2023 results about "Low altitude" patented technology

The invention relates to the technical field of low-altitude aircraft navigation, and discloses a low-altitude aircraft track real-time planning method and system. The system comprises a flight situation awareness module, a track constraint calculation module, a real-time track planning module, a conflict prediction module and a track dynamic correction module. The flight situation sensing module generates a flight situation matrix through multi-source data fusion; a track constraint calculation module extracts static obstacle contours and dynamic obstacle tracks according to the static obstacle contours and the dynamic obstacle tracks, and generates a multi-dimensional track constraint set in combination with aircraft performance parameters; the real-time flight path planning module builds a three-dimensional flight path search domain by using an adaptive space division technology, and iteratively solves an optimal flight path sequence by using an intelligent search algorithm; the conflict prediction module combines the real-time dynamic obstacle trajectory to calculate the space-time proximity, and generates a conflict early warning map; and the track dynamic correction module re-draws an obstacle avoidance constraint area according to the map, and triggers local track correction. The system improves the comprehensiveness, real-time performance and safety of flight path planning, and guarantees the stable operation of the low-altitude aircraft.

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 present invention relates to a solar azimuth estimation method and system based on multi-channel feature enhancement and region-aware attention, belonging to the technical field of intelligent navigation for low-altitude economy unmanned systems. Aiming at the problem of decreased accuracy in solar azimuth estimation based on polarization images under complex cloud cover conditions, the present invention proposes a deep learning framework integrating multi-channel features and direction-aware attention. First, based on polarization light field information acquired by a division-of-focal-plane polarization camera, a three-channel composite input feature composed of a polarization intensity map, an adaptive threshold gradient map, and high-frequency residual edge information is constructed. Second, a ResNet backbone network embedded with a squeeze-and-excitation mechanism is adopted, and a direction-aware polarization attention module is introduced to achieve adaptive fusion of multi-scale features through luminance guidance, deep feature enhancement, and a gradient edge branch.

The invention discloses a low-altitude unmanned aerial vehicle trajectory tracking and monitoring method based on a 5G-A communication sensing integrated base station, and relates to the technical field of low-altitude traffic management and communication sensing fusion, and the method comprises the steps: firstly collecting multi-source data such as a communication sensing fusion signal, environment interference and unmanned aerial vehicle attributes, and carrying out the alignment and packaging of a unified timestamp and a coordinate system into a synchronous data frame; then, deep fusion and anti-interference processing are carried out on the data frames, noise is filtered out, and pure fusion data is generated; and furthermore, real-time track calculation and motion trend prediction are carried out on pure data by utilizing multi-base-station cooperative calculation and prediction. Based on this, through a multi-target feature recognition and clustering separation mechanism, independent individual trajectories are accurately stripped from a complex mixed data stream, and compliance verification and anomaly judgment are performed on the trajectories in combination with an airspace rule base. In this way, the problems of signal interference and multi-target aliasing in a complex environment can be effectively solved, and therefore high-precision global tracking of the low-altitude unmanned aerial vehicle and real-time monitoring of abnormal behaviors are achieved.

The invention relates to the technical field of risk prediction, in particular to a collision risk prediction method of a low-altitude aircraft in high-density flight in a complex environment, which comprises the following steps: acquiring a point cloud through a three-dimensional laser radar, clustering to generate an obstacle trajectory, matching the trajectory to identify a disturbance characteristic segment, calculating a deviation angle by combining a path vector to generate a disturbance frequency graph, and calculating the collision risk of the low-altitude aircraft. And extracting a parameter modeling dynamic safety interval, and fusing multiple factors to evaluate a collision risk level. According to the method, obstacle trajectory topology is constructed through combination of three-dimensional laser radar point cloud time window division and density clustering, sudden change features are identified through trajectory similarity matching, a Gaussian kernel dynamic safety envelope is generated through combination of course offset statistics and included angle operation, and a self-matching threshold value is established through normalization parameters and radial basis weighting. The method improves the high-density flight collision prediction precision, enhances the weather and obstacle heterogeneity matching capability, reduces the misjudgment early warning delay, and achieves the multi-variable flight situation collaborative analysis.

