Helicopter low-noise flight trajectory design method and related device

Abstract

The application discloses a helicopter low-noise flight track design method and related devices, and relates to the technical field of helicopter track planning. The method adds a local or specific area cumulative sound exposure level to an evaluation function of a path optimization algorithm, and increases the weight of the cumulative sound exposure level, so that a path capable of effectively reducing the noise level of a target area or an overall area is selected, and the influence of noise on the surrounding environment is maximally reduced.

Description

Technical Field

[0001] This application relates to the field of helicopter trajectory planning technology, and in particular to a method and related apparatus for designing low-noise flight trajectories for helicopters. Background Technology

[0002] Helicopters, as aircraft capable of vertical takeoff and landing and low-altitude flight, have wide applications in both military and civilian fields. However, their intense noise has always been a significant limiting factor for the further application of rotorcraft, with helicopters being a prime example. During urban flights, the noise generated by helicopters' frequent maneuvers between buildings not only affects residents and greatly limits their civilian applications, but also makes them vulnerable to enemy detection, leading to unnecessary casualties in military operations. Therefore, helicopter noise reduction has always been a key focus of helicopter technology development and research.

[0003] Currently, helicopter global obstacle avoidance and pathfinding for complex terrains such as urban areas typically selects the shortest path based on the distance between the current node and the target point and the starting point. However, for different flight mission requirements, although the shortest path selected in this way can ensure the fastest arrival at the destination, the shortest path is not necessarily the path with the lowest noise. Summary of the Invention

[0004] The purpose of this application is to provide a method and related apparatus for designing low-noise flight trajectories for helicopters, which can design low-noise flight trajectories for helicopters and minimize the impact of noise on the surrounding environment.

[0005] To achieve the above objectives, this application provides the following solution:

[0006] Firstly, this application provides a method for designing low-noise flight trajectories for helicopters, including:

[0007] Based on the helicopter's flight mission requirements, acquire map data of the target area, helicopter performance parameters, flight constraints, and the starting and ending points of the flight path;

[0008] The map data is rasterized to obtain a rasterized map, wherein the rasterized map includes obstacle grids and feasible area grids;

[0009] Based on the helicopter performance parameters, flight constraints, and the starting and ending points of the flight path, a path optimization algorithm is used to determine the optimal flight path of the helicopter in the rasterized map. The evaluation function of the path optimization algorithm is determined based on the cumulative sound exposure level, the actual cost from the starting point to the current node, and the estimated cost from the current node to the ending point. The cumulative sound exposure level is a variable value calculated based on the noise information of the current node received by the ground noise observation point. The node corresponding to the minimum value of the evaluation function is determined as the optimal node. The optimal node is a node on the optimal flight path, and each grid in the rasterized map represents each of the nodes.

[0010] Secondly, this application provides a computer device, including: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the helicopter low-noise flight trajectory design method described above.

[0011] Thirdly, this application provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the helicopter low-noise flight trajectory design method described above.

[0012] Fourthly, this application provides a computer program product, including a computer program that, when executed by a processor, implements the helicopter low-noise flight trajectory design method described above.

[0013] According to the specific embodiments provided in this application, the following technical effects are disclosed:

[0014] This application provides a method and related apparatus for designing low-noise flight trajectories for helicopters. By incorporating the cumulative sound exposure level of a local or specific region into the evaluation function of the path optimization algorithm and increasing its weight, a path that can effectively reduce the noise level of the target area or the overall area is selected, thereby minimizing the impact of noise on the surrounding environment. Attached Figure Description

[0015] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0016] Figure 1 This is an application environment diagram of a helicopter low-noise flight trajectory design method according to Embodiment 1 of this application;

[0017] Figure 2 A flowchart illustrating a method for designing a low-noise flight trajectory for a helicopter, as provided in Embodiment 1 of this application;

[0018] Figure 3 This is a schematic diagram of a two-dimensional grid map in Embodiment 1 of this application;

[0019] Figure 4 This is a schematic diagram of the obstacle inflation process in the grid map of Embodiment 1 of this application;

[0020] Figure 5 This is a schematic diagram of the 0-1 matrix map in Embodiment 1 of this application;

[0021] Figure 6 This is a flowchart illustrating the trajectory design in Embodiment 1 of this application;

[0022] Figure 7 This is a schematic diagram of the 8-neighborhood search direction map in Embodiment 1 of this application;

