70 results about "Aerodrome" patented technology

The invention discloses a fault simulation method and equipment in a complex flight scene, belongs to the technical field of flight fault simulation, and aims to improve the fault-tolerant capability of a system and realize full-coverage simulation of the complex flight scene through state injection and fault simulation. The problems of insufficient fault scene coverage, real-time control and data reproducibility in the prior art can be solved; moreover, the method can inject the fault states of a plurality of devices in parallel in the same simulation process, and compared with a traditional method which only supports a single-device single-fault simulation mode, the method has the advantages that the capability of restoring a composite fault scene is greatly enhanced, and the method is suitable for limiting condition verification of a high-reliability aircraft system.

The invention discloses an extended-range hybrid propulsion double-source dynamic coupling energy management method, which comprises the following steps: carrying out global physical modeling on a double-source power system and a flight scene, and establishing a double-source dynamic coupling model; designing a reinforcement learning physical constraint reward function, performing optimization training on each coefficient of the reward function by adopting a QMPSO algorithm, and outputting an optimized reward function coefficient; a qualified double-source dynamic coupling model is verified, and a power distribution coefficient is optimized; outputting the optimal power distribution coefficient of the battery and the range extender; the superiority of the dual-source power cooperative control strategy in the aspects of flight economy, operation stability and system life guarantee is verified through multi-dimensional comparative analysis of each performance index. Cooperative power distribution of the battery and the range extender is achieved through dynamic coupling modeling and reinforcement learning, the flight scene load requirement is met, the system energy efficiency is improved, and the service life of parts is prolonged.

The invention discloses an aircraft trajectory forecasting method based on a deep Kupman operator, which combines deep learning with a Kupman operator theory, constructs a three-layer network to automatically extract Kupman features, introduces four physical constraints to improve the generalization ability of a model, realizes a three-section type forecasting strategy and two-channel online learning, and improves the prediction accuracy of the aircraft trajectory. The problems that a traditional method is low in prediction precision, poor in physical consistency, weak in adaptability and the like in a high-maneuverability flight scene are solved, high-precision aircraft trajectory prediction is achieved, reliable target information is provided for a seeker system, and the interception success rate is remarkably increased.

The embodiment of the invention provides a risk zoning method and device for helicopter flight, equipment and a storage medium. The method is applied to the technical field of data processing, and comprises the following steps: carrying out classification and threshold division on meteorological elements according to a fusion data set and different flight scenes to obtain a meteorological element threshold breakpoint set; based on the meteorological element threshold breakpoint set, performing frequency statistics and data synthesis on the fused data set to obtain a multi-layer meteorological element frequency data set; obtaining an initial clustering result of each meteorological element through clustering analysis according to the meteorological element frequency data set; according to the initial clustering result, carrying out risk grade division on each meteorological element to obtain a standardized single-element risk zoning result; and based on a single-element risk zoning result, adopting a multi-element fusion method to obtain a comprehensive risk zoning map. In this way, the technical problem that in the prior art, due to the fact that multiple elements jointly participate in clustering, the risk level is difficult to judge can be solved.

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 relates to the technical field of aviation safety, in particular to a fixed-wing aircraft natural icing prediction system based on a prediction model and big data analysis. Comprising a multi-source data acquisition module, a data processing module, a feature extraction module, an icing case database, a prediction model module, a prediction analysis module and an early warning result output module. The data processing module is used for preprocessing and cleaning the collected original data; the feature extraction module is used for extracting key feature variables having significant influence on natural icing of the aircraft from the processed data; the prediction model module is used for taking the extracted key feature variables as input and combining big data analysis to construct a prediction model; and the prediction analysis module is used for inputting the currently collected multi-source data into the trained prediction model for analysis, and by adopting the mode, the icing risk is predicted in advance, the operation fault-tolerant space is greatly increased, and the accurate prediction requirement in the flight scene can be met.

The invention provides a novel low-altitude traffic management method based on a satellite-based technology, and the method comprises the steps: obtaining the initial position data of a low-altitude aircraft through a satellite-based navigation system, carrying out the multi-source fusion positioning through Beidou or GPS satellite signals, and obtaining the high-precision initial positioning data; according to the initial positioning data, correcting the positioning data in real time by adopting a satellite-based enhancement system to obtain high-precision positioning data; the method comprises the following steps: acquiring flight states and route information of aircrafts through real-time communication data, and judging potential conflict risks among the aircrafts for a high-density airspace or a cross-regional flight scene to obtain conflict early warning data; and according to the conflict early warning data, extracting a target route and a speed constraint of the aircraft from the real-time communication data to obtain optimized route data. The problems of accurate positioning, stable communication, conflict avoidance, whole-process management and the like of the low-altitude aircraft in a complex environment are effectively solved, and the safety and efficiency of low-altitude flight are improved.

