Aircraft load distribution method and system based on KDtree

Abstract

This invention discloses an aircraft payload allocation method and system based on KDtree, comprising: inputting source payload, target nodes, and matching quantity; decomposing the source payload into payload vectors and source node coordinates; partitioning the source payload and target nodes according to aircraft components, and performing payload allocation only within the same partition; constructing a spatial index structure for each partition based on the target nodes within that partition; performing a nearest neighbor search for each source node within the current partition using the spatial index structure to obtain the n nearest target nodes, where n is the matching quantity; and allocating the payload vector corresponding to the current source node to the n target nodes. This invention introduces the KDtree spatial index structure for nearest neighbor search, reducing the average time for payload matching. The allocation speed is significantly improved, and the efficiency advantage becomes more prominent as the mesh becomes finer.

Description

Technical Field

[0001] This invention relates to the interdisciplinary field of aircraft load calculation and strength analysis, and in particular to an aircraft load allocation method and system based on KDtree. Background Technology

[0002] In aircraft design, load analysis is a core component of structural strength verification and validation. Throughout their lifecycle, aircraft undergo various operating conditions, including flight maneuvers, ground taxiing, landing impacts, and ground maintenance (such as lifting and hoisting). The aerodynamic, inertial, and contact loads under each condition must be obtained through precise numerical calculations or wind tunnel testing. These loads are typically defined on aerodynamic meshes, mass distribution points, or specific load extraction nodes (collectively referred to as "source nodes"), and their number can reach hundreds of thousands. Subsequent finite element analysis or static testing requires applying loads to structural mesh nodes or test loading points (collectively referred to as "target nodes"), typically numbering in the tens of thousands. Due to significant differences in the location distribution, density, and topological relationships of the two types of nodes, direct mapping is often impractical. Therefore, load allocation techniques must be employed to achieve an equivalent load transformation from source nodes to target nodes.

[0003] The core principle of load allocation lies in the equivalence principle: the total load (resultant force and resultant moment) must remain strictly consistent before and after allocation, and the load distribution should reflect the spatial characteristics of the original load as much as possible. The most commonly used equivalence method in engineering is nearest-neighbor allocation—that is, the load of each source node is allocated to its neighboring target nodes according to a certain weight, usually inversely proportional to the distance or based on shape function interpolation. Existing technologies mostly employ the global nearest-distance matching method: for each source node, all target nodes are traversed, their spatial distances are calculated, and the nearest few (such as one or a fixed number) target nodes are selected. Then, the load allocation is completed based on the reciprocal of the distance or area weighting.

[0004] While intuitive and easy to implement, this method reveals a significant performance bottleneck when handling large-scale node pairings. On current mainstream computing platforms, even with high optimization, completing a single global nearest distance assignment takes several minutes to tens of minutes. If aircraft design involves thousands of operating conditions (such as different combinations of Mach numbers, angles of attack, and sideslip angles), the total computation time will balloon to an unacceptable level, severely slowing down the design iteration cycle. Furthermore, the layout of loading points in static tests is often adjusted according to the test scheme, and the finite element mesh may also change with the level of model refinement, leading to frequent updates to the target node set, further exacerbating the burden of repetitive calculations.

[0005] Existing technologies generally neglect the use of spatial indexing structures, such as KD-trees, octrees, or Delaunay triangulation, which accelerate nearest neighbor search. However, in practice, due to physical constraints on load allocation (such as the need to ensure load vector equivalence and avoid boundary distortion) and historical reasons for engineering software implementation, most commercial or self-developed tools still remain at the global traversal level.

[0006] Therefore, there is an urgent need to provide a solution for an aircraft load allocation method and system based on KDtree. Summary of the Invention

[0007] To address the above issues, the present invention provides an aircraft load allocation method and system based on KDtree, overcoming the problem of low computational efficiency caused by the use of global nearest distance calculation in existing load allocation technologies.

