All-motion flight simulator course plan generation system based on natural language

Abstract

The invention discloses an all-moving flight simulator course plan generation system based on natural language, and relates to the technical field of flight simulation training, and the system comprises a natural language processing module which receives a natural language instruction inputted by a teacher, converts the natural language instruction into text information, and extracts key information; the course plan generation module is used for screening and generating personalized course plan scripts from the course plan template library; the system integration module is used for integrating the course plan generation module and the existing flight simulator teacher platform; and the real-time three-dimensional scene display and task timeline generation module is used for generating a three-dimensional flight simulation task scene matched with the course content according to the course plan, generating a task timeline and displaying the time arrangement and sequence of each training link. According to the method, technologies such as natural language processing and deep learning are utilized, personalized flight simulation task courses are automatically generated, an efficient, intelligent and flexible solution is provided for pilot training, and the method has a wide application prospect.

Description

Technical Field

[0001] The invention relates to the technical field of flight simulation training, and in particular to a full-motion flight simulator course plan generation system based on natural language. Background Art

[0002] Flight simulation training has been widely used in the aviation field, especially in pilot training. Traditional flight simulation systems usually rely on the experience and knowledge of instructors to guide trainees by manually setting tasks, scenarios, and timelines. With the continuous advancement of flight technology and simulation equipment, the complexity of flight simulation tasks has gradually increased, especially in flight operation training under complex weather and emergency conditions. This requires the flight training system to have not only high-precision simulation capabilities, but also flexible and efficient course design and execution functions.

[0003] In the field of flight simulation tasks, the traditional course planning method mainly relies on manual operation, which not only consumes a lot of time and manpower, but also is difficult to make personalized adjustments according to the actual situation of the students. At the same time, the existing flight simulator instructor station has problems such as single function and inconvenient interaction in course planning and scene display, which cannot meet the needs of modern flight simulation tasks for efficient and intelligent teaching. With the rapid development of the aviation industry and the continuous improvement of the quality requirements of flight simulation tasks, an innovative solution is urgently needed to improve the course plan generation and teaching auxiliary functions of flight simulators. Summary of the invention

[0004] In order to solve the above technical problems, a full-motion flight simulator course plan generation system based on natural language is provided. This technical solution solves the above problems.

[0005] In order to achieve the above purpose, the technical solution adopted by the present invention is: The full flight simulator course plan generation system based on natural language includes: Natural language processing module: used to receive natural language instructions input by the instructor and convert them into text information. It uses the voice-to-text model in the flight simulation mission field to perform high-precision recognition of the instructor's voice instructions, and uses Tencent's Hunyuan large model to deeply analyze the text information to extract key information such as course objectives, student levels, training focus, and duration requirements. Course plan generation module: The course plan generation module is electrically connected to the natural language processing module. The course plan generation module is used to screen and generate a personalized course plan script from the course plan template library according to the information parsed by the natural language processing module, convert the course plan script into an instruction format recognizable by the teacher station through a parser, and send it to the teacher station for execution; System integration module: The system integration module is electrically connected to the course plan generation module and the natural language processing module, and is used to integrate the course plan generation system with the existing flight simulator instructor station and ensure that the instructor can adjust and execute the course plan through the user interface; Real-time three-dimensional scene display and task timeline generation module: The real-time three-dimensional scene display and task timeline generation module is electrically connected to the course plan generation module. The real-time three-dimensional scene display and task timeline generation module is used to generate a three-dimensional flight simulation task scene that matches the course content according to the course plan, and generate a task timeline to display the time arrangement and sequence of each training link, and support instructors to adjust the scene and task timeline in real time through the touch screen.

[0006] Preferably, the natural language processing module specifically includes: Command input and preliminary processing unit: input natural language commands, and the voice data then enters the speech-to-text model in the flight simulation mission field; Speech-to-text model for flight simulation missions: pre-processing speech, including feature extraction and noise reduction, enhancing speech signal quality, decoding feature vectors based on acoustic and language models, and converting speech into text information; Tencent Hunyuan Large Model Semantic Parsing Unit: After the text information enters the Tencent Hunyuan Large Model, it is first pre-processed by word segmentation and part-of-speech tagging, and then semantic analysis is performed using the pre-trained model and the flight simulation mission domain knowledge graph to extract key course design information, including student level, training duration, and training subjects. This structured information is then output for use by the course plan generation module.

