Scintillation crystal array preparation and surface treatment method

Abstract

The invention relates to the technical field of process optimization, and discloses a scintillation crystal array preparation and surface treatment method, which comprises the following steps: assembling and fixing scintillation crystal strips according to a standard array arrangement configuration to obtain a scintillation crystal array initial blank; aligning the space coordinates of the surface topography original data to obtain standard surface topography data; performing morphology comparison on the standard surface morphology data and the reference standard to identify a surface defect area; performing multi-dimensional fusion on the distribution density parameter and the distribution depth parameter of the surface defect area to obtain a defect parameter; the defect parameters are mapped to a preset technological parameter matching rule base, and polishing technological parameters are determined; the surface polishing pressure distribution and the path track of the scintillation crystal array initial blank are controlled, and a scintillation crystal array of the scintillation crystal array initial blank is generated; the preparation and surface treatment efficiency of the scintillation crystal array can be improved.

Description

Technical Field

[0001] This invention relates to the field of process optimization technology, and in particular to a method for preparing and surface treating a scintillation crystal array. Background Technology

[0002] The surface treatment process of scintillation crystal arrays involves several key steps, including array assembly, morphology inspection, defect identification, and polishing process control. Existing technologies lack a systematic digital management mechanism, resulting in independent and scattered data at each stage, hindering efficient collaboration. Furthermore, the lack of a unified alignment standard for the spatial coordinates of the raw surface morphology data leads to insufficient data accuracy, failing to provide a reliable basis for subsequent defect analysis and consequently affecting the standardization and consistency of the overall processing flow.

[0003] Existing technologies have significant shortcomings in defect parameter extraction and process parameter matching, failing to effectively integrate the multi-dimensional characteristics of surface defects, resulting in incomplete and inaccurate defect assessment. Furthermore, the determination of polishing process parameters relies heavily on empirical judgment, lacking a dynamic adaptation mechanism with defect parameters. This leads to a lack of targeted control over polishing pressure distribution and path trajectory, making it difficult to achieve precise improvement of surface defects. Ultimately, this results in unstable surface treatment quality, failing to meet the production requirements of high-precision scintillation crystal arrays. Therefore, improving the fabrication and surface treatment efficiency of scintillation crystal arrays has become an urgent problem to be solved. Summary of the Invention

[0004] This invention provides a method for preparing and surface-treating a scintillation crystal array to solve the problems mentioned in the background art.

[0005] To achieve the above objectives, the present invention provides a method for fabricating and surface treating a scintillation crystal array, comprising:

[0006] S1. Under the protection of an inert atmosphere, the raw material mixture of inorganic compound raw material and rare earth metal oxide is heated in stages to obtain a homogeneous melt of the raw material mixture, and the homogeneous melt is controlled to undergo directional solidification along a preset temperature gradient direction to obtain a scintillation crystal array preform of the homogeneous melt.

[0007] S2. Collect the original surface morphology data of the scintillation crystal array blank, and align the spatial coordinates of the original surface morphology data to obtain the standard surface morphology data of the scintillation crystal array blank.

[0008] S3. Select the complete surface area in the standard surface morphology data as a reference benchmark, and compare the standard surface morphology data with the reference benchmark to identify the surface defect area of ​​the standard surface morphology data.

[0009] S4. The distribution density parameter and distribution depth parameter of the surface defect region are fused in multiple dimensions to obtain the defect parameters of the surface defect region.

[0010] S5. Map the defect parameters to a preset process parameter matching rule library to determine the polishing process parameters of the scintillation crystal array blank;

[0011] S6. Based on the polishing process parameters, control the surface polishing pressure distribution and path trajectory of the scintillation crystal array blank to generate the scintillation crystal array of the scintillation crystal array blank.

[0012] In a preferred embodiment, assembling and fixing the scintillation crystal strips according to a preset array configuration to obtain a scintillation crystal array blank of the scintillation crystal strips includes:

[0013] Based on historical scintillation crystal array data, determine the standard array configuration of the scintillation crystal strips;

[0014] According to the standard array arrangement, the axial direction and end face flatness of the scintillation crystal strip are adjusted to obtain the calibrated scintillation crystal strip;

[0015] A small amount of adhesive is filled into the splicing gaps of the calibration scintillation crystal strip, and a uniform pre-pressure is applied to the scintillation crystal strip to obtain the scintillation crystal embryo of the scintillation crystal strip;

[0016] Assemble the scintillation crystal embryos to obtain the initial scintillation crystal array of the scintillation crystal strips.

[0017] In a preferred embodiment, the step of acquiring the raw surface morphology data of the scintillation crystal array blank and aligning the spatial coordinates of the raw surface morphology data to obtain the standard surface morphology data of the scintillation crystal array blank includes:

[0018] Remove residual impurities adhering to the surface of the scintillation crystal array blank to obtain a clean scintillation crystal array blank;

[0019] The surface morphology of the clean scintillation crystal array blank is scanned, and the acquired original surface morphology information is integrated into the original surface morphology data of the clean scintillation crystal array blank.

[0020] Based on the original surface morphology data, non-collinear feature points are selected as spatial coordinate references on the clean scintillation crystal array blank with no foreign matter on the surface.

[0021] The positional correspondence between the original surface morphology data and the spatial coordinate reference is obtained by mapping the positional information in the original surface morphology data;

[0022] Based on the positional correspondence, the coordinate arrangement of the original surface morphology data is adjusted to obtain the standard surface morphology data of the scintillation crystal array blank.

[0023] In a preferred embodiment, selecting a complete surface region from the standard surface morphology data as a reference benchmark, and comparing the standard surface morphology data with the reference benchmark to identify surface defect regions in the standard surface morphology data includes:

[0024] Regions with no protrusions or depressions and continuous contours in the standard surface topography data are selected as candidate complete region data.

[0025] Examine the contour transition state of adjacent positions in the candidate complete region data;

[0026] Based on the contour transition state, regions with sudden contour transitions or broken lines in the candidate complete region data are removed to obtain the target complete region data of the standard surface topography data.

[0027] The target complete region data is used as a reference benchmark;

[0028] The standard surface morphology data is compared with the reference benchmark to identify the surface defect areas of the standard surface morphology data.

[0029] In a preferred embodiment, the step of comparing the standard surface topography data with the reference datum to identify surface defect regions in the standard surface topography data includes:

[0030] Extract the surface contour features and flatness features of the reference datum, and establish the topographic feature identifier of the reference datum;

[0031] The standard surface morphology data is split into regions to obtain the regional data of the standard surface morphology data;

[0032] The morphological feature identifiers are input into the regional data for comparison one by one, and the differences between the regional data and the morphological feature identifiers are recorded to generate the regional morphological comparison results of the standard surface morphological data.

[0033] Based on the difference information in the regional morphology comparison results, the regional data that exceeds the allowable range of the morphology feature identifier in the standard surface morphology data are filtered out to obtain the surface defect region of the standard surface morphology data.

[0034] In a preferred embodiment, the step of multi-dimensionally fusing the distribution density parameter and distribution depth parameter of the surface defect region to obtain the defect parameters of the surface defect region includes:

[0035] Obtain the distribution density information characterizing the density of defects and the distribution depth information characterizing the depth of defects in the surface defect region to form the distribution density parameter and distribution depth parameter of the surface defect region;

[0036] By removing abnormal information from the distribution density parameter and the distribution depth parameter that does not conform to the actual characteristics of the defect, the optimized distribution density parameter and the optimized distribution depth parameter of the surface defect region are obtained;

[0037] Based on the formation mechanism of surface defects, the correlation between the optimized distribution density parameter and the optimized distribution depth parameter is determined;

[0038] Based on the aforementioned correlation, the optimized distribution density parameter and the optimized distribution depth parameter are integrated collaboratively to generate the defect parameters for the surface defect region.

[0039] In a preferred embodiment, determining the correlation between the optimized distribution density parameter and the optimized distribution depth parameter based on the formation mechanism of surface defects includes:

[0040] By analyzing the formation mechanism of the surface defect region, the raw material melting state and solidification process environmental factors that affect the formation of defects are identified, and a set of factors affecting the formation of the surface defect region is obtained.

[0041] Based on the set of factors influencing defect formation, the changing trends of the optimized distribution density parameter and the optimized distribution depth parameter are analyzed to obtain a table of parameter variation patterns for the surface defect region.

[0042] Based on the parameter variation pattern table, extract the collaborative variation feature information between the optimized distribution density parameter and the optimized distribution depth parameter;

[0043] By filtering the collaborative change feature information and identifying features directly related to defect improvement, effective collaborative features of the surface defect region are obtained.

[0044] Based on the effective collaborative features, the interaction mode and correlation between the optimized distribution density parameter and the optimized distribution depth parameter are coupled to form a correlation relationship between the optimized distribution density parameter and the optimized distribution depth parameter.

