Low-altitude wind environment meteorological service monitoring and evaluation method and system based on unmanned aerial vehicle cluster

Abstract

The invention discloses a low-altitude wind environment meteorological service monitoring and evaluation method and system based on an unmanned aerial vehicle cluster. The method comprises the following steps: constructing a non-uniform three-dimensional monitoring grid, and controlling an unmanned aerial vehicle cluster to collect basic meteorological data according to a three-dimensional cruise formation; calculating flow field turbulence and a wind shear index in real time based on a sliding time window, triggering locking of an abnormal core area of the flow field when the index exceeds a limit, and controlling a cluster to be switched to a multi-layer spiral envelope formation to perform encryption sampling on the abnormal core area; meanwhile, collaborative obstacle avoidance and data correction based on dynamic wake flow perception are executed, a dynamic wake flow interference cone model is constructed, whether a rear unmanned aerial vehicle is disturbed or not is judged, and accordingly a fine adjustment displacement instruction is generated or a wake flow attenuation model is called to carry out vector denoising compensation on the actually measured wind speed; and finally, constructing a low-altitude dynamic wind field model based on the corrected data. According to the invention, the problem of data distortion caused by low monitoring precision of the low-altitude wind field and aerodynamic interference in the cluster in a complex terrain is solved.

Description

Technical Field

[0001] This invention relates to the field of meteorological monitoring and unmanned aerial vehicle (UAV) application technology, and in particular to a method and system for monitoring and evaluating low-altitude wind environment meteorological services based on UAV swarms. Background Technology

[0002] Currently, the rapid development of the low-altitude economy has placed demands on refined meteorological services in complex urban environments. Traditional ground-based meteorological stations, limited by their deployment density, struggle to capture microscale wind field changes in urban canyons; while individual meteorological drones lack multi-dimensional synchronous detection capabilities when facing sudden strong turbulence or complex flow fields. Collaborative observation using drone swarms is a development trend, but current technology faces two major bottlenecks: first, the fixed swarm formation makes it difficult to adaptively adjust sampling density based on flow field characteristics (such as the core of sudden turbulent anomalies); second, during dense formation flight, the rotor wakes of the leading drones severely interfere with the sensor data of the trailing drones, leading to measurement distortion.

[0003] Therefore, there is an urgent need for a monitoring method that can dynamically reconstruct the formation based on the flow field characteristics and effectively eliminate internal aerodynamic interference. Summary of the Invention

[0004] One of the objectives of this invention is to provide a method and system for monitoring and evaluating low-altitude wind environment meteorological services based on unmanned aerial vehicle (UAV) swarms, in order to solve the problems mentioned in the background art.

[0005] Firstly, the embodiments of the present invention provide a method for monitoring and evaluating low-altitude wind environment meteorological services based on unmanned aerial vehicle (UAV) swarms. The method is applied to a monitoring system including drone swarms, edge computing nodes, and ground command centers, and the method includes the following steps: Step 1: Based on the geographic information data of the target airspace and the requirements of meteorological monitoring tasks, construct a non-uniform three-dimensional monitoring grid, and divide the UAV cluster into main test UAVs and auxiliary test UAVs to generate an initial three-dimensional cruise formation. Step 2: Control the drone cluster to enter the target airspace according to the three-dimensional cruise formation and collect basic meteorological data including wind speed, wind direction, air pressure and temperature; During the data acquisition process, dynamic formation reconstruction and collaborative data correction based on flow field characteristics are performed in parallel: The formation dynamic reconstruction includes: maintaining a sliding time window, calculating the ratio of the standard deviation of the wind speed vector to the average wind speed within the window as the real-time flow field turbulence intensity, and when the flow field turbulence intensity is detected to exceed a preset threshold, triggering the flow field abnormal core area locking mechanism, constructing an envelope space containing inner, middle and outer multi-layer spherical surfaces with the trigger position as the geometric center, and planning the spiral orbit trajectory of each auxiliary UAV on the multi-layer spherical surfaces to satisfy the linear change of polar angle and azimuth angle with time, and controlling the cluster to switch to the multi-layer spiral envelope formation; The collaborative data correction includes: acquiring real-time flight status data of the forward UAV through a wireless ad hoc network and constructing a dynamic wake interference cone; if the coordinates of the rear UAV fall into this area, then based on the real-time thrust coefficient of the forward UAV and the measured turbulence intensity of the environmental flow field, using the wake jet model to calculate the induced wind speed increment vector generated by the forward UAV at the position of the rear UAV, and subtracting the induced wind speed increment vector from the measured wind speed vector of the rear UAV to obtain the corrected wind speed; Step 3: Map the corrected wind speed data onto the non-uniform three-dimensional monitoring grid to construct a low-altitude dynamic wind field model, and generate a low-altitude airworthiness safety index map based on the model.

[0006] Optionally, the construction of the three-dimensional monitoring grid in step one specifically includes: Acquire digital elevation models and building vector data for the target airspace; identify complex terrain areas within the target airspace based on local elevation statistical features; The three-dimensional monitoring grid is generated using a distance-weighted octree splitting algorithm, resulting in a higher grid density in complex terrain areas and building boundary layer areas compared to flat areas.

[0007] Optionally, the planning of the spiral trajectory in the formation dynamic reconstruction step specifically includes: Establish a spherical coordinate system with the geometric center of the core region of the flow field anomaly as the origin; For each spherical surface, the trajectory parameters of the auxiliary UAV are set to satisfy: the polar angle changes linearly with time, and the azimuth angle increases linearly with time, wherein the rate of change of the azimuth angle is at least ten times the rate of change of the polar angle, and a spherical spiral waypoint sequence covering the spherical surface is generated. Multiple auxiliary UAVs on the same sphere are assigned initial azimuth phases at uniform intervals to form a cage-like scanning path that does not interfere with each other.

[0008] Optionally, the generation of the low-altitude wind environment meteorological service assessment map in step three specifically includes: Based on the low-altitude dynamic wind field model, low-altitude wind shear zone, urban canyon wind zone and building wake zone are extracted. Based on the preset safety standards for low-altitude logistics routes, the airworthiness safety index of each route segment within the target airspace is calculated. Based on the airworthiness safety index, a meteorological safety situation map including recommended flight corridors and high-risk no-fly zones is drawn, forming the low-altitude wind environment meteorological service assessment map.