[0008] According to a first aspect of the present invention, an aircraft payload allocation method based on KDtree is provided, comprising: S1. Input the source load, target node, and number of matches, and decompose the source load into a load vector and source node coordinates; S2. Divide the source load and the target node into partitions according to aircraft components, and only allocate loads within the same partition; S3. For each partition, construct a spatial index structure based on the target nodes within that partition; S4. For each source node in the current partition, perform nearest neighbor search using the spatial index structure to obtain the n target nodes that are closest to the source node, where n is the number of matches; S5. Distribute the load vector corresponding to the current source node to the n target nodes; S6. Repeat S4 and S5 for each source node in the current partition until all source nodes in the partition have completed load distribution. S7. Repeat steps S3 to S6 for each partition until all partitions have completed load distribution.

[0009] In the above scheme, in step S2, the principle of partitioning includes preserving the original force transmission path; if there is no source node or no target node in a certain partition, the partition is skipped and an error message is output.

[0010] In the above scheme, step S3 involves constructing a KDtree spatial index structure. The construction process of the KDtree spatial index structure includes: recursively dividing all target nodes in the current partition into spatial segments one by one according to the three coordinate axes x, y, and z. Each segmentation uses the node corresponding to the median of the coordinate values ​​in the current dimension as the segmentation node, dividing the space into two sub-regions.

[0011] In the above scheme, in step S4, the value of the matching quantity n ranges from 10 to 100.

[0012] In the above scheme, in step S5, the load vector corresponding to the current source node is distributed to the n target nodes using the minimum deformation energy method or the least squares method, and the force equivalence and torque equivalence are satisfied during the distribution.

[0013] In the above scheme, in step S5, when using the minimum deformation energy method, a constrained optimization problem is constructed by using the Lagrange multiplier method. The constraints include resultant force constraints and resultant moment constraints, and the optimization objective is to minimize the deformation energy.

[0014] In the above scheme, the load components allocated to each target node in the minimum deformation energy method are obtained by solving a system of sixth-order linear equations. The coefficient matrix of the system of sixth-order linear equations is constructed based on the spatial coordinates and distance of each target node relative to the source node, and the right-hand side is constructed based on the resultant force and resultant moment of the load to be allocated.

[0015] In the above scheme, in step S2, the aircraft components include wings, horizontal stabilizer, vertical stabilizer, and fuselage.

[0016] In the above scheme, the source load includes aerodynamic load and / or inertial load, and the target node includes finite element calculation node or static test loading point.

[0017] According to a second aspect of the present invention, a method for implementing any of the above solutions is provided, the system comprising: The data input module is used to input the source load, target node and matching quantity, and decompose the source load into load vector and source node coordinates; The load partitioning module is used to partition the source load and the target node according to aircraft components, and to allocate load only within the same partition. The KDtree module is used to build a KDtree spatial index structure for each partition, based on the target nodes within that partition. The matching module is used to perform nearest neighbor search for each source node in the current partition using the KDtree spatial index structure to obtain the n target nodes that are closest to the source node, where n is the number of matches; The load allocation module is used to allocate the load vector corresponding to the current source node to the n target nodes; The first loop module is used to control the matching module and the load allocation module to repeatedly execute for each source node in the current partition until all source nodes in the partition have completed load allocation. The second loop control module is used to control the KDtree construction module, matching module, load allocation module and the first loop module to repeatedly execute each partition until all partitions have completed load allocation.

[0018] The beneficial effects of this invention are: 1. Significantly improved computational efficiency: This invention introduces a KDtree spatial index structure for nearest neighbor search, reducing the average time for load matching. Allocation speed is greatly improved, and the efficiency advantage becomes even more pronounced as the mesh becomes finer.

[0019] 2. The load partitioning strategy balances efficiency and physical accuracy. By partitioning aircraft components, the search scope is narrowed from the entire aircraft to a local area, further improving matching efficiency. On the other hand, it strictly limits the allocation of loads to the same partition, preserving the original force transmission path, effectively reducing the risk of load allocation distortion, and ensuring that the allocation results conform to the structural mechanical characteristics.

[0020] 3. The allocation results exhibit good load fidelity. The load distribution trend after allocation remains consistent with the original load, demonstrating good adaptability to different mesh densities and accurately transmitting the spatial distribution characteristics of the load. Experimental results show that the method of this invention can achieve high-fidelity load transfer between different discretized meshes, meeting the stringent requirements for load input accuracy in engineering strength analysis.

