46 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 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 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 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 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.

The invention relates to the technical field of aviation safety, in particular to an aircraft heavy landing prediction method and device based on a dynamic window attention mechanism, and the method comprises the steps: preprocessing the original flight data of the real-time position of a target aircraft, carrying out the weighting of a plurality of physical quantities of the real-time position, generating an importance weight value matrix of the plurality of physical quantities, and carrying out the calculation of the importance weight value matrix; generating a splicing matrix; and acquiring a position code corresponding to the real-time position based on the splicing matrix, and acquiring a time window corresponding to the real-time position according to the position code corresponding to the real-time position and the splicing matrix so as to acquire a time unit corresponding to the real-time position, generate a gravity acceleration prediction value of the real-time position and generate a heavy landing prediction result of the aircraft. According to the method, the current flight state of the target aircraft can be known in real time from a certain height to a landing point through quantile distribution of the physical quantity, and the specific value of the gravitational acceleration of the aircraft at each moment in the whole landing stage is predicted, so that more accurate early warning on a heavy landing scene is realized.

The application discloses a high-temperature battery cell replacement control method and device and a computer readable storage medium, and relates to the field of aviation safety control. The method comprises the following steps: when a high-temperature battery cell exists in a battery compartment of an aircraft, the aircraft is controlled to transfer the high-temperature battery cell to a storage compartment of the aircraft through a first channel prearranged in the aircraft; the aircraft is controlled to transfer goods in the storage compartment to the battery compartment through a second channel prearranged in the aircraft; when the battery cell temperature of the high-temperature battery cell meets preset temperature conditions, the aircraft is controlled to throw the storage compartment out; and when the throwing movement of the storage compartment meets preset parameter conditions, the storage compartment is controlled to open a parachute. The application realizes a safer scheme of high-temperature battery cell air replacement and slow descent, effectively reduces the harmfulness of high-temperature battery cells to transported goods or external environment without affecting the transportation of goods, and guarantees the safety of the aircraft and the ground.

The invention provides an aircraft risk model training data set design method based on flight simulator data, and relates to the technical field of aviation safety and flight data analysis. Comprising four steps of analog machine and QA data parameter matching, flight analog machine data validity verification based on QAR initial condition reproduction, analog machine flight risk data acquisition and analog machine risk data set modeling and QAR data verification. According to the method, a mapping relation between QAR parameters and analog machine parameters is established through parameter matching, semantic alignment is realized, then analog machine data validity verification is carried out based on QAR initial condition reproduction, the consistency of simulation data and real data is ensured, multi-source risk data is synchronously acquired by using a data extraction platform, and a high-fidelity data set is constructed; and finally, carrying out training verification by using deep learning models such as LSTM and the like. According to the method, a sufficient and high-quality risk training data set can be generated, and a reliable data basis is provided for an aircraft risk identification model.

The application provides an updating method and system for aircraft icing identification based on real-time detection of aircraft. The method comprises obtaining real-time data of the aircraft to determine icing occurrence information; constructing an icing report membership function based on the icing occurrence information, which is used to represent the spatial and temporal distribution of icing occurrence probability; and combining the icing report membership function with a basic icing index to calculate an icing potential index, which is used to identify the three-dimensional distribution of the icing area. The above method realizes accurate positioning, dynamic prediction and real-time warning of icing, and improves aviation safety and flight efficiency.

The invention belongs to the technical field of airborne computer communication and aviation safety monitoring, and provides an aircraft wireless tire pressure sensor positioning method based on channel state information, and the method is realized by relying on a wireless network constructed by sensor nodes, relay nodes and gateway nodes. Receiving a wireless signal carrying the rotation acceleration of the aircraft tire sent by the sensor node through the relay node; candidate sensor nodes of the same wheel axle are selected from the channel state information of the wireless signals, and the left and right directions of the candidate sensor nodes are judged, so that the mapping relation between the sensor nodes and tires is established and then forwarded to the gateway node; and the gateway node gathers the position information of each relay node to generate a tire pressure sensor-tire position mapping table. According to the method, accurate and dynamic matching of the position relation between the tire pressure sensor and the aircraft tire is completed, so that a pilot or a ground crew can make a quick and accurate response to the real-time abnormity of a specific tire in time, and reliable guarantee is provided for safe driving of an aircraft.

The invention belongs to the field of aviation safety management, relates to a method and a system for evaluating and managing the wind competency of a pilot based on multi-modal data, and aims to solve the problems of subjective, one-sided, passive lagging and data islanding of an existing evaluation mode. The method comprises the following steps: constructing a digital competency index library, collecting multi-source data and performing real-time verification; fusing flight quality monitoring data and audio and text data, extracting unstructured features and converting the unstructured features into multi-dimensional digital vectors; performing dynamic portrait calculation on the basis of the vector and the feature data to generate a windage deviation index; and triggering graded electronic early warning according to a comparison result of the index and a preset threshold value, and linking a scheduling system to implement blocking in case of high risk. The system correspondingly comprises an index library construction module, a data acquisition and verification module, a data fusion and feature extraction module, a risk assessment and early warning module and the like. According to the invention, objective quantification, dynamic prediction and active intervention of the wind of the pilot are realized, and the intelligent and closed-loop level of safety management is effectively improved.

