An intelligent optimization method, device, equipment and medium for an aircraft fuselage wall plate maintenance scheme

Abstract

The application discloses an intelligent optimization method, device, equipment and medium for an aircraft fuselage wall plate maintenance scheme. Its features include: identifying the maintenance damage area of the aircraft fuselage wall plate to be maintained, obtaining the finite element local submodel corresponding to the maintenance damage area; matching the patch parameters based on the maintenance damage area, and constructing a patch optimization design model for the patch of the maintenance damage area based on the patch parameters. Iterative calculation is performed based on the finite element local submodel and the patch optimization design model through the pre-established optimization script to determine the optimal patch parameters. The application effectively restores the structural strength of the fuselage wall plate, relieves the stress concentration of the damage area, significantly reduces the structural weight of the repair site, and significantly improves the lightweight design effect; and improves the traditional finite element analysis calculation efficiency, intelligently iterates and optimizes the patch parameters, and improves the efficiency and accuracy of the maintenance design.

Description

Technical Field

[0001] This invention relates to the field of aircraft structural maintenance technology, and in particular to an intelligent optimization method, apparatus, equipment and medium for aircraft fuselage panel maintenance. Background Technology

[0002] Aircraft fuselage panels are core load-bearing components of the airframe structure, and their structural integrity directly determines the aircraft's flight safety and service life. During long-term service, fuselage panels are susceptible to fatigue cracks, holes, and other damage due to complex loads, corrosive environments, and foreign object impacts, necessitating timely maintenance. Currently, standardized maintenance methods based on structural repair manuals are widely used in engineering. However, these methods rely on conservative design assumptions and do not fully consider specific damage patterns, local load environments, and structural redundancy. While they can ensure structural safety, they often lead to a significant increase in structural weight after repair, affecting not only the aircraft's aerodynamic performance but also increasing operating costs. Existing technologies, while ensuring overall structural stress consistency, involve detailed analysis of localized high-stress areas to improve maintenance accuracy. However, this approach suffers from low maintenance efficiency and lacks precise control. Summary of the Invention

[0003] This invention provides an intelligent optimization method, apparatus, equipment, and medium for aircraft fuselage panel maintenance, to solve the technical problem of increased weight caused by excessive reinforcement in traditional standardized maintenance methods in the prior art.

[0004] According to one aspect of the present invention, an intelligent optimization method for aircraft fuselage panel maintenance is provided, comprising: Identify the repair damage area of ​​the aircraft fuselage panel to be repaired, and obtain the finite element local sub-model corresponding to the repair damage area; Based on the patch parameters matched to the repair damage area, and based on the patch parameters, a patch optimization design model is constructed for the patch in the repair damage area; The optimal patch parameters are determined by iterative calculations based on the finite element local sub-model and the patch optimization design model using a pre-established optimization script.

[0005] According to another aspect of the present invention, an intelligent optimization device for aircraft fuselage panel maintenance is provided, comprising: The finite element analysis module is used to identify the repair damage area of ​​the aircraft fuselage panel to be repaired and obtain the finite element local sub-model corresponding to the repair damage area. The patch optimization design module is used to match patch parameters based on the repair damage area, and to construct a patch optimization design model for the patch in the repair damage area based on the patch parameters. The optimal patch analysis module is used to determine the optimal patch parameters by performing iterative calculations based on the finite element local sub-model and the patch optimization design model using a pre-established optimization script.

[0006] According to another aspect of the present invention, an electronic device is provided, the electronic device comprising: At least one processor; and A memory communicatively connected to the at least one processor; wherein, The memory stores a computer program that can be executed by the at least one processor, which enables the at least one processor to execute the intelligent optimization method for aircraft fuselage panel maintenance according to any embodiment of the present invention.

[0007] According to another aspect of the present invention, a computer-readable storage medium is provided, the computer-readable storage medium storing computer instructions, the computer instructions being configured to cause a processor to execute and implement the intelligent optimization method for aircraft fuselage panel maintenance schemes according to any embodiment of the present invention.

[0008] The technical solution of this invention identifies the repair damage area of ​​the aircraft fuselage panel to be repaired, obtains the corresponding finite element local sub-model of the repair damage area, and effectively reduces the amount of calculation and improves the efficiency of finite element analysis by using the sub-model; based on the repair damage area, it matches patch parameters, and constructs a patch optimization design model for the patch of the repair damage area based on the patch parameters; and by using a Bayesian optimization algorithm, it effectively restores the structural strength of the fuselage panel, alleviates stress concentration in the damaged area, and significantly reduces the structural weight of the repair part. By using a pre-established optimization script to perform iterative calculations based on the finite element local sub-model and the patch optimization design model, the optimal patch parameters are determined. This solves the technical problem of increased weight caused by over-reinforcement in traditional standardized maintenance methods, improves the efficiency of traditional finite element analysis calculations, and enhances the efficiency and accuracy of maintenance design through intelligent iterative optimization of patch parameters.

[0009] It should be understood that the description in this section is not intended to identify key or essential features of the embodiments of the present invention, nor is it intended to limit the scope of the invention. Other features of the invention will become readily apparent from the following description. Attached Figure Description

[0010] To more clearly illustrate the technical solutions in the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0011] Figure 1 A flowchart of an intelligent optimization method for aircraft fuselage panel maintenance is provided as an embodiment of the present invention; Figure 2 This is a schematic diagram of an aircraft emergency hatch wall panel structure provided in an embodiment of the present invention; Figure 3 Damage stress cloud diagrams of finite element models for fuselage panel maintenance under typical working conditions have been published. Figure 4 Damage stress cloud diagrams of the sub-model under typical working conditions were disclosed. Figure 5 The stress and strain cloud diagrams of the non-destructive sub-model, the damaged cutting sub-model, and the patch repair sub-model are disclosed. Figure 6 A flowchart illustrating an intelligent optimization method for aircraft fuselage panel maintenance, provided as an embodiment of the present invention; Figure 7 A schematic diagram of the structure of an intelligent optimization device for an aircraft fuselage panel maintenance scheme provided in an embodiment of the present invention; Figure 8 A schematic diagram of the structure of an electronic device 10 that can be used to implement an embodiment of the present invention is shown. Detailed Implementation

[0012] To enable those skilled in the art to better understand the present invention, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort should fall within the scope of protection of the present invention.

