Low-altitude pilot ai model training application system

Abstract

The application discloses a low-altitude pilot AI model training application system and relates to the technical field of aviation training.The system comprises a data module, an intelligent decision module, a flight simulation module, a communication module and an interface module.The data module comprises an airplane type database, a flight operation knowledge base, a meteorological data interface and an airspace and airport database.The airplane type database is used for storing detailed information of various airplanes eVTOL.The low-altitude pilot AI model training application system integrates the airplane type database, the flight operation knowledge base, real-time meteorological data and the airspace and airport database, thereby providing a highly simulated training environment for pilots, which can effectively improve the response capability of pilots under complex weather and special airspace conditions, significantly reduce training costs, shorten the training cycle and improve the authenticity of flight simulation through the built-in intelligent decision module to monitor and evaluate the flight state in real time.

Description

TECHNICAL FIELD

[0001] The application relates to the technical field of aviation training, in particular to an AI model training application system for low-altitude pilots. BACKGROUND

[0002] With the accelerated layout of the urban air mobility (UAM) industry, eVTOL aircraft, as a representative of the new generation of green aircraft, has entered the commercial landing stage globally. By 2025, more than 20 cities have launched air traffic pilot projects globally, and the six-rotor compound wing aircraft developed by Chinese eVTOL manufacturers has completed a cumulative 100,000 hours of flight tests. It is estimated that the market size of urban low-altitude travel will exceed 100 billion yuan by 2030. This technological innovation has put forward new requirements for pilot training systems: the unique vertical take-off and landing characteristics, distributed electric propulsion system and full-electric control mode of eVTOL make the skill transfer of traditional fixed-wing or helicopter pilots face challenges; at the same time, urban low-altitude flight needs to deal with complex scenarios such as three-dimensional obstacle avoidance of building groups, UAV cluster cooperation and dynamic airspace management, and puts forward higher requirements for the multi-modal interactive decision-making ability of pilots.

[0003] The existing training system has room for improvement in response to new demands: traditional flight training relies on a combination of physical machine flight and basic simulators, and the cost of training a single pilot is relatively high, and the actual flight proportion of special situation handling training is about 15%. The mainstream flight simulator is mostly based on preset routes and standard weather conditions to build training scenarios, and there are technical bottlenecks in complex scenario simulation and dynamic scenario construction. In addition, the existing system still has room for improvement in terms of personalized training mechanism and deep utilization of training data, and a more perfect pilot operation feature and training effectiveness correlation model needs to be built. In view of this, we propose an AI model training application system for low-altitude pilots. SUMMARY

[0004] To solve the above technical problems, the low-altitude pilot AI model training application system is provided, which solves the above problems.

[0005] To achieve the above purposes, the technical scheme adopted by the application is:

[0006] The low-altitude pilot AI model training application system comprises a data module, an intelligent decision-making module, a flight simulation module, a communication module and an interface module.

[0007] The data module comprises: an aircraft type database, a flight operation knowledge base, a meteorological data interface, and an airspace and airport database; the aircraft type database is used to store detailed information of various types of aircraft eVTOL, including: model, performance parameters, and handling characteristics; the flight operation knowledge base is used to store flight operation procedures and skills; the meteorological data interface is used to connect external meteorological sources to obtain meteorological information; and the airspace and airport database is used to store airspace and airport related data;

[0008] The intelligent decision module comprises: a flight state evaluation unit, a task planning unit, and a decision making unit; the flight state evaluation unit is used to monitor the simulated aircraft state and compare it with the standard; the task planning unit is used to make a task plan according to the knowledge base and the environment; and the decision making unit is used to make an operation decision through an artificial intelligence algorithm;

[0009] The flight simulation module is used to simulate the physical movement of the aircraft;

[0010] The communication module is used to realize communication with the simulated control environment and the local pilot;

[0011] The interface module is used to provide an interface for integration with the flight simulation training device and instructor configuration.

