235 results about "Flight training" patented technology

The invention relates to the technical field of flight training evaluation, and discloses an electroencephalogram feature and physiological index fused flight training evaluation system. The system comprises a physiological signal acquisition module which synchronously captures multichannel electroencephalogram original signals and body surface physiological index data, and the body surface physiological index data comprises an electrocardiograph R-R interval sequence, respiratory wave frequency amplitude and galvanic skin response amplitude; the multi-modal fusion module is used for analyzing an electrocardiograph R-R interval sequence to generate a heart rate variability feature vector and establishing dynamic association mapping of an electroencephalogram entropy value and a physiological feature vector; the cognitive state modeling module is used for generating a cognitive load index according to the dynamic association mapping and constructing a cognitive stability quantization matrix; the self-adaptive feedback module is used for receiving related data and dynamically adjusting simulated flight scene parameters; and the evaluation output module is used for integrating the data to generate a comprehensive training evaluation report containing a neurophysiological coordination degree score and an operation accuracy rating. According to the system, comprehensive evaluation and dynamic training adjustment of the cognitive state of the pilot are realized.

The invention relates to the field of electroencephalogram signal processing, and particularly discloses a multi-mode electroencephalogram characteristic cognitive ability evaluation system based on a neural network, comprising: an acquisition module used for synchronously acquiring whole brain signals and eye movement data during pilot simulation training; the data preprocessing module is used for primarily processing the received whole brain signals and the eye movement data, monitoring the primarily processed data in real time by utilizing a preset monitoring mechanism, and triggering to generate interaction test starting signals if preset symbolic fluctuation occurs in the whole brain signals or the eye movement data; and the test interaction sub-module is used for generating arithmetic test questions according to a preset rule after receiving the interaction test starting signal, and synchronously recording answer correlation parameters of the pilot. By adopting the technical scheme of the invention, the deep-level reaction mechanism of the brain under the stimulation of the special complex cognitive task of flight training can be fully considered, and the cognitive load change of the pilot in the simulated training can be comprehensively and accurately reflected.

The invention relates to the technical field of TEM models, provides a pilot dynamic evaluation method, system and device based on a TEM model and a storage medium, and solves the problems of low accuracy of flight training evaluation and poor adaptability of a training scheme. The method comprises the steps that flight control, physiological monitoring and cockpit voice data are acquired, and a multi-modal data stream is formed through time synchronization; performing threat perception, error management and non-technical skill three-level evaluation on the feature vector by using a TEM model, generating corresponding indexes, and fusing the indexes into a comprehensive feature vector; respectively processing the time sequence and cognitive features by means of a dual-channel long-short-term memory network, and extracting deep features; performing decision tracing by adopting an SHAP value so as to locate a capability defect node; finally, defect types are matched through a personalized improvement scheme recommendation mechanism, and self-adaptive training content is generated. According to the invention, the accuracy of flight training evaluation and the adaptability of the training scheme are improved.

The invention, which relates to the technical field of aviation training and simulation, discloses a virtual low-altitude air traffic controller AI model training system comprising a data acquisition and preprocessing module, an AI core model module and a communication and interaction module. The data acquisition and preprocessing module is used for collecting data from a simulated low-altitude radar, low-altitude weather, low-altitude geographic information and a low-altitude flight plan data source, and performing cleaning, denoising, format conversion and integration processing on the data to generate preprocessed data used by the AI core model. According to the virtual low-altitude air traffic controller AI model training system provided by the invention, the effect and quality of low-altitude flight training of a pilot are remarkably improved, and the pilot can perform a large amount of practical operation and emergency treatment training in a safe environment by simulating various real low-altitude flight scenes and complex conditions; the coping ability of the system to low-altitude flight rules, control programs and various complex conditions is enhanced, the training period is shortened, and the training cost is reduced.

The invention relates to the technical field of flight training, in particular to a flight training system. The flight joystick receives operation action data input by a target user based on a preset training instruction; the button control table receives button action data input by a target user based on a preset training instruction; the electronic equipment receives parameter setting data which is input by a target user based on a preset training instruction and is used for setting flight parameters; the multi-axis gravity seat performs six-degree-of-freedom movement based on the operation action data, the button action data and the parameter setting data so as to simulate the flight attitude of the aircraft; and the electronic equipment generates a target virtual driving scene based on the operation action data, the button action data and the parameter setting data, and controls the dynamic scene display equipment to display the target virtual driving scene. The target virtual driving scene is combined with the actual operation process. And the training effect is improved in the flight training process.

