Aviation flight training simulation system based on trajectory simulation and workload distribution

Abstract

The present invention relates to the technical field of aviation simulation training, and specifically discloses an aviation flight training simulation system based on trajectory simulation and workload distribution. The system comprises three modules: an aviation flight training simulation model construction module constructs a preliminary simulation model by debugging aircraft model data, and determines its availability through fluctuation parameter monitoring; a multi-captain seat collaboration and air traffic control scenario simulation module uses the available model for general aviation flight subjects and trajectory simulation, realizes captain seat simulation in a unique organizational manner, and solves the captain seat load distribution problem in control training; an aviation flight training effectiveness evaluation module monitors the simulation process, obtains training indicators to determine the effectiveness of the simulation; the system forms a complete closed loop from model construction to scenario simulation to effect evaluation, can improve the authenticity and efficiency of aviation training, reduce the cost and risk of real aircraft training, and is suitable for the construction of a standardized and intelligent training system in the general aviation field.

Description

Technical Field

[0001] The present invention relates to the technical field of aviation simulation training, in particular to an aviation flight training simulation system based on track simulation and workload distribution. Background Art

[0002] The current aviation flight training simulation system achieves high-precision motion simulation through flight dynamics modeling, using nonlinear six-degree-of-freedom equations combined with the characteristics of the atmosphere, engines, etc.; it uses a professional engine to build high-resolution, high-refresh rate 3D vision, and provides a realistic visual and motion experience with a six-degree-of-freedom platform; it uses sensors to collect flight data and ensures accuracy through signal processing and fusion; it also simulates weather, terrain, airports and other environments, integrates aviation regulations and flight procedures, and supports route planning and emergency drills; the hardware relies on high-performance computers, high-precision input devices and high-quality display systems, and the software adopts a modular architecture to ensure the realization and scalability of system functions; the air traffic control training simulation in the civil aviation field is all aimed at flight transport flights, and its principle is to determine fixed route routes, fixed approach procedures, fixed departure procedures, and fixed approach procedures according to the flight take-off and landing airports to simulate flight transport flights, which realizes the simulation of flight transport flights more realistically.

[0003] For example, the Chinese invention patent with publication number: CN119091724A discloses an environmental simulation system based on an aviation flight trainer, which belongs to the field of aviation flight training technology, including a processor module; a main control center; a surround display screen module; an environmental simulation module; a flight simulation module; a special effect simulation module; a scene database module; the surround display screen module and the scene database module system provide highly realistic terrain simulation, including digital elevation data and satellite image data, the environmental simulation module simulates various meteorological conditions such as cloudy, sunny, rainy, and snowy, the surround display screen module adopts a spherical screen and a multi-channel projection display system, and cooperates with an image fusion correction system to provide immersive visual display, and the special effect simulation module supports users to customize special animation effects.

[0004] For example, the Chinese invention patent with publication number CN118942310A discloses an avionics simulation system for aircraft flight simulation training, which relates to the field of simulation training technology. The system includes: a data acquisition module for acquiring radio station environment data of an airport, the radio station environment data including communication station data and navigation station data; a communication simulation module for acquiring pilot setting data, and establishing a wireless communication connection when the setting data matches the communication station data; a navigation simulation module for acquiring radio data and simulating the radio data into a navigation signal. The navigation simulation module also determines the radio bearing of the aircraft relative to the navigation station based on the navigation signal, and converts the radio bearing into a corresponding distance.

[0005] However, in the process of implementing the technical solutions of the invention in the embodiments of the present application, the above-mentioned technology has at least the following technical problems: flights in the general aviation field are characterized by "small aircraft, slow speed, low altitude, and flexible and changeable subjects", and flight missions are complex and changeable. Usually, control units cannot obtain the flight missions of the crew in advance, and the flight path cannot be simply determined by the take-off and landing airports. The flight subjects usually change during the flight. Therefore, the existing technology cannot realize the simulation of general aviation flight subjects and air traffic control simulation training for general aviation flights. Summary of the Invention

[0006] In view of the shortcomings of the existing technology, the present invention provides an aviation flight training simulation system based on trajectory simulation and workload distribution, which can effectively solve the problems involved in the above-mentioned background technology.

[0007] To achieve the above objectives, the present invention is implemented through the following technical solutions: an aviation flight training simulation system based on trajectory simulation and workload distribution, including an aviation flight training simulation model construction module, which is used to preliminarily construct a preliminary aviation flight training simulation model and monitor the operating fluctuation parameters of the preliminary aviation flight training simulation model, thereby judging whether the preliminary aviation flight training simulation model can be put into use; a multi-captain seat coordination and air traffic control scenario simulation module, which marks the preliminary aviation flight training simulation model that can be put into use as an aviation flight training simulation model, and uses the aviation flight training simulation model to perform flight subject simulation and trajectory simulation in the general aviation field, and simultaneously realize captain seat simulation, thereby solving the load problem of the simulated captain seat in control training; an aviation flight training effectiveness evaluation module, which is used to monitor the simulation process of the aviation flight training simulation model, obtain aviation flight training indicators during the simulation process, and thereby judge whether the aviation flight training simulation model can be effectively simulated.

[0008] Compared with the prior art, the embodiments of the present invention have at least the following advantages or beneficial effects:

[0009] (1) The present invention provides an aviation flight training simulation system based on trajectory simulation and workload distribution. Through the close collaboration of three modules, a full-process system covering model construction, scenario simulation, and effect verification is constructed. By accurately constructing simulation models, deeply simulating multi-pilot collaboration and air traffic control scenarios, and scientifically evaluating training effects, the present invention not only reduces the cost and risk of real-machine training, but also optimizes training content in a targeted manner, improves pilots' ability to respond in complex scenarios, and helps general aviation training develop in an efficient, safe, and intelligent direction, providing solid technical support for general aviation talent training and industry safe operation.

[0010] (2) The aviation flight training simulation model construction module lays a solid foundation for the entire simulation system by constructing a preliminary simulation model for general aviation flight training and monitoring its operation fluctuations. It can repeatedly debug based on a large amount of aircraft model data to establish an accurate general aviation aircraft model database to ensure that the model is close to the actual flight characteristics. At the same time, by monitoring and evaluating the model's operation fluctuation parameters, it strictly controls the model availability to avoid training failures due to model errors, and provides a reliable model basis for subsequent training scenario simulation and effect evaluation, thus ensuring the accuracy and stability of general aviation flight training simulation from the source.

[0011] (3) The multi-captain seat coordination and air traffic control scenario simulation module relies on the general aviation flight training simulation model to simulate flight subjects and trajectories. Through a unique captain seat simulation organization method, it solves the load problem of the simulated captain seat in control training. In order to solve the operational difficulties of turning with DME distance as the turning timing in the "through-cloud" route, it helps pilots to accurately grasp the turning timing and improve their adaptability to operations in complex environments. This module restores the multi-captain coordination and air traffic control scenarios, allowing pilots to adapt to the command and dispatch and teamwork in real flights in advance, enhance the authenticity and practicality of training, and accumulate experience for responding to actual flight missions.

[0012] (4) Aviation flight training effectiveness evaluation module, through comprehensive monitoring of the simulation process of general aviation flight training simulation model, obtains key training indicators and conducts scientific evaluation, accurately judging the effectiveness of the simulation system; on the one hand, it can timely discover the deviation between the simulation process and the actual flight effect, and provide data support for optimizing the simulation model and training plan; on the other hand, through quantitative evaluation of training effects, it ensures that pilots truly master the required flight skills in simulation training, improves the conversion rate of training results, realizes strict control of the quality of general aviation flight training, and ensures that the training goals are highly consistent with actual flight needs. BRIEF DESCRIPTION OF THE DRAWINGS

[0013] The present invention is further described with reference to the accompanying drawings. However, the embodiments in the accompanying drawings do not constitute any limitation to the present invention. A person skilled in the art can obtain other drawings based on the following drawings without creative effort.

