Flight space orientation training auxiliary system and method

Abstract

The invention discloses a flight space directional training auxiliary system and method, and the system comprises a flight task simulation module; the meteorological environment simulation module is used for simulating a meteorological scene corresponding to a flight task, and simulating the meteorological scene by establishing a multi-dimensional meteorological model and combining meteorological parameters; the meteorological environment parameter control module comprises a time step length control module which adopts a preset time step length to input meteorological parameter observation data collected in real time to the meteorological parameter setting module; the specific meteorological event control module is used for transmitting meteorological parameters of a specific meteorological event area to the meteorological parameter setting module when the airplane triggers a specific meteorological event; and the training scheme generation module is used for generating a proper training scheme according to the historical training information of the flight personnel. According to the system and the method, a real and personalized simulation training environment can be provided for the flight personnel, and the professional skills and the capability of coping with various flight conditions of the flight personnel are effectively improved.

Description

Technical Field

[0001] The present invention relates to the technical field of flight space orientation training, and more specifically, to a flight space orientation training auxiliary system and method thereof. Background Art

[0002] With the rapid development of the aviation industry, flight safety has always been a top priority. Flight space orientation training is an important training to help pilots overcome spatial orientation obstacles and improve their ability to recognize themselves, aircraft and flight environment during flight. Spiral staircase training is a common traditional method. Through the movement and changes of the human body in the fixed rollers, the wheels are rotated to train the pilots' anti-vertigo ability, strength, courage, etc., and enhance their posture, position and movement judgment ability in space. However, there are safety hazards such as pilots' slipping out of their hands and causing secondary injuries. Virtual reality technology training: This technology can be used to simulate a variety of flight scenes and complex environments, such as simulating the low visibility environment of the space station and the complex weather conditions of the aircraft, providing pilots with a close-to-real visual experience of space movement, enhancing their sense of immersion, and thus improving the training effect. Therefore, flight personnel not only need to be proficient in the operation of routine flight missions, but also need to have the ability to cope with various complex weather conditions and special flight conditions. Traditional flight training methods have many limitations. It is often difficult to fully and accurately simulate the real scenes of flight missions at different stages and special flight missions. Moreover, when simulating special flight missions such as emergency landings, aerial refueling, low-altitude flights, and formation flying, traditional training methods are unable to provide a highly realistic training environment, resulting in flight crews lacking sufficient experience in dealing with complex situations when actually performing missions.

[0003] To sum up, in order to improve the quality and effectiveness of flight training and ensure flight safety, there is an urgent need for a flight space orientation training auxiliary system that can comprehensively simulate flight missions and accurately simulate meteorological scenes. Summary of the invention

[0004] The present invention provides a flight space orientation training auxiliary system and method thereof, which can provide a comprehensive, realistic and personalized simulation training environment for flight personnel, and effectively improve the professional skills of flight personnel and their ability to cope with various flight situations.

[0005] In order to achieve these purposes and other advantages of the present invention, in a first aspect, the present invention provides a flight space orientation training auxiliary system, comprising: Flight mission simulation module, which is used to simulate flight missions at different stages and special flight missions; A meteorological environment simulation module, which is connected to the flight mission simulation module and is used to simulate the meteorological scene corresponding to the flight mission. The meteorological environment simulation module simulates the meteorological scene by establishing a meteorological model of multiple dimensions and combining the set meteorological parameters; Meteorological environment parameter control module, which includes: A time step control module, which uses a preset time step to input the meteorological parameter observation data collected in real time into the meteorological parameter setting module; A specific meteorological event control module, which is used to transmit the meteorological parameters of the specific meteorological event area to the meteorological parameter setting module when the aircraft triggers a specific meteorological event, so that the meteorological environment simulation module updates the simulated meteorological scene according to the meteorological parameters of the specific meteorological event area; A training program generation module generates a suitable training program based on the historical training information of the flight personnel, and the training program generation module is connected to the flight mission simulation module.

[0006] Preferably, an auxiliary function module is also included, which provides the flight crew with the best operating steps and suggestions based on the flight crew's current flight status and mission requirements.

[0007] Preferably, the specific meteorological event control module includes: The meteorological event recognition module uses deep learning methods to learn historical meteorological data and flight data, and can identify the meteorological parameters of the area where the aircraft is located in real time and determine whether a specific meteorological event is triggered; The meteorological parameter calculation module calculates the changing meteorological parameters for the specific meteorological event if a specific meteorological event is triggered, and inputs the calculated meteorological parameters into the meteorological parameter setting module.