[0009] Optionally, the mechanism for triggering the core region locking of the abnormal flow field in the formation dynamic reconstruction step specifically includes: Real-time monitoring of the turbulence intensity of the flow field collected by each of the main test drones; The turbulence intensity of the flow field is compared with the preset threshold. If the sampling period of the turbulence intensity of the flow field of any of the main test drones exceeds the preset threshold for a consecutive preset number of sampling periods, then the current position of the main test drone is marked as the geometric center of the core region of the flow field anomaly. Based on the geometric center, a motion logic is set where the polar angle changes linearly with time and the azimuth angle increases linearly with time with a rate of change greater than that of the polar angle, thereby generating a dynamic waypoint sequence distributed on a concentric sphere.

[0010] Optionally, the calculation of the wake interference cone region for each UAV in the cooperative obstacle avoidance and data correction step specifically includes: Using the geometric center of the forward UAV that generates the wake as the vertex; calculate the vector sum of the flight speed vector of the forward UAV and the ambient wind speed vector to obtain the composite airflow vector; With the reverse extension of the composite airflow vector as the central axis; The cone apex angle is dynamically set based on the environmental turbulence measured by the drone in front. The greater the environmental turbulence, the larger the set cone apex angle, and a spatial model of the cone is constructed. The effective extension range of the conical spatial model in the downstream direction is defined as the wake interference cone region.

[0011] Optionally, the generation of fine-tuning displacement commands in the cooperative obstacle avoidance and data correction step specifically includes: Determine whether the current coordinates of the rear UAV fall within the wake interference cone region of any of the front UAVs; If so, calculate the vertical distance vector of the rear UAV relative to the central axis; Along the vertical distance vector pointing away from the central axis, a translation component perpendicular to the central axis is generated as the fine-tuning displacement command until the rear UAV moves out of the wake interference cone region.

[0012] Optionally, the step of correcting the data using the wake jet model specifically includes: Acquire real-time thrust data and environmental turbulence of the UAV in front, and calculate the dimensionless thrust coefficient and wake expansion coefficient; Based on the thrust coefficient, the initial induced velocity of the rotor plane is calculated using momentum theory; Based on the wake expansion coefficient and Gaussian distribution function, the induced wind speed increment at the position of the UAV behind within the wake interference cone is calculated. Construct the vector form of the induced wind speed increment and remove the vector from the original measured data.

[0013] Optionally, the method further includes an energy-constrained formation recovery step: After the multi-layer spiral envelope formation has been maintained for a preset time, or when the turbulence of the flow field has decreased to below a safe threshold, the remaining energy of each UAV is evaluated. If the remaining energy meets the requirements of subsequent missions, the drone cluster is controlled to release the multi-layer spiral envelope formation and execute a formation change command to restore the three-dimensional cruise formation. If the remaining energy is insufficient, a backup mechanism is triggered, and a standby drone is dispatched to take over the position of the drone with low battery.

[0014] Secondly, the low-altitude wind environment meteorological service monitoring and evaluation system based on UAV swarm provided in the embodiments of the present invention includes: The grid construction and path planning module is used to construct a 3D monitoring grid and generate the initial 3D cruise formation of the UAV swarm; The data acquisition and transmission module is used to control the UAV cluster to collect basic meteorological data and perform spatiotemporally aligned transmission. The flow field feature perception and formation reconstruction module is used to calculate the flow field turbulence intensity and wind shear index, and to control the UAV cluster to switch to a multi-layer spiral envelope formation when the flow field anomaly core area locking mechanism is triggered. The collaborative obstacle avoidance and data correction module is used to calculate the wake interference cone region, generate fine-tuning displacement commands to avoid wake interference, and use the wake attenuation model to denoise and compensate the data. The model building and evaluation report generation module is used to build a low-altitude dynamic wind field model and generate a low-altitude wind environment meteorological service evaluation report.

[0015] The present invention has achieved the following beneficial effects: It achieves adaptive monitoring that transitions from passive cruise to active locking of the core area of ​​anomalies in the flow field; by combining physical obstacle avoidance with algorithm compensation, it solves the problem of aerodynamic interference when UAVs are densely arrayed, ensuring the authenticity of meteorological data.

[0016] Other features and advantages of the invention will be set forth in the following description, and will be apparent in part from the description, or may be learned by practicing the invention. The objects and other advantages of the invention may be realized and obtained by means of the structures particularly pointed out in the written description and the accompanying drawings.

[0017] 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

[0018] The accompanying drawings are provided to further illustrate the invention and form part of the specification. They are used in conjunction with embodiments of the invention to explain the invention and do not constitute a limitation thereof. In the drawings: Figure 1 This is a schematic diagram of a method for monitoring and evaluating low-altitude wind environment meteorological services based on unmanned aerial vehicle (UAV) swarms, as described in an embodiment of the present invention. Figure 2 This is a schematic diagram of a low-altitude wind environment meteorological service monitoring and evaluation system based on a drone swarm, as described in an embodiment of the present invention. Detailed Implementation

[0019] The preferred embodiments of the present invention will be described below with reference to the accompanying drawings. It should be understood that the preferred embodiments described herein are for illustration and explanation only and are not intended to limit the present invention.

[0020] This invention provides a method and system for monitoring and evaluating low-altitude wind environment meteorological services based on unmanned aerial vehicle (UAV) swarms. This technical solution is designed to address the urgent need for refined meteorological monitoring of complex urban terrain and unsteady low-altitude flow fields in the context of the booming low-altitude economy. This solution aims to solve problems such as insufficient spatial resolution of traditional fixed meteorological stations, lack of dynamic response capability of conventional single-airborne detection methods, and difficulty in overcoming wake interference during dense formation flight by constructing a highly dynamic and adaptive UAV swarm observation network.

[0021] This embodiment elaborates on the physical architecture of the system described in this invention. The system constructs a three-tiered collaborative sensing network at the cloud, edge, and terminal levels at the physical layer.

[0022] At the edge, i.e., the data acquisition and execution layer, this system deploys a heterogeneous drone swarm. To balance wind resistance and stability with coverage efficiency, the swarm consists of two types of drones with different functions: primary test drones and secondary test drones.

