Civil aircraft safety sensing method and system based on virtual flight

Abstract

The invention provides a civil aircraft safety perception method and system based on virtual flight, and relates to the technical field of aircraft safety perception. A dynamic and hierarchical three-dimensional wind shear field model is constructed through space-time alignment and fusion of multi-source meteorological data; deep coupling of wind disturbance and aircraft dynamic response is realized based on a small disturbance theory and a flight dynamics model; continuous and dynamic quantification of risks is realized by combining and analyzing flight parameters and a safety threshold value dynamically adjusted based on wind disturbance intensity and utilizing multi-parameter risk level weighted integration; according to the method, the risk coefficient is coupled with the three-dimensional terrain and the virtual flight trajectory, hierarchical color mapping is adopted to carry out visual space visualization, and a complete closed loop from environment modeling to risk early warning is formed, so that the identification accuracy and timeliness of the flight risk in the wind shear environment are greatly improved, and the scientificity and practicability of security situation awareness are enhanced.

Description

Technical Field

[0001] This invention relates to the field of aircraft safety perception technology, specifically to a method and system for civil aircraft safety perception based on virtual flight. Background Technology

[0002] With the increasing complexity of civil aviation flight missions and the growing uncertainty of flight environments, especially against the backdrop of frequent extreme weather conditions such as low-level wind shear and microbursts, traditional flight safety monitoring methods face numerous challenges. Existing technologies largely rely on static or empirical safety threshold determinations, lacking effective fusion of multi-source dynamic meteorological data. This makes it difficult to accurately reflect the impact of multi-scale, multi-temporal variations in wind shear environments on the dynamic response of aircraft, resulting in insufficient accuracy in identifying wind disturbance risks.

[0003] In the prior art, CN114417507A discloses a method, simulation method, and apparatus for constructing a predictive wind shear simulation scenario. The method includes: determining the current flight phase of the aircraft based on real-time calculated flight data, and obtaining the airport and runway information corresponding to the current training; determining the wind shear trigger position based on the flight phase and airport and runway information; and locating a pre-established wind shear field model at the wind shear trigger position to construct a predictive wind shear simulation scenario. Although this scheme can simulate wind shear, it does not consider the coupling between wind disturbance and flight dynamics and dynamic risk assessment, making it difficult to capture the detailed dynamic changes of the aircraft under the action of complex wind fields. Furthermore, it lacks sufficient risk quantification and spatial visualization, resulting in limited simulation accuracy and safety situation awareness capabilities.

[0004] The information disclosed in the background section is only intended to enhance the understanding of the background of this disclosure, and therefore may include information that does not constitute prior art known to those skilled in the art. Summary of the Invention

[0005] The purpose of this invention is to provide a method and system for safety perception of civil aircraft based on virtual flight, so as to solve the problems mentioned in the background art.

[0006] To achieve the above objectives, the present invention provides the following technical solution: The method for safety perception of civil aircraft based on virtual flight includes the following steps: S1: Collect multi-source meteorological data within the aircraft's flight area, construct a three-dimensional wind shear field model using interpolation algorithms, and obtain the wind speed disturbance experienced by the aircraft in different flight areas based on the wind shear field model. S2: Based on the small perturbation assumption, construct the small perturbation kinematic equations of the aircraft and couple the wind shear field model with the small perturbation kinematic equations of the aircraft to simulate the dynamic response of the aircraft under wind shear. S3: Based on the aircraft's dynamic response, acquire flight parameters in virtual flight, compare the flight parameters with the preset safety threshold range, obtain the risk coefficient corresponding to time and space, and dynamically adjust the safety threshold range based on wind speed disturbance; S4: Couple the aircraft's virtual flight trajectory with the 3D terrain data of the flight area, and visualize the risk coefficient of all trajectory points on the virtual flight trajectory.

