27 results about "Flight planning" patented technology

Methods, systems and apparatus, including computer programs encoded on computer storage media for generation of autonomous unmanned aerial vehicle flight plans based on triggered sensor information. One of the methods includes accessing information correlated from sensors monitoring features of weather events, and determining an upcoming weather event, the determination comprising one or more areas expected to be affected by the weather event. A likelihood of damage associated with the weather event is determined to be greater than a threshold in the areas. The weather event is monitored while areas in which the likelihood is greater than the threshold are updated accordingly. Subsequent to the weather event, properties to be inspected by unmanned aerial vehicles are determined based on severity information associated with the weather event. Job information is generated, the job information being associated with inspecting the determined properties, the job information including jobs each assignable to operators for implementation.

The invention relates to a method for piloting a drone (100) equipped with a surface treatment device, a three-dimensional position sensor, and a distance sensor, comprising the steps of: - Flying the drone (100) along the flight plan (400), at a distance from a structural element comprising a surface to be treated; - The flight plan (400) of the drone (100) being defined within a georeferenced point cloud (300) representing the surface to be treated; - The flight plan (400) comprising a flight path in which a set of successive positions that the drone (100) must adopt are defined, the flight path being such that the drone (100) passes within the treatment distance of the surface to be treated, the coordinates of the points of which are known; - Defining the position of the drone (100) during the flight and selectively activating the surface treatment device according to the position of the drone (100). Figure for the abstract: Fig. 3

The invention relates to a method for controlling a drone (100) provided with a surface treatment device and with position sensors, the method comprising: - flying the drone (100) according to the flight plan (400) at a distance from a surface to be treated; - the flight plan (400) of the drone (100) being defined in a georeferenced 3D point cloud (300) representing the surface to be treated; - the flight plan (400) comprising a flight path in which a set of successive positions that the drone (100) must adopt are defined, the flight path being such that the drone (100) passes at a treatment distance from the surface to be treated, the coordinates of the points of the surface being known; - defining the position of the drone (100) during flight and selectively activating the surface treatment device depending on the position of the drone (100).

A method of optimizing flights of a fleet of aircraft is provided. The method includes accessing flight plans for flights of a fleet of aircraft through an air transportation network, tracking positions and trajectories of one or more aircraft of the fleet that are currently in-flight, and applying the flight plans and the positions and trajectories to a reinforcement learning model configured to determine maneuvers that achieves a respective maximum cumulative value of an operational efficiency metric across the flights of the fleet of aircraft, one or more of the maneuvers constituting a deviation from a respective flight plan. A comparison of respective maximum cumulative values of the operational efficiency metric is performed for the aircraft of the fleet of aircraft, one of the aircraft is selected based on the comparison, and a notification of the deviation from the respective flight plan is sent to the one of the aircraft.

This invention relates to the field of low-altitude security technology, specifically a dynamic avoidance decision generation method coupled with flight missions and birds. The method includes the following steps: initializing the system communication link, pre-setting risk assessment indicators, and initiating a data acquisition task; accessing flight plan data and bird activity data to generate a standardized dataset; and calling a coupling analysis model to perform spatial situation coupling on the standardized dataset and calculate the rendezvous probability. This invention synchronizes the avoidance decision execution command to the tower dispatch system and the collision avoidance system to execute avoidance operations, dynamically updates the collision risk level, and constructs a multi-terminal collaborative closed-loop control mechanism, reducing the time delay of manual coordination. Recording the entire process data and conducting post-mortem analysis to optimize the parameters of the coupling analysis model gives the overall operating mechanism the ability to continuously iterate and evolve, cope with the complex and ever-changing low-altitude ecological environment, and improves the active defense effectiveness during aircraft takeoff, landing, and cruise phases.

The application discloses a UAV delivery and pickup collaborative path planning method for mountainous area agricultural products, which comprises the following steps: gathering and distributing delivery and pickup orders; calculating a differentiated rural revitalization factor based on order types and seasons, and giving high priority to the pickup orders of agricultural products in the harvest season; participating in the clustering distribution of orders to hubs by using the factor; adopting an improved variable neighborhood descent algorithm which is fused with a delivery and pickup collaborative operator to perform path optimization, generating a main flight plan, and synchronously generating a static backup path for each flight section; and finally dynamically selecting between the main path and the backup path according to real-time risks during execution. The application improves operation economy through delivery and pickup collaborative planning, realizes accurate assistance to farmers through seasonal factors, and guarantees flight safety through preset double paths, effectively solving the technical problems of two-way delivery difficulty of UAV logistics in mountainous areas, poor timeliness of agricultural products uplink and high flight risk.

