A nuclear fuel rod gap measurement method, device, equipment and storage medium

Abstract

This application discloses a method, apparatus, device, and storage medium for measuring the gap between nuclear fuel rods, relating to the field of industrial measurement technology. The method includes: determining the 3D point clouds of the left and right cameras of the target nuclear fuel rod assembly based on images from the left and right cameras; transforming the 3D point cloud of the left camera to the coordinate system of the right camera according to the matrix transformation relationship between the left and right cameras to obtain the source 3D point cloud; determining the point cloud registration matrix based on the source and right camera 3D point clouds using a feature matching algorithm, and aligning and fusing the source and right camera 3D point clouds using the point cloud registration matrix to obtain the target 3D point cloud of the target nuclear fuel rod assembly; and determining the fuel rod gap between at least two candidate nuclear fuel rods in the target nuclear fuel rod assembly based on the target 3D point cloud. This technical solution improves the measurement accuracy of the nuclear fuel rod gap while achieving non-contact measurement.

Description

Technical Field

[0001] This application relates to the field of industrial measurement technology, and more particularly to the field of nuclear fuel rod measurement technology, specifically to a method, apparatus, equipment, and storage medium for measuring the gap between nuclear fuel rods. Background Technology

[0002] Nuclear fuel rods are the core components of a nuclear reactor, and the precise measurement of their spacing is crucial to ensuring the reactor's safety and operational efficiency.

[0003] Currently, traditional gap measurement methods that rely on contact measurement tools or two-dimensional measurement techniques based on optical imaging have certain limitations in the high-temperature and high-radiation nuclear reactor environment. Furthermore, monocular structured light cameras are affected by the reflection of fuel rods, resulting in significant deviations in measurement accuracy. In addition, existing nuclear fuel rod gap measurement equipment and technologies have varying degrees of risks or disadvantages in terms of equipment structure complexity, operational complexity, or economic efficiency. Contact measurement also carries nuclear fuel safety risks. Summary of the Invention

[0004] This application provides a method, apparatus, device, and storage medium for measuring the gap between nuclear fuel rods, thereby improving the measurement accuracy of the gap between nuclear fuel rods while achieving non-contact measurement.

[0005] According to one aspect of this application, a method for measuring the gap between nuclear fuel rods is provided. This method is applied to a data processing center within a nuclear fuel rod gap measurement system. The nuclear fuel rod gap measurement system further includes a projector, a left camera located to the left of the projector, and a right camera located to the right of the projector. The projector and the left camera constitute a left monocular structured light measurement module; the projector and the right camera constitute a right monocular structured light measurement module; the data processing center is communicatively connected to both the left and right monocular structured light measurement modules. The method includes:

[0006] Based on the left and right camera images of the target nuclear fuel rod assembly, the three-dimensional point clouds of the left and right cameras of the target nuclear fuel rod assembly are determined; wherein, the left and right camera images are obtained by acquiring the phase difference fringe patterns projected by the projector onto the target nuclear fuel rod assembly through the left and right cameras, respectively;

[0007] Based on the matrix transformation relationship between the left and right cameras, the 3D point cloud of the left camera is transformed into the coordinate system of the right camera to obtain the source 3D point cloud; wherein, the matrix transformation relationship is determined by calibrating the coordinate systems of the left and right cameras using a calibration board;

[0008] Based on the feature matching algorithm, a point cloud registration matrix is ​​determined according to the source 3D point cloud and the right camera 3D point cloud. The point cloud registration matrix is ​​then used to align and fuse the source 3D point cloud and the right camera 3D point cloud to obtain the target 3D point cloud of the target nuclear fuel rod group.

[0009] Based on the target three-dimensional point cloud, determine the fuel rod gap between at least two candidate nuclear fuel rods in the target nuclear fuel rod group.

[0010] According to another aspect of this application, a nuclear fuel rod gap measurement device is provided, which is configured in the data processing center of a nuclear fuel rod gap measurement system; the nuclear fuel rod gap measurement system further includes a projector, a left camera located to the left of the projector, and a right camera located to the right of the projector; the projector and the left camera constitute a left monocular structured light measurement module; the projector and the right camera constitute a right monocular structured light measurement module; the data processing center is communicatively connected to both the left monocular structured light measurement module and the right monocular structured light measurement module; the device includes:

[0011] The point cloud determination module is used to determine the three-dimensional point cloud of the left camera and the three-dimensional point cloud of the right camera of the target nuclear fuel rod assembly based on the left camera image and the right camera image of the target nuclear fuel rod assembly; wherein, the left camera image and the right camera image are respectively acquired by the left camera and the right camera through the phase difference fringe pattern projected by the projector onto the target nuclear fuel rod assembly by the projector;

[0012] The point cloud conversion module is used to convert the 3D point cloud of the left camera to the coordinate system of the right camera according to the matrix conversion relationship between the left and right cameras, so as to obtain the source 3D point cloud; wherein, the matrix conversion relationship is determined by calibrating the coordinate systems of the left camera and the right camera using a calibration plate;

[0013] The point cloud alignment module is used to determine the point cloud registration matrix based on the source 3D point cloud and the right camera 3D point cloud according to the feature matching algorithm, and to align and fuse the source 3D point cloud and the right camera 3D point cloud using the point cloud registration matrix to obtain the target 3D point cloud of the target nuclear fuel rod group.

[0014] The gap measurement module is used to determine the fuel rod gap between at least two candidate nuclear fuel rods in the target nuclear fuel rod group based on the target three-dimensional point cloud.

[0015] According to another aspect of this application, an electronic device is provided, the electronic device comprising:

[0016] One or more processors;

[0017] Memory, used to store one or more programs;

[0018] When the one or more programs are executed by the one or more processors, the one or more processors implement any of the nuclear fuel rod gap measurement methods provided in the embodiments of this application.

[0019] According to another aspect of this application, a computer-readable storage medium is provided, on which a computer program is stored, which, when executed by a processor, implements any of the nuclear fuel rod gap measurement methods provided in the embodiments of this application.

[0020] According to another aspect of this application, a computer program product is provided, including a computer program that, when executed by a processor, implements any of the nuclear fuel rod gap measurement methods provided in the embodiments of this application.