The invention relates to the technical field of artificial intelligence and flight training, and particularly discloses an intelligent training method and system for consciousness of a pilot. The method comprises the following steps: constructing a multi-modal flight scene and an original task structure chart, and extracting a flight task target and a semantic tag set; guiding a pilot to carry out natural language expression, collecting a voice stream and combining a task stage to align a text; comparing the language expression with task semantics by using a language model, and identifying expression differences; generating a feedback statement based on the difference, and pushing the feedback statement to the pilot; updating a task flow and reconstructing an execution script; finally, training records are counted, the capacity label weight is updated, and a capacity growth trend graph is generated. According to the method, accurate identification of pilot task understanding deviation and language expression defects can be realized, a scene training process dynamically adaptive to a cognitive state is constructed, and scene awareness and language expression ability of a pilot are remarkably improved.

The invention discloses a flight training digital twin system, which comprises a data acquisition module (for collecting real-time data of an aircraft and an environment to enable virtual and actual conditions to be consistent), a model and simulation module (for creating an aircraft, an operating environment and an airborne equipment simulation model), and an intelligent analysis and control module (for analyzing operation and equipment states through big data, and analyzing and analyzing the real-time data of the aircraft and the environment to enable virtual and actual conditions to be consistent). Feedback and optimization suggestions are generated), and an interaction and training module (based on real-time data scoring feedback and immersive interaction-aid operation). According to the system, states and behaviors of physical entities such as an aircraft, a pilot and an environment are mapped to a virtual space through a high-precision virtual simulation technology, and a real-time interaction and dynamic feedback simulation training platform is constructed. The system integrates multi-dimensional data, can be linked with physical equipment, and assists a pilot in improving skills and enhancing the coping ability of complex conditions in a safe and controllable virtual environment by simulating a real scene and providing a personalized scheme, intelligent decision and risk prediction.

The invention relates to the technical field of running big data, provides a flight training adaptive optimization method, system and device based on running big data, and a storage medium, and solves the problems of low control precision and poor stability of high maneuvering flight training. The method comprises the following steps: acquiring attitude time sequence data, structural deformation monitoring data and historical operation data of a flight target in a maneuvering state in a current period; performing environmental interference suppression on the attitude time sequence data, and extracting motion characteristic parameters; converting the structural deformation monitoring data into deformation compensation data, and fusing the motion characteristic parameters with the deformation compensation data by using an attitude fusion algorithm; based on historical operation data, multi-period feature matching is carried out on the fusion result of the current period, and six-dimensional attitude information of the flight target is generated; and generating an optimization control instruction through the adaptive control strategy, wherein the optimization control instruction is used for optimizing the response performance of the flight target. According to the invention, the control precision and stability of high-maneuverability flight training are improved.

The invention discloses a flight training quality evaluation method based on DTW and K-means clustering, and belongs to the technical field of pilot training, and the method comprises the following steps: S1, data collection and processing; s2, data preprocessing and standard template generation; s3, DTW similarity measurement and mahalanobis distance weighting are carried out; s4, performing similarity measurement and clustering analysis; s5, generating a personalized training scheme; therefore, the invention provides a scientific, objective and efficient flight trainee quality evaluation method, the landing process of the pilot is subjected to comprehensive quantitative analysis in a data-driven manner, a more accurate and personalized flight training scheme is provided, the flight level of the flight trainee is finally improved, and the risk of flight accidents is reduced.

The invention provides a QAR data-based main pilot effective manipulation analysis method and system, and belongs to the technical field of flight training. Comprises: acquiring flight decoding data; performing initialization correction on the joystick force in the flight decoding data, and limiting a data analysis range; constructing a control stick data analysis logic, and executing the control stick data analysis logic in a limited data analysis range to analyze the initialized and corrected control stick force so as to judge whether the main pilot is effectively manipulated or not; matching the flight decoding data with the task book information to judge whether the control rate of the main pilot reaches the standard or not; and according to the effective control of the main pilot and the standard control rate, the actual effective control condition of the main pilot is judged. The method can accurately quantify the manipulation performance of the pilot and the flight condition of the captain of the instructor, and provides scientific guidance for the evaluation of the driving level of the pilot.

