Method for constructing multi-level low-altitude air route network in complex urban environment

Abstract

The application proposes a multi-level low-altitude air route network construction method in complex urban environment, relates to the technical field of low-altitude air route construction, and comprises the following steps: establishing a low-altitude flight rule library; discretizing the urban space in three dimensions to divide the airspace into three-dimensional grids; designing a risk assessment function R; dividing multiple height layers and assigning height layers to different types of aircraft; constructing a multi-level air route network; constructing a three-dimensional air route model of the airspace; based on the three-dimensional air route model of the airspace, using a hybrid iterative algorithm to optimize the air route planning of each height layer; using a B-spline curve to smooth the discrete path points generated by the hybrid iterative algorithm, verifying the flight performance of the smoothed path, and obtaining a continuous flyable path; and generating a low-altitude air route network according to the continuous flyable path. The application can solve the deficiencies of the prior art in handling complex urban environment, multi-source data integration, multi-level airspace division, and path optimization, thereby realizing safe, efficient, and flexible low-altitude flight management.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of low-altitude air route construction, and in particular to a multi-level low-altitude air route network construction method in a complex urban environment. BACKGROUND

[0002] With the rapid development of unmanned aerial vehicle technology, low-altitude aircraft are increasingly widely used in urban environments. However, the complex urban environment poses many challenges to low-altitude flight, including high-rise buildings, dense population, complex weather conditions, and communication signal interference. In order to ensure the safety and efficiency of low-altitude flight, establishing a perfect low-altitude air route network has become a hot research topic. Currently, some researchers have proposed a public air route network planning method based on obstacle Voronoi diagram and A* algorithm, which can solve the path planning problem in a complex environment to some extent.

[0003] However, with the popularization of 5G technology and the complication of low-altitude public air routes, ensuring coverage has become a new challenge. From the perspective of time and space, the existing air route network planning method has shortcomings in dealing with large-scale 5G base stations and complex low-altitude public air routes. In addition, the complexity of the urban surface environment also brings difficulties to air route network planning. Due to the lack of geographic information and low-altitude air route support, the current unmanned aerial vehicle traffic management system (UTM) faces great challenges in dealing with complex urban environments.

[0004] In order to solve these problems, some researchers have proposed a method of iteratively constructing urban low-altitude unmanned aerial vehicle air route networks. This method attempts to optimize the air route network through multiple iterations to adapt to complex urban environments. However, this method still has some limitations, such as the need to improve in dealing with dynamic changes in the environment and the needs of multiple types of aircraft.

[0005] In summary, the existing technology still has technical defects in dealing with low-altitude air route network planning in complex urban environments, mainly in the difficulty of ensuring comprehensive coverage, the lack of full consideration of complex surface environments, and the shortcomings in dealing with the needs of multiple types of aircraft and dynamic environmental changes, which limit the practical application and effect of low-altitude air route networks in complex urban environments. SUMMARY

[0006] Therefore, the present application proposes a multi-level low-altitude air route network construction method in a complex urban environment to solve the shortcomings of existing technology in dealing with complex urban environments, multi-source data integration, multi-level airspace division, and path optimization, thereby realizing safe, efficient, and flexible low-altitude flight management.

[0007] The technical solution of the present application is as follows: The present application provides a multi-level low-altitude air route network construction method in a complex urban environment, comprising:

[0008] S1 establishing a low-altitude flight rule library;

[0009] S2 based on the adaptive octree structure, discretizing the urban space in three dimensions, dividing the airspace into uneven three-dimensional grids;

[0010] S3 integrating geographic information system data, real-time weather data, population density data, and communication signal strength to form multi-source data; based on the multi-source data and the low-altitude flight rule library, designing a risk assessment function R;

[0011] S4 according to the low-altitude flight rule library, dividing the airspace from the ground to 1000 meters into multiple height layers, and assigning height layers to different types of aircraft;

[0012] S5 constructing a multi-level air route network, including a first-level air route network, a second-level air route network, a third-level air route network, a fourth-level air route network, a fifth-level air route network, and a sixth-level air route network;

[0013] S6 under the three-dimensional grid environment, based on the sixth-level air route network, constructing an airspace three-dimensional air route model, including an air route network model, a traffic node model, a take-off and landing point channel model, and a terminal air route model;

[0014] S7 based on the airspace three-dimensional air route model, using a hybrid iterative algorithm to optimize the air route planning of each height layer;

[0015] S8 using B-spline curves to smooth the discrete path points generated by the hybrid iterative algorithm, and verifying the flight performance of the smoothed path to obtain a continuous flyable path;

[0016] S9 generating a low-altitude air route network according to the continuous flyable path, including horizontal segments, vertical segments, intersection interchanges, and terminal air routes.

[0017] Based on the above scheme, preferably, the low-altitude flight rule library includes:

[0018] Flight height limit: specifies the minimum height for flying in a particular area;

[0019] Route specification: specifies the route of the aircraft in a particular area;

[0020] Flight speed limit: specifies the maximum flight speed of the aircraft in a particular area;

[0021] Air traffic control: establishing an air traffic control system to regulate the traffic order of low-altitude flight;

[0022] Emergency handling: developing a handling procedure for emergency situations;

[0023] Violation punishment: Punish the aircrafts that violate traffic rules.

[0024] On the basis of the above scheme, preferably, in step S4, the height layer division includes:

[0025] 0-20m, near-ground airspace; 20-50m, consumer aircraft isolation airspace; 50-120m, consumer aircraft suitable flight airspace; 120-150m, light, small aircraft isolation airspace; 150-200m, light, small aircraft suitable flight airspace; 200-230m, medium-sized aircraft isolation airspace; 230-300m, medium-sized aircraft suitable flight airspace; 300-400m, large aircraft isolation airspace; 400-600m, large aircraft suitable flight airspace; 600-700m, manned aircraft super-high-speed isolation airspace; 700-1000m, manned aircraft super-high-speed suitable flight airspace.

[0026] On the basis of the above scheme, preferably, in step S5, the first-level air route network is an initial air route network generated based on the ground road network; the second-level air route network is an air route network that iteratively constrains positive geographical elements based on the first-level air route network; the third-level air route network is an air route network that iteratively constrains negative geographical elements based on the second-level air route network; the fourth-level air route network is a dynamic air route network that iteratively constrains aircraft traffic demand based on the third-level air route network; the fifth-level air route network is an air route network that iteratively simulates flight testing and corrects the fourth-level air route network; and the sixth-level air route network is an air route network that iteratively tests actual flight based on the fifth-level air route network.

[0027] On the basis of the above scheme, preferably, the air route network model includes:

[0028] An elliptical cylindrical fixed layered air route pipeline is constructed to form a main air route network, including flight lane width, height and buffer zone;

[0029] The traffic node model includes:

[0030] The air route network is abstracted as a collection of traffic nodes and flight air routes, and the traffic nodes are constructed in a ring-shaped spiral interchange form, with multiple layers of rotary flight ramps set in the intersection range, and the ramps are provided with the same layer variable speed lanes outside;

[0031] The take-off and landing point lane model includes:

[0032] A conical spatial structure is used to connect the ground take-off and landing points and the air route network;

[0033] The end air route model includes:

[0034] The main air route network is connected with the ground take-off and landing facilities, emergency landing strips and related support facilities;

[0035] The end route model is connected with the main route network through a ring spiral interchange, and the interchange is a right-turn only intersection.

