5041results about "Aircraft traffic control" patented technology

The invention provides an unmanned aerial vehicle low-altitude intelligent traffic dynamic airspace management and control method and system, and relates to the technical field of intelligent control, and the method comprises the steps: taking an electronic fence geographic coordinate set as a monitoring reference boundary, fusing ADS-B data, meteorological information and an unmanned aerial vehicle equipment state, generating a real-time risk thermodynamic diagram, and outputting a grading alarm instruction; receiving a real-time risk thermodynamic diagram and a grading alarm instruction, and combining wind speed prediction and dynamic airspace occupation data; when the grading alarm instruction is triggered, executing the following operations: constructing a route feasible solution space by taking a no-fly zone and a high-risk zone in the risk thermodynamic diagram as constraint conditions; and iterating an evolutionary path population through selection, intersection and mutation operations of a genetic algorithm by taking the lowest energy consumption as an optimization target, so as to output a global final obstacle avoidance bypassing path, and issuing a route updating instruction to an unmanned aerial vehicle flight control system. According to the invention, the utilization of airspace resources is maximized on the premise of ensuring safety.

The invention discloses a low-altitude integrated management system based on a grid digital twin model and an intelligent algorithm, and relates to the technical field of low-altitude aircrafts. The method comprises the following steps: multi-source sensing data fusion and grid coding are carried out, and an airspace environment space-time database is constructed; constructing an airspace environment space-time database to carry out three-dimensional subdivision on the airspace; generating a multi-scale grid twinborn body, and associating a corresponding attribute for each level of grid according to a management requirement; generating an airspace risk thermodynamic diagram in the future 5-30 minutes; establishing a grid state change triggering rule; and generating a multi-target optimal path set by taking the grid navigation cost as a weight. According to the invention, by combining the real-time operation data of the low-altitude aircraft, the meteorological environment and other data with the artificial intelligence algorithm and the navigation rule base, the airspace traffic flow is analyzed, the flight plan is optimized, the flight route is intelligently distributed, the automatic route setting of any two points is realized, and the response speed of the low-altitude flight service system and the processing capability of various flight data are improved.

The invention belongs to the technical field of three-dimensional airspace management, and provides a three-dimensional airspace rasterizing management method and system based on multistage dynamic division, and the method comprises the steps: dividing height layers for a three-dimensional airspace, carrying out the rasterizing division of the three-dimensional airspace in each height layer, and carrying out the rasterizing division of the three-dimensional airspace; a live-action three-dimensional model of a three-dimensional airspace of a target area is constructed through multi-source data fusion, passable grids are marked, selection priority values of the passable grids are calculated through multi-source data fusion, when an aircraft is about to enter the three-dimensional airspace, route dynamic priority values of the grids are initialized, and a basic planning route of the aircraft is generated. Detecting the collision risk of the aircraft on the grids in the basic planning route, dynamically adjusting the route dynamic priority value, updating to obtain the actual planning route of the aircraft, monitoring the space between the aircrafts in the three-dimensional airspace in real time, and judging whether the collision risk exists between the aircrafts; and if there is a collision risk signal, triggering an aircraft obstacle avoidance and path re-planning strategy, and dynamically adjusting and updating the grid side length.

The invention discloses an unmanned aerial vehicle cluster intelligent cooperative control method, and the method comprises the steps: optimizing a network topology through heterogeneous unmanned aerial vehicle cluster dynamic networking and a dynamic clustering algorithm, and guaranteeing the reliability of a communication link; a layered hybrid decision architecture is designed to improve the task allocation rationality and the dynamic adaptability of the unmanned aerial vehicle cluster; a distributed control strategy network is trained by using a multi-agent near-end strategy optimization MA-PPO algorithm, and unmanned aerial vehicle cluster behavior collaboration is ensured in combination with space-time consistency constraint; an asynchronous incremental consensus protocol AICP is provided, the data transmission amount is reduced, and topology reconstruction is accelerated; real-time three-dimensional environment reconstruction and dynamic threat prediction are realized based on a neural radiation field NeRF technology; a lightweight anti-interference communication middleware is developed, and the instruction transmission stability is enhanced by adopting a space-time coding diversity technology. The method solves the problems of high delay of centralized control of the unmanned aerial vehicle cluster, poor convergence of a distributed algorithm and the like, and is suitable for high-dynamic task scenes such as urban street battle and complex terrain search.

