An integrated air-ground monitoring system is adopted, including a ground sensing module, a collaborative computing and scheduling module, an adaptive aerial acquisition module, and a multi-dimensional data fusion and analysis module. By calculating the snow layer instability index, the UAV sensor modes are adaptively adjusted to achieve multi-dimensional data fusion analysis and highly reliable communication.
It has achieved automated and accurate assessment of avalanche risk, solved the problems of blind inspection by drones and the reliability of communication links, and realized quantitative assessment and efficient early warning of avalanche risk.
Smart Images
Figure CN122170943A_ABST
Abstract
Description
Technical Field
[0001] This invention relates to an avalanche collaborative monitoring system and method, and more particularly to an integrated air-ground-space avalanche collaborative monitoring system and method. Background Technology
[0002] Even advanced avalanche monitoring systems equipped with multiple sensors generally suffer from the following deep-seated technical bottlenecks:
[0003] 1. Passive monitoring, lacking proactive investigation capabilities: Traditional systems can only "wait" for data changes. When sensor data shows anomalies, manual analysis is required to decide whether to dispatch personnel or drones for investigation. This process is delayed, and in high-risk weather, manual intervention is both dangerous and inefficient. The system itself does not possess automated "perception-decision-action" closed-loop capabilities.
[0004] 2. Limited data dimensions and monitoring blind spots: Ground-based fixed sensors (such as snow depth gauges) provide data at "points" or "lines," failing to capture the "area" information of the entire avalanche-forming area, such as the overall distribution of snow accumulation, micro-cracks, snowboard tension, and other key surface features. This results in inherent spatial blind spots in monitoring.
[0005] 3. Fragile and unreliable communication links: The monitoring stations are located in remote areas and rely heavily on a single ground communication link (such as fiber optic cable). If a disaster such as a landslide or freezing occurs and the fiber optic cable is interrupted, the entire monitoring system will be "out of contact" and fail when it is most needed.
[0006] 4. Fragmented data and lack of in-depth fusion analysis: The existing "ground-triggered - airborne early warning" model is usually a simple on / off trigger (e.g., take off when snow depth exceeds the limit), lacking adaptive adjustment to UAV data collection strategies (e.g., key scanning areas, scanning resolution, sensor mode selection), resulting in low UAV inspection efficiency and poor data relevance. Furthermore, ground data (points) and airborne data (areas) are usually displayed separately or simply overlaid, lacking fusion at the physical and mechanical level. For example, it is impossible to use the snow density measured on the ground and the snowboard thickness measured in the air, combined with terrain slope, to calculate the specific ratio of "shear strength" to "shear stress," making it impossible to quantify the risk of snowboard instability. Summary of the Invention
[0007] Purpose of the invention: The technical problem to be solved by the present invention is to provide an integrated air-space-ground avalanche collaborative monitoring system and method to address the shortcomings of the existing technology.
[0008] To address the aforementioned technical problems, this invention discloses an integrated air-space-ground avalanche collaborative monitoring system and method, the system comprising:
[0009] The system comprises a ground-based sensing module, a collaborative computing and scheduling module, an adaptive aerial data acquisition module, and a multi-dimensional data fusion and analysis module; among which...
[0010] The ground sensing module collects a set of environmental parameters for the target area and sends them to the collaborative computing and scheduling module.
[0011] The collaborative computing and scheduling module calculates the snow layer instability index of the target area. And perform hierarchical scheduling;
[0012] The adaptive aerial acquisition module acquires remote sensing data using different dominant modes according to the instructions of hierarchical scheduling.
[0013] The multi-dimensional data fusion analysis module determines the snowboard instability zone and issues an alarm based on the set of environmental parameters and remote sensing data of the target area.
[0014] Furthermore, the ground sensing module includes:
[0015] Multiple communication towers are deployed in the target area for avalanche collaborative monitoring. These towers are equipped with sensors and form a distributed monitoring network. The sensors include snow depth sensors, meteorological sensors, infrasound / microseismic sensors, and high-definition cameras. Each sensor connects to the monitoring network via an IoT module to acquire a set of environmental parameters. , means as follows:
[0016]
[0017] in, The rate of change of snow depth. For the temperature gradient of the snow layer, These are the acoustic emission characteristic values inside the snow layer. This refers to the near-ground wind speed and direction.
