Airspace simulation deduction method and system based on digital twinning

Abstract

The invention provides an airspace simulation deduction method and system based on digital twinning, and belongs to the technical field of airspace simulation, and the method comprises the steps: integrating geographic bottom plate data and airspace element data, carrying out the meshing based on an airspace grid engine, and constructing an airspace digital twinning model; receiving at least one flight plan, and mapping the flight plan to the airspace digital twin model to generate a corresponding simulation aircraft entity; based on a preset simulation engine, driving the simulation aircraft entity to perform flight simulation in the airspace digital twin model, and calculating a simulation flight situation; in the simulation process, airspace conflict detection and early warning are carried out based on the simulation flight situation and the airspace digital twin model; and outputting a simulation deduction result, performing three-dimensional visual output on the simulation flight situation data and the early warning information, and generating an analysis report including conflict statistics and airspace evaluation. The airspace resource allocation is optimized, the flight safety is improved, and intelligent and efficient management is realized.

Description

TECHNICAL FIELD

[0001] The application belongs to the technical field of airspace simulation, and particularly relates to an airspace simulation deduction method and system based on digital twinning. BACKGROUND

[0002] Airspace, especially low-altitude airspace, is a resource with three-dimensional properties, dynamic changes and scarcity. With the development of low-altitude industries such as unmanned aerial vehicle logistics, urban air traffic, and emergency rescue, the density of aircraft in the airspace has increased dramatically, and the operation scene has become increasingly complex, posing unprecedented challenges to the refinement, intelligence, and real-time nature of airspace management.

[0003] The existing airspace management and simulation deduction mainly has the following limitations: first, the traditional airspace management system is mostly based on two-dimensional plane maps or simplified three-dimensional models, which cannot accurately integrate and reflect key environmental factors such as complex terrain, high-rise buildings, and meteorological disturbances (such as low-altitude wind shear and turbulence) in the real environment, resulting in significant deviations between simulation deduction results and real flight environments, and insufficient prediction credibility. Second, existing simulations are mostly offline static deductions, which cannot receive real-time data from real aircraft to drive the simulation model. At the same time, the simulation results are difficult to feed back to the flight control system in the physical world in real time, making it impossible to achieve closed-loop management from perception to decision-making, simulation, and control, and the response is slow when dealing with unexpected situations (such as equipment failure, sudden weather changes, and temporary airspace control). Third, in dense and high-speed flight scenarios, traditional conflict detection methods based on geometric calculations have high computational overhead and cannot meet real-time requirements. Fourth, it is difficult for management decision-makers to quantitatively evaluate and visually simulate the potential impact of management decisions. SUMMARY

[0004] In a first aspect, the embodiments of the present application provide an airspace simulation deduction method based on digital twinning, comprising the following steps: S1. Integrating geographic base data and airspace element data, and performing gridding based on an airspace grid engine to construct an airspace digital twinning model; S2. Receiving at least one flight plan and mapping the flight plan to the airspace digital twinning model to generate corresponding simulation aircraft entities; S3. Based on a preset simulation engine, driving the simulation aircraft entities to fly in the airspace digital twinning model, and based on a six-degree-of-freedom dynamics model bound to the simulation aircraft entities, and in combination with real-time meteorological data obtained from an environment data interface of the airspace digital twinning model, calculating the simulation flight situation in real time; S4. During the simulation, performing airspace conflict detection and warning based on the simulation flight situation and the airspace digital twinning model; S5. Output the simulation deduction result, visualize the simulation flight situation data and early warning information in three dimensions, and generate an analysis report containing conflict statistics and airspace assessment.

[0005] Further, the step S1 comprises the following steps: S11. Obtain a digital elevation model and remote sensing images from a geographic information system as three-dimensional spatial data, obtain structured airspace element data from an airspace management system, and perform coordinate unification and format standardization processing on the obtained data; S12. Call the airspace grid engine of the Earth Discrete Global Grid System, uniformly divide the preprocessed three-dimensional geographic spatial data and airspace element data into multiple levels of grid cells, and generate global unique grid code data for each grid cell; S13. Associate the grid code data with the specific numerical values of the corresponding three-dimensional spatial data and airspace element data, and construct a digital airspace twin in memory as a unified spatial reference for simulation to perform spatial query and analysis.

[0006] Further, in step S12, the airspace grid engine performs the following calculation process for encoding any spatial point P( , , ): S121. Determine the target grid level Level according to the precision requirement of the application scenario; S122. Find the corresponding longitude and latitude span Δ and height span H as grid resolution parameters according to the target grid level Level; S123. Calculate the longitude and latitude plane grid row and column numbers (i, j) and the height level k of the spatial point P:

[0007]

[0008]

[0009] where i is the longitude and latitude plane grid row number, j is the longitude and latitude plane grid column number, k is the height level, and are the longitude and latitude coordinates of the southwest corner of the encoding region, and H is the preset level height reference; , , are the latitude, longitude, and height of the aircraft, respectively; S124. Use the Z-order space-filling curve to perform bit cross-merging on the three-dimensional index (i, j, k) to generate an integer grid code Code.

[0010] Further, the step S2 comprises the following steps: S21. Receiving and parsing the flight plan data stream in JSON or XML format from the external system, extracting the planned route, aircraft model, performance parameters and planned schedule; S22. Querying the local aircraft dynamics model library by aircraft model as the key, loading the corresponding six-degree-of-freedom dynamics model, mass characteristics, aerodynamic parameters and performance envelope into the simulation environment; S23. At the initial time of simulation, creating a simulation aircraft entity at the corresponding grid position of the airspace digital twin model according to the parsed planned route, and binding the loaded six-degree-of-freedom dynamics model and performance envelope with the simulation aircraft entity, completing the mapping from static plan to dynamic entity.

[0011] Further, the step S3 comprises the following steps: S31. Periodically advancing the simulation time by a fixed simulation step size Δt in the simulation engine; S32. For each simulation aircraft entity, within each simulation step size Δt, according to the bound six-degree-of-freedom dynamics model, synthesizing the current state vector and the real-time weather data obtained from the environment data interface of the airspace digital twin model, solving the new state vector at the next time by numerical integration method; the current state vector includes the current position, velocity and attitude; the real-time weather data includes real-time wind field vector and atmospheric density data; S33. Aggregating the new states of all simulation aircraft entities after solving, forming the global and time-stamped real-time simulation flight situation data, and publishing to the shared data area.