Embodiments of the present application provide a kind of unmanned plane flight planning method and related equipment, belong to unmanned plane technical field.The method includes: obtaining the real-time data of unmanned plane and surrounding environment;According to real-time data, space modeling is carried out, and state space model is obtained;According to state space model, dynamic preference calculation is carried out, and dynamic preference vector is obtained;According to dynamic preference vector and state space model, flight planning is carried out, and the flight plan and hovering strategy of unmanned plane are obtained.The flight safety of the unmanned plane, task execution accuracy and energy utilization rate can be improved in the embodiments of the present application.

This relates to receiving aviation data from different countries and standardizing the data into a common format for use in flight planning and / or controlling aircraft in flight. [Solution] The aeronautical navigation data standardization system (100) comprises an import module (106) configured to receive aeronautical navigation datasets (102) from ANSPs located in each country, a detection module (108) that identifies the type of data and / or the country in which the ANSP is located, a storage module (112) that selects a data standardization case to modify the data based on previous detections of the type of data for one or more previous changes to the data, and an execution module (110) that modifies the data according to the selected one or more data standardization cases. The execution module (110) modifies the format and / or syntax of the aeronautical navigation data in the dataset (102).

The present application belongs to the technical field of flight management system, and particularly relates to a kind of over point turn track calculation method.The method extracts flight plan information, calculates the turning direction according to the azimuth information of the front and rear flight segments, and further calculates the azimuth of the straight transition segment.First, the center angle corresponding to the transition arc 1 is calculated, the starting point coordinates of the straight transition segment are calculated, the end point coordinates of the straight transition segment are calculated, the turning center coordinates corresponding to the transition arc 1 are calculated, the turning center coordinates corresponding to the transition arc 2 are calculated, the turning end point coordinates corresponding to the transition arc 2 are calculated, and finally the complete track of the over point turn is calculated in combination with the first waypoint, the second waypoint and the third waypoint in the flight plan.The flight transition analysis algorithm increases the determinability and repeatability of the track, and improves the flight quality of the aircraft during the over point turn.

The present disclosure relates to an FMS configured to accept a plurality of flight legs (beyond the standard five) for consideration when developing a flight plan. Allowing an FMS to receive a plurality of flight legs, beyond just five, is achieved by calculating a lateral trajectory and a vertical trajectory based on the current state of the flight path. This may be done mid-flight or initially when a given flight plan is entered by the pilot. The sequence of leg types that are entered may be used to generate a predicted final trajectory and output a GUI (graphical user interface) of the predicted final trajectory.

The application relates to a flight plan generation method and device, computer equipment, a storage medium and a computer program product. The method comprises the following steps: determining a divertable point meeting a diversion condition of a standby airport from a waypoint; determining a leg distance based on a leg between the divertable points; determining a segmentation point of the leg in a segmentation step based on a segmentation step corresponding to the leg distance; and changing the standby airport of the leg if it is determined that there is a target segmentation point not meeting the diversion condition of the standby airport in the segmentation points of the leg in the segmentation step. The method can determine the reachable standby airport for more positions in a leg.

A system and method for predicting estimated times of arrival are disclosed. The system may include a controller configured to: receive a flight plan for an aircraft, define a ground path for the aircraft based on the flight plan with respect to an ellipsoidal model of Earth, and integrate along the ground path for each flight leg using an energy-based method to generate aircraft states. The integration may involve calculating ground speed, traversal time, fuel consumed, and gross weight of the aircraft, aggregating the traversal time of each integration step to determine a total traversal time for each waypoint, determining an estimated time of arrival at each waypoint based on the total traversal time, and directing a transmission indicative of the estimated time of arrival at each waypoint.