[0007] Preferably, the preprocessing of speech, including feature extraction and noise reduction, enhancing speech signal quality, decoding feature vectors based on acoustic models and language models, and converting speech into text information specifically includes: Extract features based on Mel frequency cepstral coefficients, obtain the extracted features, use the HMM speech recognition model, and predict the next item in the sequence based on the probability of state transition; Among them, the formula of Mel frequency cepstrum coefficient is: , In the formula, For the The calculated Mel-frequency cepstral coefficients are is the frequency index, It is the first The amplitude of the frequency point, is the total number of Mel filters, is the index of the Mel-frequency cepstral coefficients, It is pi.

[0008] Preferably, the step of obtaining the extracted features and using the HMM speech recognition model to predict the next item in the sequence based on the probability of state transition specifically includes: Among them, the HMM speech recognition model formula is: , In the formula, Indicates that given the model parameters In the case of The probability of represents the parameters of the hidden Markov model, represents all possible hidden state sequences.

[0009] Preferably, the course plan generation module specifically includes: Information receiving and template matching unit: receiving the course design intention information output by the natural language processing module, including the student level and training focus, searching for matching templates in the course plan template library, and finding a preliminary matching template; Template personalization adjustment unit: Personalize the selected templates according to specific requirements, reasonably allocate the time for theoretical explanation and simulated flight operation according to the time requirements, optimize the relevant operation steps and parameters for the key points of complex weather flight simulation tasks, determine the depth of theoretical explanation according to the level of students, and generate a detailed course plan script; Format conversion and output unit: The generated course plan script is converted into a format recognizable by the teaching station through a parser, the operation steps are converted into a touch command sequence, the level score is converted into a data format for recording and analysis, and finally the converted course plan data is output.

[0010] Preferably, the template personalization adjustment unit specifically includes: Clarify the training objectives of the course, including basic flying skills, weather emergency response, and flight operations under complex weather conditions; Conduct level assessments on students based on their basic level, including flight experience, skill mastery, and flight time, and adjust the course difficulty based on the level assessment results; Focus on important flying skills and specific flight simulation tasks according to students’ weaknesses and specific needs; Filter out the course templates that match the training objectives from the preset training templates, evaluate the adaptability of each template based on the needs and goals of the trainees, and select the best matching template for adjustment; Combine the students' schedule and training goals to allocate the time ratio between theory explanation and simulated flight. According to the duration requirement of the flight course, adjust the duration ratio of each link to ensure the balance between theory and practice. According to the students' skill level and specific needs, the steps of simulated flight operation are optimized. During the simulated flight, the flight equipment and flight environment parameters are adjusted to simulate different flight scenarios and complex weather conditions, so that students can exercise their coping ability in a real environment. During the course, collect feedback from students, understand their learning progress, problems and difficulties, and adjust the content and difficulty of subsequent courses.

[0011] Preferably, the step of evaluating the level of the trainees according to their basic level, including flight experience, skill mastery and flight time, and adjusting the course difficulty based on the level evaluation results specifically includes: The level assessment formula is: , In the formula, To rate the students' overall level. Rate your flight experience, The impact of flight experience rating on students’ comprehensive ratings. Rate your skill mastery, The degree of influence of the skill mastery score on the student's overall score, Rate the flight time, The degree of influence of flight time score on the overall score of students.

[0012] Preferably, the system integration module specifically includes: Interface layer unit: receives the generated course plan and flight simulation task, and transmits it to the flight simulator system; receives the natural language instructions input by the user, and converts them into a machine-recognizable format; transmits the course plan and the adjusted flight simulation task to the instructor station, ensuring that the instructor station can display and execute the corresponding flight simulation task; Task Scheduling and Execution Unit: Schedule flight simulation tasks and ensure the execution order of tasks according to the course plan and instructor adjustment requirements. Arrange the execution order of flight simulation tasks according to priority, schedule and resource allocation. The flight simulator instructor station works together to execute actual flight simulation tasks. User interface interaction layer unit: provides a user interface to display the course plan content, including flight simulation tasks, flight targets, and training progress, and displays the status of flight simulation tasks in real time, including execution progress and abnormal reports.