[0045] In a preferred embodiment, the calculation formula for the collaborative integration is as follows:

[0046] ;

[0047] In the formula, The defect parameters represent the surface defect region. This represents the optimized distribution density parameter of the surface defect region. This represents the optimized distribution depth parameter of the surface defect region. This represents the reference reference flatness coefficient, quantified based on the flatness characteristics of the reference reference. This represents the parameter coordination coefficient quantified based on the effective coordination characteristics.

[0048] In a preferred embodiment, the step of mapping the defect parameters to a preset process parameter matching rule library to determine the polishing process parameters of the scintillation crystal array blank includes:

[0049] Extract the core defect features that characterize the overall defect state from the defect parameters;

[0050] The core features of the defects are mapped to a preset process parameter matching rule library to filter out a set of candidate polishing process parameters that match the defect features of the scintillation crystal array blank.

[0051] By eliminating parameters in the candidate polishing process parameter set that do not match the process implementation conditions, the target polishing process parameter set for the scintillation crystal array blank is obtained;

[0052] By integrating the target polishing process parameter set, polishing process parameters that can specifically improve the surface defects of the scintillation crystal array blank are generated.

[0053] In a preferred embodiment, the step of controlling the surface polishing pressure distribution and path trajectory of the scintillation crystal array preform according to the polishing process parameters to generate the scintillation crystal array preform includes:

[0054] The pressure control features and path planning features related to surface polishing in the polishing process parameters are integrated into the process execution parameters of the scintillation crystal array blank.

[0055] Based on the location information of the surface defect region and the path planning features in the process execution parameters, a polishing path covering all defect regions and avoiding the intact surface region in the scintillation crystal array blank is planned, and a polishing path planning scheme for the scintillation crystal array blank is generated.

[0056] Based on the pressure control characteristics in the process execution parameters and the degree of defects in the surface defect region, the polishing pressure adaptation standard for different regions in the scintillation crystal array blank is determined, and the polishing pressure distribution scheme of the scintillation crystal array blank is generated.

[0057] The motion trajectory is controlled according to the polishing path planning scheme, and the pressure output is adjusted according to the polishing pressure distribution scheme to perform targeted polishing on the scintillation crystal array blank, thereby obtaining the scintillation crystal array of the scintillation crystal array blank.

[0058] Compared with the prior art, the present invention has the following beneficial effects:

[0059] 1. This invention accurately identifies defects by aligning topographic data coordinates and using the complete area as a reference. It then determines polishing parameters by fusing defect parameters across multiple dimensions and mapping them to a process library. Finally, it customizes the polishing pressure distribution and path according to the defect condition. Compared to the problems of vague defect identification and blindly setting polishing parameters in traditional processes, this invention can accurately locate defects and treat them specifically, avoiding ineffective polishing of the entire area. This improves surface treatment accuracy and product quality while further optimizing processing efficiency.

[0060] 2. This invention first accurately identifies defects, then integrates defect distribution density and depth parameters through a multi-dimensional fusion algorithm to generate accurate defect parameters; subsequently, it matches and adapts polishing processes based on defect parameters, plans paths to cover defects and avoid complete areas, adjusts polishing pressure in different areas, achieves precise defect improvement, reduces damage to complete areas, and effectively improves the stability and consistency of the final array surface quality. Attached Figure Description

[0061] Figure 1 This is a schematic flowchart illustrating a method for fabricating and surface treating a scintillation crystal array according to an embodiment of the present invention.

[0062] The realization of the objective, functional features and advantages of the present invention will be further explained in conjunction with the embodiments and with reference to the accompanying drawings. Detailed Implementation

[0063] It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention.

[0064] This application provides a method for fabricating and surface-treating a scintillation crystal array. The execution subject of this method includes, but is not limited to, at least one of the following electronic devices that can be configured to execute the method provided in this application: a server, a terminal, etc. In other words, the method for fabricating and surface-treating a scintillation crystal array can be executed by software or hardware installed on a terminal device or a server device. The server includes, but is not limited to, a single server, a server cluster, a cloud server, or a cloud server cluster. The server can be an independent server or a cloud server that provides basic cloud computing services such as cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communication, middleware services, domain name services, security services, content delivery networks (CDNs), and big data and artificial intelligence platforms.

[0065] Reference Figure 1 The diagram shown is a schematic flowchart of a scintillation crystal array fabrication and surface treatment method according to an embodiment of the present invention. In this embodiment, the scintillation crystal array fabrication and surface treatment method includes:

[0066] S1. Assemble and fix the scintillation crystal strips according to a preset array arrangement to obtain the initial blank of the scintillation crystal array of the scintillation crystal strips;

[0067] In this embodiment of the invention, assembling and fixing the scintillation crystal strips according to a preset array configuration to obtain a scintillation crystal array blank of the scintillation crystal strips includes:

[0068] Based on historical scintillation crystal array data, determine the standard array configuration of the scintillation crystal strips;

[0069] According to the standard array arrangement, the axial direction and end face flatness of the scintillation crystal strip are adjusted to obtain the calibrated scintillation crystal strip;

[0070] A small amount of adhesive is filled into the splicing gaps of the calibration scintillation crystal strip, and a uniform pre-pressure is applied to the scintillation crystal strip to obtain the scintillation crystal embryo of the scintillation crystal strip;

[0071] Assemble the scintillation crystal embryos to obtain the initial scintillation crystal array of the scintillation crystal strips.

[0072] The entire implementation process revolves around the preparation of the scintillation crystal array blank. Under the protection of an inert atmosphere, inorganic compound raw materials and rare earth metal oxides are first fully mixed to obtain a raw material mixture. Then, the raw material mixture is placed in a crucible and completely melted by segmented heating to obtain a homogeneous melt. Finally, the homogeneous melt is directionally solidified by applying a preset temperature gradient to obtain the scintillation crystal array blank of the homogeneous melt.

[0073] Historical scintillation crystal array data was collected, encompassing structural information, long-term performance records, and relevant test reports of scintillation crystal arrays deployed in various application scenarios. This data includes structural parameters such as the number and arrangement of scintillation crystal strips, the spacing between adjacent strips, the overall array dimensions, and the placement angle of the crystal strips. It also covers performance data for key performance indicators such as detection accuracy, signal transmission efficiency, structural stability, and lifespan. This historical data was systematically categorized and organized, mapping structural parameters to corresponding performance indicators. Performance differences of scintillation crystal arrays with different arrangements under the same operating conditions were compared, as well as the adaptability of the same arrangement under different conditions. The arrangement configuration that consistently delivers optimal performance across various application scenarios was selected. Key parameters for this configuration were determined, including the specific number of rows and columns of scintillation crystal strips, the relative position of each strip, the fixed spacing between adjacent strips, the overall array outline shape, and specific dimensions. This arrangement configuration was ultimately identified as the standard array configuration for scintillation crystal strips.

[0074] The scintillation crystal bar to be calibrated is placed on a high-precision positioning fixture customized according to the standard array configuration parameters. The positioning surface and limiting structure of the fixture are manufactured in strict accordance with the requirements of the standard array configuration to ensure the accuracy of the positioning reference. An optical collimator is used to align the center of both ends of the crystal bar. The extension direction of the crystal bar axis is observed through the laser beam emitted by the collimator. The direction of the crystal bar axis at this position is compared with the specification of the crystal bar axis direction in the standard array configuration. The placement angle of the crystal bar is slowly adjusted by the fine adjustment knob on the side of the fixture. After each adjustment, the axis direction is reconfirmed by the optical collimator until the laser beam shows that the crystal bar axis is completely aligned with the reference direction required by the standard configuration. The adjustment of the crystal bar axis direction is then completed. Subsequently, a precision planar grinding machine was used to process both ends of the crystal strip. The grinding disc of the grinding machine was made of high-hardness diamond material. Before grinding, a special grinding fluid was evenly applied to the surface of the grinding disc. The crystal strip, with its axial direction adjusted, was fixed on the grinding fixture, so that one end of the crystal strip was in close contact with the surface of the grinding disc. The machine was started to allow the grinding disc to rotate at a constant speed, while the grinding fixture drove the crystal strip to make uniform reciprocating motion. During the grinding process, the machine was paused at intervals, and a laser flatness tester was used to inspect the end face of the crystal strip. The planar contour image generated by the tester was used to observe whether there were any protrusions, depressions, or tilts on the end face. Based on the test results, the contact pressure between the grinding disc and the end face of the crystal strip and the grinding time were adjusted until the test results showed that the flatness of the end face of the crystal strip completely met the requirements of the standard array arrangement configuration. The same grinding and testing process was then repeated on the other end face of the crystal strip. The scintillation crystal strip after axial direction adjustment and flatness treatment of both ends is the calibrated scintillation crystal strip.

