Method and apparatus for detecting optical anisotropy of coated fuel particles and computing device

Abstract

The application discloses a coated fuel particle optical anisotropy detection method and device and computing equipment, and the method comprises the following steps: traversing a plurality of first field metallographic section images covering the whole metallographic sample to determine the image position information of each coated fuel particle and perform coordinate splicing to obtain a global coordinate map; based on the global coordinate map, the second field metallographic section image of each coated fuel particle is collected one by one; the second field metallographic section image is coated by a semantic segmentation model to define the coating, and then the coated fuel particle is subjected to measurability analysis to screen a plurality of target coated fuel particles; based on the layer definition image, the dense pyrolysis carbon layer region is extracted from the second field metallographic section image of the target coated fuel particle, and the measured region is selected therefrom to perform reflectivity measurement, so as to determine the optical anisotropy of the target coated fuel particle. The application can realize the automation and intelligentization of the coated fuel particle optical anisotropy detection process.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of computer and visual detection, and particularly relates to a coated fuel particle optical anisotropy detection method, a coated fuel particle optical anisotropy detection device and a computing device. BACKGROUND

[0002] Nuclear energy is the key to addressing global energy crisis and is an important means to guarantee national energy security, economy and greenness. High-temperature gas cooled reactor is a representative reactor type in the fourth generation of nuclear power, has characteristics such as inherent safety and modular design, and the related technology is self-controllable. Coated fuel particles are the basis for the operation of high-temperature gas cooled reactors (the smallest unit), and the number of coated fuel particles in each reactor module exceeds 4 billion. The optical anisotropy of the coated fuel particles affects the thermal conductivity and mechanical properties of the nuclear fuel element. Therefore, accurate detection of the optical anisotropy of the coated fuel particles is an important guarantee for the safe operation of the nuclear reactor.

[0003] In the prior art, the coated fuel particles are usually detected by section detection by using metallographic method. However, the coated fuel particles in the metallographic photo are small and numerous, which makes it difficult to detect the anisotropy of the coated fuel particles. In addition, the manual detection has low precision, low efficiency and poor consistency of results, and cannot meet the detection requirements of the coated fuel particles.

[0004] Therefore, there is a need for a coated fuel particle optical anisotropy detection method to solve the problems in the prior art. SUMMARY

[0005] To this end, the present application provides a coated fuel particle optical anisotropy detection method and device to solve or at least alleviate the above problems.

[0006] According to an aspect of the present application, a coated fuel particle optical anisotropy detection method is provided, which is executed in a computing device in communication connection with a microscope, and a metallographic sample with a plurality of coated fuel particles is placed on a motorized stage of the microscope, the method comprising: controlling the microscope to traverse and collect a plurality of first field metallographic cross-section images covering the entire metallographic sample; determining image position information of a plurality of coated fuel particles contained in each of the first field metallographic cross-section images; performing coordinate stitching on the image position information of all coated fuel particles contained in the plurality of first field metallographic cross-section images to obtain a global coordinate map covering all coated fuel particles in the metallographic sample; based on the global coordinate map, controlling the microscope to collect a second field metallographic cross-section image of each coated fuel particle in the metallographic sample one by one; for each of the coated fuel particles, using a semantic segmentation model to coat the second field metallographic cross-section image of the coated fuel particle to obtain a corresponding coating boundary image, and based on the coating boundary image, performing measurability analysis on the coated fuel particle to screen a plurality of target coated fuel particles meeting anisotropy measurement conditions; for each of the target coated fuel particles, based on the corresponding layer boundary image, extracting a dense pyrolytic carbon layer region from the second field metallographic cross-section image of the target coated fuel particle, and selecting a to-be-measured region from the dense pyrolytic carbon layer region; performing reflectivity measurement on the to-be-measured region to determine the optical anisotropy of the dense pyrolytic carbon layer of the target coated fuel particle.

[0007] Optionally, in the coated fuel particle optical anisotropy detection method according to the present application, selecting the to-be-measured region from the dense pyrolytic carbon layer region comprises: determining a first black point proportion in the dense pyrolytic carbon layer region, judging whether the black point is an essential attribute of the dense pyrolytic carbon layer region based on the first black point proportion; extracting a plurality of reflectivity measurement regions from the dense pyrolytic carbon layer region, and determining a second black point proportion in each of the reflectivity measurement regions; if the black point is an essential attribute of the dense pyrolytic carbon layer region, selecting a reflectivity measurement region with the most second black point proportion from the plurality of reflectivity measurement regions as the to-be-measured region; if the black point is not an essential attribute of the dense pyrolytic carbon layer region, selecting a reflectivity measurement region with the least second black point proportion from the plurality of reflectivity measurement regions as the to-be-measured region.

[0008] Optionally, in the coated fuel particle optical anisotropy detection method according to the present application, the plurality of reflectivity measurement regions are extracted from the dense pyrolytic carbon layer region, including: establishing a target coordinate system with the core center of the target coated fuel particle as the target origin on the coating boundary image of the target coated fuel particle; drawing two measurement lines along the X-axis and Y-axis directions of the target coordinate system, and extracting a plurality of regions of a predetermined size from the plurality of intersections of the two measurement lines and the dense pyrolytic carbon layer region as a plurality of reflectivity measurement regions.

[0009] Optionally, in the coated fuel particle optical anisotropy detection method according to the present application, the reflectivity measurement is performed on the to-be-measured region to determine the optical anisotropy of the dense pyrolytic carbon layer of the target coated fuel particle, including: controlling the motorized stage of the microscope to move the metallographic sample so that the center of the to-be-measured region of the target coated fuel particle is located at the center of the collection area of the microscope; controlling the microscope to switch to a third multiple polarizing objective and adjusting the field stop to the minimum so that the microscope automatically focuses to the frame of the field stop is clear; adjusting the polarizer of the microscope to generate polarized light in two directions at 0° and 90° respectively, and measuring the maximum reflectivity and minimum reflectivity of the to-be-measured region under the two polarized lights by the microphotometer as the optical anisotropy of the dense pyrolytic carbon layer of the target coated fuel particle.

[0010] Optionally, in the coated fuel particle optical anisotropy detection method according to the present application, determining the optical anisotropy of the dense pyrolytic carbon layer of the target coated fuel particle further includes: comparing the optical anisotropy of the dense pyrolytic carbon layer with the standard optical anisotropy of the standard substance with known reflectivity under the two polarized lights to determine the optical anisotropy factor of the dense pyrolytic carbon layer.

[0011] Optionally, in the coated fuel particle optical anisotropy detection method according to the present application, controlling the microscope to traverse and collect a plurality of first field of view metallographic cross-section images covering the entire metallographic sample, including: acquiring a first field of view metallographic cross-section image of the central region of the metallographic sample collected by the microscope, establishing an absolute coordinate system of the metallographic sample with the core center of any coated fuel particle in the first field of view metallographic cross-section image as an absolute origin; determining a traversal collection path covering the entire metallographic sample based on the absolute coordinate system; controlling the microscope to start from the absolute origin and traverse and collect a plurality of first field of view metallographic cross-section images covering the entire metallographic sample based on the traversal collection path.

[0012] Optionally, in the coated fuel particle optical anisotropy detection method according to the present application, based on the global coordinate map, the microscope is controlled to collect second field of view metallographic section images of each coated fuel particle in the metallographic sample one by one, comprising: based on the global coordinate map and the traversal collection path, path planning is performed on each coated fuel particle in each first field of view metallographic section image to determine a global collection path; based on the global collection path, the microscope is controlled to collect second field of view metallographic section images of each coated fuel particle in the metallographic sample one by one.