[0021] This application determines the 3D point clouds of the target nuclear fuel rod assembly from the left and right camera images. The left and right camera images are acquired by capturing the phase difference fringe patterns projected onto the target nuclear fuel rod assembly by the projector using the left and right cameras, respectively. Based on the matrix transformation relationship between the left and right cameras, the 3D point cloud from the left camera is transformed into the coordinate system of the right camera to obtain the source 3D point cloud. The matrix transformation relationship is determined after calibrating the coordinate systems of the left and right cameras using a calibration plate. Based on a feature matching algorithm, a point cloud registration matrix is ​​determined from the source and right camera 3D point clouds. This registration matrix is ​​then used to align and fuse the source and right camera 3D point clouds to obtain the target 3D point cloud of the target nuclear fuel rod assembly. Based on the target 3D point cloud, the fuel rod gap between at least two candidate nuclear fuel rods in the target nuclear fuel rod assembly is determined. This technical solution, by combining multi-view fusion technology, improves the accuracy of nuclear fuel rod gap measurement while achieving non-contact measurement. Attached Figure Description

[0022] Figure 1 This is a flowchart of a method for measuring the gap between nuclear fuel rods according to Embodiment 1 of this application;

[0023] Figure 2 This is a flowchart of a nuclear fuel rod gap measurement method according to Embodiment 2 of this application;

[0024] Figure 3 This is a schematic diagram of a nuclear fuel rod gap measuring device provided in Embodiment 3 of this application;

[0025] Figure 4 This is a schematic diagram of the structure of an electronic device for implementing the nuclear fuel rod gap measurement method of Embodiment 4 of this application. Detailed Implementation

[0026] To enable those skilled in the art to better understand the present application, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present application, and not all embodiments. Based on the embodiments in the present application, all other embodiments obtained by those of ordinary skill in the art without creative effort should fall within the scope of protection of the present application.

[0027] It should be noted that the terms "first," "second," etc., in the specification, claims, and accompanying drawings of this application are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments of this application described herein can be implemented in orders other than those illustrated or described herein. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover non-exclusive inclusion; for example, a process, method, system, product, or apparatus that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus.

[0028] Furthermore, it should be noted that the collection, storage, use, processing, transmission, provision, and disclosure of data related to the left and right camera images in the technical solution of this application all comply with the provisions of relevant laws and regulations and do not violate public order and good morals.

[0029] Example 1

[0030] Figure 1 This is a flowchart of a nuclear fuel rod gap measurement method according to Embodiment 1 of this application. This embodiment is applicable to the measurement of the gap between at least two nuclear fuel rods, and can be performed by a nuclear fuel rod gap measurement device. This device can be implemented in hardware and / or software and can be configured in a computer device, such as the data processing center of a nuclear fuel rod gap measurement system. The nuclear fuel rod gap measurement system also includes a projector, a left camera located to the left of the projector, and a right camera located to the right of the projector. The projector and the left camera constitute a left monocular structured light measurement module; the projector and the right camera constitute a right monocular structured light measurement module; the data processing center is communicatively connected to both the left and right monocular structured light measurement modules. Figure 1 As shown, the method includes:

[0031] S110. Based on the left and right camera images of the target nuclear fuel rod assembly, determine the three-dimensional point cloud of the left camera and the three-dimensional point cloud of the right camera of the target nuclear fuel rod assembly; wherein, the left camera image and the right camera image are obtained by acquiring the phase difference fringe pattern projected by the projector onto the target nuclear fuel rod assembly through the left and right cameras respectively.

[0032] In this embodiment, the target nuclear fuel rod assembly refers to the collection of fuel rods used to generate energy in a nuclear reactor. Left camera images and right camera images refer to images captured by two cameras, one on the left and one on the right, which work together in a special configuration (such as stereo vision) to capture different perspectives of the same target. A 3D point cloud is a set of spatial data consisting of many discrete points obtained through a 3D scanning device (such as a stereo camera or laser scanner); the left camera 3D point cloud is the spatial data set obtained by the left camera, and the right camera 3D point cloud is the spatial data set obtained by the right camera. A phase difference fringe pattern is a pattern used for 3D reconstruction. It is projected onto the surface of an object by a projector, and the left and right cameras capture the phase difference between the object's surface and the projected pattern, thereby obtaining the depth information of the object's surface. The phase difference fringe pattern helps extract the 3D morphology of the object's surface.

[0033] Optionally, the grayscale values ​​of pixels in the left camera image of the target nuclear fuel rod group are calculated to obtain the first grayscale value set of the left camera image; based on the first grayscale value set, the first wrapping phase set of the left camera image is determined; wherein, the first wrapping phase set is composed of the wrapping phase of each pixel in the left camera image; a multi-frequency heterodyne algorithm is used to solve the first wrapping phase set to obtain the first absolute phase set of the left camera image; based on the first absolute phase set, the three-dimensional point cloud of the target nuclear fuel rod group in the left camera coordinate system is determined.

[0034] In this embodiment, the pixel grayscale value refers to the brightness or intensity information of each pixel in the image; in a grayscale image, the value of each pixel represents different grayscale levels from black to white. The first grayscale value set refers to the data set composed of the grayscale values ​​of all pixels in the left camera image, reflecting the distribution of image brightness. The wrapped phase is the data set obtained by unwrapping the stripe pattern in the image; in this technology, the wrapped phase refers to the phase information related to the surface texture of the object, representing the phase change at different locations. The first wrapped phase set is the wrapped phase calculated based on the first grayscale value set. The multi-frequency heterodyne algorithm is an algorithm that improves the phase calculation accuracy by using stripe patterns of multiple different frequencies; in this algorithm, stripe patterns of different frequencies are used for measurement, thereby obtaining more accurate phase information through heterodyne processing (calculation of frequency difference). The first absolute phase set refers to the set of phase values ​​obtained by unwrapping the first wrapped phase set, which has physical meaning; the first absolute phase set calculated by using the multi-frequency heterodyne algorithm can provide the actual phase value of each pixel on the surface of the target object (here, the target nuclear fuel rod assembly), reflecting the depth or shape information of the object surface. The left camera coordinate system refers to the three-dimensional coordinate system associated with the left camera, used to represent three-dimensional spatial data from the left camera's perspective.

