Data-driven method for coordinating gap step in aircraft access door-to-frame joint

Abstract

This application discloses a data-driven method for coordinating the gap and step difference of the joint between an aircraft cover and frame, relating to the field of aerospace manufacturing engineering. The method includes: calculating the key geometric parameters of the cover and frame in the joint area; pre-constructing a geometric feature model library, searching the library based on the model information of the cover and frame, and directly calling the corresponding pre-stored geometric feature models to obtain the geometric feature parameters of the cover and frame respectively; inputting the measured gap value, measured step difference value, geometric feature parameters of the cover and frame, and a preset accuracy range into a pre-constructed compensation decision model to determine whether the measured gap value and measured step difference value meet the accuracy requirements. This achieves comprehensive and continuous detection of the complex contour joint state, enabling the identification of deviations in local areas and ensuring that the assembly accuracy fully meets the requirements.

Description

Technical Field

[0001] This application relates to the field of aerospace manufacturing engineering, and in particular to a data-driven method for coordinating the gap step of the joint between the aircraft cover and the frame. Background Technology

[0002] Aircraft panels are an important component of the aircraft structure, mainly used in the wings, fuselage, and tail. The panels' covers and frames are key components of the fuselage structure, primarily used for maintenance, repair, and connection of external equipment. The precision of the fit between the covers and frames affects the smoothness of the aircraft surface, aerodynamic performance, and stealth characteristics. Current technologies for coordinating aircraft cover gaps / steps rely heavily on manual experience. Tools such as feeler gauges, dial indicators, and depth gauges are used to manually measure joint gaps and step differences. Operators then judge, based on their experience, whether adjustments are needed by locally grinding the cover edges with a grinding wheel or by filling gaps with rubber gaskets, ultimately completing the assembly.

[0003] The methods described above are essentially still based on "experience-driven" approaches, and suffer from a series of problems such as unstable accuracy, low efficiency, and poor adaptability. Especially when modern aircraft hatches commonly use complex contours such as curved surfaces and variable cross-sections, traditional methods cannot collect joint contour data when faced with complex profiles of test pieces. They can only perform limited contact measurements, which are prone to local deviations, resulting in low assembly efficiency and assembly quality that is difficult to meet basic design requirements. Summary of the Invention

[0004] The purpose of this application is to provide a data-driven method for coordinating the gap step of the aircraft hatch and frame joint, which can solve the problem of "unable to collect joint contour data and only able to perform limited contact measurements".

[0005] To achieve the above objectives, this application provides the following solution: In a first aspect, this application provides a data-driven method for coordinating the gap step between the aircraft hatch and the hatch frame joint, including: Obtain the three-dimensional point cloud data of the cap and the frame; Based on the three-dimensional point cloud data, the outer contours of the lid and the frame are extracted respectively, so as to register the outer contours of the lid and the frame. Based on the spatial relationship after registration, the key geometric parameters of the cover and the frame in the joint area are calculated. The key geometric parameters are the measured values ​​of the gap and the step difference. A geometric feature model library is pre-built, which includes multiple pre-stored geometric feature models, and each pre-stored geometric feature model is uniquely associated with a corresponding model of the cap or the frame; Based on the model information of the lid and the model information of the frame, a search is performed in the geometric feature model library. If a pre-stored geometric feature model corresponding to the lid and the frame is found, the corresponding pre-stored geometric feature model is directly called to obtain the geometric feature parameters of the lid and the frame respectively. If no model matching the lid and the frame is found, a new geometric feature model of the lid and the frame is created and stored in the geometric feature model library to obtain the geometric feature parameters of the lid and the frame respectively. The measured values ​​of the gap, the measured values ​​of the step difference, the geometric feature parameters of the cover and the geometric feature parameters of the frame, and the preset accuracy range are input into a pre-built compensation decision model to determine whether the measured values ​​of the gap and the measured values ​​of the step difference meet the accuracy requirements, and output a compensation scheme when the accuracy requirements are not met. Based on the compensation scheme, corresponding compensation operations are performed to coordinate the gaps and steps in the joint area.

[0006] In one embodiment, the step of constructing the compensation decision model specifically includes: Historical assembly data of the caps and frames of various models are obtained. The historical assembly data includes historical geometric feature parameters of the caps, historical geometric feature parameters of the frames, historical measured values ​​of gaps and step differences, and corresponding historical compensation schemes. The historical assembly data is preprocessed and features are extracted to construct a training dataset; Based on the training dataset, a machine learning algorithm is used to train the model to obtain the compensation decision model.

