185 results about "Air safety" patented technology

The invention relates to the technical field of airport safety protection, and discloses an airport bird strike prevention method and system based on multi-sensor fusion. The method comprises the steps of collecting airport peripheral airspace radar detection, radio detection, laser monitoring, weather and other multi-mode sensor data, and performing time synchronization and space registration to generate a fusion data stream; features are extracted based on a dynamic weight distribution algorithm, target types are identified by using a multi-modal classification model, and birds and unmanned aerial vehicles are distinguished; constructing a dynamic risk assessment model based on a Markov chain to generate an intrusion risk level; and when the risk level exceeds a preset threshold value, triggering a multi-level alarm strategy and linking the expelling equipment to execute a self-adaptive expelling instruction. According to the method and system, through multi-sensor fusion, the monitoring precision is improved, the target is accurately recognized, the risk is evaluated in real time, efficient alarming and expelling are achieved, the airport bird strike prevention capacity is effectively improved, and aviation safety is guaranteed.

The invention discloses a plateau airport refined weather forecast system based on deep learning. The system comprises a data acquisition module, a data processing module, a model training module, a prediction output module and an evaluation and optimization module. According to the system, an improved Transform deep learning model is adopted, a high-resolution numerical forecasting product of the European mid-term weather forecasting center and airport high-frequency automatic observation data are fused, and single-point and hour-level accurate meteorological element forecasting is achieved. On the basis of a 0.4-degree global numerical forecasting product of the ECMWF, the system obtains key meteorological factors such as temperature, rainfall and wind field. For the complex terrain and extreme weather conditions of the plateau airport, the model structure is optimized, and the multi-source data fusion and transfer learning technology is introduced, so that the forecasting precision and timeliness are remarkably improved. The method effectively solves the problems that a traditional numerical forecasting mode is large in forecasting error and poor in timeliness in a plateau area, and provides intelligent guarantee for aviation safety and operation efficiency of a plateau airport.

The invention belongs to the technical field of aviation safety management and pilot efficiency evaluation, relates to a pilot multi-task processing efficiency evaluation method under a complex situation, and aims to solve the problem that a traditional method does not comprehensively consider a real flight environment and has a blank in the field of multi-task processing efficiency evaluation. The method comprises the steps that flight parameter data, eye movement data and task data of a pilot during flight are acquired and preprocessed; screening and calculating from three dimensions of attention distribution, work memory and task switching and burst task processing efficiency to obtain a key parameter data set; performing flight stage division based on flight parameter data in combination with the flight stage transfer atlas and the weighted multi-dimensional matching distance; presetting a weight matrix according to cognitive requirements of different flight stages; and matching a preset weight matrix according to a flight stage division result, and carrying out weighted fusion on each key parameter value to obtain a final performance evaluation value. In combination with multi-source data analysis, efficient evaluation of pilot multi-task processing efficiency is realized.

The invention relates to the technical field of aviation safety, and provides an NSGA-based flight ordering method, system, device, product and medium, and the method comprises the steps: obtaining the flight information of to-be-parked aircrafts, carrying out the random ordering of the to-be-parked aircrafts, and obtaining an initial population; calculating adaptive parameters so as to formulate optimization constraint conditions; under the constraint of an optimization constraint condition, performing multi-target optimization sorting on the initial population according to adaptive parameters, calculating the congestion degree of an approach sequence, performing elimination to obtain a first intermediate population, performing genetic algorithm screening to obtain a second intermediate population, and performing crossover variation to obtain a third intermediate population to obtain a target population; calculating a parking position selection parameter and a shortest taxiing path of the aircraft to be landed, thereby calculating a taxiway adaptability parameter; and calculating iteration adaptability parameters of the target population, and performing genetic algorithm iteration on the target population to obtain a target approach sequence. According to the invention, the scheduling efficiency of flight landing is effectively improved.

