637 results about "Flight speed" patented technology

The invention provides an automatic obstacle avoidance point selection and obstacle avoidance method for a photovoltaic station polled by an unmanned aerial vehicle, and the method comprises the steps: carrying out the real-time analysis of the image definition through image processing for a polling image captured by the unmanned aerial vehicle carrying different sensors, carrying out the classification and recognition of an obstacle according to the high-low difference of the definition, and optimizing the detection process of the obstacle; under the condition of different obstacle recognition speeds, the flight speed and height of the unmanned aerial vehicle are dynamically adjusted to obtain higher patrol image definition and obstacle recognition efficiency; an obstacle is recognized from the inspection image, and a recognition result is used for updating an obstacle avoidance database in the flight of the unmanned aerial vehicle; according to the obstacle avoidance database updated in real time and the current position of the unmanned aerial vehicle, an optimal obstacle avoidance path is planned, and the optimal obstacle avoidance path is transmitted to the unmanned aerial vehicle in real time for execution; and when the optimal obstacle avoidance path is executed, the unmanned aerial vehicle system continuously monitors the change of the surrounding environment, and if a new obstacle or obstacle movement is detected, the obstacle avoidance path is adjusted in real time.

The invention discloses a power construction unmanned aerial vehicle path planning method and system, and relates to the technical field of space calculation, and the method comprises the following steps: determining a target region needing power construction and a preset construction point coordinate; obtaining a topographic map of the target area based on the geographic information database, constructing a three-dimensional map model, and marking the preset construction point coordinates; generating an initial construction path of the unmanned aerial vehicle; the method comprises the following steps: acquiring meteorological data in real time through meteorological data of a meteorological station in a target area, establishing a meteorological obstacle three-dimensional model, and dividing a preset radius threshold with a meteorological obstacle as a circle center into non-flying areas according to the severity level of the meteorological obstacle; adjusting the initial construction path based on the non-flying area; the cruise height is adjusted according to the meteorological data and topographic relief, a relief tracking path is generated, and the constant relative height is kept; and combining the battery capacity, the flight speed and the task priority, dynamically distributing inspection road sections, and generating a corresponding power construction unmanned aerial vehicle flight path.

The embodiment of the invention provides a flight plan conflict detection method, device and equipment based on a low-altitude airspace grid, and is applied to the technical field of air traffic control. The method comprises the steps of constructing a low-altitude airspace grid set, planning a flight plan, dividing an airline flight stage into a plurality of flight segments, obtaining a space-time occupation grid set corresponding to each flight segment based on the flight speed and obtained flight target physical parameters, and combining to obtain a space-time occupation grid set of the airline flight stage. Based on the space-time occupancy range and the low-altitude airspace grid set, constructing a space-time occupancy grid set corresponding to other flight stages; the flight plan space-time occupation grid set is obtained through merging and compared with the historical space-time occupation grid set, if conflicts exist, conflict information is generated, and the flight plan is re-planned according to the conflict information, so that path overlapping and conflicts between tasks are effectively avoided, the turnover rate and the utilization rate of a low-altitude airspace are increased, and the flight planning efficiency is improved. And the dense flight requirement of the low-altitude unmanned aerial vehicle is met.

The invention provides a control method and system for an unmanned aerial vehicle to check and hit a ground station. The method comprises the following steps: firstly, acquiring real-time image data, carrying out terrain contour comparison, identifying a disaster risk area, secondly, generating a task mark file, compressing the task mark file into an encrypted data packet with a geographic label, then sending the encrypted data packet to a ground station, simultaneously receiving a rescue priority instruction, and adjusting the hovering height and the inspection navigational speed of the unmanned aerial vehicle. The method comprises the steps of receiving a device ready state code, sending the device ready state code to a ground station, verifying the integrity of the device ready state code to generate an action authorization identifier, controlling an unmanned aerial vehicle to fly to a safe coordinate, controlling the unmanned aerial vehicle to execute a task when a positioning signal transmitter receives a synchronous verification signal, and automatically deleting data after the task is completed; according to the technical scheme provided by the invention, rapid response, accurate execution and data closed-loop management of the unmanned aerial vehicle to the multi-target area in a disaster rescue scene are realized.