[0007] Preferably, the multi-source meteorological data includes, but is not limited to, CIRA-86 model data, NOAA data, and weather forecast data. The logic for constructing a wind shear field model based on multi-source meteorological data is as follows: Spatiotemporal alignment of multi-source meteorological data is performed, and three-dimensional wind field data is generated using a three-dimensional interpolation algorithm; The average wind speed distribution is generated based on the three-dimensional wind field data as the background wind. Then, the three-dimensional wind field data is decomposed into background wind and several layers of wind disturbance components through a spatiotemporal filtering method. A wind shear field model is constructed by weighting several sets of wind disturbance components, and its functional expression is as follows: ; In the formula , , They represent in At this moment, spatial coordinates Wind speed disturbance, wind disturbance component, and corresponding weight of wind disturbance component at the location, subscript The hierarchical index representing the wind disturbance component. This indicates the total number of levels for the wind disturbance component.

[0008] Preferably, the logic for coupling the wind shear field model with the aircraft's small perturbation kinematic equations is as follows: Based on the small perturbation assumption, the differential equations of the aircraft's flight motion are linearized and decoupled into a set of small perturbation kinematic equations in the longitudinal and lateral directions. The wind shear field model is converted from the geographic coordinate system to the aircraft's body coordinate system to obtain the wind speed disturbance in different directions under the body coordinate system. By adding the wind speed disturbance in the body coordinate system to the kinematic equations of the small disturbance, the coupling between the wind shear field model and the kinematic equations of the small disturbance is completed, generating a coupled set of equations for simulating the dynamic response of the aircraft.

[0009] Preferably, the flight parameters include, but are not limited to, airframe angle of attack, vertical speed, and airframe overload, and the logic for generating the risk coefficient is as follows: The flight parameters of the aircraft are collected at different times and spatial coordinates. The flight parameters are compared with the preset safety threshold range to calculate the deviation between the flight parameters and the safety threshold. Based on the deviation, the risk level of the flight parameters is determined. It includes 1-4 risk levels, each of which corresponds to a deviation range. The risk level is determined by which deviation range the deviation falls into. The risk level increases as the median of the deviation range increases. Each risk level corresponds to a score, and the higher the risk level, the higher the corresponding risk score. The risk levels corresponding to different flight parameters are weighted and processed to generate a risk coefficient corresponding to time and space. The calculation method is as follows: ; In the formula , They represent in Spatial coordinates at time The risk coefficient at the location and the first The risk level rating corresponding to each flight parameter. Indicates the first The weights corresponding to the various flight parameters This represents the maximum value of the risk level. Indicates the number of types of flight parameters. Index representing flight parameters, Indicates the aircraft's departure time. Indicates from arrive The time variable.

[0010] Preferably, the logic for dynamically adjusting the safety threshold range based on wind speed disturbances is as follows: according to At this moment, spatial coordinates The wind speed disturbance at a given location is calculated to determine the corresponding disturbance amplitude, and the ratio between the disturbance amplitude and the preset wind disturbance limit threshold is used as the wind shear intensity factor. The upper and lower limits of the safety threshold range are adjusted based on the wind shear intensity factor, thereby shrinking or expanding the safety threshold range.

[0011] Preferably, the wind shear intensity factor is calculated as follows: ; In the formula This represents the wind shear intensity factor. Indicates the amplitude of the disturbance. Represents norm calculation, Indicates the wind disturbance limit threshold; The calculation method for the upper and lower limits of the safety threshold range is as follows: ; ; In the formula , These represent the upper and lower limits of the adjusted safety threshold range, respectively. , These represent the upper and lower limits of the initial safety threshold range, respectively. This indicates a regulatory factor.

[0012] The preferred logic for visualizing risk coefficients is as follows: Based on the virtual flight trajectory, obtain the spatial coordinates, time, and corresponding risk coefficient of each trajectory point; The risk coefficient is divided into intervals based on the fuzzy constraint method. Each interval is mapped to a color, and the intensity of the color is positively correlated with the magnitude of the risk coefficient. The virtual flight trajectory is colored using different colors mapped by the risk coefficient, and then displayed in a three-dimensional curved mesh format in combination with the three-dimensional terrain data of the flight area.