[0035] Optionally, the grayscale values ​​of pixels in the right camera image of the target nuclear fuel rod group are calculated to obtain a second grayscale value set of the right camera image; based on the second grayscale value set, a second wrapping phase set of the right camera image is determined; wherein, the second wrapping phase set is composed of the wrapping phase of each pixel in the right camera image; a multi-frequency heterodyne algorithm is used to solve the second wrapping phase set to obtain a second absolute phase set of the right camera image; based on the second absolute phase set, the three-dimensional point cloud of the target nuclear fuel rod group in the right camera coordinate system is determined.

[0036] In this embodiment, the second grayscale value set refers to the data set composed of the grayscale values ​​of all pixels in the right camera image, reflecting the distribution of image brightness. The second wrapping phase set is the wrapping phase calculated based on the second grayscale value set. The second absolute phase set refers to the set of phase values ​​obtained by unwrapping from the second wrapping phase set, and has physical meaning; the second absolute phase set calculated using the multi-frequency heterodyne algorithm can provide the actual phase value of each pixel on the surface of the target object (here, the target nuclear fuel rod assembly), reflecting the depth or shape information of the object's surface. The right camera coordinate system refers to the three-dimensional coordinate system associated with the right camera, used to represent the three-dimensional spatial data from the right camera's perspective.

[0037] For example, firstly, the projector projects four sinusoidal fringe patterns with phases differing by π / 4. The left and right cameras simultaneously acquire and save images of the target object based on their respective camera coordinate systems. The grayscale values ​​of the pixels in the four captured sinusoidal structured light images are calculated, and the wrapping phase of the pixels can be calculated based on the grayscale values. Based on the wrapping phase of each pixel, the wrapping phase is converted into an absolute phase using the multi-frequency heterodyne method. Based on the absolute phase, a three-dimensional point cloud of the target object is established in the coordinate systems of the left and right cameras respectively.

[0038] S120. Based on the matrix transformation relationship between the left and right cameras, transform the 3D point cloud of the left camera into the coordinate system of the right camera to obtain the source 3D point cloud; wherein, the matrix transformation relationship is determined by calibrating the coordinate systems of the left and right cameras using a calibration plate.

[0039] In this embodiment, the matrix transformation relationship refers to the coordinate system relationship between the left and right cameras. Coordinate system calibration refers to accurately determining the spatial positional relationship and directional differences between different devices (such as left and right cameras) using specific methods or calibration boards. The source 3D point cloud refers to the 3D point cloud data acquired by the left camera, which has been transformed into the coordinate system of the right camera.

[0040] In one alternative implementation, the matrix transformation relationship can be determined as follows: The projector and two cameras are fixed in place. Each camera and the projector constructs a monocular structured light measurement model. The two modules are calibrated by placing a circular array calibration plate at the edge of the camera's field of view. Sixteen images of the calibration plate at different positions are simultaneously acquired from both monocular structured light measurement modules. The pixel coordinates at the center of the calibration plate are extracted. Combined with the corresponding real-world coordinates of the center pixels, the intrinsic and extrinsic parameter matrices of the camera and projector in each module are obtained. The calibration plate is placed at different positions and angles to ensure it is within the field of view of the left and right cameras. Sixteen images from the left and right cameras are simultaneously acquired. The center coordinates on the calibration plate are detected for each image to ensure a one-to-one correspondence between the centers in the left and right camera images. An extrinsic parameter transformation formula between the left and right cameras is introduced to calculate the matrix transformation relationship between them.

[0041] For example, the external parameter conversion formula is as follows:

[0042]

[0043] in, It is a three-dimensional coordinate point in the right camera coordinate system. This refers to the 3D coordinates of the point in the left camera's coordinate system. R represents the rotation matrix of the left camera relative to the right camera. t represents the translation vector of the left camera relative to the right camera.

[0044] Furthermore, based on the matrix transformation relationship between the left and right cameras, the 3D point cloud of the left camera is transformed into the coordinate system of the right camera to obtain the source 3D point cloud. This can be done by transforming the 3D point cloud generated by the left camera into the coordinate system of the right camera based on the rotation matrix R and translation vector t in the extrinsic parameter transformation formula.

[0045] S130. Based on the feature matching algorithm, determine the point cloud registration matrix according to the source 3D point cloud and the right camera 3D point cloud, and use the point cloud registration matrix to align and fuse the source 3D point cloud and the right camera 3D point cloud to obtain the target 3D point cloud of the target nuclear fuel rod group.

[0046] In this embodiment, the feature matching algorithm is a method that analyzes and compares key features (such as point distribution, surface features, etc.) in two point cloud datasets to find the correspondence between them, thereby aligning and registering the two point clouds. A point cloud registration matrix is ​​a mathematical tool for aligning point cloud data in two different coordinate systems. It typically includes rotation matrices and translation vectors, describing how to transform the source point cloud from one coordinate system to the coordinate system of the target point cloud. Alignment refers to aligning two point cloud datasets to the same coordinate system based on feature matching or the registration matrix. Fusion refers to merging the aligned two point cloud datasets into a single whole to obtain a more complete 3D shape. The target 3D point cloud is the final 3D point cloud data obtained by aligning and fusing the source 3D point cloud and the right camera 3D point cloud, representing the complete 3D shape of the target nuclear fuel rod assembly.

[0047] For example, based on the feature matching algorithm, a point cloud registration matrix is ​​determined according to the source 3D point cloud and the right camera 3D point cloud, and the point cloud is initially aligned using this matrix to obtain candidate 3D point clouds; voxel filtering is used to downsample the candidate 3D point clouds to remove redundant points in overlapping areas; finally, the candidate 3D point clouds are fused, and the fused point cloud is smoothed using the moving least squares method to obtain the target 3D point cloud of the target nuclear fuel rod group.

[0048] S140. Based on the target three-dimensional point cloud, determine the fuel rod gap between at least two candidate nuclear fuel rods in the target nuclear fuel rod group.

[0049] In this embodiment, candidate nuclear fuel rods refer to all nuclear fuel rods included in a nuclear fuel rod bank. Fuel rod gaps refer to the spatial spacing between multiple nuclear fuel rods in a nuclear reactor.