[0012] Preferably, in the data module, the calculation formula of the maximum speed of the aircraft performance parameters in the aircraft type database is: ,

[0013] wherein, Vmax is the maximum speed of the aircraft, Tmax is the maximum thrust of the aircraft, ρ is the air density, S is the wing area;

[0014] The cruising speed is determined according to the engine efficiency curve of the aircraft and the flight height factor, and is the speed range that meets the engine battery power consumption rate at a preset height;

[0015] The handling characteristics are measured by the force feedback coefficient according to the sensitivity of the control stick, when the pilot applies force , the deflection angle of the control surface is , and the value of is different for different aircraft models, which is determined by the mechanical structure and hydraulic / electrical transmission system design of the aircraft;

[0016] In the flight operation knowledge base, the take-off operation procedure includes an initial acceleration stage set according to the aircraft type on the runway, and the acceleration calculation formula is:

[0017] ,

[0018] wherein, For the aircraft acceleration, For the engine thrust, For the aircraft resistance, For the aircraft mass;

[0019] When the speed reaches the take-off decision speed , according to the aircraft lift coefficient and the speed to determine whether to meet the take-off conditions, and then perform the wheel lifting operation, wherein the expression of the aircraft lift coefficient and the speed is:

[0020] ,

[0021] In the formula, is the lift, is the airspeed, is the lift coefficient;

[0022] And throughout the process, keep the specified frequency of communication with air traffic control, report the status of the aircraft;

[0023] The landing operation procedure involves determining the glide path angle according to the airport runway conditions and weather information, guiding the aircraft through the instrument landing system, and accurately controlling the sink rate at the landing moment to meet:

[0024] ,

[0025] In the formula, is the sink rate at the landing moment, is the aircraft weight, is the glide path angle, which ensures the smooth landing of the aircraft and provides the pilot with comprehensive and accurate operation reference;

[0026] Among the weather information obtained by the weather data interface, the influence of wind speed on the flight trajectory of the aircraft is calculated through the aircraft ground speed calculation formula:

[0027] ,

[0028] Where is the ground speed, is the airspeed, is the wind speed, is the angle between the wind direction and the aircraft heading;

[0029] Air temperature affects air density, which in turn changes aircraft performance. According to the ideal gas state equation: ,

[0030] Where is the air pressure, is the gas constant, For air temperature, the parameters of aircraft engine thrust and lift at different air temperatures need to be adjusted accordingly, and the AI model of low-altitude pilots adjusts flight operation decisions in real time according to changes in meteorological factors;

[0031] In the airspace and airport database, for airspace structure information, the control area is divided according to the International Civil Aviation Organization standard;

[0032] Above a certain height layer is a high-altitude control area, and below is a low-altitude control area. Different control areas have different communication frequencies and control rules;

[0033] The flight altitude layer allocation rule follows the standard barometric altitude layer setting, which is divided at intervals of 300 meters;

[0034] In terms of airport data, the runway length and direction determine the aircraft take-off and landing performance requirements and operation mode.

[0035] Preferably, the flight state evaluation unit monitors the method steps of comparing the simulated aircraft state with the standard:

[0036] Based on the flight simulation module, real-time state data of the simulated aircraft is obtained, and standard state data corresponding to the flight phase and task is extracted from the flight operation knowledge base;

[0037] Based on the collected data, the data is preprocessed, which includes removing noise, outliers and missing values, and the cleaned data is standardized;

[0038] Compare the preprocessed real-time state data with the standard state data parameter by parameter. For each state parameter, calculate the difference between it and the standard value. According to the comparison results of each parameter, use a weighted comprehensive evaluation method to evaluate the overall flight state of the aircraft;

[0039] Generate a detailed flight state report based on the evaluation results, which includes the comparison results of each state parameter and the comprehensive evaluation index, and feed the flight state report back to the intelligent decision module and the interface module.