[0023] Figure 8 This is a schematic diagram of the spatial positioning of the secondary acoustic radiation sphere in Embodiment 1 of this application;

[0024] Figure 9 This is a schematic diagram of the secondary acoustic radiation model in Embodiment 1 of this application;

[0025] Figure 10 This is a schematic diagram of the two-dimensional linear interpolation method in Embodiment 1 of this application;

[0026] Figure 11(a) is a schematic diagram of the initial path before the Floyd method smoothing process;

[0027] Figure 11(b) is a schematic diagram of the path obtained after step (1) of the Freudian method;

[0028] Figure 11(c) is a schematic diagram of the path obtained after step (2) of the Freudian method;

[0029] Figure 12 This is a schematic diagram of the structure of a computer device provided in Embodiment 2 of this application. Detailed Implementation

[0030] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0031] Research has shown that altering flight attitude and trajectory can achieve noise reduction, which is the mainstream method for noise reduction in helicopter maneuvering. Because complex terrains such as urban areas contain many unevenly distributed obstacles, helicopters must avoid various obstacles while considering noise reduction.

[0032] While global obstacle avoidance and pathfinding methods for complex terrains such as urban areas are relatively mature, in order to quickly find the shortest path to the destination, the evaluation function typically uses the distances between the current node and the target point and the starting point as the main components. However, this calculation method can only guarantee the selection of the shortest path, and for different flight mission requirements, the path that reaches the destination fastest is not necessarily the path with the lowest noise. Moreover, even if noise information is considered in UAV noise reduction pathfinding in urban areas, the relevant solution uses a sphere containing corresponding noise information to represent the UAV, assuming that it flies at a uniform speed through buildings. The sphere only considers changes in direction, and uses this sphere to calculate local noise as another component of the evaluation function, thereby ensuring low noise levels throughout the flight while completing pathfinding from the starting point to the destination. However, the magnitude and directionality of noise change when helicopters fly in urban areas under different maneuvering states, which is one of the reasons why trajectory noise reduction is feasible. Therefore, it is not possible to directly calculate local noise using a fixed acoustic radiation sphere.

[0033] The helicopter low-noise flight trajectory design method provided in this embodiment can plan a path that effectively reduces the noise level of the target area or the overall area. It considers the helicopter's maneuvering state and noise characteristics to construct a dynamically changing secondary sound radiation model to calculate local noise. This method can more accurately determine the magnitude and directionality of noise when the helicopter is flying in urban areas under different maneuvering states, so as to more accurately evaluate and optimize its flight path. This ensures flight safety and efficiency while minimizing the impact of noise on the surrounding environment.

[0034] To make the above-mentioned objectives, features and advantages of this application more apparent and understandable, the application will be further described in detail below with reference to the accompanying drawings and specific embodiments.

[0035] Example 1

[0036] The helicopter low-noise flight trajectory design method provided in this application embodiment can be applied to, for example... Figure 1In the application environment shown, terminal 102 communicates with server 104 via a network. A data storage system can store the data that server 104 needs to process. The data storage system can be set up independently, integrated into server 104, or placed in the cloud or on another server. Terminal 102 can send map data of the target area, helicopter performance parameters, flight constraints, and the start and end positions of the flight path to server 104. After receiving the map data of the target area, helicopter performance parameters, flight constraints, and the start and end positions of the flight path, server 104 performs rasterization processing on the map data to obtain a rasterized map. Based on the helicopter performance parameters, the flight constraints, and the start and end positions of the flight path, server 104 uses a path optimization algorithm to determine the optimal flight path for the helicopter in the rasterized map. Server 104 can then feed back the obtained optimal flight path to terminal 102. In addition, in some embodiments, the helicopter low-noise flight trajectory design method can also be implemented by the server 104 or the terminal 102 separately. For example, the terminal 102 can directly process the map data of the target area, helicopter performance parameters, flight constraints, and the starting and ending positions of the flight path using the helicopter low-noise flight trajectory design method. Alternatively, the server 104 can obtain the map data of the target area, helicopter performance parameters, flight constraints, and the starting and ending positions of the flight path from the data storage system and process them using the helicopter low-noise flight trajectory design method.

[0037] The terminal 102 can be, but is not limited to, various desktop computers, laptops, smartphones, tablets, IoT devices, and portable wearable devices. IoT devices can include smart speakers, smart TVs, smart air conditioners, and smart in-vehicle devices. Portable wearable devices can include smartwatches, smart bracelets, and head-mounted devices. The server 104 can be implemented using a standalone server or a server cluster composed of multiple servers, or it can be a cloud server.