[0036] On the basis of the above scheme, preferably, the step S7 comprises:

[0037] S71, calculating a priority value according to the mission information of the aircraft, assigning a priority to the aircraft based on the priority value, and dividing the aircraft into two categories of high priority and low priority according to the priority;

[0038] S72, using a first optimization algorithm to plan a route for the high-priority aircraft;

[0039] S73, using a second optimization algorithm to plan a route for the low-priority aircraft;

[0040] In the route planning process, the constraints of the three-dimensional airspace route model are considered, including:

[0041] The main flight path is searched in the route network model, and a fixed layered pipe structure of an elliptical cylinder is followed;

[0042] The traffic node model is used to realize the conversion between different height layers and routes, and a ring spiral interchange structure is adopted;

[0043] The take-off and landing point channel model is used to plan the path of the take-off and landing stage, and a conical space structure is used;

[0044] The end route model is used to complete the precise navigation of the task area, and connects the main route network with the specific task site.

[0045] On the basis of the above scheme, preferably, the step S72 comprises:

[0046] S721, sorting the high-priority aircraft according to the priority from high to low;

[0047] S722, planning a path for each high-priority aircraft in turn according to the priority order, including:

[0048] Using a global path planning algorithm to find the fastest path from the starting point to the ending point in the three-dimensional airspace route model, while considering the flight time and risk evaluation function R;

[0049] Considering the trajectory and time scheduling of the planned high-priority aircraft, evaluating the potential conflict between the path of the current high-priority aircraft and the planned high-priority aircraft;

[0050] If there is a conflict risk, adjusting the flight plan of the current high-priority aircraft, including time adjustment, height layer adjustment and path re-planning;

[0051] The optimization path with the shortest expected arrival time is selected under the premise of meeting all constraint conditions;

[0052] S723 record the planned path and schedule of the current high-priority aircraft, and update the trajectory information of the high-priority aircraft;

[0053] S724 repeat steps S722-723 until the path planning of all high-priority aircrafts is completed.

[0054] On the basis of the above scheme, preferably, the path optimization objective function of the first optimization algorithm is as follows:

[0055]

[0056]

[0057] In the formula, is the total path cost, is the energy consumption function of the high-priority aircraft, is the risk weight factor, and are the start and end times, is the spatial position of the aircraft at time t, represents the comprehensive risk value at spatial position and time t, is the obstacle risk function, is the population density risk function, is the weather risk function, is the communication risk function, is the dynamically adjusted weight coefficient.

[0058] On the basis of the above scheme, preferably, step S73 comprises:

[0059] S731 according to the task requirements, starting point and ending point of all medium and low-priority aircrafts, a multi-objective path planning model is established, and the optimization objectives include flight distance, energy consumption and air route congestion degree;

[0060] S732 considering the constraint conditions of the three-dimensional air route model and the path and time schedule of the planned high-priority aircrafts, collision avoidance constraints and airspace accessibility constraints are established;

[0061] S733 path iterative optimization is performed on the medium and low-priority aircrafts, including:

[0062] The flight path is represented as a chromosome coding based on the sequence of air route nodes; an initial population is generated based on the shortest path algorithm;

[0063] ​​A multi-objective optimization function is used as the fitness function, and the flight distance, energy consumption and air route congestion degree are comprehensively considered;

[0064] The selection is performed by using an elite reservation strategy, a partial mapping crossover method and adaptive mutation probability are used for optimization;

[0065] S734 After the iteration reaches the preset condition, the optimal path sequence is output, and the path planning of the medium-low priority aircraft is completed.

[0066] On the basis of the above scheme, preferably, the multi-objective optimization function in the second optimization algorithm is as follows:

[0067] ,

[0068] ,

[0069] ,

[0070] ,

[0071] In the formula, is a comprehensive cost function, is a flight distance, represents a spatial distance between nodes and , is energy consumption, is the energy consumed by the aircraft from node to , is an air route congestion degree, is an air route congestion degree evaluation function of node , N is the number of nodes, is a weight coefficient;

[0072] The calculation formula of the air route congestion degree evaluation function is as follows:

[0073] ,

[0074] In the formula, is the number of aircraft passing through node in a unit time, is the maximum passing capacity of node , is the average size of the aircraft at node , is the maximum value of the aircraft size, is the cumulative priority penalty at node , is the maximum value of the priority penalty.

[0075] The present application has the following beneficial effects over the prior art:

[0076] (1) By comprehensively considering spatial discretization, multi-source data integration, multi-level spatial division, and path optimization, a safe, efficient, and flexible low-altitude flight management is achieved. This method effectively addresses the shortcomings of existing technologies in handling complex urban environments, ensuring comprehensive coverage, adapting to environmental diversity and dynamics, and significantly improves the practical application effect of low-altitude air route networks in complex urban environments;

[0077] (2) By integrating geographic information system data, real-time weather data, population density data, and communication signal strength, and combining a low-altitude flight rule library to design a risk assessment function, this comprehensive approach improves the accuracy and comprehensiveness of risk assessment;

[0078] (3) A priority-based hybrid iterative algorithm is proposed to plan air routes for high-priority and low-priority aircraft. This differentiated approach effectively balances the needs of different types of aircraft, improves the utilization efficiency of the overall air route network, and ensures the timely completion of high-priority tasks;

[0079] (4) The first optimization algorithm considers energy consumption and comprehensive risk values to achieve optimal path planning for high-priority aircraft. This method not only ensures the timely completion of high-priority tasks, but also minimizes energy consumption while ensuring flight safety, improving the overall efficiency of high-priority flight tasks;

[0080] (5) The dynamic adjustment weight coefficient mechanism introduced in the first optimization algorithm allows the system to flexibly adjust the importance of obstacle risk, population density risk, weather risk, and communication risk according to real-time conditions. This adaptive mechanism significantly improves the flexibility and adaptability of route planning, better responding to complex and changing urban flight environments;

[0081] (6) The second optimization algorithm uses a multi-objective optimization function to consider flight distance, energy consumption, and route congestion, achieving group optimization for low-priority aircraft. This method not only balances the performance requirements of individual aircraft, but also considers load balancing of the overall air route network;

[0082] (7) The congestion penalty mechanism and route congestion evaluation function introduced in the second optimization algorithm allow the system to dynamically adjust flight paths to avoid congested areas. This mechanism effectively reduces route congestion, improves the overall traffic efficiency of low-priority aircraft, and also reserves more air route resources for high-priority aircraft. BRIEF DESCRIPTION OF DRAWINGS

[0083] In order to make the technical solutions in the embodiments of the present application or the prior art clearer, the accompanying drawings needed in the embodiments or the prior art description will be briefly introduced. Obviously, the accompanying drawings in the following description only aim to some embodiments of the present application, and all other embodiments obtained by those of ordinary skill in the art without any creative work based on these drawings also belong to the protection scope of the present application.