[0021] 4. Strong applicability, covering a variety of load types and scenarios. This invention is not only applicable to aerodynamic loads but also to inertial loads. The target node can be a finite element calculation node or a static test loading point. It can be widely used in aircraft load calculation and strength analysis engineering practice, and is especially suitable for high-frequency, large-scale load loading tasks.

[0022] In summary, this invention significantly improves calculation speed while ensuring the accuracy of load equivalence, and also takes into account both physical rationality and engineering applicability. Attached Figure Description

[0023] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on the structures shown in these drawings without creative effort.

[0024] Figure 1 This is a flowchart of the method of the present invention; Figure 2 A vector diagram showing the original aerodynamic loads on the right horizontal stabilizer; Figure 3The load vector diagram for assigning the right flat tail to the finite element nodes.

[0025] The realization of the objective, functional features and advantages of the present invention will be further explained in conjunction with the embodiments and with reference to the accompanying drawings. Detailed Implementation

[0026] Exemplary embodiments will now be described in detail, examples of which are illustrated in the accompanying drawings. When the following description relates to the drawings, unless otherwise indicated, the same numerals in different drawings denote the same or similar elements. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with this disclosure. Rather, they are merely examples of apparatuses and methods consistent with some aspects of this disclosure as detailed in the appended claims.

[0027] The terms "first," "second," etc., used in this disclosure are for distinguishing similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such use of data can be interchanged where appropriate so that the embodiments of this disclosure described herein can be implemented, for example, in orders other than those illustrated or described herein.

[0028] Furthermore, the terms “comprising” and “having”, and any variations thereof, are intended to cover non-exclusive inclusion, such that a process, method, system, product, or apparatus that includes a series of steps or units is not necessarily limited to those steps or units that are explicitly listed, but may include other steps or units that are not explicitly listed or that are inherent to such process, method, product, or apparatus.

[0029] Multiple, including two or more.

[0030] And / or, it should be understood that, for the purposes of this disclosure, the term "and / or" is merely a description of the relationship between related objects, indicating that three relationships can exist. For example, A and / or B can represent three situations: A exists alone, A and B exist simultaneously, and B exists alone.

[0031] like Figure 1 As shown, one embodiment of the technical solution of the present invention provides an aircraft payload allocation method based on KDtree, including: S1, Input source payload, target node, number of matches Decompose the source load into load vectors and node coordinates ; The source load includes aerodynamic loads and / or inertial loads, and the target node includes a finite element calculation node or a static test loading point. In this embodiment, the aerodynamic load of the entire machine is used. Finite element mesh nodes Let's take an example and explain in detail.

[0032] S2, Aerodynamic loads on the entire aircraft based on aircraft components Finite element mesh nodes The aerodynamic loads of the entire machine and the finite element mesh nodes must be partitioned in the same way, and the loads are only distributed in the same partition.

[0033] Specifically, the partitions include: fuselage, left wing, right wing, left horizontal stabilizer, right horizontal stabilizer, vertical stabilizer, left flap, right flap, and all control surfaces. The partitioning principle includes preserving the original force transmission path; if there is no source node or no target node in a partition, the partition is skipped and an error message is output.

[0034] By partitioning the entire aircraft load into components, the traversal range of the nearest neighbor search in step S4 is reduced, further improving matching efficiency. More importantly, by limiting the load to be transmitted only within the same component, the partitioning ensures that the load is distributed along the original force transmission path of the aircraft structure, avoiding load distortion caused by cross-component distribution.

[0035] S3. For each partition, use the finite element mesh nodes within that partition. Construct a KDtree spatial index structure; The construction process of the KDtree spatial index structure includes: (1) Determine the segmentation dimension and segmentation points: First, select the x-axis as the segmentation dimension, and then determine the finite element mesh nodes within the segment. Sort all nodes by their x-coordinates in ascending order, and find the node corresponding to the median of the sorted array. This node becomes the root node of the current KDTree.

[0036] (2) Divide the left and right subspaces: Divide the sorted array into two parts from the median. The left half corresponds to the nodes whose x-coordinates are less than the median and is distributed in the left subspace of the dividing node; the right half corresponds to the nodes whose x-coordinates are greater than the median and is distributed in the right subspace of the dividing node.