[0007] In one embodiment, the step of creating geometric feature models of the cap and the frame to obtain the geometric feature parameters of the cap and the frame respectively includes: Based on the registered 3D point cloud data and the key geometric parameters, the geometric feature model of the lid and the frame is constructed by continuous surface fitting through reverse engineering and 3D modeling techniques. Geometric feature parameters of the cap and the frame are extracted from their geometric feature models, including external dimensions, surface curvature, contour curve parameters, reference surface information, and positioning hole coordinates.

[0008] In one embodiment, the compensation scheme includes the amount of contour grinding of the cap and the amount of gap filling compensation in the joint area between the cap and the frame.

[0009] In one embodiment, the step of extracting the outer contours of the lid and the frame based on the three-dimensional point cloud data specifically includes: An algorithm based on point cloud contour boundary search based on normal vector mutation and neighborhood density difference is used to extract the outer contours of the lid and the frame from the three-dimensional point cloud data.

[0010] In one embodiment, after performing the corresponding compensation operation based on the compensation scheme, the method further includes: The adjusted measured values ​​of the gap and the step difference are recalculated to obtain the retest results; The three-dimensional point cloud data, geometric feature parameters, compensation scheme, and retest results obtained during this assembly process are stored in a historical database for iterative optimization of the compensation decision model.

[0011] Secondly, this application also provides a data-driven gap step coordination device for the joint between the aircraft hatch and the frame, comprising: The data acquisition module acquires three-dimensional point cloud data of the lid and the frame. The contour extraction and registration module extracts the outer contours of the lid and the frame based on the three-dimensional point cloud data, and registers the outer contours of the lid and the frame. The parameter calculation module calculates the key geometric parameters of the cover and the frame in the joint area based on the registered spatial relationship. The key geometric parameters are the measured values ​​of the gap and the step difference. The model library construction module pre-builds a geometric feature model library, which includes multiple pre-stored geometric feature models. Each pre-stored geometric feature model is uniquely associated with a corresponding model of the cap or the frame. The geometric feature parameter acquisition module searches the geometric feature model library based on the model information of the lid and the model information of the frame. If a pre-stored geometric feature model corresponding to the lid and the frame is found, the corresponding pre-stored geometric feature model is directly called to obtain the geometric feature parameters of the lid and the frame respectively. If no model matching the lid and the frame is found, a new geometric feature model of the lid and the frame is created and stored in the geometric feature model library to obtain the geometric feature parameters of the lid and the frame respectively. The compensation decision module inputs the measured values ​​of the gap, the measured values ​​of the step difference, the geometric feature parameters of the cover and the geometric feature parameters of the frame, and a preset accuracy range into a pre-built compensation decision model to determine whether the measured values ​​of the gap and the measured values ​​of the step difference meet the accuracy requirements, and outputs a compensation scheme when the accuracy requirements are not met. The compensation execution module performs corresponding compensation operations based on the compensation scheme to coordinate the gaps and steps in the joint area.

[0012] Thirdly, this application also provides a computer device, including: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the above-described data-driven method for coordinating the gap step of the aircraft hatch and frame joint.

[0013] Fourthly, this application also provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the above-described data-driven method for coordinating the gap step difference between the aircraft hatch and the frame joint.

[0014] Fifthly, this application also provides a computer program product, including a computer program that, when executed by a processor, implements the above-described data-driven method for coordinating the gap difference between the aircraft hatch and the frame joint.

[0015] According to the specific embodiments provided in this application, the following technical effects are disclosed: This application provides a data-driven method for coordinating the gap and step difference of the joint between an aircraft hatch and frame. It acquires three-dimensional point cloud data of the hatch and frame, extracts their outer contours based on the point cloud data, and registers them. Based on the registered spatial relationship, it calculates the key geometric parameters of the hatch and frame in the joint area, which are the measured values ​​of the gap and step difference. Thus, by scanning to acquire the overall point cloud data of the hatch and frame and extracting their outer contours, it can further comprehensively calculate the measured values ​​of the gap and step difference in the joint area. This achieves comprehensive and continuous detection of the joint state of complex contours, ensuring that any deviations in local areas can be identified and that the assembly accuracy fully meets design requirements.