The embodiment of the invention provides an airspace fine management method and device based on acceptable risks, and a risk assessment model is constructed by creatively adopting a grid airspace division mode and fusing ground attributes, historical accidents, meteorological environments and aircraft activity multi-source data. The self-adaptive adjustment of the risk tolerance threshold is realized through a deep reinforcement learning method, and a differentiated regional access standard is established. And through combination of task grading evaluation and a risk early warning mechanism, accurate evaluation of the operation safety of the unmanned aerial vehicle is realized. A multi-objective optimization algorithm is adopted to carry out route planning, cooperative management and control are realized in cooperation with an emergency avoidance strategy, and a prevention and control mechanism is continuously optimized through risk traceability analysis. According to the method, the defects of the traditional technology in the aspects of fine management, risk assessment, collaborative management and control and the like are effectively overcome, and the safety and efficiency of urban low-altitude airspace management are remarkably improved.

A display system of an aircraft includes a display device defining a display area, a vision system for generating an image of an operating environment of the aircraft on the display area, data processors operatively coupled to the display device and the vision system, and non-transitory machine-readable memory operatively coupled to the one or more data processors and storing instructions executable by the one or more processors and configured to cause the one or more processors to, when a condition of the aircraft is met, cause an alteration of a display of flight information on the display area to declutter a cutout region in the image generated by the vision system, the cutout region devoid of imaging generated by the vision system and displaying an Approach Lighting System (ALS) of a runway of intended landing for the aircraft.

Systems and methods are provided for promoting stable aircraft approach conditions. The system comprises a display device that is onboard an aircraft and a controller in communication with the display device. The controller is configured to, by a processor: receive data that includes information relating to an action configured to stabilize an approach of the aircraft during landing thereof and a recommended timing of performing the action relative to a predetermined flight plan of the aircraft, and render a first visual element on the display device that is configured to display the action relative to the flight plan and dynamically indicate the recommended timing of performing the action relative to a geographic position of the aircraft along the flight plan.

The invention provides an unmanned aerial vehicle identification early warning method and system. The method comprises the following steps: acquiring RGB image data, thermal radiation data and spectral data of an unmanned aerial vehicle no-fly zone through a visible light camera, an infrared thermal imager and a multispectral imager; performing transmission preprocessing on the RGB image data, the thermal radiation data and the spectral data, and adaptively adjusting multi-level fusion of a fusion weight based on real-time environmental parameters to generate target fusion data; based on a deep learning model and a tracking prediction algorithm, performing unmanned aerial vehicle identification early warning on the target fusion data, and generating early warning data; and transmitting the target fusion data and the corresponding abnormal event log to a cloud server, and updating the deep learning model by adopting the target fusion data and the abnormal event log. Through cooperative work of a multi-mode sensor, visible light, infrared, multispectral and other wave bands are covered, all-weather and full-scene unmanned aerial vehicle detection is achieved, the fusion weight is adjusted in real time based on real-time environment parameters, and the accuracy of the recognition result under the complex air situation is ensured.

The invention discloses an unmanned aerial vehicle cluster communication system based on weak signal connection. According to the invention, through the intelligent cooperation mechanism of the dynamic relay path optimization module, the communication reliability in a complex environment is significantly improved. The system analyzes cluster network topology and node states in real time, combines multi-dimensional parameters such as signal stability, energy consumption efficiency and task load, dynamically screens an optimal relay node and generates an efficient transmission path. For example, in a weak signal area, the system can automatically start an adjacent high-stability node as a relay, the problem of rigidity of a traditional fixed relay strategy is avoided, meanwhile, the signal attenuation risk is avoided in advance through predictive switching, and continuous return of key data is ensured. The capability of actively adapting to network fluctuation effectively reduces the probability of communication interruption, and can ensure the non-inductive connection of task instructions and key information especially in scenes with strict requirements on real-time performance, such as search and rescue, exploration and the like.

A path planning system for an unmanned aerial vehicle in a network of unmanned aerial vehicles is disclosed. The system includes the unmanned aerial vehicles (UAVs). The system further includes a first processing circuitry configured with a particle swarm optimization component to offline generate paths for each of the UAVs by PSO to minimize path length and avoid static obstacles. The system further includes a second processing circuitry configured with a deep reinforcement learning (RL)-based planner component for each UAV, to perform real-time path planning to navigate the UAV through dynamic environmental conditions using a particular path generated by the PSO for the UAV as a consistent reference for the UAV. The system further includes a reward component to calculate a reward as part of the path planning by the deep RL-based planner component to determine potential paths and converge to an optimal path for the UAV.