[0050] In one optional implementation, after determining the fuel rod gaps between all candidate fuel rods in the target nuclear fuel rod group, the gaps between multiple candidate fuel rods and the designed fuel rod gaps can be fitted using the least squares method to reduce errors that may occur during point cloud acquisition, cylinder fitting, etc., to obtain the fitting relationship of the measured values. The fuel rod gaps between at least two candidate fuel rods in the target nuclear fuel rod group are then measured again to obtain the measured values ​​of the fuel rod gaps. Based on the fitting relationship of the measured values, the measured values ​​are fitted to obtain the final measurement result.

[0051] In this embodiment, the least squares method is a commonly used mathematical optimization method to find the optimal fitting curve or fitting function by minimizing the sum of squared errors. The fitting relationship refers to the mathematical expression obtained after data fitting, which describes the relationship between input and output variables; here, the fitting relationship describes the mathematical relationship between the gap between candidate nuclear fuel rods and other relevant data. The measured value refers to the actual measurement result of the fuel rod gap. The final measurement result is the accurate measurement value obtained after data fitting and error correction.

[0052] This embodiment of the application determines the 3D point clouds of the target nuclear fuel rod assembly from the left and right camera images. The left and right camera images are acquired by capturing the phase difference fringe patterns projected onto the target nuclear fuel rod assembly by the projector using the left and right cameras, respectively. Based on the matrix transformation relationship between the left and right cameras, the 3D point cloud from the left camera is transformed into the coordinate system of the right camera to obtain the source 3D point cloud. The matrix transformation relationship is determined after calibrating the coordinate systems of the left and right cameras using a calibration plate. Based on a feature matching algorithm, a point cloud registration matrix is ​​determined from the source and right camera 3D point clouds. This registration matrix is ​​then used to align and fuse the source and right camera 3D point clouds to obtain the target 3D point cloud of the target nuclear fuel rod assembly. Based on the target 3D point cloud, the fuel rod gap between at least two candidate nuclear fuel rods in the target nuclear fuel rod assembly is determined. This technical solution, by combining multi-view fusion technology, improves the accuracy of nuclear fuel rod gap measurement while achieving non-contact measurement.

[0053] Example 2

[0054] Figure 2This is a flowchart of a nuclear fuel rod gap measurement method according to Embodiment 2 of this application. Based on the technical solutions of the above embodiments, this embodiment refines "determining the fuel rod gap between at least two candidate nuclear fuel rods in the target nuclear fuel rod group according to the target nuclear fuel rod group" into "performing Euclidean clustering segmentation of the target three-dimensional point cloud to obtain fuel rod point clouds of at least two candidate nuclear fuel rods in the target nuclear fuel rod group; using a random sampling consensus algorithm, performing cylindrical fitting on the fuel rod point clouds to obtain cylindrical parameters of at least two candidate nuclear fuel rods; determining the fuel rod gap between at least two candidate nuclear fuel rods based on the cylindrical parameters of at least two nuclear fuel rods." It should be noted that for parts not detailed in this embodiment, please refer to the relevant descriptions in other embodiments. Figure 2 As shown, the method includes:

[0055] S210. Based on the left and right camera images of the target nuclear fuel rod assembly, determine the three-dimensional point cloud of the left camera and the three-dimensional point cloud of the right camera of the target nuclear fuel rod assembly.

[0056] S220. Based on the matrix transformation relationship between the left and right cameras, transform the 3D point cloud of the left camera into the coordinate system of the right camera to obtain the source 3D point cloud.

[0057] S230. Based on the feature matching algorithm, the point cloud registration matrix is ​​determined according to the source 3D point cloud and the right camera 3D point cloud. The point cloud registration matrix is ​​then used to align and fuse the source 3D point cloud and the right camera 3D point cloud to obtain the target 3D point cloud of the target nuclear fuel rod group.

[0058] Optionally, based on a feature matching algorithm, the source 3D point cloud and the right camera 3D point cloud are decentered and their covariance is calculated to obtain the target covariance matrix, as well as the first center point of the source 3D point cloud and the second center point of the right camera 3D point cloud; singular value decomposition is performed on the target covariance matrix to obtain the left singular vector matrix and the right singular vector matrix; the rotation matrix is ​​determined based on the left singular vector matrix and the right singular vector matrix; the translation vector is determined based on the rotation matrix, the first center point and the second center point; and the point cloud registration matrix is ​​determined based on the translation vector and the rotation matrix.

[0059] In this embodiment, decentralization involves adjusting the coordinate system of the point cloud to its geometric center (centroid) as the origin. This is achieved by subtracting the coordinates of the point cloud's center point from the coordinates of each point, ensuring the centroid of the point cloud is located at the origin. This operation helps eliminate point cloud data offset and improves registration accuracy. Covariance calculation measures the changing trend of points in the point cloud data. By calculating the covariance matrix of the point cloud data, the linear relationships, distribution, and transformation characteristics between data points can be understood. The target covariance matrix is ​​the decentralized covariance matrix of the source 3D point cloud and the right camera 3D point cloud; it describes the degree of change and distribution pattern of the target point cloud in various directions in 3D space. The first centroid refers to the centroid of the source 3D point cloud, representing the geometric center of the source point cloud data. The second centroid refers to the centroid of the right camera 3D point cloud, representing the geometric center of the target point cloud data. Singular value decomposition (SVD) is a matrix decomposition method that decomposes a matrix into the product of three matrices: the left singular vector matrix, the singular value matrix, and the right singular vector matrix. The left singular vector matrix represents the basis vector directions of the source point cloud data in the data space. The right singular vector matrix represents the basis vector directions of the target point cloud data in the data space. The rotation matrix describes the rotation transformation in space; by combining the left and right singular vector matrices, the rotation matrix is ​​obtained, indicating how the source point cloud is rotated to align with the target point cloud. The translation vector represents the amount of translation from the center point of the source point cloud (first center point) to the center point of the target point cloud (second center point); this vector defines the position adjustment of the point cloud in three-dimensional space.

[0060] S240. Perform Euclidean clustering segmentation on the target 3D point cloud to obtain the fuel rod point cloud of at least two candidate nuclear fuel rods in the target nuclear fuel rod group.