[0013] It should be noted that the terms "first," "second," etc., in the specification, claims, and accompanying drawings of this invention are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments of the invention described herein can be implemented in orders other than those illustrated or described herein. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover a non-exclusive inclusion; for example, a process, method, system, product, or apparatus that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus.

[0014] Figure 1This invention provides a flowchart of an intelligent optimization method for aircraft fuselage panel repair, applicable to repairing fatigue cracks, holes, and other damage to aircraft fuselage panels. This method can be executed by an intelligent optimization device for aircraft fuselage panel repair, which can be implemented in hardware and / or software and can be configured in an electronic device. Figure 1 As shown, the method includes: S110. Identify the repair damage area of ​​the aircraft fuselage panel to be repaired, and obtain the finite element local sub-model corresponding to the repair damage area.

[0015] The aircraft fuselage panel can be understood as any aluminum alloy fuselage panel located at any position on the aircraft fuselage. For example, taking a typical aluminum alloy fuselage panel as an example, it is an integrated structure composed of skin, stringers, and frames. The aircraft skin, as a key component in the fuselage structure that bears load and participates in the formation of the aerodynamic shape, undertakes important structural functions under flight and ground loads. Considering the requirements for damage tolerance and fatigue performance of the fuselage structure during service, the fuselage skin is usually made of 2000 series aluminum alloy materials with high fracture toughness, low crack propagation rate, and good fatigue performance. The main structural material of the fuselage panel is selected according to the different functions and load environments of each component. The main load-bearing skin uses 2024-T3 aluminum alloy (density ρ=2800 kg / m³). 3 With an elastic modulus E = 72.4 GPa, it balances good formability, fatigue resistance, and moderate strength. In stress concentration areas and at skin overlaps, a more durable 2024-T351 aluminum alloy (ρ = 2780 kg / m²) is used. 3 (E=68 GPa). The long girder, which serves as the main longitudinal load-bearing component, is made of high-strength 7075-T73511 aluminum alloy (ρ=2800 kg / m). 3 (E=71 GPa). For critical stringers with higher load-bearing requirements, 7150-T73511 aluminum alloy (ρ=2830 kg / m) with better static strength is selected. 3 (E=71 GPa). In critical connection areas such as lap joints and reinforcing corner pieces, material selection focuses on connection reliability and fatigue resistance, using 2024-T42 aluminum alloy with excellent plasticity (ρ=2850 kg / m). 3 (E=73 GPa). For reinforced pads or joints subjected to extreme localized concentrated loads, 7175-T7351 plate (ρ=2780 kg / m²) with high strength is used. 3 (E=71 GPa). Figure 2This is a structural schematic diagram of an aircraft emergency hatch wall panel structure provided in an embodiment of the present invention, as shown below. Figure 2 As shown, the aircraft emergency exit door panel consists of a front reinforcing frame, a rear reinforcing frame, a reinforcing skin, a long stringer, and a short frame.

[0016] The maintenance-damaged area can be understood as the area of ​​the aircraft fuselage panel that has been damaged due to fatigue cracks or external impacts. For example, areas of aircraft skin with damage such as holes, cracks, or wear.

[0017] The finite element local sub-model can be a finite element analysis model constructed in the repair damage area. It should be noted that when repairing damage to aircraft fuselage panels, a repair scheme using aircraft repair patches is adopted. This involves cutting the damaged area, laying a patch with properties matching the base material in the damaged area, and connecting it to the original structure via riveting to form an integral structure. In this invention, the finite element local sub-model constructed for the repair damage area is a finite element analysis model with predetermined geometric cutting features at the damage location.

[0018] Specifically, the repair damage area of ​​the aircraft fuselage panel to be repaired is identified, and the corresponding finite element local sub-model is obtained.

[0019] Optionally, in another optional embodiment of the present invention, obtaining the finite element local sub-model corresponding to the repair damage area includes: obtaining the structural parameters, material properties and load constraints of the aircraft fuselage panel to be repaired; establishing a fuselage panel repair finite element model for the aircraft fuselage panel based on the structural parameters, material properties and load constraints; and constructing a finite element local sub-model for the repair damage area based on the fuselage panel repair finite element model.

[0020] The structural parameters can be the structural data of the fuselage panel to be repaired. For example, using the fuselage panel at the emergency exit as a template, the structural parameters include: the geometric dimensions and spatial layout of each component such as the front reinforcing frame, rear reinforcing frame, reinforcing skin, stringer, and short frame; the connection relationship of each component; the location of the repair damage area on the aircraft fuselage panel; and the cutting features of the predetermined geometric shape. Among them, material properties can be the material data of the fuselage panel of the aircraft to be repaired, such as the aluminum alloy material parameters of the various components mentioned above, including elastic modulus, Poisson's ratio and plasticity relationship.

[0021] The load constraints can be understood as the load information of the aircraft fuselage panels under actual conditions. For example, taking the fuselage panel at the emergency exit as a template, the load constraints, according to CCAR25.365(d), require the aircraft structure to be designed to withstand 1.33 times the maximum set value of the decompression valve. The maximum set value of the aircraft cabin decompression valve is approximately 8.76 psi. Considering a safety factor of 1.5, for the load applied in the finite element analysis, the uniformly distributed pressure in the emergency exit area is simplified by equivalent static force, with an equivalent concentrated force of 45475 N. The support state of the fuselage section where the door is located is simulated in actual flight. The finite element load of the fuselage panel includes the equivalent concentrated force and the internal pressure applied to the inner side of the skin. The constraints are applied along the circumferential direction on the left and right boundaries of the skin, and axial displacement is also constrained. The upper side of the skin is constrained along the circumferential direction, and the lower side is fixed.

[0022] The fuselage panel repair finite element model can be a complete finite element analysis model constructed for the aircraft fuselage panel to be repaired. The fuselage panel repair finite element model is used to simulate the complete aircraft fuselage panel to be repaired and the damage existing on the fuselage panel.