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 provides an unmanned aerial vehicle path tracking control method based on chaotic encryption and disturbance observation, and belongs to the technical field of unmanned aerial vehicle control, data encryption and interference resistance. Comprising the steps that measurement data of an aircraft is encrypted through a key stream, and the key stream is obtained through Logistic map chaotic nesting encryption, chaotic system switching and random key stream coefficient distribution; transmitting the ciphertext measurement data to a controller of the aircraft for decryption based on the communication interface; generating a control instruction according to the decrypted measurement data; a chaos encryption algorithm and a disturbance observer control strategy are combined, and the dual requirements of the VTOL-FW for measurement data secure transmission and anti-disturbance control in a dynamic flight scene are met.

An unmanned aerial vehicle fine path planning method for flight domain map dynamic refinement comprises the three steps of flight scene omnidirectional elastic grid model preparation, initial flight domain map construction and flight path recursion refinement, and the method has the capability of achieving high-resolution unmanned aerial vehicle path planning in a large-range city scene; the method has lower space complexity, and the time efficiency is higher when unmanned aerial vehicle path planning is carried out in a large-range city scene.

The invention relates to a visual feature adaptation method based on flight scene category driving, and belongs to the technical field of image processing. The method comprises the following steps: establishing a scene classification data set with labels; constructing an image feature extraction algorithm set; performing feature extraction on the images of the various flight scenes in the scene classification data set by using algorithms in the algorithm set one by one, and performing feature merging and dimension reduction to obtain representative features corresponding to the various scene images; then image clustering is carried out, and representative features of all images under each algorithm are clustered into clusters with the same number of scene categories through clustering; and according to the clustering result and the actual scene classification label of the image, calculating the index of the feature adaptation degree, which is used for representing the adaptation degree of the image feature extraction algorithm for processing a certain scene image. According to the method, accurate comparison of feature adaptation degrees of different feature extraction algorithms under various flight scene categories is realized, and a scientific basis is provided for feature selection in an aircraft visual task.

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.

Aerodrome signalling system (100), comprising a plurality of signalling units (2), each comprising at least one aerodrome signalling device (9, 10), and a central communication unit (4) configured for data communication with the plurality of signalling units. The plurality of signalling units (2) each comprise a wireless data communication device (18) configured for cellular data communication and at least one sensor device (15) configured to capture measurement data in relation to one or more of: an environmental condition, a meteorological condition, a presence of an object, a movement of an object and a status condition relating to the respective signalling unit other than a status condition of the respective at least one signalling device. The at least one sensor device (15) is configured to communicate the measurement data to the wireless data communication device for transmission via a cellular network.

A system for global auto-configuration of at least one airport signaling device (11) of an aerodrome, said system comprising: a central control unit (14) and signaling devices (11), wherein the central control unit (14) is capable of communicating with the airport signaling devices (11) via the communication module (119) once a communication channel has been initiated; wherein the system is configured to communicate configuration data of at least one signaling device (11) to an Internet of Things (loT) hub (210), namely the identity code and the estimated location of the at least one signaling device; wherein the Internet of Things (loT) hub is capable of data communication with plurality of servers (220) configured to store inventory data related to a set of registered airport aeronautical signaling devices for at least one aerodrome for a predefined geographical area.

The invention provides an unmanned aerial vehicle mode switching control method and system, and relates to the technical field of mode switching control, and the method comprises the steps: constructing a flight scene vector, and carrying out the fitness evaluation; if the flight mode adaptation coefficient is smaller than or equal to the flight mode adaptation threshold value, generating a mode switching decision; switching accident suppression compensation adjustment is carried out on the mode switching decision according to the flight scene vector; loss constraint optimization is carried out on the switching adjustment first group according to the switching loss evaluation model; and a switching propagation constraint condition is introduced, propagation optimization is carried out, a switching optimization strategy is obtained, and mode switching control is carried out on the unmanned aerial vehicle. According to the method and the device, the technical problem that the flight safety of the unmanned aerial vehicle is affected due to the fact that the unmanned aerial vehicle mode switching lacks fine control and is easy to cause instability at the switching moment in the prior art is solved, the flight scene vector is constructed and the mode switching decision is made based on the fitness evaluation, the stability of the unmanned aerial vehicle mode switching is improved, and the flight safety of the unmanned aerial vehicle is improved. Therefore, the flight safety is improved.

