Neutron collimator flexible diaphragm measurement method, program product and electronic equipment

Abstract

The invention provides a neutron collimator flexible diaphragm measurement method, a program product and electronic equipment. The method comprises the following steps: acquiring a multi-angle image of a flexible diaphragm of a neutron collimator; mapping the multi-angle image to a three-dimensional space coordinate system according to the pixel information of the multi-angle image and camera parameters, and obtaining a multi-dimensional vector corresponding to a pixel point in the multi-angle image; the multi-dimensional vector is used for representing a spatial position and a light direction corresponding to the pixel point; generating a three-dimensional point cloud model by using the multi-dimensional vector and the trained neural radiation field model; according to the three-dimensional point cloud model, generating a digital twin model of the flexible diaphragm; the digital twin model is an information model based on flexible diaphragm virtual mapping; and measuring the flexible diaphragm through the digital twin model. The cooperative technology chain of the neural radiation field model and the digital twinning improves the precision of the neutron fine collimator flexible diaphragm angle measurement, and improves the efficiency of the neutron fine collimator flexible diaphragm angle measurement.

Description

Technical Field

[0001] The present application relates to the field of instrument measurement technology, and in particular to a neutron collimator flexible diaphragm measurement method, program product, and electronic equipment. Background Art

[0002] Neutron fine collimators are constructed from arrays of hundreds to thousands of flexible neutron-absorbing membranes. The angle tolerance of a single membrane layer must be strictly controlled to meet the atomic spacing resolution accuracy required by neutron scattering experiments. Current methods for measuring the angle of flexible membranes require manual calibration, resulting in low efficiency and accuracy. Summary of the Invention

[0003] The purpose of the embodiments of the present application is to provide a neutron collimator flexible diaphragm measurement method, program product, electronic device and storage medium, which are used to improve the above-mentioned problems.

[0004] In the first aspect, an embodiment of the present application provides a method for measuring a flexible diaphragm of a neutron collimator, including: acquiring multi-angle images of the flexible diaphragm of the neutron collimator; mapping the multi-angle images to a three-dimensional space coordinate system based on pixel information and camera parameters of the multi-angle images, and obtaining multi-dimensional vectors corresponding to pixel points in the multi-angle images; the multi-dimensional vectors are used to characterize the spatial position and light direction corresponding to the pixel points; using the multi-dimensional vectors and a trained neural radiation field model to generate a three-dimensional point cloud model; the three-dimensional point cloud model is used to characterize the sampling point information corresponding to the light; based on the three-dimensional point cloud model, a digital twin model of the flexible diaphragm is generated; the digital twin model is an information model based on the virtual mapping of the flexible diaphragm; and the flexible diaphragm is measured through the digital twin model.

[0005] In the aforementioned implementation process, a high-precision three-dimensional point cloud model is generated based on a multi-angle camera using a neural radiation field model. This three-dimensional point cloud model can reconstruct the surface morphology of the diaphragm, improving the data loss problem caused by the poor light transmittance and reflectivity of the diaphragm in traditional optical methods. Digital twin technology maps the state of the actual physical device through a virtual model, supporting real-time monitoring of changes in the angle of the flexible diaphragm. The collaborative technology chain of the neural radiation field model and digital twin not only improves the accuracy of the angle measurement of the flexible diaphragm of the neutron fine collimator, but also greatly improves the efficiency of the angle measurement of the flexible diaphragm of the neutron fine collimator.

[0006] Optionally, in an embodiment of the present application, the pixel information includes the pixel coordinates of the pixel points; based on the pixel information of the multi-angle image and the camera parameters, the multi-angle image is mapped to a three-dimensional space coordinate system to obtain a multi-dimensional vector corresponding to the pixel points in the multi-angle image, including: obtaining the camera parameters for acquiring the multi-angle image, and the pixel coordinates of the pixel points in the multi-angle image; the camera parameters include camera intrinsic parameters and camera extrinsic parameters; calculating the three-dimensional direction vector of the light through the camera intrinsic parameters and the pixel coordinates; determining the light direction of the pixel point based on the three-dimensional direction vector of the light; determining the spatial position corresponding to the pixel point based on the three-dimensional direction vector of the light and the camera extrinsic parameters; obtaining the multi-dimensional vector corresponding to the pixel point based on the light direction and the spatial position of the pixel point.

[0007] In this implementation, the collated and preprocessed multi-angle image data is converted into a multidimensional vector that meets the requirements of the neural radiation field model. This process uses the spatial position and viewpoint information in the image as input to generate 3D reconstruction data of the flexible membrane. This process provides accurate input data for the neural radiation field model, enabling it to accurately reconstruct a 3D point cloud model of the flexible membrane based on the 2D image information from multiple viewpoints.

[0008] Optionally, in an embodiment of the present application, a three-dimensional point cloud model is generated using a multidimensional vector and a trained neural radiation field model, including: inputting the multidimensional vector corresponding to the pixel point into the neural radiation field model to obtain parameter information of the pixel point; the parameter information includes color and density; based on the parameter information of the pixel point, a three-dimensional point cloud model is generated through volume rendering technology; volume rendering technology is used to simulate the changes of light when it propagates in three-dimensional space.

[0009] In the above implementation process, volume rendering technology simulates the real light propagation process based on the parameter information of pixel points. Multiple spatial points are sampled along each light path, and the transmittance and absorptivity of the light when passing through each point are calculated based on the density parameter, while the color contribution value is accumulated. Through integral operation, the discrete sampling points are restored to a continuous spatial material distribution, and finally a three-dimensional point cloud model with both geometric accuracy and optical realism is constructed. This method is more suitable for semi-transparent objects such as flexible membranes. It can accurately simulate the light scattering path and energy deposition distribution under neutron radiation, providing a spatial data base with atomic-level precision for subsequent digital twins, thereby improving the accuracy of flexible membrane measurements.

[0010] Optionally, in an embodiment of the present application, the neural radiation field model includes a vector input layer, a position encoding layer, a feature extraction layer, a ray casting and sampling layer, and an output layer; the vector input layer is used to convert a multidimensional vector into a feature representation; the position encoding layer is used to perform position encoding on the feature representation to obtain the encoded spatial coordinates; the feature extraction layer is used to perform feature extraction on the encoded spatial coordinates to generate feature data; wherein the feature extraction layer includes multiple layers of fully connected layers; the fully connected layer includes an occlusion attention module, which is used to dynamically shield the features of the occluded area through the diaphragm spacing data; the ray casting and sampling layer is used to perform multiple samplings on the light path corresponding to the pixel point to generate multiple sampling points; the output layer is used to output the parameter information of the sampling point; the parameter information of the sampling point is used to generate a three-dimensional point cloud model.

[0011] In the aforementioned implementation, the neural radiation field model employs a layered architecture to accurately digitally reconstruct the complex real-world light propagation process. The vector input layer is compatible with physical space three-dimensional coordinates and light direction parameters, enabling the model to naturally interface with raw data collected by various sensors. The position encoding layer uses high-frequency signal conversion technology to convert flat spatial position and direction information into detailed mathematical representations, effectively enhancing the model's ability to perceive minute structural features, such as micron-scale wrinkles on a diaphragm surface or the gradient effect of a translucent coating. The feature extraction layer, the core of the deep neural network, extracts abstract laws of light-matter interaction from the encoded data through multi-layered nonlinear transformations, including implicit physical field information such as material density distribution and scattering properties. The ray casting and sampling layer maps the abstract features from the previous steps back into physically interpretable dimensions. This layer intelligently generates density-adaptive sampling points along each ray path, automatically encrypting the point cloud in solid areas of the diaphragm and sparsely sampling in open areas, significantly improving computational efficiency. The final output layer simultaneously generates dual-channel results in both color and density, meeting visual rendering requirements and providing geometric existence criteria for subsequent point cloud reconstruction.

[0012] Optionally, in an embodiment of the present application, a digital twin model of a flexible diaphragm is generated based on a three-dimensional point cloud model, including: geometrically repairing the three-dimensional point cloud model after format conversion to obtain a repaired model; the geometric repair includes transmittance compensation and curvature optimization; assigning physical properties to the repaired model to obtain a physical entity model; the physical property assignment includes material modeling and dynamic property configuration; performing virtual environment modeling on the physical entity model to obtain a body to be simulated; the virtual environment modeling includes neutron radiation field simulation and embedded radiation thermal effects; performing multi-physical field coupling according to the parameters of the body to be simulated to generate a digital twin model of the flexible diaphragm; the multi-physical field coupling includes mechanical thermal coupling and boundary conditions.

[0013] In the aforementioned implementation process, format conversion resolves data compatibility issues, geometric repair strip models are computable, physical property assignments impart authenticity to materials, virtual environment modeling loads external stimuli, and multi-physics field coupling integrates all elements for behavioral deduction. For example, after loading the neutron radiation field on the repaired geometric model, thermal-mechanical coupling simulation can predict a 0.1mm edge warpage, thereby guiding the implementation of multiple encrypted monitoring of this area during actual measurements. Transforming the traditional R&D model that relies on physical trial and error into an efficient iteration in the digital space shortens the R&D cycle and reduces trial and error costs. Coupled simulation can also be used to predict the risk of diaphragm failure under extreme working conditions in advance.

