Multi-source fusion structure deformation measurement and calibration method and related device

Abstract

The invention discloses a multi-source fusion structure deformation measurement and calibration method and a related device, and relates to the field of structure deformation measurement and calibration, and the method comprises the steps: obtaining first deformation information after deformation collected by a fiber grating sensor disposed on a to-be-measured piece; acquiring initial state point cloud information of the to-be-tested piece scanned by the binocular vision system before deformation; after the initial state point cloud information and the first deformation information are input into a Gaussian process regression model, non-fiber grating monitoring point information in the initial state point cloud is input into the Gaussian process regression model, and deformation information corresponding to non-fiber grating monitoring points is obtained; and inputting multi-source fusion deformation information formed by the first deformation information and the deformation information corresponding to the non-fiber grating monitoring points into the trained NDT-CNN-SA network model to obtain calibrated deformation information. According to the invention, calibration can be carried out based on multi-source fusion structure deformation measurement data, and the accuracy of the structure deformation data is improved.

Description

Technical Field

[0001] The present application relates to the field of structural deformation measurement and calibration, and in particular to a multi-source fusion structural deformation measurement and calibration method and related devices. Background Art

[0002] In the context of the continuous digitalization of aerospace products, the accuracy requirements for real-time data monitoring of structural deformation are becoming higher and higher. High-precision structural deformation data can not only ensure the realization of the basic performance of aerospace products, but also improve assembly accuracy, optimize assembly processes, and build more accurate digital twins. Typical aerospace vehicles such as satellites and drones have the characteristics of long working hours, high working intensity, and high running speed. During the long-term operation of the product, the structure is prone to deformation. Long-term structural deformation may cause structural fatigue damage or fracture, affecting the work efficiency and accuracy of the product, reducing the safety and stability of the product, and even causing irreversible consequences.

[0003] At present, laser tracking, fiber Bragg grating, and visual measurement systems are generally selected for structural deformation measurement. The visual measurement system uses a camera to obtain image information of large components, and derives the position of the measured image in the visual measurement coordinate system through parallax and model parameters, so as to visualize the high-density point cloud to represent the surface information of the component. However, the deformation of the measured component product can only be obtained through repeated scanning, and the deformation information of the measured component can be obtained after data processing. The deformation of the measured component surface cannot be observed in real time in terms of time length and spatial breadth; fiber Bragg grating is a direct measurement. The sensor is pasted on the surface of the component to achieve deformation surface reconstruction. The deformation amount at the fiber Bragg grating pasting point is the most accurate, but its pasting position does not have accurate position information in the model, and the points calculated by interpolation between the micro-element arc segments of the deformation curve are different from the actual deformation of the component; the laser tracking system has a unique advantage in the unification of the measurement field coordinates due to its measurement point accuracy. Given its measurement principle, each measurement data obtains a spatial point coordinate, so the measurement efficiency of large and complex surface features is low and the data obtained is not comprehensive enough.

[0004] On this basis, in order to solve the low accuracy of fiber optic monitoring point cloud data, structural deformation measurement technology based on multi-source fusion has also become a research hotspot. This method can obtain more accurate and complete point cloud data by adding high-precision optical equipment, but it is also subject to the error between the fiber optic pasting position and the theoretical position and the accuracy of the reconstruction algorithm or the added prediction algorithm. This method can obtain the most accurate real-time structural deformation data at present, but the accuracy of the data obtained is still not comparable to that of high-precision optical equipment. Summary of the invention

[0005] The purpose of this application is to provide a multi-source fusion structural deformation measurement calibration method and related devices, which can perform calibration based on multi-source fusion structural deformation measurement data to improve the accuracy of structural deformation data.

