76 results about "Aviation safety" patented technology

The invention relates to the technical field of aviation safety, and discloses a multi-modal data fused anti-collision early warning method, device and equipment for a carrier and a medium, and the method comprises the steps: obtaining multi-modal data which comprises radar point cloud data and a photoelectric image; respectively preprocessing the radar point cloud data and the photoelectric image to obtain radar effective point cloud, a semantic mask and confidence; performing cross-modal filtering fusion based on the radar effective point cloud, the semantic mask and the confidence coefficient to obtain a multi-dimensional feature point cloud; and constructing a threat assessment model based on the multi-dimensional feature point cloud, and performing early warning on collision prevention of the carrier based on the threat assessment model. According to the invention, the automatic detection of the threat target in the flight of the aerial carrier is realized, the measurement distance is farther, the measurement precision is higher, and the problems of short anti-collision detection distance and low detection precision in the prior art are solved.

The invention relates to the field of aviation safety, and provides a flight bumping area prediction method and system based on air-ground data fusion, and the method comprises the steps: obtaining a measurement area, and obtaining a barometric altitude through meteorological data; acquiring air detection data of the measurement aircraft so as to calculate a horizontal airspeed component; checking according to the horizontal airspeed component and the ground speed component to obtain a high-altitude wind speed component; generating an airborne data matrix, calculating the distance between a measurement point in the airborne data matrix and a grid point in a measurement area to obtain a latitude and longitude distance, and performing weight accumulation to obtain a grid point wind speed; wind field deformation is calculated, a jolting index is calculated according to the wind field deformation and normalized, a normalized jolting index is obtained, and a comprehensive jolting index is obtained through the normalized jolting index; and obtaining a target jolting index according to the ground measurement jolting index and the comprehensive jolting index to realize jolting prediction. According to the invention, accurate flight bumping prediction can be realized.

The invention relates to the field of aviation safety and unmanned aerial vehicle management, and discloses a low-altitude flight real-time situation awareness method, system and device and a storage medium, and the method comprises the steps: deploying a radar group composed of a millimeter wave radar and an optical camera in an airport area, the deployment density of the radar group meeting the detection precision requirement of a small unmanned aerial vehicle, each device communicates with the real-time data processing platform through a distributed network; target detection data of the radar group, image data of the AI image recognition module, GPS positioning data and ADS-B flight data are received in real time; and processing the multi-source data through a preset data fusion algorithm, including time-space calibration of the unified timestamp and the space coordinate reference. A radar group composed of millimeter wave radars and optical cameras is deployed in an airport area, so that the problems that most traditional low-altitude monitoring systems only depend on a single radar data source, so that the omission ratio of small unmanned aerial vehicles is high, and the misjudgment rate of the small unmanned aerial vehicles and targets such as birds is high are solved.

The invention provides a bird collision channel risk prediction method, and particularly relates to the technical field of aviation safety, and the method comprises the steps: obtaining bird situation data, airplane flight data, meteorological data and other multi-source data of a preset time window in an airport channel region, and carrying out the preprocessing to obtain standardized data; and then inputting the standardized data into a multi-stream transform model to generate a prediction probability of the bird-hit channel. And according to comparison between the prediction probability and a preset risk level threshold value, dividing a current channel risk level, and triggering a corresponding alarm prevention and control decision based on the risk level, thereby effectively preventing occurrence of bird collision accidents. According to the method, the technical problem of how to effectively improve the accuracy and timeliness of bird strike risk early warning so as to reduce the aviation accident rate is solved, so that real-time accurate prediction and automatic prevention and control decision of the bird strike risk are realized, and the aviation accident risk is remarkably reduced.

