EVTOL flight student training system and method based on virtual low-altitude environment

Abstract

The invention discloses an eVTOL flight trainee training system based on a virtual low-altitude environment, which relates to the technical field of flight training and comprises a data layer, a business logic layer and a user interface layer. The data layer comprises a data source, a data synchronization center, a data transmission protocol, a data receiving end and a data storage module, the data source comprises flight student training equipment and sensor data of a low-altitude aircraft, and the data synchronization center adopts a distributed architecture to coordinate multi-component data synchronization. The invention further discloses an eVTOL flight trainee training method based on the virtual low-altitude environment. According to the eVTOL flight trainee training system and method based on the virtual low-altitude environment provided by the invention, through innovative architecture design and function implementation, the problems of integration, scheduling, synchronization, communication and the like of various elements in the virtual environment are effectively solved, an efficient and high-quality training platform is provided for eVTOL flight trainees, flight talents can be cultivated, and the eVTOL flight trainees can be trained. The application of the aircraft in the traffic field is promoted.

Description

Technical Field

[0001] The present invention relates to the field of flight training technology, and in particular to an eVTOL flight trainee training system and method based on a virtual low-altitude environment. Background Art

[0002] With the acceleration of urbanization, eVTOL aircraft, as an important part of future urban air traffic, has an increasingly broad application prospect. This emerging field has put forward higher requirements for the professional training of flight trainees. eVTOL aircraft not only need to operate flexibly in complex and changeable low-altitude environments, but also need to have the ability to fly in coordination with a variety of low-altitude aircraft. Therefore, it is particularly urgent to develop a professional training system that can efficiently simulate the real low-altitude flight environment and cover a variety of flight scenarios. The system needs to have highly integrated elements, including accurate flight simulation, environment rendering, aircraft interaction, and real-time data feedback, so as to comprehensively improve the skill level of flight trainees and their ability to deal with complex situations.

[0003] However, the existing flight training systems have significant deficiencies in simulating eVTOL flights. Traditional flight simulators are difficult to accurately restore the complexity and variability of low-altitude environments, especially when dealing with factors such as buildings, terrain obstacles, and airflow changes. In addition, the existing systems lack sufficient flexibility and synchronization in the construction of virtual environments, resulting in a single flight simulation scene and low accuracy, which cannot meet the eVTOL flight trainees' needs for high-simulation and diversified training scenarios. At the same time, there are also obvious shortcomings in factor integration, scheduling, synchronization, and communication, which limits the improvement of training effects. In this regard, we propose an eVTOL flight trainee training system and method based on a virtual low-altitude environment. Summary of the invention

[0004] In order to solve the above technical problems, an eVTOL flight student training system and method based on a virtual low-altitude environment are 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 eVTOL flight trainee training system based on a virtual low-altitude environment includes: data layer, business logic layer and user interface layer; The data layer includes a data source, a data synchronization center, a data transmission protocol, a data receiving end, and a data storage module, wherein the data source includes sensor data of flight trainee training equipment and low-altitude aircraft, the data synchronization center adopts a distributed architecture to coordinate multi-component data synchronization, the data transmission protocol supports efficient encrypted transmission, the data receiving end verifies and processes data, and the data storage module establishes a persistent storage mechanism; The business logic layer implements the functions of virtual low-altitude scene construction, aircraft simulation, student operation processing, digital pilot control, digital air traffic controller integration and training evaluation. The virtual scene construction is based on high-precision geographic information and meteorological models. The aircraft simulation includes physical property modeling of eVTOL and other low-altitude aircraft. The student operation processing analyzes the control input in real time and feeds back to the virtual environment. The digital pilot simulates collaborative flight behavior through AI algorithms, and the digital air traffic controller generates instructions and coordinates traffic according to control rules. The user interface layer provides an instructor operation interface and a student interaction interface; the system adopts a microservice architecture, splitting the functional modules into scenario management microservices, aircraft simulation microservices, student operation processing microservices, digital pilot services, digital air traffic controller services, evaluation feedback microservices and data processing microservices, and each microservice communicates through the DDS protocol; The trainee training equipment is connected to the system through a standardized interface, receiving virtual scene data and uploading operation data; digital pilots and digital air traffic controllers are integrated into the system through independent interfaces, obtaining environmental data in real time and providing feedback on control instructions.

