105 results about "Flight data" patented technology

The present application relates to the technical field of aviation safety and artificial intelligence, in particular to an ADS-B time series data adversarial sample generation method based on improved WGAN-GP, comprising the following steps: obtaining ADS-B historical flight data, extracting the longitude, latitude, height, speed and heading five-dimensional features to construct a time series data set; building an adversarial sample generation model based on an improved Wasserstein distance generative adversarial network, and configuring a multi-objective composite loss function for the generator; the present application builds a generator and a discriminator based on LSTM, designs a composite loss function that integrates adversarial loss, concealment loss and time series smoothness loss, adopts gradient penalty and asymmetric update strategy to train the model, and the generated adversarial sample has high concealment and physical consistency, can effectively avoid various unsupervised anomaly detection models, the model training is stable, and technical support is provided for building a safer air traffic control defense mechanism.

This invention proposes an efficient system and method for processing objective data from helicopter flight simulators. The system includes a data preprocessing module for preprocessing acquired complete flight data, including data fusion and time-series correction; a data segmentation module for segmenting the raw simulator objective data based on helicopter configuration, control variable changes, and flight status to obtain segmented data; a batch processing module for providing a batch processing entry point for multiple sets of segmented data, simultaneously importing multiple sets of segmented data and multiple data parameters into one or more other processing modules for processing; an outlier removal module for identifying and removing outliers from the batch-processed segmented data; a curve fitting module for correcting the data after outlier removal; and a filtering module for recovering the true information as much as possible from the segmented data containing interference.

This invention relates to the field of unmanned aerial vehicle (UAV) flight control technology, and discloses a method for adaptive adjustment control of UAV flight attitude. Addressing the problems of unstable flight and untimely or inaccurate attitude adjustment of UAVs in complex weather conditions and variable terrain, this method deploys multiple types of sensors on the UAV to collect flight data in real time, including information such as air pressure, wind speed and direction, and attitude angles, and transmits the data to a central processing unit (CPU). The CPU analyzes and processes the data based on a preset algorithm model. When it detects a deviation in flight attitude from a set value, it automatically adjusts the UAV's rotor speed, wing angle, and other actuators to achieve rapid adaptive adjustment of flight attitude. This method effectively improves the flight stability and reliability of UAVs in complex environments and can be widely applied in fields such as aerial surveying, material delivery, and environmental monitoring, ensuring efficient and safe operation of UAVs.

We provide the system. [Solution] A means of recognizing road signs by analyzing images acquired in real time from an imaging device mounted on a vehicle, A means for generating and providing voice notifications to the operator based on recognized signs, A means for accumulating individual flight data and analyzing flight tendencies based on that data, A means for generating improvement guidelines for the pilot based on the analyzed piloting tendencies, A means of optimizing insurance premiums based on piloting skills by linking piloting data with an external device, A means of providing visual notification via a display device installed inside the vehicle, A system that includes this.

The application discloses an interception intention recognition strategy simulation system and method for unmanned aerial vehicle attack and defense game, which comprises a battle simulation data generation unit, a complex environment simulation unit and an interception intention recognition and decision unit. The battle simulation data generation unit is used for generating and storing flight performance parameters and flight data of enemy and friendly unmanned aerial vehicles. The complex environment simulation unit is used for simulating natural environment and electromagnetic interference conditions applied by the enemy when obtaining flight data of enemy unmanned aerial vehicles. The interception intention recognition and decision unit is used for predicting flight data of enemy unmanned aerial vehicles, recognizing interception intention of the enemy unmanned aerial vehicles based on the predicted flight data of the enemy unmanned aerial vehicles and current flight data of friendly unmanned aerial vehicles, and taking corresponding interception countermeasures. The application improves the efficiency of algorithm design and verification and shortens the test cycle through real-time simulation.

