CN115438556B

CN115438556B - Method, device and equipment for predicting structural rigidity degradation rate of flexible inflatable aircraft

Info

Publication number: CN115438556B
Application number: CN202211402193.4A
Authority: CN
Inventors: 孟军辉; 马诺; 岳振江; 李文光; 刘莉
Original assignee: Beijing Institute of Technology BIT
Current assignee: Beijing Institute of Technology BIT
Priority date: 2022-11-10
Filing date: 2022-11-10
Publication date: 2022-12-30
Anticipated expiration: 2042-11-10
Also published as: CN115438556A

Abstract

The application relates to the field of aircrafts, in particular to a method, a device and equipment for predicting the structural rigidity degradation rate of a flexible inflatable aircraft, electronic equipment and a computer readable medium. The method comprises the following steps: establishing a first database; acquiring a fold area image of an object to be detected; carrying out modal decomposition on a fold area image of an object to be detected; determining participation modalities and contribution degrees of each participation modality based on a plurality of decomposition modalities obtained by modality decomposition and a basic modality in a first database; and calculating the overall rigidity degradation rate of the object to be measured. According to the method and the device, on the premise that the load is unknown, the rigidity degradation condition of the inflatable structure is directly predicted through the actually-known fold area form, the pneumatic load in the actual flight of the flexible inflatable aircraft is not required to be obtained, the problem that the structural rigidity is difficult to predict due to the fact that the pneumatic load is difficult to obtain in the actual flight of the flexible inflatable aircraft in the traditional scheme is solved, and the effect of predicting the rigidity degradation condition of the flexible inflatable aircraft in real time is achieved.

Description

Method, device and equipment for predicting structural rigidity degradation rate of flexible inflatable aircraft

Technical Field

The invention relates to the field of aircrafts, in particular to a method, a device and equipment for predicting the structural rigidity degradation rate of a flexible inflatable aircraft, electronic equipment and a computer readable medium.

Background

The inflatable wing is a multi-air-cavity inflatable structure which is made of high-strength composite flexible materials and is internally filled with high-pressure gas to maintain the pressure and the shape, and has the remarkable advantages of flexible and variable volume, lighter structural weight, convenience in storage and carrying, easiness in unfolding at any time, lower cost and the like. With the diversified development of the application of the aircraft, the rigid body and the flexible wing are organically combined by the inflatable wing technology, the characteristic of rigid-flexible coupling shows great application value, and the inflatable wing has military and civil dual-purpose prospects.

The whole flexible inflatable wing structure lacks rigid connection, and the aeroelasticity problem is more outstanding. Except for the common failure modes of flutter, buffeting, divergence and the like of the traditional rigid wing, the fold-buckling is related to the inflatable membrane material and the inflatable internal pressure, takes the rigidity degradation phenomenon of the membrane fold into account, and is the unique aeroelastic failure phenomenon of the flexible inflatable wing.

In the related art, the analysis method of the pleat area of the inflatable wing determines the stable state of the unit by identifying the relationship between the maximum and minimum principal stresses of the membrane unit under the loading environment. The technology can determine the area and the form of the crumple zone of the inflatable wing more efficiently, and provides the form standard of the crumple zone in the critical buckling state. However, due to the time-varying characteristic of the aerodynamic load in the actual aircraft, the method cannot give accurate aerodynamic load distribution in real time during the flying process of the inflatable wings, due to the poor compatibility of the inflatable membrane structure and the sensor, the conventional technical scheme of acquiring the pressure distribution in real time by the sensor cannot be applied.

Disclosure of Invention

In view of the above, the invention provides a method, a device and equipment for predicting the structural rigidity degradation rate of a flexible inflatable aircraft, electronic equipment and a computer readable medium, which solve the problem that the structural rigidity is difficult to predict due to the difficulty in acquiring the pneumatic load in the actual flight of an inflatable wing.

Additional features and advantages of the invention will be set forth in the detailed description which follows, or may be learned by practice of the invention.

In a first aspect of the present invention, a method for predicting a structural stiffness degradation rate of a flexible inflatable aircraft is provided, where the method includes: establishing a first database, wherein the first database is a basic database of a basic fold area geometric feature-rigidity degradation rate relation of an inflatable structure model, and each basic fold area geometric feature corresponds to a basic mode; acquiring a fold area image of an object to be detected, wherein the object to be detected is a part on the flexible inflatable aircraft; carrying out modal decomposition on the fold area image of the object to be detected to obtain a plurality of decomposition modes and the contribution degree of each decomposition mode; determining participation modalities and contribution degrees of each participation modality based on a plurality of decomposition modalities obtained by modality decomposition and a basic modality in the first database, wherein all the participation modalities are the basic modalities in the first database, and the contribution degrees of the participation modalities are obtained according to the contribution degrees of the decomposition modalities obtained by modality decomposition; and calculating the overall rigidity degradation rate of the object to be tested according to the contribution degree of each participating mode and the rigidity degradation rate of each participating mode.

Further, acquiring a wrinkle area image of the object to be measured, including: shooting an image of the surface of the object to be measured through an optical sensor; aligning the shot image through the transformation matrix; identifying a wrinkle region in the image; pooling a wrinkle area, and filtering out a fine wrinkle area to obtain a wrinkle area image of the object to be detected, wherein the fine wrinkle area is formed by processes and structural lines.

Further, performing modality decomposition on the wrinkle region image of the object to be detected to obtain a plurality of decomposition modalities and a contribution degree of each decomposition modality, including: comparing a fold region obtained by linear combination of a plurality of decomposition modalities with a geometric similarity law of a fold region image of the object to be detected based on the contribution degree of the decomposition modalities obtained by modality decomposition; if the geometric similarity of the two is greater than or equal to a preset geometric similarity threshold value, the modal decomposition is successful; and recording the decomposition modes obtained by the mode decomposition and the contribution degree of each decomposition mode.

Further, calculating the overall stiffness degradation rate of the object to be measured according to the contribution degree of each participating modality and the stiffness degradation rate of each participating modality, including: and linearly superposing the stiffness degradation rate of each participating mode, wherein the contribution degree of the participating modes is used as the weight in the linear superposition process to obtain the total stiffness degradation rate of the object to be detected.

Further, establishing a first database, comprising: establishing a finite element model of an inflatable structure model; defining a pressure load for the finite element model; calling a finite element solver in batch processing, and transiently solving the rigidity and the fold area under the pressure load; and establishing the first database according to the solved rigidity and the fold area.

Further, before performing modal decomposition on the image of the wrinkle region of the object to be measured, the method further includes: -digitizing a wrinkle region image, the digitizing comprising: discretizing the image in space coordinates; the image is discretized in magnitude.

Further, the method further comprises: judging whether the overall stiffness degradation rate of the object to be detected exceeds a preset overall stiffness degradation rate threshold value or not; if so, controlling the flexible inflatable aircraft to change at least one of the following: flight speed, flight attitude.

