A method for UAV route planning and dynamic control in urban low-altitude airspace

Abstract

The present invention discloses a method for UAV route planning and dynamic control for urban low-altitude airspace, which relates to the technical field of UAV route planning and dynamic control, including: dividing the low-altitude airspace into a basic flight layer, a buffer anti-collision layer and an emergency guarantee layer in the vertical direction; setting different areas to different route levels and restriction rules in the horizontal direction; generating a basic route network skeleton and optimizing the structure; obtaining the operating status and environmental update data of the UAV; semantically annotating each segment and node in the basic route network skeleton, embedding airspace regulations, regulatory rules, restricted flight areas, temporary control areas and recommended flight corridor information into the semantic layer of the route network; inputting the basic route network skeleton, semantic annotation information and real-time monitoring data into the urban low-altitude airspace management and decision-making platform. The present invention realizes the coordinated scheduling and avoidance between multiple UAVs, and improves the operating efficiency of the route network and the collaborative working ability of the UAVs.

Description

Technical Field

[0001] The present invention relates to the technical field of unmanned aerial vehicle (UAV) route planning and dynamic control, and in particular to a method for unmanned aerial vehicle (UAV) route planning and dynamic control in urban low-altitude airspace. Background Art

[0002] With the rapid development of drone technology, especially its widespread application in Urban Air Mobility (UAM), the frequency and density of drone use in low-altitude airspace have increased significantly, which has also brought many challenges, including the efficient use of airspace resources, the guarantee of flight safety, and the management of multi-drone collaborative operations.

[0003] When faced with high-density flight environments and dynamic airspace changes, existing UAV route planning and management methods usually rely on pre-set fixed routes, lack the ability to respond to real-time environmental changes, and are difficult to adapt to high-density aircraft and dynamic changes in complex environments, resulting in low airspace resource utilization, unable to meet real-time airspace management needs, and increasing the risk of flight conflicts. In addition, there is a lack of effective algorithm support in optimizing route structure and energy consumption management, and it is impossible to minimize energy consumption while ensuring flight safety, which affects the endurance and economy of UAVs.

[0004] To solve the above problems, the present invention proposes a UAV route planning and dynamic control method for urban low-altitude airspace, so as to realize coordinated scheduling and avoidance among multiple UAVs, improve the operating efficiency of the entire route network and the collaborative working ability of UAVs. Summary of the invention

[0005] In view of the above problems, the present invention provides a method for UAV route planning and dynamic control in urban low-altitude airspace.

[0006] According to one aspect of the present invention, a method for UAV route planning and dynamic control in urban low-altitude airspace is provided, comprising:

[0007] Step S1, constructing a multi-dimensional urban environment model based on the location of drone take-off and landing sites in the urban area, surrounding geographic information, three-dimensional building data, road traffic flow data, population density data and meteorological information, and storing the multi-dimensional urban environment model in the urban low-altitude airspace management and decision-making platform;

[0008] Step S2, according to the multi-dimensional urban environment model, urban low-altitude airspace regulations and operational requirements, the low-altitude airspace is divided into a basic flight layer, a buffer collision avoidance layer and an emergency guarantee layer in the vertical direction; the Voronoi diagram is used to divide the low-altitude airspace in the horizontal direction, and different areas are set to different route levels and restriction rules; the basic direction and flight altitude range of the reference route are determined as the starting point of the path planning, and the floating range and priority rules are set to allow the route to float within the specified altitude range, and different priorities are assigned according to the urgency and importance of the flight mission;

[0009] Step S3, using the take-off and landing address locations of the UAV as network nodes, generating a basic route network skeleton through Voronoi diagram, Delaunay triangulation or based on the shortest path search method; wherein, in the process of generating the basic route network skeleton, the three-dimensional data of the building, the high-altitude billboards, and the three-dimensional obstacle data of the restricted flying area are introduced into the cost function of the path search, and an obstacle avoidance solution is found in the three-dimensional space; a multi-level route network including trunk routes, secondary routes and emergency routes is established to ensure that path planning only occurs in the valid airspace; by automatically screening the surrounding geographic information, airspace control and meteorological information, invalid airspace is filtered, and the generated basic route network skeleton is stored in the urban low-altitude airspace management and decision-making platform;

[0010] Step S4, structurally optimizing the basic route network skeleton by using a cellular automaton and a spanning tree algorithm to generate a route network that meets the requirements of logistics efficiency and safety; wherein the objectives of structural optimization include shortest flight time, lowest energy consumption and lowest risk, and finding the best balance point among multiple optimization objectives; dynamically adjusting the use authority and priority of routes according to the road traffic flow data of each route segment and the basic route network skeleton to avoid route congestion and flight delays, and giving priority to route segments with energy consumption below a preset threshold; and coordinating flight time and route selection by sharing flight path information to avoid route conflicts and congestion;

[0011] Step S5, obtaining the UAV operation status and environmental update data in real time through sensors, communication links and UTM platform, and adjusting the path of a single or multiple UAVs in real time according to the road traffic flow data, population density data and meteorological information, so as to avoid wind field disturbances, sudden obstacles and temporary control areas; wherein, when a potential path conflict or collision risk is detected, the vertical level or horizontal track of the aircraft is automatically adjusted according to the basic route network skeleton; collaborative avoidance between multiple UAVs is achieved by integrating multi-source sensors, and the avoidance actions of UAVs are coordinated by sharing flight status and path information;

[0012] Step S6, semantically annotating each flight segment and node in the basic route network skeleton, and embedding airspace regulations, regulatory rules, restricted flight zones, temporary control zones and recommended flight corridor information into the semantic layer of the route network through a knowledge graph and a rule engine;

[0013] Step S7, input the basic route network skeleton, the semantic annotation information and the real-time monitoring data into the urban low-altitude airspace management and decision-making platform, and issue flight permits and dispatch instructions according to the mission type, risk level, the vertical and horizontal airspace divisions of the low-altitude airspace and the priority rules.

[0014] In an optional manner, the multi-dimensional urban environment model includes:

[0015] Two-dimensional ground grid data, used to record ground obstacles, road distribution and population gathering areas;

[0016] 3D building models and airspace obstacle distribution data for identifying three-dimensional obstacles, restricted and no-fly zones;

[0017] Dynamic environmental data, including real-time meteorological information, electromagnetic interference source information, wireless communication base stations and 5G cellular network node locations, where the real-time meteorological information includes wind speed, wind direction, temperature and visibility.

[0018] In an optional manner, in step S1, constructing a multi-dimensional urban environment model further includes:

[0019] Step S11, using laser radar, high-definition photogrammetry and high-precision map data to build a three-dimensional model of the city; using mobile communication signal strength data to obtain 5G cell distribution information; using an online meteorological data interface to obtain wind field parameters, visibility, air pressure and precipitation in real time;

[0020] Step S12, cleaning, denoising and projection conversion of the acquired geographic data, building models and meteorological data;

[0021] Step S13, integrating the 5G cellular network node locations and communication signal strength data within the city, and incorporating the communication network information into the environmental modeling to support real-time data transmission and coordinated flight of drones;

[0022] Step S14, real-time monitoring of electromagnetic interference sources in the urban area, and evaluating the impact on the communication and navigation systems of the drone; wherein high electromagnetic interference areas are marked in the multi-dimensional urban environment model;

[0023] Step S15, monitoring the changes of urban terrain and buildings in real time, and updating the multi-dimensional urban environment model;

[0024] Step S16, integrating the meteorological data acquired in real time into the multi-dimensional urban environment model.

[0025] In an optional manner, in step S2, the stratification of the low-altitude airspace and the division of the route level further include:

[0026] Step S21, determining the upper and lower flight altitudes of the low airspace according to regional regulations and technical standards, and dividing the airspace into a basic flight layer, a safety buffer layer, and an emergency guarantee layer;

[0027] Step S22, assigning different priorities and traffic rules to air routes in each level, and embedding the temporary restricted flight zone or preset rules issued by the supervisory authority in real time into the air routes of the corresponding level;

[0028] Step S23, assigning a priority score to the mission according to the urgency and importance of the flight mission;

[0029] Step S24, dynamically adjusting the flight permissions of each airspace level according to real-time mission requirements and airspace usage conditions;

[0030] Step S25, dynamically adjusting the functional allocation of each airspace layer according to different time periods and urban activities; in which more airspace resources are allocated to the logistics distribution layer during peak hours;

[0031] Step S26, dynamically adjust the resource allocation of each airspace level according to the importance and urgency of the task, to ensure that high-priority tasks can obtain airspace resource support when needed.

[0032] In an optional manner, in step S3, generating a basic route network skeleton further includes:

[0033] Step S31, based on the determined drone take-off and landing sites, generate a preliminary route skeleton of the urban airspace through Delaunay triangulation; use the Voronoi diagram to divide the urban area, determine the neighbor relationship of each route node, and form a basic route network skeleton;

[0034] Step S32, combining the three-dimensional building model and airspace obstacle distribution data in the generated basic route network skeleton, and optimizing the route for obstacle guidance; wherein, static obstacles such as buildings and high-altitude billboards and temporary restricted flight zones are introduced into the cost function of the path search, and the generation of the route is adjusted through a weighted mechanism to ensure that the drone can safely avoid obstacles;

[0035] Step S33, classifying the basic route network skeleton into trunk routes, secondary routes and emergency routes according to the importance and frequency of use of the routes, ensuring that the trunk routes connect the main take-off and landing sites, the secondary routes cover the medium-density areas, and the emergency routes are used for rapid response to emergency tasks and emergencies;

[0036] Step S34, planning the capacity of each route segment according to the flight density and mission type of the drones to ensure that the route carries the expected drone traffic; setting the maximum number of parallel flights of the route segment to prevent flight delays and conflicts caused by route overload;

[0037] Step S35, reserving emergency routes in the route network for rapid response to emergency tasks and emergencies;

[0038] Step S36, designing redundant routes for key route segments to ensure rapid switching to redundant routes when a failure or congestion occurs in the main route segment.

[0039] In an optional manner, in step S4, the structural optimization of the basic route network skeleton further includes:

[0040] Step S41, iteratively optimizing the initially generated basic route network skeleton to reduce redundancy and unnecessary intersections between routes;

[0041] Step S42, using an optimal spanning tree algorithm to perform connectivity analysis on the optimized basic route network skeleton to minimize the total route length and flight energy consumption;

[0042] Step S43, performing multi-objective optimization on the basic route network skeleton by using a particle swarm optimization algorithm to find the best balance point among multiple optimization objectives; wherein the optimization objectives include flight density, route intersection risk and energy consumption;

[0043] Step S44, real-time monitoring of the drone traffic of each route segment, and dynamically adjusting the route usage rights and priorities based on the traffic monitoring data; wherein, during the peak traffic period, route allocation is optimized to avoid route congestion and flight delays;

[0044] Step S45, during the route optimization process, the route segments with energy consumption lower than a preset threshold are preferentially selected to reduce the energy consumption of the UAV and extend the flight endurance;

[0045] Step S46, coordinate the flight time and route selection of the UAVs by sharing flight path information to avoid route conflicts and congestion, so as to improve the operating efficiency of the entire route network and the collaborative working ability of the UAVs.

