145 results about "Flight training" patented technology

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 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 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 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.

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.

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 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.

The utility model relates to a simulation training device for flight principle courses, and belongs to the technical field of helicopter teaching and flight training. Comprising a helicopter simulation cabin, a visual device, an instructor console, a master control and communication device and a flight principle demonstration device, and the helicopter simulation cabin comprises a simulation cabin and cabin instruments and further comprises a cabin cover, a base, a cabin central instrument panel, a control system assembly and a pilot simulation seat. The cabin instrument display module is used for virtual display of cabin instruments and visual flight information indication; the visual device is composed of a visual generation device and a visual display device. The teacher console is mainly used for controlling and setting a flight environment, a flight subject and the like; the flight principle demonstration device comprises real-time stress, torque display, manipulation and helicopter response curve storage and playback. The device is stable and reliable in operation, visual and convenient to operate, strong in training pertinence, high in simulation degree, good in visualization degree and good in popularization value.

The invention discloses a spatial orientation real-time analysis method based on eye movement, relates to the related technical field of flight training, and aims to overcome the defects of low analysis accuracy and poor adaptability in the prior art. The method comprises the following steps: acquiring pilot eye movement data through eye movement tracking equipment, acquiring head space orientation data through a flight helmet space sensor, and acquiring environment data containing instrument visual complexity and aircraft attitude from a virtual flight simulator; performing time synchronization on the three types of data; extracting features such as coordinates of a fixation point from the synchronous eye movement data; comparing the head orientation data with the reference data to generate deviation data; inputting the eye movement characteristics, the deviation data and the environment data into an evaluation model; the model dynamically adjusts parameters according to environment data, and calculates pilot space orientation capability indexes in combination with eye movement characteristics and deviation data. According to the method, accurate integration of multi-dimensional data can be realized, the analysis comprehensiveness and reliability are improved, a dynamic flight scene is adapted, and real-time accurate evaluation is realized.

The invention relates to the technical field of physiological signal recognition, in particular to a flight training quality analysis method and system based on brain magnetic resonance imaging. According to the method, fMRI data of students before and after flight training are collected, dACC and PoCGL of brain functional areas are defined, BOLD signal time sequences corresponding to the functional areas are accurately extracted, meanwhile, resting state functional connection analysis is carried out on the BOLD signal time sequences of two key brain areas before and after training by utilizing a Pearson's correlation coefficient, and a connection strength difference value with high credibility is obtained, so that the training accuracy of the students is improved. An individual training quality score is output through nerve-behavior joint modeling, a structured evaluation report is formed, training quality evaluation is converted from experience judgment to data driving and mechanism supporting, objectivity and scientificity of the system are enhanced, and compared with a traditional subjective scoring system, the system has the advantages that the training quality evaluation efficiency is improved. The index has the advantages of physiological interpretability and high repeatability, and can accurately analyze the flight training quality of trainees.

The invention belongs to the technical field of avionics, and particularly relates to a flight comment system and a design method thereof, and a data center server, an unloading terminal and a comment terminal are constructed in a local area network; the data center server is used for centrally storing data and undertaking message exchange, the unloading terminal is used for uploading data after flight to a data center, and the commenting terminal is used for displaying video data, playing back audio, simulating cabin display and carrying out 2D / 3D playback. According to the scheme provided by the invention, task commenting with data visualization can be realized, and the commenting efficiency and the flight training quality are remarkably improved.

The invention provides a method and a device for evaluating the workload of a pilot in a high-dynamic scene, and the method comprises the steps: inputting the audio and video information of a cockpit according to the real-time operation of a computer; the method comprises the following steps: decomposing uncertain actions made by a pilot when executing a task in a high-dynamic scene in a time window with a preset length into basic action elements with fine granularity and certainty; and evaluating the workload of the pilot according to the types of the basic action elements appearing in the time window with the preset length, the duration of each basic action element and the total resource consumption value of each basic action element. The method is real-time, accurate, quantitative and non-invasive, and a certain reference is provided for efficiency improvement and task arrangement optimization of new-generation aviation equipment. The method is suitable for flight training, task optimization and man-machine system design.

This invention discloses a BeiDou high-precision positioning integration method for UAV navigation teaching. By creating a virtual teaching airspace and mapping the UAV into it, virtual flight training of the UAV is achieved. The UAV body is mounted on a three-degree-of-freedom test station, allowing only three-axis rotation and no displacement. Real-time data from the UAV is collected and converted into virtual ground truth. Based on this virtual ground truth, the UAV can be controlled to interact within the virtual teaching airspace. Simultaneously, high-fidelity BeiDou satellite radio frequency signals and virtual force ratio signals are calculated based on the virtual ground truth. These calculated high-fidelity BeiDou satellite radio frequency signals and virtual force ratio signals are fed back to the UAV, enabling the UAV's flight control system to believe that the UAV is in a real flight state. This allows for high-fidelity testing and teaching verification of the complete navigation hardware and algorithms of multi-rotor UAVs in a safe, controllable, and repeatable indoor environment.

