15835 results about "Aviation" patented technology

The present invention discloses an aero engine bearing fault diagnosis method based on STFT-IncepNext, which includes the following steps: step 1: collect acceleration signals under various working conditions and fault conditions of the bearing as sample data; step 2: determine the number of sampling points of each sensor first, and perform data merging by permuting and combining data collected by sensors at different positions in the same time series, and then use short-time Fourier transform to convert the fused vibration signal into a time-frequency diagram; step 3: data preprocessing: divide into training set, test set and validation set according to fault type, and normalize the converted time-frequency diagram using average value and standard difference; step 4: build an IncepNext model, train and validate it; and step 5: use the trained IncepNext model to diagnose aero engine bearing faults and obtain a diagnosis result. The present invention can effectively identify aero engine bearing faults, can guarantee the fault identification rate, and has good accuracy.

Disclosed in the present invention are an aviation equipment reliability evaluation method and system based on a knowledge graph and model inference. The method comprises: acquiring data of human factors, equipment systems, and a working environment of aviation equipment; carrying out preprocessing and text labeling on the acquired data; inputting the labeled text information into a constructed entity relationship joint extraction model to form a high-quality structured triple of the knowledge graph; constructing an elastic knowledge graph for the aviation equipment, wherein the elastic knowledge graph comprises an online knowledge graph and an offline knowledge graph which has aviation equipment reliability; and extracting semantic features, and analyzing the similarity between the extracted features to realize indirect inference of the aviation equipment reliability. The present invention fully fuses expert experience and knowledge data, and exerts respective advantages of a human brain and machine intelligence, so as to achieve accurate analysis and prediction of aviation equipment reliability, thereby providing intelligent risk analysis, early warning and optimization suggestions for command and control personnel, and reducing a fault occurrence rate.

The invention relates to an aviation standard question and answer optimization method and system based on atlas and document data, and belongs to the field of natural language processing in the aviation field, and the method comprises the steps: S1, recognizing the intention of a user question based on a BERT model; s2, constructing an aviation enterprise standard attribute knowledge graph; s3, constructing an aviation enterprise standard text knowledge base; s4, recalling aviation enterprise standard attribute question knowledge; s5, recalling aviation enterprise standard content question knowledge; and S6, performing big model intelligent question answering based on RAG, and inputting a recall result and an original question of the user into the pre-trained big model through a retrieval enhancement generation technology to serve as knowledge reference to assist in generating answers. According to the method, the knowledge graph of the aviation enterprise standard is constructed, the large model RAG technology based on the knowledge graph is researched, the problem that entities and relations in user questions cannot be intelligently understood in the existing standard attribute information retrieval process is solved, and the accuracy and efficiency of standard attribute information question answering can be improved.

The present invention is directed to systems and methods for monitoring activities in an aviation environment. The system includes at least two monitoring units, each including at least two types of sensors, wherein: the sensors are mounted at a plurality of locations in the aviation environment. The system further includes a processing system being configured to receive said information from the sensors, to process said information to monitor and make predictions, and to combine sensor information by applying data fusion. The system is further configured to compare sensor information with predetermined safety operation criteria, and to generate an alert signal. The method of the invention includes obtaining sensor information, receiving said information from the sensors at a processing system, processing said information, comparing the processed information with predetermined safety operation criteria, and generating an alert signal.

The invention discloses a low-altitude airspace management method and system based on data analysis, and relates to the technical field of air traffic management and low-altitude airspace planning, and the method comprises the steps: 1, multi-scale dynamic grid division: adjusting the grid granularity in real time according to the aircraft density and environment complexity, generating a multi-scale grid by adopting an optimized quadtree data structure, and carrying out the multi-scale dynamic grid division; boundary smoothing processing is carried out on boundary areas of the grids with different scales; 2, intelligent airspace layering: dynamically adjusting the thickness of a height layer according to a risk score, and setting a buffer layer to enhance the flight safety; and 3, intelligent semantic annotation: giving dynamic semantic information such as risk levels and traffic priorities to the grid units, embedding airspace control regulations and flight rules, and realizing multi-source data fusion and real-time updating of semantic information. According to the method, the problems of static division limitation, boundary problem, insufficient real-time performance and the like in the prior art are solved, and the airspace resource utilization rate and the flight safety are improved.

