Aircraft aerial delivery material recovery method, device and computer equipment

Abstract

The application relates to an aircraft airdrop material recovery method, device and computer equipment. The method comprises the following steps: constructing a design variable of an optimal release point of an aircraft airdrop material; the design variable comprises a release point coordinate and a heading angle; a relationship between the design variable and a landing point position vector is established according to a motion equation of a material landing process and the design variable; a landing point evaluation function is determined according to a pre-acquired release mode, a landing point position vector corresponding to each heading angle is simulated and calculated in a way of traversing the heading angle, and a corresponding heading angle and a corresponding landing point position vector are selected according to a landing point evaluation function optimization principle; a greedy algorithm is used to solve a traveling salesman problem to traverse all landing points of the landing point position vector, so that an optimal path planning of the material recovery is obtained. According to the method, the heading angle can be dynamically adjusted according to real-time environment data, and the material release demand is met.

Description

Technical Field

[0001] This application relates to the field of flight dynamic planning technology, and in particular to a method, apparatus and computer equipment for recovering airdropped supplies from an aircraft. Background Technology

[0002] Following natural disasters (such as earthquakes, floods, and hurricanes), timely delivery of supplies (e.g., food, medicine, tents, and other necessities) to affected areas is crucial for saving lives and alleviating the disaster. Aircraft, due to their high maneuverability, independence from ground traffic conditions, and ability to quickly reach remote disaster areas, are increasingly widely used in disaster relief delivery.

[0003] In existing aircraft-based disaster relief delivery technologies, path planning based on the Traveling Salesman Problem (TSP) is a common approach. The TSP aims to find the shortest path for a traveling salesman to return to its starting city after visiting multiple cities. In disaster relief scenarios, each delivery point is considered a "city," and the aircraft departs from a supply base, visiting these points sequentially and returning to the base. However, after a natural disaster, the environment in the disaster area is often highly uncertain, including meteorological conditions (such as strong winds, heavy rain, and unstable air currents) and terrain changes (such as road damage and landslides altering the terrain). The TSP problem typically relies on static geographical data and relatively stable environmental assumptions for path planning. When faced with these uncertainties, the actual flight path may deviate significantly from the planned path, affecting the accuracy and timeliness of delivery. Summary of the Invention

[0004] Therefore, it is necessary to provide a method, apparatus, and computer equipment for recovering airdropped supplies from aircraft, addressing the aforementioned technical problems.

[0005] A method for recovering supplies airdropped by an aircraft, the method comprising:

[0006] Design variables for constructing the optimal release point for aircraft to drop supplies; the design variables include the release point coordinates and the heading angle;

[0007] Based on the motion equations of the material landing process and the aforementioned design variables, establish the relationship between the design variables and the landing point position vector;

[0008] Based on the pre-acquired delivery pattern, the landing point evaluation function is determined. By traversing the heading angles, the landing point position vector corresponding to each heading angle is simulated and calculated. Based on the optimal principle of the landing point evaluation function, the corresponding heading angle and the corresponding landing point position vector are selected.

[0009] A greedy algorithm is used to solve the Traveling Salesman Problem by traversing all landing point position vectors to obtain the optimal path planning for material recovery.

[0010] In one embodiment, the design variables for determining the optimal release point for the aircraft to deliver supplies include:

[0011] u = [x c y c , ψ]

[0012] Where u represents the design variable, x c y c ψ represents the coordinates of the release point, and ψ represents the heading angle.

[0013] In one embodiment, the equation of motion for the material landing process is as follows:

[0014]

[0015] Where vv represents the velocity vector of the center of mass of the material. The vector representing the velocity of the center of mass is denoted by ρ, where D is air resistance, g is gravitational acceleration, m is the mass of the object, ρ is air density, and R is the instantaneous projected radius of the parachute deployment. The rate of change of the projected radius.

