15 results about "Pilot training" patented technology

The invention relates to the technical field of aviation safety data analysis and pilot training optimization, provides an aviation safety data and training data integrated analysis method and system, electronic equipment and a storage medium, and solves the problems of poor pertinence of pilot training and poor foresight of safety guarantee. The method comprises the following steps: automatically extracting threats, errors and unexpected aircraft states in aviation unsafe events from multi-source heterogeneous data, and judging a management state; based on the aviation safety knowledge graph, positive and negative competency and behavior indexes corresponding to the managed and unmanaged states are identified through graph calculation; the statistical frequency is analyzed, and a priority list of the security data side is generated; carrying out weighted fusion to obtain a target training list by combining a training data statistical analysis result of the same period; and in combination with the threat priority, generating a training scene configuration scheme containing threat elements and capability items, and forming a course content, evaluation standard and resource optimization configuration scheme. According to the invention, the pertinence of pilot training is improved.

A communication system configured to estimate Channel State Information based on a channel estimator transformer model, comprising a transmitting device and a receiving device. The transmitting controller is configured to generate a generic pilot pattern set that includes a plurality of potential pilot patterns, map the generic pilot pattern set into a positional encoding indices set and a positional embedding indices set, generate a positional encoding sequence, PE for a pilot pattern map the positional encoding sequence into the generic pilot pattern thereby generating a Positional Encoding-Embedded Pilot training signal, transmit the generic pilot pattern set to the receiving device, and transmit the PEEP training signals according to the generic pilot pattern set to the receiving device. Moreover, the receiving controller is configured to receive the generic pilot pattern set, receive the transmitted PEEP training signals, and train the channel estimator transformer model based.

The utility model discloses an aviation handle clamping device, and relates to the technical field of aviation handles. The handle comprises a handle fixing seat and a handle body, the handle body comprises a straight rod section movably arranged in the middle of the top of the handle body fixing seat and an arc-shaped section connected to the top of the straight rod section, and control buttons are arranged on the two sides and the outer side face of the arc-shaped section. The clamping mechanism is detachably arranged on the edge side of the top face of the handle fixing base, and the clamping mechanism is used for fixing the straight rod section of the handle body. The detachable locking mechanism is introduced, movement of the handle body is limited in a physical mode, the handle can be rapidly and reliably fixed when a pilot trainings, free movement of the handle can be recovered only by detaching the locking mechanism when fixation needs to be removed, normal flight operation is guaranteed not to be affected, and the operation is simple and convenient. The operation is very simple, the practicability is higher, and the method is worthy of general application and popularization.

The invention discloses a predictive cooperative beam forming and resource allocation method based on multi-station information fusion, which is suitable for a cellular-free large-scale MIMO unmanned aerial vehicle communication system. According to the method, a low-overhead beam tracking frame structure is designed on a ground base station side, and position tracking of a mobile unmanned aerial vehicle user is realized by using an uplink pilot signal and based on an extended Kalman filtering distribution mode; and then, designing beam forming based on position prediction to realize downlink data transmission. Each base station returns user state estimation and covariance information in a low-overhead mode through a forward link, and the central processing unit fuses information of multiple base stations to improve position tracking precision. Furthermore, according to the method, the system effectiveness and the spectrum efficiency are taken as optimization targets, the uplink pilot frequency length, the downlink base station user association and the power distribution are jointly optimized, and a low-complexity iterative algorithm based on compressed sensing and fractional programming is used for solving. According to the invention, the pilot frequency training and backhaul overhead can be obviously reduced.

A fly-by-wire system that lets a single-engine aircraft mimic multi-engine behavior for real-flight pilot training, especially asymmetric thrust effects that create yaw / roll. A cockpit central thrust control with two independent thrust levers generates an overall power command plus a dissymmetry signal; a calculator applying a dedicated control law converts these into corrected setpoints for the rudder, left / right ailerons, and propeller parameters to reproduce multi-engine handling. Instructors may trigger a simulated engine failure and then reset back to normal single-engine operation. Optional wingtip electric thrusters powered by a battery may increase asymmetric effects while maintaining safety margin, and the system may display / record simulated multi-engine data for debriefing—reducing training cost and CO2 versus operating real twins

