Pilot training simulator based on big data and machine learning

Abstract

The invention relates to the field of flight training simulators, in particular to a pilot training simulator based on big data and machine learning, which is used for dynamically correcting a view angle by collecting pilot head action data in real time and combining with a machine algorithm, reducing the problems of simulator view angle delay and distortion, and meanwhile, improving the simulation accuracy. High-frequency motion and low-frequency motion of the motion simulation assembly and the simulation seat are combined to be closer to the simulation effect of a real flight attitude, long-term flight simulation data is used as a support, a simulation change curve is generated through big data, the training progress trend is visually displayed through the simulation change curve, and the training efficiency is improved. Different pilot training effects are obtained, a personalized training scheme is helped to be formulated, and pilot simulation training cost is reduced.

Description

Technical Field

[0001] The present invention relates to the field of flight training simulators, specifically a pilot training simulator based on big data and machine learning. Background Art

[0002] The current flight training mode basically involves learning using computer-aided training equipment, followed by classroom teaching, and then training on a synthetic procedure trainer or a flight training device to conduct cockpit familiarization, equipment operation, and flight procedure training and learning; According to the training requirements of different aircraft models, flight training equipment mainly includes high-level training equipment such as full-motion flight simulators (FFS), flight training devices (FTD), and low-level training equipment such as synthetic procedure trainers (IPT). The FFS is the trainer with the highest simulation degree for simulating the entire cockpit of a specific aircraft model, including sensory systems such as motion, visual, and sound. The FTD is an FFS that may not provide a motion system and an external cockpit scene visual system. The IPT is a device using a touch-screen virtual cockpit. Compared with other training equipment, the IPT has the advantage of low cost; Due to the high production and training costs in each link from development, procurement to operation and maintenance of the FTD, the training time allowed for a single training is short, and there is a lack of training time to fully familiarize with the cockpit, resulting in a long training time occupying the simulator. Therefore, in order to improve the efficiency of simulation training as much as possible, it is necessary to collect more comprehensive and scientific training data of pilots during the pilot simulation training process to evaluate the training conditions of different pilots, so as to more reasonably arrange the training schedule and training types of pilots, and improve the operation efficiency of flight simulation equipment at each stage. For this reason, this application proposes a solution. Summary of the Invention

[0003] In the present invention, by standardizing and analyzing flight operation data, pilot operation defects can be quickly identified, subjective errors in manual evaluation can be reduced, and supported by long-term flight simulation data, a simulation change curve is generated through big data, and the training progress trend is intuitively displayed through the simulation change curve to obtain the training effects of different pilots, helping to formulate personalized training plans and shortening the pilot simulation training cost to solve the technical defects proposed in the background art. Now, a pilot training simulator based on big data and machine learning is proposed.

[0004] The object of the present invention can be achieved by the following technical solutions: A pilot training simulator based on big data and machine learning, including a fixed base, a motion simulation component is installed above the fixed base, and an outer cabin body is movably connected to the top of the motion simulation component; A simulated seat is installed inside the outer cabin, and an interactive display cabin and a simulated operating table are also installed inside the outer cabin. The simulated seat can perform shaking simulation and tilt adjustment simulation. A camera component is integrated on the interactive display cabin. The motion simulation component can simulate the flight attitude of the aircraft by performing high-frequency reciprocating expansion and contraction. The motion simulation component, interactive display cabin and simulation seat are all controlled by a pilot training simulation system; The pilot training simulation system includes a motion acquisition module, a field of view transformation module, a dynamic output module, a simulation control module and a statistical evaluation module. The motion acquisition module can collect the head motion of the simulated training personnel, obtain the field of view angle according to the collection result, and send the field of view angle to the field of view transformation module. The field of view transformation is calculated according to the field of view angle to display the picture on the interactive display cabin. The simulation control module is used to collect flight simulation data, perform standardized analysis on the collected flight simulation data, and obtain simulation training evaluation results. The statistical evaluation module performs statistics on the simulation training evaluation results obtained each time, generates a simulation change curve based on the statistical results, realizes the evaluation of the training effect, and outputs the evaluation results of the training effect.