[0012] Further, in the step S32, the fourth-order Runge-Kutta method is used for numerical integration; The six-degree-of-freedom dynamics model includes translational dynamics equations and rotational dynamics equations, and the real-time weather data at least includes three-dimensional wind speed vector and atmospheric density, which participates in solving in the following way: Based on the aircraft state and real-time three-dimensional wind speed, the relative airspeed is calculated, and the real-time atmospheric density is used to calculate the aerodynamic force and moment; the aerodynamic force and moment are substituted into the translational dynamics equations and rotational dynamics equations to calculate the derivative of the state vector; The core position update formula of the fourth-order Runge-Kutta method is:

[0013] Wherein, And are the position vectors of the current and next time instant respectively; k1, k2, k3, k4 are the slope estimates calculated on the four intermediate states respectively constructed by stacking the increments of and the current state respectively , , ; in calculating each slope estimate, real-time weather data corresponding to the spatial position of each intermediate state is obtained from the environment data interface.

[0014] Further, the real-time weather data further includes unsteady turbulent flow data; random wind speed is generated through a turbulent flow statistical model, and the random aerodynamic force increment and moment increment caused thereby are calculated, and the random aerodynamic force increment and moment increment are added as disturbance terms to the six-degree-of-freedom dynamics equation.

[0015] Further, the step S4 has the following specific steps: S41. For each simulated aircraft entity, the instantaneous protection radius is dynamically calculated according to the instantaneous speed v, the system preset reaction time t and the safety fault tolerance constant C, and a spherical safety space following the movement of the simulated aircraft entity is constructed according to the instantaneous protection radius; S42. Through the airspace grid engine, the spherical safety space is converted into a covered grid code set, and the grid code set is marked as the safety envelope grid set of the simulated aircraft entity ; S43. A parallel computing task is started to perform two types of grid intersection judgments: Perform aircraft collision judgment: The safety envelope grid set of any two simulated aircraft entities is compared in real time and , if , it is determined that there is a collision risk; Perform airspace intrusion conflict judgment: The safety envelope grid set of each simulated aircraft entity is compared in real time with the predefined no-fly zone and restricted area grid set and , if , it is determined as airspace intrusion; S44. If a conflict is detected, structured warning information containing the conflict type, involved entities, conflict grid position and expected occurrence time is generated and published to the warning information pool.

[0016] Further, the safety fault tolerance constant C in step S41 is dynamically adjusted according to the preset risk level of the grid cell in which the aircraft is currently located:

[0017] wherein, is a base fault tolerance constant, is a risk impact coefficient, is obtained in real time from the grid cell properties of the airspace digital twin model.

[0018] Further, the step S5 comprises the following steps: S51. The three-dimensional visualization rendering engine receives and parses the simulation flight situation data and early warning information in real time through the WebSocket long connection, and performs the following dynamic graphic rendering on the visualization client: drives the simulation aircraft model to move, highlights the conflict area, and draws the flight trajectory line; S52. The visualization client provides a user interaction interface to respond to view switching, pausing / continuing simulation, and clicking on entities to view details; S53. After the simulation deduction is completed or at a specified time point, the data analysis service is automatically run, and based on the simulation flight situation data and early warning data throughout the process, a structured analysis report in PDF and Excel formats is generated, and the report content at least includes: total flight sorties, conflict event list, airspace capacity utilization rate statistics, and safety score of each route, and is automatically uploaded to a specified file server or pushed to a specified user.

[0019] In a second aspect, the embodiments of the present application also provide an airspace simulation deduction system based on digital twinning, comprising: an airspace digital twinning model construction module, configured to integrate geographic base data and airspace element data, and perform gridding based on an airspace grid engine to construct an airspace digital twinning model; a simulation entity mapping module, configured to receive at least one flight plan and map the flight plan to the airspace digital twinning model to generate corresponding simulation aircraft entities; a flight dynamics simulation engine module, configured to drive the simulation aircraft entities to perform flight simulation in the airspace digital twinning model based on a preset simulation engine, and to calculate simulation flight situations in real time according to a six-degree-of-freedom dynamics model bound to the simulation aircraft entities and in combination with real-time weather data obtained from an environment data interface of the airspace digital twinning model; a parallel conflict detection and early warning module, configured to perform airspace conflict detection and early warning based on simulation flight situations and the airspace digital twinning model during the simulation; a three-dimensional visualization and decision support module, configured to output simulation deduction results, perform three-dimensional visualization output on simulation flight situation data and early warning information, and generate an analysis report containing conflict statistics and airspace evaluation.

[0020] From the above technical solutions, the present application has the following advantages: The airspace simulation and deduction method and system based on digital twinning provided by the present application construct a highly realistic airspace simulation environment through digital twinning, integrate geographic base data and airspace element data, provide more accurate dynamic analysis support for airspace management, effectively solve the limitations of traditional two-dimensional maps or simplified three-dimensional models, support dynamic simulation and prediction of complex flight scenarios, realize simulation processing of aircraft dynamic behavior through the combination of flight plans and simulation dynamics models, support conflict detection and early warning between aircraft in complex airspace, and improve the safety of airspace operation. Through three-dimensional visualization rendering and dynamic interaction, the simulation results are intuitively presented, users can quickly analyze airspace conflicts and risks, and generate structured reports to provide reasonable basis for decision-making. Through simulation deduction and analysis, the airspace capacity, utilization rate and route safety score are evaluated to provide data support for airspace planning and optimization. BRIEF DESCRIPTION OF DRAWINGS

[0021] In order to more clearly illustrate the technical solutions of the present application, the drawings required to be used in the description will be briefly introduced below. Obviously, the drawings in the following description are only some embodiments of the present application, and other drawings can be obtained by those skilled in the art without creative labor.

[0022] Figure 1 The flowchart of the airspace simulation and deduction method based on digital twinning of the present application.