In a second aspect of the invention, a flexible inflatable aircraft structural stiffness degradation rate prediction apparatus is provided, the apparatus being configured to perform the method of the first aspect, the apparatus comprising: the database establishing unit is used for establishing a first database, the first database is a basic database of the relation between the geometric characteristics of the basic fold area of the inflatable structure model and the rigidity degradation rate, and each geometric characteristic of the basic fold area corresponds to a basic mode; the acquiring unit is used for acquiring a fold area image of an object to be detected, wherein the object to be detected is a part on the flexible inflatable aircraft; the modal decomposition unit is used for carrying out modal decomposition on the fold region image of the object to be detected to obtain a plurality of decomposition modalities and the contribution degree of each decomposition modality; the determining unit is used for determining participation modalities and the contribution degree of each participation modality based on a plurality of decomposition modalities obtained through modality decomposition and the basic modality in the first database, wherein all the participation modalities are the basic modalities in the first database, and the contribution degrees of the participation modalities are obtained according to the contribution degrees of the decomposition modalities obtained through modality decomposition; and the calculating unit is used for calculating the overall rigidity degradation rate of the object to be measured according to the contribution degree of each participating modality and the rigidity degradation rate of each participating modality.

Further, the acquisition unit includes: the shooting subunit is used for shooting the image of the surface of the object to be measured through an optical sensor; the alignment subunit is used for aligning the shot image through the conversion matrix; the identification subunit is used for identifying a wrinkle area in the image; and the pooling subunit is used for pooling the wrinkle area, filtering out a fine wrinkle area and obtaining a wrinkle area image of the object to be detected, wherein the fine wrinkle area is formed by processes and structural lines.

Further, the modal decomposition unit includes: the comparison subunit is used for comparing a fold region obtained by linear combination of the plurality of decomposition modalities with a geometric similarity law of a fold region image of the object to be detected based on the contribution degree of the decomposition modalities obtained by modality decomposition; the determining subunit is used for successfully decomposing the mode if the geometric similarity of the two is greater than or equal to a preset geometric similarity threshold; and the recording subunit is used for recording the decomposition modes obtained by the mode decomposition and the contribution degree of each decomposition mode.

Further, the calculation unit includes: and the linear superposition subunit is used for linearly superposing the stiffness degradation rate of each participating mode, wherein the contribution degree of the participating modes is used as a weight in the linear superposition process to obtain the total stiffness degradation rate of the object to be detected.

Further, the database establishing unit includes: the finite element model establishing subunit is used for establishing a finite element model of the inflatable structure model; a defining subunit, configured to define a pressure load for the finite element model; the solving subunit is used for calling a finite element solver in a batch mode and solving the rigidity and the fold area under the pressure load in a transient state; and the database establishing subunit is used for establishing the first database according to the rigidity and the wrinkle area obtained by solving.

Further, the apparatus further comprises: a wrinkle region image digitization subunit to: discretizing the image at a space coordinate before carrying out modal decomposition on the image of the fold area of the object to be detected; the image is discretized in magnitude.

Further, the apparatus further comprises: the control unit is used for controlling the flexible inflatable aircraft to change at least one of the following conditions when the overall stiffness degradation rate of the object to be tested exceeds a preset overall stiffness degradation rate threshold value: flight speed, flight attitude.

According to a third aspect of the invention, there is provided a flexible inflatable aircraft structural stiffness degradation rate prediction apparatus, the apparatus comprising: a camera; a flexible pneumatic aircraft structure stiffness degradation rate prediction apparatus for carrying out the method of the first aspect as hereinbefore described.

According to a fourth aspect of the present invention, there is provided an electronic apparatus comprising: one or more processors; storage means for storing one or more programs; when the one or more programs are executed by the one or more processors, the one or more processors are caused to implement a method as described above in the first aspect.

According to a fifth aspect of the present invention, a computer-readable medium is proposed, on which a computer program is stored which, when being executed by a processor, carries out the method as described above in the first aspect.

According to the method and the device, on the premise that the load is unknown, the rigidity degradation condition of the inflatable structure is directly predicted through the actually-known fold area form, the pneumatic load in the actual flight of the flexible inflatable aircraft is not required to be obtained, the problem that the structural rigidity is difficult to predict due to the fact that the pneumatic load is difficult to obtain in the actual flight of the flexible inflatable aircraft in the traditional scheme is solved, and the effect of predicting the rigidity degradation condition of the flexible inflatable aircraft in real time is achieved.

The solution according to the invention also brings about a number of other advantages, which will be explained in more detail in the detailed description.

It is to be understood that both the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the invention as claimed.

Drawings

In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings of the embodiments will be briefly described below, and it is apparent that the drawings in the following description only relate to some embodiments of the present invention and are not limiting on the present invention.

FIG. 1 is a flow chart of a method for predicting the structural stiffness degradation rate of a flexible inflatable aircraft according to an embodiment of the present application;

FIG. 2 is a flow chart of a method for predicting the structural stiffness degradation rate of a flexible inflatable aircraft according to an embodiment of the present application;

FIG. 3 is a schematic view of a geometric model of a gas tube as provided in an embodiment of the present application;

FIG. 4 is a schematic diagram of a finite element model of a gas tube according to an embodiment of the present application;

FIG. 5 is a schematic illustration of a pooling effect provided by an embodiment of the present application;

FIG. 6 is a schematic diagram illustrating a low-order mode of a crumple zone according to an embodiment of the present disclosure;

FIG. 7 is a schematic view of an experimental platform for an inflatable wing according to an embodiment of the present disclosure;

FIG. 8 is a schematic illustration of a finite element model of an airfoil under inflation provided by an embodiment of the present application;

FIG. 9 is a schematic diagram of a device for predicting the degradation rate of the structural stiffness of a flexible inflatable aircraft according to an embodiment of the present application;

fig. 10 is a block diagram of an electronic device provided in an embodiment of the present application;

FIG. 11 is a block diagram of a computer-readable medium provided by an embodiment of the present application.

Detailed Description

Example embodiments will now be described more fully with reference to the accompanying drawings. Example embodiments may, however, be embodied in many different forms and should not be construed as limited to the embodiments set forth herein; rather, these embodiments are provided so that this disclosure will be thorough and complete, and will fully convey the concept of example embodiments to those skilled in the art. The same reference numerals denote the same or similar parts in the drawings, and thus, a repetitive description thereof will be omitted.

Furthermore, the described features, structures, or characteristics may be combined in any suitable manner in one or more embodiments. In the following description, numerous specific details are provided to provide a thorough understanding of embodiments of the invention. One skilled in the relevant art will recognize, however, that the invention may be practiced without one or more of the specific details, or with other methods, components, devices, steps, and so forth. In other instances, well-known methods, devices, implementations or operations have not been shown or described in detail to avoid obscuring aspects of the invention.

