Low-altitude weather intelligent management system for navigable airport based on multi-source weather data fusion

Abstract

The application provides a kind of low-altitude weather intelligent management system of navigable airport based on multi-source weather data fusion, including three-dimensional grid monitoring network, for realizing three-dimensional scanning and dynamic perception of airspace weather environment by real-time acquisition of weather detection data;Data fusion system, for assimilating the real-time detection data collected and multi-source forecast data, generating high-resolution three-dimensional grid weather field covering the airspace range of the airport;Dynamic safety research module: for real-time research and judgment of the influence of current and future weather conditions on the flight safety of each aircraft type;Management system establishment module: the influence of current weather conditions on flight is visually displayed by establishing a flight management system in the form of visual charts.The application solves the problem of insufficient low-altitude weather service capability of existing navigable airport by intelligent perception, data fusion and decision-making prediction innovation system, and constructs a comprehensive weather safety protection system.

Description

Technical Field

[0001] This invention relates to the field of low-altitude flight meteorological data processing technology, specifically to a general aviation airport low-altitude meteorological intelligent management system based on multi-source meteorological data fusion, which aims to provide comprehensive and accurate meteorological support for low-altitude flight activities at general aviation airports. Background Technology

[0002] The Low-Altitude Meteorological Intelligent Service System is a comprehensive system that provides real-time, three-dimensional meteorological perception, data fusion, and decision support for low-altitude aircraft, offering safe digital protection for low-altitude activities. Against the backdrop of the rapid development of the low-altitude economy, general aviation airports, as strategic pillars of the low-altitude economic system, undertake the core function of being the operational hub for aircraft.

[0003] However, most airports rely on traditional single-point meteorological observation equipment, lacking vertical detection capabilities and exhibiting significant shortcomings in monitoring small-scale weather phenomena such as low-level wind shear and microbursts. A comprehensive, three-dimensional meteorological perception capability is generally lacking. Simultaneously, the current experience-driven operation and maintenance model cannot match the highly dynamic operational demands of frequent takeoffs and landings and long-distance flights of low-altitude aircraft, leading to a structural mismatch between meteorological services and flight safety requirements. In recent years, frequent accidents caused by meteorological factors, such as aircraft loss of control and interruptions of emergency missions, have exposed significant operational risks arising from the disruption of meteorological data links. Therefore, failure to accelerate the generational upgrade of meteorological infrastructure at general aviation airports will directly constrain the large-scale development of the low-altitude economy. Summary of the Invention

[0004] This invention provides a low-altitude meteorological intelligent management system for general aviation airports based on multi-source meteorological data fusion. Through an innovative system of intelligent sensing, data fusion, and decision prediction, it constructs a comprehensive meteorological safety protection and control system, thereby solving the problem of insufficient low-altitude meteorological service capabilities in existing general aviation airports.

[0005] The present invention achieves the above objectives through the following technical solutions:

[0006] A general aviation airport low-altitude meteorological intelligent management system based on multi-source meteorological data fusion includes:

[0007] A three-dimensional grid-based monitoring network is deployed in the airport monitoring area with various types of intelligent meteorological terminal equipment. By collecting meteorological detection data in real time, it can realize three-dimensional scanning and dynamic perception of the airspace meteorological environment.

[0008] The data fusion system is used to assimilate the collected real-time detection data and multi-source forecast data, and combine them with the terrain and building parameters near the general aviation airport to simulate and calculate the atmospheric state, generate a high-resolution three-dimensional gridded meteorological field covering the airport airspace, and realize the forecast and early warning of future meteorological conditions of the airport.

[0009] Dynamic safety assessment module: Combining the aircraft model database, it constructs a meteorological tolerance parameter system for multiple aircraft, defines the impact thresholds of various aircraft models on meteorological sensitive conditions, and assesses in real time the impact of current and future airspace meteorological conditions on the flight safety of each aircraft model;

[0010] Management system establishment module: Establish a lightweight and standardized flight management system by outputting meteorological data that affects flight safety in a result-oriented manner and defining multi-color warning thresholds, and visually displaying the degree of impact of current weather conditions on flight through charts.

[0011] According to the present invention, a low-altitude meteorological intelligent management system for general aviation airports based on multi-source meteorological data fusion includes the following: The construction of a three-dimensional gridded monitoring network comprises:

[0012] In the airport runway, apron, terminal area and airspace monitoring area, various types of intelligent meteorological terminal equipment are deployed according to the principle of effective detection range and coverage overlap, including at least 3D laser wind measurement radar, vertical laser wind measurement radar, micro-rain radar, visibility meter, small weather station and all-sky imager; among them, vertical laser wind measurement radar is deployed at different locations on the ground to form a three-dimensional wind field monitoring network.

[0013] Using deployed equipment as nodes, a three-dimensional gridded monitoring network covering the airport airspace is constructed. Kriging interpolation or inverse distance weighting method is used, combined with equipment layout and terrain elevation data, to generate a continuous three-dimensional gridded meteorological field.

[0014] By utilizing the beam scanning function of 3D laser wind radar, continuous monitoring of the horizontal wind field can be achieved at 360°; by using the pulse transmission and echo reception of vertical laser wind radar, the wind speed and wind direction profiles at vertical height can be obtained.

[0015] By combining cloud image data from the all-sky imager and precipitation particle data from the micro-rain radar, a cloud-precipitation co-distribution map is generated and overlaid onto a three-dimensional gridded meteorological field.

[0016] According to the present invention, a low-altitude meteorological intelligent management system for general aviation airports based on multi-source meteorological data fusion is provided. When constructing a three-dimensional gridded monitoring network, various types of intelligent meteorological terminal devices are deployed as spatial nodes. Each deployed device is uniquely identified and located to determine its coordinates (x, y, z) in three-dimensional space. These devices serve as nodes of the three-dimensional gridded monitoring network to construct a three-dimensional gridded monitoring network covering the airport airspace.

[0017] Based on the actual range of the airport airspace and meteorological monitoring needs, the spatial range of the three-dimensional grid monitoring is determined, including the horizontal coverage area and the vertical height range.

[0018] Establish rules for dividing the three-dimensional mesh, determine the resolution of the mesh in the horizontal and vertical directions, and divide the entire monitoring space into discrete three-dimensional mesh units according to the rules. Each mesh unit has a unique identifier.

[0019] Each intelligent meteorological terminal device collects meteorological observation data in real time according to the set data update frequency and transmits the data to the data processing center; at the data processing center, the collected raw meteorological data is preprocessed.

[0020] Three-dimensional gridded meteorological fields are generated based on the Kriging interpolation method or the inverse distance weighting method;

[0021] The meteorological parameter value of each three-dimensional grid cell is obtained by calculating the weighted average of the data from each device node, thereby constructing a continuous three-dimensional gridded meteorological field.

[0022] According to the present invention, a low-altitude meteorological intelligent management system for general aviation airports based on multi-source meteorological data fusion is provided. The simulation calculation adopts the Large Eddy Simulation (LES) technology to analyze the multi-scale motion characteristics of turbulence. The terrain-induced lift source term is introduced into the LES control equation through the terrain dynamic coupling module, and the multi-source data assimilation technology is integrated to realize the dynamic fusion of meteorological observation data and numerical forecast data.

[0023] Among them, the characteristics of the atmospheric boundary layer are accurately characterized by a hybrid wall function model for the building complex and the terrain boundary. The inlet boundary conditions are based on the fusion and reconstruction of the radar inverted wind field and downscaled data provided by the global forecast system. Using unstructured grid generation technology, the terrain data of the geographic information system and the airport building BIM model are discretized into a body-fitted computational grid with sub-meter precision, and finally a high-resolution three-dimensional gridded meteorological field covering the airport airspace is generated.

[0024] According to the present invention, a low-altitude meteorological intelligent management system for general aviation airports based on multi-source meteorological data fusion is provided. In the data fusion system, large eddy simulation (LES) technology is used to perform multi-scale analysis of atmospheric boundary layer turbulence, decomposing the turbulent motion into solvable scales. u i And at the sub-grid scale, its governing equations are:

[0025]

[0026] in, Indicates the sign of the partial derivative. x j , x i Represents spatial coordinate variables, used to describe spatial location. and They represent the solvable scales, respectively. u ivelocity components, For time variables, For solvable scale velocity components Partial derivative with respect to time t, For solvable scale velocity components Spatial coordinates The partial derivatives, For the density of the fluid, For pressure at a solvable scale For the pressure gradient term in x i Components in direction, The kinematic viscosity coefficient of the fluid. The term represents the diffusion of solvable-scale velocities due to the viscosity of the fluid. The divergence of subgrid stress reflects the influence of spatial variations in subgrid stress on the solvable scale velocity field. For terrain forces in x i The directional component is used to account for the influence of topography on atmospheric boundary layer turbulence;

[0027] For subgrid stress, calculated using the Smagorinsky model:

[0028]

[0029] in, The filtered value is the product of the velocity components. The product of the solvable scale velocity components. For the trace of sublattice stress, The symbol for Kronecker. The trace of the solvable scale strain rate tensor. is the eddy viscosity coefficient at the subgrid scale, used to quantify the momentum exchange between subgrid-scale turbulence and solvable-scale turbulence. The modulus of the solvable scale strain rate tensor. Here, Smagorinsky constant is given, and Δ is the grid filtering scale. It is a solvable scaled strain rate tensor.