[0008] Preferably, the time step control module specifically includes: A judgment module is used to judge the degree of change of meteorological parameters. If the meteorological parameters change greatly, the time step is shortened; if the meteorological parameters change slowly, the time step is increased; A time step adjustment module adjusts the time step according to the judgment result of the judgment module.

[0009] Preferably, the judgment module specifically includes: A normalization module, which is used to normalize multiple different meteorological parameter data; A weight determination module determines the weight of each meteorological parameter according to the degree of influence of different meteorological parameters on meteorological changes; The comprehensive change index calculation module calculates the comprehensive change index value through the comprehensive change index formula, which is specifically: ,in, is the comprehensive change index value, is the wind speed weight, is the wind speed change, is the temperature weight, is the temperature change, is the air pressure weight, Air pressure change; The comparison module determines that the meteorological parameter changes greatly if the comprehensive change index value C is greater than a predetermined first threshold value, and determines that the meteorological parameter changes slowly if the comprehensive change index value C is less than a predetermined second threshold value, and the second threshold value is less than the first threshold value.

[0010] Preferably, the meteorological parameter setting module integrates the meteorological parameter observation data with the simulation results of the meteorological model, and continuously adjusts the initial conditions and meteorological parameters of the meteorological model to make the simulation results of the meteorological model close to the actual meteorological situation.

[0011] Preferably, the multiple dimensions in the multiple-dimensional meteorological model include: spatial dimensions, which include horizontal and vertical directions; The time dimension, which includes: short-term changes and long-term changes; The physical process dimension, which includes: dynamic process and thermodynamic process; Among them, in the meteorological model, it is also provided with: Raindrop simulation model, which is used to simulate the generation, movement trajectory and collision effect of raindrops, and simulate the subtle changes of raindrops in complex airflow fields through numerical solution of Navier-Stokes equations; The strong wind simulation model is used to simulate the spatial distribution and changes of wind speed, wind direction and strong wind at different heights and locations, and use the large eddy simulation method to simulate the large-scale vortex structure in the wind field to capture the turbulent characteristics in strong winds.

[0012] Preferably, the meteorological event identification module specifically includes: Historical meteorological data collection module, which is used to collect parameter information under various meteorological conditions, including temperature, humidity, air pressure, wind speed, wind direction, and cloud information in different regions, seasons, and weather types; Flight data acquisition module, which includes the flight trajectory, altitude, speed, and attitude information of the aircraft under different meteorological conditions; A convolutional neural network model extracts and reduces the dimensionality of image features in meteorological data through convolutional layers and pooling layers, takes the labeled meteorological data as input, outputs predictions of meteorological events, and adjusts the weights of the convolution kernels through a back-propagation algorithm to accurately identify meteorological events; A recurrent neural network processing model is used to process the time series information in the flight data to obtain the features related to meteorological events in the flight data time series, so as to accurately identify the meteorological environment in which the aircraft is located.

[0013] In a second aspect, the present invention further provides a flight space orientation training assistance method, which is applicable to the flight space orientation training assistance system, and comprises the following steps: Generate appropriate training plans based on the flight crew's historical training information; According to the training requirements of the training program, determine the flight phases and special flight tasks to be simulated and construct flight scenarios; Build meteorological models: Build meteorological models in multiple dimensions, including spatial dimension, temporal dimension, and physical process dimension; By using the meteorological parameter setting module, combined with the meteorological parameter observation data collected in real time, initial parameters are set for the meteorological model; Use the established meteorological models and set meteorological parameters to simulate various meteorological scenarios; According to the set time step, the meteorological parameter observation data collected in real time is transmitted to the meteorological parameter setting module; Continuously monitor the aircraft's flight status and environmental information to determine whether specific meteorological events are triggered; When a specific meteorological event is detected, the meteorological parameters of the event area are obtained and transmitted to the meteorological parameter setting module, and the meteorological environment simulation module updates the simulated meteorological scene according to the specific meteorological parameters.

[0014] The present invention has at least the following beneficial effects: First, it can simulate flight missions at different stages and special flight missions, providing flight crews with diversified training support and comprehensive and targeted training scenarios, which helps improve flight crews’ ability to cope with various flight conditions.