[0014] Optionally, in an embodiment of the present application, after generating a digital twin model of the flexible diaphragm based on the three-dimensional point cloud model, the method also includes: performing static performance simulation, dynamic performance simulation and multi-physics field coupling simulation on the digital twin model in sequence to generate a measurement strategy; static performance simulation is used to simulate the deformation and stress distribution of the diaphragm under working conditions; static performance simulation includes deformation analysis and stress concentration location analysis under neutron radiation; dynamic performance simulation is used to analyze the vibration and fatigue behavior of the diaphragm under dynamic load; dynamic performance simulation includes vibration modal analysis and fatigue life prediction; multi-physics field coupling simulation is used to comprehensively analyze the mutual influence between heat, force and radiation fields, and multi-physics field coupling simulation includes thermal-mechanical coupling analysis and radiation material performance degradation; measuring the flexible diaphragm through the digital twin model, including: measuring the flexible diaphragm through the digital twin model according to the measurement strategy.

[0015] In the above implementation process, static performance simulation first provides a baseline snapshot in the spatial dimension, accurately locating high-stress areas and deformation limit points under fixed working conditions; dynamic performance simulation then introduces the time dimension, capturing transient response laws through vibration mode and fatigue life analysis; multi-physics field coupling simulation ultimately integrates interactions such as heat, force, and radiation to accurately predict the performance limits of the diaphragm in extreme environments, providing data support for subsequent measurements and key decision support.

[0016] Optionally, in an embodiment of the present application, the flexible diaphragm is measured through a digital twin model, including: simulating the geometric shape of the flexible diaphragm under different working conditions through the digital twin model, measuring the angle of the flexible diaphragm in a virtual space, and obtaining measurement data; the measurement data includes the angle of the flexible diaphragm relative to the reference plane, and the relative angle changes between different parts of the flexible diaphragm; after measuring the flexible diaphragm through the digital twin model, the method also includes: obtaining the measurement data of the flexible diaphragm, analyzing the measurement data, and generating a diaphragm angle correction scheme; and correcting the measurement data using the diaphragm angle correction scheme.

[0017] In the above implementation process, the angle deviation of the flexible diaphragm can be identified and corrected by comparing and analyzing the angle measurement of the digital twin with the actual data, so that the diaphragm always maintains a predetermined angle accuracy during operation.

[0018] Optionally, in an embodiment of the present application, the flexible diaphragm is measured through a digital twin model, including: real-time monitoring of the flexible diaphragm and uploading the detection results to a control system; if the angle of the flexible diaphragm is detected to be offset, the position of the flexible diaphragm or the application of external force is automatically adjusted through the control system.

[0019] In this implementation, the control system provides real-time feedback of the angle correction solution generated in the digital twin to the actual device, enabling the flexible diaphragm to maintain the ideal angle during operation and dynamically correct the angle based on real-time data. This process improves the accuracy and efficiency of angle correction, ensuring long-term stable operation of the device.

[0020] In the second aspect, an embodiment of the present application also provides a neutron collimator flexible diaphragm measuring device, including: an image acquisition module for acquiring multi-angle images of the flexible diaphragm of the neutron collimator; a multi-dimensional vector conversion module for mapping the multi-angle image to a three-dimensional space coordinate system according to the pixel information of the multi-angle image and the camera parameters, and obtaining a multi-dimensional vector corresponding to the pixel point in the multi-angle image; the multi-dimensional vector is used to characterize the spatial position and light direction corresponding to the pixel point; a point cloud module for generating a three-dimensional point cloud model using the multi-dimensional vector and a trained neural radiation field model; the three-dimensional point cloud model is used to characterize the sampling point information corresponding to the light; a digital twin module for generating a digital twin model of the flexible diaphragm based on the three-dimensional point cloud model; the digital twin model is an information model based on the virtual mapping of the flexible diaphragm; and a measurement module for measuring the flexible diaphragm through the digital twin model.

[0021] In a third aspect, an embodiment of the present application further provides a computer program product, including computer program instructions, which, when executed by a processor, execute the method provided by the first aspect or any one of the implementations of the first aspect.

[0022] In a fourth aspect, an embodiment of the present application further provides an electronic device comprising: a processor and a memory, wherein the memory stores computer program instructions, and when the computer program instructions are executed by the processor, the method provided by the first aspect or any one of the implementations of the first aspect is executed.

[0023] In a fifth aspect, an embodiment of the present application further provides a computer-readable storage medium, on which computer program instructions are stored. When the computer program instructions are executed by a processor, the method provided by the first aspect or any one of the implementations of the first aspect is executed.

[0024] A neutron collimator flexible diaphragm measurement method, program product, electronic device and storage medium provided in this application are used to generate a high-precision three-dimensional point cloud model based on a multi-angle camera through a neural radiation field model. The three-dimensional point cloud model can reconstruct the surface morphology of the diaphragm, improving the data loss problem caused by the poor light transmittance and reflectivity of the diaphragm in traditional optical methods. Digital twin technology maps the state of real physical equipment through a virtual model and supports real-time monitoring of changes in the angle of the flexible diaphragm. The collaborative technology chain of the neural radiation field model and digital twin not only improves the accuracy of the angle measurement of the flexible diaphragm of the neutron fine collimator, but also greatly improves the efficiency of the angle measurement of the flexible diaphragm of the neutron fine collimator. BRIEF DESCRIPTION OF THE DRAWINGS

[0025] In order to more clearly illustrate the technical solutions of the embodiments of the present application, the following is a brief introduction to the drawings required for use in the embodiments of the present application. It should be understood that the following drawings only show certain embodiments of the present application and therefore should not be regarded as limiting the scope. For ordinary technicians in this field, other relevant drawings can be obtained based on these drawings without creative work.

[0026] Figure 1 A schematic flow chart of a method for measuring a flexible diaphragm of a neutron collimator provided in an embodiment of the present application; Figure 2 A schematic diagram of a multi-angle image acquisition process provided in an embodiment of the present application; Figure 3 A schematic diagram of the structure of an electronic device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0027] The following embodiments of the technical solution of the present application will be described in detail with reference to the accompanying drawings. The following embodiments are only used to more clearly illustrate the technical solution of the present application and are therefore only examples and are not intended to limit the scope of protection of the present application.

[0028] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by those skilled in the art to which this application belongs. The terms used herein are only for the purpose of describing specific embodiments and are not intended to limit this application.

[0029] In the description of the embodiments of this application, the technical terms "first" and "second" are used only to distinguish different objects and should not be understood to indicate or imply relative importance or implicitly specify the quantity, specific order, or primary and secondary relationship of the indicated technical features. In the description of the embodiments of this application, the meaning of "plurality" is two or more, unless otherwise specifically defined.

[0030] As the core equipment of neutron scattering spectrometers (such as the China Spallation Neutron Source CSNS, the UK Spallation Neutron Source ISIS, the US Spallation Neutron Source SNS, the Japan Spallation Neutron Source J-PARC, and other facilities), the neutron fine collimator constrains the neutron optical path (initial divergence angle greater than or equal to 1 degree, less than or equal to 0.1 degree after collimation constraint) and defines the sample measurement volume. It can improve the spectrometer resolution from the nanometer level (1-10 nanometers) to the sub-angstrom level, and the experimental signal-to-noise ratio is improved by 2-3 orders of magnitude (actual measurement optimization from 10:1 to 300:1). The device consists of an array of hundreds to thousands of flexible neutron-absorbing membranes. The single-layer membranes use a polyimide organic substrate (thickness 20-75 microns, transmittance greater than or equal to 90%) with double-sided coating material. They are densely assembled at a micro-angle of 0 to 5 degrees with a spacing of less than or equal to 0.1 mm. The angle tolerance must be strictly controlled within ±0.001 degrees to ±0.05 degrees (corresponding to a dimensional error of less than or equal to 1 micron) to meet the requirements of neutron scattering experiments for the accuracy of atomic spacing resolution.

[0031] However, the special properties of flexible diaphragms lead to the following technical challenges: 1. Dynamic deformation interference: The diaphragm undergoes non-uniform deformation due to the assembly preload and neutron radiation heat load, with an angular drift Δθ greater than or equal to 0.02 degrees (40% over-tolerance), and the deformation rate cannot be captured by traditional static measurement methods; 2. Dense occlusion effect: When the diaphragm spacing is less than or equal to 0.1 mm, more than 80% of the area is blocked by adjacent diaphragms. The effective data coverage of laser triangulation (such as the Keyence LK-G5000) is less than 20%, and the occlusion shadows cause topological fractures in the reconstructed model (fracture rate greater than 65%). 3. Material optical defects: The surface reflectivity of the material coating is less than 5% (wavelength 532 nanometers), the substrate transmittance is greater than or equal to 90%, the signal-to-noise ratio attenuation of the optical sensor (such as Basler ace 2) is greater than 60%, and the feature point matching failure rate is greater than 70% (compared to a success rate of greater than 95% for rigid devices).

[0032] Traditional measurement methods have systematic flaws: 1. Indirect inference distortion: A laser tracker (such as Leica AT960) infers the angle by measuring the clamped spacer (thickness tolerance ±2 microns). Due to assembly stress relaxation (creep rate 0.1 microns / h), the actual diaphragm angle deviates from the inferred value by more than ±0.1 degrees (twice the tolerance). 2. Conflict between efficiency and accuracy: A single measurement requires manual calibration of 15-20 reference points (taking ≥30 minutes per piece). Manual calibration introduces an error of ±0.05 degrees (accounting for 50% of the total error), and cannot achieve full array synchronous measurement (a 1,000-piece array requires ≥500 hours). 3. Environmentally sensitive defects: Thermal disturbances in the neutron radiation field cause the optical sensor baseline to drift by more than 10 microns (corresponding to an angle error of ±0.03 degrees), and humidity fluctuations (greater than or equal to 30%) cause the lens distortion rate to increase by 15%.