[0030] According to the present invention, a low-altitude meteorological intelligent management system for general aviation airports based on multi-source meteorological data fusion is provided, which introduces terrain-induced lift source terms. F terrain,i The LES governing equations are modified in the following way:

[0031] Based on the Digital Elevation Model (DEM), the terrain height is... h ( x , yMapped to LES mesh nodes, spatial continuity is ensured using bilinear interpolation;

[0032] Combined with terrain slope h and wind speed u i The lift source term is represented as:

[0033]

[0034] in, For the density of the fluid, The magnitude of the wind speed is a solvable scale, i.e., the modulus of the wind speed vector. For terrain height, Terrain height Spatial coordinates The partial derivatives, For wind speed at j The directional component and the terrain j The slope in a given direction reflects the contribution of the interaction between wind speed and terrain slope to lift generation. C L n is the lift coefficient. i This is the unit vector normal to the terrain;

[0035] Near the terrain surface, the wind speed profile is adjusted using a logarithmic law:

[0036]

[0037] in, For friction speed, To indicate at altitude z The first i The adjusted average wind speed components in each direction. κ For von Kármán's constant, z 0 represents surface roughness. The height above the ground. The boundary layer height, Indicates the first i A unit vector in each direction is used to determine the direction of the wind speed component.

[0038] According to the present invention, a low-altitude meteorological intelligent management system for general aviation airports based on multi-source meteorological data fusion is provided, which employs an ensemble Kalman filter algorithm to integrate real-time observation data. D obs ( t ) and LES simulation results D LES ( t Dynamic fusion, updating the initial field X init :

[0039] From the initial field X b generate N e Disturbance samples The disturbance amplitude is based on the background error covariance. B ;

[0040] Run the LES model on each sample to obtain the prediction set. ;

[0041] Calculate the observation increment And update the sample:

[0042]

[0043] in, For the observation operator, For observation operator transpose, For the prediction set, For at any time t The mean of the predicted state vector, For at any time t The updated version i One sample, The product of the observation operator and the covariance matrix of the prediction set and its transpose. To predict the product of the set covariance matrix and the transpose of the observation operator, K ( t ) represents the Kalman gain. To predict the covariance of the set, R For observation error covariance, This is random observation noise;

[0044] Based on the assimilated initial field X init Running the LES model generates a high-resolution three-dimensional meshed meteorological field M( x , y , z , t ),include:

[0045] Wind field: Three-dimensional wind speed vector It describes the spatial location ( ) and time Wind speed conditions above, These are the three-dimensional wind speed vectors in Velocity components in three directions; Temperature field: Potential temperature θ ( x , y , z ,t This reflects the spatial location ( ) and time Potential temperature of upper air; Humidity field: specific humidity q ( x , y , z , t ), used to describe spatial location ( ) and time The humidity level of the upper air.

[0046] According to the present invention, a low-altitude meteorological intelligent management system for general aviation airports based on multi-source meteorological data fusion is provided. Addressing the complex geometric features of building complexes and terrain boundaries, a hybrid wall function model is used to accurately characterize the atmospheric boundary layer properties. Its mathematical expression is as follows:

[0047]

[0048] in: For dimensionless velocity, y + For dimensionless distance, The kinematic viscosity of the fluid. y This is the actual distance from the wall. It is the natural logarithm function. Pi It is the hyperbolic tangent function. κ For von Kármán's constant, E It is an empirical constant. Δ is the dimensionless distance corresponding to the critical point of the transition region. y + The width of the transition zone; This represents the dimensionless distance corresponding to the rough element height. C rough This is the roughness correction factor. n The roughness element shape index;

[0049] Wind field data U obtained from radar inversion radar ( x , y , z , t ) and downscaling data U provided by the Global Forecasting System GFS ( x , y , z , t The inlet boundary condition U is generated through a dynamic weight fusion algorithm. in ( x , y , z , t ):

[0050] The weight allocation is represented as follows:

[0051]

[0052] in, The weighting function for radar data during the fusion process. The weighting function for global forecast system data during the fusion process. α The attenuation coefficient is used to control the rate of change of the weighting function. Represents an exponential function. z radar For effective radar detection altitude;

[0053] The fusion result of radar inversion wind field data and downscaled data from the Global Forecasting System is expressed as follows:

[0054]

[0055] Adjust the boundary layer:

[0056] In the near-surface layer, the wind speed profile is corrected using the Monin-Obukhov similarity theory:

[0057]

[0058] in, In order to be at a high altitude z The corrected wind speed vector. e is the height above the ground. x Indicates the direction of wind speed along x Axial direction, z 0 represents surface roughness. ψ m For stability function, L It is the Obukhov length;

[0059] Using snappyHexMesh technology, the terrain data from the geographic information system and the airport building BIM model are discretized into a sub-meter precision computational grid; the LES model is run to generate a high-resolution three-dimensional meshed meteorological field M covering the airport airspace. x , y , z , t ).

[0060] According to the present invention, a low-altitude meteorological intelligent management system for general aviation airports based on multi-source meteorological data fusion is provided. The establishment of the aircraft model database includes the following core parameters:

[0061] Geometric parameters: wingspan b ,captain L fuselage heighth Rotor diameter D ;

[0062] Performance parameters: Stall speed V stall Maximum cruising speed V cruise Maximum climb rate Minimum turning radius R min ;

[0063] The meteorological tolerance parameter system for multi-element aircraft includes the following meteorological sensitive parameters:

[0064] Wind field tolerance: maximum crosswind speed V cross_max Maximum tailwind / headwind speed V head_max Maximum gust intensity;

[0065] Turbulence tolerance: maximum turbulence intensity;

[0066] Visibility tolerance: Minimum takeoff / landing visibility Vis min Minimum cruising visibility Vis cruise_min ;

[0067] Rainfall tolerance: maximum rainfall intensity R max Maximum hail diameter d hail_max ;

[0068] Temperature tolerance: Minimum start-up temperature T min Maximum operating temperature T max ;

[0069] Barometric pressure tolerance: Minimum takeoff pressure P min Maximum operating air pressure P max ;

[0070] Dimensionless processing was performed on the meteorological sensitive parameters of each aircraft model to generate meteorological safety assessment benchmark values. S base :

[0071]

[0072] in, i It is an index variable used to iterate from 1 to n All meteorological sensitive parameters, n This indicates the total number of meteorological sensitive parameters.p i For the first i Meteorological sensitive parameters, p imax This is the maximum parameter value for similar models. w i These are the weighting coefficients.

[0073] According to the intelligent management system for low-altitude meteorological data of general aviation airports based on multi-source meteorological data fusion provided by the present invention, the following real-time data are extracted through the generated high-resolution three-dimensional gridded meteorological field:

[0074] Wind field: Three-dimensional wind speed U ( x , y , z , t )=( u , v , w ), gust factor Turbulence: Turbulence intensity TI ( x , y , z , t ), turbulent integral scale L turb Visibility: based on atmospheric extinction coefficient β Calculations; Precipitation: Precipitation Types and Intensities R ( x , y , z , t Temperature: air temperature T ( x , y , z , t Dew point temperature T d ( x , y , z , t Air pressure: Sea level air pressure P sfc ( x , y , t );

[0075] For the real-time position of the aircraft ( x p , y p , z p Meteorological parameters were obtained using inverse distance-weighted interpolation.

[0076]

[0077] in d i For the first i The distance from each grid point to the aircraft N This represents the number of nearest neighbor grid points.

[0078] For each meteorological parameter p Define three levels of early warning thresholds:

[0079] Safety threshold p safe The aircraft can operate safely for a long period of time;

[0080] Attention threshold p warn Close monitoring is required as it affects flight performance.

[0081] No-fly threshold p prohibit : Immediately terminate the flight;

[0082] Calculate the real-time meteorological safety index by combining multiple parameter weights. S meteo :

[0083]

[0084] Where min is the function for finding the minimum value. p warn_i For the first i The attention threshold of the item parameter m The number of parameters involved in the evaluation.