The invention relates to the technical field of aviation safety, and provides a laser radar trailing vortex inversion method, system and device, a product and a medium. The method comprises the following steps: acquiring meteorological parameters, model parameters and ADS-B data, and calculating trailing vortex circular rector; constructing an axial scanning coordinate system; constructing a first radial speed algorithm; background wind speed is calculated through a first radial speed algorithm; constructing a second radial speed algorithm through the background wind speed and the first radial speed algorithm; determining a range gate, obtaining a corresponding detection point coordinate pair, and traversing gradient absolute values of the detection point coordinate pair in the detection point set to obtain a relative vortex core position; and obtaining a first real vortex core position, a second real vortex core position and a distance between the two positions in an area where the relative vortex core position is located, and iterating to the distance convergence between the two positions to determine a real vortex core position. According to the method, interference of background wind is effectively eliminated, and airplane wake flow axial scanning based on the laser radar is realized by calculating the real vortex core position.

The invention belongs to the technical field of artificial intelligence and aviation safety, and particularly relates to an intelligent aircraft inspection system based on a multi-modal large model. Comprising a multi-modal data acquisition module, a data preprocessing module, a multi-modal large model analysis module, a real-time fault diagnosis and risk prediction module, a cloud-based data management module and a user interaction and visualization module. Through the powerful processing capability of the large model, comprehensive analysis of various states of the aircraft is realized, and potential problems are found in time, so that the automation level and safety of aircraft inspection are improved.

The invention relates to a civil aviation data automatic classification and management method based on large model training. The method comprises the following steps: collecting navigation notification information from a plurality of official platforms; defining professional classification term dictionary interpretation according to specific use scenes and meanings in the notification information; carrying out manual annotation processing on the obtained navigation announcement data; performing large model training on the civil aviation notification information by using the marked data; deploying the trained model to a data application platform for real-time processing; the model automatically classifies the announcement into a preset professional field category according to the property of the announcement; and a manual verification system is adopted to verify and adjust the classification accuracy. The invention further relates to a corresponding device, a processor and a storage medium thereof. By adopting the civil aviation data automatic classification and management method and device based on large model training, the processor and the storage medium thereof, the efficiency of aviation safety management and operation decision can be effectively improved.

A multiple functional instrument is provided. The instrument includes an optical autocovariance function interferometer that can feature multiple fields of view to detect winds in the atmosphere. The instrument can include an infrared camera to detect atmospheric temperatures and the presence of clouds, and a detector assembly that detects the polarization of light returned to the interferometer. Data collected by the instrument can be provided to a deep and reinforcement learning algorithm for real-time prediction of clear air turbulence and other wind-based aviation safety phenomena. Moreover, predicted and actual conditions can be correlated and used to train a deep learning algorithm to enable more accurate predictions. The instrument can be carried by an aircraft or other platform and operated to detect clear air turbulence or other atmospheric phenomena, and to provide instructions regarding flight parameters including wind-aided navigation in order to minimize the effect of predicted turbulence.

The invention provides an updating method and system for aviation ice accretion identification based on aircraft real-time detection. The method comprises the following steps: acquiring real-time data of an aircraft to judge icing generation information; constructing an ice accumulation report membership function according to the ice accumulation generation information, wherein the function is used for representing the space and time distribution of the ice accumulation generation probability; and through combination of the icing report membership function and a basic icing index, an icing potential index is obtained through calculation and is used for identifying three-dimensional distribution of an icing area. According to the method, accurate positioning, dynamic prediction and real-time early warning of the accumulated ice are realized, and the aviation safety and the flight efficiency are improved.

The invention discloses a heterogeneous unmanned aerial vehicle cooperative operation air safety assessment method, and relates to the technical field of unmanned aerial vehicle cooperative operation safety, and the method comprises the steps: obtaining static parameters and operation dynamic parameters of a heterogeneous unmanned aerial vehicle; constructing a dynamic and static parameter incidence matrix based on a gradient lifting decision tree algorithm; constructing a positioning precision probability matrix based on Bayesian updating; task and operation dynamic data are collected in real time; in combination with the environment interference coefficient and the task priority weight, collision time prediction is optimized by using LSTM, and the air collision probability is calculated; and triggering the graded security risk alarm. According to the invention, refined quantitative evaluation and graded early warning of safety risks in a heterogeneous unmanned aerial vehicle cooperative scene are realized, and the problems of extensive evaluation and insufficient collaboration in the prior art are solved.

