1468 results about "Flight safety" patented technology

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 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 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 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 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 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 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.

The invention discloses an unmanned aerial vehicle cluster cooperative formation control method and system based on an unknown environment, and the method comprises the steps: setting a formation expectation position according to a tracking target position, and flexibly adjusting a specific position in a formation according to the position of the unmanned aerial vehicle, so as to construct a target point of each unmanned aerial vehicle; an optimal path from the position of an unmanned aerial vehicle to a target point is constructed, and after the optimal path is smoothed, the unmanned aerial vehicle is controlled by a model prediction controller to move along a track after the optimal path is processed; in the moving process of the unmanned aerial vehicle, whether the acceleration or height of the rear vehicle is adjusted or not is determined by setting the speed adjusting area and the position adjusting area of the front vehicle to avoid collision, so that the potential collision risk is effectively avoided, the flight safety of the unmanned aerial vehicle in a complex environment is improved, and particularly, the performance is outstanding in a dense or dynamically changing flight area.

The invention belongs to the technical field of low-altitude flight safety early warning, and provides a multi-modal data fusion method for low-altitude flight risk early warning. Comprising the steps of flight state data and meteorological data alignment processing, missing data filling, spatial feature extraction, spatial feature and flight state data fusion and space-time convolutional neural network and space-time diagram convolutional network analysis. According to the invention, the flight state and the meteorological data are fused, so that the prediction accuracy and reliability are improved; through the space-time convolutional neural network and the space-time diagram convolutional network, the space and time dependency relationship in the flight data and the meteorological data can be captured at the same time; through spatio-temporal data fusion, missing data can be accurately aligned and complemented, and the data quality and the real-time performance of the system are improved.

The invention discloses a shortest-time-based bird flock repelling effect evaluation method for an airport intelligent bird repelling system, and the method comprises the steps: S1, collecting the multi-dimensional monitoring data of an airport bird flock repelling process through a biological feature sensing system, and enabling the multi-dimensional monitoring data to comprise a repelling duration parameter, a region clearing index, and bird flock adaptability response data; s2, based on the multi-dimensional monitoring data, analyzing the spatio-temporal behavior characteristics of the bird flock through an intelligent analysis terminal, and constructing a multi-dimensional bird flock expelling effect characteristic matrix; s3, based on the feature matrix, performing dynamic weight optimization of bird flock expelling effect evaluation indexes with the shortest expelling time as a target, and generating a self-adaptive weight coefficient group; and S4, based on the self-adaptive weight coefficient group, performing panoramic evaluation on the bird flock repelling effect by adopting a time sequence perception memory network, and outputting a repelling efficiency analysis result taking the repelling timeliness as a core. While the flight safety is guaranteed, efficient utilization of bird repelling resources is realized.

The invention discloses an intelligent sensor for low-altitude flight safety guarantee. The intelligent sensor comprises a data acquisition unit, a data processing and analysis unit, a safety risk assessment module, an early warning and obstacle avoidance strategy generation unit and a communication interface, the data acquisition unit is used for collecting multi-source data in a low-altitude flight environment; the data processing and analyzing unit performs preprocessing, feature extraction and fusion analysis on the data acquired by the data acquisition unit; the security risk assessment module is based on the output of the data processing and analysis unit; the early warning and obstacle avoidance strategy generation unit automatically triggers an early warning signal when the safety risk value exceeds a preset threshold value; the method has remarkable beneficial effects in the aspects of improving safety risk assessment accuracy, realizing balance between path cost and safety risk, enhancing the autonomous obstacle avoidance capability of the aircraft, improving flight efficiency and fuel economy, promoting aviation technology innovation and development and the like.

The invention belongs to the technical field of unmanned aerial vehicle technology and intelligent operation and maintenance management, and discloses an unmanned aerial vehicle maintenance task scheduling and resource management platform and method, and the method comprises the steps that a data collection and fusion module obtains and fuses multi-source data; the fault diagnosis and prediction analysis module uses a deep learning model and a CNN-LSTM hybrid model to diagnose faults, predict life and generate a maintenance task list; an optional task dynamic priority evaluation module performs priority ranking on the maintenance task list; the intelligent scheduling decision module uses an improved NSGA-II and VNS mixed algorithm to generate an optimal allocation scheme according to priority and resource constraints; the resource dynamic management and cooperation module realizes dynamic allocation and cooperation of personnel, spare parts, tools and sites, and can integrate AR guidance; and the optional visual monitoring and feedback optimization module displays the state, feeds back data and realizes closed-loop optimization. The maintenance efficiency of the unmanned aerial vehicle can be remarkably improved, the operation cost is reduced, and the flight safety is guaranteed.