[0085] Therefore, the intelligent low-altitude meteorological management system for general aviation airports proposed in this invention, based on multi-source meteorological data fusion, provides comprehensive, accurate, and intelligent meteorological services for low-altitude flight activities at general aviation airports by integrating a three-dimensional gridded monitoring network, a data fusion system, a dynamic safety assessment module, and a management system establishment module. Compared with existing technologies, it has the following beneficial effects:

[0086] 1. This invention deploys various types of intelligent meteorological terminal equipment at key locations in and around airports, such as 3D laser wind radar, vertical laser wind radar, micro-rain radar, visibility meters, small weather stations, and all-sky imagers, constructing a three-dimensional gridded monitoring network. This network can collect meteorological data in real time, enabling three-dimensional scanning and dynamic perception of the airspace meteorological environment. It overcomes the shortcomings of traditional single-point meteorological observation equipment in vertical detection capabilities, significantly improving the comprehensiveness of meteorological monitoring. For example, it can more accurately monitor small-scale weather phenomena such as low-level wind shear and micro-downbursts, providing more reliable data support for flight safety.

[0087] 2. The data fusion system of this invention assimilates real-time detection data and multi-source forecast data, and combines this with terrain and building parameters near the general aviation airport to simulate atmospheric conditions. Large eddy simulation (LES) is used to analyze the multi-scale motion characteristics of turbulence. A terrain-induced lift term is introduced through a terrain dynamic coupling module. Multi-source data assimilation technology is integrated to achieve dynamic fusion of meteorological observation data and numerical forecast data. Simultaneously, unstructured mesh generation technology (snappyHexMesh) is used to discretize GIS terrain data and airport building BIM models (converted to STL format) into sub-meter precision body-fitted computational meshes. Finally, a high-resolution three-dimensional meshed meteorological field covering the airport airspace is generated, which can more accurately reflect the meteorological conditions of the airport airspace and provide more detailed meteorological information for flight safety.

[0088] 3. The data fusion system of this invention, through the assimilation processing of real-time detection data and multi-source forecast data, as well as the simulation calculation of atmospheric conditions, can generate more accurate forecasts of future weather conditions. By combining topographical and building parameters near general aviation airports, and considering the specificities of the local meteorological environment, the system improves the relevance of forecasts. For example, it can provide early warnings for potential localized severe convective weather around airports, giving sufficient time to adjust flight plans and reducing the occurrence of accidents such as aircraft loss of control and emergency mission interruptions caused by weather conditions.

[0089] 4. The data acquisition and processing of this invention's system are performed in real time, enabling dynamic updates of meteorological information. In the highly dynamic operating environment of dense takeoffs and landings of low-altitude aircraft and long-distance flights, timely updates of meteorological information are crucial for flight safety. The dynamic safety assessment module can acquire the latest meteorological data in real time and, in conjunction with the aircraft model database, assess the impact of current and future airspace weather conditions on the flight safety of various aircraft models, providing timely decision-making support for flight operators.

[0090] 5. The dynamic safety assessment module of this invention, combined with an aircraft database, constructs a multi-dimensional meteorological tolerance parameter system for aircraft. By simulating and comprehensively evaluating the flight performance of different aircraft models under various meteorological conditions, it delineates the impact thresholds of various aircraft models on meteorologically sensitive conditions. For example, for different types of aircraft such as UAVs, light aircraft, and helicopters, its safe flight range under different meteorological conditions such as wind speed, visibility, and precipitation is determined, making flight safety assessments more scientific and accurate.

[0091] 6. This invention's system can assess in real time the impact of current and future airspace weather conditions on the flight safety of various aircraft types, providing timely risk alerts to flight operators. When weather conditions approach or exceed the impact threshold for a particular aircraft type, the system will issue a warning, reminding operators to take appropriate measures, such as adjusting flight altitude, changing flight routes, or pausing flight, thereby effectively reducing the risk of flight accidents.

[0092] 7. The management system establishment module of this invention establishes a lightweight and standardized flight management system by outputting meteorological data affecting flight safety in a result-oriented manner and defining multi-color warning thresholds. For example, the "Safety-Attention-No-Fly" three-color warning dashboard visually displays the degree of impact of current weather conditions on flight in the form of charts, enabling flight operators and managers to quickly and accurately understand the impact of weather conditions on flight safety, simplifying management processes and improving management efficiency.

[0093] The present invention will now be described in further detail with reference to the accompanying drawings and specific embodiments. Attached Figure Description

[0094] Figure 1 This is a schematic diagram of an embodiment of the intelligent low-altitude meteorological management system for general aviation airports based on multi-source meteorological data fusion according to the present invention.

[0095] Figure 2 This is a flowchart illustrating the construction of a three-dimensional gridded monitoring network in an embodiment of an intelligent low-altitude meteorological management system for general aviation airports based on multi-source meteorological data fusion, according to the present invention.

[0096] Figure 3 This is a flowchart illustrating the application of Large Eddy Simulation (LES) technology in an embodiment of a general aviation airport low-altitude meteorological intelligent management system based on multi-source meteorological data fusion, according to the present invention.

[0097] Figure 4 This is a flowchart illustrating the dynamic fusion implementation of multi-source data assimilation technology in an embodiment of the intelligent management system for low-altitude meteorological data of general aviation airports based on multi-source meteorological data fusion according to the present invention.

[0098] Figure 5 This is a flowchart illustrating the application of the hybrid wall function model in an embodiment of the intelligent management system for low-altitude meteorological conditions at general aviation airports based on multi-source meteorological data fusion, according to the present invention.

[0099] Figure 6 This is a schematic diagram illustrating the construction of the aircraft database and the standardization of meteorological tolerance parameters in an embodiment of a general aviation airport low-altitude meteorological intelligent management system based on multi-source meteorological data fusion according to the present invention. Detailed Implementation

[0100] To make the objectives, technical solutions, and advantages of this invention clearer, the technical solutions of this invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of this invention. All other embodiments obtained by those skilled in the art based on the embodiments of this invention without creative effort are within the scope of protection of this invention.

[0101] In this document, the term "embodiment" means that a particular feature, structure, or characteristic described in connection with an embodiment may be included in at least one embodiment of this application. The appearance of this phrase in various places throughout the specification does not necessarily refer to the same embodiment, nor is it a separate or alternative embodiment mutually exclusive with other embodiments. It will be explicitly and implicitly understood by those skilled in the art that the embodiments described herein can be combined with other embodiments.

[0102] See Figures 1 to 6 This embodiment provides a low-altitude meteorological intelligent management system for general aviation airports based on multi-source meteorological data fusion, including:

[0103] The three-dimensional grid-based monitoring network consists of various types of intelligent meteorological terminal equipment deployed in the airport monitoring area (key locations in the airport and adjacent areas) to collect meteorological observation data in real time, construct an airport area grid-based monitoring system, and realize three-dimensional scanning and dynamic perception of the airspace meteorological environment.

[0104] The data fusion system is used to assimilate the collected real-time detection data and multi-source forecast data, and combine them with the terrain and building parameters near the general aviation airport to simulate and calculate the atmospheric state, generate a high-resolution three-dimensional gridded meteorological field covering the airport airspace, and realize the forecast and early warning of future meteorological conditions of the airport.

[0105] Dynamic safety assessment module: Combining the aircraft model database, it constructs a meteorological tolerance parameter system covering various aircraft such as UAVs, light aircraft, and helicopters, delineates the impact thresholds of various aircraft models on meteorological sensitive conditions, and assesses in real time the impact of current and future airspace meteorological conditions on the flight safety of each aircraft model;

[0106] Management System Establishment Module: A lightweight, standardized flight management system is established. This involves outputting meteorological data affecting flight safety in a results-based manner and defining a three-color warning threshold system: "Safe," "Attention," and "No-Fly Zone." Visual charts are used to intuitively display the impact of current weather conditions on flight. For example, assuming a wind speed impact threshold of 5-6 for a certain aircraft, safe airspace below level 5 is marked in green, attention airspace at level 5-6 in blue, and dangerous airspace above level 6 in red. A mobile application is also developed to receive real-time customized weather briefings, including future trends of key meteorological elements, aircraft compatibility recommendations, and emergency response plans. Standardized decision-making process documents are established to clarify the response mechanisms under different weather warning levels, ensuring the standardization and traceability of operational processes.