[0013] Preferably, the real-time three-dimensional scene display and task timeline generation module specifically includes: Scene and timeline initialization unit: The instructor confirms the course plan, and the system calls relevant scene elements from the 3D scene model library, including specific airport runways, weather conditions, and initial aircraft status. The system uses the graphics rendering engine to initialize and render the 3D scene. At the same time, it generates a task timeline based on the course plan, clarifies the time arrangement of each training link, including theoretical explanations and simulated flight operations, and displays the initial scene and timeline to the instructor and trainees. Scene and timeline adjustment unit: The instructor adjusts the scene and timeline through the touch screen, and the system responds after receiving the instruction and updates the scene effect and timeline; Simulator control and interaction unit: The instructor activates preset scenarios and tasks through the touch screen for training, and the system adjusts the simulator status to start training. During training, the real-time status of the simulator is fed back to the system, and the system updates the scenario display and timeline to keep them synchronized, allowing instructors and trainees to understand the operation of the simulator in real time.

[0014] Preferably, the simulator control and interaction unit specifically includes: The instructor selects and activates preset training scenarios and tasks through the touch screen operation interface. After receiving the tasks and scenarios selected by the instructor, the system loads the relevant flight simulation tasks, scenario data and training requirements. According to the selected tasks, the system automatically configures the flight simulation environment. The system adjusts the initial state of the simulator according to the mission requirements, including the current state of the aircraft, the hardware and software configuration of the simulator, and the simulator is ready, waiting for the instructor to confirm the start of training; The instructor confirms and starts the training task through the touch screen. The system sends a start signal to the simulator. The simulator starts to execute the flight simulation task according to the received start command and starts the simulation process. The system simultaneously starts the timeline to display the estimated time, key nodes and current progress of the flight simulation task. The simulator collects the status information of the aircraft and the operating status of the system in real time. The simulator transmits these real-time data to the control system through the data interface. The system receives the simulator status data in real time and updates the display information on the interface, including flight parameters, mission progress and warning information. The system dynamically updates the display of the training scenario based on the simulator’s feedback, and updates the task timeline in real time, showing the current task stage and estimated completion time; The instructor monitors the training process through the touch screen interface, including real-time viewing of the aircraft status, mission execution status and student operating performance. If the tasks and scenarios need to be adjusted during the training process, the instructor can modify the task requirements through the touch screen, select different scenarios, and synchronize them to the simulator for adjustment. The instructor issues real-time instructions through the interface, and the system immediately transmits the instructions to the simulator for execution.

[0015] Compared with the prior art, the present invention has the following beneficial effects: The present invention proposes to use technologies such as natural language processing and deep learning to automatically generate personalized flight simulation mission courses, and combines real-time three-dimensional scene and mission timeline management, flight simulator control and interaction and other functions to provide an efficient, intelligent and flexible solution for pilot training. Through the implementation of this system, not only can the training efficiency and quality be improved, but also the training cost can be significantly reduced, and ultimately the pilot's operational ability and emergency response ability can be improved, which has broad application prospects. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] Figure 1 It is a system framework diagram of the present invention. DETAILED DESCRIPTION

[0017] The following description is used to disclose the present invention so that those skilled in the art can implement the present invention. The preferred embodiments described below are only examples, and those skilled in the art may think of other obvious variations.