[0038] In one exemplary embodiment, a method for designing a low-noise flight trajectory for a helicopter is provided. This method is executed by a computer device, specifically a terminal or server, or both. In this embodiment, the method is applied to... Figure 1 Taking server 104 as an example, the explanation includes the following steps 201 to 208. Wherein:

[0039] Step 201: Based on the helicopter's flight mission requirements, acquire map data of the target area, helicopter performance parameters, flight constraints, and the starting and ending points of the flight path.

[0040] Step 202: Rasterize the map data to obtain a rasterized map;

[0041] Step 203: Based on the helicopter performance parameters, flight constraints, and the starting and ending positions of the flight path, a path optimization algorithm is used to determine the optimal flight path of the helicopter in the rasterized map. The evaluation function of the path optimization algorithm is determined based on the cumulative sound exposure level, the actual cost from the starting position to the current node, and the estimated cost from the current node to the ending position. The cumulative sound exposure level is a variable value calculated based on the noise information of the current node received by the ground noise observation point. The node corresponding to the minimum of the evaluation function is determined as the optimal node. The optimal node is a node on the optimal flight path, and each grid in the rasterized map represents each of the nodes.

[0042] To enable those skilled in the art to better understand the specific implementation process of the helicopter low-noise flight trajectory design method based on steps 201-203, the following detailed description is provided.

[0043] like Figure 2 As shown, the general process of designing low-noise flight trajectories for helicopters in urban areas includes:

[0044] 1. Urban environment modeling and initialization.

[0045] This embodiment mainly uses the raster method for urban environment modeling. The extracted map data file is rasterized using a C++ program and outputs a 0-1 matrix file, which is then read by MATLAB.

[0046] 1.1 Map data rasterization processing.

[0047] When the static environment is known, the grid method divides the environment into equally divided grids and distinguishes the areas with black and white grids. Black grids represent obstacles such as buildings, while white grids represent passable areas.

[0048] Ignoring factors such as terrain elevation, we assume the selected urban area (i.e., the target area) is rectangular, composed of a×b square grids, where:

[0049]

[0050] Where, x max and y max , where are the length and width of the selected rectangular region, and L is the side length of each raster cube. The final generated 2D raster map (i.e., rasterized map) is as follows: Figure 3 As shown.

[0051] 1.2 Obstacle expansion.

[0052] In complex terrain, not every obstacle is a regular rectangle, and the helicopter may collide with the edges of obstacles during pathfinding. Therefore, it is necessary to expand the obstacle's size. If an obstacle does not fill a grid cell, its dimensions are expanded outwards, and it is calculated as an environmental obstacle grid cell. This ensures that the moving aircraft does not collide with obstacles. Figure 4 As shown, Figure 4 The left side of the image shows the actual shape of the obstacle in a two-dimensional plane. Figure 4 The right side of the image shows the actual shape of the obstacle in the grid map after it expands.

[0053] 1.3 0-1 matrixing of the raster.

[0054] Storing and calculating the coordinates of all four vertices of each obstacle during code writing is time-consuming and resource-intensive. Therefore, it's necessary to simplify the relative position of each grid cell to reduce the amount of data involved in the calculation. One approach is to simplify the position of each grid cell to the coordinates (x_mid, y_mid) of the grid center and store them. Another approach is to convert the map into a 0-1 matrix and then reconstruct the 0-1 matrix into actual map coordinates after finding a low-noise trajectory. This embodiment chooses the 0-1 matrix method to reduce the amount of data required for obstacle detection later on.

[0055] Figure 3 The simplified 2D raster map shown is a raster (0-1 matrix map) as follows: Figure 5 As shown, 1 represents an obstacle and 0 represents a passable area.

[0056] The conversion formula between the center coordinates (x0, y0) of an obstacle grid in a real map and the coordinates (x, y) of an M-row, N-column 0-1 matrix element is as follows:

[0057]

[0058] 2. Pathfinding calculation (low-noise flight trajectory design).

[0059] After completing the environment modeling, the trajectory design begins from the starting point, and the calculation process is as follows: Figure 6 As shown, the specific steps include:

[0060] The specific steps are as follows:

[0061] Step 2.1: Use a C++ program to rasterize the extracted map data file and output a 0-1 matrix file. Then, use MATLAB to read the 0-1 matrix file and store the raster coordinates of obstacles and feasible areas into the Close and Open lists respectively.

