Aircraft composite material repair digital twin and intelligent repair method and system

Abstract

The present disclosure provides an aviation composite material repair digital twin and intelligent repair method, system and device, wherein the method comprises: transmitting the repair elements of the aviation composite material repair entity collected in real time to the aviation composite material repair digital twin; the digital twin establishes an initial visual digital model of the repair entity according to the structure parameters and damage parameters of the repair entity; the digital twin searches the design space according to the repair process parameters, predicts the key performance of different repair process parameter combinations, and obtains the target repair process parameters required for the repaired repair entity to achieve the key performance; the digital twin predicts the full-field distribution information of the key performance of the repair entity according to the repair process parameters and the initial visual digital model, and displays and analyzes; and transmitting the received target repair process parameters to the repair tool; setting the repair process according to the target repair process parameters through the repair tool, and implementing intelligent repair of the repair entity.

Description

TECHNICAL FIELD

[0001] The present disclosure belongs to the field of composite material repair digital twin, and particularly relates to an aviation composite material repair digital twin and intelligent repair method, system and equipment. BACKGROUND

[0002] With the rapid increase of the proportion of aviation aircraft whole machine composite materials, its application range gradually develops from non-bearing parts to bearing parts, such as aircraft fuselage, fairing, landing gear cabin door, spoiler, aileron and other components, which plays a more and more key role in aviation aircraft structure safety. At the same time, with the effect of service time, complex load and external environment, the damage and deterioration of aircraft composite materials are continuously accumulated. How to quickly and intelligently evaluate the aviation aircraft composite material damage and provide efficient and reliable repair scheme and repair process has become more and more important.

[0003] One of the significant requirements of aviation composite material repair is "fast", but the current aviation composite material repair has the problems of long time consumption, difficult repair and high repair cost, especially for specific structures. In addition, a large part of aviation composite material parts are easy to damage and easy to damage structures, and the diversity and complexity of aviation composite material components, structures, damage and failure modes also put strict requirements on repair process and technology, which brings outstanding difficulties to repair process design, real-time prediction and analysis of repair process and intelligent repair in actual repair process. Therefore, it is particularly important to develop a method capable of realizing intelligent repair according to the damage condition. SUMMARY

[0004] In view of the above technical problems, the present disclosure provides an aviation composite material repair digital twin and intelligent repair method, system and equipment to at least partially solve the above technical problems.

[0005] In order to solve the above technical problems, the technical scheme provided by the present disclosure is as follows:

[0006] As a first aspect of the present disclosure, an intelligent repair method using an aviation composite material repair digital twin is provided, comprising:

[0007] transmitting the repair elements of the aviation composite material repair entity collected in real time to the aviation composite material repair digital twin, wherein the repair elements include damage parameters, repair process parameters and structure parameters;

[0008] establishing an initial visual digital model of the repair entity according to the structure parameters and damage parameters of the repair entity by the digital twin, and the initial visual digital model is used to describe the structure parameters and loss parameters of the repair entity;

[0009] The digital twin is used to search the design space according to the repair process parameters, to predict the key performance of different combinations of repair process parameters, and to obtain the target repair process parameters required for the repaired repair entity to achieve the key performance;

[0010] The digital twin is used to search the design space according to the repair process parameters, to predict the key performance of different combinations of repair process parameters, and to obtain the target repair process parameters required for the repaired repair entity to achieve the key performance;

[0011] The digital twin is used to search the design space according to the repair process parameters, to predict the key performance of different combinations of repair process parameters, and to obtain the target repair process parameters required for the repaired repair entity to achieve the key performance;

[0012] As a second aspect of the present disclosure, an intelligent repair system for an aviation composite material is provided, comprising:

[0013] A real-time data transmission module is configured to transmit repair elements of the aviation composite material repair entity obtained in real time to the aviation composite material repair digital twin, wherein the repair elements include damage parameters, repair process parameters, and structure parameters;

[0014] A digital model acquisition module is configured to establish an initial visual digital model of the repair entity by the digital twin according to the structure parameters and damage parameters of the repair entity, and the initial visual digital model is used to describe the structure parameters and loss parameters of the repair entity;

[0015] An intelligent repair design module is configured to search the design space by the digital twin according to the repair process parameters, to predict the key performance of different combinations of repair process parameters, and to obtain the target repair process parameters required for the repaired repair entity to achieve the key performance;

[0016] A digital twin control module is configured to predict the full-field distribution information of the key performance of the repair entity by the digital twin according to the repair process parameters and the initial visual digital model, and to display and analyze in real time; and to transmit the received target repair process parameters to the repair tool module;

[0017] A repair tool module is configured to set the repair process according to the received target repair process parameters, and to implement the repair of the repair entity.

[0018] As a third aspect of the present disclosure, an intelligent repair device for an aviation composite material is provided, comprising the intelligent repair system for an aviation composite material described above;

[0019] An input part is configured to obtain repair information of the repair entity, and the repair information includes at least one of repair tool module information, text information, image information, and graphic information;

[0020] a storage portion configured to store a computer program, the computer program including a digital twin;

[0021] a processor configured to execute the intelligent repair method for repairing a digital twin of an aerospace composite material according to the above;

[0022] an output portion configured to output real-time repair information of the repaired entity;

[0023] a virtual-real fusion interface configured to realize virtual-real interaction between the repaired entity and the digital twin.

[0024] According to embodiments of the present disclosure, the digital twin provided by the present disclosure fully considers the diversity and complexity of the composite material components, structures, damages and failure modes, and effectively solves the problems of difficult to quickly realize target repair process design, difficult to predict and analyze the repair process and difficult to integrate intelligent repair by using the digital twin enhanced by machine learning, repair process and technology.

[0025] Specifically, the repair elements of the repaired entity of the aerospace composite material collected in real time are transmitted to the digital twin of the aerospace composite material repair, and an initial visual digital model of the repaired entity is established by using the structure parameters and damage parameters in the repair elements by the digital twin, that is, the repaired entity is converted into a digital virtual body. By using the digital twin to search the design space according to the repair process parameters, the key performance of different combinations of repair process parameters is predicted, and the target repair process parameters required by the repaired entity after repair to achieve the key performance are obtained. Then, by using the digital twin according to the repair process parameters and the initial visual digital model, the full-field distribution information of the key performance of the repaired entity can be predicted and displayed and analyzed in real time, and the received target repair process parameters are transmitted to the repair tool. The repair tool sets the repair process according to the received target repair process parameters and implements the repair of the repaired entity, so as to realize the analysis of the repair process parameters and the key performance of the composite material during the repair of the repaired entity by using the digital twin, and the digital twin can intelligently adjust the repair work of the repair tool on the repaired entity.