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 provides a vortex aircraft detection signal processing method and system based on optical fiber sensing. The method comprises the following steps: firstly, acquiring an original micro-vibration signal; secondly, performing environment self-adaptive cooperative processing on the original micro-vibration signal to generate a self-adaptive micro-vibration signal; converting the generated self-adaptive micro-vibration signal into a spatio-temporal evolution sequence based on eddy current physical characteristics; then analyzing the spatio-temporal evolution sequence to output a type discrimination result of the vortex aircraft; and finally, generating a customized detection report based on the type discrimination result. According to the technical scheme provided by the invention, the detection and identification capability of a low-altitude low-speed small target in a complex environment is improved, and reliable detection and identification of the vortex aircraft in the complex environment are also realized.

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.

Disclosed in the present invention are a low-altitude air route risk map construction method and system based on hexagonal grid cells. The method comprises: on the basis of management and control requirements for different types of airspace, forming low-altitude airspace three-dimensional hexagonal hierarchical grids by means of multi-scale partitioning of an airspace horizontal plane and multi-scale partitioning of an airspace vertical plane; constructing an unmanned aerial vehicle flight risk assessment indicator system for the low-altitude airspace three-dimensional hexagonal hierarchical grids, and after optimization, constructing an unmanned aerial vehicle operational risk assessment model based on a dynamic Bayesian network, so as to generate multi-scale grid risk values; and combining the multi-scale grid risk values with geographic location information of a region, designing a hexagonal grid code index, and generating a dynamic multi-scale low-altitude air route three-dimensional hexagonal risk map. The present invention can improve the accuracy and real-time performance of unmanned aerial vehicle flight risk assessment of grids, quickly obtain low-risk grids of an unmanned aerial vehicle in a target region, and improve the safety of air route planning.

The invention relates to the technical field of flight risk monitoring, in particular to an unmanned aerial vehicle low-altitude risk monitoring method and system based on a dynamic optimization algorithm. Acquiring flight state data of the unmanned aerial vehicle and multi-dimensional risk factor data of a current low-altitude environment; constructing a risk assessment model, assessing the flight state data and the multi-dimensional risk factor data of the unmanned aerial vehicle through the risk assessment model, and obtaining a comprehensive risk index; taking the comprehensive risk index as input, and generating an optimal monitoring strategy through a dynamic optimization algorithm; and carrying out unmanned aerial vehicle low-altitude risk monitoring according to the optimal monitoring strategy, obtaining a low-altitude risk monitoring result, carrying out iterative optimization on the optimal monitoring strategy according to the low-altitude risk monitoring result, and continuing to carry out low-altitude risk monitoring. And the safety, economy and sustainability of low-altitude risk monitoring of the unmanned aerial vehicle are improved.

The invention belongs to the technical field of intelligent unmanned systems and edge intelligent reasoning, and discloses an unmanned aerial vehicle emergency inspection system integrating low-altitude communication and edge calculation. The system is composed of a link state monitoring and scoring module, a reasoning calculation migration and model loading module, a task graph modeling and dynamic segmentation module, a multi-unmanned aerial vehicle task collaboration and trajectory continuation module, a flight state fusion and path adjustment module, a redundancy criterion driven fault-tolerant control module and a result consistency verification and data caching module. According to the method, the communication link scoring function fusing the packet loss rate, the bandwidth, the time delay and the signal-to-noise ratio is constructed, the comprehensive quality evaluation of different low-altitude communication links is realized, and the reasoning model switching and task migration strategy is driven by the scoring result, so that the communication adaptation capability of the system in a complex, dynamic or emergent scene is remarkably improved, and the system performance is improved. Compared with an existing communication mechanism depending on a fixed link or a preset priority.

The invention discloses a low-altitude wind field prediction method and system based on a space-time diagram convolutional network, and relates to the technical field of weather forecast and wind energy utilization, and the method comprises the steps: collecting wind field observation data and physical field data of all nodes of a target region, a dynamic space-time diagram is constructed based on a flow function-vorticity theory through a dynamic diagram construction module; extracting spatial information through a graph attention network to obtain a spatial feature tensor; the spatial feature tensor and the physical field data are processed by a PhysFusion-TransTCN encoder to obtain the deep spatial and temporal features of the wind field; performing hierarchical feature aggregation on the wind field deep spatial-temporal features through an output module to obtain a wind field prediction result of the target area; a wind field physical mechanism is deeply fused, multi-scale spatial-temporal feature fusion is realized, and prediction result precision and physical rationality are ensured.