[0016] In one embodiment, the method further includes: determining a landing point evaluation function based on a pre-acquired delivery pattern, including:

[0017] f(x I p1, x I p2, ..., x I p n ,u)

[0018] Where, x I p i This represents the vector indicating the landing point of the i-th material on the ground.

[0019] Alternatively, based on the pre-obtained delivery pattern, determine the landing point evaluation function, including:

[0020]

[0021] Where n is the total number of airdropped supplies, x T The coordinate vector of the target landing point.

[0022] In one embodiment, the method further includes: using a greedy algorithm to solve the traveling salesman problem by traversing all landing point position vectors to obtain the optimal path planning for material recovery as follows:

[0023]

[0024] x ij ∈{0,1}, i,j∈V

[0025] Where, d ij x represents the distance between the i-th and j-th landing points. ij Let V represent the decision variable, taking the value 1 or 0, where V represents the set of nodes, and S is a subset of the set of nodes V.

[0026] In one embodiment, the method further includes: using a greedy algorithm to solve the traveling salesman problem by traversing all landing point position vectors to obtain the optimal path planning for material recovery as follows:

[0027]

[0028] ∑k j =n

[0029] in, These are the objective functions for the path distance and time during the vehicle's j-th departure, respectively, where x is the path distance. and time Weighted sum, k j d[i][j] represents the number of landing points traversed on the j-th start, d[i][j] represents the distance between the i-th and j-th landing points, t[i][j] represents the time interval between recovering the i-th and j-th landing points, x[i] represents the i-th recovered resource, and x[i]-x[j] represents a path. Furthermore, when j ≠ i, x[i] ≠ x[j], v[x[j]] is the volume function of the j-th material to be recycled in the path sequence, and the maximum volume that the vehicle can carry is V. max d[x[i]][x[j]] represents the distance function between the i-th landing point and the j-th landing point, and t[x[i]][x[j]] is the time function between the i-th landing point and the j-th landing point.

[0030] An airdropped supplies recovery device, the device comprising:

[0031] The design variable construction module is used to construct design variables for the optimal release point of the aircraft dropping supplies; the design variables include the release point coordinates and the heading angle.

[0032] The relationship building module is used to establish the relationship between the design variables and the landing point position vector based on the motion equation of the material landing process and the design variables.

[0033] The landing evaluation module is used to determine the landing evaluation function based on the pre-acquired deployment pattern. It simulates and calculates the landing position vector corresponding to each heading angle by traversing the heading angles, and selects the corresponding heading angle and the corresponding landing position vector according to the optimal principle of the landing evaluation function.

[0034] The planning module is used to solve the Traveling Salesman Problem by traversing all landing point position vectors to obtain the optimal path planning for material recovery.

[0035] In one embodiment, the design variable construction module is also used to construct design variables for the optimal release point of the aircraft dropping supplies, including:

[0036] u = [x c ,y c ,ψ]

[0037] Where u represents the design variable, x c ,y c ψ represents the coordinates of the release point, and ψ represents the heading angle.

[0038] A computer device includes a memory and a processor, the memory storing a computer program, and the processor executing the computer program performing the following steps:

[0039] Design variables for constructing the optimal release point for aircraft to drop supplies; the design variables include the release point coordinates and the heading angle;

[0040] Based on the motion equations of the material landing process and the aforementioned design variables, establish the relationship between the design variables and the landing point position vector;

[0041] Based on the pre-acquired delivery pattern, the landing point evaluation function is determined. By traversing the heading angles, the landing point position vector corresponding to each heading angle is simulated and calculated. Based on the optimal principle of the landing point evaluation function, the corresponding heading angle and the corresponding landing point position vector are selected.

[0042] A greedy algorithm is used to solve the Traveling Salesman Problem by traversing all landing point position vectors to obtain the optimal path planning for material recovery.