[0033] In this application, the combination of computer vision and digital twin technology provides a new approach to measuring the angle of flexible membranes. NeRF (Neural Radiance Fields) technology generates a high-precision 3D model from multi-view images (e.g., 50-100 2D images captured by an industrial camera). This technology can reconstruct the membrane surface morphology (with an accuracy of ±0.01 mm), overcoming the data loss caused by the membrane's poor light transmission and reflectivity using traditional optical methods.

[0034] This collaborative technology chain of NeRF and digital twins not only improves the accuracy of angle measurement of the flexible diaphragm of the neutron fine collimator, but also realizes the automation and real-time feedback of the measurement process, greatly improving the efficiency and accuracy of angle measurement of the flexible diaphragm of the neutron fine collimator.

[0035] See Figure 1 The flowchart of a method for measuring a flexible diaphragm of a neutron collimator provided by an embodiment of the present application is shown. The method for measuring a flexible diaphragm of a neutron collimator provided by an embodiment of the present application can be applied to electronic devices, which may include physical devices such as servers, PCs, tablets, or smartphones, or virtual devices such as virtual machines or containers. The electronic device may be a single device, a combination of multiple devices, or a cluster of a large number of devices. The method for measuring a flexible diaphragm of a neutron collimator may include: Step S110: collecting multi-angle images of the flexible membrane of the neutron collimator.

[0036] Step S120: Map the multi-angle image to a three-dimensional space coordinate system based on the pixel information and camera parameters of the multi-angle image to obtain a multi-dimensional vector corresponding to the pixel point in the multi-angle image; the multi-dimensional vector is used to represent the spatial position and light direction corresponding to the pixel point.

[0037] Step S130: Generate a three-dimensional point cloud model using the multi-dimensional vector and the trained neural radiation field model; the three-dimensional point cloud model is used to represent the sampling point information corresponding to the light.

[0038] Step S140: Generate a digital twin model of the flexible diaphragm according to the three-dimensional point cloud model; the digital twin model is an information model based on the virtual mapping of the flexible diaphragm.

[0039] Step S150: measuring the flexible diaphragm through the digital twin model.

[0040] In step S110, the first step in the 3D reconstruction of the flexible membrane is image acquisition and preparation. This step aims to obtain sufficiently detailed, multi-angle 2D image data to provide accurate input for subsequent 3D reconstruction. The multi-angle images can have the same or varying image sizes, capturing the size and shape of the flexible membrane at different angles.

[0041] See Figure 2 A schematic diagram of a multi-angle image acquisition process provided by an embodiment of the present application is shown.

[0042] As an implementation method, the camera can be an industrial camera (such as the Basler ace 2 industrial camera) equipped with a preset lens to ensure a single-pixel resolution of less than or equal to 0.05 mm; the auxiliary equipment can be integrated with a circular LED polarized light source to suppress the light transmission interference of the diaphragm base (so that the transmittance is attenuated to less than or equal to 30%).

[0043] The cameras can be installed and arranged in a circular array: several cameras are arranged in a ring (e.g., eight cameras arranged with a 500mm radius), covering the entire circumference of the diaphragm with a preset angle (e.g., 0.1-degree step angle). This ensures that adjacent images overlap a preset ratio, such as 70%, to minimize data loss due to dense occlusion. The viewing angle can be 40 degrees. Of course, other array configurations are also possible, such as cameras arranged in the front, back, left, and right directions. Camera attitude calibration is then performed: a laser level (with an accuracy of ±0.01 degrees) is used to adjust the camera pitch angle to ensure that the optical axis is perpendicular to the diaphragm surface (with a tilt error of less than or equal to 0.05 degrees).

[0044] Regarding the optimization of the environment and light: light transmission suppression can be used. A black light-absorbing curtain is laid on the back of the diaphragm (to make the reflectivity less than 1%), and a polarizing filter (extinction ratio 1000:1) is used to eliminate the background light transmission noise. Dynamic fill light is also used to adaptively adjust the light source intensity based on the reflectivity of the diaphragm coating (reflectivity less than 5%). Valid feature points are extracted (extraction rate greater than 95%) through a brightness threshold segmentation algorithm (for example, the threshold can be set to 200 lux).

[0045] Image acquisition process, for example: Multispectral imaging: Synchronous acquisition of visible light and near-infrared images to enhance coating edge contrast (SNR increased by 40%); Anti-radiation interference: In neutron radiation fields (e.g., flux greater than or equal to 1×10 6 ~ 8 The camera is enclosed in a 5mm thick lead shield and embedded with a real-time noise filtering algorithm (based on wavelet transform) to ensure an image signal-to-noise ratio (SNR) of 35 decibels or greater. The basic unit of neutron flux density is "per square centimeter per second," symbolized as per square centimeter per second.

[0046] The camera can also be calibrated and corrected. Geometric calibration: using preset parameters (for example, 12×9) checkerboard calibration plate (grid size 10 mm ± 0.001 mm), the camera intrinsic parameters (focal length, distortion coefficient) and extrinsic parameters (rotation and translation matrix) are calculated through OpenCV (cross-platform computer vision library). The reprojection error after calibration is less than or equal to 0.01 mm; light transmission compensation: based on the transmittance of the diaphragm base, a light transmission compensation algorithm is introduced in the calibration stage, and the light transmission noise is separated by the background difference method (the background is modeled as a Gaussian mixture model), reducing the hole generation rate to less than 5%; dynamic distortion correction: based on temperature sensor data (accuracy ±0.1 degrees Celsius), the distortion caused by thermal expansion of the lens is corrected in real time to ensure that the image stitching error is less than or equal to 0.005 mm.

[0047] This ensures that the captured images possess high-quality resolution, uniform illumination, accurate viewing angle, and corrected distortion, providing sufficient data support for subsequent precise 3D reconstruction using NeRF technology. n images of the flexible membrane can be captured, including images 1 through n. Each image has varying sizes, documenting the size and shape of the flexible membrane at different viewing angles.

[0048] In step S120, according to the input requirements of the neural radiation field model, the pixels (points) in each image need to be converted into a 5D vector (i.e., a multidimensional vector). Each 5D vector consists of the following five elements: Spatial position ( ): This is the 3D spatial position of the image pixel, determined by the camera's extrinsic parameters (camera position and viewing angle). The spatial position corresponding to each pixel is calculated, indicating the specific location of that point in 3D space.

[0049] Light direction ( ): Indicates the direction of light from the camera's perspective to the object's surface. is the pitch angle (vertical direction), is the azimuth (horizontally). These angles are calculated based on the camera's view angle and the pixel position.

[0050] We need to obtain basic image information and camera parameters to calculate the position and orientation of each pixel in three-dimensional space. Pixel information can be expressed as the two-dimensional coordinates of each pixel, which can be written as (u, v), representing the pixel's position in the image.

[0051] Light direction (denoted as d): For each pixel, we need to calculate the unit vector from the camera to the surface of the object (i.e., the light direction). This is because each pixel of the image represents a ray of light projected from the camera to the surface of the object.

[0052] Calculate the three-dimensional direction vector of the light through the camera intrinsic parameters and pixel coordinates; according to the three-dimensional direction vector of the light, determine the light direction of the pixel point ( ); According to the three-dimensional direction vector of the light and the camera external parameters, determine the spatial position corresponding to the pixel point ( ). This results in a multi-dimensional vector, also known as a 5D vector.

[0053] In step S130, the constructed multidimensional vector is input into the pre-trained neural radiation field model. This model is essentially a deep learning network that calculates the optical properties (including color brightness and material density) of each spatial point by analyzing the position and direction information in the vector. For example, the model will determine whether a point belongs to the diaphragm surface and output the corresponding translucency characteristics. The multidimensional vector corresponding to the pixel point is input into the neural radiation field model to obtain the parameter information of the pixel point; the parameter information includes color and density. Based on the parameter information of the pixel point, a three-dimensional point cloud model is generated using volume rendering technology; volume rendering technology is used to simulate the changes in light as it propagates in three-dimensional space.

[0054] In step S140, after obtaining the three-dimensional point cloud model, the three-dimensional point cloud model is subjected to format conversion, geometric repair, physical property assignment, virtual environment modeling, and multi-physical field coupling to generate a digital twin model of the flexible diaphragm.

[0055] Format conversion is the process of converting a 3D point cloud model into a format that can be recognized by computing tools. Geometry repair intelligently repairs model defects generated during scanning or conversion. Physical property assignment injects the laws of material science into the geometric model, achieving a transition from "shape" to "physical entity." Virtual environment modeling reproduces the extreme working conditions of the diaphragm in digital space. Multi-physics field coupling is the core engine of the digital twin, which simultaneously solves the interacting physical equations. When the diaphragm is heated by neutron radiation, the heat conduction equation interacts with the thermal expansion strain in real time: changes in the temperature field cause structural deformation, and the deformation in turn reacts to the thermal boundary conditions; at the same time, the radiation embrittlement effect causes the material properties to dynamically decay, further affecting the mechanical response.

[0056] In step S150, the flexible diaphragm is measured through the digital twin model, that is, the angle measurement is performed in the virtual space. The digital twin model can be used to simulate the geometric shape of the flexible diaphragm under different working conditions, and the angle measurement of the flexible diaphragm is performed in the virtual space to obtain measurement data; the measurement data includes the angle of the flexible diaphragm relative to the reference plane, and the relative angle changes between different parts of the flexible diaphragm.