The invention relates to the fields of wind engineering, weather forecast, environmental protection and aviation, and provides a complex terrain wind field refined simulation method based on a high-resolution grid. The objective of the invention is to solve the problem of simulation errors caused by different topographic data formats and coordinate systems in complex topographic wind field simulation and to improve simulation precision and efficiency. According to the main scheme, the method comprises the following steps: generating a three-dimensional computational grid of wind mine software according to satellite image elevation data, and obtaining a three-dimensional unstructured grid for wind field CFD simulation; selecting a proper entrance boundary condition type for four side boundary surfaces and a top surface of the target area; and wind mine software is driven to carry out numerical calculation to obtain wind field information data of the refined simulation of the complex terrain. The method is used for wind field characteristic evaluation under complex terrains, and is beneficial to efficient utilization of wind energy, weather forecast, environmental protection and aviation safety.

The invention discloses a low-altitude aircraft conflict resolution method based on deep reinforcement learning and time constraint, and belongs to the technical field of aviation safety, and the method comprises the following steps: setting a static obstacle and a non-cooperative target aircraft to construct a low-altitude operation environment; constructing a state space and an action space of the aircraft in the low-altitude operation environment; constructing a reward function with time constraint; according to the reward function with the time constraint and the state space and the action space of the aircraft in the low-altitude operation environment, performing low-altitude aircraft conflict resolution training in the low-altitude operation environment based on a deep reinforcement learning model to obtain a trained deep reinforcement learning model; and acquiring the current state of the body aircraft in executing the actual conflict resolution task, and outputting a corresponding action when the reward value is the highest by using the trained deep reinforcement learning model so as to timely reach the target position while avoiding conflicts. According to the method, the problem that the conflict resolution capability and the punctuality rate of the low-altitude aircraft are insufficient is solved.

The invention relates to the technical field of aviation safety and data processing, in particular to a flight data anomaly detection method based on DEWMA-IForest, and the method comprises the following steps: carrying out the preprocessing of original flight data; initializing a smoothing coefficient and a sliding window size parameter; calculating an arithmetic mean value and a standard deviation by taking data in the sliding window as a statistical sample, and respectively taking the arithmetic mean value and the standard deviation as a DEWMA statistic initial value and a control limit calculation reference value; setting an initial control limit range; calculating the DEWMA statistical magnitude and the standard deviation of the data to be measured; dynamically updating the control limit range; and DEWMA fitting reconstruction data and a control limit are calculated. According to the method, the dynamic control limit is adapted to the time-varying property of the QAR data, so that the sensitivity of small-amplitude trend anomaly is improved; high-dimensional data dimension disasters are avoided through multi-algorithm fusion; and the abnormal criterion can be explained, so that a scientific basis is provided for aircraft operation monitoring and intelligent maintenance decision making.

The invention discloses a pilot airworthiness state sensing method based on multi-task alternate pairing learning, and relates to the technical field of aviation safety guarantee and monitoring, and the method comprises the steps: constructing a multi-task-based alternate pairing learning model, and inputting a pre-obtained video data set into the multi-task-based alternate pairing learning model for training; the multi-task-based alternate pairing learning model comprises a shared feature extraction network and three special task sub-networks, and each special task sub-network comprises an rPPG signal estimator, a continuous emotion predictor and an airworthiness state sequence predictor; a multi-task rotation pairing learning method is adopted in the training stage; and inputting a to-be-predicted video into the trained multi-task-based alternate pairing learning model for prediction, and outputting to obtain a multi-task prediction result. According to the invention, potential relations among the physiological state, the psychological state and the airworthiness state of the pilot can be learned, and the state of the pilot in the driving process can be accurately evaluated.