[0040] Preferably, the flight state evaluation unit of the intelligent decision module, when monitoring the aircraft power, calculates the remaining power according to the aircraft engine battery power consumption rate and flight time : ,

[0041] Wherein, is the remaining power, is the initial power;

[0042] When the power is lower than the safety threshold, an alarm is sent in time and the nearest suitable airport is planned for emergency landing;

[0043] When assessing aircraft attitude stability, the rate of change of the aircraft's pitch angle, roll angle, and yaw angle is measured. If the rate of change exceeds the preset stability range, the aircraft dynamics model is used to determine whether there is a risk of loss of control and corresponding adjustment measures are taken.

[0044] Preferably, when planning the optimal route, the task planning unit of the intelligent decision-making module uses Dijkstra's algorithm to find the shortest path from the starting point to the destination;

[0045] The navigation points in the airspace are regarded as nodes in a graph, and the distance between nodes is the flight cost. The path cost is weighted by comprehensively considering weather conditions, airspace restrictions and other aircraft dynamic factors.

[0046] In areas with strong headwinds, the cost of that route is increased. When avoiding busy airspace, high-cost obstacle areas are set up to plan safe and efficient flight routes to meet the needs of different mission scenarios.

[0047] Preferably, the specific method by which the decision-making unit formulates operational decisions using artificial intelligence algorithms is as follows:

[0048] Collect flight status data, mission plan data, and environmental data, and normalize the data.

[0049] Principal component analysis is used to identify the core variables that influence decision-making. These core variables include, but are not limited to, the sensitivity of velocity to heading deviation, the dynamic correlation between mission priority and flight path, and a multidimensional feature vector containing time series data and static parameters is constructed.

[0050] The model network architecture design adopts a multi-layer feedforward neural network, with the input layer corresponding to the feature dimension, the hidden layer determining the number of neurons through trial and error, and the output layer mapping to decision instructions;

[0051] A composite loss function is defined to combine flight safety and mission efficiency. An adaptive optimization algorithm is used in conjunction with backpropagation to adjust the weights, and a cross-validation strategy is adopted to train the model.

[0052] Based on the trained algorithm model, real-time data is input into the trained model to output operation instructions. The compliance of the decision is verified by combining the rule engine, and the network weights are updated by continuously collecting flight result feedback through reinforcement learning algorithm to achieve online learning.

[0053] Preferably, when the decision-making unit is trained using a reinforcement learning algorithm, a reward function is set: ,

[0054] in A positive reward is given for flight safety when an aircraft maintains a safe distance from other aircraft while not exceeding its speed limit, not falling below its stall speed, and maintaining a safe distance from other aircraft. Rewards will be given for completing the mission, arriving at the destination on time, and accurately executing air traffic control instructions; As an operational efficiency reward, a reward will be given if the aircraft flies at an economical battery power consumption rate and selects the optimal flight altitude.

[0055] Through continuous iterative training, the model can make optimal decisions in complex flight scenarios, thereby improving the rationality and accuracy of simulated aircraft behavior.

[0056] Preferably, the flight simulation module simulates the physical motion of the aircraft, specifically including:

[0057] S1: Model decomposition: The physical motion of the eVTOL aircraft is divided into rigid body dynamics, electric propulsion system power / torque calculation and atmospheric environment simulation subsystems, and non-causal modeling is performed using Modelica;

[0058] S2: Differential Equation Solving: The differential equation solving logic of multi-motor cooperative control is parsed through XML files, and a six-degree-of-freedom nonlinear equation system is established based on the aircraft mass, inertia and battery-motor response;

[0059] S3: Environmental impact calculation: Combining the urban boundary layer meteorological model, dynamically calculate the impact of low-level turbulence and heat island effect on lift;

[0060] S4: Real-time computing and interaction: Design a real-time calculation engine to interact with the flight control module through a shared memory bus, and integrate algorithms for building cluster flow field and urban canyon wind shear to correct aerodynamic parameters.