[0084] Figure 1 Flow chart of the method of the present application

[0085] Figure 2 Traffic prediction four-stage schematic diagram of the present application

[0086] Figure 3 Traffic zone division schematic diagram of the present application

[0087] Figure 4 Traffic distribution schematic diagram of the present application

[0088] Figure 5 Route network model schematic diagram of the present application

[0089] Figure 6 Traffic node coding schematic diagram of the present application

[0090] Figure 7 Traffic node model passing schematic diagram of the present application

[0091] Figure 8 Take-off and landing point channel model schematic diagram of the present application

[0092] Figure 9 End route model passing schematic diagram of the present application

[0093] Figure 10 Avoidance strategy schematic diagram of the present application DETAILED DESCRIPTION

[0094] The technical solutions in the embodiments of the present application will be described clearly and completely below in conjunction with the embodiments of the present application. Obviously, the described embodiments are only some of the embodiments of the present application, but not all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those of ordinary skill in the art without any creative work belong to the protection scope of the present application.

[0095] As shown in Figure 1 The present application provides a multi-level low-altitude route network construction method in a complex urban environment, which comprises:

[0096] S1: Establishing a low-altitude flight rule library

[0097] S2, discretize the urban space in three dimensions based on an adaptive octree structure, divide the airspace into non-uniform three-dimensional grids;

[0098] S3, integrate geographic information system data, real-time weather data, population density data, and communication signal strength to form multi-source data; based on the multi-source data and the low-altitude flight rule library, design a risk assessment function R;

[0099] S4, according to the low-altitude flight rule library, divide the airspace from the ground to 1000 meters into multiple height layers, and assign height layers to different types of aircraft;

[0100] S5, construct a multi-level air route network, including a first-level air route network, a second-level air route network, a third-level air route network, a fourth-level air route network, a fifth-level air route network, and a sixth-level air route network;

[0101] S6, construct an airspace three-dimensional route model in a three-dimensional grid environment, including a route network model, a traffic node model, a take-off and landing point channel model, and a terminal route model;

[0102] S7, based on the airspace three-dimensional route model, use a hybrid iterative algorithm to optimize the route planning of each height layer;

[0103] S8, for the discrete path points generated by the hybrid iterative algorithm, use a B-spline curve to smooth the path, and verify the flight performance of the smoothed path to obtain a continuous flyable path;

[0104] S9, according to the continuous flyable path, generate a low-altitude air route network, including horizontal segments, vertical segments, intersection interchanges, and terminal routes.

[0105] Specifically, in an embodiment of the present application, the low-altitude flight rule library includes: flight height limit: specifies the minimum height for flight in a specific area; flight route specification: specifies the flight route of the aircraft in a specific area; flight speed limit: specifies the maximum flight speed of the aircraft in a specific area; air traffic control: establishes an air traffic control system to monitor the traffic order of low-altitude flight; emergency handling: develops a handling procedure for emergency situations; and violation penalty: punishes aircraft that violate traffic rules.

[0106] The low-altitude flight rule library is the basis for the construction of the air route network, which is used to determine the height layer division standard of the airspace and the flight height limit of different types of aircraft, guide the design of the air route network, traffic nodes, take-off and landing point channels, and terminal routes, ensure compliance with aviation regulations and safety requirements, provide specification and limitation conditions for path planning, conflict resolution, and avoidance strategies, and ensure that the flight of the aircraft complies with the regulations.

[0107] Specifically, in an embodiment of the present application, an adaptive octree structure is used to discretize the urban space in three dimensions. The octree is a tree data structure used to represent three-dimensional space. Each node represents a cubic space that can be divided into eight sub-cubes. The three-dimensional discretization process is as follows: a) First, the entire urban space is considered as a large cube. b) According to the complex structures such as obstacles, buildings, etc. in the space, recursively divide the cube into smaller sub-cubes. c) In areas with high complexity (such as high-rise dense areas), more detailed division is performed. d) In open areas (such as parks, squares), larger cubes are maintained. e) Stop dividing when the preset minimum cube size or spatial complexity threshold is reached. f) Use pointers or indexes to improve the access efficiency of the tree structure. Since the adaptive method is used, the final three-dimensional grid is non-uniform. That is, complex areas have smaller and denser grid cells, while simple areas have larger and sparser grid cells.

[0108] Specifically, in an embodiment of the present application, step S3 includes:

[0109] Obtain multi-source data, including geographic information system data, real-time weather data, population density data, and communication signal strength. Geographic Information System (GIS) data: Use a spatial database (such as PostGIS) to store and manage urban three-dimensional spatial information. Real-time weather data: Obtain real-time weather information through an API interface (such as OpenWeatherMap API). Population density data: Use a population statistics database and spatial interpolation algorithm (such as Kriging) to generate dynamic population density distribution. Communication signal strength: Collect data in real time through a distributed sensor network and signal strength measurement equipment.

[0110] Use the min-max normalization method to unify data of different scales to the [0, 1] interval. For time-varying data, use sliding window technology for real-time standardization. Use Kalman filtering algorithm to fuse multi-source data, improve data accuracy and reliability. Use a spatio-temporal database to store and manage fused multi-dimensional data.

[0111] Design the mathematical model of the risk assessment function R:

[0112] ,

[0113] represents the comprehensive risk value at spatial location and time t, is the obstacle risk function, is the population density risk function, is the weather risk function, is the communication risk function, is the dynamically adjusted weight coefficient.

[0114] where each sub-function is calculated as follows: Obstacle Risk Function : Uses R-tree spatial indexing technique to quickly retrieve obstacles around a given coordinate (x, y, z). Calculates the distance of the aircraft from the nearest obstacle and maps it to a risk value using an inverse proportion function. Population Density Risk Function : Utilizes spatiotemporal interpolation algorithms (e.g., spatiotemporal Kriging) to process dynamic population density data. Maps the interpolated results to risk values, with higher population density resulting in higher risk values. Weather Risk Function : Uses numerical weather prediction models and real-time weather data updates. Considers factors such as wind speed, visibility, and more to calculate a comprehensive weather risk through a fuzzy logic system. Communication Risk Function : Based on signal propagation models and measured data, calculates the signal strength at a given location. Maps the signal strength inversely to risk values, with weaker signals resulting in higher risk. Uses machine learning methods to dynamically adjust ω1, ω2, ω3, ω4 based on historical data and current conditions. Designs a feedback mechanism to continuously optimize the weight coefficients based on actual flight data.

[0115] Optimization of Risk Assessment Function Calculation: Uses GPU acceleration for computation, such as CUDA or OpenCL, to improve the efficiency of large-scale spatial risk assessment. Implements spatial partitioning and parallel computing strategies to improve the real-time performance of risk assessment.