[0026] The digital twin of the present disclosure has the ability of full-field high-fidelity information simulation and prediction, real-time display and analysis, dynamic update and evolution of the key performance of the composite material repaired entity, fully considers the multi-scale connection of the iterative information of the digital twin, so that in the case of given composite material damage parameters, after the target repair process parameters required by the repaired entity after repair to achieve the key performance are predicted by the digital twin, the repair tool can implement the repair of the repaired entity according to the repair process set by the digital twin, realize the integration of the digital twin and the intelligent repair method, and help to improve the repair speed and accuracy of the aerospace composite material. BRIEF DESCRIPTION OF DRAWINGS

[0027] Figure 1 A flowchart of a method for intelligent repair of an aviation composite material is shown schematically;

[0028] Figure 2 A flowchart of construction of a digital twin is shown schematically;

[0029] Figure 3 A block diagram of a system for intelligent repair of an aviation composite material is shown schematically;

[0030] Figure 4 A schematic diagram of intelligent repair and life cycle management of an aviation composite material is shown schematically;

[0031] Figure 5 A block diagram of an intelligent repair device for an aviation composite material is shown schematically. DETAILED DESCRIPTION

[0032] Hereinafter, embodiments of the present disclosure will be described with reference to the accompanying drawings. It should be understood, however, that the description which follows is merely exemplary and is not intended to limit the scope of the present disclosure. In the following detailed description of the embodiments of the present disclosure, numerous specific details are set forth in order to provide a thorough understanding of the present disclosure. However, it will be apparent to one skilled in the art that one or more embodiments of the present disclosure can be practiced without these specific details. In other instances, well-known structures and functions have not been described in detail in order to avoid obscuring aspects of the present disclosure.

[0033] The terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of the present disclosure. As used herein, the term "including" "comprising" and the like are meant to be inclusive, but not limiting to the components, steps, operations and / or features that were listed. The use of "including" "comprising" and "having" also modifies the term "comprises" to include a combination of elements or ingredients unless specified otherwise.

[0034] All terms used herein (including technical and scientific terms) have meanings that are commonly understood by one of ordinary skill in the art unless otherwise defined. It should be noted that the terms used herein are to be interpreted as having a meaning that is consistent with the understanding of one of ordinary skill in the art and are not to be interpreted in an idealized or overly formal sense.

[0035] In situations where similar terminology is used in a similar manner, one of ordinary skill in the art will be aware that the terminology has a meaning that is consistent with the meaning that one of ordinary skill in the art would commonly understand the terminology to have (e.g., "a system having at least one of A, B, and C" would include, but is not limited to, a system that has A alone, a system that has B alone, a system that has C alone, a system that has both A and B, a system that has both A and C, a system that has both B and C, and / or a system that has all of A, B, and C, etc.).

[0036] It is found in the process of implementing the present disclosure that although the use of digital twins can realize the prediction and analysis of faulty devices, the digital twins used lack high-fidelity information simulation and prediction and dynamic updating capabilities, cannot effectively strengthen the multi-scale connection of digital twin iteration information, and it is also difficult to realize the effective integration of digital twins and intelligent repair equipment.

[0037] In view of the problem that it is difficult to integrate digital twins with intelligent repair methods and intelligent repair equipment in the related art, the present disclosure provides an aviation composite material repair digital twin and an intelligent repair method, system and equipment for aviation composite materials.

[0038] Figure 1 An aviation composite material repair digital twin and an intelligent repair method, system and equipment for aviation composite materials are provided.

[0039] As Figure 1 shown, the aviation composite material repair digital twin and the intelligent repair method, system and equipment for aviation composite materials provided by the present disclosure include operations S101-S105.

[0040] In operation S101, the repair elements of the aviation composite material repair entity collected in real time are transmitted to the aviation composite material repair digital twin, wherein the repair elements include damage parameters, repair process parameters and structure parameters.

[0041] In operation S102, the digital twin establishes an initial visualized digital model of the repair entity according to the structure parameters and damage parameters of the repair entity, and the initial visualized digital model is used to describe the structure parameters and loss parameters of the repair entity.

[0042] In operation S103, the digital twin performs design space search according to the repair process parameters, predicts the key performance of different combinations of repair process parameters, and obtains the target repair process parameters required for the repaired repair entity to achieve the key performance.

[0043] In operation S104, the digital twin predicts the full-field distribution information of the key performance of the repair entity in real time according to the repair process parameters and the initial visualized digital model and displays it in real time, and transmits the received target repair process parameters to the repair tool.

[0044] In operation S105, the repair tool sets the repair process according to the received target repair process parameters, and implements the repair of the repair entity.

[0045] According to an embodiment of the present disclosure, the digital twin of the present disclosure provides a virtual object composed of a group of information technologies, which can simulate the structure, environment and behavior of the composite material repair entity. The digital twin is dynamically updated throughout its life cycle by using the repair data of the repair entity, and valuable repair decisions are given, such as providing repair process parameters and guiding the repair tool to implement the repair. Specifically, the repair elements of the repair entity of the aviation composite material collected in real time are transmitted to the digital twin of the aviation composite material repair, and the initial visual digital model of the repair entity is established by using the structure parameters and damage parameters in the repair elements through the digital twin, i.e. the repair entity is converted into a digital virtual body. By using the digital twin to search the design space according to the repair process parameters, the key performance of different combinations of repair process parameters is predicted, and the target repair process parameters required by the repaired repair entity to achieve the key performance are obtained. Then, by using the digital twin according to the repair process parameters and the initial visual digital model, the full-field distribution information of the key performance of the repair entity can be predicted and displayed and analyzed in real time, and the received target repair process parameters are transmitted to the repair tool. The repair tool sets the repair process according to the target repair process parameters and implements the repair of the repair entity, so as to realize the analysis of the repair process parameters and the performance of the composite material during the repair of the aviation composite material by using the digital twin, and intelligently adjust the repair work of the repair tool, so that the repair tool repairs the composite material repair entity according to the preset repair process.

[0046] According to an embodiment of the present disclosure, in step S101, the repair entity is an entity component that has suffered damage in the manufacturing and / or service link, wherein the damage includes but is not limited to voids, delamination, debonding, scratches, cracks, impacts, lightning strikes, combustion, etc. caused by external force impact.

[0047] The composite material in the embodiment of the present disclosure is composed of at least two components, and the mechanical properties are anisotropic, such as carbon fiber laminated plates, honeycomb sandwich structures, etc. The composite material includes but is not limited to components applied to fuselage skin, equipment hatch, fairing, landing gear door, spoiler, aileron, etc.

[0048] According to an embodiment of the present disclosure, the repair elements include damage parameters, repair process parameters and structure parameters. Specifically, the structure parameters include but are not limited to material properties, geometric shape, boundary conditions, etc. of the repair entity; the damage parameters include but are not limited to damage size, damage type, damage depth, damage layer number, impact time, adhesive layer thickness, etc.; and the repair process parameters include but are not limited to medium pressure, curing temperature, curing time, repair tool path, adhesive strength, temperature rate, pressure rate, etc.