The invention discloses a self-adaptive 4D Gaussian splashing high-precision three-dimensional reconstruction system and method, and belongs to the technical field of computer graphic processing. The invention aims to realize high-precision automatic registration of multi-source heterogeneous data and improve the calculation efficiency. The method comprises the following steps: collecting multi-source heterogeneous data; constructing a multi-modal fusion registration method, which comprises the following steps: combining satellite image data and low-altitude oblique photography data to realize spatial distribution geometric coarse registration, fusing low-altitude laser radar point cloud data and ground acquisition vehicle laser radar point cloud data to realize luminosity fine registration, establishing semantic features to assist registration, and obtaining registered multi-source data; initializing a 4D Gaussian primitive and executing adaptive splashing reconstruction to obtain an optimized 4D Gaussian splashing model; designing a cloud edge cooperative computing architecture oriented to 4D Gaussian splash reconstruction, and performing distributed parallel processing on the obtained optimized 4D Gaussian splash model; and executing quality evaluation and adaptive optimization, and outputting a final adaptive 4D Gaussian splash model.

The invention discloses an intelligent traffic control method and system in a low-altitude economic environment, and relates to the technical field of intelligent traffic systems, and the method comprises the steps: collecting low-altitude aircraft and ground traffic spatio-temporal data through a multi-modal sensor network, and generating a multi-source heterogeneous data set; constructing a dynamic traffic situation map through spatio-temporal feature fusion; performing three-dimensional path planning to generate a three-dimensional guiding strategy; detecting conflicts and correcting strategies according to a preset rule base, and outputting an instruction set to distribute real-time traffic flow. The technical problem that in the low-altitude economic environment, a traditional traffic management and control method is difficult to meet the cross-domain cooperation requirement of the low-altitude aircraft and the ground traffic is solved, and the technical effects of three-dimensional cooperative management and control of the low-altitude aircraft and the ground traffic and further guaranteeing safe and efficient operation of the traffic in the low-altitude economic environment are achieved.

The invention discloses a low-altitude airway flow field sensitive area dynamic identification optimization method based on set simulation, and the method comprises the steps: building a low-altitude flow field preprocessing data base with consistent time and space based on Beidou subdivision grids and multi-source heterogeneous data fusion; constructing a low-altitude airspace digital twinning environment based on the data; based on the low-altitude airspace digital twin environment and the cellular automaton-fluid coupling model, generating a diversified flow field evolution scene covering extreme weather and equipment faults; based on a set simulation result, extracting a high-conflict probability region through a spatio-temporal clustering algorithm and quantifying region risk features; generating an air route planning scheme meeting security constraints through a multi-objective evolutionary algorithm based on the quantitative regional risk features; on the basis of a low-altitude airspace digital twin environment and an air route planning scheme, verifying the feasibility of the air route planning scheme through historical data playback and virtual-real fusion test; and according to a verification feedback result, carrying out dynamic feedback optimization on the low-altitude air route flow field sensitive area identification and air route planning scheme.

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 a multi-base-station AOA cooperative low-altitude target rapid positioning system. The system comprises an AOA measurement base station network, an AOA data preprocessing module, an adaptive weighted intersection positioning module, an extended Kalman filtering state estimation module and a data fusion and system integration module. The system synchronously measures the arrival angle of a target signal through multiple base stations, removes noise in combination with smoothing filtering and an anomaly rejection algorithm, and then solves the initial position of a target by using a self-adaptive weighted intersection algorithm based on measurement quality and geometric distribution. And then, fusing the target motion model and the measurement model by adopting an extended Kalman filtering algorithm to realize dynamic estimation and prediction of the position, the speed and the course. The system can realize high-precision and real-time positioning and continuous tracking of targets such as low-altitude unmanned aerial vehicles, small aircrafts and the like in a complex electromagnetic environment and a sight distance limited scene, and has visual display and regional alarm functions.