The application discloses a kind of flight student simulation flight training psychological stress data acquisition methods combined with virtual reality, it is related to machine learning technical field.The technical points of the present application include: collecting the skin electric signal of flight student in virtual reality environment carries out simulation flight training, and carries out feature extraction, as physiological signal feature dataset;Flight information of flight student in high dynamic simulation platform is collected and is used as flight feature dataset;Flight student carries out flight simulation training in virtual reality environment using hand throttle and joystick, and outputs hand throttle and joystick instruction as control signal dataset, while the key information of virtual reality scene is output as, constitute virtual reality scene information dataset;Psychological stress data acquisition model based on deep convolutional neural network is trained using physiological signal feature dataset, flight feature dataset, control signal dataset and virtual reality scene information dataset.

The embodiment of the invention relates to the technical field of flight management, and provides a flight plan management method and device for a flight management system, and the method comprises the steps: analyzing a navigation database file of a target protocol, and writing the analyzed navigation data into a target database, the navigation data including airports, waypoints, routes and flight program data; a flight plan creating instruction input by a target user is received through CDU simulation unit software, and the flight plan creating instruction at least comprises a takeoff airport, a target airport and waypoint numbers or route names; based on the flight plan creation instruction, extracting target data from a target database, and generating a flight plan; receiving and responding to an activation instruction input by the target user, and activating the flight plan; and controlling the target flight trainer to execute the activated flight plan based on the activated flight plan. The creation, editing, activation and dynamic over-point calculation of the flight plan can be completely reproduced in the trainer through simulation software, and the cost is reduced.

This utility model belongs to the technical field of unmanned aerial vehicle (UAV) flight training equipment, and particularly relates to a six-axis, six-rotor UAV flight training device, comprising: a base plate, a U-shaped frame fixedly installed on the top of the base plate, two rotating rods rotatably installed inside the U-shaped frame, and a connecting rod fixedly installed between the two rotating rods; a limiting component, located inside the U-shaped frame, used to limit one of the rotating rods; a first ring, fixedly installed on the top of the connecting rod, a second ring inside the first ring, a third ring inside the second ring, and grooves formed in both the first and second rings. In use, this utility model ensures that the difficulty of training can be adjusted in various ways, enabling trainees to handle more challenging flight scenarios in actual operation after training, making operation more efficient.

The invention discloses a dynamic model of a fixed-wing aircraft, and relates to the technical field of flight training. Comprising an aircraft mass center kinetic equation of the fixed wing aircraft, a mass center rotation kinetic equation, an aircraft aerodynamic characteristic calculation model, an aircraft kinetic equation resolving model, an abnormal flight state trajectory and attitude inverse solution calculation model and a ski-jump take-off and carrier landing model. Wherein the aircraft aerodynamic characteristic calculation model is used for calculating aerodynamic force and aerodynamic moment of the fixed-wing aircraft; the aircraft kinetic equation resolving model is used for resolving the aircraft mass center kinetic equation and the mass center rotation kinetic equation; the abnormal flight state trajectory and attitude inverse solution calculation model is used for inverse solution calculation of stress and torque of the fixed wing aircraft in an abnormal flight state; and the ski-jump takeoff and carrier landing model is used for solving the carrier landing arresting longitudinal load problem of the fixed-wing aircraft. The technical problems that in the prior art, the flight training process is abstract in theory and large in difficulty are solved.

The utility model provides a universal installation platform for a flight training simulator motion system, which comprises three groups of groove floors and three groups of adapter plates, and the three groups of groove floors are uniformly arranged at intervals of 120 degrees; the three groups of groove floor structures are the same in size, and the length, the width and the thickness are 4000 mm, 1200 mm and 100 mm respectively; the lower surface of each group of groove floor is a plane, N longitudinal columns of inverted T-shaped grooves and N + 1 longitudinal columns of fixing holes are formed in the upper surface of each group of groove floor at intervals, foundation bolts are arranged in the fixing holes, and each group of groove floor is fixed in a deep pit of a foundation through a plurality of foundation bolts; n is an integer; the top of the bolt hole expands outwards and is provided with a nut groove, the depth of the nut groove is not smaller than 50 mm, the foundation bolt is sleeved with a nut, the height of the nut ranges from 32 mm to 35 mm, and the head of the foundation bolt is higher than the nut by not smaller than 5 mm. First bolt holes and second bolt holes are formed in the adapter plate, the first bolt holes are arrayed and matched with the N longitudinal columns of inverted T-shaped grooves at intervals, and the second bolt holes are formed in the first bolt hole array; the adapter plate is fixedly connected with the groove floor through bolts arranged in the first bolt holes. A motion platform lower hinged support of each flight training simulator motion system is fixedly connected with the adapter plate through a bolt arranged in a second bolt hole, and then is fixedly connected with the groove floor; slope-shaped guide openings are formed in the two ends of the inverted-T-shaped groove.