[0061] S5: Multi-mode switching: Activate the rotor dynamic coupling model during vertical takeoff and landing, and switch the ground effect compensation algorithm during landing to determine stability by motor torque and fuselage sway angle;

[0062] S6: Power Redundancy and Safety: Set a power redundancy threshold and use a model predictive controller to adjust the rotor speed to maintain attitude stability;

[0063] S7: Data Comparison and Parameter Optimization: By comparing flight test and simulation data, the dynamic parameters of different eVTOL configurations are switched through a modular configuration library. The dynamic distribution strategy is optimized using an adversarial generative network, and the turbulence compensation coefficient is iteratively updated.

[0064] Preferably, in the flight simulation module, when the aircraft dynamics model calculates the aircraft's motion under various forces and moments, for lift, it uses the aircraft lift coefficient. Calculation of the expression relating to velocity;

[0065] By fitting wind tunnel test data and flight test data, different conditions were obtained. Value; Resistance:

[0066] ,

[0067] Among them, the drag coefficient It includes parasitic drag coefficient and induced drag coefficient, which are related to aircraft shape and wing aspect ratio, respectively.

[0068] Preferably, during communication between the communication module and the SATCE communication unit, the aircraft position information is sent to the control system at a frequency of 1Hz using the ADS-B data transmission protocol, with a position accuracy of within 10 meters, a speed accuracy of 0.5 knots, and an altitude accuracy of 50 feet. At the same time, control commands are received, with a command transmission delay of less than 0.5 seconds.

[0069] Compared with the prior art, the beneficial effects of the present invention are as follows:

[0070] The low-altitude pilot AI model training application system proposed in this invention provides pilots with a highly simulated training environment by integrating aircraft type databases, flight operation knowledge bases, real-time meteorological data, and airspace and airport databases. This not only effectively improves pilots' ability to cope with complex weather and special airspace conditions, but also significantly reduces training costs and shortens the training cycle. Through the built-in intelligent decision-making module, it can monitor and evaluate flight status in real time, intelligently plan routes and make decisions based on environmental and mission requirements, which greatly improves the realism and challenge of flight simulation. Through continuous iterative training using reinforcement learning algorithms, the AI ​​model can make optimal decisions in complex flight scenarios, thereby improving the rationality and accuracy of simulated aircraft behavior. It can also assess cognitive load and stress response in real time based on the trainee's biometrics, thereby developing personalized training plans. Attached Figure Description

[0071] Fig. 1 A framework diagram of a low-altitude pilot AI model training application system module.

[0072] Fig. 2 The overall architecture diagram of the AI ​​model training application system for low-altitude pilots. Detailed Implementation

[0073] The following description is intended to disclose the invention and enable those skilled in the art to implement it. The preferred embodiments described below are merely examples, and other obvious variations will occur to those skilled in the art.

[0074] Reference Figs. 1-2 As shown, the low-altitude pilot AI model training application system includes: a data module, an intelligent decision-making module, a flight simulation module, a communication module, and an interface module.

[0075] Data module

[0076] The aircraft type database stores detailed information on various eVTOL aircraft, such as model, performance parameters, and handling characteristics. This categorized storage of different aircraft provides fundamental data support for subsequent flight simulations and decision-making.

[0077] The flight operations knowledge base encompasses a wealth of flight operation procedures and techniques, including operational steps for key phases such as takeoff and landing. During takeoff, the initial acceleration phase on the runway is set according to the aircraft type, and acceleration is calculated based on factors such as engine thrust, aircraft drag, and mass. Once the speed reaches the takeoff decision speed, the relationship between the aircraft's lift coefficient and speed is used to determine if takeoff conditions are met, leading to the execution of the takeoff wheel lift. Throughout this process, communication with air traffic control is maintained at a prescribed frequency, reporting the aircraft's status. For landing, the glide slope angle is determined based on airport runway conditions and weather information. The instrument landing system guides the aircraft, precisely controlling the descent rate at the moment of landing to ensure a smooth landing, providing pilots with comprehensive and accurate operational references.