There is provided a system of monitoring an airborne training aircraft, the system comprising a processing circuitry configured to: a) receive, from one or more ground- based aircraft simulators, data indicative of, at least, respective three-dimensional (3D) positions and bearings of one or more virtual aircraft associated with the respective simulators; b) receive, from the training aircraft, data indicative of, at least, the training aircraft's 3D position and bearing; c) construct, utilizing, at least, the training aircraft's 3D position and bearing, and the respective three-dimensional (3D) positions and bearings of respective virtual aircraft, at least one of: i) imaging depicting a view, from an external point of reference, of the training aircraft and relative positions of virtual aircraft, and ii) imaging depicting at least one instrument of the training aircraft, wherein the depicted at least one instrument depicts positions of the one or more virtual aircraft relative to training aircraft.

The invention relates to a helicopter simulation system based on a six-degree-of-freedom turntable. According to the system, various attitudes and motion states of the helicopter in flight are simulated through the 6-degree-of-freedom turntable, and real-time simulation of the flight process of the helicopter is realized by combining a high-precision sensor and simulation software. Experiments show that the system can efficiently and accurately simulate the flight characteristics of the helicopter, and is suitable for pilot training and flight performance testing. The invention relates to a three-degree-of-freedom semi-physical simulation platform and an experiment method for a single-rotor unmanned aerial vehicle in the prior art. In the prior art, only algorithm simulation of a controller can be carried out, and the functions of educational training and flight training of a simulation system are not achieved. An existing simulation system is difficult to comprehensively simulate complex flight attitudes and motion states of helicopters. The invention provides a helicopter simulation system based on a six-degree-of-freedom turntable, which can effectively solve the problem.

The invention provides a pilot training demand determination method and a storage medium, relates to the technical field of aviation, and is used for accurately identifying the training demand of a driver. The method comprises the steps that multiple pieces of pilot driving data corresponding to a target pilot are acquired, the multiple pieces of pilot driving data comprise safe operation data, driving habit data or operation risk data, and the safe operation data are used for reflecting the preset safe operation state of an aircraft driven by the target pilot; the driving habit data is used for reflecting the driving habit of the target pilot facing the risk event, and the operation risk data is used for reflecting the safety risk degree of the driving behavior of the target pilot. And determining a driving style of the target pilot based on the plurality of pilot driving data. And based on the driving style of the target pilot, determining at least one training demand point to be improved by the target pilot, and obtaining a training demand corresponding to the target pilot.

This invention relates to an auxiliary system and training method suitable for jetpack training, comprising the following steps: Step S101, setting a corresponding flight route based on the training objectives set by the pilot and recording it completely; Step S102, acquiring various data of the jetpack during flight training, including real-time positioning data, real-time speed and direction, and the real-time altitude of the jetpack; Step S103, generating a real-time flight trajectory of the jetpack based on the acquired real-time data and comparing it with the set trajectory of the jetpack to analyze its deviation status; Step S104, outputting a correction signal to the pilot based on the real-time trajectory deviation status of the jetpack and the original set route. This invention sets the pilot training route using electronic equipment, freeing it from the limitations of actual terrain; simultaneously, the pilot can select the curve type and related parameters for this route, solving the problem of limited training routes due to cost constraints in previous methods.

The application relates to the technical field of aviation safety data analysis and pilot training optimization, and provides an aviation safety data and training data integrated analysis method and system, an electronic device and a storage medium, which solve the problems of poor pertinence of pilot training and poor foresight of safety guarantee. The method comprises the following steps: automatically extracting threats, errors and unexpected aircraft states in aviation unsafe events from multi-source heterogeneous data, and determining a management state; based on an aviation safety knowledge graph, graph calculation is performed to identify positive and negative competence and behavior indexes corresponding to the managed and unmanaged states; priority lists on the safety data side are generated by analyzing and counting frequencies; combined with the statistical analysis results of the training data in the same period, a targeted training list is obtained by weighted fusion; combined with the threat priority, a training scene configuration scheme containing threat elements and capability items is generated, and a course content, evaluation standard and resource optimization configuration scheme are formed. The application improves the pertinence of pilot training.