The block diagrams shown in the figures are functional entities only and do not necessarily correspond to physically separate entities. I.e. these functional entities may be implemented in the form of software, or in one or more hardware modules or integrated circuits, or in different networks and/or processor means and/or microcontroller means.

The flowcharts shown in the figures are illustrative only and do not necessarily include all of the contents and operations/steps, nor do they necessarily have to be performed in the order described. For example, some operations/steps may be decomposed, and some operations/steps may be combined or partially combined, so that the actual execution sequence may be changed according to the actual situation.

These terms are used to distinguish one element from another. Thus, a first component discussed below could be termed a second component without departing from the teachings of the present concepts. As used herein, the term "and/or" includes any and all combinations of one or more of the associated listed items.

It will be understood by those skilled in the art that the drawings are merely schematic representations of exemplary embodiments, and that the blocks or processes in the drawings are not necessarily required to practice the present invention and, therefore, are not intended to limit the scope of the present invention.

In the present application, the contribution and the engagement are the same, and the engagement of a modality are the same.

FIG. 1 is a flow chart of a method for predicting the rate of degradation of structural stiffness of a flexible inflatable aircraft according to an embodiment of the invention, as shown in FIG. 1, the method comprising:

step S101: a first database is established.

As an alternative embodiment, the establishing of the first database includes: establishing a finite element model of the inflatable structure model; defining a pressure load for the finite element model; calling a finite element solver in batch processing, and transiently solving the rigidity and the fold area under the pressure load; and establishing a first database according to the stiffness and the fold area obtained by solving.

Inflatable structural models generally employ the basic structural building units of the flexible, inflatable aircraft.

For example, in the case of the present application, the inflatable wings of the flexible inflatable aircraft are formed by a plurality of inflatable cylinders, and the inflatable cylinders are used as the inflatable structure model to build the first database.

In the case of a flexible inflatable aircraft using a plurality of basic structural building units, the inflatable structural model also uses a plurality of basic structural building units to build the first database.

As an alternative embodiment, the finite element model is created using ANSYS finite element software.

ANSYS finite element software is a multi-purpose finite element method computer design program and can be used for solving the problems of structures, fluids, electric power, electromagnetic fields, collision and the like.

The ANSYS software mainly comprises three parts: the device comprises a pre-processing module, an analysis and calculation module and a post-processing module.

The preprocessing module provides a powerful tool for solid modeling and mesh division, and a user can conveniently construct a finite element model.

Pretreatment: refers to creating solid models and finite element models.

And (3) solid model: i.e., a geometric model of the structure, which does not participate in the finite element analysis.

The establishing of the finite element model comprises defining element properties and dividing a grid.

After the element attributes are defined on the solid model and the meshes are divided, the solid model is converted into a finite element model.

Unit attributes refer to characteristics of the analyzed object that must be specified before the mesh is divided, including: material properties, cell type, and real constants. The material properties depend on the type of analysis, e.g. structural analysis is input to at least the young's modulus of the material, and thermal analysis is input to at least the thermal conductivity of the material.

The ANSYS software provides more than 100 cell types for simulating various structures and materials in engineering, for example, link series cells are used for simulating rods, beam series cells are used for simulating beams, shell series cells are used for simulating plate shells, for example, link10 can be used for simulating stay ropes, beam44 can be used for simulating thin-walled steel structural members or variable-section members, and shell41 can be used for simulating membranes. The type of the unit is suitably selected in accordance with the structure and the material.

The entity model can be directly created in ANSYS, or can be created in other software (such as CAD) and then read into ANSYS through a data interface.

The finite element model can be established by dividing a finite element mesh by a solid model, or can be established by directly establishing nodes and units.

The analysis calculation module is used for applying load and solving: and applying load and load options, setting constraint conditions and solving.

A pressure load on the inflatable structure can create a corresponding crumple zone and stiffness. The geometric characteristics of the crumple zones produced under different pressure loads are different.

Each basic fold region geometric feature corresponds to a basic mode.

In the embodiment of the application, one end of the inflatable structure model is fixedly supported, pressure loads are uniformly distributed inside and outside, uniform distribution and polynomial distribution loads are respectively defined outside the upper surface, and the load value is parameterized.

And (3) calling a finite element solver in batch processing, and performing transient solution to record the rigidity and the fold area to obtain the relation between the geometric characteristics of the basic fold area and the rigidity degradation rate of the inflatable structure model, or the relation between the load of the inflatable structure model and the geometric characteristics of the basic fold area and the rigidity degradation rate, and establishing a first database.

The first database stores the geometric characteristics of the basic fold region of the inflatable structure model and the rigidity degradation rate relation.

As an alternative embodiment, the first database stores the relationship between the load-basic fold region geometric characteristic-stiffness degradation rate of the inflatable structure model.

And the post-processing module is used for checking the analysis result and checking the result.

Step S102: and acquiring a wrinkle area image of the object to be detected.

The object to be measured is a part on a flexible inflatable aircraft, such as an inflatable wing and the like.

As an alternative embodiment, the surface of the object to be measured is coated to enhance the image recognition effect, and specifically, the surface of the object to be measured is coated with high contrast, such as a checkerboard pattern or a stripe pattern.

As an optional implementation manner, acquiring a wrinkle region image of an object to be measured includes: shooting an image of the surface of an object to be measured through an optical sensor; aligning the shot image through the transformation matrix; identifying a wrinkle region in the image; and pooling the fold area, and filtering out the fine fold area to obtain a fold area image of the object to be detected. The micro-wrinkle area is formed by processes and structural lines, is not formed by pressure load, belongs to interference, and is removed by filtering the micro-wrinkle area.

Pooling is a method of compressing an image.

The larger the image, the greater the processing speed and the recognition difficulty. By the pooling process, the size of the image can be reduced.

Pooling allows for relatively lower dimensionality by aggregating statistical processing of different features while avoiding overfitting.

Pooling retains most important information while reducing the dimensions of each feature map, and currently, there are mainly ways of maximization, averaging, addition, and the like.

The most common pooling operations are both mean pooling and maximum pooling. Average pooling: the average value of the image area is calculated and used as the pooled value of the area. Maximum pooling: and selecting the maximum value of the image area, and taking the maximum value as the pooled value of the area.

Step S103: and carrying out modal decomposition on the fold region image of the object to be detected to obtain a plurality of decomposition modalities and the contribution degree of each decomposition modality.

The characteristics of the crumple zones formed are different when different pressure load distributions are applied to the aircraft surface. Different fundamental modes have different pressure load distribution characteristics, and corresponding crumple zone image characteristics.

