1293 results about "Flight safety" patented technology

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 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 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 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 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 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 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 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 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 provides a multi-node collaborative airspace control method and device for urban low-altitude traffic. The method comprises the following steps: acquiring sensing data of a real-time sensing system of an urban low-altitude aircraft body and an urban airspace infrastructure; assigning task scheduling sorting, and determining a local congestion coefficient and a path starting and ending point pair of a current aircraft assignment task; constructing a path cost function to optimize the initial spline path, and generating a path control point sequence and a path total cost; generating a final path of the aircraft during actual execution based on dynamic path adjustment of distributed cooperative control; and mapping the final path to the urban airspace, generating a complete scheduling sequence of the aircraft in the urban airspace, and sending the complete scheduling sequence to an aircraft control system for execution. Under the background that the urban low-altitude airspace is increasingly complex, cooperation among the aircrafts can be guaranteed, the utilization efficiency of airspace resources is improved, meanwhile, flight safety is guaranteed, and therefore a foundation is laid for intelligent development of a low-altitude traffic system.

The invention discloses an unmanned aerial vehicle obstacle avoidance method based on sensing fusion and reinforcement learning, and belongs to the technical field of aviation flight control, and the method comprises the following steps: S1, sensor fusion time-space synchronization; s2, sensing fusion obstacle recognition and positioning; s3, obstacle avoidance route planning based on reinforcement learning; the low-resolution global map and a local occupation grid map with the unmanned aerial vehicle as the center are superposed to serve as multi-channel input, and the current state and the historical action sequence of the unmanned aerial vehicle are combined to be input into an intelligent agent model fused by a trained convolutional neural network and a long-short-term memory network; and an obstacle avoidance decision is output through a reward function of the agent model, and the airborne computer executes the obstacle avoidance decision to control the unmanned aerial vehicle to complete an obstacle avoidance action. Through sensor fusion, the obstacle sensing capability under different weather conditions and different flight environments is enhanced, the sensing distance is also increased, and the flight safety under extreme conditions can be ensured.

The invention discloses a multi-unmanned-aerial-vehicle high-speed dynamic flight formation control method, system and device, and relates to the technical field of multi-unmanned-aerial-vehicle cooperative formation control, and the method comprises the steps: analyzing a multi-unmanned-aerial-vehicle formation, setting a reasonable flight safety distance, and constructing a formation topological structure through an adjacent matrix and an offset; a distributed control model combining leader target driving and follower cooperation is designed, and global navigation and local formation keeping are achieved; setting a kinematics constraint function and a dynamic formation transformation mechanism to ensure that the unmanned aerial vehicle formation stably and safely advances and has an adaptive formation adjustment capability; according to the improved artificial potential field method, various resultant forces are considered, and virtual edge traction force is introduced, so that the local minimum problem of the traditional artificial potential field method is effectively solved, and the obstacle avoidance reliability of the system is enhanced.

Examples relate to a fatigue detection system to monitor and assess pilot fatigue levels during flight operations. An example system includes a wearable biometric sensor (WBS) integrated into a wristband that captures biometric data from the pilot. A processor analyzes the captured data to determine the pilot's fatigue level. When this level exceeds a predetermined threshold, the system provides an alert to the pilot through a haptic feedback mechanism in the wristband. Additionally, the system generates personalized fatigue mitigation advice, which is displayed on an electronic flight bag (EFB) application accessible to the pilot. The advice may include recommendations for taking a controlled rest, consuming caffeine, or engaging in physical activity. The system enhances flight safety by providing real-time alerts and actionable advice to combat pilot fatigue.

The invention belongs to the field of unmanned aerial vehicle emergency control systems, and relates to a risk identification and emergency method when an unmanned aerial vehicle flies in an airspace adjacent to an expressway, and the method comprises the steps: obtaining the balance state of the unmanned aerial vehicle through constructing a six-degree-of-freedom dynamic model and incorporating environmental factors and control intentions; dynamically comparing the balance state with real-time data to obtain a state deviation, deducing the fault probability of the unmanned aerial vehicle according to the state deviation, and realizing sensitive early warning of possible faults of the unmanned aerial vehicle; besides, the method constructs a double-layer linkage framework of an electromagnetic safety boundary and a physical safety boundary, realizes safety risk identification in combination with a fault probability, and ensures that a safety area between the unmanned aerial vehicle and a motorcade on a highway can be represented by a risk value along with speed, motorcade density and interference intensity; the fault probability of the unmanned aerial vehicle flying in the air is considered, when the fault probability is larger than a preset threshold value, the out-of-control state of the unmanned aerial vehicle is judged, a corresponding control strategy is adopted, and the flight safety of the unmanned aerial vehicle during partial or comprehensive out-of-control is guaranteed.

The invention relates to the technical field of safe flight of unmanned aerial vehicles, in particular to an air-ground multi-mode bird monitoring and early warning method and system for guaranteeing low-altitude safety, and the method comprises the steps: obtaining radar, visual, infrared and other multi-sensor data, and determining a first bird recognition probability, a second bird recognition probability and a third bird recognition probability through the data; and determining a final bird recognition probability based on the basic weight matrix, when the final bird recognition probability is greater than a preset threshold value, obtaining a relative distance and an included angle between the unmanned aerial vehicle and the bird, and establishing a multi-stage early warning mechanism according to the relative distance and the included angle to perform bird early warning. According to the invention, the contradiction between load limitation of small and medium-sized unmanned aerial vehicles and wide-area monitoring requirements is solved, all-weather, all-weather, all-direction, non-blind-area, all-route and real-time bird monitoring, recognition and tracking under a complex urban environment are realized, the real-time monitoring accuracy of birds is improved, early warning is performed in combination with distance and included angle information, and the real-time monitoring accuracy of the birds is improved. The collision between the unmanned aerial vehicle and birds can be more accurately and effectively avoided, and the low-altitude flight safety is guaranteed.