[0023] Figure 2 The schematic diagram of the airspace simulation and deduction system based on digital twinning of the present application. DETAILED DESCRIPTION

[0024] In the following detailed description of the specific steps of the airspace simulation and deduction method based on digital twinning, various embodiments of the present disclosure will be described more fully. The present disclosure can have various embodiments, and adjustments and changes can be made therein. However, it should be understood that there is no intention to limit various embodiments of the present disclosure to the specific embodiments disclosed herein, but the present disclosure should be understood to cover all adjustments, equivalents and / or alternatives falling within the spirit and scope of various embodiments of the present disclosure.

[0025] The present embodiment provides an airspace simulation and deduction method based on digital twinning, which realizes accurate modeling and simulation of airspace through digital twinning technology, improves flight safety, optimizes airspace management, and provides intelligent decision support.

[0026] With reference to the accompanying drawings, the technical solutions in the embodiments of the present application will be clearly and completely described below, obviously, the described embodiments are only part of the embodiments of the present application, rather than all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative labor fall within the scope of protection of the present application.

[0027] Please refer to Figure 1 Fig. 1 shows a flowchart of a method for airspace simulation and deduction based on digital twinning in an embodiment, the method comprising the following steps: S1. Integrating geographic base data and airspace element data, and performing gridding based on an airspace grid engine to construct an airspace digital twinning model; It should be noted that this step integrates geographic base data and airspace element data to provide comprehensive and dynamic three-dimensional space reference for airspace simulation; S2. Receiving at least one flight plan and mapping the flight plan to the airspace digital twinning model to generate a corresponding simulation aircraft entity; It should be noted that this step converts a static flight plan into a dynamic aircraft entity in a simulation environment, improving the real-time performance and flexibility of the simulation; S3. Based on a preset simulation engine, driving the simulation aircraft entity to fly in the airspace digital twinning model, and based on a six-degree-of-freedom dynamics model bound to the simulation aircraft entity, and combining real-time weather data obtained from an environment data interface of the airspace digital twinning model, real-time calculating a simulation flight situation; It should be noted that this step performs simulation calculation of flight state to provide dynamic situation awareness for airspace operation; S4. During the simulation process, performing airspace conflict detection and early warning based on the simulation flight situation and the airspace digital twinning model; It should be noted that this step quickly identifies collision and airspace intrusion risks to improve the real-time performance of early warning; S5. Outputting simulation deduction results, three-dimensionally visualizing simulation flight situation data and early warning information, and generating an analysis report containing conflict statistics and airspace evaluation; It should be noted that this step real-time renders aircraft motion, conflict area and trajectory line to provide intuitive visual analysis capability; automatically generates an analysis report to provide reasonable and efficient decision basis for airspace management.

[0028] This embodiment constructs an airspace model through digital twinning, fuses flight plans and real-time weather data, accurately simulates flight situation, timely detects conflicts and provides early warning, presents results through three-dimensional visualization, generates a detailed analysis report, and improves airspace management efficiency and flight safety.

[0029] Further, as a refinement and extension of the above embodiment, in order to complete the specific implementation process in the embodiment, another airspace simulation deduction method based on digital twinning is provided, which comprises the following steps: S1. Integrate the geographic base data and airspace element data, and grid based on the airspace grid engine, construct the airspace digital twinning model; the specific steps of step S1 are as follows: S11. Obtain digital elevation model and remote sensing image from geographic information system as three-dimensional space data, obtain structured airspace element data from airspace management system, and perform coordinate unification and format standardization processing on the obtained data; Exemplarily, taking the unmanned aerial vehicle logistics distribution airspace (30.0°-30.5° north latitude, 120.0°-120.5° east longitude) of a city as a target region, obtaining a digital elevation model (DEM) with a resolution of 1 m and a remote sensing image with a resolution of 0.5 m from a geographic information system (GIS) as three-dimensional space data; obtaining structured airspace element data from an airspace management system, including three no-fly zones (airport clearance area, government office area, high-voltage corridor), five planned routes, and two take-off and landing site coordinates, and converting all data to CGCS2000 coordinate system and standardizing the format to GeoJSON (vector data) and TIFF (image data); S12. Call the airspace grid engine of the Earth Discrete Global Grid System, uniformly divide the preprocessed three-dimensional geographic space data and airspace element data into multiple levels of grid cells, and generate global unique grid code data for each grid cell; In step S12, the calculation process of the airspace grid engine for encoding any spatial point P( , , ) is as follows: S121. According to the requirement of the application scene to the precision, determine the target grid level Level; S122. According to the target grid level Level, find the corresponding longitude and latitude span Δ and height span H as the grid resolution parameters; S123. Calculate the longitude and latitude plane grid row number (i, j) and height level k where the spatial point P is located:

[0030]

[0031]