[0107] The data baseline construction module standardizes the output of data from different sources, types, and time periods, establishing a unified data storage format. It supports multi-dimensional retrieval, allowing users to quickly access and view data by time interval, geographical range, or meteorological element type, thereby enabling rapid decision support. At the same time, it preserves key data to ensure data traceability and tamper-proof capabilities, providing a reliable basis for meteorological service quality assessment and accountability.

[0108] In this embodiment, when forecasting and issuing warnings of future weather conditions at the airport, the three-dimensional gridded meteorological field data is assimilated into a high-resolution numerical weather prediction model (such as CMA-MESO), and the initial field of the model is optimized using four-dimensional variational assimilation (4D-Var) technology. A short-term forecast model based on a convolutional neural network (CNN) updates the future meteorological field in rolling increments (e.g., 5 minutes). The model training data includes 10 years of historical meteorological observations and radar data, enabling warnings of hazardous weather such as wind shear and low-level turbulence up to 30 minutes in advance. An early warning platform integrating radar, satellite, numerical model, and AI forecast results provides tiered warning information to air traffic controllers and pilots through a visual interface. For example, when the wind shear intensity at the runway threshold exceeds a threshold, the system automatically triggers a red alert and recommends an alternate landing plan, while simultaneously broadcasting Weather Hazard Area (TIBA) information to surrounding flights.

[0109] In this embodiment, as Figure 2 As shown, the construction of the three-dimensional gridded monitoring network includes:

[0110] Various types of intelligent meteorological terminal equipment are deployed around airport runways, aprons, terminals, and airspace monitoring areas (adjacent to key airspace locations) according to the principles of effective detection range and coverage overlap. These include at least 3D laser wind measurement radar, vertical laser wind measurement radar, micro-rain radar, visibility meter, small weather station, and all-sky imager. Among them, vertical laser wind measurement radar is deployed at different locations on the ground to form a three-dimensional wind field monitoring network.

[0111] Furthermore, system calibration was performed on all deployed meteorological terminal equipment, including sensor accuracy calibration, time synchronization calibration, and data transmission stability testing.

[0112] Furthermore, the raw data from different devices are converted into a unified format, including at least timestamps, geographic coordinates, meteorological element values, and quality indicators;

[0113] Using deployed equipment as nodes, a three-dimensional gridded monitoring network covering the airport airspace is constructed. Kriging interpolation or inverse distance weighting method is used, combined with equipment layout and terrain elevation data, to generate a continuous three-dimensional gridded meteorological field.

[0114] By utilizing the beam scanning function of 3D laser wind radar, continuous monitoring of the horizontal wind field can be achieved at 360°; by using the pulse transmission and echo reception of vertical laser wind radar, the wind speed and wind direction profiles at vertical height can be obtained.

[0115] By combining cloud image data from the all-sky imager and precipitation particle data from the micro-rain radar, a cloud-precipitation synergistic distribution map is generated and overlaid onto a three-dimensional gridded meteorological field.

[0116] In this embodiment, when constructing a three-dimensional gridded monitoring network, various types of intelligent meteorological terminal devices are deployed as spatial nodes. Each deployed device is uniquely identified and located to determine its coordinates (x, y, z) in three-dimensional space. These devices serve as nodes of the three-dimensional gridded monitoring network to construct a three-dimensional gridded monitoring network covering the airport airspace.

[0117] Based on the actual range of the airport airspace and meteorological monitoring needs, the spatial range of the three-dimensional grid monitoring is determined, including the horizontal coverage area (such as a range with a radius of several kilometers centered on the airport) and the vertical height range (such as from the ground to the upper limit of the airport airspace).

[0118] Establish rules for dividing the 3D mesh and determine the resolution of the mesh in the horizontal and vertical directions. For example, divide the horizontal direction into a mesh cell at regular intervals (e.g., 100 meters) and the vertical direction into layers at regular heights (e.g., 50 meters). Divide the entire monitoring space into discrete 3D mesh cells according to the division rules, with each mesh cell having a unique identifier.

[0119] Each intelligent meteorological terminal device collects meteorological data in real time, such as wind speed, wind direction, temperature, humidity, air pressure, and visibility, according to a set data update frequency, and transmits the data to the data processing center. At the data processing center, the collected raw meteorological data undergoes preprocessing, including data cleaning (removing outliers and erroneous data), data conversion (converting data collected from different devices into a unified format and unit), and data interpolation (using appropriate methods to interpolate and supplement missing data points), ensuring data quality and consistency.

[0120] In this embodiment, generating a three-dimensional gridded meteorological field based on the Kriging interpolation method or the inverse distance weighting method includes:

[0121] Application of Kriging interpolation

[0122] Using the Kriging interpolation method, meteorological parameters for each three-dimensional grid cell are estimated based on the location of the equipment nodes and the collected meteorological data, taking into account spatial correlation.

[0123] First, calculate the semi-variogram between device nodes to determine the semi-variogram model parameters (such as nugget value, sill value, range, etc.).

[0124] Then, based on the semi-variogram model and the data of known device nodes, the meteorological parameters of each unknown grid cell are interpolated to generate a continuous three-dimensional gridded meteorological field.

[0125] Inverse distance weighting method application

[0126] The inverse distance weighting method is adopted, which assigns different weights to the data of each device node based on the distance from the device node to the grid cell.

[0127] The closer a device node is to a grid cell, the greater its impact on the estimation of meteorological parameters for that grid cell. By calculating the weighted average of the data from each device node, the meteorological parameter values ​​for each three-dimensional grid cell are obtained, thus constructing a continuous three-dimensional gridded meteorological field.

[0128] As new meteorological data is continuously collected, the three-dimensional gridded meteorological field is dynamically updated at set time intervals (e.g., every 5 minutes), using incremental or full updates to ensure that the meteorological field can reflect the meteorological changes in the airport airspace in real time.

[0129] The generated three-dimensional gridded meteorological field is regularly optimized and verified. The accuracy and reliability of the meteorological field are evaluated by comparing and analyzing it with actual meteorological observation data (such as manual observation data from airport meteorological stations and data from other independent meteorological monitoring equipment).

[0130] Based on the evaluation results, adjust the parameters of the interpolation method or optimize the layout of equipment nodes to continuously improve the quality of the three-dimensional gridded meteorological field.

[0131] In this embodiment, the simulation calculation uses Large Eddy Simulation (LES) technology to analyze the multi-scale motion characteristics of turbulence. The terrain-induced lift source term is introduced into the LES control equation through the terrain dynamic coupling module, and the multi-source data assimilation technology is integrated to realize the dynamic fusion of meteorological observation data and numerical forecast data.

[0132] Among them, the characteristics of the atmospheric boundary layer are accurately characterized by hybrid wall functions for the building complex and terrain boundary. The inlet boundary conditions are based on the fusion reconstruction of radar inverted wind field and downscaled data from Global Forecast System (GFS / ECMWF). Using unstructured mesh generation technology (snappyHexMesh), the geographic information system (GIS) terrain data and airport building BIM model (converted to STL format) are discretized into a body-fitted computational mesh with sub-meter precision, and finally a high-resolution gridded three-dimensional meteorological field covering the airport airspace is generated.

[0133] Specifically, in data fusion systems, such as Figure 3 As shown, the Large Eddy Simulation (LES) technique is used to perform multi-scale analysis of atmospheric boundary layer turbulence, decomposing the turbulent motion into solvable scales. u i and subgrid scale UI Its governing equation is:

[0134]

[0135] in, Indicates the sign of the partial derivative. x j , x i Represents spatial coordinate variables, used to describe spatial location. and They represent the solvable scales, respectively. u i The velocity components in three-dimensional space i , j =1,2,3, corresponding to different directions (e.g. x , y , z (direction) speed, For time variables, For solvable scale velocity components Partial derivative with respect to time t, For solvable scale velocity components Spatial coordinates The partial derivatives, For the density of the fluid, For pressure at a solvable scale For the pressure gradient term in x i Components in direction, The kinematic viscosity coefficient of the fluid. The term represents the diffusion of solvable-scale velocities due to the viscosity of the fluid. The divergence of subgrid stress reflects the influence of spatial variations in subgrid stress on the solvable scale velocity field. For terrain forces in x i The directional component is used to account for the influence of topography on atmospheric boundary layer turbulence;

[0136] For subgrid stress, calculated using the Smagorinsky model:

[0137]

[0138] in, The filtered value is the product of the velocity components. The product of the solvable scale velocity components. For the trace of sublattice stress, The symbol for Kronecker. The trace of the solvable scale strain rate tensor. is the eddy viscosity coefficient at the subgrid scale, used to quantify the momentum exchange between subgrid-scale turbulence and solvable-scale turbulence. The modulus of the solvable scale strain rate tensor. Δ is the Smagorinsky constant (default 0.18), and Δ is the grid filtering scale. It is a solvable scaled strain rate tensor.