[0018] Reference Figure 1 As shown, the natural language-based full-motion flight simulator course plan generation system includes: Natural Language Processing Module: Command input and preliminary processing unit: input natural language commands, and the voice data then enters the speech-to-text model in the flight simulation mission field; Speech-to-text model for flight simulation missions: pre-processing speech, including feature extraction and noise reduction, enhancing speech signal quality, decoding feature vectors based on acoustic and language models, and converting speech into text information; Extract features based on Mel frequency cepstral coefficients, obtain the extracted features, use the HMM speech recognition model, and predict the next item in the sequence based on the probability of state transition; Among them, the formula of Mel frequency cepstrum coefficient is: , In the formula, For the The calculated Mel-frequency cepstral coefficients are is the frequency index, It is the first The amplitude of the frequency point, is the total number of Mel filters, is the index of the Mel-frequency cepstral coefficients, is pi; Among them, the HMM speech recognition model formula is: , In the formula, Indicates that given the model parameters In the case of The probability of represents the parameters of the hidden Markov model, represents all possible hidden state sequences; Tencent Hunyuan Large Model Semantic Parsing Unit: After the text information enters the Tencent Hunyuan Large Model, it is first pre-processed by word segmentation and part-of-speech tagging, and then semantic analysis is performed using the pre-trained model and the flight simulation mission domain knowledge graph to extract key course design information, including student level, training duration, and training subjects. This structured information is then output for use by the course plan generation module.

[0019] Lesson Plan Generation Module: Information receiving and template matching unit: receiving the course design intention information output by the natural language processing module, including the student level and training focus, searching for matching templates in the course plan template library, and finding a preliminary matching template; Template personalization adjustment unit: Personalize the selected templates according to specific requirements, reasonably allocate the time for theoretical explanation and simulated flight operation according to the time requirements, optimize the relevant operation steps and parameters for the key points of complex weather flight simulation tasks, determine the depth of theoretical explanation according to the level of students, and generate a detailed course plan script; Clarify the training objectives of the course, including basic flying skills, weather emergency response, and flight operations under complex weather conditions; Conduct level assessments on students based on their basic level, including flight experience, skill mastery, and flight time, and adjust the course difficulty based on the level assessment results; The level assessment formula is: , In the formula, To rate the students' overall level. Rate your flight experience, The impact of flight experience rating on students’ comprehensive ratings. Rate your skill mastery, The degree of influence of the skill mastery score on the student's overall score, Rate the flight time, The degree to which the flight time rating affects the student's overall rating; Focus on important flying skills and specific flight simulation tasks according to students’ weaknesses and specific needs; Filter out the course templates that match the training objectives from the preset training templates, evaluate the adaptability of each template based on the needs and goals of the trainees, and select the best matching template for adjustment; Combine the students' schedule and training goals to allocate the time ratio between theory explanation and simulated flight. According to the duration requirement of the flight course, adjust the duration ratio of each link to ensure the balance between theory and practice. According to the students' skill level and specific needs, the steps of simulated flight operation are optimized. During the simulated flight, the flight equipment and flight environment parameters are adjusted to simulate different flight scenarios and complex weather conditions, so that students can exercise their coping ability in a real environment. During the course implementation, students’ feedback is collected to understand their learning progress, problems and difficulties, and to adjust the content and difficulty of subsequent courses; Format conversion and output unit: The generated course plan script is converted into a format recognizable by the teaching station through a parser, the operation steps are converted into a touch command sequence, the level score is converted into a data format for recording and analysis, and finally the converted course plan data is output.

[0020] System integration module: Interface layer unit: receives the generated course plan and flight simulation task, and transmits it to the flight simulator system; receives the natural language instructions input by the user, and converts them into a machine-recognizable format; transmits the course plan and the adjusted flight simulation task to the instructor station, ensuring that the instructor station can display and execute the corresponding flight simulation task; Task Scheduling and Execution Unit: Schedule flight simulation tasks and ensure the execution order of tasks according to the course plan and instructor adjustment requirements. Arrange the execution order of flight simulation tasks according to priority, schedule and resource allocation. The flight simulator instructor station works together to execute actual flight simulation tasks. User interface interaction layer unit: provides a user interface to display the course plan content, including flight simulation tasks, flight targets, and training progress, and displays the status of flight simulation tasks in real time, including execution progress and abnormal reports.