[0075] All calibration scintillation crystal strips are embedded one by one into the corresponding slots of a dedicated splicing fixture according to the requirements of the standard array arrangement. The size of the fixture slots is precisely matched with the cross-sectional size of the calibration scintillation crystal strips, and the arrangement of the slots completely follows the standard array arrangement. This ensures that after each calibration scintillation crystal strip is placed, a uniform splicing gap is formed between adjacent crystal strips, and the relative position of all crystal strips is completely consistent with the standard configuration. A special adhesive with low viscosity, high bonding strength, and no impact on the optical performance of the scintillation crystal is selected. The adhesive is loaded into a manual dispensing device with a miniature stainless steel needle. The inner diameter of the needle is matched with the width of the splicing gap. Holding the dispensing device, the needle is aligned with one end of the splicing gap, and the push rod of the dispensing device is slowly pushed to allow the adhesive to flow out of the needle at a uniform speed and be injected into the gap. During the injection process, the needle is slowly moved along the length of the gap to ensure that the adhesive fills the entire gap evenly. At the same time, the filling condition in the gap is observed to avoid the formation of air bubbles. If any unfilled areas are found in the gap, adhesive is injected in time to ensure that the adhesive completely covers the gap and does not overflow from the edge of the gap and contaminate the surface of the crystal strip. After the adhesive is applied, the pressure application system of the splicing fixture is activated. This system consists of multiple symmetrically distributed pressure actuators and pressure feedback sensors. Each pressure actuator corresponds to one side of the crystal array. After activation, all actuators apply pressure synchronously towards the center of the crystal array. The pressure feedback sensor monitors the pressure output value of each actuator in real time. The control system adjusts the driving force of each actuator to ensure that all calibration scintillation crystal strips are subjected to the same pressure and are evenly distributed on the side of the crystal strips. This pre-pressure state is maintained until the adhesive reaches the initial curing state. At this point, the overall structure formed by the calibration scintillation crystal strips and the adhesive filling the splicing gaps is the initial scintillation crystal array embryo of the scintillation crystal strips.

[0076] The beneficial effects are that, based on comprehensive data from historical scintillation crystal arrays, a standard array configuration that is suitable for various application scenarios and has optimal performance is identified. This provides a precise and unified basis for the subsequent processing and splicing of crystal strips, ensuring the rationality of the basic structure and the stability of the scintillation crystal array. By strictly adjusting the axial direction of the scintillation crystal strips according to the standard array configuration, and with the cooperation of high-precision positioning fixtures and optical collimators, it is ensured that the axial direction of each crystal strip perfectly matches the standard requirements. Then, through a closed-loop operation of precision planar grinding and laser detection, the flatness of the crystal strip end face is improved, resulting in calibrated scintillation crystal strips with uniform specifications and meeting the accuracy standards. This effectively reduces errors in the subsequent splicing process and ensures the overall structural accuracy and optical transmission efficiency of the array. During the splicing process, the calibration scintillation crystal strips are precisely positioned according to the standard configuration. A special adhesive injection method is used to evenly fill the splicing gaps without air bubbles or overflow. Then, a symmetrically distributed pressure system is used to apply uniform pre-pressure to ensure that the adhesive and crystal strips are fully bonded, so that the calibration scintillation crystal strips are firmly spliced ​​into a whole, avoiding loosening or gap defects at the splicing points. The resulting scintillation crystal array embryo has a regular structure and reliable connection. It not only maintains the optical performance of the scintillation crystals but also has good structural stability, which can meet the detection accuracy and long-term use requirements of subsequent applications.

[0077] S2. Collect the original surface morphology data of the scintillation crystal array blank, and align the spatial coordinates of the original surface morphology data to obtain the standard surface morphology data of the scintillation crystal array blank.

[0078] In this embodiment of the invention, the step of acquiring the original surface morphology data of the scintillation crystal array blank and aligning the spatial coordinates of the original surface morphology data to obtain the standard surface morphology data of the scintillation crystal array blank includes:

[0079] Remove residual impurities adhering to the surface of the scintillation crystal array blank to obtain a clean scintillation crystal array blank;

[0080] The surface morphology of the clean scintillation crystal array blank is scanned, and the acquired original surface morphology information is integrated into the original surface morphology data of the clean scintillation crystal array blank.

[0081] Based on the original surface morphology data, non-collinear feature points are selected as spatial coordinate references on the clean scintillation crystal array blank with no foreign matter on the surface.

[0082] The positional correspondence between the original surface morphology data and the spatial coordinate reference is obtained by mapping the positional information in the original surface morphology data;

[0083] Based on the positional correspondence, the coordinate arrangement of the original surface morphology data is adjusted to obtain the standard surface morphology data of the scintillation crystal array blank.

[0084] The entire process revolves around the surface treatment and morphology data standardization of the scintillation crystal array blank. First, residual impurities attached to the surface of the scintillation crystal array blank are removed to obtain a clean scintillation crystal array blank. Then, the surface morphology of the clean blank is scanned and the original surface morphology data is integrated. Subsequently, non-collinear feature points are selected on the clean scintillation crystal array blank without foreign matter as spatial coordinate references. Next, the position information in the original surface morphology data is matched with the spatial coordinate references to obtain the position correspondence relationship. Finally, the coordinate arrangement of the original surface morphology data is adjusted based on the position correspondence relationship to obtain the standard surface morphology data of the scintillation crystal array blank.

[0085] Before removing residual impurities, special materials need to be prepared in advance. The microfiber wiping cloth needs to be dust-free to ensure that it is free of dust and fiber shedding. The neutral cleaning solution needs to be tested for compatibility to confirm that it does not react chemically with the material of the scintillation crystal array blank and will not corrode or damage the surface of the blank.

[0086] During the wiping operation, hold the treated microfiber wiping cloth, dip it in an appropriate amount of neutral cleaning solution, ensuring that the cloth is moist but not dripping. Start from one end of the scintillation crystal array blank and slowly wipe in the same direction to the other end. For areas where impurities are easily trapped, such as edges and crevices, slow down the wiping speed appropriately and wipe twice in one direction to avoid pushing impurities inward or re-adhering them. After wiping, immediately place the blank under a deionized water rinsing device. The purified deionized water is sprayed evenly from above to fully cover the surface of the blank, rinsing away residual cleaning solution and loose impurities. After rinsing, quickly transfer the blank to an inert gas drying device. Dry inert gas is blown onto the surface of the blank at an appropriate flow rate, drying it sequentially from top to bottom and from front to back, ensuring that there is no moisture residue or visible impurities on the surface, resulting in a clean scintillation crystal array blank.

[0087] Before scanning, place the clean scintillation crystal array blank stably on the sample stage of the precision scanning equipment. Gently clamp the edge of the blank with the fixing clamp of the sample stage to ensure that the blank does not shift during the scanning process. At the same time, adjust the height and angle of the sample stage so that the surface of the blank is perpendicular to the scanning probe and the distance between the two is within the optimal scanning range of the equipment.

[0088] After the precision scanning equipment is started, the scanning probe moves slowly on the surface of the billet along a preset continuous path. During the movement, the probe continuously emits detection signals. After the signals come into contact with the surface of the billet, they are reflected back to the receiving device. The equipment records the surface undulations of each detection point in real time based on the time difference and intensity changes of the signal reflection. For raised areas, the signal reflection time is short and the intensity is high; for recessed areas, the signal reflection time is long and the intensity is low; for flat areas, the signal reflection state is stable. The information of all detection points is continuously recorded in the order of the scanning path. Then, the equipment integrates these discrete individual point information in an orderly manner to form a structured data set that can completely present the overall morphological features of the surface of the clean scintillation crystal array billet, thus obtaining the original surface morphological data of the clean scintillation crystal array billet.

[0089] View the original surface morphology data through the device's display terminal, intuitively observe the surface state of the billet reflected by the data, and screen out complete areas without foreign matter attachment, scratches, or contamination marks. Within these areas, find points with obvious and stable morphological characteristics. These points should be naturally formed on the surface of the billet, small protrusions or depressions that are not easily deformed or disappeared, to ensure the recognizability and stability of the feature points. Select three such points, and the line connecting the three points should form a stable geometric structure to ensure that the three points are not collinear. Clearly record the specific location information of each feature point in the original surface morphology data, and establish these three non-collinear feature points as the reference for subsequent coordinate positioning, thus establishing a spatial coordinate benchmark.

[0090] Using data extraction tools, the relative position information of each detection point is separated from the raw surface topography data, including the straight-line distance and azimuth angle between each point. This extracted position information is then compared one by one with the established spatial coordinate reference. Using a reference frame composed of three feature points, the specific positional relationship of each detection point relative to each reference feature point is determined, clarifying the accurate orientation of each raw data point in the reference coordinate system. Through point-by-point matching and confirmation, a clear and unique association is established between all raw surface topography data and the spatial coordinate reference, thus obtaining the positional correspondence of the raw surface topography data.

[0091] Based on the established positional correspondence, the coordinates of all detection points in the original surface morphology data are uniformly calibrated. For the original position information of each point, precise adjustments are made according to its correspondence with the spatial coordinate reference, so that all data points use the coordinate system composed of three feature points as a unified reference standard. The coordinate distribution order of each point is reorganized, and problems such as positional offset and disordered arrangement in the original data are corrected. This ensures that the adjusted coordinate arrangement can truly and accurately reflect the actual spatial morphology of the surface of the clean scintillation crystal array blank, eliminate the impact of data deviation on subsequent use, and finally obtain the standard surface morphology data of the scintillation crystal array blank.