The invention discloses a dynamic scheduling optimization method for a low-altitude logistics distribution network, and the method comprises the steps: a server side builds a multi-source sensing network through satellite remote sensing, an unmanned plane airborne sensor and ground traffic monitoring, fuses meteorological data, airspace control data and order distribution data which are collected in real time, and generates a four-dimensional space-time grid map; based on the four-dimensional space-time grid map, the server side adopts a TD3-GA hybrid intelligent algorithm to carry out path planning of the logistics distribution network; the edge calculation end generates an optimal scheduling scheme of the unmanned aerial vehicle group through a multi-objective optimization function based on the global path; and the server side performs security risk assessment on the optimal scheduling scheme by using a Bayesian network model, and dynamically adjusts a space-time routing strategy of the unmanned aerial vehicle cluster according to an assessment result. According to the method, in a large-scale unmanned aerial vehicle concurrent scheduling scene, the scheduling efficiency can be effectively improved, the response time delay is reduced, the risk prediction accuracy is improved, and the timeliness and safety of a low-altitude distribution network are remarkably improved.

The invention discloses a disaster area surveying and mapping and deep learning analysis system based on cooperation of a low-altitude unmanned aerial vehicle and satellite remote sensing, and belongs to the field of surveying and mapping and deep learning, and the system comprises the steps: carrying out the preliminary cruise scanning of a suspected disaster area through a low-altitude unmanned aerial vehicle platform, and obtaining high-resolution local image data from the suspected disaster area; marking abnormal features identified in the image to generate preliminary disaster distribution information; according to the real-time change information, in combination with time sequence image data of satellite remote sensing, calculating the dynamic evolution trend of the disaster hotspot by applying a long-short term memory network time sequence analysis algorithm, and determining the evolution direction and speed; and constructing a disaster diffusion prediction model based on data driving according to the evolution direction and speed data, adjusting an unmanned aerial vehicle cruising route and a satellite monitoring key area according to a possible diffusion path identified in a prediction path distribution diagram, and continuously tracking key nodes on the path to obtain updated monitoring data.

The invention relates to the technical field of aerospace, in particular to low-altitude traffic flow airspace-oriented real-time planning, and provides a centimeter-level precision detection network covering a low-altitude airspace by integrating multi-dimensional data sources such as radar electromagnetic feature recognition, ADS-B (Automatic Dependent Surveillance-Broadcast) automatic monitoring, Beidou or GPS (Global Positioning System) space-time reference positioning and the like. The three-dimensional trajectory and motion situation of the aircraft are solved in real time, intelligent reconstruction of an airspace sector and adaptive optimization of a flight corridor are realized by adopting a dynamic programming algorithm driven by reinforcement learning based on the real-time pose data of the aircraft, and a flight conflict prediction model constructed by combining a space-time convolutional neural network is used for predicting the flight conflict. According to the method, potential risks can be pre-judged, an optimal avoidance path can be generated, full-process digital management and control from identity verification to airspace authorization can be realized by constructing an aircraft digital identity authentication system and a dynamic access control mechanism, and modules such as a three-dimensional navigation information service module, a low-altitude digital communication private network module and an intelligent early warning and warning system module are integrated. And the guarantee of full-life-cycle service is provided for the low-altitude aircraft.

The invention discloses a bridge structure low-altitude inspection and disease assessment system and method based on deep learning, and belongs to the technical field of bridge disease detection and structure health monitoring. The system comprises a multispectral adaptive image acquisition module, a multi-scale disease detection and feature extraction module, a digital twin mapping and disease positioning module and a disease evolution prediction and maintenance decision module. The system dynamically adjusts acquisition parameters according to environmental conditions, accurately identifies diseases of different scales based on a multi-scale convolutional neural network, realizes centimeter-level accurate positioning of the diseases through a three-dimensional digital twinborn model, analyzes a disease time sequence evolution trend and generates graded maintenance suggestions, and realizes adaptive optimization through a closed-loop feedback mechanism. The method is high in environmental adaptability, high in detection precision and accurate in positioning, has disease evolution analysis capability, and provides comprehensive technical support for bridge health monitoring and intelligent management.