As an optional implementation manner, performing modality decomposition on the wrinkle region image of the object to be measured to obtain a plurality of decomposition modalities and a contribution degree of each decomposition modality, including: comparing a fold region obtained by linear combination of a plurality of decomposition modalities with a geometric similarity law of a fold region image of the object to be detected based on the contribution degree of the decomposition modalities obtained by modality decomposition; if the geometric similarity of the two is greater than or equal to a preset geometric similarity threshold value, the modal decomposition is successful; and recording the decomposition modality obtained by modality decomposition and the contribution degree of each decomposition modality. The sum of the contribution degrees of all the decomposition modes obtained by decomposition is 1.

The predetermined geometric similarity law threshold may be set as desired, for example, 90%, 92%, 95%, 96%, 98%, etc. The higher the preset geometric similarity law threshold value is, the higher the accuracy of the method is, and meanwhile, the larger the calculation amount in modal decomposition is.

For example, modal decomposition is successful, and there are 3 decomposition modalities obtained by decomposition, which are a decomposition modality m1 ', a decomposition modality m2 ', and a decomposition modality m3 ', respectively. The contribution of the decomposition mode m 1' is g ₁ (ii) a The contribution of the decomposition mode m 2' is g ₂ (ii) a The contribution of the decomposition mode m 3' is g ₃ 。g ₁ +g ₂ +g ₃ =1。

Step S104: and determining the participation modalities and the contribution degree of each participation modality based on the plurality of decomposition modalities obtained by modality decomposition and the basic modality in the first database. All the participation modes are basic modes in the first database, and the contribution degrees of the participation modes are obtained according to the contribution degrees of the decomposition modes obtained by mode decomposition.

Assume that 100 basic modalities are stored in the first database, which are the basic modality m1, the basic modality m2, … …, and the basic modality m100, respectively. The stiffness degradation rate corresponding to the basic mode m1 is t ₁ (ii) a The stiffness degradation rate corresponding to the basic mode m2 is t ₂ (ii) a … …; the stiffness degradation rate corresponding to the fundamental mode m100 is t ₁₀₀ 。

Contrasting a decomposition mode m1 ', a decomposition mode m2 ' and a decomposition mode m3 ' obtained by mode decomposition with 100 basic modes stored in a first database, respectively finding out modes consistent with the decomposition mode m1 ', the decomposition mode m2 ' and the decomposition mode m3 ' from the 100 basic modes, assuming that the basic mode m25 is consistent with the decomposition mode m1 ', the basic mode m83 is consistent with the decomposition mode m2 ', and the basic mode m46 is consistent with the decomposition mode m3 ', then the basic mode m25, the basic mode m83 and the basic mode m46 are participation modes, and the contribution degrees of the three participation modes are g < 2 > and g < 3 > respectively ₁ 、g ₂ 、g ₃ The rate of stiffness degradation is t ₂₅ 、t ₈₃ 、t ₄₆ 。

The inventor finds that the common basic modes are uniform distribution load, polynomial distribution load and sine distribution load, and a plurality of basic modes can be obtained by adjusting the peak value and/or the phase, so that the uniform distribution, the polynomial distribution and the sine distribution load with different phases of a plurality of different peak values can be stored in the first database. And during modal decomposition, the modes are decomposed according to the uniformly distributed load, the polynomial distributed load or the sine distributed load, and the obtained decomposition modes are one or more of the uniformly distributed load, the polynomial distributed load and the sine distributed load, so that the basic modes consistent with the decomposition modes can be ensured to be found in the first database, namely, the participation modes can be determined.

Step S105: and calculating the overall stiffness degradation rate of the object to be measured according to the contribution degree of each participating mode and the stiffness degradation rate of each participating mode.

As an optional implementation manner, calculating an overall stiffness degradation rate of the object to be measured according to the contribution degree of each participating modality and the stiffness degradation rate of each participating modality includes: and linearly superposing the stiffness degradation rate of each participating mode, wherein the contribution degree of the participating modes is used as the weight in the linear superposition process to obtain the total stiffness degradation rate of the object to be detected.

Overall stiffness degradation rate T of object to be measured _{General assembly} =t ₂₅ ×g ₁ +t ₈₃ ×g ₂ +t ₄₆ ×g ₃

As an alternative embodiment, the overall stiffness degradation rate T of the object to be measured is calculated _{General (1)} And then, judging whether the overall stiffness degradation rate of the object to be detected exceeds a preset overall stiffness degradation rate threshold value or not. The preset overall stiffness degradation rate threshold value is a value preset according to actual needs, and if the stiffness degradation rate of the aircraft in the flying process exceeds the value, the pressure load on the aircraft is too large, so that the safety of the flight is not facilitated. If the total stiffness degradation rate of the object to be measured exceeds a preset total stiffness degradation rate threshold value, the pressure load applied to the flexible inflatable air vehicle can be reduced by adjusting the flying speed, flying attitude and the like of the flexible inflatable air vehicle, and therefore the safety of the flexible inflatable air vehicle is guaranteed.

In the traditional method, the pressure distribution condition of the surface of the flexible inflatable aircraft is measured through a sensor, and then the rigidity degradation rate is determined through the pressure distribution condition, the core of the method lies in that the pressure distribution condition of the surface of the flexible inflatable aircraft is measured through the sensor in real time, and the pressure distribution condition of the surface of the flexible inflatable aircraft in the flight process cannot be accurately measured through the sensor in real time due to the fact that the material and the structure of the flexible inflatable aircraft are very unfavorable for the pressure measurement through the sensor in real time, so that the rigidity degradation rate of the aircraft in the flight process cannot be determined (calculated) in real time.

According to the scheme, the core is that when the flexible inflatable aircraft flies, the wrinkle area image of the surface of the flexible inflatable aircraft is obtained, the wrinkle area image is analyzed to determine the rigidity degradation rate, the pressure distribution of the surface of the flexible inflatable aircraft does not need to be measured in real time in the flying process of the flexible inflatable aircraft, and therefore the problem in the traditional method is solved.

FIG. 2 is a flowchart of a method for predicting the structural stiffness degradation rate of a flexible inflatable aircraft according to an embodiment of the invention, and the method mainly comprises the steps of establishing a database, carrying hardware, processing images, decomposing modes and inverting the stiffness degradation rate. As described in detail below.

Establishing a database: as shown in steps S201 to S204 of fig. 2, a high-precision inflatable structure model is built by a conventional finite element method, uniform distribution, polynomial and sinusoidal distribution loads with different peak values are loaded, the rigidity and the geometric characteristics of the crumple zone are recorded, and a basic database (i.e., the above-mentioned first database) of the load-crumple zone geometric characteristics-rigidity degradation rate is built.

Hardware mounting: as shown in steps S205 to S206 of fig. 2, an optical sensor and an on-board computer are installed on the platform to be measured, for obtaining image information of the surface of the inflatable structure. High contrast coatings, such as checkerboards, stripes, and the like, may be applied to the upper surface of the inflatable structure to improve image recognition.