[0092] The beneficial effects are as follows: A clean scintillation crystal array blank is obtained by wiping the surface of the blank with a dust-free microfiber cloth soaked in neutral cleaning solution in one direction, followed by rinsing with deionized water and drying with inert gas, thoroughly removing residual impurities; the clean scintillation crystal array blank is then fixed on the sample stage of a precision scanning device, and the scanning probe moves along a preset path to collect morphological information point by point. This information is then integrated to obtain the original surface morphology data of the clean scintillation crystal array blank; the clean blank area free of foreign matter is selected from the original surface morphology data, and three stable and non-collinear feature points are chosen as spatial coordinate references; the positional information of the points in the original surface morphology data is extracted and compared with the spatial coordinate references one by one to establish a clear correlation, thus obtaining the positional correspondence of the original surface morphology data; based on the positional correspondence, the coordinates of the original data are uniformly calibrated and adjusted to eliminate positional deviations, resulting in standard surface morphology data of the scintillation crystal array blank.

[0093] S3. Select the complete surface area in the standard surface morphology data as a reference benchmark, and compare the standard surface morphology data with the reference benchmark to identify the surface defect area of ​​the standard surface morphology data.

[0094] In this embodiment of the invention, selecting a complete surface region from the standard surface morphology data as a reference benchmark, and comparing the standard surface morphology data with the reference benchmark to identify surface defect regions in the standard surface morphology data includes:

[0095] Regions with no protrusions or depressions and continuous contours in the standard surface topography data are selected as candidate complete region data.

[0096] Examine the contour transition state of adjacent positions in the candidate complete region data;

[0097] Based on the contour transition state, regions with sudden contour transitions or broken lines in the candidate complete region data are removed to obtain the target complete region data of the standard surface topography data.

[0098] The target complete region data is used as a reference benchmark;

[0099] The standard surface morphology data is compared with the reference benchmark to identify the surface defect areas of the standard surface morphology data.

[0100] The step of comparing the standard surface topography data with the reference datum to identify surface defect regions in the standard surface topography data includes:

[0101] Extract the surface contour features and flatness features of the reference datum, and establish the topographic feature identifier of the reference datum;

[0102] The standard surface morphology data is split into regions to obtain the regional data of the standard surface morphology data;

[0103] The morphological feature identifiers are input into the regional data for comparison one by one, and the differences between the regional data and the morphological feature identifiers are recorded to generate the regional morphological comparison results of the standard surface morphological data.

[0104] Based on the difference information in the regional morphology comparison results, the regional data that exceeds the allowable range of the morphology feature identifier in the standard surface morphology data are filtered out to obtain the surface defect region of the standard surface morphology data.

[0105] The entire process revolves around defect identification of standard surface topography data. First, candidate complete area data without protrusions or depressions and with continuous contours are selected from the standard surface topography data. Then, the contour transition state of adjacent positions in the candidate complete area data is checked. Based on the contour transition state, area data with sudden contour turns or broken lines are eliminated to obtain target complete area data. The target complete area data is used as a reference benchmark. Finally, by comparing the morphology of the standard surface topography data with that of the reference benchmark, the surface defect areas of the standard surface topography data are identified.

[0106] Before screening candidate complete region data, the standard surface morphology data is first decomposed into multiple continuous local regions according to the regional division rules. Each local region contains a certain number of adjacent data points to ensure that the range of each region is moderate, which is convenient for detailed verification and can fully reflect the local morphology characteristics.

[0107] For each segmented local area, the surface morphology is visualized through data. The surface height of each data point within the area is observed point by point and directly compared with the surface height of adjacent points. If the height of a point is significantly higher than the surrounding points, it is determined that there is a protrusion; if the height of a point is significantly lower than the surrounding points, it is determined that there is a depression. Then, along the contour line of the area, from the starting point to the ending point, it is traced sequentially to check whether the line can connect all edge points without any interruption or gap. All local area data that have been verified to be free of protrusions and depressions and have continuous contour lines are extracted and organized into a centralized dataset according to the region order. The regions with no protrusions and depressions and continuous contours in the standard surface morphology data are used as candidate complete region data.

[0108] When checking the contour transition status, strictly follow the natural arrangement order of each data point in the candidate complete region data. Starting from the first edge point of each candidate complete region, select two adjacent edge points in sequence, focus on the contour line segment between these two points, and use visualization tools to zoom in and observe the changes in the direction of the line. Pay special attention to the smoothness of the connection between the contour extension direction of the previous point and the contour extension direction of the next point. Record whether the line transitions smoothly or has a sudden change in direction. At the same time, confirm whether the contour lines between two adjacent points are completely connected without any gaps. Complete the check of the contour transition status of all adjacent positions in groups to ensure that no group of adjacent points is missed.

[0109] Based on the previously recorded transition states of all adjacent locations, each local region in the candidate complete region data is reviewed one by one. When it is observed that the direction of the contour lines of a group of adjacent points in a local region changes drastically and there is no smooth transition in the intermediate form, it is directly determined that there is a sudden change in the contour of the region. When it is found that there is an obvious interruption gap between the contour lines of a group of adjacent points in a local region and they cannot form a continuous connection, it is determined that there is a line break in the region. The data of the regions that are determined to have a sudden change in the contour or a line break are completely separated and removed from the candidate complete region data by the data removal tool. The remaining regions that are not removed are integrated to form a new dataset, and the target complete region data of the standard surface morphology data is obtained.

[0110] The target complete area data is imported into the feature extraction system. The system automatically extracts the surface flatness features common to all areas in the dataset, i.e., uniform height features without any undulations. At the same time, it extracts the contour continuity features, i.e., the continuous features of uninterrupted edge lines, and the smooth transition features of adjacent contours, i.e., the smooth connection of adjacent lines. These extracted features are systematically integrated to clarify the specific manifestation of each feature. These features are jointly established as a unified standard scale for subsequent shape comparison, so that the target complete area data has clear and definite reference attributes, and the target complete area data is officially used as a reference benchmark.

[0111] The morphology comparison system is activated, and the various morphological feature parameters of the reference benchmark are imported into the system as comparison standards. Subsequently, the standard surface morphology data is completely input into the system. The system splits the standard surface morphology data according to the region division rules, keeping it consistent with the region division of the reference benchmark. Each local region in the standard surface morphology data is compared with the corresponding region of the reference benchmark one by one. The system checks whether the surface of the region is flat without protrusions or depressions, whether the contour is continuous without interruption, and whether the contours of adjacent positions transition smoothly. If a local region is inconsistent with the features of the reference benchmark in any comparison, that is, if there are protrusions, depressions, sudden turns in the contour, or broken lines, the system immediately marks the region. After all comparisons are completed, all marked regions are integrated to complete the identification of surface defect regions in the standard surface morphology data.

[0112] The entire process revolves around the identification of surface defect regions in standard surface topography data. First, the surface contour features and flatness features of the reference benchmark are extracted to establish topography feature identifiers. Then, the standard surface topography data is split into regions to obtain regional data. Next, the topography feature identifiers are compared with the regional data one by one and the difference information is recorded to generate regional topography comparison results. Finally, based on the difference information, regional data that exceed the allowable range of the topography feature identifiers are filtered out to obtain the surface defect regions of the standard surface topography data.

[0113] When extracting the surface profile features of the reference datum, data visualization tools are used to present the complete shape of the reference datum. Starting from the starting point of the reference datum's profile, trace along the edge lines point by point, carefully capturing whether the overall direction of each line segment is a straight line extension or a curve transition, recording the curvature and trend of the curve, and observing whether the connection between adjacent line segments is smooth and natural, clarifying the specific manifestation of the continuous state of the profile lines without interruption or abrupt turns, and also paying attention to whether the profile edges are regular, without burrs or irregular protrusions.

[0114] When extracting flatness features, the reference benchmark is divided into multiple continuous micro-detection units. All data points within each detection unit are detected one by one. The actual height information of each point is obtained through height sensing technology. The height information of each point within the same detection unit is directly compared to confirm that the height of all points is consistent and there are no protrusions or depressions higher or lower than the overall plane. At the same time, the height connection between different detection units is checked to ensure that the surface of the entire reference benchmark is in a uniform and flat state without obvious height differences.

[0115] The extracted surface contour features and flatness features are systematically sorted out. The specific judgment criteria for line direction, curvature trend, connection form and regularity in the contour features are clarified, as well as the core requirements for consistent surface height and no undulations in the flatness features. These feature information and judgment criteria are integrated into a complete set of landmark information that can be used for comparison. This set can comprehensively reflect the essential morphological attributes of the reference benchmark, and finally establish the morphological feature identifier of the reference benchmark.

