EVTOL-oriented urban air traffic route network planning method and device

Abstract

The invention relates to the technical field of route network optimization design, and discloses an eVTOL-oriented urban air traffic route network planning method, which comprises the following steps of: for route network construction of eVTOL in urban air traffic, dividing an urban low-altitude airspace structure by analyzing multiple influence factors, and defining an operation height layer of eVTOL; then rasterizing and modeling the height layer of the low-altitude airspace, performing risk analysis on the urban low-altitude airspace by combining air and ground risk factors, constructing an urban low-altitude risk assessment model, calculating the risk cost of each grid unit according to the risk assessment model, delimiting the risk level, and further constructing a visual risk grid map; through a K-means clustering algorithm, obtaining a rising and landing point position of eVTOL-oriented urban air traffic route network planning; and finally, planning an eVTOL operation path network based on an A * algorithm improved by risk cost and penalty factors. According to the method, the flight area of the eVTOL can be divided in combination with the actual operation environment of the eVTOL, the flight route network is constructed according to the flight area, and safe and orderly flight of the eVTOL is guaranteed.

Description

Technical Field

[0001] The present invention relates to the technical field of route network optimization design, and in particular to a method and device for urban air traffic route network planning for eVTOL. Background Art

[0002] Route network planning is an important part of urban air traffic and a prerequisite for the safe operation of aircraft such as eVTOL entering urban airspace. Its rationality and correctness directly determine the application and development of urban air traffic. For urban air traffic route network planning of eVTOL, a path planning method is needed that can combine risk maps and penalty factors, incorporate urban risk variables into path planning, and expand traditional single path planning to route networks.

[0003] At present, some studies have proposed the layout of the urban air traffic route network based on Air-Matrix simulation, the layout of the road network based on the roof of the building, and the layout of the route based on the layout of the road and river, and evaluated the airspace capacity and throughput of the three route planning methods. However, since the current urban low-altitude airspace management system in my country is not yet sound, most of the route network planning technologies are studied in free airspace, and the shortest flight path or route cost is used as the goal of route planning. The actual operating environment is rarely taken into account, such as the operating altitude of aircraft such as eVTOL, the urban risk environment and other important factors, resulting in poor actual application results. At the same time, there are few studies on the construction of urban low-altitude route networks, most of which only involve single technical research such as path planning and risk assessment, and lack a systematic route network planning method. Summary of the invention

[0004] In order to solve the problems and shortcomings in the above-mentioned prior art, the present invention specifically proposes a method and device for urban air traffic route network planning for eVTOL. The present invention delineates the urban low-altitude airspace structure by analyzing multiple influencing factors and clarifies the operating altitude layer of eVTOL; thereby combining air and ground risk factors to conduct risk analysis on the urban airspace, and then constructs a risk assessment model containing risk level information based on the risk assessment results; obtains the location of the vertical take-off and landing field through the K-means clustering algorithm, and obtains the take-off and landing point locations for the urban air traffic route network planning for eVTOL; finally, the A* algorithm is improved based on the risk cost to plan the eVTOL operation path network, so as to construct a route network structure that can ensure the safe and orderly flight of eVTOL.

[0005] In order to achieve the above-mentioned object of the invention, the technical solution of the present invention is as follows:

[0006] On one hand, the present invention discloses a method for planning an urban air traffic route network for eVTOL, the method comprising the following steps:

[0007] S1. Based on aircraft classification and operation tasks, the urban low-altitude airspace altitude layers are vertically divided to establish the eVTOL operation airspace structure model.

[0008] In the present invention, it can be understood that when vertically dividing the altitude layers of urban low-altitude airspace, it is necessary to analyze the operating altitude layers of aircraft such as drones or eVTOLs in combination with the operating characteristics of aircraft such as eVTOLs, the height of urban buildings, flight noise and other factors, and then combine the laws and regulations, aircraft types and mission nature to vertically divide the urban low-altitude airspace into layers, such as Figure 2 As shown, the flight altitude of eVTOL is finally set.

[0009] Regarding the height of buildings, taking Chengdu as an example, eVTOL can avoid most buildings when flying in the city's low-altitude airspace above 300m.

[0010] For flight noise, the noise received by residents in new residential areas (residential buildings are about 80m high) is analyzed. According to the ground sound pressure formula, the flight noise of eVTOL at a flight altitude of more than 125m can be less than the acceptable noise intensity of urban air traffic, which is 67dB. The expression is:

[0011]

[0012] Among them, L p is the ground sound pressure, in dB; L m is the reference distance sound level, L m =100dB; l is the distance from the ground, in meters; l m is the reference distance from the ground, l m =1m.