[0016] In this application, a geometric feature model library is pre-built and maintained. In practical applications, based on the current model information of the lid and frame, the model library is searched and matched to quickly obtain the geometric feature parameters of the lid and frame. If there is no matching model, a new model can be created and stored in the model library, thereby continuously expanding the coverage of the model library while ensuring processing efficiency.

[0017] This application inputs the measured values ​​of gaps, step differences, geometric feature parameters, and preset accuracy ranges into a pre-constructed compensation decision model to determine whether the measured values ​​of gaps and step differences meet the accuracy requirements. If the accuracy requirements are not met, a compensation scheme is output, and corresponding compensation operations are performed based on the compensation scheme to coordinate the gaps and step differences in the joint area. Thus, by inputting key geometric parameters, geometric feature parameters, and preset accuracy data into the model, automatic judgment and compensation are achieved, effectively solving the problem of low assembly accuracy caused by reliance on manual experience in traditional methods. While ensuring accuracy, assembly efficiency is significantly improved. Attached Figure Description

[0018] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0019] Figure 1 This is a flowchart illustrating a data-driven method for coordinating the gap step difference between the aircraft hatch and the hatch frame, according to an embodiment of this application. Figure 2 This is a flowchart of a data-driven method for coordinating the gap step of the aircraft hatch and frame joint according to an embodiment of this application. Figure 3 This is a schematic diagram illustrating the calculation of the gap and step difference between the aircraft hatch and frame in a data-driven method for coordinating the gap and step difference between the aircraft hatch and frame according to an embodiment of this application. Figure 4 This is a schematic diagram of the structure of a computer device provided in an embodiment of this application. Detailed Implementation

[0020] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0021] To make the above-mentioned objectives, features and advantages of this application more apparent and understandable, the application will be further described in detail below with reference to the accompanying drawings and specific embodiments.

[0022] See Figure 1 This application provides a data-driven method for coordinating the gap step between the aircraft hatch and the hatch frame joint, comprising the following steps: S100: Acquire 3D point cloud data of the lid and frame; S200: Based on 3D point cloud data, extract the outer contours of the lid and the frame respectively, and register the outer contours of the lid and the frame. S300: Based on the spatial relationship after registration, calculate the key geometric parameters of the cover and frame in the joint area. The key geometric parameters are the measured values ​​of the gap and the step difference. S400: A pre-built geometric feature model library, which includes multiple pre-stored geometric feature models. Each pre-stored geometric feature model is uniquely associated with a corresponding model of cap or frame. S500: Based on the model information of the lid and the model information of the frame, the geometric feature model library is searched. If a pre-stored geometric feature model corresponding to the lid and the frame is found, the corresponding pre-stored geometric feature model is directly called to obtain the geometric feature parameters of the lid and the frame respectively. If no model matching the lid and the frame is found, a new geometric feature model of the lid and the frame is created and stored in the geometric feature model library to obtain the geometric feature parameters of the lid and the frame respectively. S600: Input the measured values ​​of the gap, the measured values ​​of the step difference, the geometric feature parameters of the cover, the geometric feature parameters of the frame, and the preset accuracy range into the pre-built compensation decision model, determine whether the measured values ​​of the gap and the measured values ​​of the step difference meet the accuracy requirements, and output the compensation scheme when the accuracy requirements are not met. S700: Performs corresponding compensation operations based on the compensation scheme to coordinate the gaps and steps in the joint area.

[0023] In S100, before acquiring the 3D point cloud data of the aircraft panel cover and frame, it is necessary to confirm the specifications and models of the cover and frame to be assembled, and clean the surface impurities to ensure that there are no impurities or surface scratches, so as to ensure the accuracy of subsequent geometric dimension measurement data.

[0024] In this step, based on the structural features of the cap and frame, clamping fixtures adapted to each are used to position and clamp them respectively. During clamping, it is essential to ensure that key feature areas such as assembly joints, positioning holes, and reference surfaces are unobstructed to avoid hindering point cloud data acquisition. Subsequently, a high-precision 3D laser scanner is used to scan the entire feature area of ​​the clamped structural component. After scanning, the point cloud is output as a .txt or .ply file for subsequent data processing.