[0049] According to an embodiment of the present disclosure, in step S102, the initial visualized digital model is established by the digital twin according to the structure parameters and damage parameters of the repaired entity, the repaired entity is converted into a virtual object for visual display, so as to describe the structure parameters and damage parameters of the repaired entity.

[0050] According to an embodiment of the present disclosure, in step S103, the design space search is performed by the digital twin according to the repair process parameters, the key performance of the repair process parameter combination under different damage parameters is predicted, and the target repair process parameter required for the repaired repaired entity to achieve the key performance is obtained. The design space search is obtained by the following method: the high-dimensional data is converted into low-dimensional data by the digital twin using the reduced-order model, and high-fidelity simulation prediction is performed, the mean value and uncertainty of the key performance of the repaired repaired entity under the repair process parameter combination in the design space are obtained, and the key performance of the repaired repaired entity is obtained. The repair process parameter combination includes at least one combination of temperature, pressure, size, time, curing degree, tool path; the key performance includes at least one of curing degree, deformation, strain, stress, tensile strength, bearing strength, hardness, plasticity, toughness.

[0051] According to an embodiment of the present disclosure, in steps S104-S105, the digital twin obtains the full-field distribution information of the key performance of the repaired repaired entity according to the real-time collected repair process parameters and the initial visualized digital model, realizes the full-field real-time display and analysis of the repaired repaired entity in two dimensions or three dimensions; and the target repair process parameter is transmitted to the repair tool by the digital twin, and the repair tool sets the repair process according to the given target repair process parameter to repair. The repair tool can be cutting, grinding, cutting, drilling, digging, thermal curing, microwave curing, electron beam curing, light curing, laser ablation, anchoring, smearing, dust collection, etc.

[0052] According to an embodiment of the present disclosure, the intelligent repair method of the aviation composite material further comprises: adjusting the repair process of the repair tool to the repaired entity according to the repair process, so as to realize the rapid and accurate repair of the repaired entity.

[0053] According to an embodiment of the present disclosure, the intelligent repair method of the aviation composite material further comprises: monitoring the parameter change of the repaired repaired entity in the service stage by the digital twin. It can be understood that after the repair of the repaired entity is completed, the digital twin continues to receive the same load, environmental parameter change and the like of the repaired repaired entity in the service stage, provides the latest virtual information of the repaired entity, and until the repaired entity fails and obtains the failure parameters such as strength reduction degree and life, so as to realize the monitoring of the parameter change of the repaired repaired entity in the service stage by the digital twin.

[0054] According to an embodiment of the present disclosure, the intelligent repair method of the aviation composite material further comprises: dynamically updating the digital twin by using repair data of the repair entity collected in real time during the repair process, wherein the repair data comprises repair process parameters and observation data, the repair process parameters can be set repair process parameters and repair process parameters dynamically adjusted in real time according to the observation data during the repair process, and the observation data is data tested by some measuring instruments. It can be understood that the repair data of the repair entity collected in real time is used as the input of the digital twin, the actual repair process is simulated and predicted by the digital twin according to the input, for example, the set repair process parameters in the actual repair process are used as the input, the digital twin is used to simulate and determine the optimal repair process according to the repair process parameters. At the same time, the observation data in the real-time collected data and the predicted data output by the digital twin are verified by the multi-scale interaction model in the data twin, and then the digital twin is corrected, so as to realize the updating of the digital twin.

[0055] In an embodiment of the present disclosure, by updating the digital twin by using the repair data of the repair entity collected in real time during the repair process, the data twin can continuously have the updating evolution ability, and the multi-scale relationship of the iteration information of the digital twin is effectively considered, which helps the digital twin to provide more accurate repair process parameters in the subsequent repair process.

[0056] According to an embodiment of the present disclosure, the intelligent repair method of the aviation composite material further comprises: establishing an aviation composite material repair digital twin database according to the repair data of the plurality of repair entities and the corresponding digital twins, and realizing analysis, updating and evolution of the aviation composite material repair digital twin.

[0057] According to an embodiment of the present disclosure, the digital twin in the embodiment of the present disclosure is obtained by using the repair data of the repair entity sample and the mechanism model of the repair entity sample in the repair process as training data, and training the to-be-trained digital twin by using the training data and / or real-time collected observation data. Specifically, the digital twin modeling process is as shown in Figure 2 .

[0058] Figure 2 A flowchart of construction of the aviation composite material repair digital twin in the embodiment of the present disclosure is schematically shown.

[0059] As shown in Figure 2 , the construction of the digital twin in the embodiment of the present disclosure is as steps S201 to S211.

[0060] In step S201, the repair elements of the repair entity sample are obtained.

[0061] Specifically, the repair elements of the repair entity sample also include the structure parameters, the repair process parameters and the damage parameters.

[0062] In step S202 to step S203, the repair digital model and the mechanism model are established.

[0063] Specifically, the repair digital model is established with the repair elements of the repair entity sample, the mechanism model is established with the internal mechanism of the repair entity sample in the repair process, and the training data is constructed based on the repair digital model and the mechanism model.

[0064] More specifically, the repair digital model is a digital model suitable for computational processing, which is established according to the aviation composite material repair entity sample and fully reflects the repair elements of the repair entity sample; the mechanism model is a mathematical model that accurately describes the repair process, which is established according to the internal mechanism of the repair entity sample in the repair process, and the internal mechanism of the repair entity sample in the repair process includes the basic laws required by the digital twin, the thermodynamic coupling dynamics equation, the material constitutive model, the damage evolution model, the fatigue failure model, the physical meaning of the parameters in the mechanism model, and at least one of the composite material repair process. It should be noted that the basic laws required by the digital twin are laws consistent with natural laws, such as Newton's first law and Newton's second law; the composite material repair process includes but is not limited to drilling, patching, layering process, curing process, detection process, design process, and service detection.

[0065] Wherein, before the construction of the training data is completed, it is also necessary to judge whether the training data meets the high-fidelity information simulation, i.e. step S204.

[0066] In step S204, whether the actual repair process is accurately described.

[0067] Specifically, the repair digital model and the mechanism model established by using the repair entity sample are used to establish a repair model suitable for calculation processing, which comprehensively reflects the repair elements of the repair entity sample, so as to realize numerical simulation in the actual repair process. The repair model can realize high-fidelity simulation and prediction of the actual repair process. The simulation value of the repair model is output by training the repair model using the training data. If the error between the simulation value of the repair model and the actual test result is large, it is considered that the training data cannot perform high-fidelity information simulation and prediction, and cannot meet the required accuracy, that is, the repair process cannot be accurately described. At this time, it is necessary to re-establish the repair digital model and the mechanism model, and continuously iteratively adjust, so that the training data can perform high-fidelity information simulation and prediction. If the error between the simulation value of the repair model and the actual test result is small, it is considered that the repair process can be accurately described. After comprehensively considering the cost and accuracy requirements, a suitable sampling strategy is adopted to screen and construct the training data, which is more representative and suitable for the calculation processing of the repair model, and is used for the construction of the digital twin.