The invention relates to the technical field of flight training, and particularly discloses an intelligent auxiliary training method and system for the driving skill of a helicopter pilot, and the method comprises the steps: deploying a plurality of sensors in a simulated helicopter cockpit for collecting operation, physiology, eye movement and other original data; after data standardization preprocessing, constructing a trainee training state comprehensive data model by using a multi-modal data fusion algorithm; dynamically generating a simulated flight scene by using VR and AR; carrying out comprehensive evaluation by adopting a multi-modal evaluation model based on deep learning in combination with multi-source data and student scene performance, and generating a multi-dimensional report; an evaluation result is fed back in real time through multiple channels such as voice and touch, and customized training resources are pushed; and formulating an exclusive training plan by using a personalized learning path planning algorithm based on multiple training data. Through multi-source data fusion and an intelligent algorithm, the training state of the trainee is comprehensively and accurately evaluated, a personalized training scheme is provided, and the driving skill training efficiency and quality of the helicopter pilot are effectively improved.

The invention belongs to the technical field of aviation flight training, particularly relates to a multi-module collaborative flight simulator control panel and method, and solves the problems that an existing system is low in interaction efficiency, subjective in evaluation, lagged in early warning and time-consuming in comment. The system depends on traditional IOS hardware, and is provided with an intelligent voice interaction module which recognizes teacher voice through ASR, generates an instruction through intention recognition and three-level slot completion, and cooperates with double-layer flight stage recognition logic to guarantee interaction accuracy; the flight quality monitoring module operates a rule engine and an enhanced LSTM neural network in parallel, and outputs a comprehensive flight quality score in combination with an adaptive fusion algorithm; the real-time reminding module dynamically adjusts the early warning priority according to the event level and the flight stage, and multi-mode early warning is provided; and the intelligent comment module fuses the multi-source data and generates a personalized structured report according to the priority of the template. According to the invention, intelligent interaction, quantitative evaluation and automatic commenting of training are realized, and the training efficiency and evaluation objectivity are improved.

The invention relates to the technical field of aviation safety and flight training data analysis, provides a training priority determination method and system based on aviation event data statistical analysis, electronic equipment and a storage medium, and solves the problems of poor accuracy of flight training subject design and poor risk pertinence. The method comprises the following steps: extracting aviation unsafe event data of a plurality of heterogeneous data sources, extracting threat, error and unexpected aircraft states, and judging a unit management state; a group decision is combined with a distributed scoring node and block chain consensus mechanism, three-level scoring is performed on various threats from the training effect, the occurrence possibility and the consequence severity, and a threat scoring matrix is formed; the unit management rate and the actual occurrence frequency of each threat are counted, a comprehensive risk value is calculated, all threats are ranked to determine training priorities, and the threats with high priorities and related errors are embedded into the scene design of a flight training subject. According to the invention, the accuracy of flight training subject design is improved.

The invention relates to the technical field of space flight and aviation, in particular to a low-delay communication control system and method for an analog machine in a fixed flight training course. The control system comprises a multi-source sensor fusion module, a data simplification processing module, a dynamic time delay optimization communication framework, a distributed intelligent decision-making terminal, a virtual-real mapping synchronous controller, a milli delay regulation and control module and an intelligent sensing alarm interaction module. According to the invention, 4G, 5G and satellite double links are integrated, an optimal link is dynamically selected by adopting a Q-Learning algorithm, millisecond-level time delay control is realized, transmission reliability is improved through a packet loss rate compensation algorithm, multi-sensor data is fused based on an FPGA hardware accelerator and an EKF algorithm, and a data compression ratio is improved in combination with Huffman coding and Kalman filtering. The bandwidth requirement is greatly reduced, through software and hardware collaborative optimization, the end-to-end time delay is reduced to be within hundreds of milliseconds, the virtual-real synchronization precision is improved to the centimeter level, and the decision response speed is improved by two times.

