A method and system for evaluating micro-hole aperture in zoom microscopy

Abstract

The application discloses a kind of pore aperture evaluation method and system in zooming microscopic measurement, which comprises the following steps: obtaining the three-dimensional point cloud data of micro-pore obtained by zooming microscopic three-dimensional reconstruction;Micro-pore three-dimensional point cloud data is filtered and denoised by statistics, to obtain the point cloud data after denoising;Along the predetermined micro-pore axial, the point cloud data after denoising is layered, to obtain a plurality of layered cross-section point clouds;Each layered cross-section point cloud is projected to the plane perpendicular to the micro-pore axial, and envelope boundary extraction is carried out, to obtain envelope point set;Based on envelope point set, two-dimensional circle fitting is carried out, to obtain the center parameter and radius parameter of each layer;The center parameter and radius parameter of each layer are axially smoothed, and the pore aperture evaluation result is calculated according to the smoothed radius parameter.The application can reduce the influence of noise points, local abnormal points and redundant points on aperture evaluation, improve the stability and calculation efficiency of micro-pore aperture evaluation.

Description

Technical Field

[0001] This invention belongs to the field of precision optical three-dimensional measurement and geometric feature evaluation technology, specifically relating to a method and system for evaluating the aperture of micropores in zoom microscopy. Background Technology

[0002] Microporous structures are widely used in critical components such as film cooling holes for aero-engine turbine blades, injection holes for liquid engines, precision nozzles, and microfluidic devices. The size, consistency, and geometry of the micropores directly affect flow distribution, injection stability, cooling efficiency, and component reliability. Therefore, high-precision, automated, and non-contact evaluation of micropore diameters is a crucial aspect of quality control in precision manufacturing.

[0003] Existing methods for measuring micropore diameter mainly include contact measurement and non-contact optical measurement. Contact measurement typically uses measuring needles, inside gauges, or coordinate measuring machines. Although these methods can achieve a certain level of measurement accuracy in some regular hole structures, for micropore structures with small diameters, thin walls, or easily damaged edges, the probe contact can easily introduce contact deformation, scratches, or positioning errors. Furthermore, the detection efficiency is low, making it difficult to meet the needs of large-scale online inspection.

[0004] Non-contact optical measurement methods can avoid contact damage. Among them, zoom microscopy acquires multi-focal plane image sequences along the optical axis and combines them with 3D reconstruction algorithms to obtain 3D point cloud data of micropores, providing a data foundation for the 3D evaluation of micropore diameter. However, the micropore point cloud obtained by actual reconstruction is usually not an ideal cylindrical point cloud. It is easily affected by imaging noise, hole wall reflection, focal plane identification error, and 3D reconstruction error, resulting in outliers, local anomalies, and regions with uneven density in the point cloud.

[0005] Furthermore, the micropore orifice region often contains chamfers, burrs, transition surfaces, or residual points on the outer surface, while the pore wall region may also have local defects and occlusions. This makes it easy for the fitting results to be influenced by local anomalies when directly using the overall point cloud for cylindrical fitting, resulting in insufficient stability of the pore size evaluation results. If only a single cross-section is selected for two-dimensional circular fitting, the measurement results are highly dependent on the cross-section position. Once there are defects, noise, or local edge distortions at the cross-section, it is easy to cause problems with poor repeatability and insufficient representativeness.

[0006] Meanwhile, the point cloud data obtained from zoom microscope 3D reconstruction is large in scale. If all point cloud points are directly used in the fitting calculation, not only will the computational load be large, but internal redundant points will also reduce the fitting efficiency and may interfere with the accurate representation of the outer contour boundary. Therefore, the existing technology still lacks a micropore size evaluation method that can suppress the interference of abnormal points while reducing the computational load for zoom microscope micropore point cloud data, and take into account both local cross-sectional accuracy and overall pore size stability. Summary of the Invention

[0007] The technical problem to be solved by this invention is to address the shortcomings of the prior art by providing a method and system for evaluating the aperture size of micro-apertures in zoom microscopy. This method addresses the issues in existing zoom micro-aperture aperture evaluation methods, such as the susceptibility of 3D reconstructed point clouds to imaging noise, aperture wall reflection, reconstruction errors, aperture transition regions, outer surface regions, and local defects. These issues result in insufficient robustness of the overall cylindrical model fitting, sensitivity and poor representativeness of single-section 2D fitting to section position, and low efficiency of directly involving large-scale point clouds in fitting calculations. Consequently, it is difficult to simultaneously ensure the accuracy, stability, and real-time performance of aperture evaluation.

[0008] The present invention adopts the following technical solution: A method for evaluating micropore size in zoom microscopy includes the following steps: Acquire three-dimensional point cloud data of micropores. The three-dimensional point cloud data of micropores is point cloud data obtained by zoom microscopic imaging of micropores and three-dimensional reconstruction by zoom microscopic imaging. The three-dimensional point cloud data of micropores includes multiple point cloud points and the three-dimensional spatial coordinates of each point cloud point. The micropore 3D point cloud data is subjected to statistical filtering and denoising processing. For each target point in the micropore 3D point cloud data, the neighboring points of the target point are searched. Based on the comparison result of the average distance from the target point to its neighboring points and the preset distance threshold, outliers are removed to obtain the denoised point cloud data. The denoised point cloud data is subjected to axial layering processing. Along the predetermined micropore axis, the denoised point cloud data is divided into multiple layered cross-sectional point clouds according to a preset layering thickness. Envelope extraction is performed on the point cloud of each layered section, and the point cloud of the layered section is projected onto a plane perpendicular to the axis of the micropore to obtain a two-dimensional section point set. The envelope boundary of the two-dimensional section point set is extracted to obtain an envelope point set representing the outer contour of the corresponding layered section. Two-dimensional circle fitting is performed based on each set of envelope points, and the center parameters and radius parameters of the corresponding layered cross section are determined according to the geometric error from the envelope points in the set of envelope points to the fitted circle. Axial smoothing is performed on the center and radius parameters of multiple layered sections. Based on the center and radius parameters of adjacent layered sections, the center and radius parameters of the current layered section are updated smoothly to obtain the smoothed center and radius parameters. The micropore diameter evaluation result is calculated based on the radius parameters after smoothing multiple layered cross sections, wherein the micropore diameter evaluation result includes the micropore diameter obtained statistically from the radius parameters after smoothing multiple layered cross sections.