Embodiments of the present application provide a wind speed estimation method, device and aircraft. In the wind speed estimation method, flight data of the aircraft is obtained, and then a horizontal disturbance acceleration of the aircraft in a navigation coordinate system is obtained according to a vertical acceleration, a horizontal acceleration and an attitude angle of the aircraft in the navigation coordinate system in the flight data. Further, an air resistance of the aircraft in the horizontal direction is obtained according to a mass of the aircraft and the horizontal disturbance acceleration of the aircraft in the navigation coordinate system. Finally, the wind speed of the flight environment of the aircraft is obtained according to the air resistance and a speed of the aircraft relative to the navigation coordinate system, so that the wind speed of the flight environment of the aircraft can be estimated, the accuracy of the wind speed estimation is improved, and in the flight adaptation to different climate environments, only a few key parameters need to be adapted, so that the engineering practicability of the model is greatly improved.

The application provides a blockchain-based unmanned aerial vehicle airspace management method and system, and relates to the technical field of intelligent transportation systems. The blockchain-based unmanned aerial vehicle airspace management method and system comprises the following steps: S1: based on multi-source sensor data, S2: based on a trusted dynamic airspace map, S3: based on a privacy protection compliance proof and a trusted dynamic airspace map, S4: based on airspace use authorization and a trusted dynamic airspace map, and S5: based on a dynamic risk insurance policy, airspace use authorization and real-time flight data. Through the integration of multi-source sensor data and the application of Kalman filtering and long short-term memory network deep learning algorithm for real-time data fusion processing, in combination with blockchain network storage and data snapshot verification, a high-precision airspace state model is constructed, the accuracy and reliability of airspace situation awareness are significantly enhanced, and a flight plan compliance proof is generated by using a zero-knowledge proof algorithm.

This invention relates to the field of unmanned aerial vehicle (UAV) flight control technology and discloses a Kalman attitude evaluation method for UAVs. Addressing the problems of low accuracy and poor real-time performance in attitude evaluation of UAVs under complex weather conditions and varying terrain, this method deploys multiple types of sensors (barometric pressure, angular velocity, acceleration, and attitude angle sensors) on the UAV to collect flight data in real time, which is then transmitted to a central processing unit (CPU). The CPU constructs a state-space model based on the Kalman filter algorithm, performs prediction and update fusion processing on the sensor data, and optimizes the evaluation results using a PID control algorithm, achieving real-time, high-precision attitude evaluation of the UAV. This method effectively improves the attitude perception capability and stability of UAVs in complex environments and can be widely applied in aerial surveying, material delivery, environmental monitoring, and other fields, providing reliable attitude data support for efficient and safe UAV operations.

This invention relates to a method and apparatus for accurate identification of persons in distress in multiple scenarios, belonging to the field of water rescue technology. The method includes determining a search area and an optimal flight altitude based on the distress information of the persons in distress, UAV flight data, and mission requirements. This allows the UAV to acquire the most suitable video image data from its current perspective when it reaches the optimal flight altitude within the search area, improving search efficiency. Furthermore, it allows for detail restoration and degradation recovery of the current video images, resulting in clearer video images and improved video data quality. Finally, it allows for multi-scale feature extraction from the clear video images to obtain the identification results of the persons in distress, thus extracting comprehensive information about the persons in distress from the video data.

The present application belongs to the technical field of carrier aircraft attitude control, and provides an attitude adaptive control method and system based on dynamic parameter optimization, comprising the following steps: obtaining the maximum limit value of the drift amount and the drift amount early warning value of the corresponding parameters of the carrier aircraft in different flight state stages according to the historical flight database of the carrier aircraft; comparing the real-time parameters with the corresponding drift amount early warning value according to the current flight state stage of the carrier aircraft, extracting the parameters exceeding the drift amount early warning value as the parameters to be optimized and triggering the calculation and analysis process, and analyzing the change trend of each parameter to be optimized and calculating the predicted time period corresponding to the maximum limit value of the drift amount. The decision imbalance problem caused by using only a single indicator is optimized, the shortest prediction time period is set as the limited time, and all parameter optimization is required to be completed before the over-limit critical point of the most urgent parameter, thereby reducing the chain failure risk caused by the short board effect and increasing the safety.