[0057] In the aforementioned implementation process, a high-precision three-dimensional point cloud model is generated based on a multi-angle camera using a neural radiation field model. This three-dimensional point cloud model can reconstruct the surface morphology of the diaphragm, improving the data loss problem caused by the poor light transmittance and reflectivity of the diaphragm in traditional optical methods. Digital twin technology maps the state of the actual physical device through a virtual model, supporting real-time monitoring of changes in the angle of the flexible diaphragm. The collaborative technology chain of the neural radiation field model and digital twin not only improves the accuracy of the angle measurement of the flexible diaphragm of the neutron fine collimator, but also greatly improves the efficiency of the angle measurement of the flexible diaphragm of the neutron fine collimator.

[0058] In an optional embodiment, after acquiring multi-angle images, standardization processing and algorithm optimization can be used to improve image data defects caused by diaphragm transmittance (to a transmittance greater than or equal to 90%) and dense occlusion (to a spacing less than or equal to 0.1 mm), thereby reducing 3D reconstruction accuracy errors (for example, to an accuracy error less than or equal to 0.05 mm). The implementation method is as follows: 1. Image data organization Data classification: Each acquired image data must be processed to ensure that it is not confused. Therefore, the image naming convention should be clear and unambiguous. Usually, images are classified and stored by shooting angle (0 degrees, 45 degrees, 90 degrees) and neutron energy level (e.g., 0.025eV thermal neutrons, 1eV epithermal neutrons). The file naming convention is "view angle_energy_number" (e.g., "45_0.025eV_001"); 2. Image format conversion and standardization Format unification: Use image software such as ImageMagick to batch convert RAW format to 16-bit TIFF (lossless compression) to preserve the dynamic range; Resolution calibration: The resolution was unified to 300 dpi (dots per inch) using an FFmpeg script, and super-resolution areas (such as the diaphragm edge) were enhanced to 0.025 mm / pixel using the ESRGAN algorithm (magnification × 2); 3. Denoising and contrast enhancement Noise suppression: To address high-energy particle noise in neutron radiation fields (SNR ≤ 35 dB), we used non-local mean filtering (search window 21×21, similarity block 7×7) combined with wavelet threshold denoising (Symlet 8, threshold 0.05), improving the signal-to-noise ratio to ≥ 42 dB. Edge enhancement: Contrast-limited adaptive histogram equalization (CLAHE, grid 8×8, contrast limit 2.0) was applied to enhance the contrast of the coating edges (gradient value increased from 15 to 45); Transmission compensation: Based on the light-transmitting area mask (areas with brightness greater than 200 lux), Poisson image editing is used to fill holes (repair rate greater than 98%).

[0059] 4. Color correction and brightness adjustment Color chart calibration: Using a 24-color chart (embedded during the shooting phase), a polynomial regression model (third-order terms) is used to correct color casts and reduce chromatic aberration. Brightness normalization: Based on the reflectivity of the neutron-absorbing coating, adjust the image grayscale histogram to the mean to ensure brightness consistency at different viewing angles (e.g., variance less than 5); HDR synthesis: For overexposed (greater than 240) and underexposed (less than 20) areas, 16-bit HDR images are synthesized through bracketing, covering a dynamic range of 0-65535.

[0060] 5. Image cropping and edge correction When capturing images, unnecessary background or edge areas may affect image quality. To ensure that the image contains only the actual data of the flexible membrane, the image must be cropped to remove the unnecessary areas. This cropped image should contain only the key information on the membrane surface, making it easier to process and analyze.

[0061] 6. Image Registration and Alignment Before inputting images from multiple viewpoints, it is necessary to ensure that they are spatially aligned. This means that the images from different viewpoints have consistent scale and alignment. Image registration algorithms, such as feature matching and affine transformations, ensure that the feature points of all images are precisely aligned, ensuring smooth 3D reconstruction.

[0062] Through the above steps, the collected image data will have high-quality resolution, uniform lighting, clear details and uniform scale, further providing reliable basic data for subsequent 3D reconstruction processing.

[0063] Optionally, in an embodiment of the present application, the pixel information includes the pixel coordinates of the pixel points; according to the pixel information of the multi-angle image and the camera parameters, the multi-angle image is mapped to a three-dimensional space coordinate system to obtain a multi-dimensional vector corresponding to the pixel points in the multi-angle image, including: obtaining the camera parameters for acquiring the multi-angle image, and the pixel coordinates of the pixel points in the multi-angle image; the camera parameters include camera intrinsic parameters and camera extrinsic parameters; wherein the camera intrinsic parameters include focal length ( , ) and principal point coordinates ( , ), these parameters can be obtained through the camera calibration process. Camera external parameters, including the spatial position of the camera ( ) and the rotation matrix (R), which describe the position and orientation of the camera relative to the world coordinate system.

[0064] First, we introduce the calculation of light direction ( ) process.

[0065] 3D direction vector (d): For each pixel, we need to calculate the unit vector from the camera to the surface of the object (flexible membrane) (i.e. the direction of the light). This is because each pixel of the image represents a light ray projected from the camera to the surface of the object.

[0066] According to the intrinsic parameters of the camera, the three-dimensional direction vector can be calculated by the following formula:

[0067] in,( , ) are the pixel coordinates in the image, ( , ) is the principal point coordinate of the camera, ( , ) is the focal length of the camera. is the 3D direction vector of the light. Represents the parameter of the light in the x direction, Represents the parameter of the light in the y direction, Represents the parameter of the light in the z direction.

[0068] Then calculate the pitch angle of the light direction according to the three-dimensional direction vector and azimuth : According to the three-dimensional direction vector , we can calculate the pitch angle of the light and azimuth , these two angles describe the direction of the light.

[0069] Pitch angle ( ) represents the angle of the light relative to the vertical direction (in the xy plane), and its calculation formula is:

[0070] Position angle ( ) represents the angle of the light relative to the horizontal plane (along the z-axis), and its calculation formula is:

[0071] In the NeRF model, each ray contains not only direction information but also position in space. Since the image is two-dimensional and the ray propagates in three-dimensional space, we need to map the coordinates of each pixel to the position in three-dimensional space. In order to obtain the spatial coordinates ( ), we need to transform it through the camera's external parameters (camera position and rotation matrix). The following describes how to calculate the spatial position ( ) process.

[0072] Knowing the external parameters of the camera, the spatial position of the camera ( ) and the rotation matrix (R), the transformation relationship between the camera coordinate system and the world coordinate system is as follows:

[0073] in: is the three-dimensional direction vector of the light calculated previously.

[0074] Given the direction d of the light in this way, combined with the rotation and position of the camera, we can map the light from the camera coordinate system to the world coordinate system and obtain the three-dimensional spatial position of the light ( ).

[0075] The final 5D vector is: 5D = ( Through these steps, the collated and preprocessed image data is converted into a 5D vector format that meets NeRF technical requirements. This allows the spatial position and viewing angle information in the image to be used as input to generate 3D reconstruction data of the flexible membrane. This process provides accurate input data for the NeRF model, enabling it to accurately reconstruct the 3D geometry of the flexible membrane based on the 2D image information from multiple viewpoints.

[0076] In the implementation of the above embodiment, the collated and preprocessed multi-angle image data is converted into a multidimensional vector that meets the requirements of the neural radiation field model, so that the spatial position and perspective information in the image are used as input to generate three-dimensional reconstruction data of the flexible membrane. This process provides accurate input data for the neural radiation field model, enabling it to accurately reconstruct a three-dimensional point cloud model of the flexible membrane based on the two-dimensional image information from multiple perspectives.

[0077] Optionally, in an embodiment of the present application, the neural radiation field model includes a vector input layer, a position encoding layer, a feature extraction layer, a ray casting and sampling layer, and an output layer; The vector input layer is used to convert multi-dimensional vectors into feature representations. For example, a multi-dimensional vector ( ) will be input into the vector input layer of the neural radiation field model (NeRF algorithm model), where the spatial coordinates ( ) accuracy ±0.05 mm, angle ( ) with an accuracy of ±0.01 degrees. The vector input layer processes the spatial position and light direction, converting them into feature representations that can be understood by the neural network.

[0078] The position encoding layer is used to perform position encoding on the feature representation to obtain the encoded spatial coordinates. Due to the limitations of the neural network's understanding of continuous positions, NeRF uses position encoding technology in the embodiment of this application. Position encoding is achieved by encoding the spatial coordinates ( ) is processed using sine and cosine functions to increase the network's ability to perceive position changes.

[0079] The encoded spatial coordinates are transformed into a high-dimensional spatial representation, allowing the network to better capture subtle differences between locations during learning. This process is crucial for capturing complex spatial information, especially for modeling details and lighting changes.

[0080] For each spatial coordinate, the position encoding function yes:

[0081] In an optional embodiment, position encoding enhancement can also be performed: the number of spatial coordinate encoding layers L = 10, generating a dimension of 3 × 2 × 10 = 60; the number of light direction encoding layers L = 4, generating a dimension of 2 × 2 × 4 = 16. Furthermore, light-transmitting area suppression is performed: for 5D vectors with brightness greater than 200 lux, transparency weights are embedded to reduce light-transmitting noise interference (improving the signal-to-noise ratio by 40%).