[0009] Furthermore, the acquisition of the micropore three-dimensional point cloud data includes: A CCD camera is used to perform zoom microscopy imaging of micro-holes, and image sequences with different focal positions are acquired along the optical axis. The image sequence is subjected to zoom microscopic 3D reconstruction processing. Sharp points are extracted from each image and their 3D spatial coordinates are restored to obtain the 3D point cloud data of the micropore.

[0010] Further, the statistical filtering and denoising process for the micropore 3D point cloud data includes: For each target point in the microporous 3D point cloud data, search for a preset number of neighboring points of the target point in 3D space; Calculate the average distance from the target point to the preset number of neighboring points; When the average distance is greater than the preset distance threshold, the target point is identified as an outlier and removed.

[0011] Further, the axial layering process for the denoised point cloud data includes: Determine the coordinate range of the denoised point cloud data along the axis of the micropore; The number of layers is determined based on the coordinate range and the preset layer thickness; According to the coordinate values ​​of each point cloud point along the axis of the micropore, the denoised point cloud data is divided into the corresponding layered cross-sectional point cloud.

[0012] Furthermore, the micropore axis is the axis determined in the zoom microscope three-dimensional reconstruction coordinate system; In one embodiment, the micro-aperture axis is the Z-axis in the zoom microscope three-dimensional reconstruction coordinate system; The preset layer thickness is determined based on the axial step distance of the stage during zoom microscopy acquisition.

[0013] Further, the envelope extraction for each of the layered cross-sectional point clouds includes: Projecting the point cloud points in each of the layered cross-sections onto a two-dimensional plane perpendicular to the axis of the micropores yields the two-dimensional cross-section point set. The convex hull boundary is extracted from the set of points on the two-dimensional cross section; The extracted convex hull boundary points are used as the envelope point set of the corresponding layered cross section.

[0014] Further, the two-dimensional circle fitting based on each of the envelope point sets includes: Algebraic circle fitting is performed based on the envelope point set to obtain the initial center parameters and initial radius parameters of the corresponding layered cross section; Using the initial center parameter and the initial radius parameter as the starting parameters for iterative optimization, a geometric distance error objective function from the envelope point to the fitted circle is established. The objective function of geometric distance error is iteratively optimized to obtain the center parameters and radius parameters of the corresponding layered cross sections.

[0015] Furthermore, the axial smoothing process for the center and radius parameters of the multiple layered sections includes: A smooth window is formed by selecting adjacent layered sections centered on the current layered section; The radius parameters of the current layered section are obtained by weighted averaging the radius parameters of each layered section within the smoothing window. The center parameters of the current layered section are obtained by weighted averaging the center parameters of each layered section within the smoothing window.

[0016] Furthermore, the calculation of the micropore size evaluation result based on the radius parameters after smoothing multiple layered cross sections includes: The average radius parameter is obtained by statistically averaging the radius parameters of multiple smoothed layered sections. The average radius parameter is taken as twice the diameter of the micropore.

[0017] Secondly, embodiments of the present invention provide a micropore size evaluation system for zoom microscopy measurements, comprising: The point cloud acquisition module is used to acquire micro-hole three-dimensional point cloud data. The micro-hole three-dimensional point cloud data is point cloud data obtained by zoom microscopic imaging of the micro-hole and zoom microscopic three-dimensional reconstruction. The micro-hole three-dimensional point cloud data includes multiple point cloud points and the three-dimensional spatial coordinates of each point cloud point. The noise reduction module is used to perform statistical filtering noise reduction on the micro-hole three-dimensional point cloud data. For each target point in the micro-hole three-dimensional point cloud data, the neighboring points of the target point are searched. Based on the comparison result of the average distance from the target point to its neighboring points and the preset distance threshold, outliers are removed to obtain the noise-reduced point cloud data. The axial layering module is used to perform axial layering processing on the denoised point cloud data, dividing the denoised point cloud data into multiple layered cross-sectional point clouds along a predetermined micro-hole axis and according to a preset layering thickness. The envelope extraction module is used to extract the envelope of each layered cross-section point cloud, project the layered cross-section point cloud onto a plane perpendicular to the axis of the micropore to obtain a two-dimensional cross-section point set, and extract the envelope boundary of the two-dimensional cross-section point set to obtain an envelope point set representing the outer contour of the corresponding layered cross-section. The circle fitting module is used to perform two-dimensional circle fitting based on each set of envelope points, and to determine the center parameters and radius parameters of the corresponding layered section based on the geometric error from the envelope points in the set of envelope points to the fitted circle. The parameter smoothing module is used to perform axial smoothing on the center and radius parameters of multiple layered sections. Based on the center and radius parameters of adjacent layered sections, the center and radius parameters of the current layered section are updated smoothly to obtain the smoothed center and radius parameters. The pore size calculation module is used to calculate the micropore size evaluation result based on the radius parameters after smoothing multiple layered cross sections. The micropore size evaluation result includes the micropore diameter obtained by statistical analysis based on the radius parameters after smoothing multiple layered cross sections.

[0018] Thirdly, a computer device includes a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the steps of the micropore size evaluation method in the above-described zoom microscopy measurement.

[0019] Fourthly, embodiments of the present invention provide a computer-readable storage medium including a computer program, which, when executed by a processor, implements the steps of the micropore diameter evaluation method in the above-described zoom microscopy measurement.

[0020] Fifthly, a chip includes a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the steps of the micropore size evaluation method in the above-described zoom microscopy measurement.

[0021] In a sixth aspect, embodiments of the present invention provide an electronic device including a computer program, which, when executed by the electronic device, implements the steps of the micropore diameter evaluation method in the above-described zoom microscopy measurement.

[0022] Compared with the prior art, the present invention has at least the following beneficial effects: A method for evaluating micropore size in zoom microscopy first eliminates outliers through statistical filtering, then transforms the overall 3D pore wall point cloud into multiple local 2D cross-sections to reduce the risk of the overall fitting being influenced by local anomalies. Subsequently, only the outer contour envelope points of the cross-sections are extracted for circle fitting, reducing the influence of internal redundant points and non-boundary points on the fitting. Finally, axial smoothing is applied to the interlayer circle center and radius parameters, so that the pore size evaluation results are no longer dependent on a single cross-section. This setup can balance noise resistance, computational efficiency, and pore size evaluation stability.