[0018] Furthermore, the collaborative computing and scheduling module, based on the set of environmental parameters... Calculate the snow layer instability index of the target area. , means as follows:
[0019]
[0020] in, This represents the normalized rate of change in snow depth. These are the normalized acoustic emission characteristic values inside the snow layer; To determine the temperature gradient of the snow layer and near-ground wind speed and direction The calculated coupling effect factor between wind load and temperature gradient; , and These are weighting coefficients derived from historical disaster data.
[0021] Furthermore, the coupling influence factor between wind load and temperature gradient is based on the snow layer temperature gradient. and near-ground wind speed and direction The result, calculated using the unified weighted average method, is expressed as follows:
[0022]
[0023] in, and These are the weighting coefficients. This represents the normalized near-ground wind speed and direction. This represents the normalized snow layer temperature gradient.
[0024] Furthermore, the collaborative computing and scheduling module performs hierarchical scheduling, including:
[0025] Set hierarchical scheduling thresholds and ;
[0026] like If so, the current working status will be maintained, and continuous monitoring will be carried out;
[0027] like Based on the set of environmental parameters Based on the coordinates of each sensor, a Region of Interest (ROI) is generated, and an early warning-level scheduling command is issued to the adaptive aerial data acquisition module.
[0028] like Based on the set of environmental parameters Based on the coordinates of each sensor, a Region of Interest (ROI) is generated, and alarm-level scheduling commands are issued to the adaptive aerial data acquisition module.
[0029] Furthermore, the generation of the Region of Interest (ROI) includes:
[0030] Using the coordinates of each sensor as the origin, along the near-ground wind speed and direction... Wind direction A ray is established in the direction of the ray, and combined with the GIS digital elevation model (DEM), the leeward slope within the preset range downwind of the sensor is identified, that is, the area where the slope change rate changes from positive to negative. The projection area of the leeward slope is defined as the snow accumulation sector, and the snow accumulation sectors of all sensors are merged as the final generated Region of Interest (ROI).
[0031] Furthermore, the adaptive aerial acquisition module acquires remote sensing data using different dominant modes, including:
[0032] If the snow layer instability index of the target area is calculated During the process, )or If the value is the largest, then it means The index is determined by acoustic emission characteristics. or snow depth In this mode, the surface cracking / morphology dominance mode is adopted. The UAV in the adaptive aerial acquisition module activates the lidar, lowers the flight altitude to a preset value, resets the lateral overlap rate, performs point cloud scanning, and obtains terrain deformation data of the key area of interest (ROI).
[0033] If the snow layer instability index of the target area is calculated During the process, If the value is the largest, then it means The index is determined by temperature. In this mode, the thermal / wet snow dominant mode is adopted. The UAV in the adaptive aerial acquisition module activates the thermal imaging sensor to collect thermal imaging data to correct the cohesion of the snow layer, collect temperature field distribution data of the snow surface in the ROI of focus, and identify spatial differences in liquid water content.
[0034] Furthermore, the multi-dimensional data fusion analysis module determines the snowboard instability zone and issues an alarm, including:
[0035] The point cloud transmitted back by the lidar of the UAV in the adaptive aerial acquisition module is used to construct a real-time digital elevation model (DEM) of the region of interest (ROI), obtaining arbitrary grid locations. Snowboard thickness and slope angle ;
[0036] Using the Kriging interpolation method, with the positions of each sensor on the communication tower as template points, the measured average density of the snow layer was calculated. and internal friction angle Interpolation is mapped onto the surface of a real-time digital elevation model (DEM) to obtain arbitrary grid locations. snow density and internal friction angle ;
[0037] Based on the infinite slope model, the stability coefficients of all grid locations within the region of interest (ROI) are calculated. ;
[0038] Based on the stability coefficients of all grid locations within the ROI of focus The calculation results are used to generate visualizations. Contour map;
[0039] When a continuous region is detected When the area is identified as a ski instability zone, a warning report containing grid location coordinates and images is generated.
[0040] Furthermore, the calculation focuses on the stability coefficient of all grid locations within the ROI. The method is as follows:
[0041]
[0042] in, To determine the cohesion of the snow layer, if thermal imaging data is available, the cohesion of the snow layer can be determined based on the snow surface temperature distribution. Perform spatial correction. This is the acceleration due to gravity.