[0061] In this embodiment, Euclidean clustering is a distance-based algorithm used to segment a point cloud dataset into multiple independent clusters; each cluster represents a set of dense regions in the point cloud, where the points are separated by small Euclidean distances. The random sample consensus algorithm is an iterative method used to estimate a mathematical model from data containing outliers; it fits the model by randomly selecting data points and checks whether the model fits the majority of the data points.

[0062] S250. Using a random sampling consensus algorithm, cylinder fitting is performed on the fuel rod point cloud to obtain cylinder parameters for at least two candidate nuclear fuel rods.

[0063] In this embodiment, cylinder fitting uses mathematical methods to fit point cloud data into a cylindrical model. Cylinder parameters are numerical values ​​describing the geometric properties of the cylinder, and may include at least one of the following: cylinder radius, axis endpoint coordinates, and axis direction vector; wherein, axis endpoint coordinates include the coordinates of the upper and lower endpoints of the axis.

[0064] For example, the obtained point cloud is segmented by Euclidean clustering to distinguish the point cloud of each fuel rod in the point cloud set and store it in a container. The random sampling consensus algorithm is used to perform cylinder fitting on each cylindrical rod in the container, and the fitted cylinder parameters are extracted to obtain the cylinder parameters of at least two candidate nuclear fuel rods.

[0065] S260. Determine the fuel rod gap between at least two candidate nuclear fuel rods based on the cylindrical parameters of at least two nuclear fuel rods.

[0066] Optionally, for each pair of candidate nuclear fuel rods, the axis expressions of the two candidate nuclear fuel rods are determined based on the coordinates of their endpoints and the axis direction vector; the axial distance between the two candidate nuclear fuel rods is determined based on the geometric distance calculation method and the axis expressions; and the fuel rod gap between the two candidate nuclear fuel rods is determined based on the axial distance and the cylinder radius of the two candidate nuclear fuel rods.

[0067] In this embodiment, the axis endpoint coordinates are the coordinates of the two endpoints of the axis of each candidate nuclear fuel rod in three-dimensional space. The axis direction vector is a vector indicating the direction of the nuclear fuel rod's axis; it represents the direction of the nuclear fuel rod from one end to the other and is typically calculated from the coordinates of the two endpoints; this vector is used to describe the geometric orientation of the fuel rod. The axis expression refers to the geometry of the axis of the nuclear fuel rod; in three-dimensional space, the axis can be represented by a point (endpoint coordinates) and a direction vector. The axis distance refers to the geometric distance between the axes of two candidate nuclear fuel rods, i.e., the shortest distance from one axis to another. The cylinder radius is a fundamental parameter describing the cylindrical shape of the nuclear fuel rod, representing the radius of the cylinder.

[0068] For example, the axis expression can be represented by the following formula:

[0069]

[0070] in, , and It is the coordinate of an endpoint of the axis, or the coordinate of the starting point; it represents the starting position of the axis, specifically a point in three-dimensional space. , and It is the coordinate of the other endpoint of the axis, indicating the position of the other endpoint of the axis. , and It refers to the coordinates of any point on the axis. By giving the parameter t, different points on the axis can be obtained. The axis is composed of these points. As t changes, the position of the points gradually changes along the axis. When t is 0, it represents the starting point of the axis. When t is 1, it represents the ending point of the line segment.

[0071] For example, the fuel rod gap can be calculated using the following formula:

[0072]

[0073] Where L refers to the fuel rod gap and D refers to the axial distance. This refers to the radius of one of the fuel rods in the current calculation of the fuel rod gap. This refers to the radius of the other fuel rod used to calculate the fuel rod gap.

[0074] This embodiment of the application determines the 3D point clouds of the target nuclear fuel rod assembly based on the left and right camera images of the target nuclear fuel rod assembly. The left and right camera images are obtained by acquiring the phase difference fringe patterns projected onto the target nuclear fuel rod assembly by the projector using the left and right cameras, respectively. The 3D point cloud of the left camera is transformed into the coordinate system of the right camera according to the matrix transformation relationship between the left and right cameras to obtain the source 3D point cloud. The matrix transformation relationship is determined after calibrating the coordinate systems of the left and right cameras using a calibration plate. Based on a feature matching algorithm, according to... The source 3D point cloud and the right camera 3D point cloud are used to determine a point cloud registration matrix. This matrix is ​​then used to align and fuse the source and right camera 3D point clouds to obtain the target 3D point cloud for the target nuclear fuel rod group. Euclidean clustering is performed on the target 3D point cloud to obtain fuel rod point clouds for at least two candidate nuclear fuel rods in the target nuclear fuel rod group. A random sampling consensus algorithm is used to perform cylinder fitting on the fuel rod point clouds to obtain cylinder parameters for at least two candidate nuclear fuel rods. Based on the cylinder parameters of at least two nuclear fuel rods, the fuel rod gap between at least two candidate nuclear fuel rods is determined. This technical solution, by combining multi-view fusion technology, can improve the accuracy of nuclear fuel rod gap measurement while achieving non-contact measurement.

[0075] Example 3

[0076] Figure 3This is a schematic diagram of a nuclear fuel rod gap measuring device according to Embodiment 3 of this application. It is applicable to measuring the gap between at least two nuclear fuel rods. The nuclear fuel rod gap measuring device can be implemented in hardware and / or software and can be configured in a computer device, such as the data processing center of a nuclear fuel rod gap measuring system. The nuclear fuel rod gap measuring system also includes a projector, a left camera located to the left of the projector, and a right camera located to the right of the projector. The projector and the left camera constitute a left monocular structured light measurement module; the projector and the right camera constitute a right monocular structured light measurement module; the data processing center is communicatively connected to both the left and right monocular structured light measurement modules. Figure 3 As shown, the device includes:

[0077] The point cloud determination module 310 is used to determine the three-dimensional point cloud of the left camera and the three-dimensional point cloud of the right camera of the target nuclear fuel rod group based on the left camera image and the right camera image of the target nuclear fuel rod group; wherein, the left camera image and the right camera image are respectively acquired by the left camera and the right camera to the phase difference fringe pattern projected by the projector onto the target nuclear fuel rod group;

[0078] The point cloud conversion module 320 is used to convert the 3D point cloud of the left camera to the coordinate system of the right camera according to the matrix conversion relationship between the left and right cameras, so as to obtain the source 3D point cloud; wherein, the matrix conversion relationship is determined after the coordinate system of the left and right cameras is calibrated by a calibration plate;

[0079] The point cloud alignment module 330 is used to determine the point cloud registration matrix based on the source 3D point cloud and the right camera 3D point cloud according to the feature matching algorithm, and to align and fuse the source 3D point cloud and the right camera 3D point cloud using the point cloud registration matrix to obtain the target 3D point cloud of the target nuclear fuel rod group.