[0006] Preferably, the data synchronization model adopts a synchronization method combining streaming and batch, which specifically includes the following steps: Data reading and packaging: At each data source end of the training system, the data reading thread obtains the latest data from the data source in real time. After reading the data, it is packaged into standard data units. The reading thread at the data source end reads the data at a time interval of Δ Periodically collect sensor and operation data and encapsulate them into data units ,in is the timestamp, is the sensor type identifier, is a numeric vector, is the data check code; Pipeline transmission: The encapsulated data units are transmitted through multi-threaded pipelines. The reading thread and the writing thread communicate through the pipeline. The reading thread immediately sends each result data to the pipeline without waiting for other data. The pipeline capacity satisfy ,in is the number of concurrent data sources, and the transmission delay satisfies ; Batch processing: The writing thread waits at the other end of the pipeline to receive data. The writing thread is processed in a time window. Or data volume threshold Merge batches. If the current accumulated data volume satisfy or , then merge these data into one batch to generate batch ,in ; Data transmission and processing: When the data batch is ready, the writing thread transmits the batch data to the target node through the network interface via the encryption protocol. After receiving the data batch, the target node will parse and process it. After parsing, the target node updates the system status. The data consistency is verified by the verification function. Verify that is a hash function, is a cyclic redundancy check code.

[0007] Preferably, the data synchronization model is combined with a distributed lock to achieve consistency control, specifically including: Proposal Generation: When Sharing Data When an update is required, a component acts as a proposer, and the proposer generates a unique proposal number , and sends a broadcast prepare request to most recipients to the majority of acceptors; Commitment response: The acceptors are distributed on the key nodes of the system. After receiving the prepare request from the proposer, they compare the proposal number with all the proposal numbers that have been responded to. With the largest number in history ,like , then returns a promise , otherwise reject; Proposal confirmation: Acceptors are distributed on key nodes of the system. When the proposer receives confirmation from enough acceptors, it will notify all learners of the accepted proposal and select or , send an acceptance request ; Status Update: Acceptor Confirmed If not covered by a higher proposal, persist , the learner records this proposal, the learner node applies the update to the local state machine, and spreads it to the entire system through the broadcast protocol to ensure that the final consistency is satisfied .

[0008] Preferably, the virtual low-altitude scene construction function uses geographic information data and graphics rendering technology to construct a virtual low-altitude environment, including diverse terrains such as cities, mountains and rivers, as well as high-rise buildings and bridges, and simulates meteorological conditions, including wind direction and speed, rainfall, snowfall and fog, and dynamically adjusts meteorological parameters according to training needs; Supports rapid construction of a variety of low-altitude mission scenarios, including urban aerial tours, emergency medical rescue, and logistics distribution scenarios. Each scenario has specific mission objectives and environmental settings. The virtual low-altitude scene construction function is based on a three-dimensional geographic grid model ,in is the terrain elevation data, is the obstacle coordinate set; the meteorological simulation uses the Navier-Stokes equation to describe fluid dynamics: , in is the wind speed vector, is the air pressure, is the viscosity coefficient, The dynamic meteorological parameters drive the changes of the virtual environment by solving equations in real time, supporting the dynamic adjustment of wind direction, rainfall intensity and visibility.

[0009] Preferably, the aircraft simulation function accurately simulates the flight characteristics of the eVTOL aircraft, including the dynamic response and handling performance in vertical take-off and landing, hovering, and cruise flight modes, based on real physical models and performance parameters; The aircraft simulation function establishes a six-degree-of-freedom dynamic model for eVTOL: ,

[0010] in is the linear speed, is the angular velocity, For quality, is the inertia tensor, For propulsion, is the aerodynamic drag, is the torque; other aircraft simulations use simplified models ,in is the proportional gain, The preset speed.

[0011] Preferably, the flight trainee operation processing function receives the trainee's operation input on the training equipment, such as flight attitude control, speed adjustment, route planning and other operations, and converts it into aircraft actions in the virtual environment; monitors the accuracy and standardization of the trainee's operation in real time, such as judging whether the joystick input complies with the flight operation procedures, and prompts or records the operations that do not comply with the regulations; adjusts the flight environment and task difficulty in the virtual scene according to the trainee's operation feedback, such as adding sudden airflow interference, changing the destination, etc., to improve the trainee's ability to respond; The student operation processing function converts the control input into Converted into aircraft control instructions: , in is the error signal, For the expected state, is the current state; the system monitors operational compliance in real time and if the joystick input exceeds the safety threshold , an alarm is triggered and the deviation is recorded .