[0043] This invention also proposes an integrated air-space-ground avalanche monitoring method, which uses the aforementioned system for coordinated detection and includes the following steps:
[0044] Step 1: Collect a set of environmental parameters for the target area;
[0045] Step 2: Calculate the snow layer instability index of the target area based on the environmental parameter set of the target area. And perform hierarchical scheduling;
[0046] Step 3: According to the instructions of hierarchical scheduling, use UAVs to collect remote sensing data in different dominant modes;
[0047] Step 4: Based on the set of environmental parameters and remote sensing data of the target area, determine the snowboard instability zone and issue an alarm.
[0048] Beneficial effects:
[0049] 1. This invention calculates... The dominant factor of the index adaptively adjusts the sensor modes of the drone, solving the data redundancy problem caused by the "blind inspection" of drones in existing technologies.
[0050] 2. Unlike simple image overlay, this invention uses "point-to-surface fusion" to assign the physical parameters of the ground to the geometric model in the air, and calculates the stability coefficient based on physical and mechanical formulas to achieve quantitative risk assessment.
[0051] 3. A QoS-based hierarchical transmission strategy ensures that core decision data can still be reliably transmitted via satellite link even when communication is impaired. Attached Figure Description
[0052] The present invention will be further described in detail below with reference to the accompanying drawings and specific embodiments, and the advantages of the present invention in the above and / or other aspects will become clearer.
[0053] Figure 1This is a diagram of the overall logical architecture of the system of the present invention.
[0054] Figure 2 The flowchart of collaborative triggering and adaptive acquisition in this invention.
[0055] Figure 3 This is a schematic diagram illustrating the principle of stability coefficient calculation based on point-surface fusion in this invention. Detailed Implementation
[0056] I. System Module Architecture.
[0057] like Figure 1 As shown, the system includes: a ground sensing module, a collaborative computing and scheduling module, an adaptive aerial acquisition module, a multi-dimensional data fusion and analysis module, and a high-reliability communication support subsystem.
[0058] 1. Ground sensing module.
[0059] This module forms the sensing foundation of the entire system, consisting of a distributed monitoring network comprised of multiple integrated multi-functional sensor communication towers deployed in avalanche-prone areas. Each communication tower integrates a snow depth sensor, a meteorological sensor, an infrasound / microseismic sensor, and a high-definition camera. All sensors connect to the network via a 5G-A RedCap low-power IoT module, enabling low-latency data transmission.
[0060] To support subsequent calculations of the "cooperative triggering model," this module is configured to perform edge preprocessing on the raw sensor data and output a set of environmental parameters for the target area. in The rate of change of snow depth. For the temperature gradient of the snow layer, These are the acoustic emission characteristics within the snow layer (reflecting crack propagation). This refers to the near-ground wind speed and direction.
[0061] 2. Collaborative Computing and Scheduling Module
[0062] This module is the "brain" of the entire system, deployed on a server cluster in the cloud or rear command center, responsible for receiving and storing all data from the ground-based sensing modules in real time. The data, including the image data transmitted back by the aerial module, is used to construct a unified historical database.
[0063] To address the limitations of traditional collaborative mechanisms, this module employs a multi-factor weighted algorithm to calculate the Snow Layer Instability Index (STI) of the target region in real time. Using a linear weighted model ensures millisecond-level warning response on low-power embedded devices.
[0064]
[0065] in: This represents the normalized rate of change in snow depth. These are the normalized acoustic emission eigenvalues; This is the coupling influence factor between wind load and temperature gradient; , and Weighting coefficients are set based on historical disaster data.
[0066] Coupling Influence Factors of Wind Load and Temperature Gradient The calculation uses a unified weighted average method, and the specific formula is as follows:
[0067]
[0068] In the formula: For weighting coefficients (e.g., take...) =0.7, =0.3);
[0069] Wind speed normalization function: Set the start-up speed =5m / s and wind speed =25m / s, when the measured wind speed When within the interval, ;
[0070] The normalized function for the rate of temperature change reflects the increased risk of brittleness caused by a sudden drop in temperature.
[0071] The system is based on the calculated Values are used to implement tiered scheduling strategies, for example:
[0072] Warning level ( The system does not perform blind scanning of the entire area, but automatically calculates the Region of Interest (ROI) that the drone is focused on.