This application provides a method, apparatus, system, device, medium, and product for controlling unmanned aerial vehicles (UAVs), relating to the field of UAV control technology. It receives a fused feature vector and current flight data sent by the UAV; the fused feature vector is obtained by processing the user's multimodal data using a multimodal large model. Alternatively, it receives multimodal data and current flight data sent by the UAV and determines a fused feature vector of the multimodal data. Based on the fused feature vector, it determines a mission command and, based on the fused feature vector, the mission command, and the current flight data, determines a state node. The state node is input into a decision tree to obtain the target control command output by the decision tree, and the target control command is sent to the UAV. This application eliminates the need for user input in a specific manner, offering high flexibility, rapid processing of complex and dynamic commands, shortening response time, and reducing latency.

The application relates to an icing grading self-adaptive deicing method suitable for an inclined conductor, a medium and a product, a deicing operation robot is a robot integrating an unmanned aerial vehicle and a deicing robot, and the method comprises the steps of starting flight, data acquisition, icing judgment, icing type and thickness identification, parking on the conductor and deicing operation. The application adopts an integrated structure of the unmanned aerial vehicle and the deicing robot, combines multi-modal sensor data acquisition and icing judgment logic, realizes accurate identification and differential response of an icing area, solves the problem of traditional deicing "non-differential action", avoids energy consumption and conductor damage when there is no ice, and improves deicing efficiency and equipment safety. The application can realize full-process autonomous control without manual intervention, solve the problem of traditional deicing relying on manual operation and insufficient automation, greatly reduce the operation risk in extreme weather, and guarantee the stability of the power grid and energy supply.

This invention relates to the field of intelligent welding control technology, and discloses an intelligent welding control system for precast box girder frames, including an alternating magnetic field module, a laser excitation module, an interference receiving module, a phase-locked extraction module, and a main control module. The alternating magnetic field module generates a magnetic field in the molten pool region and outputs a reference signal, driving micro-oscillations at the solid-liquid interface; the laser excitation module excites ultrasonic waves, and the interference receiving module converts the ultrasonic echoes carrying oscillation characteristics into voltage signals; the phase-locked extraction module uses the reference signal for phase-locked demodulation to extract time-of-flight data; the main control module combines this data with a sound velocity distribution model to calculate the actual weld depth. This invention filters out environmental interference through phase-locked demodulation, improves calculation accuracy through sound velocity compensation, and adjusts the alternating magnetic field to generate electromagnetic lifting force when there is a risk of burn-through, achieving closed-loop intervention and preventing welding defects.

The application discloses a kind of quick fatigue analysis systems based on eye movement data, belong to fatigue monitoring and analysis field, including: data acquisition module, for obtaining the eye movement flight data and eye movement hotspot map of pilot, and the eye movement hotspot map is pre-processed with addition;Data screening module, based on the hotspot map of pre-processed with addition, using deep convolution generation counterwork network and width learning system to construct final simulation database;Fatigue judgment module, for the final simulation database and the pre-processed hotspot map of standard pilot with addition are input into FBLS-BLS network to obtain fatigue degree classification result;Result output module, for fatigue degree classification result is valued to obtain fatigue degree score, and according to fatigue degree score trigger warning.The application can dynamically adjust model parameters according to new input data to maintain network optimization, so as to realize real-time fatigue diagnosis.

The application provides a kind of photovoltaic power station inspection image processing method and system based on unmanned aerial vehicle, it is related to visual processing technical field, including: to photovoltaic power station initial inspection video carries out image processing, constructs inspection network architecture;According to the key inspection node visual features in this architecture, in combination with reference node and key inspection node position relationship, generate initialization inspection route.Extract the visual features of power station fluctuation feature image, obtain the three-dimensional height distribution of photovoltaic panel array, construct inspection route adaptive algorithm and adaptively adjust the initialization route.Fusion processing of real-time flight data and photovoltaic panel surface image data of unmanned aerial vehicle, construct dynamic flight control algorithm;In combination with dynamic flight control algorithm and inspection route optimization algorithm, obtain photovoltaic panel surface image data, and then build inspection image quality evaluation model.Using the model to evaluate the quality of the images collected in real time during the inspection process, realize the continuous improvement of inspection quality and efficiency.