[0013] Optionally, in the coated fuel particle optical anisotropy detection method according to the present application, the image position information of the plurality of coated fuel particles contained in each first field of view metallographic section image is determined, comprising: using a particle intelligent positioning model to determine the image position information of the plurality of coated fuel particles contained in each first field of view metallographic section image, the particle intelligent positioning model comprising a feature extraction network, an anchor box generation network, a proposal generation network, and a fully connected network coupled in sequence; wherein the feature extraction network is used to perform feature extraction on the first field of view metallographic section image to obtain a feature image; the anchor box generation network is used to generate an anchor box based on the feature image; the proposal generation network is used to determine a candidate proposal region based on the feature image and the anchor box; and the fully connected network is used to classify and coordinate the candidate proposal region to determine the image position information of the plurality of coated fuel particles, eliminate one or more incomplete coated fuel particles, and output a positioning mark image corresponding to the first field of view metallographic section image.

[0014] Optionally, in the coated fuel particle optical anisotropy detection method according to the present application, the coated fuel particle is analyzed for measurability based on the coating boundary image, comprising: performing integrity analysis on the coated fuel particle based on the coating boundary image to determine whether the coated fuel particle is complete; if it is determined that the coated fuel particle is complete, determining the tangent distance between the coated fuel particle and its adjacent coated fuel particle based on the coating boundary image, and determining whether the cross section of the coated fuel particle is close to the equatorial plane based on the tangent distance, if it is close to the equatorial plane, it is determined that the coated fuel particle meets the tangent requirement, and the coated fuel particle is taken as a target coated fuel particle that meets the anisotropy measurement condition.

[0015] Optionally, in the coated fuel particle optical anisotropy detection method according to the present application, the control of the microscope to traverse and collect a plurality of first field metallographic section images covering the entire metallographic sample comprises: control of the microscope to traverse and collect a plurality of first field metallographic section images covering the entire metallographic sample by using a first magnification objective lens; and the control of the microscope to collect, one by one, second field metallographic section images of each coated fuel particle in the metallographic sample comprises: control of the microscope to collect, one by one, second field metallographic section images of each coated fuel particle in the metallographic sample by using a second magnification objective lens.

[0016] According to an aspect of the present application, there is provided a coated fuel particle optical anisotropy detection device deployed in a computing device, which is communicatively connected with a microscope, and an electric stage of the microscope is adapted to place a metallographic sample having a plurality of coated fuel particles, the device comprising: a first acquisition unit adapted to control the microscope to traverse and collect a plurality of first field metallographic section images covering the entire metallographic sample; a determination unit adapted to determine image position information of a plurality of coated fuel particles contained in each of the first field metallographic section images; a stitching unit adapted to perform coordinate stitching on the image position information of all coated fuel particles contained in the plurality of first field metallographic section images, to obtain a global coordinate map covering all coated fuel particles in the metallographic sample; a second acquisition unit adapted to control the microscope to collect, one by one, second field metallographic section images of each coated fuel particle in the metallographic sample based on the global coordinate map; an analysis unit adapted to, for each of the coated fuel particles, perform coating delimitation on the second field metallographic section image of the coated fuel particle by using a semantic segmentation model, to obtain a corresponding coating delimitation image, and perform measurability analysis on the coated fuel particle based on the coating delimitation image, to screen a plurality of target coated fuel particles satisfying anisotropy measurement conditions; an extraction unit adapted to, for each of the target coated fuel particles, extract a dense pyrolytic carbon layer region from the second field metallographic section image of the target coated fuel particle based on the corresponding coating delimitation image, and select a to-be-measured region from the dense pyrolytic carbon layer region; and a measurement unit adapted to perform reflectivity measurement on the to-be-measured region, to determine optical anisotropy of the dense pyrolytic carbon layer of the target coated fuel particle.

[0017] According to an aspect of the present application, there is provided a computing device comprising: at least one processor; and a memory storing program instructions configured to be executed by the at least one processor, the program instructions comprising instructions for performing the coated fuel particle optical anisotropy detection method as described above.

[0018] According to an aspect of the present application, there is provided a computer program product comprising computer programs / instructions which, when executed by a processor, implement the method as described above.

[0019] According to an aspect of the present application, there is provided a readable storage medium storing program instructions which, when read and executed by a computing device, cause the computing device to perform the coated fuel particle optical anisotropy detection method as described above.

[0020] According to the technical solution of the present application, a coated fuel particle optical anisotropy detection method is provided. The computing device first controls the microscope to traverse and collect a plurality of first field metallographic cross-section images covering the entire metallographic sample, determines the image position information of a plurality of coated fuel particles contained in each first field metallographic cross-section image, and performs coordinate splicing on the image position information of all coated fuel particles to obtain a global coordinate map covering all coated fuel particles in the metallographic sample. Then, the global coordinate map can be used to control the microscope to collect a second field metallographic cross-section image of each coated fuel particle in the metallographic sample one by one, and perform region segmentation on the second field metallographic cross-section image to analyze the measurability of the coated fuel particle. After that, a to-be-measured region is selected from the dense pyrolytic carbon layer region of each target coated fuel particle that meets the anisotropy measurement condition. Finally, reflectivity measurement is performed on the to-be-measured region to determine the optical anisotropy of the dense pyrolytic carbon layer of the target coated fuel particle. Based on this, the present application can realize the automation and intelligentization of the optical anisotropy detection process of the dense pyrolytic carbon layer of the coated fuel particle, and the detection process is more efficient, which improves the detection precision and consistency of the detection results. Among them, the automatic collection of high-quality images of multiple fields of each coated fuel particle is realized; the coated fuel particle can be intelligently positioned, and the target coated fuel particle that meets the anisotropy measurement condition can be intelligently analyzed and screened, and the to-be-measured region for optical anisotropy detection can be intelligently screened, and then the optical anisotropy of the coated fuel particle can be accurately measured based on the to-be-measured region.

[0021] In addition, by comparing the optical anisotropy degree of the dense pyrolytic carbon layer with the standard optical anisotropy degree of the standard substance, the optical anisotropy detection result can be corrected, and then the interference factors such as environmental changes can be excluded, and the optical anisotropy detection result can be ensured to be accurate and reliable.

[0022] The above description is only a summary of the technical solution of the present application. In order to more clearly understand the technical means of the present application, the content of the specification can be implemented, and in order to make the above and other purposes, features and advantages of the present application more obvious and easy to understand, the specific embodiments of the present application are described below. BRIEF DESCRIPTION OF DRAWINGS

[0023] To the accomplishment of the foregoing and related ends, certain illustrative aspects are described herein in connection with the following description and the annexed drawings. These aspects are indicative of various ways in which the principles disclosed herein can be practiced. All aspects and equivalents thereof are intended to be within the scope of the claimed subject matter. The foregoing and other objects, features, and advantages of the present application will become more fully apparent from the following detailed description, appended claims, and accompanying drawings in which like reference numerals identify like components throughout the drawings. The detailed description is to be read with the accompanying drawings so as to enable those skilled in the art to practice the present application. Throughout the specification, like reference numerals will be understood to refer to like structures, whether appearing in other

[0024] Figure 1 A schematic diagram of a computing device 100 is shown according to an embodiment of the present application;

[0025] Figure 2 A flowchart of a coated fuel particle optical anisotropy detection method 200 is shown according to an embodiment of the present application;

[0026] Figure 3 A schematic diagram of establishing an absolute coordinate system of a metallographic sample is shown according to an embodiment of the present application;