[0073] ROI calculation is based on the location coordinates of ground sensors. With the origin as the vector along the wind direction A ray is established in the direction of the ray, and combined with the GIS digital elevation model (DEM), the leeward slope (the area where the slope change rate changes from positive to negative) within 500 to 1000 meters downwind of the sensor is identified. The projected area of the leeward slope is designated as the snow accumulation sector ROI, and the flight path of the ROI is automatically generated.
[0074] Alarm level ( It directly triggers the highest level alarm and forces the drone to start a full-element scan survey of the calculated Area of Interest (ROI) and its buffer zone extending 50-100 meters outward, including lidar, thermal imaging and visible light.
[0075] 3. Adaptive Aerial Acquisition Module
[0076] This module acts as the system's "mobile execution arm," responsible for performing high-precision, non-contact reconnaissance of potential areas of interest (ROI). It consists of an automated hangar deployed near the monitoring area, at least one multi-rotor industrial-grade UAV, and modular mission payloads. The automated hangar, equipped with environmental awareness and protection capabilities, is responsible for the UAV's automatic takeoff and landing, precise homing, contact / inductive automatic charging, and data transmission, ensuring true unmanned operation of the system. The UAV carries a three-light pod integrating a high-definition visible light camera, a thermal imaging camera, and a miniature airborne LiDAR.
[0077] Unlike traditional blind inspections along fixed routes, this module, based on instructions issued by the collaborative computing and scheduling module, performs... The dominant factor of the index is adaptively configured with collection parameters to achieve targeted data collection. For example:
[0078] Surface cracking / morphology-dominant mode: when The index is determined by acoustic emission characteristics. or snow depth Dominant (if) )or When the value is the largest, it is determined to be "surface cracking / form-dominant mode" mechanical instability (there is mechanical fracture or shear instability inside the snow layer). The UAV automatically activates the lidar, lowers the flight altitude to 30 to 50 meters relative to the ground (the normal cruise altitude is usually above 100 meters), and starts a high overlap rate (such as lateral overlap rate > 70%) point cloud scanning mode to focus on acquiring small terrain deformation data of the ROI area.
[0079] Thermal / wet snow dominant mode: when The index is determined by temperature. Dominant (if) When the value is the largest, it is determined to be a "thermal / skiing-dominated mode" (risk of surface melting or wet snow avalanche). The drone automatically activates thermal imaging to focus on collecting temperature field distribution data of the snow surface in order to identify spatial differences in liquid water content.
[0080] 4. Multidimensional data fusion and analysis module
[0081] This module, deployed on a cloud server cluster, is the core computing engine for generating the final disaster early warning conclusions of the system. It reads in real-time point-like environmental parameters (such as snow density) uploaded by ground-based sensor modules. internal friction angle Snow accumulation is based on data collected from ground monitoring towers. and temperature The physical parameters were derived through empirical physical models, referencing JORDAN R. Aone-dimensional temperature model for a snow cover: Technical documentation for SNTHERM.89 [R]. Hanover: US Army Corps of Engineers, Cold Regions Research and Engineering Laboratory, 1991., as well as the area-like remote sensing data transmitted back by the adaptive aerial acquisition module.
[0082] Unlike the simple data overlay display in existing technologies, this module achieves deep fusion of physical dimensions through the following steps:
[0083] 1) Geometric modeling: Using high-precision LiDAR point clouds transmitted back by UAVs, a real-time digital elevation model (DEM) of the target area is constructed (Reference: LIU X. Airborne LiDAR for DEM generation: Some critical issues[J]. Progress in Physical Geography, 2008, 32(1): 31-49.), accurately obtaining arbitrary grid positions. Snowboard thickness and slope angle
[0084] 2) Attribute Mapping: Utilizing the existing mature kriging interpolation algorithm (reference: OLIVER MA, WEBSTER R. Basic steps in geostatistics: The variogram and kriging [M]. Cham: Springer, 2014.), using the location of the ground sensor as a template point, the physical attributes measured on the ground (average snow density) are mapped... internal friction angle Interpolation is mapped onto the entire surface of the 3D DEM model, thereby giving the geometric model physical properties.