[0005] As a preferred embodiment of the present invention, the dynamic output module is connected to a simulation operating console, and obtains a flight simulation posture through the simulation operating console, and sends the flight simulation posture to a motion simulation component and a simulation seat. After obtaining the flight simulation posture, the motion simulation component analyzes the flight simulation posture to obtain low-frequency jitter and posture simulation, and controls the telescopic cylinder to complete the action within a set response time. The simulation seat analyzes the flight simulation posture to obtain a high-frequency jitter effect, and outputs the high-frequency jitter effect to achieve jitter simulation.

[0006] As a preferred embodiment of the present invention, the motion acquisition module acquires the motion of the simulated trainee through a camera, imports the acquired picture into a preset picture processing model for processing, marks the head position of the simulated trainee from the picture, and further marks the eyeball picture in the head position to obtain the eyeball position; The motion acquisition module takes the midpoint of the interactive display cabin in the vertical direction as the standard height and draws a horizontal line along the standard height. The motion acquisition module takes the midpoint of the interactive display cabin in the horizontal direction as the standard distance and draws a vertical line along the standard distance. The motion acquisition module records the horizontal line and the vertical line as the standard dividing line.

[0007] As a preferred embodiment of the present invention, after obtaining the eye position, the motion acquisition module connects the eye position with the horizontal line of the standard dividing line in the vertical plane to obtain a horizontal connection line, connects the eye position with the numerical line of the standard dividing line in the horizontal plane to obtain a vertical connection line, and then connects the eye position with the interactive display cabin along the normal direction to the interactive display cabin to obtain a normal connection line; The motion acquisition module calculates the angle between the horizontal connection line and the normal connection line, records it as the horizontal angle, and records the angle between the vertical connection line and the normal connection line as the vertical angle. The motion acquisition module records the vertical angle and the horizontal angle as the field of view angle.

[0008] As a preferred embodiment of the present invention, after obtaining the field of view angle, the field of view transformation module first obtains an initial display screen. The initial screen is a panoramic screen, and a part of the area directly in front is displayed through the interactive display cabin. The field of view transformation module adjusts the screen in the opposite direction according to the horizontal angle and the vertical angle in the field of view angle, so that the displayed area of the interactive display cabin moves to simulate the real picture area.

[0009] As a preferred embodiment of the present invention, the flight simulation data obtained by the simulation control module includes flight trajectory, flight risk, and risk response time. The simulation control module compares the flight trajectory with the preset trajectory to obtain the coincidence rate between the flight trajectory and the preset trajectory; The simulation control module counts the number of occurrences of flight risks to obtain the total number of risks; The simulation control module statistically analyzes the risk response time to obtain the longest risk response time and the average risk response time respectively, and makes independent threshold judgments on the longest risk response time and the average risk response time respectively, and generates a risk response delay signal and a risk response slow signal according to the judgment results.

[0010] As a preferred embodiment of the present invention, the simulation control module records the trajectory coincidence rate, the total number of risks, the longest risk response time, and the average risk response time as simulation training evaluation data, and sends the simulation training evaluation data to the statistical evaluation module.

[0011] As a preferred embodiment of the present invention, the statistical evaluation module assigns different data in the simulation training evaluation data received each time to different evaluation curves to form a trajectory coincidence rate change curve, a total number of risk change curve, a longest risk response time change curve, and an average risk response time change curve; The statistical evaluation module fits the change trends of different curves, obtains the change trends of the trajectory coincidence rate, the total number of risks, the longest risk response time, and the average risk response time, and records them for output in the training effect evaluation.

[0012] Compared with the prior art, the beneficial effects of the present invention are as follows: 1. In the present invention, by collecting the pilot's head movement data in real time and combining machine algorithms to dynamically correct the viewing angle, the problems of perspective delay and distortion in traditional simulators are solved. At the same time, through the combination of high-frequency and low-frequency movements of the motion simulation component and the simulation seat, and the linkage of the data-driven dynamic output module, the motion frequency limit of the mechanical simulator is improved, and a simulation effect closer to the real flight attitude is achieved.