[0116] Integration with Low Altitude Flight Rule Library: Designs a rule engine to convert regulations from the low altitude flight rule library into constraint conditions. During the risk assessment process, dynamically checks whether flight rules are violated and adjusts the risk values appropriately.

[0117] Specifically, in an embodiment of the present application, the airspace height profile is established on the vertical level of the airspace, and is expanded upwards according to the flight height layers of the aircraft, to establish height division in the vertical direction. The low-altitude airspace (0-1000m) is subdivided into eleven height layers for different types of aircraft to fly, specifically:

[0118] 0-20m, near-ground airspace; 20-50m, consumer aircraft isolation airspace; 50-120m, consumer aircraft suitable airspace; 120-150m, light and small aircraft isolation airspace; 150-200m, light and small aircraft suitable airspace; 200-230m, medium aircraft isolation airspace; 230-300m, medium aircraft suitable airspace; 300-400m, large aircraft isolation airspace; 400-600m, large aircraft suitable airspace; 600-700m, manned aircraft super-high-speed isolation airspace; 700-1000m, manned aircraft super-high-speed suitable airspace.

[0119] It should be noted that the airspace division of the present application is divided by true height, that is, the vertical height of the aircraft from a certain reference horizontal plane, which is a relative height division, rather than an absolute height. Specifically, it can be based on sea level or ground level, and in actual division, it is determined according to specific circumstances.

[0120] The hierarchical data structure (such as tree structure) is used to represent the 11 height layers. Each height layer node contains the following information: height range (lower limit and upper limit), applicable aircraft type, whether it is a segregated airspace, and current airspace usage status (such as real-time aircraft number, congestion degree, etc.).

[0121] Aircraft classification and height layer allocation algorithm:

[0122] a) According to the physical characteristics (such as size, weight, flight performance) of the aircraft and the task requirements, the aircraft is classified into consumer, light small, medium, large and manned aircraft.

[0123] b) Design height layer allocation algorithm: input: aircraft type, task requirement, current airspace usage status; output: allocated height layer; algorithm flow: determine the available height layer range according to the aircraft type; check if the task requirement conflicts with certain height layers (such as near-earth mission); evaluate the usage status of each height layer to avoid allocating to congested height layers; if there are multiple selectable height layers, use heuristic algorithm (such as considering minimum energy consumption) to select the optimal height layer; return the allocation result.

[0124] Specifically, in an embodiment of the present application, in step S5, the first-level air route network is an initial air route network generated based on the ground route network; the second-level air route network is an air route network that iteratively constrains positive geographical elements based on the first-level air route network; the third-level air route network is an air route network that iteratively constrains negative geographical elements based on the second-level air route network; the fourth-level air route network is a dynamic air route network that iteratively constrains aircraft traffic demand based on the third-level air route network; the fifth-level air route network is an air route network that iteratively simulates flight testing and corrects the fourth-level air route network; and the sixth-level air route network is an air route network that iteratively constrains actual flight testing based on the fifth-level air route network.

[0125] In this embodiment, before constructing the multi-level air route network, an environment map needs to be constructed, and the specific process is as follows:

[0126] The flight environment of the unmanned aerial vehicle has strong complexity. In order to ensure that the flight path can effectively avoid obstacles and safely fly, it is necessary to first identify and extract the influence boundary (including the safety clearance range) of various geographical constraint elements, and construct a geographical fence database of the unmanned aerial vehicle low-altitude flight environment, that is, an environment map. The range of geographical constraint elements is referred to the "Urban Land Classification and Planning Construction Land Standard" and "Land Use Status Classification".

[0127] 1. Positive constraint geographic elements

[0128] Parks and green spaces, water areas, roads, squares and other service facilities belong to positive constraint geographic elements available for low-altitude flight of UAVs.

[0129] 2. Negative constraint geographic elements

[0130] Adverse weather-prone areas, base station signal weak areas (communication blind areas), special restricted areas and the like belong to negative constraint geographic elements that need to be avoided by UAVs during flight.

[0131] After the environment map is constructed, the embodiment successively constructs a multi-level air route network, as follows:

[0132] The first-level air route network: The regional low-altitude air route is closely related to the ground road network, and therefore, the initial air route network is generated based on the ground road network. When determining the initial air route network, the third national land survey (hereinafter referred to as "third survey") data is used as the base, and the flight space corresponding to the polygons with the third survey attribute codes of 1003, 1004 and 1006 is selected as the range of the initial air route network.

[0133] The second-level air route network: Based on the range of the initial air route network, the flight area environment is buffered 100m outward along the normal direction of the road center line, and it is determined whether the positive constraint geographic elements are connected with the first-level air route. The ones determined as "connected" are included in the air route network range, so as to generate the second-level air route network.

[0134] The third-level air route network: After the second-level air route network based on the positive constraint elements is generated, the UAVs need to avoid sensitive geographic elements such as buildings, power lines and overpass bridges during flight. By detecting potential conflict road sections based on the negative constraint element set, the conflict road sections are re-planned, and the third-level air route network is constructed.

[0135] The fourth-level air route network: The road traffic demand prediction "four stages" include traffic generation and attraction, traffic distribution, traffic mode division and traffic assignment, as shown in Figure 2 , the research idea of the traffic demand prediction "four stages" method is used. On the basis of the third-level air route network, according to the proposed four-stage method of UAV transportation demand prediction, combined with the actual UAV traffic demand, traffic assignment (the shortest path traffic assignment method is selected) is performed on the third-level air route network, so as to form a dynamic low-altitude air route network, and the fourth-level air route network is constructed. When constructing this level of air route network, the traffic zone division diagram is as shown in Figure 3 , and the traffic assignment diagram is as shown in Figure 4 .

[0136] The 5th level air route network: in order to verify the safety and scientificity of the air route, simulation flight test needs to be carried out on the air route construction result. The simulation result is fed back to the 4th level air route network and the flight air route is generated. In the process of simulation flight, the current position of the unmanned aerial vehicle is detected and calculated in real time. If the real-time position of the unmanned aerial vehicle is in the air route, the air route is reasonable. Otherwise, the 4th level air route needs to be adjusted and corrected. In combination with the simulation result and repeated debugging, the width of the air route, the minimum turning radius of the unmanned aerial vehicle, the pitch angle of the unmanned aerial vehicle and other parameter thresholds are finally determined.

[0137] The 6th level air route network: in order to ensure the absolute safety of the unmanned aerial vehicle flight activity, the actual flight needs to be carried out on the realness of the flight area environment and the precision correction, and the communication link parameters, the modeling precision of local climate environment and the accuracy of various geographic information such as city buildings, road traffic, municipal infrastructure, terrain and power grid towers are monitored, so as to generate the 6th level air route network.

[0138] Specifically, in an embodiment of the present application, the air route network model comprises: constructing an elliptical cylindrical fixed layered air route pipeline to form a main air route network, including flight lane width, height and buffer zone; the traffic node model comprises: abstracting the air route network into a collection of traffic nodes and flight air routes, and constructing the traffic nodes into a ring spiral overpass form, and setting a multi-layer rotary flight ramp in the intersection range, and setting a same-layer variable speed lane outside the ramp; the take-off and landing point lane model comprises: adopting a conical space structure to connect the ground take-off and landing point and the air route network; the terminal air route model comprises: connecting the main air route network and the ground take-off and landing facility, the emergency landing zone and the related support facility; wherein, the terminal air route model is connected with the main air route network through the ring spiral overpass, and the overpass is a right-turn only intersection.

