297 results about "Air traffic control" patented technology

The invention belongs to the technical field of air traffic control and conduction, and particularly relates to an intelligent question answering system method for air traffic control and conduction business knowledge. According to the method and the system, the refined and context-aware information retrieval capability of the air traffic control communication service knowledge and the expert-level air traffic control communication service knowledge question and answer capability are provided, the knowledge management and application efficiency is effectively improved, and powerful support is provided for digital transformation of related services. Comprising the following steps: S1, a document content extraction and storage process; s2, a content retrieval and generation process; s3, content slice optimization is carried out through extraction and similarity calculation of adjacent paragraphs or contents, complete contents are in the same slice, and the content retrieval efficiency is improved; according to the method, the label library and the business rule library are continuously iteratively optimized, so that the provided service is always in the optimal state.

The invention relates to a critical flight identification and guarantee plan inversion method based on LTOT, and the method comprises the steps: data integration and preprocessing, critical flight identification, multi-objective optimization, guarantee plan generation, real-time monitoring and feedback, emergency response and the like. Intelligent monitoring and optimal scheduling of flight states and airport resources are realized by using the multi-layer technology and large model layer technologies such as a deep neural network, reinforcement learning, ensemble learning and natural language processing, and the system has real-time data acquisition, risk prediction, dynamic threshold adjustment and a cross-department collaboration platform, can quickly respond to emergencies, optimizes resource allocation, and improves the scheduling efficiency. The flight punctuality rate and the operation efficiency are improved, the data security and privacy are ensured, and the intelligent level and the emergency response capability of aviation scheduling are remarkably improved.

The invention provides an intelligent intelligent supervision system for civil aviation VoIP communication equipment, relates to the basic field of civil aviation air traffic control equipment, and solves the limitation problems of non-centralized supervision, imperfect supervision, non-autonomous supervision and the like existing in an existing supervision mode. The system comprises a system architecture module which adopts a layered modular design to construct a double-layer system comprising a top layer management architecture and a bottom layer operation architecture; the function service module comprises an equipment state monitoring unit, a risk assessment engine and an operation and maintenance management unit; the data processing architecture forms a multi-stage processing channel by a data acquisition layer, a data transmission layer, a data storage layer, a data calculation layer and a data application layer which are connected in sequence; the technical support system comprises a micro-service component group, a continuous integration platform and a security authentication module. The intelligent supervision network architecture of the VoIP communication equipment is subjected to brand-new autonomous design, transformation from passive response to active management is completed, and the supervision efficiency and the treatment efficiency are improved.

A method for an Unmanned Aerial Vehicle, UAV, for generating a temporary flight-plan for a region is provided. The method includes determining whether any air traffic control station is emitting a flight-plan for the region. If it is determined that no air traffic control station is emitting a flight-plan for the region, the method further includes determining a score for the UAV based on properties of the UAV and receiving scores from other UAVs within the region. Additionally, the method includes determining whether the UAV is a master UAV or a slave UAV for generating the temporary flight-plan based on the calculated score for the UAV and the received scores from the other UAVs. If it is determined that the UAV is a master UAV, the method includes performing a first task in generating the temporary flight-plan.

The invention provides an air traffic control scene instruction recommendation method and system combined with a knowledge graph, and relates to the technical field of air traffic control. The method comprises the following steps: firstly, establishing a dynamic matching relationship between current air traffic state data (including flight real-time operation parameters, airspace use state parameters and meteorological influence parameters) and an association rule set (including air traffic control rule entries, historical instruction execution constraint conditions and airspace resource allocation criteria), and generating a preliminary instruction set; adjusting the initial instruction set in real time according to real-time air traffic state change data (including flight position change parameters, airspace flow fluctuation parameters and meteorological condition updating parameters) to obtain an adjusted instruction set; and then verifying the compatibility between the adjusted instruction set and airspace use constraint conditions (including airspace capacity limiting parameters, flight interval safety parameters and equipment operation state parameters), outputting a compatible instruction set to cooperate as a target recommendation instruction set, and assisting air traffic control personnel to make a control instruction decision.