The invention belongs to the field of civil aviation, provides a civil aircraft field service operation scheduling control system, and aims to solve the problem of low cooperative efficiency of multiple links of ground service scheduling. File management is based on an Ali cloud OSS development interface, supports multi-format files, and realizes functions of permission access, encryption and the like; communication adopts an MQ / MQTT architecture to construct an IM system, and supports multi-type message transmission and http network communication; data processing packages a multi-language database interface, and internal and external network secure intercommunication is realized through a gatekeeper agent. Flight functions cover and guarantee node management and control, dynamic updating, cross-department communication, real-time flight data butt joint, task distribution of unified operation according to work types, and support of multi-terminal such as a web terminal and a handheld terminal and nailing access. Ground service multi-scene requirements can be met, and the operation efficiency and the management level are improved.

The invention provides an unmanned aerial vehicle flight authority management method and platform based on a block chain technology, and relates to the field of unmanned aerial vehicle flight management, and the method comprises the steps: constructing a block chain network, and encrypting and chaining flight authority; deploying the student model after knowledge distillation to an edge computing device for risk prediction before flight and abnormal flight identification in flight; before the unmanned aerial vehicle takes off, the flight legality is verified, pre-flight risk prediction is executed through a student model, and then a smart contract is called to perform matching verification of on-chain permission records; during execution of the flight task, an abnormal score multi-model combination mechanism is introduced to assist the student model to dynamically identify abnormal flight, and an intelligent contract execution response mechanism is triggered when an abnormal behavior is detected; after the unmanned aerial vehicle lands, the flight record is encrypted and stored in the off-chain storage system, and the flight record hash value, the off-chain storage index and the related access credential are linked. The whole-process authority management of the flight task of the unmanned aerial vehicle is realized, and the supervision efficiency is improved.

The invention discloses an unmanned aerial vehicle anti-collision method and system based on LoRa communication, and belongs to the technical field of risk avoidance of multiple unmanned aerial vehicles. The method comprises the following steps: constructing an ad hoc network communication architecture to realize flight state broadcast and synchronization; fusing the multi-source data by adopting a space-time alignment algorithm, and constructing a dynamic airspace situation map taking a local machine as a center; calculating a time window and space overlapping probability of the predicted intersection; a multi-priority dynamic negotiation protocol is considered, and autonomous decision making of the unmanned aerial vehicle cluster in a scene without a central node is supported; and dynamically adjusting the flight path, speed and height through a collaborative obstacle avoidance algorithm. Through a spread spectrum modulation technology and an adaptive power control mechanism, over-the-horizon communication coverage of more than 10km level is realized; through fusion of a kinematics prediction model and a risk quantification algorithm, millisecond identification and hierarchical response of collision threats are realized, a global optimal avoidance strategy is achieved, communication delay and calculation bottleneck brought by centralized decision are effectively avoided, and the real-time collaborative obstacle avoidance capability in a complex airspace is ensured.

The invention discloses a dynamic planning method for an air route of an urban low-altitude unmanned aerial vehicle. The method comprises the following steps: firstly, constructing an urban low-altitude environment model based on an airspace rasterization technology, fusing multi-dimensional constraint factors such as geographic data, meteorological conditions and airspace control information through multi-source environment information, and accurately calibrating the position of a take-off and landing point; secondly, a dynamic grid availability evaluation model is established in combination with environmental constraints and a real-time airspace state, and the navigation feasibility of each grid unit is quantitatively analyzed; then, an improved A * algorithm is combined with a grid availability evaluation result to generate a global optimal initial route; finally, a rolling time domain optimization strategy is introduced, and uncertain factors such as sudden obstacles and airspace dynamic limitation are responded in real time through periodic non-flight route re-planning after the unmanned aerial vehicle takes off. Through collaborative fusion of static environment modeling and a dynamic optimization mechanism, the problem of real-time planning of the air route of the unmanned aerial vehicle in the urban low-altitude complex environment is effectively solved, and the environmental adaptability and task reliability of the system are remarkably 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 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.

Systems and methods for automated unmanned aerial vehicle recognition. A multiplicity of receivers captures RF data and transmits the RF data to at least one node device. The at least one node device comprises a signal processing engine, a detection engine, a classification engine, and a direction finding engine. The at least one node device is configured with an artificial intelligence algorithm. The detection engine and classification engine are trained to detect and classify signals from unmanned vehicles and their controllers based on processed data from the signal processing engine. The direction finding engine is operable to provide lines of bearing for detected unmanned vehicles.