This invention relates to a magnetic field visual fusion detection system and method for an unmanned aerial vehicle (UAV) platform, belonging to the field of airborne magnetic detection technology. It includes an UAV platform and its mounted magnetic detection module, camera equipment, data receiver, UAV remote controller, and connected ground control terminal. The ground control terminal is configured to receive flight data, detection data, and visual images. It performs interpolation and color mapping using an interpolation algorithm to generate a magnetic anomaly distribution map. Combining the field-of-view parameters of the camera equipment, it calculates the latitude and longitude coordinates corresponding to each pixel in the visual image, obtaining a visual image with latitude and longitude mapping. The magnetic anomaly distribution map and the visual image with latitude and longitude mapping are superimposed to generate a fused positioning map. This achieves synchronous acquisition and fusion processing of multi-source data, avoiding the system complexity and equipment redundancy caused by introducing an additional host computer, and improving the portability and on-site deployment efficiency of the magnetic detection module.

This application discloses a method, system, and electronic device for flight technical quality assessment. The method includes: selecting parameter types from historical QAR data based on the maximum relevance and minimum redundancy criterion as indicators for assessing flight quality; determining the weight coefficients of each indicator based on historical QAR data; determining the standard thresholds of each indicator based on historical target flight data; training and generating a personal flight habit model corresponding to the pilot to be assessed based on the first indicator parameter value, weight coefficient, and standard threshold of the pilot to be assessed in historical QAR data; and determining the flight quality assessment result corresponding to the pilot to be assessed based on the second indicator parameter value of the pilot to be assessed in current QAR data, the personal flight habit model corresponding to the pilot to be assessed, as well as the weight coefficient and standard threshold.

This invention relates to the field of unmanned aerial vehicle (UAV) application technology, specifically a method for energy consumption prediction and path planning in heavy-payload UAV hoisting operations. The method includes the following steps: loading the original flight data and mission target point data of the heavy-payload UAV; constructing a hybrid energy consumption model coupled with physical mechanisms and residual correction; introducing a heavy-load unbalanced weighted training mechanism to train the hybrid energy consumption model, assigning significantly higher weights to samples exceeding a preset heavy-payload threshold; using an improved K-Means clustering algorithm to divide the spatial domain of discrete operation points, introducing the hybrid energy consumption model as an evaluation function into the clustering iterative closed loop to achieve region clustering based on real operation point corrections; and calling the trained hybrid energy consumption model to perform energy simulation, performing path planning and search based on the limit efficiency of a safe power threshold. This invention effectively solves the safety hazard of inaccurate endurance estimation in heavy-payload operations and maximizes hoisting efficiency within a single takeoff and landing cycle.

The application discloses a kind of simulation flight data determinacy communication demonstration system based on time sensitive network, including by end system and switch form airborne network, demonstration manager and network tester.Demonstration manager is used to set the simulation parameter of end system and switch.Simulation end system has flight simulation application module, interface service module and network driver module;It is the interaction of simulating the flight data stream of real end system in time sensitive network;Through simulating periodic flight data, the flight state synchronization interaction of application layer is simulated, the data frame of data link layer is configured and encapsulated guarantee, and the communication function of simulation end system is simulated.Simulation switch has switching module and TSN protocol configuration module;It is according to scheduling or forwarding logic processing data frame.