[0082] The feature extraction layer is used to extract features from the encoded spatial coordinates and generate feature data; wherein, the feature extraction layer includes multiple layers of fully connected layers; the fully connected layer includes an occlusion attention module, which is used to dynamically mask the features of the occluded area through the diaphragm spacing data.

[0083] The position-encoded data is then fed into the model's feature extraction layer for further processing. This layer uses an 8-layer fully connected network (FCN), rather than a traditional convolutional neural network (CNN). This layer performs nonlinear mapping and feature extraction on the position-encoded data, generating high-level features related to surface details.

[0084] Anti-occlusion design: The Occlusion-Aware Attention module is inserted into the third and fifth layers of the feature extraction layer to dynamically mask the features of the occluded area using the diaphragm spacing data (less than or equal to 0.1 mm). Feature fusion formula:

[0085] in, is the fused feature, is the original feature, is the distance between adjacent diaphragms, is the skip connection feature.

[0086] The ray casting and sampling layer is used to sample the ray path corresponding to the pixel point multiple times to generate multiple sampling points.

[0087] For each ray (one ray per pixel), the neural network samples its path multiple times. Specifically, as a ray travels from the camera's viewpoint toward the scene, the network predicts the color and density of each 3D point it passes. This collection of points forms a ray propagation model of the 3D scene. In this way, NeRF technology can simulate the propagation of light and the illumination of surfaces in the real world.

[0088] In an optional embodiment, the ray sampling optimization strategy can be hierarchical sampling: 64 points are uniformly sampled along each ray (step size Δt=0.5 mm); importance sampling: 64 points are added in the area with density gradient ▽σ>0.1, with a total of 128 sampling points / ray; or transmission compensation sampling: sparse sampling (step size Δt=2 mm) is used for the transparent area (α<0.3) to reduce invalid calculations (sampling points are reduced by 50%).

[0089] The output layer outputs parameter information for the sampling points; this information is used to generate a 3D point cloud model. For the output layer, the neural network integrates the illumination, density, and color of each sampling point through multiple fully connected layers, ultimately generating the final result for each sampling point. The output includes: color (c = (r, g, b)), which represents the color of light passing through the surface of the object, and density (σ), which represents the transparency or light absorption at that point. This output data provides all the necessary parameter information for subsequent 3D rendering.

[0090] In the implementation of the above-mentioned embodiment, the neural radiation field model adopts a layered architecture to achieve accurate digital reconstruction of the complex real-world light propagation process. The vector input layer is compatible with the three-dimensional coordinates of physical space and light direction parameters, enabling the model to naturally interface with raw data collected by various sensors. The position encoding layer uses high-frequency signal conversion technology to convert flat spatial position and direction information into detailed mathematical representations, effectively improving the model's ability to perceive tiny structural features, such as micron-scale wrinkles on the diaphragm surface or the gradient effect of a translucent coating. The feature extraction layer, as the core of the deep neural network, extracts abstract laws of light-matter interaction from the encoded data through multi-layer nonlinear transformations, including implicit physical field information such as material density distribution and scattering properties. The ray casting and sampling layer maps the abstract features from the previous steps back into physically interpretable dimensions. This layer intelligently generates density-adaptive sampling points along each ray path, automatically encrypting the point cloud in solid areas of the diaphragm and sparsely sampling in open areas, significantly improving computational efficiency. The final output layer simultaneously generates dual-channel results of color and density, meeting visual rendering requirements and providing geometric existence criteria for subsequent point cloud reconstruction.

[0091] The following describes the training strategy for the neural radiation field model. The training parameters, including the hardware platform, optimizer, batch size, and training cycle, can be configured based on actual needs. For example, the hardware platform can be an NVIDIA A100 GPU (80GB of video memory), with distributed training on four nodes; the optimizer can be Adam (learning rate 5×10⁻¹⁴, exponentially decaying to 1×10⁻¹⁵); the batch size can be 128 samples per ray, with 4096 rays per batch, and the training cycle can be 200,000 iterations (approximately 48 hours).

[0092] Loss function: During training, the difference between the 3D scene generated by the model and the actual image is calculated using a loss function. A commonly used loss function is the mean squared error (MSE), which aims to minimize the error between the network-generated image and the real image.

[0093] The loss function formula is:

[0094] in, is the pixel value generated by the model, is the pixel value of the real image, N is the total number of pixels in the image, and i represents the pixel point.

[0095] Dynamic training strategies can be used, such as progressive training: for the first 50,000 iterations, low-resolution images (1368×912) with encoding layers L=6; for the next 150,000 iterations, full-resolution images (5472×3648) with encoding layers L=10. Or occlusion-aware training: update the patch spacing mask every 10,000 iterations; the weight of the sample points in the occluded area is reduced to 0.1× the normal value; During training, anti-interference enhancement techniques can be incorporated, such as neutron radiation field adaptation: 20% high-noise images (SNR = 30dB) are mixed into the training data to improve the model's resistance to radiation interference; the PSNR of noisy images is consistently greater than or equal to 28dB. Thermal deformation compensation can also be employed: temperature deformation data (ΔT = 5 degrees Celsius expansion) is injected every 60 minutes, dynamically updating the extrinsic parameter matrices R and T; the post-deformation reconstruction error fluctuates to less than 0.005 mm.

[0096] Optionally, in an embodiment of the present application, a three-dimensional point cloud model is generated using a multi-dimensional vector and a trained neural radiation field model, including: The multidimensional vector corresponding to each pixel is input into the neural radiation field model to obtain pixel parameter information, including color and density. Based on this pixel parameter information, a three-dimensional point cloud model is generated using volume rendering technology, which simulates the changes in light as it propagates in three-dimensional space.

[0097] After the multidimensional vector corresponding to the pixel is input into the neural radiation field model, the model gradually analyzes the physical behavior of light in three-dimensional space through a layered information processing mechanism. The multidimensional vector first carries the two-dimensional position of the pixel and its corresponding three-dimensional light direction. After the high-frequency signal is enhanced by the position encoding layer, subtle spatial features such as ripples on the diaphragm surface or gradual changes in coating thickness are converted into recognizable mathematical patterns. The feature extraction layer analyzes the interaction between light and matter through the weights of the deep neural network. The final output layer generates the physical parameters of each spatial sampling point: the color parameter records the visible spectrum reflectance characteristics of the light at that point, intuitively presenting the appearance of the material; the density parameter quantifies the degree of material aggregation at that location, determining the attenuation intensity of light when it penetrates.

[0098] Volume rendering combines the color and density of each sample point to generate the color of each ray. Volume rendering simulates the changes in light as it propagates through three-dimensional space, ultimately producing an image of the three-dimensional scene.

[0099] Rendering equations such as:

[0100] in: is the final color of the light. is the density of each sampling point, indicating the degree to which light is absorbed. is the color (RGB value) of the sampling point, predicted by the network. Transmittance is the ratio of light that is not absorbed or scattered when it passes through a certain area. represents the time point. Among them, the transmittance It can be calculated from the density of each sampling point and is defined as:

[0101] The color of the light is gradually accumulated each time it is calculated. Suppose we start from a starting point and sample multiple sampling points along the direction of the light. Each sampling point will have a contribution value, which is determined by its color and density value. The accumulation process can be achieved by the following steps: Light from the camera passes through multiple sampling points, and the color and density of each point will affect the final color.

[0102] The color contribution of each sampling point is weighted, and points with higher transmittance (higher density) have a greater impact on the final result.

[0103] Through step-by-step calculation, the composite color of all light from the camera's perspective to the object's surface along the direction of the light is finally obtained.

[0104] The following is a specific implementation of the volume rendering formula: At each sampling point, the final light color (RGB) and density (σ) can be calculated by weighting. For example, NeRF uses the following cumulative calculation formula to process the rendering of each light:

[0105] in, is the transmittance through the current sampling point. is the density at that point. is the color of the point. is the transmittance of the next sampling point corresponding to the current sampling point, and i represents the sampled pixel.

[0106] This process gradually accumulates the contribution of each sampling point and finally synthesizes the color of the light.

[0107] Post-processing then generates a 3D model (with industrial-grade precision). The first step is point cloud generation: Depth map extraction: Based on the volume rendering results, a 16-bit depth map (resolution 5472×3648, accuracy 0.01 mm) is generated. Point cloud construction: Back-projection is used to calculate the 3D coordinates of each pixel, generating approximately 20 million points for a single membrane.

[0108] Mesh reconstruction and optimization are then performed, including Poisson reconstruction: setting the octree depth and smoothing iterations for 5 times to generate a closed mesh (approximately 5 million facets); mesh optimization: Laplace smoothing: iterating 3 times to eliminate aliasing (curvature radius error less than 0.02 mm); edge refinement: local subdivision of areas with a curvature radius greater than or equal to 0.1 mm (subdivision level 3); hole repair: filling in missing facets based on a transmittance compensation mask (repair rate greater than 95%).

[0109] After mesh reconstruction and optimization, model verification and correction can also be performed. For example, accuracy benchmarking: laser scanner comparison error is less than or equal to 0.05 mm; automated defect detection: model faults are identified through the CNN classification network (ResNet-50) (detection rate greater than 99%); thermal deformation compensation: injecting temperature deformation coefficients to dynamically adjust mesh vertex coordinates.

[0110] The model can then be verified experimentally, with the following aspects of the verification: accuracy indicators: 3D model geometric error is less than or equal to 0.05 mm (ISO 10360 standard); angular measurement error is ±0.01 degrees (compared with laser interferometer); and / or efficiency indicators: single diaphragm reconstruction time is less than or equal to 8 minutes (including post-processing); point cloud to mesh conversion is less than or equal to 2 minutes; and / or anti-interference ability: model integrity under neutron radiation field is greater than 98%; the artifact rate in the transparent area is less than 1%.