The invention belongs to the technical field of aviation flight training, particularly relates to a flight training trajectory generation method fusing an attention mechanism, and solves the problems that in the prior art, diversified, personalized and high-quality flight training trajectories are difficult to generate, and the high-quality training requirement cannot be met. According to the method, flight training multi-source data is obtained and subjected to preprocessing and dynamic weight fusion to generate fusion data, an encoder in a prediction model processes a single track fragment in the fusion data through a random feature attention mechanism, real-time aerodynamic parameters are fused into a key matrix, an attention score is adjusted through an aerodynamic consistency coefficient, and then submerged space representation is obtained; the decoder generates an initial prediction flight training trajectory sequence in combination with the submerged space representation and the noise vector; and finally, suppressing high-frequency noise by using speed self-adaptive cut-off frequency and weighted sinc filtering, and carrying out nonlinear filtering to obtain a final predicted flight training trajectory sequence. According to the method, diversified, personalized and high-quality flight training trajectories are generated, and the high-quality training requirement is met.

The invention discloses a flight trainee attention and error behavior detection method based on a multi-modal physiological signal, and the method comprises the steps: obtaining the electrocardiogram data, eye movement data and electroencephalogram data of a flight trainee in a flight training process, and subjective evaluation data; based on subjective evaluation data, dividing flight student attention distribution conditions into a high grade, a middle grade and a low grade by adopting a clustering algorithm; performing feature optimization processing on the physiological data, and constructing a physiological feature attention level flight behavior associated data set; constructing an attention distribution level judgment model and a flight error behavior detection and early warning model; and outputting an attention level judgment result and a flight error behavior detection and early warning result. According to the method, association mapping of the multi-modal physiological signals and flight performance is realized, an attention grading standard for a flight training specific scene is established, and conversion from passive response to active early warning is realized.

The invention discloses a civil aviation pilot manual manipulation proficiency quantitative evaluation method and device, and the method comprises the steps: S1, setting a research flight stage which comprises a stage from takeoff to climbing, a stage from approaching to landing, and intercepting the QAR data of the research flight stage; and S2, intercepting data from a takeoff stage to a climbing stage and an approaching stage to a landing stage, and performing quantitative evaluation on the manipulation proficiency degree. According to the invention, the high manual operation intensity and difficulty of the takeoff-to-climbing stage and the approaching-to-landing stage are taken as the research flight stage, and multi-aspect evaluation values can be respectively obtained according to the takeoff-to-climbing stage and the approaching-to-landing stage; meanwhile, the evaluation result from the takeoff to the climbing stage, the evaluation result from the approaching to the landing stage and the comprehensive evaluation result from the research flight stage are obtained through weighted fusion, staged, aspect-divided and multi-fusion manual manipulation quantitative evaluation is achieved, and the flight training effect and the operation skill of a pilot can be improved.

The invention provides a flight trainer instrument display system based on vector graphics, a dynamic rendering method, computer equipment and a storage medium, and the method comprises the steps: obtaining a cockpit instrument layout, vector graphics elements, a carrier real-time state and environment meteorological terrain information; light field information is generated by processing meteorological topographic data and converted into optical adjustment parameters, and then ambient light compensation data is generated. And mapping the real-time state data of the carrier to the corresponding vector graphic elements to update the display attribute, and calculating the accurate position of each element in the display coordinate system in combination with the layout information. And predicting the rendering time of the next frame by using a long-short-term memory model, and dynamically optimizing the graphic detail hierarchy. And finally, fusing the updated display attributes, the optimized details, the position coordinates and the compensation data for rendering, and outputting a high-quality instrument image to a display screen. According to the invention, the display quality and rendering efficiency of instrument display in a dynamic environment are improved.

The invention relates to the technical field of civil aviation training, in particular to a civil aviation training quality monitoring system based on a digital twin model, and the system comprises a data collection unit; the state analysis unit is used for determining a training state according to the posture change value and the track dispersion of the training task; the selection analysis unit is used for determining a to-be-detected point selection mode according to the training state; the image division unit is used for determining a sub-image division mode according to the abrupt change pixel point distribution coefficient; the quality confirmation unit is used for determining an image quality value confirmation mode according to the analysis condition; the processing and analyzing unit is used for determining a processing mode according to the image quality value; the image quality of the flight training simulation scene can be guaranteed, and then the training quality is guaranteed.