[0037] (3) Recursive subspace partitioning: The above process is recursively executed on the left and right subspaces respectively, but the partitioning dimension is rotated to the y-axis. That is, in the next level of recursion, the subspace is sorted by the y-coordinate and partitioned by the median; in the next level, it is partitioned by the z-axis; and then it rotates back to the x-axis. The space is partitioned recursively along the three coordinate axes of x, y, and z one by one. Each partitioning is based on the node corresponding to the median of the coordinate values ​​in the current dimension, dividing the space into two sub-regions.

[0038] (4) Termination condition: The recursive segmentation continues until the number of nodes in each subspace is no greater than a preset threshold (usually 1 or 16, and the threshold is 16 in this embodiment).

[0039] Through the above process, KDtree organizes discrete target nodes in 3D space into a balanced binary tree. Each tree node corresponds to a target node, and its left and right subtrees correspond to the sub-regions on either side of that node in space, respectively. This structure allows subsequent nearest neighbor searches to quickly eliminate a large number of irrelevant nodes through pruning strategies. It transforms the nearest neighbor search for each subsequent source node from a global linear scan into a logarithmic search based on a tree structure, fundamentally reducing the time complexity of the matching process.

[0040] While there are many commonly used methods for spatial indexing structures of 3D point cloud data, such as KDtree, octree, R-tree, and uniform grid, this invention adopts KDtree because it offers high pruning efficiency in 3D point cloud scenarios. KDtree can quickly eliminate a large number of irrelevant nodes through pruning strategies, transforming nearest neighbor search from a global linear scan into a logarithmic search based on a tree structure. This is an advantage that other methods lack. Octtrees are less adaptable to non-uniformly distributed point cloud data, R-trees are more complex to implement and are more suitable for region queries and boundary indexing of spatial objects, and uniform grids have a single, fixed mesh, making them unsuitable for all partitions. Therefore, this invention selects KDtree as the spatial indexing structure to achieve efficient matching of source and target nodes during load allocation.

[0041] S4. For each source node within the current partition, perform a nearest neighbor search using the KDtree spatial index structure to obtain the n closest target nodes to the source node, where n is the number of matches; the number of matches n ranges from 10 to 100. Preferably, n is 50. On the one hand, 50 target nodes distributed around the source node are sufficient to accurately fit the spatial distribution of the local load (forming the basis for three-dimensional surface interpolation); on the other hand, this number ensures good numerical stability in solving the 6×6 linear equation system in step S5. If n is too small, it may not fully reflect the characteristics of the local load distribution; if n is too large, it may introduce target nodes that are far away, thus reducing the locality accuracy of load allocation and increasing computational overhead.

[0042] Taking fuselage partitioning as an example: aerodynamic load nodes are Finite element mesh nodes are The corresponding spatial index structure is .right Each node in ,use The nearest neighbor search method, to obtain distance Recent There are nodes, and the point set is... .

[0043] Compared to existing technologies where each source node needs to traverse all target nodes to calculate the distance, this invention utilizes a pre-constructed KDtree for nearest neighbor search. By using the tree structure to quickly locate candidate regions and prune irrelevant regions, the search efficiency is greatly improved.

[0044] S5. After matching is completed, the load vector corresponding to the current source node is assigned to the n target nodes, and the assignment satisfies force equivalence and torque equivalence. Taking the chassis partition as an example again: Corresponding load vector The point set obtained by loading into S4 The loading requirement is that force and moment are equivalent. Available methods include the minimum deformation energy method and the least squares method. The basic principle of the minimum deformation energy method is to find a unique load distribution scheme that minimizes the deformation energy of the target node under all constraints satisfying force and moment equivalence. This is equivalent to transferring the load from the source node to the surrounding target nodes in an energy-optimal way, resulting in a smooth and non-singular distribution. The basic idea of ​​the least squares method is to minimize a certain norm (such as the L2 norm) of the distributed load under the constraints satisfying force and moment equivalence. The solution framework of this method is similar to that of the minimum deformation energy method—it also requires constructing a constrained optimization problem and transforming it into a system of linear equations using the Lagrange multiplier method. Those skilled in the art can choose the appropriate method from the above based on actual engineering needs.