[0006] To achieve the above objectives, this application provides the following solutions:

[0007] In a first aspect, the present application provides a multi-source fusion structural deformation measurement calibration method, comprising:

[0008] Construct a global coordinate system;

[0009] Acquiring first deformation information collected by a fiber grating sensor arranged on the test piece after deformation; the fiber grating sensor is arranged on the test piece based on the global coordinate system;

[0010] Acquire the initial state point cloud information of the test piece before deformation scanned by the binocular vision system; the binocular vision system is arranged based on the global coordinate system;

[0011] After the initial state point cloud information and the first deformation information are input into the Gaussian process regression model, the non-fiber Bragg grating monitoring point information in the initial state point cloud is input into the Gaussian process regression model to obtain the deformation information corresponding to the non-fiber Bragg grating monitoring point; the first deformation information and the deformation information corresponding to the non-fiber Bragg grating monitoring point constitute multi-source fusion deformation information;

[0012] The multi-source fusion deformation information is input into the trained NDT-CNN-SA network model to obtain the calibrated deformation information; the NDT-CNN-SA network model body includes a NDT module, a CNN model, a self-attention mechanism module and a perceptron connected in series.

[0013] In a second aspect, the present application provides a multi-source fusion structural deformation measurement calibration method device, comprising:

[0014] Coordinate system construction module, used to construct the global coordinate system;

[0015] A first data acquisition module, used for acquiring first deformation information collected by a fiber grating sensor arranged on the test piece after deformation; the fiber grating sensor is arranged on the test piece based on the global coordinate system;

[0016] A second data acquisition module is used to obtain the initial state point cloud information of the test piece before deformation scanned by the binocular vision system; the binocular vision system is arranged based on the global coordinate system;

[0017] A Gaussian regression analysis module, for inputting the initial state point cloud information and the first deformation information into a Gaussian process regression model, and then inputting the non-fiber Bragg grating monitoring point information in the initial state point cloud into the Gaussian process regression model to obtain deformation information corresponding to the non-fiber Bragg grating monitoring point; the first deformation information and the deformation information corresponding to the non-fiber Bragg grating monitoring point constitute multi-source fusion deformation information;

[0018] The deformation calibration module is used to input the multi-source fusion deformation information into the trained NDT-CNN-SA network model to obtain the calibrated deformation information; the NDT-CNN-SA network model body includes a NDT module, a CNN model, a self-attention mechanism module and a perceptron connected in series.

[0019] In a third aspect, the present application 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-mentioned multi-source fusion structural deformation measurement calibration method.

[0020] In a fourth aspect, the present application provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the above-mentioned multi-source fusion structural deformation measurement calibration method.

[0021] In a fifth aspect, the present application provides a computer program product, including a computer program, which implements the above-mentioned multi-source fusion structural deformation measurement calibration method when executed by a processor.

[0022] According to the specific embodiments provided in this application, this application discloses the following technical effects:

[0023] The present application provides a multi-source fusion structural deformation measurement calibration method and related devices. In order to improve the accuracy of multi-source fusion real-time structural deformation data and fill the technical gap of multi-source fusion deformation measurement without a calibration method, a neural network framework based on NDT-CNN-SA is proposed. The multi-source fusion deformation measurement structure is calibrated through high-precision point cloud data obtained by a binocular scanner, thereby improving data fidelity and achieving higher-precision multi-source fusion deformation measurement monitoring. BRIEF DESCRIPTION OF THE DRAWINGS

[0024] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings required for use in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0025] Figure 1This is an application environment diagram of a multi-source fusion structural deformation measurement calibration method in one embodiment of the present application;

[0026] Figure 2 A schematic diagram of a flow chart of a multi-source fusion structural deformation measurement calibration method provided in one embodiment of the present application;

[0027] Figure 3 A schematic diagram of a technical route of a multi-source fusion structural deformation measurement calibration method provided in one embodiment of the present application;

[0028] Figure 4 A schematic diagram of the structure of the NDT-CNN-SA network model provided in one embodiment of the present application;

[0029] Figure 5 A schematic diagram of the structure of an NDT module provided in one embodiment of the present application;

[0030] Figure 6 A schematic diagram of the structure of a CNN module provided in one embodiment of the present application;

[0031] Figure 7 A schematic diagram of the structure of a self-attention mechanism module provided in one embodiment of the present application;

[0032] Figure 8 A schematic diagram of the structure of a sensor provided in one embodiment of the present application;

[0033] Fig. 9 A schematic diagram of functional modules of a multi-source fusion structural deformation measurement and calibration device provided in another embodiment of the present application.