[0085] 3) Mechanical stability calculation: such as Figure 3 As shown, based on the infinite slope model, the stability coefficient of the entire network is calculated. calculate:
[0086]
[0087] in, The cohesion of the snow layer is determined by a ground-based monitoring tower based on air temperature and snow type (e.g., dry and wet snow) using a pre-set parameter table. If thermal imaging data is available, it is determined based on the snow surface temperature distribution. Values are spatially corrected (the closer the temperature is) (The cohesion of wet snow is significantly reduced). This is the acceleration due to gravity.
[0088] The system generates visualizations based on the calculation results. Contour map. When a continuous region is detected. When the shear strength is less than the shear stress, the area is determined to be the snowboard instability zone, and a high-confidence warning report containing precise coordinates and images is automatically generated.
[0089] 5. High-reliability communication subsystem
[0090] This subsystem provides a "never-disconnected" data transmission channel for the entire monitoring network. At the physical layer, it constructs a heterogeneous dual-link architecture of 5G public network and satellite / microwave, and at the control layer, it innovatively introduces a dynamic hierarchical transmission mechanism based on QoS (Quality of Service) awareness.
[0091] 1) Heterogeneous dual-link architecture
[0092] Primary channel: Utilizes a wide-coverage 5G terrestrial public network, featuring high bandwidth and low latency, for transmitting massive amounts of raw monitoring data.
[0093] Backup channel: Equipped with a high-throughput satellite communication terminal (such as Ka-band) or a long-distance microwave relay link. Capable of withstanding geological disasters, used for transmitting critical commands and results data under extreme conditions.
[0094] 2) Dynamic hierarchical transmission strategy
[0095] In response to the limited bandwidth and high cost of satellite links, the system no longer employs a simple full handover, but instead performs content-aware adaptive transmission based on the real-time status of the link.
[0096] Link quality detection: The system monitors the packet loss rate of the primary 5G link in real time. and round-trip delay .
[0097] Normal broadband mode (when) <threshold The system determines the health of the link, and all raw data collected from the ground and air are transmitted back to the cloud in real time via the 5G network for storage and detailed modeling.
[0098] Limited narrowband mode (when) > In the event of a threshold or complete 5G outage: The system automatically switches to a backup satellite / microwave link and immediately initiates edge-side data compression strategies. This blocks large volumes of raw video and point cloud backhaul; only key feature data processed by edge computing (such as computed data) is transmitted. Risk distribution heatmap Index, alarm control commands, and low-resolution situation screenshots.
[0099] II. Spatiotemporal Fusion-Based Collaborative Monitoring Method
[0100] The core of this invention lies in its automated collaborative monitoring workflow. This method achieves accurate assessment of avalanche risk through a closed-loop process of "perception-computation-action-fusion." Figure 2 As shown, the specific steps are as follows:
[0101] Step 1: Continuous sensing of multi-dimensional ground parameters.
[0102] The ground-based sensing module collects the rate of change of snow depth in the target area in real time. Snow layer temperature gradient Acoustic emission characteristics and wind load vector
[0103] Step Two: Instability Index ( )Calculation and hierarchical triggering
[0104] The collaborative computing and scheduling module uses formulas based on the received parameters.
[0105] Calculate the snow layer instability index in real time.
[0106] 1) If The system maintains a low-power standby state;
[0107] 2) If (Warning level), proceed to step three;
[0108] 3) If (Alarm level) directly triggers the highest level alarm (notifying relevant departments to close roads) and simultaneously proceeds to step three, using drones to obtain detailed images and geometric data of the disaster area to assist in rescue decision-making;
[0109] Step 3: Region of Interest (ROI) deduction and strategy generation.
[0110] The system uses the location coordinates of ground sensors and wind direction vectors By combining GIS topographic data, snow accumulation sectors are extrapolated to determine the key areas of interest (ROI) for drones. Simultaneously, based on the factors leading to... Increased dominant factor, adaptive generation of acquisition strategy:
[0111] 1) If acoustic emission The system takes the lead in generating the "LiDAR+ high-density scan" command.
[0112] 2) If the temperature It takes the lead in generating "thermal imaging + multispectral" commands.
[0113] Step 4: Adaptive aerial data collection by UAV.
[0114] The adaptive aerial data acquisition module receives commands, and the UAV automatically takes off and heads to the ROI area to perform non-contact, high-precision data acquisition. During the acquisition process, if the communication link quality degrades (packet loss rate > 100%), the UAV may fail to acquire the necessary data. It automatically initiates edge compression and only transmits key feature data back.