[0023] Optionally, during the establishment of the finite element model for fuselage panel repair, thin-walled components such as skin, stringers, bulkheads, and patches are discretized using S4R shell elements. By reasonably defining contact relationships, the actual riveting structure is simulated, and cutting features of predetermined geometry are introduced in the repair damage area to simulate the damaged repair cutting parts. The material properties of each component are assigned according to the aerospace aluminum alloy standard, including elastic modulus, Poisson's ratio, and plasticity relationship. The boundary conditions are set according to the actual constraint state under the ultimate airtight load condition (0.1205 MPa).

[0024] Optionally, after constructing the finite element model for fuselage panel repair, a local finite element sub-model can be constructed for the repair damage area based on the finite element model for fuselage panel repair using finite element technology for local refinement analysis.

[0025] Specifically, the structural parameters, material properties, and load constraints of the aircraft fuselage panel to be repaired are obtained; a finite element model for fuselage panel repair is established based on the structural parameters, material properties, and load constraints; and a finite element local sub-model is constructed for the damaged area based on the finite element model for fuselage panel repair.

[0026] Optionally, in another optional embodiment of the present invention, the step of constructing the finite element local sub-model for the repair damage area based on the fuselage panel repair finite element model includes: In the finite element model for fuselage panel repair, a local region corresponding to the repair damage area is extracted to establish a sub-model; the model calculation results of the repair damage area in the finite element model for fuselage panel repair are applied as boundary conditions to the sub-model; stress distribution and displacement distribution analysis are performed on the sub-model by establishing a non-destructive sub-model, a damage cutting sub-model, and a patch repair sub-model; if the stress distribution and displacement distribution analysis is successfully verified, the sub-model is determined as the finite element local sub-model.

[0027] The sub-model can be a finite element sub-model corresponding to the repair damage area. It should be noted that in the fuselage panel repair finite element model, a local area is defined with the repair damage area as the center, and this area is extracted from the fuselage panel repair finite element model to obtain the sub-model.

[0028] Optionally, after obtaining the sub-model, the model calculation results (i.e., displacement calculation results) of the fuselage panel repair finite element model in the repair damage area are applied as boundary conditions to the sub-model, so that the sub-model inherits the overall structural stress state of the fuselage panel repair finite element model. The displacement calculation results are used to represent the actual effect of the global structure of the aircraft fuselage panel on the repair damage area in the fuselage panel repair finite element model.

[0029] For example, Figure 3 Damage stress cloud diagrams of finite element models for fuselage panel maintenance under typical working conditions have been published. Figure 4 The damage stress cloud diagram of the sub-model under typical working conditions is disclosed, such as... Figure 3 and Figure 4 As shown: After updating the boundary conditions of the sub-model, the applicability of the sub-model in local analysis was evaluated. The stress cloud maps of the sub-model and the global model in the same loss cutting area were compared and analyzed. The two models have good consistency in stress distribution pattern and the location of high stress area. The stress concentration area has some difference in local stress amplitude between the sub-model and the global model, but the overall stress distribution characteristics and changing trend are consistent. This shows that the sub-model can reasonably reflect the stress response of local area under typical working conditions on the basis of inheriting the global stress state.

[0030] The non-destructive sub-model can be a finite element mesh model of the local area corresponding to the repair damaged area that is undamaged; the damage cutting sub-model can be a finite element mesh model of the local area corresponding to the repair damaged area that is damaged and has undergone damage cutting; and the patch repair sub-model can be a finite element mesh model of the local area corresponding to the repair damaged area that is damaged and has been repaired using a patch. It should be noted that the non-destructive sub-model, the damage cutting sub-model, and the patch repair sub-model are used to verify the effectiveness of patch repair for repairing damaged areas.

[0031] Optionally, the overall geometry of the non-destructive sub-model, the damaged cutting sub-model, and the patch repair sub-model can be kept the same as that of the sub-model to reduce the impact of model scale differences on numerical results.

[0032] Optionally, in this invention, when establishing a non-destructive sub-model, a damaged cutting sub-model, and a patch repair sub-model for a sub-model, it is necessary to pre-construct a non-destructive geometric model, a damaged cutting geometric model, and a patch repair geometric model for the sub-model, and ensure that the geometric parameters of the non-destructive geometric model, the damaged cutting geometric model, and the patch repair geometric model are consistent with the geometric dimensions of the sub-model.

[0033] Optionally, the airtight working condition ultimate load is applied to the non-destructive sub-model, the damaged cutting sub-model, and the patch repair sub-model respectively, and then stress distribution and displacement distribution analysis are performed to obtain the stress cloud map and strain cloud map corresponding to the non-destructive sub-model, the damaged cutting sub-model, and the patch repair sub-model respectively. By comparing the stress and strain contour maps of the non-destructive sub-model, the damaged cutting sub-model, and the patch repair sub-model, the effectiveness of patch repair is verified. If the stress and strain contour maps of the patch repair sub-model significantly reduce the stress in the damaged cutting area compared to those of the damaged cutting sub-model, effectively alleviating stress concentration at the damage site; and if the displacement at the notch is significantly reduced, with the maximum displacement effectively decreased compared to the maximum displacement of the damaged sub-model, and the high displacement area significantly shrunk with a smoother displacement distribution, then the patch repair is considered effective, and the stress and displacement distribution analysis is considered successful, thus the sub-model is determined as a finite element local sub-model. If the stress and strain contour maps of the patch repair sub-model show little change in stress and displacement in the damaged cutting area compared to those of the damaged cutting sub-model, then the patch repair is considered ineffective, and the stress and displacement distribution analysis is considered a failure, requiring a new sub-model to be established.

[0034] For example, Figure 5 The stress and strain contour plots of the non-destructive sub-model, the damaged cutting sub-model, and the patch repair sub-model are disclosed, such as... Figure 5 As shown: By comparing the undamaged sub-model and the damaged cutting sub-model, the maximum stress value in the damaged cutting area increases, and the stress distribution range on both sides of the damaged cutting area shows a significant diffusion trend compared to the undamaged state, indicating that the introduction of damage changes the local load transmission path; the maximum displacement in the damaged sub-model increases.

[0035] After patch repair, the stress in the damaged area of ​​the patch repair sub-model was significantly reduced, and the stress concentration at the damage site was effectively alleviated. In the area with drastic thickness changes at the lower end of the patch, the sudden change in thickness led to increased stress at that location. The displacement at the notch was significantly reduced; the maximum displacement was less than that in the damage sub-model, and the high displacement area was significantly shrunk, with a more gradual displacement distribution.

