117 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 a data driving-based route flight safety risk assessment method. The assessment method comprises the steps of obtaining historical QAR data and performing data preprocessing; constructing a flight safety risk index system which comprises a plurality of index events influencing the flight safety in the predetermined flight scene, dividing the index events into a plurality of risk levels, and setting a corresponding risk index coefficient for each risk level; carrying out standardization processing on the risk index coefficient; calculating information entropies of the plurality of index events, and determining weight coefficients of the plurality of index events according to an entropy weight method; and for the flight to be assessed, calculating a flight safety assessment risk value of the flight. According to the method, the QAR data can be comprehensively utilized to establish a safety index monitoring model of complete team operation, an airline and other related parties can be helped to more comprehensively grasp the safety risk of an aircraft in the course operation, and the safety risk in the course operation can be objectively and comparatively evaluated.

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 electronic fence positioning method and system applied to low-altitude flight control, and belongs to the technical field of data analysis. According to the embodiment of the invention, the low-altitude flight fence space is constructed and the flight early warning quantitative characteristics are set, so that the fine management basis of low-altitude flight is realized. The electronic fence monitoring route and the space-time cross sensing data thereof are acquired to determine the local management and control area, so that airspaces with different functions and risks can be accurately focused, and the management and control efficiency is improved. Alarm data of different monitoring routes are determined according to flight early warning quantitative characteristics, differences of tasks of different routes in the same area are considered, the method can adapt to complex and diversified flight scenes, and misjudgment is reduced. And after the trigger condition is responded, the monitoring risk decision feature is determined and the flight is controlled, so that the abnormal condition of the low-altitude aircraft can be handled in time in a targeted manner, the safety and orderliness of the low-altitude flight are effectively ensured, and potential threats to specific areas (such as airports and activity sites) are avoided.

The invention discloses an unmanned aerial vehicle dynamic environment real-time obstacle avoidance method and device based on visual auxiliary B-spline trajectory optimization, and particularly relates to the field of unmanned aerial vehicle trajectory optimization. Comprising the steps of obtaining a depth image of a flight scene, and establishing a voxel map based on the depth image; recognizing an obstacle in the depth image, and determining the position of the obstacle; generating an initial trajectory of the unmanned aerial vehicle, parameterizing the initial trajectory, and determining a control point of the initial trajectory; taking the intersection part of the initial track and the obstacle as a collision section, and forming a collision set by all control points in the collision section; finding a replacement path in the voxel map by using A *; searching a path point corresponding to each control point in the collision set in the replacement path for replacement to obtain an initial obstacle avoidance trajectory; and optimizing the trajectory by adopting an optimization algorithm to obtain an obstacle avoidance trajectory. The problem that in the prior art, the requirement for computing resources is high is solved.

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 discloses an aircraft fly-by-wire teaching simulation system, which comprises a flight operation table, a simulation computer and a comprehensive display system, the flight operation platform is in communication connection with the simulation computer; and the comprehensive display system is electrically connected with the simulation computer, and the comprehensive display system is used for displaying teaching contents of the fly-by-wire control system of the airplane, virtual flight scenes and virtual cabin instruments. A virtual reality technology is adopted, and technical means such as computer simulation and system modeling are comprehensively adopted, so that flight trainees can more intuitively understand theoretical knowledge such as basic composition, working principle, control rate calculation and control instruction response of an aircraft telex system, and the aircraft telex system can be simulated through control simulation training. Feedback of each functional module of the aircraft telex system to an aircraft control instruction is sensed in an immersive manner, the telex system theoretical knowledge level of a pilot can be improved, meanwhile, the actual installation loss is reduced, and economy and safety are achieved.

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.

The invention relates to the technical field of civil aircraft cockpit design, in particular to a new aircraft type workload assessment airworthiness conformity verification method based on an independent assessment strategy. According to the method, a flight scene set is defined, a verification scene set, a reference scene set and a comparison scene set are selected from the flight scene set, and a tested unit is selected according to the background and experience of the unit. Objective data and subjective data collected by the test flight and the simulator test are used for analyzing the fidelity and rationality of the workload. And comprehensively analyzing multi-party data to confirm whether the workload of the flight crew is safe and acceptable, thereby providing comprehensive evaluation and description for the seaworthiness of a new aircraft type.