[0013] S2. Carry out raster modeling on the divided low-altitude airspace height layers, calculate the risk cost of each grid unit according to the urban low-altitude risk assessment model, and then define the risk level of the grid unit according to the risk cost to construct a visual risk grid map.

[0014] Furthermore, the step S2 specifically includes the following steps:

[0015] S21. Use a three-dimensional grid to divide the urban low-altitude airspace, wherein the grid containing obstacles and no-fly zones is marked as a non-expandable grid, and the grid of other airspaces that allow eVTOL flights is marked as an expandable grid;

[0016] S22. Combine air risk and ground risk to establish the following urban low-altitude risk assessment model:

[0017]

[0018] Among them, C is the risk cost; R is the risk value; k is the adjustment coefficient; R' is the safety level of urban low-altitude eVTOL operation, in (person / h); q is the risk factor of densely populated areas; Under normal circumstances, densely populated areas and general areas are divided into two groups based on the population density of 2×10 4 Person / km 2 As the boundary, each area uses different formulas to calculate the risk cost according to the different population densities;

[0019] Furthermore, the risk value is composed of air risk and ground risk, and the calculation expression is as follows:

[0020] R=δ1R S +δ2R G Formula (2);

[0021] Among them, δ1 and δ2 are weight coefficients, R s For air risks, R G For ground risks;

[0022] Furthermore, the calculation expression of the air risk is as follows:

[0023] R S (t) = C A (t)+γ1MET Formula (3);

[0024] Among them, R s (t) is an air risk, and R s (t)∈(0,1]; C A (t) is the air collision risk cost, γ1 is the adjustment coefficient, and MET is the meteorological threat cost;

[0025] Furthermore, the meteorological threat cost MET is determined by wind speed value and rainfall intensity, and the calculation expression is:

[0026] MET=MET1+MET2 Formula (14);

[0027] Among them, MET1 is the meteorological threat value corresponding to wind speed, and MET2 is the meteorological threat value corresponding to rainfall intensity;

[0028] Furthermore, the calculation expression of the ground risk is as follows:

[0029] R G =PS=γ3D S M formula (4);

[0030] Among them, R G is the ground risk, and R G ∈(0,1]; P is the accident rate; S is the severity of the accident; γ3 is the adjustment coefficient; DS is the severity of the eVTOL aircraft falling, that is, the risk of falling; M is the number of people hit by the eVTOL aircraft falling, that is, the size of the crowd in the area;

[0031] The calculation expression of the severity of the eVTOL aircraft crash is as follows:

[0032] D S =λLI Formula (15);

[0033] Among them: λ is the crash rate of eVTOL aircraft; L is the probability of casualties after the collision; I is the degree of injury to the personnel after the collision;

[0034] The calculation expression of the crowd size M in the area is as follows:

[0035] M = Aρ Formula (16);

[0036] Where A is the ground impact area where the eVTOL aircraft falls; ρ is the ground population density in the area at time t;

[0037] S23. Based on the urban low-altitude risk assessment model established in step S22, the risk cost of each grid unit under each altitude layer is calculated, and then the following risk matrix is ​​established:

[0038]

[0039] Among them, C k is the risk matrix, r(o i,j,k ) indicates that the center point of the grid is o i,j,k The risk cost corresponding to the grid cell;

[0040] Then determine the risk level of eVTOL operation in urban low-altitude airspace, and refer to the following evaluation criteria:

[0041]

[0042] Finally, a risk map is constructed based on the risk matrix and risk levels. Different grid cells with different risk levels are marked on the risk map with different colors. Figure 5 As shown, high-risk grid cells are marked in red, medium-risk and low-risk grid cells are marked in yellow and green, respectively.

[0043] S3. The location of the vertical take-off and landing field is obtained through the K-means clustering algorithm, and then the take-off and landing point location for the urban air traffic route network planning for eVTOL is obtained.

[0044] In the present invention, for the K vertical take-off and landing site locations required to be established in the urban transportation area, the traditional K-means clustering algorithm is used to perform cluster analysis on the city's demand points, and the K cluster center locations are determined as the vertical take-off and landing site locations.

[0045] Furthermore, the demand points of a city can be understood as N families in an area who need to take eVTOL, an urban air transportation mode, so there are N demand points in this area. The role of the traditional Kmeans algorithm can be understood as dividing the N demand points in a certain area into K areas, and the K value is the number of vertical take-off and landing sites required.