Image processing: as shown in steps S207 to S210 of fig. 2, the wrinkle area image is digitized, and the positive inflation wing image is processed through a transformation matrix by methods such as point operation, filtering, global optimization, etc., so as to cluster and identify wrinkles formed on the surfaces of all inflation wings. Pooling the wrinkled area, and filtering the fine wrinkled area formed by the process and the structural lines to finally obtain the main geometric distribution of the wrinkled area.

And (3) modal decomposition: as shown in steps S211 to S212 of fig. 2, a POD method is used to perform modality decomposition on the digital image obtained after the image processing step is performed, the order of modality extraction is that the geometric similarity law of the wrinkle region obtained by linear combination of the extracted modality (i.e., the decomposition modality) and the image information is greater than or equal to 95% (i.e., the preset geometric similarity threshold is set to 95%), and the participation modality and the participation degree are determined and recorded according to the decomposition modality and the basic modality in the first database.

For example, modal decomposition is successful (geometric similarity between the folded region obtained by linear combination of the decomposition modalities and the image information is equal to or greater than 95%), and there are 3 decomposition modalities obtained by decomposition, namely, decomposition modality m1 ', decomposition modality m2 ', and decomposition modality m3 '. The contribution of the decomposition mode m 1' is g ₁ (ii) a The contribution of the decomposition mode m 2' is g ₂ (ii) a The contribution of the decomposition mode m 3' is g ₃ 。g ₁ +g ₂ +g ₃ =1。

Assume that 100 fundamental modalities are stored in the first database, which are the fundamental modality m1, the fundamental modality m2, … …, and the fundamental modality m100, respectively. The stiffness degradation rate corresponding to the basic mode m1 is t ₁ (ii) a The stiffness degradation rate corresponding to the basic mode m2 is t ₂ (ii) a … …; the stiffness degradation rate corresponding to the fundamental mode m100 is t ₁₀₀ 。

Inversion stiffness degradation rate: according to the contribution degrees of the participating modes and the linear superposition of the corresponding degradation rates, the overall stiffness degradation rate of the inflatable wing is obtained as shown in steps S213 to S214 of FIG. 2. Overall stiffness degradation rate T of object to be measured _{General assembly} =t ₂₅ ×g ₁ +t ₈₃ ×g ₂ +t ₄₆ ×g ₃ 。

Example 1: the present embodiment takes a standard model ETFE tube of an inflation structure as an example to illustrate the specific process of the present invention

Step 1: a geometric model of the inflation tube is established as shown in fig. 3.

Step 2: and establishing a finite element model of the inflation tube. According to the characteristics of the ETFE standard model, a Shell41 unit is selected for modeling, and the thickness is 0.2mm, as shown in figure 4.

Shell41 is a three-dimensional unit with membrane strength in-plane but no bending strength out-of-plane. This is specific to the housing structure, since its unit bending is minor. The unit has three degrees of freedom at each node: movement along the x, y, z axes of the nodes. The cells have varying thicknesses, strain strengths, large deviations and material choices.

And 3, step 3: boundary conditions are defined. One end of the inflation tube is fixedly supported (all degrees of freedom are restrained), pressure loads are uniformly distributed inside and outside, uniform and polynomial distribution loads are respectively defined outside the upper surface, and load values are parameterized.

And 4, step 4: and (3) calling a finite element solver in batch processing, and transiently solving and recording the rigidity and the wrinkle area to establish a database (namely the first database). The fold region unit is distinguished as follows:

Fw=σ ₁ σ ₂ /|σ ₁ σ ₂ |，

σ ₁ representing the maximum principal stress, σ, of the cell ₂ Representing the minimum principal stress of the cell.

When in useF _w When = 1, the cell wrinkles.

And 5: the method comprises the following steps of solving a wrinkle area of the inflation tube under any complex load by finite elements, and digitizing an image of the wrinkle area, wherein the wrinkle area comprises two main stages: a. discretizing the image in space coordinates, namely sampling the image. According to the geometric complexity and the size specification of the inflatable structure, the target resolution is initially selected, and the target resolution is selected to be 64 x 512 due to the fact that the structure of the inflatable structure and the wrinkle distribution are simple. b. Discretized in amplitude, i.e. grey level quantization (rounding). Quantized to an 8-bit image according to pixel color. At this time, the simulated image can be represented by a 64 × 512-dimensional matrix, and the matrix elements are the gray values of the pixels at the corresponding positions. Finally, the mixture is subjected to a pooling treatment, as shown in FIG. 5.

Step 6: and (3) performing modal decomposition on the corrugated area of the inflation tube under the complex load by adopting a POD method, wherein the POD method has the following theory:

measuring the same phenomenon, each measurement comprising a large numbernVector of real termsx _k (first stepkAnnRow-column vectors) to discover interdependencies in the data and reduce the data set to parametersr << nCan be described as an optimization problem as follows:

E|x-Px|^2 --> min

Xis a real spaceR _n The random real vector of (a) is,Eas desired. Is rank ofrProjection operator ofPCan be expressed as:

P=VU,UV=Ir

setting matrixWThe above problem is then for covariance matricesrThe principal eigenvector has a solution:

W=Wx=Exx’

optimal orthogonal projection:

P=VV’,V=[v1,v2,…,Vr]

the mode of the crumpled zone obtained by decomposition is shown in fig. 6.

And 7: and calling a database, determining participation modes, and linearly combining the degradation rates corresponding to the participation modes of all orders to obtain a predicted value of the stiffness degradation rate. The results were compared using finite element simulation as a standard with an error of about 6.71%.

Example 2: the present example illustrates the effectiveness of the present invention in complex inflatable structures using an inflatable wing as an example

Step 1: the experimental platform for the inflatable wings is built and comprises an air compressor, the inflatable wings, a base, an optical sensor, a balance weight and a load, and is shown in fig. 7, wherein A is the air compressor, B is the inflatable wings, C is a support, D is the optical sensor, and E is the load.

Step 2: and establishing a finite element model of the inflatable wing. Shell41 cells were selected for modeling, 0.2mm thick, as shown in FIG. 8.

And step 3: boundary conditions are defined. The inflatable tube is fixedly supported at one section, pressure loads are uniformly distributed inside and outside, uniformly distributed and polynomial distributed loads are respectively defined outside the upper surface, and load values are parameterized.

And 4, step 4: and (4) calling a finite element solver in batch processing, performing transient solution to record the rigidity and the fold area, and establishing a database.

And 5: and carrying out noise reduction treatment on the wrinkle area image of the inflatable wing shot by the optical sensor through mean value filtering to obtain a real wrinkle area of the inflatable wing, and carrying out pooling treatment on the actual wrinkle area of the inflatable wing.

Step 6: and digitizing the image of the corrugated area, and performing modal decomposition on the corrugated area of the inflation tube under the complex load by adopting a POD (wafer POD) method.

POD method pseudo code:

% 1.1 defines the snapshot set

energy=0.999999;

G_X=X;

% 1.2 singular value decomposition of the snapshot set

[ U _ X _1, S _X _1, V _X ] = svd (G _ X)%, U _ X _1 is an eigenvector, S _ X _1 is a matrix of eigenvalues in descending order, and V _ X is an orthogonal matrix.