[0036] Specifically, a sub-model is established by extracting the local region corresponding to the repair damage area from the finite element model of fuselage panel repair; the model calculation results of the repair damage area of ​​the finite element model of fuselage panel repair are applied as boundary conditions to the sub-model; stress distribution and displacement distribution analysis are performed on the sub-model by establishing a non-destructive sub-model, a damage cutting sub-model, and a patch repair sub-model; if the stress distribution and displacement distribution analysis is successfully verified, the sub-model is determined as a local sub-model of finite element.

[0037] Optionally, in this invention, for each fuselage panel repair finite element model and its corresponding finite element local sub-model, identification information is set for the fuselage panel repair finite element model. The identification information identifies the fuselage panel repair finite element model and its corresponding finite element local sub-model. The identification information and the fuselage panel repair finite element model and its corresponding finite element local sub-model are stored. In the next optimization process, the fuselage panel repair finite element model or the finite element local sub-model can be directly called for analysis.

[0038] S120. Based on the patch parameters matched to the repair damage area, and based on the patch parameters, construct a patch optimization design model for the patch in the repair damage area.

[0039] The patch parameters can be the length, width, and thickness of the repair patch. It should be noted that, based on aircraft maintenance experience or operating manuals, a patch parameter is selected as the initial patch, and intelligent optimization design is performed for that patch.

[0040] Among them, the patch optimization design model can be a mathematical model established for repairing damaged areas.

[0041] Specifically, based on the patch parameters matched to the repair damage area, and based on the patch parameters, a patch optimization design model is constructed for the patch in the repair damage area.

[0042] In this invention, the step of constructing an optimized design model for the patch in the repair damage area based on the patch parameters includes: Using the patch parameters as design variables and minimizing the patch volume as the optimization objective, the patch optimization design model is constructed by setting maximum stress constraints and maximum displacement constraints.

[0043] Optionally, the initial patch parameters can be used as design variables for optimization, the minimum patch volume can be set as the optimization objective, and maximum stress and maximum displacement constraints can be set as constraints to construct a patch optimization design model. For example, the expression for the patch optimization design model is: Find X = (I i ,w i,t i ) Min M=M(X) Among them, I i ,w i ,t i Let X be the length, width, and thickness of the patch; M be the patch volume, and MinM is the minimum patch volume; F is the load vector borne by the patch, describing the set of external forces at all nodes of the patch repair sub-model; K is the overall stiffness matrix of the patch structure, describing the distribution of the entire patch repair structure's ability to resist deformation; F and K are obtained through optimization calculations based on the finite element local sub-model; X il and X iU Upper and lower limits for design variables set by the user; σ cr For maximum stress constraint; u cr This is a maximum displacement constraint.

[0044] After repair using a patch, the maximum stress constraint during the optimization process can be obtained using the safety margin formula, which is: Among them, R el The yield strength of the material is 345 MPa; M s For safety margin, it is set to 1.3; the calculated maximum stress constraint is 150MPa.

[0045] The critical stress value for skin buckling under compression was calculated using the classic plate and shell stability formula, yielding a maximum stress constraint of 157.8 MPa. The formula for calculating plate and shell stability is as follows: Where K is the buckling coefficient, E is the elastic modulus of the skin material, and V e t is the Poisson's ratio of the skin material. s The thickness of the skin is set to 2.6mm, b s The width of the skin is set to 107mm.

[0046] Based on the above two calculation results, the maximum stress constraint is finally taken as 150MPa.

[0047] Specifically, the patch parameters are used as design variables, and the optimization objective is to minimize the patch volume. Maximum stress constraints and maximum displacement constraints are set to construct an optimal design model for the patch.

[0048] S130. The optimal patch parameters are determined by iterative calculation based on the finite element local sub-model and the patch optimization design model using a pre-established optimization script.

[0049] Optionally, the optimization script can be a pre-developed script for automating the optimization of patch parameters. It should be noted that the optimization script uses a combination of Abaqus and Python scripts to achieve automatic coupling between finite element simulation and the Bayesian optimization algorithm. This enables intelligent optimization of patch parameters by automatically calling local sub-models of the finite element method, extracting simulation results, and updating the patch optimization surrogate model of the Bayesian optimization program. The Bayesian optimization program can be a program written in Matlab based on the Bayesian optimization algorithm.

[0050] Specifically, the optimal patch parameters are determined by iterative calculations based on the finite element local sub-model and patch optimization design model using a pre-established optimization script.

[0051] The technical solution of this invention identifies the repair damage area of ​​the aircraft fuselage panel to be repaired, obtains the corresponding finite element local sub-model of the repair damage area, and effectively reduces the amount of calculation by using the sub-model, thereby improving the efficiency of finite element analysis. Based on the repair damage area, patch parameters are matched, and based on the patch parameters, an optimized patch design model is constructed for the patch in the repair damage area. A Bayesian optimization algorithm is used to effectively restore the structural strength of the fuselage panel, alleviate stress concentration in the damaged area, and significantly reduce the structural weight of the repaired part. Through a pre-established optimization script, iterative calculations are performed based on the finite element local sub-model and the patch optimized design model to determine the optimal patch parameters. This solves the technical problem of increased weight caused by over-reinforcement in traditional standardized maintenance methods, improves the calculation efficiency of traditional finite element analysis, and enhances the efficiency and accuracy of maintenance design through intelligent iterative optimization of patch parameters.

[0052] Figure 6 This is a flowchart illustrating an intelligent optimization method for aircraft fuselage panel maintenance, provided as an embodiment of the present invention. The relationship between this embodiment and the previous embodiments is to specifically describe the automated intelligent optimization process. Figure 6 As shown, the method includes: S610. Identify the repair damage area of ​​the aircraft fuselage panel to be repaired, and obtain the finite element local sub-model corresponding to the repair damage area.

[0053] S620. Based on the patch parameters matched to the repair damage area, and based on the patch parameters, construct a patch optimization design model for the patch in the repair damage area.

