1807 results about "Flight safety" patented technology

The invention relates to the technical field of unmanned aerial vehicles, and discloses an inspection unmanned aerial vehicle flight control management method, which comprises the following steps: step 1, an unmanned aerial vehicle obtains environment three-dimensional point cloud data through a laser radar, captures terrain visual information through an RGB camera and a depth camera, obtains real-time position and attitude information of the unmanned aerial vehicle through a GPS and an inertial measurement unit, and obtains the real-time position and attitude information of the unmanned aerial vehicle; and fusing the collected data to construct a spatial grid map, and taking the grid map as input data of path planning. A high-precision space grid map is generated through three-dimensional environment perception and modeling in combination with an unmanned aerial vehicle multi-sensor fusion technology, and a path is updated in real time by using dynamic environment data, and an air route is planned and optimized, so that path dynamic adjustment and energy consumption minimization of the unmanned aerial vehicle in a complex dynamic environment are realized; and the effects of improving the task execution efficiency, reducing the flight energy consumption and improving the flight safety are achieved.

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

The invention relates to the technical field of unmanned aerial vehicles, in particular to a flight mission planning and management system and method for cooperation of multiple unmanned aerial vehicles, and the method comprises the steps: decomposing a flight region of an unmanned aerial vehicle based on a flight mission of the unmanned aerial vehicle, and dividing the flight region; path planning is carried out in the flight area of the unmanned aerial vehicle, global flight path planning is carried out by adopting a genetic algorithm, and local flight path planning is carried out by adopting an A * algorithm after the global path planning; an obstacle avoidance strategy of the unmanned aerial vehicle in the flight process is constructed, and after the obstacle avoidance strategy is triggered, local flight path planning is carried out again; according to unmanned aerial vehicle flight task constraints, a priority judgment criterion of multiple unmanned aerial vehicle tasks is formulated, and unmanned aerial vehicle resources are allocated and dispatched. The intelligent level of unmanned aerial vehicle task planning can be remarkably improved, the adaptability to complex environments is enhanced, flight safety and efficient task execution are ensured, and a landing technical support is provided for actual application of the unmanned aerial vehicle.

The invention discloses an unmanned aerial vehicle trajectory tracking system based on multi-sensor fusion, which relates to the technical field of unmanned system autonomous navigation and control, and comprises an unmanned aerial vehicle data acquisition module, an unmanned aerial vehicle feature extraction module, a trajectory prediction deviation scoring module, a trajectory dynamic correction module and a trajectory monitoring module. The unmanned aerial vehicle data acquisition module acquires and preprocesses unmanned aerial vehicle and GPS data, the unmanned aerial vehicle feature extraction module constructs a trajectory tracking feature sequence table, the trajectory prediction deviation scoring module outputs three-dimensional trajectory prediction coordinates and deviation scores, and the trajectory dynamic correction module generates correction parameters. Based on the judgment logic, the judgment logic is converted into an unmanned aerial vehicle correction instruction and attitude feedback quantity, and the track monitoring module constructs a visual interface. According to the method, the trajectory tracking precision of the unmanned aerial vehicle in a complex environment is remarkably improved, real-time detection and dynamic correction of abnormal deviation are realized, the state monitoring capability is enhanced by a three-dimensional visual interface, the trajectory tracking error is reduced, the misjudgment rate is reduced, and the flight safety and the control efficiency are guaranteed.

The invention provides a satellite navigation and atmosphere fusion positioning method and system based on an inertial navigation system, and relates to the field of navigation. The method comprises the following steps: firstly, acquiring multi-source navigation original data, including inertial navigation data, satellite navigation data and atmosphere information data; performing feature extraction through deep learning to obtain multi-modal feature data; and performing quality evaluation on the satellite navigation data, and judging whether the satellite navigation signal is effective or not. When the satellite navigation signal is effective, carrying out combined navigation calculation by adopting a federated filtering algorithm, and establishing an atmospheric information error model; and when the satellite navigation signal is invalid, the error model is utilized to compensate the atmosphere information, and is fused with inertial navigation data, so that high-precision navigation is realized, and the flight safety of the unmanned aerial vehicle in a complex environment is guaranteed.