[0139] Introducing terrain-induced lift source term F terrain,i The LES governing equations are modified in the following way:

[0140] Based on the Digital Elevation Model (DEM), the terrain height is... h ( x , y The data is mapped to LES grid nodes, and bilinear interpolation is used to ensure spatial continuity.

[0141] Combined with terrain slope h and wind speed u i The lift source term is represented as:

[0142]

[0143] in, For the density of the fluid, The magnitude of the wind speed is a solvable scale, i.e., the modulus of the wind speed vector. For terrain height, Terrain height Spatial coordinates The partial derivatives, For wind speed at j The directional component and the terrain j The slope in a given direction reflects the contribution of the interaction between wind speed and terrain slope to lift generation. C L n is the lift coefficient (default 0.05). i This is the unit vector for terrain normal.

[0144] Near the terrain surface ( z ≤0.1 H , H (For boundary layer height), the wind speed profile is adjusted using a logarithmic law:

[0145]

[0146] in, For friction speed, To indicate at altitude z The first i One direction (usually in three-dimensional space), i The adjusted average wind speed components can be taken as 1, 2, and 3 (corresponding to the three orthogonal directions respectively). κ is the von Kármán constant (0.4). z 0 represents surface roughness. The height above the ground. The boundary layer height, Indicates the first i A unit vector in each direction is used to determine the direction of the wind speed component.

[0147] like Figure 4 As shown, the Ensemble Kalman Filter (EnKF) algorithm is used to process real-time observation data. D obs ( t ) and LES simulation results D LES ( t Dynamic fusion, updating the initial field X init :

[0148] From the initial field X b generate N e Disturbance samples The disturbance amplitude is based on the background error covariance. B .

[0149] Run the LES model on each sample to obtain the prediction set. .

[0150] Calculate the observation increment And update the sample:

[0151]

[0152] in, For the observation operator, For observation operator transpose, For the prediction set, For at any time t The mean of the predicted state vector, For at any time t The updated version i One sample, The product of the observation operator and the covariance matrix of the prediction set and its transpose. To predict the product of the set covariance matrix and the transpose of the observation operator, K ( t ) represents the Kalman gain. To predict the covariance of the set, R For observation error covariance, This is random observation noise.

[0153] Based on the assimilated initial field X init Running the LES model generates a gridded meteorological field M covering the airport airspace. x , y , z , t ),include:

[0154] Wind field: Three-dimensional wind speed vector It describes the spatial location ( ) and time Wind speed conditions above, These are the three-dimensional wind speed vectors in Velocity components in three directions; Temperature field: Potential temperature θ ( x , y , z , t This reflects the spatial location ( ) and time Potential temperature of upper air; Humidity field: specific humidity q ( x , y, z , t ), used to describe spatial location ( ) and time The humidity level of the upper air.

[0155] The data is stored in NetCDF format, supports dynamic retrieval by time, altitude, and geographic range, and generates interactive 3D visualization products through volume rendering technology.

[0156] In this embodiment, as Figure 5 As shown, considering the complex geometric features of the building complex and terrain boundary, a hybrid wall function (HWF) model is used to accurately characterize the atmospheric boundary layer properties. Its mathematical expression is as follows:

[0157]

[0158] in: For dimensionless velocity, For dimensionless distance, The kinematic viscosity of the fluid. This is the actual distance from the wall. It is the natural logarithm function. Pi It is the hyperbolic tangent function. κ =0.4 is the von Kármán constant. E =9.8 is an empirical constant. Δ is the dimensionless distance (default value 11.6) corresponding to the critical point of the transition region. y + =5 represents the width of the transition zone; This represents the dimensionless distance corresponding to the rough element height. C rough This is a roughness correction factor (0.5 for building surfaces, 0.2 for vegetation). n The roughness element shape index is 1.0 for cubes and 0.7 for cylinders.

[0159] Wind field data U obtained from radar inversion radar ( x , y , z , t Downscaling data U provided by the Global Forecasting System (GFS / ECMWF) GFS ( x , y , z , t The inlet boundary condition U is generated through a dynamic weight fusion algorithm. in ( x ,y , z , t ):

[0160] The weight allocation is represented as follows:

[0161]

[0162] in, The weighting function for radar data during the fusion process. The weighting function for global forecast system data during the fusion process. α =0.1m 1 The attenuation coefficient is used to control the rate of change of the weighting function. Represents an exponential function. z radar =2000m is the effective detection altitude of the radar.

[0163] The fusion result of radar inversion wind field data and downscaled data from the Global Forecasting System is represented as follows:

[0164]

[0165] Adjust the boundary layer:

[0166] In the near-surface layer ( z (≤100m), the wind speed profile was corrected using the Monin-Obukhov similarity theory:

[0167]

[0168] in, In order to be at a high altitude z The corrected wind speed vector. e is the height above the ground. x Indicates the direction of wind speed along x Axial direction, z 0 represents the surface roughness (0.01 m for airport runways and 1.0 m for building complexes). ψ m For stability function, L It is the Obukhov length.

[0169] Using snappyHexMesh technology, GIS terrain data (GeoTIFF format) and airport building BIM model (converted to STL format) are discretized into sub-meter precision computational grids. The specific process includes:

[0170] Background mesh generation: Generate a hexahedral background mesh within the computational domain Ω, with a minimum resolution Δ. x min=50m, maximum resolution Δ xmax =1000m.

[0171] Geometric feature extraction:

[0172] Terrain surface: DEM contour lines are extracted using the Marching Cubes algorithm to generate a triangulated surface mesh;

[0173] Building surfaces: Directly load the BIM model in STL format, preserving all detailed features (such as windows and eaves).

[0174] Prism layers are generated on building and terrain surfaces, with a total thickness of [missing information]. H bl =10m, number of floors N bl =10, growth rate r =1.2, to ensure y +<1 satisfies the LES wall resolution requirement.

[0175] Based on the above grid and boundary conditions, the LES model is run to generate a three-dimensional meteorological field M covering the airport airspace. x , y , z , t ),include:

[0176] Wind field: Three-dimensional wind speed U ( x , y , z , t )=( u , v , w ), resolution Δ x =Δ y =0.5m, Δ z min = 0.1m (near the ground);

[0177] Turbulent characteristics: turbulent kinetic energy k ( x , y , z , t and dissipation rate P ( x , y , z , t );

[0178] Thermal parameters: potential temperature θ ( x , y , z , t ) and specific moisture q (x , y , z , t ).

[0179] The data is stored in VTK format and supports dynamic visualization of wind field vectors and turbulence isosurfaces through tools such as ParaView.

[0180] In this embodiment, Large Eddy Simulation (LES) is used to analyze the multi-scale motion characteristics of turbulence, capturing turbulent structures at different scales in the atmosphere. The hybrid wall function model is used to accurately characterize the atmospheric boundary layer properties. Combining the two allows for a more accurate description of the complex turbulent motion within the atmospheric boundary layer. For example, in airport airspace, near-surface turbulence has a significant impact on flight safety. LES can simulate large-scale turbulent eddies, while hybrid wall functions can accurately characterize the turbulent characteristics near the walls, thus achieving a precise simulation of the turbulent motion of the entire atmospheric boundary layer.

[0181] Therefore, Large Eddy Simulation (LES) turbulence analysis provides a fundamental method for simulating turbulent motion; the introduction of a terrain-induced lift source term considers the influence of terrain on airflow; the mixed wall function model accurately characterizes the atmospheric boundary layer properties, providing accurate boundary conditions for LES simulation and terrain-influence simulation; and multi-source data dynamic assimilation utilizes real-time detection data and multi-source forecast data, combined with the preceding simulation results, to generate a more accurate, high-resolution, three-dimensional gridded meteorological field. Through the synergistic effect of these four steps, the system can provide more reliable meteorological services for low-altitude flight activities at general aviation airports, ensuring flight safety.