[0139] A specific example is described as follows:

[0140] (1) Air route network model

[0141] The aircraft performs flight tasks at the corresponding flight altitude layer according to the type of the aircraft, and the flight route is an elliptical cylindrical fixed layered pipeline structure composed of a starting point, an ending point and a flight altitude. During flight, the flight altitude, direction and speed of the aircraft need to be limited and required, so as to reduce the complexity of the flight mode inside the air lane structure and improve the flight safety. The flight lane width is determined according to the type and automation level of the aircraft and other performances, and the aircraft can fly in the flight lane according to the minimum safety distance. At the same time, a certain width buffer zone needs to be set around the air lane, and no other air lane or obstacle is allowed in the buffer zone. The buffer zones of different air lanes can overlap. Figure 5 As shown in Figure 5This design showcases a multi-layered flight path structure, with each layer representing a specific flight altitude. This layered design allows different types of aircraft or aircraft with different missions to operate at their respective altitude layers, effectively reducing the risk of vertical collisions. Each aircraft's flight path is designed in a cylindrical shape, a shape that better suits the aircraft's motion characteristics than a simple rectangular channel, especially during turns. The flight paths of all aircraft collectively constitute the flight path for that layer. To simplify management and identification by automated systems, the flight paths are designed with rectangular channel shapes. The cross-section is also rectangular, providing ample operational space and ensuring clear boundaries. Figure 5 In this model, W represents the flight path width, H represents the flight path altitude, R represents the aircraft's safe clearance, Di represents the lateral clearance, and Li represents the track safety clearance. Although the model specifies a fixed path structure, adjustments to W (path width) and H (path altitude) can accommodate the needs of different aircraft types. Buffer zones are established around each path to prevent other paths or obstacles from entering, further increasing the safety margin. The overlapping design of these buffer zones ensures both safety and improves space utilization efficiency.

[0142] (2) Traffic node model

[0143] The airway network can be abstracted as a collection of traffic nodes and flight routes. For example, a 3×3 airway network can be abstracted into a network of 9 traffic nodes and 24 one-way segments. Figure 6 As shown, assuming the aircraft flies in the air according to the right-hand rule, the route segment is coded based on the flight path and heading:

[0144]

[0145] The airway network nodes are designed as ring-shaped spiral grade-separated interchanges, with multiple layers of spiral flight ramps within the intersection area. Variable speed lanes (acceleration or deceleration lanes) are located outside the ramps at the same level. When an aircraft passes through a traffic node, it must first ascend or descend vertically to the ramp height, then ascend or descend via the ring-shaped spiral grade-separated interchange to the ramp height in the target direction, and finally ascend or descend vertically back to the original lane height via the variable speed lane (acceleration or deceleration lane) at that height.

[0146] like Figure 7 As shown, Figure 7At least five different altitude layers are shown, including the main route layer (layer ①) and multiple import and export layers (layers ②, ③, ④, ⑤). The aircraft flying west to east horizontally flies to the deceleration channel port of the route layer (layer ①), vertically descends to the deceleration channel port of the west import channel layer (layer ④), and horizontally flies at a certain distance to the ramp port of the west import channel layer (layer ④), after the spiral ramp, enters the east export channel layer (layer ②), and horizontally accelerates at a certain distance to the acceleration channel port of the layer (layer ②), and vertically descends to the route layer (layer ①). Similarly, the aircraft flying east to west horizontally flies to the deceleration channel port of the route layer (layer ①), vertically descends to the deceleration channel port of the east import channel layer (layer ⑤), and horizontally flies at a certain distance to the ramp port of the east import channel layer (layer ⑤), after the spiral ramp, enters the west export channel layer (layer ③), and horizontally accelerates at a certain distance to the acceleration channel port of the layer (layer ③), and vertically descends to the route layer (layer ①). For Figure 7 For illustration, assume that two aircraft A and B meet head-on in the east-west direction and need to pass through this intersection. 1. Aircraft A (west to east): When approaching the intersection in the route layer (layer ①), enter the deceleration channel. Vertically descend to the west import channel layer (layer ④). Continue to decelerate in layer ④ deceleration channel. Enter the spiral ramp and ascend to the east export channel layer (layer ②). Accelerate in the acceleration channel of layer ②. Finally, vertically descend back to the route layer (layer ①) and continue eastward. 2. Aircraft B (east to west): When approaching the intersection in the route layer (layer ①), enter the deceleration channel. Vertically descend to the east import channel layer (layer ⑤). Continue to decelerate in layer ⑤ deceleration channel. Enter the spiral ramp and ascend to the west export channel layer (layer ③). Accelerate in the acceleration channel of layer ③. Finally, vertically descend back to the route layer (layer ①) and continue westward. Aircraft A and B are always in different altitude layers throughout the process, completely avoiding the risk of collision. The spiral ramp design allows aircraft to smoothly change direction and altitude, reducing sharp turns and sudden altitude changes. This design can easily accommodate more directional traffic flow by simply adding corresponding import and export layers.

[0147] (3) Take-off and landing point channel model

[0148] As shown in Figure 8 , considering that part of the manned aircraft must accelerate to a certain speed during take-off to enter the corresponding layer for normal flight, the take-off trajectory is not completely vertical, so the take-off and landing point channel airspace is set as a "cone" space for the aircraft to safely pass through the unmanned aircraft layer.

[0149] (4) End route model

[0150] As shown in Figure 9As shown, the terminal route is the last kilometer route connecting the city route and the ground take-off and landing facility, the emergency landing strip and the related support facilities, to ensure that the aircraft can be assisted in time or can be safely landed nearby when a failure or abnormal situation occurs during flight, and its setting is closely related to the position of the ground take-off and landing facility, the emergency landing strip and the like. The terminal route and the city route network are connected through a ring spiral interchange, and the interchange is a right-turn only intersection, and only the right-turn route needs to pass through the spiral interchange.

[0151] Specifically, in an embodiment of the present application, step S7 comprises:

[0152] S71 calculating a priority value according to the task information of the aircraft, assigning a priority to the aircraft based on the priority value, and dividing the aircraft into two categories of high priority and medium-low priority according to the priority;

[0153] Specifically, when calculating the priority value, the importance, urgency and time sensitivity of the task are considered, and the priority values of the aircrafts are calculated in a weighted summation manner. According to the calculated priority value, a suitable threshold value is set, and the high priority is divided into high priority above the threshold value, and the medium-low priority below the threshold value.