The invention discloses a low-altitude patrol system and method for a smart city AI unmanned aerial vehicle, particularly relates to the technical field of unmanned aerial vehicle traffic control, and is used for solving the problem that a dynamic coupling effect caused by airflow interaction is not fully considered when an existing unmanned aerial vehicle cooperatively flies in a dense area. By monitoring the flight state and the relative position of the unmanned aerial vehicle in real time, the dense degree of the unmanned aerial vehicle in a target area is judged, and if the dense degree exceeds a safety threshold value, interaction between the unmanned aerial vehicle and surrounding airflow is analyzed through a nonlinear dynamic model, and possible dynamic coupling effect hidden dangers are evaluated; meanwhile, in combination with phase-space reconstruction and multi-scale analysis, potential interference of ground reflection airflow on the flight path of the unmanned aerial vehicle is evaluated; and whether overall traffic coordination optimization of the target area is carried out is judged, and the trajectory adjustment priority is determined according to the flight path complexity of each unmanned aerial vehicle, so that the flight path conflict is effectively avoided, and the flight safety and execution efficiency of the unmanned aerial vehicle cluster are improved.

The invention provides an aircraft digital twinning situation deduction and risk assessment method in a complex meteorological environment, and belongs to the technical field of data processing, the aircraft digital twinning situation deduction and risk assessment method in the complex meteorological environment comprises the following steps: collecting and processing weather data covering parameters such as wind direction and wind speed; obtaining aircraft performance parameters and building a database; and inputting and analyzing the flight plan. By establishing a three-dimensional deduction model including dynamics, weather influence and environment models, aircraft situations under different weathers are deduced, and flight safety, performance and task risks are evaluated. The method has the advantages that weather factors are comprehensively and accurately analyzed, and influences are quantified through data acquisition and processing and a meteorological model; the system is suitable for various aircrafts, can integrate navigation and control systems, and realizes remote monitoring management; risk assessment is scientific and reliable, multiple factors are integrated, powerful support is provided for flight decision making, and aircraft safety and task success rate in complex weather are effectively improved.

The invention relates to the field of unmanned aerial vehicle environment perception, in particular to a multi-mode environment perception method and system for a low-altitude medical unmanned aerial vehicle. The method comprises the following steps: collecting a multi-modal data stream, carrying out adaptive data optimization processing, and constructing a multi-modal fusion data set; performing environment multi-level obstacle identification and evaluation on the multi-modal fusion data set to generate a threat mapping environment map; multi-dimensional environment parameters are collected based on the unmanned aerial vehicle, wind field time-varying prediction and safe flight area calculation are performed based on the threat mapping environment map, and a flight area map is constructed; performing multi-position collision risk assessment based on the multi-modal fusion data set to generate collision risk coefficients of different positions; and carrying out safe flight constraint analysis on the flight area map according to the collision risk coefficient, and extracting an optimal flight path. According to the invention, in combination with real-time environment data, comprehensive flight path planning is provided, and the flight safety and task completion efficiency of the unmanned aerial vehicle are improved.

The invention relates to the technical field of unmanned aerial vehicle control, in particular to an unmanned aerial vehicle self-adaptive navigation system which comprises an inertial navigation system, a visible light sensor, a data fusion module, a database and a navigation control module. The inertial navigation system provides attitude, speed and acceleration data; the visible light sensor collects an image and obtains environment feature points through deep learning algorithm processing to realize visual positioning; and the data fusion module fuses two kinds of sensor data by using a dynamic weighted Kalman filtering algorithm, and dynamically adjusts the weight according to the environment to optimize the positioning information. The map library and the feature library store environment data to assist in positioning and dynamically update; the navigation control module generates an instruction according to the fused positioning information, and automatically adjusts a flight strategy by considering various factors based on an adaptive control algorithm. The system improves the positioning precision and environmental adaptability, enhances the flight safety and stability, is high in intelligence and autonomy degree, has flexibility and expandability, and can be widely applied to the field of unmanned aerial vehicles.