The invention relates to the technical field of aviation safety data analysis and pilot training optimization, provides an aviation safety data and training data integrated analysis method and system, electronic equipment and a storage medium, and solves the problems of poor pertinence of pilot training and poor foresight of safety guarantee. The method comprises the following steps: automatically extracting threats, errors and unexpected aircraft states in aviation unsafe events from multi-source heterogeneous data, and judging a management state; based on the aviation safety knowledge graph, positive and negative competency and behavior indexes corresponding to the managed and unmanaged states are identified through graph calculation; the statistical frequency is analyzed, and a priority list of the security data side is generated; carrying out weighted fusion to obtain a target training list by combining a training data statistical analysis result of the same period; and in combination with the threat priority, generating a training scene configuration scheme containing threat elements and capability items, and forming a course content, evaluation standard and resource optimization configuration scheme. According to the invention, the pertinence of pilot training is improved.

The invention relates to the technical field of aviation safety and flight training data analysis, provides a training priority determination method and system based on aviation event data statistical analysis, electronic equipment and a storage medium, and solves the problems of poor accuracy of flight training subject design and poor risk pertinence. The method comprises the following steps: extracting aviation unsafe event data of a plurality of heterogeneous data sources, extracting threat, error and unexpected aircraft states, and judging a unit management state; a group decision is combined with a distributed scoring node and block chain consensus mechanism, three-level scoring is performed on various threats from the training effect, the occurrence possibility and the consequence severity, and a threat scoring matrix is formed; the unit management rate and the actual occurrence frequency of each threat are counted, a comprehensive risk value is calculated, all threats are ranked to determine training priorities, and the threats with high priorities and related errors are embedded into the scene design of a flight training subject. According to the invention, the accuracy of flight training subject design is improved.

The invention discloses an intelligent bird strike prevention and control method and system based on multi-source data, and relates to the field of aviation safety, in particular to the intelligent bird strike prevention and control method based on the multi-source data. The intelligent bird strike prevention and control method based on the multi-source data comprises the steps that aircraft position data are collected; acquiring bird position data; preprocessing the data; constructing a hybrid space-time convolutional neural network, a long-short-term memory network and a physical constraint aircraft trajectory prediction model; constructing a bird trajectory prediction model of a mixed long and short-term memory network and a graph neural network; constructing a collision detection model combined with a mahalanobis distance; risk grade evaluation; and risk grading response. According to the invention, the problems that the data structure is single and different air defense measures are not taken for different risk levels in the prior art are solved. Therefore, the probability of bird strike accidents is effectively reduced, and the safety performance of air transportation is improved.

The invention belongs to the field of natural language processing, particularly relates to an aviation safety accident report analysis method based on topic modeling and a word co-occurrence network, and aims to solve the problems that an existing topic modeling method is limited in understanding ability in aviation safety accident text analysis and cannot quantitatively reveal key causes. The method comprises the steps that a report text is acquired and preprocessed; based on a preset semantic fusion enhanced topic modeling engine keyword, obtaining a semantic fusion enhanced feature vector, and extracting a topic structure by adopting an improved deep embedding clustering model; and constructing an aviation safety accident word co-occurrence network based on a dynamic cosine similarity threshold value, performing network analysis, and determining key risk factors. According to the method, structured topic mining and deep learning semantic extraction are dynamically fused, and a network analysis method is combined, so that deep analysis and risk identification of the aviation safety accident report are realized, and the limitation of a traditional method on capturing text deep semantic information and subtle semantic difference is broken through.

The invention discloses an obstruction light control method and system in a low-altitude flight environment and a lamp, and relates to the technical field of aviation safety, and the method comprises the steps: obtaining a current obstruction light parameter corresponding to each aircraft based on preset obstruction light division information in a target flight region; based on the preset flight parameter corresponding to each aircraft, the current obstacle light parameter is adjusted, and an obstacle light initial optimization parameter is obtained; obtaining obstacle light regulation and control environment information corresponding to each aircraft, and obtaining obstacle light adjustment information based on a preset obstacle light parameter corresponding rule and the obstacle light regulation and control environment information; according to the obstacle light adjustment information, the obstacle light initial optimization parameters are adjusted, and environment suitable obstacle light parameters are obtained; and controlling the corresponding obstruction lights to perform dynamic adjustment based on the environment suitable obstruction light parameters. The method has the effect of improving the control efficiency of the aviation obstruction beacon.

