61 results about "Flight dynamics" patented technology

The invention discloses an end-to-end obstacle avoidance method considering flight dynamics of a fixed-wing unmanned aerial vehicle, and the method comprises the steps: constructing a multi-source data synchronous collection module, and obtaining a monocular depth image and inertial navigation state data which are aligned in time sequence; an enhanced visual perception coding module is designed, and spatial position information of a tiny obstacle is reserved at the tail end of feature extraction through an asymmetric convolution strategy and a parallel spatial attention mechanism; constructing a state fusion and time sequence reasoning module, performing long and short time sequence modeling on the multi-modal features by using an encoder with a causal mask mechanism, and outputting potential decision variables; and constructing a kinematics constraint trajectory generation module, and mapping the neural network output into a three-dimensional control trajectory conforming to flight mechanics limitation. The problems that spatial information of a traditional convolutional neural network is seriously lost, an end-to-end model easily generates an illusion trajectory exceeding a physical limit and the like are solved, and the flight safety and robustness of the fixed-wing unmanned aerial vehicle in a complex low-altitude environment are remarkably improved.

The invention relates to a tilt rotorcraft semi-physical simulation system and method, and relates to the technical field of wind tunnel flight test semi-physical simulation. Based on the initialized system with the test parameters set, six-component balance load, wind speed and attitude data are collected in real time, and the collected key physical data are filtered and then sent to the flight simulation subsystem; the flight simulation subsystem carries out calculation based on a flight dynamics model, a rotor wing power model, a control instruction preprocessing model and the received data and outputs an actuation instruction; the actuator receives an actuation instruction and drives the aircraft model, and the airborne sensor feeds back new attitude data to form a control closed loop; the flight simulation subsystem sends state data to the 3D visual simulation module in real time to realize synchronization of control and visual; in this way, the stable state under the combination of different tilting angles and flight speeds is recorded, and a tilting transition corridor is obtained through fitting. Therefore, simulation precision, test safety and iteration efficiency are taken into consideration, and fused semi-physical simulation is formed.

A method for controlling anti-skid braking in civil aircraft involves constructing an anti-skid braking model and flight dynamics and kinematics models of the aircraft to analyze the forces acting on the aircraft system and obtain the corresponding forces and torques. For the anti-skid braking system, a BSMC controller is designed to control the braking coefficient μ. Brake This invention controls the braking force to ultimately control the anti-skid braking system of civil aircraft. It uses a backstepping slip mode controller (BSMC) to control the aircraft's braking commands, ensuring the system maintains the optimal slip ratio during braking, maximizing braking efficiency, reducing wheel wear, and minimizing the cost of the aircraft's braking system.

The invention belongs to the technical field of synthetic aperture radars, and relates to a bistatic SAR receiver flight path planning method, electronic equipment and a storage medium. The method comprises the following steps: establishing motion models of a transmitter, a moving target and a receiver; establishing a three-dimensional proportional guidance model, and establishing a motion differential equation of the receiver driven by the enhanced proportional guidance law as a guidance flight path; establishing a mixed integer nonlinear programming model taking the optimal approximation guide flight path as a target function, and taking an imaging constraint, a guide terminal constraint, a flight dynamics constraint and a system communication constraint as constraint conditions; and solving the mixed integer nonlinear programming model to obtain the optimal flight path of the receiver meeting the guidance and imaging dual constraints. According to the method, the small miss distance guidance precision of the maneuvering target can be ensured in the terminal guidance stage; the terminal guidance performance and the SAR imaging quality are both considered, the feasible solution space is greatly expanded, and the feasibility and optimization performance of trajectory planning under complex constraint conditions are improved.

The invention provides an unmanned aerial vehicle supervision system based on an electronic license plate. The system comprises a terminal layer, a network layer, a service platform and a supervision platform. Wherein the terminal layer comprises at least one unmanned aerial vehicle provided with an electronic license plate, and the electronic license plate is used for collecting flight dynamic data of the unmanned aerial vehicle in real time and supervising the unmanned aerial vehicle according to the flight dynamic data and electronic fence information issued by the supervision platform; the network layer is used for reporting the flight dynamic data to a service platform and synchronizing the electronic fence information to each electronic license plate; the business platform is used for performing data analysis on the flight dynamic data and reporting the flight dynamic data to the supervision platform; and the supervision platform is used for formulating electronic fence information, generating a flight path according to the flight dynamic data, and supervising the unmanned aerial vehicle according to the flight path. According to the invention, the unmanned aerial vehicle can be better supervised.