[0014] Figure 1 This is a schematic diagram of system module connections of the present invention.

[0015] Figure 2 It is a schematic diagram of the motion fluctuation evaluation process of the preliminary simulation model of the present invention.

[0016] Figure 3 It is a schematic diagram of the secondary optimization process of the preliminary simulation model of the present invention.

[0017] Figure 4It is a schematic diagram of the flight subject simulation and track simulation process of the present invention.

[0018] Figure 5 It is a schematic diagram of the effective evaluation process of simulation training of the present invention.

[0019] Figure 6 The figure is a schematic diagram of the early warning process of the aviation flight training simulation model of the present invention. DETAILED DESCRIPTION

[0020] The technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, rather than all the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of the present invention.

[0021] See Figure 1 As shown, an embodiment of the present invention provides a technical solution: an aviation flight training simulation system based on trajectory simulation and workload distribution, including an aviation flight training simulation model construction module, a multi-captain seat collaboration and air traffic control scenario simulation module, an aviation flight training effectiveness evaluation module and a simulation database.

[0022] The aviation flight training simulation model construction module and the multi-captain seat collaboration and air traffic control scenario simulation module are connected, and the multi-captain seat collaboration and air traffic control scenario simulation module and the aviation flight training effectiveness evaluation module are connected. The three are collectively connected to the simulation database; the above-mentioned simulation database is used to store various parameters involved in the aviation flight training simulation system based on trajectory simulation and workload distribution.

[0023] The aviation flight training simulation model construction module is used to preliminarily construct the aviation flight training preliminary simulation model and monitor the operating fluctuation parameters of the aviation flight training preliminary simulation model, so as to determine whether the aviation flight training preliminary simulation model can be put into use; the multi-captain seat coordination and air traffic control scenario simulation module marks the aviation flight training preliminary simulation model that can be put into use as an aviation flight training simulation model, and uses the aviation flight training simulation model to perform flight subject simulation and trajectory simulation in the general aviation field, and realize captain seat simulation at the same time, so as to solve the load problem of the simulated captain seat in control training; the aviation flight training effectiveness evaluation module is used to monitor the simulation process of the aviation flight training simulation model, obtain the aviation flight training indicators during the simulation process, and determine whether the aviation flight training simulation model can be effectively simulated.

[0024] This system uses the DRS2008 (Digital Radar Simulation) system, the 2008 version of the Digital Radar Simulation System, to simulate the ADS-B monitoring system. The above-mentioned preliminary construction of the preliminary simulation model for aviation flight training refers to obtaining data of various aircraft models through aircraft model manuals and production data statistics, and using the system's built-in "AIRCRAFT PARAMATERS" module to repeatedly debug aircraft parameters, thereby constructing a preliminary simulation model for aviation flight training that integrates aircraft dynamics models, visual simulation systems, control input processing, and evaluation and feedback systems.

[0025] Specifically, the operating fluctuation parameters of the preliminary simulation model for aviation flight training are monitored, and the specific analysis process is as follows: the operating fluctuation parameters of the preliminary simulation model for aviation flight training include the physical engine step jitter factor of the preliminary simulation model for aviation flight training, the normal overload peak-to-valley difference factor of the preliminary simulation model for aviation flight training, and the visual brightness gradient integral factor of the preliminary simulation model for aviation flight training.

[0026] The above-mentioned physical engine step jitter factor represents the relative level of the physical engine step jitter duration of the preliminary simulation model for aviation flight training and the defined physical engine step jitter duration, and its value is the ratio of the two; the above-mentioned normal overload peak-to-valley difference factor represents the relative level of the normal overload peak-to-valley difference value of the preliminary simulation model for aviation flight training and the defined normal overload peak-to-valley difference value, and its value is the ratio of the two; the above-mentioned visual brightness gradient integral factor represents the difference between the visual brightness gradient integral value of the preliminary simulation model for aviation flight training and the defined visual brightness gradient integral value, and intuitively reflects the degree of deviation between the two.

[0027] The influence coefficients are introduced from the simulation database to quantify the influence of the physical engine step jitter factor, the normal overload peak-to-valley difference factor, and the visual brightness gradient integral factor on the preliminary simulation model operation fluctuation assessment value. The various degrees are integrated to obtain the preliminary simulation model operation fluctuation assessment value. The preliminary simulation model operation fluctuation assessment value represents the degree of fluctuation during the operation of the preliminary simulation model for aviation flight training. The specific evaluation method is as follows:

[0028]

[0029] Wherein, KLOP is the operational fluctuation assessment value of the preliminary simulation model, DSF is the physical engine step size jitter factor of the preliminary simulation model for aviation flight training, GHJ is the physical engine step size jitter duration of the preliminary simulation model for aviation flight training, KLP is the defined physical engine step size jitter duration preset in the simulation database, CVB is the normal overload peak-to-valley difference factor of the preliminary simulation model for aviation flight training, UJF is the normal overload peak-to-valley difference value of the preliminary simulation model for aviation flight training, BDA is the defined normal overload peak-to-valley difference value preset in the simulation database, UFN is the scene brightness gradient integral factor of the preliminary simulation model for aviation flight training, MJK is the scene brightness gradient integral value of the preliminary simulation model for aviation flight training, LGB is the reference scene brightness gradient integral value preset in the simulation database, θ1 is the influence coefficient corresponding to the physical engine step size jitter factor preset in the simulation database, θ2 is the influence coefficient corresponding to the normal overload peak-to-valley difference factor preset in the simulation database, and θ3 is the influence coefficient corresponding to the scene brightness gradient integral factor preset in the simulation database.

[0030] The above-mentioned physics engine step jitter duration indicates the degree of deviation between the actual calculation time of each time step and the ideal fixed step length when the physics engine executes the simulation. The step length monitoring is enabled through the engine configuration, and the timestamp is collected in real time using the programming interface to calculate the absolute value of the actual step length minus the theoretical value; the above-mentioned normal overload peak-to-valley difference refers to the absolute difference between the maximum value (peak value) and the minimum value (valley value) of the normal overload during the simulation process. Monitoring points are selected in the simulation model to collect normal overload data in real time. The maximum and minimum values ​​are extracted by traversing and analyzing the time series, and the absolute difference is calculated to obtain the peak-to-valley difference; the above-mentioned scene brightness gradient integral value refers to the overall amplitude of brightness changes in the scene. The scene is rendered by the simulation engine and brightness data is extracted. The gradient operator is used to calculate the brightness change rate of each pixel, and the gradient modulus values ​​of the entire image are summed to obtain the scene brightness gradient integral value.

[0031] The above-mentioned definition of the physical engine step jitter duration indicates the maximum value of the physical engine step jitter duration within the specified range; the above-mentioned definition of the normal overload peak-to-valley difference indicates the maximum value of the normal overload peak-to-valley difference within the specified range; the above-mentioned reference scene brightness gradient integral value indicates the standard value of the scene brightness gradient integral value as a reference.

[0032] The simulation database stores the mapping relationships between the physical engine step jitter factor, normal overload peak and valley factor, and visual brightness gradient integral factor and their influence coefficients respectively; among them, the influence coefficient of each factor refers to the degree of influence of the unit value corresponding to each factor on the preliminary simulation model operation fluctuation assessment value; when the above three factors are input into the simulation database, the database will generate the corresponding influence coefficient based on the preset mapping rules, and the numerical range of each coefficient is strictly controlled between 0 and 1.

[0033] The jitter of the physics engine step size will cause high-frequency noise in the acceleration calculation, resulting in the peak-to-valley difference (the difference between the maximum and minimum values) of the normal overload being amplified or distorted; the jitter of the physics engine step size will increase the overload peak-to-valley difference, causing the vision system to misjudge it as a "high-overload scene" and incorrectly adjust the brightness gradient, thereby causing the vision brightness gradient integral factor to deviate and the visual simulation to be out of touch with the real overload scene; when the vision brightness gradient integral factor increases (such as entering the cloud shadow area or the day-night transition period), the scene brightness mutation rate intensifies, resulting in high-frequency peaks and valleys in the normal overload alternating in a short period of time, and the peak-to-valley difference will increase accordingly.