[0032] Wherein, i is the longitude and latitude plane grid row number, j is the longitude and latitude plane grid column number, k is the height level, and Here are the latitude and longitude coordinates of the starting point in the southwest corner of the coding area, and H is the preset level height reference. , , These are the latitude, longitude, and altitude of the aircraft, respectively. S124. Using the Z-order space filling curve, the three-dimensional index (i,j,k) is bit-crossed and merged to generate an integer grid code Code; For example, the spatial grid engine of the Geo-Discrete Global Grid System (G-DGGS) is invoked to grid the preprocessed data: Because drone logistics scenarios require centimeter-level precision control, the target grid level is set to Level=15; According to the preset parameter table based on Level=15, the latitude and longitude span Δ=0.0001° (approximately 11m×11m) and the height span H=5m; Starting from the southwest corner of the target area (30.0°N, 120.0°E) =30.0° =120.0°), for a certain takeoff and landing field center point P ( =120.2°, =30.3°, Calculate grid coordinates (=50m): line number i = floor( )=3000 Column number j = floor( )=2000 Height k = floor( )=10 Using a Z-order space filling curve, the three-dimensional index (3000, 2000, 10) is bit-crossed and merged; for example, 3000 is converted to binary "101110111000", 2000 is converted to binary "011111010000", and 10 is converted to binary "1010". After cross-merging according to the Z-order rules, a unique integer grid code Code=3000200010 is generated. S13. Associate the grid-coded data with the specific values ​​of the corresponding three-dimensional spatial data and spatial element data, and construct a digital spatial twin in memory as a unified spatial benchmark for simulation in order to perform spatial query and analysis. For example, the grid code "3000200010" is associated with the DEM elevation value (e.g., 25m), remote sensing image pixel value, and airspace features (e.g., take-off and landing field attributes) of the grid cell to build a digital airspace twin in memory, which supports quick querying of information such as terrain height and no-fly zone range of the area through the grid code; S2. Receive at least one flight plan and map the flight plan to the airspace digital twin model to generate the corresponding simulated aircraft entity; The specific steps of step S2 are as follows: S21. Receive and parse the JSON or XML format flight plan data stream from the external system, and extract the planned route, aircraft model, performance parameters and planned timetable; For example, receiving a JSON-formatted flight plan pushed by a logistics company via API, and parsing key information: Planned route: From takeoff and landing field A ( =120.2°, =30.3°) to takeoff and landing field B ( =120.4°, =30.4°) Aircraft Model: DJI Matrice 350 RTK Performance parameters: Maximum speed 20m / s, flight time 30min Scheduled timetable: Departure time 10:00, estimated arrival time 10:15; S22. Using the aircraft model as the keyword, query the local aircraft dynamics model library and load the corresponding six-degree-of-freedom dynamics model, mass characteristics, aerodynamic parameters and performance envelope into the simulation environment; For example, using "DJI Matrice 350 RTK" as the keyword, the local aircraft dynamics model library is queried, and the corresponding six-degree-of-freedom dynamics model (e.g., containing a mass of 15kg and a moment of inertia matrix), aerodynamic parameters (e.g., lift coefficient CL=0.8, drag coefficient CD=0.05), and performance envelope (e.g., speed 0-20m / s, altitude 0-120m) are loaded into the simulation environment; S23. At the initial moment of the simulation, based on the resolved planned flight path, create the simulated aircraft entity at the corresponding grid position of the airspace digital twin model, and bind the loaded six-degree-of-freedom dynamic model and performance envelope to the simulated aircraft entity to complete the mapping from static plan to dynamic entity; For example, at the initial moment of the simulation (e.g., 10:00), based on the starting point of the planned flight path, a simulated aircraft entity is created at the position corresponding to the mesh code "3000200010" in the airspace digital twin model. The loaded six-degree-of-freedom model is bound to the entity, and the initial state is set (e.g., position (120.2°, 30.3°, 50m), speed 0m / s, attitude angle (0°, 0°, 0°)). S3. Based on the preset simulation engine, drive the simulation aircraft entity to fly in the airspace digital twin model, and calculate the simulation flight situation in real time according to the six-degree-of-freedom dynamics model bound with the simulation aircraft entity and in combination with the real-time meteorological data obtained from the environment data interface of the airspace digital twin model; The specific steps of step S3 are as follows: S31. The simulation engine periodically advances the simulation time at a fixed simulation step Δt (for example, a typical value is 50 ms); Exemplarily, the simulation engine periodically advances the simulation time at a fixed step Δt=50 ms, that is, the simulation state is updated 20 times per second; S32. For each simulation aircraft entity, in each simulation step Δt, according to the bound six-degree-of-freedom dynamics model, the current state vector and the real-time meteorological data obtained from the environment data interface of the airspace digital twin model are integrated to solve the new state vector at the next moment by a numerical integration method; the current state vector includes the current position, velocity and attitude; the real-time meteorological data includes real-time wind field vector and atmospheric density data; In step S32, the fourth-order Runge-Kutta method is used for numerical integration; The six-degree-of-freedom dynamics model includes translational dynamics equations and rotational dynamics equations, and the real-time meteorological data at least includes a three-dimensional wind speed vector and atmospheric density, which are involved in solving in the following way: The relative airspeed is calculated based on the aircraft state and the real-time three-dimensional wind speed, and the real-time atmospheric density is used to calculate the aerodynamic force and moment; the aerodynamic force and moment are substituted into the translational dynamics equations and the rotational dynamics equations to calculate the derivative of the state vector; The core position update formula of the fourth-order Runge-Kutta method is:

[0033] Wherein, and are the position vectors at the current moment and the next moment respectively; k1, k2, k3, k4 are the slope estimates calculated based on the six-degree-of-freedom dynamics equations at four intermediate states respectively formed by superimposing , , , , increments on the current state and the current state The random wind speed is generated by a turbulence statistical model, and the random aerodynamic force increment and moment increment caused thereby are calculated, and the random aerodynamic force increment and moment increment are added to the six-degree-of-freedom dynamics equation as disturbance terms; Exemplarily, for the "DJI Matrice 350 RTK" simulation entity, in the first simulation step: The current state vector : position (e.g. 120.2°, 30.3°, 50m), velocity 5m / s (e.g. take-off acceleration phase), attitude angle (e.g. 0°, 0°, 90°) (i.e. heading east) Real-time meteorological data is obtained from the airspace digital twin model environment interface: wind field vector (e.g. eastward wind speed 3m / s, northward wind speed 1m / s), atmospheric density 1.225kg / m 3 ; The six-degree-of-freedom dynamics equation is solved by the fourth-order Runge-Kutta method, and the intermediate state slope is calculated: k1: based on The position increment slope is calculated (e.g. x direction 0.00005° / ms, y direction 0.00001° / ms, z direction 0m / ms) k2: Superimposed The post-computation slope is (x direction 0.000052° / ms) k3: Superimposed The post-computation slope is (x direction 0.000051° / ms) k4: Superimposed The post-computation slope is (x direction 0.000053° / ms) Substitute the position update formula , the next time position (120.200026°, 30.300005°, 50m) is obtained, and the velocity is increased to 6m / s; Specifically, the correlation solving process of the six-degree-of-freedom dynamics equation and the meteorological data is as follows: In step S32, the core of driving the simulation aircraft entity is to perform state solving based on a six-degree-of-freedom dynamics model. The model is composed of translational dynamics equations and rotational dynamics equations, and the aerodynamic force, moment and external disturbance in the model are accurately corrected through real-time meteorological data obtained from the airspace digital twin model environment data interface: First, the form of the six-degree-of-freedom dynamics equation is The translational dynamics equation is expressed in the body coordinate system as:

[0034] Wherein, m is the mass of the aircraft, is the acceleration vector in the body frame, is the aerodynamic force vector, is the engine thrust vector, is the gravity vector, is the disturbance force introduced by the weather data; The rotational dynamics equation is expressed in the body frame as:

[0035] where, is the inertia tensor of the aircraft, is the angular acceleration vector in the body frame, is the angular velocity vector, is the aerodynamic moment vector, is the thrust moment vector, is the disturbance moment introduced by the weather data; Then the way weather data is associated with the dynamics equation Weather data is embedded in the dynamics equation by modifying the aerodynamic force / moment calculation and introducing additional disturbance terms, including: Relative airspeed calculation: Aerodynamic force calculation relies on the airspeed of the aircraft relative to the airflow; let the velocity of the aircraft in the body frame be The real-time three-dimensional wind speed in the ground frame is The velocity of the aircraft in the body frame is converted to the body frame by the attitude conversion matrix Then the relative airspeed is:

[0036] The relative airspeed is used to calculate the aerodynamic force and moment; Atmospheric density correction: Real-time atmospheric density obtained from the environmental data interface is directly used for aerodynamic force and thrust calculation, the formula is:

[0037] where, is the reference area, is the aerodynamic coefficient vector, which is a function of the angle of attack , side slip angle ; Unsteady weather disturbance modeling: For unsteady weather such as turbulence, random wind speed is generated by the Dryden or von Karman model , and the random aerodynamic force increment and moment increment caused by the random wind speed​ , the aerodynamic force increment is added to the dynamics equation , the moment increment is added to the dynamics equation ; Finally, the state solving process combined with numerical integration When the fourth-order Runge-Kutta method is used for numerical integration in step S32, the calculation processes of the slope estimates k1, k2, k3, and k4 are all closely coupled with real-time meteorological data: When k1 is calculated, the current state and the meteorological data corresponding to the current time and position are used to calculate the derivative of the dynamics equation ; When k1, k2, k3, and k4 are calculated, the intermediate states , , are used, and real-time meteorological data for the spatial positions corresponding to the intermediate states are reacquired from the environmental data interface to calculate the derivative values and ; In the above manner, the coupling of the aircraft dynamics model and the spatiotemporally varying meteorological field is achieved, ensuring that the simulation and deduction results realistically reflect the real flight environment; S33. The new states of all simulated aircraft entities after solving are aggregated to form global, time-stamped real-time simulation flight situation data, which is published to the shared data area; By way of example, after completing one simulation step, the new states of all simulated aircrafts (assuming a total of 5 aircrafts) are aggregated to generate global flight situation data with a time stamp of “10:00:00.050”, which includes the positions, speeds, and attitudes of the aircrafts, and is published to the shared data area for calling by the conflict detection module; S4. During the simulation process, airspace conflict detection and warning are performed based on the simulation flight situation and the airspace digital twin model; The specific steps of step S4 are as follows: S41. For each simulated aircraft entity, the instantaneous protective radius is dynamically calculated according to the instantaneous speed v, the system preset reaction time t, and the safety tolerance constant C, and a spherical safety space following the movement of the simulated aircraft entity is constructed according to the instantaneous protective radius; The safety tolerance constant C in step S41 is dynamically adjusted according to the preset risk level of the grid cell in which the aircraft is currently located:

[0038] wherein is the basic tolerance constant, Risk impact coefficient, from the grid cell attributes of the airspace digital twin model in real time; Exemplarily, for the "DJI Matrice 350 RTK" simulation entity, the real-time speed v = 15 m / s, the system preset reaction time t = 4 s, the current grid cell risk level = 0.3 (ordinary logistics airspace, medium risk), the basic fault tolerance constant = 15 m, the risk impact coefficient = 0.5, according to the formula , the safety fault tolerance constant C = 15 (1 + 0.50.3) = 17.25 m is calculated; and according to , the instantaneous protection radius R = 15 * 4 + 17.25 = 77.25 m is obtained, and a spherical safety space with a radius of 77.25 m is constructed with the current position as the center; S42. Through the airspace grid engine, the spherical safety space is converted into a covered grid code set, and the grid code set is marked as a safety envelope grid set of the simulation aircraft entity ; Exemplarily, through the airspace grid engine, the spherical safety space is converted into a covered grid code set, which contains 12 grid cells in total and is marked as a safety envelope grid set Gsafe = {3000200010, 3000200011,..., 3000200021}; S43. Start a parallel computing task to perform two types of grid intersection judgments: Perform inter-aircraft collision judgment: Real-time comparison of the safety envelope grid set of any two simulation aircraft entities and , if ∩ ≠∅, it is judged that there is a collision risk; Perform airspace intrusion conflict judgment: Real-time comparison of the safety envelope grid set of each simulation aircraft entity and the predefined no-fly zone, restricted area grid set , if , it is judged as airspace intrusion; Exemplarily, start a parallel computing task to perform two types of conflict judgment: Inter-aircraft collision judgment: Real-time comparison of the safety envelope grid of this entity and another unmanned aerial vehicle (model JiFei P40, = {3000200018,..., 3000200028}) is found and The intersection of the two sets is {3000200018, 3000200019} determines that there is a collision risk; airspace intrusion conflict determination: compare with the no-fly zone grid set (Airport clearance zone grid code "3000199900-3000199950"), no intersection, no airspace intrusion risk; S44. If a conflict is detected, generate structured warning information containing conflict type, involved entities, conflict grid location, and expected occurrence time, and publish to the warning information pool; Exemplarily, generate structured warning information: { "Conflict Type": "Inter-aircraft collision risk", "Involved Entities": ["DJI Matrice 350 RTK (ID: D-001)", "JiFei P40 (ID: J-002)"], "Conflict Grid Location": ["3000200018", "3000200019"], "Expected Occurrence Time": "10:08:30", "Risk Level": "Medium" } Publish the warning information to the warning information pool; S5. Output simulation deduction results, three-dimensional visualization of simulation flight situation data and warning information, and generate analysis report containing conflict statistics and airspace assessment; The specific steps of step S5 are as follows: S51. The three-dimensional visualization rendering engine receives and parses simulation flight situation data and warning information in real time through WebSocket long connection, and performs dynamic graphic rendering on the visualization client as follows: Drive the simulation aircraft model to move, highlight the conflict area, and draw the flight trajectory line; Exemplarily, the three-dimensional visualization rendering engine receives situation data and warning information through WebSocket long connection, and performs dynamic rendering on the large-screen command cabin client: Drive the "DJI Matrice 350 RTK" model to fly along the route, and update the position and attitude in real time; Highlight the conflict grid "3000200018-3000200019" in red, and label the conflict type; Draw a blue trajectory line from the takeoff point to the current position, and the thickness of the trajectory line changes with the speed (the faster the speed, the thicker the line); S52. The visualization client provides a user interaction interface, responding to view angle switching, pausing / resuming simulation, and clicking on an entity to view details; Exemplarily, the visualization client supports a large-screen command cabin, displaying a global three-dimensional situation, and supporting mouse-dragging view angle switching (top view, side view); Pausing / resuming simulation is performed, and the detailed parameters of the "D-001" entity are viewed (70% of the remaining power, and an estimated endurance of 18 minutes); The operation and maintenance personnel mobile terminal: receives early warning push and clicks to jump to the conflict area map view; S53. After the simulation deduction is completed or at a specified time point, the data analysis service is automatically run, based on the simulation flight situation data and early warning data throughout the process, to generate a structured analysis report in PDF and Excel formats, and the report content at least includes: Total flight times, conflict event list, air space capacity utilization rate statistics, and safety score of each route, and is automatically uploaded to a specified file server or pushed to a specified user; Exemplarily, after the simulation deduction is completed (10:15), an analysis report is automatically generated: Total flight times: 5 times Conflict event list: 1 medium collision risk (D-001 and J-002), no airspace intrusion Airspace capacity utilization rate: current airspace capacity 20 times, utilization rate 25% Safety score of each route: D-001 route safety score 82 points (penalty item is 1 medium conflict) The report is automatically uploaded to the enterprise file server in PDF and Excel formats, and is pushed to the air space administrator mailbox.