[0115] Step 5: Point-area data fusion based on physical mapping
[0116] After receiving the data from the air, the multidimensional data fusion and analysis module performs the following fusion operations:
[0117] 1) Construct a high-precision digital elevation model (DEM) of the snow surface using LiDAR point clouds to obtain the slope. and thickness ;
[0118] 2) Using Kriging interpolation, the density measured on the ground is... and internal friction angle Mapped onto the corresponding network of the DEM model;
[0119] 3) Based on the infinite slope model, calculate the stability coefficient of the entire field mesh. .
[0120] Step Six: Risk Quantitative Assessment and Distribution
[0121] The system according to Distribution map, when a continuous region is detected When the value is less than 1, it is automatically identified as a snowboard instability zone, and an early warning report containing precise three-dimensional coordinates and predicted collapse amount is generated and sent to the control terminal through a dual-mode communication link.
[0122] Example
[0123] A high-altitude avalanche-prone area. The system deployment includes: Ground monitoring tower A (integrating multi-functional sensors) located at an altitude of 3200m, an automated hangar at the foot of the mountain (containing UAV C and a three-light pod), and a cloud-based collaborative computing platform.
[0124] Step 1: Ground-based multi-dimensional parameter sensing (data input)
[0125] In the early morning of [Date], affected by a severe cold wave, the sensors at monitoring tower A collected the following real-time data set. :
[0126] 1) Rate of change of snow depth ( The snow depth sensor showed an additional 15cm of snow accumulation in the past hour; the normalized value is as follows. =0.8 (indicating heavy snowfall).
[0127] 2) Acoustic emission characteristic values ( The infrasound sensor captured weak low-frequency vibrations, and the normalized values were... =0.6 (indicating some internal stress relief)
[0128] 3) Wind load ( Northwest wind, average wind speed 18m / s.
[0129] 4) Temperature gradient ( Temperatures dropped sharply, and no signs of snow melting were observed.
[0130] Step 2: Instability Index Calculation and Triggering (Algorithm Logic)
[0131] Cloud-based collaborative computing and scheduling module utilizes The formula is analyzed. Weighting coefficients are set: =0.5 (snowfall weight) =0.3 (acoustic emission weight). =0.2 (meteorological weight) calculation process:
[0132] =0.5*0.8+0.3*0.6+0.2*0.7(wind load factor)=0.72
[0133] Trigger determination:
[0134] Set a first-level threshold =0.7. Because (0.72) > (0.7) The system determines that there is a potential risk and triggers the collaboration mechanism.
[0135] Step 3: ROI Deduction and Strategy Generation (Intelligent Decision Making)
[0136] The system, combining GIS topographic data analysis, determined that northwest winds (18 m / s) would cause a significant amount of snow to be transported to the leeward slope on the southeast side. The system automatically identified the southeast slope as the region of interest (ROI). Because... Mainly caused by snowfall ( ) and acoustic emission ( The dominant factor suggests that the risk type is "dry snow slab-like mechanical instability" rather than "wet snow thermal instability". Therefore, the system generates the instruction "UAV take off, activate the LiDAR payload, and perform high-density point cloud scanning".
[0137] Step 4: UAV Adaptive Data Acquisition and Communication Support (Action Execution)
[0138] Drone C automatically left the warehouse and flew to the ROI area to perform LiDAR scanning. During the data transmission, a sudden increase in the 5G link packet loss rate to 15% was detected (exceeding [percentage missing]). (Threshold). The system immediately triggers a tiered transmission strategy: suspending the return of the original point cloud file, extracting sparse feature points at the edge of the drone, and only transmitting feature data back via a backup satellite link to ensure that the command center can see the approximate terrain outline.
[0139] Step 5: Data Fusion Based on Physical Mapping (Deep Analysis)
[0140] After the data is recovered, the multidimensional data fusion and analysis module processes it.
[0141] 1. Geometric Modeling: LiDAR data shows the slope at a certain point in the ROI region. And form a thickness (Geometric parameters from an aerial drone LiDAR).