[0023] The main testing drone, serving as the sensing core and communication backbone of the cluster, preferably employs a six- or eight-rotor industrial-grade flight platform with a wheelbase between 800 mm and 1200 mm. Its main fuselage structure is integrally molded from high-modulus carbon fiber composite material to ensure structural rigidity in turbulent environments with wind speeds of level 7 (13.9-17.1 m / s) or even higher, preventing high-frequency sensor vibration caused by arm deformation. The main testing drone's power system has been specially optimized, employing a high-voltage (e.g., 12S or 14S lithium polymer battery) drive to a low-KV, high-torque brushless motor, coupled with a large-diameter, silent carbon fiber propeller, providing a thrust-to-weight ratio greater than 2.0. For sensing payloads, the main testing drone carries a research-grade, high-precision meteorological sensor array. The core wind speed measurement device is a three-dimensional ultrasonic anemometer and wind vane. Unlike traditional mechanical anemometers, ultrasonic anemometers calculate flow velocity using the time difference of ultrasonic pulses propagating in a fluid. They offer advantages such as no inertia, high response frequency (above 100Hz), and no starting wind speed limitations, making them ideal for capturing low-altitude instantaneous turbulence. To eliminate interference from the rotor downwash, the sensor is mounted on a vertically erected carbon fiber support at the top of the fuselage. The support height has been rigorously verified using computational fluid dynamics (CFD) simulations to ensure the probe is positioned within a location where airflow disturbances in the rotor-induced velocity field are minimal. Furthermore, the main testing UAV integrates a miniature multi-beam lidar to acquire detailed outlines of buildings and obstacle point clouds at close range (e.g., within 50 meters), assisting in obstacle avoidance flight.

[0024] As a sensing extension of the swarm, auxiliary testing drones utilize a lightweight quadcopter platform. Their small size and high agility allow them to penetrate narrow city streets or building corners for detection. These drones are equipped with hot-wire anemometers or pitot tube micropressure sensors based on microelectromechanical systems (MEMS) technology. While their individual accuracy is slightly lower than that of the main testing drone, their sheer numbers allow them to construct a high-density spatial sampling array.

[0025] All UAVs are equipped with redundant flight control computers, high-precision inertial measurement units (IMUs), and RTK (real-time dynamic differential) positioning modules that support the BeiDou / GPS / Galileo multi-mode satellite navigation systems. This ensures that the UAVs can maintain three-dimensional positioning accuracy and attitude awareness accuracy even in complex urban canyon environments.

[0026] At the edge computing and communication layer, the system deploys edge computing nodes at high points (such as communication towers and rooftops of tall buildings) or on mobile command vehicles around the monitoring area. These nodes integrate high-gain omnidirectional or directional antenna arrays, multi-standard wireless communication gateways, and high-performance edge servers (equipped with GPU accelerator cards). The primary function of the edge nodes is to establish highly reliable data links between the UAV swarm and the ground, and to perform data cleaning and real-time early warning computation. Due to the massive amount of data generated by the UAV swarm (including high-frequency waveform data, video streams, and radar point clouds), the edge nodes perform noise reduction, compression, and feature extraction on the raw data, only transmitting key meteorological parameters and alarm information back to the cloud. This reduces bandwidth consumption on the backbone network and lowers the system's closed-loop response latency.

[0027] On the cloud side, at the command and decision-making level, the ground command center is deployed on a cloud computing platform, running a large-scale Geographic Information System (GIS), a meteorological fluid dynamics simulation engine, and a mission scheduling and management system. The command center is responsible for macro-level mission planning, training and updating complex models, storing and mining historical data, and ultimately distributing visualized meteorological service assessment reports to users.

[0028] This embodiment details the specific implementation process of environmental perception and grid construction in step one of the methods.

[0029] At the start of the mission, the system first retrieves multi-source geographic information data of the target airspace, including not only basic two-dimensional satellite imagery, but also high-precision digital elevation models (DEMs) and detailed three-dimensional building models of the city (LOD3 or LOD4 level). By analyzing this data, the system constructs a digital virtual airspace environment.

[0030] Based on this, the system performs intelligent environmental airworthiness analysis. The system utilizes computational geometry algorithms to identify hard and soft constraint regions within the airspace.

[0031] Hard-constrained areas, also known as non-airworthiness zones, include physical spaces around buildings, high-voltage power line corridors, no-fly zones (such as airspace above government offices), and airspace above densely populated areas. The system will inflate the building vector model to generate a safety buffer layer surrounding the building, marking it as an impassable physical obstacle.

[0032] Soft-constrained regions, also known as complex terrain regions, refer to areas that significantly disturb airflow. The system calculates the rate of change of the normal vector of the terrain surface and the surface roughness coefficient. For example, in a narrow passage formed by two high-rise buildings, the system identifies it as a strong wind zone prone to the "narrowing effect"; on the leeward side of the high-rise buildings, it identifies it as a turbulent region prone to karman vortex streets or cavity backflow; and at the boundary between land and water, it identifies it as a region prone to thermal circulation. For the identification of complex terrain regions, the system employs a judgment algorithm based on local elevation statistical characteristics. Specifically, the system defines a sliding sampling window (e.g., 50m × 50m) on the digital elevation model (DEM), calculates the standard deviation of the elevation values ​​of all pixels within the window, and if this standard deviation exceeds a preset terrain roughness threshold (e.g., 5 meters), the area covered by the window is identified as a complex terrain region prone to mechanical turbulence, and is then subjected to high-density meshing in the subsequent mesh generation step.

[0033] Based on the above analysis results, the system employs a feature-driven non-uniform octree mesh partitioning algorithm. Specifically, this embodiment uses a distance-weighted octree splitting algorithm. The system sets the base mesh size to L0. When the Euclidean distance from the mesh center point to the nearest obstacle surface is less than a preset threshold, the mesh is octetted. This judgment is recursively applied to the sub-mesh until the mesh size is less than the minimum resolution limit. This non-uniform partitioning ensures extremely high spatial resolution in the boundary layer region near the building surface. The specific splitting criterion is: calculating the Euclidean distance from the mesh center to the nearest obstacle surface. If the grid side length The value satisfies (in This is the resolution factor, for example, 1.5. This is a preset distance offset constant (e.g., a value that is a natural constant, in meters). It is the natural logarithm, in the formula and If the values ​​are all in meters, the grid is deemed insufficiently precise, and an octet operation is performed. This logarithmic criterion ensures that the grid density increases exponentially with decreasing distance, thus accurately covering the building boundary layer.

[0034] In open, flat background areas with relatively stable airflow, the system generates a large-scale sparse grid. For example, the grid cells are cubes with sides ranging from 50 to 100 meters. This ensures the capture of large-scale background wind fields while avoiding the waste of computational resources.