[0054] S630. Determine the corresponding patch parameter group based on hypercube sampling; perform dynamic simulation on the patch parameter group using the optimization script based on the finite element local sub-model and the patch optimization design model to determine the patch performance data group.

[0055] The patch parameter set can be a set of patch parameter data automatically generated by hypercube sampling. It should be noted that hypercube sampling can generate multiple parameter combinations consisting of patch length, patch width, and patch thickness.

[0056] Optionally, the patch performance data set can be a set of patch parameter performance data obtained from simulation. This set serves as the initial training sample for the Bayesian optimization program. It should be noted that the dynamic simulation can use the patch parameter data set as its foundation to calculate the maximum stress, maximum displacement, and patch volume after repair for each patch parameter. Each patch parameter, along with the simulated stress, displacement, and patch volume, is then used as a parameter in the patch performance data set as a training sample.

[0057] Specifically, the corresponding patch parameter set is determined based on hypercube sampling; the patch parameter set is dynamically simulated using the finite element local sub-model and the patch optimization design model through optimization scripts to determine the patch performance data set.

[0058] S640. The optimal patch parameters are determined by iterative optimization based on the patch performance data set using a Bayesian optimization program.

[0059] Optionally, the patch performance data set can be used as the initial training sample for Bayesian optimization. The Bayesian optimization program reads the patch performance data set and can use the patch volume as the target output, gradually reducing the output patch volume data value to achieve intelligent optimization. Finally, it obtains the patch parameters that have smaller maximum stress and maximum displacement values ​​and smaller volume under the constraint conditions, which are used as the optimal patch parameters.

[0060] Specifically, the optimal patch parameters are determined by iterative optimization based on the patch performance data set using a Bayesian optimization program.

[0061] Optionally, in another optional embodiment of the present invention, the step of determining the optimal patch parameters by iterative optimization based on the patch performance data set using a Bayesian optimization program includes: The patch optimization surrogate model is updated based on the Bayesian optimization program and the patch performance data set, and patch parameters are generated. The patch parameters are dynamically simulated using the optimization script to determine patch performance data. The patch performance data is then updated to the patch performance data set. If the updated patch performance data set meets a preset convergence condition, the patch parameters corresponding to the patch performance data are determined as the optimal patch parameters.

[0062] The patch optimization surrogate model can be a tool component in the Bayesian optimization program, specifically a Gaussian process surrogate model for the Bayesian optimization algorithm. This surrogate model can be used to predict a set of stresses, displacements, and patch volumes based on any given set of patch lengths, widths, and thicknesses. It should be noted that another component in the Bayesian optimization program is a sampling function, used to iteratively optimize a set of patch lengths, widths, and thicknesses that minimize the patch volume, based on the patch performance data set.

[0063] The preset convergence condition can be set such that the change in the target volume in the ten iterations before and after optimization is less than 0.1%.

[0064] Optionally, in this invention, in each iteration, the patch performance data set is input into a Bayesian optimization program. The Bayesian optimization program determines whether the patch performance data set meets a preset convergence condition. If the preset convergence condition is not met, the patch optimization surrogate model is updated based on the patch performance data set. An optimal patch parameter is found through a data acquisition function, and an optimization script is called to perform dynamic simulation on the patch parameter to obtain the patch performance data corresponding to the patch parameter. The patch performance data is added to the patch performance data set to update the patch performance data set. The patch performance data updated in the 10 most recent iterations of the patch parameter is taken to determine whether the preset convergence condition is met. If the preset convergence condition is met, the patch parameter generated in this iteration is taken as the optimal patch parameter. If the preset convergence condition is not met, the next iteration process is entered until the preset convergence condition is met and the optimal patch parameter is obtained. The patch parameter generated in this iteration must meet the maximum stress constraint and the maximum displacement constraint; if the maximum stress constraint and the maximum displacement constraint are not met, the iteration optimization is repeated.

[0065] Specifically, the Bayesian optimization program updates the patch optimization surrogate model based on the patch performance data set and generates patch parameters; the patch parameters are dynamically simulated using an optimization script to determine the patch performance data; the patch performance data is then updated to the patch performance data set, and if the updated patch performance data set meets the preset convergence conditions, the patch parameters corresponding to the patch performance data are determined as the optimal patch parameters.

[0066] Optionally, in another alternative embodiment of the present invention, it further includes: Based on the optimal patch parameters, tensile load and shear load are calculated to determine the tensile load and shear load; based on the preset rivet type, preset rivet arrangement rule, tensile load and shear load, the number of fastener columns is calculated to determine the number of fastener columns; based on the number of fastener columns, the optimal patch parameters are updated to determine the target patch parameters.

[0067] The preset rivet type can be a rivet connecting the skin and the patch. For example, the preset rivet type is NAS1097KE rivet, and the design load of the skin material is 3170N. The preset rivet arrangement rule can be the arrangement rule for the connection between the patch and the skin based on the optimal patch parameters; for example, the preset rivet arrangement rule is that there are 3 rivets in each row, the distance between the rivets is 4D, the edge distance is 2D, and D is 4mm.

[0068] The tensile load can be a circumferential or axial load on the fuselage generated during the connection of the patch and skin; the shear load can be a load perpendicular to the direction of the tensile load generated during the connection of the patch and skin. For example, the tensile load is transmitted through P... tensile To represent, the shear load is through P shear The tensile load is calculated as follows: Among them, F tu For ultimate tensile strength, F ty The tensile yield strength, ultimate tensile strength, and tensile yield strength are obtained from tensile tests on the skin material. W represents the notch width, and t represents the skin thickness.

[0069] The shear load is calculated as follows: Among them, F su This represents the ultimate shear strength, which can be calculated from the ultimate tensile strength.

[0070] The number of fastener columns can be calculated from the preset rivet type, preset rivet arrangement rules, tensile load, and shear load. The number of fastener columns is used to indicate the number of rivet columns connecting the skin and the patch.

[0071] Among them, the target patch parameters can be transformed from the optimal patch parameters into the final patch parameters that can be implemented in the project.

[0072] For example, with optimal patch parameters of 52mm, 54mm, and 2.7mm, and tensile and shear loads of 21060N and 12420N respectively, the number of rivets per row, n, is 3. The number of fastener rows is represented by N. The design load of the rivets is selected as 3170N, and the calculated number of fastener rows is 3. The specific calculation formula is as follows: The calculated target patch parameters are 148mm, 150mm, and 2.7mm.