[0068] In steps S205-S211, a digital twin to be trained is constructed to obtain a digital twin.

[0069] Specifically, the digital twin includes a statistical inference model, a multi-scale interaction model and a data assimilation model, wherein the digital twin is trained by the following steps S206-S211:

[0070] A repair digital model reflecting the repair elements of the repair entity sample is established according to the repair entity sample, and a mechanism model describing the repair process is established according to the internal mechanism of the repair entity sample in the repair process;

[0071] The repair digital model and the mechanism model of the repair entity sample are used as training data and mechanism constraints of the digital twin to be trained;

[0072] The statistical inference model in the digital twin to be trained is trained according to the training data, the observation data of the repair entity sample collected in real time and the prior knowledge data in the mechanism model, and the prediction data is output;

[0073] Under the constraint of the mechanism model, the loss value is calculated according to the prediction data and the observation data of the repair entity sample collected in real time, the loss result is obtained, the parameters of the multi-scale interaction model in the digital twin to be trained are iteratively adjusted, and the multi-scale interaction model and the statistical inference model of the digital twin are obtained; and

[0074] The new observation data of the repair entity sample collected in real time is input into the data assimilation model in the digital twin to be trained for updating, and the data assimilation model of the digital twin is obtained;

[0075] The statistical inference model, the multi-scale interaction model and the data assimilation model in the digital twin are integrated to obtain the digital twin.

[0076] According to an embodiment of the present disclosure, the statistical inference model is based on a dynamic Bayesian network, and the digital twin to be trained is trained according to observation data of a repaired entity sample collected in real time and prior knowledge (i.e., basic laws required by the digital twin), an inference of a repair working condition outside an assimilation range is made, and the inference of the unknown working condition is expressed in a probabilistic form, so as to realize prediction and prediction of the overall working condition of the repaired entity sample.

[0077] According to an embodiment of the present disclosure, the multi-scale interaction model is based on a dynamic Bayesian network, and a loss value is calculated according to prediction data and observation data of a repaired entity sample collected in real time under the constraint of a mechanism model, a loss result is obtained, parameters of the multi-scale interaction model in the digital twin to be trained are iteratively adjusted by using the loss result, and a multi-scale interaction model and a statistical inference model of the digital twin are obtained, so as to ensure mechanism coordination of output information of the digital twin in multiple scales.

[0078] It can be understood that, according to the multi-scale mechanism between input, output and observation data of different scales, the internal mechanism of the repaired entity sample in the repair process is combined, that is, the multi-scale mechanism and the statistical method fuse the multi-scale mechanism constraint in the calculation and prediction of the digital twin, the high-fidelity collaborative integration between the digital twin and the multi-scale and multi-physical model is realized, and the state and analysis of the repaired entity sample under different scales are given, wherein the multi-scale can be macroscopic, microscopic and mesoscopic physical quantities.

[0079] According to an embodiment of the present disclosure, the data assimilation model is constructed by using a probabilistic graph model or a dynamic Bayesian network-Markov network, new observation data is fused on the basis of dynamic running of the digital twin to be trained by considering data spatio-temporal distribution and model uncertainty, dynamic updating of the digital twin to be trained is realized, and a data assimilation model of the digital twin is obtained. The data spatio-temporal distribution includes: repair data distribution characteristics associated with repair entity coordinates and courses based on a unified spatio-temporal reference; and the model error includes uncertainty caused by noise and deviation due to the fact that experiments, mechanisms and simulations are not completely consistent with the real situation.

[0080] Then, the statistical inference model, the multi-scale interaction model and the data assimilation model in the digital twin are integrated to obtain the digital twin. In the case that the obtained digital twin can realize data assimilation, statistical inference and multi-scale interaction functions during the repair process according to the given repair entity of the composite material, a digital twin suitable for repairing the repair entity is obtained, which has the characteristics of virtual-real cooperation, data fusion, iterative optimization, error quantification and intelligent decision-making, and lays a foundation for improving the repair level of the repair entity of the aviation composite material.

[0081] According to the embodiments of the present disclosure, after obtaining the digital twin, the repair entity can be converted into a virtual object by using the digital twin, and an intelligent repair scheme suitable for the repair entity can be designed through the virtual object. Then, according to the intelligent repair scheme, the repair of the repair entity is implemented by using the digital twin to control the repair tool, realizing the virtual-real interaction between the repair entity and the virtual object. The digital twin provided by the present disclosure fully considers the diversity and complexity of the components, structure, damage and failure mode of the composite material during the construction process, and enhances the repair process and technology of the digital twin by using the machine learning algorithm, effectively solving the problems that it is difficult to quickly realize the target repair process design, predict and analyze the repair process and integrate the intelligent repair in the actual repair process.

[0082] In the embodiments of the present disclosure, the digital twin constructed by the above method replaces the high-fidelity engineering simulation that can be realized by the repair digital model and mechanism model of the aviation composite material, and can realize the two-dimensional or three-dimensional full-field real-time prediction of the key performance of the repair entity, and is used for accelerating the prediction and design space search. At the same time, the digital twin provided by the present disclosure has the ability of updating and evolution by using the data assimilation model, and the multi-scale interaction model can effectively strengthen the multi-scale connection of iterative information. The key performance of the repair entity includes at least one of the curing degree, deformation, strain, stress, tensile strength, bearing strength, hardness, plasticity and toughness.

[0083] According to the embodiments of the present disclosure, the digital twin can be used for accelerating the prediction and design space search by the following steps:

[0084] The digital twin to be trained is relatively accurate in approximating high nonlinearity by statistical methods and machine learning algorithms with relatively loose training data according to a parameterized or non-parameterized reduced-order model, and realizes high-fidelity information simulation. On this basis, combined with a data assimilation model, a statistical inference model and a multi-scale interaction model, the digital twin obtained in the design space under a repair process parameter combination (such as any point in the design space) provides a predicted mean and a predicted uncertainty of the key performance of the repaired repair entity, so as to obtain the key performance of the repaired repair entity, wherein the repair process parameter combination includes at least one of temperature, pressure, size, time, curing degree and tool path.

[0085] Based on the above-mentioned intelligent repair method for aviation composite materials, the disclosure also provides an intelligent repair system for aviation composite materials, which will be described in detail below Figure 3 The device is described in detail.

[0086] Figure 3 The structure block diagram of the intelligent repair system for aviation composite materials in the embodiment of the disclosure is schematically shown.

[0087] As Figure 3 shown, the intelligent repair system for aviation composite materials includes a real-time data transmission module 310, a digital model acquisition module 320, an intelligent repair design module 330, a digital twin control module 340 and a repair tool module 350.

[0088] Specifically, the real-time data transmission module 310 is configured to transmit the repair elements of the repair entity of the aviation composite material collected in real time to the digital twin of the aviation composite material repair, wherein the repair elements include damage parameters, repair process parameters and structure parameters. In an embodiment, the real-time data transmission module 310 can be used to perform the operation S101 described above, which will not be described here.