[0034] The larger the physics engine step jitter factor, the lower the physics engine calculation stability (such as fluctuations in the interval for solving the dynamic equations), which directly leads to increased model operation fluctuations; the larger the normal overload peak-to-valley difference factor, the more unstable the model operation state will be, pushing up the operation fluctuation assessment value; the larger the scene brightness gradient integral factor, the greater the deviation of the scene brightness gradient from the standard value (such as incorrect simulation of brightness mutations in strong light scenes), which can easily trigger chain fluctuations in the model dynamic parameters, and ultimately increase the operation fluctuation assessment value.

[0035] To determine whether the preliminary simulation model for aviation flight training can be put into use, the specific judgment process is: compare the preliminary simulation model operation fluctuation assessment value with the preliminary simulation model operation fluctuation assessment threshold preset in the simulation database; the above-mentioned preliminary simulation model operation fluctuation assessment threshold represents the maximum value of the preliminary simulation model operation fluctuation assessment value within the specified range.

[0036] If the preliminary simulation model operation fluctuation evaluation value is less than or equal to the preliminary simulation model operation fluctuation evaluation threshold, then the preliminary simulation model for aviation flight training is judged to be able to be put into use, and the preliminary simulation model for aviation flight training is marked as an aviation flight training simulation model; if the preliminary simulation model operation fluctuation evaluation value is greater than the preliminary simulation model operation fluctuation evaluation threshold, then the preliminary simulation model for aviation flight training is judged not to be able to be put into use, and the preliminary simulation model for aviation flight training is optimized at the same time. The specific optimization process is: based on the preliminary simulation model operation fluctuation evaluation value and the preliminary simulation model operation fluctuation evaluation threshold, the preliminary simulation model operation fluctuation evaluation deviation value is obtained, and the numerical integration step reduction coefficient is matched based on the preliminary simulation model operation fluctuation evaluation deviation value, thereby reducing the numerical integration step of the dynamic model, refining the time interval, significantly improving the simulation calculation accuracy, accurately capturing the dynamic changes of the aircraft under complex maneuvers and extreme working conditions, and avoiding state jump errors; at the same time, enhancing numerical stability, preventing model oscillation or divergence caused by excessive calculation step size, and ensuring the simulation reliability of complex scenarios such as multi-aircraft collaboration.

[0037] The above-mentioned acquisition of the preliminary simulation model operation fluctuation assessment deviation value refers to the result of subtracting the preliminary simulation model operation fluctuation assessment value from the preliminary simulation model operation fluctuation assessment threshold value; the above-mentioned matching of the numerical integration step reduction coefficient based on the preliminary simulation model operation fluctuation assessment deviation value, the specific matching process is: the simulation database pre-stores the mapping relationship between the preliminary simulation model operation fluctuation assessment deviation value interval and the numerical integration step reduction coefficient, enters the actual deviation value and then matches the corresponding interval, obtains the reduction coefficient of the interval, and multiplies it by the original integration step to obtain the adjusted numerical integration step; the above-mentioned numerical integration step reduction coefficient is less than 1, which indicates the proportion of the reduction in the numerical integration step of the dynamic model.

[0038] The optimized preliminary simulation model operation fluctuation evaluation value is obtained and marked as the simulation model secondary fluctuation evaluation value, and at the same time, it is determined whether the preliminary simulation model for aviation flight training should be optimized secondary.

[0039] Figure 2 As shown in the schematic diagram of the preliminary simulation model motion fluctuation evaluation process of the present invention, the process starts with obtaining the preliminary simulation model operation fluctuation evaluation value and comparing it with the preliminary simulation model operation fluctuation evaluation threshold: if the preliminary simulation model operation fluctuation evaluation value is less than or equal to the preliminary simulation model operation fluctuation evaluation threshold, it is determined that the preliminary simulation model can be put into use without adjustment; if the preliminary simulation model operation fluctuation evaluation value is greater than the preliminary simulation model operation fluctuation evaluation threshold, the numerical integration step of the dynamic model is reduced based on the preliminary simulation model operation fluctuation evaluation deviation value, and then the simulation model secondary fluctuation evaluation value is obtained. If the simulation model secondary fluctuation evaluation value is less than or equal to the preliminary simulation model operation fluctuation evaluation threshold, the model is updated and put into use; otherwise, the preliminary simulation model for aviation flight training is optimized secondary.

[0040] Specifically, it is determined whether to perform secondary optimization on the preliminary simulation model of aviation flight training. The specific judgment process is: comparing the secondary fluctuation evaluation value of the simulation model with the preliminary simulation model operation fluctuation evaluation threshold; if the secondary fluctuation evaluation value of the simulation model is less than or equal to the preliminary simulation model operation fluctuation evaluation threshold, then it is determined not to perform secondary optimization on the preliminary simulation model of aviation flight training, and at the same time, the preliminary simulation model of aviation flight training is updated and put into use, and the preliminary simulation model of aviation flight training is marked as an aviation flight training simulation model; the above-mentioned updating of the preliminary simulation model of aviation flight training refers to updating the adjusted numerical integration step size to the preliminary simulation model of aviation flight training.

[0041] If the secondary fluctuation evaluation value of the simulation model is greater than the operating fluctuation evaluation threshold of the preliminary simulation model, it is determined that the preliminary simulation model for aviation flight training is subject to secondary optimization. The specific optimization process is as follows: based on the secondary fluctuation evaluation value of the simulation model and the operating fluctuation evaluation threshold of the preliminary simulation model, the secondary fluctuation evaluation deviation value of the simulation model is obtained; based on the secondary fluctuation evaluation deviation value of the simulation model, the numerical integration step size is quadratically reduced by matching the coefficient of the numerical integration step size, thereby further reducing the numerical integration step size of the dynamic model; based on the secondary fluctuation evaluation deviation value of the simulation model, the random seed reset period increase coefficient of the particle system is matched, thereby increasing the random seed reset period of the particle system of the visual system, which can significantly enhance the randomness and authenticity of particle effects in complex weather scenes, avoid the singleness of training scenes caused by repetitive patterns, help pilots deal with various natural phenomena, reduce the reset calculation load, optimize rendering efficiency, and ensure the smoothness of the picture under high-load scenes; and form a synergy with the high-precision dynamics model to ensure that the particle motion and the aircraft state changes maintain visual consistency, ultimately achieving a dual improvement in immersive training experience and complex actual combat scene simulation capabilities.

[0042] The above-mentioned acquisition of the simulation model secondary fluctuation assessment deviation value refers to the result of subtracting the simulation model secondary fluctuation assessment deviation value from the preliminary simulation model operation fluctuation assessment threshold; the above-mentioned matching of the numerical integration step size quadratic reduction coefficient based on the simulation model secondary fluctuation assessment deviation value, the specific matching process is: the simulation database pre-stores the mapping relationship between the simulation model secondary fluctuation assessment deviation value interval and the numerical integration step size quadratic reduction coefficient, after the actual deviation value is input into the database, the system automatically matches the corresponding deviation interval, obtains the reduction coefficient corresponding to the interval, and then multiplies the coefficient by the original numerical integration step size to obtain the adjusted integration step size, so as to achieve the goal of reducing the dynamic model integration step size according to the deviation ratio; the above-mentioned numerical integration step size quadratic reduction coefficient is less than 1, which means the ratio of reducing the numerical integration step size of the dynamic model, and the numerical integration step size quadratic reduction coefficient is less than the numerical integration step size reduction coefficient; the above-mentioned matching of the particle system random seed reset cycle improvement coefficient based on the simulation model secondary fluctuation assessment deviation value, the specific matching The matching process is as follows: a mapping relationship between the particle system random seed reset period improvement coefficient corresponding to the secondary fluctuation evaluation deviation value interval of each simulation model is pre-stored in the simulation database, the simulation model secondary fluctuation evaluation deviation value is input into the database, the system matches the corresponding deviation interval, obtains the improvement coefficient of the interval, multiplies it by the original reset period, and obtains the adjusted reset period, so as to realize that the random seed reset period of the visual system particle system is increased according to the deviation multiple; the above-mentioned particle system random seed reset period improvement coefficient is greater than 1, indicating that the random seed reset period of the particle system of the visual system needs to be increased by a multiple; the above-mentioned particle system random seed reset period of the visual system refers to the particle system (a rendering module used to simulate dynamic effects such as smoke, flames, rain and snow) in the aviation flight training simulation visual system by setting a random seed (a set of initial values ​​that determines the generation rules of random characteristics such as particle motion and distribution) to control the randomness of the particle effect, and the reset period refers to the time interval for the system to automatically update the random seed.