[0023] Furthermore, image sequences at different focal positions were acquired using a CCD camera along the optical axis, and the resulting microscopic 3D reconstruction yielded 3D point cloud data of the micropores. This non-contact method for obtaining the spatial coordinates of the micropore walls avoids damage to the micropore edges or walls caused by contact measurements, while also providing a 3D data foundation for subsequent axial layering and cross-sectional fitting.

[0024] Furthermore, outliers exhibit greater spatial isolation relative to their surrounding point cloud, allowing for the identification of imaging noise and the reconstruction of anomalies and scattered points through neighborhood average distance. This setting reduces the interference of outliers on envelope extraction and circle fitting results.

[0025] Furthermore, the large-scale 3D point cloud is decomposed into multiple local 2D cross-sectional data, so that the subsequent circle fitting is performed within the local cross-section. This reduces the amount of data to be calculated per operation and avoids the overall fitting being affected by the transition area of ​​the orifice or local missing areas.

[0026] Furthermore, the micro-aperture axis is defined in the three-dimensional reconstruction coordinate system, and in one embodiment, the Z-axis is used. Simultaneously, the layer thickness is determined based on the stage axial step distance. This ensures that the layering direction is consistent with the zoom microscopy acquisition process, and that the layer thickness matches the actual sampling interval, avoiding excessively thick layers leading to overlapping cross-sectional features, or excessively thin layers resulting in insufficient points per layer.

[0027] Furthermore, by projecting the layered cross-sectional point cloud onto a two-dimensional plane and extracting the envelope point set using convex hull boundary extraction, the convex hull boundary points can characterize the outer contour of the cross-section, eliminating redundant points inside the cross-section, making circle fitting mainly dependent on the geometric information of the outer boundary. This setting can reduce the participation of redundant points in the calculation, improve fitting efficiency, and enhance the specificity of aperture boundary evaluation.

[0028] Furthermore, algebraic circle fitting is used to obtain the initial center and radius, which are then used as the starting point for iterative optimization of geometric distance error. Algebraic circle fitting quickly obtains reasonable initial values, reducing the risk of geometric optimization getting stuck in unstable iterations or slow convergence; then, geometric distance error optimization improves the matching degree between the final center and radius parameters and the actual cross-sectional boundary.

[0029] Furthermore, a weighted average of the center and radius parameters is performed using a smoothing window centered on the current layer section. The walls of real micropores exhibit a certain degree of continuity along the axial direction, while single-layer fitting results may fluctuate due to noise, changes in point cloud density, or local missing values. Smoothing parameters between adjacent layers can suppress random fluctuations between layers and improve the consistency of pore size evaluation results.

[0030] Furthermore, the radius parameters of multiple smoothed layered sections are statistically averaged, and twice this average is taken as the micropore diameter. This avoids the pore diameter result being determined by a single section, allowing the final micropore diameter to reflect the comprehensive geometric characteristics of multiple axial positions, thereby improving the representativeness and repeatability of pore diameter evaluation. This method is suitable for micropore point cloud data with local missing or local anomalies.

[0031] It is understood that the beneficial effects of the second to sixth aspects mentioned above can be found in the relevant descriptions in the first aspect mentioned above, and will not be repeated here.

[0032] In summary, this invention removes outliers through statistical filtering, processes the point cloud in layers along the micropore axis, performs circle fitting on the envelope points of each layer, and then calculates the pore diameter after smoothing the center and radius parameters between layers. This reduces the impact of noise, pore anomalies, and internal redundant points on the evaluation results, and improves the stability, accuracy, and computational efficiency of micropore diameter measurement.

[0033] The technical solution of the present invention will be further described in detail below with reference to the accompanying drawings and embodiments. Attached Figure Description

[0034] Figure 1 This is the overall flowchart of the present invention.

[0035] Figure 2 This is a schematic diagram of axial layering of micropore point cloud data in step S3 of the present invention; Figure 3 This is a schematic diagram of the envelope extraction and two-dimensional circle fitting of the two-dimensional cross-sectional point cloud data in steps S4 and S5 of the present invention. Figure 4 A schematic diagram of a computer device provided in an embodiment of the present invention; Figure 5 This is a block diagram of a chip provided in one embodiment of the present invention.

[0036] Among them, 60. Computer equipment; 61. Processor; 62. Memory; 63. Computer program; 600. Electronic device; 610. Processing unit; 620. Storage unit; 6201. Random access memory unit; 6202. Cache memory unit; 6203. Read-only memory unit; 6204. Program / utility; 6205. Program module; 630. Bus; 640. Display unit; 650. Input / output interface; 660. Network adapter; 700. External device. Detailed Implementation

[0037] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of the present invention. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0038] In the description of this invention, it should be understood that the terms "comprising" and "including" indicate the presence of the described features, integrals, steps, operations, elements and / or components, but do not exclude the presence or addition of one or more other features, integrals, steps, operations, elements, components and / or collections thereof.

[0039] It should also be understood that the terminology used in this specification is for the purpose of describing particular embodiments only and is not intended to limit the invention. As used in this specification and the appended claims, the singular forms “a,” “an,” and “the” are intended to include the plural forms unless the context clearly indicates otherwise.

[0040] It should also be further understood that the term "and / or" as used in this specification and the appended claims refers to any combination and all possible combinations of one or more of the associated listed items, and includes such combinations. For example, A and / or B can represent three cases: A alone, A and B simultaneously, and B alone. Additionally, the character " / " in this invention generally indicates that the preceding and following objects have an "or" relationship.

[0041] It should be understood that although terms such as first, second, third, etc., may be used in the embodiments of the present invention to describe the preset range, these preset ranges should not be limited to these terms. These terms are only used to distinguish the preset ranges from one another. For example, without departing from the scope of the embodiments of the present invention, the first preset range may also be referred to as the second preset range, and similarly, the second preset range may also be referred to as the first preset range.

[0042] Depending on the context, the word "if" as used here can be interpreted as "when," "when," "in response to determination," or "in response to detection." Similarly, depending on the context, the phrase "if determination" or "if detection (of the stated condition or event)" can be interpreted as "when determination," "in response to determination," "when detection (of the stated condition or event)," or "in response to detection (of the stated condition or event)."

[0043] The accompanying drawings illustrate various structural schematic diagrams according to embodiments disclosed in this invention. These drawings are not to scale, and some details have been enlarged for clarity, and some details may have been omitted. The shapes of the various regions and layers shown in the drawings, as well as their relative sizes and positional relationships, are merely exemplary and may deviate from reality due to manufacturing tolerances or technical limitations. Furthermore, those skilled in the art can design regions / layers with different shapes, sizes, and relative positions as needed.