The invention provides an aviation ADS-B anomaly detection method and system based on semi-supervised joint modeling, and belongs to the technical field of aviation safety monitoring. The method comprises the following steps: receiving and screening ADS-B data, and extracting NACp, position, speed and time features; denoising and screening low-confidence samples through Hampel filtering and a Gaussian mixture model; using trajectory clustering and NACp precision clustering to construct a dual anomaly labeling set; fusing point-level, trajectory-level and space grid features to form a multi-dimensional feature set; generating a high-confidence pseudo label based on multi-model voting, and dynamically training and inferring in combination with a LightGBM semi-supervised model; and outputting an abnormal level and visualizing the abnormal level through a thermodynamic diagram. Through semi-supervised learning and multi-modal clustering, the problems of label scarcity and category imbalance are solved, and the detection precision is improved; in combination with heterogeneous calculation and dynamic iterative optimization, efficient real-time processing is realized, and support is provided for aviation safety monitoring in complex space weather.

The invention discloses a civil aircraft cable laying quality evaluation method and system based on a 3D scanning reconstruction technology. The quality evaluation method comprises the following steps: 1) data acquisition; 2) cable segmentation; 3) performing three-dimensional reconstruction; 4) calculating parameters; 5) quality evaluation; and 6) performing man-machine interaction. According to the method, a computer vision technology and a three-dimensional reconstruction method are combined, and a high-precision, high-efficiency, objective and consistent solution is provided for cabin cable laying quality evaluation, so that the quality and efficiency of aircraft manufacturing are improved, and a more reliable guarantee is provided for aviation safety.

The invention belongs to the field of aviation safety technology and flight data application, and discloses a precursor capture and effectiveness evaluation method and system for monitoring aviation unsafe incidents in real time, and the method comprises the steps: S0, carrying out the FOQA based on a flight quality monitoring standard of a target aircraft type, a standard operation program manual file, an FOQA file, a FOQA file and an FOQA file; a set of flight state parameters and a set of pilot manipulation parameters are determined. The method comprises the following steps: S1, constructing a precursor capture model fusing wavelet transform convolution and a modern time domain convolution network, extracting a general mode of an aviation unsafe event and pilot manipulation characteristics, and converting a packet-level label into a precursor probability of each moment through example pooling; and S2, acquiring a specific unsafe event over-limit label in combination with an FOQA monitoring standard, and comprehensively evaluating the effectiveness of the captured precursor through a disturbance curve method and a reinforcement learning intelligent agent simulation method. According to the invention, through fusion of a plurality of technologies, comprehensive monitoring, early warning and evaluation means are provided for aviation unsafe events, and the flight safety can be effectively improved.

The invention relates to the technical field of low-altitude atmosphere monitoring, and particularly discloses a low-altitude meteorological digital twinborn reconstruction method based on three-dimensional entity data modeling, seamless connection from a regional macroscopic wind field to an urban microscopic flow field is realized through combination of a mesoscale meteorological model and a computational fluid mechanics model, and the low-altitude meteorological digital twinborn reconstruction method based on three-dimensional data modeling of a real city is realized. The physical effect of surface features on airflow is directly considered, errors of a CFD grid model are continuously corrected through real-time observation data, the uncertainty of pure numerical simulation is made up, the capturing capacity of a space-time mutation phenomenon is improved, a hidden rule in the data is mined through an intelligent prediction model, and the real-time prediction model is established. According to the method, the precision of urban boundary layer airflow simulation can be remarkably improved, and a more accurate meteorological guarantee service is provided for the fields of atmospheric environment monitoring, urban planning, aviation safety and the like.