[0015] Second, by establishing a multi-dimensional meteorological model and combining it with the meteorological parameters set by the meteorological parameters to simulate the meteorological scene, the meteorological scene corresponding to the flight mission can be accurately simulated. This allows flight personnel to be exposed to various meteorological conditions that may be encountered in real flights during training, enhance their familiarity and response capabilities for flight operations in different meteorological environments, and improve the authenticity and effectiveness of training.

[0016] Third, use the preset time step to input the real-time collected meteorological parameter observation data to ensure that the data used by the meteorological environment simulation module is timely and accurate, closer to the actual meteorological changes in flight, and further improve the authenticity of the training. When the aircraft triggers a specific meteorological event, the meteorological parameters of the specific area can be transmitted to the meteorological parameter setting module in a timely manner to achieve dynamic updates of the meteorological scene. This enables flight personnel to obtain a real-time adjusted meteorological environment when facing sudden meteorological events during training, enhance response training for emergencies, and improve emergency response capabilities in actual flights.

[0017] Fourth, appropriate training plans are generated based on the flight crew's historical training information, which realizes the personalized customization of the training plan. According to the flight crew's actual level and training experience, more targeted training tasks are arranged to improve the training effect and promote the improvement of flight crew's capabilities.

[0018] Other advantages, objectives and features of the present invention will be embodied in part through the following description, and in part will be understood by those skilled in the art through study and practice of the present invention. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] Figure 1 It is a schematic diagram of the relationship structure of the flight space orientation training auxiliary system described in the present invention. DETAILED DESCRIPTION

[0020] The present invention is further described in detail below in conjunction with the accompanying drawings so that those skilled in the art can implement the invention with reference to the description.

[0021] It should be understood that the terms such as “having”, “including” and “comprising” used herein do not exclude the existence or addition of one or more other elements or combinations thereof.

[0022] like Figure 1 As shown, the present invention provides a flight space orientation training auxiliary system, comprising: Flight mission simulation module, which is used to simulate flight missions at different stages and special flight missions; A meteorological environment simulation module, which is connected to the flight mission simulation module and is used to simulate the meteorological scene corresponding to the flight mission. The meteorological environment simulation module simulates the meteorological scene by establishing a meteorological model of multiple dimensions and combining the set meteorological parameters; Meteorological environment parameter control module, which includes: A time step control module, which uses a preset time step to input the meteorological parameter observation data collected in real time into the meteorological parameter setting module; A specific meteorological event control module, which is used to transmit the meteorological parameters of the specific meteorological event area to the meteorological parameter setting module when the aircraft triggers a specific meteorological event, so that the meteorological environment simulation module updates the simulated meteorological scene according to the meteorological parameters of the specific meteorological event area; A training program generation module generates a suitable training program based on the historical training information of the flight personnel, and the training program generation module is connected to the flight mission simulation module.

[0023] In the above embodiment, the flight mission simulation module can simulate flight missions at different stages, such as take-off phase simulation, cruise phase simulation and landing phase simulation. Special flight missions, such as emergency simulation, exemplary, engine failure, bird strike incident, emergency landing, aerial refueling, etc. Flight environment simulation mainly includes meteorological environment simulation, geographical environment simulation and time environment simulation. The meteorological environment simulation module is used to simulate the meteorological scene corresponding to the flight mission, such as sunny, cloudy, overcast, rainstorm, thunderstorm, strong wind, icing conditions, etc. The meteorological parameter setting module is used to set the parameter data set when simulating the meteorological scene, such as temperature, air pressure, humidity, wind speed and wind direction, cloud cover and cloud height, etc. For geographical environment simulation and time environment simulation, this embodiment does not make specific restrictions. For example, the geographical environment simulation module is used to simulate different geographical environments, such as plain areas, mountainous areas, ocean areas, plateau areas, etc.; the time environment simulation module is used to simulate the time environment, such as, day, night; or different seasons, such as spring, summer, autumn and winter. This embodiment focuses on the improvement of meteorological environment simulation.