[0043] A computer-readable storage medium having a computer program stored thereon, the computer program performing the following steps when executed by a processor:

[0044] Design variables for constructing the optimal release point for aircraft to drop supplies; the design variables include the release point coordinates and the heading angle;

[0045] Based on the motion equations of the material landing process and the aforementioned design variables, establish the relationship between the design variables and the landing point position vector;

[0046] Based on the pre-acquired delivery pattern, the landing point evaluation function is determined. By traversing the heading angles, the landing point position vector corresponding to each heading angle is simulated and calculated. Based on the optimal principle of the landing point evaluation function, the corresponding heading angle and the corresponding landing point position vector are selected.

[0047] A greedy algorithm is used to solve the Traveling Salesman Problem by traversing all landing point position vectors to obtain the optimal path planning for material recovery.

[0048] The aforementioned methods, devices, computer equipment, and storage media for airdropped supplies, by incorporating the heading angle, can optimize the accuracy of supplies delivery based on the motion equations of the landing process, ensuring supplies land more precisely in designated disaster areas and avoid terrain obstacles. Simultaneously, this variable helps optimize the distribution of landing points, thereby shortening ground recovery paths and improving rescue efficiency. Regarding environmental uncertainties, the heading angle can be dynamically adjusted based on real-time environmental data to ensure the smooth execution of supplies delivery missions. Furthermore, by flexibly adjusting the heading angle according to the different supplies needs of various disaster areas, diverse requirements can be met, and in terms of resource utilization, aircraft energy consumption and equipment utilization rates can be optimized, enabling aircraft to play a greater role in disaster relief. Attached Figure Description

[0049] Figure 1 This is a flowchart illustrating a method for recovering supplies dropped by an aircraft in one embodiment.

[0050] Figure 2 This is a schematic diagram illustrating the coordinate system definition for the airdrop release process in one embodiment;

[0051] Figure 3 This is a structural block diagram of an aircraft-dropped supplies recovery device in one embodiment;

[0052] Figure 4 This is an internal structural diagram of a computer device in one embodiment. Detailed Implementation

[0053] To make the objectives, technical solutions, and advantages of this application clearer, the following detailed description is provided in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the scope of this application.

[0054] In one embodiment, such as Figure 1 As shown, a method for recovering supplies airdropped by an aircraft is provided, including the following steps:

[0055] Step 102: Construct design variables for the optimal release point for the aircraft to drop supplies.

[0056] Design variables include the release point coordinates and the heading angle.

[0057] In this step, different heading angles result in different distributions of material landing points. Therefore, designing the heading angle can significantly improve the distribution results, making it easier to recover materials. On the other hand, real-time planning of the heading angle can also ensure the accuracy of landing point distribution in the face of uncertainties in the deployment scenario.

[0058] Step 104: Based on the motion equations of the material landing process and the design variables, establish the relationship between the design variables and the landing point position vector.

[0059] In this step, since the landing point of the deployed materials is affected by various factors, we consider the motion equation of the material landing process. By calculating the mapping relationship between the release point coordinates and the ground through the motion equation of the material landing process, we can accurately predict the landing point information when changing the heading angle, which facilitates the efficiency of subsequent ground recovery.

[0060] Step 106: Based on the pre-acquired delivery mode, determine the landing point evaluation function, simulate and calculate the landing point position vector corresponding to each heading angle by traversing the heading angles, and select the corresponding heading angle and the corresponding landing point position vector according to the optimal principle of the landing point evaluation function.

[0061] In this step, different landing point evaluation functions can be designed for different delivery modes. The landing point evaluation function is an evaluation index that evaluates the closeness between the landing point and the expected landing point. Since the relationship between the design variables and the landing point position vector is established in advance, simulation calculation can be used to traverse each heading angle within the heading angle constraint to obtain the predicted landing point position vector. Then, the landing point is evaluated according to the landing point evaluation function, thereby optimizing to obtain the best heading angle.