This invention discloses an aircraft flight status identification method and system based on ADS-B, comprising: acquiring ADS-B data of the aircraft in real time and caching the ADS-B data in a high-speed cache database; extracting the latest ADS-B data points of the aircraft from the high-speed cache database at set time intervals, and storing multiple consecutively extracted latest ADS-B data points into a fixed-length queue of a preset length to form a temporal ADS-B data sequence; calculating estimated parameters for status determination by combining the aircraft's flight dynamic data with preset geospatial information; and identifying the current flight stage of the aircraft through a state machine model based on the temporal ADS-B data sequence and the estimated parameters, wherein the current flight stage includes one of taxiing, takeoff, climb, cruise, descent, approach, and landing. The method and system of this application improve the accuracy, continuity, and real-time performance of flight status identification.

The invention belongs to the technical field of unmanned aerial vehicle control, and provides an unmanned aerial vehicle linear flight control method and device. The method comprises the following steps: collecting flight state parameters and environment interference parameters of an unmanned aerial vehicle, wherein the flight state parameters comprise at least one of a three-dimensional position, a flight speed, a course angle, a lateral offset distance and an attitude angle; according to the flight state parameters and the environment interference parameters, constructing an unmanned aerial vehicle linear flight dynamics model, linear flight constraint conditions and a fusion reward function; a linear flight simulation environment constructed based on the unmanned aerial vehicle linear flight dynamics model; based on a reinforcement learning algorithm, the linear flight constraint condition and the fusion reward function, performing interactive training on the unmanned aerial vehicle and the linear flight simulation environment to determine a linear flight control strategy; and controlling the unmanned aerial vehicle to fly linearly according to the linear flight control strategy. According to the invention, high-precision, high-stability and energy-saving autonomous linear flight control can be realized, dependence on an accurate model is significantly reduced, and complex environmental interference is effectively coped with.

The invention discloses a method for dynamically correcting departure time based on flights, which comprises the following steps: acquiring running state information and time parameters of a target flight in real time, and calculating corrected departure time according to a preset initial calculation rule based on the running state information and the time parameters after judging that preset calculation triggering time is reached; presetting a trigger event for updating the corrected departure time, monitoring the change of the running state information of the flight, and recalculating and updating the corrected departure time according to an updating calculation rule corresponding to the trigger event after judging that the trigger event is met; and outputting the calculated or updated corrected departure time. According to the method, the most appropriate preparation time of the flight can be automatically calculated and updated in real time along with the change of the flight state, so that the problems that manual estimation is inaccurate and the plan cannot be changed are effectively solved, the waiting time of an airplane on the ground is shortened, the flight punctuality rate can be improved, and the airport operation is smoother and more efficient.

The invention belongs to the technical field of flight dynamics modeling in aircraft design, and particularly relates to a dynamics modeling method for an overwater tilting wing aircraft. Comprising the following steps: step 1, modeling a wing section based on a blade element theory, dividing an unmanned aerial vehicle into three parts including a propeller, the wing and a fuselage, and carrying out an experiment or CFD calculation to obtain aerodynamic data; 2, geometric parameters of a wing micro-section, a control surface, a propeller and a wing tilting shaft are obtained; 3, calculating wing aerodynamic force, propeller aerodynamic force and fuselage aerodynamic force according to the aerodynamic force data and the geometric parameters; 4, hydrodynamic force is calculated, and the hydrodynamic force comprises buoyancy, inertia force and wave disturbance force.

The embodiment of the specification provides a kind of based on quasi-static electric quantity discrete electric aircraft reachable domain evaluation method and system, wherein, method includes: the quasi-static dimension reduction conversion of electric aircraft dynamics model, according to quasi-static hypothesis, simplify electric aircraft dynamics equation, it is decoupled as horizontal plane kinematics equation and push resistance balance and lift weight balance equation constraint, reduce state quantity dimension, obtain dimension reduction flight dynamics model;The electric quantity discrete conversion of dimension reduction flight dynamics model, the form of relative to time dynamics equation is converted into relative to electric quantity consumption value, determines simulation interval and discrete grid;Based on the dimension reduction flight dynamics model, by setting roll angle numerical sequence, numerical integration simulation is carried out on the simulation interval and discrete grid, and the horizontal plane maneuvering reachable domain boundary of electric aircraft is obtained.