%1.3 determination of the optimal orthogonal base from the energy values

%1.3.1 Total energy value

Defining a loop and traversing S _ X _1;

end

accumulating to obtain a total energy value;

%1.3.2 determination of orthogonal basis

Defining a loop and traversing S _ X _1;

calculating an orthogonal base;

end

% looping when the total energy ratio corresponding to the selected orthogonal vector is less than a specified value

Define a loop, trial and error _ x

end

% 2.2. Solving the coefficient vector

Define cycle, traverse X

Define a cycle, traverse U _ X

Operation of an element in a _ X

end

Outputting orthogonal basis, coefficient vectors

Digital image of the first three-order mode of the gas tube:

in the first stage:

0 0 0 0 0 0 0 0

0 1 1 1 1 1 1 0

1 1 1 1 1 1 1 1

1 0 0 0 0 0 0 1

and a second stage:

1 1 1 1 1 1 1 1

1 1 1 0 0 1 1 1

1 1 1 1 1 1 1 1

1 1 1 0 0 1 1 1

1 1 1 1 1 1 1 1

1 1 1 0 0 1 1 1

1 1 1 1 1 1 1 1

1 1 1 0 0 1 1 1

1 1 1 1 1 1 1 1

1 1 1 0 0 1 1 1

1 1 1 1 1 1 1 1

1 1 1 0 0 1 1 1

1 1 1 1 1 1 1 1

1 1 0 0 0 0 1 1

1 1 1 1 1 1 1 1

1 1 0 0 0 0 1 1

1 1 1 1 1 1 1 1

1 0 0 0 0 0 0 1

1 1 1 1 1 1 1 1

1 0 0 0 0 0 0 1

1 1 1 1 1 1 1 1

1 0 0 0 0 0 0 1

1 1 1 1 1 1 1 1

0 0 0 0 0 0 0 0

third order:

1 1 1 1 1 1 1 1

1 1 0 0 0 0 1 1

1 1 1 0 0 1 1 1

1 1 1 1 1 1 1 1

1 1 1 0 0 1 1 1

1 1 1 1 1 1 1 1

1 1 1 0 0 1 1 1

1 1 1 1 1 1 1 1

1 1 1 0 0 1 1 1

1 1 1 1 1 1 1 1

1 1 1 0 0 1 1 1

1 1 1 1 1 1 1 1

1 1 1 0 0 1 1 1

1 1 1 1 1 1 1 1

1 1 1 0 0 1 1 1

1 1 1 1 1 1 1 1

1 1 1 0 0 1 1 1

1 1 0 0 0 0 1 1

1 1 1 1 1 1 1 1

and 7: and calling a database, determining participation modes, and linearly combining the degradation rates corresponding to the participation modes of all orders to obtain a predicted value of the stiffness degradation rate. The results are compared by taking the static experiment as a standard, and the error is about 8.02 percent.

As can be seen from the embodiment 1 and the embodiment 2, the scheme error of the method is small, and the accuracy of predicting the rigidity degradation rate of the aircraft structure in real time is high.

The method mentioned in the background art only gives out the buckling critical state through experience, the characteristics of the wrinkle area when the stiffness degradation rate reaches 100%, the actual relation between the wrinkle area and the stiffness of the inflatable structure cannot be established, and the time history of the stiffness degradation of the inflatable wing in the wrinkle development process cannot be accurately described, so that the method is difficult to be applied to the inflatable wing aircrafts with different safety margins.

According to the scheme, the rigidity degradation state of the structure is determined through the geometric shape and position characteristics of the complex fold area, the actual relation between the fold area and the rigidity of the inflatable structure can be established, and the real-time prediction of the rigidity degradation rate of the inflatable wing under the complex working condition is realized through establishing the explicit mathematical relationship between the fold area and the rigidity degradation rate based on image recognition.

The solution of the present application is quite different from the technical route of the method mentioned in the background section. In the background art, a finite element method is adopted for judging wrinkles, accurate load input is required, and a wrinkle area is used as output. The method directly predicts the rigidity degradation state of the inflatable structure on the premise of unknown load through the actually known fold area form.

When the scheme of the application predicts the stiffness degradation rate, the image information is used as input, the online application of the aircraft can be realized, the structural stability of the aircraft can be predicted in real time, if the overall stiffness degradation rate of the aircraft is found to exceed a preset overall stiffness degradation rate threshold value, the flying speed, flying attitude and the like of the aircraft can be adjusted, the pressure load applied to the aircraft is reduced, and the efficiency and the safety of tasks are improved.

On the premise of high time resolution of the sensor, the continuous change process of the structural rigidity degradation rate under the time-varying load can be given, a basis can be provided for correction of a rigidity item in a structural dynamics equation, and the method is finally used for efficient analysis of aeroelastic problems such as flutter, gust response and the like.

The method and the device can replace the image information processing by adopting finite element simulation, and have higher universality in the aspect of numerical simulation.

FIG. 9 is a schematic diagram of a device for predicting the degradation rate of the structural rigidity of a flexible inflatable aircraft according to an embodiment of the application. The device includes:

the database establishing unit 10 is configured to establish a first database, where the first database is a basic database of a relationship between a geometric characteristic of a basic wrinkle region of an inflatable structure model and a stiffness degradation rate, and each geometric characteristic of the basic wrinkle region corresponds to a basic mode.

The acquiring unit 20 is configured to acquire a wrinkle area image of an object to be detected, where the object to be detected is a component on a flexible inflatable aircraft.

The modal decomposition unit 30 is configured to perform modal decomposition on the wrinkle region image of the object to be measured, so as to obtain a plurality of decomposition modalities and a contribution degree of each decomposition modality.

The determining unit 40 is configured to determine, based on a plurality of decomposition modalities obtained by modality decomposition and a basic modality in the first database, participation modalities and contribution degrees of each participation modality, where all the participation modalities are the basic modalities in the first database, and the contribution degrees of the participation modalities are obtained according to the contribution degrees of the decomposition modalities obtained by modality decomposition.

And the calculating unit 50 is used for calculating the overall rigidity degradation rate of the object to be measured according to the contribution degree of each participating modality and the rigidity degradation rate of each participating modality.

According to the method and the device, on the premise that the load is unknown, the rigidity degradation condition of the inflatable structure is directly predicted through the actually-known fold area form, the aerodynamic load in the actual flight of the flexible inflatable aircraft does not need to be obtained, the problem that the structural rigidity is difficult to predict due to the fact that the aerodynamic load is difficult to obtain in the actual flight of the flexible inflatable aircraft in the traditional scheme is solved, and the effect of predicting the structural rigidity degradation condition of the flexible inflatable aircraft in real time is achieved.