[0025] In S200, the steps of extracting the outer contours of the lid and the frame based on the three-dimensional point cloud data are as follows: the three-dimensional point cloud data of the lid and the frame obtained in the above steps are preprocessed, including point cloud filtering, noise reduction, simplification and other operations, so as to extract the outer contours of the lid and the frame from the three-dimensional point cloud data by using a point cloud contour boundary search algorithm based on normal vector mutation and neighborhood density difference.

[0026] It should be noted that the point cloud contour boundary search algorithm is used to automatically and accurately identify the external contour boundary of an object from a dense 3D point cloud. The point cloud contour boundary search algorithm of this application identifies and extracts the contour boundary of a 3D object by collaboratively detecting abrupt changes in surface normal vectors and differences in local point density in the point cloud.

[0027] The registration process for the outer contours of the outlet cover and the outlet frame involves the following steps: After extracting the independent outer contours of the outlet cover and the outlet frame separately, the two independent contour point clouds are precisely aligned to the same coordinate system through coordinate transformation based on a common physical reference (such as positioning holes) to complete the registration. The purpose is to restore the spatial relationship between the outlet cover and the outlet frame in actual assembly, laying the foundation for subsequent accurate calculation of the gap and step difference between the outlet cover and the outlet frame.

[0028] In S300, see Figure 3 The positional relationship between two points A and B on the lid and frame is used to calculate the gap and step difference values ​​for the entire range using the following formulas for calculating gap g and step difference s. Alternatively, the gap and step difference values ​​for a selected range can be calculated according to actual needs. Those skilled in the art can set these values ​​according to actual conditions.

[0029] The formulas for calculating the gap and step difference are: in, Indicates the distance between two points. Indicates the order difference. Indicates the gap; the spatial coordinates of point A are ( , , ), and lies on a contour; the spatial coordinates of point B are ( , , ), and located on another contour, with the two points in a directly opposite position.

[0030] In S400, a geometric feature model library is pre-built, comprising multiple pre-stored geometric feature models, each uniquely corresponding to a specific model of cap or frame. During the construction of the geometric feature model library, geometric feature parameters are extracted for each type of cap and frame to be assembled, resulting in multiple sets of geometric feature parameter data. The data is then classified and validated, outliers are removed, and key missing information is supplemented to ensure the accuracy and completeness of the parameter set. Subsequently, a parametric modeling method is used to construct a geometric feature mathematical model corresponding one-to-one with the cap and frame model based on the validated geometric feature parameter data. During the modeling process, a mapping relationship must be established between the model and the specimen model, as well as between the model and the corresponding geometric feature parameters. That is, the pre-stored geometric feature model not only contains complete geometric structure information but also includes model identification, parameter traceability labels, and other information for identifying the cap or frame.

[0031] In S500, the model information of the lid and the model information of the frame are searched in the geometric feature model library. If a pre-stored geometric feature model corresponding to the lid and the frame is found, the corresponding pre-stored geometric feature model is directly called to obtain the geometric feature parameters of the lid and the frame respectively. If no model matching the lid and the frame is found, a new geometric feature model of the lid and the frame is created and stored in the geometric feature model library to obtain the geometric feature parameters of the lid and the frame respectively.

[0032] The model information of the new test piece is compared with the geometric feature mathematical models stored in the model library, and a search is performed by model. In some other embodiments, error thresholds for other key parameters can be set to verify whether the fit is complete. These key parameters are mainly the key identity features of the test piece, used to identify the information of the test piece, such as key positioning base dimensions, etc. If there is a pre-stored geometric feature model that perfectly matches the new test piece, the model is directly called to access the subsequent accuracy judgment, compensation scheme generation and other processing flows, without having to carry out surface fitting and modeling work again, which greatly shortens the processing cycle of a single set of caps. If there is no matching model in the model library, a new geometric feature model is created according to the process, and it is associated with the corresponding geometric feature parameters and model information and then included in the model library to continuously enrich the coverage of the model library.

[0033] Specifically, the steps of creating geometric feature models of the cap and frame to obtain their geometric feature parameters include: based on the registered 3D point cloud data and key geometric parameters, continuous surface fitting is performed through reverse engineering and 3D modeling techniques to construct geometric feature models of the cap and frame; from the geometric feature models of the cap and frame, the geometric feature parameters of the cap and frame are extracted respectively. The geometric feature parameters include external dimensions, surface curvature, contour curve parameters, reference surface information, and positioning hole coordinates.