[0044] This invention provides a method for evaluating the aperture of micropores in zoom microscopy. For micropore point cloud data obtained during 3D reconstruction in zoom microscopy, the method performs layered processing along the micropore axis, extracts envelope points representing the outer contour of the micropore cross-section in each layer, and performs two-dimensional circle fitting based on these envelope points. Furthermore, the center and radius parameters of the fitted circles are smoothed axially, ultimately achieving a stable and accurate evaluation of the micropore aperture. By introducing a layered processing and envelope-constrained circle fitting strategy, the influence of noise points and local anomalies on the fitting results is effectively reduced. While ensuring measurement accuracy, the method significantly improves computational efficiency, exhibits good noise resistance and stability, and is particularly suitable for real-time processing of large-scale point cloud data and measurement applications of complex micropore structures.

[0045] Please see Figure 1 The present invention provides a method for evaluating the pore size of micropores in zoom microscopy, comprising the following steps: S1. Acquisition of 3D point cloud data for micropores: A CCD camera is used to perform zoom microscopic imaging of the micropore, acquiring a sequence of images with different focal positions along the optical axis. The image sequence is then subjected to zoom microscopic 3D reconstruction processing. Sharp points are extracted from each image, and their 3D spatial coordinate information is recovered, thereby obtaining the 3D point cloud data of the micropore. The point cloud data is represented as follows:

[0046] in, , , Representing points respectively The coordinates in a spatial rectangular coordinate system.

[0047] S2, Noise Reduction Processing of Point Cloud Data Please see Figure 2The denoised micropore 3D point cloud data is divided into multiple layered cross-sectional point clouds along a predetermined micropore axis. Each layered cross-sectional point cloud includes point cloud points located within the corresponding axial interval. Through this layered processing, the overall micropore point cloud is converted into multiple local cross-sectional point clouds, allowing the subsequent circle fitting process to be performed separately within each local cross-section. This method reduces the possibility of the overall fitting being affected by the orifice transition region, outer surface region, and local anomalies, and reduces the amount of data required for a single fitting operation.

[0048] For the point cloud data Statistical filtering is performed to reduce the impact of outliers and noise points on subsequent geometric fitting. For any point in the point cloud... Search for its three-dimensional space Let the nearest neighbor be denoted as _n_1. Calculation points Average distance to its neighboring points:

[0049] Set distance threshold ,when At that time, the corresponding points are identified as outliers and removed, thus obtaining the denoised point cloud set:

[0050] S3, Axial layering of micropore point clouds Denoising point cloud data Layered processing is performed along the axial direction of the micropores to transform the three-dimensional point cloud data into multiple local cross-sectional point clouds, which facilitates subsequent circle fitting in a two-dimensional plane.

[0051] Suppose the point cloud data is along the axis (in one implementation it is...) The range on the axis is Set the layer thickness to (In one embodiment, the) It can be determined based on the axial step distance of the stage during zoom microscopy acquisition. Layered point cloud data is defined as:

[0052] The total number of floors is:

[0053] Hierarchical processing can decompose a large point cloud into multiple small datasets, making subsequent computation more efficient and improving the robustness of the fitting process to local anomalies.

[0054] Please see Figure 3For each layered cross-sectional point cloud, it is first projected onto a two-dimensional plane perpendicular to the micropore axis to obtain a two-dimensional cross-sectional point set. Then, the envelope boundary of the two-dimensional cross-sectional point set is extracted to obtain an envelope point set that can characterize the outer contour of the cross-section. Finally, a two-dimensional circle fitting is performed based on the envelope point set to obtain the center parameter and radius parameter of the layered cross-section. By selecting only the outer contour envelope points of the cross-section to participate in the circle fitting, the influence of redundant points inside the cross-section on the fitting result can be reduced, allowing the fitted circle to better correspond to the boundary contour of the micropore cross-section.

[0055] In a preferred embodiment, before performing envelope boundary extraction or two-dimensional circle fitting, the chamfers, burrs, residual points on the outer surface, and abnormal envelope points can be removed according to the preset effective axial range, radius abnormality threshold, or cross-sectional point number threshold, so as to avoid the outer noise points pulling the fitted circle.

[0056] S4. Envelope Extraction of Layered Section Point Clouds For each layer of point cloud data Extract the envelope point set that can characterize the outer contour of the micropore cross section of the layer.

[0057] First, the first The layer point cloud data is projected onto a plane perpendicular to the micropore axis (in one embodiment, ...). (Plane), forming a two-dimensional cross-sectional point set:

[0058] Then, for the two-dimensional point set Boundary extraction is performed. In one implementation, a convex hull algorithm is used to obtain the convex hull boundary points of the two-dimensional point set. The resulting set of boundary points is defined as the envelope point set of the cross-section of that layer.

[0059] By extracting envelope points, only the outer contour points that are representative of the circular boundary are retained, reducing the interference of redundant points inside the cross section on the fitting results.

[0060] S5. Two-dimensional circle fitting based on envelope points Based on envelope point set , for the The cross-section of the layer is fitted with a circle to obtain the coordinates of the center and the radius parameters of the circle.

[0061] To improve the convergence speed and stability of the circle fitting process, an algebraic circle fitting method is first used to process the envelope point set to obtain initial estimates of the circle's center and radius. Let the center of the fitted circle be... , radius is In one implementation, the Kåsa circle fitting method is used, by minimizing the following algebraic error function:

[0062] Obtain the center coordinates and radius parameters for each layer.

[0063] Solve for parameters And thus obtain the initial values ​​of the center and radius of the circle:

[0064] After obtaining the initial parameters, the initial center and radius are used as the starting values ​​for iterative optimization to further establish the objective function for the geometric distance error from the point to the circle:

[0065] An iterative optimization algorithm is then used to minimize the objective function. This is achieved by using the coordinates of the circle's center. and radius The process is iteratively updated until the error function converges or the preset number of iterations is reached, thus obtaining the final circle fitting parameters for the cross-section of that layer.

[0066] By combining the above-mentioned "algebraic initial value + geometric distance optimization" approach, the convergence efficiency and stability of the optimization process can be improved while ensuring fitting accuracy.

[0067] S6. Axial smoothing of layered fitting parameters To eliminate fluctuations in fitting parameters between adjacent layers due to noise, changes in point cloud density, or local missing data, the center and radius parameters obtained from fitting each layer are smoothed axially.