[0039] In this embodiment, a Level=15 high-precision air space digital twin model is constructed for a certain city's unmanned aerial vehicle logistics air space, 11m×11m×5m grid-level control is realized, based on a six-degree-of-freedom dynamics model and a fourth-order Runge-Kutta method, the unmanned aerial vehicle flight state is accurately simulated, the simulation step is 50ms, which meets the real-time requirement, through dynamic safety envelope and parallel conflict detection, the collision risk between aircrafts is successfully identified, the early warning response time is <100ms, combined with multi-terminal visualization and structured report, the air space administrator is provided with full-process support from simulation to early warning to decision-making, and the operation safety and management efficiency of the low-altitude logistics air space are improved.

[0040] It should be understood that the size of the serial number of each step in the above embodiment does not mean the order of execution, the execution order of each process should be determined according to its function and inherent logic, and should not constitute any limitation on the implementation process of the embodiment of the present application.

[0041] As Figure 2As shown, the following is an embodiment of a space domain simulation deduction system based on digital twinning provided by the embodiments of the present disclosure, which belongs to the same inventive concept as the space domain simulation deduction method based on digital twinning described above. Details not described in the embodiment of the space domain simulation deduction system based on digital twinning can be referred to the embodiment of the space domain simulation deduction method based on digital twinning described above.

[0042] The system comprises: a space domain digital twinning model construction module, configured to integrate geographic base data and space domain element data, and perform gridding based on a space domain grid engine to construct a space domain digital twinning model; a simulation entity mapping module, configured to receive at least one flight plan and map the flight plan to the space domain digital twinning model to generate a corresponding simulation aircraft entity; a flight dynamics simulation engine module, configured to drive the simulation aircraft entity to perform flight simulation in the space domain digital twinning model based on a preset simulation engine, and calculate a simulation flight situation in real time according to a six-degree-of-freedom dynamics model bound to the simulation aircraft entity and in combination with real-time meteorological data obtained from an environment data interface of the space domain digital twinning model; a parallel conflict detection and early warning module, configured to perform space domain conflict detection and early warning based on the simulation flight situation and the space domain digital twinning model during the simulation; a three-dimensional visualization and decision support module, configured to output simulation deduction results, perform three-dimensional visualization output on the simulation flight situation data and early warning information, and generate an analysis report containing conflict statistics and space domain evaluation.

[0043] The embodiments of the present disclosure realize accurate space domain modeling and simulation, improve flight safety, optimize space domain management, and provide intelligent decision support through the interaction and cooperation of the space domain digital twinning model construction module, the simulation entity mapping module, the flight dynamics simulation engine module, the parallel conflict detection and early warning module, and the three-dimensional visualization and decision support module.

[0044] The electronic device related in the embodiments of the present application does not constitute a limitation on the electronic device. The electronic device can include more or fewer components than those shown, or a combination of some components, or different arrangement of components. In the embodiments of the present application, the electronic device includes but is not limited to a laptop computer, a desktop computer, a workstation, a personal digital assistant, a server, a blade server, a mainframe computer, and other suitable computers. The electronic device can also represent various forms of mobile devices, such as a personal digital processor, a cellular phone, a smart phone, a wearable device, and other similar computing devices. The components shown herein, their connections, and relationships, and their functions, are meant to be examples only, and are not intended to limit the implementations of the embodiments of the present application described and / or claimed herein.

[0045] The electronic device can include a processor, an external memory interface, an internal memory, a universal serial bus (USB) interface, a charging management module, a power management module, a battery, a wireless communication module, an audio module, a speaker, a microphone, a sensor module, a key, a camera, a display screen, and a SIM card interface, etc.

[0046] It can be understood that the structure shown in the embodiments of the present application does not constitute a specific limitation on the electronic device. In other embodiments of the present application, the electronic device can include more or fewer components than those shown, or a combination of some components, or split some components, or different arrangement of components. The components shown can be implemented in hardware, software, or a combination of software and hardware.

[0047] The processor can include one or more processing units, such as: the processor can include a central processing unit (CPU), an application processor (AP), a modem processor, a graphics processing unit (GPU), an image signal processor (ISP), a controller, a memory, a video codec, a digital signal processor (DSP), a baseband processor, and / or a neural-network processing unit (NPU), etc. Different processing units can be independent devices, or can be integrated in one or more processors.

