238 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 discloses an unmanned helicopter control method based on a fuzzy neural network extended state observer, and belongs to the technical field of transverse unmanned helicopter flight control, and the structure of a transverse unmanned helicopter flight controller comprises an FNNESO-RBSC controller, a control strategy and a transverse unmanned helicopter flight dynamics model; the FNNESO-RBSC controller calculates transverse, longitudinal, course and vertical channel control quantities according to system input and output information, the control quantities are distributed to all control surfaces of the transverse unmanned helicopter through a control strategy and act on a controlled object of the transverse unmanned helicopter, object state response of the transverse unmanned helicopter is changed under the action of aerodynamic force, and the control surfaces of the transverse unmanned helicopter are controlled. And the target flight state is controlled. According to the method, the fuzzy neural network is adopted to transform the extended state observer, and the adaptability of the extended state observer is improved, so that the unknown total disturbance needing to be compensated in the backstepping control law is more effectively obtained.

The invention discloses a stable control method for a deformation process of an unmanned aerial vehicle in a complex environment, and relates to the technical field of unmanned aerial vehicle flight control. The state-space equation is used for describing flight dynamics parameters in the deformation process of the unmanned aerial vehicle in the complex environment; constructing a disturbance observer based on the state-space equation; the disturbance observer is used for observing a lumped disturbance quantity caused by a complex environment and wing tip folding deformation; constructing a sliding mode surface equation according to the attitude angle tracking error; constructing an attitude stability controller according to the disturbance observer and the sliding mode surface equation; and based on the Lyapunov stability theory, performing stable control on the unmanned aerial vehicle by using an attitude stability controller. The lumped disturbance quantity caused by the complex environment and wing tip folding deformation is considered during flight control of the unmanned aerial vehicle, and the control precision of the deformable unmanned aerial vehicle under the complex environment condition can be improved.

The invention provides a distributed electric propulsion aircraft lateral course model prediction control method and system, and the method comprises the steps: completing the six-degree-of-freedom linear state space modeling of a distributed electric propulsion aircraft, obtaining the aerodynamic parameters of the aircraft, and constructing a flight dynamics model containing multiple propellers; under the flight dynamics model of the multiple propellers, according to a flight task instruction and state variables of the current position and speed of the airplane, calculating an expected flight path, and converting the expected flight path into an attitude angle; the edge computing platform computes an optimal control instruction based on the flight state variable and the attitude angle, and the target attitude is realized by integrating the propeller, the control surface and the flight state constraint; and the edge computing platform realizes the optimal control of the lateral heading attitude of the aircraft according to the optimal control instruction. The precision, stability and fault tolerance of flight control are remarkably improved, and an efficient and reliable solution is provided for practical application of model prediction control in a distributed electric propulsion aircraft.

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 belongs to the technical field of unmanned aerial vehicle accompanying flight, and particularly relates to a guiding system based on a vehicle-mounted accompanying flight unmanned aerial vehicle, which comprises the steps of carrying out state calculation on the unmanned aerial vehicle, judging whether the unmanned aerial vehicle reaches a take-off condition, checking battery power endurance of the unmanned aerial vehicle, whether a communication link is stable, and whether sending and receiving states of a control instruction are normal. The vehicle-mounted interaction platform is in signal transmission with the vehicle-mounted terminal, obtains the position information and state of the unmanned aerial vehicle in real time, maps the position information and state to a vehicle-mounted terminal screen and keeps a real-time interaction state with the unmanned aerial vehicle, and the vehicle-mounted terminal obtains the flight normal state information and the flight dynamic information of the unmanned aerial vehicle in real time and controls the unmanned aerial vehicle according to the two-state information of the unmanned aerial vehicle. The vehicle-mounted terminal can select to take over or the vehicle-mounted trusteeship platform to perform trusteeship, and the control system can send a landing instruction. According to the invention, exploration operation tasks in a large area can be completed more quickly and autonomously, dangers possibly foreseen by vehicles are avoided or eliminated, the driving safety is improved, and the dependence of the unmanned aerial vehicle on manual control is reduced.

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 provides a flight data packet data augmentation method, system and medium of a flight simulator. The flight data packet data augmentation method adopts a flight data packet data augmentation method based on the principle of flight mechanics, combines a Markov chain model and a historical flight data packet, performs data augmentation on the basis of real flight data, determines the state transition probability according to the historical actual flight data, then uses the Markov chain model to simulate the sequence change of the flight state, generates a flight state sequence, and calculates the flight attitude, speed, attitude angle, thrust and other changes in different flight stages in each state in combination with a random process, so as to generate flight data that is more in line with actual physical laws, ensure that the changes in engine parameters match the flight state of an aircraft, and constrain a physical model based on flight dynamics and aircraft parameters, so as to better simulate the dynamic changes and coherence in the flight process, and improve the authenticity and scientificity of the augmented data.

The invention relates to the technical field of non-controlled airspace intelligent operation management, in particular to an urban-level low-altitude airspace intelligent operation supervision method and system, and the method comprises the following steps: receiving task data indicating a flight plan of an aircraft, the flight plan comprising planned flight time and a planned route; whether the airspace capacity corresponding to the planned flight time can meet the flight requirement of an operator or not is determined, if not, first information indicating re-planning of a flight plan is generated and fed back to the operator, and the first information comprises recommended flight time and a recommended route; receiving dynamic data indicative of a flight course of the aircraft within the airspace; and determining whether the aircraft deviates from the planned route based on the flight route, and if yes, generating second information indicating to return to the route and feeding back the second information to the aircraft. According to the invention, through perception and acquisition of aircraft basic data, aircraft flight dynamic data and aircraft state data, digital services of airspace and flight are provided, and unified supervision of aircrafts can be realized.