The invention provides a collaborative flight path planning method and system applied to low-altitude aircrafts, relates to the technical field of flight control of the low-altitude aircrafts, and aims to construct a three-dimensional dynamic flight environment model aiming at the problems that a low-altitude flight environment is complicated and multiple aircrafts are difficult to collaborate. Low-altitude area obstacle distribution, real-time weather and multi-aircraft task demand information are covered. Generating an initial cooperative flight path set based on the three-dimensional dynamic flight environment model and the aircraft performance parameters, performing cooperative conflict verification on the initial cooperative flight path set, detecting an overlapping region of preset flight paths of different aircrafts in a space-time dimension, generating a path adjustment instruction according to a verification result in combination with task priority ranking, and performing cooperative conflict verification on the path adjustment instruction. The trajectory with conflicts is corrected, safe and efficient cooperative flight of multiple low-altitude aircrafts is achieved, and the overall performance and task execution efficiency of the low-altitude aircrafts in a complex environment are improved.

The invention discloses an intelligent low-altitude unmanned aerial vehicle obstacle avoidance path planning method and system. The method comprises the steps of multi-modal data acquisition, space-time alignment fusion, three-dimensional dynamic trajectory reconstruction, obstacle avoidance path planning and system verification. The invention belongs to the technical field of path planning, and particularly relates to an intelligent low-altitude unmanned aerial vehicle obstacle avoidance path planning method and system.According to the scheme, an incremental dynamic updating mechanism is adopted, and the three-dimensional trajectory reconstruction precision is improved through a double-correction mechanism of hidden space normal estimation and global discretization constraint; bEV feature optimization fusion is adopted to generate a double-enhanced feature map; constructing a mixed bounding box for the trajectory prediction points, calculating a dynamic minimum distance, designing a dynamic safety threshold fusing the real-time speed of the unmanned aerial vehicle and the priority, constructing a space-time coupling risk index in combination with the potential collision time and the priority to complete refined risk grading, and constructing a reinforcement learning model of priority weighted rewards; the problem that the passing right distribution is not uniform in the low-altitude multi-priority unmanned aerial vehicle obstacle avoidance process is solved.

The invention provides an urban low-altitude unmanned aerial vehicle instant distribution scheduling and path planning method considering demand prediction. The method comprises the following steps: establishing a three-dimensional environment map of an unmanned aerial vehicle distribution system by adopting a grid-topology hybrid modeling strategy based on three-dimensional urban environment map data; according to the three-dimensional environment map of the unmanned aerial vehicle distribution system, the starting point and the target point of the unmanned aerial vehicle, the optimal flight path of the unmanned aerial vehicle, the time required by the optimal path and the transportation cost are obtained by using an unmanned aerial vehicle path planning algorithm of space-time improvement A *; and according to the order data, the unmanned aerial vehicle performance parameters, the optimal flight path of the unmanned aerial vehicle, the time required by the optimal path and the transportation cost, using the CNN-LSTM-Attention order prediction model and the task allocation model to output a task allocation scheme including a virtual order and an actual order. Accurate spatial data support is provided for path planning of the unmanned aerial vehicle, the reasonability of low-altitude logistics distribution path planning is improved, and finer data support is provided for urban airspace management.

The invention provides a low-altitude space path planning method and system, and relates to the technical field of low-altitude space management and path planning, and the method comprises the steps: S1, dividing a target airspace into a plurality of levels of three-dimensional voxels, and generating a unified-level three-dimensional code for each voxel; s2, voxelizing a task starting point, a task ending point and constraint elements based on unified hierarchical three-dimensional coding to generate a confinement three-dimensional corridor so as to limit a subsequent search space; s3, in the confinement three-dimensional corridor, hierarchical path search from the coarse hierarchy to the target hierarchy is executed, and a discrete path is output; s4, when the dynamic change of the environment is detected, performing increment re-planning on the basis of the discrete path, and updating the affected local path; and S5, smooth processing and safety verification are carried out on the discrete path or the updated path, and a final continuous flight path is generated. According to the invention, the real-time performance, the safety and the verifiability of the low-altitude flight task path can be realized.

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.