[0048] The processor can be the nerve center and command center of the electronic device. The controller can generate operation control signals according to instruction operation codes and timing signals, and complete the control of fetching and executing instructions.

[0049] The memory in the processor can also be configured to store instructions and data. In some embodiments, the memory in the processor is a cache memory. The memory can store instructions or data that have just been used or recycled by the processor. If the processor needs to use the instructions or data again, it can directly call them from the memory. This avoids repeated access and reduces the waiting time of the processor, thereby improving the efficiency of the system.

[0050] The electronic device realizes the integration of the integrated geographic base data and airspace element data based on the airspace simulation deduction method of the digital twin of the application, and performs gridding based on the airspace grid engine to construct an airspace digital twin model; receives at least one flight plan and maps the flight plan to the airspace digital twin model to generate a corresponding simulation aircraft entity; based on a preset simulation engine, drives the simulation aircraft entity to fly in the airspace digital twin model, and according to a six-degree-of-freedom dynamics model bound to the simulation aircraft entity and in combination with real-time weather data obtained from an environment data interface of the airspace digital twin model, calculates a simulation flight situation in real time; in the simulation process, airspace conflict detection and early warning are performed based on the simulation flight situation and the airspace digital twin model; the simulation deduction result is output, the simulation flight situation data and the early warning information are output in three dimensions, and an analysis report containing conflict statistics and airspace evaluation is generated, achieving the beneficial effects of constructing an airspace model through a digital twin, fusing flight plans and real-time weather data, accurately simulating a flight situation, timely detecting conflicts and giving early warnings, presenting results through three-dimensional visualization, generating a detailed analysis report, and improving airspace management efficiency and flight safety.

[0051] In the storage medium provided in the application, a program product capable of realizing the airspace simulation deduction method based on the digital twin is stored.

[0052] The airspace simulation deduction method based on digital twinning comprises: integrating geographic base data and airspace element data, and performing gridding based on an airspace grid engine to construct an airspace digital twinning model; receiving at least one flight plan, and mapping the flight plan to the airspace digital twinning model to generate a corresponding simulation aircraft entity; based on a preset simulation engine, driving the simulation aircraft entity to perform flight simulation in the airspace digital twinning model, and calculating a simulation flight situation in real time according to a six-degree-of-freedom dynamics model bound to the simulation aircraft entity and in combination with real-time meteorological data obtained from an environment data interface of the airspace digital twinning model; in the simulation process, performing airspace conflict detection and early warning based on the simulation flight situation and the airspace digital twinning model; and outputting simulation deduction results, performing three-dimensional visualization output on simulation flight situation data and early warning information, and generating an analysis report containing conflict statistics and airspace evaluation.

[0053] In some possible implementation manners, the airspace simulation deduction method based on digital twinning of the present disclosure can be implemented in the form of a program product, which comprises program codes for causing a terminal device to perform the steps according to various exemplary embodiments of the present disclosure described in the above “Exemplary Method” section of the present specification when the program product is run on the terminal device.

[0054] The storage medium of the present disclosure can adopt any combination of one or more readable media. The readable medium can be a readable signal medium or a readable storage medium. The readable storage medium may, for example, be but is not limited to an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, device or component, or any combination of the above. More specific examples (non-exhaustive list) of readable storage media include an electrical connection having one or more wires, a portable disc, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the above.

[0055] The above description of disclosed embodiments enables a person skilled in the art to implement or use the present application. Various modifications to these embodiments will be apparent to those skilled in the art, and the general principles defined herein can be implemented in other embodiments without departing from the spirit or scope of the present application. Therefore, the present application will not be limited to these embodiments shown herein, but will conform to the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A spatial domain simulation and extrapolation method based on digital twins, characterized in that, Includes the following steps: S1. Integrate geographic baseline data and airspace element data, and grid them based on the airspace grid engine to build an airspace digital twin model; S2. Receive at least one flight plan and map the flight plan to the airspace digital twin model to generate the corresponding simulated aircraft entity; S3. Based on the preset simulation engine, drive the simulated aircraft entity to perform flight simulation in the airspace digital twin model, and calculate the simulated flight status in real time according to the six-degree-of-freedom dynamic model bound to the simulated aircraft entity and combined with the real-time meteorological data obtained from the environmental data interface of the airspace digital twin model. S4. During the simulation process, airspace conflict detection and early warning are carried out based on the simulated flight situation and the airspace digital twin model; S5. Output simulation results, output simulated flight situation data and early warning information in three dimensions, and generate an analysis report including conflict statistics and airspace assessment.

2. The spatial domain simulation and deduction method based on digital twins according to claim 1, characterized in that, The specific steps of step S1 are as follows: S11. Obtain digital elevation models and remote sensing images from geographic information systems as three-dimensional spatial data, obtain structured airspace element data from airspace management systems, and perform coordinate unification and format standardization processing on the obtained data. S12. Call the spatial grid engine of the discrete global grid system of the Earth to divide the preprocessed three-dimensional geospatial data and spatial element data into multi-level grid units, and generate globally unique grid code data for each grid unit. S13. Associate the grid-coded data with the specific values ​​of the corresponding three-dimensional spatial data and spatial element data, and construct a digital spatial twin in memory as a unified spatial benchmark for simulation in order to perform spatial query and analysis.

3. The spatial domain simulation and deduction method based on digital twins according to claim 2, characterized in that, In step S12, the spatial grid engine performs a test on any spatial point P( , , The encoding calculation process is as follows: S121. Determine the target mesh level based on the accuracy requirements of the application scenario; S122. Based on the target grid level, find the corresponding latitude and longitude span Δ and height span H, which are used as grid resolution parameters; S123. Calculate the latitude and longitude plane grid row and column number (i,j) and height layer k of spatial point P: Where i represents the row number of the latitude and longitude planar grid, j represents the column number of the latitude and longitude planar grid, and k represents the altitude level. and Here are the latitude and longitude coordinates of the starting point in the southwest corner of the coding area, and H is the preset level height reference. , , These are the latitude, longitude, and altitude of the aircraft, respectively. S124. Using the Z-order space filling curve, the three-dimensional index (i,j,k) is bit-crossed and merged to generate an integer grid code.

