16534 results about "Flight vehicle" patented technology

The invention discloses a low-altitude integrated management system based on a grid digital twin model and an intelligent algorithm, and relates to the technical field of low-altitude aircrafts. The method comprises the following steps: multi-source sensing data fusion and grid coding are carried out, and an airspace environment space-time database is constructed; constructing an airspace environment space-time database to carry out three-dimensional subdivision on the airspace; generating a multi-scale grid twinborn body, and associating a corresponding attribute for each level of grid according to a management requirement; generating an airspace risk thermodynamic diagram in the future 5-30 minutes; establishing a grid state change triggering rule; and generating a multi-target optimal path set by taking the grid navigation cost as a weight. According to the invention, by combining the real-time operation data of the low-altitude aircraft, the meteorological environment and other data with the artificial intelligence algorithm and the navigation rule base, the airspace traffic flow is analyzed, the flight plan is optimized, the flight route is intelligently distributed, the automatic route setting of any two points is realized, and the response speed of the low-altitude flight service system and the processing capability of various flight data are improved.

The invention belongs to the technical field of three-dimensional airspace management, and provides a three-dimensional airspace rasterizing management method and system based on multistage dynamic division, and the method comprises the steps: dividing height layers for a three-dimensional airspace, carrying out the rasterizing division of the three-dimensional airspace in each height layer, and carrying out the rasterizing division of the three-dimensional airspace; a live-action three-dimensional model of a three-dimensional airspace of a target area is constructed through multi-source data fusion, passable grids are marked, selection priority values of the passable grids are calculated through multi-source data fusion, when an aircraft is about to enter the three-dimensional airspace, route dynamic priority values of the grids are initialized, and a basic planning route of the aircraft is generated. Detecting the collision risk of the aircraft on the grids in the basic planning route, dynamically adjusting the route dynamic priority value, updating to obtain the actual planning route of the aircraft, monitoring the space between the aircrafts in the three-dimensional airspace in real time, and judging whether the collision risk exists between the aircrafts; and if there is a collision risk signal, triggering an aircraft obstacle avoidance and path re-planning strategy, and dynamically adjusting and updating the grid side length.

The invention discloses a low-altitude aircraft take-off and landing platform site selection optimization method. The method comprises the steps that real-time dynamic data and static GIS data including urban traffic flow data, meteorological data, landform data, environment data and POI data are acquired; constructing an urban three-dimensional digital model according to the static GIS data, and constructing a digital twin model according to the three-dimensional digital model and the real-time dynamic data; constructing environment constraint conditions and safety constraint conditions of candidate take-off and landing platform positions in the digital twin model; performing multi-objective optimization on the digital twin model through a particle swarm optimization algorithm, and generating an optimal candidate take-off and landing platform site selection scheme set meeting environment constraint conditions and safety constraint conditions; and dynamically updating the digital twin model according to real-time data feedback, and dynamically adjusting the site selection scheme of the take-off and landing platform. According to the method, the virtual city model is constructed through the digital twin, an accurate simulation environment and real-time feedback are provided for site selection optimization, and the accuracy and feasibility of a site selection scheme are improved.

The invention discloses an aircraft flow field prediction method and system of a multi-region physical drive neural network, and the method comprises the steps: constructing a continuous region mask and high-dimensional physical parameter sampling system, carrying out the global sampling of high-dimensional physical parameters through employing a Latin hypercube sampling method, and carrying out the space division through combining with a KMeans clustering algorithm; inputting the space coordinates, the continuous area mask, the wall surface distance and the physical condition parameters into an AMPD model, and generating a boundary layer mask, an eddy current mask and a physical residual error; inputting the boundary layer mask and the eddy current mask into a physical constraint driven loss function system, and establishing a multi-target residual minimization loss function for training an AMPD model; based on the multi-target residual error minimization loss function and the physical residual error, training an AMPD model by adopting a course learning training strategy; wing surface flow field reconstruction is carried out through the trained AMPD model, aircraft flow field prediction is completed, and high-precision and high-efficiency intelligent prediction of wing streaming is achieved.

