373 results about "Flight planning" patented technology

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 embodiment of the invention provides a flight plan conflict detection method, device and equipment based on a low-altitude airspace grid, and is applied to the technical field of air traffic control. The method comprises the steps of constructing a low-altitude airspace grid set, planning a flight plan, dividing an airline flight stage into a plurality of flight segments, obtaining a space-time occupation grid set corresponding to each flight segment based on the flight speed and obtained flight target physical parameters, and combining to obtain a space-time occupation grid set of the airline flight stage. Based on the space-time occupancy range and the low-altitude airspace grid set, constructing a space-time occupancy grid set corresponding to other flight stages; the flight plan space-time occupation grid set is obtained through merging and compared with the historical space-time occupation grid set, if conflicts exist, conflict information is generated, and the flight plan is re-planned according to the conflict information, so that path overlapping and conflicts between tasks are effectively avoided, the turnover rate and the utilization rate of a low-altitude airspace are increased, and the flight planning efficiency is improved. And the dense flight requirement of the low-altitude unmanned aerial vehicle is met.

The invention provides an unmanned aerial vehicle terrain acquisition method, system and device and a storage medium, and relates to the technical field of unmanned aerial vehicles, and the method comprises the steps: determining the number of power supply nodes needed by a target region and the position of each power supply node according to the terrain features of the target region; according to the position of each power supply node, the target area is divided into a plurality of sub-areas, the number of unmanned aerial vehicles required by each sub-area is determined according to the area of each sub-area, and the unmanned aerial vehicles of the corresponding number are distributed to each sub-area; determining an initial flight plan of each unmanned aerial vehicle by combining the residual electric quantity of each unmanned aerial vehicle in each sub-region and the position of a power supply node in each sub-region; and determining electromagnetic interference intensities of different positions in each sub-region according to the region characteristics of each sub-region, adjusting the initial flight plan according to the electromagnetic interference intensities of different positions in each sub-region, and determining a target flight plan of each unmanned aerial vehicle. The technical effect of the invention is that the terrain acquisition work can be reliably completed.

The invention provides an aircraft digital twinning situation deduction and risk assessment method in a complex meteorological environment, and belongs to the technical field of data processing, the aircraft digital twinning situation deduction and risk assessment method in the complex meteorological environment comprises the following steps: collecting and processing weather data covering parameters such as wind direction and wind speed; obtaining aircraft performance parameters and building a database; and inputting and analyzing the flight plan. By establishing a three-dimensional deduction model including dynamics, weather influence and environment models, aircraft situations under different weathers are deduced, and flight safety, performance and task risks are evaluated. The method has the advantages that weather factors are comprehensively and accurately analyzed, and influences are quantified through data acquisition and processing and a meteorological model; the system is suitable for various aircrafts, can integrate navigation and control systems, and realizes remote monitoring management; risk assessment is scientific and reliable, multiple factors are integrated, powerful support is provided for flight decision making, and aircraft safety and task success rate in complex weather are effectively improved.

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.

The embodiment of the invention provides an unmanned aerial vehicle dynamic deployment method and equipment, and the method comprises the steps: introducing a flight plan, a flight path line and a space-time envelope of flight, predicting the flight path of an unmanned aerial vehicle at a specific time, mastering the full view of the flight process in the whole flight plan, and carrying out the management and control of the capacity of the flight path as a resource, thereby achieving the dynamic deployment of the unmanned aerial vehicle. According to the method, the flight plan is deployed based on the capacity control result, conflict detection is carried out on space-time conflicts possibly existing in the flight process in combination with the flight control area and the flight plans of other unmanned aerial vehicles, and the flight plan is further adjusted in combination with the conflict detection result, so that accurate deployment of the flight of the unmanned aerial vehicles is realized. And the flight efficiency and the airspace utilization rate of the unmanned aerial vehicle are effectively improved.