[0062] Step 108: Use a greedy algorithm to solve the traveling salesman problem by traversing all landing point position vectors to obtain the optimal path planning for material recovery.

[0063] After the supplies land, since all landing points are optimized by adjusting the heading angle, a greedy algorithm can be used to solve the traveling salesman problem by traversing all landing point position vectors, thus achieving the optimal path planning for supplies recovery.

[0064] In the aforementioned method of airdropping and recovering supplies, by incorporating the heading angle, the accuracy of the delivery can be optimized based on the motion equations of the supplies' landing process, ensuring that supplies land more precisely in the designated disaster area and avoid terrain obstacles. Simultaneously, this variable helps optimize the distribution of landing points, thereby shortening the ground recovery path and improving rescue efficiency. Regarding environmental uncertainties, the heading angle can be dynamically adjusted based on real-time environmental data to ensure the smooth execution of supply delivery missions. Furthermore, by flexibly adjusting the heading angle according to the different supply needs of various disaster areas, diverse requirements can be met, and in terms of resource utilization, the energy consumption and equipment utilization rate of drones can be optimized, enabling drones to play a greater role in disaster relief.

[0065] In one embodiment, the flight altitude of the aircraft is generally fixed and is not considered an optimization design variable. During the airdrop of supplies using an uncontrolled parachute, the parachute system lacks autonomous maneuverability; therefore, the only selectable design variables are the airdrop location and the aircraft's flight direction. The design variables for optimizing the uncertainty of the optimal release point are written as:

[0066] u = [x c y c , ψ]

[0067] Where u represents the design variable, x c y c This represents the coordinates of the release point, and ψ represents the heading angle, as shown in the example below. Figure 2 As shown.

[0068] The objective function and constraints for optimizing the uncertainty of the airdrop release point need to be determined based on the specific application scenario, and their calculation requires integration with a dynamic model of the material airdrop process. In one embodiment, the equation of motion for the material landing process is:

[0069]

[0070] Where, vv represents the velocity vector of the center of mass of the material. The vector representing the velocity of the center of mass is denoted by ρ, where D is air resistance, g is gravitational acceleration, m is the mass of the object, ρ is air density, and R is the instantaneous projected radius of the parachute upon deployment. The rate of change of the projected radius.

[0071] Specifically, air resistance D can be further expressed as

[0072]

[0073] In the formula, C d Let S be the air resistance coefficient, S be the drag reference area, and let w be the wind speed vector. Then v ∞ It can be represented as v ∞ =wv. The additional mass is generated by the forced motion of the air inside the parachute canopy, and can usually be expressed as the mass of the air contained in a sphere with the same projected radius as the parachute multiplied by a constant coefficient k. a to indicate

[0074]

[0075] When wind interference is present, the main parachute deployment dynamics have a significant impact on landing time and drop point. Based on a summary of extensive experimental data, an empirical formula is proposed for the change of drag area with deployment time during the parachute deployment process:

[0076]

[0077] Among them, C d S0 is the steady-state resistance area after inflation is complete, t0 is the opening time, η is the ratio of the projected area when straightened to the projected area when fully extended, and t is the time from the start of inflation to the current moment.

[0078] Assuming the parasol's projected shape is approximately circular during inflation, the rate of change of the parasol's projected radius over time can be further obtained.

[0079] R0 is the projected radius of the canopy after it is fully inflated.

[0080] Based on the above results, projecting the vector form of the equations of motion onto the Earth system yields the set of ordinary differential equations for the motion dynamics of the center of mass of the object-umbrella system.

[0081] In another embodiment, the purpose of the airdrop mission is to ensure that the cargo is as close as possible to ground recovery personnel upon landing, facilitating collection by ground personnel. Let x be the reference point. I p i Let be the vector of the landing position of the i-th item on the ground. Then the landing position evaluation function can be expressed as:

[0082] f(x I p1, x I p2, ..., x I p n ,u)

[0083] In one specific embodiment, n represents the total number of airdropped goods. Taking a fixed-point airdrop as an example, the landing point evaluation function can be calculated as follows:

[0084] Where x T Let x be the coordinate vector of the target landing point. Under uncertain conditions, x I p i All are random variables.