The invention relates to a method and system for deflating an oxygen mask. The method for throwing the oxygen mask comprises the steps that the current flight scene S of an aircraft is determined, wherein the flight scene S comprises a ground station-crossing and take-off stage S1, a climbing or cruising stage S2 and a descending and landing stage S3; calculating a cabin height warning value ZCW based on the flight scene S and the cabin height value ZC; judging a jettisoning height threshold value ZD based on the cabin height alarm value ZCW; in any flight scenario, the cabin height value ZC is compared to the jettisoning height threshold ZD, and when the cabin height value ZC is greater than the jettisoning height threshold ZD, the oxygen mask is automatically jettisoned.

The invention discloses an aircraft trajectory prediction method and device and a readable storage medium, and the method comprises the steps: inputting historical flight trajectory data of a to-be-predicted aircraft into a trajectory prediction model, and carrying out the bidirectional gating feature extraction of the historical flight trajectory data, and obtaining historical trajectory features; performing guidance law classification prediction on the historical trajectory features to obtain a prediction guidance law type; determining a corresponding guidance parameter regression branch according to the prediction guidance law type, and carrying out guidance law parameter prediction on the historical trajectory features based on the guidance parameter regression branch to obtain a prediction guidance parameter value; and performing flight path extrapolation on the to-be-predicted aircraft according to the prediction guidance law type and the prediction guidance parameter value to obtain a flight path prediction result. The guidance law type and the guidance parameters are predicted in a data-driven mode, trajectory extrapolation is performed through the physical model, the theoretical completeness of the physical model and the flexibility of the data-driven model are effectively combined, and the prediction precision in a high-dynamic flight scene is improved.

The embodiment of the invention provides a large-scale low-altitude flight safety dynamic calculation parallel scheduling method and device, and is applied to the technical field of low-altitude airspace safety management. The method comprises the following steps of: gridding a low-altitude airspace based on a geographic coordinate system to generate geographic space grids, and performing grid coding on each grid; dividing each grid into a calculation area and a buffer area; the calculation area is a core operation unit and is used for real-time dynamic calculation and parallel scheduling of large-scale low-altitude flight safety, and the buffer area is a data interaction hub and is used for independently loading and caching grid data of adjacent areas; and according to the aircraft position information and the security envelope range, matching the aircraft to the calculation area to which the aircraft belongs, and routing the aircraft data to the corresponding buffer area. On the basis, inter-partition calculation decoupling can be achieved through cooperative work of the calculation area and the buffer area, the low-altitude flight safety dynamic calculation efficiency is remarkably improved, and therefore a real-time and efficient safety calculation solution is provided for a high-density low-altitude flight scene.

The invention discloses a low-altitude unmanned aerial vehicle intelligent supervision system and method based on multi-source fusion, and relates to the technical field of unmanned aerial vehicle supervision, and the supervision method comprises the following steps: capturing each flight path of an unmanned aerial vehicle, and generating a corresponding flight record; collecting flight data, confirming a flight scene and performing anomaly evaluation; acquiring an expected flight route of any flight record, and analyzing the deviation condition of the flight record; carrying out abnormal feature extraction on flight data of any flight record, and analyzing the influence condition of each abnormal feature on the deviation condition; setting corresponding deviation alarm thresholds for different flight scenes according to the abnormal feature inclusion condition of any flight record; generating a real-time flight record whenever the unmanned aerial vehicle flies in real time, and confirming a deviation alarm threshold value of the real-time flight record; and analyzing the real-time deviation condition of the flight route, and carrying out anomaly identification on the flight route of the real-time flight record.

The invention provides a flight potential standardization evaluation method based on rapid teaching and flight path assistance, and relates to the technical field of flight simulation evaluation. The method comprises the following steps: firstly, establishing a target capability model including understanding and accepting capability, control capability, attention capability and strain capability, and compiling a simulated flight test according to a cognitive diagnosis theory; constructing a scene library based on the target capability model, configuring track assistance for each flight scene, and marking excitation intensity and discriminability parameters of each flight scene for each capability; short-time rapid pre-teaching is implemented according to a standardized teaching script before evaluation; during evaluation, a multi-capability probe is embedded in a flight scene, evaluation data is collected, a capability estimation value is updated based on a capability-scene matching model, a subsequent flight scene is adaptively selected or scene parameters are adjusted according to the capability estimation value to generate a personalized examination path, a comprehensive score is calculated, and a four-dimensional capability profile map is output. The objectivity of the evaluation result can be improved.