An aero-engine augmented reality virtual-real fusion method based on depth prior scene reconstruction comprises the following steps: firstly, collecting data of an aero-engine look-around RGB image and a depth image, calculating a camera frame pose by using an SFM algorithm and obtaining a sparse point cloud, and optimizing the camera pose by using an ICP algorithm and the point cloud converted from the depth image to obtain an aligned prior dense point cloud; gaussian points are initialized based on the priori dense point cloud and the sparse point cloud, and random generation of 3D Gaussian is limited by using the boundary of the dense point cloud as geometric constraint; through the three-dimensional reconstruction of the adjacent multi-view enhancement strategy, the spatial and visual consistency is improved, and an aero-engine model is reconstructed; then understanding the reconstructed aero-engine model based on semantic segmentation, identifying and marking core components, and finally performing virtual-real fusion visualization on the reconstructed aero-engine through augmented reality to guide training, assembling and maintenance operations. According to the invention, rapid high-quality three-dimensional reconstruction and augmented reality virtual-real fusion visualization of the aero-engine are realized.

The invention provides an aircraft and satellite cluster collaborative navigation positioning accurate control method and system, and relates to the technical field of aviation, and the method comprises the steps: an aircraft receives a multi-system navigation signal from a satellite cluster, carries out the fusion processing of multi-source navigation information through employing a self-adaptive Kalman filtering algorithm, and carries out the navigation positioning of a satellite cluster based on the fused multi-source navigation information. Calculating the accurate position of the aircraft by adopting a differential positioning technology; and transmitting the optimal control instruction to a flight control system of the aircraft based on the accurate position of the aircraft in combination with a preset flight plan, calculating a correction instruction by using a satellite-ground cooperative trajectory correction algorithm when trajectory deviation is detected, and transmitting the correction instruction to the aircraft through a high-frequency broadband communication system, and closed-loop accurate control of the track of the aircraft is realized.

An engine for aeromechanical instability abatement includes a sensor configured to capture data from rotating blades of the engine system, a airflow effector device, and an engine controller. The engine controller is configured to control the airflow effector device according to a nominal schedule, detect, based on a signal from the sensor indicating a vibration amplitude of the rotating blades within a frequency band, an incipient instability condition, in response to the incipient instability condition being present, determine a modified control parameter for at least one of the airflow effector device, and control the airflow effector device according to the modified control parameter, deviating from the nominal schedule.

The invention provides an aviation video stream target identification processing method and system, relates to the technical field of image processing, artificial intelligence and aviation monitoring, and aims to realize the target identification processing of an aviation video stream through preprocessing modal data such as video, infrared and radar and combining an adaptive weighted fusion technology, a multi-stage visual feature extraction and a global convolution optimization technology. The system can extract target features from multi-level and multi-modal data; and a historical motion track and future motion prediction are further combined, the system dynamically corrects the target position, and finally the consistency of multi-modal data is ensured through cross validation. According to the method, the recognition precision and robustness of the system to weak and small targets are enhanced, missing detection and false detection are reduced, the adaptability is high, the requirement for computing resources is low, and the method is suitable for target recognition and tracking tasks in a complex dynamic environment.

The invention discloses an aviation converter fault detection method based on an information fusion algorithm, and the method comprises the steps: collecting the operation data of an aviation converter, and constructing a data set; performing time information extraction and space information extraction on the data in the data set, and performing feature fusion on time features and space features based on a variational auto-encoder to obtain spatio-temporal data; constructing a Bayesian deep learning model, and training and optimizing the Bayesian deep learning model; inputting the spatio-temporal data into the trained and optimized Bayesian deep learning model for processing, outputting a numerical value representing a fault occurrence probability, and taking a variance based on the probability numerical value as uncertainty estimation; presetting a probability threshold, and when the output probability is greater than the threshold, determining that the converter has a fault; otherwise, judging that the operation state is normal; a more accurate and reliable diagnosis result is provided for fault detection, and the method has high adaptability and robustness in practical application.