[0034] Fig.10 A schematic diagram of the structure of a computer device provided in one embodiment of the present application. DETAILED DESCRIPTION

[0035] The following will be combined with the drawings in the embodiments of the present application to clearly and completely describe the technical solutions in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of this application.

[0036] In order to make the above-mentioned objects, features and advantages of the present application more obvious and easy to understand, the present application is further described in detail below with reference to the accompanying drawings and specific implementation methods.

[0037] The multi-source fusion structural deformation measurement calibration method provided in the embodiment of the present application is specifically a multi-source fusion structural deformation measurement calibration method based on NDT-CNN-SA network, which can be applied to Figure 1In the application environment shown. Among them, the terminal 102 communicates with the server 104 through the network. The data storage system can store the data that the server 104 needs to process. The data storage system can be set up separately, integrated on the server 104, or placed on the cloud or other servers. The terminal 102 can send the global coordinate system information, the first deformation information after deformation collected by the fiber grating sensor arranged on the test piece, and the initial state point cloud information of the test piece before deformation scanned by the binocular vision system to the server 104. After the server 104 receives the relevant data, the server 104 inputs the initial state point cloud information and the first deformation information into the Gaussian process regression model, and then inputs the non-fiber grating monitoring point information in the initial state point cloud into the Gaussian process regression model to obtain the deformation information corresponding to the non-fiber grating monitoring point; the first deformation information and the deformation information corresponding to the non-fiber grating monitoring point constitute the multi-source fusion deformation information; the multi-source fusion deformation information is input into the trained NDT-CNN-SA network model to obtain the calibrated deformation information. The server 104 can feed back the obtained calibrated deformation information to the terminal 102. In addition, in some embodiments, the multi-source fusion structural deformation measurement calibration method can also be implemented by the server 104 or the terminal 102 alone, such as the terminal 102 can directly perform deformation information calibration processing on the global coordinate system information, the first deformation information after deformation collected by the fiber Bragg grating sensor arranged on the test piece, and the initial state point cloud information of the test piece before deformation scanned by the binocular vision system, or the server 104 can obtain the global coordinate system information, the first deformation information after deformation collected by the fiber Bragg grating sensor arranged on the test piece, and the initial state point cloud information of the test piece before deformation scanned by the binocular vision system from the data storage system, and perform deformation information calibration processing.

[0038] The terminal 102 may be, but is not limited to, various desktop computers, laptop computers, smart phones, tablet computers, IoT devices, and portable wearable devices. The IoT devices may be smart speakers, smart TVs, smart air conditioners, smart vehicle-mounted devices, etc. The portable wearable devices may be smart watches, smart bracelets, head-mounted devices, etc. The server 104 may be implemented as an independent server or a server cluster consisting of multiple servers, or may be a cloud server.

[0039] In an exemplary embodiment, Figure 2 and Figure 3 As shown, a multi-source fusion structure deformation measurement calibration method is provided. The method is executed by a computer device, and can be executed by a computer device such as a terminal or a server alone, or can be executed by a terminal and a server together. In the embodiment of the present application, the method is applied to Figure 1 The server 104 in the example is used for explanation, and the steps include the following steps 201 to 205.

[0040] in:

[0041] Step 201, constructing a global coordinate system.

[0042] In order to achieve synchronous collaboration between various digital devices and tooling, it is necessary to establish accurate relative positional relationships between various digital devices and tooling values ​​through a laser tracker. If the measured deformation area of ​​the measured component is relatively small, it is also possible not to use a laser tracker to construct a global coordinate system.

[0043] Step 202, obtaining first deformation information collected by a fiber grating sensor arranged on the test piece after deformation; the fiber grating sensor is arranged on the test piece based on the global coordinate system.