[0046] In an optional manner, in step S5, the real-time path adjustment and the path conflict further include:

[0047] Step S51, when a sudden obstacle or high-density area is detected, an avoidance path is generated using a local path replanning algorithm; the global path and the local path are combined to ensure the continuity and safety of the flight;

[0048] Step S52, in the autonomous decision-making module integrated on the UAV side, the flight path or flight altitude is automatically adjusted according to the conflict detection result and path replanning, so that the UAV can perform real-time avoidance and path optimization without relying on the central control system;

[0049] Step S53, coordinating the avoidance actions of the UAVs by sharing the flight status and path information, ensuring the safe and orderly flight of multiple UAVs in the high-density airspace;

[0050] In an optional manner, in step S6, semantic annotation and embedding further includes:

[0051] Step S61, performing risk level assessment on each route segment and route node according to flight density, obstacle density, population density and environmental sensitivity; and classifying the route segment into low risk, medium risk and high risk levels according to the level assessment results;

[0052] Step S62, setting the passage priority of the route segment according to the risk level of the route segment and the importance of the flight mission; wherein the high-priority route is used for emergency rescue and high-priority missions, and the low-priority route is used for conventional logistics distribution;

[0053] Step S63, calculating the flight energy cost for each route segment according to the route length, flight altitude change and weather conditions; using the energy cost as a parameter for route optimization to guide the UAV to select a more energy-efficient flight path;

[0054] Step S64, marking the noise sensitivity of the route segment according to the noise sensitivity of the area through which the route passes; wherein the UAV is guided to adopt a low-noise flight mode in the noise-sensitive area to reduce noise interference to the public;

[0055] Step S65, setting priorities and applicable scopes according to different types of airspace control rules; in the semantic annotation process, high priority rules are applied first to ensure that the drone follows the most stringent flight regulations;

[0056] Step S66, when different semantic rules conflict, automatically identify and apply preset priorities or resolution strategies to ensure that the drone follows consistent and conflict-free flight rules.

[0057] In an optional manner, in step S7, the issuing of flight permission and dispatch instruction further includes:

[0058] Step S71, automatically generating a flight permit based on the drone's mission type, route priority, and real-time airspace conditions, and issuing a flight instruction through the drone's communication system to ensure that the drone performs its flight mission on a legal and safe route;

[0059] Step S72, classifying the flight missions according to their nature and urgency, wherein different types of missions correspond to different scheduling strategies and route allocation rules;

[0060] Step S73, dynamically adjusting the priority of the UAV mission according to the real-time mission requirements and airspace usage; wherein, when an emergency mission occurs, route resources are reallocated to ensure that the emergency mission can be executed first;

[0061] Step S74, using V2V and V2 communication methods to achieve coordinated scheduling among multiple drones to avoid flight conflicts and airspace congestion;

[0062] Step S75, based on the priority scheduling algorithm and resource allocation algorithm, optimize the task allocation and route selection of the UAV to ensure that high-priority tasks obtain priority route resources and scheduling support;

[0063] Step S76, recording the flight data of the UAV in real time, wherein the flight data includes the flight path, flight altitude, flight speed and avoidance action;

[0064] Step S77, dynamically adjusting the validity period and authority scope of the flight permit according to the real-time airspace usage and the flight status of the UAV; wherein, during the flight, the flight permit is extended, shortened or revoked according to the real-time status of the UAV and environmental changes;

[0065] Step S78, dynamically assigning task priorities based on real-time task requirements and airspace resource conditions; wherein, when high-priority tasks appear, the scheduling order and route resource allocation of low-priority tasks are automatically adjusted;

[0066] Step S79, dynamically adjust the scheduling strategy and route allocation according to real-time flight data and airspace usage conditions.

[0067] In an optional manner, in step S79, the dynamically adjusting the scheduling strategy and route allocation further includes:

[0068] Step S791, real-time monitoring of drone traffic and route usage in the city's low-altitude airspace, and analysis of congestion and potential conflict points on the routes;

[0069] Step S792, dynamically adjusting the capacity and traffic rules of the route according to the real-time monitoring data to alleviate route congestion and reduce flight conflicts;

[0070] Step S793, re-evaluating the priorities of all tasks when an emergency task or incident occurs, and reallocating route resources according to the new priorities;

[0071] Step S794, based on the real-time position and flight speed of the UAV, predict the route usage in the future period of time, and adjust the route resource scheduling and flight permission in advance.

[0072] According to the solution provided by the present invention, the grid granularity is adjusted in real time according to the density of aircraft and the complexity of the environment through dynamic grid division technology to generate a multi-scale grid model. The grid is refined in areas with dense aircraft to improve navigation accuracy, and the grid is expanded in areas with simpler environments to optimize the computing burden, reduce the risk of conflict, and improve the efficiency of flight mission planning. Through energy cost calculation and energy consumption optimization strategies, the drone is guided to choose a more energy-saving flight path, reduce energy consumption, and extend flight endurance. Through multi-drone collaborative optimization and collaborative avoidance mechanisms, collaborative scheduling and avoidance between multiple drones are achieved, route conflicts and airspace congestion are avoided, and the operating efficiency of the entire route network and the collaborative working ability of drones are improved.

[0073] The above description is only an overview of the technical solution of the present invention. In order to more clearly understand the technical means of the present invention, it can be implemented according to the contents of the specification. In order to make the above and other purposes, features and advantages of the present invention more obvious and easy to understand, the specific implementation methods of the present invention are listed below. BRIEF DESCRIPTION OF THE DRAWINGS

[0074] Various other advantages and benefits will become apparent to those of ordinary skill in the art by reading the detailed description of the preferred embodiments below. The accompanying drawings are only for the purpose of illustrating the preferred embodiments and are not to be considered as limiting the present invention. Moreover, the same reference symbols are used throughout the accompanying drawings to represent the same components. In the accompanying drawings:

[0075] Figure 1 A schematic diagram of the process of the method for UAV route planning and dynamic control in urban low-altitude airspace according to an embodiment of the present invention is shown;

[0076] Figure 2 A schematic diagram of the process of data collection and environment modeling according to an embodiment of the present invention is shown;

[0077] Figure 3 A schematic diagram of the airspace stratification and route level division process according to an embodiment of the present invention is shown;

[0078] Figure 4 A schematic diagram of a basic route network construction process according to an embodiment of the present invention is shown;

[0079] Figure 5 A schematic diagram of the route optimization and flow control process of an embodiment of the present invention is shown;

[0080] Figure 6 A schematic diagram of the real-time dynamic path adjustment and conflict management process according to an embodiment of the present invention is shown;

[0081] Figure 7 A schematic diagram of the semantic annotation and regulatory rule embedding process according to an embodiment of the present invention is shown;

[0082] Figure 8 A schematic diagram of the decision-making and scheduling execution process of an embodiment of the present invention is shown. DETAILED DESCRIPTION

[0083] The exemplary embodiments of the present invention will be described in more detail below with reference to the accompanying drawings. Although the exemplary embodiments of the present invention are shown in the accompanying drawings, it should be understood that the present invention can be implemented in various forms and should not be limited by the embodiments set forth herein. On the contrary, these embodiments are provided in order to enable a more thorough understanding of the present invention and to enable the scope of the present invention to be fully communicated to those skilled in the art.

[0084] Figure 1 The flowchart of the method for UAV route planning and dynamic control in urban low-altitude airspace according to an embodiment of the present invention is shown. Figure 1 As shown, the following steps are included:

[0085] Step S1, constructing a multi-dimensional urban environment model based on the location of drone take-off and landing sites in the urban area, surrounding geographic information, three-dimensional building data, road traffic flow data, population density data and meteorological information.

[0086] In this embodiment, the location data of multiple drone take-off and landing sites that have been determined in the urban area, as well as the surrounding geographic information, three-dimensional building data, road traffic flow data, population density data and meteorological information are obtained; wherein, the multi-dimensional urban environment model includes: two-dimensional ground grid data: recording ground obstacles, road distribution and population gathering areas; three-dimensional building models and airspace obstacle distribution data: used to identify three-dimensional obstacles, restricted and no-fly zones; dynamic environmental data: including real-time meteorological information (wind speed, wind direction, temperature, visibility), electromagnetic interference source information, wireless communication base stations and 5G cellular network node locations.

[0087] Specifically, Figure 2 As shown, step S1 includes the following sub-steps:

[0088] Step S11: Multi-source sensor fusion

[0089] Use LiDAR, high-definition photogrammetry and high-precision map data to build a three-dimensional urban model; use mobile communication signal strength data to obtain 5G cell distribution information; and use the online meteorological data interface to obtain wind field parameters (wind speed, wind direction), visibility, air pressure and precipitation conditions in real time.

[0090] Step S12: Data preprocessing and standardization

[0091] The acquired geographic data, building models and meteorological data are cleaned, denoised and projected; flight-related parameters (such as maximum allowable speed, load capacity, and endurance) and environmental parameters (such as regional noise sensitivity and population density) are standardized to form a unified data interface.

[0092] Step S13: Communication network integration

[0093] Integrate 5G cellular network node locations and communication signal strength data within the city to ensure that drones can maintain stable communication connections during flight; incorporate communication network information into environmental modeling to support real-time data transmission and collaborative flight of drones.

[0094] Step S14: Electromagnetic interference monitoring

[0095] Real-time monitoring of electromagnetic interference sources in urban areas (such as high-voltage towers, radio stations, etc.) to evaluate their impact on drone communication and navigation systems; marking high electromagnetic interference areas in the environmental model to guide drones to avoid or take corresponding anti-interference measures when selecting flight paths.

[0096] Step S15: Dynamic terrain change monitoring

[0097] Monitor changes in urban terrain and buildings in real time, and update the three-dimensional terrain model in a timely manner through the drone's own sensors or external monitoring equipment; ensure that the airspace model can reflect the latest geographical environment and guide the drone to fly safely.

[0098] Step S16: Real-time meteorological data fusion

[0099] Integrate real-time meteorological data (such as wind speed, wind direction, rainfall, lightning, etc.) into the environmental model to dynamically adjust the flight path planning; in severe weather conditions, guide the UAV to adopt corresponding flight strategies, such as lowering the flight altitude, delaying the flight time or selecting an alternate route.

[0100] The present invention uses laser radar (LiDAR), high-definition photogrammetry and high-precision map data to build a three-dimensional urban model, combines mobile communication signal strength data to obtain 5G cell distribution information, and obtains wind field parameters such as wind speed, wind direction, visibility, air pressure and precipitation in real time through an online meteorological data interface.

[0101] Wherein, step S11 includes the following sub-steps:

[0102] Step S111: LiDAR

[0103] Scan urban areas with ground or aerial LiDAR devices to obtain high-density point cloud data. Ground LiDAR devices include fixed scanners, while aerial LiDAR devices can be mounted on drones or helicopters. The collected point cloud data is processed using point cloud processing algorithms such as registration, classification, and filtering to generate accurate 3D terrain and building models. Specific steps include point cloud registration to eliminate scanning errors, point cloud classification to distinguish different types of objects, and filtering to remove noise and stray points.

[0104] Step S112: High-definition photogrammetry

[0105] Use high-definition camera equipment to take multi-angle aerial photos and obtain high-resolution image data, covering every corner of the city, ensuring the overlap and coverage of image data. Use structured light measurement or multi-view stereo matching technology to convert two-dimensional image data into three-dimensional models to supplement the details that LiDAR data cannot capture, such as the texture and color of buildings.

[0106] Step S113: High-precision map data

[0107] Introduce existing high-precision urban map data sources, such as OpenStreetMap (OSM) and Google Maps API, to obtain information such as road networks, building outlines, landmark locations, etc. Fuse high-precision map data with 3D models generated by LiDAR and photogrammetry, and use geographic information system (GIS) tools for spatial alignment and data matching to improve the comprehensiveness and accuracy of the model.