[0035] After identifying complex terrain areas, the system automatically triggers a grid refinement mechanism. Grid cells undergo multi-level recursive splitting, with their size decreasing exponentially. For example, in areas with funneling effects, the grid is subdivided to 5 meters or even smaller; in building corners or edge separation zones, the grid resolution can reach the 1-meter level. This high-density grid enables precise capture of drastic changes in wind speed gradients and minute-scale vortex structures.

[0036] In the final generated grid data structure, each node not only contains three-dimensional spatial coordinates, but also terrain attribute markers (such as near-wall, canyon center, open area). These attributes will guide subsequent UAV path planning and sampling strategies.

[0037] This embodiment describes in detail the basic data acquisition and processing flow in step two of the method. This is fundamental to ensuring the scientific validity and accuracy of meteorological data.

[0038] Once the drone swarm enters the monitoring grid, it initiates a full-element data acquisition mode. To ensure data consistency in the distributed system, the swarm employs a high-precision time synchronization mechanism based on IEEE 1588 PTP (Precise Time Protocol). The master drone acts as the master clock source, periodically broadcasting synchronization messages, while each slave drone calibrates its local crystal oscillator, suppressing time synchronization errors to the microsecond level.

[0039] Under a unified clock trigger (e.g., 100Hz), the drone synchronously reads meteorological sensor data and flight status data.

[0040] However, the wind speed directly measured by the sensors is a relative wind speed with respect to the aircraft's coordinate system, including both the ambient wind speed and the drone's own speed. To obtain the absolute wind speed with meteorological significance, the onboard processor must perform real-time vector calculations. This calculation process follows the principles of rigid body kinematics, as detailed below: The first step is coordinate system rotation. Using the real-time attitude angles (pitch, roll, yaw) measured by the IMU, a rotation matrix is ​​constructed. The relative wind speed vector in the sensor coordinate system is then projected and transformed to a geographic coordinate system (such as the North-East-Earth (NED) coordinate system).

[0041] The second step is motion compensation. The three-dimensional ground speed vector of the UAV, calculated by fusion of RTK-GPS and IMU, is subtracted from the projected relative wind speed vector.

[0042] The third step is attitude disturbance correction. Considering that the UAV will disturb the surrounding flow field during maneuvers (such as large-angle tilting), the system introduces an aerodynamic disturbance correction coefficient table based on wind tunnel experiments. Based on the current attitude angle and collective pitch (throttle), the correction factor is obtained from the table, and the solution results are fine-tuned to eliminate the obstruction and inducing effects of the fuselage on the airflow.

[0043] During data processing, the rotation matrix is ​​first constructed using the attitude quaternions measured by the IMU. The relative airflow vector measured by the meteorological sensor in the aircraft's coordinate system is projected onto the Northeast Earth (NED) geographic coordinate system. Then, based on the principle of vector composition, the projected relative airflow vector (i.e., air velocity relative to the aircraft) is vector-added with the UAV's ground velocity vector measured by RTK. This is based on the principle of relative motion. Through vector synthesis operations, the absolute environmental wind speed vector is decoupled from the observations and restored.

[0044] After the above processing, the system outputs a standard meteorological data frame with spatiotemporal tags. This data frame includes: a high-precision UTC timestamp, latitude and longitude, altitude, triaxial wind speed, temperature, air pressure, humidity, and data quality flags. The data is transmitted back to the edge nodes in real time via a wireless ad hoc network. To address the instability of the communication link, the system employs a data breakpoint resumption and adaptive compression strategy. When link quality degrades, the system prioritizes sending critical low-frequency statistical data (such as average wind speed), temporarily storing high-frequency waveform data in onboard memory, and retransmitting it after the link is restored, ensuring the integrity of the data link.

[0045] This embodiment illustrates the working process of flow field feature perception and anomaly core locking.

[0046] During the normal cruise phase, the onboard AI chip of the main test drone continuously runs a flow field feature extraction algorithm. This algorithm maintains a sliding time window and calculates two key flow field statistics in real time: flow field turbulence intensity and wind shear index.

[0047] The turbulence intensity is calculated as follows: within a sliding window, the standard deviation of the wind speed vector is divided by the average wind speed. To more accurately capture anisotropic turbulence, the system also calculates the turbulence components in the longitudinal, lateral, and vertical directions separately.

[0048] The wind shear index is calculated by using data from drones during vertical ascent / descent, or from drones at adjacent altitudes, to determine the rate of change of the wind speed vector with distance.

[0049] The system has preset tiered trigger thresholds. When the monitored data is stable, the cluster maintains a sparse cruise formation.

[0050] Once a primary monitoring drone detects that the turbulence intensity or wind shear index exceeds the warning threshold for N consecutive cycles (N being a positive integer), the system determines that it has captured the core region of the abnormal flow field, corresponding to the core of a micro-downburst, a strong wake of a building, or a convective front of hot or cold air. For example, in this embodiment, the turbulence intensity threshold is set to 0.15, and the wind shear index threshold is set to 0.12. .

[0051] At this moment, the system immediately activates the flow field anomaly core region locking mechanism and performs the following actions: Anomaly core localization: Mark the location of the main test drone where the anomaly was detected as the geometric center of the anomaly core.

[0052] Formation switching: When the command is issued, the cluster switches from cruise mode to fine scan mode.

[0053] Multi-layer spiral envelope generation: The system calculates the geometric parameters of the multi-layer spiral envelope formation. Several concentric spheres (e.g., radii of 20m, 40m, and 60m) are defined with the anomaly core as the center. The auxiliary UAV is scheduled to specific phase points on these spheres. Specifically, the construction process of the multi-layer spiral envelope formation is as follows: The system establishes a local spherical coordinate system with the geometric center of the locked flow field anomaly core region as the origin. First, the number of envelope layers is determined (e.g., inner, middle, and outer layers). For each spherical layer, the flight trajectory of the auxiliary UAV is defined as a spherical spiral. This trajectory is calculated based on the projection of the Archimedean spiral onto the sphere, satisfying the geometric constraints that the polar angle changes linearly with time and the azimuth angle rotates rapidly with time. The system uniformly staggers the initial phase angles of multiple auxiliary UAVs (e.g., 120 degrees) to ensure that at the same time, the UAVs are evenly distributed in different azimuths around the anomaly core, thereby achieving a comprehensive tomographic scan of the turbulent air mass. The dynamic waypoint sequence of each auxiliary UAV is calculated. These waypoints are not static coordinates but continuous spatial coordinates generated by time discretization sampling based on the system's preset spherical spiral model. By controlling the time interval of the waypoints, the system precisely constrains the tangential flight velocity of the UAVs on the spiral trajectory, enabling them to perform dynamic scanning while maintaining the envelope formation.