[0068] A weighted average method is used to smooth the parameters of adjacent layers:

[0069] in, These are the weighting coefficients. To smooth the half-width of the window, the center parameter... The smoothing method is the same as the radius parameter, which yields the smoothed center coordinates. .

[0070] S7. Calculation of micropore size Based on the smoothed radius parameters of each layer Calculate the final pore size of the micropores. The smoothed radii of each layer are statistically averaged, and twice this average is taken as the micropore diameter.

[0071] Through the above steps, the method of this invention can achieve efficient and stable pore size evaluation when processing large-scale micropore point cloud data obtained from zoom microscopy 3D reconstruction. This method has advantages such as strong noise resistance, high computational efficiency, high accuracy, and wide applicability, and can provide reliable technical support for the engineering inspection and precision measurement of micropore structures.

[0072] In another embodiment of the present invention, a micropore diameter evaluation system for zoom microscopy is provided. This system can be used to implement the above-mentioned micropore diameter evaluation method for zoom microscopy. Specifically, the micropore diameter evaluation system for zoom microscopy includes a point cloud acquisition module, a noise reduction module, an axial layering module, an envelope extraction module, a circle fitting module, a parameter smoothing module, and a pore diameter calculation module.

[0073] The point cloud acquisition module is used to acquire micro-hole three-dimensional point cloud data. The micro-hole three-dimensional point cloud data is point cloud data obtained by zoom microscopic imaging of the micro-hole and zoom microscopic three-dimensional reconstruction. The micro-hole three-dimensional point cloud data includes multiple point cloud points and the three-dimensional spatial coordinates of each point cloud point. The noise reduction module is used to perform statistical filtering noise reduction on the micro-hole three-dimensional point cloud data. For each target point in the micro-hole three-dimensional point cloud data, the neighboring points of the target point are searched. Based on the comparison result of the average distance from the target point to its neighboring points and the preset distance threshold, outliers are removed to obtain the noise-reduced point cloud data. The axial layering module is used to perform axial layering processing on the denoised point cloud data, dividing the denoised point cloud data into multiple layered cross-sectional point clouds along a predetermined micro-hole axis and according to a preset layering thickness. The envelope extraction module is used to extract the envelope of each layered cross-section point cloud, project the layered cross-section point cloud onto a plane perpendicular to the axis of the micropore to obtain a two-dimensional cross-section point set, and extract the envelope boundary of the two-dimensional cross-section point set to obtain an envelope point set representing the outer contour of the corresponding layered cross-section. The circle fitting module is used to perform two-dimensional circle fitting based on each set of envelope points, and to determine the center parameters and radius parameters of the corresponding layered section based on the geometric error from the envelope points in the set of envelope points to the fitted circle. The parameter smoothing module is used to perform axial smoothing on the center and radius parameters of multiple layered sections. Based on the center and radius parameters of adjacent layered sections, the center and radius parameters of the current layered section are updated smoothly to obtain the smoothed center and radius parameters. The pore size calculation module is used to calculate the micropore size evaluation result based on the radius parameters after smoothing multiple layered cross sections. The micropore size evaluation result includes the micropore diameter obtained by statistical analysis based on the radius parameters after smoothing multiple layered cross sections.

[0074] This invention provides a terminal device comprising a processor and a memory. The memory stores a computer program, which includes program instructions. The processor executes the program instructions stored in the computer storage medium. The processor may be a Central Processing Unit (CPU), or other general-purpose processors, graphics processing units (GPUs), tensor processing units (TPUs), digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. It is the computing and control core of the terminal, suitable for implementing one or more instructions, specifically suitable for loading and executing one or more instructions to achieve a corresponding method flow or function. The processor described in this embodiment can be used in the operation of a micropore aperture evaluation method in zoom microscopy, including: Acquire three-dimensional point cloud data of micropores. The three-dimensional point cloud data of micropores is point cloud data obtained by zoom microscopic imaging of micropores and three-dimensional reconstruction by zoom microscopic imaging. The three-dimensional point cloud data of micropores includes multiple point cloud points and the three-dimensional spatial coordinates of each point cloud point. The micropore 3D point cloud data is subjected to statistical filtering and denoising processing. For each target point in the micropore 3D point cloud data, the neighboring points of the target point are searched. Based on the comparison result of the average distance from the target point to its neighboring points and the preset distance threshold, outliers are removed to obtain the denoised point cloud data. The denoised point cloud data is subjected to axial layering processing. Along the predetermined micropore axis, the denoised point cloud data is divided into multiple layered cross-sectional point clouds according to a preset layering thickness. Envelope extraction is performed on the point cloud of each layered section, and the point cloud of the layered section is projected onto a plane perpendicular to the axis of the micropore to obtain a two-dimensional section point set. The envelope boundary of the two-dimensional section point set is extracted to obtain an envelope point set representing the outer contour of the corresponding layered section. Two-dimensional circle fitting is performed based on each set of envelope points, and the center parameters and radius parameters of the corresponding layered cross section are determined according to the geometric error from the envelope points in the set of envelope points to the fitted circle. Axial smoothing is performed on the center and radius parameters of multiple layered sections. Based on the center and radius parameters of adjacent layered sections, the center and radius parameters of the current layered section are updated smoothly to obtain the smoothed center and radius parameters. The micropore diameter evaluation result is calculated based on the radius parameters after smoothing multiple layered cross sections, wherein the micropore diameter evaluation result includes the micropore diameter obtained statistically from the radius parameters after smoothing multiple layered cross sections.

[0075] Please see Figure 4 The terminal device is a computer device. In this embodiment, the computer device 60 includes a processor 61, a memory 62, and a computer program 63 stored in the memory 62 and executable on the processor 61. When executed by the processor 61, the computer program 63 implements the micropore size evaluation method in the zoom microscopy measurement embodiment. To avoid repetition, details are omitted here. Alternatively, when executed by the processor 61, the computer program 63 implements the functions of each model / unit in the micropore size evaluation system in the zoom microscopy measurement embodiment. To avoid repetition, details are omitted here.

[0076] Computer device 60 can be a desktop computer, laptop, handheld computer, cloud server, or other computing device. Computer device 60 may include, but is not limited to, a processor 61 and a memory 62. Those skilled in the art will understand that... Figure 4 This is merely an example of computer device 60 and does not constitute a limitation on computer device 60. It may include more or fewer components than shown, or combine certain components, or different components. For example, computer device may also include input / output devices, network access devices, buses, etc.