[0062] Step 2.2: Input the parameters required for the pathfinding calculation, including the starting position, ending position, helicopter performance parameters, flight constraints, etc., and store the starting position in the Close list.

[0063] Step 2.3: Start the pathfinding process from the starting point.

[0064] Step 2.3.1: Taking the current node as the parent node P, in two-dimensional space, select the nodes in the neighborhood that are in the Open list as candidate child nodes p. i .

[0065] Pathfinding algorithms based on grid methods typically perform pathfinding in a two-dimensional plane, usually using 8-neighborhood or 16-neighborhood networks. Figure 7 This is a schematic diagram of the 8-neighborhood search direction used in this embodiment, with the center point being the parent node P and the surrounding eight nodes being the child nodes p. i .

[0066] Before pathfinding begins, given the coordinates (x, y) of the starting point in a 2D grid, when planning the path, the starting point is the current node (parent node) in the loop, and the candidate nodes around it (neighborhood) are its child nodes p. i When the optimal child node is selected based on the evaluation function and denoted as p... best Then, p best Add it to the already found path; the current loop is complete. p best As the parent node of the next loop, select the next p from its neighborhood. best Until the destination is found.

[0067] Step 2.3.2: Calculate each candidate child node p according to the evaluation function. i Given the cost, select the optimal child node (the node with the lowest cost) and store it in the path list and the Close list. Then, use this child node p as the new parent node P. Repeat steps 2.3.1-2.3.2 until the parent node P becomes the endpoint.

[0068] 2.3.2.1 Improvement of the evaluation function.

[0069] In commonly used pathfinding algorithms, taking the A* algorithm as an example, there are two main aspects to its improvement: one is to improve the search neighborhood, and the other is to improve the evaluation function. This embodiment aims to generate helicopter flight paths that can reduce urban noise, and adds noise as one of the evaluation criteria to the evaluation function as an improvement.

[0070] The traditional A* algorithm uses Manhattan distance and other parameters as key components of its evaluation function to quickly find the shortest path to the target point. However, such a path is not necessarily the one with the least noise impact. Therefore, the cumulative sound exposure level C(n) is added to the evaluation function as an improvement. The improved evaluation function is as follows:

[0071] f(n)=w1*g(n)+w2*h(n)+w3*C(n)(3);

[0072] In the formula, C(n) represents the local noise level, and the weighting coefficients can be adjusted according to different task requirements in practice. f(n) represents the estimated cost of reaching the target state from the initial state via state node n in the grid map. g(n) is the actual cost from the initial state to node n in the state space, and h(n) is the estimated cost of the optimal path from state node n to the target state. w1, w2, and w3 are weighting coefficients, which can be adjusted according to different task requirements in practice. Including C(n) as part of the evaluation function can effectively find a low-noise flight path that can quickly reach the target location.

[0073] 2.3.2.2 Calculation of cumulative acoustic exposure level.

[0074] Helicopters generate noise during flight. When navigating in a grid, it is necessary to calculate the evaluation function for all eight child nodes in the current neighborhood. Therefore, it is impractical to calculate the urban sound exposure level after global pathfinding. Thus, this embodiment chooses to use the cumulative sound exposure level of the local area to participate in the calculation of the evaluation function.

[0075] In the pathfinding algorithm based on the grid method and 8-neighborhood, the path has been discretized into multiple path segments connecting different grids. Let sel(s) be the cumulative sound exposure level when the helicopter flies to the s-th path segment, and let L be the sound exposure level of the s-th segment. E (s), then the calculation model is:

[0076]

[0077] In the formula, t s (s) represents the time it takes for the helicopter to reach the starting point of the route, t e (s) represents the time it takes for the helicopter to reach the end of this route, which is also the starting point of the next route. s (t) represents the instantaneous sound pressure at a specific ground noise observation point at that moment, P ref This is a reference sound pressure level.

[0078] For different purposes or mission requirements, ground noise observation points can be fixed at specific locations or certain locations to study the noise impact of specific areas. During helicopter flight, the location of noise receiving points may also change as the helicopter moves.