The invention discloses an unmanned aerial vehicle low-altitude route intelligent planning and cooperation system and method based on multi-source perception and edge calculation, and relates to the technical field of unmanned aerial vehicle route planning. Comprising a multi-source data sensing module, a data processing module, a planning execution module and a feedback monitoring module, and the multi-source data sensing module is used for acquiring meteorological data and airborne data and generating an initial data set; the data processing module is used for generating an environment model and inputting the initial data set into the environment model to obtain physical prediction data and obstacle information data; the planning execution module is used for generating a planning path according to the physical prediction data and the obstacle information data and controlling the unmanned aerial vehicle group to fly; and the feedback monitoring module is used for monitoring the flight state of the unmanned aerial vehicle group in real time and feeding data back to the ground control unit. According to the invention, the flight safety, efficiency and cooperative capability of the unmanned aerial vehicle group are improved.

The invention relates to the technical field of unmanned aerial vehicle control, in particular to a data-driven unmanned aerial vehicle flight control system real-time defect prediction system and method, and provides the following scheme that the system comprises a data acquisition module, a sensor verification module, a defect prediction module, a credibility analysis module and a flight control optimization module. And the sensor verification module identifies sensor data exceptions and distinguishes fault types through data consistency, time sequence exception detection and environment correlation analysis. And the defect prediction module analyzes the verified data by using machine learning and predicts potential faults. And the credibility analysis module verifies the prediction result and generates a credibility score. And the flight control optimization module selects an optimization control strategy according to the credibility score, adjusts flight parameters or executes safe operations such as return flight and landing. According to the method, the fault identification and prediction capability of the unmanned aerial vehicle in a complex environment is improved, and the flight safety and the system reliability are enhanced.

The invention relates to the technical field of path planning, and discloses an unmanned aerial vehicle path planning method based on large-range cruise, which comprises the steps of geographic information acquisition, geographic information processing, regional discretization, starting point and target point setting, feasible path search and path optimization. Geographic information collection is comprehensive and accurate, data of terrains, obstacles, no-fly zones and the like are covered, and a foundation is laid for path planning; regional discretization fits the characteristics of an unmanned aerial vehicle, cells are divided according to the minimum turning radius and the effective range of a sensor, and flight safety and perception are guaranteed. Feasible path searching is efficient and reasonable, and the optimal path is searched by well-designed cost and heuristic functions and considering multiple factors; path optimization is effective, based on a B spline curve smoothing method, curvature and length are reduced, energy consumption is reduced, and abrasion is reduced; safety verification is added, collision and cross-zone risks can be accurately judged, and safety guarantee is enhanced.

The invention discloses a small unmanned aerial vehicle narrow-distance space safety patrol method, relates to the technical field of space monitoring, and is used for solving the problem that a patrol path of an unmanned aerial vehicle in a transformer substation is not clearly generated. According to the substation equipment layout, inspection points are divided into safety points, dangerous points and transition points, an inspection path is optimized through a shortest path algorithm, flight safety and task efficiency and energy consumption minimization are ensured, inspection tasks are allocated to unmanned aerial vehicles according to priorities, emergency tasks are ensured to be processed preferentially, and then environment changes are monitored in real time, so that the safety of the substation equipment is ensured. According to the method, the unmanned aerial vehicle can rapidly and accurately arrive at the new dangerous point through the real-time monitoring of the unmanned aerial vehicle, new dangerous points and obstacles are timely identified, the flight path is dynamically adjusted through transition point optimization, it is ensured that the unmanned aerial vehicle can rapidly and accurately arrive at the new dangerous points, finally, high-precision positioning of the unmanned aerial vehicle in a narrow-distance space is ensured through combination of RTK and a vision-assisted positioning system, the flight state is dynamically corrected through a PID control algorithm, and it is ensured that the path is accurate and stable.

