12 results about "Air traffic management" patented technology

Disclosed are a method for designing a codebook for A2A communication between UAMs that takes into account the unique topology of a UAM corridor based on the spatial characteristics of the UAM corridor, and an apparatus for the same. First, based on the structure of the UAM corridor, the density of UAMs and the communication range required for inter-UAM communication, a UAM distribution function representing the distribution of UAMs located in the UAM corridor is obtained. A beam width of a beam pattern to be generated by each codeword in the codebook is set Based on the obtained UAM distribution function. Codewords optimized for the hardware of the wireless communication system installed are designed based on each ideal beam pattern that produces the set beam width.

There is provided a UATM service apparatus included in a plurality of UATM service apparatuses for implementing a multiplex UATM network. The apparatus comprises a UATM network connector configured to share control data with other UATM network connectors connected to the multiplex UATM network based on a pre-determined authority; and a UATM service provider configured to perform control processing on at least one of a plurality of control objects or control areas separated from all control objects or control areas for the multiplex UATM network according to a pre-determined logical operation criterion or a pre-determined physical space criterion, and collect the control data by a pre-determined criterion or modify the control data by a pre-determined criterion to share the collected control data or the modified control data with other UATM service apparatuses through the UATM network connector and the other UATM network connectors.

This invention discloses a method and system for predicting the performance of air traffic controllers in high-altitude areas based on smart bracelets. It relates to the fields of air traffic management, high-altitude medicine, and wearable computing. The method includes collecting physiological data such as heart rate, blood oxygen, and blood pressure via a smart bracelet; simultaneously acquiring environmental data such as altitude and visibility; extracting reaction time and error rate from voice data; performing data cleaning and alignment; inputting multimodal data into a deep fusion neural network model to calculate comprehensive risk indicators; triggering alerts and visually outputting the status when the indicators exceed dynamic thresholds, while simultaneously generating automated intervention suggestions. This invention, specifically designed for the low-oxygen environment of high altitudes, achieves deep attentional fusion of physiological, environmental, and behavioral data, constructing a dedicated prediction model suitable for high-altitude airports and forming a complete closed loop of "perception-prediction-intervention," providing an intelligent and non-intrusive proactive management tool for air traffic control safety.

Generally discussed herein are systems, apparatuses, and methods for simulating an airspace including a method that receives a plurality of flight intent data inputs from a plurality of sources including service suppliers of unmanned aircraft systems (UAS) traffic management (UTM), advanced air mobility (AAM) and conventional air traffic management (ATM). The plurality of flight intent data inputs include, UTM flight intent volumes, UTM flight intent trajectories, conventional flight plans, conventional flight trajectories and an airspace design configuration. The method includes generating a center line route corresponding to each of the plurality of flight intent data inputs; generating a flight volume for each the plurality of flight intent data inputs; generating a four-dimensional trajectory based upon the center line route and the flight volume; and verifying the four-dimensional trajectory against constraints and potential conflicts.

The application relates to the technical field of unmanned aerial vehicle air traffic management, and discloses an airspace network flight simulation system, which comprises a multi-source data fusion module, a height section graph generation module, an algorithm selection module, a path planning module, a cooperative scheduling module and a visualization module, and a simulation verification module. The multi-source data fusion module dynamically integrates geographic information data by adopting a time-weighted fusion algorithm. The height section graph generation module adaptively adjusts a grid resolution and introduces a safety buffer zone to generate an obstacle distribution graph. The algorithm selection module intelligently matches an optimal path algorithm based on a machine learning prediction model. The path planning module generates a high-quality trajectory by adopting a hierarchical strategy in combination with global planning and local optimization. The cooperative scheduling module constructs a distributed architecture and guarantees the safe cooperation of multiple machines by means of predictive conflict detection and negotiation solution strategies. The visualization module provides an augmented reality interface. The simulation verification module verifies in a digital twin environment. The application provides a reliable solution for urban low-altitude traffic management.

The application belongs to the technical field of artificial intelligence and flight path simulation, and discloses a flight path simulation method and system based on large model fine tuning, which comprises the following steps: extracting original flight path data from multi-source heterogeneous data, performing spatio-temporal alignment processing and normalized representation, converting continuous motion state parameters into discrete feature word units, and constructing a flight path sequence text stream; designing a decoder architecture based on a self-attention mechanism, configuring a rotating position embedding module; constructing a parameter-efficient fine tuning module, injecting a low-rank decomposition adaptation operator, and integrating a physical prior encoder to modulate the attention weight distribution; performing multi-criteria joint target function fine tuning; in the reasoning stage, a controlled random sampling strategy is adopted, and constraint checking is performed through an online motion school verification module. The scheme of the application realizes diversified generation while maintaining the physical fidelity of the flight path, and can be applied to air traffic management simulation and tactical training simulation scenes.