[0079] For fast pathfinding, the noise evaluation function can also be simplified. For example, the formula for calculating C(n) can be simplified to:

[0080]

[0081] Where i represents the i-th path segment; n represents the total number of path segments (including all completed paths plus the n-th path currently being evaluated); t e (i) represents the time it takes for the sound pressure level generated at the end of the i-th path segment to reach the receiving point; t s (i) represents the time it takes for the sound pressure level generated at the starting point of the i-th path segment to reach the receiving point; p i (t) represents the sound pressure level received at time t of the i-th path propagation segment. s to t e The summation is a simplification of formula (5), and the summation of the second i=1:n is C(n), which is a simplification of formula (4).

[0082] The calculation process of instantaneous sound pressure at ground noise observation points is the same as the sound exposure level calculation process based on the two-stage sound radiation sphere method.

[0083] The main improvement in the trajectory design method of this embodiment lies in the addition of a noise calculation component to the evaluation function. Considering computational efficiency and other issues, this method employs a rotor noise radiation model that combines the concept of a point sound source with the directional characteristics of rotor noise propagation. A secondary sound radiation sphere replaces the actual blade sound source to transmit rotor noise information and calculate the local ground sound exposure level. The specific process of calculating the sound exposure level of the helicopter at a certain trajectory point is as follows:

[0084] (1) Based on the helicopter's status and spatial position information, the inverse simulation method is used to calculate the spatial position parameters, motion parameters, and rotor noise characteristic parameters of the control vectors of each control point on the helicopter's flight trajectory.

[0085] During unsteady helicopter flight, its flight state changes constantly, meaning the flight state differs at the start and end points of each discrete trajectory segment. Therefore, control points are set at the midpoint of each discrete trajectory segment, and each control point is assigned a corresponding control vector. The control vector contains information such as the helicopter's spatial position and flight parameters, used for subsequent selection, positioning, and noise calculation of the acoustic sphere. The control vector includes: t: the time when the helicopter reaches the control point; (x, y, z): the helicopter's spatial position parameters (the grid center coordinates (x, y) in a 2D grid map, and (x, y, z) in a 3D grid map); γ eff The equivalent flight trajectory angle of the helicopter, and α tpp Together used for locating the acoustic radiation sphere; dV / dt: helicopter flight acceleration; (μ, α) tpp The helicopter's forward ratio and rotor disk tilt angle are also characteristic parameters of the secondary acoustic radiation sphere, which can be directly used to select the secondary acoustic radiation sphere for each discrete control point.

[0086] (2) Select the starting point of the flight trajectory, select the corresponding secondary acoustic radiation ball according to the rotor noise characteristic parameters of the starting point, and perform spatial positioning and azimuth positioning of the secondary acoustic radiation ball according to parameters such as spatial position.

[0087] (μ,α tpp This is used to determine the corresponding secondary acoustic radiation sphere.

[0088] (x,y,z) (in a two-dimensional raster map, it is generally (x,y)), α tpp With γ eff These three parameters are used for the spatial positioning of the acoustic radiation sphere, specifically as follows: Figure 8 As shown.

[0089] After determining the acoustic radiation model, it needs to be placed in the correct spatial location, and the direction of the acoustic radiation sphere needs to be adjusted according to the azimuth angle of the aircraft (i.e. the direction the aircraft is facing). This ensures that the simulated sound wave propagation path matches the actual situation, thereby more accurately predicting and analyzing the rotor noise generated by the aircraft during flight.

[0090] (3) Select a ground noise observation point, determine the corresponding secondary sound radiation point based on the location of the ground noise observation point and the spatial location of the trajectory control point, and calculate the noise information of the radiation point using a two-dimensional linear interpolation method. Finally, map the corresponding noise radiation information to the ground noise observation point. The secondary sound radiation model and the two-dimensional linear interpolation method are as follows: Figure 9 and Figure 10 As shown.

[0091] According to the point source noise attenuation formula, the noise level received by the ground noise observation point is:

[0092] L(r) = L(ro) - Ageometry (7);

[0093] In the formula, L(r) represents the noise sound pressure level received at the ground noise observation point. o ) represents the noise sound pressure level at the secondary sound source radiation point; r represents the distance from the ground noise observation point to the center of the rotor hub. o This represents the radius of the second-order acoustic radiation sphere. A geometry This represents the propagation loss over a spherical surface. The noise sound pressure level attenuates with increasing distance as follows:

[0094] Ageometry = 20lg(r / ro)(8).