[0044] Based on the global coordinate system, the optical fiber position is pasted according to the theoretical design plan. At this time, the coordinates of the initial state optical fiber monitoring position point are known quantities.

[0045] During the monitoring process, the optical fiber sensor uses direct contact measurement to obtain real-time deformation information of the surface of the measured object. At this time, the deformation information obtained by the optical fiber sensor is expressed as the change in wavelength, and then converted into coordinate form.

[0046] Step 203, obtaining the initial state point cloud information of the test piece before deformation scanned by the binocular vision system; the binocular vision system is arranged based on the global coordinate system.

[0047] Based on the global coordinate system, binocular scanning point cloud is used to perform visual scanning on the initial state product to obtain the initial state point cloud information of the initial state test piece.

[0048] Step 204, after the initial state point cloud information and the first deformation information are input into the Gaussian process regression model, the non-fiber Bragg grating monitoring point information in the initial state point cloud is input into the Gaussian process regression model to obtain the deformation information corresponding to the non-fiber Bragg grating monitoring point; the first deformation information and the deformation information corresponding to the non-fiber Bragg grating monitoring point constitute multi-source fusion deformation information.

[0049] Based on the unified data format of the data source, Gaussian process regression analysis is used to realize the fusion of low-precision deformation data of fiber Bragg grating monitoring and initial state point cloud data of binocular scanning. The coordinates of the fiber Bragg grating monitoring points before and after deformation are used to perform data Gaussian process regression training, and the hyperparameters and Gaussian process model are determined as prior inputs. Then, the undeformed points (points without fiber Bragg grating sensors) in the zero load state (initial state before deformation) of the binocular scanning are fused and predicted, and different kernel functions are selected to obtain the deformation data of the points without fiber Bragg grating sensors.

[0050] Step 205, input the multi-source fusion deformation information into the trained NDT-CNN-SA network model to obtain calibrated deformation information; Figure 4 As shown, the NDT-CNN-SA network model body includes an NDT module, a CNN model, a self-attention mechanism module and a perceptron connected in series.

[0051] The multi-source fusion deformation information of Gaussian process regression is input into the trained NDT-CNN-SA network model. The model can output real-time calibration to an accuracy close to that of the binocular vision scanning measurement point cloud, thereby achieving multi-source fusion deformation measurement calibration.

[0052] Among them, the NDT module divides the multi-source fused deformable point cloud into grids of specified size according to the occupied space distribution, and calculates the multidimensional normal distribution parameters of each grid. The normal distribution N(u, C) of each NDT unit consists of the mean vector and the covariance matrix C, which is defined as:

[0053]

[0054] where x k = 1, ..., n is a 3D point in each cell, that is, each point in the multi-source fusion deformation point cloud. Figure 5 What is shown is the visualization of the NDT principle, which meshes the multi-source fused deformed point cloud and obtains the mean matrix and covariance matrix of the points in the grid.

[0055] like Figure 6 As shown in the figure, the CNN module is a feature extraction module. The mean vector and covariance matrix data obtained by the NDT module are input into the module. The module uses three one-dimensional convolutional layers to extract features from the point cloud. In order to avoid offset and deviation, a batch normalization layer BN is added to standardize the output of the one-dimensional convolutional layer so that the output has a similar distribution. Finally, an activation function is used for nonlinear changes, and the standardized results are processed to retain positive values ​​and enhance the nonlinear expression ability of the network. Figure 6 As shown, the CNN model includes a plurality of CNN units connected in series; each CNN unit includes a first convolutional layer, a first batch normalization layer, and a first activation layer connected in series in sequence.