[0027] Figure 4 A schematic diagram of traversing a collection path is shown according to an embodiment of the present application;

[0028] Figure 5 A schematic diagram of positioning a mark image is shown according to an embodiment of the present application;

[0029] Figure 6 A schematic diagram of a global collection path is shown according to an embodiment of the present application;

[0030] Figure 7 An effect diagram of a coating boundary image corresponding to a second field of view metallographic cross-section image is shown according to an embodiment of the present application;

[0031] Figure 8 A schematic diagram of determining a black point proportion in a dense pyrolytic carbon layer region is shown according to an embodiment of the present application;

[0032] Figure 9 A schematic diagram of extracting a plurality of reflectivity measurement regions from a dense pyrolytic carbon layer region and selecting a to-be-measured region is shown according to an embodiment of the present application;

[0033] Figure 10 A schematic diagram of a visual metallographic Map image of a metallographic sample generated according to an embodiment of the present application is shown;

[0034] Figure 11 A structural diagram of a particle intelligent positioning model 1100 is shown according to an embodiment of the present application;

[0035] Figure 12An architecture diagram of a particle intelligent positioning model provided according to an embodiment of the present invention is shown;

[0036] Figure 13 A schematic diagram of the structure of a semantic segmentation model 1300 provided according to an embodiment of the present invention is shown;

[0037] Figure 14 A schematic diagram of tangential analysis of coated fuel particles according to an embodiment of the present invention is shown;

[0038] Figure 15 A schematic diagram of an optical anisotropy detection device 1500 for coated fuel particles provided according to an embodiment of the present invention is shown. Detailed Implementation

[0039] Exemplary embodiments of the invention will now be described in more detail with reference to the accompanying drawings. While exemplary embodiments of the invention are shown in the drawings, it should be understood that the invention may be implemented in various forms and should not be limited to the embodiments set forth herein. Rather, these embodiments are provided so that this invention will be thorough and complete, and will fully convey the scope of the invention to those skilled in the art.

[0040] To address the problems of difficulty, low accuracy, low efficiency, and insufficient consistency in the detection of anisotropy of coated fuel particles in existing technologies, this invention proposes a method for detecting the optical anisotropy of coated fuel particles. This method automates and intelligentizes the detection process, making the detection more efficient and improving both accuracy and consistency.

[0041] The optical anisotropy detection method for coated fuel particles in this embodiment of the invention can be executed in a computing device.

[0042] The embodiments of the present invention will now be described in detail with reference to the accompanying drawings.

[0043] Figure 1 A schematic diagram of a computing device 100 according to an embodiment of the present invention is shown. Figure 1 As shown, in a basic configuration, computing device 100 includes at least one processing unit 102 and system memory 104. According to one aspect, depending on the configuration and type of the computing device, the processing unit 102 may be implemented as a processor. System memory 104 includes, but is not limited to, volatile memory (e.g., random access memory), non-volatile memory (e.g., read-only memory), flash memory, or any combination of such memories. According to one aspect, system memory 104 includes an operating system 105.

[0044] According to an aspect, the operating system 105 is suitable for controlling the operation of the computing device 100, for example. Furthermore, examples are practiced in conjunction with a graphics library, other operating systems, or in connection with any other application program, and is not limited to any particular application or system. This detailed description in its entirety, refrained in connection with the drawings, describes exemplary features and the Figure 1 The basic configuration is shown in FIG. 1 by those components within the dashed line. According to an aspect, the computing device 100 has additional features or functionality. For example, according to an aspect, the computing device 100 includes additional data storage devices (removable and / or non-removable) such as, for example, magnetic disks, optical disks, or tape. Such additional storage is illustrated in FIG. 1 by the removable storage 109 and the non-removable storage 110. Figure 1

[0045] As stated above, according to an aspect, program modules 103 are stored in the system memory 104, in accordance with an aspect. According to an aspect, the program modules 103 can include one or more application programs, the application programs are not limited by the present application, for example, the application programs can include: email and contacts applications, word processing applications, spreadsheet applications, database applications, slide presentation applications, drawing or computer-aided applications, web browser applications, and the like.

[0046] According to an aspect, the program modules 103 can include a plurality of program instructions suitable for performing the coated fuel particle optical anisotropy detection method 200 of the present application, so that the computing device 100 is configured to perform the coated fuel particle optical anisotropy detection method 200 of the present application.

[0047] According to an aspect, the program modules 103 can include a coated fuel particle optical anisotropy detection apparatus 1500, the coated fuel particle optical anisotropy detection apparatus 1500 can be configured to perform the coated fuel particle optical anisotropy detection method 200 of the present application.

[0048] According to an aspect, examples can be practiced with electronic circuits comprising discrete electronic elements, packaged or integrated electronic chips containing logic gates, a circuit utilizing a microprocessor, or by implementing the examples on a single chip, with all the components integrated therein. For example, a memory management unit can be implemented by logic gates in a logic circuit, by a microprocessor, or by a combination of both, among other possibilities. Figure 1 ​Each or many of the components illustrated in FIG. 1 can be practiced on a system-on-a-chip (SOC) integrated on a single integrated circuit in accordance with examples. According to one aspect, such an SOC device can include one or more processing units, graphics units, communications units, system virtualization units, and various application functionality all of which are integrated (or "burned") onto the chip substrate according to an embodiment. When operating via an SOC, the functionality described herein can be operated via application-specific logic integrated with other components of the computing device 100 on the single integrated circuit (chip). Embodiments of the application can also be practiced using other technologies that now exist or are developed in the future, including, but not limited to, mechanical, optical, fluidic, and quantum technologies. In addition, embodiments of the application can be practiced within a general computer system or in any other circuits or systems.

[0049] According to one aspect, the computing device 100 can also have one or more input device(s) 112 such as a keyboard, a mouse, a pen, a voice input device, a touch input device, etc. Output device(s) 114 such as a display, speakers, a printer, etc. can also be included. The aforementioned devices are examples and others can be used. The computing device 100 can include one or more communication connections 116 allowing communications with other computing devices 118. Examples of suitable communication connections 116 include, but are not limited to: RF transmitter, receiver, and / or transceiver circuitry; universal serial bus (USB), parallel, and / or serial ports.

[0050] The term computer readable media as used herein includes computer storage media. Computer storage media can include volatile and nonvolatile, removable and non-removable media implemented in any method or technology for storage of information such as computer readable instructions, data structures, or program modules 103. The system memory 104, the removable storage device 109, and the non-removable storage device 110 are all computer storage media examples (i.e., memory storage.) Computer storage media can include Random Access Memory (RAM), Read-Only Memory (ROM), Electronically Erasable Programmable Read-Only Memory (EEPROM), flash memory or other memory technology, CD-ROM, digital versatile disks (DVD) or other optical storage, magnetic cassettes, magnetic tape, magnetic disk storage or other magnetic storage devices, or any other medium that can be used to store information and which can be accessed by computing device 100. According to one aspect, any such computer storage media can be part of the computing device 100. Computer storage media does not include a modulated data signal or other propagated data signal.

[0051] According to one aspect, the communication medium is implemented by computer-readable instructions, data structures, program modules 103, or other data in a modulated data signal (e.g., a carrier wave or other transmission mechanism), and includes any information transmission medium. According to one aspect, the term "modulated data signal" describes a signal having one or more sets of characteristics or altered in a manner that encodes information in the signal. By way of example and not limitation, the communication medium includes wired media such as wired networks or direct wired connections, and wireless media such as acoustic, radio frequency (RF), infrared, and other wireless media.