[0012] Preferably, in the digital pilot control function, the digital pilot autonomously drives other low-altitude aircraft to fly in a virtual environment according to a preset flight plan and real-time environmental information; The digital pilot makes flight decisions based on air traffic rules and actual flight conditions, including avoiding other aircraft and choosing the optimal flight path; By adjusting the behavior strategy of the digital pilot and simulating other aircraft pilots of different levels and styles, a variety of training scenarios can be provided for students. Digital pilot uses deep reinforcement learning model , whose policy network outputs action : , in For the Critic network, is the exploration rate, is Gaussian noise; state Contains environmental data, aircraft status and mission objectives, reward function Weighted safety distance and range efficiency .

[0013] Preferably, the control functions of digital air traffic controllers include: generating control instructions based on virtual low-altitude traffic conditions and control rules; managing voice and data link communications using speech synthesis and other technologies; real-time monitoring of situations and optimizing control decisions; and quickly coordinating resources from all parties to implement rescue plans in simulated emergency situations to improve trainees' response capabilities. Digital air traffic controllers generate control instructions based on conflict detection algorithms and use the speed obstacle method to predict aircraft trajectory conflicts: , If the minimum approach distance , then generate a height adjustment instruction ; Voice communication uses the WaveNet model to synthesize command audio ,in is a latent variable, Encode the text.

[0014] Preferably, the training evaluation function records various operational data of trainees in simulated flight, including flight trajectory, flight attitude changes, and reaction time to emergencies; By comparing the preset evaluation criteria and excellent flight operation cases, the students' performance is comprehensively evaluated. The evaluation indicators cover flight skills, decision-making ability, and safety awareness. Generate detailed evaluation reports, including trainees’ strengths and weaknesses, and provide personalized improvement suggestions and follow-up training plans; The training evaluation function is scored by weighting multiple indicators: , in , , , weight ; The evaluation report generates decision tree classification results and marks the operation defect nodes.

[0015] The eVTOL flight trainee training method based on a virtual low-altitude environment is used to implement the eVTOL flight trainee training system based on a virtual low-altitude environment, comprising: System initialization: Loading geographic data into memory matrix , initialize the microservice instance and register it with the service registration center; Device connection: Establish a secure channel through the TLS1.3 protocol and verify the device certificate ; Simulation training: The digital pilot selects actions according to the Markov decision process, and the digital air traffic controller Update regulatory directives; Evaluation feedback: Use the gradient boosting tree model to analyze operational data and generate personalized improvement paths .

[0016] Compared with the prior art, the present invention has the following beneficial effects: The eVTOL flight trainee training system and method based on a virtual low-altitude environment proposed in the present invention effectively solves the problems of integration, scheduling, synchronization and communication of various elements in a virtual environment through innovative architecture design and function implementation, and provides an efficient and high-quality training platform for eVTOL flight trainees, which is helpful to cultivate high-quality eVTOL flight talents and promote the widespread application of eVTOL aircraft in the future transportation field. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] Figure 1 This is the architecture diagram of the virtual low-altitude environment training system;

[0018] Figure 2 It is a data flow diagram of the present invention;

[0019] Figure 3 This is an example diagram of a simulation scenario of the present invention;

[0020] Figure 4 It is a data synchronization flow chart of the present invention. DETAILED DESCRIPTION

[0021] 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.

[0022] Reference Figure 1 As shown in the figure, the eVTOL flight trainee training system based on the virtual low-altitude environment includes a data layer, a business logic layer, and a user interface layer. Each layer cooperates with each other to jointly realize the functions of the flight trainee training system.

[0023] Data Layer: Data source: The data source of student training equipment (such as joysticks, pedals and other operating data acquisition devices) and other low-altitude aircraft data sources (such as various sensors) are the source of data and provide raw data for the entire system.

[0024] Data synchronization center: As the core hub of data processing, internal data verification, format conversion and batch processing modules ensure the accuracy and validity of data. For example, the data verification module can check the integrity and accuracy of data, the data format conversion module can unify data in different formats, and the data batch processing module can group data for processing and improve efficiency.

[0025] Data transmission protocols: Protocols such as DDS ensure the secure and efficient transmission of data in the system.

[0026] Data receiving end: mainly receives data from the data transmission protocol, and interacts with the student training equipment to achieve two-way data transmission, such as sending virtual low-altitude scene data to the student training equipment and receiving student operation data at the same time.

[0027] Data storage: used to store and read data, ensure data persistence, and provide historical data support for the system.

[0028] Business logic layer: Scene management microservice: responsible for managing virtual scene related information, such as scene switching, scene parameter settings, etc., and works closely with the aircraft simulation microservice to provide it with scene information.

[0029] Aircraft simulation microservice: includes eVTOL simulation submodules and other low-altitude aircraft simulation submodules, simulates the flight characteristics and behaviors of different types of aircraft, receives scenario information from the scenario management microservice and operation instructions from the student operation processing microservice, and feeds back flight data to the evaluation feedback microservice.