The invention discloses a low-altitude economic intelligent scheduling method based on multi-dimensional data fusion and dynamic topological optimization, and belongs to the technical field of aircraft air traffic control, and the method comprises the following steps: S1, collecting and fusing multi-dimensional data, including meteorological data, equipment data, airspace data, task data and historical data, S2, constructing a dynamic airspace topological network based on the fused data, the topology network comprises a vertex set and an edge set, vertexes represent unmanned aerial vehicles, and edges represent cooperative paths between the unmanned aerial vehicles, S3, generating a global optimal scheduling strategy through a multi-objective optimization algorithm according to the dynamic airspace topology network, S4, executing the global optimal scheduling strategy, updating historical data and model parameters after a task is completed, and S5, executing the global optimal scheduling strategy according to the updated historical data and model parameters. And closed-loop feedback is formed. Multi-target dynamic balance of unmanned aerial vehicle dispatching safety, efficiency and energy consumption is achieved through multi-dimensional data fusion and dynamic topological optimization, the system supports full-process closed-loop automation, and the accident rate and resource consumption are remarkably reduced.

The invention relates to the technical field of low-altitude safety, and provides an aircraft optimal trajectory screening method and system based on adjustable phase quantum search. Candidate trajectories are generated according to flight mission constraints, cost values are calculated, and Boolean markers are distributed based on cost threshold values to identify high-quality solutions; performing iterative search and measurement on the balanced quantum superposition state of the coding track index by utilizing a Grover quantum search algorithm based on the adjusted search phase to obtain an output index; and mapping the index into a track, obtaining an optimized track through classic local optimization, adaptively adjusting the phase parameter and the cost threshold value based on performance evaluation, and repeatedly executing until an end condition is met, thereby determining an optimal track. On-line reconfiguration of a strategy is realized by introducing an adjustable phase mechanism, a feedback closed loop is established to realize adaptive optimization of parameters, and the real-time requirements of scenes such as urban air traffic control and unmanned aerial vehicle clusters are met.

The invention relates to the technical field of traffic control, in particular to an airspace situation prediction method and system based on a self-adaptive grid, and the method comprises the following steps: collecting the information of all aircrafts in a current airspace, including the number, speed, x coordinate, y coordinate, z coordinate, course, pitch angle and time of the aircrafts; according to the x coordinate, the y coordinate and the z coordinate of the aircraft, mapping the acquired information of the aircraft into grids of a pre-constructed airspace basic grid network, and obtaining an input feature sequence; and inputting the input feature sequence into an airspace grid situation prediction model based on 3D CNN-GCN-Transform, and outputting to obtain an airspace situation prediction result. According to the invention, accurate prediction of airspace situation change can be realized, and technical support is provided for air traffic control.

The invention provides a voice instruction intelligent analysis method and system applied to an air traffic control scene, relates to the technical field of air traffic control, and aims at the characteristics of voice instruction interaction between a controller and a flight crew in the air traffic control scene to construct an instruction interaction expectation model. The instruction interaction expectation model comprises an instruction sequence association rule and a semantic constraint condition set which are generated based on historical controlled call data, and an interaction feature set of speaker identity features, instruction component time sequence features, voice intonation fluctuation features and the like in a real-time capture voice interaction process; inputting an instruction interaction expectation model for dynamic matching and deviation identification, generating an interaction deviation analysis result, triggering an intention clarification and instruction confirmation process based on the analysis result, obtaining a complete instruction component set, generating an instruction analysis result meeting an air traffic control data exchange standard after structured conversion processing, and pushing the instruction analysis result to an air traffic control automation system. The accuracy and efficiency of air traffic control voice instruction analysis are effectively improved.