The invention discloses a flight student pressure assessment system based on fusion of heart rate variability and multi-modal data, and relates to the technical field of flight training. The data acquisition module is used for acquiring heart rate variability data, electroencephalogram data, galvanic skin data and behavior data of a flight student, and constructing a data set after preprocessing; the feature extraction module is used for extracting features from the data set and comprises the steps of extracting time domain features and frequency domain features from heart rate variability, calculating phase synchronism of different electroencephalogram channel signals based on a phase locking value, extracting energy features of each frequency band and decomposing a skin electric signal on different time scales based on wavelet transform; and extracting a baseline value and fluctuation amplitude characteristics. According to the method, the limitation of a single index is broken through by integrating the heart rate variability physiological signal and the limb movement behavior data; the system assists students in cognitive pressure, personalized guidance of coaches and mechanism optimization training, promotes flight training to be changed from experience driving to data driving, and provides early warning measures for high-risk flight students.

A rotorcraft flight training method for a rotorcraft comprising at least two engines and at least one rotor providing lift helping to keep the rotorcraft in the air. The method comprises at least the following steps: flying the rotorcraft in a mode wherein each engine supplies driving power to the rotor; and activating a first command to start a training phase simulating asymmetric flight, the training phase comprising a first flight phase comprising a first control operation, with a controller, controlling the at least two engines and a first display operation for displaying, with a display, information carrying at least two simulated powers.

The invention provides a flight simulation stress assessment method and system based on MR fused AI, and a medium, and relates to the technical field of stress assessment, and the method comprises the steps: constructing a virtual flight environment, fusing a flight simulator with the virtual flight environment, carrying out flight control simulation in the virtual flight environment, and constructing a flight training platform; constructing a stress evaluation model; acquiring real-time physiological data, control data of a flight simulator and simulated flight state data when a pilot executes simulated flight in different flight scenes and different flight stages on a complete time axis in the flight training platform; the real-time physiological data, the control data and the simulated flight state data are extracted to be used for representing mutation characteristics of the cardiovascular stress level, the stress state of the pilot is recognized through a stress evaluation model, and the stress state of the pilot, the flight scene and the flight stage of the stress state and the corresponding stress degree are determined. According to the method, pre-flight stress assessment can be effectively carried out.

The invention discloses a training aircraft flight training simulator system in the field of aircraft training simulator systems, which comprises a cabin and instrument indicating system, a visual imaging display system, a flight resolving system, a control and control load system, a cabin buffeting system, a sound system, a console system and a computer network, the cabin and instrument indicating system is mainly composed of a cabin simulation system and a cabin instrument system. The visual imaging display system is displayed on the display distributor and the cabin and instrument indicating system through a computer network and the flight resolving system in an imaging mode. According to the invention, flight simulation training of sliding, takeoff, turning, level flight, gliding, landing and takeoff and landing routes under four meteorological conditions can be simulated; the special effect flight simulation training such as circling, diving, jumping, sharp rising turning, rolling, sharp circling descending, sideslip, horizontal 8-shaped, weight giving, inclined weight giving and semi-rolling reversing can be implemented.

The invention provides a flight training simulation and evaluation method and system for special situation disposal, and belongs to the technical field of aviation flight training, and the system comprises a special situation scene intelligent generation module, a digital twin simulation module, a multi-modal data acquisition module, an intelligent evaluation module, a self-adaptive training scheduling module and a visual redisk module. By intelligently generating a special situation scene, collecting multi-modal data in real time, performing multi-dimensional quantitative evaluation based on a deep learning algorithm, and dynamically adjusting a training scheme according to an evaluation result, accurate training and scientific evaluation of the special situation disposal ability of the pilot are realized. According to the method, the problems of lack of systematicness of special situation scene generation, high subjectivity of an evaluation process, non-fine training feedback and the like in traditional flight training are solved, the scientificity and efficiency of special situation disposal training are improved, the special situation disposal ability of a pilot can be accurately enhanced, and a guarantee is provided for aviation safety.

The invention relates to the technical field of driving simulation training, in particular to a pilot driving simulation training method and device, electronic equipment and a storage medium. Receiving at least one flight training subject corresponding to the target pilot; pilot behavior data and flight control data input by a target pilot for each flight training subject are acquired; acquiring flight parameter data of the target training aircraft based on the pilot behavior data and the flight control data change; and inputting the flight training subject, the flight parameter data, the pilot behavior data and the flight control data into a preset evaluation model, and outputting a target score corresponding to the target pilot. And the accuracy of the obtained target score corresponding to the target pilot is ensured. Because there is no need for a teacher to score a target pilot based on subjective judgments. According to the method, a scientific and objective standard is provided for evaluation of the target pilot, and an accurate target score is obtained in the training process of the target pilot driving model.