Optionally, the obtaining unit 20 includes: shooting subunit, aligning subunit, identifying subunit and pooling subunit.

And the shooting subunit is used for shooting the image of the surface of the object to be measured through the optical sensor.

And the alignment subunit is used for aligning the obtained images through the conversion matrix.

And the identifying subunit is used for identifying the wrinkle area in the image.

And the pooling subunit is used for pooling the wrinkle area and filtering out a fine wrinkle area to obtain a wrinkle area image of the object to be detected, wherein the fine wrinkle area is formed by a process and a structural line.

Optionally, the modal decomposition unit 30 includes: a comparison subunit, a determination subunit and a recording subunit.

And the comparison subunit is used for comparing the fold region obtained by linear combination of the plurality of decomposition modalities with the geometric similarity law of the fold region image of the object to be detected based on the contribution degree of the decomposition modalities obtained by modality decomposition.

And the determining subunit is used for successfully decomposing the mode if the geometric similarity laws of the two are greater than or equal to a preset geometric similarity law threshold value.

And the recording subunit is used for recording the decomposition modalities obtained by modality decomposition and the contribution degree of each decomposition modality.

Optionally, the calculation unit 50 includes: and linearly superposing the subunits.

And the linear superposition subunit is used for linearly superposing the stiffness degradation rates of the participation modes, wherein the contribution degrees of the participation modes are used as weights in the linear superposition process to obtain the total stiffness degradation rate of the object to be detected.

Optionally, the database establishing unit 10 includes: the method comprises a finite element model establishing subunit, a defining subunit, a solving subunit and a database establishing subunit.

And the finite element model establishing subunit is used for establishing a finite element model of the inflatable structure model.

A defining subunit for defining a pressure load for the finite element model.

And the solving subunit is used for calling a finite element solver in batch processing and solving the rigidity and the wrinkle area under the pressure load in a transient state.

And the database establishing subunit is used for establishing a first database according to the rigidity and the fold area obtained by solving.

Optionally, the apparatus further comprises: and the fold area image digitalizing subunit. The fold region image digitizing subunit is configured to: discretizing an image at a space coordinate before carrying out modal decomposition on the image of the fold area of the object to be detected; the image is discretized in magnitude.

Optionally, the apparatus further comprises: a control unit. The control unit is used for controlling the flexible inflatable air vehicle to change at least one of the following conditions when the overall stiffness degradation rate of the object to be tested exceeds a preset overall stiffness degradation rate threshold value: flight speed, flight attitude.

Fig. 10 is a block diagram of an electronic device provided in an embodiment of the present application.

An electronic device 700 according to this embodiment of the present disclosure is described below with reference to fig. 10. The electronic device 700 shown in fig. 10 is only an example and should not bring any limitation to the functions and the scope of use of the embodiments of the present disclosure.

As shown in fig. 10, electronic device 700 is embodied in the form of a general purpose computing device. The components of the electronic device 700 may include, but are not limited to: at least one processing unit 710, at least one memory unit 720, a bus 730 that connects the various system components (including the memory unit 720 and the processing unit 710), a display unit 740, and the like.

Wherein the storage unit stores program code that can be executed by the processing unit 710 to cause the processing unit 710 to perform the steps according to various exemplary embodiments of the present disclosure described in this specification.

The memory unit 720 may include readable media in the form of volatile memory units, such as a random access memory unit (RAM) 7201 and/or a cache memory unit 7202, and may further include a read only memory unit (ROM) 7203.

The memory unit 720 may also include a program/utility 7204 having a set (at least one) of program modules 7205, such program modules 7205 including, but not limited to: an operating system, one or more application programs, other program modules, and program data, each of which, or some combination thereof, may comprise an implementation of a network environment.

Bus 730 may be any representation of one or more of several types of bus structures, including a memory unit bus or memory unit controller, a peripheral bus, an accelerated graphics port, a processing unit, or a local bus using any of a variety of bus architectures.

The electronic device 700 may also communicate with one or more external devices 700' (e.g., keyboard, pointing device, bluetooth device, etc.), such that a user can communicate with devices with which the electronic device 700 interacts, and/or any devices (e.g., router, modem, etc.) with which the electronic device 700 can communicate with one or more other computing devices. Such communication may occur via an input/output (I/O) interface 750. Also, the electronic device 700 may communicate with one or more networks (e.g., a Local Area Network (LAN), a Wide Area Network (WAN), and/or a public network such as the internet) via the network adapter 760. The network adapter 760 may communicate with other modules of the electronic device 700 via the bus 730. It should be appreciated that although not shown in the figures, other hardware and/or software modules may be used in conjunction with the electronic device 700, including but not limited to: microcode, device drivers, redundant processing units, external disk drive arrays, RAID systems, tape drives, and data backup storage systems, among others.

Through the above description of the embodiments, those skilled in the art will readily understand that the exemplary embodiments described herein may be implemented by software, or by software in combination with necessary hardware. Therefore, as shown in fig. 11, the technical solution according to the embodiment of the present disclosure may be embodied in the form of a software product, which may be stored in a non-volatile storage medium (which may be a CD-ROM, a usb disk, a removable hard disk, etc.) or on a network, and includes several instructions to enable a computing device (which may be a personal computer, a server, or a network device, etc.) to execute the above method according to the embodiment of the present disclosure.

The software product may employ any combination of one or more readable media. The readable medium may be a readable signal medium or a readable storage medium. A readable storage medium may be, for example, but not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination of the foregoing. More specific examples (a non-exhaustive list) of the readable storage medium include: an electrical connection having one or more wires, a portable disk, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.

The computer readable storage medium may include a propagated data signal with readable program code embodied therein, for example, in baseband or as part of a carrier wave. Such a propagated data signal may take many forms, including, but not limited to, electro-magnetic, optical, or any suitable combination thereof. A readable storage medium may be any readable medium that is not a readable storage medium and that can communicate, propagate, or transport a program for use by or in connection with an instruction execution system, apparatus, or device. Program code embodied on a readable storage medium may be transmitted using any appropriate medium, including but not limited to wireless, wireline, optical fiber cable, RF, etc., or any suitable combination of the foregoing.

Program code for carrying out operations for the present disclosure may be written in any combination of one or more programming languages, including an object oriented programming language such as Java, C + + or the like and conventional procedural programming languages, such as the "C" programming language or similar programming languages. The program code may execute entirely on the user's computing device, partly on the user's device, as a stand-alone software package, partly on the user's computing device and partly on a remote computing device, or entirely on the remote computing device or server. In the case of a remote computing device, the remote computing device may be connected to the user computing device through any kind of network, including a Local Area Network (LAN) or a Wide Area Network (WAN), or may be connected to an external computing device (e.g., through the internet using an internet service provider).