[0182] In this embodiment, as Figure 6 As shown, the model database construction and parameter standardization include:

[0183] Model Classification and Parameter Collection:

[0184] Establish a database covering diverse aircraft types, including drones, light aircraft, and helicopters, containing the following core parameters:

[0185] Geometric parameters: wingspan b (m), Captain L (m) fuselage height h (m), rotor diameter D (m, for helicopter use only);

[0186] Performance parameters: Stall speed V stall (m / s), maximum cruising speed V cruise (m / s), maximum rate of ascent (m / s), minimum turning radius Rmin (m);

[0187] The meteorological tolerance parameter system for multi-element aircraft includes the following meteorological sensitive parameters:

[0188] Wind field tolerance: maximum crosswind speed V cross_max (m / s), maximum tailwind / headwind speed V head_max (m / s), maximum gust intensity V_{gust_max} (m / s);

[0189] Turbulence tolerance: Maximum turbulence intensity (TI_{max} (k is calculated as: TI = \sqrt{\frac{2k}{U^2}} \times 100%));

[0190] Visibility tolerance: Minimum takeoff / landing visibility Vis min (m) Minimum cruising visibility Vis cruise_min (m);

[0191] Rainfall tolerance: maximum rainfall intensity R max (mm / h), maximum hail diameter d hail_max (mm);

[0192] Temperature tolerance: Minimum start-up temperature T min (°C), Maximum operating temperature T max (°C);

[0193] Barometric pressure tolerance: Minimum takeoff pressure P min (hPa), maximum operating pressure P max (hPa).

[0194] Dimensionless processing was performed on the parameters of each aircraft model to generate meteorological safety assessment benchmark values. S base :

[0195]

[0196] in, i It is an index variable used to iterate from 1 to n All meteorological sensitive parameters, n This indicates the total number of meteorological sensitive parameters. p i For the first i Meteorological sensitive parameters (such as)V cross_max ), p imax This is the maximum parameter value for similar models. w i Weighting coefficients (determined through the analytic hierarchy process, e.g., crosswind weights) w cross =0.3, turbulence weight w TI =0.2).

[0197] The following real-time data were extracted from the generated high-resolution gridded meteorological field:

[0198] Wind field: Three-dimensional wind speed U ( x , y , z , t )=( u , v , w ), gust factor Turbulence: Turbulence intensity TI ( x , y , z , t ), turbulent integral scale L turb (m); Visibility: based on atmospheric extinction coefficient β calculate: Vis = β 3.912 (m); Precipitation: Precipitation type (rain / snow / hail) and intensity R ( x , y , z , t (mm / h); Temperature: Air temperature T ( x , y , z , t (°C), dew point temperature T d ( x , y , z , t (°C); Air pressure: Sea level air pressure P sfc ( x , y , t (hPa).

[0199] For the real-time position of the aircraft ( x p , y p ,z p Meteorological parameters were obtained using inverse distance-weighted interpolation (IDW).

[0200]

[0201] in d i For the first i The distance from each grid point to the aircraft N The number of nearest neighbor grid points (take) N =8).

[0202] For each meteorological parameter p Define three levels of early warning thresholds:

[0203] Safety threshold p safe The aircraft can operate safely for a long time (e.g.) V cross ≤0.3 V stall );

[0204] Attention threshold p warn Close monitoring is required, as it may affect flight performance (e.g., 0.3). Vstall < V cross ≤0.5 V stall );

[0205] No-fly threshold p prohibit : Immediately terminate the flight (e.g.) V cross >0.5 V stall ).

[0206] Calculate the real-time meteorological safety index by combining multiple parameter weights. S meteo :

[0207]

[0208] Where min is the function for finding the minimum value. p warn_i For the first i The attention threshold of the item parameter m The number of parameters participating in the evaluation (take) m =8). Three-color warning level: Safe (green): S meteo ≤0.5; Attention (yellow): 0.5< S meteo ≤0.8; No-fly zone (red):S meteo >0.8.

[0209] In this embodiment, the weather safety index is updated every 10 seconds for all aircraft within the airspace. S meteo The warning is then pushed to the airborne terminal via ADS-B or 5G link, displaying the current warning level and exceeding parameters (e.g., "Crosswind exceeding limit: 12 m / s > 10 m / s"). The airspace is dynamically delineated using three colors.

[0210] Safe Zone (Green): All meteorological parameters meet the safety thresholds, allowing free flight;

[0211] Concern Zone (Yellow): At least one parameter has reached the concern threshold, and the flight altitude or heading needs to be adjusted to avoid risks;

[0212] No-fly zone (red): If at least one parameter reaches the no-fly threshold, the pilot must return to base or make an emergency landing. The airspace designation results are overlaid on the electronic flight bag (EFB) as a GIS layer, allowing pilots to adjust flight routes in real time.

[0213] In this embodiment, a database linking meteorological parameters with flight accidents / abnormal events is established, and high-risk parameter combinations are identified using a logistic regression model:

[0214]

[0215] in, Y =1 indicates that a security incident has occurred. β i For parameter coefficients, The independent variable represents various meteorological parameters or other factors that may affect flight safety. This refers to the number of independent variables, i.e., the total number of meteorological parameters or other influencing factors participating in the logistic regression model. The intercept term of the logistic regression model is a constant, representing the threshold value when all independent variables are equal. p i When both are 0, it is the logarithm of the ratio of the probability of the event occurring to the probability of it not occurring. P ( Y =1): Indicates that a security incident has occurred. Y The probability of (=1).

[0216] Dynamically adjust thresholds: Based on historical data, update warning thresholds quarterly.

[0217]

[0218] in α =0.2 is the correction factor. The original warning threshold, Δp j For the first j The parameter exceeded the limit in this event. This represents the total number of historical events, that is, the number of relevant events recorded within a past period. This is the average value of parameter exceeding the limit in historical events.

[0219] In this embodiment, a unified meteorological safety data exchange format (MSDF) is defined, which includes the following fields: aircraft ID, location (longitude / latitude / altitude), timestamp, meteorological parameters (wind speed / visibility / precipitation, etc.), and warning level (safe / concern / no-fly zone).

[0220] The collected meteorological data (wind, turbulence, visibility, etc.) are normalized to generate the Comprehensive Meteorological Impact Index (CMII), ranging from 0 to 100. The index is divided into three intervals: 0-50 is safe, 51-75 is of concern, and 76-100 is no-fly zone, corresponding to three-color warning levels.

[0221] Based on the CMII index and aircraft weather tolerance parameters, a structured report is automatically generated, including:

[0222] Description of the impact of current weather conditions on flight performance (e.g., "Crosswind exceeds limits, course adjustment recommended");

[0223] Weather forecast for the next 2 hours (e.g., "Visibility will continue to decrease to 3km");

[0224] Recommended operating procedures (e.g., "climb to 1500 meters to avoid the low-altitude turbulence zone").

[0225] When overlaying a weather warning layer onto an electronic map (such as OpenStreetMap), the following visualization rules apply:

[0226] Safe zone (green): 80% transparency, displays the "Safe" label;

[0227] Attention area (yellow): Semi-transparent wavy texture with flashing edges;

[0228] No-fly zone (red): solid fill, with a diagonal no-entry symbol superimposed.

[0229] Based on data transmitted back from the airborne equipment, the aircraft's position and status are dynamically marked on the map:

[0230] Green icon: In the safe zone;

[0231] Yellow icon: In the watch zone, the current out-of-limit parameters are displayed on the edge of the icon (such as "wind speed 12m / s").

[0232] Red icon: The area is in a no-fly zone. The icon flashes and triggers an audible and visual alarm.

[0233] It integrates auxiliary analysis tools such as bar charts (showing the duration of each warning level), radar charts (comparing the impact of different meteorological parameters), and heat maps (marking high-risk areas).

[0234] Edge computing devices are deployed in airport control towers or near-Earth orbit satellites to achieve localized data processing and reduce cloud transmission latency to less than 50ms. Containerization technology (Docker) is used to encapsulate various functional modules, supporting one-click deployment and elastic scaling. A "one-click report generation" function is provided, automatically exporting a PDF document containing warning screenshots, data tables, and operation suggestions. Voice command interaction is supported (such as "show changes to no-fly zones in the next hour"), with a response time of less than 1 second.

[0235] A feedback entry point is embedded in the visual interface to collect pilots' and controllers' evaluations of the accuracy of the warnings (1-5 star rating). For warning cases with three consecutive ratings below 3 stars, the algorithm review process is automatically triggered. The matching degree between actual flight data and warning records for each aircraft type is statistically analyzed monthly. If the false alarm rate exceeds 5%, the warning thresholds for the corresponding meteorological parameters are adjusted. When a new aircraft type is connected, an initial threshold library is quickly generated through simulated flight tests (such as CFD airflow simulation).

[0236] In summary, this invention deploys various types of intelligent meteorological terminal equipment, such as 3D laser wind radar, vertical laser wind radar, micro-rain radar, visibility meters, small weather stations, and all-sky imagers, at key locations in and around airports, constructing a three-dimensional gridded monitoring network. This network can collect meteorological data in real time, enabling three-dimensional scanning and dynamic perception of the airspace meteorological environment. It overcomes the shortcomings of traditional single-point meteorological observation equipment in vertical detection capabilities, significantly improving the comprehensiveness of meteorological monitoring. For example, it can more accurately monitor small-scale weather phenomena such as low-level wind shear and micro-downbursts, providing more reliable data support for flight safety.