[0108] Step S114: Mobile communication signal strength data

[0109] The communication receiving equipment carried by the drone can measure and record the mobile communication signal strength in real time, including 4G and 5G signals. The coverage and signal quality of the 5G cell can be analyzed, the signal strength distribution map can be drawn, and weak signal areas and blind spots can be identified to provide reference for flight path planning.

[0110] Step S115: Online meteorological data interface

[0111] Access multiple meteorological data sources, such as the Meteorological Bureau API and commercial meteorological service providers, to obtain real-time meteorological parameters such as wind speed, wind direction, visibility, air pressure, precipitation, lightning activity, etc. Adopt data caching and prediction algorithms to integrate meteorological data from different sources, eliminate redundancy and conflict, and ensure the continuity and stability of information.

[0112] The present invention cleans, denoises and projects the acquired geographic data, building models and meteorological data, and standardizes flight-related parameters (such as maximum allowable speed, load capacity, and endurance) and environmental parameters (such as regional noise sensitivity and population density) to form a unified data interface.

[0113] Step S12 includes the following sub-steps:

[0114] Step S121: Data cleaning and denoising

[0115] Filtering algorithms such as median filtering and Gaussian filtering are used to remove noise from geographic data and sensor data to improve data quality. At the same time, outlier detection methods such as statistical analysis and machine learning algorithms are used to identify and remove abnormal data points to ensure data reliability.

[0116] Step S122: Projection conversion

[0117] Convert geographic data from different sources into a unified geographic coordinate system, such as WGS84 or UTM, to ensure spatial consistency of the data. Use geographic information system (GIS) software to spatially align and project data, eliminate spatial offset and deformation, and ensure spatial correspondence between data layers.

[0118] Step S123: Parameter standardization processing

[0119] The maximum permissible speed, load capacity, endurance and other flight-related parameters of drones are normalized to facilitate comprehensive evaluation in the model. At the same time, environmental parameters such as regional noise sensitivity and population density are standardized to ensure the comparability and consistency of different indicators in the comprehensive evaluation.

[0120] Step S124: Unify data interface

[0121] Use standardized data formats such as GeoJSON, XML, JSON and interface protocols such as RESTful API to achieve unified access and interaction of data. Develop data conversion tools to import pre-processed data into a unified database or data warehouse to support subsequent data analysis and modeling.

[0122] The present invention integrates the location of 5G cellular network nodes and communication signal strength data in the city to ensure that the drone maintains a stable communication connection during flight. At the same time, the communication network information is incorporated into the environmental modeling to support the real-time data transmission and coordinated flight of the drone.

[0123] Step S13 includes the following sub-steps:

[0124] Step S131: 5G cellular network node location integration

[0125] By cooperating with mobile communication operators, the geographical location and coverage of all 5G cellular network nodes in the city are obtained, and GIS tools are used for visualization, the signal strength distribution of the 5G network is analyzed, and the signal coverage in the drone flight path is determined.

[0126] Step S132: Communication signal strength data integration

[0127] The communication module carried by the drone monitors and records the changes in communication signal strength on the flight path in real time, and uses the signal strength data to optimize the flight path, avoiding areas with weak signals or disconnections, and ensuring the continuity and reliability of data transmission.

[0128] Step S133: Incorporating communication network information into environmental modeling

[0129] The location and signal strength information of 5G network nodes are embedded in the multi-dimensional environmental model to form a communication network layer, which serves as a reference for flight path planning and real-time adjustment. The network topology analysis method is used to evaluate the load and redundancy of the communication network, improve the fault tolerance of the communication network, and ensure the reliable operation of the UAV communication system.

[0130] The present invention monitors and evaluates in real time the impact of various electromagnetic interference sources that the communication and navigation systems of unmanned aerial vehicles may be subjected to in a complex electromagnetic environment in a city.

[0131] Step S14 includes the following sub-steps:

[0132] Step S141: Identifying electromagnetic interference sources

[0133] Use spectrum analyzers and electromagnetic sensors to scan the electromagnetic spectrum in urban areas and identify major interference sources such as high-voltage towers, radio stations, and industrial equipment. Use a database management system to record the frequency band, transmission power, working status, and other information of each interference source and establish an electromagnetic interference source database.

[0134] Step S142: Interference strength assessment

[0135] Signal strength measurement and interference analysis algorithms are used to evaluate the potential impact of various interference sources on the drone's communication and navigation systems, generate electromagnetic interference intensity maps, and mark high and low interference areas.

[0136] Step S143: Environmental model annotation and path planning

[0137] High electromagnetic interference areas are marked in the multi-dimensional environment model as constraints for flight path planning. Combined with path planning algorithms such as the A* algorithm and the Dijkstra algorithm, flight paths are designed to avoid high interference areas, or anti-interference technologies such as frequency hopping and adaptive modulation are used when necessary to ensure the stability of communication and navigation.

[0138] The present invention aims at the dynamic changes of urban terrain and buildings, and ensures the flight safety of the UAV and the accuracy of path planning through real-time monitoring and updating.

[0139] Step S15 includes the following sub-steps:

[0140] Step S151: Dynamic monitoring equipment

[0141] Integrate multiple sensors such as LiDAR, HD cameras, and inertial measurement units (IMUs) on drones to collect real-time information on terrain and building changes in the surrounding environment. At the same time, deploy ground sensors and surveillance cameras to provide auxiliary data and enhance the accuracy and coverage of change detection.

[0142] Step S152: Real-time data processing and model updating

[0143] Using change detection algorithms such as differential image analysis and point cloud comparison, we can identify the addition, movement or destruction of terrain and buildings. Through the incremental update mechanism, we can quickly integrate the latest change information into the 3D terrain model to ensure the real-time and accuracy of the model.

[0144] Step S153: Safe flight guidance

[0145] Based on the latest 3D terrain model, the flight path of the drone is adjusted in real time to avoid new obstacles or dangerous areas. It realizes two-way information interaction between the drone and the ground control system, and timely feedback of terrain change information improves overall flight safety.

[0146] The present invention dynamically integrates real-time meteorological data into the environmental model to guide the adjustment of the UAV's flight path and ensure flight safety and mission execution efficiency.

[0147] Step s16 includes the following sub-steps:

[0148] Step S161: Acquisition and processing of meteorological data

[0149] Through the online meteorological data interface, real-time meteorological parameters such as wind speed, wind direction, rainfall, lightning activity, etc. are continuously obtained, and data fusion technology is used to integrate meteorological data from different sources to eliminate redundancy and conflict, ensuring the accuracy and consistency of meteorological information.

[0150] Step S162: Integrate meteorological data with environmental model

[0151] Embed real-time weather data in the multi-dimensional environmental model to form a dynamic weather layer for flight path planning and decision support. Use the forecast model driven by weather data to estimate the weather trend in the future and adjust the flight strategy in advance.

[0152] Step S163: Flight path planning and adjustment

[0153] Based on real-time weather data, the flight path is dynamically optimized to avoid bad weather areas or adjust the flight altitude to adapt to changes in wind speed and direction. When extreme weather conditions such as strong winds, heavy rain, lightning, etc. are detected, the emergency response mechanism is automatically triggered to instruct the drone to lower the flight altitude, delay the flight time or choose an alternate route to ensure flight safety.

[0154] Step S164: Intelligent decision support

[0155] By integrating machine learning algorithms, analyzing historical meteorological data and flight records, predicting potential meteorological risks, and optimizing flight strategies, it can also achieve real-time data sharing and collaborative decision-making between drones and ground control centers, improving the intelligence level and adaptability of the overall flight system.

[0156] According to the urban low-altitude airspace regulations and operational requirements, the present invention divides the low-altitude airspace into a basic flight layer, a buffer and collision avoidance layer, and an emergency guarantee layer in the vertical direction; divides it horizontally by grids or Voronoi diagrams, and sets different route levels and restriction rules for different areas; determines the basic direction and flight altitude range of the reference route as the starting point for all further path planning; sets a floating range and priority rules, allows the route to float within a specified altitude range, and assigns different priorities according to the urgency and importance of the flight mission.

[0157] Step S2, according to the multi-dimensional urban environment model, urban low-altitude airspace regulations and operational requirements, the low-altitude airspace is divided into a basic flight layer, a buffer and collision avoidance layer and an emergency protection layer in the vertical direction.

[0158] In this embodiment, according to urban low-altitude airspace regulations and operational requirements, the low-altitude airspace is divided vertically into a basic flight layer, a buffer and collision avoidance layer, and an emergency guarantee layer; horizontally, it is divided by grids or Voronoi diagrams, and different route levels and restriction rules are set for different areas; the basic direction and flight altitude range of the reference route are determined as the starting point for all further path planning; the floating range and priority rules are set to allow the route to float within the specified altitude range, and different priorities are assigned according to the urgency and importance of the flight mission.

[0159] Among them, Figure 3 As shown, step S2 includes the following sub-steps:

[0160] Step S21: Layering benchmark setting

[0161] The upper and lower flight altitudes of the low airspace are determined according to national or regional laws and technical standards, and the airspace is divided into the basic flight layer (such as 30m-60m), the safety buffer layer (such as 60m-80m) and the emergency guarantee layer (such as 80m-100m) based on this reference; the specific altitudes are only for examples and can be adjusted dynamically.

[0162] Step S22: Route level and priority setting

[0163] Different priorities and traffic rules are assigned to the air routes within each layer. For example, the basic flight layer is used for conventional logistics drones, and the emergency support layer is used for emergency rescue drones to have priority passage; temporary restricted flight zones or special rules issued in real time by regulators are embedded in the routes of the corresponding layers.

[0164] Step S23: Task priority scoring

[0165] Assign corresponding mission priority scores based on the urgency and importance of different flight missions; incorporate mission priority scores into risk score calculations to affect route allocation and priority setting.

[0166] Step S24: Dynamic rights management

[0167] Based on real-time mission requirements and airspace usage, the flight permissions of each airspace level are dynamically adjusted; for example, during the peak period of emergency rescue missions, the authority of the emergency support layer is temporarily increased to increase its airspace resource allocation.

[0168] Step S25: Dynamic allocation of functional levels

[0169] Dynamically adjust the functional allocation of each airspace level according to different time periods and urban activities; for example, during peak hours, allocate more airspace resources to the logistics distribution layer, reduce the resource usage of the emergency support layer, and improve the overall airspace utilization efficiency.

[0170] Step S26: Airspace resource priority management

[0171] Establish a priority management mechanism for airspace resources, dynamically adjust resource allocation at each airspace level based on the importance and urgency of the mission, and ensure that high-priority missions can obtain sufficient airspace resource support when needed.

[0172] The present invention provides a method for hierarchical airspace management, which sets the upper and lower limits of the flight altitude of the low airspace according to national or regional laws and technical standards, and divides the airspace into multiple flight levels (such as basic flight layer, safety buffer layer, and emergency guarantee layer).

[0173] Step S21 includes the following sub-steps:

[0174] Step S211: Analysis of regulations and standards

[0175] According to the relevant laws and technical standards of the country or region in the target area, the legal flight altitude range of low-altitude airspace is systematically analyzed and determined. By collecting and analyzing the applicable aviation management regulations and technical specifications, the upper and lower flight altitude limits of low-altitude airspace are clarified to ensure the legality and compliance of airspace stratification.