[0021] Real-time 3D scene display and task timeline generation module: Scene and timeline initialization unit: The instructor confirms the course plan, and the system calls relevant scene elements from the 3D scene model library, including specific airport runways, weather conditions, and initial aircraft status. The system uses the graphics rendering engine to initialize and render the 3D scene. At the same time, it generates a task timeline based on the course plan, clarifies the time arrangement of each training link, including theoretical explanations and simulated flight operations, and displays the initial scene and timeline to the instructor and trainees. Scene and timeline adjustment unit: The instructor adjusts the scene and timeline through the touch screen, and the system responds after receiving the instruction and updates the scene effect and timeline; Simulator control and interaction unit: The instructor activates preset scenarios and tasks through the touch screen for training, and the system adjusts the simulator status to start training. During training, the real-time status of the simulator is fed back to the system, and the system updates the scenario display and timeline to keep in sync, allowing instructors and trainees to understand the operation of the simulator in real time; The instructor selects and activates preset training scenarios and tasks through the touch screen operation interface. After receiving the tasks and scenarios selected by the instructor, the system loads the relevant flight simulation tasks, scenario data and training requirements. According to the selected tasks, the system automatically configures the flight simulation environment. The system adjusts the initial state of the simulator according to the mission requirements, including the current state of the aircraft, the hardware and software configuration of the simulator, and the simulator is ready, waiting for the instructor to confirm the start of training; The instructor confirms and starts the training task through the touch screen. The system sends a start signal to the simulator. The simulator starts to execute the flight simulation task according to the received start command and starts the simulation process. The system simultaneously starts the timeline to display the estimated time, key nodes and current progress of the flight simulation task. The simulator collects the status information of the aircraft and the operating status of the system in real time. The simulator transmits these real-time data to the control system through the data interface. The system receives the simulator status data in real time and updates the display information on the interface, including flight parameters, mission progress and warning information. The system dynamically updates the display of the training scenario based on the simulator’s feedback, and updates the task timeline in real time, showing the current task stage and estimated completion time; The instructor monitors the training process through the touch screen interface, including real-time viewing of the aircraft status, mission execution status and student operating performance. If the tasks and scenarios need to be adjusted during the training process, the instructor can modify the task requirements through the touch screen, select different scenarios, and synchronize them to the simulator for adjustment. The instructor issues real-time instructions through the interface, and the system immediately transmits the instructions to the simulator for execution.

[0022] In summary, the advantages of the present invention are: The present invention greatly simplifies the flight simulator course planning process through natural language interaction technology, improves teaching preparation efficiency, reduces the workload of instructors, and enables them to devote more energy to teaching guidance; Utilizing advanced model technology and a rich library of curriculum plan templates, it is possible to quickly generate personalized curriculum plans that meet teaching objectives and students’ actual conditions, thus improving the pertinence and effectiveness of flight simulation tasks and helping to improve students’ learning outcomes and pass rates. The system is highly compatible and scalable, and can seamlessly adapt to full-motion flight simulators of different brands and models and various flight simulators. It also supports customized development based on customers’ specific needs, meeting diverse teaching scenarios and training requirements, and providing flight simulation mission organizations with a more flexible and efficient teaching assistance solution. The optional real-time 3D scene display and mission timeline generation module further enhance the intuitiveness and flexibility of teaching, allowing instructors and trainees to more intuitively understand the training content and process, improve the authenticity and immersion of training, and help trainees better master flight skills and improve training quality; The improved simulator control and interaction method replaces natural language commands with touch screen operations, which improves the accuracy and convenience of operations and reduces the risk of operational errors caused by natural language comprehension errors. It also improves the instructor's operational experience and efficiency during training, making the teaching process smoother and more stable.

[0023] The above shows and describes the basic principles, main features and advantages of the present invention. Those skilled in the art should understand that the present invention is not limited to the above embodiments. The above embodiments and descriptions only describe the principles of the present invention. The present invention may be subject to various changes and improvements without departing from the spirit and scope of the present invention. These changes and improvements fall within the scope of the present invention. The scope of protection claimed by the present invention is defined by the attached claims and their equivalents.