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 discloses an unmanned aerial vehicle scene interaction method and device based on digital twinning and a storage medium, and relates to the technical field of digital twinning and simulation, and the method comprises the steps: obtaining environment data of a target scene, and constructing a scene twinning model of the target scene based on the environment data; the unmanned aerial vehicle simulation model is imported into the scene twin model, a target scene interaction model is obtained, and the unmanned aerial vehicle simulation model comprises a flight dynamics module, a flight control module and a multi-sensor simulation module; in the target scene interaction model, interaction simulation of the unmanned aerial vehicle and the twin scene is carried out according to flight data in a target module, and the target module comprises at least one of a flight dynamics module, a flight control module and a multi-sensor simulation module. According to the invention, accurate simulation and interactive training of the unmanned aerial vehicle in a highly real virtual environment are realized, complexity and diversity of the real world can be reflected in simulation, and the accuracy and effectiveness of performance evaluation and training of the unmanned aerial vehicle are improved.

The invention discloses an energy substitution-based efficient generation method for an optimal trajectory of aerodynamic-assisted orbit drop burn-up, which belongs to the field of aerospace and comprises the following steps of: establishing a spacecraft aerodynamic-assisted orbit drop flight dynamics model, extracting a motion equation in a longitudinal plane, converting an independent variable into a variable in an energy form, and generating the optimal trajectory of the aerodynamic-assisted orbit drop burn-up. Obtaining a height and energy decoupling differential equation, and parameterizing the change of the aerodynamic acceleration; designing a segmented parameterization expression of the track angle with respect to energy, and realizing high-precision fitting of the optimal track angle change; based on the fitting function, deriving an expression of terminal energy in different situations, and further predicting the trajectory speed; and corresponding speeds are calculated at different height points, a burnup optimal reference trajectory is efficiently generated, and it is guaranteed that the spacecraft is transferred to a target orbit with low burnup. According to the energy substitution-based efficient generation method for the optimal burnup trajectory of pneumatic auxiliary rail descending, provided by the invention, efficient generation of the subsequent optimal burnup trajectory can be realized no matter in a height descending section or a height ascending section.

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 an unmanned aircraft and pilot management and control method and management and control system, and the method specifically comprises the steps: collecting the product identification information of an unmanned aircraft, judging that the product identification information is illegal if the product identification information cannot be obtained, and further judging whether the real-name registration is needed or not if the product identification information can be obtained; if the real-name registration information is needed, acquiring real-name registration information, if the real-name registration information cannot be acquired, judging whether the real-name registration information is illegal, if the real-name registration information can be acquired or does not need to be real-name registration, acquiring flight dynamic data to judge whether the flight dynamic data is in a suitable flight airspace range, and if the flight dynamic data is legal; otherwise, further collecting flight authorization information, and if no authorization exists, determining that the flight is illegal; and if authorization is obtained, judging whether the reported flight plan data is met, if not, judging that the flight plan data is illegal, and if yes, judging that the flight plan data is legal. The management and control system comprises a memory, a processor and a computer program capable of running on the processor and executing the management and control method. The method is wide in applicability and low in application threshold, the accuracy of legal / illegal identification can be greatly improved, and the management and control safety is improved.

The invention discloses a helicopter flight dynamics model which comprises an acting force determination and mass center motion equation, a helicopter rotation motion equation, a helicopter aerodynamic calculation equation, a numerical solution of the equations and a motion transformation equation of a related object. The model provides core data for the flight of the whole helicopter by establishing a helicopter centroid kinetic equation and a kinetic equation of rotation around the centroid, calculating aerodynamic characteristics of the helicopter, performing real-time numerical calculation on the helicopter kinetic equation, and determining parameters such as the position and attitude of the helicopter through motion relation calculation and coordinate transformation. According to the invention, the difficult problems of aerodynamic characteristics, operation stability characteristics, flight performance, various flight action control principles and the like of the rotor wing in the flight principle theory of the helicopter are well solved. And the theoretical teaching effect is greatly improved, the theoretical quality and training benefits of flight personnel are improved, the formation of fighting capacity is accelerated, and the military benefits are remarkable.

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.

The invention discloses a flight data monitoring method and device, electronic equipment and a storage medium, the method and device are applied to the electronic equipment, and specifically, all key information of global flights of all bases of a target airline company is collected based on a preset data collection rule; processing the key information based on a preset data processing rule set to obtain flight dynamic information and ground clothing guarantee information; and pushing the flight dynamic information and / or the ground clothing guarantee information to related personnel based on an information pushing rule. According to the scheme, related personnel can obtain the flight dynamic information and the ground clothing guarantee information in time, so that monitoring of the full state of the flight and the guarantee process of the flight is achieved, the related personnel can guarantee the flight in time according to the informatization data, and the guarantee efficiency of the flight is improved.

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 an unmanned aerial vehicle conflict prediction method based on a safety interval progressive decision tree. The method comprises the following steps: establishing an unmanned aerial vehicle safety interval parameter library; the method comprises the following steps: establishing a coordinate conversion model parameter library for converting a WGS-84 coordinate system and a GLONASS-PZ90 coordinate system into a CGCS2000 coordinate system, and packaging a constructed Boolea model into a coordinate conversion function; analyzing the flight dynamic data and performing standardized conversion; airspace type unmanned aerial vehicle conflict prediction is carried out, and prediction results are divided into a to-be-observed data set, a focus attention data set and an alarm prompt data set according to conflict risk levels; calculating the time when the unmanned aerial vehicle pair in the focus attention data set possibly reaches the alarm prompt level; and calculating the predicted collision time of the conflicting unmanned aerial vehicle in the alarm prompt data set. According to the method, the maximum utilization of airspace resources can be realized, and the flight flux in the airspace is effectively improved.

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.