[0237] Furthermore, the data fusion system of this invention assimilates the collected real-time detection data with multi-source forecast data and combines it with the terrain and building parameters near the general aviation airport to simulate and calculate the atmospheric state. Large eddy simulation (LES) technology is used to analyze the multi-scale motion characteristics of turbulence. A terrain-induced lift source term is introduced through a terrain dynamic coupling module. Multi-source data assimilation technology is integrated to achieve dynamic fusion of meteorological observation data and numerical forecast data. Simultaneously, using unstructured mesh generation technology (snappyHexMesh), the Geographic Information System (GIS) terrain data and the airport building BIM model (converted to STL format) are discretized into a sub-meter precision body-fitted computational mesh. Finally, a high-resolution three-dimensional meshed meteorological field covering the airport airspace is generated, which can more accurately reflect the meteorological conditions of the airport airspace and provide more detailed meteorological information for flight safety.

[0238] Furthermore, the data fusion system of this invention, through assimilation processing of real-time detection data and multi-source forecast data, as well as simulation calculations of atmospheric conditions, can generate more accurate forecasts of future weather conditions. By incorporating topographical and building parameters near general aviation airports, and considering the unique characteristics of local meteorological environments, the system improves the relevance of forecasts. For example, it can provide early warnings for potential localized severe convective weather around airports, offering ample time for flight plan adjustments and reducing the occurrence of accidents such as aircraft loss of control and emergency mission interruptions due to weather conditions.

[0239] Furthermore, the data acquisition and processing of this invention's system are performed in real time, enabling dynamic updates of meteorological information. In the highly dynamic operating environment of dense takeoffs and landings and long-distance flights of low-altitude aircraft, timely updates of meteorological information are crucial for flight safety. The dynamic safety assessment module can acquire the latest meteorological data in real time and, in conjunction with the aircraft model database, assess the impact of current and future airspace weather conditions on the flight safety of various aircraft models, providing timely decision-making support for flight operators.

[0240] Furthermore, the dynamic safety assessment module of this invention, combined with an aircraft model database, constructs a multi-dimensional meteorological tolerance parameter system for aircraft. Through simulation analysis and comprehensive evaluation of the flight performance of different aircraft models under various meteorological conditions, it defines the impact thresholds of various aircraft models on meteorologically sensitive conditions. For example, for different types of aircraft such as UAVs, light aircraft, and helicopters, its safe flight range under different meteorological conditions such as wind speed, visibility, and precipitation is determined, making flight safety assessments more scientific and accurate.

[0241] Furthermore, the system of this invention can assess in real time the impact of current and future airspace weather conditions on the flight safety of various aircraft types, providing timely risk warnings to flight operators. When weather conditions approach or exceed the impact threshold of a certain aircraft type, the system will issue an early warning, reminding operators to take appropriate measures, such as adjusting flight altitude, changing flight route, or pausing flight, thereby effectively reducing the risk of flight accidents.

[0242] Furthermore, the management system establishment module of this invention establishes a lightweight and standardized flight management system by outputting meteorological data affecting flight safety in a result-oriented manner and defining multi-color warning thresholds. For example, the "Safety-Attention-No-Fly" three-color warning dashboard visually displays the degree of impact of current weather conditions on flight in the form of charts, enabling flight operators and managers to quickly and accurately understand the impact of weather conditions on flight safety, simplifying management processes and improving management efficiency.

[0243] Furthermore, this invention allows for the development of mobile applications that provide real-time, customized weather briefings. These briefings include future trends in key meteorological elements, aircraft compatibility recommendations, and emergency response plans. Flight operators can use these briefings to prepare for flights in advance and quickly implement emergency response plans in the event of sudden weather events, thus improving their ability to cope with weather risks.

[0244] Furthermore, this invention establishes standardized decision-making process documents, clearly defining response mechanisms under different meteorological warning levels, ensuring the standardization and traceability of operational processes. In the event of meteorological-related accidents or anomalies, responsibility can be determined and experience summarized by reviewing the decision-making process documents and relevant meteorological data, providing a reference for subsequent flight safety management.

[0245] Therefore, the intelligent low-altitude meteorological management system for general aviation airports based on multi-source meteorological data fusion of the present invention effectively improves the capability and level of low-altitude meteorological services for general aviation airports through multi-faceted innovation and technology integration, providing strong protection for low-altitude flight safety, and has significant economic and social benefits.

[0246] The technical features of the above embodiments can be combined in any way. For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0247] The above embodiments are merely preferred embodiments of the present invention and should not be construed as limiting the scope of protection of the present invention. Any non-substantial changes and substitutions made by those skilled in the art based on the present invention shall fall within the scope of protection claimed by the present invention.

Claims

1. An intelligent management system for low-altitude weather of navigable airports based on multi-source weather data fusion, characterized in that, It comprises: A three-dimensional gridding monitoring network, which deploys various types of intelligent meteorological terminal equipment in the monitoring area of the airport, realizes three-dimensional scanning and dynamic perception of the airspace meteorological environment by real-time collection of meteorological detection data; A data fusion system for assimilating the collected real-time detection data with multi-source prediction data, and combining the terrain and building parameters near the navigable airport to simulate and calculate the atmospheric state, generate a high-resolution three-dimensional gridding meteorological field covering the airspace range of the airport, and realize the prediction and warning of future meteorological conditions of the airport; A dynamic safety research and judgment module: combined with the aircraft model database, a multi-element aircraft meteorological tolerance parameter system is constructed, the influence threshold of various aircraft models on meteorological sensitive conditions is determined, and the influence of the current and future meteorological conditions of the airspace on the flight safety of various aircraft models is judged in real time; A management system establishment module: a flight management system is established, the meteorological data affecting flight safety are output, multi-color warning thresholds are determined, and the influence of the current meteorological conditions on flight is visually displayed in the form of charts and tables; The simulation calculation uses large eddy simulation LES technology to analyze the multi-scale motion characteristics of turbulence, introduces the terrain-induced lift source term in the LES control equation through the terrain dynamic coupling module, and integrates multi-source data assimilation technology to realize the dynamic fusion of meteorological observation data and numerical prediction data; Among them, the hybrid wall function model is used to accurately characterize the atmospheric boundary layer characteristics, and the inlet boundary condition is reconstructed based on the fusion of radar inversion wind field and downscaling data provided by the global prediction system; Unstructured grid generation technology is used to discretize the terrain data of geographic information system and the BIM model of airport buildings into sub-millimeter precision body calculation grid, and finally a high-resolution three-dimensional gridding meteorological field covering the airspace of the airport is generated.

2. The system of claim 1, wherein, The construction of the three-dimensional gridding monitoring network comprises: In the monitoring area of the airport runway, the apron, the terminal building and the airspace, various types of intelligent meteorological terminal equipment are deployed according to the effective detection range and the overlapping principle, including at least 3D laser wind radar, vertical laser wind radar, micro rain radar, visibility meter, small weather station and all-sky imager; Among them, vertical laser wind radars are deployed at different positions on the ground to form a three-dimensional wind field monitoring network; Taking the deployed equipment as nodes, a three-dimensional gridding monitoring network covering the airspace of the airport is constructed, and a continuous three-dimensional gridding meteorological field is generated by using Kriging interpolation or inverse distance weighting method combined with equipment layout and terrain elevation data; The beam scanning function of the 3D laser wind radar is used to realize continuous monitoring of the 360° horizontal wind field; The wind speed and direction profile at vertical height is obtained by pulse emission and echo reception of the vertical laser wind radar; Combined with the cloud data of the all-sky imager and the precipitation particle data of the micro rain radar, a cloud-precipitation collaborative distribution map is generated and superimposed into the three-dimensional gridding meteorological field.

3. The system of claim 2, wherein: In the construction of three-dimensional grid monitoring network, various types of intelligent meteorological terminal devices are deployed as spatial nodes, each deployed device is uniquely identified and located, and its coordinates (x, y, z) in three-dimensional space are determined. These devices serve as nodes of the three-dimensional grid monitoring network and construct a three-dimensional grid monitoring network covering the airport airspace; According to the actual range of the airport airspace and the meteorological monitoring requirements, the spatial range of the three-dimensional grid monitoring is determined, including the coverage area in the horizontal direction and the height range in the vertical direction; The division rules of the three-dimensional grid are formulated, the resolution of the grid in the horizontal and vertical directions is determined, and the entire monitoring space is divided into discrete three-dimensional grid units according to the division rules, each grid unit has a unique identification; Each intelligent meteorological terminal device collects meteorological detection data in real time according to the set data update frequency and transmits the data to the data processing center; in the data processing center, the collected raw meteorological data is preprocessed; Based on the Kriging interpolation method or inverse distance weighting method, a three-dimensional grid meteorological field is generated.