[0056] The Self-Attention Mechanism module is a mechanism used in deep learning to process sequence data. Its core idea is to generate a weight for each position by calculating the degree of association between each position and other positions, so as to adaptively adjust the importance of each input feature, thereby improving the expressiveness of the entire network. Figure 7 A structural diagram of the self-attention mechanism module is shown. Figure 7 Medium, Fp Represents the feature vector output by the CNN feature extraction module; Indicates F p After the transposition change of the specified dimension, in the process of forward propagation, the attention scores used to indicate the importance of each sequence are obtained by Calculate and obtain weight init and bias init Represents the initialized weights and bias terms. Figure 7 In , weight and bias without subscripts represent weight and bias. Apply Softmax to normalize the attention scores to obtain the attention weights, which put the elements of the feature vector in the range [0,1] in the specified dimension and sum them to 1. The calculation formula is: Matmul is matrix multiplication.

[0057] Multilayer Perceptron (MLP) is used to apply a series of linear and nonlinear transformations on the input features, map the output of this layer to the desired output size, and learn more complex and abstract representations in the feature space of the input data to obtain better prediction performance. Figure 8 As shown, the perceptron includes a plurality of perceptual units connected in series, a third convolutional layer and a concatenation layer; each perceptual unit includes a second convolutional layer, a second batch normalization layer and a second activation layer connected in series in sequence.

[0058] Implementing the above steps 201 to 205 provides a calibration method for the multi-source fusion structural deformation monitoring system, filling the gap of no calibration method for multi-source fusion structural deformation measurement. Compared with the previous use of a multi-source fusion system alone, the calibration method compares the deformation point cloud after calibration with the actual scanned deformation point cloud. The standard deviation of the profile under different loads is below 0.1, and the percentage of point clouds with deviation values ​​within the confidence interval of ±0.1 can be 97%. The accuracy after calibration is higher than the multi-source fusion measurement accuracy.

[0059] In another exemplary embodiment of the present application, the present invention adds an NDT-CNN-SA network model after the Gaussian process regression model, and the network is a single-input, single-output calibration network. The data input into the constructed network includes a large amount of multi-source fused deformation sample information with different deformation amounts and a deformation point cloud monitored by a binocular vision scanning device after deformation (the deformation monitoring accuracy is very high). The two are at the same time, and the accuracy of the multi-source fused deformation sample information is relatively low. It is used as the point cloud to be calibrated, and the point cloud information measured by the binocular vision scanning is used as a high-precision label point cloud. The network uses these two types of data as input and output data to train the constructed neural network. Therefore, if Figure 3As shown, before executing the step of "inputting the multi-source fusion deformation information into the trained NDT-CNN-SA network model", it includes:

[0060] (1) Obtain multi-source fusion deformation sample information and the corresponding scanning deformation sample information after the deformation of the measured component obtained by scanning the binocular vision system.

[0061] (2) Taking the multi-source fused deformation sample information as input and the scanned deformation sample information as label, the NDT-CNN-SA network model is trained.

[0062] (3) When the model loss error converges or the number of training iterations reaches the preset number of iterations, the trained NDT-CNN-SA network model is obtained.

[0063] The present application also provides an application scenario, which applies the above-mentioned multi-source fusion structural deformation measurement calibration method. Specifically: The multi-source fusion structural deformation measurement calibration method provided in this embodiment can be applied in the digital assembly process. As the state of the assembly process changes, the product will deform and need to be monitored. In order to improve the accuracy of the monitoring, the monitoring is calibrated, especially for large composite material components. The present invention is mainly used in scenarios where composite material components are easily deformed during assembly.

[0064] Based on the same inventive concept, the embodiment of the present application also provides a multi-source fusion structural deformation measurement calibration device for implementing the multi-source fusion structural deformation measurement calibration method involved above. The implementation scheme for solving the problem provided by the device is similar to the implementation scheme recorded in the above method, so the specific limitations in one or more embodiments of the multi-source fusion structural deformation measurement calibration device provided below can refer to the limitations of the multi-source fusion structural deformation measurement calibration method above, and will not be repeated here.

[0065] In an exemplary embodiment, Fig. 9 As shown, a multi-source fusion structural deformation measurement and calibration device is provided, comprising:

[0066] The coordinate system construction module M1 is used to construct the global coordinate system.