The invention discloses a real-time aviation flow prediction method based on aviation situation awareness, and the method comprises the steps: constructing a flow data set fusing spatial-temporal characteristics, and achieving the efficient conversion from trajectory data to aviation flow data based on time slice division; extracting multi-source spatio-temporal features; a light GBM is adopted to construct a prediction model, and the core lies in that a Tweedie loss function is used to model the zero expansion and long tail distribution characteristics of flow data, and the robustness of the model is ensured through five-fold cross validation; establishing a multi-dimensional evaluation system; in the application stage, the airspace is divided into dynamic grids, and flight traffic in a plurality of minutes in the future in the airspace is predicted in real time in combination with radar return trajectory data. The method supports dynamic feature extraction and flow prediction based on real-time radar data, can effectively support an aviation flow management and control decision through actual measurement verification, can quickly respond when the airspace flow changes, meets the timeliness demand of dynamic management and control of air traffic control, and remarkably improves the scientificity and response speed of air traffic control decision.

A system and a method include a monitoring control unit configured to compare an initial flight plan, as generated by a flight planner for an aircraft, and an assessed flight plan, as determined by air traffic control. The monitoring control unit is further configured to determine one or more alternate initial flight plans that differ from the initial flight plan in response to determining one or more differences between the initial flight plan and the assessed flight plan.

The invention discloses an unmanned area unmanned aerial vehicle non-visual flight dynamic threat assessment management and control method, and belongs to the technical field of air traffic control systems, and the method comprises the steps: obtaining the multi-source sensing data in the flight process of an unmanned aerial vehicle in real time, and generating the state reference parameters of the unmanned aerial vehicle; generating a multi-source quantitative threat data set based on the multi-source sensing data; the state reference parameters of the unmanned aerial vehicle and the multi-source quantitative threat data set are fused to generate a comprehensive flight risk assessment result; generating an adaptive flight management and control strategy based on the comprehensive flight risk assessment result; generating a dynamic adjustment control instruction set based on the adaptive flight control strategy; and performing feedback adjustment based on the execution effect of the dynamic adjustment control instruction set. The technical means of quantifying multi-source threats, carrying out comprehensive risk assessment, generating a self-adaptive control strategy and carrying out feedback optimization based on an execution effect are adopted, so that the autonomous flight safety and intelligent level of the unmanned aerial vehicle in a complex dynamic environment can be remarkably improved.

The invention provides an air traffic control dialogue type speech recognition method and device based on multi-task learning. Cooperative optimization of voice segmentation and content recognition is realized by constructing a joint learning model of a voice feature representation learning module and a voice activity detection module. The method comprises the following steps: firstly, designing a segmented shielding comparison pre-training strategy, and constructing a supervision signal through a product quantization codebook, so that a speech feature representation learning module learns fine-grained acoustic features in unlabeled data; secondly, a double-branch joint training mode fine tuning model is designed, a main branch optimizes a voice recognition task through connection time sequence classification loss, an auxiliary branch improves voice segment boundary detection precision through binary cross entropy loss, and meanwhile, a dynamic task weight adjustment strategy is combined to balance a multi-target optimization direction; and finally, innovatively introducing a dynamic local window attention mechanism, and focusing context modeling of an effective voice segment according to a real-time segmentation index.

The invention provides a dynamic path planning method and system based on a generative adversarial network model, and the method comprises the steps: firstly receiving a flight task instruction of an air traffic control system and an airspace real-time operation data set, inputting the flight task instruction and the airspace real-time operation data set into a generator unit of a generative adversarial network, and generating a plurality of candidate dynamic flight paths; and calling a discriminator unit to evaluate the candidate dynamic flight paths and output comprehensive evaluation values by taking the airspace control constraint rule and the path operation efficiency index as references, feeding back the candidate dynamic flight path with the highest comprehensive evaluation value to the generator unit to adjust parameters, and repeating evaluation and adjustment operations until a preset optimization threshold value is reached. And the final path is sent to an air traffic control command system, so that the airspace utilization rate and the flight operation efficiency can be improved, and the flight safety is guaranteed.