4. The system of claim 1, wherein: In the data fusion system, the large eddy simulation LES technology is used to analyze the atmospheric boundary layer turbulence in multiple scales, and the turbulence movement is divided into resolvable scale u i and sub-grid scale, and the control equation is: wherein denotes the partial derivative symbol, x j , x i denotes a spatial coordinate variable for describing a spatial position, and denote the resolvable scale u i velocity components, is a time variable, is the resolvable scale velocity component with respect to time t, is the resolvable scale velocity component with respect to the spatial coordinate , is the density of the fluid, is the resolvable scale pressure, is the component of the pressure gradient term in x i direction, is the kinematic viscosity coefficient of the fluid, is the viscous term, which represents the diffusion of the resolvable scale velocity due to the viscous action of the fluid, is the divergence of the subgrid stress, which reflects the influence of the spatial variation of the subgrid stress on the resolvable scale velocity field, is the component of the terrain force in x i direction, which is used to consider the influence of the terrain on the atmospheric boundary layer turbulence; For subgrid stresses, the Smagorinsky model is used: wherein, is a filtered value of the velocity component product, is a resolvable scale velocity component product, is a trace of the subgrid stress, is a Kronecker symbol, is a trace of the resolvable scale strain rate tensor, is a subgrid scale eddy viscosity coefficient, quantifying the momentum exchange between the subgrid scale turbulence and the resolvable scale turbulence, is a norm of the resolvable scale strain rate tensor, is a Smagorinsky constant, and Δ is a grid filter scale, is a resolvable scale strain rate tensor.

5. The system of claim 4, wherein: Introducing terrain-induced lift source terms F terrain,i The LES control equations are modified by The terrain height h is mapped to the LES grid nodes based on a digital elevation model DEM, ensuring spatial continuity with a bilinear interpolation. h x y ​​​ Combining terrain slope h and wind speed u i The lift source term is represented as: wherein, is the density of the fluid, is the wind speed magnitude in the resolvable scale, i.e. the modulus of the wind speed vector, is the terrain height, is the terrain height is the partial derivative of the spatial coordinate with respect to the terrain height, is the component of the wind speed in the direction j and the slope of the terrain in the direction j reflects the contribution of the interaction between the wind speed and the terrain slope to the lift, C L is the lift coefficient, n i is the terrain normal unit vector; Near the terrain surface, a logarithmic law is used to adjust the wind speed profile: in, For friction speed, To indicate at altitude z The first i The adjusted average wind speed components in each direction.

6. The system of claim 5, wherein: For von Kármán's constant, z 0 represents surface roughness. h' is the height above the ground, and h' is the boundary layer height. Indicates the first i A unit vector in each direction is used to determine the direction of the wind speed component.

7. The system of claim 5, wherein: The ensemble Kalman filter algorithm is used to dynamically fuse real-time observation data D obs ( t ) with LES simulation results D LES ( t ) to update the initial field X init : from the initial field X init generating N e one perturbation sample , the perturbation amplitude being based on the background error covariance B ; Running LES model for each sample, resulting in a set of predictions ; Computing the observation increment and updating the sample: in, For the observation operator, For observation operator transpose, For the prediction set, For at any time t The mean of the predicted state vector, For at any time t The updated version i One sample, The product of the observation operator and the covariance matrix of the prediction set and its transpose. To predict the product of the set covariance matrix and the transpose of the observation operator, K ( t ) represents the Kalman gain. To predict the covariance of the set, R For observation error covariance, This is random observation noise; based on the assimilated initial field X init , running a LES model to generate a high-resolution three-dimensional gridded meteorological field M( x , y , z , t ) covering the airspace of the airport, comprising: Wind field: three-dimensional wind velocity vector , describes the wind speed situation at spatial position ( ) and time , , respectively, is the velocity component of the three-dimensional wind velocity vector in three directions; Temperature field: potential temperature For complex geometric features of building groups and terrain boundaries, a hybrid wall function model is used to accurately represent the atmospheric boundary layer characteristics, and its mathematical expression is: ( x , y , z , t ), reflects the potential temperature value of the air at spatial position ( ) and time ; Humidity field: specific humidity q ( x , y , z , t ), used to describe the humidity condition of the air at spatial position ( ) and time . The weight distribution is represented as: The fusion result of radar inversion wind field data and global forecast system downscaling data is represented as: where: is the dimensionless velocity, y + is the dimensionless distance, y is the actual distance from the wall, is the natural logarithm function, is the circle constant, is the hyperbolic tangent function, Adjust the boundary layer: is the von Karman constant, E is the empirical constant, is the dimensionless distance corresponding to the critical point of the transition region, Δ y + is the transition region width; is the dimensionless distance corresponding to the roughness element height, C rough is the roughness correction factor, n is the roughness element shape exponent; Wind field data U based on radar inversion radar ( x , y , z , t ) and downscaling data U provided by global forecast system GFS ( x , y , z , t ) to generate the entrance boundary conditions U in ( x , y , z , t ) through a dynamic weight fusion algorithm In the near-surface layer, the Monin-Obukhov similarity theory is used to correct the wind speed profile: wherein, is a weight function for radar data in the fusion process, is a weight function for global forecast system data in the fusion process, α is a decay coefficient for controlling the rate of change of the weight function, represents an exponential function, z radar is a radar effective detection height; 8. The system of any one of claims 1 to 7, wherein: The establishment of the aircraft type database includes the following core parameters: The meteorological tolerance parameter system of multi-element aircrafts includes the following meteorological sensitive parameters: wherein is the height above ground z is the wind speed vector corrected at height is the height above ground, e x denotes the direction of the wind speed along x the axis direction, z 0 is the surface roughness, Turbulence tolerance: maximum turbulence intensity; m is the stability function, L is the Obukhov length; The terrain data of geographic information system and the BIM model of airport buildings are discretized into sub-meter precision computational mesh by snappyHexMesh technology, and the high-resolution three-dimensional meshed meteorological field M( x , y , z , t ) covering the airport airspace is generated by running LES model. Vis Vis Geometric parameters: wingspan b , fuselage length L 1, fuselage height h 1, rotor diameter D ; Performance parameter: stall speed V stall ; maximum cruising speed V cruise ; maximum rate of climb ; minimum turn radius R min ; 9. The system of claim 8, wherein: Wind field tolerance: maximum crosswind speed V cross_max , maximum head / tail wind speed V head_max , maximum gust intensity; Through the generated high-resolution three-dimensional grid meteorological field, the following real-time data is extracted: Visibility tolerance: minimum takeoff / landing visibility TI min , minimum cruise visibility Air pressure cruise_min ; Rainfall tolerance: maximum rainfall intensity R max , maximum hailstone diameter d hail_max ; Temperature tolerance: minimum start-up temperature T min , maximum operating temperature T max ; Pressure tolerance: minimum takeoff pressure P min , maximum operating pressure P max ; The weather sensitive parameters of each model are dimensionless processed to generate weather safety evaluation benchmark values S base : wherein, i is an index variable for traversing all weather sensitive parameters from 1 to n 1, n 1 represents the total number of weather sensitive parameters, p i is the weather sensitive parameter of the i th item, p imax is the maximum value of the same type of parameter, w i is the weight coefficient. ​ ​ Wind field: three-dimensional wind speed U x , y , z , t )=( u , v , w ), gust factor ; Turbulence: Turbulence intensity ​ x y z t Turbulence integral scale L turb Visibility: Calculated based on atmospheric extinction coefficient β Precipitation: Precipitation type and intensity R x y z t Temperature: Air temperature T x y z t Dew point temperature T d x y z t ;​​​​​​​​​​​​​​​​ ​ ： Sea level pressure P sfc ( x , y , t ) For the real-time position of the aircraft x p , y p , z p , the weather parameters are obtained by inverse distance weighted interpolation. wherein d i is the distance from the i N is the number of nearest neighbor grid points;​ For each meteorological parameter p a three-level warning threshold is defined: Safety threshold p safe : the aircraft can be operated safely for a long time; Attention threshold p warn : Close monitoring required, impact on flight performance; No-fly threshold p prohibit : immediately terminate flight; Combining multi-parameter weight, calculate real-time weather safety index S meteo : wherein min is a minimum function, p warni is the first i attention threshold for the m is the number of parameters involved in the evaluation.

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