Claims

1. A full-flight simulator course plan generation system based on natural language, characterized in that: include: Natural language processing module: used to receive natural language instructions input by the instructor and convert them into text information. It uses the voice-to-text model in the flight simulation mission field to perform high-precision recognition of the instructor's voice instructions, and uses Tencent's Hunyuan large model to deeply analyze the text information to extract key information such as course objectives, student levels, training focus, and duration requirements. Course plan generation module: The course plan generation module is electrically connected to the natural language processing module. The course plan generation module is used to screen and generate a personalized course plan script from the course plan template library according to the information parsed by the natural language processing module, convert the course plan script into an instruction format recognizable by the teacher station through a parser, and send it to the teacher station for execution; System integration module: The system integration module is electrically connected to the course plan generation module and the natural language processing module, and is used to integrate the course plan generation system with the existing flight simulator instructor station and ensure that the instructor can adjust and execute the course plan through the user interface; Real-time three-dimensional scene display and task timeline generation module: The real-time three-dimensional scene display and task timeline generation module is electrically connected to the course plan generation module. The real-time three-dimensional scene display and task timeline generation module is used to generate a three-dimensional flight simulation task scene that matches the course content according to the course plan, and generate a task timeline to display the time arrangement and sequence of each training link, and support instructors to adjust the scene and task timeline in real time through the touch screen.

2. The natural language-based full-flight simulator course plan generation system according to claim 1, characterized in that: The natural language processing module specifically includes: Command input and preliminary processing unit: input natural language commands, and the voice data then enters the speech-to-text model in the flight simulation mission field; Speech-to-text model for flight simulation missions: pre-processing speech, including feature extraction and noise reduction, enhancing speech signal quality, decoding feature vectors based on acoustic and language models, and converting speech into text information; Tencent Hunyuan Large Model Semantic Parsing Unit: After the text information enters the Tencent Hunyuan Large Model, it is first pre-processed by word segmentation and part-of-speech tagging, and then semantic analysis is performed using the pre-trained model and the flight simulation mission domain knowledge graph to extract key course design information, including student level, training duration, and training subjects. This structured information is then output for use by the course plan generation module.

3. The natural language-based full-flight simulator course plan generation system according to claim 2, characterized in that: The speech is preprocessed, It includes feature extraction and noise reduction, enhancing the quality of speech signals, decoding feature vectors based on acoustic models and language models, and converting speech into text information. Specifically, it includes: Extract features based on Mel frequency cepstral coefficients, obtain the extracted features, use the HMM speech recognition model, and predict the next item in the sequence based on the probability of state transition; Among them, the formula of Mel frequency cepstrum coefficient is: , In the formula, For the The calculated Mel-frequency cepstral coefficients are is the frequency index, It is the first The amplitude of the frequency point, is the total number of Mel filters, is the index of the Mel-frequency cepstral coefficients, It is pi.

4. The natural language-based full-flight simulator course plan generation system according to claim 3, characterized in that: The extracted features are obtained, and the HMM speech recognition model is used to predict the next specific item in the sequence based on the probability of state transition. include: Among them, the HMM speech recognition model formula is: ； In the formula, Indicates that given the model parameters In the case of The probability of represents the parameters of the hidden Markov model, represents all possible hidden state sequences.

5. The natural language-based full-flight simulator course plan generation system according to claim 4, characterized in that: The course plan generation module specifically includes: Information receiving and template matching unit: receiving the course design intention information output by the natural language processing module, including the student level and training focus, searching for matching templates in the course plan template library, and finding a preliminary matching template; Template personalization adjustment unit: Personalize the selected templates according to specific requirements, reasonably allocate the time for theoretical explanation and simulated flight operation according to the time requirements, optimize the relevant operation steps and parameters for the key points of complex weather flight simulation tasks, determine the depth of theoretical explanation according to the level of students, and generate a detailed course plan script; Format conversion and output unit: The generated course plan script is converted into a format recognizable by the teaching station through a parser, the operation steps are converted into a touch command sequence, the level score is converted into a data format for recording and analysis, and finally the converted course plan data is output.