[0095] (4) Calculate the noise of the rotor to all ground observation points when the helicopter is located at a certain trajectory control point by cycling through the ground noise observation points, and then perform noise post-processing according to specific requirements.

[0096] Different post-processing methods can be used depending on the requirements. When there is only one ground noise observation point, the sound exposure level (sel) of that ground noise observation point can be calculated. When there are multiple ground noise observation points, the sound exposure level of each ground noise observation point can be calculated separately, and then the average can be calculated.

[0097] (5) Calculation ends.

[0098] 3. Path smoothing processing.

[0099] In this embodiment, the Floyd method is used to remove redundant optimal nodes in the optimal flight path, and then B-spline curves are used to smooth the optimal path after removing nodes to obtain the smoothed optimal path.

[0100] The Freudian smoothing process mainly consists of two core steps: first, merging all nodes located on the same straight line; and second, eliminating nodes, with the specific steps as follows:

[0101] (1) For the initial path (i.e. the optimal flight path) shown in Figure 11(a), if there are 3 or more adjacent nodes collinear, the middle node is removed, and only the first and last nodes of each collinear node are kept. The kept nodes are then added to a new list in sequence to obtain the path shown in Figure 11(b).

[0102] (2) Start from the beginning of the new list. Take the line connecting the current node p and its neighboring node p+1 as a candidate path. If the candidate path satisfies the maximum turning angle constraint and avoids obstacles, then delete node p+1, and let node p+2 continue to connect with node p and judge; otherwise, keep node p+1, and add node p and node p+1 to the path list. Repeat the step "take the line connecting the current node p and its neighboring node p+1 as a candidate path" until the current node is the last node, and get the path shown in Figure 11(c).

[0103] 4. Export flight trajectory data.

[0104] The generated smoothed optimal path is input into the inverse simulation program to obtain the maneuver state data of the smoothed optimal path. This data is then stored together with the smoothed optimal path data to generate a complete flight trajectory data file.

[0105] 5. Noise calculation and analysis.

[0106] The generated complete flight trajectory data file is imported into the ground noise calculation program to generate sound exposure level contour maps and sound pressure-time history maps, which are then analyzed.

[0107] The helicopter low-noise flight trajectory design method provided in this embodiment has the following advantages:

[0108] 1) It enables helicopters to navigate in complex terrain with low noise.

[0109] The evaluation function of commonly used pathfinding algorithms, taking the A* algorithm as an example, is as follows:

[0110] f(n) = g(n) + h(n) (9);

[0111] In the formula, f(n) represents the cost estimate of reaching the target state from the initial state via state node n in the grid map, g(n) is the actual cost from the initial state to node n in the state space, and h(n) is the estimated cost of the optimal path from state node n to the target state. When finding the shortest path to the target point in the shortest time, g(n) and h(n) are usually represented by Manhattan distance, Euclidean distance, and diagonal distance. However, depending on different flight requirements, g(n) and h(n) may also include other information, such as fuel consumption.

[0112] For low-noise trajectories, the sound exposure level (sel) of a local or specific region is added as a component of the evaluation function, and its weight is increased, thereby selecting a path that can effectively reduce the noise level of the target area or the overall area. The improved evaluation function is shown in equation (3).

[0113] 2) It enables rapid calculation of noise under complex helicopter maneuvers.

[0114] The magnitude and directionality of helicopter noise vary under different maneuvers. Calculating noise using direct methods based on the FW-H (Fortz-Williams-Hawkings) equations is computationally intensive and time-consuming. Therefore, a noise calculation method based on a second-order acoustic radiating sphere is employed. Furthermore, inverse simulation methods can be used to obtain the characteristic parameters corresponding to the trajectory control points, allowing for the selection of the appropriate acoustic radiating sphere. This approach ensures both computational efficiency and consideration of the magnitude and directionality of noise under different maneuvers.

[0115] This application also provides an application scenario in which the aforementioned helicopter low-noise flight trajectory design method is applied. Specifically, the helicopter low-noise flight trajectory design method provided in this embodiment can be applied to helicopter flight scenarios. Helicopter flight scenarios include takeoff, cruise, and landing. The helicopter low-noise flight trajectory design method provided in this embodiment can be applied to the takeoff and landing phases. By optimizing the flight trajectory, the noise generated by the helicopter during takeoff and landing can be significantly reduced, thereby reducing the impact on the surrounding environment and residents. In addition, this design method can also be applied to the cruise phase, further improving the overall flight performance of the helicopter and passenger comfort. In summary, the helicopter low-noise flight trajectory design method provided in this embodiment plays an important role in several key phases, providing effective technical support for low-noise helicopter flight.