The invention relates to the technical field of unmanned aerial vehicle positioning, in particular to an unmanned aerial vehicle positioning method and system combining environment characteristics. According to the technical scheme, the method comprises the steps that multi-modal data of the environment where the unmanned aerial vehicle is located are collected through a multi-source heterogeneous sensor, and the multi-modal data comprise GNSS signal data, visual image data, laser radar point cloud data, inertia measurement data and electromagnetic interference monitoring data; and carrying out cooperative processing on the multi-modal data by utilizing the fusion deep learning model. A comprehensive basis is provided for subsequent processing through multi-source data collection, data quality and feature matching reliability are improved through fusion processing, timely and accurate positioning is guaranteed through real-time calculation, safe and efficient flight is achieved through a decision-making model based on accurate positioning, and the flight speed is high. The method not only solves the traditional technical problems of signal shielding, interference, dynamic interferent influence and contradiction between real-time performance and precision in a complex environment, but also shows excellent positioning stability and adaptability in various complex scenes.

The invention relates to the technical field of low-altitude aircraft obstacle avoidance, and discloses an autonomous obstacle avoidance method and system for a low-altitude intelligent dynamic monitoring aircraft, and the method comprises the steps: carrying out the state vector construction of a position coordinate, a flight speed and a course angle of a low-altitude aircraft cluster, and obtaining a state space of the low-altitude aircraft cluster; performing Bezier curve calculation and genetic optimization on the control point sequence in the low-altitude aircraft cluster state space to obtain an optimal detection route parameter; performing geometric distance calculation and double-point removal processing according to the waypoint sequence of the optimal detection route to obtain a patrol airspace equation; performing multi-level reward function calculation through a hierarchical reinforcement learning framework to obtain a high-low layer decision network; time sequence differential learning and strategy gradient optimization are carried out based on the high-low layer decision network to obtain a distributed control strategy, so that global optimization control of the low-altitude aircraft cluster is realized, and stable operation of the low-altitude aircraft cluster in an unknown wind disturbance environment is ensured.

The invention discloses an electric power engineering defect diagnosis method based on AI image recognition and a block chain technology, and relates to the technical field of electric power engineering defect diagnosis, and the method comprises the following steps: setting the initial flight rate of an unmanned plane according to the monitoring demands and field conditions of an electric power line, it is ensured that the unmanned aerial vehicle can cover all key areas of the power line in the inspection process, and meanwhile the inspection efficiency is kept. According to the invention, by intelligently adjusting the flight rate of the unmanned aerial vehicle, the unmanned aerial vehicle can stay in a potential abnormal area for a longer time, and detailed acquisition of temperature data is ensured, so that tiny temperature changes, especially tiny temperature anomalies caused by electric arcs and electrical overload, can be captured more accurately. According to the intelligent adjusting mechanism, in the inspection process, the inspection efficiency of the whole process can be guaranteed, accurate monitoring of the key area can be guaranteed, and the risk of detail loss or temperature anomaly missing detection caused by the too high flight speed is reduced.

The invention discloses a radio frequency source positioning method based on aerial unmanned aerial vehicles, which relates to the field of radio, and comprises the following steps: deploying at least three unmanned aerial vehicles with positioning devices and radio frequency receiving functions, and acquiring flight height, flight speed, current position and detected radio frequency signals; obtaining frequency values and amplitudes of different sub-signals; obtaining a radio amplitude radiated to each unmanned aerial vehicle by each radio frequency source; obtaining the horizontal distance from each radio frequency source to each unmanned aerial vehicle; and establishing a distance azimuth equation set, and solving through a generalized inverse matrix calculation method to obtain the position of each radio frequency source. The characteristics of low cost and high flexibility of the unmanned aerial vehicles are fully utilized, through joint detection of the multiple unmanned aerial vehicles, limitation of complex terrain environments is avoided, multiple radio frequency sources can be detected at the same time, influence of flight attitudes of the unmanned aerial vehicles is avoided, and the requirements of various application scenes are met.