[0154] S72 using a first optimization algorithm for route planning for the high-priority aircraft;

[0155] S73 using a second optimization algorithm for route planning for the medium-low-priority aircraft;

[0156] In the route planning process, the constraint conditions of the three-dimensional airspace route model are considered, including: searching for the main flight path in the route network model, following the fixed layered pipe structure of the elliptical cylinder; using the traffic node model to realize the conversion between different height layers and routes, using a ring spiral interchange structure; planning the path of the take-off and landing stage through the take-off and landing point channel model, using a conical space structure; using the terminal route model to complete the accurate navigation of the task area, connecting the main route network and the specific task site.

[0157] In this embodiment, step S72 comprises:

[0158] S721 sorting the high-priority aircrafts according to the priority from high to low;

[0159] S722, in order of priority, plan a path for each high-priority aircraft, including: using a global path planning algorithm to find the fastest path from the starting point to the ending point in the three-dimensional airspace route model, while considering the flight time and risk assessment function R; considering the trajectory and time schedule of the planned high-priority aircraft, evaluating the potential conflict between the path of the current high-priority aircraft and the planned high-priority aircraft; if there is a conflict risk, adjust the flight plan of the current high-priority aircraft, including time adjustment, height layer adjustment and path re-planning; select the optimized path with the shortest expected arrival time under the premise of meeting all constraint conditions;

[0160] S723 record the planned path and schedule of the current high-priority aircraft, and update the trajectory information of the high-priority aircraft;

[0161] S724 repeat steps S722-723 until the path planning of all high-priority aircraft is completed.

[0162] The path optimization objective function of the first optimization algorithm is as follows:

[0163] ,

[0164] ,

[0165] In the formula, is the total path cost, is the energy consumption function of the high-priority aircraft, is the risk weight factor, and are the start and end times, is the spatial position of the aircraft at time t, represents the comprehensive risk value at spatial position and time t, is the obstacle risk function, is the population density risk function, is the weather risk function, is the communication risk function, is a dynamically adjusted weight coefficient.

[0166] Specifically, the energy consumption function calculation formula is as follows:

[0167] ,

[0168] In the formula, is the basic power consumption, which is proportional to the mass m of the aircraft, is the basic power coefficient; is the power required to overcome air resistance, , is the air density at height h, is the drag coefficient, A is the aircraft frontal area, and v(t) is the flight speed at time t; is the power required to change height, g is the acceleration of gravity, is the vertical speed, positive for ascent and negative for descent; is the wind effect on energy consumption, , is the wind effect coefficient, is the wind speed at time t, is the angle between the wind direction and the flight direction; is the additional power consumption due to the load, , is the load power coefficient, is the load mass at time t.

[0169] In this embodiment, the energy consumption function is a continuous function of time, representing the real-time energy consumption rate of high-priority aircraft during flight. At each time point t, considering the energy consumption and the comprehensive risk R of the current position (x, y, z), the total cost of the entire flight process is calculated by integration, achieving a balance between safety and efficiency.

[0170] In this embodiment, step S73 includes:

[0171] S731 According to the task requirements, starting point and ending point of all medium and low priority aircraft, a multi-objective path planning model is established, and the optimization objectives include flight distance, energy consumption and air route congestion degree;

[0172] S732 Considering the constraint conditions of the three-dimensional air route model, as well as the paths and time schedules of the planned high-priority aircraft, collision avoidance constraints and airspace accessibility constraints are established;

[0173] Specifically, this embodiment is to prioritize planning high-priority aircraft paths, and when planning medium and low-priority aircraft paths, more constraints are considered. The following is a specific embodiment description:

[0174] Collision avoidance constraints:

[0175] 1) Spatiotemporal separation constraint: for any two aircraft i and j, at any time t, the distance between them must be greater than the safety distance D safe : ; wherein and represent the position vectors of aircraft i and j at time t, respectively.

[0176] 2) Velocity vector constraint: the direction of relative velocity must be within a safety angle θ from the direction of relative position vector safe : where and denote the velocity vector of aircraft i and j at time t, respectively.

[0177] 3) Altitude separation constraint: the vertical separation between aircraft in different altitude layers must be greater than a minimum safety altitude H safe : where and denote the flight altitude of aircraft i and j at time t, respectively.

[0178] Airspace reachability constraint:

[0179] 1) Altitude layer constraint: aircraft must fly between its assigned altitude layer H min and H max : where denotes the flight altitude of aircraft at time t.

[0180] 2) No-fly zone constraint: for any no-fly zone k, aircraft cannot enter the zone at any time t: where denotes the position of aircraft at time t, and denote the center and radius of no-fly zone k, respectively.

[0181] 3) Air route corridor constraint: aircraft must fly within a predefined elliptical cylindrical air route corridor: where (x(t), y(t)) denotes the horizontal position of aircraft at time t, denotes the horizontal position of air route centerline at that time, a and b denote the horizontal and vertical semi-axes length of the ellipse, respectively.

[0182] 4) Turning radius constraint: the turning radius of aircraft must be greater than a minimum safety turning radius R min : where v(t) and a(t) denote the velocity and acceleration vector of aircraft at time t, respectively.

[0183] 5) Communication coverage constraint: aircraft must always remain within communication coverage: where p(t) denotes the position of aircraft at time t, p base denotes the position of communication base station, and R comm denotes the communication coverage radius.

[0184] S733 Path iteration optimization for medium and low priority aircraft, including: representing the flight path as a chromosome coding based on a sequence of waypoints; generating an initial population based on the shortest path algorithm; taking a multi-objective optimization function as the fitness function, considering flight distance, energy consumption and route congestion; using an elite reservation strategy for selection, using a partial mapping crossover method, and combining adaptive mutation probability for optimization.

[0185] Specifically, the iterative optimization uses a genetic algorithm for optimization, and the elite reservation strategy, partial mapping crossover method, and adaptive mutation probability enhance the algorithm optimization ability, as follows:

[0186] The elite reservation strategy is a method to ensure that the best individuals are not lost during evolution. In each generation, a certain number of best individuals (elites) are directly copied to the next generation. These elite individuals do not participate in crossover and mutation operations, and their original characteristics are maintained. The steps include: determine the elite proportion (usually 1-5% of the population size). At the end of each generation, sort all individuals according to the fitness function. The top elite individuals are directly copied to the next generation. The remaining positions are filled by regular selection, crossover and mutation operations.

[0187] The partial mapping crossover method (PMX) is a crossover operation specifically designed for permutation problems. A portion of the genes is exchanged between two parent chromosomes. Conflicts that may arise after crossover are resolved through mapping relationships to ensure that the generated offspring are still valid permutations. The steps include: randomly select two crossover points to determine the crossover region. Copy the crossover region of one parent directly to the same position in the offspring. From the other parent, find the genes that have not been copied. If the original positions of these genes are already occupied, find new positions according to the mapping relationship. Fill the remaining genes into the empty positions of the offspring.