[0085] In summary, after establishing the motion equations for the cargo landing process and the landing point evaluation function, in continuous cargo airdrops, the parachute of the next cargo is released and deployed immediately after the previous cargo leaves the aircraft. Therefore, the time of the previous cargo's exit determines the initial airdrop location of the next cargo. Due to errors in actual flight, the actual initial airdrop release point may differ from the calculated airdrop release point. The error in the airdrop release point is related to the transport aircraft's current flight direction. To investigate the impact of the transport aircraft's heading angle on the multi-point cargo drop and recovery problem, considering the influence of wind speed and uncertainties, we can use a step size of 1° to traverse all flight directions and calculate the scattering range of airdrop release points using the airdrop planning method under given conditions.

[0086] In one embodiment, the problem of recovering landed supplies can be viewed as a typical Traveling Salesman Problem (TSP), a classic combinatorial optimization problem in graph theory. It can be transformed into finding a Hamiltonian cycle with minimum weight in a weighted, completely undirected graph, such that the total weight of the cycle is minimized. Find the shortest traversal order that minimizes the following objective function:

[0087]

[0088] Where t i The indexes 1 to n represent the landing points of the airdropped cargo, and d(t) represents the number of landing points of the airdropped cargo. i , t i+1 d(t0, t) represents the distance between landing point i and landing point i+1. i ) and d(t n t0 and t0 represent the distance from the base to the first cargo landing point and the distance to the destination after the last cargo is recovered, respectively.

[0089] The shortest traversal order for the vehicle collecting supplies, which departs from the base, visits each target location exactly once, and finally returns to the starting point, can be represented as F = (X[1], X[2], X[3], ..., X[n]).

[0090]

[0091] ∑k j =n

[0092] These are the objective functions for path distance and time during the vehicle's and departure processes, respectively, where x is the path distance. and time The weighted sum is the objective function to be optimized. k j Let be the number of landing points traversed on the j-th departure, d[i][j] represent the distance between the i-th and j-th landing points, t[i][j] represent the time interval between recovering the i-th and j-th landing points, x[i] is the i-th recovered item, and x[i]-x[j] represents a path. Furthermore, when j ≠ i, x[i] ≠ x[j], v[x[j]] is the volume function of the i-th item to be collected in the path sequence, and the maximum volume that the delivery vehicle can carry is V. max d[x[i]][x[j]] represents the distance function between the i-th landing point and the j-th landing point, and t[x[i]][x[j]] is the time function between the i-th landing point and the j-th landing point. t[x[i]][x[j]] is derived based on d[x[i]][x[j]], and the driving speed takes into account the terrain and road conditions in the mountainous area, with different driving speeds for different road sections.

[0093] The present invention addresses the TSP (Transportation-Shortest-Package) problem of parachute-dropped cargo recovery. The problem can be described as follows: an aircraft drops cargo via parachute at regular intervals from high altitude. Ground-based logistics personnel depart from a base, collect all cargo using vehicles, and return to the base. Vehicles have a fixed volume, and the cargo to be recovered has a fixed volume; only a fixed volume of cargo can be recovered at a time. After the vehicles are full, they must return to the base to unload before departing again. The traversal of cargo landing points is not a simple straight-line connection, but rather follows feasible roads in the actual terrain, influenced by road conditions and topography. The shortest path for cargo recovery is described in graph theory terms as: finding a minimum-weight Hamiltonian cycle in a weighted complete graph. Let G(V, E) be a weighted complete graph, V = (1, 2, ..., n) be the vertex set, and E be the edge set. The distances between vertices are known: (d ij >0, d ii =0, i, j∈V).