The invention relates to the technical field of aviation safety monitoring, in particular to an aircraft cargo hold fire alarm detection system based on AI anomaly recognition and thermal imaging linkage, and the system comprises a visible light image collection module which is used for obtaining a real-time visible light image in an aircraft cargo hold; the infrared thermal imaging acquisition module is used for acquiring multi-region and multi-channel thermal imaging images in the cargo hold and constructing a thermal distribution diagram; the smoke and gas sensing device is used for monitoring the concentration change of hazardous gas in the air and outputting continuous detection data; the multi-modal AI identification processing module is used for carrying out fusion analysis on the multi-modal data; the pod linkage verification module is used for performing external verification sampling on the target area when the multi-modal analysis result is low in confidence but has risks, and forming a final judgment result in an auxiliary manner; and the alarm output module is used for converting the final judgment result into an alarm signal, sending the alarm signal to a cockpit HUD or a main control system through a flight control system, and automatically switching a prompt mode according to the fire behavior grade.

The invention provides a method and a system for determining a fault reason of an aircraft and electronic equipment, relates to the technical field of aviation safety and artificial intelligence, and aims to improve the accuracy of reasoning the fault reason of the aircraft. The method is applied to an airborne terminal, and comprises the following steps: acquiring a target operation parameter of an aircraft in a flight process; a light-weight early warning model is called to conduct reasoning on the target operation parameters, fault early warning signals and fault confidence of the fault early warning signals are obtained, the light-weight early warning model is obtained by utilizing knowledge distillation to conduct knowledge migration from a teacher model to a light-weight student model, the teacher model is a target multi-mode large model, and the student model is a target multi-mode large model; the target multi-modal large model is obtained based on multi-modal data sample training; sending a fault early warning signal to a ground service platform in response to the fact that the fault confidence is greater than a confidence threshold; a fault reason sent by the ground service platform is received, and the fault reason is obtained by calling the target multi-mode large model to conduct reasoning on the fault early warning signal.

The invention provides a flight training simulation and evaluation method and system for special situation disposal, and belongs to the technical field of aviation flight training, and the system comprises a special situation scene intelligent generation module, a digital twin simulation module, a multi-modal data acquisition module, an intelligent evaluation module, a self-adaptive training scheduling module and a visual redisk module. By intelligently generating a special situation scene, collecting multi-modal data in real time, performing multi-dimensional quantitative evaluation based on a deep learning algorithm, and dynamically adjusting a training scheme according to an evaluation result, accurate training and scientific evaluation of the special situation disposal ability of the pilot are realized. According to the method, the problems of lack of systematicness of special situation scene generation, high subjectivity of an evaluation process, non-fine training feedback and the like in traditional flight training are solved, the scientificity and efficiency of special situation disposal training are improved, the special situation disposal ability of a pilot can be accurately enhanced, and a guarantee is provided for aviation safety.

The invention discloses a flight safety warning method based on pilot operation intention recognition, which comprises the following steps: S1, collecting EEG data of a subject in each stage of simulated flight through an electroencephalogram signal data collection device; s2, carrying out preprocessing operation on the collected EEG data; s3, extracting and fusing the electroencephalogram signals by constructing a convolutional gradient attention network, and mapping fused features to an operation intention of a pilot; s4, calculating safety risk factors according to the deviation between the flight state and the preset flight envelope, the pilot operation intention and the external environment factors; s5, comparing the risk factor with a preset risk threshold, and dividing the risk into a high level, a middle level and a low level; and S6, the cockpit touch alarm system triggers multi-mode alarm in the cockpit according to the risk level, and adjusts the flight state in real time. The flight accident risk caused by human factors can be reduced, and the aviation safety is improved.