The present invention provides an unmanned aerial vehicle-based electric power patrol inspection control method and system. The method comprises: creating a flight path on the basis of an electric power patrol inspection area, and collecting images and position information of electric power devices by means of various types of devices carried by an unmanned aerial vehicle, the images and the position information being transmitted to a receiving terminal at a ground station in a wireless transmission mode; the ground station receiving the images and the position information that are collected in real time, processing and analyzing the images to obtain analytic status, such as defects, damage and corrosion, of the electric power devices; and, on the basis of the analytic status from the ground station, using the position information as distinguishing features to generate an electronic patrol inspection report, providing service lives, maintenance costs and maintenance measures of the electric power devices on the basis of the electronic patrol inspection report, and notifying maintenance staff of the position information of the electric power devices. The system comprises an unmanned aerial vehicle control module, an image processing and analysis module and an electronic patrol inspection report generation module. The present invention is of great significance to maintenance and management of electric power devices.

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 relates to a low-altitude flight dynamic safety guarantee method and system based on airspace gridding, and relates to the technical field of low-altitude airspace management, and the method comprises the steps: constructing a gridding airspace; on the basis of the gridding airspace, labeling network attributes for each grid unit in real time and evaluating a low-altitude safety state; according to network attributes, extracting a grid center point of an airspace grid which is evaluated to be in a low-risk range as a safe flight point, and dividing the gridding airspace into a plurality of safe triangular grids; and according to the predicted trajectory of the aircraft, selecting a candidate point with the lowest risk cost from adjacent triangles of the safety triangular mesh, and introducing a risk cost function to dynamically adjust the distribution of flight path points. According to the invention, the flight path of the aircraft group can be dynamically adjusted according to the environment change, and the stability and safety of cluster task execution are improved.

The invention discloses an unmanned aerial vehicle collision risk early warning method and system based on a dynamic space grid, and relates to the technical field of aircraft control, and the method comprises the steps: obtaining the historical operation data of a target region based on a preset space-time collection constraint, and extracting a space and unmanned aerial vehicle reference index set; constructing a dynamic space grid comprising an analysis grid and a shared grid; taking the shared grid as a data acquisition constraint, traversing the analysis grid to preliminarily screen the analysis object, and obtaining an associated object set containing the subject machine and the object machine; and performing collision assessment based on a preset discrimination condition, and outputting a collision risk early warning result. Therefore, the technical effects of improving early warning accuracy and early warning efficiency and facilitating efficient parallel early warning are achieved.

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 relates to the technical field of unmanned aerial vehicles, and discloses a low-altitude unmanned aerial vehicle conflict detection method and system based on trajectory prediction, a terminal and a storage medium, and the method comprises the steps: obtaining space-time flow trajectory data of an unmanned aerial vehicle, carrying out the preprocessing of the space-time flow trajectory data, and obtaining target space-time flow trajectory data; performing behavior characteristic division on the target space-time flow trajectory data according to a preset division rule to obtain behavior characteristic constraint information; fusing the target space-time flow trajectory data and the behavior feature constraint information to obtain a space-time fusion input sequence, and inputting the space-time fusion input sequence into a mixed trajectory prediction neural network model for prediction to obtain a plurality of trajectory prediction points; and establishing a plurality of three-dimensional mixed bounding box models according to the plurality of trajectory prediction points, and performing conflict detection on the plurality of three-dimensional mixed bounding box models to obtain a conflict detection result. According to the method, collision conflicts of the multiple unmanned aerial vehicles are detected according to the tracks of the unmanned aerial vehicles, and accurate detection of collision of the unmanned aerial vehicles is realized.

The invention provides a low-altitude economic area traffic control optimization method and system, and relates to the technical field of traffic control, and the method comprises the steps: carrying out the spatial division of a low-altitude economic area, generating a multi-layer air corridor topological structure, and mapping the traffic flow data to each layer of air corridor; according to the air corridor capacity constraint of each layer, drawing up a first flight control condition; according to the same-layer air corridor safety distance constraint, a second flight control condition is drawn up; and performing traffic scheduling control in the low-altitude economic area by using the first flight control condition and the second flight control condition. The technical problem that the traffic management and control efficiency of the low-altitude economic area is low due to the fact that the traffic density of the low-altitude economic airspace is increased and airspace resources are difficult to distribute effectively in the prior art is solved, and the traffic management and control efficiency of the low-altitude economic area is improved by comprehensively considering the capacity constraint of the airspace corridors and the safety distance constraint between the airspace corridors on the same layer. And the traffic control efficiency of the low-altitude economic area is improved.