In an embodiment, a method includes receiving, at a compute device of a unmanned aerial vehicle (UAV) that is included within a plurality of unmanned aerial vehicles (UAVs) associated with a site, a representation of a flight plan (1) between a start location at a start time and an end location at an end time, and (2) for the UAV. The method further includes causing the UAV to autonomously fly based on the flight plan for the UAV in response to the UAV receiving the signal with the representation of the flight plan for the UAV.

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 provides a flight management positioning point generation display method and system. In the method, a pilot determines a reference point and a geometric relation according to a flight instruction, inputs the reference point and the geometric relation through a positioning point information panel on a flight management system display, a navigation display displays an intersection of the geometric relation and a continuous horizontal trajectory of a current flight plan as a positioning point, the flight management system acquires ETA / DTG / fuel prediction data of the positioning point according to the reference point and the geometric relation, and displays the positioning point on the flight management system display. The application allows the pilot to dynamically create radial lines, distance circles and projection points and other geometric elements based on any navigation database reference point, automatically calculate intersections of the elements and the current horizontal flight plan, and visualize, manage and insert the intersections into the flight plan as virtual positioning points, thereby significantly reducing the pilot's workload and improving flight safety and navigation accuracy.

In some aspects, the techniques described herein relate to a method including: receiving, at a processor, a blocked airspace request from a utilizing organization (UO), the blocked airspace request identifying airspace; filtering, by the processor, the blocked airspace request using a cross-domain guard; transmitting, by the processor, the filtered blocked airspace request to a data sharing platform for authorization; storing, by the processor, the blocked airspace request in a database; receiving, by the processor, a network message from a flight planning system, the network message including at least one flight path; and providing, by the processor, a status of the blocked airspace in response to the network message.

The application discloses a time window-based double-layer low-altitude air route network flight plan deployment method and system, and relates to the low-altitude unmanned aerial vehicle flight field. The application establishes a flight route layered network calculation structure which comprehensively considers time intervals and capacities; establishes a deployment method which does not change flight routes, considers time intervals, simultaneously considers capacity limitations, task priorities, aircraft performance and does not cause new conflicts; and establishes a plan deployment logic based on consideration of plan execution states, so as to support dynamic deployment of flight plans.

An unmanned aerial vehicle (UAV) includes a sprayer configured to generate a pressurized fluid flow and a nozzle configured to receive the pressurized fluid from the sprayer and to generate a spray fan to apply the fluid to a surface. The UAV includes sensors and a control unit to control both flight of the UAV and spraying by the sprayer. The fluid can be stored onboard the UAV in a reservoir or can be remotely stored and pumped to the UAV. The UAV control unit can be preloaded with a spray plan and a flight plan, or the UAV can be controlled by a user.

The present application provides a kind of based on artificial intelligence's oil refining area environment monitoring method, device, electronic equipment and storage medium, it is related to oil refining area environment monitoring technical field, by monitoring planning, determine vertical monitoring line;Current area wind speed is detected, and hierarchical cross monitoring flight planning is carried out, and flight planning route is generated;Spectral measurement and data analysis of multiple pollutants are carried out;Pollution emission flux is calculated;Demand interaction is carried out based on artificial intelligence technology, and multiple pollution emission fluxes are visually processed.The method can effectively improve the intelligent degree of oil refining area environment monitoring, reduce the monitoring time, improve the data processing efficiency, timely respond to the environmental monitoring demand of oil refining area, realize fast, accurate environmental monitoring and pollution display.

We provide control systems and other equipment that enable safe operation management in a variety of situations. [Solution] The memory unit 601 stores the position of the aircraft. The decision unit (flight plan decision unit 603) uses the communication difficulty distance, which indicates the length of the self-position estimation difficulty region, which is a region in the flight path where it is difficult for an individual aircraft to estimate its own position, to determine the number of aircraft that can estimate their own position. The creation unit (flight plan decision unit 603) uses the number of aircraft, the flight path, and the position of the aircraft to determine the aircraft that will travel along the flight path and create a flight plan for each aircraft. The output unit 604 outputs the flight plan and information about the aircraft that will travel along the flight path.