[0067] The first data acquisition module M2 is used to acquire first deformation information collected by a fiber Bragg grating sensor arranged on the test piece after deformation; the fiber Bragg grating sensor is arranged on the test piece based on the global coordinate system.

[0068] The second data acquisition module M3 is used to obtain the initial state point cloud information of the test piece before deformation scanned by the binocular vision system; the binocular vision system is arranged based on the global coordinate system.

[0069] The Gaussian regression analysis module M4 is used to input the initial state point cloud information and the first deformation information into the Gaussian process regression model, and then input the non-fiber Bragg grating monitoring point information in the initial state point cloud into the Gaussian process regression model to obtain the deformation information corresponding to the non-fiber Bragg grating monitoring point; the first deformation information and the deformation information corresponding to the non-fiber Bragg grating monitoring point constitute multi-source fusion deformation information.

[0070] The deformation calibration module M5 is used to input the multi-source fusion deformation information into the trained NDT-CNN-SA network model to obtain the calibrated deformation information; the NDT-CNN-SA network model body includes a NDT module, a CNN model, a self-attention mechanism module and a perceptron connected in series.

[0071] In an exemplary embodiment, a computer device is provided. The computer device may be a server or a terminal. The internal structure diagram thereof may be as follows: Fig.10 As shown. The computer device includes a processor, a memory, an input / output interface (Input / Output, referred to as I / O) and a communication interface. The processor, the memory and the input / output interface are connected through a system bus, and the communication interface is connected to the system bus through the input / output interface. The processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program and a database. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The database of the computer device is used to store multi-source fusion structure deformation measurement calibration data. The input / output interface of the computer device is used to exchange information between the processor and an external device. The communication interface of the computer device is used to communicate with an external terminal through a network connection. When the computer program is executed by the processor, a multi-source fusion structure deformation measurement calibration method is implemented.

[0072] Those skilled in the art will understand that Fig.10 The structure shown in the figure is only a block diagram of a part of the structure related to the solution of the present application, and does not constitute a limitation on the computer device to which the solution of the present application is applied. The specific computer device may include more or fewer components than those shown in the figure, or combine certain components, or have a different arrangement of components.

[0073] In an exemplary embodiment, a computer device is further provided, including a memory and a processor, wherein a computer program is stored in the memory, and the processor implements the steps in the above-mentioned method embodiments when executing the computer program.

[0074] In an exemplary embodiment, a computer-readable storage medium is provided, storing a computer program, and when the computer program is executed by a processor, the steps in the above method embodiments are implemented.

[0075] In an exemplary embodiment, a computer program product is provided, including a computer program, and when the computer program is executed by a processor, the steps in the above method embodiments are implemented.

[0076] 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, stored data, displayed data, 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 relevant data must comply with relevant regulations.

[0077] Those of ordinary skill in the art can understand that all or part of the processes in the above-mentioned embodiment methods can be completed by instructing the relevant hardware through a computer program, and the computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to the memory, database or other medium used in the embodiments provided in the present 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 may be in various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM).

[0078] The database involved in each embodiment provided in this application may include at least one of a relational database and a non-relational database. The non-relational database may include a distributed database based on blockchain, etc., but is not limited thereto. The processor involved in each embodiment provided in this application may be a general-purpose processor, a central processing unit, a graphics processor, a digital signal processor, a programmable logic device, a data processing logic device based on quantum computing, etc., but is not limited thereto.

[0079] The technical features of the above embodiments may be combined arbitrarily. To make the description concise, 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.

[0080] This article uses specific examples to illustrate the principles and implementation methods of this application. The description of the above embodiments is only used to help understand the method and core ideas of this application. At the same time, for those skilled in the art, according to the ideas of this application, there will be changes in the specific implementation methods and application scope. In summary, the content of this specification should not be understood as limiting this application.

