164 results about "Flight dynamics" patented technology

The invention relates to a low-altitude flight dynamic safety guarantee method and system based on airspace gridding, and relates to the technical field of low-altitude airspace management, and the method comprises the steps: constructing a gridding airspace; on the basis of the gridding airspace, labeling network attributes for each grid unit in real time and evaluating a low-altitude safety state; according to network attributes, extracting a grid center point of an airspace grid which is evaluated to be in a low-risk range as a safe flight point, and dividing the gridding airspace into a plurality of safe triangular grids; and according to the predicted trajectory of the aircraft, selecting a candidate point with the lowest risk cost from adjacent triangles of the safety triangular mesh, and introducing a risk cost function to dynamically adjust the distribution of flight path points. According to the invention, the flight path of the aircraft group can be dynamically adjusted according to the environment change, and the stability and safety of cluster task execution are improved.

The invention discloses a dual-ring active-disturbance-rejection trajectory tracking control method for a four-rotor unmanned aerial vehicle, and the method comprises the steps: representing a dynamic coupling effect of the unmanned aerial vehicle caused by the load mass change and dynamic center-of-gravity shift through a six-degree-of-freedom flight dynamic model containing an asymmetric inertia matrix; an outer ring position control loop and an inner ring attitude control loop are constructed on the basis of an active disturbance rejection controller, and the following control cycles are executed in real time on the basis of integrating an extended state observer: the outer ring position control loop generates a position control signal and an attitude expected angle according to an expected track; the inner loop attitude control loop generates a control quantity for driving the unmanned aerial vehicle according to the attitude expected angle, and the two loops cooperatively work through decoupling logic to realize expected trajectory tracking; estimating and compensating unmodeled dynamics and external interference of the unmanned aerial vehicle in real time through the extended state observer; and dynamically adjusting parameters of the active-disturbance-rejection controller by adopting a particle swarm optimization algorithm.

The invention relates to the technical field of flight simulation, and particularly discloses an airplane departure flight path simulation method and system based on a BADA model, and the method comprises the steps: obtaining airplane type data in a BADA database, the starting position coordinates of an airplane and airplane departure control parameters, and constructing a flight dynamics model of the airplane in a climbing stage; calculating an external atmospheric parameter of the current height of the aircraft, thrust and resistance of the aircraft, a target indicated airspeed corresponding to a flight stage, and a climbing parameter, a turning parameter and a fuel flow of the aircraft; and calculating a course angle, a vacuum velocity position coordinate and the like of the aircraft at the next moment, carrying out iterative calculation, and drawing a simulated flight path of the aircraft by utilizing the position coordinate. According to the invention, the performance change of the aircraft in each flight stage is dynamically simulated, and the flight mode and parameters are adjusted in real time according to the flight state, so that the behavior of the aircraft in a complex flight environment can be simulated more accurately, and the simulation precision and adaptability are improved.

The invention discloses a machine learning-based flight wheel block removal time prediction method, and relates to the technical field of flight prediction, and the method comprises the steps: collecting flight preorder state data, airport resource distribution data and meteorological data in real time, and generating an original data set through multi-source heterogeneous data fusion; constructing spatio-temporal features including a preorder flight delay propagation chain, a stand-vehicle conflict energy matrix and a meteorological attenuation factor, and screening and optimizing a feature set through distribution drift detection; training and verifying the Bayesian depth quantile regression model, and outputting a prediction result with a confidence interval; and combining the airport Internet of Things positioning feedback optimization feature set and parameters to generate a prediction deviation diagnosis report. According to the method, a preorder flight delay propagation chain and a stand-vehicle conflict energy matrix are constructed, flight dynamics, resource allocation and weather attenuation factors are embedded into a unified spatial-temporal feature space, and the problem of feature information loss caused by data isolation is solved.

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 the technical field of flight safety of civil aircrafts, in particular to a flight trajectory prediction method under the condition of air high-speed target hood perforation, which comprises the following steps: acquiring calculation coefficient parameter data of a target flight dynamics model, establishing a trajectory prediction framework based on a three-degree-of-freedom target flight dynamics model and target real-time flight data; the method comprises the following steps: performing numerical simulation on a flow field of a hood perforation area through fluid mechanics analysis software to obtain speed field and pressure field data, establishing a hood perforation flow field pneumatic analysis model by adopting a high-order polynomial fitting method, and calculating Mach number and pressure distribution after hood perforation; dynamically updating aerodynamic coefficient data and an aerodynamic parameter database; the speed and pressure distribution data are introduced into the three-degree-of-freedom target flight dynamics model for registration updating, a prediction track is obtained, the prediction precision is improved by 30%-50%, and especially under the condition of a large angle of attack, the prediction error is controlled to be within 5% from 20% or above.