[0045] The minimum deformation energy method is used as follows: The load is applied to n target nodes, ensuring that the forces and moments are equivalent and the deformation energy is minimized. The Lagrange multiplier method is used to solve this constrained optimization problem.

[0046] Deformation energy function: (1) in, , , The load has three components. It is the Euclidean distance between the target node and the source node. It is a hypothetical stiffness (complete theory, numerical values ​​do not affect the results).

[0047] Introducing six Lagrange multipliers: , , Corresponding resultant force constraints, , , Stress moment constraint.

[0048] Resultant force constraint: , , (2) Resultant moment constraint: , , (3) Construct the Lagrangian function: (4) Take the partial derivative with respect to each force component and set it to zero: (5) (6) (7) Therefore, we can conclude that: (8) (9) (10) Substituting the above expression into the six constraint equations, we obtain the following about , , , , , A system of linear equations. To simplify the writing, the following summation quantity is defined (all summations are derived from...). arrive ): , , , , , , , , , (11) Substitute into the resultant force equation (e.g.) direction): (12) Right now (13) Similarly, (14) (15) Substitute into the torque equation (with) (Taking the axial moment as an example) (16) Will , Substitute the expression: (17) Summarized as follows: (18) Right now (19) Similarly, Directional moment equation: (20) Substituting, we get: (twenty one) Right now (twenty two) Directional moment equation: (twenty three) Substituting, we get: (twenty four) Right now (25) Thus, we have obtained a system of linear equations concerning the six multipliers. This system of equations can be written in matrix form. ,in coefficient matrix for Right end item The matrix elements consist of the sums of the aforementioned quantities. The solution can be achieved using Gaussian elimination, least squares, or pseudo-inverse methods. For this problem, least squares is preferred as it effectively solves matrix singularity problems.

[0049] Step S5 combines the strong engineering constraint of force and torque equivalence with the variational principle of minimizing deformation energy in mechanics to construct a constrained quadratic optimization problem. More importantly, it uses the Lagrange multiplier method to precisely transform it into a system of linear equations (6×6 matrix) with only six unknowns. This transforms the originally complex iterative optimization problem into a simple algebraic problem that can be solved analytically, ensuring both the accuracy of physical equivalence and maintaining extremely high computational efficiency.

[0050] The deformation energy function is set such that the deformation energy generated by the load on the target node increases cubically with increasing distance from the source node. During the optimization process, the system automatically and smoothly distributes most of the load to neighboring nodes while strictly satisfying moment balance. There is no need to manually set the attenuation coefficient or cutoff radius, which has a clear physical meaning.

[0051] The sixth-order linear equation system is solved using the least squares method. The numerical stability of the least squares solution is demonstrated, ensuring convergence of load distribution even under extremely harsh grid conditions.

[0052] S6. Repeat S4 and S5 for each source node in the current partition until all source nodes in the partition have completed load distribution. S7. Repeat steps S3 to S6 for each partition until all partitions have completed load distribution.

[0053] According to a second aspect of the present invention, a method for implementing any of the above solutions is provided, the system comprising: The data input module is used to input the source load, target node and matching quantity, and decompose the source load into load vector and source node coordinates; The load partitioning module is used to partition the source load and the target node according to aircraft components, and to allocate load only within the same partition. The KDtree module is used to build a KDtree spatial index structure for each partition, based on the target nodes within that partition. The matching module is used to perform nearest neighbor search for each source node in the current partition using the KDtree spatial index structure to obtain the n target nodes that are closest to the source node, where n is the number of matches; The load allocation module is used to allocate the load vector corresponding to the current source node to the n target nodes; The first loop module is used to control the matching module and the load allocation module to repeatedly execute for each source node in the current partition until all source nodes in the partition have completed load allocation. The second loop control module is used to control the KDtree construction module, matching module, load allocation module and the first loop module to repeatedly execute each partition until all partitions have completed load allocation.

[0054] Example To verify the actual engineering effect of the method of the present invention, this embodiment compares and verifies it from two dimensions: computational efficiency and load distribution fidelity.