The invention relates to the technical field of aircraft part detection, and particularly discloses an intelligent detection device for aircraft part processing, which comprises a box-type base, a mounting groove, a simulation mounting mechanism, a test mechanism, a top reset mechanism, a top shell, a controller, temperature adjusting equipment and humidity adjusting equipment, the simulation mounting mechanism can simulate flight vibration; the testing mechanism is arranged at the top end of the mounting groove and located on the rear side of the mounting groove, and the testing mechanism can simulate the using action of a passenger; the top resetting mechanism is arranged above the testing mechanism and can be used for resetting assistance and cyclic testing. Through the detection mode of scene simulation and automatic operation, the problem that a traditional single vibration or isolated action test is disjointed with an actual scene is solved, the detection efficiency and reliability are improved, the performance of the seat in an extreme environment is reflected more accurately, and a core guarantee is provided for aviation safety.

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 relates to the field of aviation safety, in particular to a low-altitude flight safety co-guidance system, which comprises a flight airspace delimiting module, an image intelligent identification module, a position determination module, a collision risk judgment module, a route planning module and a management database, and is characterized in that an identification model is constructed through a convolutional neural network, and a flight airspace image is acquired; the method comprises the following steps: identifying a moving object and a fixed obstacle in a flight airspace, performing position marking on the moving object and the fixed obstacle in a flight airspace image, predicting a movement track of the moving object in the flight airspace image, further judging whether the aircraft has a collision risk, and determining the collision risk position of the aircraft. A heuristic search algorithm is utilized to output a safe flight path fragment, the whole process from environment perception to risk response is covered, and the possibility of collision accidents of the aircraft is greatly reduced.

The invention relates to the technical field of aviation safety prediction, and discloses a bumping prediction correction method and system based on aircraft geometric configuration and shipping space information, electronic equipment and a storage medium, and the method comprises the steps: obtaining input features, carrying out the cross-aircraft-type feature enhancement through comparative learning, generating an enhanced feature vector, and carrying out the recognition of the enhanced feature vector; and performing structured combination on the enhanced feature vector, the distance from the shipping space to the lift center, the normal overload value of the shipping space and the distance from the target seat to the lift center, fusing model embedded vectors and spatial position codes, generating a plurality of feature marks, and inputting the feature marks into an attention model adjusted by a geometric perception mechanism to perform spatial dependency relationship modeling. Outputting morphological parameters for representing an overload distribution form; through parameterized curve constraint calculation, obtaining a preliminary normal overload prediction value of the target seat; and performing adaptive weighted fusion based on the rule constraint prediction value, and outputting a normal overload prediction correction value of the target seat. According to the invention, the overload value of any target seat in the cabin can be accurately predicted.

The invention relates to the technical field of computer vision and aviation safety, and discloses an airport clearance area unmanned aerial vehicle intrusion monitoring and positioning method based on improved YOLOv8n, and the method comprises the steps: constructing an improved YOLOv8n detection model, enhancing multi-scale feature fusion through integrating BiFPN at a neck network, and introducing an attention mechanism at a prediction head to improve feature selectivity; deploying the trained and optimized model at an intelligent sensing node, and detecting and acquiring pixel coordinates of the unmanned aerial vehicle in real time; the geographic coordinates and the attitude parameters of the nodes are fused, and the three-dimensional geographic coordinates of the unmanned aerial vehicle are calculated through space coordinate transformation in combination with a camera model and target prior information; and finally, intelligent alarm and visual display are realized. According to the method, the detection capability of the tiny unmanned aerial vehicle target under the complex background is effectively improved, an integrated solution from image recognition to precise geographic positioning is realized, and the method has the advantages of accurate detection, precise positioning and reliable system.

The aviation safety oversight software will be utilized by the civil aviation authority to monitor the safety performance of aviation service providers. This software will first classify the air operating companies and then create an electronic file to update the regulatory processes of the civil aviation authority according to the applicable air operating companies and the aviation safety performance indicators of the country. The software aims to streamline the implementation of the regulatory processes of the Civil Aviation Authority. It will use statistical analysis to convert safety data into safety information for better analysis and decision-making.