A method for controlling a plurality of drones to survey a location, the method comprising, at a computing system: automatically generating preliminary flight plans for a plurality of drones to survey the location based on a 3D model; receiving survey data from the plurality of drones as the plurality of drones are surveying the location based on the preliminary flight plans; updating the 3D model based on the survey data received from the plurality of drones; and automatically updating at least a portion of the flight plans based on the updated 3D model

The invention discloses a low-altitude aircraft air traffic management method and system, and relates to the technical field of aircraft air traffic management, and the method comprises the following steps: a traffic management system formulates the flight path, speed, course and formation of each unmanned plane according to a preset flight plan and performance demands before the flight performance begins; by monitoring the flight state of the unmanned aerial vehicle in real time and combining external disturbances such as wind speed and airflow, the path and speed of the unmanned aerial vehicle deviating from the trajectory can be dynamically adjusted, trajectory deviation caused by wind speed change is effectively prevented, the flight formation is kept stable, the relative distance is prevented from being too close or disordered, and the collision risk is reduced. Meanwhile, the deep learning model intelligently evaluates the flight consistency deviation, identifies the unmanned aerial vehicle deviating from the trajectory, and adjusts the path in time when the deviation exceeds a threshold value, thereby ensuring the flight safety, preventing the collision caused by the trajectory deviation, and guaranteeing the stability and safety of the group flight.

A system for generating an environment for an operation using a set of assets includes processor(s) configured to obtain data associated with task(s) to be performed by a set of assets, wherein: 1) the set of assets comprises semi-autonomous drones and 2) the data associated with the task(s) comprises other drone flight plan(s); determine a discretized representation of the geographic location, wherein the discretized representation comprises discrete elements each corresponding to a volume associated with the geographic location; annotate the discretized representation with the other drone flight plan(s) to create an annotated representation; determine a first flight plan of one drone, wherein the first flight plan is determined based on the annotated representation; and communicate information pertaining to the first flight plan to at least one other asset.

The invention provides an airspace simulation deduction method and system based on digital twinning, and belongs to the technical field of airspace simulation, and the method comprises the steps: integrating geographic bottom plate data and airspace element data, carrying out the meshing based on an airspace grid engine, and constructing an airspace digital twinning model; receiving at least one flight plan, and mapping the flight plan to the airspace digital twin model to generate a corresponding simulation aircraft entity; based on a preset simulation engine, driving the simulation aircraft entity to perform flight simulation in the airspace digital twin model, and calculating a simulation flight situation; in the simulation process, airspace conflict detection and early warning are carried out based on the simulation flight situation and the airspace digital twin model; and outputting a simulation deduction result, performing three-dimensional visual output on the simulation flight situation data and the early warning information, and generating an analysis report including conflict statistics and airspace evaluation. The airspace resource allocation is optimized, the flight safety is improved, and intelligent and efficient management is realized.

Techniques for flight path optimization include obtaining map information defining mapping coordinates of a geographical region, obtaining historical UAV-obtained flight information related to a first autonomous flight of a UAV in the geographical region based on a first flight plan, storing the map information, the historical UAV obtained-flight information, the first flight plan, and current values of a set of external factors separate from the flight information, and generating a second flight plan for a second autonomous flight of the UAV. The generating may include processing the map information, the historical UAV-obtained flight information, and the current values of the set of external factors, and updating the first flight plan based on the processing to generate the second flight plan. The second flight plan may be transmitted to the UAV for a second autonomous flight of the UAV in the geographical region.

A system for filtering and presenting notifications received by an aircraft includes a processor, a display device, a communication device, and computer-readable memory. The computer-readable memory encoded with instructions that, when executed by the processor, cause the system to perform the following steps. The system receives one or more broadcast notifications. The notifications are, for example, Notices to Air Missions (NOTAMs). The system filters the one or more broadcasted notifications based upon applicability to a present flight plan to generate applicable notifications. The system filters the applicable notifications based upon one or more filtering criteria to assign a criticality to each of the applicable notifications. The system display, via the display device, the applicable notifications based upon the criticality of each of the applicable notifications. The system outputs alternate flight plans due to restrictions on the present flight plan.