[0142] 2. Using Kriging interpolation, the density of new snow measured at tower A was... and internal friction angle Mapped onto the snowboard model. (Physical parameters mapped from ground sensors)
[0143] 3. Other parameters derived from model derivation
[0144] Snow layer cohesion (Based on the deduction of ground temperature gradient and snow type, dry snowboards usually have low cohesion)
[0145] 4. Mechanical calculations: Substitute into the formula for the stability of an infinite slope to calculate:
[0146]
[0147] Step 6: Publish the conclusions
[0148] The calculation results show This indicated that the snowboard was in a state of mechanical instability and could collapse at any time. The system automatically generated a red alert report, along with the snowboard's precise three-dimensional coordinates and predicted volume, and pushed it to the emergency management department, successfully guiding the preventative closure of relevant roads.
[0149] In its specific implementation, this application provides a computer storage medium and a corresponding data processing unit. The computer storage medium is capable of storing a computer program, which, when executed by the data processing unit, can run the invention's content regarding an integrated air-space-ground avalanche collaborative monitoring system and method, as well as some or all of the steps in various embodiments. The storage medium can be a magnetic disk, optical disk, read-only memory (ROM), or random access memory (RAM), etc.
[0150] Those skilled in the art will clearly understand that the technical solutions in the embodiments of the present invention can be implemented using computer programs and their corresponding general-purpose hardware platforms. Based on this understanding, the technical solutions in the embodiments of the present invention, or the parts that contribute to the prior art, can be embodied in the form of computer programs, i.e., software products. These computer program software products can be stored in a storage medium and include several instructions to cause a device containing a data processing unit (which may be a personal computer, server, microcontroller, MCU, or network device, etc.) to execute the methods described in various embodiments or certain parts of the embodiments of the present invention.
[0151] This invention provides a concept and method for an integrated air-space-ground avalanche monitoring system. Many methods and approaches exist for implementing this technical solution; the above description is merely a preferred embodiment. It should be noted that those skilled in the art can make various improvements and modifications without departing from the principles of this invention, and these improvements and modifications should also be considered within the scope of protection of this invention. All components not explicitly stated in this embodiment can be implemented using existing technologies.
Claims
1. A space-air-ground integrated avalanche collaborative monitoring system, characterized in that, include: The system comprises a ground-based sensing module, a collaborative computing and scheduling module, an adaptive aerial data acquisition module, and a multi-dimensional data fusion and analysis module; among which... The ground sensing module collects a set of environmental parameters for the target area and sends them to the collaborative computing and scheduling module. The collaborative computing and scheduling module calculates the snow layer instability index of the target area. And perform hierarchical scheduling; The adaptive aerial acquisition module acquires remote sensing data using different dominant modes according to the instructions of hierarchical scheduling. The multi-dimensional data fusion analysis module determines the snowboard instability zone and issues an alarm based on the set of environmental parameters and remote sensing data of the target area.
2. The integrated air-space-ground avalanche monitoring system according to claim 1, characterized in that, The ground sensing module includes: Multiple communication towers are deployed in the target area for avalanche collaborative monitoring. These towers are equipped with sensors and form a distributed monitoring network. The sensors include snow depth sensors, meteorological sensors, infrasound / microseismic sensors, and high-definition cameras. Each sensor connects to the monitoring network via an IoT module to acquire a set of environmental parameters. , means as follows: ； in, The rate of change of snow depth. For the temperature gradient of the snow layer, These are the acoustic emission characteristic values inside the snow layer. This refers to the near-ground wind speed and direction.
3. The integrated air-space-ground avalanche monitoring system according to claim 2, characterized in that, The collaborative computing and scheduling module, based on the set of environmental parameters... Calculate the snow layer instability index of the target area. , means as follows: ； in, This represents the normalized rate of change in snow depth. These are the normalized acoustic emission characteristic values inside the snow layer; To determine the temperature gradient of the snow layer and near-ground wind speed and direction The calculated coupling effect factor between wind load and temperature gradient; , and These are weighting coefficients derived from historical disaster data.
4. The integrated air-space-ground avalanche monitoring system according to claim 3, characterized in that, The coupling effect factor of wind load and temperature gradient is based on the snow layer temperature gradient. and near-ground wind speed and direction The result, calculated using the unified weighted average method, is expressed as follows: ； in, and These are the weighting coefficients. This represents the normalized near-ground wind speed and direction. This represents the normalized snow layer temperature gradient.