The application discloses a kind of based on bluetooth beacon equipment automatic collection flight guarantee node data method and system, method includes: in the each guarantee node area of flight ground guarantee process, respectively install bluetooth beacon equipment, each bluetooth beacon equipment is associated with the corresponding guarantee node area by identity coding;For guarantee personnel configuration terminal equipment, terminal equipment is configured to when entering or leaving the signal coverage boundary of any bluetooth beacon equipment, automatically obtain the identity coding of this bluetooth beacon equipment and generate trigger data upload to background system;Background system is in real time to interface flight dynamic information system, according to the trigger data received determination corresponding guarantee node area, and associate guarantee node area current matching flight information and guarantee task information.The application realizes the full automation collection and real-time matching of flight guarantee node time data by the dynamic interaction of bluetooth beacon equipment and terminal equipment, significantly improves data accuracy and guarantee efficiency.

The application relates to the technical field of administration and supervision, and discloses a pilot multi-dimensional capability collaborative evaluation method based on a multi-modal heterogeneous graph and decoupling prediction, which comprises the following steps: acquiring multi-modal original data flow in an evaluation task; loading a standard operation procedure knowledge base and converting the same into a logical reference graph; constructing a heterogeneous information graph model integrating causal logic; calculating manifold deviation values between the model and the logical reference graph, modulating node weights on the basis of the manifold deviation values to generate enhanced feature flow; separating the enhanced feature flow into independent dimension-specific representation vectors by using a decoupling prediction system; and outputting an evaluation result based on preset weights. The application embeds standard operation procedure constraints into the bottom layer of a graph model, forces the system to learn the causal mapping between operation behaviors and supervision standards, and eliminates interference between physiological signals and flight dynamics characteristics.

The invention provides an abnormal behavior detection method and system based on flight mission context awareness, and is applied to the technical field of data processing. The method comprises the following steps: processing flight dynamic data and task protocol data of an unmanned aerial vehicle, obtaining a task type by analyzing a MASSIONITEM field in a task protocol, and generating a dynamic tag; combining the GPS positioning data with normalized mutual information and random forest importance score to screen task sensitive features, and generating a task related feature subset; processing the task related feature subsets based on a random forest model, training an exclusive classification model for each task type, and generating an exclusive classification model of each type of tasks; and predicting the real-time flight state data of the unmanned aerial vehicle based on the exclusive classification model of each task to obtain probability distribution of each task type, performing judgment according to a set threshold, and generating an unmanned aerial vehicle abnormal behavior judgment result.

The invention discloses an unmanned aerial vehicle flight dynamic monitoring method and system based on a three-dimensional map, and relates to the technical field of unmanned aerial vehicle monitoring. The method comprises the following steps: acquiring a three-dimensional map of a target area and low-altitude airspace control requirements, and constructing a low-altitude three-dimensional partition model for describing allowed activities of an unmanned aerial vehicle; historical flight data are collected, comprehensive heat values of the partition units are calculated through a multi-factor weighted evaluation model, and a three-dimensional heat map is generated; defining a layout area, and solving an optimal layout scheme by adopting a multi-objective optimization algorithm by taking the maximization of the weighted total coverage amount and the minimization of the monitoring point number as optimization objectives; monitoring equipment is arranged according to the scheme, and three-dimensional dynamic monitoring of the target area is achieved. According to the method, the problems of multiple monitoring blind areas, unreasonable resource allocation, low monitoring efficiency and the like in a traditional method are effectively solved, and refined and intelligent management of airspace resources is realized.

This invention provides a convex optimization design method and system for eVTOL based on power kit selection, belonging to the field of vertical takeoff and landing aircraft design technology. The convex optimization design method includes: obtaining the parameters of the power kit and continuous flight dynamic parameters; constructing aerodynamic models, load characteristic models, battery discharge models, and efficiency models for the eVTOL cruise phase based on the parameters to determine constraints; constructing a convex optimization objective function that minimizes energy consumption per unit range based on the models; solving the convex optimization objective function using the interior-point method to obtain optimal weights and continuous parameters; and obtaining the optimal power kit and mass parameters of each system based on the constraints and optimal weights. This invention achieves simultaneous optimization of discrete power kit selection and continuous parameters under complex constraints, significantly improving optimization accuracy while meeting the strict safety and performance constraints of the entire eVTOL flight profile.