[0034] For example, based on the processed 3D point cloud data and the calculated key geometric parameters such as gaps and step differences in the joint area, reverse engineering and 3D modeling techniques are used to combine the registered point cloud data with these key geometric parameters to fit a continuous surface, thus constructing a surface model. This model transforms discrete point cloud data into a structured geometric model, laying the foundation for subsequent extraction of core geometric features, integration of cap geometric information, and construction of a standardized dataset. Furthermore, complete geometric information of the cap and frame to be assembled is obtained, including the cap and frame dimensions, surface curvature, joint gaps and step differences, contour curve parameters, datum plane information, and spatial coordinates of the positioning holes. The datum plane information is designed and defined in 3D space, serving as the spatial orientation of the assembly reference plane.

[0035] In S600, the preset precision range is the assembly precision range of the access panel and the access frame specified according to the model, that is, based on the allowable range of joint gap error and the allowable range of step difference error, a precision judgment system is established as the basis for subsequent precision evaluation. This precision judgment system is determined by quantitative means. During actual detection, multiple measurement points are selected along the joint, and the measured data of each point is compared with the allowable range. If the data of all measurement points are within the allowable range, it is determined that the assembly precision is qualified. As long as the data of any one measurement point exceeds the allowable range, it is determined as unqualified and compensation adjustment needs to be carried out. Those skilled in the art can set the precision range according to the specific model and assembly requirements of the access panel and the access frame. The assembly requirements can be set according to the design drawings, process specifications or industry standards of the access panel and the access frame, without specific limitations.

[0036] In the embodiment of the present application, the steps of inputting the measured gap value, the measured step difference value, the geometric feature parameters and the preset precision range into a pre-constructed compensation decision model to determine whether the measured gap value and the measured step difference value meet the precision requirements and outputting a compensation scheme when the precision requirements are not met are as follows. The specific judgment process is as follows: See Figure 2 As shown, based on the compensated decision model that has been trained and verified, first, the measured gap value, the measured step difference value, the geometric feature parameters and the preset precision range of the joint area of the current specimen are used as inputs, and the model determines whether both the gap and step difference indicators meet the precision requirements. If the model outputs a decision of "meeting the precision range", no fitting is required and it can directly enter the assembly link; if the model outputs a decision of "not meeting the precision range", including both exceeding the tolerance or any one exceeding the tolerance, further based on the difference between the above-mentioned measured value and the preset precision range (that is, the actual amount of exceeding the tolerance), the model outputs a specific compensation scheme, including the amount of profile grinding and the amount of gap filling compensation, to perform the corresponding compensation.

[0037] In the embodiment of the present application, the steps of constructing the compensation decision model include: The steps of constructing the compensation decision model include: obtaining the historical assembly data of access panels and access frames of multiple models. The historical assembly data includes the historical geometric feature parameters of the access panel, the historical geometric feature parameters of the access frame, the historical measured values of the gap and the step difference, and the corresponding historical compensation schemes; preprocessing and feature extraction are performed on the historical assembly data to construct a training data set; based on the training data set, a machine learning algorithm is used for model training to obtain the compensation decision model.

[0038] Specifically, historical assembly data is preprocessed, including outlier removal, missing value supplementation, and data standardization, to ensure data validity and consistency. Geometric features of the specimens (such as the contour dimensions and shape parameters of the assembly joint area) and key features such as assembly gaps and step differences are quantified and extracted to construct the feature dataset required for model training. A machine learning algorithm is selected, and the model is trained using the preprocessed and quantized dataset. The model's learning performance is enhanced by iteratively optimizing the model's hyperparameters. An independent validation dataset is used to validate and evaluate the trained model. By calculating the error between the predicted and actual results, it is determined whether the model can accurately output assembly decisions (grinding amount, gap compensation amount, etc.) that meet the accuracy requirements of gaps and step differences based on the structural component input information. After repeated training and validation, once the model's prediction accuracy reaches the preset evaluation standard, the trained machine learning decision model is obtained.

[0039] It should be noted that the final standardized geometric model dataset includes STL files for different models of caps and frames, standardized parameter tables for features such as contours, reference planes, and positioning holes corresponding to each model, and a unified set of key geometric parameters such as seam gaps and step differences. In addition, it includes auxiliary information such as model identification and modeling accuracy, thereby enabling the model's generalization ability.