The computer readable medium carries one or more programs which, when executed by a device, cause the computer readable medium to perform the functions of: establishing a first database, wherein the first database is a basic database of a basic fold area geometric characteristic-rigidity degradation rate relation of an inflatable structure model, and each basic fold area geometric characteristic corresponds to a basic mode; acquiring a fold area image of an object to be detected, wherein the object to be detected is a part on the flexible inflatable aircraft; carrying out modal decomposition on the fold region image of the object to be detected to obtain a plurality of decomposition modalities and the contribution degree of each decomposition modality; determining participation modalities and contribution degrees of each participation modality based on a plurality of decomposition modalities obtained by modality decomposition and a basic modality in the first database, wherein all the participation modalities are the basic modalities in the first database, and the contribution degrees of the participation modalities are obtained according to the contribution degrees of the decomposition modalities obtained by modality decomposition; and calculating the overall rigidity degradation rate of the object to be measured according to the contribution degree of each participating mode and the rigidity degradation rate of each participating mode.

Those skilled in the art will appreciate that the modules described above may be distributed in the apparatus as described in the embodiments, and that corresponding changes may be made in one or more apparatus that are unique from the embodiments. The modules of the above embodiments may be combined into one module, or further split into multiple sub-modules.

Through the above description of the embodiments, those skilled in the art will readily understand that the exemplary embodiments described herein may be implemented by software, or by software in combination with necessary hardware. Therefore, the technical solution according to the embodiments of the present disclosure may be embodied in the form of a software product, which may be stored in a non-volatile storage medium (which may be a CD-ROM, a usb disk, a removable hard disk, etc.) or on a network, and includes several instructions to enable a computing device (which may be a personal computer, a server, a mobile terminal, or a network device, etc.) to execute the method according to the embodiments of the present disclosure.

Exemplary embodiments of the present disclosure are specifically illustrated and described above. It is to be understood that the present disclosure is not limited to the precise arrangements, instrumentalities, or instrumentalities described herein; on the contrary, the disclosure is intended to cover various modifications and equivalent arrangements included within the spirit and scope of the appended claims.

Claims

1. A method for predicting the degradation rate of the structural rigidity of a flexible inflatable aircraft, which is characterized by comprising the following steps:

establishing a first database, wherein the first database is a basic database of a basic fold area geometric characteristic-rigidity degradation rate relation of an inflatable structure model, and each basic fold area geometric characteristic corresponds to a basic mode;

acquiring a fold area image of an object to be detected, wherein the object to be detected is a part on the flexible inflatable aircraft;

carrying out modal decomposition on the fold region image of the object to be detected to obtain a plurality of decomposition modalities and the contribution degree of each decomposition modality;

determining participation modalities and contribution degrees of each participation modality based on a plurality of decomposition modalities obtained by modality decomposition and a basic modality in the first database, wherein all the participation modalities are the basic modalities in the first database, and the contribution degrees of the participation modalities are obtained according to the contribution degrees of the decomposition modalities obtained by modality decomposition;

and calculating the overall rigidity degradation rate of the object to be tested according to the contribution degree of each participating mode and the rigidity degradation rate of each participating mode.

2. The method of claim 1, wherein acquiring the image of the wrinkled area of the object to be measured comprises:

shooting an image of the surface of the object to be measured through an optical sensor;

aligning the shot image through the transformation matrix;

identifying a wrinkle region in the image;

pooling a wrinkle area, and filtering out a fine wrinkle area to obtain a wrinkle area image of the object to be detected, wherein the fine wrinkle area is formed by processes and structural lines.

3. The method according to claim 1, wherein performing modal decomposition on the wrinkle region image of the object to be tested to obtain a plurality of decomposition modalities and a contribution degree of each decomposition modality includes:

comparing a fold region obtained by linear combination of a plurality of decomposition modalities with a geometric similarity law of a fold region image of the object to be detected based on the contribution degree of the decomposition modalities obtained by modality decomposition;

if the geometric similarity of the two is greater than or equal to a preset geometric similarity threshold value, the modal decomposition is successful;

and recording the decomposition modality obtained by modality decomposition and the contribution degree of each decomposition modality.

4. The method according to claim 1, wherein calculating the overall stiffness degradation rate of the object to be tested according to the contribution degree of each participating modality and the stiffness degradation rate of each participating modality comprises:

and linearly superposing the stiffness degradation rate of each participating mode, wherein the contribution degree of the participating modes is used as the weight in the linear superposition process to obtain the total stiffness degradation rate of the object to be detected.

5. The method of claim 1, wherein building a first database comprises:

establishing a finite element model of the inflatable structure model;

defining a pressure load for the finite element model;

calling a finite element solver in batch processing, and transiently solving the rigidity and the fold area under the pressure load;

and establishing the first database according to the solved rigidity and the fold area.

6. The method according to claim 1, wherein before the performing modal decomposition on the image of the crumple zone of the object to be tested, the method further comprises: the image of the crumple zone is digitized,

the crumple zone image digitization comprises: discretizing the image in space coordinates; the image is discretized in magnitude.

7. The method of claim 1, further comprising:

judging whether the overall stiffness degradation rate of the object to be detected exceeds a preset overall stiffness degradation rate threshold value or not;

if so, controlling the flexible inflatable aircraft to change at least one of the following: flight speed, flight attitude.

8. A flexible inflatable aircraft structural stiffness degradation rate prediction apparatus, the apparatus comprising:

the system comprises a database establishing unit, a data analysis unit and a data analysis unit, wherein the database establishing unit is used for establishing a first database, the first database is a basic database of the relation between the geometric characteristics of basic fold areas and the rigidity degradation rate of an inflatable structure model, and each geometric characteristic of the basic fold area corresponds to a basic mode;

the acquiring unit is used for acquiring a fold area image of an object to be detected, wherein the object to be detected is a part on the flexible inflatable aircraft;

the modal decomposition unit is used for carrying out modal decomposition on the fold region image of the object to be detected to obtain a plurality of decomposition modalities and the contribution degree of each decomposition modality;

the determining unit is used for determining participation modalities and the contribution degree of each participation modality based on a plurality of decomposition modalities obtained through modality decomposition and the basic modality in the first database, wherein all the participation modalities are the basic modalities in the first database, and the contribution degrees of the participation modalities are obtained according to the contribution degrees of the decomposition modalities obtained through modality decomposition;

and the calculating unit is used for calculating the overall rigidity degradation rate of the object to be measured according to the contribution degree of each participating modality and the rigidity degradation rate of each participating modality.

9. A flexible inflatable aircraft structural stiffness degradation rate prediction apparatus, the apparatus comprising:

a camera;

a flexible pneumatic aircraft structure stiffness degradation rate prediction device for performing the method of any one of claims 1-7.

10. An electronic device, comprising:

one or more processors;

storage means for storing one or more programs;

when executed by the one or more processors, cause the one or more processors to implement the method of any one of claims 1-7.

11. A computer-readable medium, on which a computer program is stored, which, when being executed by a processor, carries out the method according to any one of claims 1-7.