The invention relates to the technical field of artificial intelligence and aviation income management, in particular to an AI-driven flight dynamic pricing and intelligent bargaining system and an implementation method thereof. Comprising a passenger intention analysis module, an intention order hanging module, an income management docking module, an intelligent bargaining engine module, a transaction feedback module, a third-party task entrusting module and a block chain evidence storage module. Pricing is accurately matched with passenger budget, time preference and service requirements, an intelligent bargaining engine supports multiple rounds of interaction, passengers can trigger re-pricing by adjusting conditions such as time range and shipping space selection, and a third-party task price-withstanding mechanism provides a cost reduction channel for the passengers and improves the efficiency of the passengers. Block chain evidence storage ensures that bargaining records, transaction data and price information cannot be tampered, and the problems that in traditional transactions, data tracing is difficult, and dispute handling is complex are solved.

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.

The application discloses a helicopter flight dynamics model parameter error correction method, comprising the following steps: S1, parameterizing the discrete form of helicopter aerodynamic data to obtain multiple aerodynamic parameters and form a flight dynamics parameterization model; S2, performing trimming error analysis on the parameterization model, determining each main input parameter through sensitivity analysis, and using a trimming error value matrix and a trimming error fitting coefficient matrix about the forward flight speed to represent the model trimming error; and S3, taking the trimming error value matrix and the trimming error fitting coefficient matrix as the input of a neural network to identify each error. The application parameterizes the discrete aerodynamic data to obtain the flight dynamics parameterization model, makes the parameters more explicit, adopts the input parameter error cross combination mode to calculate and generate training samples, and identifies the error of each main input parameter by using the neural network, so that the calculation cost is effectively reduced, and the correction efficiency and accuracy are improved.

The invention provides a civil aircraft safety perception method and system based on virtual flight, and relates to the technical field of aircraft safety perception. A dynamic and hierarchical three-dimensional wind shear field model is constructed through space-time alignment and fusion of multi-source meteorological data; deep coupling of wind disturbance and aircraft dynamic response is realized based on a small disturbance theory and a flight dynamics model; continuous and dynamic quantification of risks is realized by combining and analyzing flight parameters and a safety threshold value dynamically adjusted based on wind disturbance intensity and utilizing multi-parameter risk level weighted integration; according to the method, the risk coefficient is coupled with the three-dimensional terrain and the virtual flight trajectory, hierarchical color mapping is adopted to carry out visual space visualization, and a complete closed loop from environment modeling to risk early warning is formed, so that the identification accuracy and timeliness of the flight risk in the wind shear environment are greatly improved, and the scientificity and practicability of security situation awareness are enhanced.

The invention belongs to the technical field of civil transportation aviation airport aircraft monitoring, and discloses a panoramic aircraft monitoring system and method based on video stitching and an electronic map. The method comprises the following steps: acquiring a vehicle position, a personnel position, aircraft information and flight dynamic information, and acquiring high-definition camera video stream data by a video gateway; performing video stitching processing on the acquired high-definition camera video stream data; the electronic map GIS server provides an electronic map service; carrying out panoramic video fusion processing on the electronic map and the spliced video, calculating the position information of the aircraft, and completing personnel management, vehicle management, flight dynamic information management and monitoring mode management; and reporting normal monitoring and exception handling conditions to the server through the client. According to the invention, a video fusion and electronic map function complementarity technology is adopted, all-weather panoramic video safety monitoring of the aircraft in different modes is realized, and invading personnel and vehicles can be actively found and alarmed.

The invention discloses a multi-unmanned aerial vehicle formation control method and system based on deep reinforcement learning, relates to the field of unmanned aerial vehicle control, and aims to solve the problem that high robustness, safety and reliability are difficult to ensure when unmanned aerial vehicles in existing formation cope with model uncertainty and multi-source interference. Comprising the steps of 1, building an external airflow interference model by considering external airflow interference faced by an unmanned aerial vehicle when the unmanned aerial vehicle passes through a narrow channel to fly; 2, constructing an unmanned aerial vehicle flight dynamics model influenced by external airflow interference; 3, a PD controller is used as a basic controller, and a UDE-based controller is adopted to estimate and compensate the model uncertainty and multi-source interference; and 4, constructing a state space and an action space for unmanned aerial vehicle robust control optimization based on deep reinforcement learning, and constructing a segmented reward function which focuses on position tracking, attitude stability and control smoothness and safety, thereby improving the adaptive ability of the unmanned aerial vehicle under complex interference.