The application relates to the technical field of aviation safety management, and discloses an obstacle evaluation method and system based on airport clearance management. The obstacle evaluation method based on airport clearance management comprises the following steps: S1, real-time data acquisition, including: high-frequency scanning of an airport clearance area by means of a laser radar to generate high-density three-dimensional point cloud data, and then constructing a clearance area digital twin base map through point cloud data processing technology; accessing ADS-B signals to analyze aircraft position data in real time; fusing the aircraft position data with the digital twin base map to construct a spatial relationship atlas of temporary obstacles and aircrafts; and collecting meteorological data of the airport based on sensors. Through dynamic evaluation models such as a spatial intrusion factor and a height conflict factor, the application quantifies obstacle risks and correlates aircraft operation states, so that the system can automatically identify the sudden threats of temporary obstacles, and the deficiency that a traditional static model cannot adapt to dynamic scenes is made up.

The invention belongs to the technical field of aviation safety and weather prediction crossing, and discloses an air route turbulence intensity prediction method and system based on multi-source data assimilation and time-varying weighted collection.The method comprises the steps that multi-source data are directionally collected, inversion and turbulence diagnosis index calculation are conducted on the multi-source data based on different stages of flight, and the multi-source data are obtained; a turbulence dissipation rate EDR index is calculated; the method comprises the following steps: by taking a temperature-pressure wind field inverted by multi-source data as background data and a temperature-pressure wind field inverted by QAR data as observation data, realizing multi-source data assimilation through Kalman filtering, generating a high-temporal-spatial-resolution meteorological data set on an air route, and outputting an error covariance matrix representing the accuracy of an assimilation result; high-resolution assimilation data is used as input, an EDR index obtained by inverting a vertical wind frequency through QAR data is used as a reference label, a weight coefficient of a turbulence diagnosis index is optimized in real time through a machine learning method, a time-varying weighted set turbulence intensity index is constructed, and turbulence intensity prediction confidence is output.

The application discloses a kind of dual-kinetics Koopman operator flight trajectory anomaly detection method and system.The method is first to flight trajectory time series data is standardized and difference processing, and then based on singular value decomposition, system state variable is automatically separated into slow state subsystem and fast state subsystem;Local Koopman operator is independently constructed to two subsystems, and nonlinear dynamics is mapped into linear evolution process;From the operator, a variety of dynamically interpretable dynamics indexes are extracted, and the dynamics comprehensive distance is calculated by a weighted fusion module;Finally, based on statistical threshold, the anomaly is determined and the score is output.The application separates the mechanism of fast and slow dynamics, significantly improves the detection sensitivity of transient anomaly, while having good interpretability, high detection accuracy and low computing resource requirement, suitable for aviation safety monitoring and airborne real-time detection.

The application relates to the field of aviation safety, in particular to a low-altitude flight safety coordination system, which comprises a flight airspace demarcation module, an image intelligent recognition module, a position determination module, a collision risk judgment module, a route planning module and a management database. The system constructs an identification model through a convolutional neural network, acquires a flight airspace image, recognizes mobile objects and fixed obstacles in the flight airspace, marks the positions of the mobile objects and the fixed obstacles in the flight airspace image, predicts the motion trajectory of the mobile objects in the flight airspace image, judges whether the aircraft has a collision risk, determines the collision risk position of the aircraft, outputs a safe flight path segment by using a heuristic search algorithm, covers the whole process from environment perception to risk response, and greatly reduces the possibility of collision accidents of the aircraft.

The application belongs to the technical field of aviation safety and biomedical engineering, and discloses a navigation maintenance personnel fatigue risk estimation system and method, which comprises collecting objective data, subjective data and multi-life sign data; extracting and fusing subjective and objective fatigue cause factors, and outputting fatigue risk assessment results; pre-processing the multi-life sign data and extracting time-frequency characteristics; performing correlation analysis and difference analysis on the time-frequency characteristics of the multi-life sign data, establishing a fatigue state recognition model based on the multi-life sign data, and outputting fatigue cause factors corresponding to each physiological channel; using a subjective and objective weighting method to weight and fuse the fatigue risk assessment results and the fatigue cause factors, and outputting real-time fatigue risk prediction results; the application also provides a system for realizing the above method; through subjective and objective data fusion, multi-physiological channel recognition and dynamic weight distribution of multi-cause factors, real-time estimation of the fatigue risk of navigation maintenance personnel is realized.