[0073] The technical solution of this invention identifies the repair damage area of ​​the aircraft fuselage panel to be repaired, obtains the corresponding finite element local sub-model of the repair damage area, and effectively reduces the amount of calculation by using the sub-model, thereby improving the efficiency of finite element analysis. Based on the repair damage area, patch parameters are matched, and based on the patch parameters, an optimized patch design model is constructed for the patch in the repair damage area. A Bayesian optimization algorithm is used to effectively restore the structural strength of the fuselage panel, alleviate stress concentration in the damaged area, and significantly reduce the structural weight of the repaired part. Through a pre-established optimization script, iterative calculations are performed based on the finite element local sub-model and the patch optimized design model to determine the optimal patch parameters. This solves the technical problem of increased weight caused by over-reinforcement in traditional standardized maintenance methods, improves the calculation efficiency of traditional finite element analysis, and enhances the efficiency and accuracy of maintenance design through intelligent iterative optimization of patch parameters.

[0074] Figure 7 This is a schematic diagram of an intelligent optimization device for an aircraft fuselage panel maintenance scheme provided in an embodiment of the present invention. Figure 7 As shown, the device includes: a finite element analysis module 710, a patch optimization design module 720, and an optimal patch analysis module 730; wherein, The finite element analysis module 710 is used to identify the repair damage area of ​​the aircraft fuselage panel to be repaired and obtain the finite element local sub-model corresponding to the repair damage area. The patch optimization design module 720 is used to match patch parameters based on the repair damage area and construct a patch optimization design model for the patch in the repair damage area based on the patch parameters. The optimal patch analysis module 730 is used to determine the optimal patch parameters by performing iterative calculations based on the finite element local sub-model and the patch optimization design model using a pre-established optimization script.

[0075] The technical solution of this invention identifies the repair damage area of ​​the aircraft fuselage panel to be repaired, obtains the corresponding finite element local sub-model of the repair damage area, and effectively reduces the amount of calculation by using the sub-model, thereby improving the efficiency of finite element analysis. Based on the repair damage area, patch parameters are matched, and based on the patch parameters, an optimized patch design model is constructed for the patch in the repair damage area. A Bayesian optimization algorithm is used to effectively restore the structural strength of the fuselage panel, alleviate stress concentration in the damaged area, and significantly reduce the structural weight of the repaired part. Through a pre-established optimization script, iterative calculations are performed based on the finite element local sub-model and the patch optimized design model to determine the optimal patch parameters. This solves the technical problem of increased weight caused by over-reinforcement in traditional standardized maintenance methods, improves the calculation efficiency of traditional finite element analysis, and enhances the efficiency and accuracy of maintenance design through intelligent iterative optimization of patch parameters.

[0076] Optionally, the device further includes a finite element model construction module; the finite element model construction module is used for: Obtain the structural parameters, material properties, and load constraints of the aircraft fuselage panel to be repaired; Based on the structural parameters, material properties, and load constraints, a finite element model for the maintenance of the aircraft fuselage panel is established. Based on the aforementioned finite element model for fuselage panel repair, a local finite element sub-model is constructed for the repair damage area.

[0077] The finite element model construction module is specifically used for: A sub-model is established by extracting the local region corresponding to the repair damage area from the finite element model of the fuselage panel repair; The model calculation results of the repair damage area of ​​the fuselage panel repair finite element model are applied to the sub-model as boundary conditions. For the sub-model, a non-destructive sub-model, a damaged cutting sub-model, and a patch repair sub-model are established, and stress distribution and displacement distribution analysis are performed. If the stress distribution and displacement distribution analysis is successfully verified, the sub-model is determined as the finite element local sub-model.

[0078] Optionally, the patch optimization design module 720 is specifically used for: Using the patch parameters as design variables and minimizing the patch volume as the optimization objective, the patch optimization design model is constructed by setting maximum stress constraints and maximum displacement constraints.

[0079] Optionally, the optimal patch analysis module 730 is specifically used for: The corresponding patch parameter set is determined based on hypercube sampling; The optimization script is used to perform dynamic simulation on the patch parameter set based on the finite element local sub-model and the patch optimization design model to determine the patch performance data set. The optimal patch parameters are determined by iterative optimization based on the patch performance data set using a Bayesian optimization program.

[0080] Optionally, the optimal patch analysis module 730 is specifically used for: The patch optimization proxy model is updated based on the Bayesian optimization procedure and the patch performance data set, and patch parameters are generated. The patch parameters are dynamically simulated using the optimization script to determine the patch performance data. The patch performance data is updated to the patch performance data group. If the patch performance data group meets the preset convergence condition, the patch parameters corresponding to the patch performance data are determined as the optimal patch parameters.

[0081] Optionally, the device further includes a skin adapter module; the skin adapter module is used for: Based on the optimal patch parameters, tensile load and shear load are calculated respectively to determine the tensile load and shear load; The number of fastener rows is calculated and determined based on the preset rivet type, preset rivet arrangement rules, tensile load and shear load. The optimal patch parameters are updated based on the number of fastener columns to determine the target patch parameters.

[0082] The intelligent optimization device for aircraft fuselage panel maintenance provided in this embodiment of the invention can execute the intelligent optimization method for aircraft fuselage panel maintenance provided in any embodiment of the invention, and has the corresponding functional modules and beneficial effects of the method.

[0083] Figure 8 A schematic diagram of an electronic device 10 that can be used to implement embodiments of the present invention is shown. The electronic device is intended to represent various forms of digital computers, such as laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The electronic device can also represent various forms of mobile devices, such as personal digital assistants, cellular phones, smartphones, wearable devices (e.g., helmets, glasses, watches, etc.), and other similar computing devices. The components shown herein, their connections and relationships, and their patterns are merely illustrative and are not intended to limit the implementation of the invention described and / or claimed herein.