The invention discloses a fan blade transportation and installation system and method and a flight device, and belongs to the technical field of intelligent transportation of fan blades. The system comprises a flight transportation module, an intelligent navigation control module, an installation auxiliary module and a scheduling module; the flight transportation module is used for suspending and loading the fan blades, and suspending, flying and transporting the fan blades according to a preset flight route; the intelligent navigation control module is used for monitoring a flight path, collecting flight data and environment data in a flight process, predicting potential obstacles according to the flight data and the environment data, and optimizing a flight strategy; the mounting auxiliary module is used for aligning and fastening the blades and the fan hub; the scheduling module is used for determining a flight plan according to the meteorological data, the performance of the flight transportation module and the priority of the transportation installation task. By combining a plurality of transportation, installation and dispatching modules, efficient and safe transportation and installation of the fan blades are achieved, and the transportation and installation difficulty and cost are reduced.

The invention is suitable for the technical field of smart civil aviation, and provides a flight plan dynamic generation method and system in severe weather, and the method comprises the following steps: obtaining data, including origin-destination and plan time of a flight, an air route network, and aircraft performance of the flight; on the basis of meteorological radar data, according to the risk threshold value, the future communication condition of the air route network is predicted; constructing a flight plan dynamic generation model library based on different scheduling priorities; and according to control conditions, a solver is used for carrying out serialization solution on each model, and the most adaptive model and the corresponding optimal strategy are determined by comparing the closeness of positive and negative ideal distances between a model result and the best worst scheme. According to the invention, flight plan dynamic generation model libraries with different scheduling priorities are established, a multi-scale scheme evaluation recommendation technology is discussed, and technical support is provided for daily dispatchers to carry out flight plan intelligent management.

The invention discloses a signal source tracking-oriented Gaussian process space-time modeling and unmanned aerial vehicle flight planning method, which comprises the following steps of: firstly, acquiring RSSI (Received Signal Strength Indicator) data of a signal source by a sensor carried by an unmanned aerial vehicle, and recording corresponding position information and timestamps to form a space-time data set; based on the data set, space-time modeling of signal intensity is carried out by using a Gaussian process, the future position of a signal source is predicted, and the challenge of the position change of the signal source in a dynamic environment is overcome. And then, in combination with a designed flight planning algorithm, calculating a target position of the unmanned aerial vehicle at the next moment according to a signal intensity distribution prediction result. The flight planning method is divided into an exploration stage and a tracking stage, and the path of the unmanned aerial vehicle is dynamically optimized by adopting an overall variance reduction (IVR) evaluation function and a likelihood weighting (LW) evaluation function respectively. According to the invention, through spatial-temporal distribution modeling of signal intensity and flight path optimization, accurate tracking of the unmanned aerial vehicle on the mobile signal source is realized, and hardware dependence and realization cost are effectively reduced.

The invention provides a flow control method based on segmented flight route splicing data, and the method comprises the following steps: S1, carrying out the dynamic flight path perception and segmented detection stage, and analyzing the ADS-B broadcast data and flight plan of an aircraft in real time; s2, a flight path prediction and resource pre-allocation stage: predicting the flight path of the aircraft in the future 10 seconds based on the hybrid model and the flight route, inputting a historical flight path, meteorological data and an airspace control instruction, and outputting a flight path probability distribution diagram; according to a prediction result, an optimal network resource allocation scheme is dynamically calculated through reinforcement learning, a target function is minimum switching delay, and satellite link delay compensation and spectrum resource conflict avoidance are constrained; s3, in a multi-mode network cooperative switching stage, a flow table rule is issued to a target network node through the SDN controller 300 ms before a route splicing point, and a double-link redundant channel is established; a QUIC protocol optimized fast session migration technology is adopted, transmission layer connection is maintained during physical layer switching, and zero-loss switching is achieved.

A method and an unmanned aerial vehicle for identifying and remediating a gas leak. The method includes flying the unmanned aerial vehicle towards a possible location of the gas leak, then detecting a first condition indicative of the gas leak with a first sensor and sensing environmental conditions near the gas leak with an environmental sensor. The method includes receiving, by a controller, a signal representing the first condition indicative of the gas leak and a signal representing the environmental conditions. The method includes determining, based on the first condition indicative of the gas leak and the environmental conditions near the gas leak, a flight plan including an ignition zone. The method includes positioning, based on the flight plan, the unmanned aerial vehicle relative to the ignition zone and discharging, by an ignition mechanism, an ignition source into the gas leak to ignite the gas leak.