[0094] The mathematical model for the TSP (Traffic Supplement) problem can be written in linear programming form:

[0095]

[0096] x ij ∈{0,1}, i, j∈V

[0097] Among them, di j Let xi represent the distance between the i-th and j-th landing points. j Let V represent the decision variable, taking the value 1 or 0, where V represents the set of nodes, and S is a subset of the set of nodes V.

[0098] It should be understood that, although Figure 1 The steps in the flowchart are shown sequentially as indicated by the arrows, but these steps are not necessarily executed in the order indicated by the arrows. Unless otherwise specified herein, there is no strict order in which these steps are executed, and they can be performed in other orders. Figure 1 At least some of the steps in the process may include multiple sub-steps or multiple stages. These sub-steps or stages are not necessarily completed at the same time, but can be executed at different times. The execution order of these sub-steps or stages is not necessarily sequential, but can be executed in turn or alternately with other steps or at least some of the sub-steps or stages of other steps.

[0099] In one embodiment, such as Figure 3 As shown, an airdropped supplies recovery device for aircraft is provided, comprising: a design variable construction module 302, a relationship construction module 304, a landing evaluation module 306, and a planning module 308, wherein:

[0100] Design variable construction module 302 is used to construct design variables for the optimal release point of the aircraft dropping supplies; the design variables include the release point coordinates and the heading angle.

[0101] The relationship building module 304 is used to establish the relationship between the design variables and the landing point position vector based on the motion equation of the material landing process and the design variables.

[0102] The landing evaluation module 306 is used to determine the landing evaluation function based on the pre-acquired delivery mode. It uses a traversal of heading angles to simulate and calculate the landing position vector corresponding to each heading angle, and selects the corresponding heading angle and the corresponding landing position vector according to the optimal principle of the landing evaluation function.

[0103] Planning module 308 is used to solve the Traveling Salesman Problem by traversing all landing point position vectors to obtain the optimal path planning for material recovery.

[0104] In one embodiment, the design variable construction module 302 is further used to construct design variables for the optimal release point of the aircraft dropping supplies, including:

[0105] u = [x c y c , ψ]

[0106] Where u represents the design variable, x c y c ψ represents the coordinates of the release point, and ψ represents the heading angle.

[0107] In one embodiment, the equation of motion for the material landing process is:

[0108]

[0109] Where, vv represents the velocity vector of the center of mass of the material. The vector representing the velocity of the center of mass is denoted by ρ, where D is air resistance, g is gravitational acceleration, m is the mass of the object, ρ is air density, and R is the instantaneous projected radius of the parachute upon deployment. The rate of change of the projected radius.

[0110] In one embodiment, the landing evaluation module 306 is further configured to determine a landing evaluation function based on a pre-acquired deployment pattern, including:

[0111] f(x I p1, x I p2, ..., x I p n ,u)

[0112] Where, xI p i This represents the vector indicating the landing point of the i-th material on the ground.

[0113] Alternatively, based on the pre-acquired delivery pattern, determine the landing point evaluation function, including:

[0114]

[0115] Where n is the total number of airdropped supplies, x T The coordinate vector of the target landing point.

[0116] In one embodiment, the planning module 308 is further configured to use a greedy algorithm to solve the traveling salesman problem by traversing all landing point position vectors to obtain the optimal path planning for material recovery as follows:

[0117]

[0118] x ij ∈{0,1}, i,j∈V

[0119] Where, d ij x represents the distance between the i-th and j-th landing points. ij Let V represent the decision variable, taking the value 1 or 0, where V represents the set of nodes, and S is a subset of the set of nodes V.