[0080] The gap measurement module 340 is used to determine the fuel rod gap between at least two candidate nuclear fuel rods in the target nuclear fuel rod group based on the target three-dimensional point cloud.

[0081] This embodiment of the application determines the 3D point clouds of the target nuclear fuel rod assembly from the left and right camera images. The left and right camera images are acquired by capturing the phase difference fringe patterns projected onto the target nuclear fuel rod assembly by the projector using the left and right cameras, respectively. Based on the matrix transformation relationship between the left and right cameras, the 3D point cloud from the left camera is transformed into the coordinate system of the right camera to obtain the source 3D point cloud. The matrix transformation relationship is determined after calibrating the coordinate systems of the left and right cameras using a calibration plate. Based on a feature matching algorithm, a point cloud registration matrix is ​​determined from the source and right camera 3D point clouds. This registration matrix is ​​then used to align and fuse the source and right camera 3D point clouds to obtain the target 3D point cloud of the target nuclear fuel rod assembly. Based on the target 3D point cloud, the fuel rod gap between at least two candidate nuclear fuel rods in the target nuclear fuel rod assembly is determined. This technical solution, by combining multi-view fusion technology, improves the accuracy of nuclear fuel rod gap measurement while achieving non-contact measurement.

[0082] Optionally, the gap measurement module 340 includes:

[0083] The point cloud segmentation unit is used to perform Euclidean clustering segmentation on the target 3D point cloud to obtain the fuel rod point cloud of at least two candidate nuclear fuel rods in the target nuclear fuel rod group.

[0084] The cylinder fitting unit is used to perform cylinder fitting on the fuel rod point cloud using a random sampling consensus algorithm to obtain cylinder parameters for at least two candidate nuclear fuel rods.

[0085] A gap measurement unit is used to determine the fuel rod gap between at least two candidate nuclear fuel rods based on the cylindrical parameters of at least two nuclear fuel rods.

[0086] Optionally, the cylinder parameters include the cylinder radius, axis endpoint coordinates, and axis direction vector; correspondingly, the gap measurement unit is specifically used for:

[0087] For each pair of candidate nuclear fuel rods, the axis expressions of the two candidate nuclear fuel rods are determined based on the coordinates of their endpoints and the axis direction vector.

[0088] Based on the geometric distance calculation method, the axial distance between the two candidate nuclear fuel rods is determined according to the axial expression of the two candidate nuclear fuel rods;

[0089] The fuel rod gap between the two candidate nuclear fuel rods is determined based on the axial distance and the cylindrical radius of the two candidate nuclear fuel rods.

[0090] Optional, the point cloud determination module 310 is specifically used for:

[0091] The pixel grayscale values ​​of the left camera image of the target nuclear fuel rod group are calculated to obtain the first grayscale value set of the left camera image.

[0092] Based on the first grayscale value set, determine the first wrapping phase set of the left camera image; wherein, the first wrapping phase set is composed of the wrapping phase of each pixel in the left camera image;

[0093] A multi-frequency heterodyne algorithm is used to solve the first package phase set to obtain the first absolute phase set of the left camera image;

[0094] Based on the first absolute phase set, determine the left camera 3D point cloud of the target nuclear fuel rod assembly in the left camera coordinate system.

[0095] Optionally, the point cloud determination module 310 is also specifically used for:

[0096] The pixel grayscale values ​​of the right camera image of the target nuclear fuel rod group are calculated to obtain the second grayscale value set of the right camera image.

[0097] Based on the second grayscale value set, determine the second wrapping phase set of the right camera image; wherein, the second wrapping phase set is composed of the wrapping phase of each pixel in the right camera image;

[0098] A multi-frequency heterodyne algorithm is used to solve the second wrapper phase set to obtain the second absolute phase set of the right camera image;

[0099] Based on the second absolute phase set, determine the three-dimensional point cloud of the target nuclear fuel rod assembly in the right camera coordinate system.

[0100] Optional, the point cloud alignment module 330 is specifically used for:

[0101] Based on the feature matching algorithm, the source 3D point cloud and the right camera 3D point cloud are decentered and covariance is calculated respectively to obtain the target covariance matrix, as well as the first center point of the source 3D point cloud and the second center point of the right camera 3D point cloud.

[0102] Singular value decomposition is performed on the target covariance matrix to obtain the left singular vector matrix and the right singular vector matrix;

[0103] Determine the rotation matrix based on the left and right singular vector matrices;

[0104] Determine the translation vector based on the rotation matrix, the first center point, and the second center point;

[0105] The point cloud registration matrix is ​​determined based on the translation vector and rotation matrix.

[0106] The nuclear fuel rod gap measuring device provided in this application embodiment can execute the nuclear fuel rod gap measuring method provided in any embodiment of this application, and has the corresponding functional modules and beneficial effects for executing each nuclear fuel rod gap measuring method.

[0107] According to embodiments of this application, this application also provides an electronic device, a readable storage medium, and a computer program product.

[0108] Example 4

[0109] Figure 4 This is a schematic diagram of the electronic device 410 implementing the nuclear fuel rod gap measurement method of the embodiments of this application. The electronic device is intended to represent various forms of digital computers, such as laptop computers, desktop computers, workbenches, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The electronic device may also represent various forms of mobile devices, such as personal digital processors, cellular phones, smartphones, wearable devices (such as helmets, glasses, watches, etc.), and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely examples and are not intended to limit the implementation of the present application described and / or claimed herein.