The invention discloses a multi-channel meteorological risk early warning information self-adaptive targeted publishing system and method based on low-altitude flight, and solves the problems that existing low-altitude meteorological early warning is low in temporal-spatial resolution, single in risk identification and insufficient in information pertinence. The system integrates air-based, space-based, foundation, social and topographic data, and generates a low-altitude meteorological dynamic database through fusion; micro-scale risks such as wind shear are extracted and graded through an AI model, a digital twin simulation response is constructed in combination with the state of the aircraft, and a risk grid model and coordinates are output; calculating a comprehensive risk index, generating evasion guidance, converting into multi-role early warning information, generating a three-dimensional thermodynamic diagram, and finally adaptively selecting a publishing channel according to user roles, terminals and network states. According to the invention, the accuracy and timeliness of low-altitude flight meteorological risk early warning are improved, and the safety of low-altitude flight is guaranteed.

The invention relates to the technical field of unmanned aerial vehicles, and discloses a gas-electric hybrid composite wing unmanned aerial vehicle system, which comprises an intelligent energy management module, a gas-electric hybrid composite wing unmanned aerial vehicle control module and a gas-electric hybrid composite wing unmanned aerial vehicle control module, the self-adaptive flight control module is used for optimizing the flight attitude, dynamically adjusting the flight route and improving the flight safety; the task adaptive scheduling module is used for adaptively adjusting a task execution strategy according to the task category, the load condition and the environmental parameters; the data monitoring and remote control module is used for collecting the operation state of the unmanned aerial vehicle in real time and storing and analyzing flight data; the intelligent safety management module is used for improving the autonomous safety of the unmanned aerial vehicle; and the environment perception optimization module is used for perceiving external environment factors in real time. According to the invention, the intelligent energy management module realizes intelligent scheduling of fuel and electricity power, and dynamically optimizes the power distribution of the battery and fuel oil, so that the flight time of the unmanned aerial vehicle is greatly prolonged.

The invention provides a navigation airport low-altitude meteorological intelligent management system based on multi-source meteorological data fusion, and the system comprises a three-dimensional grid monitoring network which is used for achieving the three-dimensional scanning and dynamic perception of an airspace meteorological environment through real-time collection of meteorological detection data; the data fusion system is used for carrying out assimilation processing on the collected real-time detection data and multi-source forecast data to generate a high-resolution three-dimensional gridding meteorological field covering an airport airspace range; the dynamic safety research and judgment module is used for researching and judging the influence of the current and future meteorological conditions of the airspace on the flight safety of each aircraft type in real time; and the management system establishment module is used for visually displaying the influence degree of the current meteorological condition on the flight through a visual chart by establishing a flight management system. According to the invention, a comprehensive meteorological safety protection system is constructed through an innovative system of intelligent perception, data fusion and decision pre-judgment, so that the problem of insufficient low-altitude meteorological service capability of an existing navigation airport is solved.

The invention discloses a low-altitude logistics unmanned aerial vehicle path planning method and system, and relates to the technical field of unmanned aerial vehicle logistics transportation, and the method comprises the steps: firstly, carrying out the optimization through calculating a preliminary path and combining the terrain and weather information; a sensor is used for detecting and dynamically avoiding obstacles; secondly, data transmission between the unmanned aerial vehicle and the ground control center is achieved, and the ground control center carries out monitoring, control and data processing. According to the low-altitude logistics unmanned aerial vehicle path planning system, efficient path planning is realized through multi-module cooperation. According to the invention, the flight safety, path planning accuracy and energy utilization efficiency of the unmanned aerial vehicle can be improved, the operation cost is reduced, and the system adapts to complex environmental conditions.

The invention provides an intelligent autonomous obstacle avoidance and path optimization system for an AI unmanned aerial vehicle, and aims to realize efficient autonomous obstacle avoidance and optimal path planning of the unmanned aerial vehicle in a complex environment by integrating a plurality of sensor data fusion technologies, edge calculation processing and an advanced artificial intelligence algorithm. The system comprises a sensing module, a data processing module, an artificial intelligence decision module, an execution module and a communication module. Wherein the sensing module acquires environmental data in real time through various sensors such as a laser radar, a high-definition camera, an infrared sensor and an ultrasonic sensor; the data processing module performs data preprocessing and fusion by using edge computing equipment to generate a high-precision environment model; the artificial intelligence decision module comprises an obstacle avoidance sub-module and a path optimization sub-module which are respectively based on a deep learning algorithm and a reinforcement learning algorithm to realize identification and avoidance of dynamic and static obstacles and optimal path planning; the execution module controls the flight attitude, speed and course of the unmanned aerial vehicle according to the decision instruction; the communication module realizes real-time data communication between the unmanned aerial vehicle and a ground control station or other unmanned aerial vehicles, and supports cooperative flight and task allocation. According to the system, through combination of multi-sensor fusion and an advanced algorithm, the flight safety of the unmanned aerial vehicle is improved, the task execution efficiency is remarkably improved, and the system has wide application prospects and remarkable technical advantages.