[0052] In an embodiment of the invention, a computing device 100 is configured to perform the coated fuel particle optical anisotropy detection method 200 of the invention. The computing device 100 includes one or more processors and one or more readable storage media storing program instructions that, when executed by the one or more processors, cause the computing device to perform the coated fuel particle optical anisotropy detection method 200 of the present invention.

[0053] Figure 2 A schematic flowchart of a method 200 for detecting the optical anisotropy of coated fuel particles according to an embodiment of the present invention is shown. The method 200 for detecting the optical anisotropy of coated fuel particles can be executed in a computing device (e.g., the aforementioned computing device 100).

[0054] In an embodiment of the present invention, the computing device 100 is communicatively connected to a microscope. The computing device 100 can cooperate with the microscope to automatically perform optical anisotropy detection on the dense pyrolytic carbon layer (including an inner dense pyrolytic carbon layer and an outer dense pyrolytic carbon layer) of the fuel particles. Before performing method 200, a metallographic sample having multiple fuel particles coated on it can be placed on the electric stage of the microscope in advance.

[0055] It should be noted that the coated fuel particles include the core (uranium dioxide core) and multiple coating layers covering the core. The multiple coating layers, from the outside to the inside, include a loose pyrolytic carbon layer, an inner dense pyrolytic carbon layer, a pyrolytic silicon carbide layer, and an outer dense pyrolytic carbon layer.

[0056] like Figure 2 As shown, the optical anisotropy detection method 200 for coated fuel particles includes the following steps 210 to 270.

[0057] Step 210: The computing device 100 can control the microscope to acquire multiple first-field metallographic cross-sectional images covering the entire metallographic sample (metallographic section). It should be noted that the metallographic sample contains multiple coated fuel particles.

[0058] Specifically, the computing device 100 can control the microscope to traverse to collect a plurality of first field metallographic section images covering the entire metallographic sample using a first magnification objective. The first magnification objective corresponds to the first field metallographic section images. For example, the first magnification objective can be a 5x objective, and accordingly, the first field can be a 5x field.

[0059] In some embodiments, before performing step 210, the computing device 100 can pre-establish an absolute coordinate system of the metallographic sample. Figure 3 A schematic diagram of establishing an absolute coordinate system of a metallographic sample according to an embodiment of the present application is shown. Specifically, referring to Figure 3 The computing device 100 can control the microscope to collect a first field metallographic section image (for example, a 5x field) of a central region of the metallographic sample and obtain the first field metallographic section image of the central region of the metallographic sample collected by the microscope. Subsequently, any core center of a coated fuel particle in the first field metallographic section image can be taken as an absolute origin (i.e., an origin of the absolute coordinate system of the metallographic sample) to establish the absolute coordinate system of the metallographic sample. It should be noted that, in order to distinguish from the target coordinate system in the following, the origin of the absolute coordinate system is referred to as an “absolute origin” here.

[0060] Subsequently, the computing device 100 can determine a traversal collection path covering the entire metallographic sample based on the absolute coordinate system. The traversal collection path is used to indicate that the microscope traverses the entire metallographic sample to collect a plurality of first field metallographic section images covering the entire metallographic sample. Here, Figure 4 A schematic diagram of a traversal collection path according to an embodiment of the present application is shown. Subsequently, the computing device 100 can control the microscope to traverse to collect a plurality of first field metallographic section images covering the entire metallographic sample based on the traversal collection path, starting from the absolute origin. Here, it should be noted that, before imaging, each collection point of the metallographic sample can be moved to the laser sensor of the microscope for focusing distance marking, so as to avoid the image clarity from being reduced due to the uneven surface of the metallographic sample.

[0061] Step 220: The computing device 100 can determine image position information (coordinates) of a plurality of coated fuel particles contained in each first field metallographic section image. In this step 220, part of the incomplete coated fuel particles in the first field metallographic section image can be removed at the same time.

[0062] It should be noted that, by determining the image position information of the plurality of coated fuel particles contained in the first field metallographic section image, a positioning marker image corresponding to the first field metallographic section image can be obtained. Figure 5 A schematic diagram of a positioning marker image according to an embodiment of the present application is shown.

[0063] In some embodiments, in step 220, the computing device 100 can utilize a particle intelligent positioning model to determine the image position information of the plurality of coated fuel particles contained in each first-view metallurgical cross-section image, while rejecting some of the incomplete coated fuel particles. The particle intelligent positioning model will be described in the following embodiments.

[0064] Step 230, the computing device 100 can perform coordinate stitching on the image position information of all the coated fuel particles contained in the plurality of first-view metallurgical cross-section images, to obtain a global coordinate map covering all the coated fuel particles (all the coated fuel particles remaining after rejecting some of the incomplete coated fuel particles) in the metallurgical sample.

[0065] Step 240, the computing device 100 can control the microscope to collect the second-view metallurgical cross-section image of each coated fuel particle in the metallurgical sample one by one based on the global coordinate map. It should be pointed out that the second-view metallurgical cross-section image only contains a single complete coated fuel particle.

[0066] Specifically, the computing device 100 can control the microscope to collect the second-view metallurgical cross-section image of each coated fuel particle in the metallurgical sample one by one based on the global coordinate map. Here, the second-view metallurgical cross-section image corresponds to the second magnification objective lens. In the embodiments of the present application, the second magnification is greater than the first magnification, and correspondingly, the second view is smaller than the first view. For example, the first magnification can be 5 times, and the second magnification can be 10 times, i.e., the first magnification objective lens can be a 5 times objective lens, and the second magnification objective lens can be a 10 times objective lens, and correspondingly, the second view can be a 10 times view.

[0067] In some embodiments, the computing device 100 can perform path planning on each coated fuel particle in each first-view metallurgical cross-section image based on the global coordinate map and the above-mentioned traversal collection path, to determine a global collection path. Here, Figure 6 A schematic diagram of the global collection path according to the embodiments of the present application is shown. It should be pointed out that the global collection path is used to indicate that the microscope collects the second-view metallurgical cross-section image of each coated fuel particle in the metallurgical sample one by one. Further, the computing device 100 can control the microscope (using the second magnification objective lens) to collect the second-view metallurgical cross-section image of each coated fuel particle in the metallurgical sample one by one based on the global collection path.

[0068] At step 250, the computing device 100 can utilize the semantic segmentation model to perform coating delimitation on the second-view metallographic cross-section image of each coated fuel particle to obtain a corresponding coating delimitation image (i.e., a segmentation mask image), and then can perform measurability analysis (including integrity analysis and tangentiality analysis) on the coated fuel particle based on the coating delimitation image, so as to screen a plurality of target coated fuel particles satisfying the anisotropy measurement condition from all coated fuel particles of the metallographic sample.

[0069] Herein, Figure 7 An effect schematic diagram of the coating delimitation image corresponding to the second-view metallographic cross-section image in an embodiment of the present application is shown. As shown in the diagram, Figure 7 The coating delimitation image contains a plurality of segmented coatings (corresponding to the core and each coating layer of the coated fuel particle, respectively).

[0070] Specifically, when performing the measurability analysis at step 250, the computing device 100 can first perform integrity analysis on the coated fuel particle (the coated fuel particle in the second-view metallographic cross-section image) based on the corresponding coating delimitation image to determine whether the coated fuel particle is complete (whether it meets the integrity requirement). Herein, the integrity requirement is as follows: the core of the coated fuel particle is not detached and complete, each coating layer has no obvious detachment and crack, and the boundary line of each coating layer is clear and tightly combined. That is, if it is determined that the core of the coated fuel particle is not detached and complete, each coating layer has no obvious detachment and crack, and the boundary line of each coating layer is clear and tightly combined, it can be determined that the coated fuel particle is complete.