Systems, methods, and non-transitory computer readable media for identifying a cockpit alert context during an aircraft mission are provided. The system includes a controller configured to: store message content in a clearance look-up table for one or more clearance messages received from air traffic control (ATC) during the aircraft mission that contain cockpit data about which an avionic system in an aircraft may later sense and generate a cockpit system alert for display on a cockpit display device; monitor a plurality of avionic systems in the aircraft for a cockpit system alert; retrieve an ATC clearance message from the clearance look-up table that corresponds to a detected cockpit system alert when a cockpit system alert is detected; and signal an aircraft display device to display the ATC clearance message in a visually distinguishable manner that indicates that the displayed ATC clearance message relates to a detected cockpit system alert.

The invention discloses a dynamic planning method for an approaching track of a vertical take-off and landing airport. The method comprises the following steps of: constructing a structured airspace which takes the airport as a center and comprises a plurality of layers of annular waiting rings; assigning a serial number to the aircraft requesting approach and assigning the serial number to a specific waiting sector; dynamically planning an interlayer transfer track based on a multi-constraint optimization model; the airspace state is monitored in real time, and dynamic re-planning is triggered in case of emergencies; and finally generating a final approach trajectory to guide the aircraft to complete landing. According to the method, a funnel-shaped structured airspace which is formed by a plurality of layers of annular waiting circles and takes an airport as a center is constructed, a traditional disordered approaching airspace is converted into an air corridor with a clear spatial level, and each waiting circle is further divided into independently managed sector seats; a plurality of vertical take-off and landing aircrafts can obtain isolated waiting positions in a three-dimensional airspace, so that the complexity of air traffic control command is greatly simplified, and the aircrafts are prevented from scrambling and flying conflicts in the airspace above an airport.

A method for communication between an air traffic control system and a communication module, the communication method includes a step of determining environmental optimization time slots based on the air traffic absorption capacities of the various flight information regions, the environmental optimization time slots at least partially covering one or more flight information regions, each environmental optimization time slot having an associated efficiency level; a step of negotiation, between the pilot via the communication module and the air traffic control system, to negotiate changes to the flight plan on the basis of the environmental optimization time slots and the environmental optimization levels of the environmental optimization time slots.

The invention provides a flight time adjusting method and device, electronic equipment and a storage medium, and the method comprises the steps: carrying out the all-day data fusion of the basic capacity of an airport, the temporary air traffic control capacity and the ground guarantee capability, and taking the capacity with the minimum value as the capacity fusion data in each preset time period; based on the guarantee plan data, determining the total flight plan amount in each preset time period; performing evaluation processing based on the capacity fusion data and the total flight plan amount, and determining the flight calling-out amount of each airline in the excessive time period and the flight calling-in amount of each airline in the spare time period; and scoring each flight of each airline in the excess time period, and determining a plurality of timing flights based on the timing score of each flight of each airline and the number of scheduled flights of the airlines. Intelligent recommendation of the flight time adjustment scheme is realized, the flight time adjustment efficiency is improved, the influence of flight delay is minimized, and the problem of low manual operation efficiency is solved.

The embodiment of the invention discloses a flow refined prediction method driven by air traffic flow pattern recognition, and relates to the technical field of air traffic situation awareness and flow management. According to the method, spatial-temporal characteristics in a flight path are efficiently extracted, a local flow mode is accurately identified, a graph model is constructed based on a representative flow mode, and fine-grained prediction of traffic flow in an airspace is realized, so that the fineness and dynamic response capability of local region regulation and control are greatly improved. The method is especially suitable for deep analysis of flight path data in an airspace sector, and provides accurate auxiliary decision support for air traffic control, route planning and airspace design.