5. The integrated air-space-ground avalanche monitoring system according to claim 4, characterized in that, The collaborative computing and scheduling module performs hierarchical scheduling, including: Set hierarchical scheduling thresholds and ; like If so, the current working status will be maintained, and continuous monitoring will be carried out; like Based on the set of environmental parameters Based on the coordinates of each sensor, a Region of Interest (ROI) is generated, and an early warning-level scheduling command is issued to the adaptive aerial data acquisition module. like Based on the set of environmental parameters Based on the coordinates of each sensor, a Region of Interest (ROI) is generated, and alarm-level scheduling commands are issued to the adaptive aerial data acquisition module.
6. The integrated air-space-ground avalanche monitoring system according to claim 5, characterized in that, The generation of the Region of Interest (ROI) includes: Using the coordinates of each sensor as the origin, along the near-ground wind speed and direction... Wind direction A ray is established in the direction of the ray, and combined with the GIS digital elevation model (DEM), the leeward slope within the preset range downwind of the sensor is identified, that is, the area where the slope change rate changes from positive to negative. The projection area of the leeward slope is defined as the snow accumulation sector, and the snow accumulation sectors of all sensors are merged as the final generated Region of Interest (ROI).
7. The integrated air-space-ground avalanche monitoring system according to claim 6, characterized in that, The adaptive aerial acquisition module acquires remote sensing data using different dominant modes, including: If the snow layer instability index of the target area is calculated During the process, )or If the value is the largest, then it means The index is determined by acoustic emission characteristics. or snow depth In this mode, the surface cracking / morphology dominance mode is adopted. The UAV in the adaptive aerial acquisition module activates the lidar, lowers the flight altitude to a preset value, resets the lateral overlap rate, performs point cloud scanning, and obtains terrain deformation data of the key area of interest (ROI). If the snow layer instability index of the target area is calculated During the process, If the value is the largest, then it means The index is determined by temperature. In this mode, the thermal / wet snow dominant mode is adopted. The UAV in the adaptive aerial acquisition module activates the thermal imaging sensor to collect thermal imaging data to correct the cohesion of the snow layer, collects the temperature field distribution data of the snow surface in the ROI of focus, identifies the spatial differences in liquid water content, and keeps the lidar in the basic mode to obtain terrain geometry data.
8. The integrated air-space-ground avalanche monitoring system according to claim 7, characterized in that, The multi-dimensional data fusion analysis module determines the snowboard instability zone and issues an alarm, including: The point cloud transmitted back by the lidar of the UAV in the adaptive aerial acquisition module is used to construct a real-time digital elevation model (DEM) of the region of interest (ROI), obtaining arbitrary grid locations. Snowboard thickness and slope angle ; Using the Kriging interpolation method, with the positions of each sensor on the communication tower as template points, the measured average density of the snow layer was calculated. and internal friction angle Interpolation is mapped onto the surface of a real-time digital elevation model (DEM) to obtain arbitrary grid locations. snow density and internal friction angle ; Based on the infinite slope model, the stability coefficients of all grid locations within the region of interest (ROI) are calculated. ; Based on the stability coefficients of all grid locations within the ROI of focus The calculation results are used to generate visualizations. Contour map; When a continuous region is detected When the area is identified as a ski instability zone, a warning report containing grid location coordinates and images is generated.
9. The integrated air-space-ground avalanche monitoring system according to claim 8, characterized in that, The calculation focuses on the stability coefficients of all grid locations within the Region of Interest (ROI). The method is as follows: ； in, To determine the cohesion of the snow layer, if thermal imaging data is available, the cohesion of the snow layer can be determined based on the snow surface temperature distribution. Perform spatial correction. This is the acceleration due to gravity.
10. A method for integrated air-space-ground avalanche monitoring, characterized in that, Coordinated detection using any of the systems described in claims 1-9 includes the following steps: Step 1: Collect a set of environmental parameters for the target area; Step 2: Calculate the snow layer instability index of the target area based on the environmental parameter set of the target area. And perform hierarchical scheduling; Step 3: According to the instructions of hierarchical scheduling, use UAVs to collect remote sensing data in different dominant modes; Step 4: Based on the set of environmental parameters and remote sensing data of the target area, determine the snowboard instability zone and issue an alarm.