A coastal airport intelligent ecological regulation and control system based on digital twinborn and multi-objective optimization of the present invention belongs to the technical field of intelligent airport operation management and environmental safety control, and comprises a state reconstruction module for obtaining an original real-time data set of a physical sensor and an environmental prediction state generated by a digital twinborn model, performing weighted fusion based on a deviation metric between the data set and the environmental prediction state to generate a fused environmental state; the risk assessment module is used for quantitatively predicting a flight safety risk and an ecological toughness risk based on the fusion environment state, and determining dynamic weights corresponding to the two risks according to a preset rule to obtain a weighted risk assessment result; and the collaborative decision optimization module is used for solving a multi-objective optimization problem taking the risk weighted sum as an objective function based on the weighted risk assessment result so as to generate an optimal regulation and control instruction, and all-weather and high-reliability environment state acquisition is ensured.

The invention relates to the technical field of digital data processing, and discloses an aircraft safety data multi-source heterogeneous data acquisition method, which comprises the following steps of: synchronously controlling multiple types of sensors to concurrently execute physical obstacle three-dimensional investigation, micro-meteorological wind field construction and space electromagnetic spectrum surveying and mapping operation through a single unmanned aerial vehicle platform; and integrating the acquired three types of heterogeneous data into a three-dimensional spatio-temporal data base with a unified timestamp in real time. According to the method, endogenous space-time consistency of multi-source safety data at a collection source is ensured, information dislocation during traditional data fusion is avoided, a data base can truly reflect the instant coupling state of multiple risks, meanwhile, the exploration positioning capacity of the dynamic risks is arranged at a collection end in advance, and the data collection efficiency is improved. And an accurate and reliable decision basis is provided for flight safety assessment.

The invention relates to the technical field of unmanned aerial vehicle low-altitude flight safety monitoring, in particular to a low-altitude safe flight data monitoring system and method based on multi-source perception. The specific implementation process comprises the following steps: constructing a feature vector analysis module, fusing multi-source heterogeneous data through a space-time correlation model, and analyzing a flight feature vector; constructing a safe flight route planning module, and planning a safe flight route on the drawn flight safety map according to the flight feature vector; a risk factor correction module is constructed, when it is monitored that the deviation of the flight route exceeds a threshold value, a risk factor causing the deviation is deduced and quantified into a correction parameter, and dynamic re-planning of the flight route is triggered. According to the method, the flight feature vector and the flight safety map are combined, so that the low-altitude flight data of the unmanned aerial vehicle are monitored, and the continuous safety and the risk avoidance capability of the unmanned aerial vehicle in task execution in a complex, dynamic and unknown environment are effectively improved.

The invention relates to the technical field of flight control, and discloses a flight control optimization method and system, electronic equipment and a storage medium, and the method comprises the steps: obtaining flight state data, and executing data standardization and integrity processing; constructing an axial attention dynamic model to establish a state transition relationship; configuring a deep reinforcement learning framework and defining a multi-objective optimization mechanism; in response to the processed flight state data, network parameters are iteratively updated through an interactive training process based on the axial attention dynamic model and the deep reinforcement learning framework, and gradient optimization of a flight control strategy is realized; and in response to the trained flight state data, outputting a dynamic association model of environmental factors and control parameters, and generating a flight adjustment instruction set for deployment. Through the fusion architecture of axial attention and deep reinforcement learning, the modeling efficiency is improved, the calculation complexity is reduced, meanwhile, the generalization ability is high, the control precision is finally improved, and the flight safety is enhanced.

The invention relates to the technical field of automatic obstacle avoidance, and provides an automatic obstacle avoidance method and system for a low-altitude aircraft. The method comprises the following steps: acquiring flight state information in real time; acquiring obstacle information through an environment sensing module; based on the flight state information and the obstacle information, calculating a relative position and a relative speed with the obstacle, and determining a potential collision risk area; if the potential collision risk exists, based on a preset obstacle avoidance strategy library, an obstacle avoidance strategy is retrieved in combination with a risk area, and an obstacle avoidance control instruction is generated; and transmitting the control instruction to a flight controller, and controlling the aircraft to execute automatic obstacle avoidance. The technical problems that an existing low-altitude aircraft is lagged in obstacle avoidance response in a complex environment and difficult to accurately avoid obstacles in real time are solved, and the technical effects that collision risk dynamic pre-judgment is achieved through multi-source information fusion and intelligent strategy matching, and the autonomous obstacle avoidance success rate and flight safety of the low-altitude aircraft are improved are achieved.

The invention belongs to the technical field of low-altitude navigation safety monitoring, and particularly relates to a low-altitude navigation multi-source heterogeneous data adaptive fusion method for multiple scenes, which is a low-altitude navigation multi-source heterogeneous data adaptive fusion method for multiple scenes such as urban air traffic, low-altitude logistics and emergency rescue. Efficient integration of multiple types of monitoring data and position output of the trusted aircraft can be achieved, and accurate data support is provided for low-altitude navigation anomaly recognition and risk deduction. The method comprises the following specific steps: constructing a low-altitude navigation scene classification system and fusion demand mapping, collecting and preprocessing multi-source heterogeneous data, quantifying data credibility and resolving conflicts, constructing a hierarchical adaptive fusion framework and outputting a fusion result. According to the method, efficient and accurate fusion of multi-source data in different scenes is realized by constructing a layered scene adaptation framework and a credibility fusion model, and finally, a high-credibility aircraft position is output, so that reliable data support is provided for low-altitude navigation anomaly recognition and risk management and control.