[0120] In one embodiment, the planning module 308 is further configured to use a greedy algorithm to solve the traveling salesman problem by traversing all landing point position vectors to obtain the optimal path planning for material recovery as follows:

[0121]

[0122] ∑k j =n

[0123] in, These are the objective functions for path distance and time during the vehicle's and departure processes, respectively, where x is the path distance. and time Weighted sum, k j d[i][j] represents the number of landing points traversed on the j-th start, d[i][j] represents the distance between the i-th and j-th landing points, t[i][j] represents the time interval between recovering the i-th and j-th landing points, x[i] represents the i-th recovered resource, and x[i]-x[j] represents a path. Furthermore, when j ≠ i, x[i] ≠ x[j], v[x[j]] is the volume function of the j-th material to be recycled in the path sequence, and the maximum volume that the vehicle can carry is V. maxd[x[i]][x[j]] represents the distance function between the i-th landing point and the j-th landing point, and t[x[i]][x[j]] is the time function between the i-th landing point and the j-th landing point.

[0124] Specific limitations regarding the aircraft-dropped supplies recovery device can be found in the limitations on the aircraft-dropped supplies recovery method described above, and will not be repeated here. Each module in the aforementioned aircraft-dropped supplies recovery device can be implemented entirely or partially through software, hardware, or a combination thereof. These modules can be embedded in or independent of the processor in a computer device in hardware form, or stored in the memory of a computer device in software form, so that the processor can call and execute the corresponding operations of each module.

[0125] In one embodiment, a computer device is provided, which may be a terminal, and its internal structure diagram may be as follows: Figure 4 As shown, the computer device includes a processor, memory, network interface, display screen, and input devices connected via a system bus. The processor provides computing and control capabilities. The memory includes non-volatile storage media and internal memory. The non-volatile storage media stores the operating system and computer programs. The internal memory provides an environment for the operation of the operating system and computer programs in the non-volatile storage media. The network interface is used to communicate with external terminals via a network connection. When the computer program is executed by the processor, it implements a method for recovering air-dropped supplies from an aircraft. The display screen can be an LCD screen or an e-ink screen. The input devices can be a touch layer covering the display screen, buttons, a trackball, or a touchpad on the computer device's casing, or an external keyboard, touchpad, or mouse.

[0126] Those skilled in the art will understand that Figure 4 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.

[0127] In one embodiment, a computer device is provided, including a memory and a processor, the memory storing a computer program, the processor executing the computer program to implement the steps of the method described above.

[0128] In one embodiment, a computer-readable storage medium is provided having a computer program stored thereon, which, when executed by a processor, implements the steps of the method described above.

[0129] 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, storage, databases, or other media used in the embodiments provided in this application can include non-volatile and / or volatile memory. Non-volatile memory can include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM), or flash memory. Volatile memory can include random access memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in various forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), dual data rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous link DRAM (SLDRAM), Rambus direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM), etc.

[0130] 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.

[0131] The embodiments described above are merely illustrative of several implementation methods of this application, and while the descriptions are relatively specific and detailed, they should not be construed as limiting the scope of the invention patent. It should be noted that those skilled in the art can make various modifications and improvements without departing from the concept of this application, and these all fall within the protection scope of this application. Therefore, the protection scope of this patent application should be determined by the appended claims.