Claims

1. A multi-source fusion structural deformation measurement calibration method, characterized in that: include: Construct a global coordinate system; Acquiring first deformation information collected by a fiber grating sensor arranged on the test piece after deformation; The fiber grating sensor is arranged on the test piece based on the global coordinate system; Obtaining the initial state point cloud information of the test piece before deformation scanned by the binocular vision system; The binocular vision system is arranged based on the global coordinate system; After inputting the initial state point cloud information and the first deformation information into the Gaussian process regression model, inputting the non-fiber Bragg grating monitoring point information in the initial state point cloud into the Gaussian process regression model to obtain the deformation information corresponding to the non-fiber Bragg grating monitoring point; The first deformation information and the deformation information corresponding to the non-fiber grating monitoring point constitute multi-source fusion deformation information; The multi-source fusion deformation information is input into the trained NDT-CNN-SA network model to obtain the calibrated deformation information; the NDT-CNN-SA network model body includes a NDT module, a CNN model, a self-attention mechanism module and a perceptron connected in series.

2. The multi-source fusion structural deformation measurement calibration method according to claim 1 is characterized in that: Before executing the step of "inputting the multi-source fusion deformation information into the trained NDT-CNN-SA network model", it includes: Acquire multi-source fusion deformation sample information and corresponding scanned deformation sample information after deformation of the measured component scanned by the binocular vision system; Taking multi-source fused deformation sample information as input and scanned deformation sample information as label, the NDT-CNN-SA network model is trained; When the model loss error converges or the number of training iterations reaches the preset number of iterations, the trained NDT-CNN-SA network model is obtained.

3. The multi-source fusion structural deformation measurement calibration method according to claim 1, characterized in that: The output of the NDT module is the mean vector and the covariance matrix; wherein the expressions of the mean vector and the covariance matrix are: Where, μ, C represent the mean vector and covariance matrix of an NDT unit respectively; x k =1,…,n is the three-dimensional point in each cell.

4. The multi-source fusion structural deformation measurement calibration method according to claim 1, characterized in that: The CNN model includes a plurality of CNN units connected in series; each CNN unit includes a first convolutional layer, a first batch normalization layer and a first activation layer connected in series in sequence.

5. The multi-source fusion structural deformation measurement calibration method according to claim 1, characterized in that: The perceptron includes a plurality of perceptual units, a third convolutional layer and a concatenation layer connected in series; each perceptual unit includes a second convolutional layer, a second batch normalization layer and a second activation layer connected in series in sequence.

6. A multi-source fusion structural deformation measurement and calibration device, characterized in that: include: Coordinate system construction module, used to construct the global coordinate system; A first data acquisition module is used to acquire first deformation information collected by a fiber grating sensor arranged on the test piece after deformation; The fiber grating sensor is arranged on the test piece based on the global coordinate system; The second data acquisition module is used to obtain the initial state point cloud information of the test piece before deformation scanned by the binocular vision system; The binocular vision system is arranged based on the global coordinate system; A Gaussian regression analysis module, for inputting the initial state point cloud information and the first deformation information into a Gaussian process regression model, and then inputting the non-fiber Bragg grating monitoring point information in the initial state point cloud into the Gaussian process regression model to obtain deformation information corresponding to the non-fiber Bragg grating monitoring point; The first deformation information and the deformation information corresponding to the non-fiber grating monitoring point constitute multi-source fusion deformation information; The deformation calibration module is used to input the multi-source fusion deformation information into the trained NDT-CNN-SA network model to obtain the calibrated deformation information; the NDT-CNN-SA network model body includes a NDT module, a CNN model, a self-attention mechanism module and a perceptron connected in series.

7. A computer device comprising: 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 multi-source fusion structural deformation measurement calibration method according to any one of claims 1 to 5.

8. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the multi-source fusion structural deformation measurement calibration method according to any one of claims 1 to 5 is implemented.

9. A computer program product, comprising a computer program, characterized in that When the computer program is executed by a processor, the multi-source fusion structural deformation measurement calibration method according to any one of claims 1 to 5 is implemented.

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