[0054] Dynamic scanning: The auxiliary drone does not remain stationary, but instead orbits along a spiral trajectory on a sphere. This movement allows the sensor to perform tomographic imaging of the core area of ​​the anomaly from all directions (360-degree azimuth and different elevation angles).

[0055] Specifically, the generation of the dynamic waypoint sequence follows a spherical spiral scan logic to ensure trajectory continuity and coverage. The system establishes a spherical coordinate system with the geometric center of the flow field anomaly core region as the origin. For each envelope layer, the processor performs the following calculation steps: First, the total scanning time and starting point are set. Second, the polar angle (latitude direction) is controlled to change linearly with time, allowing the UAV trajectory to advance uniformly from the top to the bottom of the sphere. Simultaneously, the azimuth angle (longitude direction) is controlled to increase linearly with time, and its rate of change is set to a preset multiple (e.g., more than 10 times) of the polar angle's rate of change, thus enabling the UAV to perform high-speed orbiting around the sphere's center while moving along the meridian. Finally, the system calculates the spherical coordinates at each moment. Convert to Cartesian coordinate system Waypoint instructions are given, and initial azimuth phases with uniform intervals are assigned to different auxiliary UAVs on the same sphere to form a cage-like spiral scanning path that does not interfere with each other.

[0056] Establish a spherical coordinate system with the anomaly core as the origin. To avoid multiple auxiliary testing drones from colliding at startup (… A physical collision occurs at the pole of the sphere. This invention employs a spherical spiral trajectory algorithm with polar angle bias. For the first... Sphere (radius) The first on ) The trajectory control equation for the auxiliary measurement drone is:

[0057] in, Let Cartesian coordinates be the coordinates of the UAV in a spherical coordinate system. The flight time is calculated from the moment the spiral scan mode is initiated; For the first The radius of the sphere; The polar angle scanning angular velocity, Angular velocity of azimuth rotation (set) ); This is the initial azimuth phase; crucially, The initial polar angle bias is non-zero (e.g., setting the initial polar angle bias to zero). interval Distribute equally (UAVs). This strategy ensures that the cluster is initially distributed in a cage-like pattern across different latitudes, geometrically eliminating the risk of congestion and collisions at the poles.

[0058] Through this mechanism, the system can construct a detailed three-dimensional structure inside turbulent air masses, analyze its vortex intensity, scale, and dissipation rate, and provide the most direct evidence for flight safety assessment.

[0059] This embodiment details the key technologies for resolving aerodynamic interference in dense formation flight.

[0060] When multi-rotor drone swarms perform high-density spiral envelope missions, the strong downwash (wake) generated by the leading drone is the biggest source of interference. If a trailing drone flies into the wake of the leading drone, not only will its flight attitude become unstable, but the measured wind speed data will also be completely distorted (the wind measured will be from the leading drone, not the ambient wind).

[0061] The system introduces a dynamic wake interference cone model to solve this problem.

[0062] For each drone, the system constructs a virtual cone-shaped space in real time to represent its wake region, which includes: Cone apex: Located at the center of gravity of the drone.

[0063] The cone's axis direction is strictly along the reverse extension of the currently measured composite airflow vector. The composite airflow vector is defined as the vector difference between the ambient wind speed vector and the UAV's flight speed vector (i.e., the relative airflow vector). This means that if the crosswind is strong, the wake will be blown off course, and the interference cone will also deflect accordingly.

[0064] Cone apex angle and length: dynamically calculated based on the current rotor thrust coefficient, air density, and environmental turbulence. The greater the thrust, the stronger the wake; the greater the environmental turbulence, the faster the wake spreads, resulting in a larger apex angle but a shorter length.

[0065] Specifically, the semi-apex angle of the cone The calculation follows empirical formulas. ,in Use the base expansion angle (e.g., 5°). The expansion coefficient, The intensity of environmental turbulence.

[0066] The effective extension range (i.e., the axial cutoff distance of the wake influence zone) is defined as: extending downstream from the rotor center along the wake axis to the maximum induced wind speed on the central axis. Spatial locations where the wind speed decreases to 5% of the current ambient background wind speed (or decreases to below 0.5 m / s).

[0067] The system uses a high frequency (e.g., 50Hz) to detect whether each rear aircraft falls into the interference cone of any preceding aircraft. Detection scenarios include: Scenario 1: Physical Avoidance. If it is determined that the system is about to enter the interference zone and the surrounding space allows, the system generates a fine-tuning displacement command. The vertical distance vector from the rear aircraft to the axis of the interference cone is calculated, and the rear aircraft is driven to translate outward along the direction of this vector. Since it only needs to move out of the cone boundary, the displacement is usually very small (a few meters) and will not disrupt the overall formation structure.

[0068] Scenario 2: Data Correction. If space constraints (such as between narrow buildings) prevent avoidance, or if the area is located in a weakly affected region at the edge of the wake, the system performs data correction. The system calls a wake velocity deficit model (such as the Jensen model or a Gaussian distribution model). Input the thrust of the preceding aircraft, the relative position of the two aircraft, and the relative distance. The model estimates the induced velocity vector generated by the preceding aircraft at that position. The system executes a wake correction algorithm based on momentum theory and Gaussian diffusion, with the following specific steps: The first step is thrust coefficient calculation. The system obtains the real-time motor speed of the UAV in front through the inter-machine communication link, determines the real-time thrust value based on the pre-calibrated motor performance curve (speed-thrust mapping table), and calculates the dimensionless real-time thrust coefficient by combining the current air density and rotor sweep area.

[0069] The second step is to calculate the axial velocity deficit. Based on momentum theory, the average induced velocity of the forward UAV's rotor plane is determined. Then, based on the projected distance of the following UAV on the wake axis of the forward UAV, the center axial velocity deficit at that location is calculated. This calculation follows the wake diffusion law, that is, the center deficit gradually decreases as the axial distance increases.