Claims

1. A method for recovering supplies airdropped from an aircraft, characterized in that, The method includes: Design variables for constructing the optimal release point for aircraft to drop supplies; the design variables include the release point coordinates and the heading angle; Based on the motion equations of the material landing process and the aforementioned design variables, establish the relationship between the design variables and the landing point position vector; Based on the pre-acquired delivery pattern, the landing point evaluation function is determined. By traversing the heading angles, the landing point position vector corresponding to each heading angle is simulated and calculated. Based on the optimal principle of the landing point evaluation function, the corresponding heading angle and the corresponding landing point position vector are selected. A greedy algorithm is used to solve the Traveling Salesman Problem by traversing all landing point position vectors to obtain the optimal path planning for material recovery; A greedy algorithm is used to solve the Traveling Salesman Problem by traversing all landing point position vectors to obtain the optimal path planning for resource recovery, including: A greedy algorithm is used to solve the Traveling Salesman Problem by traversing all landing point position vectors to obtain the optimal path planning for material recovery as follows: x ij ∈{0,1},i,j∈V Where, d ij x represents the distance between the i-th and j-th landing points. ij Let V represent the decision variable, taking the value 1 or 0, where V represents the set of nodes and S is a subset of the set of nodes V. A greedy algorithm is used to solve the Traveling Salesman Problem by traversing all landing point position vectors to obtain the optimal path planning for resource recovery. This also includes: A greedy algorithm is used to solve the Traveling Salesman Problem by traversing all landing point position vectors to obtain the optimal path planning for material recovery as follows: ∑k j =n in, These are the objective functions for the path distance and time during the vehicle's j-th departure, respectively, where x is the path distance. and time Weighted sum, k j Let be the number of landing points traversed on the j-th start, d[i][j] represent the distance between the i-th and j-th landing points, t[i][j] represent the time interval between recovering the i-th and j-th landing points, x[i] is the i-th recovered resource, and x[i]-x[j] represents a path. Furthermore, when j ≠ i, x[i] ≠ x[j], v[x[j]] is the volume function of the j-th material to be recycled in the path sequence, and the maximum volume that the vehicle can carry is V. max d[x[i]][x[j]] represents the distance function between the i-th landing point and the j-th landing point, and t[x[i]][x[j]] is the time function between the i-th landing point and the j-th landing point.

2. The method according to claim 1, characterized in that, The design variables for determining the optimal release point for aircraft to deliver supplies include: u=[x c ,y c ,ψ] Where u represents the design variable, x c ,y c ψ represents the coordinates of the release point, and ψ represents the heading angle.

3. The method according to claim 1, characterized in that, The equation of motion for the material landing process is: Where v represents the velocity vector of the center of mass of the material. The vector representing the velocity of the center of mass is denoted by ρ, where D is air resistance, g is gravitational acceleration, m is the mass of the object, ρ is air density, and R is the instantaneous projected radius of the parachute upon deployment. The rate of change of the projected radius.

4. The method according to claim 2, characterized in that, Based on the pre-acquired delivery pattern, determine the landing point evaluation function, including: f(x I p1,x I p2,...,x I p n ,u) Where, x I p i This represents the vector indicating the landing point of the i-th material on the ground. Alternatively, based on the pre-acquired delivery pattern, determine the landing point evaluation function, including: Where n is the total number of airdropped supplies, x T The coordinate vector of the target landing point.

5. A device for recovering supplies dropped from an aircraft, characterized in that, The apparatus for implementing the aircraft airdropped supplies recovery method according to any one of claims 1 to 4, the apparatus comprising: The design variable construction module is used to construct design variables for the optimal release point of the aircraft dropping supplies; the design variables include the release point coordinates and the heading angle. The relationship building module is used to establish the relationship between the design variables and the landing point position vector based on the motion equation of the material landing process and the design variables. The landing evaluation module is used to determine the landing evaluation function based on the pre-acquired deployment pattern. It simulates and calculates the landing position vector corresponding to each heading angle by traversing the heading angles, and selects the corresponding heading angle and the corresponding landing position vector according to the optimal principle of the landing evaluation function. The planning module is used to solve the Traveling Salesman Problem by traversing all landing point position vectors to obtain the optimal path planning for material recovery.

6. The apparatus according to claim 5, characterized in that, The design variable building module also includes design variables for constructing the optimal release point for the aircraft to drop supplies. u=[x c ,y c ,ψ] Where u represents the design variable, x c ,y c ψ represents the coordinates of the release point, and ψ represents the heading angle.

7. A computer device comprising a memory and a processor, wherein the memory stores a computer program, characterized in that, When the processor executes the computer program, it implements the steps of the method according to any one of claims 1 to 4.

8. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by a processor, it implements the steps of the method according to any one of claims 1 to 4.

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