[0116] Example 2

[0117] This embodiment provides a computer device, which may be a server or a terminal, and its internal structure diagram may be as follows. Figure 12As shown, the computer device includes a processor, memory, input / output (I / O) interfaces, and a communication interface. The processor, memory, and I / O interfaces are connected via a system bus, and the communication interface is also connected to the system bus via the I / O interfaces. The processor provides computational and control capabilities. The memory includes a non-volatile storage medium and internal memory. The non-volatile storage medium stores the operating system, computer programs, and a database. The internal memory provides an environment for the operation of the operating system and computer programs in the non-volatile storage medium. The database stores any data from the helicopter low-noise flight trajectory design method. The I / O interfaces are used for exchanging information between the processor and external devices. The communication interface is used for communicating with external terminals via a network connection. When the computer program is executed by the processor, it implements the helicopter low-noise flight trajectory design method provided in Embodiment 1.

[0118] Those skilled in the art will understand that Figure 12 The structure shown is merely a block diagram of a portion of the structure related to the present application and does not constitute a limitation on the computer device to which the present application is applied. Specific computer devices may include more or fewer components than those shown in the figure, or combine certain components, or have different component arrangements.

[0119] Example 3

[0120] This embodiment provides a computer device, including a memory and a processor. The memory stores a computer program, and when the processor executes the computer program, it implements the helicopter low-noise flight trajectory design method provided in Embodiment 1.

[0121] Example 4

[0122] This embodiment provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the helicopter low-noise flight trajectory design method provided in Embodiment 1.

[0123] Example 5

[0124] This embodiment provides a computer program product, including a computer program that, when executed by a processor, implements the helicopter low-noise flight trajectory design method provided in Embodiment 1.

[0125] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, data stored, data displayed, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties, and the collection, use and processing of the relevant data must comply with relevant regulations.

[0126] Those skilled in the art will understand that all or part of the processes in the methods of the above embodiments can be implemented by a computer program instructing related hardware. The computer program can be stored in a non-volatile computer-readable storage medium, and when executed, it can include the processes of the embodiments of the above methods. Any references to memory, databases, or other media used in the embodiments provided in this application can include at least one of non-volatile and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetic random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. By way of illustration and not limitation, RAM can take many forms, such as Static Random Access Memory (SRAM) or Dynamic Random Access Memory (DRAM).

[0127] The databases involved in the embodiments provided in this application may include at least one type of relational database and non-relational database. Non-relational databases may include, but are not limited to, blockchain-based distributed databases. The processors involved in the embodiments provided in this application may be general-purpose processors, central processing units, graphics processing units, digital signal processors, programmable logic devices, quantum computing-based data processing logic devices, etc., and are not limited to these.

[0128] The technical features of the above embodiments can be combined in any way. For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0129] This document uses specific examples to illustrate the principles and implementation methods of this application. The descriptions of the above embodiments are only for the purpose of helping to understand the methods and core ideas of this application. Furthermore, those skilled in the art will recognize that, based on the ideas of this application, there will be changes in the specific implementation methods and application scope. Therefore, the content of this specification should not be construed as a limitation of this application.

Claims

1. A method for designing a low-noise flight trajectory for a helicopter, characterized in that, The method for designing low-noise flight trajectories for helicopters includes: Based on the helicopter's flight mission requirements, acquire map data of the target area, helicopter performance parameters, flight constraints, and the starting and ending points of the flight path; The map data is rasterized to obtain a rasterized map; Based on the helicopter performance parameters, flight constraints, and the starting and ending points of the flight path, a path optimization algorithm is used to determine the optimal flight path of the helicopter in the rasterized map. The evaluation function of the path optimization algorithm is determined based on the cumulative sound exposure level, the actual cost from the starting point to the current node, and the estimated cost from the current node to the ending point. The cumulative sound exposure level is a variable value calculated based on the noise information of the current node received by the ground noise observation point. The node corresponding to the minimum value of the evaluation function is determined as the optimal node. The optimal node is a node on the optimal flight path, and each grid in the rasterized map represents each of the nodes. The calculation expression for the cumulative acoustic exposure level is as follows: Where C(n) is the cumulative sound exposure level when the helicopter flies to the nth segment of the path; L E (i) represents the sound exposure level of the i-th path segment; t s (i) represents the time it takes for the helicopter to reach the starting point of the i-th segment of the path; t e (i) represents the time it takes for the helicopter to reach the end of the i-th segment of the path; p s (t) represents the instantaneous sound pressure at a specific observation point at time t on the i-th path segment; p ref For reference sound pressure level; The expression for the evaluation function is: f(n)=w1*g(n)+w2*h(n)+w3*C(n); Where f(n) is the cost estimate of reaching the target state from the initial state via state node n in the rasterized map; g(n) is the actual cost from the initial state to node n in the state space; h(n) is the estimated cost from state node n to the target state; C(n) is the cumulative sound exposure level; w1, w2, and w3 are weighting coefficients.