The invention relates to the technical field of unmanned aerial vehicle control, in particular to an unmanned aerial vehicle dynamic obstacle avoidance method based on multi-sensor fusion. Comprising the steps of receiving an image sequence, a depth point cloud set, a pose state parameter and a flight speed parameter; calculating an illumination distortion gradient value and a speckle noise entropy value based on the image sequence and the depth point cloud set; performing coordinate registration and feature extraction on the image sequence, the depth point cloud set and the pose state parameter, and outputting a visual feature set and a radar feature set; generating a weighted visual feature set and a weighted radar feature set through a weight adjustment function, and generating a joint heterogeneous feature tensor through alignment splicing; and generating a body attitude and thrust control instruction by using the combined heterogeneous feature tensor through a probability prediction model and a model prediction control algorithm device. According to the method, through cross-domain multiplexing and deep coupling of the body physical parameters in the data stream, error accumulation and decision delay caused by multi-stage series calculation are avoided, and global performability of an obstacle avoidance task is facilitated.

The invention discloses a hypersonic variable wingspan aircraft attitude control method based on reinforcement learning, aiming at the characteristics of complex flight environment, strong nonlinearity of control input and aerodynamic characteristics, strong coupling and the like of a hypersonic variable configuration aircraft. The method comprises the following steps: acquiring discrete relationships between variables such as wingspan length, rudder deflection angle, flight attitude and flight speed and aerodynamic parameters through a computational fluid mechanics method, and then fitting the discrete relationships by utilizing a neural network method to realize aerodynamic parameter identification under different flight working conditions and appearance conditions; secondly, establishing a dynamic model of the hypersonic variable-wingspan aircraft by using an aerodynamic parameter identification network; and finally, realizing attitude control of the hypersonic variable-wingspan aircraft based on a DQN reinforcement learning method. The method can effectively solve the problems that a traditional control method is complex in controller design and strict in model precision requirement, and simulation experiment results prove that the method can effectively achieve attitude angle tracking.

The present invention relates to the field of electronic fence technology, specifically to an electronic fence monitoring method and system for unmanned aerial vehicles, comprising the following steps: based on the real-time positioning data of the unmanned aerial vehicle, by analyzing the positioning changes of the unmanned aerial vehicle within a period of time, calculating the flight speed of the unmanned aerial vehicle, and analyzing the change trend of the flight speed of the unmanned aerial vehicle according to the flight speed data of the unmanned aerial vehicle within a period of time, and obtaining the speed information of the unmanned aerial vehicle. The present invention can analyze the distance between the unmanned aerial vehicle and the electronic fence in real time and estimate the crossing time by continuously tracking the flight speed and flight direction, thereby providing scientific data support for preventing potential safety risks, and by adjusting the warning threshold and matching the reaction time of the operator, improving the adaptability and practicality of the early warning, making the warning more timely and effective, significantly reducing the possibility of false alarms and missed alarms, enhancing the response speed and accuracy of the unmanned aerial vehicle electronic fence system, and effectively preventing the illegal flight of the unmanned aerial vehicle and the occurrence of safety accidents.

The invention provides an unmanned aerial vehicle testing method, device and equipment, belongs to the technical field of unmanned aerial vehicles, and solves the problems that a traditional unmanned aerial vehicle is low in testing efficiency, long in time consumption and high in testing cost, and the accuracy is difficult to guarantee. The method comprises the steps of generating a test instruction and sending the test instruction to the unmanned aerial vehicle; acquiring flight state data of the unmanned aerial vehicle; processing the flight state data to obtain performance data, the performance data including at least one of endurance time, a climbing rate, a maximum voyage, a maximum flight height and a maximum flight speed; and inputting the flight state data and the performance data into a flight state test model for processing to obtain test data. According to the scheme, the testing efficiency and accuracy of the unmanned aerial vehicle are improved, and the testing cost is reduced.

The invention provides a low-altitude heterogeneous unmanned aerial vehicle flight plan pre-tactical diversified deployment method and system, and belongs to the technical field of air traffic management and flight supervision information, and the method comprises the steps: generating an initial flight plan of a single machine, and deducing a process flight path of the initial flight plan; performing multi-aircraft track conflict detection, and identifying static and dynamic flight conflicts; heterogeneous unmanned aerial vehicle multi-factor priority evaluation is carried out, and the space resource use sequence of unmanned aerial vehicles is determined; diversified strategies such as flight routes, take-off time and flight speed are designed, and optimal release is achieved. According to the method, the optimal conflict resolution of the heterogeneous unmanned aerial vehicles in the low-altitude airspace in the pre-tactical stage is realized, the conflict of multiple unmanned aerial vehicles on limited airspace time and space use is effectively avoided, and the requirements of reasonable use and continuous safety of low-altitude airspace resources are met.