[0024] In the above embodiment, the meteorological environment simulation module simulates the meteorological scene by establishing a meteorological model of multiple dimensions and combining the set meteorological parameters. The meteorological model of multiple dimensions includes spatial dimension, time dimension, and physical process dimension, so as to realize the all-round and three-dimensional simulation of the meteorological scene. In the spatial dimension, the horizontal grid division and the vertical stratification can accurately describe the distribution of meteorological parameters in different regions and at different heights. For example, the high-resolution grid can capture the urban heat island effect. In the time dimension, it simulates both short-term weather changes from hours to days and long-term meteorological trends from years to decades. It can predict the short-term rainfall caused by the cold front crossing and analyze the changes in the frequency and intensity of extreme weather events under global climate change. The physical process dimension comprehensively considers power and heat, and can accurately simulate the formation of wind, precipitation mechanism and the influence of atmospheric chemical reactions on meteorology. According to different flight missions, corresponding meteorological parameters are set. For example, in the flight mission in the tropics, high temperature, high humidity and possible tropical cyclone related parameters are set to simulate the real meteorological environment of the area, so that the pilot can feel the actual situation of flying under specific meteorological conditions. The time step control module uses a preset time step to input the real-time collected meteorological parameter observation data (such as satellite cloud images, ground meteorological station data, or high-altitude detection data, etc.) into the meteorological parameter setting module to ensure that the meteorological model can obtain the latest data in a timely manner. When meteorological conditions change, such as the rapid movement of cold fronts, the data is updated according to the set time step, so that the simulated meteorological scene fits the actual meteorological changes. Flight personnel can experience the dynamic evolution of meteorological conditions in real time during training, rather than static simulation based on outdated data, which enhances the authenticity and timeliness of training. When the aircraft triggers a specific meteorological event, the specific meteorological event control module quickly transmits the meteorological parameters of the event area to the meteorological parameter setting module to update the meteorological scene. Exemplarily, when the aircraft enters the cumulonimbus cloud area, it immediately captures the meteorological parameters such as strong convection, lightning, and drastic temperature changes in the cumulonimbus cloud area, and updates the simulation scene. This enables flight personnel to truly face and respond to sudden meteorological events during training, and improve their emergency response capabilities under complex meteorological conditions.

[0025] The meteorological environment simulation module is connected to the flight mission simulation module, and can simulate corresponding meteorological scenarios according to different flight missions. For example, when simulating the training of low-altitude flight missions in mountainous areas, the complex mountain valley winds, terrain waves and other meteorological conditions can be simulated; for transoceanic flight missions, the changeable weather over the ocean, such as tropical cyclones and severe convective weather, can be simulated. This kind of meteorological simulation combined with flight missions provides personalized training for pilots, enhances their adaptability to different flight environments and missions, and comprehensively improves their flying skills and professional quality.

[0026] In one specific implementation, an auxiliary function module is also included, which provides the flight crew with the best operating steps and suggestions based on the flight crew's current flight status and mission requirements.

[0027] In the above embodiment, during the training process, the auxiliary function module can provide real-time operation guidance. For example, when the flight crew performs a certain operation, the auxiliary function module will give the best operation steps and suggestions according to the current flight status and mission requirements. In addition, the flight crew's wrong behavior can also be corrected in time, and the flight crew can be informed of the wrong operation through voice prompts, pop-up warnings, etc., and provide correct operation demonstrations.

[0028] In one specific implementation, the specific meteorological event control module includes: The meteorological event recognition module uses deep learning methods to learn historical meteorological data and flight data, and can identify the meteorological parameters of the area where the aircraft is located in real time and determine whether a specific meteorological event is triggered; The meteorological parameter calculation module calculates the changing meteorological parameters for the specific meteorological event if a specific meteorological event is triggered, and inputs the calculated meteorological parameters into the meteorological parameter setting module.

[0029] Specifically, the meteorological event identification module specifically includes: Historical meteorological data collection module, which is used to collect parameter information under various meteorological conditions, including temperature, humidity, air pressure, wind speed, wind direction, and cloud information in different regions, seasons, and weather types; Flight data acquisition module, which includes the flight trajectory, altitude, speed, and attitude information of the aircraft under different meteorological conditions; A convolutional neural network model extracts and reduces the dimensionality of image features in meteorological data through convolutional layers and pooling layers, takes the labeled meteorological data as input, outputs predictions of meteorological events, and adjusts the weights of the convolution kernels through a back-propagation algorithm to accurately identify meteorological events; A recurrent neural network processing model is used to process the time series information in the flight data to obtain the features related to meteorological events in the flight data time series, so as to accurately identify the meteorological environment in which the aircraft is located.