The invention relates to the technical field of adaptive control, in particular to an unmanned aerial vehicle adaptive flight control system and method based on multi-modal fusion, and the system comprises an airspace analysis module, a signal screening control module, a state recognition module, a parameter matching module and a feedback adjustment module. According to the method, through a multi-source task data analysis and trend signal screening mechanism, the accuracy and real-time performance of path planning and adjustment are improved, through combination of channel fluctuation trend identification and abnormal state locking, the stability of sensing data input is enhanced, and the identification accuracy of an unbalance state is analyzed and optimized by using attitude change direction consistency. The attitude error trend and control parameter segment similarity comparison mode is adopted, parameter matching has dynamic responsiveness, control weight configuration is adjusted through collaborative analysis of the path direction component and the attitude adjustment trend, rhythm self-adaptive adjustment of aircraft control output is achieved, and the control precision of the aircraft is improved. And the dynamic adjustment capability and the safety redundancy response capability of flight control are enhanced.

The invention relates to the technical field of low-altitude aircraft navigation, and discloses a low-altitude aircraft track real-time planning method and system. The system comprises a flight situation awareness module, a track constraint calculation module, a real-time track planning module, a conflict prediction module and a track dynamic correction module. The flight situation sensing module generates a flight situation matrix through multi-source data fusion; a track constraint calculation module extracts static obstacle contours and dynamic obstacle tracks according to the static obstacle contours and the dynamic obstacle tracks, and generates a multi-dimensional track constraint set in combination with aircraft performance parameters; the real-time flight path planning module builds a three-dimensional flight path search domain by using an adaptive space division technology, and iteratively solves an optimal flight path sequence by using an intelligent search algorithm; the conflict prediction module combines the real-time dynamic obstacle trajectory to calculate the space-time proximity, and generates a conflict early warning map; and the track dynamic correction module re-draws an obstacle avoidance constraint area according to the map, and triggers local track correction. The system improves the comprehensiveness, real-time performance and safety of flight path planning, and guarantees the stable operation of the low-altitude aircraft.

The invention relates to the technical field of data management, in particular to a low-altitude economic flight data management method and system based on edge computing, and the system comprises edge computing nodes deployed on an aircraft and a ground base station, a sensor for collecting multi-source data, a module for preprocessing the data, and an annular data queue caching mechanism. The system comprises a composite biological feature code generation module, a distributed storage edge node, an abnormal fluctuation feature extraction module, an equipment health state and trend analysis and calculation module, a virtual power supply manager, a main power supply, a standby power supply, an abnormal behavior monitoring module and a defense strategy library. After preprocessing and caching, feature codes are generated and encrypted distributed storage is carried out, dynamic resource adjustment is realized through state evaluation and a digital twinning and optimization algorithm, a neural network is utilized to manage a power supply, data security is guaranteed in combination with abnormal monitoring, and a whole-process management system is formed.

The present disclosure relates generally to flight control of electric aircraft and other powered aerial vehicles. In one embodiment, a method is disclosed, comprising: receiving a descent rate command from a pilot input device, determining a proximity of each propeller of at least two propellers to a vortex ring state; and controlling the aircraft's descent rate to be less than the commanded descent rate when at least one of the at least two propellers is within a first threshold proximity to the vortex ring state.

The invention discloses an aircraft surface flow field intelligent reconstruction method and system based on multi-source data fusion, and belongs to the technical field of flow field intelligent prediction. Acquiring multi-source sensor data on the surface of the aircraft, and constructing a multi-source data set; a flow field reconstruction model based on Transform is constructed; a multi-source data set is adopted to train a flow field reconstruction model; and outputting an aircraft surface flow field reconstruction result based on the trained flow field reconstruction model, and visually displaying the aircraft surface flow field reconstruction result. Based on the strong nonlinear mapping capability of the deep learning model, through training of a large amount of sensor data, high-order correlation characteristics among pressure, heat flow and friction resistance stress can be automatically extracted, through fusion of sparse discrete data, a continuous physical field of the whole surface of the aircraft can be effectively reconstructed, and the method is suitable for the aircraft. The limitation of a traditional analytical model in dealing with a strong nonlinear problem is broken through, and the precision and reliability of aircraft flow field prediction are improved.