[0176] Step S212: Flight level division

[0177] According to the flight altitude range determined in step S211, the low airspace is divided into a basic flight layer, a safety buffer layer, and an emergency protection layer. For example, the basic flight layer is set to 30 meters to 60 meters, the safety buffer layer is 60 meters to 80 meters, and the emergency protection layer is 80 meters to 100 meters. The specific altitude range can be dynamically adjusted according to actual needs to adapt to different flight missions and environmental changes.

[0178] Step S213: Dynamic adjustment mechanism

[0179] A dynamic adjustment mechanism based on real-time monitoring data is established, and the altitude range of each flight layer is automatically optimized using data such as drone flight density and urban activity intensity. Through adaptive algorithms, the stratification benchmark is adjusted in real time to improve airspace utilization efficiency and flight safety.

[0180] This invention proposes a route priority setting method based on flight level, which assigns different priorities and traffic rules to each route channel according to different airspace levels (such as basic flight level, emergency support level, etc.). By matching specific tasks (such as logistics distribution or emergency rescue) with airspace levels, reasonable priority allocation of UAV tasks is achieved, and combined with regulatory requirements or temporary flight restrictions, the smooth execution of UAV flight missions is ensured.

[0181] Step S22 includes the following sub-steps:

[0182] Step S221: Route channel definition

[0183] Multiple air routes are planned at each flight level, covering flight paths in different directions and altitudes. According to the nature of the flight mission, the air routes are classified into conventional logistics routes, emergency rescue routes, and regulatory routes, etc., to ensure that the air route requirements of different types of missions are effectively met.

[0184] Step S222: Priority allocation

[0185] The priority of each airway channel is set according to the urgency and importance of the flight mission. For example, the emergency rescue route is given the highest priority to ensure priority passage in an emergency. The conventional logistics route is second, and the priority of the supervision route is dynamically adjusted according to actual needs.

[0186] Step S223: Rule embedding and dynamic adjustment

[0187] Embed temporary restricted flight zones or special rules issued by regulatory agencies in real time into routes at the corresponding level, dynamically adjust the access rights and priorities of route channels, and establish a real-time rule update mechanism to ensure the timeliness and accuracy of route level and priority settings.

[0188] The present invention provides a task priority scoring method, which analyzes the urgency and importance of flight tasks and assigns a priority score to each task. This scoring mechanism will affect the allocation of UAV routes and the scheduling of airspace resources, ensuring that high-priority tasks can be given priority when airspace resources are limited, thereby improving the efficiency and safety of flight scheduling.

[0189] Step S23 includes the following sub-steps:

[0190] Step S231: Formulate scoring criteria

[0191] Develop a scoring standard based on the urgency and importance of flight missions. By evaluating the urgency of the mission (such as rescue missions, logistics distribution, monitoring missions) and its importance (such as public safety, commercial value, legal compliance), different missions are given corresponding priority scores.

[0192] Step S232: Score calculation

[0193] The weighted scoring formula is used to calculate the comprehensive task priority score, the formula is as follows:

[0194] Task priority score = w1 × urgency score + w2 × importance score

[0195] Among them, w1 + w2 = 1, and the weight coefficient can be adjusted according to specific needs.

[0196] Step S233: Scoring Application

[0197] Incorporate the mission priority score into the overall risk score calculation to affect the allocation and priority setting of routes. Dynamically adjust the mission priority score based on real-time mission requirements and environmental changes to ensure the real-time and accuracy of the score.

[0198] The present invention proposes a dynamic authority management method based on real-time mission requirements and airspace usage. By dynamically adjusting the flight authority of each airspace level, it is ensured that different missions obtain corresponding flight authority at different airspace levels. For example, in an emergency rescue mission, the flight authority of the emergency support layer can be temporarily increased to meet the demand for airspace resources in emergencies, thereby improving the flexibility and efficiency of mission execution.

[0199] Step S24 includes the following sub-steps:

[0200] Step S241: Permission definition and classification

[0201] Define different flight permission categories, such as regular permission, emergency permission, and temporary permission, based on flight mission type and airspace level. Set levels for different types of permissions, such as high, medium, and low, to ensure that high-priority missions have an advantage in resource allocation.

[0202] Step S242: Real-time permission adjustment

[0203] Monitor the mission requirements and usage status of the current airspace in real time, and identify changes in the requirements of high-priority missions. Based on the real-time monitoring results, dynamically adjust the flight permissions of each airspace layer. For example, during the peak period of emergency rescue missions, temporarily increase the permissions of the emergency support layer and increase its airspace resource allocation.

[0204] Step S243: Permission adjustment mechanism

[0205] Utilize automated control systems to automatically adjust permissions based on preset rules and real-time data, reduce human intervention, and improve adjustment efficiency. Ensure that high-priority tasks are given priority in permission adjustments, and avoid low-priority tasks occupying key airspace resources.

[0206] The present invention provides a method for dynamically allocating airspace functional levels based on time periods and urban activities. By dynamically adjusting the functional allocation of airspace levels according to different time periods and changes in urban airspace activities, for example, more airspace resources are allocated to the logistics distribution layer during peak hours to reduce the resource occupation of the emergency support layer, thereby improving the utilization efficiency and scheduling flexibility of airspace resources.

[0207] Step S25 includes the following sub-steps:

[0208] Step S251: Time period and activity analysis

[0209] The allocation priority of airspace resources is divided according to different time periods (such as peak hours and off-peak hours) and urban activities (such as large-scale events and emergencies). Real-time monitoring of urban activities, such as traffic flow and frequency of public activities, is used as the basis for dynamic allocation of airspace resources.

[0210] Step S252: Function allocation strategy

[0211] During peak traffic hours, more airspace resources will be allocated to the logistics distribution layer to improve logistics efficiency and reduce the waste of airspace resources. When an emergency occurs, the functional allocation of the airspace layer will be dynamically adjusted to give priority to the airspace resources of the emergency support layer and ensure timely response to rescue missions.

[0212] Step S253: Dynamic allocation algorithm

[0213] Adaptive allocation algorithms are used to dynamically adjust the functional allocation of each airspace level based on real-time monitoring data and preset strategies to optimize the utilization efficiency of airspace resources. At the same time, load balancing measures are used to ensure the reasonable allocation of airspace resources in different time periods and activities to avoid overloading or idleness of resources at a certain level.

[0214] The present invention provides an airspace resource priority management mechanism, which dynamically adjusts the allocation of airspace resources according to the importance and urgency of flight missions. This mechanism can ensure that high-priority tasks receive sufficient support when airspace resources are tight, thereby improving the efficiency of task execution, ensuring flight safety, optimizing the use of airspace resources, and avoiding interference of low-priority tasks with high-priority tasks.

[0215] Step S26 includes the following sub-steps:

[0216] Step S261: Establishing a priority management mechanism

[0217] Define the priority categories of different airspace resources, such as high priority resources, medium priority resources, and low priority resources, based on the importance and urgency of the mission. Develop detailed resource priority management rules to clarify the priority and usage rules of different priority tasks in resource allocation.

[0218] Step S262: Dynamic resource allocation

[0219] Monitor the use of airspace resources and mission requirements in real time to identify resource shortages and surpluses. Based on the real-time monitoring results, dynamically adjust the allocation of airspace resources to ensure that high-priority tasks can obtain sufficient resource support when needed.

[0220] Step S263: Conflict resolution mechanism

[0221] Establish a resource conflict identification and resolution mechanism to identify resource contention and usage conflicts between tasks of different priorities. Develop conflict resolution strategies, such as resource reallocation, task delay or cancellation, to ensure that the resource needs of high-priority tasks are met first.

[0222] The present invention uses the known take-off and landing sites as network nodes through the basic route network construction steps, combines three-dimensional obstacle data, generates a multi-level route network, ensures that route planning only occurs within the valid airspace, and realizes obstacle avoidance through a dynamic weighting mechanism.

[0223] Step S3, using the take-off and landing address locations of the drone as network nodes, generating a basic route network skeleton through a Voronoi diagram, Delaunay triangulation or a shortest path search method.

[0224] In this embodiment, known take-off and landing sites are used as network nodes, and the basic route network skeleton is generated through Voronoi diagram, Delaunay triangulation or graph theory-based shortest path search method (such as A*, Dijkstra algorithm); in the generation process, three-dimensional obstacle data such as buildings, high-altitude billboards, and restricted flight zones are introduced into the cost function of the path search, and obstacle avoidance solutions are found in three-dimensional space through a dynamic weighting mechanism; a multi-level route network including trunk routes, secondary routes and emergency routes is established; it is ensured that path planning only occurs in valid airspace, and invalid airspace is filtered out by automatically screening geographical conditions, airspace control and meteorological conditions.

[0225] Among them, Figure 4 As shown, step S3 includes the following sub-steps:

[0226] Step S31: Route skeleton generation

[0227] Based on the determined UAV take-off and landing sites, the basic route network skeleton is generated through Voronoi diagram, Delaunay triangulation or graph theory-based shortest path search method (such as A* algorithm, Dijkstra algorithm).

[0228] Step S32: Path optimization for obstacle guidance

[0229] In the generated route skeleton, the three-dimensional building model and airspace obstacle distribution data are combined to optimize the obstacle-guided path of the route to ensure that the drone can safely avoid obstacles.

[0230] Step S33: Multi-level route classification

[0231] The basic route network is classified into trunk routes, secondary routes and emergency routes according to the importance and frequency of use of the routes, ensuring that route planning only occurs within the valid airspace.

[0232] Step S34: Route capacity planning

[0233] The capacity of each route segment is planned based on the drone flight density and mission type to ensure that the route can carry the expected drone traffic.

[0234] Step S35: Emergency route configuration

[0235] Emergency routes are reserved in the route network for rapid response to emergency tasks and emergencies, ensuring that the drone can reach its destination quickly in an emergency.

[0236] Step S36: Route redundancy design and maintenance

[0237] Redundant routes are designed for key routes to ensure that when the main route fails or is congested, it can quickly switch to the redundant route to ensure the continuity and reliability of drone flights. At the same time, the route network is regularly maintained and updated, and the route structure and allocation are optimized based on drone flight data and airspace usage.

[0238] The present invention generates a basic route network skeleton based on the determined UAV take-off and landing sites through Voronoi diagram, Delaunay triangulation or a shortest path search method based on graph theory (such as A* algorithm, Dijkstra algorithm).

[0239] Step S31 includes the following sub-steps:

[0240] Step S311: Voronoi diagram division

[0241] The Voronoi diagram is used to divide the urban area, determine the proximity relationship of each route node, and form the skeleton structure of the basic route network.

[0242]

[0243] in, For point The Voronoi region of represents a point on the plane (in coordinates), represents a two-dimensional Euclidean space, represents the i route node, represents the j route node, Indicates x to The Euclidean distance of Indicates x to The Euclidean distance of .

[0244] Step S312: Delaunay triangulation

[0245] The preliminary skeleton of the route network is generated through Delaunay triangulation to ensure the connectivity and coverage of the route network.

[0246] Delaunay triangulation condition: The hollow circle does not contain any other points.

[0247] Step S313: Shortest path search

[0248] Adopt A* algorithm or Dijkstra algorithm to search the shortest path on the generated skeleton and build the basic route network.

[0249]

[0250] Among them, we is the weight of edge e, representing the cost of the route segment.

[0251] The present invention combines the three-dimensional building model and airspace obstacle distribution data in the generated route skeleton to optimize the obstacle-guided path of the route, ensuring that the UAV can safely avoid obstacles.