[0046] S4. Combined with the risk cost, the A* algorithm improved by the repeated path penalty factor is adopted to obtain multiple optimized paths between the take-off and landing points that strike a balance between the flight cost and the route similarity, and finally construct a route network suitable for eVTOL operation.

[0047] In the present invention, step S4 specifically includes the following steps:

[0048] S41. First, constrain the flight altitude and maximum range of the eVTOL. The calculation expressions are as follows:

[0049] 300<h e <1000 Formula (5);

[0050]

[0051] L≤L max Formula (7);

[0052] Among them, h e is the flight altitude of eVTOL, L is the flight path of eVTOL; L max is the maximum range of eVTOL; (x i ,y i ) are the coordinates of the center point of the grid cell that the eVTOL passes during flight;

[0053] It can be understood that the flight path is the path between the starting point and the end point, that is, the path of a certain route;

[0054] S42. Combining the risk cost to improve the heuristic term of the A* algorithm, the following calculation expression of the heuristic function is obtained:

[0055] f(n)=g(n)+h(n)+C(n) Formula (8);

[0056] Among them, g(n) is the distance from the node to be expanded to the starting point, h(n) is the distance from the node to be expanded to the target position, C(n) = r(o i,j,k), i = 1, 2, 3 ..., j = 1, 2, 3 ..., k = 1, 2, 3 ..., l (l is the distance of the aircraft from the ground);

[0057] Assume that the starting point of the eVTOL flight is A(x0, y o ), the flight node to be expanded during the flight is B(x i ,y i ), the flight end point is C(x j ,y i ), the distance h(n) from the node to be expanded to the target location is comprehensively evaluated using the Euclidean distance. The calculation expression is as follows:

[0058]

[0059] S43. Improve the A* algorithm using penalty factors. After planning an optimized path, a penalty factor is introduced to adjust the path distance cost of the grid cells that the path passes through. The calculation expression is as follows:

[0060] H(n)=δ·[g(n) Oi +h(n) Oi ] Formula (10);

[0061] Where H(n) represents the adjusted path distance cost; δ represents the penalty factor, g(n) Oi 、h(n) Oi The optimized path passes through node O i The path cost of

[0062] Furthermore, the calculation expression of the penalty factor is:

[0063]

[0064] Among them, S max is the maximum similarity of the two expected planning paths and 0<S max <1; is a preset positive coefficient;

[0065] After introducing the penalty factor, the expression of the heuristic function is as follows:

[0066] f(n) x =H(n)+C(n)=δ·[g(n) Oi +h(n) Oi ]+C(n) Formula (12);

[0067] Among them, f(n) x represents the heuristic function for planning the xth route;

[0068] S44. Finally, the improved A* algorithm is used to obtain multiple optimized routes for the same set of take-off and landing points. Based on the risk map, different take-off and landing points are traversed and looped separately to obtain a multi-path optimized route set for multiple groups of take-off and landing points, and finally an air traffic route network is formed.

[0069] In the present invention, the specific process of using the improved A* algorithm to traverse and obtain a multi-path optimization route set of multiple groups of take-off and landing points is as follows:

[0070] Define the starting point and end point of a task area as A c , B d , c and d are the numbers of the starting point and the end point in the area, c = 1, 2, 3, ..., d = 1, 2, 3, ...; i is the number of paths expected to be searched for a pair of starting points and end points, and j is the number of paths currently searched; P n Starting point A c To the end point B d The nth flight path, L n P n The total length of the flight path; C mn P m and P n The overlap of the flight paths, i.e. the total length of the overlapping area of ​​the two routes, n = m-1, m-2, ..., 1; S mn is the similarity between the mth optimized route and the nth optimized route, 0<S mn ≤1.0. The specific steps of the algorithm are:

[0071] Step 1. Set the maximum similarity S according to the requirements max , positive coefficient and the value of the expected search path number i, at which time the searched path number j = 0. Read the risk map of the kth height layer to obtain the environmental information risk cost information.

[0072] Step 2. Search for starting point A c , End point B d Corresponding flight path. Use the improved heuristic function (Formula (12)) to plan a path from the starting point A1 to the end point B1, and the flight path is recorded as P1. The specific process of this step is:

[0073] (1) Create two lists, Openlist and Closelist. Openlist is used to store nodes that have been searched but not expanded, and Closelist is used to store expanded nodes.