The invention discloses a group aircraft collaborative perception decision control method and system in a complex high-dynamic environment, and relates to the technical field of cluster aircrafts, and the method comprises the steps: obtaining the performance parameters of a target group aircraft, building a task scene model in combination with the geographic information of a target task area, and building a target optimization function. Iteratively optimizing an aircraft and task allocation scheme through a graph neural network algorithm, outputting a task allocation result of each aircraft, and then according to a flight path constraint condition, analyzing a matching degree between a flight cost generated in a flight path and a corresponding task demand; and generating a globally optimal task allocation and flight path cooperation scheme. Task distribution and path planning are dynamically adjusted by sensing task requirements and environment changes in real time and combining performance parameters of all aircrafts, and compared with a traditional static method, the method can quickly respond to dynamic changes of task scenes, and task distribution reasonability and path planning effectiveness are ensured.

A docking system is disclosed for an unmanned aerial vehicle (UAV). The docking system includes a base station that is configured to receive the UAV and a pedestal that is configured to support the base station in an elevated position. The pedestal defines an interior space that is configured in correspondence with an outer contour of the base station such that the base station is positionable within the pedestal to protect the base station during nonuse.

The invention discloses a macroscopic progressive damage intelligent evaluation method driven by multi-scale microscopic damage observation, and the method comprises the steps: selecting a plurality of representative materials to carry out a microscopic mechanical test, and observing the damage evolution process under different loading conditions through MicroCT to obtain a microscopic mechanical test result; simulating a crack state based on a micro-mechanical model to reproduce a micro-mechanical test result, and performing material parameter correction of simulation calculation by taking test data as a constraint to obtain a micro-numerical modeling calculation result; and determining a representative volume element (RVE) scale based on a microscopic numerical modeling calculation result, and describing a macroscopic damage behavior by utilizing a continuous damage mechanical model so as to obtain evolution relation data of a macroscopic damage variable, stress and strain. According to the method, the damage behaviors of metal and composite material structures under complex working conditions can be accurately predicted, and the safety and reliability of aircraft structures can be improved.

A craft includes a hull, a wing, a hydrofoil, and a control system. The wing is configured to generate upwards aero lift as air flows past the wing to facilitate wing-borne flight of the craft. The hydrofoil is configured to generate upwards hydrofoil lift during a first mode of operation as water flows past the hydrofoil to facilitate hydrofoil-borne movement of the craft through the water. While the craft is hydrofoil-borne, the control system is configured to determine the upwards aero lift generated by the wing. The control system is further configured to control the hydrofoil to generate downwards hydrofoil lift to counteract the upwards aero lift generated by the wing that maintains the hydrofoil at least partially submerged in the water while the determined upwards aero lift is below a threshold lift.

A base station may include a base body, a landing pad, and a base station opening and closing structure. The base station opening and closing structure may include an open state and a closed state. In the open state, the base station opening and closing structure may provide a landing space for an aerial vehicle to land on the landing pad, and in the closed state, the base station opening and closing structure may provide an accommodating space to accommodate the aerial vehicle. During a process of the base station opening and closing structure transitioning from the open state to the closed state, the base station opening and closing structure may be configured to propel some blades of the aerial vehicle partially protruding out of the accommodating space to fold or retract so as to accommodate the aerial vehicle within the accommodating space.

An introduced autonomous aerial vehicle can include multiple cameras for capturing images of a surrounding physical environment that are utilized for motion planning by an autonomous navigation system. In some embodiments, the cameras can be integrated into one or more rotor assemblies that house powered rotors to free up space within the body of the aerial vehicle. In an example embodiment, an aerial vehicle includes multiple upward-facing cameras and multiple downward-facing cameras with overlapping fields of view to enable stereoscopic computer vision in a plurality of directions around the aerial vehicle. Similar camera arrangements can also be implemented in fixed-wing aerial vehicles.