The embodiment of the invention discloses a formation control method, device and equipment for a distributed vertical fixed-wing unmanned aerial vehicle cluster and a storage medium, and relates to the technical field of unmanned aerial vehicle formation control. The method comprises the following steps: receiving topological position information of a formation, and determining a planned path; determining a first flight speed according to the current position information and the expected position information in the planned path; determining a second flight speed according to the current position information, the expected position information and topological position information in the formation; determining a third flight speed according to the current position information and the position relationship with other unmanned aerial vehicles in the formation; calculating a target flight speed based on the first flight speed, the second flight speed and the third flight speed; according to the target flight speed, the formation is controlled, and the stability and safety of the vertical fixed-wing unmanned aerial vehicle cluster formation are effectively guaranteed.

The invention relates to the technical field of aerospace, and discloses an ALOT-based low-altitude intelligent obstacle avoidance route planning system, which comprises a data collection module, a data calculation module, a feedback module, a feedback optimization module and an optimization management module, and is characterized in that the data collection module is responsible for collecting various environmental information required in the flight process of an unmanned aerial vehicle; the data calculation module performs calculation and analysis by using an algorithm based on the data collected by the data collection module to generate a preliminary dynamic route planning model, the feedback module performs real-time monitoring and feedback on a calculation result of the data calculation module and evaluates the flight state of the unmanned aerial vehicle, and the feedback optimization module adjusts a flight strategy according to a feedback result. According to the method, obstacle avoidance and route planning are optimized, optimization measures for adjusting the flight speed and changing the route are put forward in time, an optimization management module manages and implements the optimization measures, it is ensured that the unmanned aerial vehicle flies safely and efficiently according to the optimized route, meanwhile, the implementation effect of the optimization measures is monitored, and effective adjustment is conducted.

The invention discloses an unmanned aerial vehicle cluster relay system optimization method, and aims to significantly reduce the overall outage probability of the system by performing adaptive optimization on cooperative position deployment and flight trajectories of multiple unmanned aerial vehicles under multiple actual constraint conditions of flight speed, safety distance, flight boundary and the like. Therefore, the reliability and the service quality of the multi-unmanned aerial vehicle relay communication network are improved. According to the method, a Markov decision process (MDP) model is constructed, a multi-unmanned aerial vehicle trajectory optimization problem is converted into a multi-agent reinforcement learning problem, and efficient optimization of unmanned aerial vehicle trajectories is realized by using a centralized training-distributed execution framework, so that the outage probability of a communication system is minimized.

The invention provides a path planning and energy management cooperative regulation and control method for a multi-agent unmanned aerial vehicle, and the method comprises the following steps: building a kinetic model based on a six-degree-of-freedom motion equation of a four-rotor unmanned aerial vehicle, and building an energy consumption model through combining the power of a motor and the discharge characteristics of a battery; sensing the state of the unmanned aerial vehicle in real time, defining a state space and an action space of a deep reinforcement learning algorithm, and generating navigation points by using the deep reinforcement learning algorithm; receiving a delivery request, and dynamically allocating tasks according to the state of the unmanned aerial vehicle by adopting a deep reinforcement learning algorithm; performing path planning on the unmanned aerial vehicle for task allocation by adopting an A * algorithm according to the navigation points in combination with the multi-dimensional cost, and obtaining an optimal path; dispersing the optimal path into a speed instruction, and dynamically adjusting the course angle and the flight speed of the unmanned aerial vehicle through a PID controller; through combination of deep reinforcement learning and an A * algorithm, the problems of path planning and energy management of the multi-agent quad-rotor unmanned aerial vehicle logistics distribution system in complex terrains are effectively solved.