The invention relates to the technical field of aviation safety, in particular to a short-distance parallel runway collision risk assessment method based on influence of front and side wind, which comprises collision risk assessment, Bayesian parameter estimation, linear regression analysis, dynamic front and side wind fitting and Monte Carlo analog simulation. The problem of small sample parameter estimation is solved by introducing a Bayesian parameter estimation method, crosswind parameters are calculated by combining linear regression analysis, positive crosswind data are fitted by applying Weibull distribution, and an influence rule of positive crosswind and an initial interval on collision probability is quantified through Monte Carlo simulation. According to the short-distance parallel runway collision risk assessment method based on the influence of the positive and crosswind, on the basis of a traditional collision model, a re-flight condition is incorporated into the model, complex scenes such as crosswind and re-flight are considered, a dynamic interval adjustment model is fused, and a new collision risk model is established; the crosswind grading minimum interval standard is provided for airline companies and air traffic control operation management, and the runway safety and the airport operation capacity are improved.

The invention belongs to the technical field of aviation safety information processing and intelligent retrieval, particularly relates to an aviation safety knowledge mixed retrieval system based on a creative distributed database, and solves the problem that the prior art cannot meet the requirements of aviation safety information processing accuracy, efficiency and reliability. The method comprises the following steps: firstly, depending on a Peng and Feiteng trust and creative server and an OceanBase distributed database, matching an integrated storage engine and a physical collaborative distribution module, and constructing a multi-type data fusion storage base; then realizing precise semantic retrieval, weak network off-line data management and increment synchronization through double-engine cooperation of an intelligent engine layer, fusion of a domain-specific embedded model and an RRF optimization algorithm and combination of a finite-state machine and a positive and negative counter algorithm; and the application service layer standardizes an API and a multi-tenant isolation strategy, and adapts to multi-terminal and third-party system integration. The accuracy, efficiency and reliability of aviation safety information processing are remarkably improved.

The invention provides an aircraft landing turbulence index determination method and device, a storage medium and equipment, and belongs to the field of civil aviation industry aviation safety. The method comprises the following steps: acquiring QAR data of all flights of aircrafts of the same type in the same airport in low-altitude and near-earth stages; respectively calculating a pilot operation amplitude mean value and a wind field related variable per second of each flight aircraft based on the QAR data; searching matched data in flight data of all flights of aircrafts by using a K-nearest neighbor method for data of each second before landing of the aircrafts based on wind field related variables to form a data set, and calculating a quantitative index of a pilot operation amplitude fluctuation degree based on the data set; fitting the quantitative index of the pilot operation amplitude fluctuation degree based on the wind field related variable, and constructing a semi-parameter regression model; and obtaining a turbulence index calculation formula based on the semi-parameter regression model. According to the method, the accuracy of the turbulence index is improved, and the quantitative index of the influence of turbulence on the aircraft can be reflected more accurately.

The invention relates to the technical field of aviation safety, in particular to a parachute type slow descent and tail end buffering emergency lifesaving system and method for an ultra-low-altitude aircraft. The system comprises an umbrella-shaped slow descent module, a tail end buffer module, a detection and control module and an emergency energy discarding module. When the detection and control module judges that the aircraft is out of control, the emergency energy discarding module is triggered to throw away the battery pack, meanwhile, the umbrella-shaped slow descent module is triggered to pop out of the canopy, and the canopy is rapidly inflated through high-pressure gas, so that the aircraft is stably and slowly descended; before touching the ground, the gas generator of the tail end buffering module enables the buffering air bag to expand rapidly, impact energy is absorbed through compression deformation and controllable pressure relief, and safe landing is achieved. The problem that traditional lifesaving equipment cannot effectively work in the ultra-low-altitude environment is solved, the system has the advantages of being rapid in response, high in reliability and good in buffering effect, and the safety of the ultra-low-altitude aircraft is remarkably improved.

This invention relates to the field of aviation safety monitoring technology, and discloses an aircraft overload prediction method, system, electronic equipment, and storage medium. The method includes: calculating air density based on flight environment parameters using the wet air state equation; calculating the true airspeed based on flight environment parameters and positioning parameters using vector synthesis; calculating the standard engine thrust based on EDR parameters; calculating the actual engine thrust using an environmental correction model; calculating lift and drag using an aerodynamic model; calculating the instantaneous overload value based on the balance relationship between the synthesized total normal force and gravity; predicting future overload sequences using a deep learning model based on an input sequence composed of historical EDR parameters, flight environment parameters, and instantaneous overload values; counting the number of times the instantaneous overload value exceeds the overload threshold within a set time window, calculating the overload frequency per unit time, and outputting a turbulence intensity index. This invention can improve the conversion accuracy, prediction efficiency, and reliability of aircraft overload prediction.