[0074] (2) Search for the path from starting point A1 to end point B1, and put starting point A1 in Openlist;

[0075] (3) Determine whether the starting point A1 is the target point. If so, end the search; otherwise, visit the nodes near the starting point A1, put the non-obstacle points in the Openlist, calculate their cost estimates using equation (8) and put the starting point A1 in the Closelist;

[0076] (4) Select the expansion node O with the lowest cost estimate i , O i Remove from Openlist and put in Closelist, if O i For the specified target point, the path planning ends and the algorithm runs, otherwise proceed to the next step;

[0077] (5) Set node O i As the current node, visit the surrounding nodes of the current node. For the surrounding child nodes that are not in the Closelist, add them to the Openlist and calculate the cost;

[0078] (6) Return to step (4) until the target point is reached;

[0079] (7) Save the expanded nodes and the f value of each node, backtrack and save the path.

[0080] Step 3. Determine whether the expected number of searched paths has been reached. j = j + 1, if j ≥ i, the search for the flight path of the starting point and the end point is completed, and go to Step 6; if j < i, continue the search, and go to Step 4.

[0081] Step 4. Multiply the flight cost of the grid area traversed by the flight path P1 by the penalty factor δ. According to formula (12), the adjusted heuristic function is f(n)2, and the flight path P2 is searched.

[0082] Step 5. Calculate the path similarity between flight paths P1 and P2. If S 21 >S max The flight path search for the starting point and the end point is completed, and the process goes to Step 6; otherwise, the process goes to Step 3 to continue searching the path.

[0083] Step 6. Plan the flight paths of the remaining starting points and end points. When the flight path search for the pair of starting points and end points is completed, search for the flight paths of another pair of starting points and end points until all pre-planned starting points and end points are searched.

[0084] Step7. The traversal algorithm ends, save the map, and backtrack all paths.

[0085] Based on the same inventive concept, the present invention also proposes an urban air traffic route network planning device for eVTOL. Since the principle of solving the problem by the device is similar to that of the route network planning method, the implementation of the device can refer to the implementation of the method, and the repeated parts will not be repeated. As used below, the terms "unit" or "module" can be a combination of software and / or hardware that implements predetermined functions. Although the device described below is preferably implemented in software, the implementation of hardware, or a combination of software and hardware, is also possible and conceivable. The device may include:

[0086] The low-altitude airspace altitude layer division module is used to vertically divide the urban low-altitude airspace altitude layers according to aircraft classification and operation tasks, and establish an eVTOL operation airspace structure model;

[0087] The visual risk grid map construction module is used to perform grid modeling based on the divided low-altitude airspace altitude layers, calculate the grid unit risk cost based on the urban low-altitude risk assessment model, and then define the grid unit risk level based on the risk cost to construct a visual risk grid map;

[0088] The take-off and landing point location planning module is used to obtain the location of the vertical take-off and landing field through the K-means clustering algorithm, and then obtain the take-off and landing point location for the urban air traffic route network planning for eVTOL;

[0089] The route network planning module is used to combine risk cost and adopt the A* algorithm improved by repeated path penalty factor to obtain multiple optimized paths between take-off and landing points that strike a balance between flight cost and route similarity, and finally build a route network suitable for eVTOL operation.

[0090] The system, device, model or unit described in the above scheme can be implemented by a computer chip or entity, or by a product with a certain function. For the convenience of description, in this specification, the above device is described in various units according to their functions. Of course, when implementing the present invention, the functions of each unit can be implemented in the same or multiple software and / or hardware.

[0091] Furthermore, another aspect of the present invention provides a computer device, which includes a processor, an input device, an output device and a memory, and the processor, the input device, the output device and the memory are interconnected; wherein the memory is used to store a computer program, the computer program includes program instructions, and the processor is configured to call the program instructions to implement the steps of the above-mentioned route network planning method.

[0092] Furthermore, another aspect of the present invention provides a computer-readable storage medium, wherein the computer-readable storage medium stores a computer program, wherein the computer program includes program instructions, and when the program instructions are executed by a processor, the processor executes the steps of the above-mentioned route network planning method.

[0093] In the present invention, the processor may be a central processing unit (CPU). The processor may also be other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, or a combination of the above chips.

[0094] The memory, as a non-transitory computer-readable storage medium, can be used to store non-transitory software programs, non-transitory computer executable programs and units, such as the corresponding program units in the above method of the present invention. The processor executes various functional applications of the processor and works data processing by running the non-transitory software programs, instructions and modules stored in the memory, that is, implementing the steps of the above route network planning method.