[0013] Compared with the prior art, the beneficial effects of the present invention are: This invention constructs a dynamic, hierarchical three-dimensional wind shear field model by spatiotemporal alignment and fusion of multi-source meteorological data, and achieves deep coupling between wind disturbance and aircraft dynamic response based on small disturbance theory and flight dynamics model. Combining the acquisition of flight parameters in virtual flight with safety thresholds dynamically adjusted based on wind disturbance intensity, it uses multi-parameter risk level weighted integral to achieve continuous and dynamic quantification of risk. By coupling the risk coefficient with three-dimensional terrain and virtual flight trajectory, and using hierarchical color mapping for intuitive spatial visualization, a complete closed loop from environmental modeling to risk warning is formed, which greatly improves the accuracy and timeliness of flight risk identification under wind shear environment, and enhances the scientific nature and practicality of safety situation awareness. Attached Figure Description

[0014] Figure 1 This is a schematic diagram of the overall method flow of the present invention; Figure 2 A schematic diagram of the precise constraint method; Figure 3 This is a schematic diagram of the fuzzy constraint method; Figure 4 This is a schematic diagram illustrating the safety status of a virtual flight trajectory. Detailed Implementation

[0015] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to specific embodiments.

[0016] It should be noted that, unless otherwise defined, the technical or scientific terms used in this invention should have the ordinary meaning understood by one of ordinary skill in the art to which this invention pertains. The terms "first," "second," and similar terms used in this invention do not indicate any order, quantity, or importance, but are merely used to distinguish different components. Terms such as "comprising" or "including" mean that the element or object preceding the word encompasses the elements or objects listed following the word and their equivalents, without excluding other elements or objects. Terms such as "connected" or "linked" are not limited to physical or mechanical connections, but can include electrical connections, whether direct or indirect. Terms such as "upper," "lower," "left," and "right" are used only to indicate relative positional relationships; when the absolute position of the described object changes, the relative positional relationship may also change accordingly.

[0017] Example: Please see Figures 1-4 The present invention provides a technical solution: A method for safety perception of civil aircraft based on virtual flight, characterized by the following specific steps: S1: Collect multi-source meteorological data within the aircraft's flight area, including but not limited to CIRA-86 model data, NOAA data, and weather forecast data. Utilize interpolation algorithms to construct a three-dimensional wind shear field model, and based on this model, obtain the wind speed disturbances experienced by the aircraft at different locations (such as micro-downbursts, low-altitude abrupt wind changes, etc.). Combining the classic CIRA-86 atmospheric model, NOAA measured data, and real-time weather forecasts, this approach covers atmospheric information at different time and spatial scales, compensating for the limitations of single data sources and improving the completeness and accuracy of wind field data.

[0018] The logic for constructing a wind shear field model based on multi-source meteorological data is as follows: Spatiotemporal alignment of multi-source meteorological data is performed, and three-dimensional wind field data is generated using a three-dimensional interpolation algorithm; An average wind speed distribution is generated based on 3D wind field data to serve as the background wind. Then, a spatiotemporal filtering method is used to decompose the 3D wind field data into background wind and several layers of wind disturbance components. Different levels of wind disturbance components reflect different physical processes, such as atmospheric circulation, topographic ventilation effects, and localized strong convection. Each wind disturbance component has its own spatial and temporal characteristics, and their superposition and combination form the overall wind disturbance. By decomposing the 3D wind field into background wind and multiple disturbance levels (such as macroscopic weather system disturbances, mesoscale topographic effects, and microscopic downbursts), wind shear characteristics at different scales can be more precisely characterized.

[0019] A wind shear field model is constructed by weighting several sets of wind disturbance components, and its functional expression is as follows: ; In the formula , , They represent in At this moment, spatial coordinates Wind speed disturbance, wind disturbance component, and corresponding weight of wind disturbance component at the location, subscript The hierarchical index representing the wind disturbance component. This represents the total number of levels for the wind disturbance components. The weights corresponding to the wind disturbance components satisfy the normalization condition, i.e.: ; By combining historical observations and weather forecasts, the frequency and intensity of different disturbance components in specific geographical areas and weather conditions are statistically analyzed. The frequency and typical intensity distribution of each type of disturbance component in the region and under the conditions are also statistically analyzed. The disturbances are weighted according to their probability of occurrence and intensity, with higher frequency and intensity disturbance components usually given larger weights. This determines the initial weight distribution. Subsequently, the weight values ​​can be dynamically adjusted using probabilistic models or machine learning methods (such as Bayesian networks, Markov models, etc.) based on real-time weather conditions and aircraft positions, thereby enhancing the model's adaptability.