The application provides a kind of intelligent flight training system, it is related to intelligent flight training technical field, the system includes: if target user is located in copilot position, according to the time consumption of each virtual voice corresponding instruction executed by target user in the preset sliding time window TH, determine the average time consumption of target user in TH Virtual voice corresponding instruction is executed;If average time consumption is less than first preset time consumption or greater than second preset time consumption, then obtain next to be played virtual voice file, using first preset virtual voice processing method to process next to be played voice file, to obtain corresponding reverse virtual voice file;Reverse virtual voice file is played through the voice playing device;In the application, since reverse virtual voice file is generated based on the duration of instruction execution of target user, it does not have a certain regularity, so that when simulating for copilot to target user, it is more in line with real flight scene.

The invention relates to the technical field of flight training data analysis and pilot driving ability evaluation, in particular to a training plane flight data recording and analyzing system. Comprising a data acquisition module used for synchronously acquiring flight state time sequence data and pilot operation input time sequence data so as to generate a high-dimensional original data stream; the feature extraction module is used for identifying a flight stage and extracting a group of intermediate variables according to the high-dimensional original data stream and a preset flight task knowledge base; the quantitative evaluation module is used for calculating a group of capability evaluation parameters according to the group of intermediate variables; the vector generation module is used for generating a capability feature vector according to the group of capability evaluation parameters; and the capability portrait generation module is used for generating a continuous manipulation capability evolution portrait according to the capability feature vector sequence which is generated by the vector generation module and is arranged according to the time sequence. According to the method, the objectivity, the consistency and the fineness of evaluation are remarkably improved.

The utility model discloses a flight training simulator cabin mechanical structure which is composed of a front skin, a light shield, a windshield frame, an opening frame, a framework assembly, a rear skin, a seat support, a floor, a first equipment support, a second equipment support, side skins, trundles and a cover plate. The side skins are arranged on the two sides of the framework assembly, the light shield and the windshield frame are arranged on the front side of the upper end of the framework assembly, the opening frame is arranged at the upper end of the framework assembly, the seat support, the floor, the first equipment support and the second equipment support are arranged in the cabin, and the trundles are arranged at the bottom of the cabin. The bottom cover plate is arranged on the side face of the bottom of the framework assembly. According to the simulator cabin designed by the utility model, the internal and external structures of a real aircraft are restored to the greatest extent, and corresponding equipment can be mounted in the cabin frame to restore the driving state of the real aircraft, so that a pilot can quickly find the real driving state, the pilot is accelerated to be familiar with driving, and the efficiency is improved.

The invention discloses a training flight path optimization method and system in combination with low-altitude training and transportation in an airport and a medium. Relates to the technical field of air transportation and flight training. Improvement is carried out on the basis of the prior art, a built training flight airspace grid model finely expresses training flight airspace information, the fine degree of airspace resource management is improved, a multi-target optimization model comprehensively considering safety, economy and green targets is built, and through reasonable setting of constraint conditions, the optimization efficiency of airspace resource management is improved. The optimization result better meets the actual flight training requirement, and the risk, oil consumption and pollutant emission of training flight are effectively reduced; based on the improved artificial bee colony algorithm, the solving efficiency and quality are improved, a better solution can be quickly found in a complex multi-objective optimization problem, and reliable algorithm support is provided for training flight trajectory optimization.

The application provides a pilot attention allocation training method, device and equipment and a storage medium, and belongs to the field of flight training. The method comprises the following steps: in the case that a pilot wears a head-mounted eye tracker for training, obtaining a fixation point video of the pilot and recording the triggering conditions of observable items; dividing the fixation point video into interest areas, performing data analysis on the interest areas, and determining the access times, Pearson correlation coefficients, pupil diameter change scatter plots, Markov transition matrices, access sequences and time lengths, in-cabin and out-of-cabin access times, cockpit saccade mode models and the pupil diameter change rates of the pilot; further determining the attention transfer portrait and the attention allocation score of the pilot; and adjusting the attention allocation training bias of the pilot according to the attention allocation score and the attention transfer portrait. In this way, the limitations of traditional subjective judgment can be overcome, and the training evaluation is more scientific and operable.

The application relates to the technical field of actuator simulation, and particularly discloses a simulation method of an airplane control surface servo actuator considering the influence of hinge torque, which receives external current control commands, feedback control surface position and speed data through an external system interface and a closed loop control module, and provides a fault setting and an aerodynamic force data interface; enters a current control command processing module to perform fault simulation processing on the current control commands received in step 1, including simulation of hydraulic fault, electromagnetic valve blockage, command dead zone and control signal out-of-control; and the hinge torque coupling module is used to couple the dynamically changing hinge torque influence factor with the actuator motion speed, so that the defects that the load is simplified as a constant torque in the existing model are overcome, the influence of the aerodynamic environment on the control surface motion is truly reflected, the simulation result is closer to the actual flight state, and a more reliable reference basis is provided for airplane development, ground test and flight training.