The invention provides a method and a system for generating mass unmanned aerial vehicle flight simulation data. The method comprises the following steps: S101, collecting data; s102, recording and storing data; s103, processing the data; s104, carrying out data modeling; s105, generating data; s106, outputting the data; and S107, adjusting the data in real time. According to the invention, various virtual weather conditions, terrains and obstacles can be created, and different flight scenes and coping strategies can be tested; detailed flight data including position, speed and the like of the unmanned aerial vehicle are provided to help comprehensive understanding of flight behaviors of the unmanned aerial vehicle; data modeling is carried out through the combination of three loss functions, the performance and training speed of a data model are improved, and sample features and convergence gradients are rapidly extracted.

The invention discloses a fleet behavior prediction method based on a potential function and a Bayesian network and a medium. The fleet behavior prediction method comprises the following steps: establishing a topological relation of an unmanned aerial vehicle fleet; constructing an unmanned aerial vehicle potential energy field function and external regulation and control force; setting an adaptive obstacle avoidance distance, and establishing collision avoidance constraints, planning track continuity constraints and dynamics feasibility constraints; dividing the cluster by using a K-means algorithm; constructing a dynamic Bayesian network, and predicting a cluster behavior; setting a behavior prior probability; preprocessing the data to obtain required information; dynamically adjusting the weight of each behavior probability; and updating and normalizing the behavior probability. According to the method, cluster division and behavior identification results can be given under discrete data input (single moment) and continuous data input (multi-moment) conditions, good convergence time and stability are achieved, the requirement for stable output of a behavior identification algorithm in a flight scene is met, and meanwhile the behavior identification efficiency is improved. And algorithm identification results in a specific flight scene can be traced.

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.

This document relates to a method for detecting an aircraft in the landing phase at an aerodrome, in particular uncontrolled, the aerodrome comprising at least one runway (V1) having at least two ends (E1, E2), and by means of an optronic system comprising at least one primary camera (18, 18a, 18b, 18c, 18d) whose primary angular aperture (Pa, Pb, Pc, Pd) is directed towards a first end (E1) of the runway (V1), the method comprising determining the presence or not, in an end-of-approach region (104) or end-of-departure region, of an object compatible with an aircraft, and where appropriate analyzing the path of the detected object by comparing its current position with a history of the positions of objects detected in an analysis zone (104).

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 utility model discloses an intelligent deformation double-wing-head vertical take-off and landing aircraft, and relates to the technical field of aircrafts. The intelligent transformation double-wing-head vertical take-off and landing aircraft comprises an aircraft body, vertical take-off and landing mechanisms, folding and unfolding mechanisms and at least two wing heads, and the vertical take-off and landing mechanisms are arranged on the two sides of the aircraft body and used for driving the aircraft body to take off and land vertically; the folding and unfolding mechanism is arranged at the top of the machine body; the at least two wing heads are separably arranged on the folding and unfolding mechanism, the at least two wing heads are distributed at intervals in the vertical direction, and the folding and unfolding mechanism is used for folding or unfolding the wing heads. According to the intelligent transformable double-wing-head vertical take-off and landing aircraft, the aircraft body takes off and lands vertically, so that the aircraft does not need to slide during take-off, and the requirement for a flight site is not high; when the aircraft breaks down, the wing heads can provide lift force for the aircraft body and buffer the aircraft body, so that the aircraft body slowly descends in a gliding state, safe falling of the aircraft body is guaranteed, and direct crash of the aircraft body is avoided.

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 relates to the technical field of information technology and environmental perception, and discloses an unmanned aerial vehicle blind landing area autonomous detection method based on monocular vision, which can perform data processing only by using optical video data acquired by monocular vision without relying on landmark identification and ground priori knowledge. An elevation model of a flight scene is established, a three-dimensional terrain is analyzed autonomously, and various terrain factors are comprehensively considered to determine a safe landing area.