[0252] According to the content, step S32 includes the following sub-steps:

[0253] Step S321: Cost function definition

[0254] Static obstacles such as buildings and high-altitude billboards as well as information on temporary restricted flight zones are introduced into the cost function of path search, and the generation of routes is adjusted through a weighted mechanism to ensure that drones can safely avoid obstacles.

[0255]

[0256] Among them, C is the total cost, D is the distance cost, O is the obstacle avoidance cost, E is the energy consumption cost, and α, β, and γ are weight coefficients used to balance the importance of each cost.

[0257] Step S322: Dynamic weighting mechanism application

[0258] According to actual flight requirements and environmental changes, the weight coefficients α, β, and γ are dynamically adjusted to achieve adaptive path optimization for different flight scenarios.

[0259] The present invention classifies the basic route network according to the importance and frequency of use of the routes, and divides it into main routes, secondary routes and emergency routes, ensuring that route planning only occurs within the effective airspace.

[0260] Step S33 includes the following sub-steps:

[0261] Step S331: Main route setting

[0262] Ensure that the main routes connect the main take-off and landing sites, cover high-density flight areas, and meet the flight needs of conventional logistics drones.

[0263] Trunk route characteristics: Trunk routes have high priority and high capacity, supporting the parallel flight of a large number of drones.

[0264] Step S332: Secondary route setting

[0265] The routes covering medium-density areas in the basic route network are divided into secondary routes to meet the needs of medium-density flight areas.

[0266] Secondary route characteristics: Secondary routes have medium priority and medium capacity and are suitable for medium-density flight missions.

[0267] Step S333: Emergency route setting

[0268] The routes reserved for emergency tasks and emergencies in the basic route network are divided into emergency routes to ensure that the drone can reach its destination quickly in an emergency.

[0269] Emergency route characteristics: Emergency routes have the characteristics of highest priority and fast passage, ensuring the efficient response capability of the drone in emergency situations.

[0270] The present invention plans the capacity of each route section according to the flight density and mission type of the UAVs to ensure that the route can carry the expected UAV traffic.

[0271] Step S34 includes the following sub-steps:

[0272] Step S341: Flight density analysis

[0273] Analyze the flight density of each route segment and determine the capacity demand of the route segment.

[0274]

[0275] Among them, Nmax is the maximum number of parallel flights in the route segment, Q is the flow capacity of the route segment, and Tflight is the flight time of the UAV in the route segment.

[0276] Step S342: Setting the number of parallel flights

[0277] According to the flight density analysis results, the maximum number of parallel flights for each route segment is set to prevent flight delays and conflicts caused by route overload.

[0278] The present invention reserves an emergency route in the route network for rapid response to emergency tasks and emergencies, ensuring that the UAV can quickly reach the destination in an emergency.

[0279] Step S35 includes the following sub-steps:

[0280] Step S351: Emergency route design

[0281] Design the path and width of the emergency route to ensure it has high priority and fast passage characteristics.

[0282] Emergency route characteristics: Emergency routes should avoid major obstacles and high-density flight areas and have a straight flight path to shorten flight time.

[0283] Step S352: Establishment of emergency response mechanism

[0284] Establish an emergency response mechanism to ensure that emergency routes can be activated in a timely manner when emergency tasks occur, and safeguard the rapid response capability of drones.

[0285] Response time requirement: The emergency response mechanism should ensure that the time from the triggering of the emergency task to the activation of the emergency route does not exceed the preset threshold Tresponse.

[0286] The present invention designs redundant routes for key route sections to ensure that when the main route section fails or is congested, it can quickly switch to the redundant route to ensure the continuity and reliability of the UAV flight. At the same time, the route network is regularly maintained and updated, and the route structure and allocation are optimized based on the UAV flight data and airspace usage.

[0287] Step S36 includes the following sub-steps:

[0288] Step S361: Redundant route design

[0289] Redundant routes are designed for key sections to ensure that when the main route section fails or is congested, it can be quickly switched to the redundant route to ensure the continuity and reliability of the UAV flight.

[0290] Redundant route characteristics: Redundant routes should have similar coverage and capacity as primary routes to ensure that when primary routes are unavailable, redundant routes can undertake corresponding flight missions.

[0291] Step S362: Route maintenance and optimization

[0292] The route network is maintained and updated regularly, and the route structure and allocation are optimized based on drone flight data and airspace usage.

[0293] Maintenance frequency: Route maintenance should be performed according to a preset time period TmaintenanceT_{\text{Maintenance}}Tmaintenance, such as daily, weekly or monthly.

[0294] Application of optimization algorithms: Use optimization algorithms (such as genetic algorithms and simulated annealing algorithms) to optimize the route network and improve the adaptability and long-term stability of the route network.

[0295]

[0296] Among them, we is the optimization weight of route segment e, which represents the cost or efficiency index of the route segment.

[0297] The present invention uses improved cellular automata and spanning tree algorithms to optimize the structure of the initial route network through route optimization and traffic control steps, reduce redundant routes and unnecessary intersections, and perform multi-objective optimization through heuristic algorithms, ultimately generating an optimized route network that meets logistics efficiency and safety requirements. At the same time, the drone traffic of each route segment is monitored in real time, and the route usage authority and priority are dynamically adjusted through traffic monitoring data to avoid route congestion and flight delays.

[0298] Step S4, optimizing the structure of the basic route network skeleton by using a cellular automaton and a spanning tree algorithm to generate a route network that meets the requirements of logistics efficiency and safety.

[0299] In this embodiment, an improved cellular automaton and a minimum spanning tree (MST) algorithm are introduced to optimize the structure of the initial route network, thereby reducing redundant routes and unnecessary intersections. Heuristic algorithms (such as ant colony optimization and particle swarm optimization) are used to iteratively optimize the routes while considering flight density, route intersection risk and energy consumption, so as to generate an optimized route network that meets logistics efficiency and safety requirements. A multi-objective optimization strategy is adopted, which comprehensively considers the shortest flight time, the lowest energy consumption and the lowest risk, and finds the best balance point among multiple optimization objectives through a trade-off method. The UAV traffic of each route segment is monitored in real time, and the route usage rights and priorities are dynamically adjusted through traffic monitoring data to avoid route congestion and flight delays. Route segments with lower energy consumption are given priority to reduce the energy consumption of UAVs and improve economy and environmental friendliness. Collaborative optimization among multiple UAVs is considered, and flight path information is shared to coordinate flight time and route selection to avoid route conflicts and congestion.

[0300] Among them, Figure 5 As shown, step S4 includes the following sub-steps:

[0301] Step S41: Cellular automaton optimization

[0302] The improved cellular automaton algorithm is used to iteratively optimize the initially generated route network to reduce redundancy and unnecessary intersections between routes and improve the overall efficiency of the network.

[0303] Step S42: Application of spanning tree algorithm

[0304] The optimal spanning tree algorithm (such as Kruskal algorithm or Prim algorithm) is used to perform connectivity analysis on the optimized route network to ensure the redundancy and reliability of the network and generate an optimal spanning tree to minimize the total route length and flight energy consumption.

[0305] Step S43: Heuristic algorithm iterative optimization

[0306] Ant colony optimization algorithm and particle swarm optimization algorithm are introduced to perform multi-objective optimization of the route network, considering factors such as flight density, route intersection risk and energy consumption, to generate an optimized route network that meets logistics efficiency and safety requirements.

[0307] Step S44: Multi-objective optimization strategy

[0308] In the process of route optimization, multi-objective optimization strategies such as shortest flight time, lowest energy consumption and lowest risk are comprehensively considered, and the best balance point is found among multiple optimization objectives through a trade-off method. The specific method is as follows:

[0309] Define multiple optimization objectives, including shortest flight time, lowest energy consumption and lowest risk, to ensure that the route network is efficient and safe while meeting multiple requirements. The mathematical expression of the optimization objective is:

[0310]

[0311] Among them, T is the flight time, E is the energy consumption, R is the risk, w1, w2, w3 are weight coefficients, satisfying w1+w2+w3=1.

[0312] By using trade-off methods such as weighted sum method and multi-objective genetic algorithm, the best balance point is found among multiple optimization objectives to achieve multi-dimensional optimization of the route network:

[0313] Weighted sum method: by calculating the comprehensive cost w1×T+w2×E+w3×R w 1× T + w 2× E + w 3× R , select the route configuration with the minimum comprehensive cost as the optimal solution.

[0314] Multi-objective genetic algorithm: Utilizes the selection, crossover and mutation operations of the genetic algorithm to search for route configurations that meet multiple optimization objectives and generate a set of Pareto optimal solutions.

[0315] Step S45: Real-time flow monitoring and regulation

[0316] Monitor the drone traffic of each route section in real time, and dynamically adjust the route usage rights and priority based on traffic monitoring data to avoid route congestion and flight delays.

[0317] Step S46: Energy consumption optimization strategy

[0318] During the route optimization process, priority is given to route sections with lower energy consumption to reduce the energy consumption of the drone, extend the flight endurance, and improve economy and environmental friendliness.

[0319] Step S47: Multi-UAV collaborative optimization

[0320] Considering the collaborative optimization among multiple UAVs, by sharing flight path information, coordinating flight time and route selection, avoiding route conflicts and congestion, and improving the operating efficiency of the entire route network and the collaborative working capabilities of UAVs.

[0321] The present invention adopts an improved cellular automaton algorithm to iteratively optimize the initially generated route network, reduce redundancy and unnecessary intersections between routes, and improve the overall efficiency of the network.

[0322] Step S41 includes the following sub-steps:

[0323] Step S411: Cellular automaton model construction

[0324] A route optimization model based on cellular automata is constructed, state transition rules and neighborhood relationships are defined, and the dynamic evolution of the route network is simulated.

[0325] State definition: Define the state of each grid cell, such as occupied, unoccupied, conflicting, etc.

[0326] Transition rules: Define the transition rules of grid cell states based on the current state and neighborhood state, such as the rules for eliminating redundant routes.

[0327] Step S412: Eliminate redundant routes

[0328] By adjusting the transfer rules of the cellular automaton, the redundancy and unnecessary intersections between routes are reduced, and the structure of the route network is optimized.

[0329] Redundant route identification: Use route density and number of intersections to identify redundant route segments.

[0330] Redundant route removal: Automatically remove redundant route segments based on transfer rules to keep the route network simple.

[0331] The present invention utilizes an optimal spanning tree algorithm (such as the Kruskal algorithm or the Prim algorithm) to perform connectivity analysis on the optimized route network, ensure the redundancy and reliability of the network, and generate an optimal spanning tree to minimize the total route length and flight energy consumption.

[0332] Step S42 includes the following sub-steps:

[0333] Step S421: Connectivity analysis

[0334] Connectivity analysis is performed on the optimized route network to ensure that the various parts of the route network can be interconnected and have good redundancy and reliability.

[0335] Connectivity detection algorithm: Apply the depth-first search (DFS) or breadth-first search (BFS) algorithm to detect the connectivity of the route network.

[0336] Step S422: Optimal spanning tree construction

[0337] The Kruskal algorithm or Prim algorithm is used to generate the optimal spanning tree to minimize the total route length and flight energy consumption.

[0338]

[0339] Among them, we is the optimization weight of route segment e, which represents the cost or efficiency index of the route segment.

[0340] The present invention introduces ant colony optimization algorithm and particle swarm optimization algorithm to perform multi-objective optimization on the route network, taking into account factors such as flight density, route intersection risk and energy consumption, and generates an optimized route network that meets logistics efficiency and safety requirements.