The invention discloses a method and system for automatically collecting flight guarantee node data based on Bluetooth beacon devices, and the method comprises the steps: installing the Bluetooth beacon devices at all guarantee node regions of a flight ground guarantee process, and enabling each Bluetooth beacon device to be associated with the corresponding guarantee node region through an identity code; terminal equipment is configured for the guarantee personnel, and the terminal equipment is configured to automatically obtain the identity code of the Bluetooth beacon equipment when entering or leaving the signal coverage boundary of any Bluetooth beacon equipment, generate trigger data and upload the trigger data to a background system; and the background system is in real-time butt joint with the flight dynamic information system, judges the corresponding guarantee node area according to the received trigger data, and associates the currently matched flight information and guarantee task information of the guarantee node area. Through dynamic interaction of the Bluetooth beacon device and the terminal device, full-automatic acquisition and real-time matching of flight guarantee node time data are realized, and data accuracy and guarantee efficiency are remarkably improved.

The double-water-outlet water tank structure structurally comprises a water tank body, a flow stabilizing mechanism is arranged in the water tank body and used for achieving dynamic flow stabilization of water flow under the working condition that 30 L of water is stored, and the influence of water flow disturbance on output stability is eliminated, and two water outlets are formed in the bottom or the side wall of the water tank body in a spaced mode; the water outlets are arranged at preset intervals in the circumferential direction or the axial direction so as to be matched with the pod space of the unmanned aerial vehicle, flight dynamic balance and operation safety are guaranteed, each water outlet is provided with a sealing assembly in a sealed mode, each water outlet is correspondingly connected with an independent sealing pipeline in a sealed mode, two independent water flow conveying channels are formed, uniform flow distribution is achieved, and the photovoltaic cleaning effect is guaranteed.

Disclosed in the present invention is a safety separation progressive decision tree-based unmanned aerial vehicle conflict prediction method, comprising: establishing an unmanned aerial vehicle safety separation parameter library; establishing a coordinate transformation model parameter library for transforming a WGS-84 coordinate system or a GLONASS-PZ90 coordinate system to a CGCS2000 coordinate system, and encapsulating a constructed Bursa-Wolf model into a coordinate transformation function; parsing flight dynamics data and performing standardized transformation; performing airspace-based unmanned aerial vehicle conflict prediction, and classifying prediction results by conflict risk level into a monitoring dataset, a close-monitoring dataset, and a warning-alert dataset; and calculating the time for an unmanned aerial vehicle pair in the close-monitoring dataset to potentially reach a warning-alert level, and calculating the estimated time to collision for a conflicting unmanned aerial vehicle pair in the warning-alert dataset. The present invention can maximize space resource utilization in an airspace, thereby effectively increasing the air traffic flow within the airspace.

The present application provides a helicopter pilot manipulation intention fuzzy reasoning sling swing angle feedback anti-swing method, comprising: step 1, establishing a helicopter sling flight dynamics model; step 2, establishing a flight control system model integrating a sling swing angle feedback control law CAF and a fuzzy module, coupling and integrating the helicopter sling flight dynamics model to construct a helicopter sling flight closed-loop system model; step 3, detailed design of the fuzzy module, obtaining pilot manipulation intention by using a fuzzy reasoning method according to the sling flight state and pilot manipulation history; step 4, real-time adjusting the sling swing angle feedback control law configuration according to the pilot manipulation intention to eliminate conflicts, and verifying the effectiveness through human-machine-sling coupled closed-loop system simulation. The present application has good command tracking effect and good sling swing damping, and can improve the manned helicopter sling flight quality.

The application discloses a kind of aircraft maneuvering flight control strategy self-learning training method and system, it is related to the control technical field of combat aircraft, including: based on the engine performance and flight dynamics performance of target combat aircraft, the flight dynamics model of target combat aircraft is established;Based on flight dynamics model and reinforcement learning algorithm, the reinforcement learning agent for building the control strategy of control target combat aircraft;With the initial strategy controlled combat aircraft as the opponent, the reinforcement learning agent is trained, and the trained reinforcement learning agent is obtained;Based on self-game training method, by periodically replacing the trained reinforcement learning agent as the opponent of target combat aircraft, the reinforcement learning agent is repeatedly trained to reach the target training period, and the target reinforcement learning agent is obtained.The application alleviates the technical problems that the prior art cannot obtain a wide range of effective countermeasures.