A method includes receiving, by an air traffic control (ATC) system and from an unmanned aerial system (UAS), a routing request, the routing request including a first start position and a first end position, generating a three-dimensional path based on a directed airspace reservation graph and the routing request, the path beginning at the first start position, concluding at the first end position, and including a set of path segments, modifying the directed airspace reservation graph to include at least part of the set of path segments, and causing the UAS to travel from the first start position to the first end position in accordance with the at least part of the set of path segments.

A system and method include a monitoring control unit configured to compare an initial flight plan, as generated by a flight planner for an aircraft, and an assessed flight plan, as determined by air traffic control. The monitoring control unit is configured to determine one or more differences between the initial flight plan and the assessed flight plan. The monitoring control unit is configured to output a notification signal to the flight planner regarding the one or more differences between the initial flight plan and the assessed flight plan.

The invention discloses an aircraft recovery scheduling method and device based on a self-adaptive Lagrange multiplier, and relates to the technical field of aviation traffic control automation, and the method comprises the steps: building a constrained Markov decision process model of an aircraft recovery scheduling problem; introducing and initializing a Lagrangian multiplier by using a Lagrangian relaxation technology, and converting the constrained Markov decision process model into an unconstrained optimization problem to obtain a Lagrangian function; and performing main dual collaborative iterative optimization based on a Carlo demander-Kuhn-Tucker condition, and adaptively adjusting the Lagrange multiplier until the Lagrange multiplier converges to obtain an optimal strategy. According to the method, the constraint weight is dynamically adjusted in the main dual collaborative iteration mechanism, so that the rationality and accuracy of large-scale cluster recovery scheduling are improved.

The invention relates to a track prediction method based on multi-resolution hierarchical reasoning. The method comprises the steps that firstly, historical track data are acquired and preprocessed, and a track prediction data set is obtained; then, a track prediction model based on multi-resolution hierarchical reasoning is constructed, and the track prediction model comprises a track encoder module oriented to trend extraction, a probability type key point predictor module based on a mixed density network, a dynamic sampling module, a linear interpolation module based on learnable residual errors and a Transformer decoder module; then, inputting the track prediction data set into the track prediction model based on multi-resolution hierarchical reasoning for model training, and optimizing model parameters in combination with an uncertainty loss function; and finally, pre-processing real-time track data, inputting the pre-processed real-time track data into the trained track prediction model based on multi-resolution hierarchical reasoning, and outputting to obtain a future track prediction result. And the prediction performance and precision of the flight path in a complex air traffic control scene are improved.

A method for reducing cost of airflight, the method comprising reducing cost of Air Traffic Control (ATC) and / or reducing maintenance cost of the Air Traffic Control (ATC)'s radar system. Reducing of maintenance cost may include reducing operational cost of estimation, based on data, of bias (e.g.) in the Air Traffic Control (ATC) radar system including at least once, basing the estimation of bias (e.g.) at least partly on data from commercial aircraft / s, to reduce or eliminate need for flying dedicated calibration aircraft to provide a source of data for the bias estimation and / or parameter Calibration.

The invention provides a flight management system based on cloud-edge collaboration and an air route planning method thereof. The flight management system comprises an airborne end, a cloud platform and an edge computing node, the route planning method of the flight management system comprises the following steps: step 1, calculating an optimal flight path by a cloud platform, and predicting a meteorological condition on the flight route and corresponding real-time airspace information; 2, the cloud platform completes the route optimization of the current flight according to the data in the step 1, and sends the data to an edge computing node and an airborne end; step 3, when an emergency occurs, the edge computing node computes a corresponding release path and issues the corresponding release path to the airborne end and the air traffic control center, and the air traffic control center determines whether the aircraft executes the release path or not; according to the method, the defects of a traditional FMS in the aspects of dynamic planning capability, computing power limitation and the like can be overcome, and an extensible, high-reliability and low-cost solution is provided for a next-generation flight management system.