[0111] During the implementation of the above embodiment: volume rendering technology simulates the real light propagation process based on the parameter information of the pixel points. Multiple spatial points are sampled along each light path, and the transmittance and absorptivity of the light when it passes through each point are calculated based on the density parameter, and the color contribution value is accumulated at the same time. The discrete sampling points are restored to a continuous spatial material distribution through integral operations, and finally a three-dimensional point cloud model with both geometric accuracy and optical authenticity is constructed. This method is more suitable for translucent objects such as flexible diaphragms, and can accurately simulate the light scattering path and energy deposition distribution under neutron radiation, providing a spatial data base with atomic-level accuracy for subsequent digital twins, thereby improving the accuracy of flexible diaphragm measurements.

[0112] Optionally, in an embodiment of the present application, generating a digital twin model of the flexible diaphragm according to the three-dimensional point cloud model includes: Perform geometric repair on the converted 3D point cloud model to obtain a repaired model; geometric repair includes light transmission compensation and curvature optimization.

[0113] The 3D mesh model (5 million facets) generated by NeRF was converted to STEP format and compressed to Parasolid format (the number of facets was reduced to 2 million, and the accuracy was retained to ±0.01 mm). The converted data was then verified: the point cloud and mesh model were compared using the ICP algorithm (Iterative Closest Point), and the registration error was less than or equal to 0.02 mm.

[0114] Transmission compensation: For holes in translucent areas (e.g., brightness greater than 200 lux), a non-manifold topology repair algorithm is used to fill in missing patches (e.g., repair rate greater than 95%). Curvature optimization: For bends with a radius of curvature greater than or equal to 0.1 mm, the Catmull-Clark subdivision algorithm (subdivision level 3) is applied to achieve a smaller curvature continuity error.

[0115] The repaired model is assigned physical properties to obtain a physical entity model; the physical property assignment includes material modeling and dynamic property configuration.

[0116] Material modeling includes elastic modulus and neutron absorption coating. Elastic modulus: The anisotropic parameters of the flexible membrane substrate (polyimide) are determined according to requirements and calibrated through nanoindentation experiments; the neutron absorption coating is used to set the coating thickness, density and thermal expansion coefficient.

[0117] Dynamic property configuration includes temperature-dependent parameters and stress relaxation model: Temperature-dependent parameters: relate the elastic modulus to temperature through the Arrhenius equation; while the stress relaxation model uses the generalized Maxwell model (three relaxation times can be set).

[0118] A virtual environment model is constructed for the physical entity model to obtain the object to be simulated. This includes neutron radiation field simulation and embedded radiative thermal effects. This includes constructing a neutron flux distribution field based on the Monte Carlo Particle Transport (MCNP) code. An exemplary mesh accuracy of 0.1 mm and a flux gradient error of 5% or less are achieved. This also includes embedded radiative thermal effects for calculating neutron energy deposition power.

[0119] Based on the parameters of the object to be simulated, multi-physics coupling is performed to generate a digital twin model of the flexible diaphragm. Multi-physics coupling includes mechanical-thermal coupling and boundary conditions. Mechanical-thermal coupling involves the simultaneous calculation of thermal expansion and stress distribution using a bidirectional coupling solver. Boundary conditions include fixed boundaries, such as the fixed edge of the diaphragm, and dynamic loads, such as the neutron impact force and loading frequency.

[0120] It is understandable that the specific parameters for generating the digital twin model can be set according to actual needs, and the present application embodiment does not limit this. During the implementation of the above-mentioned embodiment, format conversion resolves data compatibility issues, geometric repair strip models are computable, physical property assignments impart authenticity to materials, virtual environment modeling loads external stimuli, and multi-physics field coupling integrates all elements for behavioral deduction. For example, after loading the neutron radiation field onto the repaired geometric model, thermal-mechanical coupling simulation can predict edge warping of 0.1 mm, thereby guiding the implementation of multiple encrypted monitoring of this area during actual measurements. This transforms the traditional R&D model that relies on physical trial and error (e.g., a single neutron irradiation experiment costs over 500,000 yuan) into efficient iteration in the digital space, shortening the R&D cycle and reducing trial and error costs. Coupled simulation can also be used to predict the risk of diaphragm failure under extreme working conditions in advance.

[0121] After the digital twin model is created, multi-physics field coupling simulation technology is used to predict and optimize the behavior of the flexible diaphragm under complex environments such as neutron radiation and temperature changes. The following is an introduction to the simulation process: Optionally, in the embodiment of the present application, after generating a digital twin model of the flexible diaphragm according to the three-dimensional point cloud model, the method further includes: The digital twin model is subjected to static performance simulation, dynamic performance simulation and multi-physics field coupling simulation in sequence to generate a measurement strategy; static performance simulation is used to simulate the deformation and stress distribution of the diaphragm under working conditions; static performance simulation includes deformation analysis and stress concentration location analysis under neutron radiation; dynamic performance simulation is used to analyze the vibration, fatigue and other behaviors of the diaphragm under dynamic loads; dynamic performance simulation includes vibration modal analysis and fatigue life prediction; multi-physics field coupling simulation is used to comprehensively analyze the mutual influence between heat, force and radiation fields, and multi-physics field coupling simulation includes thermal-mechanical coupling analysis and radiation material performance degradation.

[0122] The goal of static performance simulation is to predict the deformation and stress distribution of the diaphragm under fixed working conditions.

[0123] Static performance simulation includes deformation analysis under neutron radiation: for example, simulation of neutron beam (flux 1×10 6 ~ 8 The system detects thermal expansion effects on the membrane caused by neutron energy deposition when irradiated at 1000 sq cm per second. For example, the coating's temperature rises after neutron absorption, causing the membrane edge to warp by 0.1 mm. High-risk areas with deformation exceeding 0.05 mm are flagged by the system.

[0124] Static performance simulation also includes stress concentration location: for example, identifying the stress peak (e.g., 85 MPa) at the diaphragm bend (e.g., a crease with a curvature radius of 0.1 mm), which is close to the material yield limit (90 MPa), indicating the need to optimize the structural design.

[0125] The goal of dynamic response simulation is to analyze the vibration, fatigue and other behaviors of the diaphragm under dynamic loads.

[0126] Dynamic response simulation includes vibration modal analysis: for example, simulating the vibration mode of the diaphragm under a 10Hz neutron impact force found that the first-order natural frequency was 120Hz (which is prone to resonate with external loads). By increasing the stiffness of the supporting structure, the frequency was increased to 150Hz to avoid the risk of resonance.

[0127] Static performance simulation also includes fatigue life prediction: for example, based on 100 million cyclic loads (simulating 10 years of operation), the interface between the coating and the substrate is predicted to be the fatigue crack initiation zone, with a lifespan of only 5 years; by thickening the coating to 60 microns, the lifespan is extended to 8 years.

[0128] The goal of physical field coupling simulation is to integrate the mutual influence of thermal, force and radiation fields.

[0129] The coupled physics simulation includes a thermomechanical coupling analysis: When neutron radiation causes a local temperature rise of 50 degrees Celsius, the diaphragm deforms by 0.03 mm due to thermal expansion. Simultaneously, the high temperature causes the elastic modulus of the polyimide to drop by 5%, further exacerbating the deformation. The system uses a pre-compensation algorithm to reversely correct this deformation.

[0130] Physical field coupling simulation also includes radiation-material performance degradation: under simulation of long-term neutron irradiation, coating embrittlement causes the elastic modulus to drop by 10%, and the cumulative deformation error increases by 0.02 mm, triggering a maintenance prompt to automatically thicken the coating.

[0131] Through the above simulation process, the digital twin can accurately predict the performance limits of the diaphragm in extreme environments and guide design optimization, ultimately achieving industrial-grade reliability with an error of less than or equal to 0.05 mm, providing key decision support for practical applications.

[0132] Measuring a flexible diaphragm using a digital twin model involves: Measuring the flexible diaphragm using the digital twin model based on a measurement strategy. For example, if simulation predicts stress concentration at a bend, a measurement strategy might include adding strain gauges to the R0.1mm area. This measurement strategy improves the capture rate of key data. For another example, if simulation predicts thermal expansion deformation to be a gradient distribution, a measurement strategy might include using full-field laser scanning instead of single-point thickness measurement, thereby improving deformation measurement accuracy.

[0133] During the implementation of the above embodiment: static performance simulation first provides a baseline snapshot in the spatial dimension, accurately locating high stress areas and deformation limit points under fixed working conditions; dynamic performance simulation then introduces the time dimension, capturing transient response laws through vibration mode and fatigue life analysis; multi-physics field coupling simulation ultimately integrates interactions such as heat, force, and radiation to accurately predict the performance limits of the diaphragm in extreme environments, providing data support for subsequent measurements and key decision support.

[0134] The simulation process maps the complexity of the physical world into a virtual space for rehearsal and decision-making, ultimately outputting quantitative results that can guide engineering practice, or measurement strategies. This can improve accuracy, reduce costs, and mitigate risks in subsequent actual measurements.