[0095] The memory may include a program storage area and a data storage area, wherein the program storage area may store an operating system, an application required for at least one function; the data storage area may store data created by the processor, etc. In addition, the memory may include a high-speed random access memory, and may also include a non-transient memory, such as at least one disk storage device, a flash memory device, or other non-transient solid-state storage device. In some embodiments, the memory may optionally include a memory remotely arranged relative to the processor, and these remote memories may be connected to the processor via a network. Examples of the above-mentioned network include, but are not limited to, the Internet, an intranet, a local area network, a mobile communication network, and combinations thereof.

[0096] The one or more units are stored in the memory, and when executed by the processor, the steps of the above-mentioned route network planning method are performed.

[0097] Those skilled in the art will appreciate that the present invention can be provided as a method, system, or computer program product. Therefore, the present invention can take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware aspects.

[0098] Beneficial effects of the present invention:

[0099] 1. The present invention incorporates urban airspace risk factors into path planning. The planned route can take into account both path cost and risk cost to a certain extent, and plans a "shorter path and lower risk" route, thereby improving the safety of future eVTOL operations in urban airspace.

[0100] 2. The present invention can also expand single-route planning to multi-route planning, build an air traffic route network suitable for eVTOL flight, improve the operating efficiency of eVTOL, and provide some help for the further sustainable and green development of future urban airspace. BRIEF DESCRIPTION OF THE DRAWINGS

[0101] The foregoing and following detailed description of the present invention will become more apparent when read in conjunction with the following drawings, in which:

[0102] Figure 1 It is a schematic diagram of the process of the present invention;

[0103] Figure 2 It is a schematic diagram of the structure of demarcation of low-altitude airspace in the city of the present invention;

[0104] Figure 3 This is a flowchart of the implementation of the improved A* algorithm of the present invention;

[0105] Figure 4 It is a preliminary risk grid map of airspace in an embodiment of the present invention;

[0106] Figure 5 It is the urban airspace risk grid map of the embodiment of the present invention;

[0107] Figure 6 This is a plan diagram of a city air traffic route network according to an embodiment of the present invention; DETAILED DESCRIPTION

[0108] In order to enable those skilled in the art to better understand the technical solutions in the present invention, the technical solutions for achieving the purpose of the present invention will be further described below through several specific embodiments. It should be noted that the technical solutions claimed for protection in the present invention include but are not limited to the following embodiments. Based on the embodiments in the present invention, all other embodiments obtained by those skilled in the art without creative work shall fall within the scope of protection of the present invention.

[0109] In order to verify the effectiveness and applicability of the urban air traffic route network planning method for eVTOL proposed in this invention, some areas of Chengdu were selected as the analysis objects to plan the air traffic route network.

[0110] Plan the flight path of the eVTOL from an altitude of 300m in the airspace of a certain urban area in Chengdu at 19:00 on June 27, 2022. According to the airspace structure model, the true altitude of 300m is used as the flight altitude of the eVTOL. At this flight altitude, a 10km×10km urban environment area in Chengdu is selected for simulation, and the area is gridded with 200m×200m to form 50×50, a total of 2500 grid areas.

[0111] Assume that the take-off point coordinates of the eVTOL are A1(1,1) and A2(24,1), the landing point coordinates are B1(50,50) and B2(1,50), and the flight altitude is h e =300m, the length of the planned path Taking EHang's unmanned manned aircraft EH216-S, which has obtained the world's first type certificate (TC) for the eVTOL (electric vertical take-off and landing aircraft) project, as a reference object, its parameter information is shown in Table 1 below.

[0112] Table 1 Basic parameters of EH216-S aircraft performance characteristics

[0113]

[0114] The meteorological conditions of the area selected in this embodiment starting from 0:00 on June 27, 2022 are shown in Table 2 below.

[0115] Table 2 Meteorological conditions record

[0116]

[0117] According to relevant literature, in this embodiment, in the process of risk assessment calculation in the area, the urban low-altitude risk assessment model calculation expression is set, and the urban low-altitude eVTOL operation safety level R' is taken as 1.0×10 -6 ; The value of adjustment coefficient k is 5.0×10 4 ; The value of the hazard coefficient q in densely populated areas is 0.5.

[0118] Furthermore, in the risk value calculation expression, the values ​​of the two weight coefficients δ1 and δ2 are 0.4 and 0.6 respectively.