The application discloses a launch vehicle flight data intelligent interpretation and attitude stability analysis method and system, relates to the launch vehicle flight data processing and attitude analysis technical field, wherein the launch vehicle flight data intelligent interpretation and attitude stability analysis method comprises the following steps: standardizing preprocessing of multi-source data in the flight process of a launch vehicle to obtain a standardized flight data set; obtaining fusion attitude feature data based on the standardized flight data set, and constructing a dynamic self-adaptive interpretation threshold to complete intelligent interpretation of the fusion attitude feature data; calculating an attitude stability margin value by fusing the fusion attitude feature data, the dynamic self-adaptive interpretation threshold and a dynamic correction coefficient of an attitude real-time change rate, so as to realize quantitative grading and determination of the flight attitude stability of the launch vehicle. The application can improve the accuracy of attitude analysis and the timeliness of early warning, realize fine active control of the attitude, and effectively guarantee the flight attitude safety and controllability of the launch vehicle.

This invention discloses a resource pre-scheduling method, apparatus, terminal, and medium based on flight intent prediction, relating to the field of low-altitude communication. The method involves: acquiring real-time flight plan data of a target aircraft; generating a standard intent vector based on the real-time flight plan data, which includes basic flight data and experience quality requirement data; acquiring a network state diagram of the current low-altitude communication grid; using an intent resource mapping model to determine a resource demand tensor heatmap based on the network state diagram and the standard intent vector; adjusting the resolution of the current low-altitude communication grid based on the resource demand tensor heatmap and a preset high-load threshold to determine a reconstructed low-altitude communication grid; and pre-scheduling the grid communication resources based on the standard intent vector and the reconstructed low-altitude communication grid. Therefore, this method effectively solves the problem of lag in resource scheduling of low-altitude communication grids in existing technologies, making it difficult to adapt to the needs of low-altitude scenarios.

This application belongs to the field of cockpit layout, and specifically relates to a dual-stick ergonomic evaluation structure based on mission scenarios, including a cockpit, a bottom platform, a control console, a stick head, and a display screen. Both the cockpit and control console are located on the bottom platform, with the stick head connected to the control console, and the display screen located on the outside of the bottom platform. The display screen is electrically connected to the control console. The control console houses a flight control simulation system and an information processing system. The flight control simulation system outputs the pilot's flight control information to the information processing system, which processes the flight control information, generates corresponding flight data, and transmits it to the display screen. This allows for dual-stick switching ergonomic testing before the development of the control stick and throttle lever products, enabling timely problem identification and design iteration. It offers significant advantages in terms of agility, versatility, and low-cost evaluation, providing support for dual-stick design iteration and evaluation verification throughout the aircraft's entire lifecycle.

A quantitative flight test data gathering system is configured to, during flight test operation of an aircraft, obtain, from one or more flight data sensors, from a flight control system, or both, one or more feedback signals indicative of a state of the aircraft. The quantitative flight test data gathering system is configured to generate, based at least in part on the feedback signals, one or more signals associated with a flight test maneuver. The quantitative flight test data gathering system is configured to provide the one or more signals as input to the flight control system to cause one or more control laws of the flight control system to generate control signals to cause the aircraft to operate according to the flight test maneuver.

The application discloses a fuel flow prediction method and system based on physical constraints and data driving, and relates to the technical field of aircraft performance modeling and flight data analysis; the system comprises a data acquisition module, a benchmark thrust calculation module, a degradation correction module, a fusion calculation module, a physical constraint module and a fuel flow calculation module which are sequentially operated; the technical key points are as follows: through a working condition perception fusion weight function, the physical model and the data driving correction model are adaptively allocated weights under different flight stages, and the prediction accuracy under complex working conditions is improved; by introducing a physical consistency constraint based on Newton's second law, in the absence of real thrust labels, the aircraft flight acceleration is used for weak supervision training, and the reliability and physical interpretability of the model are significantly improved; the engine thrust and fuel flow can be predicted with high accuracy, low deviation and strong physical consistency in the actual operating environment.