The invention relates to the technical field of low-altitude meteorological science and aviation safety, in particular to a low-altitude turbulence prediction method based on multi-source observation data. The method is realized based on multi-source data, and specifically comprises the steps of data acquisition and preprocessing, unification of observation data of different data sources to a target height layer and realization of space-time matching, calculation of turbulent kinetic energy by physical characteristic quantity, wind shear, turbulence intensity weighted calculation, design of a height weight function of exponential attenuation, and calculation of turbulence intensity through weighted combination. Therefore, turbulence intensity distribution in a two-dimensional vertical plane is obtained. Through systematic multi-source data fusion, characteristic quantity extraction based on a physical mechanism, intelligent weight design and reliability evaluation, high-precision two-dimensional and three-dimensional low-altitude turbulence prediction is realized, and powerful technical support is provided for low-altitude flight safety. Meanwhile, the adaptability scheme of the single-source data ensures the practicability of the method under different equipment configuration conditions, and the method has a wide application prospect.

The invention relates to the technical field of aviation safety and weather prediction, in particular to an aircraft bumping prediction method, system and equipment based on decoupling type physical constraint. Aligning pre-collected multi-time-sequence weather forecast data and airplane EDR data on the same space-time grid, and converting the data into probabilistic features including a mean value and a standard deviation; for sparse airplane EDR data, introducing an atmospheric physical model correction term based on a wind shear and turbulence energy dissipation rate relationship for filling, and obtaining a probabilistic input tensor; inputting the probabilistic input tensor into a pre-constructed and trained self-correlation attention model fused with physical constraints; and extracting a contribution degree matrix from the attention weight of the trained model, quantifying the contribution ratio of each forecast time point to a prediction result, and outputting a probabilistic prediction result of the airplane bumping and the corresponding contribution degree matrix as a final prediction result. A reliable basis is provided for flight decision making, and the accuracy and robustness of airplane bumping prediction are improved.

The invention discloses an aviation safety report classification method, device and equipment and a storage medium, and the method comprises the steps: obtaining a historical data set, including a text data set and an image data set, of an aviation safety report; constructing a security report text classification model according to the text data set; constructing a security report image classification model according to the image data set; obtaining a to-be-classified aviation safety report, obtaining a text classification result by adopting the safety report text classification model according to the text data, and obtaining an image classification result by adopting the safety report image classification model according to the image data; performing quality evaluation on the text data and the image data, and determining a text weight and an image weight according to an evaluation result; and based on the text weight and the image weight, performing weighted summation on the text classification result and the image classification result to obtain a classification result of the to-be-classified aviation safety report. According to the invention, the accuracy of aviation safety report classification is effectively improved.

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.

The invention discloses a backtracking verification method and device for a navigation announcement analysis result and electronic equipment, and relates to the field of artificial intelligence. The method comprises the following steps: acquiring an analysis result of a target system on a navigation announcement; determining announcement key information from the analysis result through a backstepping model, and reconstructing and generating a target announcement based on the announcement key information; detecting the similarity between the target announcement and the navigation announcement; dependency grammar analysis is carried out on the target announcement and the navigation announcement to obtain an analysis result, and the dependency grammar analysis is used for analyzing a word dependency relationship and a grammar role relationship between the target announcement and the navigation announcement; and determining the analysis accuracy of the target system to the navigation announcement according to the similarity and the analysis result. According to the method and the device, the technical problem that the verification efficiency of the navigation announcement analysis result is relatively low and the aviation safety is influenced due to the fact that the expert rule is mainly utilized to verify the navigation announcement analysis result in the prior art is solved.