The application provides an airport ground traffic passenger flow prediction method and system, which is applied to the field of prediction data processing; the application forms a dynamic data pool by integrating flight dynamic data, traffic flow data, weather forecast data and passenger movement data, ensures that the system has a comprehensive and timely information base, enables the system to dynamically update and timely reflect the real-time conditions of the airport and its surroundings, improves the prediction accuracy and response speed, and simultaneously ensures that only when the data is complete and up-to-date, the subsequent prediction and scheduling operation is triggered, avoids inaccurate prediction caused by insufficient or outdated data, and improves the reliability of system decision-making.

The invention discloses an airport apron aircraft push-out sorting method. The method comprises the following steps: acquiring flight dynamic data and gate position distribution data of a plurality of aircrafts in a to-be-sorted aircraft set; generating a preliminary push-out sorting sequence for the to-be-sorted aircraft set; sending the preliminary push-out sorting sequence to a three-dimensional visual simulation system, carrying out sliding path planning on each aircraft based on the predicted push-out time, and carrying out conflict detection on the sliding paths of all aircrafts in a preset future time period; receiving a conflict detection result, and if the conflict detection result indicates that a path conflict exists, adjusting the preliminary push-out sorting sequence based on the conflict detection result to generate a conflict-free target push-out sorting sequence; and outputting the target push-out sorting sequence to a display interface to serve as a control instruction reference for visual display. The airport apron operation efficiency and control safety can be improved, and the airport apron control system is suitable for various airport apron operation scenes.

The invention discloses an intelligent airport surrounding traffic flow prediction method and system. The method comprises the steps of traffic data acquisition, heterogeneous feature extraction, hierarchical feature integration optimization, cost response loss design and airport surrounding traffic flow prediction. The invention belongs to the field of traffic flow prediction, and particularly relates to an intelligent airport peripheral traffic flow prediction method and system.According to the scheme, a flight anchoring space-time attention unit is designed to guide attention weights to incline towards flight peak periods and key road network areas, accurate coupling of fine-grained space-time features and flight dynamic states is achieved, and the traffic flow prediction accuracy is improved. The airport scene pertinence is optimized; a time period and error direction two-dimensional cost matrix is constructed, unit error cost of different scenes is quantified clearly, and peak period prediction precision is improved; meanwhile, a flight density correction term is introduced, error cost differences in the same time period are accurately distinguished, refined punishment is achieved, and high-value sub-scenes are focused; and the prediction effect of the traffic flow around the airport is improved.

The invention belongs to the technical field of airport luggage systems, and particularly relates to a system and a method for dynamically adjusting early arrival resources and containerized resources. The system comprises a resource dynamic adjusting and planning system, wherein the resource dynamic adjusting and planning system is in communication connection with a real-time data collector, a data collection prediction model and a data solver; the data acquisition prediction model acquires data including flight data, luggage system data, early arrival resources, container resources and passenger and luggage data in an airport flight timetable, and after model construction is carried out, an early arrival resource allocation scheme and a container resource allocation scheme are obtained; and the resource dynamic adjustment planning system is used for receiving real-time latest flight dynamic and actual luggage flow data, comparing actual data with a model prediction scheme, continuously calibrating and optimizing model parameters by utilizing feedback circulation, and realizing rolling type dynamic planning of early arrival resources and container resources. The invention provides a system and a method for dynamically adjusting early arrival resources and containerized resources. The system and the method are used for dynamically adjusting the early arrival resources and the containerized resources.

The application discloses an AI-driven aviation consumable demand prediction and intelligent inventory optimization method and system, integrates multi-dimensional heterogeneous data such as flight dynamic scheduling, component service working conditions, 3D printing consumable attributes and a supply chain, constructs a collaborative prediction model through an AI algorithm, greatly improves the demand prediction accuracy compared with a traditional time series analysis method, effectively solves the problem of insufficient prediction accuracy in a complex aviation scene, and provides reliable data support for inventory optimization.