4. The spatial domain simulation and deduction method based on digital twins according to claim 1, characterized in that, The specific steps of step S2 are as follows: S21. Receive and parse the JSON or XML format flight plan data stream from the external system, and extract the planned route, aircraft model, performance parameters and planned timetable; S22. Using the aircraft model as the keyword, query the local aircraft dynamics model library and load the corresponding six-degree-of-freedom dynamics model, mass characteristics, aerodynamic parameters and performance envelope into the simulation environment; S23. At the initial moment of the simulation, based on the resolved planned flight path, create the simulated aircraft entity at the corresponding grid position of the airspace digital twin model, and bind the loaded six-degree-of-freedom dynamic model and performance envelope to the simulated aircraft entity to complete the mapping from static plan to dynamic entity.

5. The spatial domain simulation and deduction method based on digital twins according to claim 4, characterized in that, The specific steps of step S3 are as follows: S31. The simulation engine periodically advances the simulation time with a fixed simulation step size Δt; S32. For each simulated aircraft entity, within each simulation step Δt, based on the bound six-degree-of-freedom dynamic model, synthesize the current state vector. In addition, real-time meteorological data obtained from the environmental data interface of the airspace digital twin model are used to solve for the new state vector at the next moment through numerical integration. The current state vector This includes current position, velocity, and attitude; the real-time meteorological data includes real-time wind field vectors and atmospheric density data. S33. Aggregate the new states of all simulated aircraft entities after calculation to form global, timestamp-consistent real-time simulated flight situation data, and publish it to the shared data area.

6. The spatial domain simulation and deduction method based on digital twins according to claim 5, characterized in that, In step S32, the fourth-order Runge-Kutta method is used for numerical integration; The six-degree-of-freedom dynamic model includes translational and rotational dynamic equations. The real-time meteorological data includes at least three-dimensional wind speed vectors and atmospheric density, and participates in the solution process in the following ways: The relative airspeed is calculated based on the aircraft's state and real-time three-dimensional wind speed, and the aerodynamic forces and moments are calculated using real-time atmospheric density. The aerodynamic forces and moments are then substituted into the translational and rotational dynamic equations to calculate the derivative of the state vector. The core position update formula for the fourth-order Runge-Kutta method is as follows: in, and These are the current and next time position vectors, respectively; k1, k2, k3, and k4 are based on the six-degree-of-freedom dynamic equations and represent the current state. and current status Superimpose , , The slope estimates are calculated for the four intermediate states formed by the increment; when calculating each slope estimate, real-time meteorological data corresponding to the spatial location of each intermediate state must be obtained from the environmental data interface.

7. The spatial domain simulation and deduction method based on digital twins according to claim 5, characterized in that, The specific steps of step S4 are as follows: S41. For each simulated aircraft entity, dynamically calculate the instantaneous protection radius based on the instantaneous velocity v, the system's preset reaction time t, and the safety tolerance constant C. And based on the instantaneous protection radius, a spherical safety space that follows the movement of the simulated aircraft entity is constructed; S42. Using the airspace mesh engine, the spherical safety space is converted into the covered mesh coding set, and the mesh coding set is marked as the safety envelope mesh set of the simulated aircraft entity. ; S43. Start the parallel computing task and perform the intersection judgment of the two types of grids: Perform inter-aircraft conflict assessment: Real-time comparison of the safety envelope mesh sets of any two simulated aircraft entities and ,like If so, it is determined that there is a risk of collision; Perform airspace intrusion conflict assessment: Real-time comparison of the safety envelope mesh set of each simulated aircraft entity With predefined no-fly zone and restricted zone grid sets ,like If so, it is determined to be an airspace intrusion; S44. If a conflict is detected, generate a structured warning message containing the conflict type, involved entities, conflict grid location, and expected occurrence time, and publish it to the warning message pool.

8. The spatial domain simulation and deduction method based on digital twins according to claim 5, characterized in that, In step S41, the safety tolerance constant C is based on the preset risk level of the grid cell in which the aircraft is currently located. Make dynamic adjustments: in, Based on the fault tolerance constant, This is the risk impact coefficient. Obtained in real time from the grid cell properties of the spatial domain digital twin model.

9. The spatial domain simulation and deduction method based on digital twins according to claim 7, characterized in that, The specific steps of step S5 are as follows: The S51 3D visualization rendering engine receives and parses simulated flight situation data and early warning information in real time via a WebSocket long connection, and performs the following dynamic graphics rendering on the visualization client: Drive the simulated aircraft model to move, highlight conflict areas, and draw flight trajectory lines; S52. The visualization client provides a user interaction interface, responding to viewpoint switching, pausing / resuming simulation, and selecting entities to view details; S53. After the simulation is completed or at a specified time, automatically run the data analysis service to generate a structured analysis report in PDF and Excel formats based on the simulation flight situation data and early warning data throughout the process. The report content should include at least: The system automatically uploads total flight counts, conflict event lists, airspace capacity utilization statistics, and safety scores for each route to a designated file server or pushes them to a designated user.

10. A spatial simulation and deduction system based on digital twins, characterized in that, include: The airspace digital twin model building module is used to integrate geographic base data and airspace element data, and to build an airspace digital twin model based on the airspace grid engine. The simulation entity mapping module is used to receive at least one flight plan and map the flight plan to the airspace digital twin model to generate the corresponding simulated aircraft entity; The flight dynamics simulation engine module is used to drive the simulated aircraft entity to perform flight simulation in the airspace digital twin model based on a preset simulation engine. It also calculates the simulated flight status in real time based on the six-degree-of-freedom dynamic model bound to the simulated aircraft entity and real-time meteorological data obtained from the environmental data interface of the airspace digital twin model. The parallel conflict detection and early warning module is used to perform airspace conflict detection and early warning based on the simulated flight situation and the airspace digital twin model during the simulation process. The 3D visualization and decision support module is used to output simulation results, visualize simulated flight situation data and early warning information in 3D, and generate analysis reports that include conflict statistics and airspace assessment.

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