[0078] The meteorological data interface connects to external meteorological sources to obtain meteorological information. Wind speed affects aircraft flight trajectory, and this effect is reflected by calculating ground speed using a specific formula. Temperature affects air density, which in turn changes aircraft performance. The system adjusts aircraft engine thrust and lift parameters accordingly based on the ideal gas law at different temperatures, enabling the low-altitude pilot AI model to adjust flight operation decisions in real time based on changes in meteorological factors.

[0079] The airspace and airport database stores airspace and airport-related data. Regarding airspace structure information, controlled area divisions are based on ICAO standards. Areas above a specific altitude are considered high-altitude controlled areas, while those below are considered low-altitude controlled areas. Different controlled areas have different communication frequencies and control rules. Furthermore, flight altitude allocation follows standard barometric altitude settings, divided in 300-meter intervals. Aircraft must fly at the designated altitudes when planning flight routes to avoid conflicts. In airport data, runway length and orientation determine aircraft takeoff and landing performance requirements and operating procedures.

[0080] Intelligent decision-making module

[0081] The flight status assessment unit acquires real-time status data of the simulated aircraft based on the flight simulation module and extracts standard status data corresponding to the flight phase and mission from the flight operation knowledge base. First, the collected data is preprocessed to remove noise, outliers, and missing values, followed by standardization. Then, the preprocessed real-time status data is compared parameter by parameter with the standard status data, calculating the difference between each status parameter and the standard value. A weighted comprehensive evaluation method is then used to assess the overall flight status of the aircraft. For example, when monitoring the aircraft's battery power, the remaining battery power is calculated based on the aircraft engine battery power consumption rate and flight time. When the battery power falls below a safe threshold, an alarm is issued promptly, and the nearest suitable airport is planned for an emergency landing. When assessing the aircraft's attitude stability, the rate of change of the aircraft's pitch, roll, and yaw angles is measured. If these exceed the preset stability range, the aircraft dynamics model is used to determine if there is a risk of loss of control and corresponding adjustment measures are taken. Finally, a detailed flight status report is generated and fed back to the intelligent decision-making module and the interface module.

[0082] When planning the optimal flight route, the mission planning unit uses the Dijkstra algorithm to find the shortest path from the starting point to the destination. Navigation points in the airspace are treated as nodes in a graph, and the distance between nodes represents the flight cost. The path cost is weighted by comprehensively considering weather conditions, airspace restrictions, and other aircraft dynamics. For example, the cost of this path segment is increased in areas with strong headwinds, and high-cost obstacle areas are set to avoid busy airspace, thereby planning a safe and efficient flight route to meet the needs of different mission scenarios.

[0083] The decision-making unit first collects and normalizes flight status data, mission plan data, and environmental data. Then, it uses principal component analysis to identify core variables influencing decision-making and constructs a multi-dimensional feature vector containing time-series data and static parameters. The model's network architecture employs a multi-layer feedforward neural network. The number of hidden layer neurons is determined through trial and error, and the output layer is mapped to decision commands. A composite loss function is defined to integrate flight safety and mission efficiency. An adaptive optimization algorithm combined with backpropagation is used to adjust weights, and a cross-validation strategy is employed to train the model. After training, real-time data is input into the model to output operational commands. A rule engine is used to verify decision compliance. Furthermore, a reinforcement learning algorithm continuously collects flight result feedback and updates network weights to achieve online learning. When using reinforcement learning for training, a reward function is set that includes rewards for flight safety, mission completion, and operational efficiency. Through iterative training, the model makes optimal decisions in complex flight scenarios, improving the rationality and accuracy of simulated aircraft behavior.