[0030] Student operation processing microservice: processes the student's operation input on the training equipment, passes the operation data to the aircraft simulation microservice, and sends the operation data to the evaluation feedback microservice for analysis.

[0031] Digital pilot service: Control the flight of other low-altitude aircraft through a digital pilot (AI model), exchange flight control information with the aircraft simulation microservice, and feed back flight status data to the evaluation feedback microservice.

[0032] Digital air traffic controller service: Generates control instructions based on information in the virtual low-altitude environment and sends them to the digital pilot service and aircraft simulation microservice. It also receives aircraft status feedback and communication information to adjust control strategies in real time.

[0033] Evaluation feedback microservice: conducts a comprehensive evaluation of student operation data, aircraft flight data, etc., and passes the evaluation data to the data processing microservice for storage and analysis.

[0034] Data processing microservice: responsible for processing and storing various types of data in the system, interacting with the data storage of the data layer to ensure effective data management.

[0035] User interface layer: Instructor operation interface: Instructors use this interface to manage and monitor the training system, such as setting up training courses, adjusting training parameters, etc.

[0036] Scene interaction interface: provides students with an interface for interacting with the virtual low-altitude environment, displaying flight scenes, aircraft status and other information.

[0037] eVTOL simulation submodule (from the perspective of student training equipment): Students perform simulated flight operations on this device. The device interacts with the system through a standardized data interface, receives virtual scene data and sends student operation data.

[0038] Reference Figure 2 As shown, the data flow includes: 1. Data collection and transmission

[0039] Data sources: including aircraft sensors (such as position and attitude sensors, etc.) and student operating devices (joysticks, pedals), etc. The data they generate is the starting point of the entire process.

[0040] Data reading and encapsulation: Read the data from the data source and encapsulate it, converting it into a format suitable for subsequent processing. The encapsulated data unit is sent to the pipeline transmission part.

[0041] Pipeline transmission: The reading thread continuously obtains the latest data from the data source and sends it to the pipeline. The writing thread receives the data at the other end of the pipeline and processes it in batches. This mechanism ensures orderly transmission and efficient processing of data.

[0042] Data batch processing: When certain conditions (such as time interval or data volume threshold) are met, multiple data units are merged into a batch and then sent to the target node (such as the data processing module on the server side) through the network interface.

[0043] 2. Process based on distributed lock implementation Proposer initiates proposal: When a data batch arrives at the target node and involves shared data update, the proposer (such as a digital air traffic controller or student training device) sends a prepare request to the majority of acceptors, which contains the proposal number and proposal content.

[0044] The acceptor processes the preparation request: After receiving the request, the acceptor compares the proposal number with the proposal number that has been responded to. If the received number is larger, it accepts the proposal and promises not to accept proposals with smaller numbers. Then it replies to the acceptor with the subsequent content of the preparation request:

[0045] The proposer sends an acceptance request: After receiving the commitment from the majority of acceptors, the proposer sends an acceptance request, in which the proposal value is usually the last proposal value accepted by the majority of acceptors. If it does not exist, the new proposal value is used.

[0046] Acceptor confirms acceptance request: After receiving the acceptance request, if the acceptor has not made a commitment to a larger numbered proposal, it accepts the proposal and confirms the acceptance, and then notifies the learner of the accepted proposal.

[0047] Learner propagation proposal: The learner records the proposal and applies the data updates in it to the local state machine to keep it consistent with the overall state of the system, and then propagates the updated data to other relevant components in the system to ensure data synchronization of the entire system.

[0048] Reference Figure 3 As shown, the simulation scenarios include: 1. Virtual low-altitude environment

[0049] Terrain and obstacles: Diverse terrains (cities, mountains, rivers) and obstacles (high-rise buildings, bridges) create complex and realistic flight scenarios, providing students with a close-to-real training environment and honing their flying skills in dealing with different terrains and obstacles.

[0050] Aircraft display: Various types of aircraft (eVTOL, helicopters, drones) are distributed in the virtual environment. Their flight direction and attitude changes show the simulated flight status. The numbers or names are easy to distinguish and identify, helping students understand the operation of different aircraft in the same environment.

[0051] Illustration of weather conditions: Complex weather conditions (wind direction and speed, rainfall, snowfall, fog) are presented through graphics and annotations. Dynamically adjustable weather parameters allow students to adapt to various weather conditions and improve their flying capabilities in complex weather environments.