[0116] Before splitting the standard surface morphology data, the basic principles of splitting must be clearly defined. The region division logic of the reference benchmark should be used as a guide to ensure that the size of the split regions matches the detection units of the reference benchmark. Based on the spatial distribution pattern of the standard surface morphology data, and using consecutive adjacent data points as a foundation, multiple local regions of consistent size and clear boundaries are sequentially divided from the starting position of the data. Each local region contains a fixed number of consecutive data points, ensuring that each region can completely cover the surface morphology of its corresponding spatial location. During the division process, the region boundaries are strictly controlled to avoid region overlap or data omission. After division, each region is uniquely identified according to its spatial position in the standard surface morphology data, resulting in the regional data of the standard surface morphology data.

[0117] The established morphological feature labels are completely imported into a dedicated data comparison system. This system has feature matching and difference recognition functions. Then, the regional data of standard surface morphology data are entered into the system one by one according to the previous label order. For each input regional data, the system first activates the contour feature comparison module to compare the edge line direction, curvature trend, and connection shape of the regional data with the contour features in the morphological feature labels segment by segment to determine the degree of fit between the two. Next, the flatness feature comparison module is activated to detect the height distribution of each point in the regional data and perform a comprehensive comparison with the flatness features in the morphological feature labels to confirm whether the height is consistent.

[0118] During the comparison process, the system captures and records in real time all differences between the regional data and the morphological feature markers in terms of contour or flatness, and clarifies the specific manifestations of the differences, such as the contour lines deviating from the preset direction, having sudden turns, broken lines, or the appearance of bulges or depressions on the surface. At the same time, it accurately records the specific spatial location and range of the differences. After all regional data has been compared, the system organizes and summarizes the comparison results of each region, the corresponding differences, and the location information according to the region marker order to form a complete structured report and generate the regional morphological comparison results of the standard surface morphological data.

[0119] The acceptable range is defined by the shape feature identifier, which indicates a continuous and regular contour without abrupt turns or breaks, a uniform and flat surface without undulations. Each difference in the shape comparison results of the regions is checked one by one. For each difference, it is determined whether the corresponding region data deviates from the acceptable range. When the contour lines of the region data deviate significantly from the preset direction of the shape feature identifier, or there are sudden turns, line breaks, or surface protrusions or depressions that are inconsistent with the flatness feature, the region data is directly determined to have exceeded the acceptable range of the shape feature identifier.

[0120] All data from areas deemed to be outside the permissible range are filtered out and organized according to their spatial position in the standard surface morphology data. This ensures that each area outside the permissible range is accurately identified without omission. Finally, these filtered data are integrated to form a dataset that fully reflects the location and extent of all defects in the standard surface morphology data, thus obtaining the surface defect areas of the standard surface morphology data.

[0121] The beneficial effects are as follows: standard surface morphology data is divided into multiple local regions, and regions without protrusions or depressions and with continuous contours are checked one by one to form candidate complete region data; according to the arrangement of points in the candidate complete region data, adjacent edge points are selected one by one to observe the smoothness of contour line connection and complete the check of the contour transition state of adjacent positions; based on the contour transition state record, regions with sudden contour turns or broken lines are eliminated, and the remaining data are integrated to obtain target complete region data; the surface flatness, contour continuity, and smooth transition features of the target complete region data are extracted, integrated, and established as comparison standards, and the target complete region data is used as a reference benchmark; using the reference benchmark as the comparison standard, the morphology features of the standard surface morphology data and the reference benchmark are compared region by region, inconsistent regions are marked, and surface defect regions of the standard surface morphology data are identified.

[0122] S4. The distribution density parameter and distribution depth parameter of the surface defect region are fused in multiple dimensions to obtain the defect parameters of the surface defect region.

[0123] In this embodiment of the invention, the step of multi-dimensionally fusing the distribution density parameter and distribution depth parameter of the surface defect region to obtain the defect parameters of the surface defect region includes:

[0124] Obtain the distribution density information characterizing the density of defects and the distribution depth information characterizing the depth of defects in the surface defect region to form the distribution density parameter and distribution depth parameter of the surface defect region;

[0125] By removing abnormal information from the distribution density parameter and the distribution depth parameter that does not conform to the actual characteristics of the defect, the optimized distribution density parameter and the optimized distribution depth parameter of the surface defect region are obtained;

[0126] Based on the formation mechanism of surface defects, the correlation between the optimized distribution density parameter and the optimized distribution depth parameter is determined;

[0127] Based on the aforementioned correlation, the optimized distribution density parameter and the optimized distribution depth parameter are integrated collaboratively to generate the defect parameters for the surface defect region.

[0128] Determining the correlation between the optimized distribution density parameter and the optimized distribution depth parameter based on the formation mechanism of surface defects includes:

[0129] By analyzing the formation mechanism of the surface defect region, the raw material melting state and solidification process environmental factors that affect the formation of defects are identified, and a set of factors affecting the formation of the surface defect region is obtained.

[0130] Based on the set of factors influencing defect formation, the changing trends of the optimized distribution density parameter and the optimized distribution depth parameter are analyzed to obtain a table of parameter variation patterns for the surface defect region.

[0131] Based on the parameter variation pattern table, extract the collaborative variation feature information between the optimized distribution density parameter and the optimized distribution depth parameter;

[0132] By filtering the collaborative change feature information and identifying features directly related to defect improvement, effective collaborative features of the surface defect region are obtained.

[0133] Based on the effective collaborative features, the interaction mode and correlation between the optimized distribution density parameter and the optimized distribution depth parameter are coupled to form a correlation relationship between the optimized distribution density parameter and the optimized distribution depth parameter.

[0134] The calculation formula for the collaborative integration is as follows:

[0135] ;

[0136] In the formula, The defect parameters represent the surface defect region. This represents the optimized distribution density parameter of the surface defect region. This represents the optimized distribution depth parameter of the surface defect region. This represents the reference reference flatness coefficient, quantified based on the flatness characteristics of the reference reference. This represents the parameter coordination coefficient quantified based on the effective coordination characteristics.

[0137] A comprehensive delineation of the surface defect region is conducted to clarify its specific coverage area on the surface of the scintillation crystal array blank. The surface defect region is divided into multiple equally sized detection units using a uniform division method. Defects within each detection unit are counted one by one, and the actual number of defects in each unit is statistically analyzed to reflect the density of defects per unit area, thus forming the distribution density information of the surface defect region. Simultaneously, a high-precision depth detection tool is used to contact the bottom of each defect one by one, measuring the vertical distance from the surface to the deepest point of each defect, accurately recording the depth data of each defect, forming the distribution depth information of the surface defect region. The distribution density information and distribution depth information are then organized into structured data forms to obtain the distribution density parameters and distribution depth parameters of the surface defect region.

[0138] The actual observation data of the surface defect area is collected to clarify the density distribution pattern and depth variation characteristics of defects during their natural formation and development. This is used as the basis for judging abnormal information. Each detection unit data in the distribution density parameter is checked one by one. If the number of defects in a certain detection unit is much higher or lower than the general level of the surrounding units and does not match the actual observed defect distribution, it is judged as abnormal information. Similarly, each defect depth data in the distribution depth parameter is checked. If the depth of a defect exceeds the thickness range of the crystal itself, or differs greatly from the conventional depth of similar defects, and there is no reasonable cause for its formation, it is judged as abnormal information. These judged abnormal information are completely removed from the distribution density parameter and distribution depth parameter. The remaining data is reorganized and confirmed to obtain the optimized distribution density parameter and optimized distribution depth parameter of the surface defect area.

[0139] This study delves into the formation mechanism of surface defects, clarifying the correlation between defect generation and factors such as the melting state of raw materials, solidification process, and environmental conditions. It examines the typical patterns of change in defect density and depth under the influence of these factors; for example, when raw materials are unevenly melted, areas with denser defects are often accompanied by deeper defects. Based on this mechanism, the study compares and analyzes the data trends of optimized distribution density and optimized distribution depth parameters. It observes whether areas with higher defect distribution density show corresponding changes in distribution depth, and examines whether there are correlation characteristics in the distribution density of areas with deeper distribution depth. By summarizing these data correlations, the study clarifies the corresponding changes between optimized distribution density and optimized distribution depth parameters, thus determining the correlation between the two.

[0140] Based on established correlations, the optimized distribution density parameters and optimized distribution depth parameters are integrated collaboratively. According to the positional correspondence of each detection unit, the optimized distribution density data of that unit is matched with the corresponding optimized distribution depth data to ensure that the density and depth information of each region correspond one-to-one. At the same time, the inherent logic between the data is sorted out according to the correlations, so that the integrated parameters can comprehensively reflect the collaborative characteristics of defect density and depth. It includes both the defect distribution density of each region and the defect depth status of the corresponding region, forming a complete, coherent data set that can comprehensively characterize the overall state of the surface defect region and generate defect parameters for the surface defect region.