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 discloses a kind of network connection unmanned plane route planning and route smoothing method, including the following steps: flight planning step: by network connection technology, obtain the starting point and the end point of the route of unmanned plane and corresponding latitude and longitude coordinates, determine the route planning area of unmanned plane;According to the flight performance and task demand of unmanned plane, determine flight height, and obtain the flight communication quality data of unmanned plane under the flight height by network connection technology;Collect and set flight environment data;According to communication quality data and flight environment data, establish the reward function of reinforcement learning, output optimal planning route by reinforcement learning algorithm;The beneficial effects of the application are: by network connection technology, realize the real-time communication of unmanned plane and ground control center, improve the flexibility and real-time performance of route planning;By reinforcement learning algorithm, consider communication quality data and flight environment data comprehensively, output optimal planning route, improve the safety and efficiency of route.

This invention discloses a dynamic filtering method for meteorological radar data based on spatiotemporal elements, comprising: Step 1, collecting raw meteorological radar data and generating meteorological radar reflectivity grid data; Step 2, constructing a dynamic meteorological radar reflectivity dataset; Step 3, collecting aircraft surveillance data and obtaining aircraft dynamic parameters; collecting aircraft flight plan data and obtaining aircraft plan parameters; Step 4, determining the aircraft flight stage by combining the aircraft dynamic parameters and plan parameters; Step 5, setting a spatiotemporal filtering window according to the aircraft flight stage; Step 6, filtering meteorological radar reflectivity data that meets the conditions from the raw meteorological radar reflectivity grid data according to the spatiotemporal window. The filtered meteorological radar data can achieve full coverage monitoring of the area surrounding the aircraft, fully meeting the pilot's real-time monitoring needs for the surrounding meteorological environment and providing reliable support for safe flight of aircraft in hazardous weather conditions.

This invention provides an optimization method for low-altitude flight plan approval based on four-dimensional spatiotemporal capacity constraints. The method first collects and standardizes four-dimensional profile data of approved and newly applied flight plans, divides the low-altitude airspace into four-dimensional units, and initializes capacity thresholds. Then, it maps approved plans to these four-dimensional units, calculates capacity occupancy, and checks the capacity constraints of newly applied plans. Subsequently, it detects conflict types and classifies conflict areas into risk levels. Next, it sorts conflict-affected plans according to task priority, and uses single or combined strategies of time, altitude, and speed, along with optimization algorithms, to achieve minimum disturbance adjustment. Finally, it outputs approval opinions. This invention achieves dynamic and precise assessment of low-altitude airspace spatiotemporal capacity, avoids flight conflicts at the source, improves approval efficiency and airspace resource utilization, and is suitable for large-scale, multi-task parallel operation scenarios in low-altitude environments. It can be applied to various low-altitude operation management platforms.

Flight planning and / or optimizing flight plans can be performed using ground-based systems with little or no instrumented data, providing efficient communications between aircraft and ground-based systems. Estimates of airspeeds (i.e., derived airspeed) can be derived from transponder telemetry, which is optionally collected independently of flight planning. Alternative (adjusted or revised or modified) flight plans can be generated based on derived airspeed, weather, traffic, etc., and / or performance data specific to an individual aircraft. Aircraft performance models can be updated (e.g., fuel consumption) based on monitoring an aircraft over time. Winds aloft data can be updated based on monitoring of one or more aircraft. Data can be selectively identified and uplinked to aircraft based on certain criteria (e.g., data relevant to geographic areas and altitudes proximate flight path as indicated by flight plan).

This invention relates to the field of UAV communication and mobile edge computing optimization technology, specifically to a closed-loop joint optimization method and system for multi-UAV mobile edge computing systems. The system operates in discrete time slots: acquiring network state including UAV location, user task parameters, and channel information; outputting system performance prediction values ​​for "state-candidate flight decision" by a supervised prediction model; searching and outputting flight decisions in the flight decision space based on the prediction values ​​by a heuristic planner; generating resource scheduling actions for each UAV under flight decision constraints to complete task unloading, computation, and collaborative migration, and obtaining real rewards under these constraints; and writing interaction samples into a replay dataset to update the supervised prediction model and reinforcement learning strategy online, forming a closed-loop joint optimization of flight planning, resource execution, and feedback update, reducing candidate flight evaluation overhead, improving task completion rate, reducing service latency, and improving resource utilization.