[0071] If it is determined that the coated fuel particle is complete, the tangentiality analysis on the coated fuel particle based on the coating delimitation image can be continued, specifically as follows: the tangential distance between the coated fuel particle (i.e., the measured coated fuel particle) and its adjacent coated fuel particle can be determined based on the coating delimitation image, and based on the tangential distance, it is determined whether the cross-section (machining surface) of the measured coated fuel particle is close to the equatorial plane, if it is close to the equatorial plane, it is determined that the measured coated fuel particle meets the tangentiality requirement, and the measured coated fuel particle is taken as a target coated fuel particle satisfying the anisotropy measurement condition. It should be noted that when the tangential distance between the measured coated fuel particle and its adjacent coated fuel particle is small enough, it can be determined that the machining surface of the measured coated fuel particle is close to the equatorial plane, at this time, the thickness measurement value is close to the true value, meeting the tangentiality requirement.

[0072] At step 260, the computing device 100 can extract a dense pyrolytic carbon layer region from the second-view metallographic cross-section image of each target coated fuel particle based on the corresponding layer delimitation image, and select a measured region (i.e., the measured region of the target coated fuel particle) from the dense pyrolytic carbon layer region.

[0073] Step 270, for each target coated fuel particle, the computing device 100 can perform reflectivity measurement on the to-be-measured region of the target coated fuel particle (specifically, the reflectivity of the to-be-measured region under polarized light in different directions can be measured) to determine the optical anisotropy of the dense pyrolytic carbon layer of the target coated fuel particle. In this way, the optical anisotropy detection of the target coated fuel particle is realized automatically and intelligently.

[0074] Figure 8 A schematic diagram for determining the black point proportion in the dense pyrolytic carbon layer region according to an embodiment of the present application is shown. Figure 9 A schematic diagram for extracting a plurality of reflectivity measurement regions from the dense pyrolytic carbon layer region and selecting a to-be-measured region according to an embodiment of the present application is shown.

[0075] In some embodiments, with reference to Figure 8 and Figure 9 In step 260, the specific way of selecting the to-be-measured region from the dense pyrolytic carbon layer region is as follows: first, determine the black point proportion (first black point proportion) in the dense pyrolytic carbon layer region, judge whether the black point is the essential attribute of the dense pyrolytic carbon layer region based on the first black point proportion, for example, if the first black point proportion is within the target proportion interval, it can be determined that the black point is the essential attribute of the dense pyrolytic carbon layer region; otherwise, it is determined that the black point is not the essential attribute of the dense pyrolytic carbon layer region. It should be pointed out that the target proportion interval can be set according to actual conditions, and the present application does not make specific limitations thereon. Subsequently, a plurality of reflectivity measurement regions can be extracted from the dense pyrolytic carbon layer region. Then, the black point proportion analysis of each reflectivity measurement region can be performed in the following way: determine the black point proportion (second black point proportion) in each reflectivity measurement region, if it is determined in the above step that the black point is the essential attribute of the dense pyrolytic carbon layer region, the reflectivity measurement region with the largest second black point proportion can be selected from the plurality of reflectivity measurement regions as the to-be-measured region; if it is determined in the above step that the black point is not the essential attribute of the dense pyrolytic carbon layer region, the reflectivity measurement region with the smallest second black point proportion can be selected from the plurality of reflectivity measurement regions as the to-be-measured region.

[0076] In one specific embodiment, with reference to Figure 9 The specific way of extracting a plurality of reflectivity measurement regions from the dense pyrolytic carbon layer region is as follows: first, a target coordinate system is established with the core center of the target coated fuel particle as the target origin on the coating boundary image of the target coated fuel particle. Then, two measurement lines can be drawn along the X-axis and Y-axis directions of the target coordinate system, and a plurality of (8) regions of a predetermined size can be extracted from the plurality of (8) intersection points of the two measurement lines and the dense pyrolytic carbon layer region as a plurality of reflectivity measurement regions. The predetermined size is, for example, 4 μm*4 μm.

[0077] In addition, if none of the above-mentioned reflectivity measurement regions meets the measurement requirement, two new measurement lines can be obtained by rotating the two measurement lines (ensuring that the two measurement lines are perpendicular to each other) with the target origin of the target coordinate system as the center, and new reflectivity measurement regions of a predetermined size can be extracted based on the intersection of the two new measurement lines and the dense pyrolytic carbon layer region, and the black point proportion analysis of the new reflectivity measurement regions is performed until a to-be-measured region meeting the measurement requirement is obtained.

[0078] In some embodiments, in step 270, the computing device 100 performs reflectivity measurement on the to-be-measured region of the target coated fuel particle to determine the optical anisotropy of the dense pyrolytic carbon layer of the target coated fuel particle in the following specific manner:

[0079] First, the computing device 100 can control the motorized stage of the microscope to move the metallographic sample so that the center of the to-be-measured region of the target coated fuel particle is located at the center of the collection area of the microscope.

[0080] Subsequently, the computing device 100 can control the microscope to switch to a third multiple polarizing objective lens and adjust the field diaphragm to the minimum so that the microscope is automatically focused to the frame of the field diaphragm. It should be noted that the third multiple is greater than the second multiple, for example, the third multiple can be 50 times, and the third multiple polarizing objective lens is a 50 times polarizing objective lens.

[0081] Further, the computing device 100 can adjust the polarizer of the microscope to generate polarized light in two directions at 0° and 90°, respectively, and can measure the maximum reflectivity and the minimum reflectivity of the to-be-measured region of the target coated fuel particle under the two polarized lights through the microphotometer, and obtain the maximum reflectivity and the minimum reflectivity from the microphotometer, and take the maximum reflectivity and the minimum reflectivity as the optical anisotropy A1 of the dense pyrolytic carbon layer of the target coated fuel particle.

[0082] In addition, the optical anisotropy A1 of the dense pyrolytic carbon layer can also be compared with the standard optical anisotropy A of the standard substance with known reflectivity under the above-mentioned two polarized lights to determine the optical anisotropy factor of the dense pyrolytic carbon layer. Specifically, the optical anisotropy factor can be determined by the following formula: OPTAF=A1 / A. Wherein, A1 represents the optical anisotropy of the dense pyrolytic carbon layer, and A represents the standard optical anisotropy of the standard substance. In this way, by comparing the optical anisotropy of the dense pyrolytic carbon layer with the standard optical anisotropy of the standard substance, the optical anisotropy detection result can be corrected, and further, the interference factors such as environmental changes can be excluded, and the optical anisotropy detection result can be ensured to be accurate and reliable.

[0083] Figure 10 A schematic diagram showing a visual metallographic Map of a metallographic sample generated according to an embodiment of the present application is shown. In some embodiments, as shown in FIG. 2, the metallographic sample is a coated fuel particle, and the to-be-measured region of the target coated fuel particle is a dense pyrolytic carbon layer.Figure 10 As shown, after optical anisotropy detection is performed on each target coated fuel particle in the metallographic sample, the optical anisotropy detection data of the target coated fuel particle (including the optical anisotropy degree and the optical anisotropy factor of the dense pyrolytic carbon layer of the target coated fuel particle) can be recorded, and a visual metallographic Map of the metallographic sample can be generated based on the optical anisotropy detection data of all target coated fuel particles in the metallographic sample, so as to trace each target coated fuel particle based on the visual metallographic Map.