The present invention relates to the field of machine learning, and relates to a system for recognizing aircraft maneuvers on the basis of deep learning. The system is aimed at tasks of understanding tactical actions in air combat games, and performs operations of detection, recognition, analysis, etc., for independent and continuous maneuvering actions of an aircraft. The recognition and analysis system comprises a data collection module, a data pre-processing module, a data normalization module, a model training module, a model prediction module, and a result visualization module. Aircraft maneuver trajectory data is collected by means of an aircraft maneuver trajectory collection apparatus in a sample data collection module, and then the collected aircraft maneuver trajectory data is preprocessed in the data preprocessing module and the data normalization module, so that the aircraft maneuver trajectory data meets requirements during model training and prediction. For new continuous-maneuver samples, each independent maneuver in the samples and corresponding start and end time are predicted by using a continuous-maneuver recognition model which is obtained by means of training; and finally, the result visualization module generates recognition visualization results of predicted maneuver samples.

The invention provides a low-altitude airspace dynamic decision-making system based on a multi-modal large model, and relates to the technical field of low-altitude airspace traffic management.The low-altitude airspace dynamic decision-making system is characterized in that a first thermal disturbance coverage map, a second shear wind high-risk area map, a third turbulence risk drainage basin map and a fourth sudden obstacle prediction area map are generated by a map construction module; the aircraft can judge whether the aircraft enters the dangerous area in real time and assess the safety of the flight path according to the superposed risk value. When the superposition risk value # imgabs0 # of the flight path section passing through the jth area in the first original flight path of the ith aircraft is in a middle risk section, the system generates a first-level avoidance strategy, and the height, the speed and the course of the aircraft are adjusted; and when the superposed risk value # imgabs1 # is a high-risk section, the system generates a secondary avoidance strategy, and bypasses a high-risk area by remarkably increasing the flight height and reducing the speed.

The invention relates to an aviation big data intelligent analysis method based on trajectory anomaly detection, and belongs to the technical field of aviation data processing. The method comprises the following steps: acquiring operation state data and flight parameters of an aircraft, and extracting aerodynamic parameters of an aircraft type and deviation data corresponding to horizontal navigation and vertical navigation; carrying out stream batch integrated processing on the data through a real-time processing engine, and carrying out space-time reference unification to obtain standardized data; performing data cleaning, abnormal point correction and missing value interpolation on the standardized data; performing pneumatic-performance correlation analysis after data processing, and quantifying the influence of parameter deviation on the climbing performance through a dynamic response model to obtain a strong correlation coefficient; performing coupling analysis through the climbing rate attenuation prediction model to obtain an aircraft climbing performance anomaly prediction result; and generating a correction instruction according to the persistent climb rate anomaly of the performance anomaly prediction result. The efficient and accurate analysis of the aviation big data is realized, and the safety and reliability of the operation of the aircraft are effectively improved.

The invention discloses a multi-channel meteorological risk early warning information self-adaptive targeted publishing system and method based on low-altitude flight, and solves the problems that existing low-altitude meteorological early warning is low in temporal-spatial resolution, single in risk identification and insufficient in information pertinence. The system integrates air-based, space-based, foundation, social and topographic data, and generates a low-altitude meteorological dynamic database through fusion; micro-scale risks such as wind shear are extracted and graded through an AI model, a digital twin simulation response is constructed in combination with the state of the aircraft, and a risk grid model and coordinates are output; calculating a comprehensive risk index, generating evasion guidance, converting into multi-role early warning information, generating a three-dimensional thermodynamic diagram, and finally adaptively selecting a publishing channel according to user roles, terminals and network states. According to the invention, the accuracy and timeliness of low-altitude flight meteorological risk early warning are improved, and the safety of low-altitude flight is guaranteed.

The invention relates to an aircraft surface damage area segmentation method based on adaptive multi-modal fusion, belongs to the technical field of nondestructive testing, and solves the technical problem of low segmentation precision of an aircraft skin curved surface falling damage area. Obtaining a grey-scale map, a depth map and a point cloud map of the aircraft surface damage skin; synthesizing the grey-scale map, the depth map and the point cloud map, and performing preprocessing, feature extraction, feature conversion, image display and projection and weighted fusion on the synthesized grey-scale map, the synthesized depth map and the synthesized point cloud map to obtain a total edge probability map of skin damage; and performing targeted screening, continuity detection, connectivity detection, convex hull construction, marking and skin falling damage area division on the total edge probability graph. The method is used for aircraft skin health monitoring, maintenance guarantee and operation and maintenance.