[0040] In S700, corresponding compensation operations are performed based on the compensation scheme to coordinate the gaps and differences in the joint area. The compensation scheme includes the amount of contour grinding of the cap and the amount of gap filling compensation in the joint area between the cap and the frame. The specific compensation operations can be as follows: For example, contour grinding amount refers to the amount of material that needs to be removed from the edge of the cap through machining to meet the step difference requirements. Based on precise values ​​output from the model, it is typically converted into G-code executable by a CNC machine tool. This code guides the tool to perform precise, minute grinding at specific locations on the cap's contour, thereby reducing the height of the cap relative to the frame and ensuring a flush surface after assembly. Clearance compensation amount refers to the thickness or number of shims added between the cap and the frame to meet clearance requirements. By specifying the shim's installation position and inserting shims of a specific size, the fit clearance is adjusted to meet design standards.

[0041] In this embodiment of the application, after performing the corresponding compensation operation based on the compensation scheme, the method further includes: recalculating the adjusted gap measurement value and step difference measurement value to obtain the retest result; storing the three-dimensional point cloud data, geometric feature parameters, compensation scheme and retest result of this assembly process into the historical database for iterative optimization of the compensation decision model.

[0042] This application retains and feeds back the scanning data, feature data, compensation schemes, and retest results of each assembly to a historical database. New data is then used to iteratively optimize the decision-making model, improving its accuracy. Thus, by feeding back the complete data chain of each assembly to the historical database, the system can use continuously accumulating real production data to iteratively train the decision-making model, improving its precision and ultimately achieving automated and continuous improvement in assembly accuracy and efficiency.

[0043] This solution utilizes 3D scanning to collect complete data and machine learning to generate precise compensation schemes, eliminating reliance on manual experience and significantly improving the precision of step and gap accuracy in hatch assembly, as well as batch-to-batch and cross-batch consistency, meeting the high aerodynamic and stealth requirements of aircraft. Simultaneously, by leveraging machine learning to predict and automatically output compensation schemes, it greatly reduces the number of assembly adjustments, shortens the assembly cycle for a single set, and lowers part scrap rates and process changeover costs, achieving improved assembly efficiency and reduced manufacturing costs. Assembly data is transformed into reusable resources, shortening the process changeover cycle for different hatch models, and data feedback is reused to continuously optimize the model, forming a positive cycle of precision and efficiency. Replacing traditional manual-driven models with data-driven approaches drives the transformation of aerospace manufacturing towards precision and intelligence.

[0044] Based on the same inventive concept, this application also provides a data-driven device for coordinating the gap step difference between an aircraft hatch and its frame. The solution provided by this device is similar to the solution described in the above method. Therefore, the specific limitations of one or more embodiments of the data-driven device for coordinating the gap step difference between an aircraft hatch and its frame provided below can be found in the limitations of the data-driven method for coordinating the gap step difference between an aircraft hatch and its frame described above, and will not be repeated here.

[0045] This application also provides a data-driven gap step coordination device for the joint between the aircraft hatch and the frame, comprising:

[0046] The data acquisition module acquires 3D point cloud data of the lid and frame; The contour extraction and registration module extracts the outer contours of the lid and the frame based on 3D point cloud data, and registers the outer contours of the lid and the frame. The parameter calculation module calculates the key geometric parameters of the cap and frame in the joint area based on the registered spatial relationship. The key geometric parameters are the measured values ​​of the gap and the step difference. The model library construction module pre-builds a geometric feature model library, which includes multiple pre-stored geometric feature models. Each pre-stored geometric feature model is uniquely associated with a corresponding model of cap or frame. The geometric feature parameter acquisition module searches the geometric feature model library based on the model information of the lid and the model information of the frame. If a pre-stored geometric feature model corresponding to the lid and the frame is found, the corresponding pre-stored geometric feature model is directly called to obtain the geometric feature parameters of the lid and the frame respectively. If no matching model is found, a new geometric feature model of the lid and the frame is created and stored in the geometric feature model library to obtain the geometric feature parameters of the lid and the frame respectively. The compensation decision module inputs the measured values ​​of the gap, the measured values ​​of the step difference, the geometric feature parameters of the lid, the geometric feature parameters of the frame, and the preset accuracy range into the pre-built compensation decision model, determines whether the measured values ​​of the gap and the measured values ​​of the step difference meet the accuracy requirements, and outputs a compensation scheme when the accuracy requirements are not met. The compensation execution module performs corresponding compensation operations based on the compensation scheme to coordinate the gaps and differences in the joint area.