[0084] Figure 11 A structural schematic diagram of a particle intelligent positioning model 1100 is shown. Figure 12 A structural schematic diagram of a particle intelligent positioning model is shown.

[0085] As shown in Figure 11 and Figure 12 The particle intelligent positioning model 1100 includes a feature extraction network (i.e., a backbone network), an anchor box generation network, a proposal generation network, and a fully connected network, which are coupled in sequence.

[0086] The feature extraction network is configured to perform feature extraction on the first field-of-view metallographic cross-section image to obtain a feature image. The anchor box generation network is configured to generate sparse anchor boxes based on the feature image. The proposal generation network is configured to determine candidate proposal regions based on the feature image and the anchor boxes. The fully connected network is configured to classify and coordinate the candidate proposal regions to determine image position information of a plurality of coated fuel particles (complete coated fuel particles), while one or more incomplete coated fuel particles can be removed, and then a positioning mark image corresponding to the first field-of-view metallographic cross-section image can be output.

[0087] That is, in step 220, the computing device 100 determines the image position information of the plurality of coated fuel particles contained in each first-view metallographic cross-section image using the particle intelligent positioning model 1100, and the specific process is as follows: inputting the first-view metallographic cross-section image into the particle intelligent positioning model, first performing feature extraction on the first-view metallographic cross-section image through a feature extraction network to obtain a feature image. The feature image output by the feature extraction network is input into an anchor box generation network, and the anchor box generation network can generate sparse anchor boxes based on the feature image. The feature image and the anchor boxes can be input into a proposal generation network, and the proposal generation network can determine candidate proposal regions based on the feature image and the anchor boxes. The candidate proposal regions can be input into a fully connected network, and the fully connected network can classify and coordinate the candidate proposal regions (using a classifier and a position regression network) to determine the image position information (positioning marks) of the plurality of coated fuel particles, and one or more incomplete coated fuel particles can be removed, and then a positioning mark image corresponding to the first-view metallographic cross-section image can be output.

[0088] It can be seen that, according to the particle intelligent positioning model in the embodiment of the present application, accurate intelligent positioning of coated fuel particles and accurate removal of partial incomplete particles can be achieved.

[0089] Figure 13 A structural schematic diagram of a semantic segmentation model 1300 provided by an embodiment of the present application is shown. As shown in the figure, Figure 13 The semantic segmentation model 1300 includes a feature extraction module, a context fusion module, and a boundary refinement module coupled in sequence.

[0090] Among them, the second-view metallographic cross-section image of the coated fuel particles is input into the semantic segmentation model 1300, and the second-view metallographic cross-section image is processed by the semantic segmentation model 1300 (the feature extraction module, the context fusion module, and the boundary refinement module) to obtain a coating boundary image corresponding to the second-view metallographic cross-section image. As shown in the figure, Figure 7 The coating boundary image contains a plurality of segmented coatings (corresponding to the core and each cladding layer of the coated fuel particles, respectively).

[0091] It should be noted that, in the present application, the context fusion module is designed by stacking convolution layers in the channel direction and combining residual structures to achieve multi-scale feature extraction and depth fusion of local image information. On this basis, a weight map considering the coated fuel particle dataset is introduced, and the context fusion module and the boundary refinement module are integrated together to achieve step-by-step refined segmentation of the coated fuel particle image. In this way, the semantic segmentation model of the present application can achieve refined segmentation of the coated fuel particle image (the second-view metallographic cross-section image) and accurately extract the core and each cladding layer of the coated fuel particle.

[0092] Figure 14 A schematic diagram of tangency analysis of coated fuel particles according to an embodiment of the present invention is shown.

[0093] like Figure 14 As shown, in some embodiments, when performing tangency analysis on coated fuel particles based on coating boundary images, the following method can be used to determine the tangential distance between a coated fuel particle (i.e., the coated fuel particle under test) and its neighboring coated fuel particles: First, on the metallographic sample, with the center of gravity of the coated fuel particle under test as the center and a radius of 1.38 mm, multiple neighboring coated fuel particles are searched upwards. Then, from these neighboring coated fuel particles, the three closest neighboring coated fuel particles to the coated fuel particle under test, and the angle between each neighboring coated fuel particle and the coated fuel particle under test, are identified and recorded. Next, on the coating boundary image, three rays are emitted based on the aforementioned center and the three recorded angles, and the intersection point of each ray with the outermost layer of each coated fuel particle is determined. Each ray forms two adjacent intersection points with the outermost layers of the coated fuel particle under test and its neighboring coated fuel particles, and the shortest distance between these two adjacent intersection points can be determined as the tangential distance between the coated fuel particle under test and its neighboring coated fuel particles. Finally, the angle of the above records can be slightly changed, and the process can be repeated in the form of small angle changes to obtain different intersection points, ensuring that the tangent distance is the shortest distance between the two arcs of the outer edges of adjacent particles.

[0094] Furthermore, the minimum tangential distance can be used to determine whether the cross-section (processed surface) of the tested coated fuel particle is close to its equatorial plane, thereby ensuring the reliability of the measurement results. If it is close to the equatorial plane, it can be determined that the tested coated fuel particle meets the tangency requirement, and the tested coated fuel particle can be used as the target coated fuel particle that meets the anisotropic measurement conditions.

[0095] Figure 15 A schematic diagram of an optical anisotropy detection device 1500 for coated fuel particles according to an embodiment of the present invention is shown. The optical anisotropy detection device 1500 for coated fuel particles can be deployed in a computing device 100, and the optical anisotropy detection device 1500 for coated fuel particles is configured to perform the optical anisotropy detection method 200 for coated fuel particles of the present invention.

[0096] In an embodiment of the present invention, the computing device 100 is communicatively connected to a microscope, and a metallographic sample with multiple fuel-coated particles can be placed on the microscope's motorized stage.

[0097] like Figure 15As shown, in the embodiment of the present application, the coated fuel particle optical anisotropy detection device 1500 comprises a first acquisition unit 1510, a determination unit 1520, a splicing unit 1530, a second acquisition unit 1540, an analysis unit 1550, an extraction unit 1560 and a measurement unit 1570 connected in sequence.

[0098] The first acquisition unit 1510 is configured to control the microscope to traverse and collect a plurality of first field metallographic section images covering the entire metallographic sample.

[0099] The determination unit 1520 is configured to determine the image position information of a plurality of coated fuel particles contained in each first field metallographic section image.

[0100] The splicing unit 1530 is configured to coordinate splice the image position information of all coated fuel particles contained in the plurality of first field metallographic section images, to obtain a global coordinate map covering all coated fuel particles in the metallographic sample.

[0101] The second acquisition unit 1540 is configured to control the microscope to collect a second field metallographic section image of each coated fuel particle in the metallographic sample one by one based on the global coordinate map.

[0102] The analysis unit 1550 is configured to, for each coated fuel particle, use a semantic segmentation model to coat the second field metallographic section image of the coated fuel particle, to obtain a corresponding coating boundary image, and based on the coating boundary image, to perform measurability analysis on the coated fuel particle, to screen a plurality of target coated fuel particles satisfying anisotropy measurement conditions.

[0103] The extraction unit 1560 is configured to, for each target coated fuel particle, based on the corresponding layer boundary image, extract a dense pyrolytic carbon layer region from the second field metallographic section image of the target coated fuel particle, and select a to-be-measured region from the dense pyrolytic carbon layer region.

[0104] The measurement unit 1570 is configured to perform reflectivity measurement on the to-be-measured region, to determine the optical anisotropy of the dense pyrolytic carbon layer of the target coated fuel particle.