The invention discloses an unmanned aerial vehicle continuous speed space gesture control system and method based on a somatosensory glove. The system comprises a gesture data acquisition module, a data transmission module and a flight control execution module. The gesture data acquisition module comprises a palm data acquisition module and a palm data resolving module. The current attitude quaternion is used for calculating the thumb orientation to control the flight direction, the finger bending angle is converted into the flight speed, the thumb direction is used as the flight direction, the finger bending degree is converted into the flight speed, unmanned aerial vehicle glove control of continuous speed direction space and large and small space is achieved, computing resource overhead is reduced, and the working efficiency is improved. Meanwhile, the limitation of a traditional unmanned aerial vehicle remote controller is overcome, the control efficiency is improved, the method has the advantages of being visual and convenient to operate, rapid in response, accurate in control and the like, the method is suitable for flexible control scenes of the unmanned aerial vehicle, interaction immersion and friendliness are enhanced, and user experience and flight safety are improved.

The invention discloses a tilt rotor aircraft flight control method in a tilt transition stage, and the method comprises the steps: determining a reverse tilt state point which is a waypoint of which the to-be-fly distance to the starting point of a multi-rotor mode descent stage is greater than or equal to the shortest design deceleration distance and the height is H; and when the tilt rotor aircraft is located at the reverse tilt state point, controlling the tiltable rotors to tilt reversely according to the tilt angle instruction, and controlling the tilt rotor aircraft to fly forward according to the reverse tilt transition flight speed. In the reverse tilting transition stage, the tilting angle rate is calculated based on the to-be-fly distance, and corresponding flight control strategies are executed for different stages in the reverse tilting transition stage in combination with comparison of the to-be-fly distance and the forward distance threshold value and the tilting angle range, so that accurate control over the position of the aircraft in the reverse tilting transition stage is achieved.

The invention belongs to the technical field of aircraft simulation, and particularly relates to an aircraft longitudinal maneuvering load simulation method and device. The method comprises the following steps: S1, carrying out 1g level flight balancing on the aircraft, and determining a balancing attack angle of the aircraft and a balancing elevator skewness of the aircraft; s2, determining the aerodynamic force of the aircraft; s3, dynamic response solving is carried out on aerodynamic force of the aircraft, and the aerodynamic attack angle, normal overload, the flight speed and the pitch angle speed are obtained; s4, acquiring a first elevator skewness calculated by the stability augmentation system according to the pneumatic attack angle and the normal overload, and superposing a second elevator skewness given by the elevator instruction to obtain an actually required elevator skewness; and S5, re-determining the aerodynamic force of the aircraft according to the flight speed, the pitch angle speed, the aerodynamic angle of attack and the actually required elevator skewness until the simulation is finished. The effect of the stability augmentation system in the process of determining the longitudinal maneuvering load of the aircraft is considered, the simulation process is simple, and the precision is high.

The invention discloses a multi-unmanned aerial vehicle cooperation real-time data acquisition and transmission trajectory optimization method and system, and the method comprises the steps: 1, building a multi-unmanned aerial vehicle cooperation real-time data acquisition and transmission system model for a large-scale wireless sensor network data acquisition and transmission scene, and constructing a completion time minimization problem; 2, the sensor nodes are clustered, the clustering center is determined to be the position of a data acquisition point, then a task area is divided according to the communication radius between the unmanned aerial vehicle and the base station and the communication radius between the unmanned aerial vehicles, and the acquisition points located in the same area are responsible for the same unmanned aerial vehicle; 3, converting the optimization problem constructed in the step 1 into a new optimization problem about the hovering points, the hovering time, the flight speed and the collection sequence of the multiple unmanned aerial vehicles through trajectory discretization; and 4, solving the new optimization problem converted in the step 3 through a point matching-based trajectory planning algorithm to obtain the flight trajectory of the multiple unmanned aerial vehicles.

The invention belongs to the technical field of aircraft simulation, and relates to a flight simulation method and device under gust load considering a downwash effect. The method comprises the following steps: S1, carrying out 1g level flight balancing on the aircraft, and determining a balancing attack angle of the aircraft and a balancing elevator skewness of the aircraft; s2, superposing the aerodynamic coefficient increment brought by the downwash angle of attack to the aerodynamic coefficient of the aircraft body, and determining the aerodynamic force of the aircraft; s3, dynamic response solving is conducted on aerodynamic force of the aircraft, the flight speed is obtained, the corrected speed of the aircraft is determined after the flight speed is superposed with the wind speed of the vertical gust field, and the aerodynamic attack angle and the flight Mach number are determined according to the corrected speed; and S4, re-determining the aerodynamic force of the aircraft according to the flight Mach number, the pitch angle speed, the aerodynamic angle of attack, the given wind speed of the vertical gust field and the elevator skewness, and circulating the steps until the simulation is finished. The method is simple in simulation process and high in precision, and meets the requirements of engineering practice.