[0055] 1. Comparison of computational efficiency In the process of allocating the aerodynamic loads of the entire aircraft to the finite element nodes, the time consumption of the method of this invention is compared with that of the global nearest distance calculation method in the prior art. Both methods are executed under the same hardware environment and the same data scale. Table 1 shows the comparison of the allocation time between the allocation method of this invention and the global nearest distance calculation method.

[0056] Table 1

[0057] As shown in Table 1, under the same allocation scale (approximately 600,000 aerodynamic load nodes and approximately 7,000 finite element target nodes), the allocation time of the method of this invention is 320 seconds, while the global nearest distance calculation method takes 1030 seconds. The allocation speed of the method of this invention is approximately 3.2 times that of the global nearest distance calculation method, demonstrating a significant efficiency improvement. It is foreseeable that if higher-precision strength calculations or static tests are performed, the number of finite element mesh nodes will further increase (e.g., from 7,000 to tens of thousands or even hundreds of thousands). In this case, the time taken by the global nearest distance calculation method will increase linearly with the number of target nodes, while the time taken by the method of this invention will only increase logarithmically, further widening the efficiency gap between the two. Therefore, this invention is particularly suitable for large-scale, high-frequency aircraft load allocation engineering tasks.

[0058] Load distribution fidelity verification This embodiment verifies the load fidelity of the method of the present invention by comparing the load vector distribution of the right horizontal tail component before and after distribution.

[0059] Figure 2 This is the original aerodynamic load vector diagram for the right horizontal stabilizer, representing the original distribution of the aerodynamic loads input in step S1 across the right horizontal stabilizer partition. Each arrow in the diagram represents an aerodynamic force vector at an aerodynamic load node; the direction and length of the arrow indicate the direction and relative magnitude of the load, respectively. From... Figure 2 It can be clearly observed that: the leading edge of the right tail ( Figure 2 The load vector in the region near the positive y-axis is significantly longer than that in the trailing edge region, and the load vector near the wing root (fuselage side) is significantly longer than that near the wingtip (away from the fuselage side). In other words, the original aerodynamic load distribution characteristics are: large leading-edge load, small trailing-edge load; large wing root load, small wingtip load.

[0060] Figure 3 The load vector diagram after the right horizontal tail is assigned to the finite element nodes shows the result of the original aerodynamic load being assigned to each node of the right horizontal tail finite element mesh after complete processing by steps S1 to S7 of the method of this invention. Arrows are used in the figure to indicate the load vectors assigned to each finite element node.

[0061] contrast Figure 2 and Figure 3It can be observed that although there is a significant difference between the spatial density of aerodynamic load nodes and the spatial density of finite element nodes, aerodynamic load nodes are usually relatively sparsely distributed (due to the discrete characteristics of aerodynamic computation mesh), while finite element nodes are relatively densely distributed (due to the requirements of mesh accuracy in structural analysis). However, the load distribution after allocation maintains the same trend as the original load distribution: that is, the leading edge load is greater than the trailing edge load, and the load near the wing root is greater than the load near the wingtip.

[0062] The comparative results demonstrate that the method of this invention can accurately transfer the spatial distribution characteristics of the load from sparse load points to dense finite element meshes, and the significant difference in density between source and target nodes does not lead to distortion of the load distribution. This indicates that the method of this invention has good mesh adaptability and load distribution fidelity, and can achieve high-fidelity load transfer between different discretized meshes, meeting the stringent requirements of engineering strength analysis for load input accuracy.

[0063] Based on the above two verifications, the method of the present invention outperforms the existing technology in both key performance dimensions of computational efficiency and allocation accuracy, and can effectively support the practical engineering needs of large-scale, high-frequency load allocation in modern aircraft design.

[0064] It should be noted that, in this document, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Unless otherwise specified, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes that element.

[0065] The sequence numbers of the above embodiments of the present invention are for descriptive purposes only and do not represent the superiority or inferiority of the embodiments.

[0066] Through the above description of the embodiments, those skilled in the art can clearly understand that the above implementation methods can be implemented by means of software plus necessary general-purpose hardware platforms. Of course, they can also be implemented by hardware, but in many cases the former is a better implementation method. Based on this understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product is stored in a storage medium (such as ROM / RAM, magnetic disk, optical disk), and includes several instructions to cause a terminal (which may be a mobile phone, computer, server, air conditioner, or network device, etc.) to execute the methods described in the various embodiments of the present invention.