The invention discloses an unmanned aerial vehicle path planning and obstacle avoidance optimization method based on an improved elite colony algorithm, and the method comprises the following steps: carrying out three-dimensional grid environment modeling and obstacle generation, and obtaining a discrete model of a whole three-dimensional space according to a layering + two-dimensional grid method; elite strategy and path generation: ants select paths by using a random proportion strategy, set a transition probability function and introduce a fluctuation coefficient to prevent excessive concentration of path weights caused by pheromone volatilization; dynamic volatilization rate self-adaptive adjustment: carrying out self-adaptive adjustment according to pheromone distribution, and constructing a dynamic volatilization rate model; path cost calculation and dynamics constraint are integrated, a multi-objective planning method is applied, the path length and flight stability are optimized at the same time, and a path cost function is constructed; and performing path post-processing and smooth optimization, including processing a balanced path to improve the flyability and performing collision detection, thereby establishing an unmanned aerial vehicle dynamics constraint model, and ensuring the flight safety on the premise of path optimization.

The invention relates to the technical field of flight control quality evaluation, and discloses a flight control quality evaluation method and system based on artificial intelligence, and the method comprises the following steps: S101, obtaining multi-source flight data in real time; s102, extracting flight state characteristics, cognitive load characteristics of a pilot and environment state characteristics according to the multi-source flight data; s103, determining key control parameters and key flight state parameters, and constructing a space-time diagram; s104, recognizing the cognitive load state through a clustering algorithm, and dynamically adjusting the weight of a corresponding edge in the space-time diagram to generate a dynamic space-time diagram; s105, space-time sensitive features of the nodes are extracted, a comprehensive feature vector is obtained through calculation, and a flight control quality score is output; and S106, grading, and taking corresponding operation measures. According to the invention, real-time, accurate and dynamic flight control quality evaluation is realized, and flight safety and control quality evaluation scientificity are effectively improved.

The invention discloses a low-altitude flight real-time monitoring method and system based on 5G communication, and belongs to the technical field of low-altitude aircraft monitoring, and the method comprises the following steps: carrying out the classification and parameterization representation of low-altitude aircrafts, and constructing an aircraft model library; acquiring multi-source heterogeneous data of the aircraft based on the aircraft model library; performing feature extraction on the multi-source heterogeneous data, and constructing a three-dimensional model of a flight environment; based on the three-dimensional model of the flight environment, combining aircraft characteristics to construct a multi-aircraft motion model; on the basis of the multi-aircraft motion model, predicting a motion track of each aircraft; and when the motion trails of the multiple aircrafts have a conflict risk, monitoring and early warning are carried out, and an avoidance strategy is adopted to carry out avoidance. According to the method, the low-altitude flight environment perception capability and the risk assessment accuracy are remarkably improved, and powerful support is provided for guaranteeing the low-altitude flight safety.

The invention discloses a pilot cognitive load monitoring and early warning method based on electroencephalogram and eye movement data, and the method comprises the steps: 1) designing a pilot different cognitive load state induction experiment paradigm in a simulated flight task scene, and obtaining a multi-modal physiological signal and task performance of a testee during an experiment; wherein the multi-mode physiological signals comprise electroencephalogram signals and eye movement signals; 2) preprocessing the multi-modal physiological signals and task performance, extracting corresponding feature indexes, and fusing and selecting the feature indexes to construct an optimal feature subset; and 3) constructing a cognitive state classification and discrimination model based on the optimal feature subset so as to carry out monitoring and early warning on the cognitive load of the pilot. According to the invention, the cognitive load level of the pilot can be monitored and evaluated, especially the sudden change point of the overload state of the cognitive load is pre-warned, and the flight safety is improved.