[0043] Obtain the preliminary simulation model operation fluctuation evaluation value after the secondary optimization, mark it as the simulation model three-time fluctuation evaluation value, and determine whether to issue an early warning for the preliminary simulation model of aviation flight training.

[0044] Furthermore, it is determined whether to issue an early warning to the preliminary simulation model of aviation flight training. The specific judgment process is: comparing the three fluctuation evaluation values ​​of the simulation model with the preliminary simulation model operation fluctuation evaluation threshold; if the three fluctuation evaluation values ​​of the simulation model are less than or equal to the preliminary simulation model operation fluctuation evaluation threshold, then it is determined that no early warning is issued to the preliminary simulation model of aviation flight training, and at the same time, the preliminary simulation model of aviation flight training is updated and put into use, and the preliminary simulation model of aviation flight training is marked as an aviation flight training simulation model; the above-mentioned updating of the preliminary simulation model of aviation flight training refers to updating the adjusted numerical integration step size and particle system random seed reset period to the preliminary simulation model of aviation flight training.

[0045] If the three fluctuation evaluation values ​​of the simulation model are greater than the preliminary simulation model operation fluctuation evaluation threshold, it is determined that an early warning will be issued to the preliminary simulation model of aviation flight training; the above-mentioned early warning to the preliminary simulation model of aviation flight training refers to sending the early warning information to the operation and maintenance platform.

[0046] Figure 3 As shown in the schematic diagram of the secondary optimization process of the preliminary simulation model of the present invention, the numerical integration step size of the dynamic model is further reduced based on the secondary fluctuation evaluation deviation value of the simulation model, and the random seed reset period of the particle system of the visual system is increased; finally, the tertiary fluctuation evaluation value of the simulation model is obtained: if the tertiary evaluation value of the simulation model is less than or equal to the preliminary simulation model operation fluctuation evaluation threshold, the preliminary simulation model is updated and put into use; if the tertiary evaluation value of the simulation model is greater than the preliminary simulation model operation fluctuation evaluation threshold, a warning signal is issued, and the flight subject simulation and track simulation are entered at the same time.

[0047] Specifically, the aviation flight training simulation model is used to simulate flight subjects and track in the general aviation field. The specific analysis process is as follows: the flight subject simulation in the general aviation field includes five flight subjects: one turn on a cloud-penetrating route, three turns on a cloud-penetrating route, visual route, airspace flight, and DME arc flight.

[0048] It should be explained that DRS2008 is mainly developed for flight operations; general aviation flight subjects such as DME arc, "through the cloud" route, rectangular route, one-turn timing, three-turn timing, etc. are different from transport aviation operations. Flight subjects are flexible and changeable during flight, and the current flight simulation method cannot complete the simulation; after analysis and research, the main implementation methods of this system are: fixed-point track method, segment combination and using DME distance as the one-turn timing of the "through the cloud" route; the above-mentioned fixed-point track method refers to collecting corresponding position points (including horizontal position and flight altitude) for connection, simulating commonly used complete flight tracks or commonly used partial track segments, and saving these commonly used fixed points as track factors in the database for easy recall as needed; the above-mentioned segment combination refers to real-time selection of different track segments (track factors) for combination according to commonly used control instructions And connect them to simulate a smooth actual flight trajectory; the above-mentioned "Piercing Clouds" route with DME distance as the turning timing refers to general aviation flight. The "Piercing Clouds" route is the main training content of the flight subject, and it is also the core control means for control and command to coordinate flight conflicts; usually after the aircraft takes off, the pilot makes an initial report to the controller while ascending on one side. The controller usually commands the aircraft to join the "Piercing Clouds" route. The turning timing of the "Piercing Clouds" route is an extremely important means of adjusting flight conflicts. Generally, the distance between the aircraft and the local VOR / DME station is used as the turning timing, for example: 1200 meters along one side, 4.5 nautical miles plus the "Piercing Clouds" route; for this control instruction, the traditional method in the control simulator cannot be quickly implemented; this system is designed to use a distance based on a certain DME station as the turning timing to realize joining the "Piercing Clouds" route on one side.

[0049] In the cloud-penetrating route, the distance based on the DME station is used as the turning timing to realize the one-turn and three-turn cloud-penetrating routes.

[0050] The first turn at the current position of the "Chuanyun" route refers to the fact that in the actual general aviation control process, after the aircraft takes off from the runway, the controller commands the aircraft to ascend along one side. It is not possible to immediately determine whether the aircraft joins the procedure and observe the airspace dynamics. When specific conditions are met, the controller will require the aircraft to immediately turn at the current position to join the "Chuanyun" route, and no longer use the DME distance as the turning timing. For this type of instruction, the DME distance is determined according to the current position of the aircraft to implement the turn; the DME distance is used as the timing for the third turn of the "Chuanyun" route. It refers to the fact that in general aviation flight, the "Chuanyun" route is the main training content of the flight subject, and it is also the core control method of control and command. It is usually After takeoff, the aircraft ascends along one side and performs a procedure turn, joins the procedure flight, and reports to the controller after passing the distant tower. The controller issues control instructions based on relevant flight conflicts in the airspace and relevant calculations. The aircraft is usually required to fly along the back side of the distant tower, and the distance between the aircraft and the local VOR / DEM station is used as the timing for three turns. For example: Aircraft reports to the tower, a certain aircraft has passed the distant tower, and the controller issues an instruction: descend 900 meters, make a procedure turn at 5.5 nautical miles, and report to the tower. For this control instruction, the traditional method in the control simulator cannot be quickly implemented; this system is designed to use the distance to a certain DME station as the turning timing to achieve three turns and four turns.

[0051] The three turns at the current position of the "Chuanyun" route refers to the actual general aviation control process, in which the aircraft joins the "Chuanyun" route procedure after taking off from the runway. After passing the distant station at the specified altitude, the controller directs the aircraft to fly along the back-to-station track specified by the distant station. The timing of the aircraft's three turns cannot be judged immediately. The airspace dynamics are observed. When the relevant conditions are met, the controller will require the aircraft to immediately make three turns at the current position to join the "Chuanyun" route and then fly toward the station. The DME distance is no longer used as the turning timing. For this type of instruction, the DME distance is judged according to the aircraft's current position to implement the three turns.

[0052] In the control simulation environment, ADS-B map is mapped to realize ADS-B environment simulation.

[0053] It should be explained that the ADS-B map is a map presented based on the Automatic Dependent Surveillance-Broadcast (ADS-B) technology. In addition to displaying conventional DRS2008 system control information, it can also display the terrain around the airport airspace, and display relevant information such as plains and mountains in different colors. By presenting the aircraft's position and related information on the map in a clear and intuitive manner, it is convenient for controllers to quickly interpret and grasp the airspace conditions; this has a strong situational awareness scenario for controllers, and improves their more comprehensive understanding of the operating status of aircraft in the airspace; the corresponding map data is exported from the map database of the ADS-B system on the production line, spliced ​​into a whole map, written into the DRS2008 system and accurately positioned, further improving the degree of simulation, making the operation interface closer to the actual working environment, and improving the operating status of aircraft in the airspace of the simulation system.