The invention provides an unmanned aerial vehicle array type aviation multi-component electric field and magnetic field cooperative detection system and detection method. The detection system comprises an unmanned aerial vehicle array, a transmitting unit, a plurality of receiving units and a ground auxiliary unit. The unmanned aerial vehicle array comprises a master unmanned aerial vehicle and a plurality of slave unmanned aerial vehicles; the transmitting unit is used for transmitting primary electromagnetic field pulses; the transmitting unit comprises a loop source transmitting device, and the loop source transmitting device comprises a current transmitter, a transmitting coil and a compensating coil; the plurality of receiving units are respectively mounted under the master unmanned aerial vehicle and the slave unmanned aerial vehicle and are used for acquiring electromagnetic data after the primary electromagnetic field pulse acts on the to-be-detected area, and the electromagnetic data comprise a horizontal component electric field signal and a three-component magnetic field signal; each receiving unit comprises a capacitive electric field sensor, a three-component coil magnetic field sensor and an electromagnetic data receiver; and the ground auxiliary unit is used for controlling acquisition of the horizontal component electric field signal and the three-component magnetic field signal, and performing electromagnetic data processing to obtain underground space electrical information of the detection area.

The invention relates to the technical field of aviation communication, and discloses a true north calibration system and method for an aviation airborne laser communication equipment alignment system, and the method comprises the steps: synchronously collecting flight data through an IMU sensor and a double-antenna GPS at a preset sampling frequency, and outputting the real-time attitude angle information of an aircraft through an IMU; filtering and de-noising the synchronously acquired IMU and double-antenna GPS data, and carrying out noise suppression and deviation compensation on accelerometer and gyroscope data output by the IMU by adopting a Kalman filtering algorithm; based on the preprocessed sensor data, calculating an initial course angle by measuring relative position change and phase difference between antennas by using a double-antenna GPS (Global Positioning System); the calibration controller transmits the calculated true north direction information to an alignment system of the laser communication equipment in real time through a communication interface; and the attitude change and external environment disturbance of the aircraft are continuously monitored in the whole flight process. The method has the advantage that the alignment performance and the communication stability of the laser communication equipment are improved.

The invention relates to an aviation turbine blade service damage monitoring method based on digital twinning, belongs to the technical field of aero-engines, and solves the problems that an existing detection algorithm is high in calculation complexity and low in efficiency and precision. Determining a to-be-monitored turbine blade physical entity, and collecting turbine blade environment information; a digital twin is constructed on the basis of a physical entity, the turbine blade three-dimensional model construction module constructs a turbine blade three-dimensional model according to the physical entity, and the turbine blade operation environment simulation module performs environment simulation on the model according to real-time load, rotation speed and environment temperature data of a turbine blade. And the turbine blade defect evolution prediction module identifies blade surface defects of the model at each moment by using an intelligent defect detection algorithm to obtain the category, position information and size information of the defects, and performs evolution simulation and danger coefficient analysis. According to the invention, real-time on-line, high-precision and high-efficiency detection of the surface defects of the aviation turbine blade is realized, and the service life of the turbine blade is prolonged.

Unfaired fan for aeronautical propulsion. Unfaired propulsive fan (100) comprising at least a first row of blades (110) capable of rotating about a central axis (X). Each of the blades (110) of the first row comprises a streamlined body extending radially from an inner radius Ri to an outer radius Re with respect to the central axis (X) and comprising an intrados (111) and an extrados (112) connecting a leading edge (BA) to a trailing edge (BF). The trailing edge (BF) of the profiled body of each blade (110) of the first row of blades (110) has teeth with spacings (λk) between adjacent tooth apexes that decrease monotonically in a radial direction towards the outer radius Re and the profiled body of each blade (110) of the first row of blades (110) has an activity factor FA between 100 and 225. Figure for the abstract: Fig. 2.

The invention relates to the technical field of aeronautical equipment fault detection, and discloses an aeronautical converter fault feature extraction and diagnosis method based on multi-source information, which is characterized in that time sequence data and spatial distribution data are respectively collected and preprocessed, and the accuracy and the stability of the data are improved. Time feature extraction, spatial feature extraction, frequency spectrum acquisition and feature extraction are respectively carried out on the preprocessed data to realize fusion of multi-source information; information from different types of sensors and different feature domains is integrated, and multi-dimensional information of the equipment operation state is fully excavated, so that the accuracy and reliability of fault detection are improved. And finally, performing envelope detection on the signal to obtain an envelope line of signal amplitude change, and analyzing characteristic parameters of the envelope to detect fault information or pulse type fault characteristics in the modulation signal. And based on a deep convolutional neural network, learning a complex relationship between the envelope features and the fault, and realizing the diagnosis of the aviation airborne converter fault.