[0105] It should be pointed out that the first acquisition unit 1510, the determination unit 1520, the splicing unit 1530, the second acquisition unit 1540, the analysis unit 1550, the extraction unit 1560 and the measurement unit 1570 are respectively configured to perform the aforementioned steps 210-270. Here, the specific execution logic of each unit can be referred to the description of steps 210-270 in the method 200 described above, which will not be repeated here.

[0106] According to the coated fuel particle optical anisotropy detection method 200 in the embodiment of the present application, the computing device first controls the microscope to traverse to collect a plurality of first field metallographic cross-section images covering the entire metallographic sample, determines the image position information of a plurality of coated fuel particles contained in each first field metallographic cross-section image, and performs coordinate splicing on the image position information of all coated fuel particles to obtain a global coordinate map covering all coated fuel particles in the metallographic sample. Then, the global coordinate map can be used to control the microscope to collect a second field metallographic cross-section image of each coated fuel particle in the metallographic sample one by one, and perform region segmentation on the second field metallographic cross-section image to analyze the measurability of the coated fuel particle. After that, a to-be-measured region is selected from the dense pyrolytic carbon layer region of each target coated fuel particle that meets the anisotropy measurement condition. Finally, reflectivity measurement is performed on the to-be-measured region to determine the optical anisotropy of the dense pyrolytic carbon layer of the target coated fuel particle. Based on this, the present application can realize the automation and intelligentization of the optical anisotropy detection process of the dense pyrolytic carbon layer of the coated fuel particle, and the detection process is more efficient, which improves the detection precision and consistency of the detection results. Among them, the automatic collection of high-quality images of multiple fields of each coated fuel particle is realized; the coated fuel particle can be intelligently positioned, and the target coated fuel particle meeting the anisotropy measurement condition can be intelligently analyzed and screened, and the to-be-measured region for optical anisotropy detection can be intelligently screened, and then the precise measurement of the optical anisotropy of the coated fuel particle based on the to-be-measured region is realized.

[0107] In addition, by comparing the optical anisotropy degree of the dense pyrolytic carbon layer with the standard optical anisotropy degree of the standard substance, the optical anisotropy detection result can be corrected, and then the interference factors such as environmental changes can be excluded, and the optical anisotropy detection result can be ensured to be accurate and reliable.

[0108] Further, the embodiments of the present application also disclose that: A9, the method in any one of A1-A8, wherein the measurability analysis on the coated fuel particles based on the coating boundary image comprises: performing integrity analysis on the coated fuel particles based on the coating boundary image to determine whether the coated fuel particles are complete; if it is determined that the coated fuel particles are complete, determining the tangent distance between the coated fuel particles and adjacent coated fuel particles thereof based on the coating boundary image, determining whether the cross section of the coated fuel particles is close to the equatorial plane based on the tangent distance, if the cross section is close to the equatorial plane, determining that the coated fuel particles meet the tangent requirement, and taking the coated fuel particles as target coated fuel particles meeting the anisotropy measurement condition. A10, the method in any one of A1-A9, wherein the control of the microscope to traverse and collect a plurality of first field metallographic section images covering the entire metallographic sample comprises: control of the microscope to traverse and collect a plurality of first field metallographic section images covering the entire metallographic sample by using a first magnification objective lens; and control of the microscope to collect second field metallographic section images of each coated fuel particle in the metallographic sample one by one, comprising: control of the microscope to collect second field metallographic section images of each coated fuel particle in the metallographic sample one by one by using a second magnification objective lens. B13, a computer program product comprising computer programs / instructions, wherein the computer programs / instructions are executed by a processor to implement the method in any one of A1-A10. C14, a readable storage medium storing program instructions, which, when read and processed by a computing device, cause the computing device to process the method in any one of A1-A10.

[0109] The various techniques described herein can be implemented in connection with hardware or software or, where appropriate, with a combination of both. Thus, the methods and apparatus of the present application, or certain aspects or portions thereof, can take the form of program code (i.e., instructions) embodied in tangible media, such as removable hard disks, USB flash drives, floppy diskettes, CD-ROMs, ROMs, or any other machine-readable storage medium wherein, when the program code is loaded into an internal memory of the machine such as a computer, the machine becomes an apparatus for practicing the subject application.

[0110] Where a program code is executed on a programmable computer, the mobile terminal generally includes a processor, a processor-readable storage medium (including volatile and non-volatile memory and / or storage elements), at least one input device, and at least one output device. The memory is configured to store program code; the processor is configured to execute instructions in the program code stored in the memory to perform the coated fuel particle optical anisotropy detection method of the present application.

[0111] By way of example, and not limitation, readable media can include volatile and non-volatile, removable and non-removable media implemented in a method or technology for storage of information such as computer readable instructions, data structures, program modules or other data. Computer readable media is accessible by a computer. However, computer readable media is not comprised of propagated signals per se. Combinations of the above should also be included within the scope of readable media.

[0112] In the description provided herein, numerous specific details are set forth. However, it is understood that embodiments of the application can be practiced without these specific details. In some instances, well-known methods, structures and techniques have not been described in detail in order to not obscure the understanding of this description.

[0113] In the description provided herein, numerous specific details are set forth. However, it is understood that embodiments of the application can be practiced without these specific details. In some instances, well-known methods, structures and techniques have not been described in detail in order to not obscure the understanding of this description.

[0114] Similarly, it is to be understood that the mechanical details of the example embodiments of the application can also include any of the mechanical details of the example embodiments of the application described above, or combinations of such details.

[0115] Those skilled in the art will understand that the modules, or units, or components of the devices in the examples disclosed herein can be arranged in a device as described in the examples, or alternatively can be located in one or more devices different from the devices in the examples. The modules in the foregoing examples can be combined as a module or further divided into multiple sub-modules.

[0116] Unless otherwise stated, the use of ordinal terms such as "first", "second", "third", etc., to describe a common object indicates a different instance of like objects, rather than an explicitly identified object.