[0119] Furthermore, in the air risk calculation expression, C A (t) The mid-air collision risk cost is calculated based on the spherical collision template and the Reich collision model, which is a conventional method in this field; γ1 is the adjustment coefficient of MET, which keeps MET∈(0,1] valid.

[0120] Furthermore, in the ground risk calculation expression, the parameter λ of the eVTOL aircraft crash rate is taken as 9.648×10-7 ; γ3 is R G The adjustment factor is to make R G ∈(0,1] holds true; the probability of casualties L after a person is hit can be calculated by comprehensively considering the density of ground shelters and the flight kinetic energy of eVTOL; the degree of injury I after a person is hit can be obtained by measuring the severity of injuries corresponding to different types of injuries and the required impact energy; the ground impact area A is obtained by comprehensively considering the geometric dimensions of the falling eVTOL aircraft, the falling angle and the height of the ground personnel; the ground population density ρ is obtained by jointly training a crowd density prediction model (CNN-LSTM) based on past crowd density data using a convolutional neural network (CNN) and a long short-term memory network (LSTM).

[0121] Through field investigation, we know that there are no buildings with a height greater than 300m in this area and it is far away from the airport area. However, since some grid units in this area are close to no-fly zones such as schools and museums, there is no obstacle grid in this area but there is a no-fly zone grid. The preliminary risk grid map of the airspace is as follows: Figure 4 As shown, the white area is the expandable grid and the black area is the non-expandable grid.

[0122] Based on the urban low-altitude risk assessment model and combined with relevant reference materials and empirical values, the risk level evaluation indicators of the area at an altitude of 300m at 19:00 on June 27, 2022 are shown in Table 3 below.

[0123] Table 3 Risk level classification results

[0124]

[0125] The meteorological data in Table 2 are input into the BP neural network model to predict the local meteorological conditions of the airspace at 19:00. According to the meteorological threat risk model and combined with experience, the meteorological threat value of the airspace at 19:00 is obtained as shown in Table 4 below.

[0126] Table 4. Weather threat prediction results of BP neural network model

[0127]

[0128] According to the air risk calculation expression and the ground risk calculation expression, combined with the relevant parameters in the above table, the air risk and ground risk calculation results of the selected area are obtained, and then the risk calculation results are substituted into the urban low-altitude risk assessment model to calculate the corresponding risk cost of each grid unit under each altitude layer, and finally the risk cost matrix of the 300m altitude layer of the area is obtained. Finally, the risk level of each grid unit in the area is divided according to the risk cost matrix and risk level evaluation index, and the risk map of the selected area at 19:00 on June 27, 2022 is generated, as shown in the figure below: Figure 5 shown.

[0129] Using the improved A* algorithm based on risk cost and the introduction of repeated path penalty factor, a multi-path simulation experiment was conducted on two groups of starting points (A1, A2) and end points (B1, B2) in the risk map containing risk cost information, and the flight path of eVTOL was obtained as follows: Figure 6 As shown in the figure, the initial parameter is the maximum similarity S of the route. max is 0.8, the expected search path is i=3, and the positive coefficient The route network planning results are as follows: Figure 6 The path parameter data is shown in Table 6. After the path is planned and saved using the improved A* algorithm, the length of the path is the corresponding path cost. The simulation results show that the A* algorithm improved by the repeated path penalty factor can plan multiple flight paths with low risk costs for multiple groups of starting points and end points. The planned paths can avoid high-risk areas with high risk costs such as ground crowds and buildings. When it is impossible to avoid high-risk areas, the improved A* algorithm can select areas with lower risk costs.

[0130] Table 6 Path parameters for multi-path planning with multiple starting points and end points

[0131]

[0132] It can be seen from Table 6 that for the route between the starting point A1 and the terminal B1, the flight path 2 planned by the present method is compared with the flight path 1, and the path similarity between the two is 10.31%, and the flight path 3 is compared with the flight path 1, and the path similarity between the two is 8.82%. Further, for the route between the starting point A2 and the terminal B2, the flight path 5 planned by the present method is compared with the flight path 4, and the path similarity between the two is 6.68%, and the flight path 6 is compared with the flight path 4, and the path similarity between the two is 0.00%. Therefore, the method of the present invention can not only plan multiple routes between the same landing and take-off points, but also the multiple routes planned have low overlap, and can take into account certain path costs and risk costs.