[0341] Step S43 includes the following sub-steps:

[0342] Step S431: Application of ant colony optimization algorithm

[0343] The Ant Colony Optimization (ACO) algorithm is used to optimize the structure of the route network and path selection while considering the flight density and route intersection risk.

[0344]

[0345] in, is the updated pheromone concentration from node i to node j, is the pheromone concentration before updating from node i to node j, is the pheromone volatilization rate, is the cumulative sum of pheromones released by all ants on path ij.

[0346] Step S432: Application of particle swarm optimization algorithm

[0347] The particle swarm optimization algorithm (PSO) is used to further optimize the route network and improve the efficiency and accuracy of route planning.

[0348] Particle initialization: Initialize the position and velocity of the particle swarm to represent different route configurations.

[0349] Speed ​​and position update: According to the individual optimal position and the global optimal position, the speed and position of the particle are updated, the optimal solution space is searched, and the global optimality of the route planning is ensured.

[0350]

[0351] in, is the velocity of particle i at iteration k, is the position of particle i, k represents the number of iterations, is the individual optimal position of particle i, g is the global optimal position, w is the inertia weight, is the acceleration constant, Is a random number.

[0352] The present invention uses real-time dynamic path adjustment and conflict control steps, a deep reinforcement learning algorithm and a rule-based conflict detection mechanism to perform real-time path adjustment for single or multiple drones, avoid wind field disturbances, sudden obstacles and temporary control areas, and ensure flight safety.

[0353] Step S5, obtaining the UAV operation status and environmental update data in real time through sensors, communication links and UTM platforms, and adjusting the paths of single or multiple UAVs in real time according to the road traffic flow data, population density data and meteorological information to avoid wind field disturbances, sudden obstacles and temporary control areas.

[0354] In this embodiment, the UAV operation status and environmental update data are obtained in real time through sensors, communication links and UTM (Unmanned Traffic Management) platform; based on the deep reinforcement learning algorithm (DRL) and the rule-based conflict detection mechanism, the path of a single or multiple UAVs is adjusted in real time to avoid wind field disturbances, sudden obstacles and temporary control areas; when potential path conflicts or collision risks are detected, the vertical level or horizontal track of the aircraft is automatically adjusted through local path rerouting and autonomous decision-making mechanisms to ensure flight safety; by integrating multi-source sensors (such as radar, camera, infrared sensor, etc.), the environmental perception ability of the UAV is enhanced, and the detection and recognition ability of dynamic obstacles is improved; collaborative avoidance between multiple UAVs is achieved, and the avoidance actions of the UAVs are coordinated by sharing flight status and path information to ensure safe and orderly flight in high-density airspace.

[0355] Among them, Figure 6 As shown, step S5 includes the following sub-steps:

[0356] Step S51: Deep reinforcement learning model training

[0357] The path optimization model based on deep reinforcement learning (DRL) takes as input real-time traffic data, weather information, and airspace usage. Through training, the model can predict the best flight path in a dynamic environment and optimize the flight path of the drone. The optimization goal of the DRL model can be expressed as:

[0358]

[0359] in, For the status Take action Instant rewards, is a discount factor used to measure the impact of future rewards, is the time step, is the total time step.

[0360] Step S52: Local path replanning

[0361] During flight, if a potential path conflict or collision risk is detected, the system will automatically adjust the aircraft's vertical level or horizontal track through local path replanning and autonomous decision-making mechanisms to ensure flight safety. This includes using sensors and real-time data to detect sudden obstacles on the flight path, and quickly generating an avoidance path based on the local path replanning algorithm to ensure that the drone can avoid dangerous areas in time.

[0362] Step S53: Implementation of autonomous decision-making mechanism

[0363] The drone is integrated with an autonomous decision-making module, which automatically adjusts the flight path or altitude based on conflict detection results and path replanning suggestions. This enables the drone to perform real-time avoidance and path optimization without relying on a central control system. The autonomous decision-making module supports the drone to autonomously adjust the flight path and altitude, ensuring rapid response capabilities in emergency situations.

[0364] Step S54: Environmental perception enhancement

[0365] In order to improve the drone's ability to detect and identify dynamic obstacles, the system integrates multi-source sensors (such as radar, camera, infrared sensor, etc.) to enhance the drone's environmental perception ability. By obtaining real-time information about the surrounding environment, the drone can more accurately identify and classify dynamic obstacles such as pedestrians, vehicles and temporary obstacles, thereby improving the avoidance effect.

[0366] Step S55: Collaborative avoidance mechanism

[0367] In a multi-UAV environment, it is crucial to achieve coordinated avoidance between UAVs. By sharing flight status and path information, the system coordinates the avoidance actions of UAVs to ensure safe and orderly flight in high-density airspace. Using V2V (Vehicle-to-Vehicle) communication technology, UAVs can share flight status and path information in real time, ensuring that each UAV can make independent avoidance decisions based on shared information, avoiding the delays and single-point failure risks of centralized decision-making.

[0368] The present invention uses intelligent semantic annotation technology to dynamically assign risk level, traffic priority and regulatory rules semantic information to grid units to build an intelligent semantic grid. At the same time, airspace regulations, no-fly zones, temporary flight restrictions (TFRs) and recommended flight corridors (corridors) are embedded in the semantic layer of the route network through knowledge graphs and rule engines, enabling drones to dynamically perceive and comply with semantic information when performing tasks.

[0369] Step S6, semantically annotate each flight segment and node in the basic route network skeleton, and embed airspace regulations, regulatory rules, restricted flight zones, temporary control zones and recommended flight corridor information into the semantic layer of the route network through the knowledge graph and rule engine.

[0370] In this embodiment, semantic annotation is performed on each flight segment and node in the route network, including risk level, priority, energy consumption cost, flight speed limit and noise sensitivity information; airspace regulations, no-fly zones, temporary flight restrictions (TFR) and recommended flight corridors are embedded in the semantic layer of the route network through knowledge graphs and rule engines; dynamic perception and compliance of semantic information by drones when performing tasks are achieved; risk level assessment is performed on each route segment and route node, the passage priority of the route segment is set, and the flight energy consumption cost and noise sensitivity are calculated to ensure that the drone chooses an energy-saving and low-noise flight path; a rule conflict detection and resolution mechanism is established to ensure that the drone follows consistent and conflict-free flight rules.

[0371] Among them, Figure 7 As shown, step S6 includes the following sub-steps:

[0372] Step S61: Risk level assessment

[0373] A risk level assessment is conducted on each route segment and route node, taking into account factors including flight density, obstacle density, population density and environmental sensitivity. Based on the assessment results, the route segments are divided into low risk, medium risk and high risk levels.

[0374] Step S62: Priority setting

[0375] The priority of the route segment is set according to the risk level of the route segment and the importance of the flight mission. High-priority routes are used for emergency rescue and high-priority missions, and low-priority routes are used for regular logistics distribution, etc.

[0376] Step S63: Energy consumption cost calculation

[0377] The flight energy cost is calculated for each route segment, taking into account factors such as route length, flight altitude changes and weather conditions. The energy cost is used as an important parameter for route optimization to guide the drone to choose a more energy-efficient flight path.

[0378] Step S64: Noise sensitivity labeling

[0379] According to the noise sensitivity of the areas the route passes through (such as residential areas, hospitals, schools, etc.), the route sections are marked with noise sensitivity, guiding drones to adopt low-noise flight modes in noise-sensitive areas to reduce noise interference to the public.

[0380] Step S65: Rule priority management

[0381] According to different types of airspace control rules, set their priorities and scope of application to ensure that drones follow the most stringent flight regulations.

[0382] Step S66: Semantic rule conflict resolution

[0383] Establish a rule conflict detection and resolution mechanism to ensure that drones follow consistent and conflict-free flight rules. When different semantic rules conflict, automatically identify and apply the preset priority or resolution strategy.

[0384] The present invention evaluates the risk level of each route segment and route node, taking into account factors including flight density, obstacle density, population density and environmental sensitivity, and divides the route segments into low risk, medium risk and high risk levels according to the evaluation results.

[0385] Step S61 includes the following sub-steps:

[0386] Step S611: Risk factor analysis

[0387] Analyze factors such as flight density, environmental complexity score and mission priority score to determine the risk score of each route segment.

[0388] R 总 =w1×R 密 +w2×R 复 +w3×R 优

[0389] Among them, R 总 is the comprehensive risk score, R 密 Score the aircraft density, R 复 is the environmental complexity score, is the task priority score, and w1+w2+w3=1.

[0390] Step S612: Risk level classification

[0391] Based on the risk score, the route segments are divided into low risk, medium risk and high risk levels.

[0392]

[0393] in, and It is the risk level threshold, which is set according to actual needs.

[0394] The present invention calculates the flight energy consumption cost for each route segment, taking into account factors including route length, flight altitude changes and meteorological conditions, and uses the energy consumption cost as an important parameter for route optimization to guide the UAV to select a more energy-efficient flight path.

[0395] According to the content, step S63 includes the following sub-steps:

[0396] Step S631: Calculation of route segment energy consumption

[0397] The energy consumption cost of each route segment is calculated based on the route length, flight altitude changes and weather conditions.

[0398]

[0399] in, For energy consumption, is the route segment distance, is the altitude variation of the route segment, and is the weight coefficient, which is used to balance the impact of distance and altitude changes on energy consumption.

[0400] Step S632: Energy consumption cost application

[0401] The calculated energy consumption cost is incorporated into the route optimization process to guide the UAV to choose a more energy-efficient flight path, thereby improving economy and environmental friendliness.

[0402] The present invention marks the noise sensitivity of the route section according to the noise sensitivity of the area through which the route passes (such as residential areas, hospitals, schools, etc.), guides the drone to adopt a low-noise flight mode in the noise-sensitive area, and reduces noise interference to the public. According to the content, step S64 includes the following sub-steps:

[0403] Step S641: Noise-sensitive area identification

[0404] Identify the noise-sensitive areas that the route passes through and mark the noise sensitivity of the route segment.

[0405]

[0406] Step S642: Low noise flight mode guidance

[0407] Guide drones to adopt low-noise flight modes in noise-sensitive areas to reduce noise disturbance to the public. For example, by adjusting flight speed and altitude to reduce the noise level generated by flight.

[0408] V 低噪音 =k×V 标准

[0409] Among them, V 低噪音 is the low noise flight speed, k<1 is the speed adjustment coefficient, V 标准 This is the flight speed in normal flight mode.

[0410] The present invention sets priorities and applicable scopes according to different types of airspace control rules to ensure that drones follow the most stringent flight regulations.

[0411] Step S65 includes the following sub-steps:

[0412] Step S651: Classification of airspace control rules

[0413] Classify and set priorities based on different types of airspace control rules (such as permanent restricted flight zones, temporary restricted flight zones, route restrictions, etc.).

[0414]

[0415] Step S652: Priority application mechanism

[0416] During the semantic annotation process, high priority rules are applied first to ensure that the drone follows the most stringent flight regulations.

[0417] Highest priority → Medium priority → Low priority

[0418] The present invention establishes a rule conflict detection and resolution mechanism to ensure that the drone follows consistent and conflict-free flight rules. When different semantic rules conflict, the preset priority or resolution strategy is automatically identified and applied.

[0419] According to the content, step S66 includes the following sub-steps:

[0420] Step S661: Conflict detection

[0421] Automatically detect conflicts between different semantic rules and identify the impact of conflicting rules on the flight path.

[0422]

[0423] Among them, Ri and Rj are different semantic rules.

[0424] Step S662: Conflict resolution strategy application

[0425] Apply conflict resolution strategies based on preset priority weights or condition priorities to ensure drones follow consistent and conflict-free flight rules.

