103 results about "Aviation safety" patented technology

The invention relates to a construction method of a Ti-SA model for pilot situational awareness assessment, belongs to the technical field of aviation safety and situational awareness, and solves the technical problems that an existing perception model cannot assess the situational awareness ability of a pilot in real time, and selected data cannot adapt to a complex environment, so that the effectiveness is poor. Comprising the steps that S1, eye movement trace time sequence data and AOI fixation time sequence data in the simulated flight process of multiple pilots are acquired to serve as sample data, and the sample data and generated corresponding category labels form a training data set; wherein the category label is a level label of the scene awareness of the pilot; step S2, constructing a Ti-SA model used for pilot scene awareness assessment; and training the Ti-SA model by using the training data set until the trained Ti-SA model is obtained when the loss function converges. The method achieves the real-time evaluation of the situational awareness capability of the pilot through the Ti-SA model.

The invention discloses a small-probability aviation unsafe incident cause analysis and online monitoring method and system based on flight data, and belongs to the field of aviation safety technology and flight data application. The method comprises the following steps: S0, determining an input parameter set through an aircraft flight quality monitoring standard, and constructing a risk identification architecture I based on weak supervised learning; s1, based on the risk identification architecture I, obtaining a risk identification network I and a quantitative risk value, and obtaining a high-risk time point and an inducement by using statistical analysis; and S2, inheriting the ability of the risk identification network I to identify the risk of the unsafe event to a risk identification network II by using a knowledge distillation method, obtaining the risk identification network II for online monitoring, and carrying out online risk detection based on the risk identification network II for online monitoring.

The invention relates to a flight data anomaly detection method and system based on dual-view enhanced Transform, and belongs to the technical field of aviation safety. According to the invention, the task of flight safety anomaly detection is expanded beyond a traditional overrun event, aiming at the subtle anomaly which is often not detected in the flight process; according to the fact that QAR data is a typical form of time series data, a flight safety problem is re-expressed as an anomaly detection task in time series analysis; in addition, an unsupervised anomaly detection model specially designed for QAR data is designed in order to solve the challenge of excessively depending on expert marking data in flight safety research. And finally, designing a time sequence anomaly detection model architecture based on reconstruction according to the feature sparse characteristic of the QAR data. According to the method, the condition of deviating from the normal flight mode can be identified in time, early warning information is provided for airlines, experts are effectively assisted in analyzing abnormal flight behaviors, and the method has a wide application prospect.

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, in particular to an undercarriage safety pin and airspeed sleeve recovery integrity recognition method and device, and the recognition method comprises the steps: obtaining a real-time image of the interior of a tool box; the tool box is used for placing a plurality of safety pins and a plurality of airspeed sleeves recovered from the undercarriage, and a plurality of first areas for placing the safety pins and a plurality of second areas for placing the airspeed sleeves are arranged in the tool box; based on a target detection model, identifying whether each first area in the real-time image has a safety pin and whether each second area has an airspeed sleeve, and if yes, determining that all safety pins and airspeed sleeves are completely recovered; wherein the target detection model is a deep learning model which is obtained through a pre-training process and has adaptive weight parameters. According to the invention, the recovery integrity of the undercarriage safety pin and the airspeed sleeve can be accurately identified in real time.

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 present application relates to the technical field of aviation safety. Disclosed is a method for assessing the impact of flicker and glare of a photovoltaic panel on aviation safety. The method comprises: a specular-reflection analysis model calculating beam irradiances of specular reflections of sunlight on focusing reflectors in photovoltaic panels at different distances, and corresponding sizes of reflected images of the sun, which reflected images are observed by human eyes, and further calculating different light-source angular subtenses, and human-eye retinal irradiances of the specular reflections, and assessing the probability and risk of visual persistence on the basis of the different light-source angular subtenses and the corresponding retinal irradiances; a diffuse-reflection analysis model calculating diffuse-reflection irradiances of the sunlight at different diffuse-reflection source distances, and further calculating human-eye retinal irradiances of corresponding diffuse reflections; and in view of the retinal irradiances of the specular reflections and the retinal irradiances of the diffuse reflections, a safety impact analysis model analyzing aviation safety impact factor values corresponding to the flicker and glare of the specular reflections of the focusing reflectors. Further disclosed is a system for assessing the impact of flicker and glare of a photovoltaic panel on aviation safety.