2. The helicopter low-noise flight trajectory design method according to claim 1, characterized in that, The process of acquiring noise information specifically includes: Based on the real-time flight status and spatial position information of the helicopter, the control vector of each control point on the helicopter flight path is calculated using the inverse simulation method. The control vector includes the time when the helicopter moves to the control point, spatial position parameters, equivalent flight trajectory angle, flight acceleration, advance ratio and rotor disk tilt angle. The corresponding secondary acoustic radiation sphere is determined based on the advance ratio and rotor disk tilt angle of each control point; The spatial position and orientation of the corresponding secondary acoustic radiation sphere are determined based on the spatial position parameters of each control point, the equivalent flight trajectory angle, and the rotor disk tilt angle. The secondary acoustic radiation point on the target secondary acoustic radiation sphere is determined based on the location of the ground noise observation point and the spatial location parameters of each control point, wherein the target secondary acoustic radiation sphere is a secondary acoustic radiation sphere with a determined spatial location and orientation; Two-dimensional linear interpolation was used to calculate the noise information at the secondary sound radiation point; The noise information received by the ground noise observation point is calculated based on the noise information of the secondary sound radiation point.

3. The helicopter low-noise flight trajectory design method according to claim 2, characterized in that, The expression for noise information is: L(r)=L(ro)-A geometry ; A geometry =20lg(r / ro); Where L(r) represents the noise sound pressure level received at the ground noise observation point; L(ro) represents the noise sound pressure level at the secondary sound source radiation point; A geometry denoted by spherical propagation loss; r represents the distance from the ground noise observation point to the center of the propeller hub; ro represents the radius of the secondary acoustic radiation sphere.

4. The helicopter low-noise flight trajectory design method according to claim 1, characterized in that, After performing the step "based on the helicopter performance parameters, the flight constraints, and the start and end positions of the flight path, determine the optimal flight path of the helicopter in the rasterized map using a path optimization algorithm", the helicopter low-noise flight trajectory design method further includes: The Freudian method is used to remove redundant optimal nodes on the optimal flight path to obtain the optimal path with nodes removed. The redundant optimal nodes include the intermediate optimal nodes on the target line segment excluding the first and last optimal nodes and the second optimal nodes on the potential line segment that meet the conditions. The target line segment is a line segment composed of at least 3 collinear nodes on the optimal flight path. The potential line segment is a line segment composed of two adjacent optimal nodes on the optimal flight path. The conditions are that the turning angle of the potential line segment meets the maximum turning angle constraint and the potential line segment can avoid obstacles. The second optimal node is the optimal node that is relatively far back on the potential line segment. The optimal path for removing nodes is smoothed using B-spline curves to obtain the smoothed optimal path.

5. The helicopter low-noise flight trajectory design method according to claim 4, characterized in that, After performing the step "smoothing the optimal path for node removal using B-spline curves to obtain the smoothed optimal path", the helicopter low-noise flight trajectory design method further includes: The optimal path for smoothing is then subjected to inverse simulation to obtain the helicopter's maneuvering state data. Based on the optimal path for smoothing and the maneuvering status data, a contour map of acoustic exposure level and a sound pressure-time history map are generated.

6. A computer device, comprising: A memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that the processor executes the computer program to implement the helicopter low-noise flight trajectory design method according to any one of claims 1-5.

7. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by the processor, it implements the helicopter low-noise flight trajectory design method according to any one of claims 1-5.

8. A computer program product, comprising a computer program, characterized in that, When the computer program is executed by the processor, it implements the helicopter low-noise flight trajectory design method according to any one of claims 1-5.

Images