The utility model provides a portable four-rotor unmanned aerial vehicle which comprises a vehicle body, four vehicle arms and four rotor assemblies, and one end of each vehicle arm is provided with one rotor assembly; a containing space is formed in the fuselage body, a first resistance reduction structure is formed at the front end of the fuselage body, and second resistance reduction structures are formed on the two sides of the fuselage body; a supporting structure is arranged at the bottom of the vehicle body, and the other end of each vehicle arm is connected with the supporting structure in a locking mode through a first locking piece. The four-rotor unmanned aerial vehicle has the advantages that wind resistance in the flying process can be effectively reduced, and therefore the flying speed of the four-rotor unmanned aerial vehicle can be increased; according to the four-rotor unmanned aerial vehicle, all the vehicle arms can be folded by simply disassembling the first locking pieces, and therefore the four-rotor unmanned aerial vehicle can be conveniently stored and transported.

The invention provides an attitude control method and device for a vertical take-off and landing unmanned aerial vehicle, equipment and a medium, relates to the technical field of flight control law design, and is used for solving the problem of how to realize rapid transition of the vertical take-off and landing unmanned aerial vehicle from a horizontal state to a vertical state. The method comprises the following steps: determining a current stage of the vertical take-off and landing unmanned aerial vehicle in a transition process according to a pitch angle and a flight speed of the vertical take-off and landing unmanned aerial vehicle; the transition process refers to the process of transition from a horizontal state to a vertical state; determining a pitch angle speed control target according to the current stage; a proportional integral differential control law structure is adopted, and a pitching control surface control instruction is determined according to the pitching angular velocity control target and the pitching angular velocity; the vertical take-off and landing unmanned aerial vehicle can be controlled to be rapidly transited to a vertical state from a horizontal state through a pitching control surface control instruction.

The invention relates to a tilt-rotor aircraft trajectory optimization method and device, equipment and a medium. The method comprises the following steps: establishing a flight equation of a distributed power tilt-rotor aircraft, formulating flight criteria of forward transition and backward transition, and constructing a dynamic flight feasible region; in the dynamic flight feasible region, establishing a allowance feasible reference line which takes the flight speed as an independent variable and takes the tilt angle as a dependent variable, and constructing a longitudinal motion model; defining an objective function of transition process trajectory optimization in combination with a allowance feasible reference line and performance requirements, and defining an actuator range constraint and a system performance constraint; an aircraft is endowed with an initial state, a nonlinear dynamic optimal control problem is converted into a Bolza type optimal control problem, and a longitudinal motion model meeting flight criteria and ideal control variables meeting actuator range constraints and system performance constraints are solved to minimize a target function. By adopting the method, the transition safety of the tilt-rotor aircraft can be improved.

The invention discloses a flight control method for a tilt transition stage of a tilt-rotor aircraft. The flight control method comprises the following steps: constructing a tilt corridor of the tilt-rotor aircraft; acquiring the vertical control quantity of the aircraft in the tilting transition stage in real time; the tilt angle corresponding to the upper boundary of the tilt corridor serves as a tilt angle feed-forward instruction of the aircraft in the tilt transition stage; a tilt angle beta is obtained; taking the flight speed of the upper boundary of the tilting corridor corresponding to the tilting angle beta as a flight speed instruction of the aircraft in the tilting transition stage; correspondingly obtaining a final accelerator instruction of the tilting rotor; and performing flight control on the aircraft in the tilting transition stage. The tilt angle can be determined based on the tilt corridor so as to correspondingly obtain the final accelerator instruction of the tilt rotor, meanwhile, the flight height is kept unchanged at the moment of the tilt transition stage, and the flight speed is controlled within the preset range, so that the height and speed control precision of the aircraft is improved, and the control precision of the aircraft is improved. And therefore, the controllability and the safety in the tilting flight process are improved.