[0020] When performing spatiotemporal alignment of data, time alignment is performed first. The specific steps include: Convert the timestamps of all data to a unified time zone and time format, such as unifying them all to UTC time. For data with different time sampling frequencies, time interpolation methods (linear interpolation, spline interpolation, etc.) are used to interpolate the data to a unified time node. For example, if one data source updates once per hour and another data source updates once every 30 minutes, the hourly data is interpolated to generate 30-minute interval data. Finally, based on the time range and resolution required for flight simulation, a data time window is selected, and data from time points that are out of range or invalid are removed.

[0021] After time alignment is completed, spatial alignment is performed. The specific steps include: Convert all data to a unified geospatial reference system (such as WGS84 geographic coordinate system) to ensure the consistency of location coordinates; For data with different resolutions, spatial resampling techniques (nearest neighbor, bilinear, bicubic interpolation, etc.) are used to map the data to a unified spatial grid. For example, coarser resolution satellite data is resampled to a high resolution DEM grid. Finally, depending on the data type and purpose, the latitude and longitude coordinates are converted into spatial rectangular coordinates (such as ENU or NED coordinate systems) to adapt to subsequent three-dimensional interpolation calculations.

[0022] In this step, high-fidelity, dynamic wind speed disturbance vectors are provided for the aircraft dynamics wind disturbance input through multi-source data fusion and multi-scale decomposition. This ensures that the flight dynamics model can accurately respond to actual wind shear at different flight stages, improves the simulation credibility of virtual flight, and enables subsequent dynamic adjustment of safety thresholds and risk accumulation calculation, thereby enhancing the overall environmental adaptability and risk discrimination capability of the system.

[0023] S2: Based on the small perturbation assumption, construct the small perturbation kinematic equations of the aircraft, and couple the wind shear field model with the small perturbation kinematic equations of the aircraft to simulate the dynamic response of the aircraft under wind shear.

[0024] The logic for coupling the wind shear field model with the aircraft's small perturbation kinematic equations is as follows: Based on the small perturbation assumption, the differential equations of the aircraft's flight motion are linearized and decoupled into a set of small perturbation kinematic equations in the longitudinal and lateral directions. The wind shear field model is converted from the geographic coordinate system to the aircraft's body coordinate system to obtain the wind speed disturbance in different directions under the body coordinate system. By adding the wind speed disturbance in the body coordinate system to the kinematic equations of the small disturbance, the coupling between the wind shear field model and the kinematic equations of the small disturbance is completed, generating a coupled set of equations for simulating the dynamic response of the aircraft.

[0025] Specifically, geographic coordinate systems are typically defined as East-North-Sky (ENU) or North-East-Down (NED) coordinate systems. Taking the NED coordinate system as an example, wind speed disturbances in a wind shear field model can be expressed in this coordinate system as follows: ; In the formula This represents the wind speed disturbance in the NED coordinate system. These represent the components of wind speed disturbance in this coordinate system.

[0026] For the body coordinate system, it is usually defined as follows: with the aircraft's center of mass as the origin. The positive axis is along the direction of the aircraft's nose (forward). The positive axis is along the right side of the fuselage. The positive axis runs along the bottom of the fuselage.

[0027] The wind shear field model is transformed from the geographic coordinate system to the aircraft's body coordinate system using a transformation matrix. This transformation matrix can be achieved through a rotation matrix. , , The product is: ; in: ; ; ; In the formula , , These represent the aircraft's pitch angles (around...) (axis rotation), roll angle (around) (axis rotation), yaw angle (around) (axis rotation) Wind speed disturbance is represented in the body coordinate system as: ; Right now: ; In the formula This represents the wind speed disturbance in the body coordinate system. These represent the components of wind speed disturbance in this coordinate system. Aligning with the aircraft's direction of travel directly affects the aircraft's approach speed. Pointing to the right side of the aircraft affects sideslip and yaw motion. Perpendicular to the aircraft body, it affects pitch and yaw movements.

[0028] Based on the small perturbation linear theory of aircraft (i.e., the small perturbation assumption) and flight dynamics analysis, the six degrees of freedom (3 translational: forward and backward) are usually considered as... ,about ,up and down 3 rotations: roll , looking up ,yaw It is divided into two relatively weakly coupled subsystems: Longitudinal motion subsystem: mainly includes state variables such as the aircraft's forward and backward speeds, pitch angles, and pitch rate; the control channels are mainly the elevator and throttle. Lateral motion subsystem: mainly includes roll angle, yaw angle and corresponding angular velocity, and the control channels are aileron and rudder.