[0052] 2. Demonstration of digital air traffic controller and digital pilot

[0053] Digital air traffic controller console: includes a display screen (displaying the virtual low-altitude environment situation), communication equipment (voice and data link communication) and operation buttons (generating control instructions), manages air traffic through information interaction to ensure safe and orderly flight.

[0054] Aircraft controlled by digital pilots: Special markings or colors distinguish them from aircraft operated by trainees. Their flight trajectory and attitude changes reflect preset flight strategies and algorithms, simulating different pilot behaviors, providing trainees with diverse training scenarios, and improving their ability to coordinate flight with other aircraft.

[0055] 3. Pilot student perspective display (optional)

[0056] Trainee training equipment perspective: Displays the virtual low-altitude scene images that trainees see on the training equipment, including aircraft instrument displays (speed, altitude, attitude, etc.) and local views (terrain, obstacles, other aircraft, etc.), allowing trainees to intuitively experience the training content and enhance the sense of immersion in the training.

[0057] Reference Figure 4 As shown in the figure, the data synchronization process includes:

[0058] 1. Data collection and preprocessing stage

[0059] Data sources: include aircraft sensors (including position, speed, attitude sensors, etc.), student operating equipment (joystick and pedals), and meteorological data modules and terrain data modules. These data sources provide various key data during the flight process, such as the real-time status of the aircraft, student operating actions, meteorological conditions, and terrain information, etc. They are the starting point of the entire data synchronization process and provide a rich and diverse data foundation for subsequent processing.

[0060] Data cleaning and labeling module: The raw data obtained from the data source first enters this module. The data cleaning process is used to remove noise and outliers in the sensor data, and to process errors or invalid information in the operation data to ensure the accuracy and reliability of the data. The data labeling process classifies and labels the cleaned data, for example, labeling the student operation data according to different operation types so that the subsequent model can better understand and process the data and prepare for the effective use of the data.

[0061] 2. Model construction and training phase

[0062] Hybrid model structure: The hybrid model based on RNN and LSTM is the core part of the entire data synchronization. The input side of the model receives pre-processed data, which contains information such as aircraft status, student operations, and environment, providing comprehensive data input for the model. The RNN layer can effectively extract time series features in the data and learn the dependencies of data in the time dimension through its unique neuron structure and cyclic connection method. The LSTM layer uses mechanisms such as input gates, forget gates, and output gates to further filter and manage information, solve long-term dependency problems, and better capture long-term patterns and trends in the data.

[0063] Training process: The training set is used to train the model. The training process is started by inputting the training data into the RNN layer. The model continuously adjusts internal parameters to minimize the prediction error. Inside the model, data flows from the RNN layer to the LSTM layer to achieve data transmission and processing between different layers. Finally, the predicted synchronization data or data difference value is obtained on the output side for subsequent data synchronization operations. The test set is used to evaluate the performance of the model on unseen data. By comparing the prediction results on the output side with the real data of the test set, various evaluation indicators (such as the root mean square error, mean absolute error, etc. mentioned later) are used to judge the accuracy and generalization ability of the model to ensure the reliability of the model in practical applications.

[0064] 3. Data synchronization and update stage

[0065] Real-time data input and processing: During the operation of the system, the data source continuously generates real-time data (such as new data continuously collected by aircraft sensors during flight and real-time operation data of trainees, etc.). These data are directly input to the input side of the model in the form of dotted arrows and participate in model processing together with pre-processed data. Based on these real-time data and trained model parameters, the model predicts the synchronization adjustment amount or synchronized data, thereby realizing the synchronization processing of real-time data and ensuring that the data in the system is always kept up to date and accurate.

[0066] Data update and distribution: The data update module receives the predicted synchronization data or data difference value output by the model, and applies this data to the actual system through its internal update calculation process. Specifically, the updated data is distributed to the virtual environment rendering module to update the display of the virtual flight scene in real time so that students can see the same picture as the actual flight status; it is sent to the aircraft status update module to update the aircraft status information in the simulation system to ensure the accuracy of the simulated flight; it is also passed to the digital air traffic controller information receiving module to provide the air traffic controller with the latest aircraft status and environmental information so that it can make accurate control decisions and realize data synchronization and collaborative work between the components of the entire system.

[0067] 4. Model evaluation and adjustment stage

[0068] Monitoring points and data collection: Multiple monitoring points such as monitoring point 1 and monitoring point 2 are set at key locations in the system. They are distributed on the data flow path from the data source to the model input, from the model output to the data update module, and between the various components of the system. These monitoring points are responsible for collecting various information related to model performance and data synchronization effects, such as data transmission delay, model prediction accuracy, data consistency and other indicators. By monitoring this information in real time, possible problems in the system can be discovered in a timely manner, such as data transmission interruption, excessive model prediction deviation, untimely data update, etc., providing a basis for subsequent adjustment decisions.