[0070] The third step is radial attenuation calculation. The vertical radial distance of the UAV behind the wake centerline is calculated. In this embodiment, a Gaussian distribution function is used to describe the velocity profile within the wake cross-section. The vertical radial distance is substituted into the Gaussian function to calculate the velocity attenuation factor at the current position. The variance parameter of the Gaussian function is dynamically adjusted by the environmental turbulence intensity measured by the UAV ahead. The greater the turbulence intensity, the faster the wake spreads, and the flatter the Gaussian distribution.

[0071] The fourth step is vector correction. The calculated central axis velocity deficit is multiplied by the radial velocity attenuation factor to obtain the induced wind speed scalar. Next, the induced wind speed vector is constructed along the reverse extension of the composite airflow vector from the preceding UAV. Finally, a vector subtraction operation is performed, subtracting this induced wind speed vector from the wind speed vector measured by the rear UAV's sensors to obtain the corrected wind speed.

[0072] Correcting the data using a wake jet model specifically includes the following steps: Regarding thrust data acquisition: The system pre-stores a database of motor performance based on bench test calibration. During flight, the processor reads the motor's input voltage in real time. and rotational speed Get real-time thrust by looking up the table and according to Calculate the dimensionless thrust coefficient in real time.

[0073] Step A, State Synchronization and Spatiotemporal Backtracking: Considering the physical delay in the transmission of wake turbulence from the front drone to the rear drone, the processor first calculates the projected distance of the rear drone on the airflow axis of the front drone. And calculate the transmission delay time. ,in, The effective convective velocity for wake transmission (usually the component of the ambient wind speed along the wake axis). The system calls upon the drone ahead... Historical thrust data at any given moment is used in subsequent calculations to ensure the accuracy of causal relationships.

[0074] Step B, Initial Induced Velocity Calculation: For the hovering or low-speed conditions that the UAV may experience, the average induced velocity of the rotor plane is calculated using momentum theory. Real-time thrust is obtained through the rotational speed feedback from the electronic speed controller. ,but ,in air density, This represents the rotor disk area.

[0075] Step C, Central Axis Velocity Field Reconstruction: The wake is treated as a fully developed turbulent jet. The downstream distance is calculated based on jet similarity theory. Maximum induced wind speed on the central axis :

[0076] in The rotor diameter, This is the jet attenuation constant (typical value 6.2). This formula accurately describes the physical characteristic that the jet velocity decreases hyperbolically with distance.

[0077] Step D, Environmental Adaptive Radial Distribution Calculation: An environmental feedback mechanism is introduced, based on the real-time environmental turbulence intensity measured by the main testing UAV. (Percentage), dynamically calculated wake expansion coefficient The stronger the environmental turbulence, the faster the wake spreads. Therefore, the jet half-width at that location is calculated. The location of the drone behind (radial distance) ) induced wind speed increment Follows a Gaussian distribution: .

[0078] Construct the induced wind speed vector along the reverse extension of the forward UAV composite airflow vector. Before performing the removal, the onboard processor first utilizes the IMU attitude matrix. The wind speed in the body coordinate system measured by the sensors of the drone behind it. Projecting onto the Northeastern Dignity (NED) geographic coordinate system yields... Then, a vector subtraction is performed in the NED coordinate system: This allows us to obtain the true ambient wind speed after removing wake interference.

[0079] The system substitutes the radial distance of the UAV behind it in the wake coordinate system into the above logic to calculate the specific induced airflow velocity vector at that location.

[0080] The final correction step is as follows: the processor performs vector calculations, subtracts the calculated induced airflow velocity vector from the original wind speed vector measured by the drone behind, thereby restoring the true environmental wind speed vector.

[0081] This embodiment describes the data processing and output stage in step three of the method.

[0082] The ground command center aggregates massive amounts of data points (including sparse data from the cruise phase and encrypted data from the anomaly core locking phase) after spatiotemporal alignment and wake correction. Before data alignment, the onboard processor must perform rigorous vector calculations to obtain the data. The specific process includes: First, constructing a rotation matrix from the airframe coordinate system to the geographic navigation coordinate system based on the real-time roll, pitch, and yaw angles output by the inertial measurement unit (IMU); second, left-multiplying the relative airflow velocity vector measured by the meteorological sensors by the rotation matrix and projecting it onto the geographic navigation coordinate system; third, reading the UAV's three-dimensional ground speed vector calculated by the RTK module, and performing vector synthesis (vector subtraction) between the projected relative velocity vector and the ground speed vector to reconstruct the environmental wind speed vector after eliminating the influence of airframe motion.

[0083] The system utilizes anisotropic kriging interpolation or a physically constrained neural network (PINN) algorithm to map discrete sampling point data onto the three-dimensional mesh constructed in step one, reconstructing a continuous low-altitude dynamic wind field model. This model includes not only wind speed and direction but also fine-grained features such as turbulence intensity and vertical velocity components. Specifically, the physically constrained neural network (PINN) is constructed as a fully connected deep network, with spatiotemporal coordinates as input. The output is a wind speed vector. and air pressure To ensure physical realism, the network's loss function... Defined as .in These are the weighting coefficients for the physical constraint terms. This is due to the error in the measured data, and The residuals are physical equations, specifically including the continuity equations for incompressible fluids. And the residual norm of the Navier-Stokes equations. By minimizing this loss function, the system ensures that the wind field data reconstructed in the unsampled spatial domain still strictly follows the laws of hydrodynamic conservation.

[0084] Based on the model, the system extracts key risk areas, including: Low-level wind shear zone: The region where the spatial gradient of the wind speed vector exceeds the safety threshold.

[0085] Urban canyon wind zone: a channel where the flow velocity is abnormally increased due to the funnel effect.

[0086] Turbulent wake zone of a building: the area of ​​backflow and vortex shedding on the leeward side of a large building.

[0087] The system, combined with a pre-built airworthiness standard library for low-altitude aircraft (containing wind envelopes for different aircraft types), calculates the airworthiness safety index for each planned route. This index comprehensively considers four dimensions: mean wind, gust wind, wind shear, and turbulence. Specifically, the airworthiness safety index is calculated using a multi-factor weighted normalization model. The system selects mean wind speed, gust wind coefficient, flow field turbulence intensity, and vertical wind shear index as four core evaluation dimensions. First, an S-shaped function is used to map the measured physical quantities of each dimension to a dimensionless risk score ranging from 0 to 1. Second, the weights are determined based on the Analytic Hierarchy Process (AHP), and considering the safety characteristics of low-altitude airspace, the vertical wind shear index and turbulence intensity are given relatively high weights (e.g., 0.35 each). ,in These are the normalized wind speed, turbulence intensity, and shear index, respectively. These are the weighting coefficients for wind speed, turbulence intensity, and shear index, respectively. This index directly reflects the meteorological safety of the airspace; the lower the score, the higher the risk.