[0077] The processor 61 may be a Central Processing Unit (CPU), or other general-purpose processors, graphics processing units (GPUs), tensor processing units (TPUs), digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. A general-purpose processor may be a microprocessor or any conventional processor.

[0078] The memory 62 can be an internal storage unit of the computer device 60, such as a hard disk or memory of the computer device 60. The memory 62 can also be an external storage device of the computer device 60, such as a plug-in hard disk, smart media card (SMC), secure digital (SD) card, flash card, etc. equipped on the computer device 60.

[0079] Furthermore, the memory 62 may include both internal storage units of the computer device 60 and external storage devices. The memory 62 is used to store computer programs and other programs and data required by the computer device. The memory 62 can also be used to temporarily store data that has been output or will be output.

[0080] Please see Figure 5 The terminal device is an electronic device 600, which is manifested in the form of a general-purpose computing device. The components of the electronic device may include, but are not limited to: at least one processing unit 610, at least one storage unit 620, a bus 630 connecting different platform components (including storage unit 620 and processing unit 610), a display unit 640, etc.

[0081] The storage unit stores program code, which can be executed by the processing unit 610 to perform the steps described in the method section of this specification according to various exemplary embodiments of the present invention. For example, the processing unit 610 can perform actions such as... Figure 1 The steps are shown in the figure.

[0082] Storage unit 620 may include readable media in the form of volatile storage units, such as random access memory (RAM) 6201 and / or cache memory 6202, and may further include read-only memory (ROM) 6203.

[0083] Storage unit 620 may also include a program / utility 6204 having a set (at least one) program module 6205, such program module 6205 including but not limited to: operating system, one or more application programs, other program modules and program data, each or some combination of these examples may include an implementation of a network environment.

[0084] Bus 630 can represent one or more of several types of bus structures, including a memory cell bus or memory cell controller, a peripheral bus, a graphics acceleration port, a processing unit, or a local bus using any of the multiple bus structures.

[0085] Electronic device 600 can also communicate with one or more external devices 700 (e.g., keyboard, pointing device, Bluetooth device, etc.), and with one or more devices that enable a user to interact with electronic device 600, and / or with any device that enables electronic device 600 to communicate with one or more other computing devices (e.g., router, modem). This communication can be performed via input / output interface 650. Furthermore, electronic device 600 can also communicate with one or more networks (e.g., local area network, wide area network, and / or public network, such as the Internet) via network adapter 660. Network adapter 660 can communicate with other modules of electronic device 600 via bus 630. It should be understood that, although not shown in the figures, other hardware and / or software modules can be used in conjunction with electronic device 600, including but not limited to: microcode, device drivers, redundant processing units, external disk drive arrays, RAID systems, tape drives, and data backup storage platforms.

[0086] This invention also provides a storage medium, specifically a computer-readable storage medium, which is a memory device in a terminal device for storing programs and data. It is understood that the computer-readable storage medium here can include both built-in storage media in the terminal device and extended storage media supported by the terminal device; it can be any tangible medium containing or storing a program that can be used by or in conjunction with an instruction execution system, apparatus, or device. The computer-readable storage medium provides storage space that stores the terminal's operating system. Furthermore, the storage space also stores one or more instructions suitable for loading and execution by a processor, which can be one or more computer programs (including program code). More specific examples of the computer-readable storage medium include: an electrical connection with one or more wires, a portable disk, a hard disk, random access memory, read-only memory, erasable programmable read-only memory, optical fiber, portable compact disk read-only memory, optical storage device, magnetic storage device, or any suitable combination thereof.

[0087] Computer-readable storage media also include data signals propagated in baseband or as part of a carrier wave, carrying readable program code. Such propagated data signals can take various forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination thereof. A readable storage medium can also be any readable medium other than a readable storage medium that can send, propagate, or transmit a program for use by or in connection with an instruction execution system, apparatus, or device. The program code contained on the readable storage medium can be transmitted using any suitable medium, including but not limited to wireless, wired, optical fiber, radio frequency, etc., or any suitable combination thereof.

[0088] Program code for performing the operations of this invention can be written in any combination of one or more programming languages, including object-oriented programming languages ​​such as Java and C++, and conventional procedural programming languages ​​such as C or similar languages. The program code can execute entirely on the user's computing device, partially on the user's device, as a standalone software package, partially on the user's computing device and partially on a remote computing device, or entirely on a remote computing device or server. In cases involving remote computing devices, the remote computing device can be connected to the user's computing device via any type of network, including a local area network (LAN) or a wide area network (WAN), or it can be connected to an external computing device (e.g., via the Internet using an Internet service provider).

[0089] One or more instructions stored in a computer-readable storage medium can be loaded and executed by a processor to implement the corresponding steps of the micropore size evaluation method in the above embodiments related to zoom microscopy measurement; one or more instructions in the computer-readable storage medium are loaded and executed by the processor in the following steps: Acquire three-dimensional point cloud data of micropores. The three-dimensional point cloud data of micropores is point cloud data obtained by zoom microscopic imaging of micropores and three-dimensional reconstruction by zoom microscopic imaging. The three-dimensional point cloud data of micropores includes multiple point cloud points and the three-dimensional spatial coordinates of each point cloud point. The micropore 3D point cloud data is subjected to statistical filtering and denoising processing. For each target point in the micropore 3D point cloud data, the neighboring points of the target point are searched. Based on the comparison result of the average distance from the target point to its neighboring points and the preset distance threshold, outliers are removed to obtain the denoised point cloud data. The denoised point cloud data is subjected to axial layering processing. Along the predetermined micropore axis, the denoised point cloud data is divided into multiple layered cross-sectional point clouds according to a preset layering thickness. Envelope extraction is performed on the point cloud of each layered section, and the point cloud of the layered section is projected onto a plane perpendicular to the axis of the micropore to obtain a two-dimensional section point set. The envelope boundary of the two-dimensional section point set is extracted to obtain an envelope point set representing the outer contour of the corresponding layered section. Two-dimensional circle fitting is performed based on each set of envelope points, and the center parameters and radius parameters of the corresponding layered cross section are determined according to the geometric error from the envelope points in the set of envelope points to the fitted circle. Axial smoothing is performed on the center and radius parameters of multiple layered sections. Based on the center and radius parameters of adjacent layered sections, the center and radius parameters of the current layered section are updated smoothly to obtain the smoothed center and radius parameters. The micropore diameter evaluation result is calculated based on the radius parameters after smoothing multiple layered cross sections, wherein the micropore diameter evaluation result includes the micropore diameter obtained statistically from the radius parameters after smoothing multiple layered cross sections.