[0141] This study delves into the entire fabrication process of scintillation crystal array preforms, systematically analyzing the formation mechanism of surface defect regions by combining the specific morphology and distribution characteristics of surface defects. The focus is on the raw material melting stage, exploring the impact of the uniformity of raw material mixing and the stability of the melting temperature on defect formation. It clarifies that uneven raw material melting leads to local component aggregation and thus defect formation, while temperature fluctuations induce internal stress in the melt, resulting in defects. During solidification, the study examines the uniformity of solidification rate and the stability of the temperature gradient, confirming that excessively rapid solidification can cause atomic arrangement disorder and defect formation, while an unstable temperature gradient can lead to crystal growth imbalance and defects. Simultaneously, environmental factors during the fabrication process are investigated, including whether the purity of the inert atmosphere meets standards and whether the ambient temperature and humidity remain constant. It clarifies that impurities in the atmosphere can trigger chemical reactions and form defects, and that temperature and humidity fluctuations affect the surface tension of the melt, leading to defects. These empirically verified raw material melting states, solidification processes, and environmental factors are systematically reviewed to ensure that each factor is directly related to defect formation, ultimately integrating them into a set of influencing factors for surface defect formation.

[0142] Based on the set of factors influencing defect formation, the changes in optimized distribution density and depth parameters were observed for each factor. For the factor of uneven raw material melting, the optimized distribution density and depth parameters in different regions were compared and analyzed, recording the increasing / decreasing trend of the density parameter and the changes in the depth parameter under the influence of this factor. For the factor of excessively fast solidification rate, the corresponding parameter changes were tracked and observed to determine whether the density parameter showed a concentrated upward trend and whether the depth parameter showed an accompanying deepening change. For the case of insufficient purity of the ambient atmosphere, the direction and amplitude of parameter fluctuations under the influence of this factor were recorded. The changing trends of the two parameters corresponding to each influencing factor were recorded in detail, categorized and organized according to the type of influencing factor, clearly presenting the correspondence between different influencing factors and parameter changes, forming a well-structured and detailed table of parameter change patterns for surface defect areas.

[0143] Based on the parameter variation table, the correlation between the changes in the optimized distribution density parameter and the optimized distribution depth parameter corresponding to each influencing factor is analyzed row by row. When an influencing factor causes the optimized distribution density parameter to show an increasing trend, the corresponding optimized distribution depth parameter is simultaneously checked to see if it shows a deepening trend; when the optimized distribution density parameter shows a decreasing trend, the optimized distribution depth parameter is observed to see if it becomes shallower. For the parameter changes under the influence of each influencing factor, the synchronicity and matching degree of the changes are recorded in detail. For example, when the density parameter increases significantly, does the depth parameter also increase significantly? When the density parameter fluctuates slightly, does the depth parameter show a corresponding slight change? These specific manifestations of the synchronous changes of the two parameters are extracted one by one to ensure that each extracted feature clearly reflects the synergistic change relationship between the two, thus obtaining the synergistic change feature information of the surface defect area.

[0144] The core objective of defect improvement is to reduce defect density and depth. Based on this objective, synergistic change features are selected. Each synergistic change feature is analyzed to determine whether it can directly guide defect improvement. Features indicating simultaneous increases in density and depth are directly relevant to defect improvement because improving such features requires addressing both density and depth. Features reflecting simultaneous decreases in density and shallowing can be used as a basis for judging the improvement effect and are also directly relevant. Features that only reflect parameter changes but have no practical guiding significance for defect improvement are eliminated. After comprehensive verification, the effective synergistic features of the surface defect area are obtained.

[0145] Based on the core foundation of effective synergistic characteristics, this study delves into the interaction between the optimized distribution density parameter and the optimized distribution depth parameter. For the effective synergistic characteristic of "increased density accompanied by increased depth," it clarifies that the interaction is that the increased defect depth provides the spatial conditions for increased density, while the increased density further exacerbates the depth extension. For the characteristic of "decreased density accompanied by shallower depth," it is determined that the interaction is that the shallower depth restricts density aggregation, while the decreased density promotes a decrease in depth. Furthermore, the study details the correlation between the two parameters under different effective synergistic characteristics, such as the drastic unidirectional changes under the influence of uneven raw material melting, and the synchronous, gradual changes after the solidification rate stabilizes. These interaction mechanisms and correlation manifestations are systematically coupled and integrated into a complete description that comprehensively reflects the intrinsic relationship between the two parameters, ultimately establishing the correlation between the optimized distribution density parameter and the optimized distribution depth parameter.

[0146] These are the defect parameters of the surface defect region, which are obtained by the coordinated integration of optimized distribution density parameters, optimized distribution depth parameters, reference baseline flatness coefficient, and parameter coordination coefficient. It is the optimized distribution density parameter of the surface defect region, which is obtained by dividing the surface defect region into detection units, counting them, and removing abnormal information. It is the optimized distribution depth parameter of the surface defect region, which is obtained by measuring the defect depth with a high-precision depth detection tool and removing abnormal information; It is the reference flatness coefficient, which is obtained by detecting the height distribution of the reference surface and confirming that the surface is uniform and flat. It is the parameter synergy coefficient, which is obtained by quantifying the effective synergy features that are directly related to defect improvement after screening out the synergy change feature information.

[0147] This formula is used to integrate the optimized distribution density parameter and the optimized distribution depth parameter of the surface defect region, and combine them with the reference flatness coefficient and parameter coordination coefficient to generate defect parameters that comprehensively characterize the overall state of the surface defect region. This enables a quantitative description of the comprehensive state of the defect and provides accurate data support for subsequent targeted improvement of surface defects.

[0148] When the optimized distribution density parameter increases, the defect parameter will also increase; when the optimized distribution depth parameter increases, the defect parameter will also increase; changes in the reference flatness coefficient and parameter synergy coefficient will affect the calculation results of the defect parameter. Overall, increasing the optimized distribution density parameter and the optimized distribution depth parameter will cause the defect parameter to show an increasing trend, reflecting that the defect degree in the surface defect area is more serious.

[0149] The beneficial effects are that by dividing the detection unit count and high-precision depth measurement, the distribution density and distribution depth information of the surface defect area can be accurately obtained, forming distribution density parameters and distribution depth parameters that can truly reflect the defect state. After eliminating abnormal information that does not match the actual characteristics of the defect, accurate and reliable optimized distribution density parameters and optimized distribution depth parameters are obtained. The relationship between the two is clarified by combining the surface defect formation mechanism. Then, through collaborative integration, defect parameters that comprehensively characterize the overall state of the surface defect area are generated, providing accurate and comprehensive data support for subsequent targeted treatment of surface defects.

[0150] S5. Map the defect parameters to a preset process parameter matching rule library to determine the polishing process parameters of the scintillation crystal array blank;

[0151] In this embodiment of the invention, the step of mapping the defect parameters to a preset process parameter matching rule library to determine the polishing process parameters of the scintillation crystal array blank includes:

[0152] Extract the core defect features that characterize the overall defect state from the defect parameters;

[0153] The core features of the defects are mapped to a preset process parameter matching rule library to filter out a set of candidate polishing process parameters that match the defect features of the scintillation crystal array blank.

[0154] By eliminating parameters in the candidate polishing process parameter set that do not match the process implementation conditions, the target polishing process parameter set for the scintillation crystal array blank is obtained;

[0155] By integrating the target polishing process parameter set, polishing process parameters that can specifically improve the surface defects of the scintillation crystal array blank are generated.

[0156] When extracting core defect features, a comprehensive and systematic review of all parameters characterizing the surface defects of the scintillation crystal array blank is first conducted. This review clarifies that these defect parameters specifically encompass various aspects, including defect type (protrusions, depressions, or contour fractures), size range (length and depth of defect extension on the surface), distribution density (number of defects per unit area), and morphological characteristics (regularity or irregularity of defect edges). Subsequently, the influence of each parameter on the overall defect state is analyzed to determine which parameters are crucial in determining the essential attributes of the defect, which directly reflect the core impact of the defect on the crystal surface quality, and which parameters are key features that must be addressed in subsequent polishing processes. For example, defect type directly determines the polishing treatment method, size range affects the control of polishing intensity, and distribution density relates to the key areas covered by polishing; these key pieces of information must be prioritized. The selected key information is then integrated and summarized to ensure that each core dimension has corresponding features, forming a specific information set that comprehensively and accurately characterizes the overall defect state. Ultimately, the core defect features characterizing the overall defect state are obtained from the defect parameters.

[0157] The pre-defined process parameter matching rule base is built upon a large amount of practical polishing process data. The base stores a rich set of correspondences between defect features and polishing process parameters. Each correspondence has been verified in practice, clearly defining the types of polishing process parameters and specific applicable scenarios suitable for particular defect features. For example, for protrusion-type defects, the rule base records the corresponding ranges of polishing force, polishing head rotation speed, and other parameters; for contour fracture-type defects, it clarifies the suitable polishing path and polishing time, among other parameters. After the extracted core defect features are fully input into the rule base, each corresponding relationship is meticulously compared, starting from the first entry of the rule base, according to the specific manifestation of the core defect features. The focus is on verifying whether the core features such as defect type and size range completely match the entries in the rule base. Once a matching entry is found, the corresponding polishing process parameters are extracted. This comparison continues until all relevant entries in the rule base are covered. All polishing process parameters corresponding to matching entries are collected and then preliminarily sorted according to their functional categories, such as force parameters, rotation speed parameters, and time parameters, forming a candidate polishing process parameter set that conforms to the defect characteristics of the scintillation crystal array blank.