[0047] In one exemplary embodiment, a computer device is provided, which may be a server or a terminal, and its internal structure diagram may be as follows. Figure 4 As shown, this computer device includes a processor, memory, input / output (I / O) interfaces, and a communication interface. The processor, memory, and I / O interfaces are connected via a system bus, and the communication interface is also connected to the system bus via the I / O interfaces. The processor provides computational and control capabilities. The memory includes non-volatile storage media and internal memory. The non-volatile storage media stores the operating system, computer programs, and databases. The internal memory provides the environment for the operating system and computer programs stored in the non-volatile storage media to run. The I / O interfaces are used for exchanging information between the processor and external devices. The communication interface is used for communicating with external terminals via a network connection.

[0048] Those skilled in the art will understand that Figure 4 The structure shown is merely a block diagram of a portion of the structure related to the present application and does not constitute a limitation on the computer device to which the present application is applied. Specific computer devices may include more or fewer components than those shown in the figure, or combine certain components, or have different component arrangements.

[0049] In one exemplary embodiment, a computer device is also provided, including a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to implement the steps in the above-described method embodiments.

[0050] In one exemplary embodiment, a computer-readable storage medium is provided storing a computer program that, when executed by a processor, implements the steps in the above-described method embodiments.

[0051] In one exemplary embodiment, a computer program product is provided, including a computer program that, when executed by a processor, implements the steps in the above-described method embodiments.

[0052] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, data stored, data displayed, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties, and the collection, use and processing of the relevant data must comply with relevant regulations.

[0053] Those skilled in the art will understand that all or part of the processes in the above embodiments can be implemented by a computer program instructing related hardware. The computer program can be stored in a non-volatile computer-readable storage medium. When executed, the computer program can include the processes of the embodiments described above. Any references to memory, databases, or other media used in the embodiments provided in this application can include at least one of non-volatile and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetic random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. By way of illustration and not limitation, RAM can take many forms, such as Static Random Access Memory (SRAM) or Dynamic Random Access Memory (DRAM).

[0054] The databases involved in the embodiments provided in this application may include at least one type of relational database and non-relational database. Non-relational databases may include, but are not limited to, blockchain-based distributed databases. The processors involved in the embodiments provided in this application may be general-purpose processors, central processing units, graphics processing units, digital signal processors, programmable logic devices, quantum computing-based data processing logic devices, etc., and are not limited to these.

[0055] The technical features of the above embodiments can be combined in any way. For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0056] This document uses specific examples to illustrate the principles and implementation methods of this application. The descriptions of the above embodiments are only for the purpose of helping to understand the methods and core ideas of this application. Furthermore, those skilled in the art will recognize that, based on the ideas of this application, there will be changes in the specific implementation methods and application scope. Therefore, the content of this specification should not be construed as a limitation of this application.

Claims

1. A data-driven based method for coordinating gap step-off of an aircraft access door to access frame joint, the method comprising: include: Obtain the three-dimensional point cloud data of the cap and the frame; Based on the three-dimensional point cloud data, the outer contours of the lid and the frame are extracted respectively, so as to register the outer contours of the lid and the frame. Based on the spatial relationship after registration, the key geometric parameters of the cover and the frame in the joint area are calculated. The key geometric parameters are the measured values ​​of the gap and the step difference. A geometric feature model library is pre-built, which includes multiple pre-stored geometric feature models, and each pre-stored geometric feature model is uniquely associated with a corresponding model of the cap or the frame; Based on the model information of the lid and the model information of the frame, a search is performed in the geometric feature model library. If a pre-stored geometric feature model corresponding to the lid and the frame is found, the corresponding pre-stored geometric feature model is directly called to obtain the geometric feature parameters of the lid and the frame respectively. If no model matching the lid and the frame is found, a new geometric feature model of the lid and the frame is created and stored in the geometric feature model library to obtain the geometric feature parameters of the lid and the frame respectively. The measured values ​​of the gap, the measured values ​​of the step difference, the geometric feature parameters of the cover, the geometric feature parameters of the frame, and the preset accuracy range are input into a pre-built compensation decision model to determine whether the measured values ​​of the gap and the measured values ​​of the step difference meet the accuracy requirements, and output a compensation scheme when the accuracy requirements are not met. Based on the compensation scheme, corresponding compensation operations are performed to coordinate the gaps and steps in the joint area.