The invention provides an anti-vibration and anti-airflow inertial sensor module suitable for a flight scene, and relates to the technical field of inertial measurement. The inertial sensor module comprises a rectangular core body, twelve sensor chips, a digital circuit and a damping structure, wherein each sensor chip comprises an accelerometer and a gyroscope; the rectangular core body, the twelve sensor chips and the digital circuit are nested in the damping structure and are not in contact with the interior of the damping structure; the rectangular core body comprises six outer side surfaces, and for each outer side surface of the rectangular core body, two sensor chips are arranged on the outer side surface to obtain X-axis data and Y-axis data of the position of the outer side surface; the digital circuit is arranged in a preset area on the outer side face of the rectangular core body and used for determining acceleration data and rotation acceleration data of the position where the inertial sensor module is located in a three-dimensional space according to the X-axis data and the Y-axis data obtained by the twelve sensor chips. The anti-vibration performance and the measurement precision of the inertial sensor module can be improved.

The invention discloses a non-ideal environment-oriented adaptive closed-loop flight control ICD protocol simulation verification system and method, and belongs to the technical field of software information security analysis, and the non-ideal environment-oriented adaptive closed-loop flight control ICD protocol simulation verification system comprises a basic modeling and simulation module which is used for constructing a flight control system virtual prototype; the non-ideal environment injection module is used for injecting disturbance models of a physical layer, a link layer and a node layer into the virtual bus and the node; the dynamic scene driving module is used for dynamically generating ICD data according to the flight scene data; the intelligent monitoring and analysis module is internally provided with an anomaly detector based on a graph neural network and outputs a structured diagnosis report; and the self-adaptive optimization guide module is internally provided with an optimization strategy engine based on reinforcement learning, generates optimization suggestions and selectively and automatically adjusts configuration to initiate re-verification. The reliability and the research and development efficiency of a flight control system are remarkably improved by simulating a real operation environment in a high-fidelity manner in a design stage.

The invention relates to the technical field of flight models, in particular to a method and device for building a flight scene physical model, and the device comprises a frame, a lower supporting plate, a sand table plate, an elevator, a flight simulation suite, and an upper supporting plate. The flight simulation suite comprises a suspension device, a driver and an aircraft model; the suspension device and the driver are attracted through magnetic force and installed on the lower supporting plate and the upper supporting plate in a clamped mode, the aircraft model is upwards connected to a winding mechanism of the suspension device through a silk thread, the height of the aircraft model in the Z direction is adjusted by controlling the winding mechanism to wind and unwind the suspension silk thread, and the driver drives the suspension device to move in the plane. Therefore, the plane space positions of the aircraft models in the X direction and the Y direction are changed, the lifter is transferred between the upper supporting plate and the lower supporting plate, and the multiple sets of aircraft models can be stored or added into display activities.

The invention discloses an intelligent pilot post competency monitoring device and method. The device comprises a monitoring cloud platform display unit, a display unit, a control unit, a data collecting and processing unit and a post competency system. The pilot post competency assessment method effectively solves the technical problems of discontinuous monitoring, non-objective assessment and non-intercommunication of data in the existing pilot post competency assessment method. According to the method, the strong relevance between training investment and efficient improvement of safety performance is improved, low-cost and efficient pilot post competency evaluation is realized, the method adapts to variable requirements of future flight scenes, and a scientific, accurate and flexible selection and evaluation solution is provided.

The invention discloses a multi-source data fusion positioning monitoring method and system for a low-altitude aircraft. The method comprises the following steps: collecting multi-source data, carrying out abnormal data processing, and then carrying out space-time alignment; dividing the Beidou absolute position data into different scene regions, preliminarily fusing the aligned multi-source data in the scene regions, configuring basic weights of the multi-source data in different flight scenes, and calculating dynamic weights of the multi-source data in real time based on equivalent variances of the multi-source data to obtain real-time comprehensive weights of the multi-source data; and performing state space fusion based on the preliminary fusion positioning information and the real-time comprehensive weight of each piece of multi-source data. According to the method, the pain point that a single data source is easily interfered, drifted or invalid is solved, and meanwhile, the real-time weight of each multi-source data can be intelligently optimized according to a task environment, real-time data quality and a task instruction, so that a fusion positioning result is corrected, and reliable monitoring in dynamic tasks such as emergency search and rescue, logistics transportation and the like is guaranteed.