[0158] Before eliminating mismatched parameters, the specific details of the process implementation conditions must be fully clarified. This includes the operational capabilities of the existing polishing equipment, such as the stable output range of polishing intensity, the adjustable polishing head speed range, and the supported polishing path modes; the limitations of the polishing materials that can be used, such as the hardness grade of the polishing compound, the size and specifications of the abrasive particles, and the compatibility requirements between the material and the crystal surface; and the specific requirements of the production environment, such as the stable range of ambient temperature and humidity and the cleanroom standard. Then, each parameter in the candidate polishing process parameter set is checked one by one. First, it is checked whether the parameter is within the adjustable range of the existing polishing equipment to ensure that the equipment can accurately achieve the set value of the parameter. Next, it is confirmed whether the parameter is compatible with the characteristics of the selected polishing material to avoid material failure or damage to the crystal surface due to improper parameters. Finally, it is verified whether the parameter can be successfully implemented under the current production environment conditions and whether the parameter effect will be affected by environmental factors. Any parameter that does not meet the process implementation conditions, such as exceeding the equipment's control range, conflicting with material characteristics, or not meeting environmental requirements, is directly eliminated from the candidate polishing process parameter set. The remaining parameters need to be fully checked again to confirm that all parameters meet the process implementation conditions and are complete, and then the target polishing process parameter set for the scintillation crystal array blank is formed.

[0159] A comprehensive and detailed analysis of the target polishing process parameter set was conducted. First, following the polishing process implementation flow, the parameters in the target set were categorized into different types, such as pre-polishing parameters, main polishing parameters, and fine polishing parameters, clearly defining the specific polishing stage corresponding to each parameter. Next, the target objects of each parameter were determined; for example, certain force parameters specifically target protruding defects, while certain rotation speed parameters optimize contour smoothness, ensuring that each parameter can specifically improve its corresponding surface defects. During this process, the coordination between various parameters was crucial. For example, adjusting the connection between main polishing parameters and fine polishing parameters ensured that the effect of parameters in the previous stage laid the foundation for the next stage, while avoiding control conflicts or functional duplication between different parameters. Through classification, clarifying functions, and coordination, the various parameters were integrated into a logically coherent, highly targeted, and mutually compatible complete system. This system can be directly applied to actual polishing operations, accurately corresponding to the surface defect types and states of the scintillation crystal array blank, ultimately generating polishing process parameters that can specifically improve the surface defects of the scintillation crystal array blank.

[0160] The beneficial effects are as follows: By systematically sorting out defect parameters and accurately extracting the core features characterizing the comprehensive state of defects, a precise basis is provided for matching subsequent polishing process parameters, ensuring that parameter matching does not deviate from the essence of the defects; by comparing and screening one by one with a preset process parameter matching rule library, candidate polishing process parameter sets that meet the defect characteristics can be quickly identified, improving the efficiency and accuracy of parameter matching; by strictly verifying the process implementation conditions and eliminating mismatched parameters, the final target polishing process parameter set is guaranteed to be practically operable, avoiding process failure due to parameter and condition conflicts; finally, by scientifically integrating the target polishing process parameter set, highly targeted and logically coherent polishing process parameters are generated, which can accurately target various surface defects of the scintillation crystal array blank, effectively improve the crystal surface quality, and enhance the finished product qualification rate and performance of the scintillation crystal array blank.

[0161] S6. Based on the polishing process parameters, control the surface polishing pressure distribution and path trajectory of the scintillation crystal array blank to generate the scintillation crystal array of the scintillation crystal array blank.

[0162] In this embodiment of the invention, the step of controlling the surface polishing pressure distribution and path trajectory of the scintillation crystal array preform according to the polishing process parameters to generate the scintillation crystal array preform includes:

[0163] The pressure control features and path planning features related to surface polishing in the polishing process parameters are integrated into the process execution parameters of the scintillation crystal array blank.

[0164] Based on the location information of the surface defect region and the path planning features in the process execution parameters, a polishing path covering all defect regions and avoiding the intact surface region in the scintillation crystal array blank is planned, and a polishing path planning scheme for the scintillation crystal array blank is generated.

[0165] Based on the pressure control characteristics in the process execution parameters and the degree of defects in the surface defect region, the polishing pressure adaptation standard for different regions in the scintillation crystal array blank is determined, and the polishing pressure distribution scheme of the scintillation crystal array blank is generated.

[0166] The motion trajectory is controlled according to the polishing path planning scheme, and the pressure output is adjusted according to the polishing pressure distribution scheme to perform targeted polishing on the scintillation crystal array blank, thereby obtaining the scintillation crystal array of the scintillation crystal array blank.

[0167] From the polishing process parameters designed to specifically improve surface defects in scintillation crystal array preforms, pressure control features and path planning features directly related to surface polishing are precisely extracted. Pressure control features encompass core information such as pressure adjustment logic and stable output requirements during polishing, while path planning features include key aspects such as the basic principles of path design and coverage methods. These two types of features are systematically organized and integrated according to the execution logic and sequence of the polishing process, ensuring coordination and compatibility between pressure control features and path planning features without logical conflicts. This results in a set of parameters that directly guides the polishing operation, yielding the process execution parameters for the scintillation crystal array preform.

[0168] First, the location information of surface defect areas is clearly defined. Using the previously acquired surface defect area data, the specific coordinate range, edge boundary, and relative positional relationship between each defect area on the surface of the scintillation crystal array blank are accurately marked. Combining the path planning characteristics in the process execution parameters, the requirements for path design, such as coverage integrity and smooth movement, are clarified. Starting from an edge area on the blank surface, the path direction is rationally planned according to the distribution of adjacent defect areas. First, the closer defect areas are covered, and then the path is gradually extended to other defect areas to ensure that the planned path can completely cover all marked defect areas. At the same time, when encountering intact areas on the surface during the path design process, the path direction is adjusted to bypass them, avoiding damage to the intact areas during polishing. Finally, a detailed path route plan is formed, generating a polishing path planning scheme for the scintillation crystal array blank.

[0169] A thorough analysis of the pressure control characteristics in the process execution parameters was conducted to clarify the core logic of pressure regulation, the achievable pressure variation range, and the key requirements for stable pressure output. Simultaneously, the defect severity of surface defect areas was comprehensively assessed, categorized according to the depth, size, and severity of defects, distinguishing between areas with severe, moderate, and minor defects. Pressure control characteristics were matched with defect severity; for areas with severe defects, appropriate pressure standards were determined to ensure polishing results; for areas with moderate defects, moderate pressure standards were established to balance polishing efficiency and surface protection; and for areas with minor defects, mild pressure standards were set to avoid over-polishing. The polishing pressure standards for each area were clearly defined and summarized according to area location to generate a polishing pressure distribution scheme for the scintillation crystal array preform.

[0170] The polishing path planning scheme and polishing pressure distribution scheme are fully integrated into the polishing equipment's control system. After the equipment starts, the motion actuators move strictly according to the route set in the polishing path planning scheme, receiving control signals in real time to adjust the direction, speed, and trajectory of movement, ensuring that each path segment precisely fits the defect area and does not deviate from the preset route. Simultaneously, the equipment's pressure regulation system, based on the polishing pressure distribution scheme, synchronously switches the corresponding polishing pressure adaptation standard as the motion actuators move to different areas, precisely adjusting the pressure output to ensure that the pressure applied to the defect area meets the preset requirements. During the polishing process, the equipment continuously monitors the motion trajectory and pressure output status, promptly correcting minor deviations to ensure that the entire polishing operation strictly follows both schemes, specifically addressing each defect area, ultimately completing the polishing operation and obtaining the scintillation crystal array blank.

[0171] The beneficial effects are that by integrating the pressure control features and path planning features in the polishing process parameters to form process execution parameters, a precise guideline is provided for subsequent polishing operations. The polishing path planned by combining the location information of surface defect areas with the path planning features can completely cover all defect areas and avoid intact areas of the surface, thus avoiding damage to intact parts. Based on the polishing pressure distribution scheme determined by the pressure control features and the degree of defects, precise pressure adaptation can be achieved for different defect areas. Furthermore, by strictly controlling the motion trajectory according to the path planning scheme and adjusting the pressure output according to the pressure distribution scheme, the surface defects of the scintillation crystal array blank can be effectively improved, and finally, a scintillation crystal array with excellent surface quality can be obtained.

[0172] It will be apparent to those skilled in the art that the present invention is not limited to the details of the exemplary embodiments described above, and that the present invention can be implemented in other specific forms without departing from the spirit or essential characteristics of the present invention.