The invention discloses an intelligent ticket business recommendation method and system based on dynamic flight data, and relates to the technical field of intelligent ticket business recommendation, the intelligent ticket business recommendation method based on the dynamic flight data comprises the following specific steps: step 1, based on basic data, associated data and derivative data contained in a dynamic flight, establishing a dynamic ticket business recommendation model; the method comprises the steps of collecting multi-dimensional dynamic flight data and completing data preprocessing, 2, constructing user portrait data by integrating user real-time preferences and basic demands, and dynamically updating the user portrait data based on fixed duration or behavior recognition, and 3, based on the real-time user portrait and flight dynamic data as input, adopting a random forest algorithm to identify the flight data in real time. And step 4, setting a comprehensive recommendation score based on the feature scores, determining flight ticket business data to be matched, and screening data with high matching degree with the user as recommended ticket business data, and by capturing user demand changes in real time, it is ensured that recommended contents are highly matched with user demands.

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 discloses a parachute flight performance prediction method based on follow-up coupling flow field analysis, and the method is suitable for a parafoil and a traditional round parachute. Parachute flight flow field aerodynamic characteristics and flight dynamic characteristics are coupled by using a body-attached dynamic grid and a self-adaptive flow field boundary technology, real-time changes of a parachute streaming flow field along with body motion are accurately reflected, and the attitude, the speed and the flight trajectory of the parachute are automatically solved and obtained. According to the method, on one hand, real-time aerodynamic characteristics and motion characteristics of the parafoil are coupled, the coupling influence of flight attitude speed change on aerodynamic force and aerodynamic torque of the parafoil is reflected, and the calculation precision is guaranteed; and meanwhile, the change of the streaming flow field of the parachute system is decoupled into the change of the relative incoming flow and the change of the posture, so that the redundancy of flow field grids and the complex grid reconstruction are avoided, and the simulation calculation efficiency is improved.

The embodiment of the invention discloses a method and device for determining route information, a storage medium and an aircraft, and relates to the technical field of aircraft route planning, and the method comprises the steps: obtaining a plurality of planned routes; determining turning time consumption of the aircraft at any waypoint according to the waypoint positions of any waypoint, the previous waypoint and the next waypoint for any waypoint between the starting point and the ending point in any planned route; determining action time consumption of the aircraft at any waypoint according to the navigation action; determining the corresponding leg time consumption according to the leg distance from any waypoint to the next waypoint; and according to all turning time consumption, action time consumption and leg time consumption in any planned route, determining route time consumption corresponding to any planned route so as to plan a preset navigation task. According to the scheme, more comprehensive and accurate route information estimation can be provided, meanwhile, the flight path can be better planned, the actual flight dynamics can be better considered, and route tasks can be executed more smoothly and efficiently.

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 discloses a task scheduling method and device, equipment and a storage medium, and belongs to the technical field of flight maintenance task scheduling. The method comprises the following steps: collecting flight dynamic data, personnel qualification data, personnel state data and maintenance task information, and generating an input data set; based on a mixed integer nonlinear programming model, multi-objective optimization is carried out on the input data set, an initial task scheduling scheme is generated, and multiple objectives comprise task delay minimization, personnel work equalization, personnel moving time minimization and personnel fatigue minimization; when the flight dynamic data, the personnel state data or the maintenance task information are detected to change, generating a task rescheduling scheme based on a dynamic planning algorithm and a simulated annealing algorithm; pushing the task rescheduling scheme to maintenance personnel, and receiving task execution feedback data; and based on the task execution feedback data, a task scheduling strategy is updated through a deep Q network algorithm. According to the embodiment of the invention, the task scheduling efficiency and accuracy can be improved.

The invention provides an atmospheric data analysis method based on observability analysis, belongs to the field of aircraft navigation, guidance and control, and aims at solving the problem that the precision of traditional atmospheric data measurement is reduced under the condition of a complex flow field or a sensor fault. In the prior art, due to lack of strict theoretical support, estimation errors and convergence problems may occur in a transonic stage. According to the method, an atmospheric data state space model is established through flight dynamics, and atmospheric data estimation is carried out by adopting an extended Kalman filter based on the model; in the estimation process, observability analysis is executed to verify the reliability of an estimation result; and finally, optimizing the system model according to an analysis result. According to the method, the dependence of an existing method on an empirical model is reduced, the convergence and the stability of an estimation result are ensured, and the adaptability and the robustness of a system in a complex environment are remarkably improved.