The invention discloses a flight vision obstacle avoidance method for a low-altitude inspection unmanned aerial vehicle, particularly relates to the technical field of image processing and computer vision, and is used for solving the problem of how to stably detect and reliably estimate the distance and contact time of such obstacles on the premise of only depending on airborne vision and being limited in calculation power and time delay. A space-time skeleton of a linear obstacle is constructed and continuously tracked in the strip-shaped candidates, and a grading distance and contact time are given in combination with an imaging time sequence difference and projection drift; uncertainties generated by the detection side and the self-motion are unified to the same coordinate and time mark, and risk density and safety envelope are formed through recursion; and then a path is updated according to a fixed priority of'deceleration-bypassing-still hovering 'by taking the risk as a hard constraint, so that high recall and stable distance evaluation and executable real-time avoidance can still be realized for a slender and swinging real obstacle under the conditions of only dependence on airborne vision and limited computing power and time delay. And the near loss event occurrence rate and the meaningless braking ratio are obviously reduced.

The invention discloses a low-altitude traffic flow collaborative awareness and conflict prediction method and system based on a multi-modal large model. The method comprises the following steps: collecting multi-source heterogeneous data of an aircraft, fusing and mapping the multi-source heterogeneous data to a space-time grid with dynamically adjustable granularity based on a real-time traffic situation, and realizing adaptive characterization; the method comprises the following steps of: constructing a robust special prediction model by introducing aircraft dynamics constraint and airspace rule knowledge as priori and performing fine adjustment by adopting a loss function containing an adversarial regularization item based on a pre-trained large model of the body-equipped agent; inputting spatial-temporal characteristics into the model, outputting behavior intentions and probability trajectories in an end-to-end manner, constructing an incomplete information game deduction model based on predicted trajectories, and calculating a comprehensive collision risk index fusing trajectory uncertainty, a motion state and a game deduction result; and generating a hierarchical early warning and collaborative avoidance strategy, and performing network distribution. According to the method, the accuracy, the robustness and the decision intelligence of conflict prediction under the low-altitude dense traffic flow are remarkably improved.

The invention relates to the technical field of aircrafts, and discloses a noise-considered urban low-altitude unmanned aerial vehicle path optimization method, which comprises the steps of urban environment rasterization and environment parameter setting; constructing an unmanned aerial vehicle noise attenuation model; calculating a noise interference critical distance and building sensitivity weight assignment; solving a noise interference grid matrix; a low-altitude unmanned aerial vehicle path planning model; and improving A * algorithm design. According to the method, the urban environment is rasterized, environmental parameters are set, an outdoor noise attenuation model is established by integrating geometric divergence, atmospheric absorption, ground effect and other factors, the propagation law of unmanned aerial vehicle noise in a complex environment is accurately described, the overall noise exposure and the sensitive area noise exposure are remarkably reduced while the flight safety is guaranteed, and the unmanned aerial vehicle noise attenuation performance is improved. The calculation efficiency is improved through the critical distance, path redundancy is avoided, collaborative optimization of noise control and operation efficiency is achieved, and technical support is provided for standardized operation of urban ultra-low-altitude unmanned aerial vehicles.

The invention discloses an intelligent change detection method for non-aligned two-time-phase images of an inspection unmanned aerial vehicle, and relates to the technical field of unmanned aerial vehicle image processing and change detection, and the method comprises the steps: obtaining two-phase images collected by a low-altitude unmanned aerial vehicle under a fixed route and same sensor parameters; a lightweight registration model is constructed and trained, feature point matching is utilized to predict matching point pairs, a homography matrix is calculated, and accurate registration of non-aligned images is achieved; and constructing and training a change detection model based on image pair interaction feature fusion, analyzing the aligned image after registration, and outputting a change information binary image. The method can effectively solve the problem of non-alignment caused by position and angle differences during two-time-phase image acquisition of an unmanned aerial vehicle, and the technical problems of low precision, poor robustness and insufficient calculation efficiency of a traditional method in change detection, effectively improves the automation level of low-altitude safety monitoring of ground highways and railways, reduces the maintenance cost, and improves the safety of the unmanned aerial vehicle. The important application value is realized.