The embodiment of the invention provides an aircraft stall early warning method and device, electronic equipment and a storage medium, and relates to the technical field of aviation safety. The method comprises the steps of obtaining flight data of an aircraft; the flight data comprises flight state parameters and meteorological environment parameters; inputting the flight data into a stall early warning model, and determining a stall risk assessment result of the aircraft; the stall early warning model is used for predicting the stall risk of the aircraft based on the action relationship between the flight state parameters and the meteorological environment parameters; and outputting a stall risk assessment result. The embodiment of the invention can be used for flight safety monitoring and decision support, can realize accurate early warning of stall risks in a complex dynamic flight environment, and reduces the occurrence probability of stall accidents.

The invention discloses a method for realizing digital signal processing of a VOR receiver based on an FPGA (Field Programmable Gate Array) technology, which relates to the technical field of radio navigation and digital signal processing and comprises the following steps of: constructing a VOR full-digital simulation signal source, and simulating signal input in a VOR system; performing digital down-conversion and filtering processing on the received radio frequency signal, and separating a variable phase signal and a reference phase signal through a digital filter; calculating a phase difference by adopting a zero-cross detection method; all the modules are integrated into an FPGA system, and performance testing is carried out. According to the invention, not only can the angle measurement precision, real-time performance and anti-interference capability of the VOR receiver be improved, but also the defects of large temperature drift and large size of the traditional analog receiver can be overcome, the characteristics of reconfigurability and parallel processing of the FPGA are utilized, the requirements of miniaturization and high reliability of modern aviation navigation equipment are met, the upgrading and updating of the full-digital VOR technology in the aviation field are promoted, and the application prospect is wide. And powerful technical support is provided for safe aviation flight.

This invention discloses a multi-dimensional data-driven quantitative assessment method and system for team situational awareness, relating to the fields of aviation safety and human factors engineering. The technical problem it addresses is that existing multi-dimensional data suffers from inconsistent dimensions and a lack of quantitative team collaboration, making it difficult to directly assess team situational awareness. This invention collects evaluation data and eye-tracking data from team members during task execution. Based on subjective data, it calculates individual situational awareness scores and constructs a team collaboration coefficient. Using the weighted average of individual situational awareness scores as a benchmark, it applies the team collaboration coefficient nonlinearly to obtain a comprehensive team situational awareness score. This score is then standardized and correlated with the eye-tracking data to obtain the analytical results. This invention achieves a quantitative assessment of team situational awareness by fusing subjective and objective data, providing a reference for evaluating team cognitive status.

The invention belongs to the field of aviation safety and flight data processing, particularly relates to a flight data-based aircraft re-flight climbing gradient determination method, and aims to solve the problem that the existing aircraft re-flight climbing gradient is inaccurate. The method comprises the following steps: based on flight data of a target aircraft, identifying a re-flight action starting moment and a re-flight performance reconstruction moment; based on the flight data, non-standard atmospheric environment factors and local geographical environment factors influencing the geometric height of the aircraft are corrected, the corrected geometric height of the target aircraft is obtained, and a corrected flight track is reconstructed in combination with the real-time horizontal position of the target aircraft; and according to the re-flight motion starting moment, the re-flight performance reconstruction moment and the corrected flight path, determining a re-flight net climbing gradient of the target aircraft in a re-flight initial stage formed from the re-flight motion starting moment to the re-flight performance reconstruction moment. According to the method, the re-flight net climbing gradient containing the initial height loss can be quantified, and the actual re-flight performance of the target aircraft is truly restored.