The invention discloses an aircraft maneuvering flight control strategy self-learning training method and system, and relates to the technical field of combat aircraft control, and the method comprises the steps: building a flight dynamics model of a target combat aircraft based on the engine performance and flight dynamics performance of the target combat aircraft; based on the flight dynamics model and a reinforcement learning algorithm, constructing a reinforcement learning agent for controlling a control strategy of the target combat aircraft; taking the combat aircraft controlled by the initial strategy as a confronting opponent, training the reinforcement learning agent to obtain a trained reinforcement learning agent; based on a self-game training method, a trained reinforcement learning agent is periodically replaced by a confronting opponent of a target combat aircraft, the reinforcement learning agent is repeatedly trained to reach a target training period, and a target reinforcement learning agent is obtained. The technical problem that in the prior art, a wide effective confrontation strategy is difficult to obtain is solved.

The invention discloses a scheduling method, system and equipment for air-ground combined transport, and the method comprises the steps: firstly obtaining vehicle transport capacity information, flight dynamic information and order information, and the order information comprises an order departure place and collection time, and an order destination and arrival time; according to the vehicle transport capacity information, the flight dynamic information and the order information, determining an order segmented transportation mode, the segmented transportation mode including air transportation and land transportation; a scheduling model is constructed in a segmented mode according to an order segmented transportation mode, and air-ground combined transportation is carried out according to a result of the scheduling model; according to the invention, the order information is obtained, air transportation and land transportation are modeled in a segmented manner according to the actual business scene of the order information, and different transportation modes are coupled, so that the logistics efficiency is improved, and the scheduling is more accurate and reasonable.

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.

The invention discloses an approach stage flight safety assessment method combined with pilot control skill influence, which comprises the following steps: in an aircraft approach stage, calculating an aircraft approach real-time trajectory according to a flight dynamics model; according to the real-time approaching track and the ideal approaching track of the airplane, acquiring the flight path deviation of the airplane; correcting the flight path deviation of the aircraft in real time by combining a pilot manipulated variable probability distribution model and a control matrix in a flight dynamics model; after the aircraft completes approaching, the landing deviation between the actual landing point and the ideal landing point of the aircraft is calculated, and whether the landing deviation exceeds the safety range or not is judged; if the safety range is exceeded, heavy flight is prompted; otherwise, landing is executed. The approach stage flight safety assessment method combining pilot control skill influence can provide reliable reference for aircraft approach stage flight safety risk assessment and pilot skill level assessment.

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 present invention relates to the field of aircraft flight dynamics technology, and discloses a method for analyzing the safe flight boundaries of a deformable long-endurance unmanned aerial vehicle (UAV), including: establishing a closed-loop nonlinear flight dynamics model, combining a simulation algorithm and a flight state vector, and being able to accurately simulate the flight state of the UAV and dynamically adjust the flight path. By expanding the radial grid and the spanwise grid along the unit vector of the flight state, a high-dimensional state envelope is generated, and the safe flight boundary in the form of an implicit function is extracted using polynomial fitting, which can comprehensively and accurately describe the safe flight area of the aircraft. The present invention ensures that the UAV always remains within the safe flight area by real-time evaluation of the relationship between the flight state and the safety boundary, effectively improving the accuracy, stability and safety of flight control, especially in applications in complex environments, and can dynamically respond to various changes that may occur during the flight process, thereby improving the autonomous flight capability and safety of the aircraft.

The invention discloses an air-ground amphibious platform trajectory planning method based on modal characteristics, and relates to the field of trajectory planning. The method comprises the following steps: acquiring information data; constructing a dynamic model of the land-air amphibious platform based on the information data; the dynamical model comprises a crawler-type differential kinematic model and a ducted flight dynamical model; on the basis of the dynamic model, according to the differential flatness characteristic and the air-ground modal characteristic, air-ground acceleration dynamic sampling is carried out, and sampling trajectory data is obtained; a quasi-Newton method is adopted, iterative solution is carried out according to the sampling trajectory data, air-ground differentiation trajectory optimization is carried out, and optimized trajectory data are obtained; the optimized trajectory data is used for realizing trajectory planning of the air-ground amphibious platform. The air-ground amphibious platform aims to make full use of modal characteristics and improve the maneuverability.

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 application discloses a kind of cloud end collaborative aviation work load distribution method and system, belong to aviation transport technical field;Including: collecting flight dynamics, personnel qualification, physiological state and environmental data;Adaptive threshold denoising in sliding window median, uniform timestamp, based on fuzzy time window association fusion, construct load evaluation dataset;Multi-modal load prediction model fusion time series network, graph convolution network based on historical co-protection frequency normalized edge weight and attention mechanism, modeling inter-post load conduction, predict future load;With load variance, task balance, fatigue risk and cost as target, introduce adaptive crossover mutation optimization scheduling;Using cumulative prospect theory to evaluate fatigue, multidimensional weighted calculation basic score and deviation, distinguish the prospect value mapping level of income loss domain;According to the flow of flight dynamic adjustment safety threshold, over-limit early warning intervention.