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 discloses a safety interval calculation method for an unmanned aerial vehicle and a manned aircraft, and relates to the technical field of aviation safety. The safe interval calculation method comprises the following steps: step 1, firstly obtaining the length, wingspan and height of a first target, then obtaining the length, wingspan and height of a second target, marking the first target as a mass point at the moment, and making up for an error caused by volume loss when the first target is regarded as the mass point. A new collision model is used for carrying out quantitative analysis on the collision probability of a manned aerial vehicle and an unmanned aerial vehicle so as to find the relation between the flight interval and the collision probability and calibrate a reasonable safety interval, and the requirement of the safety interval with a fixed value for the airspace cost is high. Therefore, the dynamic safety interval is found by adopting interval division iterative calculation, and a more flexible dynamic safety interval between the unmanned aerial vehicle and the manned vehicle is given on the basis, so that the construction of a fusion flight system is accelerated.

The invention discloses an air traffic controller cognitive load detection method based on a neural network. The method belongs to the field of artificial intelligence and aviation safety management, and comprises the following steps: dynamically inducing a cognitive load through a task induction and interaction module based on a real air traffic control worker working scene (such as aircraft take-off and landing command, flight scheduling and the like), and feeding back a task completion state and a cognitive load level in real time; the electroencephalogram signal processing module carries out time-frequency domain feature extraction on the collected signals, global feature modeling is optimized by utilizing an agency attention mechanism, and the modeling capability and time causal consistency of a long-time sequence are improved by combining the optimized time sequence memory module and the causal convolution module; and the classification network outputs the cognitive load level of the air traffic keeper, and visual feedback is carried out through the evaluation module. According to the method, the real-time performance and accuracy of cognitive load detection are improved, and technical support is provided for aviation safety management and task optimization.

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 belongs to the technical field of open domain multi-modal classification, and provides an open domain multi-modal identification method for aviation safety hot events. According to the method, the text features and the image features of the aviation safety hot events are extracted, meanwhile, a memory enhancement module is introduced, a scene guide pseudo event filter is designed, final classification is obtained by combining source events and pseudo events, and more effective help is provided for coping with and managing the aviation safety hot events. Compared with the prior art, the multi-modal aviation security hotspot event classification task in a complex scene can be effectively solved. Meanwhile, the influence of unknown events on classification is considered, known events are distinguished from the unknown events, and the problem that the classification precision is reduced by the unknown events is avoided.

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 technical field of aviation safety, natural language processing and artificial intelligence, and particularly relates to an intelligent extraction method for general information of an aviation safety report based on an accident cause table. The method aims at solving the problem that an existing aviation safety information risk identification technology cannot efficiently and accurately identify the risk type and the risk reason of an aviation report. The method comprises the steps that an aviation accident report is acquired, translated and corrected, and a Chinese aviation data set is constructed; preprocessing data in the data set; converting the preprocessed text into numerical vectors and clustering the numerical vectors to generate accident cause clusters; performing subject naming and hierarchical division on the accident cause cluster to obtain various types of accident cause tables; performing fine tuning training on the BERT classification model; and acquiring a newly input aviation safety information text and a cause table corresponding to the newly input aviation safety information text, and obtaining an optimal accident matching cause by using a Ball-tree nearest neighbor algorithm. According to the invention, efficient and accurate identification of the risk type and the risk reason of the aviation report is realized.

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 discloses an ILS navigation method, device and equipment based on double channels and a storage medium, and relates to the field of aviation navigation. The specific implementation scheme comprises the following steps: receiving a radio frequency signal; performing frequency mixing processing on the radio frequency signal through a radio frequency processing module to obtain an intermediate frequency signal; respectively processing the intermediate frequency signal through a first processing module and a second processing module to respectively obtain first navigation data and second navigation data; determining a first comparison result through a first processing module according to the first navigation data, the second navigation data and a preset threshold value; determining a second comparison result through a second processing module according to the first navigation data, the second navigation data and a preset threshold value; and determining navigation data output by the ILS system according to the first comparison result and the second comparison result. The risk of navigation information error caused by single-channel failure of the ILS system can be reduced, the safety requirement is met, and the reliability of aviation safety is 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.