[0067] The embodiments of the present invention have been described above with reference to the accompanying drawings. However, the present invention is not limited to the specific embodiments described above. The specific embodiments described above are merely illustrative and not restrictive. Those skilled in the art can make many other forms under the guidance of the present invention without departing from the spirit and scope of the claims. All of these forms are within the protection scope of the present invention.

Claims

1. An aircraft payload allocation method based on KDtree, characterized in that, include: S1. Input the source load, target node, and number of matches, and decompose the source load into a load vector and source node coordinates; S2. Divide the source load and the target node into partitions according to aircraft components, and only allocate loads within the same partition; S3. For each partition, construct a spatial index structure based on the target nodes within that partition; S4. For each source node in the current partition, perform a nearest neighbor search using the spatial index structure to obtain the n target nodes that are closest to the source node, where n is the number of matches; S5. Distribute the load vector corresponding to the current source node to the n target nodes; S6. Repeat S4 and S5 for each source node in the current partition until all source nodes in the partition have completed load distribution. S7. Repeat steps S3 to S6 for each partition until all partitions have completed load distribution.

2. The aircraft load allocation method based on KDtree according to claim 1, characterized in that, In step S2, the principles of partitioning include preserving the original force transmission path; if there is no source node or no target node in a partition, the partition is skipped and an error message is output.

3. The aircraft load allocation method based on KDtree according to claim 1, characterized in that, In step S3, a KDtree spatial index structure is constructed. The construction process of the KDtree spatial index structure includes: recursively dividing all target nodes in the current partition into spatial segments one by one according to the three coordinate axes x, y, and z. Each segmentation uses the node corresponding to the median of the coordinate values ​​in the current dimension as the segmentation node, dividing the space into two sub-regions.

4. The aircraft load allocation method based on KDtree according to claim 1, characterized in that, In step S4, the number of matches n ranges from 10 to 100.

5. The aircraft load allocation method based on KDtree according to claim 1, characterized in that, In step S5, the load vector corresponding to the current source node is distributed to the n target nodes using the minimum deformation energy method or the least squares method, while satisfying force equivalence and torque equivalence during the distribution.

6. The aircraft load allocation method based on KDtree according to claim 5, characterized in that, In step S5, when using the minimum deformation energy method, a constrained optimization problem is constructed using the Lagrange multiplier method. The constraints include resultant force constraints and resultant moment constraints, and the optimization objective is to minimize the deformation energy.

7. The aircraft load allocation method based on KDtree according to claim 5, characterized in that, In the minimum deformation energy method, the load components assigned to each target node are obtained by solving a system of sixth-order linear equations. The coefficient matrix of the sixth-order linear equations is constructed based on the spatial coordinates and distance of each target node relative to the source node, and the right-hand side is constructed based on the resultant force and resultant moment of the load to be assigned.

8. The aircraft load allocation method based on KDtree according to claim 1, characterized in that, In step S2, the aircraft components include wings, horizontal stabilizer, vertical stabilizer, and fuselage.

9. The aircraft load allocation method based on KDtree according to claim 1, characterized in that, The source load includes aerodynamic loads and / or inertial loads, and the target node includes a finite element calculation node or a static test loading point.

10. An aircraft payload allocation system based on KDtree, characterized in that, The system for implementing the method as described in any one of claims 1-9 includes: The data input module is used to input the source load, target node and matching quantity, and decompose the source load into load vector and source node coordinates; The load partitioning module is used to partition the source load and the target node according to aircraft components, and to allocate load only within the same partition. The KDtree module is used to build a KDtree spatial index structure for each partition, based on the target nodes within that partition. The matching module is used to perform nearest neighbor search for each source node in the current partition using the KDtree spatial index structure to obtain the n target nodes that are closest to the source node, where n is the number of matches; The load allocation module is used to allocate the load vector corresponding to the current source node to the n target nodes; The first loop module is used to control the matching module and the load allocation module to repeatedly execute for each source node in the current partition until all source nodes in the partition have completed load allocation. The second loop control module is used to control the KDtree construction module, matching module, load allocation module and the first loop module to repeatedly execute each partition until all partitions have completed load allocation.

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