[0084] Flight simulation module

[0085] First, the model is decomposed, dividing the physical motion of the eVTOL aircraft into rigid body dynamics, electric propulsion system power / torque calculation, and atmospheric environment simulation subsystems, using Modelica for non-causal modeling. Next, the differential equation solution logic for multi-motor cooperative control is parsed using XML files, establishing a six-degree-of-freedom nonlinear equation system based on aircraft mass, inertia, and battery-motor response. Then, combined with an urban boundary layer meteorological model, the impact of low-altitude turbulence and heat island effect on lift is dynamically calculated. A real-time calculation engine is designed, interacting with the flight control module via a shared memory bus, integrating algorithms for building cluster flow field and urban canyon wind shear to correct aerodynamic parameters. During vertical takeoff and landing, the rotor dynamic coupling model is activated, and the ground effect compensation algorithm is switched during landing, with stability determined by motor torque and fuselage yaw angle. A power redundancy threshold is set, and a model predictive controller is used to adjust rotor speed to maintain attitude stability. Finally, flight test and simulation data are compared. A modular configuration library enables switching of dynamic parameters for different eVTOL configurations, generative adversarial networks optimize the power distribution strategy, and turbulence compensation coefficients are iteratively updated to achieve effective simulation of the aircraft's physical motion.

[0086] Communication module

[0087] During communication between the communication module and the SATCE communication unit, the ADS-B data transmission protocol is used to send aircraft position information to the air traffic control system at a frequency of 1Hz, with a position accuracy within 10 meters, a speed accuracy of 0.5 knots, and an altitude accuracy of 50 feet. Simultaneously, air traffic control commands are received, with a command transmission delay of less than 0.5 seconds. This ensures that the simulated aircraft responds promptly to air traffic control commands, coordinates with other aircraft and the air traffic control system, conforms to actual air traffic control rules and procedures, provides pilots with a realistic airspace traffic scenario, and effectively improves pilots' communication and coordination capabilities in actual flight.

[0088] Interface module

[0089] The interface module is mainly used to provide an interface for integration with flight simulation training equipment and for instructor configuration, which facilitates the connection between the system and external devices and the instructor's settings and management of the training process, so that the entire training system can better adapt to different training needs and scenarios.

[0090] The foregoing has shown and described the basic principles, main features, and advantages of the present invention. Those skilled in the art should understand that the present invention is not limited to the above embodiments. The embodiments and descriptions in the specification are merely principles of the invention. Various changes and modifications can be made to the invention without departing from its spirit and scope, and all such changes and modifications fall within the scope of the claimed invention. The scope of protection of the present invention is defined by the appended claims and their equivalents.

Claims

1. A low-altitude pilot AI model training application system, characterized in that, Comprise: Data module, intelligent decision module, flight simulation module, communication module and interface module; The data module comprises: an aircraft type database, a flight operation knowledge base, a meteorological data interface and an airspace and airport database; the aircraft type database is used for storing detailed information of various types of aircraft eVTOL, the detailed information comprising: model, performance parameter and maneuvering characteristic; the flight operation knowledge base is used for storing flight operation procedures and skills; the meteorological data interface is used for connecting external meteorological sources to obtain meteorological information; the airspace and airport database is used for storing airspace and airport related data; The intelligent decision module comprises: a flight state evaluation unit, a task planning unit and a decision making unit; the flight state evaluation unit is used for monitoring the simulation aircraft state and comparing with the standard; the task planning unit is used for making task plan according to the knowledge base and environment; the decision making unit is used for making operation decision through artificial intelligence algorithm; The flight simulation module is used for simulating the physical motion of the aircraft, specifically comprising: S1: model decomposition: the physical motion of eVTOL aircraft is divided into rigid body dynamics, electric propulsion system power / torque calculation and atmospheric environment simulation subsystem, and non-causal modeling is adopted by using Modelica; S2: differential equation solving: the differential equation solving logic of multi-motor cooperative control is parsed through XML file, and six-degree-of-freedom nonlinear equation set is established based on aircraft mass, inertia and battery-motor response; S3: environmental impact calculation: combined with urban boundary layer meteorological model, the influence of low-altitude turbulence and heat island effect on lift is dynamically calculated; S4: real-time calculation and interaction: a real-time calculation engine is designed, data is interacted with the flight control module through a shared memory bus, and building group flow field and urban canyon wind shear algorithm is integrated to correct aerodynamic parameters; S5: multi-modal switching: in the vertical take-off and landing stage, the rotor power coupling model is activated, and the ground effect compensation algorithm is switched during landing, and the stability is determined by motor torque and body swing angle; S6: power redundancy and safety: set the power redundancy threshold, and use the model predictive controller to adjust the rotor speed to maintain attitude stability; S7: data comparison and parameter optimization: compare the flight test data and simulation data, switch the kinetic parameters of different eVTOL configurations through the modular configuration library, optimize the power distribution strategy by using the generative adversarial network, and iteratively update the turbulence compensation coefficient; The communication module is used for realizing communication with the simulation control environment and the local pilot; The interface module is used for providing integration with the flight simulation training equipment and instructor configuration interface.