The invention discloses an ADS-B (Automatic Dependent Surveillance-Broadcast)-based unmanned aerial vehicle and manned aerial vehicle fusion airspace monitoring method, and belongs to the technical field of air traffic control. According to the method, attitude vectors of a manned aerial vehicle are collected through a ground ADS-B receiver and an unmanned aerial vehicle broadcast double channel, and relative measurement vectors of a self-sensing sensor of the unmanned aerial vehicle are combined and converted into absolute attitude vectors under a global coordinate system; carrying out weighted fusion on the signal credibility based on the three types of acquisition channels to obtain an accurate attitude vector; extracting position, speed and course angle change sequence features, and calculating various flight intention probabilities; predicting a mixed future position and a covariance based on the intention probability; and finally, constructing a space kernel function and introducing a directional amplification factor to generate an intention risk field. The attitude vector precision is improved through multi-source information fusion, modeling is optimized in combination with the intention probability and the directivity risk amplification mechanism, and fusion airspace monitoring accuracy and risk assessment reliability are remarkably improved.

The invention discloses a dynamic combination site optimization method and device for an air traffic control multi-point positioning system, and the method comprises the steps: building a site dynamic weight evaluation model, so as to determine a site weight; establishing a geometric accuracy factor lookup table for dynamically extracting geometric accuracy factors of the site combination; and determining an optimal site combination by adopting a self-adaptive combination optimization algorithm. According to the method, through a dynamic site optimization mechanism, site signal quality, geometric distribution and redundancy are evaluated in real time, site combinations participating in positioning are adjusted in a self-adaptive mode, and robustness in a complex scene is improved; according to the method, a multi-objective optimization model based on dynamic weight is adopted, and parameters such as a signal time difference of arrival error, a geometric accuracy factor and computing resource consumption are combined, so that rapid screening of an optimal site combination is realized, the computing load is effectively reduced, and the positioning reliability is guaranteed.

The invention relates to the technical field of network security, in particular to an air traffic control system network attack path optimization method and system, and the method comprises the steps: determining a network attack target host node, and obtaining a directed path and path cost between host nodes according to node network and vulnerability information in an air traffic control system network threat knowledge graph. A bidirectional ant colony algorithm is adopted, the algorithm comprises a forward ant colony and a reverse ant colony, the forward ant colony starts from a starting host node, the reverse ant colony starts from a target host node at the same time, the pheromone concentration of each path is updated through a PPO algorithm according to behaviors and rewards of the forward ant colony and the reverse ant colony, and the next action of the ant colony is updated accordingly; and the optimal attack path from the start to the target host node is obtained.

The invention relates to the technical field of aviation simulation training, and particularly discloses an aviation flight training simulation system based on flight path simulation and workload distribution, which comprises three modules: an aviation flight training simulation model construction module constructs a preliminary simulation model by debugging model data, and judges the availability of the preliminary simulation model through fluctuation parameter monitoring; the multi-captain seat cooperation and air traffic control scene simulation module applies an available model to general aviation flight subject and track simulation, captain seat simulation is realized in a specific organization mode, and the problem of captain seat load distribution in control training is solved; the aviation flight training effectiveness evaluation module monitors the simulation process and obtains a training index to judge the simulation effectiveness; the system forms a complete closed loop from model construction to scene simulation to effect evaluation, can improve the authenticity and efficiency of aviation training, reduces the cost and risk of real aircraft training, and is suitable for standardized and intelligent training system construction in the general aviation field.

An aviation anomaly detection system may include an interface configured to receive audio communications between an air traffic control station and a plurality of aircraft, a speech-to-text converter configured to convert the received audio communications from the interface to text data, and a processor. The processor may be configured to determine at least one aviation anomaly from the text data with a variational autoencoder (VAE) deep learning model, and generate an alert based upon the at least one aviation anomaly.