[0135] Optionally, in an embodiment of the present application, measuring the flexible diaphragm using a digital twin model includes: The digital twin model simulates the geometric shape of the flexible diaphragm under different working conditions, and measures the angle of the flexible diaphragm in virtual space to obtain measurement data. The measurement data includes the angle of the flexible diaphragm relative to the reference plane and the relative angle changes between different parts of the flexible diaphragm. After measuring the flexible diaphragm through the digital twin model, the method further includes: obtaining measurement data of the flexible diaphragm, analyzing the measurement data, generating a diaphragm angle correction scheme; and correcting the measurement data using the diaphragm angle correction scheme.

[0136] The goal of this embodiment is to precisely measure the angle of a flexible diaphragm using a digital twin. The virtual measurements are then compared with real-world angle standards to guide actual diaphragm angle adjustment. This allows the angle data measured by the digital twin to provide an effective basis for real-world diaphragm angle correction, thereby improving the diaphragm's accuracy and stability during operation.

[0137] The digital twin model is used to measure the angle of the flexible diaphragm in virtual space. By simulating the diaphragm's geometry under different operating conditions, the digital twin accurately calculates the diaphragm's angle data. This process not only considers the diaphragm's initial angle but also any changes in angle during use due to environmental and temperature fluctuations.

[0138] Virtual Angle Measurement: By accurately calculating the diaphragm's angle changes in virtual space, we can obtain the diaphragm's angle data under different working conditions. This includes the diaphragm's angle relative to the reference plane, as well as the relative angle changes between different parts of the diaphragm.

[0139] Angle deviation calculation: Measure the angle in the virtual model and compare it with the preset standard angle, calculate the angle deviation and obtain error data.

[0140] The virtual measurement results are then compared with the actual diaphragm angle data to perform a deviation analysis. This process helps the system identify and quantify the angular errors that occur during the actual use of the diaphragm.

[0141] Data comparison: The digital twin's measurement results are compared with the angle data obtained from sensors or other measurement tools in the actual device. By comparing the results, the system can calculate the difference between the actual diaphragm angle and the standard angle.

[0142] Error source analysis: Analyze the root cause of the error, which may be angle deviation caused by external environmental factors (such as temperature changes), non-idealities of diaphragm materials, external forces, etc.

[0143] Based on the comparative analysis results, the system generates an error correction plan and provides guidance for angle correction of the actual equipment. This correction plan can be implemented through design optimization or operational adjustment.

[0144] Correction solution generation: By analyzing the measurement results, a diaphragm angle correction solution is generated, including design adjustments and operation optimization solutions.

[0145] Correction execution: The actual equipment is adjusted according to the correction plan provided by the digital twin to ensure that the diaphragm returns to the standard angle.

[0146] During the implementation of the above embodiment, the angle deviation of the flexible diaphragm can be identified and corrected by comparing and analyzing the angle measurement of the digital twin with the actual data, so that the diaphragm always maintains a predetermined angle accuracy during operation.

[0147] Optionally, in an embodiment of the present application, the flexible diaphragm is measured through a digital twin model, including: real-time monitoring of the flexible diaphragm and uploading the detection results to a control system; if the angle of the flexible diaphragm is detected to be offset, the position of the flexible diaphragm or the application of external force is automatically adjusted through the control system.

[0148] The control system transmits virtual correction results directly to the actual equipment through a real-time feedback mechanism, automatically adjusting the diaphragm angle. This process ensures that the diaphragm can quickly respond and correct deviations during actual operation.

[0149] Real-time feedback mechanism: The sensor monitors the diaphragm angle changes in real time, and the control system automatically transmits the feedback results to the actual device to ensure that the diaphragm angle is maintained within the predetermined standard range.

[0150] Automatic correction operation: The system automatically adjusts operating parameters or performs correction operations based on feedback data to restore the actual diaphragm to the standard angle and avoid errors caused by human intervention.

[0151] Through real-time monitoring, the control system continuously acquires diaphragm angle data and automatically adjusts it based on varying operating conditions. This dynamic adjustment ensures the diaphragm always maintains the ideal angle, even as operating conditions change.

[0152] Real-time angle monitoring: The system continuously tracks the angle of the diaphragm and compares it with the standard angle. Any deviation will be immediately detected and fed back to the control system.

[0153] Dynamic adjustment: When the diaphragm angle is detected to be offset, the system will immediately automatically adjust the diaphragm position or external force through closed-loop control to ensure that it returns to the preset ideal angle.

[0154] In the implementation of the above embodiment, the control system provides real-time feedback of the angle correction solution generated in the digital twin to the actual device, enabling the flexible diaphragm to maintain the ideal angle during operation and dynamically correct the angle based on real-time data. This process improves the accuracy and efficiency of angle correction, ensuring long-term stable operation of the device.

[0155] The embodiment of the present application provides a neutron collimator flexible diaphragm measuring device, comprising: An image acquisition module, used for acquiring multi-angle images of the flexible membrane of the neutron collimator; The multi-dimensional vector conversion module is used to map the multi-angle image to a three-dimensional spatial coordinate system based on the pixel information of the multi-angle image and the camera parameters, and obtain the multi-dimensional vector corresponding to the pixel point in the multi-angle image; the multi-dimensional vector is used to represent the spatial position and light direction corresponding to the pixel point; The point cloud module is used to generate a three-dimensional point cloud model using multi-dimensional vectors and a trained neural radiation field model; the three-dimensional point cloud model is used to represent the sampling point information corresponding to the light; The digital twin module is used to generate a digital twin model of the flexible diaphragm based on the three-dimensional point cloud model; the digital twin model is an information model based on the virtual mapping of the flexible diaphragm; A measurement module is used to measure the flexible diaphragm using a digital twin model.

[0156] Optionally, in an embodiment of the present application, the neutron collimator flexible diaphragm measuring device, the pixel information includes the pixel coordinates of the pixel points; the multi-dimensional vector conversion module is used to obtain the camera parameters for acquiring multi-angle images, and the pixel coordinates of the pixel points in the multi-angle images; the camera parameters include camera intrinsic parameters and camera extrinsic parameters; the three-dimensional direction vector of the light is calculated through the camera intrinsic parameters and the pixel coordinates; the light direction of the pixel point is determined according to the three-dimensional direction vector of the light; the spatial position corresponding to the pixel point is determined according to the three-dimensional direction vector of the light and the camera extrinsic parameters.

[0157] Optionally, in an embodiment of the present application, a neutron collimator flexible diaphragm measuring device and a point cloud module are used to input the multidimensional vector corresponding to the pixel point into the neural radiation field model to obtain parameter information of the pixel point; the parameter information includes color and density; based on the parameter information of the pixel point, a three-dimensional point cloud model is generated through volume rendering technology; volume rendering technology is used to simulate the changes of light when it propagates in three-dimensional space.

[0158] Optionally, in an embodiment of the present application, a neutron collimator flexible diaphragm measuring device, a neural radiation field model includes a vector input layer, a position encoding layer, a feature extraction layer, a ray casting and sampling layer, and an output layer; the vector input layer is used to convert a multidimensional vector into a feature representation; the position encoding layer is used to perform position encoding on the feature representation to obtain the encoded spatial coordinates; the feature extraction layer is used to perform feature extraction on the encoded spatial coordinates to generate feature data; wherein, the feature extraction layer includes multiple layers of fully connected layers; the fully connected layer includes an occlusion attention module, and the occlusion attention module is used to dynamically shield the features of the occluded area through the diaphragm spacing data; the ray casting and sampling layer is used to perform multiple samplings on the light path corresponding to the pixel point to generate multiple sampling points; the output layer is used to output the parameter information of the sampling point; the parameter information of the sampling point is used to generate a three-dimensional point cloud model.

[0159] Optionally, in an embodiment of the present application, a neutron collimator flexible diaphragm measuring device and a digital twin module are used to geometrically repair the three-dimensional point cloud model after format conversion to obtain a repaired model; the geometric repair includes transmittance compensation and curvature optimization; the repaired model is assigned physical properties to obtain a physical entity model; the physical property assignment includes material modeling and dynamic property configuration; the physical entity model is modeled in a virtual environment to obtain a body to be simulated; the virtual environment modeling includes neutron radiation field simulation and embedded radiation thermal effects; multi-physical field coupling is performed according to the parameters of the body to be simulated to generate a digital twin model of the flexible diaphragm; multi-physical field coupling includes mechanical thermal coupling and boundary conditions.

[0160] Optionally, in an embodiment of the present application, the neutron collimator flexible diaphragm measuring device also includes a simulation module for performing static performance simulation, dynamic performance simulation and multi-physics field coupling simulation on the digital twin model in sequence to generate a measurement strategy; static performance simulation is used to simulate the deformation and stress distribution of the diaphragm under working conditions; static performance simulation includes deformation analysis and stress concentration location analysis under neutron radiation; dynamic performance simulation is used to analyze the vibration, fatigue and other behaviors of the diaphragm under dynamic loads; dynamic performance simulation includes vibration modal analysis and fatigue life prediction; multi-physics field coupling simulation is used to comprehensively analyze the mutual influence between heat, force and radiation fields, and multi-physics field coupling simulation includes thermal-mechanical coupling analysis and radiation material performance degradation; measuring the flexible diaphragm through the digital twin model includes: measuring the flexible diaphragm through the digital twin model according to the measurement strategy.

[0161] Optionally, in an embodiment of the present application, the neutron collimator flexible diaphragm measuring device and the measuring module are used to simulate the geometric shape of the flexible diaphragm under different working conditions through a digital twin model, perform angle measurement of the flexible diaphragm in a virtual space, and obtain measurement data; the measurement data includes the angle of the flexible diaphragm relative to the reference plane, and the relative angle change between different parts of the flexible diaphragm; after measuring the flexible diaphragm through the digital twin model, the method also includes: obtaining the measurement data of the flexible diaphragm, analyzing the measurement data, and generating a diaphragm angle correction scheme; and correcting the measurement data using the diaphragm angle correction scheme.