[0069] Adjustment decision and model optimization: The information collected by the monitoring points is summarized in the adjustment decision module, which analyzes and evaluates this information through a logical judgment process. If the model performance is detected to be degraded (such as the prediction error exceeds the preset threshold) or the data synchronization is abnormal (such as the data update delay is too long or the data is inconsistent), the adjustment decision module will make corresponding decisions according to the preset rules. On the one hand, it will send instructions to the model to fine-tune the model parameters, and optimize the model's data processing capabilities and improve prediction accuracy by adjusting the internal parameters of the RNN layer and the LSTM layer; on the other hand, the new or adjusted training data is fed back to the training set so that the model can learn the latest data patterns and change trends, so as to continuously adapt to various situations during the operation of the system, realize the continuous optimization of the model and the dynamic adjustment of the data synchronization process, and ensure the stable operation of the entire flight student training system and the high-quality synchronization of data.

[0070] The specific implementation method is: In terms of hardware configuration, if a local server is used, a high-performance workstation or server host should be selected, equipped with a multi-core high-performance CPU, large-capacity memory, high-speed solid-state drive and professional graphics card to meet the needs of complex scene rendering and large-scale data processing. If a cloud server is used, a cloud service instance with high computing power, large memory and high-speed network bandwidth should be selected. The training equipment for students can be a professional simulation cockpit equipped with a high-resolution display, precise flight joystick and pedals, or a high-performance PC with flight simulation peripherals. The network equipment uses a high-speed Ethernet switch or an enterprise-level wireless router to ensure stable and high-speed data transmission.

[0071] In terms of software installation and configuration, install the postgreSQL database on the server, optimize performance and design data structures, and create relevant data tables to store aircraft data, scene data, student operation records, etc. Develop various microservice programs, use appropriate programming languages ​​(such as C++, Python, etc.) and development frameworks, deploy them on the server, and configure the DDS communication environment. Install customized client software on the student training device, which can establish a stable connection with the server, receive and display virtual scene data, collect student operation data and transmit it to the server.

[0072] Collect detailed performance data of eVTOL and various low-altitude aircraft, including fuselage structural parameters, power system characteristics, flight control system parameters, etc., and build models through experimental tests and data provided by manufacturers. Obtain high-precision low-altitude geographic information data, such as terrain data obtained through satellite remote sensing, aerial surveying, etc., and perform digital processing and optimization. Collect meteorological data and establish meteorological models that can simulate meteorological changes under different climatic conditions. Sort out air traffic rules and various flight mission processes to provide a basis for digital pilots and digital air traffic controllers.

[0073] Instructors select appropriate training scenarios and tasks based on the training syllabus and the students’ actual level. For example, they choose simple flight missions in open areas for beginners. As students’ skills improve, they gradually increase the complexity of the scenarios and the difficulty of the tasks.

[0074] After the trainee enters the training device and logs into the system, the instructor starts the simulation training. During the training process, the instructor can monitor the trainee's operation, virtual scene status, digital pilot behavior and digital air traffic controller instructions in real time through the instructor operation interface, and adjust the scene parameters, traffic flow or provide guidance as needed.

[0075] Students perform simulated flight operations in accordance with flight operating procedures and mission requirements, experience flight challenges in different flight environments and mission scenarios, learn the skills of coordinated flight with other low-altitude aircraft, and emergency response methods for dealing with emergencies (such as aircraft failures, bad weather, air traffic conflicts, etc.), and follow the instructions of digital air traffic controllers.

[0076] After each simulated flight, the system automatically generates an evaluation report and transmits it to the trainee's training equipment and instructor's terminal. The instructor analyzes the trainee's problems based on the evaluation results, such as lack of proficiency in flight operations, unclear understanding of mission objectives, and inadequate execution of control instructions, and adjusts the training plan accordingly, adding special exercises or theoretical explanations.

[0077] Regularly collect feedback from trainees and instructors on the system and optimize and improve system performance, functions and scenarios. For example, based on trainees' feedback on the authenticity of the scenarios, improve the terrain and weather simulation effects; based on instructors' needs for training management, improve the instructor operation interface functions; based on the accuracy feedback of digital air traffic controller simulation, optimize control rules and algorithms to improve training efficiency and quality.