The invention relates to an autonomous energy-saving soaring flight path planning method for a small-sized low-cost aircraft, belongs to the technical field of aircraft trajectory planning, solves the problem of low-cost wind field energy acquisition of the small-sized low-cost aircraft in the prior art, and comprises the following steps: S1, configuring a sensor for the aircraft, and measuring through the sensor to obtain observation parameters; s2, establishing a state vector of the aircraft; s3, establishing an aerodynamic force model, introducing a dynamic equation and a state transition equation, and performing accurate modeling on aerodynamic force; s4, performing multi-source data fusion by adopting extended Kalman filtering, establishing an extended Kalman filter of a nonlinear system, and executing real-time wind vector high-precision sensing; and S5, performing global wind field modeling, estimating a wind field environment, and performing energy-obtaining flight path planning to obtain an optimal energy-obtaining soaring flight path planning scheme.

A land to air vehicle with lower extendable wings and upper extendible wings, a body, a front hood fan, a rear fan and wheels. The wheels include wheel bases, exterior tires circumscribing the wheel bases, and a fan wheel assembly within each wheel base. The wheel assembly includes extendible fans, a rotor outer rim with teeth for holding a stator inner rim, a propeller shroud configured to house a variety of fan blades, at least one fan within the propeller shroud, a hinge fixedly attached to the outer rotor rim and the propeller shroud, a wheel motor platform and a wheel motor. The hinge is structured to allow the propeller shroud with fans to extend distally from the wheel base interior to navigate the land to air vehicle.

The invention relates to the technical field of risk prediction, in particular to a collision risk prediction method of a low-altitude aircraft in high-density flight in a complex environment, which comprises the following steps: acquiring a point cloud through a three-dimensional laser radar, clustering to generate an obstacle trajectory, matching the trajectory to identify a disturbance characteristic segment, calculating a deviation angle by combining a path vector to generate a disturbance frequency graph, and calculating the collision risk of the low-altitude aircraft. And extracting a parameter modeling dynamic safety interval, and fusing multiple factors to evaluate a collision risk level. According to the method, obstacle trajectory topology is constructed through combination of three-dimensional laser radar point cloud time window division and density clustering, sudden change features are identified through trajectory similarity matching, a Gaussian kernel dynamic safety envelope is generated through combination of course offset statistics and included angle operation, and a self-matching threshold value is established through normalization parameters and radial basis weighting. The method improves the high-density flight collision prediction precision, enhances the weather and obstacle heterogeneity matching capability, reduces the misjudgment early warning delay, and achieves the multi-variable flight situation collaborative analysis.

The invention discloses a method for realizing multi-link switching and redundancy transmission of a low-altitude aircraft and a medium, and belongs to the technical field of low-altitude aircrafts, and the method comprises the steps: constructing communication links between the low-altitude aircraft and a ground control center, and dynamically detecting the quality index of each communication link; calculating a comprehensive quality evaluation value of the communication link according to the detected quality index; determining the state of the communication link according to the comprehensive quality evaluation value of the communication link; and performing multi-link switching and redundancy transmission according to the communication link state. According to the method, link state grading, dynamic strategy switching and key data redundancy guarantee are achieved, communication stability and system resource optimization are both considered, and robustness and real-time performance of aircraft communication are improved.

The invention discloses an aircraft cabin environment personalized adjustment method based on sentiment analysis, and the method comprises the steps: collecting the facial expression, voice waveform and environment parameters of a passenger in real time through a cabin multi-source sensor, and generating a standardized physiological signal matrix and anonymized voice features through noise reduction and feature extraction; inputting the physiological signal matrix and the voice features into a pre-trained deep learning model, outputting an emotion index and a classification label, and updating model parameters through a federal learning framework; dynamically generating temperature, humidity and oxygen concentration adjusting instructions and dynamic weights by adopting a fuzzy reasoning system in combination with the emotion indexes and passenger preset preferences; environment adjustment is executed through a closed-loop control system, and environment parameter errors are fed back; synchronously updating a fuzzy inference system rule base and deep learning model parameters by utilizing reinforcement learning in combination with environmental parameter errors and emotion index changes, and completing optimization of a closed loop; according to the invention, real-time dynamic regulation and control and continuous adaptation optimization of the personalized cabin environment can be realized.