[0054] Flight operations basically have fixed standard approach and departure routes. This system uses the simulation methods corresponding to the transportation-based radar control simulation system to simulate flight approach and departure routes. General aviation flight is very different from flight flight. General aviation flight has many types of subjects, frequent transitions between subjects, and very high flexibility. The analysis and classification of flight subjects are very critical to the success of the simulation. According to the analysis of characteristics, this system divides general aviation flights into the following categories: traditional approach and departure routes, local training routes, airspace flights (including joining, exiting and airspace training routes), DME arc routes (including single and double arcs and their joining and exiting routes) and temporary holding routes.

[0055] To further realize the simulation of the captain's seat, the specific implementation process is: to realize the simulation of the captain's seat through a unique organizational method, the unique organizational method is specifically: one controller seat corresponds to multiple simulated captain seats, that is, one simulated controller's instructions are divided and executed by multiple simulated captains; in the aviation flight training simulation system, a controller's control instruction contains multiple simulator operation commands, and the captain's seat completes the operation through multiple control instructions. In the aviation flight training simulation system, the complete meaning of the implicit information control instruction is analyzed, and the complete simulation is realized in the aviation flight training simulation system through the implicit information control instruction.

[0056] It should be noted that general aviation flights have many different characteristics, so the control instructions issued by controllers often contain default implicit information. This implicit instruction is not allowed in airline transport flight scenarios, but in general aviation flight scenarios, it has specific significance for the smooth and smooth conduct of control and flight, and has been used for many years. Therefore, the simulation system needs to analyze the complete instruction meaning of the control instructions with implicit information and fully simulate it in the system. Therefore, in the simulation system, a control instruction of the controller may contain multiple simulator operation commands, and the captain's seat may need to issue multiple control instructions to complete.

[0057] This system operates based on general aviation flight subjects. The control instructions are very different from the flight operation system and contain some default implicit instructions. This greatly increases the requirements for the captain's seat and also greatly increases the workload of the captain's seat. Developers need to analyze the load of the captain's seat to control it within an appropriate range.

[0058] The implementation of control instructions in the DRS2008 system mainly considers the executability of the instructions by the captain's seat. The control based on flight training has great flexibility and higher load requirements for the captain's seat. The human factors of the captain's seat have great constraints on the operation of the training system. The implementation of this system in flight subjects mainly considers the load of the captain's seat to simulate various flight subjects. The designed control instructions must not only be concise and clear, easy to understand, and easy for the captain's seat operator to operate; they must also cover most situations and have a certain degree of flexibility. After a lot of analysis, investigation, and statistics, this system has designed a relatively complete set of instructions that are closely integrated with flight practice, such as Table 1 "Cloud-penetrating" route command-R1, Table 2 "Cloud-penetrating" cloud-penetrating route command-R2 and Table 3 NDB cloud-penetrating route commands.

[0059] Table 1 Commands on the “Cloud Piercing” route - R1

[0060]

[0061] Table 2 Commands on the “Cloud Piercing” Route - R2

[0062]

[0063] Note: When the aircraft turns to the final leg, if it wants to land, the captain needs to input the blind landing command.

[0064] Table 3 Commands for NDB cloud-penetrating routes

[0065]

[0066] In the traditional radar control simulation environment training mode, one controller corresponds to one captain seat. However, for general aviation flights, due to the wide variety of flight subjects and flexible changes during flight, the simulation flight control is also complicated, and the captain seat's ground-to-air communication will inevitably increase. All these will lead to an increase in the captain seat's workload. Therefore, when the flight volume in a certain airspace is slightly larger, a single captain seat will inevitably exceed the simulated captain load, resulting in a decline in training quality or even making it difficult to implement. Therefore, the system analyzes the controller's implicit control instructions and designs unique simulated captain control instructions to reduce the captain seat workload. Furthermore, a design The "one-to-many" training mode is adopted, in which one simulated controller corresponds to multiple simulated captains, that is, the instructions of one simulated controller are divided and executed by multiple simulated captains to reduce the workload of a single captain seat. This greatly reduces the workload of a single captain seat under large flight traffic, thereby ensuring training under large flight traffic. After many debugging and verifications, the workload of the captain seat is no more than 6 to 8 flights / person. Therefore, in training, the captain seat configuration is organized according to the total number of aircraft in the practice to ensure the quality of training. However, it also brings about the problem of detailed coordination and division of labor among multiple captain seats and the allocation of ground-to-air communication channels, which need to be standardized in the production of relevant practice scripts and rules.

[0067] Figure 4 As shown in the flowchart of the flight subject simulation and track simulation process of the present invention, the aviation flight training simulation link is entered to carry out flight subject simulation and track simulation in the general aviation field; wherein, the flight subject simulation includes five subjects: one turn of cloud-penetrating route, three turns of cloud-penetrating route, visual route, airspace flight and DME arc flight: in the cloud-penetrating route simulation, the distance based on the DME station is used as the turning timing to realize one-turn and three-turn operations; in the control simulation environment, the ADS-B map is mapped to realize ADS-B environment simulation; in the captain seat simulation, the organizational method of "one controller seat corresponds to multiple simulated captain seats" is adopted, that is, the instructions of a simulated controller are divided and executed by multiple simulated captains, and at the same time, it is necessary to parse the implicit information in the control instruction, and fully implement the multiple simulator operation commands contained in a control instruction, and then obtain the effective evaluation value of the simulation training.

[0068] Specifically, the aviation flight training indicators during the simulation process are obtained, and the specific analysis process is as follows: the aviation flight training indicators during the simulation process include the course deviation integral factor of the aviation flight training simulation model, the state recovery time factor of the aviation flight training simulation model, and the communication instruction clarity factor of the aviation flight training simulation model.

[0069] The above-mentioned localizer deviation integral factor represents the difference between the localizer deviation integral value of the aviation flight training simulation model and the reference localizer deviation integral value, which directly reflects the degree of deviation between the two; the above-mentioned state recovery time factor represents the difference between the state recovery time of the aviation flight training simulation model and the reference state recovery time, which directly reflects the degree of deviation between the two; the above-mentioned communication instruction clarity factor represents the relative level of the communication instruction clarity of the aviation flight training simulation model and the defined communication instruction clarity, and its value is the ratio of the two.

[0070] The influence coefficients are introduced from the simulation database to quantify the influence of the course deviation integral factor, the state recovery time factor, the communication instruction clarity factor, and the preliminary simulation model operation fluctuation evaluation value on the simulation training effectiveness evaluation value. The influence degrees are coupled to obtain the simulation training effectiveness evaluation value. The simulation training effectiveness evaluation value indicates the degree of consistency between the simulation results and the actual flight training effect. The specific evaluation method is as follows:

[0071]

[0072] Wherein, GBXW is the effective evaluation value of simulation training, KLOP is the operational fluctuation evaluation value of the preliminary simulation model, MAN is the localizer deviation integral factor of the aviation flight training simulation model, BJO is the localizer deviation integral value of the aviation flight training simulation model, PLA is the reference localizer deviation integral value preset in the simulation database, CXH is the state recovery time factor of the aviation flight training simulation model, OPX is the state recovery duration of the aviation flight training simulation model, FGH is the reference state recovery duration preset in the simulation database, BEG is the communication instruction clarity factor of the aviation flight training simulation model, GXC is the communication instruction clarity of the aviation flight training simulation model, DLK is the defined communication instruction clarity preset in the simulation database, β1 is the influence coefficient corresponding to the localizer deviation integral factor preset in the simulation database, β2 is the influence coefficient corresponding to the state recovery time factor preset in the simulation database, β3 is the influence coefficient corresponding to the communication instruction clarity factor preset in the simulation database, and β4 is the influence coefficient corresponding to the operational fluctuation evaluation value of the preliminary simulation model preset in the simulation database.