The invention relates to the technical field of aviation safety management, and discloses an obstacle assessment method and system based on airport clearance management. An obstacle assessment method based on airport clearance management comprises the following steps: S1, real-time data acquisition: performing high-frequency scanning on an airport clearance zone by using a laser radar to generate high-density three-dimensional point cloud data, and constructing a clearance zone digital twin base map through a point cloud data processing technology; an ADS-B signal is accessed, and aircraft position data are analyzed in real time; the aircraft position data and the digital twin base map are fused, and a spatial relation map of the temporary obstacle and the aircraft is constructed; meteorological data of an airport is collected based on a sensor. According to the invention, through dynamic evaluation models such as space intrusion factors and height conflict factors, obstacle risks are quantified and aircraft operation states are associated, so that the system can automatically identify sudden threats of temporary obstacles, and the defect that a traditional static model cannot adapt to dynamic scenes is overcome.

The invention relates to the technical field of aviation safety monitoring, provides a flight personnel operation state identification method and system based on multi-modal data fusion, and solves the problems of high misjudgment rate and poor environmental adaptability in the prior art. The method comprises the following steps: synchronously acquiring electroencephalogram signals of a flight personnel, angular velocity of a head inertial measurement unit, an eye movement track and pitch angle and roll angle parameters of an aircraft; dynamically segmenting the electroencephalogram signal to construct an electroencephalogram dynamic topological graph, and generating a head stability index; extracting wave persistence features of the forehead lobe brain region, and performing weighted fusion on the wave persistence features and the head stability index to generate a cognitive conflict feature vector; the eye movement track coordinates are converted into a cabin three-dimensional coordinate system to generate space codes, and space-time feature bodies are constructed through space-time convolution kernel fusion space distribution and track density gradients; and identifying the operation state to be normal, early-warning or out-of-control three levels. According to the invention, accurate grading early warning of the operation state of the flight personnel is realized, and the real-time performance and the accuracy of flight safety monitoring are remarkably improved.

The invention discloses an aircraft fuel tank composite inerting system and method based on liquid hydrogen cold energy gradient utilization, and belongs to the technical field of aviation safety. The system comprises a liquid hydrogen supply module, a membrane method nitrogen-rich gas generation module and a fuel tank active cooling module; the membrane method nitrogen-rich gas generation module comprises an electric compressor, a first micro-channel heat exchanger and an air separation membrane assembly; the electric compressor, a hot side channel of the first micro-channel heat exchanger and the air separation membrane assembly are sequentially connected, and a nitrogen-rich gas outlet of the air separation membrane assembly is connected with an oil tank of an airplane. The oil tank active cooling module comprises a second micro-channel heat exchanger and a cold plate; the cold plate is arranged in the oil tank; a hot side channel of the second micro-channel heat exchanger is connected with the cold plate to form a cooling medium circulation loop; the liquid hydrogen supply module, a cold side channel of the first micro-channel heat exchanger and a cold side channel of the second micro-channel heat exchanger are sequentially connected. The invention has the advantages of high energy efficiency, safety, reliability, strong environmental adaptability and the like.

The present application belongs to the field of aviation safety management, and relates to a pilot style competence evaluation and management method and system based on multi-modal data, aiming to solve the problems of subjective and one-sided, passive lag and data island of the existing evaluation method. The present application includes: constructing a digital competence index library, collecting multi-source data and performing real-time verification; fusing flight quality monitoring data, audio and text data, extracting unstructured features and converting them into multi-dimensional digital vectors; performing dynamic portrait calculation based on the vectors and feature data to generate a style deviation index; triggering a graded electronic early warning according to the comparison result of the index and the preset threshold value, and linking the scheduling system to implement blocking when the risk is high. The system correspondingly includes index library construction, data collection and verification, data fusion and feature extraction, risk assessment and early warning modules. The present application realizes the objective quantification, dynamic prediction and active intervention of pilot style, effectively improving the intelligentization and closed-loop level of safety management.