The invention relates to the technical field of unmanned aerial vehicle route planning, and discloses a low-altitude route risk map construction method and system based on artificial intelligence, and the system comprises a low-altitude environment perception constraint module, a low-altitude route feature extraction module, a dynamic risk constraint judgment module, a low-altitude route map construction module, and a low-altitude route risk analysis module. And the optimal path calibration output module is used for determining a low-altitude air route flight area by acquiring a first constraint condition of low-altitude air route environment perception, extracting key feature parameters from low-altitude air route data, performing second constraint condition analysis on the low-altitude air route, intelligently planning a low-altitude air route route according to an analysis result, and constructing a low-altitude air route map. According to the method, the low-altitude air route is acquired, the constant direction line risk analysis is performed on the low-altitude air route to calibrate the optimal path, the optimal path is output to the user terminal, the path three-dimensional display is performed in the low-altitude air route map, and the execution efficiency and the safety of the flight task are improved.

The invention discloses an unmanned aerial vehicle intrusion real-time alarm and countering guidance system based on RID information. The system comprises a multi-source information acquisition and fusion module, an intrusion detection and threat evaluation module, a real-time alarm and countering strategy generation module, a feedback evaluation and strategy iteration module, a signal loss prediction and compensation module, a comprehensive processing terminal module and the like. The system establishes a three-dimensional geofence model by analyzing remote identification information of the unmanned aerial vehicle and fusing radar, photoelectric, electromagnetic and other multi-source data, and performs dynamic judgment and multi-factor threat grading on a target intrusion state. Multi-mode alarm is automatically triggered according to the threat level, and a hierarchical countering scheme from broadcast alarm and electromagnetic interference to physical capture is generated; and when the RID signal is lost, the system realizes continuous tracking and state recovery of the target by using a motion prediction and probability association algorithm.

The invention relates to the technical field of air traffic management, and particularly provides an airport scene aircraft taxiing conflict real-time prediction and dynamic scheduling method, which comprises the following steps: collecting the motion state of an aircraft and airport road network data, and generating the original trajectory of the aircraft by adopting a hidden Markov model; predicting a future taxiing trajectory of the aircraft to obtain a predicted trajectory; screening potential conflict aircrafts by adopting a dynamic R-Tree spatial index technology, judging whether tracks between the potential conflict aircrafts are intersected or not, and generating a dynamic risk value; executing a dynamic scheduling decision based on the dynamic risk value, and generating an aircraft bypass path by adopting a path generation algorithm; rolling time domain control is adopted, and a local path is re-planned based on data obtained in real time. According to the method, prediction of the taxiing trajectory of the aircraft is fully considered, the risk value of the taxiing conflict of the aircraft is pre-judged in advance, and dynamic path adjustment is performed, so that the labor cost is reduced, and the efficiency and safety of airport ground operation are improved.

The invention discloses an unmanned aerial vehicle detection method based on multi-band radio spectrum analysis, and the method can recognize the flight control behaviors of a target, such as hovering, cruising or hovering, through the extraction of a signal track and the introduction of a behavior modeling mechanism, and effectively overcomes the defect that the prior art lacks the dynamic behavior analysis capability. A multi-node synchronization mechanism and a TDOA positioning method are combined, so that high-precision traceability of a target space position is realized; meanwhile, risk level assessment is carried out based on signal features, a track mode and a space region, so that the system has intelligent early warning capability. According to the whole scheme, a full-link unmanned aerial vehicle detection method from multi-frequency receiving, frequency spectrum identification, behavior judgment, target positioning to risk output is constructed, and the problems of single frequency band, rough identification means, lack of tracking and evaluation capability and the like in the prior art are effectively solved; and the practicability and the accuracy of the unmanned aerial vehicle detection system in a complex airspace environment are remarkably 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.

A method for determining improved flight trajectory. The method includes receiving one or more weather parameters to determine contrail forecast data; receiving one or more flight parameters associated with at least one aircraft 204 to determine flight data thereof; receiving flight schedule including at least one flight plan of at least one aircraft; analyzing at least one flight plan to determine at least one navigational avoidance between at least two aircraft; determining contrail likelihood associated with at least one aircraft; altering one or more flight parameters to determine improved flight trajectory for at least one flight plan; sending at least one flight plan including improved flight trajectory to at least one aircraft; and validating improved flight trajectory using imagery data, when at least one aircraft flies according to at least one flight plan including improved flight trajectory. A system for determining improved flight trajectory.