[0188] Adaptive mutation probability is a method of dynamically adjusting the mutation rate to balance global exploration and local development. The mutation probability is dynamically adjusted according to the evolution state of the population. When the population diversity decreases, increase the mutation probability to promote exploration. When good solutions are found, reduce the mutation probability for local optimization. The steps include: at the beginning of each generation, calculate the average fitness and minimum fitness of the population. For each individual, calculate its mutation probability based on its fitness value. Use the calculated mutation probability for mutation operation. The calculation formula of adaptive mutation probability is as follows:

[0189] ,

[0190] where, the mutation probability of the current individual; is the maximum mutation probability; is the minimum mutation probability; fitness value of the current individual; minimum fitness value in the current population; average fitness value of the current population.

[0191] S734 After the iteration reaches the preset condition, output the optimal path sequence, complete the path planning of the medium-low priority aircraft.

[0192] The multi-objective optimization function in the second optimization algorithm is as follows:

[0193] ,

[0194] ,

[0195] ,

[0196] ,

[0197] In the formula, is the comprehensive cost function, is the flight distance, represents the spatial distance between nodes and , is the energy consumption, is the energy consumed by the aircraft from node to , is the air congestion degree, is the air congestion degree evaluation function at node , N is the number of nodes, is the weight coefficient.

[0198] It should be noted that in the present embodiment, is the sum of discrete, which calculates the total energy consumption between all adjacent nodes on the entire flight path. The comprehensive cost function considers the flight distance D, energy consumption and air congestion degree C three key factors. By introducing the weight coefficient , this function can flexibly adjust the relative importance of these three factors to adapt to different task requirements and environmental conditions. Flight distance D is calculated by accumulating the spatial distance between adjacent nodes, directly affecting flight time and efficiency. Energy consumption considers the energy consumption of the aircraft throughout the path, which helps to optimize battery usage and prolong flight time. Air congestion degree C assesses the congestion of each node on the path, helping to avoid traffic congestion areas and improve overall system efficiency and safety.

[0199] Air congestion degree evaluation function The calculation formula is as follows:

[0200] ,

[0201] In the formula, is the number of aircraft passing through the node in a unit of time, is the maximum passing capacity of the node , is the average size of the aircraft at the node , is the maximum value of the aircraft size, is the cumulative priority penalty at the node , is the maximum value of the priority penalty.

[0202] In this embodiment, the air route congestion evaluation function combines three key factors: the number of aircraft passing through , the average size of the aircraft , and the cumulative priority penalty . By comparing and weighting these factors with their respective maximum values, this function can comprehensively evaluate the congestion level of the node. The number of aircraft passing through reflects the traffic flow, the average size takes into account the space occupation of different types of aircraft, and the cumulative priority penalty introduces additional consideration for high-priority aircraft. This design not only effectively identifies and avoids congested areas, but also provides a path for high-priority aircraft when necessary, improving the flexibility and responsiveness of the entire system.

[0203] Specifically, in an embodiment of the present application, the hybrid iterative algorithm generates discrete path points. B-spline curves are used to interpolate these discrete points to generate smooth continuous curves. The advantage of B-spline curves is that they can maintain the overall shape of the path while providing smooth transitions. The smoothed path is verified for flight performance to ensure that the path meets the dynamics constraints of the aircraft. The verification content includes: a) whether the turning radius meets the minimum turning radius requirement of the aircraft; b) whether the climb and descent rates are within the performance range of the aircraft; c) whether the speed change conforms to the acceleration limit of the aircraft; if it is found that part of the path does not meet the flight performance requirements, local adjustment is needed. After smoothing and verification, a continuous path that meets the performance requirements of the aircraft is obtained. This path not only guarantees smoothness, but also ensures flyability.

[0204] Specifically, in an embodiment of the present application, based on the continuous flyable path, a complete low-altitude air route network is generated, which includes the following components:

[0205] Horizontal leg: a straight or curved flight part of the aircraft within the same altitude layer. It includes the main air route and the branch air route.​

[0206] Vertical leg: The climbing or descending part of the aircraft between different altitude layers. Used to connect different altitude layers.

[0207] Intersection interchange: According to the traffic node model described earlier, it adopts a ring spiral interchange form. Multi-layer revolving flyover ramps are set within the intersection range. The ramps are equipped with the same layer variable speed airways outside. This design can effectively handle the intersection of airways of different directions and altitudes.

[0208] Terminal airway: Connects the main airway network with ground take-off and landing facilities, emergency landing strips and related support facilities. It adopts a conical spatial structure to connect the ground take-off and landing points with the airway network in the air. It is connected with the main airway network through a ring spiral interchange. This interchange is a right-turn-only intersection to simplify traffic flow and improve safety.

[0209] Specifically, in another embodiment of the present application, when constructing a low-altitude airway network, an air avoidance strategy can also be set, as shown in Figure 10

[0210] According to the priority type, it is divided into three types:

[0211] (1) Coordination avoidance based on cooperative communication monitoring, conflict resolution strategy between aircrafts of the same priority and equipment assembly level under the condition of real-time duplex communication.

[0212] (2) Active avoidance based on established rules, low-priority or low-airway-use-right aircrafts actively give way to high-priority aircrafts under the condition of cooperative monitoring.

[0213] (3) Autonomous perception avoidance under non-cooperative conditions, under normal operating conditions, aircrafts based on airborne sensing equipment perceive non-network "black flight" targets or malfunctioning aircrafts and actively avoid them.

[0214] ​It should be noted that the purpose of the present application is to gradually improve and optimize the air route network by constructing a multi-level iterative air route network. Starting from the initial air route based on the ground road network, the results of positive and negative constraint geographical elements, traffic demand prediction, simulation test and actual flight test are considered in turn, and finally a 6th level air route network considering various factors is formed. This step-by-step iterative method can fully consider the complexity of urban environment, including geographical constraints, traffic demand, safety and other aspects, to ensure that the final air route network is practical and safe. On this basis, hybrid iterative optimization is carried out to further improve the efficiency and safety of the air route network. By dividing the aircraft into high priority and low priority, different optimization algorithms are used to better balance the needs of different types of aircraft, while considering factors such as air route congestion and energy consumption. This multi-level, multi-stage optimization method can fully consider various complex factors of low-altitude flight, and finally construct a safe, efficient and flexible low-altitude air route network to lay the foundation for the development of future urban air traffic.

[0215] The above only describes the preferred embodiments of the present application and is not intended to limit the present application. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application shall be included in the protection scope of the present application.