[0133] The present invention can reduce the risk of conflict between aircraft by avoiding similarities between routes, especially in complex airspace environments. The uniqueness of each route helps aircraft maintain a safe distance, thereby reducing the possibility of potential mid-air approaches or collisions. In addition, the design of different routes can better meet the needs of various flight missions. In the face of emergencies or dynamic environmental changes, multiple routes with low similarity can provide more options and adjustment space.

[0134] In summary, the present invention delineates the structure of urban low-altitude airspace by analyzing multiple influencing factors, and clarifies the operating altitude layer of eVTOL; thus, the risk analysis of urban airspace is carried out in combination with air and ground risk factors, and then a risk assessment model containing risk level information is constructed based on the risk assessment results; then the location and number of vertical take-off and landing fields of eVTOL are clarified, and the take-off and landing point locations for the urban air traffic route network planning for eVTOL are obtained; finally, the A* algorithm improved based on risk cost and introduction of repeated path penalty factor is adopted to plan the eVTOL operation path network, so as to construct a route network structure that can ensure the safe and orderly flight of eVTOL. The present invention can divide the eVTOL's flyable area and construct a flight route network in combination with the actual operating environment of eVTOL, ensure the safe and orderly flight of eVTOL, and provide reference suggestions and data basis for the urban low-altitude airspace planning and urban air traffic development in my country.

[0135] In the description of the present invention, it is necessary to understand that the terms "center", "longitudinal", "lateral", "front", "back", "left", "right", "vertical", "horizontal", "top", "bottom", "inside", "outside" and the like indicate orientations or positional relationships based on the orientations or positional relationships shown in the accompanying drawings, and are only for the convenience of describing the present invention and simplifying the description, rather than indicating or implying that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and therefore cannot be understood as an obstacle to the scope of protection of the present invention.

[0136] In the description of the present invention, it is also necessary to explain that, unless otherwise clearly specified and limited, the terms "setting", "installation" and "connection" should be understood in a broad sense, for example, it can be a fixed connection, a detachable connection, or an integral connection; it can be a mechanical connection or an electrical connection; it can be a direct connection, or it can be an indirect connection through an intermediate medium, or it can be the internal communication of two components. For ordinary technicians in this field, the specific meanings of the above terms in the present invention can be understood according to specific circumstances.

[0137] The above description is only a preferred embodiment of the present invention and does not constitute any form of hindrance to the present invention. Any simple modification or equivalent changes made to the above embodiments based on the technical essence of the present invention shall fall within the protection scope of the present invention.

Claims

1. A method for planning urban air traffic route network for eVTOL, characterized in that: The method comprises the following steps: S1. Based on aircraft classification and operation tasks, the urban low-altitude airspace altitude layers are vertically divided to establish the eVTOL operation airspace structure model; S2. The divided low-altitude airspace height layers are rasterized and modeled, and the risk cost of each grid unit is calculated according to the urban low-altitude risk assessment model. Then, the risk level of the grid unit is determined according to the risk cost, and a visual risk grid map is constructed; S3. Obtain the location of the vertical take-off and landing field through the K-means clustering algorithm, and then obtain the take-off and landing point location for the urban air traffic route network planning for eVTOL; S4. Combined with the risk cost, the A* algorithm improved by the repeated path penalty factor is adopted to obtain multiple optimized paths between the take-off and landing points that strike a balance between the flight cost and the route similarity, and finally construct a route network suitable for eVTOL operation.

2. The method for urban air traffic route network planning for eVTOL according to claim 1, characterized in that: In step S1, the operating altitude layer of the eVTOL is analyzed in combination with the operating characteristics of the eVTOL, the height of urban buildings and the flight noise, and finally the flight altitude of the eVTOL is set, and then the altitude layer of the urban low-altitude airspace is vertically divided.

3. The method for urban air traffic route network planning for eVTOL according to claim 1, characterized in that: The step S2 specifically includes: S21. Use a three-dimensional grid to divide the urban low-altitude airspace, mark the grid containing obstacles and no-fly zones as non-expandable grids, and mark the grids of other airspaces as expandable grids; S22. Combine air risk and ground risk to establish the following urban low-altitude risk assessment model: Among them, C is the risk cost, R is the risk value, k is the adjustment coefficient, R' is the safety level of urban low-altitude eVTOL operation, and q is the hazard factor of densely populated areas; Furthermore, the risk value is composed of air risk and ground risk, and the calculation expression is as follows: R=δ1R S +δ2R G expression(2); Among them, δ1 and δ2 are weight coefficients, R s For air risks, R G For ground risks; S23. Based on the urban low-altitude risk assessment model, the risk cost of each grid unit at each height layer is calculated, a risk matrix is ​​established according to the risk cost, and the risk level is divided according to the risk matrix, and finally a risk map is constructed.