[0426] Priority weight adjustment: adjust the application order of rules according to the preset priority weight.

[0427]

[0428] Conditional priority application: Under certain conditions, some rules have higher priority, ensuring that key rules are executed first.

[0429]

[0430] The present invention inputs the optimized routes, dynamically annotated semantic information and real-time monitoring data into the urban low-altitude airspace management and decision-making platform through decision-making and scheduling execution steps, performs flight mission classification, flight permission management and dynamic adjustment of mission priority, and ensures that UAVs perform flight missions on legal and safe routes. At the same time, through a multi-UAV collaborative scheduling algorithm, the flight sequence and route allocation of UAVs are optimized to avoid flight conflicts and airspace congestion.

[0431] Step S7, input the basic route network skeleton, the semantic annotation information and the real-time monitoring data into the urban low-altitude airspace management and decision-making platform, and issue flight permits and dispatch instructions according to the mission type, risk level, the vertical and horizontal airspace divisions of the low-altitude airspace and the priority rules.

[0432] In this embodiment, the optimized routes, dynamically annotated semantic information and real-time monitoring data are input into the urban low-altitude airspace management and decision-making platform; flight permits are issued and dispatch instructions are issued according to the mission type (such as delivery, inspection, rescue), risk level and traffic priority; multi-party collaborative communication (V2V, V2I) technology is used to ensure the orderly operation of multiple drones in complex urban scenarios; flight permits are automatically generated according to the mission type, route priority and real-time airspace status of the drone, and flight instructions are issued through the drone communication system to ensure that the drone performs flight missions on legal and safe routes; the priority of drone tasks is dynamically adjusted to ensure that emergency tasks can be executed first; the flight data of the drone is recorded in real time and fed back to the central management platform for flight behavior analysis and system optimization; flight missions are classified, and priority-based scheduling algorithms and resource allocation algorithms are used to optimize task allocation and route selection to ensure that high-priority tasks obtain priority resources; scheduling strategies and route allocation are dynamically adjusted to improve system adaptability and response speed to ensure response to emergencies and dynamically changing flight needs.

[0433] Among them, Figure 8 As shown, step S7 includes the following sub-steps:

[0434] Step S71: Data input

[0435] The optimized routes, dynamically annotated semantic information and real-time monitoring data are input into the urban low-altitude airspace management and decision-making platform to provide data support for flight mission decision-making and scheduling.

[0436] Input data = {optimized route, semantic information, monitoring data}

[0437] Step S72: Flight mission classification

[0438] Flight missions are classified according to their nature and urgency, such as logistics distribution, inspection and monitoring, emergency rescue, etc. Different types of missions correspond to different scheduling strategies and route allocation rules.

[0439] Step S73: Flight permission management

[0440] Based on the drone's mission type, route priority and real-time airspace conditions, flight permissions are automatically generated, and flight instructions are issued through the drone communication system to ensure that the drone performs its flight mission on a legal and safe route.

[0441] Step S74: Dynamic adjustment of task priority

[0442] Based on real-time mission requirements and airspace resource conditions, task priorities are dynamically allocated. When high-priority tasks occur, the scheduling order and route resource allocation of low-priority tasks are automatically adjusted to ensure timely completion of critical tasks.

[0443] Step S75: Coordinated dispatch of multiple drones

[0444] V2V (Vehicle-to-Vehicle) and V2I (Vehicle-to-Infrastructure) communication technologies are used to achieve collaborative scheduling among multiple drones. Through collaborative scheduling algorithms, the flight sequence and route allocation of drones are optimized to avoid flight conflicts and airspace congestion.

[0445] Step S76: Scheduling algorithm application

[0446] Priority-based scheduling and resource allocation algorithms are used to optimize the task allocation and route selection of drones, ensuring that high-priority tasks receive priority route resources and scheduling support.

[0447] Step S77: Flight data recording and feedback

[0448] The flight data of the drone is recorded in real time, including flight path, flight altitude, flight speed and avoidance action, and the flight data is fed back to the central management platform for flight behavior analysis and system optimization.

[0449] Step S78: Dynamic adjustment of flight permission

[0450] The validity period and scope of authority of the flight permit are dynamically adjusted based on the real-time airspace usage and the flight status of the UAV. During the flight, the flight permit is extended, shortened or revoked in a timely manner based on the real-time status of the UAV and environmental changes to ensure the flexibility and safety of the flight mission.

[0451] Step S79: Real-time scheduling optimization

[0452] According to real-time flight data and airspace usage, the scheduling strategy and route allocation are dynamically adjusted to improve the adaptability and response speed of the system. Through the adaptive scheduling mechanism, emergencies and dynamically changing flight needs can be responded to.

[0453] The present invention classifies flight missions according to their nature and urgency, such as logistics distribution, inspection and monitoring, emergency rescue, etc. Different types of missions correspond to different scheduling strategies and route allocation rules.

[0454] Step S72 includes the following sub-steps:

[0455] Step S721: Task type identification

[0456] Identify the type and urgency of the flight mission based on the mission description and requirements.

[0457] Task category = f(task description, task requirements)

[0458] Step S722: Scheduling strategy correspondence

[0459] According to different types of tasks, corresponding scheduling strategies and route allocation rules are applied to ensure that various tasks can obtain appropriate route resources according to their needs.

[0460]

[0461] The present invention automatically generates a flight permit according to the mission type, route priority and real-time airspace status of the UAV, and issues a flight instruction through the UAV communication system to ensure that the UAV performs the flight mission on a legal and safe route.

[0462] Step S73 includes the following sub-steps:

[0463] Step S731: Automatically generate flight permission

[0464] Automatically generate corresponding flight permissions based on mission type and route priority.

[0465] Flight permission = g(mission type, route priority, airspace status)

[0466] Among them, g is the flight permission generation function, which generates flight permission based on mission type, route priority and airspace conditions.

[0467] Step S732: Flight instructions are issued

[0468] The generated flight permission and flight instructions are sent to the drone through the drone communication system to ensure that the drone performs the flight mission in accordance with the permission.

[0469] Flight instruction = h (flight permission)

[0470] Among them, h is the flight instruction generation function, which generates specific flight instructions according to the flight permission.

[0471] In an optional manner, in step S79, the dynamically adjusting the scheduling strategy and route allocation further includes:

[0472] Step S791, real-time monitoring of drone traffic and route usage in the city's low-altitude airspace, and analysis of congestion and potential conflict points on the routes;

[0473] Step S792, dynamically adjusting the capacity and traffic rules of the route according to the real-time monitoring data to alleviate route congestion and reduce flight conflicts;

[0474] Step S793, re-evaluating the priorities of all tasks when an emergency task or incident occurs, and reallocating route resources according to the new priorities;

[0475] Step S794, based on the real-time position and flight speed of the UAV, predict the route usage in the future period of time, and adjust the route resource scheduling and flight permission in advance.

[0476] In this embodiment, when an emergency task or incident occurs, the task priority is quickly re-evaluated and route resources are adjusted to ensure that key tasks are handled with priority. By predicting route usage in the future, route resource scheduling and flight permissions are adjusted in advance to reduce flight delays.

[0477] Compared with the prior art, the present invention has the following beneficial effects:

[0478] 1. Refined airspace management: Through dynamic grid division technology, the grid size is adjusted in real time according to the density of aircraft and the complexity of the environment to generate a multi-scale grid model. The grid is refined in areas with dense aircraft to improve navigation accuracy, and the grid is expanded in areas with simpler environments to optimize the computing burden, reduce the risk of conflict, improve the efficiency of flight mission planning, and lay the foundation for accurate data for subsequent management.

[0479] 2. Improve flight safety: Multi-scale grid division effectively reduces the risk of conflict between aircraft and between aircraft and the environment. Grid boundary smoothing ensures coherence and consistency, avoiding safety hazards caused by inaccurate grid division. The risk score in intelligent airspace stratification accurately reflects the airspace risk, and the high-risk areas are stratified accordingly, and reasonable buffer layers are set. With dynamic adjustments, the probability of accidents such as aircraft collisions is significantly reduced, ensuring the safety and reliability of all aspects of flight.

[0480] 3. Enhanced adaptability and flexibility: Data collection integrates multi-source sensor data, with wide coverage and high accuracy, and can adapt to complex airspace environments. Data preprocessing ensures the efficiency and reliability of input data; dynamic grid size calculation flexibly adjusts the grid granularity based on data to adapt to different airspace scenarios, such as dense activities or low-complexity areas. Risk score calculation can dynamically adjust weights and update scores as needed, hierarchically adjust each airspace level to optimize as needed, design and optimize the buffer layer as needed, and semantic annotation is updated as the airspace changes in real time, fully meeting the dynamic changes in the airspace.

[0481] 4. Optimize energy consumption and economy: Through energy cost calculation and energy optimization strategy, guide drones to choose more energy-saving flight paths, reduce energy consumption, extend flight time, and improve economy and environmental friendliness.

[0482] 5. Support multi-UAV collaborative operation: Through multi-UAV collaborative optimization and collaborative avoidance mechanism, collaborative scheduling and avoidance between multiple UAVs can be achieved to avoid route conflicts and airspace congestion, and improve the operating efficiency of the entire route network and the collaborative working ability of UAVs.

[0483] According to the solution provided by the present invention, the grid granularity is adjusted in real time according to the density of aircraft and the complexity of the environment through dynamic grid division technology to generate a multi-scale grid model. The grid is refined in areas with dense aircraft to improve navigation accuracy, and the grid is expanded in areas with simpler environments to optimize the computing burden, reduce the risk of conflict, and improve the efficiency of flight mission planning. Through energy cost calculation and energy consumption optimization strategies, the drone is guided to choose a more energy-saving flight path, reduce energy consumption, and extend flight endurance. Through multi-drone collaborative optimization and collaborative avoidance mechanisms, collaborative scheduling and avoidance between multiple drones are achieved, route conflicts and airspace congestion are avoided, and the operating efficiency of the entire route network and the collaborative working ability of drones are improved.

[0484] Those skilled in the art will appreciate that the modules in the devices in the embodiments may be adaptively changed and arranged in one or more devices different from the embodiments. The modules or units or components in the embodiments may be combined into one module or unit or component, and further may be divided into multiple submodules or subunits or subcomponents. All features disclosed in this specification (including the accompanying claims, abstracts and drawings) and all processes or units of any method or device disclosed in this manner may be combined in any combination, except that at least some of such features and / or processes or units are mutually exclusive. Unless otherwise expressly stated, each feature disclosed in this specification (including the accompanying claims, abstracts and drawings) may be replaced by an alternative feature that provides the same, equivalent or similar purpose. In addition, those skilled in the art will appreciate that, although some embodiments herein include certain features included in other embodiments but not other features, the combination of features of different embodiments is meant to be within the scope of the present invention and to form different embodiments. For example, in the following claims, any one of the claimed embodiments may be used in any combination. The present invention may be implemented by means of hardware including several different elements and by means of a suitably programmed computer. In a unit claim that lists several means, several of these means may be embodied by the same hardware item. The steps in the above embodiments should not be understood as limiting the execution order unless otherwise specified.