A method of operating an energy management system for a fleet of aircraft, can include receiving a desired flight plan database for the fleet of aircraft, defining at least a desired flight plan for each of the fleet of aircraft and a location of each of the fleet of aircraft, and generating a power source inventory distribution plan for the at least a subset of the desired flight plans for the fleet of aircraft.

A flight planning system and method include one or more control units configured to determine, from ensemble data, a plurality of flight plans for an aircraft from a departure location to an arrival location. The ensemble data includes an aggregation of data from multiple data sources. In at least one example, the one or more control units are further configured to determine a preferred flight plan from the plurality of flight plans.

A system for electric aircraft fleet management for at least an electric aircraft is provided. The system includes a computing device communicatively connected to at least an electric aircraft, wherein the computing device is configured to receive measured data from the at least an electric aircraft, generate the aircraft performance model output as a function of the measured data, transmit the aircraft performance model output to a user, receive a flight plan from the user; and manage the at least an electric aircraft using the flight plan.

The invention discloses an aircraft simulation data generation and situation deduction method based on a flight plan, and the method comprises the steps: firstly constructing an analysis model which is used for receiving a flight plan generated by a front end and carrying out the preprocessing, and then carrying out the aircraft situation deduction based on the information of each flight segment. The method comprises the following steps: generating an independent aircraft class, attaching different state attributes to the aircraft class, establishing an aircraft climbing and descending model, and continuously updating the state attributes of the aircraft class according to real-time deduction data. And finally, designing cyclic output of the aircraft simulation situation data once every 4 seconds. The method aims at providing assistance for work of a controller and improving situation awareness and real-time state monitoring of the aircraft in the operation stage. Meanwhile, an aircraft driver is assisted to make a decision autonomously, and control over the aircraft by the driver and information interaction between the driver and a controller are enhanced; and enough reaction time can be reserved for a driver and a controller at the emergency moment.

The invention discloses a report generation method and device, equipment and a storage medium, and relates to the technical field of air traffic management, and the method comprises the steps: obtaining the current navigation data and meteorological data of a target aircraft, and constructing corresponding target data based on the navigation data and meteorological data; the navigation data comprises radar data and flight plan data of a target aircraft; determining the flight conflict probability of the target aircraft according to the target data through a preset conflict probability prediction model; the preset conflict probability prediction model is constructed based on a long and short term memory network; determining a conflict risk level of the target aircraft based on the flight conflict probability of the target aircraft and preset historical conflict data; and generating a conflict report of the target aircraft based on the flight conflict probability and the conflict risk level. Through deep fusion of the large language model and air traffic control field knowledge, a more comprehensive conflict analysis basis can be provided, automatic report generation is realized, and the accuracy of conflict detection is significantly improved.

The invention provides a generator and battery power distribution method and device based on model prediction control, and belongs to the technical field of power distribution. The method provided by the invention comprises the following steps: loading a preset dynamic model library; generating and calibrating a total load power prediction sequence based on the flight plan and the rotor aerodynamic power model; calling the dynamic model library to predict and control the rolling optimization to output the optimal power instruction sequence of the generator; in the rolling optimization execution process, bus voltage dynamic characteristics are monitored in real time, a graded response mechanism is started based on a turboshaft engine dynamic model when load sudden change occurs, and the power of a generator and the power of a battery are adjusted; in the load abrupt change response process, rolling optimization is executed again based on the adjusted power and the corresponding system state, meanwhile, feed-forward compensation is carried out on the bus voltage deviation, and the voltage is controlled to be within a stable interval set by rolling optimization by adjusting the power of the generator; and circularly executing until the flight task is finished, and determining the generator power and the battery power.

Systems, computer readable medium and methods for fully autonomous drone flight are disclosed. Example methods include taking off, navigating in accordance with a flight plan, and navigating the autonomous drone to land. The autonomous drone performs flight plans with only an initial command for the autonomous drone to fly and, in some examples, an indication of a landing space such as an open hand presented under the autonomous drone. After an initial fly command, the autonomous drone is not controlled by a remote-control device and does not receive any additional commands to complete the flight plan. The autonomous drone enters a lower energy state while flying where the wireless connections are turned off since the autonomous drone does not respond to commands during flight.