2. The low altitude pilot AI model training application system according to claim 1, wherein, In the data module, the maximum speed calculation formula for the aircraft performance parameters in the aircraft type database is: , wherein Vmax is the maximum speed of the aircraft, Tmax is the maximum thrust of the aircraft, ρ is the air density, S is the wing area; The cruising speed is determined according to the engine efficiency curve and the flight altitude factor, and the speed range that meets the engine battery power consumption rate at the preset altitude; The handling qualities are measured in terms of stick sensitivity by a force feedback coefficient When the pilot applies a force , the control surface deflection angle , for different aircraft models , is different, depending on the mechanical structure of the aircraft and the hydraulic / electrical transmission system design; In the flight operation knowledge base, the take-off operation procedure includes an initial acceleration phase on the runway set according to the type of the aircraft, and the acceleration calculation formula is: , wherein is the aircraft acceleration, is the engine thrust, is the aircraft drag, is the aircraft mass; When the speed reaches the takeoff decision speed After that, according to the aircraft lift coefficient and the speed to determine whether to meet the takeoff conditions, and then perform the lift wheel operation, wherein the aircraft lift coefficient and the speed expression is: , wherein is the lift, is the airspeed, is the lift coefficient; And in the whole process, keep the specified frequency communication with air traffic control and report the aircraft state; The landing operation procedure involves determining the glide path angle according to the airport runway conditions and meteorological information , guiding the aircraft by the instrument landing system, and accurately controlling the sink rate at the landing moment , meeting: , In the formula, is the sinking rate at the landing moment, is the weight of the airplane, is the angle of the glide path, which ensures the smooth landing of the airplane and provides the pilot with comprehensive and accurate operation reference; Among the weather information obtained by the weather data interface, the influence of the wind speed on the flight trajectory of the aircraft is calculated through the aircraft ground speed calculation formula: ; wherein Vg is ground speed, Va is airspeed, Vw is wind speed, is the angle between the wind direction and the aircraft heading; Air temperature affects air density, which in turn changes aircraft performance. According to the ideal gas state equation: , wherein is the air pressure, is the gas constant, is the air temperature, the parameters of the aircraft engine thrust and lift need to be adjusted accordingly at different air temperatures, and the low-altitude pilot AI model adjusts the flight operation decision in real time according to the change of meteorological factors; In the airspace and airport database, for airspace structure information, the control area is divided according to the International Civil Aviation Organization standard; Above a certain height layer is the high-altitude control area, and below is the low-altitude control area, different control areas have different communication frequencies and control rules; The flight altitude layer distribution rule follows the standard barometric altitude layer setting, and is divided at intervals of 300 meters. In terms of airport data, runway length and direction determine the performance requirements and operating mode of the aircraft.

3. The low altitude pilot AI model training application system of claim 1, wherein, The flight state evaluation unit monitors the simulated aircraft state and compares it with the standard as follows: Real-time state data of the simulated aircraft is obtained based on the flight simulation module, and standard state data corresponding to the flight phase and task is extracted from the flight operation knowledge base. Based on the collected data, the data is preprocessed, including removing noise, outliers and missing values, and the cleaned data is standardized. The preprocessed real-time state data is compared with the standard state data parameter by parameter. For each state parameter, the difference between it and the standard value is calculated. Based on the comparison results of each parameter, a weighted comprehensive evaluation method is used to evaluate the overall flight state of the aircraft. A detailed flight state report is generated based on the evaluation results, which includes the comparison results of each state parameter and the comprehensive evaluation index. The flight state report is fed back to the intelligent decision-making module and the interface module.