[0088] The final result is a low-altitude wind environment meteorological service assessment report. The core of the report is a three-dimensional visualized meteorological safety situation map, which includes: The red area indicates a high-risk no-fly zone (index < 60), where there is strong shear or uncontrollable turbulence.

[0089] The yellow area indicates a restricted passage zone (60 < index < 80), where it is recommended to reduce speed or allow only high-performance aircraft to pass.

[0090] The green area indicates a recommended flight corridor (index > 80), where airflow is stable and tailwinds can be utilized for energy conservation.

[0091] The report also provides specific time window recommendations, such as "It is recommended to avoid the CBD core area between 14:00 and 16:00".

[0092] When performing energy-intensive spiral envelope and wind-resistant hovering missions, the drone's power consumption is extremely high. The system has a built-in sophisticated energy management module.

[0093] Before resuming cruise formation after the core region of the abnormal flow field disappears (turbulence intensity drops), or when the formation maintenance time reaches its limit, the system performs a full-scale energy assessment.

[0094] The system reads BMS (Battery Management System) data and, combined with return distance and current wind conditions, predicts the remaining mission capability of each drone. The specific energy assessment model is as follows: ,in This is the current battery level. For current wind-resistant hovering power, For cruise power, For the return distance, The estimated remaining hovering operation time for the mission. This represents the average flight speed of the drone when it returns to home. If... If the value is below the safety threshold, the substitute mechanism will be triggered.

[0095] If all drones have sufficient power, the system will issue a command, and the cluster will orderly release the envelope and restore the initial cruise formation.

[0096] If a drone (often the main test drone in the inner wind-resistant layer) is found to have insufficient power, the system will trigger a dynamic replacement mechanism.

[0097] The system dispatches standby drones in nearby airspace or at ground charging stations to take off. The standby drone flies to the low-battery drone's position and performs an aerial handover: the standby drone takes over and begins data collection; after verification, the low-battery drone withdraws and returns to base. This relay mechanism ensures the continuity of the monitoring grid, enabling uninterrupted 24 / 7 operation.

[0098] This embodiment demonstrates the application process of the system through a specific scenario.

[0099] Scenario: A cross-sea bridge needs to be inspected by drone, but there are often strong winds and wind around the bridge towers at the bridge site, so a micro-meteorological assessment is required first.

[0100] Initialization: Import a high-precision map of the bridge and surrounding sea area. The system identifies the bridge towers and cable-stayed cable areas as complex terrain and generates a high-density body-fitting mesh. Plan a patrol formation of 10 UAVs (2 main and 8 auxiliary).

[0101] Detection: The cluster flew along the bridge axis. When it approached the main bridge tower, the main measuring drone detected a sharp increase in wind speed variance, indicating the presence of a bridge tower shedding vortex.

[0102] Lockdown: The system triggers an abnormal core lockdown. The cluster transforms into a hemispherical spiral envelope formation surrounding the rear side of the bridge tower.

[0103] Correction: Under strong crosswinds, the wake interference cone deflects. The system calculates in real time and instructs the UAV behind to perform minor lateral avoidance maneuvers, while simultaneously correcting the model for data slightly affected by interference.

[0104] Results: A model of the Karman vortex street behind the bridge tower was constructed. The assessment report indicated that there is periodic strong turbulence within 200 meters behind the bridge tower, and recommended that the inspection drone approach from the upwind direction or detour to pass at least 300 meters away.

[0105] Furthermore, the system software adopts a modular microservice architecture, mainly including: The grid construction and path planning module is responsible for GIS data parsing, octree grid generation, and A* path search.

[0106] The data acquisition and transmission module is responsible for sensor-level driving, PTP time synchronization, coordinate system transformation, and data compression and encapsulation.

[0107] The flow field feature perception and formation reconstruction module is responsible for calculating turbulence intensity / shear index in real time, running the core anomaly judgment logic, and solving the target point of the spiral formation.

[0108] The collaborative obstacle avoidance and data correction module is responsible for maintaining the wake cone model of the entire fleet, performing collision detection, generating avoidance commands, and executing data denoising algorithms.

[0109] The model building and evaluation report generation module is responsible for data aggregation, spatial interpolation and flow field reconstruction, calculation of airworthiness index, rendering of 3D situation map and generation of PDF report.

[0110] Obviously, those skilled in the art can make various modifications and variations to this invention without departing from its spirit and scope. Therefore, if these modifications and variations fall within the scope of the claims of this invention and their equivalents, this invention also intends to include these modifications and variations.

Claims

1. A method for monitoring and evaluating low-altitude wind environment meteorological services based on unmanned aerial vehicle (UAV) swarms, characterized in that, The method is applied to a monitoring system including drone swarms, edge computing nodes, and ground command centers, and the method includes the following steps: Step 1: Based on the geographic information data of the target airspace and the requirements of meteorological monitoring tasks, construct a non-uniform three-dimensional monitoring grid, and divide the UAV cluster into main test UAVs and auxiliary test UAVs to generate an initial three-dimensional cruise formation. Step 2: Control the drone cluster to enter the target airspace according to the three-dimensional cruise formation and collect basic meteorological data including wind speed, wind direction, air pressure and temperature; During the data acquisition process, dynamic formation reconstruction and collaborative data correction based on flow field characteristics are performed in parallel: The formation dynamic reconstruction includes: maintaining a sliding time window, calculating the ratio of the standard deviation of the wind speed vector to the average wind speed within the window as the real-time flow field turbulence intensity, and when the flow field turbulence intensity is detected to exceed a preset threshold, triggering the flow field abnormal core area locking mechanism, constructing an envelope space containing inner, middle and outer multi-layer spherical surfaces with the trigger position as the geometric center, and planning the spiral orbit trajectory of each auxiliary UAV on the multi-layer spherical surfaces to satisfy the linear change of polar angle and azimuth angle with time, and controlling the cluster to switch to the multi-layer spiral envelope formation; The collaborative data correction includes: acquiring real-time flight status data of the forward UAV through a wireless ad hoc network and constructing a dynamic wake interference cone; if the coordinates of the rear UAV fall into this area, then based on the real-time thrust coefficient of the forward UAV and the measured turbulence intensity of the environmental flow field, using the wake jet model to calculate the induced wind speed increment vector generated by the forward UAV at the position of the rear UAV, and subtracting the induced wind speed increment vector from the measured wind speed vector of the rear UAV to obtain the corrected wind speed; Step 3: Map the corrected wind speed data onto the non-uniform three-dimensional monitoring grid to construct a low-altitude dynamic wind field model, and generate a low-altitude airworthiness safety index map based on the model.