[0030] In the above implementation, the core components of the convolutional neural network model (CNN) are the convolution layer and the pooling layer. The convolution layer performs convolution operations on meteorological data (such as satellite cloud images) by designing convolution kernels of different sizes and weights. Exemplarily, a 3×3 convolution kernel scans the satellite cloud image pixel by pixel, multiplies the pixel value of the local area by the convolution kernel weight and sums them to obtain a new feature value. This process can extract local features in the satellite cloud image, such as the edge and texture of the cloud layer. The superposition of convolution layers with multiple different convolution kernels can extract richer and more complex image features. The pooling layer is used to reduce the data dimension. For example, the point with the largest pixel value is selected in the 2×2 area, which not only retains the main features, but also reduces the amount of data and improves the calculation efficiency. For the collected historical meteorological data, it must be processed before it can be used. Exemplarily, for the satellite cloud image in the meteorological data, it is first converted into a format suitable for CNN processing, generally a three-dimensional tensor, including the height, width and number of channels of the image. The labeled satellite cloud image data is divided into a training set, a validation set and a test set according to a certain ratio. During the training process, the training set data is continuously input into the convolutional neural network model, and the convolutional layer and pooling layer perform feature extraction and dimensionality reduction on the data in turn. For example, a 100×100 pixel satellite cloud image is input. After being processed by several convolutional layers and pooling layers, the data may be compressed into a 10×10 feature map, which contains the most important feature information in the satellite cloud image. The annotated meteorological data (such as different types of cloud images marked as cumulonimbus clouds, fair clouds, etc.) is used as input, and the convolutional neural network model outputs the prediction results of meteorological events. Through the back propagation algorithm, the error between the prediction result and the true label is back propagated to each layer of the network to adjust the weight and bias value of the convolution kernel. For example, if the convolutional neural network model mistakenly predicts cumulonimbus clouds as fair clouds, the back propagation algorithm will calculate how to adjust the convolution kernel weight so that a more accurate prediction can be made the next time a similar cumulonimbus cloud image is encountered. After multiple iterations of training, the convolutional neural network model gradually learns the characteristic patterns of different meteorological events in satellite cloud images, so that meteorological events can be accurately identified. The recurrent neural network processing model (RNN) is characterized by the existence of cyclic connections between its hidden layers, which can process time series data. At each time step, it receives the input of the current moment and the output of the hidden layer at the previous moment, and calculates the output of the hidden layer at the current moment through a nonlinear activation function, so that the RNN can remember the previous information and process the time series information in the flight data. For flight data, such as the altitude, speed, attitude and other sequence data of the aircraft over a period of time, it is sorted in chronological order into a format suitable for RNN input. It is also divided into training set, validation set and test set. During the training process, the training set data is input into the RNN model in sequence according to the time step.For example, the aircraft records flight data once a minute within 10 minutes, and the data of these 10 time steps are input into the RNN in sequence. The input of each time step will be calculated in combination with the output of the hidden layer at the previous moment. Similar to CNN, training is performed through the back propagation algorithm, but in RNN, back propagation is carried out by time step, that is, starting from the error of the last time step, it is gradually back propagated to each time step, and the weight and bias of the network are adjusted in each time step. For example, during the training process, if the model predicts the flight status of the aircraft when it encounters thunderstorm weather at a certain moment, the back propagation algorithm will trace back along the time step and adjust the parameters of the network in each time step, so that the RNN model can better handle the relationship between meteorological events and flight status in this time series. After a lot of training, RNN can learn the features related to meteorological events in the flight data time series, so as to accurately identify the meteorological environment in which the aircraft is located. First, use CNN to extract image features from meteorological data such as satellite cloud images to obtain a feature vector, and then input this feature vector into the RNN together with the time series of flight data. In this way, we can make full use of CNN's ability to extract image features and RNN's ability to process time series data to improve the accuracy of meteorological event recognition.

[0031] In one specific implementation, the time step control module specifically includes: A judgment module is used to judge the degree of change of meteorological parameters. If the meteorological parameters change greatly, the time step is shortened; if the meteorological parameters change slowly, the time step is increased; A time step adjustment module adjusts the time step according to the judgment result of the judgment module.