Claims

1. A method for detecting optical anisotropy of coated fuel particles, executed in a computing device communicatively connected to a microscope, an electrodynamic stage of the microscope being adapted to place a metallographic sample having a plurality of coated fuel particles thereon, the method comprising: controlling the microscope to traverse and collect a plurality of first field metallographic cross-section images covering the entire metallographic sample; determining image position information of a plurality of coated fuel particles contained in each of the first field metallographic cross-section images; performing coordinate stitching on the image position information of all coated fuel particles contained in the plurality of first field metallographic cross-section images to obtain a global coordinate map covering all coated fuel particles in the metallographic sample; based on the global coordinate map, controlling the microscope to collect a second field metallographic cross-section image of each coated fuel particle in the metallographic sample one by one; for each of the coated fuel particles, performing coating delimitation on the second field metallographic cross-section image of the coated fuel particle by using a semantic segmentation model to obtain a corresponding coating delimitation image, and performing measurability analysis on the coated fuel particle based on the coating delimitation image to screen a plurality of target coated fuel particles satisfying anisotropy measurement conditions; for each of the target coated fuel particles, extracting a dense pyrolytic carbon layer region from the second field metallographic cross-section image of the target coated fuel particle based on the corresponding layer delimitation image, and selecting a to-be-measured region from the dense pyrolytic carbon layer region; performing reflectivity measurement on the to-be-measured region to determine optical anisotropy of the dense pyrolytic carbon layer of the target coated fuel particle. Selecting a to-be-measured region from the dense pyrolytic carbon layer region comprises: determining a first black point proportion in the dense pyrolytic carbon layer region, and judging whether the black point is an essential attribute of the dense pyrolytic carbon layer region based on the first black point proportion; extracting a plurality of reflectivity measurement regions from the dense pyrolytic carbon layer region, and determining a second black point proportion in each of the reflectivity measurement regions; if the black point is the essential attribute of the dense pyrolytic carbon layer region, selecting a reflectivity measurement region with the most second black point proportion from the plurality of reflectivity measurement regions as the to-be-measured region; if the black point is not the essential attribute of the dense pyrolytic carbon layer region, selecting a reflectivity measurement region with the least second black point proportion from the plurality of reflectivity measurement regions as the to-be-measured region. Extracting a plurality of reflectivity measurement regions from the dense pyrolytic carbon layer region comprises: establishing a target coordinate system on the coating delimitation image of the target coated fuel particle with a core center of the target coated fuel particle as a target origin; drawing two measurement lines along X-axis and Y-axis directions of the target coordinate system, and extracting a plurality of regions with a predetermined size from a plurality of intersection points of the two measurement lines and the dense pyrolytic carbon layer region as a plurality of reflectivity measurement regions. Performing reflectivity measurement on the to-be-measured region to determine optical anisotropy of the dense pyrolytic carbon layer of the target coated fuel particle comprises: ​ ​ ​ ​ 2. The method of claim 1, wherein, ​ ​ ​ ​ ​ 3. The method of claim 2, wherein, ​ ​ ​ 4. The method of any one of claims 1-3, wherein, ​ controlling the motorized stage of the microscope to move the metallographic sample so that the center of the target area of the coated fuel particle to be measured is located at the center of the collection area of the microscope; controlling the microscope to switch to a third multiple polarizing objective and adjusting the field stop to the minimum so that the microscope is automatically focused to the frame of the field stop being clear; adjusting the polarizer of the microscope to generate polarized light in two directions at 0° and 90° respectively, and measuring the maximum reflectivity and minimum reflectivity of the target area under the two polarized light by the microphotometer as the optical anisotropy of the dense pyrolytic carbon layer of the target coated fuel particle.

5. The method of claim 4, wherein, determining the optical anisotropy of the dense pyrolytic carbon layer of the target coated fuel particle, further comprising: comparing the optical anisotropy of the dense pyrolytic carbon layer with the standard optical anisotropy of the standard substance with known reflectivity under the two polarized light to determine the optical anisotropy factor of the dense pyrolytic carbon layer.

6. The method of any one of claims 1-3, wherein, controlling the microscope to traverse and collect a plurality of first field metallographic cross-section images covering the entire metallographic sample, including: acquiring the first field metallographic cross-section image of the central region of the metallographic sample collected by the microscope, taking the center of the core of any coated fuel particle in the first field metallographic cross-section image as an absolute origin, and establishing an absolute coordinate system of the metallographic sample; determining a traversal collection path covering the entire metallographic sample based on the absolute coordinate system; controlling the microscope to traverse and collect a plurality of first field metallographic cross-section images covering the entire metallographic sample based on the traversal collection path.

7. The method of any one of claims 1-3, wherein, based on the global coordinate map, controlling the microscope to collect second field metallographic cross-section images of each coated fuel particle in the metallographic sample one by one, including: based on the global coordinate map and the traversal collection path, path planning is performed for each coated fuel particle in each first field metallographic cross-section image to determine a global collection path; based on the global collection path, controlling the microscope to collect second field metallographic cross-section images of each coated fuel particle in the metallographic sample one by one.

8. The method of any one of claims 1-3, wherein, determining the image position information of a plurality of coated fuel particles contained in each of the first field metallographic cross-section images, including: using a particle intelligent positioning model to determine the image position information of a plurality of coated fuel particles contained in each of the first field metallographic cross-section images, the particle intelligent positioning model including a feature extraction network, an anchor box generation network, a proposal generation network, and a fully connected network coupled in sequence; wherein the feature extraction network is configured to perform feature extraction on the first field metallographic cross-section image to obtain a feature image; the anchor box generation network is configured to generate an anchor box based on the feature image; the proposal generation network is configured to determine a candidate proposal region based on the feature image and the anchor box; the fully connected network is configured to classify and coordinate the candidate proposal region to determine the image position information of a plurality of coated fuel particles, eliminate one or more incomplete coated fuel particles, and output a positioning marker image corresponding to the first field metallographic cross-section image.

9. The method of any one of claims 1-3, wherein, performing measurability analysis on the coated fuel particles based on the coating boundary image, including: performing integrity analysis on the coated fuel particles based on the coating boundary image to determine whether the coated fuel particles are intact; if it is determined that the coated fuel particles are intact, determining a tangential distance between the coated fuel particles and its adjacent coated fuel particles based on the coating boundary image, determining whether a cross section of the coated fuel particles is close to an equatorial plane based on the tangential distance, if the cross section is close to the equatorial plane, determining that the coated fuel particles meet tangentiality requirements, and taking the coated fuel particles as target coated fuel particles meeting anisotropy measurement conditions.

10. The method of any one of claims 1-3, wherein, controlling the microscope to iteratively collect a plurality of first field metallographic cross-section images covering the entire metallographic sample, including: controlling the microscope to iteratively collect a plurality of first field metallographic cross-section images covering the entire metallographic sample using a first magnification objective; controlling the microscope to iteratively collect a plurality of first field metallographic cross-section images covering the entire metallographic sample using a first magnification objective; controlling the microscope to iteratively collect a plurality of first field metallographic cross-section images covering the entire metallographic sample using a first magnification objective; 11. An optical anisotropy detection device for coated fuel particles, deployed in a computing device, the computing device being in communication connection with a microscope, and an electrically driven stage of the microscope being adapted to place a metallographic sample having a plurality of coated fuel particles, the device comprising: a first acquisition unit adapted to control the microscope to iteratively collect a plurality of first field metallographic cross-section images covering the entire metallographic sample; a determination unit adapted to determine image position information of a plurality of coated fuel particles contained in each of the first field metallographic cross-section images; a stitching unit adapted to coordinate stitch the image position information of all coated fuel particles contained in the plurality of first field metallographic cross-section images to obtain a global coordinate map covering all coated fuel particles in the metallographic sample; a second acquisition unit adapted to control the microscope to iteratively collect a plurality of first field metallographic cross-section images covering the entire metallographic sample using a first magnification objective; an analysis unit adapted to, for each of the coated fuel particles, perform coating boundary determination on the second field metallographic cross-section image of the coated fuel particles using a semantic segmentation model to obtain a corresponding coating boundary image, and perform measurability analysis on the coated fuel particles based on the coating boundary image to screen a plurality of target coated fuel particles meeting anisotropy measurement conditions; an extraction unit adapted to, for each of the target coated fuel particles, extract a dense pyrolytic carbon layer region from the second field metallographic cross-section image of the target coated fuel particle based on the corresponding coating boundary image, and select a to-be-measured region from the dense pyrolytic carbon layer region; a measurement unit adapted to perform reflectivity measurement on the to-be-measured region to determine optical anisotropy of the dense pyrolytic carbon layer of the target coated fuel particle.

12. A computing device, comprising: at least one processor; and a memory having stored thereon program instructions configured to be processed by the at least one processor, the program instructions comprising instructions for processing the method of any one of claims 1-10.

13. A computer program product comprising computer program instructions, wherein, The computer program instructions, when executed by a processor, implement the method of any one of claims 1-10.

14. A readable storage medium having stored thereon program instructions, which when read and processed by a computing device, cause the computing device to process the method of any one of claims 1-10.

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