The invention belongs to the technical field of aircraft simulation, and particularly relates to an aircraft maneuvering load simulation method and device considering a downwash effect. The method comprises the following steps: S1, carrying out 1g level flight balancing on the aircraft, and determining a balancing attack angle of the aircraft and a balancing elevator skewness of the aircraft; s2, superposing the aerodynamic coefficient increment brought by the downwash angle of attack to the aerodynamic coefficient of the aircraft body, and determining the aerodynamic force of the aircraft; s3, dynamic response solving is carried out on aerodynamic force of the aircraft, and the aerodynamic attack angle, the flight speed and the pitch angle speed are obtained; s4, calculating elevator skewness at the current moment according to elevator instruction input; and S5, re-determining the aerodynamic force of the aircraft according to the flight speed, the pitch angle speed, the aerodynamic angle of attack and the elevator skewness until the simulation is finished. According to the method, the effect of the downwash influence in the aircraft maneuvering load determination process is reasonably considered, the simulation process is simple, the precision is high, and the engineering practice requirement is met.

The invention provides a bird body collecting device for engine bird impact test speed calibration, which comprises a mounting bottom plate and a buffer collector, and is characterized in that the collector comprises a collecting guide pipe, and the guide pipe comprises a vertical section, a bent section and a horizontal section which are communicated in sequence; a buffer assembly is arranged at the bottom of the vertical section and fixed to the bottom plate, and a bird body divider is arranged at an opening of the horizontal section. The front cabin is fixedly connected with the opening of the horizontal section, a speed monitor and an electromagnetic switch device are arranged in the front cabin, an anti-splashing cabin door is arranged at the inlet end of the front cabin, the electromagnetic switch device is connected with the anti-splashing cabin door, and the speed monitor is used for monitoring the flight speed of a bird body and is connected with the electromagnetic switch device; when the speed signal is monitored, the electromagnetic switch device is triggered to control the anti-splashing cabin door to be closed. The device has the advantages of miniaturization, automation, good buffering effect, easiness in operation and the like, and solves the problems of large bird impact, difficulty in residue collection and the like during bird speed calibration.

The invention discloses a meta universe task offloading and resource scheduling method based on deep reinforcement learning, and relates to the technical field of wireless communication, and the method comprises the steps: 1, putting an unmanned aerial vehicle cluster in a user dense region, and enabling a fixed edge server to obtain the position coordinates of the fixed edge server, each user and each unmanned aerial vehicle; 2, a user sends a rendering request to the fixed edge server through the unmanned aerial vehicle cluster; step 3, constructing an optimization target, converting the optimization target into a Markov decision process, solving the optimization target through a PPO algorithm, and obtaining a horizontal flight rate, a flight direction and a vertical direction rate of the unmanned aerial vehicle and a computing resource set available for edge equipment; step 4, the fixed edge server issues a decision result to the user and the unmanned aerial vehicle, and the user and the unmanned aerial vehicle execute a decision; and step 5, after the unmanned aerial vehicle completes the rendering task, returning a result to the user. The method has the characteristics of multi-node collaborative optimization, energy consumption optimization and resource utilization rate optimization.

The invention discloses an unmanned aerial vehicle route planning method based on capital construction data. The method comprises the following steps: step 1, data acquisition and arrangement; 2, performing three-dimensional modeling; step 3, preliminarily generating a route; 4, checking and optimizing the air route; and 5, simulating test flight. Through introduction of real-time dynamic data and application of an intelligent algorithm, the scheme effectively prevents the unmanned aerial vehicle from encountering obstacles and unsuitable areas in the flight process, significantly reduces the flight risk, can update the flight path plan in real time, ensures that the flight path of the unmanned aerial vehicle keeps adaptability under changing meteorological conditions and traffic flow, and improves the flight speed. The flight task is more flexible and efficient, and the flight time and energy consumption of the unmanned aerial vehicle can be reduced through an intelligent optimization algorithm and scheme, so that the task execution efficiency is improved, the operation cost is reduced, and higher economy and benefits are provided for the application of the unmanned aerial vehicle.