[0078] 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. The eVTOL flight trainee training system based on a virtual low-altitude environment is characterized by: include: Data layer, business logic layer and user interface layer; The data layer includes a data source, a data synchronization center, a data transmission protocol, a data receiving end, and a data storage module, wherein the data source includes sensor data of flight trainee training equipment and low-altitude aircraft, the data synchronization center adopts a distributed architecture to coordinate multi-component data synchronization, the data transmission protocol supports efficient encrypted transmission, the data receiving end verifies and processes data, and the data storage module establishes a persistent storage mechanism; The business logic layer implements the functions of virtual low-altitude scene construction, aircraft simulation, student operation processing, digital pilot control, digital air traffic controller integration and training evaluation. The virtual scene construction is based on high-precision geographic information and meteorological models. The aircraft simulation includes physical property modeling of eVTOL and other low-altitude aircraft. The student operation processing analyzes the control input in real time and feeds back to the virtual environment. The digital pilot simulates collaborative flight behavior through AI algorithms, and the digital air traffic controller generates instructions and coordinates traffic according to control rules. The user interface layer provides an instructor operation interface and a student interaction interface; The system adopts a microservice architecture, splitting the functional modules into scenario management microservices, aircraft simulation microservices, student operation processing microservices, digital pilot services, digital air traffic controller services, evaluation feedback microservices, and data processing microservices. Each microservice communicates through the DDS protocol. The trainee training equipment is connected to the system through a standardized interface, receiving virtual scene data and uploading operation data; Digital pilots and digital air traffic controllers are integrated into the system through independent interfaces to obtain environmental data in real time and provide feedback on control instructions.

2. The eVTOL flight trainee training system and method based on a virtual low-altitude environment according to claim 1 is characterized in that: The data synchronization model adopts a synchronization method that combines streaming and batch synchronization, and specifically includes the following steps: Data reading and packaging: At each data source end of the training system, the data reading thread obtains the latest data from the data source in real time. After reading the data, it is packaged into standard data units. The reading thread at the data source end reads the data at a time interval of Δ Periodically collect sensor and operation data and encapsulate them into data units ,in is the timestamp, is the sensor type identifier, is a numeric vector, is the data check code; Pipeline transmission: The encapsulated data units are transmitted through multi-threaded pipelines. The reading thread and the writing thread communicate through the pipeline. The reading thread immediately sends each result data it reads to the pipeline without waiting for other data. The pipeline capacity satisfy ,in is the number of concurrent data sources, and the transmission delay satisfies ; Batch processing: The writing thread waits at the other end of the pipeline to receive data. The writing thread is processed in a time window. Or data volume threshold Merge batches. If the current accumulated data volume satisfy or , then merge these data into one batch to generate batch ,in ; Data transmission and processing: When the data batch is ready, the writing thread transmits the batch data to the target node through the network interface via the encryption protocol. After receiving the data batch, the target node will parse and process it. After parsing, the target node updates the system status. The data consistency is verified by the verification function. Verify that is a hash function, is a cyclic redundancy check code.

3. The eVTOL flight trainee training system based on a virtual low-altitude environment according to claim 1, characterized in that: The data synchronization model combines distributed locks to achieve consistency control, specifically including: Proposal Generation: When Sharing Data When an update is required, a component acts as a proposer, and the proposer generates a unique proposal number , and sends a broadcast prepare request to most recipients to the majority of acceptors; Commitment response: The acceptors are distributed on the key nodes of the system. After receiving the prepare request from the proposer, they compare the proposal number with all the proposal numbers that have been responded to. With the largest number in history ,like , then returns a promise , otherwise reject; Proposal confirmation: Acceptors are distributed on key nodes of the system. When the proposer receives confirmation from enough acceptors, it will notify all learners of the accepted proposal and select or , send an acceptance request ; Status Update: Acceptor Confirmed If not covered by a higher proposal, persist , the learner records this proposal, the learner node applies the update to the local state machine, and spreads it to the entire system through the broadcast protocol to ensure that the final consistency is satisfied .

4. The eVTOL flight trainee training system based on a virtual low-altitude environment according to claim 1, characterized in that: The virtual low-altitude scene construction function uses geographic information data and graphics rendering technology to construct a virtual low-altitude environment, including diverse terrains such as cities, mountains and rivers, as well as high-rise buildings and bridges. It simulates meteorological conditions, including wind direction and speed, rainfall, snowfall and fog, and dynamically adjusts meteorological parameters according to training needs; Supports rapid construction of a variety of low-altitude mission scenarios, including urban aerial tours, emergency medical rescue, and logistics distribution scenarios. Each scenario has specific mission objectives and environmental settings. The virtual low-altitude scene construction function is based on a three-dimensional geographic grid model ,in is the terrain elevation data, is the obstacle coordinate set; the meteorological simulation uses the Navier-Stokes equation to describe fluid dynamics: ， in is the wind speed vector, is the air pressure, is the viscosity coefficient, The dynamic meteorological parameters drive the changes of the virtual environment by solving equations in real time, supporting the dynamic adjustment of wind direction, rainfall intensity and visibility.