The invention belongs to the technical field of aviation energy design, and particularly provides an aviation energy power device fault intelligent inquiry system and method based on a knowledge graph. The method comprises the following steps: firstly, showing a complex data structure and a knowledge relationship to a user in a visual and dynamic manner through knowledge extraction, knowledge graph database construction, knowledge graph visualization and other methods, and constructing a knowledge graph basic framework; according to the method, the depth information of the target object can be extracted from the images from different angles, the fault characteristics of the equipment can be intuitively displayed, and the fault state of the equipment can be visually represented by extracting the depth information of the target object from the images from different angles. The evolution of the equipment state can be tracked through the change of the three-dimensional space; a conversion algorithm of the knowledge graph and the Bayesian network is realized through the Bayesian network, and rapid reasoning of fault causes is realized in combination with a Bayesian network reasoning algorithm; and finally, based on an intelligent inquiry system of a large model, a deep learning technology and a large-scale training model are utilized, and previous fault data and professional knowledge of the aviation energy power device are combined to provide intelligent auxiliary diagnosis support.

The invention discloses an air ticket recommendation method and device based on a large language model and electronic equipment, and relates to the field of artificial intelligence and the field of aviation services. The method comprises the following steps: in response to an air ticket query request initiated by a user, obtaining a service scene prompt word, a session history and service information through an intelligent agent; obtaining preference information of a user and state information of each route; a basic template is generated according to the service scene cue word, knowledge information is extracted from the session history, the service information, the preference information of the user and the state information of each route, the basic template is filled with the knowledge information, and a target knowledge cue word is obtained; and processing the air ticket query request according to the target knowledge cue through the large language model, and generating at least one piece of recommended air ticket information for the user. The technical problems that in the prior art, the air ticket recommendation accuracy for the user is low, and the user experience feeling is poor are solved.

Disclosed are devices, systems and methods for mission-adaptable aerial vehicle. In some aspects, a mission-adaptable aerial vehicle includes a configuration having swappable, manipulatable, and interchangeable sections and components connectable by a connection and fastening system able to be modified by an end-user in the field. In some embodiments, a mission-adaptable aerial vehicle can be configured to include a main center body extending along a longitudinal direction, a wing with a lateral cross-sectional airfoil shape, and / or stabilizer and control surface structures with corresponding cross-sectional airfoil shapes.

The invention discloses a multi-aircraft 4D trajectory prediction method based on a graph attention hybrid neural network, and belongs to the technical field of aviation. Historical trajectory implicit information of different aircrafts in an airspace is extracted by utilizing Transform and a long-short-term memory network, the aircrafts in the airspace are regarded as nodes, an aircraft connection network graph is constructed, and modeling is performed on the aircraft implicit information and the aircraft connection network graph through a graph attention network so as to capture interaction information between the aircrafts. The graph attention network integrates the track implicit information of all aircrafts in the airspace and shares and updates the track implicit information, finally decodes the track implicit feature information of the aircrafts through two decoders based on the long and short term memory network, fuses the output of the two decoders, and outputs a prediction track through a full connection layer. Compared with a method which does not consider aircraft space interaction information, the accuracy of flight trajectory prediction is improved, and an accurate trajectory prediction technology provides support for a subsequent risk perception technology and a conflict resolution technology.

The invention discloses an aircraft clear sky bumping prediction method, device, equipment, medium and product, and relates to the field of aeronautical meteorology, the method comprises the following steps: obtaining a sample set, the sample set comprising a plurality of samples, one sample comprising bumping observation data when a sample aircraft flies at the same time and in the same space and values of meteorological data; calculating the value of each CAT diagnosis index according to the value of each meteorological data corresponding to the sample to obtain a CAT diagnosis value sequence corresponding to the sample; constructing a total data set according to the CAT diagnosis value sequence corresponding to each sample and the jolt observation data corresponding to each sample; sequentially performing data quality control, replacement of the bumping observation data with the turbulence label and oversampling processing on the total data set to obtain a data set; the W-KNN model is trained and verified through the data set to obtain a clear sky bumping prediction model, the clear sky bumping prediction model is used for predicting the clear sky bumping of the aircraft to be predicted, and the accuracy of the clear sky bumping prediction result can be improved.