2. The data-driven based method for coordinating gap step-off of an aircraft access door to frame joint according to claim 1, wherein, The steps for constructing the compensation decision model specifically include: Historical assembly data of the caps and frames of various models are obtained. The historical assembly data includes historical geometric feature parameters of the caps, historical geometric feature parameters of the frames, historical measured values ​​of gaps and step differences, and corresponding historical compensation schemes. The historical assembly data is preprocessed and features are extracted to construct a training dataset; Based on the training dataset, a machine learning algorithm is used to train the model to obtain the compensation decision model.

3. The data driven based method for coordinating gap step-off of aircraft access door and frame joint of claim 1, wherein, The step of creating geometric feature models of the cap and the frame to obtain the geometric feature parameters of the cap and the frame respectively includes: Based on the registered 3D point cloud data and the key geometric parameters, the geometric feature model of the lid and the frame is constructed by continuous surface fitting through reverse engineering and 3D modeling techniques. Geometric feature parameters of the cap and the frame are extracted from their geometric feature models, including external dimensions, surface curvature, contour curve parameters, reference surface information, and positioning hole coordinates.

4. The data-driven method for coordinating the gap step between the aircraft hatch and the hatch frame according to claim 1, characterized in that, The compensation scheme includes the amount of contour grinding of the cap and the amount of gap filling compensation in the joint area between the cap and the frame.

5. The data-driven method for coordinating the gap step between the aircraft hatch and the hatch frame according to claim 1, characterized in that, The step of extracting the outer contours of the lid and the frame based on the three-dimensional point cloud data specifically includes: An algorithm based on point cloud contour boundary search based on normal vector mutation and neighborhood density difference is used to extract the outer contours of the lid and the frame from the three-dimensional point cloud data.

6. The data-driven method for coordinating the gap step between the aircraft hatch and the hatch frame according to claim 1, characterized in that, After performing the corresponding compensation operation based on the compensation scheme, the process further includes: The adjusted measured values ​​of the gap and the step difference are recalculated to obtain the retest results; The three-dimensional point cloud data, geometric feature parameters, compensation scheme, and retest results obtained during this assembly process are stored in a historical database for iterative optimization of the compensation decision model.

7. A data-driven device for coordinating the gap step between an aircraft hatch and its frame, characterized in that, include: The data acquisition module acquires three-dimensional point cloud data of the lid and the frame. The contour extraction and registration module extracts the outer contours of the lid and the frame based on the three-dimensional point cloud data, and registers the outer contours of the lid and the frame. The parameter calculation module calculates the key geometric parameters of the cover and the frame in the joint area based on the registered spatial relationship. The key geometric parameters are the measured values ​​of the gap and the step difference. The model library construction module pre-builds a geometric feature model library, which includes multiple pre-stored geometric feature models. Each pre-stored geometric feature model is uniquely associated with a corresponding model of the cap or the frame. The geometric feature parameter acquisition module searches the geometric feature model library based on the model information of the lid and the model information of the frame. If a pre-stored geometric feature model corresponding to the lid and the frame is found, the corresponding pre-stored geometric feature model is directly called to obtain the geometric feature parameters of the lid and the frame respectively. If no model matching the lid and the frame is found, a new geometric feature model of the lid and the frame is created and stored in the geometric feature model library to obtain the geometric feature parameters of the lid and the frame respectively. The compensation decision module inputs the measured values ​​of the gap, the measured values ​​of the step difference, the geometric feature parameters of the cover, the geometric feature parameters of the frame, and a preset accuracy range into a pre-built compensation decision model, determines whether the measured values ​​of the gap and the measured values ​​of the step difference meet the accuracy requirements, and outputs a compensation scheme when the accuracy requirements are not met. The compensation execution module performs corresponding compensation operations based on the compensation scheme to coordinate the gaps and steps in the joint area.

8. A computer device, comprising: A memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that the processor executes the computer program to implement the data-driven method for coordinating the gap step of the aircraft hatch and frame joint according to any one of claims 1-6.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that, When executed by a processor, the computer program implements the data-driven method for coordinating the gap step of the aircraft hatch and frame joint as described in any one of claims 1-6.

10. A computer program product, comprising a computer program, characterized in that, When executed by a processor, the computer program implements the data-driven method for coordinating the gap step of the aircraft hatch and frame joint as described in any one of claims 1-6.

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