[0032] Specifically, the judgment module specifically includes: A normalization module, which is used to normalize multiple different meteorological parameter data; A weight determination module determines the weight of each meteorological parameter according to the degree of influence of different meteorological parameters on meteorological changes; The comprehensive change index calculation module calculates the comprehensive change index value through the comprehensive change index formula, which is specifically: ,in, is the comprehensive change index value, is the wind speed weight, is the wind speed change, is the temperature weight, is the temperature change, is the air pressure weight, Air pressure change; The comparison module determines that the meteorological parameter changes greatly if the comprehensive change index value C is greater than a predetermined first threshold value, and determines that the meteorological parameter changes slowly if the comprehensive change index value C is less than a predetermined second threshold value, and the second threshold value is less than the first threshold value.

[0033] In the above embodiment, the normalization module is used to normalize multiple different meteorological parameter data. For example, for temperature T, the minimum value is set to T min , the maximum value is T max , the normalized temperature is , so that all meteorological parameters are on the same scale. Different meteorological parameters have different degrees of influence on meteorological changes. For example, in severe convective weather, changes in wind speed and vertical wind shear may have a greater impact on flight safety than relative humidity. The weight of each meteorological parameter is determined according to the degree of influence of different meteorological parameters on meteorological changes. Since meteorological parameters do not exist in isolation, but influence each other. For example, rising temperature may lead to increased evaporation of water vapor, which in turn affects humidity and air pressure. Exemplarily, a machine learning algorithm can be used to learn the association pattern between parameters. For example, in a neural network model, through a large amount of data training, the model automatically learns the complex relationship between parameters such as temperature, humidity, and air pressure. According to the normalized parameter values ​​and weights, a comprehensive change index is calculated. The larger the comprehensive change index is, the more drastic the comprehensive change degree of the meteorological parameters is. If the comprehensive change index value C is greater than the predetermined first threshold, it is determined that the meteorological parameter changes greatly. If the comprehensive change index value C is less than the predetermined second threshold, it is determined that the meteorological parameter changes slowly, and the second threshold is less than the first threshold. If the comprehensive change index value C is less than the predetermined first threshold value and greater than the predetermined second threshold value, the time step is not changed. The first threshold value and the second threshold value can be specifically limited according to the actual situation, and this embodiment does not make specific limitations. In the meteorological environment simulation, a smaller time step can be used, set at the millisecond level, to ensure that changes in meteorological parameters can be reflected in the meteorological scene in real time.

[0034] In one specific implementation manner, the meteorological parameter setting module integrates the meteorological parameter observation data with the simulation results of the meteorological model, and continuously adjusts the initial conditions and meteorological parameters of the meteorological model to make the simulation results of the meteorological model close to the actual meteorological conditions.

[0035] In one specific embodiment, the multiple dimensions of the multiple-dimensional meteorological model include: spatial dimensions, which include horizontal and vertical directions; The time dimension, which includes: short-term changes and long-term changes; The physical process dimension includes: dynamic process, thermal process; Among them, in the meteorological model, it is also provided with: Raindrop simulation model, which is used to simulate the generation, movement trajectory and collision effect of raindrops, and simulate the subtle changes of raindrops in complex airflow fields through numerical solution of Navier-Stokes equations; The strong wind simulation model is used to simulate the spatial distribution and changes of wind speed, wind direction and strong wind at different heights and locations, and use the large eddy simulation method to simulate the large-scale vortex structure in the wind field to capture the turbulent characteristics in strong winds.

[0036] In the above-mentioned embodiment, the raindrop simulation model can set the size distribution and density of raindrops according to different rainfall intensities. For example, in a rainstorm, the raindrops are larger and have a higher density. At the same time, the falling speed of raindrops in the air is also considered to be affected by gravity, air resistance and wind force, and the motion trajectory of raindrops is calculated in real time, so that it presents a real falling effect in the simulated scene. When simulating raindrops, in addition to considering gravity, air resistance and wind force, more complex physical models are introduced. For example, the influence of the surface tension and deformation of raindrops on their motion trajectory is considered. And through the numerical solution of the Navier-Stokes equation, the subtle changes of raindrops in a complex airflow field are simulated, making the simulation of raindrops more realistic. The strong wind simulation model can use the principles of fluid mechanics to establish a three-dimensional wind field model, and simulate the spatial distribution and changes of strong winds by calculating the wind speed and wind direction at different heights and positions. And the large eddy simulation method is used to simulate the large-scale vortex structure in the wind field to capture the turbulent characteristics in strong winds, thereby improving the simulation accuracy of the impact on the flight attitude of the aircraft in strong winds.