The invention provides an aviation fastener service life prediction method and system based on stress analysis, and the method comprises the steps: collecting a dynamic stress data set of a target fastener in a service environment, carrying out the three-dimensional stress field reconstruction of the dynamic stress data, and obtaining a three-dimensional stress field model; the method comprises the following steps: generating stress distribution characteristics including an axial stress gradient, a radial stress cumulant and a tangential stress fluctuation coefficient, and then calling a pre-trained life prediction model to carry out nonlinear degradation analysis on the stress distribution characteristics to obtain a residual life prediction value and a key failure area identifier of a target fastener. Further, on the basis of the incidence relation between the temperature fluctuation sequence and the material thermal expansion coefficient, service environment compensation correction is conducted on the residual life prediction value, and finally, a fastener maintenance strategy set containing a stress release path adjustment scheme and a surface strengthening treatment scheme is generated according to the key failure area identifier; and precise prediction and effective maintenance of the service life of the aviation fastener are realized.

Disclosed are devices, systems and methods for mission-adaptable aerial vehicle. In some aspects, a mission-adaptable aerial vehicle includes a configuration having swappable, manipulatable, and interchangeable sections and components connectable by a connection and fastening system able to be modified by an end-user in the field. In some embodiments, a mission-adaptable aerial vehicle can be configured to include a main center body extending along a longitudinal direction, a wing with a lateral cross-sectional airfoil shape, and / or stabilizer and control surface structures with corresponding cross-sectional airfoil shapes.

The invention relates to an aviation big data intelligent analysis method based on trajectory anomaly detection, and belongs to the technical field of aviation data processing. The method comprises the following steps: acquiring operation state data and flight parameters of an aircraft, and extracting aerodynamic parameters of an aircraft type and deviation data corresponding to horizontal navigation and vertical navigation; carrying out stream batch integrated processing on the data through a real-time processing engine, and carrying out space-time reference unification to obtain standardized data; performing data cleaning, abnormal point correction and missing value interpolation on the standardized data; performing pneumatic-performance correlation analysis after data processing, and quantifying the influence of parameter deviation on the climbing performance through a dynamic response model to obtain a strong correlation coefficient; performing coupling analysis through the climbing rate attenuation prediction model to obtain an aircraft climbing performance anomaly prediction result; and generating a correction instruction according to the persistent climb rate anomaly of the performance anomaly prediction result. The efficient and accurate analysis of the aviation big data is realized, and the safety and reliability of the operation of the aircraft are effectively improved.

The invention provides a region-level aviation flow prediction method based on Mamb-GCN, and belongs to the technical field of air traffic flow prediction, and the method comprises the steps: constructing a Mamb-GNC collaborative network model; historical flight path data of a target airspace is collected, the target airspace is divided into space grids, the number of aircrafts in each space grid is counted, and the time feature and the space feature of each aircraft are coded to construct a space-time tensor; constructing a dynamic adjacency matrix and a dynamic weight map based on the 8-neighborhood topology of the space grid; inputting the space-time tensor and the dynamic weight graph into a Mamba-GCN collaborative network model for training, and optimizing model parameters; and preprocessing the aviation trajectory data of the target airspace acquired in real time, and inputting the preprocessed aviation trajectory data into the trained Mamba-GCN collaborative network model to obtain an aviation flow prediction result. According to the method, the long-time dependence of the aviation flow in the time dimension and the grid correlation in the space dimension can be captured, and the prediction efficiency is high.

The invention belongs to the technical field of aviation accessory production, and particularly relates to a welding machining system and method for assembling a combined aviation aluminum shell. The inner supporting plate bending assembly and the inner supporting plate punch forming assembly are jointly used for conducting punching machining on the upper side and the lower side of the joint, a protruding plate which is folded inwards is formed in the punching forming process, and the inner supporting plate bending assembly is used for bending the protruding plate to form an inner supporting plate; the inner side and the outer side of the joint can be welded at the same time through the joint welding assembly; by means of the inner supporting plate punching assembly, an installation hole facilitating installation of a threading plate through a bolt can be formed and machined in the inner supporting plate, in this way, main components of the combined type aviation aluminum shell can be reliably clamped, and reliable anti-deformation supporting is provided in the trepanning and welding process; two sides of the weld are uniformly heated, and internal stress is relatively balanced, so that deformation is reduced, and the overall quality of the weld is improved.