[0029] When coupling the wind shear field model with the kinematic equations of small perturbations. , These two components work together on the longitudinal motion subsystem because the longitudinal velocity and pitch angle are affected by forward and vertical wind speed disturbances, thus influencing both the aircraft's climb and velocity changes; and It primarily acts on the lateral motion subsystem, affecting sideslip angle and yaw motion.

[0030] Therefore, the coupled equations used to simulate the dynamic response of an aircraft are expressed as: Vertical: ; In the formula , , , These represent the speed increment, angle of attack increment, pitch angle increment, and pitch angle angular velocity increment, respectively. , , , Let these represent the first derivatives of the velocity increment, angle of attack increment, pitch angle increment, and pitch angle angular velocity increment with respect to time, respectively. , These represent the increments in throttle and elevator, respectively. , , These represent the longitudinal Jacobian matrix, the control input matrix, and the wind disturbance coupling matrix, respectively. Horizontal: ; In the formula , , , These represent the increments of sideslip angle, roll angle, roll angle angular velocity, and yaw angle angular velocity, respectively. , , , Let represent the first derivatives of the sideslip angle increment, roll angle increment, roll angle angular velocity increment, and yaw angle angular velocity increment with respect to time, respectively. , These represent the aileron increment and the rudder increment, respectively. , , These represent the lateral Jacobian matrix, the control input matrix, and the wind disturbance coupling matrix, respectively.

[0031] The increments refer to the deviations (i.e., disturbances) relative to the steady-state or equilibrium state. These can be directly measured by aircraft sensors (such as inertial measurement units, pitot tubes, attitude sensors, etc.) to obtain actual parameters. In virtual flight, they can be automatically calculated by simulation software based on the difference between the model state and the equilibrium state. The Jacobian matrix is ​​a linear mapping matrix of the aircraft's longitudinal state variables to its own rate of change, used to describe the coupling relationship between states such as velocity increment, angle of attack, pitch angle, and pitch angular velocity. The control input matrix describes the influence of control inputs such as elevator and throttle on the rate of change of longitudinal flight state. The wind disturbance coupling matrix describes the influence of external wind speed disturbances on the rate of change of aircraft state, and is a mapping relationship between wind disturbance inputs and the dynamic system. All three matrices are partial derivative matrices obtained by linearizing the nonlinear aircraft dynamics model at the steady-state flight point, and can be calculated by combining aerodynamic data, experiments, and numerical simulations.

[0032] In this step, by coupling the three-dimensional wind shear field model constructed from multi-source meteorological data with the aircraft dynamic model, a complete link from the environmental wind field to flight dynamics is realized. This can realistically reflect the impact of complex wind disturbances on flight status. Moreover, based on the small disturbance assumption, the nonlinear flight dynamics equations are linearized and divided into longitudinal and lateral subsystems. This not only enables the analysis and quantification of the impact of wind disturbances in different directions on flight safety, meeting the needs of multi-dimensional safety situational awareness, but also simplifies the computational complexity, making it suitable for real-time or rapid simulation.

[0033] S3: Based on the aircraft's dynamic response, acquire flight parameters in virtual flight, including but not limited to airframe angle of attack, vertical speed, and airframe overload. Compare the flight parameters with preset safety threshold ranges to obtain risk coefficients corresponding to time and space, and dynamically adjust the safety threshold ranges based on wind speed disturbances.

[0034] The logic for generating the risk coefficient is as follows: The flight parameters of the aircraft are collected at different times and spatial coordinates. The flight parameters are compared with the preset safety threshold range to calculate the deviation between the flight parameters and the safety threshold. Based on the deviation, the risk level of the flight parameters is determined. The risk levels are divided into four levels from 1 to 4 according to their severity and assigned values. Each risk level corresponds to a score, and the score of the risk level is directly proportional to the degree of deviation. The risk levels corresponding to different flight parameters are weighted and processed to generate a risk coefficient corresponding to time and space. The calculation method is as follows: ; In the formula , They represent in Spatial coordinates at time The risk coefficient at the location and the first The risk level rating corresponding to each flight parameter. Indicates the first The weights corresponding to the various flight parameters This represents the maximum value of the risk level, i.e. , Indicates the number of types of flight parameters. Index representing flight parameters, Indicates the aircraft's departure time. This represents a time infinitesimal element.