4. The method for urban air traffic route network planning for eVTOL according to claim 3, characterized in that: The calculation expression of the air risk is as follows: R S (t) = C A (t)+γ1MET Formula (3); Among them, C A (t) is the air collision risk cost, γ1 is the adjustment coefficient, and MET is the meteorological threat cost.

5. The method for urban air traffic route network planning for eVTOL according to claim 3, characterized in that: The calculation expression of the ground risk is as follows: R G =PS=γ3D S M formula (4); Among them, P is the accident rate, S is the accident severity, γ3 is the adjustment coefficient, and D S is the severity of the eVTOL aircraft crash, and M is the number of people hit by the eVTOL aircraft crash.

6. The method for urban air traffic route network planning for eVTOL according to claim 1, characterized in that: In step S3, for the K vertical take-off and landing site locations required to be established in the urban transportation area, a traditional K-means clustering algorithm is used to perform cluster analysis on the city's demand points, and the K cluster center locations are determined as the vertical take-off and landing site locations.

7. The method for urban air traffic route network planning for eVTOL according to claim 1, characterized in that: The step S4 specifically includes: S41. Constrain the flight altitude and maximum range of eVTOL, including: 300<h e <1000 Formula (5); L≤L max Formula (7); Among them, h e is the flight altitude of eVTOL, L is the flight path of eVTOL; L max is the maximum range of eVTOL; (x i ,y i ) are the coordinates of the center point of the grid cell that the eVTOL passes during flight; S42. Inspiration term for improving A* algorithm by combining risk cost: f(n)=g(n)+h(n)+C(n) Formula (8); Among them, g(n) is the distance from the node to be expanded to the starting point, h(n) is the distance from the node to be expanded to the target position, and C(n) is the risk capital of the grid unit corresponding to the node to be expanded; Assume the starting point of eVTOL flight is A(x0, y o ), the flight node to be expanded is B(x i ,y i ), the flight end point is C(x j ,y i ), use the Euclidean distance to comprehensively evaluate the distance from the node to be expanded to the target location: S43. After planning an optimized path, a penalty factor is introduced to adjust the path distance cost of the grid cells passed by the path: Where H(n) represents the adjusted path distance cost; δ represents the penalty factor, The optimized path passes through node O i The path cost of Furthermore, the calculation expression of the penalty factor is: Among them, S max is the maximum similarity of the two expected planning paths and 0<S max <1; is a preset positive coefficient; After introducing the penalty factor, the heuristic function expression is as follows: Among them, f(n) x represents the heuristic function for planning the xth route; S44. Use the improved A* algorithm to obtain multiple optimized routes for the same set of take-off and landing points. Based on the risk map, traverse and loop different take-off and landing points separately to obtain a multi-path optimized route set for multiple groups of take-off and landing points, and finally form an air traffic route network.

8. An urban air traffic route network planning device for eVTOL, the device is used to implement the route network planning method according to any one of claims 1 to 7, characterized in that: include: The low-altitude airspace altitude layer division module vertically divides the urban low-altitude airspace altitude layers according to aircraft classification and operation tasks, and establishes an eVTOL operation airspace structure model; The visual risk grid map construction module performs grid modeling based on the divided low-altitude airspace altitude layers, calculates the grid unit risk cost based on the urban low-altitude risk assessment model, and then defines the grid unit risk level based on the risk cost to construct a visual risk grid map; The take-off and landing point location planning module obtains the location of the vertical take-off and landing field through the K-means clustering algorithm, and then obtains the take-off and landing point locations for the urban air traffic route network planning for eVTOL; The route network planning module combines risk cost with the A* algorithm improved by repeated path penalty factor to obtain multiple optimized paths between take-off and landing points that strike a balance between flight cost and route similarity, and finally constructs a route network suitable for eVTOL operation.

9. A computer device, comprising a memory, a processor, and a computer program stored in the memory and executable in the processor, wherein when the processor executes the computer program, the route network planning method according to any one of claims 1 to 7 is implemented.

10. A computer-readable storage medium storing a computer program, wherein the computer program, when executed in a computer processor, implements the route network planning method according to any one of claims 1 to 7.

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