The invention relates to a low-altitude flight dynamic safety guarantee method and system based on airspace gridding, and relates to the technical field of low-altitude airspace management, and the method comprises the steps: constructing a gridding airspace; on the basis of the gridding airspace, labeling network attributes for each grid unit in real time and evaluating a low-altitude safety state; according to network attributes, extracting a grid center point of an airspace grid which is evaluated to be in a low-risk range as a safe flight point, and dividing the gridding airspace into a plurality of safe triangular grids; and according to the predicted trajectory of the aircraft, selecting a candidate point with the lowest risk cost from adjacent triangles of the safety triangular mesh, and introducing a risk cost function to dynamically adjust the distribution of flight path points. According to the invention, the flight path of the aircraft group can be dynamically adjusted according to the environment change, and the stability and safety of cluster task execution are improved.

The invention discloses a curved surface quadrilateral dominant grid generation method, device and equipment for an aircraft and a storage medium, and relates to the technical field of computational fluid mechanics, and the method comprises the steps: creating a surface unstructured grid based on surface grid generation parameters of a CAD file of the aircraft, carrying out the conformal mapping of the surface unstructured grid to a parameter domain plane, and calculating the vertex compression ratio of each grid vertex; selecting the current starting point with the minimum compression ratio and the maximum to-be-compared vertex in the adjacency list, and determining the current to-be-processed vertex with the minimum compression ratio in the adjacent vertexes and the advancing direction; determining to-be-processed discrete points according to the current compression ratio and the coordinate information to generate a current isoline, if the current compression ratio does not exceed a threshold value, iteratively updating the current starting point, and skipping to the step of determining the current to-be-processed vertex again; the discrete points are projected to the outer-layer isolines according to the isoline sequence, and the curved-surface quadrilateral dominant grid is generated based on the distance proportion between each discrete point and the projection point, so that the generation efficiency of the curved-surface quadrilateral grid is improved.

The invention provides a vortex aircraft detection signal processing method and system based on optical fiber sensing. The method comprises the following steps: firstly, acquiring an original micro-vibration signal; secondly, performing environment self-adaptive cooperative processing on the original micro-vibration signal to generate a self-adaptive micro-vibration signal; converting the generated self-adaptive micro-vibration signal into a spatio-temporal evolution sequence based on eddy current physical characteristics; then analyzing the spatio-temporal evolution sequence to output a type discrimination result of the vortex aircraft; and finally, generating a customized detection report based on the type discrimination result. According to the technical scheme provided by the invention, the detection and identification capability of a low-altitude low-speed small target in a complex environment is improved, and reliable detection and identification of the vortex aircraft in the complex environment are also realized.

The invention discloses an unmanned aerial vehicle wind resistance control method and system based on wind field sensing, and relates to the technical field of aircraft wind resistance control and wind field sensing. The method comprises the following steps: acquiring local wind field information around an aircraft in real time through wind speed and direction sensors arranged in a distributed manner, and performing fusion processing to form three-dimensional wind field distribution; generating a feedforward control quantity based on the wind field disturbance identification and short-term prediction model; feedforward control and a feedback control law of a flight control system are fused to form a composite control strategy, and flight attitude stabilization and control response optimization of the unmanned aerial vehicle under complex wind disturbance are realized. Correspondingly, the system comprises a flow field sensing and data processing module, a disturbance identification and feedforward control module and a flight control execution module, and has the functions of wind field real-time sensing, disturbance prediction, control preposition deployment and the like. According to the invention, the wind resistance and autonomous flight safety of the unmanned aerial vehicle in a low-altitude complex wind field environment can be remarkably improved.