[0090] The databases involved in the embodiments provided in this application may include at least one type of relational database and non-relational database. Non-relational databases may include, but are not limited to, blockchain-based distributed databases. The processors involved in the embodiments provided in this application may be general-purpose processors, central processing units, graphics processing units, digital signal processors, programmable logic devices, quantum computing-based data processing logic devices, etc., and are not limited to these.

[0091] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of the present invention. The components of the embodiments of the present invention described and shown in the accompanying drawings can generally be arranged and designed in various different configurations. Therefore, the following detailed description of the embodiments of the present invention provided in the accompanying drawings is not intended to limit the scope of the claimed invention, but merely to illustrate selected embodiments of the invention. All other embodiments obtained by those skilled in the art based on the embodiments of the present invention without inventive effort are within the scope of protection of the present invention.

[0092] This invention utilizes a series of techniques to evaluate the pore size by performing statistical filtering for noise reduction, axial layering, envelope extraction, two-dimensional circle fitting, axial parameter smoothing, and pore size statistical calculation on the 3D point cloud data obtained from zoom microscopy 3D reconstruction. This eliminates the reliance on a single-stage fitting of the entire point cloud or a single cross-section. Statistical filtering removes outliers, reducing the impact of noise and reconstruction anomalies on the fitting results. Axial layering transforms the 3D point cloud into multiple local cross-sectional point clouds, minimizing interference from the orifice transition region, outer surface region, and locally missing regions on the overall fitting. Envelope point extraction preserves the outer contour features of the cross-section, reducing the involvement of redundant internal points in the calculation. Two-dimensional circle fitting based on envelope points obtains the center and radius parameters of each layered cross-section. Axial smoothing suppresses inter-layer parameter fluctuations, allowing the final pore size evaluation result to comprehensively reflect the geometric features of multiple axial positions. Therefore, this invention offers advantages such as good noise resistance, low computational cost, stable pore size evaluation results, good repeatability, and suitability for automated detection.

[0093] In summary, the present invention provides a method and system for evaluating the aperture of micropores in zoom microscopy. It acquires three-dimensional point cloud data of micropores through zoom microscopic three-dimensional reconstruction and employs statistical filtering for noise reduction before aperture evaluation. This removes outliers caused by imaging noise, aperture wall reflection, and reconstruction errors, reducing the interference of outliers on subsequent fitting. Furthermore, the present invention performs layered processing of the point cloud along a predetermined micro-hole axis, transforming the overall three-dimensional micropore point cloud into multiple local cross-sectional point clouds. This avoids the overall cylindrical model fitting being affected by the transition zone at the aperture, the outer surface region, or locally missing areas. By extracting an envelope point set from each cross-sectional point cloud, only boundary points that characterize the outer contour of the cross-section are retained for two-dimensional circle fitting, reducing the computational load of internal redundant points and improving fitting efficiency. Finally, the center and radius parameters of each layer are smoothed axially so that the final aperture result does not depend on a single cross section, but comprehensively reflects the geometric characteristics of multiple cross sections, thereby improving the accuracy, stability, repeatability and engineering applicability of micropore aperture evaluation.

[0094] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the above-described division of functional units and modules is merely an example. In practical applications, the above functions can be assigned to different functional units and modules as needed, that is, the internal structure of the device can be divided into different functional units or modules to complete all or part of the functions described above. The functional units and modules in the embodiments can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit. Furthermore, the specific names of the functional units and modules are only for easy differentiation and are not intended to limit the scope of protection of this application. The specific working process of the units and modules in the above system can be referred to the corresponding process in the foregoing method embodiments, and will not be repeated here.

[0095] In the above embodiments, the descriptions of each embodiment have different focuses. For parts that are not described in detail or recorded in a certain embodiment, please refer to the relevant descriptions of other embodiments.

[0096] Those skilled in the art will recognize that the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed in this invention can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementations should not be considered beyond the scope of this invention.

[0097] In the embodiments provided by this invention, it should be understood that the disclosed devices / terminals and methods can be implemented in other ways. For example, the device / terminal embodiments described above are merely illustrative. For instance, the division of modules or units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be through some interfaces; the indirect coupling or communication connection between devices or units may be electrical, mechanical, or other forms.

[0098] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0099] Furthermore, the functional units in the various embodiments of the present invention can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit.

[0100] If the integrated module / unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, all or part of the processes in the methods of the above embodiments of the present invention can also be implemented by a computer program instructing related hardware. The computer program can be stored in a computer-readable storage medium, and when executed by a processor, it can implement the steps of the various method embodiments described above. The computer program includes computer program code, which can be in the form of source code, object code, executable files, or certain intermediate forms. The computer-readable medium can include: any entity or device capable of carrying the computer program code, a recording medium, a USB flash drive, a portable hard drive, a magnetic disk, an optical disk, a computer memory, a read-only memory (ROM), a random-access memory (RAM), an electrical carrier signal, a telecommunication signal, and a software distribution medium, etc.

[0101] This application is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus, and computer program products according to embodiments of this application. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart... Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0102] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0103] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0104] The above content is only for illustrating the technical concept of the present invention and should not be construed as limiting the scope of protection of the present invention. Any modifications made to the technical solution based on the technical concept proposed in this invention shall fall within the scope of protection of the claims of this invention.