The invention discloses an unmanned aerial vehicle wind resistance control method and system based on wind field sensing, and relates to the technical field of aircraft wind resistance control and wind field sensing. The method comprises the following steps: acquiring local wind field information around an aircraft in real time through wind speed and direction sensors arranged in a distributed manner, and performing fusion processing to form three-dimensional wind field distribution; generating a feedforward control quantity based on the wind field disturbance identification and short-term prediction model; feedforward control and a feedback control law of a flight control system are fused to form a composite control strategy, and flight attitude stabilization and control response optimization of the unmanned aerial vehicle under complex wind disturbance are realized. Correspondingly, the system comprises a flow field sensing and data processing module, a disturbance identification and feedforward control module and a flight control execution module, and has the functions of wind field real-time sensing, disturbance prediction, control preposition deployment and the like. According to the invention, the wind resistance and autonomous flight safety of the unmanned aerial vehicle in a low-altitude complex wind field environment can be remarkably improved.

The invention relates to the technical field of unmanned aerial vehicle autonomous obstacle avoidance, and discloses an unmanned aerial vehicle autonomous obstacle avoidance optimization method based on a laser radar. According to the method, a real-time point cloud data stream in a flight environment of the unmanned aerial vehicle is obtained through a laser radar scanning module, wherein the real-time point cloud data stream comprises three-dimensional position information and a dynamic movement track of an obstacle; and constructing an obstacle space distribution model based on the real-time point cloud data stream to represent the geometrical shape and the motion state of the obstacle. A reinforcement learning algorithm is adopted, a collision risk value is calculated based on the position information and the motion trail in the model, and an obstacle avoidance path optimization instruction is generated according to the risk value and used for adjusting the flight direction and speed of the unmanned aerial vehicle. And when the instruction is executed, flight path feedback data is recorded in real time, and parameters of the obstacle space distribution model are updated based on the feedback data. According to the method, the adaptability and reliability of autonomous obstacle avoidance of the unmanned aerial vehicle in a complex environment are improved, and the flight safety is guaranteed.

The invention provides an unmanned aerial vehicle flight conflict detection, deduction analysis and solution system. The system comprises a data acquisition and preprocessing module which is used for acquiring and preprocessing low-altitude basic environment data, flight plan data and unmanned aerial vehicle real-time operation data; the unmanned aerial vehicle operation risk assessment module is used for realizing multi-dimensional unmanned aerial vehicle flight environment comprehensive risk assessment analysis and searching for an optimal solution of flight conflicts in the flight process of the unmanned aerial vehicle; the unmanned aerial vehicle operation risk alarm module is used for providing an unmanned aerial vehicle risk alarm service and an unmanned aerial vehicle risk solution by combining different monitoring scenes and risk configurations of the system; and the unmanned aerial vehicle deduction simulation module is used for simulating different unmanned aerial vehicle flight scenes. According to the invention, the flight safety and management efficiency of the unmanned aerial vehicle in the low-altitude environment are realized by combining the beforehand deduction optimization and the in-event real-time conflict monitoring of the flight plan of the unmanned aerial vehicle, the burden of manual monitoring is reduced, and the method has wide application prospect and practical value.

The invention relates to flight path planning of aircrafts, in particular to a collaborative flight path planning method and system for a low-altitude multi-aircraft mixed take-off and landing field, and the method comprises the steps: carrying out the three-dimensional subdivision of an airspace based on a Beidou grid code, and carrying out the real-time mapping of an airspace state of a three-dimensional grid unit; dividing the three-dimensional grid units according to the grid state; constructing a geometric track generation rule, and eliminating a track crossing risk; introducing a time dimension on the basis of a three-dimensional space, carrying out four-dimensional space-time conflict detection on all tracks, and carrying out conflict resolution according to a four-dimensional space-time conflict detection result; a double-queue dynamic scheduling mechanism is constructed, and the aircraft is guided to land safely through queue separation and polling distribution; according to the technical scheme provided by the invention, the defects that the avoidance path of the low-priority aircraft is difficult to reasonably plan and the flight safety of the aircraft in the take-off and landing stage cannot be effectively guaranteed in the prior art can be effectively overcome.