The invention belongs to the technical field of flight control, and particularly relates to an aircraft maneuvering action automatic identification method based on a gradient boosting decision tree, and the method comprises the steps: selecting two maneuvering actions; establishing a training sample set; inputting the training sample set into a recognition model, solving an optimal distance between the two sequences by the recognition model by adopting a dynamic time planning method, and judging whether the sequence to be detected is a target maneuvering action or not; if yes, inputting the identified target maneuvering action data into the gradient boosting decision tree model to obtain the category of the target maneuvering action; typical maneuvering actions in pilot training can be quickly and accurately recognized, and target action data are automatically extracted, so that subsequent data analysis and processing work is facilitated. The method is fast in operation speed and high in accuracy, can accurately extract and classify the identified target, and has high engineering practical value.

The present application relates to the technical field of unmanned aerial vehicle training simulation, in particular to a kind of unmanned aerial vehicle pilot training teaching simulation system based on local micro-meteorological environment.It includes: local micro-meteorological environment modeling module: obtain geography, meteorology, case data and carry out pretreatment, carry out micro-meteorological field calculation and dynamic update, after visual rendering, show local micro-meteorological scene;Unmanned aerial vehicle flight physical modeling module: establish unmanned aerial vehicle dynamics model, and carry out equipment performance attenuation simulation;Multi-dimensional evaluation and feedback module: multi-dimensional evaluation is carried out to local micro-meteorological scene, and feedback output suggestion;Real-time interaction and rendering module: through the hardware of adaptation, operation information interaction is carried out, and according to the information of interaction, the rendering of scene display is carried out.The present application realizes the high-precision simulation of local micro-meteorological environment, constructs micro-meteorological-flight physical real-time coupling model, establishes multi-dimensional quantitative evaluation system, provides personalized operation suggestion, and improves training pertinence.

The embodiment of the invention provides an aircraft tail floor mopping risk assessment method, and relates to the technical field of aviation safety. The method comprises the steps of obtaining flight data of a target flight; extracting risk assessment features from the flight data; the risk assessment features comprise flight state features, operation behavior features and time sequence analysis features; the time sequence analysis feature is used for representing a change trend or a dynamic sequence of one or more flight parameters in a preset flight stage; inputting the risk assessment features into a risk assessment model to obtain a predicted grounding pitch angle output by the risk assessment model; the risk assessment model can represent a linear relation and a non-linear relation between the risk assessment characteristics and the grounding pitch angle; and determining tail floor mopping risk information of the target flight based on the predicted grounding pitch angle. The method and the device can be used in flight quality monitoring and pilot training evaluation processes, and are used for solving the problems of dependence on artificial experience and low evaluation accuracy in the prior art.

A system for flight training includes one or more non-transitory computer-readable memories storing instructions one or more processors executing the instructions to perform operations. The operations include initializing a state and a cognitive load parameter for respective pilot training tasks; determining a subset of the pilot training tasks to include in a first data structure; generating the first data structure including the subset of pilot training tasks; receiving respective scores for the pilot training tasks from a device; updating the respective states of the subset of pilot training tasks based on the received scores; recalibrating the respective cognitive load parameters of the subset of pilot training tasks; updating, based on the recalibrated respective cognitive loads, states of at least one of the subset of pilot training tasks and at least one pilot training task not in the subset; and updating a second data structure based on the updated states.

The application discloses a flight simulator somatosensory simulation method based on multi-model predictive control, relates to the technical field of flight simulators, and uses a flight scene identifier to monitor flight parameters from a flight simulator in real time, identifies a flight scene, guides a somatosensory predictive model switcher and a motion platform constraint strategy switcher, and makes them smoothly switch to the best somatosensory predictive model and motion platform constraint strategy that match the current scene and the working space of the motion platform, are transported to a high-performance model predictive controller to output optimal control parameters to a motion platform actuator, and finally integrate the somatosensory simulation method that can effectively utilize the working space of the motion platform and provide the high somatosensory fidelity required by flight training under different flight task scenes. The application widens the application scene of a single somatosensory simulation method, guarantees the high somatosensory fidelity of pilot training under multiple scenes, and has important significance for improving the training quality of pilots in a flight simulator.