The invention relates to the technical field of aviation, in particular to a low-altitude flight micrometeorological intelligent decision-making system based on multi-source sensing and AI fusion, and aims at solving the problems of difficulty in local disturbance identification, insufficient meteorological data granularity, weak flight task adaptation and the like in urban and complex terrain low-altitude flight scenes. According to the system, multi-dimensional meteorological data such as wind speed, wind direction, temperature and humidity are acquired through cooperation of fixed nodes and an air-based unmanned aerial vehicle, a risk area is dynamically identified in combination with a disturbance index model, and meteorological perception with minute-level and hectometer-level resolution is realized. The system supports intelligent perception scheduling, edge AI processing and multi-link communication transmission based on task characteristics, has task adaptability evaluation, flight path optimization, risk early warning and closed-loop learning capabilities, is suitable for scenes such as low-altitude airspace management, flight examination and approval, urban air traffic and the like, and remarkably improves the safety and intelligent level of low-altitude operation.

The invention belongs to the technical field of data processing, relates to an aircraft multi-mode instruction conflict resolution method and system, and aims to solve the problems of high conflict detection time delay and priority strategy staticization of a traditional method. The method comprises the steps that after a multi-mode instruction is received, semantic feature extraction and time sequence alignment are completed through a three-layer attention mechanism and a dynamic time warping algorithm, and a structured instruction set is generated; evaluating the priority of each instruction by combining a four-dimensional dynamic weight system with reinforcement learning; inputting the instruction set with the priority into a six-tuple model to identify a conflict type and an instruction set; and a three-level progressive arbitration method is adopted to resolve conflicts, an optimal execution sequence is generated, and the four-dimensional dynamic weight system is fed back and optimized according to an execution result. According to the method, the real-time performance and the dynamic adaptability of multi-mode instruction conflict resolution are remarkably improved, and reliable guarantee is provided for aviation instruction interaction safety.

The invention relates to the technical field of aviation information, in particular to a flight data change publishing system and method, and the system comprises a flight state monitoring module, a network modeling and analysis module, a flight influence prediction module, an optimization scheduling decision module, a scheduling execution and state updating module, and an information publishing and updating module. According to the method, the state change of the flights and the airport is monitored in real time, passengers and airport operators are ensured to obtain real-time flight information, key nodes and influence chains in an aviation network are identified by analyzing the mutual influence relationship among the flights and constructing an interaction relationship graph among the flights, and flight faults and changes are predicted and coped with. According to the method, system analysis is carried out on the influence of flight change, the flexibility and efficiency of flight scheduling are optimized, inconvenience to passengers and operation chaos caused by inaccurate information and untimely updating are reduced, and the experience and satisfaction degree of the passengers are enhanced by updating an airport information system and navigation facilities and pushing real-time updating to the passengers.

The invention discloses a high-precision surveying and mapping method based on surveying and mapping aerial photography, and relates to the technical field of surveying and mapping engineering. S2, selecting aerial photography equipment; s3, making a flight plan; s4, arranging ground control points; s5, aerial photography data acquisition; s6, data preprocessing and image splicing; s7, performing aerial triangulation and model reconstruction; s8, performing multi-sensor data fusion; s9, data analysis and evaluation; and S10, outputting and applying the result. According to the high-precision surveying and mapping method based on surveying and mapping aerial photography, the high-precision real-time image correction and enhancement technology is combined, and the efficiency and precision of a surveying and mapping task can be remarkably improved; the flight path can be quickly optimized through quantum calculation, and the breadth and quality of image acquisition are ensured; real-time image correction and enhancement process image data in the flight process, the workload of post-processing is reduced, and the surveying and mapping process is more intelligent, efficient and flexible.

The invention belongs to the field of geophysical exploration, and particularly relates to an aviation natural source electromagnetic method complex medium parallel three-dimensional forward modeling method. Comprising the following steps: S1, constructing a three-dimensional geological geometric model of a research area; s2, generating a model unstructured tetrahedral mesh; s3, setting anisotropic conductivity tensor and magnetic conductivity of the model unstructured tetrahedral mesh; s4, constructing a frequency domain electric field control equation; s5, adopting a vector finite element discrete electric field control equation; s6, setting boundary conditions; s7, solving a finite element linear equation; s8, calculating three components of an electric field and a magnetic field at the measuring point and the base station; and S9, calculating impedance, apparent resistivity, phase and tipper data at the measuring point. According to the method, a three-dimensional forward modeling algorithm is provided for the aviation natural field source electromagnetic method, a basis is provided for research of the aviation natural field source electromagnetic method theory and inversion interpretation of actual observation data, and then the aviation natural field source electromagnetic method can better serve the application fields of mineral exploration, engineering exploration and the like.