[0037] Another embodiment of the present invention further provides a flight space orientation training assistance method, which is applicable to the flight space orientation training assistance system, and comprises the following steps: S1. Generate a suitable training plan based on the historical training information of the flight crew.

[0038] Among them, the historical training information of flight crews is analyzed, and personalized training plans are generated based on the flight crews' skill levels, training goals and flight mission requirements, clarifying the training content, sequence, intensity and time schedule.

[0039] S2. According to the training requirements of the training plan, determine the flight phases and special flight missions to be simulated and construct the flight scenarios.

[0040] Among them, according to the training needs, the flight phases to be simulated are clearly defined, such as take-off, cruising, landing, etc., as well as special flight missions, such as emergency landing, aerial refueling, etc. According to the selected mission type, the corresponding flight environment is constructed, including selecting a suitable airport, setting flight routes, and simulating different geographical environments (plains, mountains, oceans, etc.).

[0041] S3. Build a meteorological model: Build a meteorological model in multiple dimensions, including spatial dimension, temporal dimension, and physical process dimension.

[0042] S4. Setting initial parameters for the meteorological model through a meteorological parameter setting module in combination with meteorological parameter observation data collected in real time.

[0043] S5. Use the established meteorological model and set meteorological parameters to simulate various meteorological scenarios.

[0044] Among them, various meteorological scenarios such as sunny days, rainy days, thunderstorms, ice, etc. are simulated using the established meteorological models and set parameters.

[0045] S6. According to the set time step, the meteorological parameter observation data collected in real time is transmitted to the meteorological parameter setting module.

[0046] Among them, a suitable time step is preset to control the input frequency of meteorological parameter observation data. According to the set time step, the meteorological parameter observation data collected in real time is transmitted to the meteorological parameter setting module to ensure that the meteorological model can obtain the latest data in time.

[0047] S7. Continuously monitor the aircraft's flight status and environmental information to determine whether specific meteorological events are triggered, such as entering a thunderstorm area or encountering strong wind shear.

[0048] S8. When a specific meteorological event is detected, the meteorological parameters of the event area are obtained and transmitted to the meteorological parameter setting module. The meteorological environment simulation module updates the simulated meteorological scene according to the specific meteorological parameters to present a more realistic sudden meteorological condition to the pilot.

[0049] In the above embodiments, a comprehensive, realistic and personalized simulation training environment can be provided for flight crews, effectively improving the flight crews' professional skills and their ability to cope with various flight situations.

[0050] The number of devices and processing scales described here are used to simplify the description of the present invention. Applications, modifications and variations of the present invention will be obvious to those skilled in the art.

[0051] Although the embodiments of the present invention have been disclosed as above, they are not limited to the applications listed in the specification and the implementation modes, and they can be fully applied to various fields suitable for the present invention. For those familiar with the art, additional modifications can be easily implemented. Therefore, without departing from the general concept defined by the claims and the scope of equivalents, the present invention is not limited to the specific details and the illustrations shown and described herein.

Claims

1. Flight space orientation training auxiliary system, characterized in that: include: Flight mission simulation module, which is used to simulate flight missions at different stages and special flight missions; A meteorological environment simulation module, which is connected to the flight mission simulation module and is used to simulate the meteorological scene corresponding to the flight mission. The meteorological environment simulation module simulates the meteorological scene by establishing a meteorological model of multiple dimensions and combining the set meteorological parameters; Meteorological environment parameter control module, which includes: A time step control module, which uses a preset time step to input the meteorological parameter observation data collected in real time into the meteorological parameter setting module; A specific meteorological event control module, which is used to transmit the meteorological parameters of the specific meteorological event area to the meteorological parameter setting module when the aircraft triggers a specific meteorological event, so that the meteorological environment simulation module updates the simulated meteorological scene according to the meteorological parameters of the specific meteorological event area; A training program generation module generates a suitable training program based on the historical training information of the flight personnel, and the training program generation module is connected to the flight mission simulation module.

2. The flight space orientation training auxiliary system according to claim 1, characterized in that: It also includes an auxiliary function module, which provides the flight crew with the best operating procedures and suggestions based on the flight crew's current flight status and mission requirements.

3. The flight space orientation training auxiliary system according to claim 1, characterized in that: The specific meteorological event control module includes: The meteorological event recognition module uses deep learning methods to learn historical meteorological data and flight data, and can identify the meteorological parameters of the area where the aircraft is located in real time and determine whether a specific meteorological event is triggered; The meteorological parameter calculation module calculates the changing meteorological parameters for the specific meteorological event if a specific meteorological event is triggered, and inputs the calculated meteorological parameters into the meteorological parameter setting module.