This invention discloses a method for analyzing the coupled influence of structural deformation and detection performance on sensor aircraft, belonging to the fields of aircraft design and aeroelasticity. The specific steps of the method are as follows: establishing an aerodynamic and structural model of the aircraft; establishing state-space aerodynamic equations; defining a flight dynamics model; obtaining rigid-elastic coupled dynamic equations based on the Lagrange equations; performing aeroelastic dynamic response calculations through coupled solution to obtain the physical deformation of the wing structure; and calculating antenna performance based on this deformation to analyze the impact of structural deformation on detection performance. This invention, by establishing a rigid-elastic coupled aeroelastic model, achieves a full-process coupled analysis from aeroelastic excitation to structural response and then to antenna performance changes. It solves the problem that traditional methods cannot accurately consider the impact of structural deformation on detection performance under actual flight conditions, providing a more accurate analytical means for the integrated design of sensor aircraft.

The invention provides a flight dynamic deduction method and device, computer equipment and a storage medium, and belongs to the field of airport planning, and the method comprises the steps: obtaining flight plan data, airport resource capacity data and airport flow limit data in the next 24 hours, and constructing a parameter set of three simulation scenes of normal operation, large-area flight cancellation and airport closing; based on the parameter set, a discrete event simulation framework is adopted, and the whole-process state transition of each flight is pushed and deduced according to time through the operation logic of three subjects of collaborative air traffic control, an airport and an airline company; flight operation data in the deduction process are recorded, and flight operation indexes and a visual chart are generated. According to the deduction process, the problem of multi-agent cooperation in a complex operation environment can be effectively solved, the operation efficiency is improved, meanwhile, the emergency disposal capacity is enhanced, and the dynamic deduction characteristic can help managers to quickly recognize potential conflicts and optimize a resource allocation scheme.

This invention relates to the field of civil aircraft flight safety technology, and in particular to a method for predicting the flight trajectory of a high-speed target under conditions of visor perforation. The method includes: acquiring the calculated coefficient parameter data of the target's flight dynamics model; establishing a trajectory prediction framework based on a three-degree-of-freedom target flight dynamics model and real-time target flight data; numerically simulating the flow field in the visor perforation area using fluid dynamics analysis software to acquire velocity and pressure field data; establishing an aerodynamic analysis model of the visor perforation flow field using a high-order polynomial fitting method; calculating the Mach number and pressure distribution after visor perforation; dynamically updating the aerodynamic coefficient data and aerodynamic parameter database; and introducing the velocity and pressure distribution data into the three-degree-of-freedom target flight dynamics model for registration and updating to obtain the predicted trajectory. The prediction accuracy is improved by 30%–50%, especially under high angle-of-attack conditions, reducing the prediction error from over 20% to within 5%.

This invention discloses an adaptive navigation method and system for urban low-altitude delivery drones. The method includes: constructing a spatial non-uniform wind field environment model, a flight dynamics model, and a propulsion power model; constructing a multimodal state vector containing kinematic guidance, visual obstacle information, and physical enhancement information; inputting the multimodal state vector into a policy network and mapping the output to executable flight control commands; constructing a gating function based on the current battery state of charge; coordinating task propulsion and aerodynamic energy-saving rewards to generate an instantaneous reward signal; training the policy network accordingly; and outputting target delivery navigation commands to execute the autonomous navigation task. Because this invention explicitly introduces local wind conditions, aerodynamic costs, and remaining energy into the decision-making process through wind field energy consumption coupling modeling and a power-driven reward coordination mechanism, it effectively suppresses high-energy consumption in complex wind areas and control jitter, achieving wind-adaptive energy-saving navigation that balances task timeliness and endurance safety under locally observable conditions.

The embodiment of the specification discloses an airport vehicle scheduling method and device based on flight dynamics, equipment and medium, relates to the technical field of vehicle scheduling, and the method comprises the following steps: receiving at least one airport vehicle scheduling request information, so as to obtain the real-time position data of a plurality of airport vehicles corresponding to the scheduling vehicle type and the real-time flight data of the target service flight based on the airport vehicle scheduling request information, wherein the airport vehicle scheduling request comprises a target service flight and a scheduling vehicle type; based on the real-time flight data, determining the flight priority parameter of the target service flight, so as to determine the vehicle dynamic electronic fence parameter corresponding to each scheduling vehicle type through the flight priority parameter and the scheduling vehicle type; determining the candidate vehicle set through the real-time position data of the vehicle and the corresponding vehicle dynamic electronic fence parameter, so as to screen the target scheduling vehicle in the candidate vehicle set for airport vehicle scheduling.