[0013] 2. In the present invention, by standardizing and analyzing flight operation data, pilot operation defects can be quickly identified, subjective errors in manual evaluation can be reduced, and supported by long-term flight simulation data, simulation change curves are generated through big data, and the training progress trend is intuitively displayed through the simulation change curves, and the training effects of different pilots are obtained, which helps to formulate personalized training plans, shorten the training cycle, and reduce the simulation training cost of pilots. BRIEF DESCRIPTION OF THE DRAWINGS

[0014] For the convenience of those skilled in the art to understand, the present invention will be further described below with reference to the accompanying drawings.

[0015] Figure 1 is a front view structural schematic diagram of the present invention; Figure 2 is a structural schematic diagram of the interactive display cabin of the present invention.

[0016] Figure 3 is a system block diagram of the present invention; Figure 4 is a system flow chart of the present invention.

[0017] In the figure: 1. Outer cabin body; 2. Motion simulation component; 3. Fixed base; 4. Interactive display cabin; 5. Simulation seat; 6. Simulation operation console. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0018] The technical solutions of the present invention will be clearly and completely described below in conjunction with the embodiments. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. All other embodiments obtained by those of ordinary skill in the art without creative efforts based on the embodiments of the present invention belong to the scope of protection of the present invention.

[0019] Embodiment 1: Please refer to Figure 1- Figure 4 As shown, a pilot training simulator based on big data and machine learning includes a fixed base 3. Above the fixed base 3, a motion simulation component 2 is installed. The top of the motion simulation component 2 is movably connected to an outer cabin 1; Inside the outer cabin 1, a simulation seat 5 is installed. Inside the outer cabin 1, an interactive display cabin 4 and a simulation operation console 6 are also installed. The simulation seat 5 can perform jitter simulation and tilt adjustment simulation. The interactive display cabin 4 is integrated with a camera component. The motion simulation component 2 can simulate the flight attitude of an aircraft by performing high-frequency reciprocating telescoping; The motion simulation component 2, the interactive display cabin 4, and the simulation seat 5 are all controlled by a pilot training simulation system. The pilot training simulation system includes an action acquisition module, a vision transformation module, a dynamic output module, a simulation management and control module, and a statistical evaluation module.

[0020] The dynamic output module in the pilot training simulation system is connected to the simulation operation console 6, and obtains the flight simulation attitude through the simulation operation console 6, and sends the flight simulation attitude to the motion simulation component 2 and the simulation seat 5. After obtaining the flight simulation attitude, the motion simulation component 2 analyzes the flight simulation attitude, obtains low-frequency jitter and attitude simulation, and controls the telescopic cylinder to complete the action within the set response time. The simulation seat 5 analyzes the flight simulation attitude, obtains the high-frequency jitter effect, and outputs the high-frequency jitter effect to achieve jitter simulation.