[0162] Optionally, in an embodiment of the present application, the neutron collimator flexible diaphragm measuring device, the control and adjustment module, is used to measure the flexible diaphragm through a digital twin model, including: real-time monitoring of the flexible diaphragm and uploading the detection results to the control system; if it is detected that the angle of the flexible diaphragm is offset, the position of the flexible diaphragm or the application of external force is automatically adjusted through the control system.

[0163] It should be understood that this device corresponds to the aforementioned embodiment of the method for measuring a neutron collimator flexible diaphragm and is capable of performing all of the steps involved in the aforementioned method embodiment. The specific functions of this device can be found in the description above; to avoid repetition, a detailed description is omitted here. The device includes at least one software functional module that can be stored in a memory in the form of software or firmware or embedded in the device's operating system (OS).

[0164] See Figure 3The electronic device 300 provided in the embodiment of the present application includes a processor 310 and a memory 320, wherein the memory 320 stores machine-readable instructions executable by the processor 310, and when the machine-readable instructions are executed by the processor 310, the method described above is performed.

[0165] Figure 3 Each component shown in the figure can be implemented using hardware, software, or a combination thereof. Electronic device 300 may be a physical device, such as a server or a PC, or a virtual device, such as a virtual machine or a virtualized container. Furthermore, electronic device 300 is not limited to a single device and may also be a combination of multiple devices or a cluster consisting of a large number of devices.

[0166] An embodiment of the present application further provides a storage medium, on which a computer program is stored. When the computer program is run by a processor, the above method is executed.

[0167] Among them, the storage medium can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as static random access memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic storage, flash memory, magnetic disk or optical disk.

[0168] An embodiment of the present application also provides a computer program product, including computer program instructions, which execute the above method when executed by a processor.

[0169] In the several embodiments provided in the embodiments of the present application, it should be understood that the disclosed devices and methods can also be implemented in other ways. The device embodiments described above are merely illustrative. For example, the flowcharts and block diagrams in the accompanying drawings show the possible architectures, functions, and operations of the devices, methods, and computer program products according to the multiple embodiments of the embodiments of the present application. In this regard, each box in the flowchart or block diagram can represent a module, a program segment, or a portion of code, and the module, program segment, or a portion of code contains one or more executable instructions for implementing the specified logical functions. It should also be noted that in some alternative implementations, the functions marked in the boxes can also occur in a different order than the order marked in the accompanying drawings. For example, two consecutive boxes can actually be executed substantially in parallel, and they can sometimes be executed in the opposite order, depending on the functions involved. It should also be noted that each box in the block diagram and / or flowchart, and the combination of boxes in the block diagram and / or flowchart, can be implemented using a dedicated hardware-based system that performs the specified function or action, or can be implemented using a combination of dedicated hardware and computer instructions.

[0170] In addition, the functional modules in each embodiment of the present application can be integrated together to form an independent part, or each module can exist independently, or two or more modules can be integrated to form an independent part.

[0171] The above description is only an optional implementation method of the embodiment of the present application, but the protection scope of the embodiment of the present application is not limited to this. Any technician familiar with this technical field can easily think of changes or replacements within the technical scope disclosed in the embodiment of the present application, and they should all be covered by the protection scope of the embodiment of the present application.

Claims

1. A method for measuring a neutron collimator flexible diaphragm, characterized in that: include: Acquire multi-angle images of the flexible membrane of the neutron collimator; Mapping the multi-angle image to a three-dimensional space coordinate system based on pixel information of the multi-angle image and camera parameters to obtain multi-dimensional vectors corresponding to pixel points in the multi-angle image; The multidimensional vector is used to represent the spatial position and light direction corresponding to the pixel point; Generate a three-dimensional point cloud model using the multi-dimensional vector and the trained neural radiation field model; the three-dimensional point cloud model is used to represent the sampling point information corresponding to the light; Generate a digital twin model of the flexible diaphragm according to the three-dimensional point cloud model; the digital twin model is an information model based on the virtual mapping of the flexible diaphragm; The flexible diaphragm is measured by the digital twin model.

2. The method according to claim 1, characterized in that The pixel information includes pixel coordinates of pixel points; mapping the multi-angle image to a three-dimensional space coordinate system based on the pixel information of the multi-angle image and camera parameters to obtain a multi-dimensional vector corresponding to the pixel points in the multi-angle image includes: Obtaining camera parameters for capturing the multi-angle images and pixel coordinates of pixel points in the multi-angle images; the camera parameters include camera intrinsic parameters and camera extrinsic parameters; Calculating the three-dimensional direction vector of the light by using the camera intrinsic parameters and the pixel coordinates; Determining the light direction of the pixel point according to the three-dimensional direction vector of the light; Determining a spatial position corresponding to the pixel point according to the three-dimensional direction vector of the light and the camera extrinsic parameter; According to the light direction and spatial position of the pixel point, a multidimensional vector corresponding to the pixel point is obtained.

3. The method according to claim 1, characterized in that The generating of a three-dimensional point cloud model by using the multi-dimensional vector and the trained neural radiation field model includes: Inputting the multidimensional vector corresponding to the pixel point into the neural radiation field model to obtain parameter information of the pixel point; the parameter information includes color and density; The three-dimensional point cloud model is generated according to the parameter information of the pixel points through volume rendering technology; the volume rendering technology is used to simulate the changes of light when it propagates in three-dimensional space.

4. The method according to claim 1, wherein in, The neural radiation field model includes a vector input layer, a position encoding layer, a feature extraction layer, a ray casting and sampling layer, and an output layer; The vector input layer is used to convert the multidimensional vector into a feature representation; The position encoding layer is used to perform position encoding on the feature representation to obtain the encoded spatial coordinates; The feature extraction layer is used to extract features from the encoded spatial coordinates to generate feature data; wherein the feature extraction layer includes multiple fully connected layers; the fully connected layers include an occlusion attention module, and the occlusion attention module is used to dynamically mask the features of the occluded area through the diaphragm spacing data; The ray casting and sampling layer is used to perform multiple sampling on the ray path corresponding to the pixel point to generate multiple sampling points; The output layer is used to output parameter information of the sampling points; the parameter information of the sampling points is used to generate the three-dimensional point cloud model.

5. The method according to claim 1, wherein Generating a digital twin model of the flexible diaphragm according to the three-dimensional point cloud model includes: Performing geometric repair on the format-converted three-dimensional point cloud model to obtain a repaired model; the geometric repair includes light transmission compensation and curvature optimization; Assigning physical properties to the repaired model to obtain a physical entity model; the physical property assignment includes material modeling and dynamic property configuration; Performing virtual environment modeling on the physical entity model to obtain a body to be simulated; the virtual environment modeling includes neutron radiation field simulation and embedded radiation heat effect; Multi-physics field coupling is performed according to the parameters of the body to be simulated to generate a digital twin model of the flexible diaphragm; the multi-physics field coupling includes mechanical thermal coupling and boundary conditions.

6. The method according to claim 1, wherein After generating a digital twin model of the flexible diaphragm according to the three-dimensional point cloud model, the method further includes: The digital twin model is sequentially subjected to static performance simulation, dynamic performance simulation, and multi-physics coupling simulation to generate a measurement strategy; the static performance simulation is used to simulate the deformation and stress distribution of the diaphragm under working conditions; the static performance simulation includes deformation analysis and stress concentration location analysis under neutron radiation; the dynamic performance simulation is used to analyze the vibration and fatigue behavior of the diaphragm under dynamic loads; the dynamic performance simulation includes vibration modal analysis and fatigue life prediction; the multi-physics coupling simulation is used to comprehensively analyze the mutual influence between heat, force, and radiation fields, and the multi-physics coupling simulation includes thermal-mechanical coupling analysis and radiation material performance degradation; The measuring the flexible diaphragm by using the digital twin model includes: measuring the flexible diaphragm by using the digital twin model according to the measurement strategy.

7. The method according to claim 1, characterized in that The measuring of the flexible diaphragm by using the digital twin model includes: Simulating the geometric shape of the flexible diaphragm under different working conditions using the digital twin model, measuring the angle of the flexible diaphragm in virtual space, and obtaining measurement data; the measurement data includes the angle of the flexible diaphragm relative to a reference plane and the relative angle changes between different parts of the flexible diaphragm; After measuring the flexible diaphragm using the digital twin model, the method further includes: The measurement data of the flexible diaphragm is obtained, the measurement data is analyzed, and a diaphragm angle correction scheme is generated; and the measurement data is corrected using the diaphragm angle correction scheme.

8. The method according to claim 7, characterized in that The measuring of the flexible diaphragm by using the digital twin model includes: Performing real-time monitoring on the flexible diaphragm and uploading the detection results to a control system; If it is detected that the angle of the flexible diaphragm is offset, the position of the flexible diaphragm or the external force applied thereto is automatically adjusted by the control system.

9. A computer program product, characterized in that The method comprises computer program instructions, which are used to execute the method according to any one of claims 1 to 8 when the computer program instructions are executed by a processor.

10. An electronic device, characterized in that: include: A processor and a memory, wherein the memory stores computer program instructions, and when the computer program instructions are executed by the processor, the method according to any one of claims 1 to 8 is executed.

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