[0089] The digital model acquisition module 320 is configured to establish an initial visual digital model of the repair entity by the digital twin according to the structure parameters and damage parameters of the repair entity, and the initial visual digital model is used to describe the structure parameters and loss parameters of the repair entity, and the repair entity is converted into a virtual object for visual display, so as to describe the structure parameters and damage parameters of the repair entity. In an embodiment, the digital model acquisition module 320 can be used to perform the operation S102 described above, which will not be described here.

[0090] The intelligent repair design module 330 is configured to perform design space search according to the repair process parameters by the digital twin, predict the key performance of the repair process parameters under different damage parameters, and obtain the target repair process parameters required for the repaired repair entity to achieve the key performance. In an embodiment, the intelligent repair design module 330 can be configured to perform the operation S103 described above, and details are not repeated here.

[0091] The digital twin control module 340 is configured to predict the full-field distribution information of the key performance of the repair entity and display in real time according to the repair process parameters and the initial visualized digital model by the digital twin, and transmit the received target repair process parameters to the repair tool module 350. In an embodiment, the digital twin control module 340 can be configured to perform the operation S104 described above, and details are not repeated here.

[0092] The repair tool module 350 is configured to set the repair process according to the received target repair process parameters, and implement the repair of the repair entity. In an embodiment, the repair tool module 350 can be configured to perform the operation S105 described above, and details are not repeated here.

[0093] In an embodiment of the present disclosure, the real-time data transmission module is adopted to transmit the repair elements of the aviation repair entity collected in real time to the digital twin, the digital twin establishes the initial visualized digital model of the repair entity according to the structure parameters and the damage parameters in the repair elements of the repair entity by the digital model acquisition module, and the two-dimensional or three-dimensional conversion of the repair entity to the virtual object is realized by the digital twin. The digital twin can perform design space search according to the repair process parameters by the intelligent repair design module, predict the key performance under different repair process combinations, and determine the target repair process parameters of the repair entity. The digital twin transmits the obtained target repair process parameters to the repair tool module by the digital twin control module, and guides the repair tool module to implement the repair. Meanwhile, the digital twin control module can also predict the full-field distribution information of the key performance of the repair entity according to the repair process parameters and the initial visualized digital model, and display and analyze in real time, so as to timely adjust the repair process of the repair tool module, thereby realizing the virtual-real collaboration, data fusion and intelligent decision of the digital twin and the intelligent repair system, and solving the problem that the digital twin and the intelligent repair system are difficult to integrate.

[0094] According to an embodiment of the present disclosure, the structure parameters include but are not limited to material properties, geometric shape, boundary conditions, etc. of the repair entity; the damage parameters include but are not limited to damage size, damage type, damage depth, damage layer number, impact time, adhesive thickness, etc.; and the repair process parameters include but are not limited to medium pressure, curing temperature, curing time, repair tool path, adhesive strength, temperature rate, pressure rate, etc.

[0095] According to an embodiment of the present disclosure, the real-time data transmission module 310 comprises: a damage scanning and identifying module and a sensor.

[0096] Specifically, the damage scanning and identifying module obtains damage parameters through image recognition processing technology by scanning damage characteristics of the damage site, wherein the damage scanning and identifying module can be an instrument related to digital image, ultrasound, laser, and thermal imaging. The damage parameters can describe defects using shape functions, extract defect features using image segmentation technology, quickly count the types and volume fractions of defects, judge the damage causes, and convert them into damage degree ratings as damage parameters input into the digital twin.

[0097] The sensor is used to measure repair process parameters of the repair process, for example, the sensor can be a temperature sensor, a pressure sensor, etc., which will not be described in detail here.

[0098] According to an embodiment of the present disclosure, the intelligent repair design module 330 further comprises:

[0099] The updating module is used to dynamically update the digital twin by using real-time collected repair data of the repair entity accumulated in the repair process, wherein the repair data includes repair process parameters and observation data.

[0100] According to an embodiment of the present disclosure, the repair tool module 350 comprises at least one of the following:

[0101] The modular repair unit, the non-destructive testing unit, the composite material pretreatment unit, the curing repair unit, the unmanned aerial vehicle repair unit, and the repair vehicle repair unit. The repair module 350 provides the intelligent repair design module 330 to implement more accurate repair on the repair entity. The repair tool module 350 can be manual, assisted by a mechanical arm, or digital.

[0102] For example, the repair tool module 350 includes cutting, polishing, cutting, drilling, patching, thermal curing, microwave curing, electron beam curing, light curing, laser ablation, anchoring, smearing, dust collection, etc.

[0103] The modular repair unit: according to different damage sites of the aircraft, prefabricate general standard site structural members, and give a series of standard repair components with the same or similar configuration under different damage degrees.

[0104] The non-destructive testing unit: uses a variety of non-destructive testing technologies such as knocking, digital image correlation, thermal imaging, ultrasound, X-ray imaging, and array sensors to detect the damage area of the repair entity.

[0105] The composite material pretreatment unit: through cutting, polishing, cutting, drilling, patching, and dust collection, it removes water, oil, fuel, dust, or other foreign matter in the damage area of the repair entity.

[0106] Solidification repair unit: using solidification resin material system such as thermal curing, microwave curing, electron beam curing, light curing, anchoring, smearing, injection and other technologies, through improving the efficiency of multi-crosslinking reaction, the curing speed of resin, reducing the use of thermal adhesive, realizing the efficient and high-performance repair of composite material repair entity damage area.

[0107] Unmanned aerial vehicle repair unit: using unmanned aerial vehicle repair module of composite material repair entity detection technology and solidification technology, realizing single unmanned aerial vehicle large area, high mobility, multi-unmanned aerial vehicle intelligent cluster rapid detection and repair in field repair environment.

[0108] Repair vehicle repair unit: integrating the intelligent repair system of aviation composite materials into a specific vehicle to realize the basic function of restoring the damaged structure of the repair entity in a short time in the field repair environment.

[0109] According to the embodiment of the present disclosure, the digital twin control module 340 further comprises:

[0110] The adjustment unit is configured to adjust the repair process of the repair tool module 350 on the repair entity according to the repair process.

[0111] The adjustment unit of the digital twin control module 340 adjusts the repair process of the repair tool module 350 on the repair entity in at least one of the following ways: single-chip digital automatic control, full-pressure starting, voltage ramp starting, voltage step starting, and current limiting starting, to ensure the precision, reliability, safety and stability of the on-site repair process parameters and the operation of the repair tool module 350. The digital twin control module 340 realizes the unmanned and intelligent repair of the composite material repair entity by online analysis, evaluation and dynamic optimization during the repair process, and improves the repair efficiency of the composite material repair entity.

[0112] According to the embodiment of the present disclosure, the intelligent repair system of aviation composite materials further comprises:

[0113] The life cycle management module is configured to monitor the parameter changes of the repaired repair entity in the service stage in real time by using the digital twin.