[0035] As can be seen from the formula for calculating the risk coefficient, the integral reflects the cumulative effect of risk over time, effectively describing the trend of risk accumulation throughout the flight; that is, the longer the flight, the greater the likelihood of safety risks. The denominator is the theoretical maximum value of the risk level accumulated over time, used to normalize the final risk coefficient. The higher the risk coefficient, the higher the risk.

[0036] The deviation between flight parameters and the safety threshold range can be calculated using the following formula: ; In the formula Indicates the first The deviation between certain flight parameters and the safety threshold range, Indicates the first Various flight parameters, , They represent the first The upper and lower limits of the safety threshold range corresponding to a certain flight parameter. This can be based on the ratio between the deviation and the safety threshold range, i.e. The risk level is determined by the ratio. For example, a ratio of 0 to 0.1 is considered a risk level 1 with a risk score of 1; 0.1 to 0.2 is considered a risk level 2 with a risk score of 2; 0.2 to 0.3 is considered a risk level 3 with a risk score of 3; and 0.3 to 0.4 is considered a risk level 4 with a risk score of 4. The specific ratio can be determined by expert experience.

[0037] The logic for dynamically adjusting the safety threshold range based on wind speed disturbances is as follows: according to At this moment, spatial coordinates The wind speed disturbance at a given location is calculated to determine the corresponding disturbance amplitude, and the ratio between the disturbance amplitude and the preset wind disturbance limit threshold is used as the wind shear intensity factor. The upper and lower limits of the safety threshold range are adjusted based on the wind shear intensity factor, thereby shrinking or expanding the safety threshold range.

[0038] The wind shear intensity factor is calculated as follows: ; In the formula This represents the wind shear intensity factor. Indicates the amplitude of the disturbance. Represents norm calculation, This represents the wind disturbance limit threshold. The larger the wind shear intensity factor, the stronger the wind speed disturbance experienced by the aircraft.

[0039] The calculation method for the upper and lower limits of the safety threshold range is as follows: ; ; In the formula , These represent the upper and lower limits of the adjusted safety threshold range, respectively. , These represent the upper and lower limits of the initial safety threshold range, respectively. This represents an adjustment factor greater than 0, which allows the safety threshold range to shrink as the wind shear intensity factor increases. The specific value of this factor is determined by expert experience. The dynamic threshold shrinkage mechanism ensures that under extreme wind disturbance conditions, the upper limit of the safety threshold range decreases and the lower limit increases, thereby improving the corresponding risk level score and facilitating timely safety measures to reduce the probability of accidents.

[0040] In this step, by continuously collecting aircraft flight parameters and comparing them with the safety threshold range dynamically adjusted based on wind shear disturbances, a comprehensive risk coefficient is formed by weighting and accumulating the risk levels of multiple flight parameters. This enables real-time dynamic perception of flight risks, enhances the accuracy and completeness of risk assessment, and dynamically adjusts the upper and lower limits of the safety threshold according to the wind shear intensity factor. This tightens the threshold when wind disturbances are strong, makes risk judgment more stringent, enhances sensitivity and adaptability to extreme wind environments, and helps to take timely safety measures to reduce the probability of accidents.

[0041] S4: Couple the aircraft's virtual flight trajectory with the 3D terrain data of the flight area, and visualize the risk coefficient of all trajectory points on the virtual flight trajectory.

[0042] The logic for visualizing risk coefficients is as follows: Based on the virtual flight trajectory, obtain the spatial coordinates, time, and corresponding risk coefficient of each trajectory point; The risk coefficient is divided into intervals based on the fuzzy constraint method, with each interval mapped to a color, and the intensity of the color is positively correlated with the magnitude of the risk coefficient. The risk coefficient can also be divided into four levels with corresponding color mappings to reflect different safety states, for example: 1: Normal - Green, 2: Warning - Orange, 3: Alarm - Red, 4: Disaster - Black. The safety color blocks (i.e., the colors corresponding to the safety states) are defined as follows: Normal - Green: Parameters are normal and will not affect flight safety; Warning - Orange: Parameters are abnormal and require attention, but not immediate action; Alarm - Red: Parameters are severely out of limit and require immediate action; Disaster - Black: An accident has occurred, causing casualties and property damage.