[0084] like Figure 8 As shown, the electronic device 10 includes at least one processor 11 and a memory, such as a read-only memory (ROM) 12 or a random access memory (RAM) 13, communicatively connected to the at least one processor 11. The memory stores computer programs executable by the at least one processor. The processor 11 can perform various appropriate actions and processes based on the computer program stored in the ROM 12 or loaded from storage unit 18 into the RAM 13. The RAM 13 can also store various programs and data required for the operation of the electronic device 10. The processor 11, ROM 12, and RAM 13 are interconnected via a bus 14. An input / output (I / O) interface 15 is also connected to the bus 14.

[0085] Multiple components in electronic device 10 are connected to I / O interface 15, including: input unit 16, such as keyboard, mouse, etc.; output unit 17, such as various types of displays, speakers, etc.; storage unit 18, such as disk, optical disk, etc.; and communication unit 19, such as network card, modem, wireless transceiver, etc. Communication unit 19 allows electronic device 10 to exchange information / data with other devices through computer networks such as the Internet and / or various telecommunications networks.

[0086] Processor 11 can be a variety of general-purpose and / or special-purpose processing components with processing and computing capabilities. Some examples of processor 11 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various special-purpose artificial intelligence (AI) computing chips, various processors running machine learning model algorithms, digital signal processors (DSPs), and any suitable processor, controller, microcontroller, etc. Processor 11 performs the various methods and processes described above, such as intelligent optimization methods for aircraft fuselage panel maintenance programs.

[0087] In some embodiments, the intelligent optimization method for aircraft fuselage panel maintenance can be implemented as a computer program tangibly contained in a computer-readable storage medium, such as storage unit 18. In some embodiments, part or all of the computer program can be loaded and / or installed on electronic device 10 via ROM 12 and / or communication unit 19. When the computer program is loaded into RAM 13 and executed by processor 11, one or more steps of the intelligent optimization method for aircraft fuselage panel maintenance described above can be performed. Alternatively, in other embodiments, processor 11 can be configured to perform the intelligent optimization method for aircraft fuselage panel maintenance by any other suitable means (e.g., by means of firmware).

[0088] Various embodiments of the systems and techniques described above herein can be implemented in digital electronic circuit systems, integrated circuit systems, field-programmable gate arrays (FPGAs), application-specific integrated circuits (ASICs), application-specific standard products (ASSPs), systems-on-a-chip (SoCs), payload-programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various embodiments may include implementations in one or more computer programs that can be executed and / or interpreted on a programmable system including at least one programmable processor, which may be a dedicated or general-purpose programmable processor, capable of receiving data and instructions from a storage system, at least one input device, and at least one output device, and transmitting data and instructions to the storage system, the at least one input device, and the at least one output device.

[0089] Computer programs used to implement the methods of the present invention can be written in any combination of one or more programming languages. These computer programs can be provided to a processor of a general-purpose computer, a special-purpose computer, or other programmable data processing device, such that when executed by the processor, the computer programs cause the patterns / operations specified in the flowcharts and / or block diagrams to be implemented. The computer programs can be executed entirely on a machine, partially on a machine, as a standalone software package partially on a machine and partially on a remote machine, or entirely on a remote machine or server.

[0090] In the context of this invention, a computer-readable storage medium can be a tangible medium that may contain or store a computer program for use by or in conjunction with an instruction execution system, apparatus, or device. A computer-readable storage medium may include, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, apparatus, or devices, or any suitable combination thereof. Alternatively, a computer-readable storage medium may be a machine-readable signal medium. More specific examples of machine-readable storage media include electrical connections based on one or more wires, portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fibers, portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination thereof.

[0091] To provide interaction with a user, the systems and techniques described herein can be implemented on an electronic device having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to the user; and a keyboard and pointing device (e.g., a mouse or trackball) through which the user provides input to the electronic device. Other types of devices can also be used to provide interaction with the user; for example, feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user can be received in any form (including sound input, voice input, or tactile input).

[0092] The systems and technologies described herein can be implemented in computing systems that include backend components (e.g., as data servers), or middleware components (e.g., application servers), or frontend components (e.g., user computers with graphical user interfaces or web browsers through which users can interact with implementations of the systems and technologies described herein), or any combination of such backend, middleware, or frontend components. The components of the system can be interconnected via digital data communication of any form or medium (e.g., communication networks). Examples of communication networks include local area networks (LANs), wide area networks (WANs), blockchain networks, and the Internet.

[0093] A computing system can include clients and servers. Clients and servers are generally located far apart and typically interact through communication networks. The client-server relationship is created by computer programs running on the respective computers and having a client-server relationship with each other. The server can be a cloud server, also known as a cloud computing server or cloud host, which is a hosting product within the cloud computing service system to address the shortcomings of traditional physical hosts and VPS servers, such as high management difficulty and weak business scalability.

[0094] It should be understood that the various forms of processes shown above can be used, with steps reordered, added, or deleted. For example, the steps described in this invention can be executed in parallel, sequentially, or in different orders, as long as the desired result of the technical solution of this invention can be achieved, and this is not limited herein.

[0095] This embodiment provides a computer-readable storage medium storing a computer program thereon. When executed by a processor, the program implements the steps of an intelligent optimization method for aircraft fuselage panel maintenance as provided in any embodiment of the present invention. The method includes: Identify the repair damage area of ​​the aircraft fuselage panel to be repaired, and obtain the finite element local sub-model corresponding to the repair damage area; Based on the patch parameters matched to the repair damage area, an optimized patch design model is constructed for the patch in the repair damage area based on the patch parameters.

[0096] The optimal patch parameters are determined by iterative calculations based on the finite element local sub-model and the patch optimization design model using a pre-established optimization script.

[0097] The computer storage medium of this invention can be any combination of one or more computer-readable media. A computer-readable medium can be a computer-readable signal medium or a computer-readable storage medium. A computer-readable storage medium can be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination thereof. More specific examples of computer-readable storage media (a non-exhaustive list) include: an electrical connection having one or more wires, a portable computer disk, a hard disk, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, portable compact disk read-only memory (CD-ROM), optical storage device, magnetic storage device, or any suitable combination thereof. In this document, a computer-readable storage medium can be any tangible medium that contains or stores a program that can be used by or in conjunction with an instruction execution system, apparatus, or device.