5. The eVTOL flight trainee training system based on a virtual low-altitude environment according to claim 1, characterized in that: The aircraft simulation function accurately simulates the flight characteristics of the eVTOL aircraft, including power response and control performance in vertical take-off and landing, hovering, and cruise flight modes, based on real physical models and performance parameters; The aircraft simulation function establishes a six-degree-of-freedom dynamic model for eVTOL: ， in is the linear speed, is the angular velocity, For quality, is the inertia tensor, For propulsion, is the aerodynamic drag, is the torque; other aircraft simulations use simplified models ,in is the proportional gain, The preset speed.

6. The eVTOL flight trainee training system based on a virtual low-altitude environment according to claim 1, characterized in that: The flight trainee operation processing function receives the trainee's operation input on the training equipment, such as flight attitude control, speed adjustment, route planning and other operations, and converts it into aircraft actions in the virtual environment; monitors the accuracy and standardization of the trainee's operation in real time, such as judging whether the joystick input complies with the flight operation procedures, and prompts or records the operations that do not comply with the regulations; adjusts the flight environment and task difficulty in the virtual scene according to the trainee's operation feedback, such as adding sudden airflow interference, changing the destination, etc., to improve the trainee's ability to respond; The student operation processing function converts the control input into Converted into aircraft control instructions: ， in is the error signal, For the expected state, is the current state; the system monitors operational compliance in real time and if the joystick input exceeds the safety threshold , an alarm is triggered and the deviation is recorded .

7. The eVTOL flight trainee training system based on a virtual low-altitude environment according to claim 1, characterized in that: In the digital pilot control function, the digital pilot autonomously drives other low-altitude aircraft to fly in a virtual environment according to a preset flight plan and real-time environmental information; The digital pilot makes flight decisions based on air traffic rules and actual flight conditions, including avoiding other aircraft and choosing the optimal flight path; By adjusting the behavior strategy of the digital pilot and simulating other aircraft pilots of different levels and styles, a variety of training scenarios are provided for students. Digital pilot uses deep reinforcement learning model , whose policy network outputs action : ， in For the Critic network, is the exploration rate, is Gaussian noise; state Contains environmental data, aircraft status and mission objectives, reward function Weighted safety distance and range efficiency .

8. The eVTOL flight trainee training system based on a virtual low-altitude environment according to claim 1, characterized in that: The control functions of digital air traffic controllers include: generating control instructions based on virtual low-altitude traffic conditions and control rules; managing voice and data link communications using speech synthesis and other technologies; monitoring situations in real time and optimizing control decisions; and quickly coordinating resources from all parties to implement rescue plans in simulated emergency situations to improve trainees' response capabilities. Digital air traffic controllers generate control instructions based on conflict detection algorithms and use speed obstacle methods to predict aircraft trajectory conflicts: ， If the minimum approach distance , then generate a height adjustment instruction ; Voice communication uses the WaveNet model to synthesize command audio ,in is a latent variable, Encode the text.

9. The eVTOL flight trainee training system based on a virtual low-altitude environment according to claim 1, characterized in that: The training evaluation function records various operational data of trainees in simulated flight, including flight trajectory, flight attitude changes, and reaction time to emergencies; By comparing the preset evaluation criteria and excellent flight operation cases, the students' performance is comprehensively evaluated. The evaluation indicators cover flight skills, decision-making ability, and safety awareness. Generate detailed evaluation reports, including trainees’ strengths and weaknesses, and provide personalized improvement suggestions and follow-up training plans; The training evaluation function is scored by weighting multiple indicators: ， in , , , weight ; The evaluation report generates decision tree classification results and marks the operation defect nodes.

10. The eVTOL flight trainee training method based on a virtual low-altitude environment is characterized by: A system for implementing an eVTOL flight trainee training system based on a virtual low-altitude environment as claimed in any one of claims 1 to 9, comprising: System initialization: Loading geographic data into memory matrix , initialize the microservice instance and register it with the service registration center; Device connection: Establish a secure channel through the TLS1.3 protocol and verify the device certificate ; Simulation training: The digital pilot selects actions according to the Markov decision process, and the digital air traffic controller Update regulatory directives; Evaluation feedback: Use the gradient boosting tree model to analyze operational data and generate personalized improvement paths .

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