6. The natural language-based full-flight simulator course plan generation system according to claim 5, characterized in that: The template personalization adjustment unit specifically includes: Clarify the training objectives of the course, including basic flying skills, weather emergency response, and flight operations under complex weather conditions; Conduct level assessments on students based on their basic level, including flight experience, skill mastery, and flight time, and adjust the course difficulty based on the level assessment results; Focus on important flying skills and specific flight simulation tasks according to students’ weaknesses and specific needs; Filter out the course templates that match the training objectives from the preset training templates, evaluate the adaptability of each template based on the needs and goals of the trainees, and select the best matching template for adjustment; Combine the students' schedule and training goals to allocate the time ratio between theory explanation and simulated flight. According to the duration requirement of the flight course, adjust the duration ratio of each link to ensure the balance between theory and practice. According to the students' skill level and specific needs, the steps of simulated flight operation are optimized. During the simulated flight, the flight equipment and flight environment parameters are adjusted to simulate different flight scenarios and complex weather conditions, so that students can exercise their coping ability in a real environment. During the course, collect feedback from students, understand their learning progress, problems and difficulties, and adjust the content and difficulty of subsequent courses.

7. The natural language-based full-flight simulator course plan generation system according to claim 6, characterized in that: The students will be assessed based on their basic level, including flight experience, skill mastery and flight time. The course difficulty will be adjusted based on the assessment results. include: The level assessment formula is: ; In the formula, To rate the students' overall level. Rate your flight experience, The impact of flight experience rating on students’ comprehensive ratings. Rate your skill mastery, The degree of influence of the skill mastery score on the student's overall score, Rate the flight time, The degree of influence of flight time score on the overall score of students.

8. The natural language-based full-flight simulator course plan generation system according to claim 7, characterized in that: The system integration module specifically includes: Interface layer unit: receives the generated course plan and flight simulation task, and transmits it to the flight simulator system; receives the natural language instructions input by the user, and converts them into a machine-recognizable format; transmits the course plan and the adjusted flight simulation task to the instructor station, ensuring that the instructor station can display and execute the corresponding flight simulation task; Task Scheduling and Execution Unit: Schedule flight simulation tasks and ensure the execution order of tasks according to the course plan and instructor adjustment requirements. Arrange the execution order of flight simulation tasks according to priority, schedule and resource allocation. The flight simulator instructor station works together to execute actual flight simulation tasks. User interface interaction layer unit: provides a user interface to display the course plan content, including flight simulation tasks, flight targets, and training progress, and displays the status of flight simulation tasks in real time, including execution progress and abnormal reports.

9. The natural language based full flight simulator course plan generation system according to claim 8, characterized in that: The real-time three-dimensional scene display and task timeline generation module specifically includes: Scene and timeline initialization unit: the teacher confirms the course plan, and the system calls relevant scene elements from the 3D scene model library; Scene and timeline adjustment unit: The instructor adjusts the scene and timeline through the touch screen, and the system responds after receiving the instruction and updates the scene effect and timeline; Simulator control and interaction unit: The instructor activates preset scenarios and tasks through the touch screen for training, and the system adjusts the simulator status to start training. During training, the real-time status of the simulator is fed back to the system, and the system updates the scenario display and timeline to keep them synchronized, allowing instructors and trainees to understand the operation of the simulator in real time.

10. The natural language based full flight simulator course plan generation system according to claim 9, characterized in that: The simulator control and interaction unit specifically includes: The instructor selects and activates preset training scenarios and tasks through the touch screen operation interface. After receiving the tasks and scenarios selected by the instructor, the system loads the relevant flight simulation tasks, scenario data and training requirements. According to the selected tasks, the system automatically configures the flight simulation environment. The system adjusts the initial state of the simulator, the hardware and software configuration of the simulator according to the requirements of the task, and the simulator is ready, waiting for the instructor to confirm the start of training; The instructor confirms and starts the training task through the touch screen. The system sends a start signal to the simulator. The simulator starts to perform the flight simulation task according to the received start command, starts the simulation process, and the system starts the timeline synchronously. The simulator collects aircraft status information and system operating status in real time, and checks aircraft status, mission execution and trainee operating performance in real time.

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