[0043] The virtual flight trajectory is colored using different colors mapped by the risk coefficient, and then displayed in a three-dimensional curved mesh format in combination with the three-dimensional terrain data of the flight area.

[0044] like Figures 2-3 As shown, in traditional precise constraint methods, when the risk coefficient exceeds the safety constraint, the corresponding safety status changes from "normal" to "warning." However, the risk coefficient level classification used in this scheme is actually a fuzzy constraint method: when the risk coefficient exceeds safety constraint 1, the corresponding safety status changes from "normal" to "alert"; when the risk coefficient exceeds safety constraint 2, the corresponding safety status changes to "warning." By introducing more safety states—that is, "fuzzy" constraint intervals—between normal flight and accidents, pilots and relevant researchers can more accurately grasp the aircraft's safety status, and this also facilitates the calculation and construction of subsequent perception methods.

[0045] like Figure 4 As shown, the diagram displays eight virtual flight paths, each with a risk factor indicating its safety status. The vertical axis represents flight altitude, while the horizontal and vertical axes represent the planar size of the flight area. White dots mark the starting points of each path, and white diamonds indicate the ending points. All paths originate from the same starting point, descending at maximum operating speed from 30,000 feet. During flight, the descent rate and aircraft heading change, resulting in different paths and safety statuses. The diagram also displays actual terrain, rendered as a 3D curved mesh at corresponding locations. A color map represents terrain altitude; to prevent confusion between the color map and safety status colors, a grayscale color map is used, with lower grayscale levels indicating higher altitudes. This image allows for a direct and intuitive analysis and perception of the aircraft's virtual flight path and safety status.

[0046] In this step, the risk coefficient is directly applied to the virtual flight trajectory through color mapping, making the risk level clearly visible in three-dimensional space. This enables the rapid identification of high-risk areas. Furthermore, the three-dimensional grid display of the flight area terrain integrates risk information with terrain features, facilitating a deeper understanding of the impact of terrain on flight safety. This aids in the development of flight strategies and safety measures, thereby improving the overall management level.

[0047] The above formulas are all dimensionless calculations. The formulas are derived from software simulations based on a large amount of collected data to obtain the most recent real-world results. The preset parameters in the formulas are set by those skilled in the art according to the actual situation.

[0048] The above embodiments can be implemented, in whole or in part, by software, hardware, firmware, or any other combination thereof. When implemented in software, the above embodiments can be implemented, in whole or in part, as a computer program product. Those skilled in the art will recognize that the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented by electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution.

[0049] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment, depending on actual needs.

[0050] The above description is merely a specific embodiment of this application, but the scope of protection of this application is not limited thereto. Any changes or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in this application should be included within the scope of protection of this application.

Claims

1. A virtual flight based civil aircraft safety awareness method, characterized in that, The specific steps include: S1: Collecting multi-source weather data in the flight area of the aircraft, constructing a wind shear field model on a three-dimensional space using an interpolation algorithm, and obtaining the wind speed disturbance suffered by the aircraft in different flight areas based on the wind shear field model; S2: Based on the small disturbance assumption, a small disturbance kinematic equation set of the aircraft is constructed, and the wind shear field model is coupled with the small disturbance kinematic equation set of the aircraft to simulate the dynamic response of the aircraft under the action of wind shear; S3: Based on the dynamic response of the aircraft, the flight parameters in the virtual flight are obtained, the flight parameters are compared with the preset safety threshold interval, the risk coefficient corresponding to time-space is obtained, and the safety threshold interval is dynamically adjusted based on the wind speed disturbance; S4: The virtual flight trajectory of the aircraft is coupled with the three-dimensional terrain data of the flight area, and the risk coefficients of all trajectory points on the virtual flight trajectory are visualized.