Claims

1. A method for constructing a multi-level low-altitude air route network in a complex urban environment, characterized in that, Comprise: S1 establish low-altitude flight rule base; S2 based on adaptive octree structure to city space for three-dimensional discretization, airspace is divided into uneven three-dimensional grid; S3 integration of geographic information system data, real-time weather data, population density data, communication signal strength, form multi-source data; based on multi-source data and low-altitude flight rule base, design risk assessment function R; S4 according to low-altitude flight rule base, airspace from the ground to 1000 meters is divided into multiple height layer, for different types of aircraft to allocate height layer; S5 build multi-level air route network, including the first level air route network, the second level air route network, the third level air route network, the fourth level air route network, the fifth level air route network and the sixth level air route network; S6 in three-dimensional grid environment, based on the sixth level air route network to build airspace three-dimensional air route model, including air route network model, traffic node model, take-off and landing point channel model and terminal air route model; S7 based on airspace three-dimensional air route model, using hybrid iterative algorithm to optimize the air route planning of each height layer; Step S7 comprises: S71 according to the task information of the aircraft to calculate the priority value, based on the priority value to give priority to the aircraft, according to the priority to divide the aircraft into high priority and low priority two kinds; S72 the first optimization algorithm is used for air route planning of high priority aircraft; S73 the second optimization algorithm is used for air route planning of low priority aircraft; Wherein, in the process of air route planning, considering the constraint conditions of airspace three-dimensional air route model, including: Search the main flight path in the air route network model, follow the fixed layered pipe structure of elliptical cylinder; Use traffic node model to realize the conversion between different height layers and air routes, adopt ring-like spiral interchanges structure; Through take-off and landing point channel model to plan the path of take-off and landing stage, use conical space structure; Use terminal air route model to complete the accurate navigation of task area, connect the main air route network and specific task site; According to the hierarchical structure of multi-level air route network, select the appropriate air route level for planning; Step S72 comprises: S721 high priority aircraft is sorted according to priority from high to low; S722 according to the priority order, in turn for each high priority aircraft to plan path, including: Use global path planning algorithm to find the fastest path from the starting point to the end point in the airspace three-dimensional air route model and multi-level air route network, considering flight time and risk assessment function R; Consider the trajectory and time scheduling of the planned high priority aircraft, evaluate the potential conflict between the path of the current high priority aircraft and the planned high priority aircraft; If there is conflict risk, adjust the flight plan of the current high priority aircraft, including time adjustment, height layer adjustment and path re-planning; Under the premise of meeting all the constraint conditions, select the optimization path with the shortest expected arrival time; S723 record the planning path and time schedule of the current high priority aircraft, update the trajectory information of the high priority aircraft; S724 repeat steps S722-723 until the path planning of all high priority aircraft is completed; The path optimization objective function of the first optimization algorithm is as follows: ， wherein is the total path cost, is the energy consumption function of a high-priority aircraft, is the risk weight factor, and are the start and end times, is the spatial position of the aircraft at time t, denotes the spatial position and the integrated risk value at time t, is the obstacle risk function, is the population density risk function, is the meteorological risk function, is the communication risk function, is a dynamically adjusted weight coefficient; S8 A B-spline curve is used to smooth the discrete path points generated by the hybrid iterative algorithm, and the flight performance of the smoothed path is verified to obtain a continuous flyable path; S9 A low-altitude air route network is generated based on the continuous flyable path, including horizontal segments, vertical segments, interchange at intersections, and terminal routes.

2. The multi-level low-altitude air route network construction method in a complex urban environment of claim 1, wherein, The low-altitude flight rule library includes: Flight height limit: specifies the minimum height for flight in a specific area; Route specification: specifies the route of the aircraft in a specific area; Flight speed limit: specifies the maximum flight speed of the aircraft in a specific area; Air traffic control: establishes an air traffic control system to regulate the traffic order of low-altitude flight; Emergency handling: develop a handling procedure for emergency situations; Penalty for violation: punish aircraft that violate traffic rules.

3. The multi-level low-altitude air route network construction method in a complex urban environment of claim 2, wherein, In step S4, the height layer division includes: 0-20m, near-ground airspace; 20-50m, consumer aircraft isolation airspace; 50-120m, consumer aircraft suitable flight airspace; 120-150m, light and small aircraft isolation airspace; 150-200m, light and small aircraft suitable flight airspace; 200-230m, medium aircraft isolation airspace; 230-300m, medium aircraft suitable flight airspace; 300-400m, large aircraft isolation airspace; 400-600m, large aircraft suitable flight airspace; 600-700m, manned aircraft super-high-speed isolation airspace; 700-1000m, manned aircraft super-high-speed suitable flight airspace.

4. The multi-level low-altitude air route network construction method in a complex urban environment of claim 1, wherein, In step S5, the first-level air route network is an initial air route network generated based on the ground route network; the second-level air route network is an air route network that iteratively constrains positive geographical elements based on the first-level air route network; the third-level air route network is an air route network that iteratively constrains negative geographical elements based on the second-level air route network; the fourth-level air route network is a dynamic air route network that iteratively constrains aircraft traffic demand based on the third-level air route network; the fifth-level air route network is an air route network that iteratively simulates flight testing and makes corrections based on the fourth-level air route network; and the sixth-level air route network is an air route network that iteratively constrains actual flight testing based on the fifth-level air route network.

5. The multi-level low-altitude air route network construction method in a complex urban environment of claim 2, wherein, The air route network model includes: A fixed layered air route pipeline in the shape of an elliptical cylinder is constructed to form a main air route network, including flight lane width, height, and buffer zone; The traffic node model includes: The air route network is abstracted as a collection of traffic nodes and flight air routes, and the traffic nodes are constructed in a ring-shaped spiral interchange form, with multiple layers of rotary flight ramps in the intersection range, and the ramps are provided with variable speed lanes of the same layer outside; The take-off and landing point lane model includes: A conical spatial structure is used to connect the ground take-off and landing points with the air route network; The terminal route model includes: The main air route network is connected with ground take-off and landing facilities, emergency landing strips, and related support facilities; The terminal route model is connected with the main air route network through a ring-shaped spiral interchange, which is a right-turn only intersection.

6. The multi-level low-altitude air route network construction method in a complex urban environment of claim 5, wherein, Step S73 includes: S731 A multi-objective path planning model is established based on the task requirements, starting points, and ending points of all medium and low priority aircraft, and the optimization objectives include flight distance, energy consumption, and route congestion degree; S732 Considering the constraints of the three-dimensional airspace route model, as well as the path and time schedule of the planned high-priority aircraft, establish the collision avoidance constraints and airspace accessibility constraints; S733 Iteratively optimize the path of the medium and low-priority aircraft, including: Express the flight path as a chromosome coding based on the sequence of route nodes; generate an initial population based on the shortest path algorithm; Use a multi-objective optimization function as the fitness function, and comprehensively consider the flight distance, energy consumption, and route congestion degree; Use the elite reservation strategy for selection, use the partial mapping crossover method, and combine the adaptive mutation probability for optimization; S734 After the iteration reaches the preset condition, output the optimal path sequence, and complete the path planning of the medium and low-priority aircraft.

7. The multi-level low-altitude air route network construction method in a complex urban environment of claim 6, wherein, The multi-objective optimization function in the second optimization algorithm is as follows: ， ， ， ， In the formula, is a comprehensive cost function, is a flight distance, represents a spatial distance between nodes and , is an energy consumption, is an energy consumed by the aircraft from the node to , is a route congestion degree, is a route congestion degree evaluation function at the node , and N is a number of nodes, is a weight coefficient; Route congestion degree evaluation function The calculation formula is as follows: ， wherein is the node the number of aircraft passing through in a unit of time, is the node the maximum passing capacity, is the node the average size of aircraft at the node, is the maximum value of aircraft size, is the node the cumulative priority penalty at the node, is the maximum value of priority penalty.

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