4. The flight space orientation training auxiliary system according to claim 1, characterized in that: The time step control module specifically includes: A judgment module is used to judge the degree of change of meteorological parameters. If the meteorological parameters change greatly, the time step is shortened; if the meteorological parameters change slowly, the time step is increased; A time step adjustment module adjusts the time step according to the judgment result of the judgment module.

5. The flight space orientation training auxiliary system according to claim 4, characterized in that: The judgment module specifically includes: A normalization module, which is used to normalize multiple different meteorological parameter data; A weight determination module determines the weight of each meteorological parameter according to the degree of influence of different meteorological parameters on meteorological changes; The comprehensive change index calculation module calculates the comprehensive change index value through the comprehensive change index formula, which is specifically: ,in, is the comprehensive change index value, is the wind speed weight, is the wind speed change, is the temperature weight, is the temperature change, is the air pressure weight, Air pressure change; The comparison module determines that the meteorological parameter changes greatly if the comprehensive change index value C is greater than a predetermined first threshold value, and determines that the meteorological parameter changes slowly if the comprehensive change index value C is less than a predetermined second threshold value, and the second threshold value is less than the first threshold value.

6. The flight space orientation training auxiliary system according to claim 5, characterized in that: The meteorological parameter setting module integrates the meteorological parameter observation data with the simulation results of the meteorological model, and continuously adjusts the initial conditions and meteorological parameters of the meteorological model to make the simulation results of the meteorological model close to the actual meteorological situation.

7. The flight space orientation training auxiliary system according to claim 1, characterized in that: The multiple dimensions of the multi-dimensional meteorological model include: spatial dimensions, which include horizontal and vertical directions; The time dimension, which includes: short-term changes and long-term changes; The physical process dimension, which includes: dynamic process and thermodynamic process; Among them, in the meteorological model, it is also provided with: Raindrop simulation model, which is used to simulate the generation, movement trajectory and collision effect of raindrops, and simulate the subtle changes of raindrops in complex airflow fields through numerical solution of Navier-Stokes equations; The strong wind simulation model is used to simulate the spatial distribution and changes of wind speed, wind direction and strong wind at different heights and locations, and use the large eddy simulation method to simulate the large-scale vortex structure in the wind field to capture the turbulent characteristics in strong winds.

8. The flight space orientation training auxiliary system according to claim 3, characterized in that: The meteorological event identification module specifically includes: Historical meteorological data collection module, which is used to collect parameter information under various meteorological conditions, including temperature, humidity, air pressure, wind speed, wind direction, and cloud information in different regions, seasons, and weather types; Flight data acquisition module, which includes the flight trajectory, altitude, speed, and attitude information of the aircraft under different meteorological conditions; A convolutional neural network model extracts and reduces the dimensionality of image features in meteorological data through convolutional layers and pooling layers, takes the labeled meteorological data as input, outputs predictions of meteorological events, and adjusts the weights of the convolution kernels through a back-propagation algorithm to accurately identify meteorological events; A recurrent neural network processing model is used to process the time series information in the flight data to obtain the features related to meteorological events in the flight data time series, so as to accurately identify the meteorological environment in which the aircraft is located.

9. A flight space orientation training assistance method, applicable to the flight space orientation training assistance system according to claims 1 to 8, characterized in that: The following steps are involved: Generate appropriate training plans based on the flight crew's historical training information; According to the training requirements of the training program, determine the flight phases and special flight tasks to be simulated and construct flight scenarios; Build meteorological models: Build meteorological models in multiple dimensions, including spatial dimension, temporal dimension, and physical process dimension; By using the meteorological parameter setting module, combined with the meteorological parameter observation data collected in real time, initial parameters are set for the meteorological model; Use the established meteorological models and set meteorological parameters to simulate various meteorological scenarios; According to the set time step, the meteorological parameter observation data collected in real time is transmitted to the meteorological parameter setting module; Continuously monitor the aircraft's flight status and environmental information to determine whether specific meteorological events are triggered; When a specific meteorological event is detected, the meteorological parameters of the event area are obtained and transmitted to the meteorological parameter setting module, and the meteorological environment simulation module updates the simulated meteorological scene according to the specific meteorological parameters.

Images