[0173] This application embodiment can acquire and process relevant data based on artificial intelligence technology. Artificial intelligence is the theory, method, technology, and application system that uses digital computers or machines controlled by digital computers to simulate, extend, and expand human intelligence, perceive the environment, acquire knowledge, and use that knowledge to obtain optimal results.

[0174] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention.

Claims

1. A method for preparing and surface treating an array of scintillation crystals, comprising: providing a plurality of scintillation crystals; providing a plurality of metal particles; and applying the plurality of metal particles to the plurality of scintillation crystals. The method comprises: S1, assembling and fixing the scintillation crystal strips according to a standard array arrangement configuration to obtain a scintillation crystal array initial blank of the scintillation crystal strips; S2, collecting surface topography original data of the scintillation crystal array initial blank, and aligning spatial coordinates of the surface topography original data to obtain standard surface topography data of the scintillation crystal array initial blank; S3, selecting a surface complete area in the standard surface topography data as a reference datum, and performing topography comparison between the standard surface topography data and the reference datum to identify a surface defect area of the standard surface topography data; S4, performing multi-dimensional fusion on a distribution density parameter and a distribution depth parameter of the surface defect area to obtain a defect parameter of the surface defect area; S5, mapping the defect parameter to a preset process parameter matching rule library to determine a polishing process parameter of the scintillation crystal array initial blank; S6, controlling surface polishing pressure distribution and path trajectory of the scintillation crystal array initial blank according to the polishing process parameter to generate a scintillation crystal array of the scintillation crystal array initial blank.

2. A method of preparing and surface treating a scintillating crystal array as defined in claim 1, wherein, The assembling and fixing of the scintillation crystal strips according to the standard array arrangement configuration to obtain the scintillation crystal array initial blank of the scintillation crystal strips comprises: determining a standard array arrangement configuration of the scintillation crystal strips according to historical scintillation crystal array data; adjusting an axis direction and an end face flatness of the scintillation crystal strips according to the standard array arrangement configuration to obtain calibrated scintillation crystal strips of the scintillation crystal strips; filling a trace amount of adhesive at a splicing gap of the calibrated scintillation crystal strips, and applying uniform pre-pressure to the scintillation crystal strips to obtain scintillation crystal embryos of the scintillation crystal strips; assembling the scintillation crystal embryos to obtain a scintillation crystal array initial embryo of the scintillation crystal strips.

3. A method of preparing and surface treating a scintillating crystal array as defined in claim 1, wherein, The collecting of the surface topography original data of the scintillation crystal array initial blank and the aligning of spatial coordinates of the surface topography original data to obtain the standard surface topography data of the scintillation crystal array initial blank comprises: removing residual impurities attached to a surface of the scintillation crystal array initial blank to obtain a clean scintillation crystal array initial blank of the scintillation crystal array initial blank; scanning a surface topography of the clean scintillation crystal array initial blank, and integrating obtained surface topography original information into surface topography original data of the clean scintillation crystal array initial blank; selecting non-collinear feature points as spatial coordinate datums on the clean scintillation crystal array initial blank without foreign matters on a surface according to the surface topography original data; performing position correspondence between position information in the surface topography original data and the spatial coordinate datums to obtain a position correspondence relationship of the surface topography original data; adjusting coordinate arrangement of the surface topography original data based on the position correspondence relationship to obtain the standard surface topography data of the scintillation crystal array initial blank.

4. The method of claim 1, wherein the method further comprises the step of: The selecting of the surface complete area in the standard surface topography data as the reference datum and the performing of the topography comparison between the standard surface topography data and the reference datum to identify the surface defect area of the standard surface topography data comprises: ​ Screening the area without protrusions, without depressions and with continuous profile in the standard surface topography data as candidate complete area data; Checking the profile transition state of adjacent positions in the candidate complete area data; According to the profile transition state, eliminating the area data with sudden profile turning and line breaking in the candidate complete area data to obtain the target complete area data of the standard surface topography data; Taking the target complete area data as a reference benchmark; Performing topography comparison on the standard surface topography data and the reference benchmark to identify the surface defect area of the standard surface topography data.

5. A method of preparing and surface treating a scintillating crystal array as defined in claim 4, wherein, The topography comparison on the standard surface topography data and the reference benchmark to identify the surface defect area of the standard surface topography data, comprising: Extracting the surface profile features and flatness features of the reference benchmark to establish the topography feature identifier of the reference benchmark; Splitting the standard surface topography data by area to obtain the area data of the standard surface topography data; Inputting the topography feature identifier into the area data for comparison one by one and recording the difference information between the area data and the topography feature identifier to generate the area topography comparison result of the standard surface topography data; According to the difference information in the area topography comparison result, screening the area data exceeding the allowed range of the topography feature identifier in the standard surface topography data to obtain the surface defect area of the standard surface topography data.

6. A method of preparing and surface treating a scintillating crystal array as defined in claim 1, wherein, The multi-dimensional fusion of the distribution density parameter and the distribution depth parameter of the surface defect area to obtain the defect parameter of the surface defect area, comprising: Obtaining the distribution density information representing the defect density and the distribution depth information representing the defect depth in the surface defect area to form the distribution density parameter and the distribution depth parameter of the surface defect area; Eliminating abnormal information in the distribution density parameter and the distribution depth parameter that does not conform to the actual characteristics of defects to obtain the optimized distribution density parameter and the optimized distribution depth parameter of the surface defect area; According to the formation mechanism of surface defects, determining the correlation between the optimized distribution density parameter and the optimized distribution depth parameter; Based on the correlation, the optimized distribution density parameter and the optimized distribution depth parameter are integrated to generate the defect parameter of the surface defect area.

7. A method of preparing and surface treating a scintillating crystal array as defined in claim 6, wherein, The correlation between the optimized distribution density parameter and the optimized distribution depth parameter is determined according to the formation mechanism of surface defects, comprising: By analyzing the formation mechanism of the surface defect area, the raw material melting state and the solidification process environmental factors affecting the generation of defects are sorted out to obtain a defect formation influencing factor set of the surface defect area; According to the defect formation influencing factor set, the change trend of the optimized distribution density parameter and the optimized distribution depth parameter is analyzed to obtain a parameter change rule table of the surface defect area; Based on the parameter change rule table, the cooperative change feature information between the optimized distribution density parameter and the optimized distribution depth parameter is extracted; Screening the features in the synergistic change feature information that are directly related to defect improvement to obtain effective synergistic features of the surface defect region; Based on the effective synergistic features, coupling the interaction mode and the correlation between the optimization distribution density parameter and the optimization distribution depth parameter to form the correlation between the optimization distribution density parameter and the optimization distribution depth parameter.

8. A method of preparing and surface treating a scintillating crystal array as defined in claim 7, wherein, The calculation formula of the synergistic integration is as follows: ； wherein represents a defect parameter of the surface defect region, represents an optimized distribution density parameter of the surface defect region, represents an optimized distribution depth parameter of the surface defect region, represents a reference flatness coefficient quantified according to the flatness characteristic of the reference datum, represents a parameter synergy coefficient quantified according to the effective synergy characteristic.

9. The method of claim 1, wherein the array of scintillating crystals is prepared and surface treated by the steps of: The mapping of the defect parameters to the pre-set process parameter matching rule library to determine the polishing process parameters of the scintillation crystal array blank includes: ​ Extracting the defect core features representing the comprehensive state of defects in the defect parameters; Mapping the defect core features to the pre-set process parameter matching rule library to screen out the candidate polishing process parameter set that meets the defect features of the scintillation crystal array blank; Eliminating the parameters in the candidate polishing process parameter set that do not match the process implementation conditions to obtain the target polishing process parameter set of the scintillation crystal array blank; Integrating the target polishing process parameter set to generate a polishing process parameter that can improve the surface defects of the scintillation crystal array blank.

10. A method of preparing and surface treating an array of scintillating crystals as defined in claim 1, wherein, According to the polishing process parameters, controlling the surface polishing pressure distribution and path trajectory of the scintillation crystal array blank to generate the scintillation crystal array of the scintillation crystal array blank includes: Integrating the pressure control features and path planning features related to surface polishing in the polishing process parameters into the process execution parameters of the scintillation crystal array blank; Based on the position information of the surface defect region and the path planning features in the process execution parameters, planning a polishing path of the scintillation crystal array blank that covers all defect regions and avoids surface complete regions to generate a polishing path planning scheme for the scintillation crystal array blank; According to the pressure control features in the process execution parameters and the defect degree of the surface defect region, determining the polishing pressure adaptation standard of different regions in the scintillation crystal array blank to generate a polishing pressure distribution scheme for the scintillation crystal array blank; According to the polishing path planning scheme, controlling the motion trajectory, and according to the polishing pressure distribution scheme, adjusting the pressure output to perform targeted polishing on the scintillation crystal array blank to obtain the scintillation crystal array of the scintillation crystal array blank.

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