The invention relates to the technical field of unmanned aerial vehicles, and discloses an unmanned aerial vehicle low-altitude complex obstacle detection and avoidance system based on multi-sensor fusion, and the system comprises a multi-mode sensing module which is used for synchronously collecting heterogeneous sensing data of the surrounding environment of an unmanned aerial vehicle; and the sensing fusion processing module is in communication connection with the multi-mode sensing module and is used for carrying out timestamp alignment, space coordinate system unification and deep fusion processing on the heterogeneous sensing data, and all-weather and high-reliability environment sensing is realized through multi-sensor fusion and hardware synchronization. The system has dynamic obstacle prediction and three-dimensional risk map construction capabilities, and realizes crossing from passive obstacle avoidance to active foresight obstacle avoidance. And a safe and smooth flight path is generated in combination with hierarchical planning and model prediction control, so that the autonomous flight safety, reliability and intelligent level of the unmanned aerial vehicle in a low-altitude complex environment are fundamentally improved.

The invention discloses an aircraft intelligent maintenance and diagnosis system, and relates to the technical field of aircraft maintenance and diagnosis, and the system comprises a data collection module which is used for collecting various data in the actual operation of an aircraft. According to the invention, a digital twin model construction module is utilized to accurately simulate the operation state and fault condition of the aircraft, early discovery and accurate diagnosis of potential faults are realized, the pertinence and effectiveness of maintenance are improved, immersive fault experience and maintenance guidance are provided for maintenance personnel through fusion of VR / AR technologies, and the maintenance efficiency is improved. The maintenance accuracy and efficiency are improved, pre-diagnosis and maintenance scheme verification are carried out on the digital twin model, unnecessary disassembly and detection work is avoided, the maintenance time and labor cost are reduced, potential safety hazards can be eliminated in time through accurate fault diagnosis and an effective maintenance scheme, the flight safety of an aircraft is improved, and the safety of the aircraft is improved. And the maintenance scheme is comprehensively evaluated and optimized, so that the performance and the structural integrity of the maintained aircraft are ensured.

The invention belongs to the field of electromechanical control, and discloses a redundancy control method and system for an electric pump and a transmission system.The redundancy control method comprises the steps that onboard state signals, flight control system state signals and first hydraulic pressure and second hydraulic pressure of each hydraulic sub-system in a hydraulic system are collected in real time; an electric pump closing instruction and a hydraulic pressure logic judgment instruction are output through an on-board state threshold value and first electric pump control logic according to the on-board state signal, and the electric pump is closed according to the electric pump closing instruction; and a flight control system state signal, the first hydraulic pressure and the second hydraulic pressure are obtained, and each electric pump is automatically controlled through a low-pressure threshold value and a pressure safety threshold value by adopting second electric pump control logic, a hydraulic pipeline protection mechanism and an electric pump protection mechanism. According to the invention, the starting of each electric pump can be automatically controlled, a hydraulic passage can be protected, and the flight safety is ensured.

The invention relates to a flight data anomaly detection method and system based on dual-view enhanced Transform, and belongs to the technical field of aviation safety. According to the invention, the task of flight safety anomaly detection is expanded beyond a traditional overrun event, aiming at the subtle anomaly which is often not detected in the flight process; according to the fact that QAR data is a typical form of time series data, a flight safety problem is re-expressed as an anomaly detection task in time series analysis; in addition, an unsupervised anomaly detection model specially designed for QAR data is designed in order to solve the challenge of excessively depending on expert marking data in flight safety research. And finally, designing a time sequence anomaly detection model architecture based on reconstruction according to the feature sparse characteristic of the QAR data. According to the method, the condition of deviating from the normal flight mode can be identified in time, early warning information is provided for airlines, experts are effectively assisted in analyzing abnormal flight behaviors, and the method has a wide application prospect.