[0114] According to the embodiment of the present disclosure, the intelligent repair system of aviation composite materials further comprises:

[0115] The digital twin database is configured to establish an aviation composite material repair digital twin database according to the repair data of a plurality of repair entities and the corresponding digital twins, and realize the analysis, updating and evolution of the aviation composite material repair digital twin.

[0116] In the embodiments of the present disclosure, the intelligent repair system for aviation composites provided by the present disclosure applies big data and mechanism combination technology, realizes mass energy and production data knowledge graph, and helps to make the implicit knowledge of manufacturing enterprises explicit.

[0117] According to the embodiments of the present disclosure, the intelligent repair system for aviation composites includes a digital twin construction module for constructing a digital twin, the digital twin is a repair digital model established by a repair element of a repair entity sample and a mechanism model established by the repair entity sample in a repair process, and the digital twin is obtained by training a to-be-trained digital twin using training data and / or real-time collected observation data.

[0118] Specifically, the digital twin construction module includes a statistical inference model, a multi-scale interaction model and a data assimilation model, and the digital twin is obtained by the following steps:

[0119] A repair digital model reflecting a repair element of a repair entity sample is established according to the repair entity sample, and a mechanism model describing a repair process is established according to an internal mechanism of the repair entity sample in the repair process.

[0120] The repair digital model and the mechanism model of the repair entity sample are used as training data and mechanism constraints of the to-be-trained digital twin.

[0121] The statistical inference model of the to-be-trained digital twin is trained according to the training data, the observation data of the repair entity sample collected in real time and the prior knowledge data in the mechanism model, and prediction data is output.

[0122] Under the constraint of the mechanism model, a loss value is calculated according to the prediction data and the observation data of the repair entity sample collected in real time, a loss result is obtained, parameters of the multi-scale interaction model of the to-be-trained digital twin are iteratively adjusted using the loss result, and the multi-scale interaction model and the statistical inference model of the digital twin are obtained.

[0123] The new observation data of the repair entity sample collected in real time is input into the data assimilation model of the to-be-trained digital twin for updating, and the data assimilation model of the digital twin is obtained.

[0124] The statistical inference model, the multi-scale interaction model and the data assimilation model in the digital twin are integrated to obtain the digital twin.

[0125] According to the embodiments of the present disclosure, the new observation data can be repair data, the repair data includes repair process parameters and observation data, and the observation data can be data detected by an instrument.

[0126] According to embodiments of the present disclosure, any of the real-time data transmission module 310, the digital model acquisition module 320, the intelligent repair design module 330, the digital twin control module 340, and the repair tool module 350 can be combined in one module for implementation, or any of them can be split into multiple modules. Alternatively, at least part of the function of one or more of these modules can be combined with at least part of the function of other modules, and implemented in one module. According to embodiments of the present disclosure, at least one of the real-time data transmission module 310, the digital model acquisition module 320, the intelligent repair design module 330, the digital twin control module 340, and the repair tool module 350 can be at least partially implemented as a hardware circuit, such as a field programmable gate array (FPGA), a programmable logic array (PLA), a system on chip, a system on board, a system on package, an application specific integrated circuit (ASIC), or any other reasonable way of integrating or packaging a circuit, etc. hardware or firmware, or any one of software, hardware and firmware or a suitable combination of any of them. Alternatively, at least one of the real-time data transmission module 310, the digital model acquisition module 320, the intelligent repair design module 330, the digital twin control module 340, and the repair tool module 350 can be at least partially implemented as a computer program module that can perform corresponding functions when executed.

[0127] Figure 4 An illustrative diagram of the intelligent repair and life cycle management of the aerospace composite material in the embodiments of the present disclosure is schematically shown.

[0128] As shown in Figure 4 The intelligent repair and life cycle management of the aerospace composite material in the embodiments of the present disclosure includes: a given aerospace composite material repair task 401, an aerospace composite material intelligent repair process 402, and an aerospace composite material repair entity whole life cycle management 403, wherein the intelligent repair process 402 includes: an aerospace composite material repair entity and an aerospace composite material intelligent repair design device 4021, a digital twin 4022, and a virtual-real fusion interface 4023.

[0129] The aviation composite material repair entity includes aviation composite material repair structures such as laminated plates and honeycomb laminated plates. The aviation composite material intelligent repair design device includes repair modules such as detection, drilling, patching, layering, heating blankets, vacuum bags, digital repair devices, curing units, large-area and high-mobility non-destructive testing units, and composite material repair vehicles. In addition, the aviation composite material intelligent repair design device includes related character, image, graphic, sound input parts, storage, processors, and output parts. The virtual-real fusion interface 4023 is used for the virtual-real interaction expansion function of the aviation composite material repair entity, the aviation composite material intelligent repair design device 4021, and the digital twin 4022, such as wireless data transmission and life cycle management.

[0130] Specifically, after receiving the aviation composite material repair task 401, the aviation composite material intelligent repair process 402 is carried out. During the repair process, the digital twin is constructed, the intelligent repair of the repair entity is implemented by using the digital twin, and the full life cycle management 403 of the aviation composite material repair entity is implemented by using the digital twin during the service process. The full life cycle management includes the management during the repair process of the repair entity and the life management of the repaired repair entity during the service stage.

[0131] Figure 5 A block diagram of the aviation composite material intelligent repair device in the embodiment of the present disclosure is schematically shown.

[0132] As shown in Figure 5 The aviation composite material intelligent repair device 500 according to the embodiment of the present disclosure includes a processor 501, which can perform various appropriate actions and processes according to programs stored in a read-only memory (ROM) 502 or programs loaded from a storage part 508 to a random access memory (RAM) 503. The processor 501 can include, for example, a general-purpose microprocessor (such as a CPU), an instruction set processor, and / or a related chipset, and / or a special-purpose microprocessor (such as an application-specific integrated circuit (ASIC)), and the like. The processor 501 can also include on-board memory for cache use. The processor 501 can include a single processing unit or multiple processing units for performing different actions of the aviation composite material intelligent repair method flow according to the embodiment of the present disclosure.

[0133] In the RAM 503, various programs and data required for the operation of the smart repair device 500 for aviation composite materials, such as a digital twin, are stored. The processor 501, the ROM 502, and the RAM 503 are connected to each other through the bus 504. The processor 501 performs various operations of the smart repair method flow for aviation composite materials according to the embodiments of the present disclosure by executing the programs in the ROM 502 and / or the RAM 503. It should be noted that the programs can also be stored in one or more memories other than the ROM 502 and the RAM 503. The processor 501 can also perform various operations of the smart repair method flow for aviation composite materials according to the embodiments of the present disclosure by executing the programs stored in the one or more memories.