Claims

1. A method for evaluating micropore size in zoom microscopy, characterized in that, Includes the following steps: Acquire three-dimensional point cloud data of micropores. The three-dimensional point cloud data of micropores is point cloud data obtained by zoom microscopic imaging of micropores and three-dimensional reconstruction by zoom microscopic imaging. The three-dimensional point cloud data of micropores includes multiple point cloud points and the three-dimensional spatial coordinates of each point cloud point. The micropore 3D point cloud data is subjected to statistical filtering and denoising processing. For each target point in the micropore 3D point cloud data, the neighboring points of the target point are searched. Based on the comparison result of the average distance from the target point to its neighboring points and the preset distance threshold, outliers are removed to obtain the denoised point cloud data. The denoised point cloud data is subjected to axial layering processing. Along the predetermined micropore axis, the denoised point cloud data is divided into multiple layered cross-sectional point clouds according to a preset layering thickness. Envelope extraction is performed on the point cloud of each layered section, and the point cloud of the layered section is projected onto a plane perpendicular to the axis of the micropore to obtain a two-dimensional section point set. The envelope boundary of the two-dimensional section point set is extracted to obtain an envelope point set representing the outer contour of the corresponding layered section. Two-dimensional circle fitting is performed based on each set of envelope points, and the center parameters and radius parameters of the corresponding layered cross section are determined according to the geometric error from the envelope points in the set of envelope points to the fitted circle. Axial smoothing is performed on the center and radius parameters of multiple layered sections. Based on the center and radius parameters of adjacent layered sections, the center and radius parameters of the current layered section are updated smoothly to obtain the smoothed center and radius parameters. The micropore diameter evaluation result is calculated based on the radius parameters after smoothing multiple layered cross sections, wherein the micropore diameter evaluation result includes the micropore diameter obtained statistically from the radius parameters after smoothing multiple layered cross sections.

2. The method for evaluating micropore size in zoom microscopy according to claim 1, characterized in that, The acquisition of micropore 3D point cloud data includes: A CCD camera is used to perform zoom microscopy imaging of micro-holes, and image sequences with different focal positions are acquired along the optical axis. The image sequence is subjected to zoom microscopic 3D reconstruction processing. Sharp points are extracted from each image and their 3D spatial coordinates are restored to obtain the 3D point cloud data of the micropore.

3. The method for evaluating micropore size in zoom microscopy according to claim 1, characterized in that, The statistical filtering and noise reduction process for the micropore 3D point cloud data includes: For each target point in the microporous 3D point cloud data, search for a preset number of neighboring points of the target point in 3D space; Calculate the average distance from the target point to the preset number of neighboring points; When the average distance is greater than the preset distance threshold, the target point is identified as an outlier and removed.

4. The method for evaluating micropore size in zoom microscopy according to claim 1, characterized in that, The axial layering process for the denoised point cloud data includes: Determine the coordinate range of the denoised point cloud data along the axis of the micropore; The number of layers is determined based on the coordinate range and the preset layer thickness; According to the coordinate values ​​of each point cloud point along the axis of the micropore, the denoised point cloud data is divided into the corresponding layered cross-sectional point cloud.

5. The method for evaluating micropore size in zoom microscopy according to claim 4, characterized in that, The micropore axis is the axis determined in the zoom microscope three-dimensional reconstruction coordinate system; In one embodiment, the micro-aperture axis is the Z-axis in the zoom microscope three-dimensional reconstruction coordinate system; The preset layer thickness is determined based on the axial step distance of the stage during zoom microscopy acquisition.

6. The method for evaluating micropore size in zoom microscopy according to claim 1, characterized in that, The envelope extraction for each of the layered cross-sectional point clouds includes: Projecting the point cloud points in each of the layered cross-sections onto a two-dimensional plane perpendicular to the axis of the micropores yields the two-dimensional cross-section point set. The convex hull boundary is extracted from the set of points on the two-dimensional cross section; The extracted convex hull boundary points are used as the envelope point set of the corresponding layered cross section.

7. The method for evaluating micropore size in zoom microscopy according to claim 1, characterized in that, The step of performing two-dimensional circle fitting based on each set of envelope points includes: Algebraic circle fitting is performed based on the envelope point set to obtain the initial center parameters and initial radius parameters of the corresponding layered cross section; Using the initial center parameter and the initial radius parameter as the starting parameters for iterative optimization, a geometric distance error objective function from the envelope point to the fitted circle is established. The objective function of geometric distance error is iteratively optimized to obtain the center parameters and radius parameters of the corresponding layered cross sections.

8. The method for evaluating micropore size in zoom microscopy according to claim 1, characterized in that, The axial smoothing process for the center and radius parameters of multiple layered sections includes: A smooth window is formed by selecting adjacent layered sections centered on the current layered section; The radius parameters of the current layered section are obtained by weighted averaging the radius parameters of each layered section within the smoothing window. The center parameters of the current layered section are obtained by weighted averaging the center parameters of each layered section within the smoothing window.

9. The method for evaluating micropore size in zoom microscopy according to claim 1, characterized in that, The calculation of micropore size evaluation results based on the radius parameters after smoothing multiple layered cross sections includes: The average radius parameter is obtained by statistically averaging the radius parameters of multiple smoothed layered sections. The average radius parameter is taken as twice the diameter of the micropore.

10. A micropore size evaluation system for zoom microscopy, characterized in that, include: The point cloud acquisition module is used to acquire micro-hole three-dimensional point cloud data. The micro-hole three-dimensional point cloud data is point cloud data obtained by zoom microscopic imaging of the micro-hole and zoom microscopic three-dimensional reconstruction. The micro-hole three-dimensional point cloud data includes multiple point cloud points and the three-dimensional spatial coordinates of each point cloud point. The noise reduction module is used to perform statistical filtering noise reduction on the micro-hole three-dimensional point cloud data. For each target point in the micro-hole three-dimensional point cloud data, the neighboring points of the target point are searched. Based on the comparison result of the average distance from the target point to its neighboring points and the preset distance threshold, outliers are removed to obtain the noise-reduced point cloud data. The axial layering module is used to perform axial layering processing on the denoised point cloud data, dividing the denoised point cloud data into multiple layered cross-sectional point clouds along a predetermined micro-hole axis and according to a preset layering thickness. The envelope extraction module is used to extract the envelope of each layered cross-section point cloud, project the layered cross-section point cloud onto a plane perpendicular to the axis of the micropore to obtain a two-dimensional cross-section point set, and extract the envelope boundary of the two-dimensional cross-section point set to obtain an envelope point set representing the outer contour of the corresponding layered cross-section. The circle fitting module is used to perform two-dimensional circle fitting based on each set of envelope points, and to determine the center parameters and radius parameters of the corresponding layered section based on the geometric error from the envelope points in the set of envelope points to the fitted circle. The parameter smoothing module is used to perform axial smoothing on the center and radius parameters of multiple layered sections. Based on the center and radius parameters of adjacent layered sections, the center and radius parameters of the current layered section are updated smoothly to obtain the smoothed center and radius parameters. The pore size calculation module is used to calculate the micropore size evaluation result based on the radius parameters after smoothing multiple layered cross sections. The micropore size evaluation result includes the micropore diameter obtained by statistical analysis based on the radius parameters after smoothing multiple layered cross sections.

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