The invention provides a construction method of an urban low-altitude wind field digital twinning system, which comprises the following steps: constructing an urban basic road network skeleton and performing region division, generating a building block model with real textures by using oblique photography data, and forming an urban three-dimensional space geometric model library; capturing atmospheric information in real time through a radar to generate high-resolution three-dimensional wind field scanning data covering a target area; processing detection data of the laser wind finding radar, performing simulation calculation on a wind field in combination with an urban three-dimensional space geometric model, and generating sub-meter gridding dynamic wind field data of an urban low-altitude area; and performing three-dimensional reduction and vivid representation on the obtained dynamic wind field data by using a visual rendering technology to form an interactive urban low-altitude wind field digital twin system. By integrating multi-scale modeling, laser wind finding radar, wind field simulation and visual rendering technologies, real-time monitoring, dynamic simulation and visual display of an urban low-altitude three-dimensional wind field are achieved, and low-altitude flight safety and operation efficiency are improved.

The invention discloses a rotor fault positioning and power reconstruction system and method for a composite wing unmanned aerial vehicle, and belongs to the technical field of unmanned aerial vehicle fault-tolerant control. The system comprises a data acquisition module, a fault verification module and a power redistribution module, and is characterized in that firstly, a rotor fault-attitude change direction symbol mapping table is established according to a rotor physical layout and attitude dynamics relationship, and a fault type is identified; after symbol matching is met, a dynamic threshold energy verification function is constructed for judgment, and whether the rotor has a real fault or not is judged through a fault dual verification model of symbol matching and energy verification; a control distribution matrix is dynamically reconstructed according to the verified fault rotor position, power compensation is provided by coordinating normal rotors, and stable control over the flight attitude is achieved. The invention provides an effective method for reducing the false alarm rate and the fault response time of the sensor, so that a control system can quickly respond and recover the flight attitude when the rotor wing suddenly fails, the attitude control stability is ensured, and the flight safety is remarkably improved.

The invention provides an aircraft digital twinning situation deduction and risk assessment method in a complex meteorological environment, and belongs to the technical field of data processing, the aircraft digital twinning situation deduction and risk assessment method in the complex meteorological environment comprises the following steps: collecting and processing weather data covering parameters such as wind direction and wind speed; obtaining aircraft performance parameters and building a database; and inputting and analyzing the flight plan. By establishing a three-dimensional deduction model including dynamics, weather influence and environment models, aircraft situations under different weathers are deduced, and flight safety, performance and task risks are evaluated. The method has the advantages that weather factors are comprehensively and accurately analyzed, and influences are quantified through data acquisition and processing and a meteorological model; the system is suitable for various aircrafts, can integrate navigation and control systems, and realizes remote monitoring management; risk assessment is scientific and reliable, multiple factors are integrated, powerful support is provided for flight decision making, and aircraft safety and task success rate in complex weather are effectively improved.

The invention provides an unmanned aerial vehicle group path planning method and system based on environment analysis, and relates to the technical field of unmanned aerial vehicle path planning. Unmanned aerial vehicle operation data are collected, unmanned aerial vehicle operation abnormity is analyzed based on the unmanned aerial vehicle operation data, an unmanned aerial vehicle group flight path is collected, and the flight distance in the unmanned aerial vehicle flight process is obtained; the method comprises the steps of screening out a flight risk interval based on a flight interval in a flight process of an unmanned aerial vehicle, collecting weather data, analyzing flight abnormity generated by the flight risk interval under the weather influence based on the weather data, and obtaining unmanned aerial vehicle group density in a set range when the unmanned aerial vehicle generates the flight abnormity based on an unmanned aerial vehicle group flight path. The flight influence of flight abnormity on the unmanned aerial vehicle group is analyzed based on the unmanned aerial vehicle group density, the optimal flight path of the unmanned aerial vehicle group is screened out based on the flight influence of the flight abnormity on the unmanned aerial vehicle group, potential flight risks such as out-of-control, course deviation and even crash of the unmanned aerial vehicles can be predicted, and therefore the flight safety is improved.