2. The method according to claim 1, characterized in that, The construction of the three-dimensional monitoring grid in step one specifically includes: Acquire digital elevation models and building vector data for the target airspace; identify complex terrain areas within the target airspace based on local elevation statistical features; The three-dimensional monitoring grid is generated using a distance-weighted octree splitting algorithm, resulting in a higher grid density in complex terrain areas and building boundary layer areas compared to flat areas.

3. The method according to claim 1, characterized in that, The planning of the spiral trajectory in the formation dynamic reconstruction step specifically includes: Establish a spherical coordinate system with the geometric center of the core region of the flow field anomaly as the origin; For each spherical surface, the trajectory parameters of the auxiliary UAV are set to satisfy: the polar angle changes linearly with time, and the azimuth angle increases linearly with time, wherein the rate of change of the azimuth angle is at least ten times the rate of change of the polar angle, and a spherical spiral waypoint sequence covering the spherical surface is generated. Multiple auxiliary UAVs on the same sphere are assigned initial azimuth phases at uniform intervals to form a cage-like scanning path that does not interfere with each other.

4. The method for monitoring and evaluating low-altitude wind environment meteorological services based on UAV swarms according to claim 1, characterized in that, The generation of the low-altitude wind environment meteorological service assessment map in step three specifically includes: Based on the low-altitude dynamic wind field model, low-altitude wind shear zone, urban canyon wind zone and building wake zone are extracted. Based on the preset safety standards for low-altitude logistics routes, the airworthiness safety index of each route segment within the target airspace is calculated. Based on the airworthiness safety index, a meteorological safety situation map including recommended flight corridors and high-risk no-fly zones is drawn, forming the low-altitude wind environment meteorological service assessment map.

5. The method for monitoring and evaluating low-altitude wind environment meteorological services based on UAV swarms according to claim 1, characterized in that, The mechanism for locking the core region of the abnormal flow field during the dynamic formation reconstruction step specifically includes: Real-time monitoring of the turbulence intensity of the flow field collected by each of the main test drones; The turbulence intensity of the flow field is compared with the preset threshold. If the sampling period of the turbulence intensity of the flow field of any of the main test drones exceeds the preset threshold for a consecutive preset number of sampling periods, then the current position of the main test drone is marked as the geometric center of the core region of the flow field anomaly. Based on the geometric center, a motion logic is set where the polar angle changes linearly with time and the azimuth angle increases linearly with time with a rate of change greater than that of the polar angle, thereby generating a dynamic waypoint sequence distributed on a concentric sphere.

6. The method for monitoring and evaluating low-altitude wind environment meteorological services based on UAV swarms according to claim 1, characterized in that, The calculation of the wake interference cone region for each UAV in the collaborative obstacle avoidance and data correction step specifically includes: Using the geometric center of the forward UAV that generates the wake as the vertex; calculate the vector sum of the flight speed vector of the forward UAV and the ambient wind speed vector to obtain the composite airflow vector; With the reverse extension of the composite airflow vector as the central axis; The cone apex angle is dynamically set based on the environmental turbulence measured by the drone in front. The greater the environmental turbulence, the larger the set cone apex angle, and a spatial model of the cone is constructed. The effective extension range of the conical spatial model in the downstream direction is defined as the wake interference cone region.

7. The method for monitoring and evaluating low-altitude wind environment meteorological services based on UAV swarms according to claim 6, characterized in that, The generation of fine-tuning displacement commands in the collaborative obstacle avoidance and data correction steps specifically includes: Determine whether the current coordinates of the rear UAV fall within the wake interference cone region of any of the front UAVs; If so, calculate the vertical distance vector of the rear UAV relative to the central axis; Along the vertical distance vector pointing away from the central axis, a translation component perpendicular to the central axis is generated as the fine-tuning displacement command until the rear UAV moves out of the wake interference cone region.

8. The method according to claim 1, characterized in that, The specific steps of correcting the data using the wake jet model include: Acquire real-time thrust data and environmental turbulence of the UAV in front, and calculate the dimensionless thrust coefficient and wake expansion coefficient; Based on the thrust coefficient, the initial induced velocity of the rotor plane is calculated using momentum theory; Based on the wake expansion coefficient and Gaussian distribution function, the induced wind speed increment at the position of the UAV behind within the wake interference cone is calculated. Construct the vector form of the induced wind speed increment and remove the vector from the original measured data.

9. The method for monitoring and evaluating low-altitude wind environment meteorological services based on UAV swarms according to claim 1, characterized in that, The method also includes an energy-constrained formation recovery step: After the multi-layer spiral envelope formation has been maintained for a preset time, or when the turbulence of the flow field has decreased to below a safe threshold, the remaining energy of each UAV is evaluated. If the remaining energy meets the requirements of subsequent missions, the drone cluster is controlled to release the multi-layer spiral envelope formation and execute a formation change command to restore the three-dimensional cruise formation. If the remaining energy is insufficient, a backup mechanism is triggered, and a standby drone is dispatched to take over the position of the drone with low battery.

10. A low-altitude wind environment meteorological service monitoring and assessment system based on unmanned aerial vehicle (UAV) swarms, characterized in that, The system includes: The grid construction and path planning module is used to construct a 3D monitoring grid and generate the initial 3D cruise formation of the UAV swarm; The data acquisition and transmission module is used to control the UAV cluster to collect basic meteorological data and perform spatiotemporally aligned transmission. The flow field feature perception and formation reconstruction module is used to calculate the flow field turbulence intensity and wind shear index, and to control the UAV cluster to switch to a multi-layer spiral envelope formation when the flow field anomaly core area locking mechanism is triggered. The collaborative obstacle avoidance and data correction module is used to calculate the wake interference cone region, generate fine-tuning displacement commands to avoid wake interference, and use the wake attenuation model to denoise and compensate the data. The model building and evaluation report generation module is used to build a low-altitude dynamic wind field model and generate a low-altitude wind environment meteorological service evaluation report.

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