[0073] It needs to be explained that the above-mentioned localizer deviation integral value refers to the accumulated value of the deviation of the actual track of the aircraft during flight from the predetermined localizer over time, which is obtained by collecting lateral deviation data from the onboard navigation system and calculating it through numerical integration; the above-mentioned state recovery time refers to the time it takes for the system to adjust from an abnormal state to a normal state after being disturbed, which is obtained by marking the start and recovery time points of the abnormality and calculating the time difference; the above-mentioned communication instruction clarity refers to the degree to which the instructions in the communication are correctly understood by the recipient, which is quantified by comparing the integrity and semantic error rate of the original instructions and the received signal.

[0074] The above-mentioned reference localizer deviation integral value indicates the standard value of the localizer deviation integral value as a reference; the above-mentioned reference state recovery time indicates the standard value of the state recovery time as a reference; the above-mentioned defined communication command clarity indicates the minimum value of the communication command clarity within the specified range.

[0075] The simulation database stores the mapping relationships between the course deviation integral factor, state recovery time factor, communication command clarity factor, and preliminary simulation model operation fluctuation assessment value and their influence coefficients; among them, the influence coefficient of each factor refers to the degree of influence of the unit value corresponding to each factor on the effective assessment value of simulation training; when the above four factors are input into the simulation database, the database will generate the corresponding influence coefficient based on the preset mapping rules, and the numerical range of each coefficient is strictly controlled between 0 and 1.

[0076] The clarity of communication instructions directly affects the pilot's reception and execution of instructions. When the clarity decreases, such as unclear voice instructions or data transmission errors, it will cause pilot operation delays or errors, which will cause the flight trajectory to deviate from the standard route and the heading deviation integral to increase accordingly. The accuracy and timeliness of instruction transmission are the basis for the system's rapid response. If the clarity of communication instructions is insufficient, the system may misjudge the instructions or receive delays, triggering an abnormal state. For example, in the transmission of emergency obstacle avoidance instructions, low instruction clarity will cause system response errors and prolong the recovery time from abnormality to stability. When the cumulative heading deviation is too large, it means that the farther the flight trajectory deviates from the standard route, the more frequently the navigation system needs to calculate correction instructions, increasing the computing load of the physical engine and avionics system, and thus causing the simulation model operation fluctuations to intensify. The navigation system needs to re-plan the route. This process involves recalculating model parameters and switching system states, thereby prolonging the time it takes for the system to recover and stabilize.

[0077] The larger the course deviation integral factor, the greater the difference between the course deviation integral value of the simulation model and the reference value, that is, the lower the track control accuracy, and the deviation from the route control requirements of actual flight training. This will reduce the effectiveness of simulation training on the pilot's route operation skills, thereby resulting in a decrease in the effective evaluation value of simulation training; the larger the state recovery time factor, the greater the difference between the state recovery time of the simulation model and the reference time, that is, the slower the system recovers stability after an abnormal disturbance, and the worse the stability and dynamic response capability of the simulation system; the larger the communication instruction clarity factor, the closer the communication instruction clarity of the simulation model is to the defined value, the higher the communication quality, the pilot can accurately receive and execute instructions, and it is more in line with the communication requirements in actual flight training, thereby improving the effectiveness of simulation training and increasing the effective evaluation value; the larger the preliminary simulation model operation fluctuation evaluation value, the more drastic the parameter fluctuation during the simulation model operation, and the more serious the problems such as decreased physical calculation stability and distortion of the dynamic model, resulting in an increase in the deviation between the simulation training results and the actual flight training effect, which reduces the effective evaluation value.

[0078] Furthermore, it is determined whether the aviation flight training simulation model can be effectively simulated. The specific judgment process is: comparing the simulation training effective evaluation value with the first effective evaluation value preset in the simulation database; if the simulation training effective evaluation value is greater than or equal to the first effective evaluation value, then it is determined that the aviation flight training simulation model can be effectively simulated, and the aviation flight training simulation process is continuously monitored; if the simulation training effective evaluation value is less than the first effective evaluation value, then it is determined that the aviation flight training simulation model cannot be effectively simulated, and the aviation flight training simulation process is optimized.

[0079] It should be noted that the above-mentioned first effective evaluation value represents a value extracted from the simulation database and used to determine whether to optimize the aviation flight training simulation process.

[0080] Specifically, the aviation flight training simulation process is optimized. The specific optimization process is as follows:

[0081] The effective evaluation value of the simulation training is compared with the second effective evaluation value preset in the simulation database; if the effective evaluation value of the simulation training is less than the first effective evaluation value and greater than the second effective evaluation value, the first effective evaluation deviation value is obtained based on the effective evaluation value of the simulation training and the first effective evaluation value, and the visual LOD switching threshold increase coefficient is matched based on the first effective evaluation deviation value, thereby increasing the visual LOD switching threshold of the aviation flight training simulation model, extending the rendering distance of high-detail models, reducing visual faults, and enhancing the visual accuracy of key scenes such as takeoff and landing; it can also reduce training deviations caused by rendering errors, adapt to different hardware configurations, and when the effective evaluation value of the simulation training is in a specific range, the visual details are targetedly optimized to correct operation deviations, forming a positive cycle to improve training effects.

[0082] The above-mentioned second effective evaluation value represents a discriminative value in the simulation database used to define the degree of optimization of the aviation flight training simulation process, and the second effective evaluation value is less than the first effective evaluation value; the above-mentioned acquisition of the first effective evaluation deviation value refers to the result of subtracting the simulation training effective evaluation value from the first effective evaluation value; the above-mentioned matching of the increase coefficient of the visual LOD switching threshold based on the first effective evaluation deviation value, the specific matching process is: the simulation database pre-stores the mapping relationship between the first effective evaluation deviation value interval and the visual LOD switching threshold increase coefficient, and after inputting the first effective evaluation deviation value, the corresponding interval is matched to obtain the increase coefficient of the interval, and multiplying it by the original threshold to obtain the adjusted LOD switching threshold; the above-mentioned visual LOD switching threshold increase coefficient is greater than 1, indicating that the visual LOD switching threshold of the aviation flight training simulation model needs to be increased by a multiple.

[0083] If the effective evaluation value of the simulation training is less than or equal to the second effective evaluation value, a second effective evaluation deviation value is obtained based on the effective evaluation value and the second effective evaluation value. The texture MIPMAP offset reduction coefficient is matched based on the second effective evaluation deviation value, thereby reducing the matched texture MIPMAP offset of the aviation flight training simulation model. This allows distant objects to use higher-resolution texture maps (such as runway markings), thereby improving overall visual clarity and realism, and avoiding delays or deviations in pilot recognition of environmental features due to texture blur. A control signal dead-band compensation gain increase coefficient is matched based on the second effective evaluation deviation value, thereby increasing the control signal dead-band compensation gain of the aviation flight training simulation model. This allows subtle pilot operations to be more sensitively converted into simulation system responses (such as control surface deflections and power adjustments), effectively alleviating operational lag caused by delayed or blunted control signals. The combination of these two factors can optimize the simulation training experience from two dimensions: "visual feedback accuracy" and "operation response sensitivity." The former enhances the accuracy of environmental recognition, while the latter improves the immediacy of control inputs. Together, these factors help pilots more accurately correct operational deviations when the evaluation value is low, pushing training effectiveness back into the effective range.

[0084] The above-mentioned second effective evaluation deviation value refers to the result of subtracting the simulation training effective evaluation value from the second effective evaluation value; the above-mentioned matching of the texture MIPMAP offset reduction coefficient based on the second effective evaluation deviation value, the specific matching process is: the simulation database pre-stores the mapping relationship between the second effective evaluation deviation value interval and the texture MIPMAP offset reduction coefficient, and after inputting the second effective evaluation deviation value, the corresponding interval is matched to obtain the reduction coefficient of the interval, and multiplying it by the original offset to obtain the adjusted texture MIPMAP offset; the above-mentioned matching of the control signal dead zone compensation gain increase coefficient based on the second effective evaluation deviation value, the specific matching process is: the simulation database pre-stores the mapping relationship between the second effective evaluation deviation value interval and the control signal dead zone compensation gain increase coefficient, and after inputting the second effective evaluation deviation value, the corresponding interval is matched to obtain the increase coefficient of the interval, and multiplying it by the original gain value to obtain the adjusted control signal dead zone compensation gain; the above-mentioned control signal dead zone compensation gain increase coefficient is greater than 1, indicating that the control signal dead zone compensation gain of the aviation flight training simulation model needs to be increased by a multiple.