[0098] Computer-readable signal media may include data signals propagated in baseband or as part of a carrier wave, carrying computer-readable program code. Such propagated data signals may take various forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination thereof. Computer-readable signal media may also be any computer-readable medium other than computer-readable storage media, capable of sending, propagating, or transmitting programs for use by or in connection with an instruction execution system, apparatus, or device.

[0099] Program code contained on a computer-readable medium may be transmitted using any suitable medium, including but not limited to: wireless, wire, optical fiber, RF, etc., or any suitable combination thereof.

[0100] Computer program code for performing the operations of this invention can be written in one or more programming languages ​​or a combination thereof, including object-oriented programming languages ​​such as Java, Smalltalk, and C++, as well as conventional procedural programming languages ​​such as "C" or similar programming languages. The program code can be executed entirely on the user's computer, partially on the user's computer, as a standalone software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In cases involving remote computers, the remote computer can be connected to the user's computer via any type of network, including a local area network (LAN) or a wide area network (WAN), or it can be connected to an external computer (e.g., via the Internet using an Internet service provider).

[0101] Those skilled in the art will understand that the modules or steps of the present invention described above can be implemented using general-purpose computing devices. They can be centralized on a single computing device or distributed across a network of multiple computing devices. Optionally, they can be implemented using computer-executable program code, thereby allowing them to be stored in a storage device for execution by a computing device, or they can be fabricated as separate integrated circuit modules, or multiple modules or steps can be fabricated as a single integrated circuit module. Thus, the present invention is not limited to any particular combination of hardware and software.

[0102] It should be understood that the various forms of processes shown above can be used, with steps reordered, added, or deleted. For example, the steps described in this invention can be executed in parallel, sequentially, or in different orders, as long as the desired result of the technical solution of this invention can be achieved, and this is not limited herein.

[0103] The specific embodiments described above do not constitute a limitation on the scope of protection of this invention. Those skilled in the art should understand that various modifications, combinations, sub-combinations, and substitutions can be made according to design requirements and other factors. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of this invention should be included within the scope of protection of this invention.

Claims

1. An intelligent optimization method for aircraft fuselage panel maintenance, characterized in that, include: Identify the repair damage area of ​​the aircraft fuselage panel to be repaired, and obtain the finite element local sub-model corresponding to the repair damage area; Based on the patch parameters matched to the repair damage area, and based on the patch parameters, a patch optimization design model is constructed for the patch in the repair damage area; The optimal patch parameters are determined by iterative calculations based on the finite element local sub-model and the patch optimization design model using a pre-established optimization script.

2. The method according to claim 1, characterized in that, The process of obtaining the finite element local sub-model corresponding to the repair damage area includes: Obtain the structural parameters, material properties, and load constraints of the aircraft fuselage panel to be repaired; Based on the structural parameters, material properties, and load constraints, a finite element model for the maintenance of the aircraft fuselage panel is established. Based on the aforementioned finite element model for fuselage panel repair, a local finite element sub-model is constructed for the repair damage area.

3. The method according to claim 2, characterized in that, The construction of the finite element local sub-model for the repair damage area based on the fuselage panel repair finite element model includes: A sub-model is established by extracting the local region corresponding to the repair damage area from the finite element model of the fuselage panel repair; The model calculation results of the repair damage area of ​​the fuselage panel repair finite element model are applied to the sub-model as boundary conditions. For the sub-model, a non-destructive sub-model, a damaged cutting sub-model, and a patch repair sub-model are established, and stress distribution and displacement distribution analysis are performed. If the stress distribution and displacement distribution analysis is successfully verified, the sub-model is determined as the finite element local sub-model.

4. The method according to claim 1, characterized in that, The process of constructing an optimized design model for the patch in the repair damage area based on the patch parameters includes: Using the patch parameters as design variables and minimizing the patch volume as the optimization objective, the patch optimization design model is constructed by setting maximum stress constraints and maximum displacement constraints.

5. The method according to claim 1, characterized in that, The step of determining the optimal patch parameters through iterative calculations based on the finite element local sub-model and the patch optimization design model using a pre-established optimization script includes: The corresponding patch parameter set is determined based on hypercube sampling; The optimization script is used to perform dynamic simulation on the patch parameter set based on the finite element local sub-model and the patch optimization design model to determine the patch performance data set. The optimal patch parameters are determined by iterative optimization based on the patch performance data set using a Bayesian optimization program.

6. The method according to claim 5, characterized in that, The step of determining the optimal patch parameters through iterative optimization based on the patch performance data set using a Bayesian optimization program includes: The patch optimization proxy model is updated based on the Bayesian optimization procedure and the patch performance data set, and patch parameters are generated. The patch parameters are dynamically simulated using the optimization script to determine the patch performance data. The patch performance data is updated to the patch performance data group. If the patch performance data group meets the preset convergence condition, the patch parameters corresponding to the patch performance data are determined as the optimal patch parameters.

7. The method according to claim 1, characterized in that, Also includes: Based on the optimal patch parameters, tensile load and shear load are calculated respectively to determine the tensile load and shear load; The number of fastener rows is calculated and determined based on the preset rivet type, preset rivet arrangement rules, tensile load and shear load. The optimal patch parameters are updated based on the number of fastener columns to determine the target patch parameters.

8. An intelligent optimization device for aircraft fuselage panel maintenance, characterized in that, include: The finite element analysis module is used to identify the repair damage area of ​​the aircraft fuselage panel to be repaired and obtain the finite element local sub-model corresponding to the repair damage area. The patch optimization design module is used to match patch parameters based on the repair damage area, and to construct a patch optimization design model for the patch in the repair damage area based on the patch parameters. The optimal patch analysis module is used to determine the optimal patch parameters by performing iterative calculations based on the finite element local sub-model and the patch optimization design model using a pre-established optimization script.

9. An electronic device, characterized in that, The electronic device includes: At least one processor; and A memory communicatively connected to the at least one processor; wherein, The memory stores a computer program that can be executed by the at least one processor, the computer program being executed by the at least one processor to enable the at least one processor to perform the intelligent optimization method for the aircraft fuselage panel maintenance scheme according to any one of claims 1-7.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores computer instructions that are used to cause a processor to execute the intelligent optimization method for the aircraft fuselage panel maintenance scheme according to any one of claims 1-7.

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