[0134] According to the embodiments of the present disclosure, the smart repair device 500 for aviation composite materials can further include an input / output (I / O) interface 505, which is also connected to the bus 504. The smart repair device 500 for aviation composite materials can further include one or more of the following components connected to the input / output (I / O) interface 505: an input portion 506 including a virtual-real fusion interface (not shown) for virtual-real interaction of the repair entity with the digital twin; an input portion 506 including a repair tool device, a character input device, a sound input device, a graphics device, an image input device, etc., wherein the input portion 506 is used to obtain repair information of the repair entity, and the repair information includes at least one of repair tool module information, character information, image information, and graphics information, wherein the repair tool device can be a detection, drilling, patching, layering, curing device, etc., and the character input device can be a related character; an output portion 507 such as a cathode ray tube (CRT), a liquid crystal display (LCD), a printer, a plotter, an image device, a voice device, a magnetic recording device, etc., and a speaker device, etc., which is used to output real-time repair information of the repair entity; a storage portion 508 including a hard disk, etc.; and a communication portion 509 including a network interface card such as a LAN card, a modem, etc. The communication portion 509 performs communication processing via a network such as the Internet. A driver 510 is also connected to the input / output (I / O) interface 505 as needed. A removable medium 511 such as a magnetic disk, an optical disk, a magneto-optical disk, a semiconductor memory, etc. is mounted on the driver 510 as needed, so that a computer program read therefrom is installed in the storage portion 508 as needed.

[0135] The present disclosure also provides a computer readable storage medium, which can be included in the device / apparatus / system described in the above embodiments, or can exist independently without being assembled into the device / apparatus / system. The above computer readable storage medium carries one or more programs, which, when executed, implement the intelligent repair method of the aviation composite material according to the embodiments of the present disclosure.

[0136] According to the embodiments of the present disclosure, the computer readable storage medium can be a non-volatile computer readable storage medium, which can include, but is not limited to, a portable computer diskette, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any appropriate combination thereof. In the present disclosure, the computer readable storage medium can be any tangible medium that contains or stores a program, which can be used by or in connection with an instruction execution system, apparatus, or device. For example, according to the embodiments of the present disclosure, the computer readable storage medium can include one or more memories of the ROM 502 and / or the RAM 503 described above and / or one or more memories other than the ROM 502 and the RAM 503.

[0137] The embodiments of the present disclosure also include a computer program product, which includes a computer program containing program codes for executing the methods shown in the flowcharts. When the computer program product is run in a computer system, the program codes are used to make the computer system implement the intelligent repair method of the aviation composite material provided by the embodiments of the present disclosure.

[0138] The above functions defined in the system of the embodiments of the present disclosure are performed when the computer program is executed by the processor 501. According to the embodiments of the present disclosure, the above described systems, modules, units, etc. can be implemented by computer program modules.

[0139] In one embodiment, the computer program can rely on tangible storage media such as optical storage media, magnetic storage media, etc. In another embodiment, the computer program can also be transmitted, distributed, downloaded and installed in the form of signals on a network medium, and be downloaded and installed through the communication part 509 and / or installed from the detachable medium 511. The program codes contained in the computer program can be transmitted by any appropriate network medium, including but not limited to wireless, wired, etc., or any appropriate combination thereof.

[0140] In such embodiments, the computer program can be downloaded and installed from the network via the communication section 509, and / or installed from the removable media 511. When the computer program is executed by the processor 501, the above-described functions defined in the system of the embodiments of the present disclosure are executed. According to the embodiments of the present disclosure, the system, device, apparatus, module, unit, and the like described above can be implemented by the computer program modules.

[0141] According to the embodiments of the present disclosure, the program code for executing the computer program provided by the embodiments of the present disclosure can be written in any combination of one or more programming languages, and specifically, these computer programs can be implemented using high-level procedural and / or object-oriented programming language, and / or assembly / machine language. The programming language includes, but is not limited to, such as Java, C++, python, "C" language, or similar programming language. The program code can be executed entirely on the user computing device, partially on the user device, partially on a remote computing device, or entirely on a remote computing device or server. In the case involving a remote computing device, the remote computing device can 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 can be connected to an external computing device (for example, connected through the Internet by using an Internet service provider).

[0142] The flowcharts and block diagrams in the drawings illustrate the architecture, functionality, and operation of possible implementations of systems, methods, and computer program products according to various embodiments of the present disclosure. In this regard, each block in the flowcharts or block diagrams can represent a module, a segment, or a portion of code, which contains one or more executable instructions for implementing the specified logical function. It should also be noted that in some alternative implementations, the functions noted in the blocks can occur in a different order than that shown in the figures. For example, two blocks noted in succession can actually be executed substantially concurrently, or they can sometimes be executed in reverse order, depending on the functionality involved. It should also be noted that each block in the flowcharts or block diagrams, and combinations of blocks in the flowcharts or block diagrams, can be implemented by dedicated hardware-based systems that perform the specified functions or operations, or can be implemented by a combination of dedicated hardware and computer instructions.

[0143] Those skilled in the art can understand that the features described in various embodiments of the present disclosure and / or claims can be combined or / and integrated, even if such combinations or integrations are not explicitly described in the present disclosure. In particular, the features described in various embodiments of the present disclosure and / or claims can be combined and / or integrated in various combinations, without departing from the spirit and teachings of the present disclosure. All such combinations and / or integrations are within the scope of the present disclosure.

Claims

1. An intelligent repair method for repairing a digital twin using an aeronautical composite material, comprising: transmitting repair elements of an aeronautical composite material repair entity collected in real time to an aeronautical composite material repair digital twin, wherein the repair elements include damage parameters, repair process parameters, and structure parameters; establishing an initial visualized digital model of the repair entity by the digital twin according to the structure parameters and damage parameters of the repair entity, the initial visualized digital model being used to describe the structure parameters and damage parameters of the repair entity; performing design space search by the digital twin according to the repair process parameters, predicting key performances of different repair process parameter combinations, and obtaining target repair process parameters required for the repaired repair entity to achieve key performances; real-time predicting full-field distribution information of key performances of the repair entity by the digital twin according to the repair process parameters and the initial visualized digital model, and real-time displaying and analyzing; and transmitting the received target repair process parameters to a repair tool; setting a repair process by the repair tool according to the received target repair process parameters, and implementing repair of the repair entity.

2. The method of claim 1, wherein, The repair entity is an entity component that has suffered damage during manufacturing and / or service. The composite material is composed of at least two components and has anisotropic mechanical properties.

3. The method of claim 1, wherein, The design space search is obtained by the following way: obtaining the key performances of the repaired repair entity by the digital twin using a reduced-order model in the design space under the repair process parameter combination; wherein the repair process parameter combination includes at least one combination of temperature, pressure, size, time, degree of curing, and tool path; the key performances include at least one of degree of curing, deformation, strain, stress, tensile strength, impact strength, hardness, plasticity, and toughness.

4. The method of claim 1, further comprising: controlling the repair process of the repair tool on the repair entity according to the repair process.

5. The method of claim 4, further comprising: real-time monitoring parameter changes of the repaired repair entity during the service stage by the digital twin.