[0085] This segmented adjustment strategy implements differentiated optimization schemes based on different intervals of effective evaluation values ​​of simulation training: when the evaluation value is between the first and second effective evaluation values, the visual accuracy of key targets is improved by increasing the scene LOD switching threshold, solving moderate simulation deviations while avoiding resource waste; when the evaluation value is less than or equal to the second effective evaluation value, the texture MIPMAP offset is simultaneously reduced and the control signal dead zone compensation gain is increased, strengthening optimization from the dual dimensions of visual clarity and operational sensitivity to solve severe distortion problems; this strategy accurately matches the severity of the problem with graded response, dynamically allocates computing resources, and forms a closed-loop feedback through progressive intervention, effectively improving training effectiveness and realism while ensuring system performance.

[0086] The optimized simulation training effective evaluation value is obtained, marked as the simulation training secondary effective evaluation value, and it is determined whether to issue an early warning on the effectiveness of the aviation flight training simulation model.

[0087] Figure 5As shown in the schematic diagram of the simulation training effective evaluation process of the present invention, the obtained simulation training effective evaluation value is hierarchically compared with the first effective evaluation value and the second effective evaluation value: if the simulation training effective evaluation value is greater than or equal to the first effective evaluation value, it is determined that no adjustment is required and the aviation flight training simulation process continues to be monitored; if the simulation training effective evaluation value is less than the first effective evaluation value but greater than the second effective evaluation value, the view LOD (level of detail) switching threshold is increased based on the first effective evaluation deviation value to optimize the scene rendering accuracy; if the simulation training effective evaluation value is less than or equal to the second effective evaluation value, the texture MIPMAP offset is reduced (to improve texture clarity) and the control signal dead zone compensation gain is increased (to optimize operation response sensitivity) based on the second effective evaluation deviation value, thereby improving the training experience through dual measures, and then determining whether to issue a warning for the effectiveness of the aviation flight training simulation model.

[0088] Furthermore, it is determined whether to issue a warning for the effectiveness of the aviation flight training simulation model. The specific judgment process is: comparing the second effective evaluation value of the simulation training with the first effective evaluation value; if the second effective evaluation value of the simulation training is greater than or equal to the first effective evaluation value, then it is determined not to issue a warning for the effectiveness of the aviation flight training simulation model, and at the same time, the aviation flight training simulation model is re-updated, and whether the updated aviation flight training simulation model is stable is re-determined; if the second effective evaluation value of the simulation training is less than the first effective evaluation value, then it is determined to issue a warning for the effectiveness of the aviation flight training simulation model.

[0089] It should be further explained that the above-mentioned re-updating of the aviation flight training simulation model refers to updating the optimized parameters (such as the control signal dead zone compensation gain) into the aviation flight training simulation model; the above-mentioned re-judgment of whether the updated aviation flight training simulation model is stable refers to re-obtaining the preliminary simulation model operation fluctuation assessment value, and indirectly reflecting the stability of the aviation flight training simulation model by judging the degree of fluctuation of the preliminary simulation model operation, thereby entering into a cycle optimization; the above-mentioned early warning of the effectiveness of the aviation flight training simulation model refers to sending the early warning information to the operation and maintenance platform.

[0090] Figure 6As shown in the schematic diagram of the early warning process of the aviation flight training simulation model of the present invention, after obtaining the secondary effective evaluation value of the simulation training, a final comparison is performed with the first effective evaluation value: if the secondary evaluation value of the simulation training is greater than or equal to the first effective evaluation value, no early warning is required, the simulation model is updated again, and the stability evaluation of the updated aviation flight training simulation model is started again to form an iterative closed loop of "optimization-verification"; if the secondary evaluation value of the simulation training is less than the first effective evaluation value, it is determined that there is an abnormality in the training process, and an early warning signal is issued to prompt relevant personnel to intervene and investigate the problem; this link is centered on model update and stability evaluation to ensure the continued reliability of the aviation flight training simulation system, and finally the process end node is used as the termination mark of the entire evaluation and adjustment process.

[0091] The above content is merely an example and explanation of the structure of the present invention. Those skilled in the art may make various modifications or additions to the described specific embodiments or replace them in a similar manner. As long as they do not deviate from the structure of the invention or exceed the scope defined by the present invention, they should all fall within the scope of protection of the present invention.

Claims

1. An aviation flight training simulation system based on trajectory simulation and workload distribution, characterized by: include: An aviation flight training simulation model construction module is used to preliminarily construct a preliminary aviation flight training simulation model and monitor the operational fluctuation parameters of the preliminary aviation flight training simulation model, thereby determining whether the preliminary aviation flight training simulation model can be put into use; The operational fluctuation parameters of the preliminary aviation flight training simulation model include a physical engine step jitter factor of the preliminary aviation flight training simulation model, a normal overload peak-to-valley difference factor of the preliminary aviation flight training simulation model, and a visual brightness gradient integral factor of the preliminary aviation flight training simulation model; The physical engine step jitter factor represents the ratio of the physical engine step jitter duration of the preliminary simulation model for aviation flight training to the defined physical engine step jitter duration; The normal overload peak-to-valley difference factor represents the ratio of the normal overload peak-to-valley difference value of the preliminary simulation model for aviation flight training to the defined normal overload peak-to-valley difference value; The visual brightness gradient integral factor represents the degree of deviation between the visual brightness gradient integral value calculated by the preliminary simulation model for aviation flight training and the defined visual brightness gradient integral value; Influence coefficients are introduced from the simulation database to quantify the degree of influence of the physical engine step jitter factor, the normal overload peak-to-valley difference factor, and the visual brightness gradient integral factor on the preliminary simulation model operation fluctuation assessment value. The various degrees are integrated to obtain the preliminary simulation model operation fluctuation assessment value, where the preliminary simulation model operation fluctuation assessment value represents the degree of fluctuation when the preliminary simulation model for aviation flight training is running. The multi-pilot seat collaboration and air traffic control scenario simulation module marks the preliminary aviation flight training simulation model that can be put into use as an aviation flight training simulation model. Through the aviation flight training simulation model, flight subject simulation and trajectory simulation in the general aviation field are carried out, and the captain seat simulation is realized at the same time, thereby solving the load problem of the simulated captain seat in air traffic control training. The aviation flight training effectiveness evaluation module is used to monitor the simulation process of the aviation flight training simulation model, obtain the aviation flight training indicators during the simulation process, and thus determine whether the aviation flight training simulation model can effectively simulate.

2. The aviation flight training simulation system based on trajectory simulation and workload distribution according to claim 1, characterized in that: The monitoring of the operational fluctuation parameters of the preliminary aviation flight training simulation model further includes determining whether the preliminary aviation flight training simulation model can be put into use: The specific judgment process of judging whether the preliminary simulation model for aviation flight training can be put into use is as follows: comparing the preliminary simulation model operation fluctuation assessment value with the preliminary simulation model operation fluctuation assessment threshold value preset in the simulation database; If the preliminary simulation model operation fluctuation assessment value is less than or equal to the preliminary simulation model operation fluctuation assessment threshold, it is determined that the aviation flight training preliminary simulation model can be put into use, and the aviation flight training preliminary simulation model is marked as an aviation flight training simulation model; If the preliminary simulation model operation fluctuation assessment value is greater than the preliminary simulation model operation fluctuation assessment threshold, it is determined that the preliminary simulation model for aviation flight training cannot be put into use, and the preliminary simulation model for aviation flight training is optimized at the same time. The specific optimization process is: based on the preliminary simulation model operation fluctuation assessment value and the preliminary simulation model operation fluctuation assessment threshold, a preliminary simulation model operation fluctuation assessment deviation value is obtained, and based on the preliminary simulation model operation fluctuation assessment deviation value, a numerical integration step size reduction coefficient is matched, thereby reducing the numerical integration step size of the dynamics model; The optimized preliminary simulation model operation fluctuation evaluation value is obtained and marked as the simulation model secondary fluctuation evaluation value, and at the same time, it is determined whether the preliminary simulation model for aviation flight training should be optimized secondary.