[0021] Embodiment 2: Please refer to Figure 1 - Figure 4 As shown, the pilot training simulation system includes an action acquisition module, a vision transformation module, a dynamic output module, a simulation management and control module, and a statistical evaluation module; The action acquisition module takes the midpoint of the interactive display cabin 4 in the vertical direction as the standard height, and draws a horizontal line along the standard height. The action acquisition module takes the midpoint of the interactive display cabin 4 in the horizontal direction as the standard distance, and draws a vertical line along the standard distance. The action acquisition module records the horizontal line and the vertical line as the standard dividing line; The action acquisition module collects the actions of the simulation training personnel through the camera, and imports the collected pictures into a preset picture processing model for processing, marks the head position of the simulation training personnel from the pictures, and further marks the eye pictures in the head position to obtain the eye position; After obtaining the eye position, the action acquisition module connects the eye position with the horizontal line of the standard dividing line in the vertical plane to obtain a horizontal connection line, connects the eye position with the vertical line of the standard dividing line in the horizontal plane to obtain a vertical connection line, and then connects the eye position with the interactive display cabin 4 along the normal direction to the interactive display cabin 4 to obtain a normal connection line; The motion acquisition module calculates the angle between the horizontal connection line and the normal connection line, records it as the horizontal angle, and records the angle between the vertical connection line and the normal connection line as the vertical angle. The motion acquisition module records the vertical angle and the horizontal angle as the field of view angle; The motion acquisition module sends the field of view angle to the field of view transformation module, and the field of view transformation calculates according to the field of view angle. Specifically: After obtaining the field of view angle, the field of view transformation module first obtains the initial display screen. The initial screen is a panoramic screen, and a part of the area in the front is displayed through the interactive display cabin 4. The field of view transformation module adjusts the screen in the opposite direction according to the horizontal angle and the vertical angle in the field of view angle, so that the displayed area of the interactive display cabin 4 moves, simulating the real picture area, realizing the display correction of the picture on the interactive display cabin 4, and improving the authenticity of the picture displayed on the interactive display cabin 4; The simulation control module is used to collect flight simulation data, perform standardized analysis on the collected flight simulation data. The flight simulation data obtained by the simulation control module includes flight trajectory, flight risk, and risk response time. The simulation control module compares the flight trajectory with the preset trajectory to obtain the coincidence rate between the flight trajectory and the preset trajectory; The simulation control module counts the number of occurrences of flight risks to obtain the total number of risks; The simulation control module statistically analyzes the risk response time to obtain the longest risk response time and the average risk response time respectively, and independently judges the thresholds of the longest risk response time and the average risk response time. If the longest risk response time is greater than the set threshold, a risk response delay signal is generated. If the longest risk response time is not greater than the set threshold, no response is made. If the average risk response time is greater than the set threshold, a risk response slow signal is generated. If the average risk response time is not greater than the set threshold, no response is made; The simulation control module records the risk response delay signal and the risk response slow signal as the simulation training evaluation result, and directly outputs it after the end of the current training; The simulation control module records the trajectory coincidence rate, the total number of risks, the longest risk response time, and the average risk response time as the simulation training evaluation data, and sends the simulation training evaluation data to the statistical evaluation module; The statistical evaluation module statistically analyzes each obtained simulation training evaluation result, distributes different data in each received simulation training evaluation data to different evaluation curves, and forms a trajectory coincidence rate change curve, a total number of risks change curve, a longest risk response time change curve, and an average risk response time change curve; The statistical evaluation module fits the change trends of different curves to obtain the change trends of the trajectory coincidence rate, the total number of risks, the longest risk response time, and the average risk response time. When the change trend of the trajectory coincidence rate is positive, the change trend of the total number of risks is negative, and the change trends of the longest risk response time and the average risk response time are negative, it indicates excellent training results, and the training performance of the pilot improves. When the change trend of the trajectory coincidence rate is negative, the change trend of the total number of risks is positive, and the change trends of the longest risk response time and the average risk response time are positive, it indicates poor training results, and the training performance of the pilot declines, and it is recorded and output for the training effect evaluation.

[0022] The preferred embodiments of the present invention disclosed above are only used to help illustrate the present invention. The preferred embodiments do not describe all the details in detail, nor do they limit the present invention to only the specific implementation manners. Obviously, many modifications and variations can be made according to the content of this specification. These embodiments are selected and specifically described in this specification to better explain the principle and practical application of the present invention, so that those skilled in the art can well understand and utilize the present invention. The present invention is only limited by the claims and their full scope and equivalents.

Claims

1. A pilot training simulator based on big data and machine learning, comprising a fixed base (3), characterized in that: A motion simulation component (2) is installed above the fixed base (3), and the top of the motion simulation component (2) is movably connected to the outer cabin (1); A simulation seat (5) is installed inside the outer cabin (1), and an interactive display cabin (4) and a simulation operating console (6) are also installed inside the outer cabin (1); the simulation seat (5) is capable of performing shaking simulation and tilt adjustment simulation; a camera assembly is integrated on the interactive display cabin (4); and the motion simulation assembly (2) is capable of simulating the flight attitude of an aircraft by performing high-frequency reciprocating extension and retraction; The motion simulation component (2), the interactive display cabin (4) and the simulation seat (5) are all controlled by a pilot training simulation system; The pilot training simulation system comprises a motion collection module, a field of view transformation module, a dynamic output module, a simulation control module and a statistical evaluation module. The motion collection module can collect the head motion of the simulated training personnel, obtain the field of view angle according to the collection result, and send the field of view angle to the field of view transformation module. The field of view transformation is calculated according to the field of view angle to perform display correction on the picture on the interactive display cabin (4); The simulation control module is used to collect flight simulation data, perform standardized analysis on the collected flight simulation data, and obtain simulation training evaluation results. The statistical evaluation module performs statistics on the simulation training evaluation results obtained each time, generates a simulation change curve based on the statistical results, realizes the evaluation of the training effect, and outputs the evaluation results of the training effect.