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 aerial vehicle route planning method and device, equipment, a product and a storage medium. Comprising the following steps: determining a first grid bitmap of at least one unmanned aerial vehicle route planning area; the first grid bitmap comprises identification information of grids; the identification information represents whether the grid is released after a preset time when the grid is occupied; acquiring planning request information of the first unmanned aerial vehicle under the condition that the first unmanned aerial vehicle needs to adjust the route; determining a second grid bitmap corresponding to the at least one unmanned aerial vehicle route planning area according to the identification information and the planning request information; based on the first grid bitmap and the second grid bitmap, whether the first unmanned aerial vehicle passes the course adjustment is judged, and a judgment result is obtained; whether the first unmanned aerial vehicle travels according to the adjusted route is determined according to the judgment result, the efficiency and flexibility of intelligent flight planning and grid distribution are achieved, and meanwhile the probability that the same grid is seized by multiple aircrafts is reduced, and the situation that the unmanned aerial vehicles lose communication exists and the route conflicts among the unmanned aerial vehicles are reduced.

The invention provides a self-organizing flight planning method based on behavior parameter dynamic adjustment, and belongs to the technical field of unmanned aerial vehicle flight planning. Each unmanned aerial vehicle obtains and updates own state and environmental threat information through a sensor, and establishes a cluster member level interaction model to interact neighbor motion states; comprehensively considering the interaction between members in the cluster and the interaction between the members and the external environment, respectively calculating an internal interaction effect and an external interaction effect, and further obtaining an overall interaction effect; on the basis of an asynchronous optimization criterion triggered by an event, online optimization adjustment is carried out on a behavior parameter set of the unmanned aerial vehicle in combination with an optimization guard interval, a flight state is predicted through a motion model, an optimal parameter is determined by means of a local performance evaluation function, a final control input driving state is generated and updated, and autonomous flight planning is realized. According to the method, the online cooperative flight requirement of the unmanned aerial vehicle cluster in a dynamic unknown environment is met.

The invention provides a collaborative interval management conversion program optimization method based on flight demand uncertainty. The method comprises the following steps: determining an uncertainty variable of a collaborative interval management conversion program CMCP optimization problem; estimating probability density function key parameters of normal distribution of the uncertainty variables according to historical data; constructing a CMCP opportunity constrained programming model and equivalent transformation thereof; and solving the converted CMCP opportunity constraint programming model based on a particle swarm algorithm. According to the method, the flight demand uncertainty factor, namely the uncertainty of the flight point passing time, is considered. Under the condition that the actual flight passing time is uncertain and interfered, the flight operation based on the optimization result can keep good airspace performance, so that the actual execution effect of the flight plan cannot be seriously fluctuated due to the uncertainty of the flight passing time.

The present disclosure provides techniques for dynamic utilization of aircraft based on environmental conditions. A proposed flight plan for an aircraft is received. Environment data representing a set of environmental conditions at a source airport indicated in the proposed flight plan is collected. Weather data representing a set of environmental conditions at a destination airport indicated in the proposed flight plan is collected. Operation data related to the aircraft indicated in the proposed flight plan is received. Aircraft engine degradation of the aircraft is dynamically simulated based on the collected environment data and the received operation data using a trained machine learning (ML) model. The simulated aircraft engine degradation is output.

An aircraft includes a flight plan and contingency data including a plurality of contingency landing sites. An aircraft communication system communicates with a ground control station via a communication link and detects a lost communication scenario in response to degradation in the communication link. An aircraft flight control system controls the aircraft en route according to the flight plan. In response to detecting the lost communication scenario, the flight control system routes the aircraft back to a last known location in which the communication link was not degraded. In response to failure to re-establish the communication link, the flight control system selects a landing site based on a current aircraft location relative to a plurality of potential landing sites including an origin airport, a destination airport, and a first continency landing site. The flight control system controls the aircraft to approach and land at the selected landing site.