The utility model relates to a low-light-intensity obstacle lamp for a wind driven generator, and aims to solve the problems of difficulty and potential safety hazards caused by a traditional mounting method. The lamp is compact in design and is directly attracted to the pipe wall of the wind driven generator through the magnet, an additional support is not needed, the installation process of high-altitude operation is simplified, and risks are reduced. The lamp body is composed of a rod body and a shell, an annular groove is formed in the surface of the rod body and used for installing a sealing ring, and rainwater is prevented from entering the supporting rod. One end of the rod body is connected with the condensing lens lampshade, more than two LED light sources are arranged in the rod body, and good aviation safety indication is provided. A driving circuit board is arranged in the shell, and a plug is arranged to be connected with an external circuit. The adjustable magnet frames are arranged on the two sides of the shell, the distance between the magnet and the pipe wall can be adjusted according to different wall thicknesses, and universality is enhanced. The installation safety and convenience are improved, the adaptability and the protection level of products are enhanced, and a new solution is provided for installation of the aviation obstruction beacon in the wind power generation industry.

The utility model provides a 360-degree light-emitting high-light-intensity aviation obstruction light which comprises a main support and obstruction lights installed on the main support, the number of the obstruction lights is more than three, the obstruction lights are installed outwards in a surrounding mode, the irradiation angle of the obstruction lights is 360 degrees, the obstruction lights are installed on the main support in a rotating mode in the vertical direction, and the obstruction lights are arranged on the main support. The function of adjusting the vertical light-emitting angle of the obstruction lamp in a single direction is achieved, and the actual installation scene requirement is met. The three obstruction lights are installed on the main support and installed outwards in a surrounding mode, 360-degree all-dimensional light emitting is achieved, the visual blind area of a traditional lamp is eliminated, and aviation safety is improved. The obstruction lights can rotate around the main support, a user can flexibly adjust the vertical light-emitting angle of each obstruction light according to the actual installation environment and requirements, and the best warning effect is ensured. According to the design, the applicability of the lamp is improved, and the installation and maintenance processes are simplified.

The method for quickly inverting aircraft wake vortex parameters based on mobile platform laser remote sensing comprises the following steps: a laser radar system scans a wake vortex influence area, and measurement data are subjected to quality control processing; a wake vortex core position and a vortex core radius are estimated; a wind speed of an arbitrary measurement point A located in the wake vortex range is subjected to vector decomposition, and the wind speed is expressed as a function of a wake vortex circulation parameter W 、 V 、 U A background wind speed and a laser radar motion speed are eliminated by using a non-wake vortex area measurement point; a difference between radial velocities of two measurement points in and out of a wake vortex influence range in the same laser emission direction is expressed as a function of a wake vortex circulation; and a turbulent disturbance is weakened by multiple measurements in the wake vortex influence range, and then a least square method is used to fit and solve the wake vortex circulation. The present application can accurately invert and correct aircraft wake vortex parameters by using less observation data. The calculation result is fast and accurate, and can provide scientific and reliable data support for aviation safety and aircraft cooperative operation.

The invention provides an evidence-driven large language model MISRA C rule review method and system for aviation safety. According to the method, formalized feature extraction is carried out from four aspects of grammar structure features, semantic structure features, logic analysis features and preprocessing behavior features for target codes according to various rules of MISRA C forcing class rules, and diagnosis information is diagnosed in combination with an industrial-grade compiler; generating a structured evidence set comprising abstract syntax tree node statistical features, code context structure features, control flow graph features, function internal data flow analysis features, symbol and type table features and macro definition analysis features; and based on the structured evidence set and the large language model, executing evidence tracing, rule matching and logical reasoning according to a preset thinking chain process, and outputting a structured judgment result containing illegal rule numbers, evidence description and code positions. According to the method, the false alarm rate can be remarkably reduced while the high rule coverage rate and the detection accuracy are kept.

The present invention provides a method and device for identifying and analyzing a pilot's landing ability, and belongs to the field of aviation safety and safety management. The method comprises: obtaining flight data before the aircraft lands; processing the pitch rate in the flight data by a pitch rate sequence change point detection algorithm to obtain the peaks and troughs of the pitch rate; and determining the dynamic behavior of the aircraft when approaching the ground according to the peaks and troughs of the pitch rate. The present invention proposes for the first time a pilot landing ability identification system based on a combination of change point detection and pattern recognition with data drive, which has broad application prospects and significant practical value.