The application provides a kind of low airspace collaborative management method and system based on digital twinning, comprising: based on the real-time state data of aircraft obtained, analyzing aircraft maneuverability, flight envelope characteristic parameters, characteristic parameters include maximum speed, maximum acceleration, minimum turning radius, utilize kinematics and dynamics model, depict different aircraft movement law;Combined with real-time flight data of aircraft, through digital twinning technology, real-time synchronization aircraft motion trajectory and attitude change in virtual environment, accurately reproduce aircraft dynamic behavior, provide high-fidelity digital model for flight conflict detection, flight path prediction;For aircraft motion characteristic difference, construct multi-level, multi-granularity digital twinning model, digital twinning model includes airspace structure model at macro level, aircraft group behavior model at mesoscopic level and single aircraft dynamic model at micro level.

The application provides a seat protection method, device, electronic equipment and computer readable medium. The method receives a seat protection request. If the trigger cause of the seat protection request is to change the passenger reservation record of a specific passenger, the method reads flight data, passenger reservation record data, seat protection rule data and seat map data in response to the seat protection request. Then, according to the original seat data corresponding to the original seat of the specific passenger in the passenger reservation record and the seat protection rule data, the specific passenger is allocated a new seat on the target flight. Since the new seat map of the source flight and the new seat map of the target flight are not regenerated when the passenger reservation record of a single specific passenger is changed, the seat protection process is simpler than the existing seat protection process, and the change of the seats booked by the passengers except the specific passenger due to the regeneration of the new seat map is avoided, thereby improving the processing efficiency of the seat protection request and the passenger experience.

This invention discloses a robust flight control system for unmanned helicopters in complex environments based on digital twins, relating to the field of unmanned helicopter technology. It includes an environmental perception data acquisition module, an environmental field quantization module, an aerodynamic digital evolution module, a control command optimization module, and a robust control execution module. By constructing a flight threat field oriented towards wind anisotropy and opportunity constraints, a five-layer environmental risk model is established, generating dynamic flight threat density field data. Joint modeling is performed based on flight data and a reduced-order hydrodynamic model, and adaptive evolution of aerodynamic characteristics is achieved through a dynamic parameter calibration mechanism. Combining the aerodynamic model and the threat density field, a multi-objective optimization function is constructed with trajectory deviation, energy consumption, and safety constraints as objectives, and reinforcement learning is used for policy training. This invention enables adaptive, energy-saving, and safe flight control of unmanned helicopters in strong disturbance and multi-threat environments, improving the robustness of flight control in complex environments.

The embodiment of the application provides a multi-airport non-intrusion area alarm detection method in a remote tower mode, relates to the technical field of airport non-intrusion area alarm detection, and comprises the following steps: acquiring airport data, historical flight data of an airplane and real-time flight data of the airplane; based on takeoff / landing information of the airplane at the airport, the airport data is associated with the historical flight data and the real-time flight data to obtain associated training data and associated detection data; based on the associated training data, a model self-supervision training data set is constructed; an integrated neural network model is constructed, and the integrated neural network model is trained based on the model self-supervision training data set; data feature extraction is performed based on the associated detection data to obtain feature data; and the feature data is input into the trained integrated neural network model for detection to obtain a detection result. The application realizes alarm detection on a multi-airport non-intrusion area in a remote tower mode.

This invention belongs to the field of UAV detection technology. Addressing the problems of existing methods neglecting inherent scale variations and complex spatiotemporal dependencies in flight data, and relying on fixed thresholds leading to high false alarm rates, this invention proposes a UAV flight data anomaly detection method based on multi-scale spatiotemporal graph convolutional networks. The method includes the following steps: acquiring and preprocessing UAV flight data records; establishing a standardized dataset; constructing a multi-scale spatiotemporal graph convolutional network model; inputting the standardized dataset into the multi-scale spatiotemporal graph convolutional network model; extracting multi-scale features from the input sample sequences to capture spatiotemporal dependencies in the flight data; and employing an adaptive threshold strategy to dynamically adjust the threshold for anomaly detection targets within a sliding window to obtain the final anomaly detection result. This invention effectively achieves multi-scale feature extraction and captures spatiotemporal dependencies, and dynamically adjusts the detection threshold within a sliding window based on local statistical information, significantly reducing the false alarm rate.