Claims

1. A method for UAV route planning and dynamic control in urban low-altitude airspace, characterized in that: include: Step S1, constructing a multi-dimensional urban environment model based on the location of drone take-off and landing sites in the urban area, surrounding geographic information, three-dimensional building data, road traffic flow data, population density data and meteorological information, and storing the multi-dimensional urban environment model in the urban low-altitude airspace management and decision-making platform; Step S2, according to the multi-dimensional urban environment model, urban low-altitude airspace regulations and operational requirements, the low-altitude airspace is divided into a basic flight layer, a buffer collision avoidance layer and an emergency guarantee layer in the vertical direction; the Voronoi diagram is used to divide the low-altitude airspace in the horizontal direction, and different areas are set to different route levels and restriction rules; the basic direction and flight altitude range of the reference route are determined as the starting point of the path planning, and the floating range and priority rules are set to allow the route to float within the specified altitude range, and different priorities are assigned according to the urgency and importance of the flight mission; Step S3, using the take-off and landing address locations of the UAV as network nodes, generating a basic route network skeleton through a Voronoi diagram, Delaunay triangulation or a shortest path search method; Step S4, optimizing the structure of the basic route network skeleton by using a cellular automaton and a spanning tree algorithm to generate a route network that meets logistics efficiency and safety requirements; Step S5, obtaining the UAV operation status and environmental update data in real time through sensors, communication links and UTM platforms, and adjusting the paths of single or multiple UAVs in real time according to the road traffic flow data, population density data and meteorological information, so as to avoid wind field disturbances, sudden obstacles and temporary control areas; Step S6, semantically annotating each flight segment and node in the basic route network skeleton, and embedding airspace regulations, regulatory rules, restricted flight zones, temporary control zones and recommended flight corridor information into the semantic layer of the route network through a knowledge graph and a rule engine; Step S7, input the basic route network skeleton, the semantic annotation information and the real-time monitoring data into the urban low-altitude airspace management and decision-making platform, and issue flight permits and dispatch instructions according to the mission type, risk level, the vertical and horizontal airspace divisions of the low-altitude airspace and the priority rules.

2. The method for UAV route planning and dynamic control in urban low-altitude airspace according to claim 1 is characterized in that: The multi-dimensional urban environment model includes: Two-dimensional ground grid data, used to record ground obstacles, road distribution and population gathering areas; 3D building models and airspace obstacle distribution data for identifying three-dimensional obstacles, restricted and no-fly zones; Dynamic environmental data, including real-time meteorological information, electromagnetic interference source information, wireless communication base stations and 5G cellular network node locations, where the real-time meteorological information includes wind speed, wind direction, temperature and visibility.

3. The method for UAV route planning and dynamic control in urban low-altitude airspace according to claim 1 is characterized in that: In step S1, the building of a multi-dimensional urban environment model further includes: Step S11, using laser radar, high-definition photogrammetry and high-precision map data to build a three-dimensional model of the city; using mobile communication signal strength data to obtain 5G cell distribution information; using an online meteorological data interface to obtain wind field parameters, visibility, air pressure and precipitation in real time; Step S12, cleaning, denoising and projection conversion of the acquired geographic data, building models and meteorological data; Step S13, integrating the 5G cellular network node locations and communication signal strength data within the city, and incorporating the communication network information into the environmental modeling to support real-time data transmission and coordinated flight of drones; Step S14, real-time monitoring of electromagnetic interference sources in the urban area, and evaluating the impact on the communication and navigation systems of the drone; wherein high electromagnetic interference areas are marked in the multi-dimensional urban environment model; Step S15, monitoring the changes of urban terrain and buildings in real time, and updating the multi-dimensional urban environment model; Step S16, integrating the meteorological data acquired in real time into the multi-dimensional urban environment model.

4. The method for UAV route planning and dynamic control in urban low-altitude airspace according to claim 1 is characterized in that: In step S2, the stratification of the low-altitude airspace and the division of the route level further include: Step S21, determining the upper and lower flight altitudes of the low airspace according to regional regulations and technical standards, and dividing the airspace into a basic flight layer, a safety buffer layer, and an emergency guarantee layer; Step S22, assigning different priorities and traffic rules to air routes in each level, and embedding the temporary restricted flight zone or preset rules issued by the supervisory authority in real time into the air routes of the corresponding level; Step S23, assigning a priority score to the mission according to the urgency and importance of the flight mission; Step S24, dynamically adjusting the flight permissions of each airspace level according to real-time mission requirements and airspace usage conditions; Step S25, dynamically adjusting the functional allocation of each airspace layer according to different time periods and urban activities; in which more airspace resources are allocated to the logistics distribution layer during peak hours; Step S26, dynamically adjust the resource allocation of each airspace level according to the importance and urgency of the task, to ensure that high-priority tasks can obtain airspace resource support when needed.

5. The method for UAV route planning and dynamic control in urban low-altitude airspace according to claim 1 is characterized in that: In step S3, generating a basic route network skeleton further includes: Step S31, based on the determined drone take-off and landing sites, generate a preliminary route skeleton of the urban airspace through Delaunay triangulation; use the Voronoi diagram to divide the urban area, determine the neighbor relationship of each route node, and form a basic route network skeleton; Step S32, combining the three-dimensional building model and airspace obstacle distribution data in the generated basic route network skeleton, and optimizing the route for obstacle guidance; wherein, static obstacles such as buildings and high-altitude billboards and temporary restricted flight zones are introduced into the cost function of the path search, and the generation of the route is adjusted through a weighted mechanism to ensure that the drone can safely avoid obstacles; Step S33, classifying the basic route network skeleton into trunk routes, secondary routes and emergency routes according to the importance and frequency of use of the routes, ensuring that the trunk routes connect the main take-off and landing sites, the secondary routes cover the medium-density areas, and the emergency routes are used for rapid response to emergency tasks and emergencies; Step S34, planning the capacity of each route segment according to the flight density and mission type of the drones to ensure that the route carries the expected drone traffic; setting the maximum number of parallel flights of the route segment to prevent flight delays and conflicts caused by route overload; Step S35, reserving emergency routes in the route network for rapid response to emergency tasks and emergencies; Step S36, designing redundant routes for key route segments to ensure rapid switching to redundant routes when a failure or congestion occurs in the main route segment.

6. The method for UAV route planning and dynamic control in urban low-altitude airspace according to claim 1 is characterized in that: In step S4, the structural optimization of the basic route network skeleton further includes: Step S41, iteratively optimizing the initially generated basic route network skeleton to reduce redundancy and unnecessary intersections between routes; Step S42, using an optimal spanning tree algorithm to perform connectivity analysis on the optimized basic route network skeleton to minimize the total route length and flight energy consumption; Step S43, performing multi-objective optimization on the basic route network skeleton by using a particle swarm optimization algorithm to find the best balance point among multiple optimization objectives; wherein the optimization objectives include flight density, route intersection risk and energy consumption; Step S44, real-time monitoring of the drone traffic of each route segment, and dynamically adjusting the route usage rights and priorities based on the traffic monitoring data; wherein, during the peak traffic period, route allocation is optimized to avoid route congestion and flight delays; Step S45, during the route optimization process, the route segments with energy consumption lower than a preset threshold are preferentially selected to reduce the energy consumption of the UAV and extend the flight endurance; Step S46, coordinate the flight time and route selection of the UAVs by sharing flight path information to avoid route conflicts and congestion, so as to improve the operating efficiency of the entire route network and the collaborative working ability of the UAVs.

7. The method for UAV route planning and dynamic control in urban low-altitude airspace according to claim 1 is characterized in that: In step S5, the real-time path adjustment and the path conflict further include: Step S51, when a sudden obstacle or high-density area is detected, an avoidance path is generated using a local path replanning algorithm; the global path and the local path are combined to ensure the continuity and safety of the flight; Step S52, in the autonomous decision-making module integrated on the UAV side, the flight path or flight altitude is automatically adjusted according to the conflict detection result and path replanning, so that the UAV can perform real-time avoidance and path optimization without relying on the central control system; Step S53, coordinate the avoidance actions of the UAVs by sharing the flight status and path information to ensure the safe and orderly flight of multiple UAVs in high-density airspace.

8. The method for UAV route planning and dynamic control in urban low-altitude airspace according to claim 1 is characterized in that: In step S6, semantic annotation and embedding further include: Step S61, performing risk level assessment on each route segment and route node according to flight density, obstacle density, population density and environmental sensitivity; and classifying the route segment into low risk, medium risk and high risk levels according to the level assessment results; Step S62, setting the passage priority of the route segment according to the risk level of the route segment and the importance of the flight mission; wherein the high-priority route is used for emergency rescue and high-priority missions, and the low-priority route is used for conventional logistics distribution; Step S63, calculating the flight energy cost for each route segment according to the route length, flight altitude change and weather conditions; using the energy cost as a parameter for route optimization to guide the UAV to select a more energy-efficient flight path; Step S64, marking the noise sensitivity of the route segment according to the noise sensitivity of the area through which the route passes; wherein the UAV is guided to adopt a low-noise flight mode in the noise-sensitive area to reduce noise interference to the public; Step S65, setting priorities and applicable scopes according to different types of airspace control rules; in the semantic annotation process, high priority rules are applied first to ensure that the drone follows the most stringent flight regulations; Step S66, when different semantic rules conflict, the preset priority or resolution strategy is automatically identified and applied to ensure that the drone follows consistent and conflict-free flight rules.

9. The method for UAV route planning and dynamic control in urban low-altitude airspace according to claim 1 is characterized in that: In step S7, the issuance of flight permission and dispatch instruction further includes: Step S71, automatically generating a flight permit based on the drone's mission type, route priority, and real-time airspace conditions, and issuing a flight instruction through the drone's communication system to ensure that the drone performs its flight mission on a legal and safe route; Step S72, classifying the flight missions according to their nature and urgency, wherein different types of missions correspond to different scheduling strategies and route allocation rules; Step S73, dynamically adjusting the priority of the UAV mission according to the real-time mission requirements and airspace usage; wherein, when an emergency mission occurs, route resources are reallocated to ensure that the emergency mission can be executed first; Step S74, using V2V and V2 communication methods to achieve coordinated scheduling among multiple drones to avoid flight conflicts and airspace congestion; Step S75, based on the priority scheduling algorithm and resource allocation algorithm, optimize the task allocation and route selection of the UAV to ensure that high-priority tasks obtain priority route resources and scheduling support; Step S76, recording the flight data of the UAV in real time, wherein the flight data includes the flight path, flight altitude, flight speed and avoidance action; Step S77, dynamically adjusting the validity period and authority scope of the flight permit according to the real-time airspace usage and the flight status of the UAV; wherein, during the flight, the flight permit is extended, shortened or revoked according to the real-time status of the UAV and environmental changes; Step S78, dynamically assigning task priorities based on real-time task requirements and airspace resource conditions; wherein, when high-priority tasks appear, the scheduling order and route resource allocation of low-priority tasks are automatically adjusted; Step S79, dynamically adjust the scheduling strategy and route allocation according to real-time flight data and airspace usage conditions.

10. The method for UAV route planning and dynamic control in urban low-altitude airspace according to claim 9 is characterized in that: In step S79, the dynamic adjustment of the scheduling strategy and route allocation further includes: Step S791, real-time monitoring of drone traffic and route usage in the city's low-altitude airspace, and analysis of congestion and potential conflict points on the routes; Step S792, dynamically adjusting the capacity and traffic rules of the route according to the real-time monitoring data to alleviate route congestion and reduce flight conflicts; Step S793, re-evaluating the priorities of all tasks when an emergency task or incident occurs, and reallocating route resources according to the new priorities; Step S794, based on the real-time position and flight speed of the UAV, predict the route usage in the future period of time, and adjust the route resource scheduling and flight permission in advance.

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