3. The aviation flight training simulation system based on trajectory simulation and workload distribution according to claim 2, characterized in that: The specific process of determining whether to perform secondary optimization on the preliminary simulation model for aviation flight training is as follows: Compare the secondary fluctuation assessment value of the simulation model with the operational fluctuation assessment threshold of the preliminary simulation model; If the secondary fluctuation evaluation value of the simulation model is less than or equal to the preliminary simulation model operation fluctuation evaluation threshold, it is determined that the preliminary aviation flight training simulation model will not be secondary optimized, and the preliminary aviation flight training simulation model is updated and put into use, and the preliminary aviation flight training simulation model is marked as an aviation flight training simulation model; If the secondary fluctuation evaluation value of the simulation model is greater than the operating fluctuation evaluation threshold of the preliminary simulation model, it is determined that the preliminary simulation model for aviation flight training is to be secondary optimized. The specific optimization process is: based on the secondary fluctuation evaluation value of the simulation model and the operating fluctuation evaluation threshold of the preliminary simulation model, the secondary fluctuation evaluation deviation value of the simulation model is obtained; based on the secondary fluctuation evaluation deviation value of the simulation model, a quadratic reduction coefficient of the numerical integration step is matched, thereby further reducing the numerical integration step of the dynamic model; based on the secondary fluctuation evaluation deviation value of the simulation model, a coefficient of increasing the random seed reset period of the particle system is matched, thereby increasing the random seed reset period of the particle system of the visual system; Obtain the preliminary simulation model operation fluctuation evaluation value after the secondary optimization, mark it as the simulation model three-time fluctuation evaluation value, and determine whether to issue an early warning for the preliminary simulation model of aviation flight training.

4. The aviation flight training simulation system based on trajectory simulation and workload distribution according to claim 3, characterized in that: The specific process of determining whether to issue an early warning to the preliminary simulation model of aviation flight training is as follows: Compare the three-time fluctuation assessment value of the simulation model with the fluctuation assessment threshold of the preliminary simulation model operation; If the three fluctuation evaluation values ​​of the simulation model are less than or equal to the preliminary simulation model operation fluctuation evaluation threshold, it is determined that no warning is issued for the preliminary simulation model of aviation flight training, and the preliminary simulation model of aviation flight training is updated and put into use, and the preliminary simulation model of aviation flight training is marked as an aviation flight training simulation model; If the three fluctuation evaluation values ​​of the simulation model are greater than the preliminary simulation model operation fluctuation evaluation threshold, it is determined that an early warning will be issued for the preliminary simulation model of aviation flight training.

5. The aviation flight training simulation system based on trajectory simulation and workload distribution according to claim 1, characterized in that: The specific analysis process of performing flight subject simulation and track simulation in the general aviation field through the aviation flight training simulation model is as follows: The flight subject simulation in the general aviation field includes five flight subjects: cloud-penetrating route with one turn, cloud-penetrating route with three turns, visual route, airspace flight and DME arc flight; In the cloud-penetrating route, the distance based on the DME station is used as the turning timing to realize the cloud-penetrating route one turn and cloud-penetrating route three turns; In the control simulation environment, ADS-B map is mapped to realize ADS-B environment simulation.

6. The aviation flight training simulation system based on trajectory simulation and workload distribution according to claim 1, characterized in that: The specific implementation process of the captain seat simulation is as follows: The captain seat simulation is achieved through a unique organizational method. Specifically, one controller seat corresponds to multiple simulated captain seats, that is, one simulated controller's instructions are divided into multiple simulated captains to execute; In the aviation flight training simulation system, a control instruction from the controller contains multiple simulator operation commands, and the captain's seat completes the operation through multiple control instructions. In the aviation flight training simulation system, the complete meaning of the implicit information control instruction is analyzed, and a complete simulation is achieved in the aviation flight training simulation system through the implicit information control instruction.

7. The aviation flight training simulation system based on trajectory simulation and workload distribution according to claim 1, characterized in that: The specific analysis process of obtaining the aviation flight training indicators during the simulation process is as follows: The aviation flight training indicators obtained during the simulation process include a course deviation integral factor of the aviation flight training simulation model, a state recovery time factor of the aviation flight training simulation model, and a communication instruction clarity factor of the aviation flight training simulation model; Influence coefficients are introduced from the simulation database to quantify the influence of the course deviation integral factor, state recovery time factor, communication instruction clarity factor and the preliminary simulation model operation fluctuation evaluation value on the effective evaluation value of simulation training. The various influence degrees are coupled to obtain the effective evaluation value of simulation training. The effective evaluation value of simulation training indicates the degree of consistency between the simulation results and the actual flight training effect.

8. The aviation flight training simulation system based on trajectory simulation and workload distribution according to claim 1, characterized in that: The specific judgment process of whether the aviation flight training simulation model can effectively simulate is as follows: Comparing the simulation training effective evaluation value with the first effective evaluation value preset in the simulation database; If the simulation training effectiveness evaluation value is greater than or equal to the first effectiveness evaluation value, it is determined that the aviation flight training simulation model is capable of effective simulation, and the aviation flight training simulation process is continuously monitored; If the simulation training effectiveness evaluation value is less than the first effectiveness evaluation value, it is determined that the aviation flight training simulation model cannot be effectively simulated, and the aviation flight training simulation process is optimized.

9. The aviation flight training simulation system based on trajectory simulation and workload distribution according to claim 8, characterized in that: The aviation flight training simulation process is optimized, and the specific optimization process is as follows: Comparing the simulation training effective evaluation value with a second effective evaluation value preset in the simulation database; If the simulation training effective evaluation value is less than the first effective evaluation value and greater than the second effective evaluation value, obtaining a first effective evaluation deviation value based on the simulation training effective evaluation value and the first effective evaluation value, and matching a visual LOD switching threshold increase coefficient based on the first effective evaluation deviation value, thereby increasing the visual LOD switching threshold of the aviation flight training simulation model; If the simulation training effective evaluation value is less than or equal to the second effective evaluation value, obtaining a second effective evaluation deviation value based on the simulation training effective evaluation value and the second effective evaluation value, matching a texture MIPMAP offset reduction coefficient based on the second effective evaluation deviation value, thereby reducing a matching texture MIPMAP offset of the aviation flight training simulation model, and matching a control signal dead zone compensation gain increase coefficient based on the second effective evaluation deviation value, thereby increasing a control signal dead zone compensation gain of the aviation flight training simulation model; The optimized simulation training effective evaluation value is obtained, marked as the simulation training secondary effective evaluation value, and it is determined whether to issue an early warning on the effectiveness of the aviation flight training simulation model.

10. The aviation flight training simulation system based on trajectory simulation and workload distribution according to claim 9, characterized in that: The specific judgment process of whether to issue a warning on the effectiveness of the aviation flight training simulation model is as follows: Comparing the second effective evaluation value of the simulation training with the first effective evaluation value; If the second validity evaluation value of the simulation training is greater than or equal to the first validity evaluation value, it is determined that no warning is issued for the validity of the aviation flight training simulation model, and the aviation flight training simulation model is updated, and whether the updated aviation flight training simulation model is stable is re-determined; If the second effective evaluation value of the simulation training is less than the first effective evaluation value, it is judged that the effectiveness of the aviation flight training simulation model is warned.

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