[0110] like Figure 4 As shown, the electronic device 410 includes at least one processor 411 and a memory, such as a read-only memory (ROM) 412 or a random access memory (RAM) 413, communicatively connected to the at least one processor 411. The memory stores computer programs executable by the at least one processor. The processor 411 can perform various appropriate actions and processes based on the computer program stored in the ROM 412 or loaded from storage unit 418 into the RAM 413. The RAM 413 may also store various programs and data required for the operation of the electronic device 410. The processor 411, ROM 412, and RAM 413 are interconnected via a bus 414. An input / output (I / O) interface 415 is also connected to the bus 414.

[0111] Multiple components in electronic device 410 are connected to I / O interface 415, including: input unit 416, such as keyboard, mouse, etc.; output unit 417, such as various types of displays, speakers, etc.; storage unit 418, such as disk, optical disk, etc.; and communication unit 419, such as network card, modem, wireless transceiver, etc. Communication unit 419 allows electronic device 410 to exchange information / data with other devices through computer networks such as the Internet and / or various telecommunications networks.

[0112] Processor 411 can be a variety of general-purpose and / or special-purpose processing components with processing and computing capabilities. Some examples of processor 411 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various special-purpose artificial intelligence (AI) computing chips, various processors running machine learning model algorithms, a digital signal processor (DSP), and any suitable processor, controller, microcontroller, etc. Processor 411 performs the various methods and processes described above, such as the nuclear fuel rod gap measurement method.

[0113] In some embodiments, the nuclear fuel rod gap measurement method may be implemented as a computer program tangibly contained in a computer-readable storage medium, such as storage unit 418. In some embodiments, part or all of the computer program may be loaded and / or installed on electronic device 410 via ROM 412 and / or communication unit 419. When the computer program is loaded into RAM 413 and executed by processor 411, one or more steps of the nuclear fuel rod gap measurement method described above may be performed. Alternatively, in other embodiments, processor 411 may be configured as the nuclear fuel rod gap measurement method by any other suitable means (e.g., by means of firmware).

[0114] Various embodiments of the systems and techniques described above herein can be implemented in digital electronic circuit systems, integrated circuit systems, field-programmable gate arrays (FPGAs), application-specific integrated circuits (ASICs), application-specific standard products (ASSPs), systems-on-a-chip (SoCs), payload-programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various embodiments may include implementations in one or more computer programs that can be executed and / or interpreted on a programmable system including at least one programmable processor, which may be a dedicated or general-purpose programmable processor, capable of receiving data and instructions from a storage system, at least one input device, and at least one output device, and transmitting data and instructions to the storage system, the at least one input device, and the at least one output device.

[0115] Computer programs used to implement the methods of this application may be written in any combination of one or more programming languages. These computer programs may be provided to the processor of a general-purpose computer, a special-purpose computer, or other programmable nuclear fuel rod gap measuring device, such that when executed by the processor, the computer programs cause the functions / operations specified in the flowcharts and / or block diagrams to be implemented. The computer programs may be executed entirely on a machine, partially on a machine, or as a standalone software package, partially on a machine and partially on a remote machine, or entirely on a remote machine or server.

[0116] In the context of this application, a computer-readable storage medium can be a tangible medium that may contain or store a computer program for use by or in conjunction with an instruction execution system, apparatus, or device. A computer-readable storage medium can be, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, apparatus, or devices, or any suitable combination of the foregoing. Alternatively, a computer-readable storage medium can be a machine-readable signal medium. More specific examples of machine-readable storage media include electrical connections based on one or more wires, portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination of the foregoing.

[0117] To provide interaction with a user, the systems and techniques described herein can be implemented on an electronic device having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to the user; and a keyboard and pointing device (e.g., a mouse or trackball) through which the user provides input to the electronic device. Other types of devices can also be used to provide interaction with the user; for example, feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user can be received in any form (including sound input, voice input, or tactile input).

[0118] The systems and technologies described herein can be implemented in computing systems that include backend components (e.g., as data servers), or middleware components (e.g., application servers), or frontend components (e.g., user computers with graphical user interfaces or web browsers through which users can interact with implementations of the systems and technologies described herein), or any combination of such backend, middleware, or frontend components. The components of the system can be interconnected via digital data communication of any form or medium (e.g., communication networks). Examples of communication networks include local area networks (LANs), wide area networks (WANs), blockchain networks, and the Internet.

[0119] A computing system can include clients and servers. Clients and servers are generally located far apart and typically interact through communication networks. The client-server relationship is created by computer programs running on the respective computers and having a client-server relationship with each other. The server can be a cloud server, also known as a cloud computing server or cloud host, which is a hosting product within the cloud computing service system to address the shortcomings of traditional physical hosts and VPS services, such as high management difficulty and weak business scalability.

[0120] It should be understood that the various forms of processes shown above can be used to rearrange, add, or delete steps. For example, the steps described in this application can be executed in parallel, sequentially, or in different orders, as long as the desired result of the technical solution of this application can be achieved, and this is not limited herein.

[0121] The specific embodiments described above do not constitute a limitation on the scope of protection of this application. Those skilled in the art should understand that various modifications, combinations, sub-combinations, and substitutions can be made according to design requirements and other factors. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of this application should be included within the scope of protection of this application.

Claims

1. A method for measuring the gap between nuclear fuel rods, characterized in that, A data processing center is applied to a nuclear fuel rod gap measurement system; the nuclear fuel rod gap measurement system further includes a projector, a left camera located to the left of the projector, and a right camera located to the right of the projector; the projector and the left camera constitute a left monocular structured light measurement module; the projector and the right camera constitute a right monocular structured light measurement module; the data processing center is communicatively connected to both the left and right monocular structured light measurement modules; the method includes: Based on the left and right camera images of the target nuclear fuel rod assembly, the three-dimensional point clouds of the left and right cameras of the target nuclear fuel rod assembly are determined; wherein, the left and right camera images are obtained by acquiring the phase difference fringe patterns projected by the projector onto the target nuclear fuel rod assembly through the left and right cameras, respectively; Based on the matrix transformation relationship between the left and right cameras, the 3D point cloud of the left camera is transformed into the coordinate system of the right camera to obtain the source 3D point cloud; wherein, the matrix transformation relationship is determined by calibrating the coordinate systems of the left and right cameras using a calibration board; Based on the feature matching algorithm, a point cloud registration matrix is ​​determined according to the source 3D point cloud and the right camera 3D point cloud. The point cloud registration matrix is ​​then used to align and fuse the source 3D point cloud and the right camera 3D point cloud to obtain the target 3D point cloud of the target nuclear fuel rod group. Based on the target three-dimensional point cloud, determine the fuel rod gap between at least two candidate nuclear fuel rods in the target nuclear fuel rod group.