2. The virtual flight based civil aircraft safety awareness method of claim 1, wherein: The multi-source weather data includes but is not limited to CIRA-86 model data, NOAA data, and weather forecast data. The logic for constructing the wind shear field model based on multi-source weather data is: Align the multi-source weather data in time and space, generate three-dimensional wind field data using a three-dimensional interpolation algorithm; Generate an average wind speed distribution based on the three-dimensional wind field data as background wind, and then decompose the three-dimensional wind field data into background wind and several layers of wind disturbance components through a time-space filtering method; Weight several groups of wind disturbance components to construct the wind shear field model, and the function expression is: ； In the formula , , They represent in At this moment, spatial coordinates Wind speed disturbance, wind disturbance component, and corresponding weight of wind disturbance component at the location, subscript The hierarchical index representing the wind disturbance component. This indicates the total number of levels for the wind disturbance component.

3. The virtual flight based civil aircraft safety awareness method of claim 2, wherein: The logic for coupling the wind shear field model with the small disturbance kinematic equation set of the aircraft is: According to the small disturbance assumption, the flight motion differential equation of the aircraft is linearized and decoupled into a small disturbance kinematic equation set in the longitudinal and lateral directions; Convert the wind shear field model from the geographic coordinate system to the body coordinate system of the aircraft to obtain the wind speed disturbance in different directions in the body coordinate system; Add the wind speed disturbance in the body coordinate system to the small disturbance kinematic equation set to complete the coupling between the wind shear field model and the small disturbance kinematic equation set, and generate a coupled equation set for simulating the dynamic response of the aircraft.

4. The virtual flight based civil aircraft safety awareness method of claim 1, wherein: The flight parameters include but are not limited to the body angle of attack, vertical speed, and body overload. The generation logic of the risk coefficient is: Collect the flight parameters of the aircraft at different time instants and spatial coordinates, compare the flight parameters with the preset safety threshold interval to calculate the deviation degree of the flight parameters from the safety threshold, and determine the risk level to which the flight parameters belong based on the deviation degree; There are 1-4 risk levels, each risk level corresponds to a deviation degree interval, the risk level of each deviation degree is determined by judging which deviation degree interval it falls into, and the level of the risk level increases with the increase of the deviation degree interval value. Each risk level corresponds to a score, and the higher the level of the risk level, the higher the corresponding risk level score; Weight the risk level scores corresponding to different flight parameters to generate a risk coefficient corresponding to time-space, and the calculation method is: ； In the formula , They represent in Spatial coordinates at time The risk coefficient at the location and the first The risk level rating corresponding to each flight parameter. Indicates the first The weights corresponding to the various flight parameters The maximum score representing the risk level. Indicates the number of types of flight parameters. Index representing flight parameters, Indicates the aircraft's departure time. Indicates from arrive The time variable between.

5. The virtual flight based civil aircraft safety awareness method of claim 2, wherein: The logic for dynamically adjusting the safety threshold interval based on the wind speed disturbance is: According to The wind speed disturbance at the spatial coordinates corresponding disturbance amplitude, and a ratio between the disturbance amplitude and a preset wind disturbance limit threshold is taken as a wind shear intensity factor. The upper and lower limits of the safety threshold interval are adjusted based on a wind shear intensity factor, so as to realize contraction or expansion of the safety threshold interval.

6. The virtual flight based civil aircraft safety awareness method of claim 5, wherein: The wind shear intensity factor is calculated in the following manner: ； wherein represents the wind shear intensity factor, represents the perturbation amplitude, represents the norm calculation, represents the wind perturbation limit threshold; The upper and lower limits of the safety threshold interval are calculated in the following manner: ； ； wherein , denote the adjusted upper and lower limit of the safety threshold interval, respectively, , denote the initial upper and lower limit of the safety threshold interval, respectively, denotes the adjustment factor.

7. The virtual flight based civil aircraft safety awareness method of claim 1, wherein: The logic for visualizing the risk coefficient is as follows: Based on the virtual flight trajectory, the spatial coordinates, time, and corresponding risk coefficient of each trajectory point are obtained. Based on the fuzzy constraint method, the risk coefficient is divided into intervals, each interval is mapped to a color, and the depth of the color is positively correlated with the size of the risk coefficient. The virtual flight trajectory is colored using different colors mapped by the risk coefficient, and is displayed in the form of a three-dimensional curved surface grid in combination with the three-dimensional terrain data of the flight area.

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