The invention discloses a predetermined time adaptive control method for a wing-riding separation variant gliding aircraft, and the method specifically comprises the following steps: firstly, constructing a time scale function, and effectively avoiding the problems of unlimited increase of control quantity and singularity; secondly, designing a preset time sliding mode surface based on the time scale function; thirdly, providing a neural network parameter identification strategy, and converting a system identification problem into a parameter estimation problem so as to adapt to a flight scene with uncertain aerodynamic parameters; and finally, designing a preset time self-adaptive sliding mode controller, and realizing convergence in specified time under the condition of ensuring that the system state is not dependent on an initial condition. The adaptive gain of the controller can be dynamically adjusted according to the system environment, jitter can be inhibited, tracking errors can be reduced, and the influence caused by centralized uncertainty can be weakened; in conclusion, the control method not only realizes accurate attitude tracking, but also achieves preset time convergence characteristics irrelevant to initial conditions and control parameters, and has excellent quick responsiveness, stability and robustness.

The present invention relates to a method for UAV Dubins path planning based on piecewise linear mixed-integer programming. The method includes: constructing a speed control model according to the flight scenario of the UAV. The speed control model includes: a total time minimization function and a mixed-integer linear programming model. Constructing piecewise linear constraints for the flight path according to the pose coordinates of the UAV at the current moment. Inputting the speed variable of the UAV at the current moment into the total time minimization function according to the piecewise linear constraints for quadratic constraints to obtain the total time. Planning the Dubins path of the UAV through the mixed-integer linear programming model according to the total time. Using this method can improve the path planning efficiency and accuracy of the UAV in complex environments.

The invention discloses a stealth aircraft maneuvering avoidance method and system under a quasi six-degree-of-freedom assumption, and the method comprises the steps: S100: obtaining an azimuth angle theta corresponding to an RCS peak in an RCS curve, and determining a high RCS region of an azimuth angle domain as [theta-w / 2, theta + w / 2], where w is the width of the RCS peak; s200, determining a high RCS flight path segment on the initial flight path; s300, setting a first turning point, a second turning point and a third turning point, and taking the first turning point, the second turning point and the third turning point as sequentially adjacent track control points to generate a maneuvering track; and S400, the aircraft flies along the initial flight path, flies along the maneuvering flight path when reaching the first turning point, and returns to the initial flight path and continues to fly along the initial flight path when reaching the third turning point. According to the invention, the exposure risk of the aircraft in a real three-dimensional flight scene can be reduced.

The invention discloses a moire dynamic rendering method and device for an aircraft simulator instrument, and belongs to the technical field of aircraft simulators, data processing and artificial intelligence. The method comprises the following steps: based on real-time data of an aircraft simulator, utilizing a self-attention mechanism to generate a dynamic weight of each physical quantity during moire rendering; utilizing the dynamic weight of each physical quantity to calculate characteristic parameters of moire; and carrying out moire rendering and display on an instrument of the aircraft simulator by utilizing the characteristic parameters. According to the method, moire rendering can be carried out in an instrument, the moire is used for representing the flight scene state, and the multi-step abstract conversion cost of mapping a numerical value into a physical state and mapping the physical state into a flight situation by a pilot can be eliminated by using visual features; according to the invention, a pilot can instantly sense the change of the flight environment from the instrument, and in an emergency flight state, the pilot can quickly and accurately make a flight decision by using intuitive feeling, so that the user experience can be improved.

The embodiment of the disclosure provides a large-scale low-altitude flight safety dynamic calculation parallel scheduling method and device, which is applied to the technical field of low-altitude airspace safety management. The method comprises the following steps: generating a geographical space grid by griding a low-altitude airspace based on a geographical coordinate system, and performing grid coding on each grid; dividing each grid into a calculation area and a buffer area; the calculation area is a core operation unit, which is used for real-time dynamic calculation and parallel scheduling of large-scale low-altitude flight safety, and the buffer area is a data interaction hub, which is used for independently loading and caching grid data of adjacent areas; according to the position information and the safety envelope range of the aircraft, the aircraft is matched to the calculation area to which the aircraft belongs, and the aircraft data is routed to the corresponding buffer area. Based on this, the calculation decoupling between the partitions can be realized through the cooperative work of the calculation area and the buffer area, the dynamic calculation efficiency of the low-altitude flight safety is significantly improved, and therefore a real-time and efficient safety calculation solution is provided for a high-density low-altitude flight scene.