4. The low altitude pilot AI model training application system of claim 3, wherein, The flight state evaluation unit of the intelligent decision module, when monitoring the power of the aircraft, calculates the remaining power according to the power consumption rate of the engine battery of the aircraft And the time of flight ,​ wherein, is the remaining amount of power, is the initial amount of power; When the power is lower than the safety threshold, an alarm is sent out in time and the nearest suitable airport is planned for emergency landing. When evaluating the attitude stability of the aircraft, the change rates of pitch angle, roll angle and yaw angle are measured. If the change rates exceed the preset stable range, it is judged whether there is a loss of control risk based on the aircraft dynamics model, and appropriate adjustment measures are taken.

5. The low altitude pilot AI model training application system of claim 1, wherein, The task planning unit of the intelligent decision-making module uses Dijkstra's algorithm to find the shortest path from the starting point to the destination when planning the optimal route. The navigation points in the airspace are regarded as nodes of a graph, and the distance between nodes is the flight cost. The path cost is weighted by considering weather conditions, airspace restrictions and other aircraft dynamic factors. In areas with strong headwinds, the cost of this section of the path is increased, and when avoiding busy airspace, a high-cost obstacle region is set, so as to plan a safe and efficient flight route that meets the needs of different task scenarios.

6. The low altitude pilot AI model training application system of claim 1, wherein, The decision-making unit formulates operation decisions through artificial intelligence algorithms as follows: Flight state data, task planning data and environmental data are collected and normalized. The principal component analysis technique is used to identify core variables that affect decision-making, including speed sensitivity to heading deviation, task priority and dynamic correlation of flight path. A multi-dimensional feature vector containing time series data and static parameters is constructed. The model network architecture is designed using a multi-layer feedforward neural network. The input layer corresponds to the feature dimension, the number of neurons in the hidden layer is determined by trial and error, and the output layer is mapped to the decision instruction. A composite loss function is defined to integrate flight safety and task efficiency. An adaptive optimization algorithm is used to adjust the weights combined with backpropagation. The model is trained using a cross-validation strategy. Based on the trained algorithm model, real-time data is input into the trained model to output operation instructions. The decision compliance is verified by a rule engine, and the network weights are updated through reinforcement learning algorithm to realize online learning.

7. The low altitude pilot AI model training application system according to claim 6, wherein, When the decision-making unit trains by using the reinforcement learning algorithm, a reward function is set: ; wherein a flight safety reward, positive reward is given when the aircraft maintains non-excessive speed, non-lower than the stall speed, and safe separation from other aircraft; a mission completion reward, reward is given for timely arrival at the destination, accurate execution of air traffic control instructions, etc.; an operation efficiency reward, reward is given if the aircraft flies at an economic battery power consumption rate, and selects the optimal flight altitude layer; Through continuous iteration and training, the model makes optimal decisions in complex flight scenarios, improving the rationality and accuracy of the simulated aircraft behavior.

8. The low altitude pilot AI model training application system of claim 1, wherein, In the flight simulation module, the aircraft dynamics model, in calculating the motion of the aircraft under the action of various forces and moments, for the lift, calculates the lift coefficient as a function of the speed. The drag values in different states are fitted by wind tunnel test data and flight test data values; drag: ; where, for airspeed, for wing area, drag coefficient includes parasite drag coefficient and induced drag coefficient, which are related to the aircraft configuration and wing aspect ratio, respectively.

9. The low altitude pilot AI model training application system of claim 1, wherein, In the communication process, the communication module and the SATCE communication unit use ADS-B data transmission protocol to send the aircraft position information to the control system at a frequency of 1 Hz, with a position accuracy of within 10 meters, a speed accuracy of 0.5 knots, and a height accuracy of 50 feet. Meanwhile, the control instructions are received, and the instruction transmission delay is less than 0.5 seconds.

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