2. The method according to claim 1, characterized in that, The step of determining the fuel rod gap between at least two candidate fuel rods in the target nuclear fuel rod group, based on the target nuclear fuel rod group, includes: Perform Euclidean clustering on the target 3D point cloud to obtain fuel rod point clouds of at least two candidate nuclear fuel rods in the target nuclear fuel rod group; A random sampling consensus algorithm is used to fit the point cloud of the fuel rods into a cylinder to obtain the cylinder parameters of the at least two candidate nuclear fuel rods. The fuel rod gap between the at least two candidate nuclear fuel rods is determined based on the cylindrical parameters of the at least two nuclear fuel rods.

3. The method according to claim 2, characterized in that, The cylinder parameters include the cylinder radius, coordinates of the axis endpoints, and the axis direction vector; correspondingly, determining the fuel rod gap between the at least two candidate nuclear fuel rods based on the cylinder parameters of the at least two nuclear fuel rods includes: For each pair of candidate nuclear fuel rods, the axis expressions of the two candidate nuclear fuel rods are determined based on the coordinates of their endpoints and the axis direction vector. Based on the geometric distance calculation method, the axial distance between the two candidate nuclear fuel rods is determined according to the axial expression of the two candidate nuclear fuel rods; The fuel rod gap between the two candidate nuclear fuel rods is determined based on the axial distance and the cylindrical radius of the two candidate nuclear fuel rods.

4. The method according to claim 1, characterized in that, Based on the left camera image of the target nuclear fuel rod assembly, determine the three-dimensional point cloud of the left camera of the target nuclear fuel rod assembly, including: The pixel grayscale values ​​of the left camera image of the target nuclear fuel rod group are calculated to obtain the first grayscale value set of the left camera image. Based on the first grayscale value set, a first wrapping phase set of the left camera image is determined; wherein, the first wrapping phase set is composed of the wrapping phase of each pixel in the left camera image; A multi-frequency heterodyne algorithm is used to solve the first package phase set to obtain the first absolute phase set of the left camera image; Based on the first absolute phase set, determine the left camera 3D point cloud of the target nuclear fuel rod assembly in the left camera coordinate system.

5. The method according to claim 1, characterized in that, Based on the right camera image of the target nuclear fuel rod assembly, determine the three-dimensional point cloud of the right camera of the target nuclear fuel rod assembly, including: The pixel grayscale values ​​of the right camera image of the target nuclear fuel rod group are calculated to obtain the second grayscale value set of the right camera image. Based on the second grayscale value set, a second wrapping phase set of the right camera image is determined; wherein, the second wrapping phase set is composed of the wrapping phase of each pixel in the right camera image; A multi-frequency heterodyne algorithm is used to solve the second package phase set to obtain the second absolute phase set of the right camera image; Based on the second absolute phase set, the three-dimensional point cloud of the target nuclear fuel rod assembly in the right camera coordinate system is determined.

6. The method according to claim 1, characterized in that, Based on the feature matching algorithm, a point cloud registration matrix is ​​determined according to the source 3D point cloud and the right camera 3D point cloud, including: Based on the feature matching algorithm, the source 3D point cloud and the right camera 3D point cloud are decentered and covariance is calculated respectively to obtain the target covariance matrix, as well as the first center point of the source 3D point cloud and the second center point of the right camera 3D point cloud. Singular value decomposition is performed on the target covariance matrix to obtain the left singular vector matrix and the right singular vector matrix; Determine the rotation matrix based on the left singular vector matrix and the right singular vector matrix; The translation vector is determined based on the rotation matrix, the first center point, and the second center point; The point cloud registration matrix is ​​determined based on the translation vector and the rotation matrix.

7. A nuclear fuel rod gap measuring device, characterized in that, A data processing center is configured in a nuclear fuel rod gap measurement system; the nuclear fuel rod gap measurement system further includes a projector, a left camera located to the left of the projector, and a right camera located to the right of the projector; the projector and the left camera constitute a left monocular structured light measurement module; the projector and the right camera constitute a right monocular structured light measurement module; the data processing center is communicatively connected to both the left and right monocular structured light measurement modules; the device includes: The point cloud determination module is used to determine the three-dimensional point cloud of the left camera and the three-dimensional point cloud of the right camera of the target nuclear fuel rod assembly based on the left camera image and the right camera image of the target nuclear fuel rod assembly; wherein, the left camera image and the right camera image are respectively acquired by the left camera and the right camera through the phase difference fringe pattern projected by the projector onto the target nuclear fuel rod assembly by the projector; The point cloud conversion module is used to convert the 3D point cloud of the left camera to the coordinate system of the right camera according to the matrix conversion relationship between the left and right cameras, so as to obtain the source 3D point cloud; wherein, the matrix conversion relationship is determined by calibrating the coordinate systems of the left camera and the right camera using a calibration plate; The point cloud alignment module is used to determine the point cloud registration matrix based on the source 3D point cloud and the right camera 3D point cloud according to the feature matching algorithm, and to align and fuse the source 3D point cloud and the right camera 3D point cloud using the point cloud registration matrix to obtain the target 3D point cloud of the target nuclear fuel rod group. The gap measurement module is used to determine the fuel rod gap between at least two candidate nuclear fuel rods in the target nuclear fuel rod group based on the target three-dimensional point cloud.

8. An electronic device, characterized in that, include: One or more processors; Memory, used to store one or more programs; When the one or more programs are executed by the one or more processors, the one or more processors implement the nuclear fuel rod gap measurement method as described in any one of claims 1-6.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that, When executed by the processor, the program implements the nuclear fuel rod gap measurement method as described in any one of claims 1-6.

10. A computer program product comprising a computer program that, when executed by a processor, implements the nuclear fuel rod gap measurement method according to any one of claims 1-6.

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