6. The method of claim 4 or 5, further comprising: dynamically updating the digital twin using repair data of the repair entity collected in real time during the repair process, wherein the repair data includes repair process parameters and observation data.

7. The method of claim 6, further comprising: establishing an aeronautical composite material repair digital twin database according to repair data of multiple repair entities and corresponding digital twins accumulated, realizing analysis, updating, and evolution of the digital twin.

8. The method of claim 1, wherein, The digital twin is obtained by training a to-be-trained digital twin using training data of a repair digital model established based on repair elements of a repair entity sample and a mechanism model established based on the repair entity sample during a repair process, and / or observation data collected in real time.

9. The method of claim 8, wherein, The digital twin comprises a statistical inference model, a multi-scale interaction model and a data assimilation model, and is trained by the following steps: A repair digital model reflecting repair elements of the repair entity sample is established according to the repair entity sample, and a mechanism model describing a repair process is established according to an internal mechanism of the repair process of the repair entity sample; The repair digital model and the mechanism model of the repair entity sample are used as training data and mechanism constraints of the to-be-trained digital twin; The statistical inference model in the to-be-trained digital twin is trained according to the training data, observation data of the repair entity sample collected in real time and prior knowledge data in the mechanism model, and prediction data is output; Under the constraint of the mechanism model, a loss value is calculated according to the prediction data and the observation data of the repair entity sample collected in real time, a loss result is obtained, parameters of the multi-scale interaction model in the to-be-trained digital twin are iteratively adjusted by using the loss result, and a multi-scale interaction model and a statistical inference model of the digital twin are obtained; And New observation data of the repair entity sample collected in real time is input into the data assimilation model in the to-be-trained digital twin for updating, and a data assimilation model of the digital twin is obtained; The statistical inference model, the multi-scale interaction model and the data assimilation model in the digital twin are integrated, and the digital twin is obtained.

10. The method of claim 9, wherein, The internal mechanism of the repair entity sample in the repair process comprises at least one of the following: Basic laws, thermodynamic coupling dynamics equations, material constitutive models, damage evolution models, fatigue failure models required by the digital twin, physical meanings of parameters in the mechanism model, and composite material repair processes; The basic laws required by the digital twin are laws conforming to natural laws, including Newton's first law and Newton's second law.

11. An intelligent repair system for an aeronautical composite material, comprising: A real-time data transmission module for transmitting repair elements of an aeronautical composite material repair entity collected in real time to an aeronautical composite material repair digital twin, wherein the repair elements include damage parameters, repair process parameters and structure parameters; A digital model acquisition module for establishing an initial visual digital model of the repair entity by the digital twin according to the structure parameters and damage parameters of the repair entity, the initial visual digital model being used to describe the structure parameters and damage parameters of the repair entity; An intelligent repair design module for searching a design space by the digital twin according to the repair process parameters, predicting key performances of different combinations of repair process parameters, and obtaining target repair process parameters required by the repaired repair entity to achieve key performances; A digital twin control module for predicting full-field distribution information of key performances of the repair entity by the digital twin according to the repair process parameters and the initial visual digital model, and displaying and analyzing the information in real time; and transmitting the received target repair process parameters to a repair tool module; A repair tool module is configured to set a repair process according to the received target repair process parameters, and implement the repair of the repair entity.

12. The system of claim 11, wherein, The system comprises a digital twin construction module configured to construct the digital twin. The digital twin is trained by using training data and / or real-time observation data, wherein the training data comprises a repair digital model of a repair element of the repair entity sample and a mechanism model of the repair entity sample in a repair process.

13. The system of claim 12, wherein, The digital twin construction module comprises a statistical inference model, a multi-scale interaction model and a data assimilation model, and the digital twin is trained by the following steps: A repair digital model reflecting a repair element of the repair entity sample is constructed according to the repair entity sample, and a mechanism model describing a repair process is constructed according to an internal mechanism of the repair entity sample in the repair process. The repair digital model and the mechanism model of the repair entity sample are used as training data and mechanism constraints of the digital twin to be trained. The statistical inference model of the digital twin to be trained is trained according to the training data, observation data of the repair entity sample collected in real time and prior knowledge data in the mechanism model, and prediction data is output. Under the constraint of the mechanism model, a loss value is calculated according to the prediction data and the observation data of the repair entity sample collected in real time, a loss result is obtained, and the parameters of the multi-scale interaction model of the digital twin to be trained are iteratively adjusted by using the loss result, so as to obtain the multi-scale interaction model and the statistical inference model of the digital twin. And New observation data of the repair entity sample collected in real time is input into the data assimilation model of the digital twin to be trained for updating, so as to obtain the data assimilation model of the digital twin. The statistical inference model, the multi-scale interaction model and the data assimilation model in the digital twin are integrated to obtain the digital twin.

14. The system of claim 11, wherein, The real-time data transmission module comprises a damage scanning and identification module and a sensor. The damage scanning and identification module obtains damage parameters through image recognition processing technology by scanning damage characteristics of a damage site. The sensor is configured to measure repair process parameters of a repair process.

15. The system of claim 11, wherein, The intelligent repair design module further comprises an updating module configured to dynamically update the digital twin by using repair data of the repair entity collected in real time in the repair process, wherein the repair data comprises repair process parameters and observation data.

16. The system of claim 11, wherein, The repair tool module comprises at least one of the following: A modular repair unit, a non-destructive testing unit, a composite material pretreatment unit, a curing repair unit, an unmanned aerial vehicle repair unit and a repair vehicle repair unit.

17. The system of claim 11, wherein, The digital twin control module further comprises: An adjusting unit configured to adjust and control the repair tool module to repair the repair entity according to the repair process. The adjustment unit of the digital twin control module regulates the way of the repair tool module to repair the repair entity, including at least one of the following: single-chip microcomputer digital automatic control, full-pressure starting, voltage ramp starting, voltage step starting, and current limiting starting.

18. The system of claim 17, further comprising: a life cycle management module for monitoring the parameter changes of the repaired repair entity in the service stage in real time by using the digital twin.

19. The system of claim 18, further comprising: a digital twin database for establishing an aviation composite material repair digital twin database according to the accumulated repair data of multiple repair entities and the corresponding digital twins, and realizing analysis, updating and evolution of the aviation composite material repair digital twin.

20. An intelligent repair equipment for aviation composite materials, comprising the system of any one of claims 11-19: an input part for obtaining repair information of the repair entity, the repair information including at least one of repair tool module information, text information, image information and graphic information; a storage part for storing a computer program, the computer program containing the digital twin; a processor for executing the method according to any one of claims 1-10; an output part for outputting real-time repair information of the repair entity; a virtual-real fusion interface for virtual-real interaction between the repair entity and the digital twin.

21. The equipment of claim 20, wherein: the input part includes at least one of a repair tool device, a text input device, a sound input device, and an image input device; the output part includes at least one of a display, a printer, a plotter, an image device, a voice device, and a magnetic recording device.

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