The application provides an airport low-altitude flight track playback analysis method and system, and relates to the technical field of air traffic management. The method comprises the following steps: acquiring low-altitude flight track data, constructing a hybrid neural network model to filter and process the low-altitude flight track data to generate intermediate flight track data; identifying key track feature data and non-key track data in the intermediate flight track data through an SVM classifier and respectively performing lossless compression and lossy compression to generate playback flight track data; designing a collaborative computing framework of CPU, GPU and FPGA, and distributing the playback flight track data to multiple track computing nodes for collaborative processing; adopting a storage architecture based on consistent hashing to partition and store the playback flight track data, and designing a multi-level index mechanism to support the retrieval of the playback flight track data, so that the track playback precision can be improved, the data processing efficiency can be optimized, and the system storage and computing cost can be reduced.

This invention relates to a dynamic air traffic control method based on a multi-tree evolutionary programming algorithm, belonging to the field of air traffic management technology. This invention constructs a dual-knowledge-tree collaborative architecture, independently encoding and co-evolving the two sub-tasks of route selection and flight priority ranking, thereby achieving overall optimization of the control strategy. It designs a structurally constrained mutation operator and a two-stage parent selection mechanism based on behavioral similarity to improve evolutionary efficiency and offspring quality while ensuring the semantic validity of the rules. Through a low-level heuristic simulation environment, the knowledge tree is mapped into executable control instructions, ultimately generating an efficient air traffic control scheme adapted to the dynamic airspace environment.

The present application relates to the technical field of air traffic management and intelligent optimization, and particularly relates to an airport parking space adaptive multi-objective intelligent allocation system and method, which comprises a heuristic initialization module, an adaptive parameter control module, a constraint-aware genetic operation module and a comprehensive decision output module. The final allocation scheme is obtained by inputting basic data into the corresponding modules. Specifically, the initial population is generated by using heuristic rules to meet the constraints of aircraft type matching and time conflict-free. Then, the parameter adaptive adjustment is performed by dynamically adjusting the crossover and mutation probabilities according to the population diversity. Through constraint crossover, feasible neighborhood mutation and greedy repair, the multi-objective intelligent decision is made, and the executable scheme is directly output by normalization and weighted scoring. The present application effectively breaks through the bottleneck of traditional algorithms, can significantly improve the utilization rate of airport parking space resources, reduce operating costs and carbon emissions, and improve passenger service experience, thereby providing efficient and feasible technical support for airport intelligent scheduling and green operation.

The application discloses a kind of approach and departure flight operation and flight plan multi-time scale deviation measurement method, belong to air traffic management technical field, including steps: constructing approach and departure flight flow time series;Coarse-grained approach and departure flight flow time series;Calculate the sequence mode distribution probability in coarse-grained time series;Calculate the permutation entropy PE value of sequence mode distribution;Calculate the IMPE value of approach and departure flight flow time series;Define and quantify the deviation of multi-scale approach and departure flight flow, draw multi-scale deviation curve;Quantitative analysis similarity relationship between the deviation of different airports;Single-airport deviation evolution analysis plane is constructed, and evolution relationship is analyzed;Define and calculate the trough characteristics of cross-day deviation;Analysis the similarity between the cross-day deviation of approach and departure flight flow of different airports.The application can solve the problems existing in the measurement of the deviation measurement, such as single measurement dimension, insufficient collaborative analysis capability, difficulty in completely covering special operation period, and incomplete description of deviation chain.

The application provides an aircraft approach sequencing and scheduling method and device for multiple landing points, and a medium, and relates to the field of air traffic management.The method comprises the following steps: acquiring airspace positions, flight data of multiple aircrafts to be landed, and landing positions of multiple landing points; determining initial service queues of the landing points based on the airspace positions of the aircrafts and the landing positions of the landing points; optimizing the initial service queues to determine optimized service queues corresponding to the initial service queues; updating landing points of at least one target aircraft determined based on a cross-zone index based on idle time slots of the aircrafts in the determined optimized service queues to obtain target landing points; and determining target service queues of the landing points based on the target landing points of the target aircrafts.The application improves airspace efficiency and scheduling flexibility through partitioning, flow distribution, optimization scheduling, and dynamic updating of landing points based on idle time slots and cross-zone indexes.

The present application relates to the technical field of unmanned aerial vehicle flight control and air traffic management, in particular to an unmanned aerial vehicle air traffic anti-collision route planning method based on a particle swarm algorithm, comprising obtaining real-time state data, inherent physical structure attributes and environmental meteorological data of a local unmanned aerial vehicle and adjacent unmanned aerial vehicles in a target airspace; fusing motion vector information, inherent physical structure attributes and environmental meteorological data to construct a dynamic fluid disturbance field model, and solving spatial form features and time decay features of a fluid wake danger zone; using an unmanned aerial vehicle dynamics model to evaluate maneuvering margin decay and generate an out-of-control probability prediction value, and constructing a composite artificial potential field containing entity geometric repulsion and wake fluid repulsion; combining the composite artificial potential field and a preset flight mission target to output an optimal four-dimensional flight trajectory; generating flight control instruction data packets based on the optimal four-dimensional flight trajectory to form an anti-collision route planning closed loop; the present application significantly enhances risk perception capability in complex scenarios.