The invention relates to the technical field of aviation simulation training, and particularly discloses an aviation flight training simulation system based on flight path simulation and workload distribution, which comprises three modules: an aviation flight training simulation model construction module constructs a preliminary simulation model by debugging model data, and judges the availability of the preliminary simulation model through fluctuation parameter monitoring; the multi-captain seat cooperation and air traffic control scene simulation module applies an available model to general aviation flight subject and track simulation, captain seat simulation is realized in a specific organization mode, and the problem of captain seat load distribution in control training is solved; the aviation flight training effectiveness evaluation module monitors the simulation process and obtains a training index to judge the simulation effectiveness; the system forms a complete closed loop from model construction to scene simulation to effect evaluation, can improve the authenticity and efficiency of aviation training, reduces the cost and risk of real aircraft training, and is suitable for standardized and intelligent training system construction in the general aviation field.

This invention relates to the fields of aviation flight training, human factors engineering, and intelligent decision support. Specifically, it discloses a multimodal intelligent monitoring and decision support system and method for flight training, comprising: a multimodal physiological data acquisition module for non-invasive real-time acquisition of pilot physiological signals; a flight operation and situation data synchronization module for acquiring aircraft status, control inputs, and external environmental data; a data fusion and feature extraction module for synchronously processing data and extracting fused feature vectors; an intelligent assessment and decision support model library for outputting status assessments, operational trend predictions, and decision support information based on machine learning algorithms; and a personalized decision support and feedback module for providing real-time feedback or decision suggestions to pilot and instructor terminals. This invention enables objective, quantitative, and real-time monitoring of pilots' physiological and psychological states, provides intelligent decision support, and improves training efficiency and flight safety.

The invention relates to the technical field of interval efficiency evaluation, in particular to an interval efficiency evaluation method in combination with possibility degree measurement. The invention discloses an interval efficiency evaluation method in combination with possibility degree measurement, and particularly relates to improvement of efficiency evaluation and data envelope analysis evaluation methods in the field of civil aviation flight training, comprising evaluation of potential efficiency change by an interval efficiency model, reflection of training preference by an attitude function, and training efficiency comprehensive sorting based on the possibility degree measurement. According to the method, a profit model and a non-profit model are constructed, the profit model pays attention to the maximum efficiency of trainees, and the non-profit model considers the efficiency distribution of the whole flight training system to obtain a flight training efficiency interval; the attitude function is combined with the possibility degree measurement, so that the evaluation technology based on the possibility degree measurement is improved, the full sorting of interval efficiency is realized, and the identification degree and the difference of efficient trainee training results are improved.

The invention discloses an unmanned aerial vehicle, a control method thereof and a storage medium, and relates to the technical field of unmanned aerial vehicles, and the control method of the unmanned aerial vehicle comprises the steps: obtaining current flight data and a flight training task of the unmanned aerial vehicle; and under the condition that the training deviation exists between the current flight data and the flight training task, generating an operation guide instruction based on the training deviation, so as to guide a user to adjust the control operation of the unmanned aerial vehicle according to the operation guide instruction, and at least eliminating part of the training deviation. The technical problem that the unmanned aerial vehicle operation training efficiency is low is solved.

The invention discloses a psychological state monitoring method and system based on a deep learning voice emotion vector, which can accurately measure the psychological state of a pilot in a simulated flight training process, and overcome the problems of strong subjectivity, complex equipment, easy interference, non-deep voice emotion analysis and the like in the existing measurement method. And a powerful guarantee is provided for flight safety. The method comprises the following steps: acquiring voice data of a tested person, and performing audio processing on the acquired voice data to obtain an effective voice audio file set after audio segmentation and de-muting; adopting a pre-trained deep voice emotion calculation model to extract a high-dimensional voice emotion vector of each audio clip; and sequentially inputting the extracted voice emotion vectors into the trained psychological state evaluation model to generate psychological characteristic indexes.