2. The pilot training simulator based on big data and machine learning according to claim 1, characterized in that: The dynamic output module is connected to the simulation operation table (6), and obtains the flight simulation posture through the simulation operation table (6), and sends the flight simulation posture to the motion simulation component (2) and the simulation seat (5); after obtaining the flight simulation posture, the motion simulation component (2) analyzes the flight simulation posture to obtain low-frequency jitter and posture simulation, and controls the telescopic cylinder to complete the action within a set response time; the simulation seat (5) analyzes the flight simulation posture to obtain a high-frequency jitter effect, and outputs the high-frequency jitter effect to achieve jitter simulation.

3. The pilot training simulator based on big data and machine learning according to claim 1, characterized in that: The motion acquisition module acquires the motion of the simulated trainee through a camera, imports the acquired images into a preset image processing model for processing, marks the head position of the simulated trainee from the images, and further marks the eyeball images in the head position to obtain the eyeball position; The motion acquisition module uses the midpoint of the interactive display cabin (4) in the vertical direction as the standard height and draws a horizontal line along the standard height. The motion acquisition module uses the midpoint of the interactive display cabin (4) in the horizontal direction as the standard distance and draws a vertical line along the standard distance. The motion acquisition module records the horizontal line and the vertical line as a standard dividing line.

4. The pilot training simulator based on big data and machine learning according to claim 3, characterized in that: After obtaining the eyeball position, the action acquisition module connects the eyeball position and the horizontal line of the standard dividing line in a vertical plane to obtain a horizontal connecting line, connects the eyeball position and the numerical line of the standard dividing line in a horizontal plane to obtain a vertical connecting line, and then connects the eyeball position and the interactive display cabin (4) along the normal direction to the interactive display cabin (4) to obtain a normal connecting line; The motion acquisition module calculates the angle between the horizontal connecting line and the normal connecting line, records it as the horizontal angle, and records the angle between the vertical connecting line and the normal connecting line as the vertical angle. The motion acquisition module records the vertical angle and the horizontal angle as the field of view angle.

5. The pilot training simulator based on big data and machine learning according to claim 1, characterized in that: After obtaining the field of view angle, the field of view transformation module first obtains an initial display picture, which is a panoramic picture, and displays a part of the area directly in front through the interactive display cabin (4). The field of view transformation module adjusts the picture in opposite directions according to the horizontal angle and the vertical angle in the field of view angle, so that the picture area displayed by the interactive display cabin (4) moves, simulating a real picture area.

6. The pilot training simulator based on big data and machine learning according to claim 1, characterized in that: The flight simulation data acquired by the simulation control module includes a flight trajectory, a flight risk, and a risk response time. The simulation control module compares the flight trajectory with a preset trajectory to obtain an overlap rate between the flight trajectory and the preset trajectory. The simulation control module counts the number of flight risk occurrences to obtain the total number of risks; The simulation management and control module counts the risk response time to obtain the longest risk response time and the average risk response time, and makes independent threshold judgments on the longest risk response time and the average risk response time, and generates a risk response delay signal and a risk response slow signal according to the judgment results.

7. The pilot training simulator based on big data and machine learning according to claim 1, characterized in that: The simulation management and control module records the trajectory overlap rate, the total number of risks, the longest risk response time and the average risk response time as simulation training evaluation data, and sends the simulation training evaluation data to the statistical evaluation module.

8. The pilot training simulator based on big data and machine learning according to claim 7, characterized in that: The statistical evaluation module allocates different data in the simulation training evaluation data received each time to different evaluation curves to form a trajectory overlap rate change curve, a total risk number change curve, a maximum risk response time change curve and an average risk response time change curve; The statistical evaluation module fits the changing trends of different curves to obtain the changing trends of trajectory overlap rate, total risk times, longest risk response time and average risk response time, and records them for output as training effect evaluation.

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