Method for flattening and fixing pressurizing frame type water sample plate with pressing rib and related device

Abstract

The embodiment of the invention relates to the field of water quality detection, and provides a method for flattening and fixing a pressurized frame type water sample plate with a pressing rib and a related device, and the method comprises the following steps: obtaining water sample spot position contour data on a water sample plate to be treated; according to the water sample spot position contour data, performing partition pressure adaptation processing on a pressing frame with a pressing rib of the to-be-processed water sample plate to obtain partition pressure configuration data; controlling the pressurizing frame to apply pressure and fix the to-be-treated water sample plate according to the partition pressure configuration data to obtain a partition pressure-applying fixed water sample plate; performing multi-dimensional flatness redundancy detection processing on the partitioned pressure-applying fixed water sample plate to obtain multi-dimensional flatness detection data; and according to the multi-dimensional flatness detection data, the partition pressure-applying fixed water sample plate is adjusted, a target flat fixed water sample plate is obtained, and the accuracy and effectiveness of flattening and fixing the pressurized frame type water sample plate with the pressing rib can be improved.

Description

Technical Field

[0001] This application relates to the fields of data processing and water quality testing technology, specifically to a method and related device for flattening and fixing a pressure frame water sample plate with pressing ribs. Background Technology

[0002] In the field of laser liquid analysis, laser water quality analyzers use lasers to bombard spots formed after liquid drying, and then use a spectrometer to detect heavy metals, non-metals, and other components in these spots. This method offers advantages such as speed and the absence of chemical reagents. The water sample preparation stage typically uses a flexible membrane (such as a zinc membrane) as a substrate. A predetermined volume of liquid is dropped onto the water sample preparation area of ​​the flexible membrane and dried to form spots. The flexible membrane supporting the spots then needs to be flattened and fixed to ensure the stability of the spot morphology during laser bombardment, thereby guaranteeing the accuracy of the spectral data. However, current methods for flattening and fixing the flexible membrane supporting the spots mostly rely on manual operation or simple mechanical clamping, making it difficult to precisely control the flatness of the flexible membrane. This can easily lead to problems such as local wrinkles and displacement of the flexible membrane, resulting in low accuracy and effectiveness of the flattening and fixing process. Therefore, improving the accuracy and effectiveness of flattening and fixing pressure frame-type water sample plates with pressure ribs has become an urgent problem to be solved. Summary of the Invention

[0003] This application provides a method and related apparatus for flattening and fixing a pressure frame water sample plate with pressing ribs, which can improve the accuracy and effectiveness of flattening and fixing the pressure frame water sample plate with pressing ribs.

[0004] The first aspect of this application provides a method for leveling and fixing a pressure frame type water sample plate with pressing ribs. This method includes: Obtain the location and contour data of the water sample spots located on the water sample plate to be processed; Based on the water sample spot location contour data, the pressure frame with pressing ribs of the water sample plate to be treated is subjected to zonal pressure adaptation processing to obtain zonal pressure configuration data. The control pressurization frame applies pressure to the water sample to be treated according to the zone pressure configuration data, thereby obtaining a zone-pressurized and fixed water sample; Multi-dimensional flatness redundancy detection processing was performed on the zoned pressure-fixed water sample plate to obtain multi-dimensional flatness detection data; The water sample plate with zoned pressure is adjusted and processed based on multi-dimensional flatness detection data to obtain the target flatness and fixed water sample plate.

[0005] A second aspect of this application provides a pressure frame type water sample plate leveling and fixing device with pressing ribs, the pressure frame type water sample plate leveling and fixing device with pressing ribs includes: The acquisition unit is used to acquire the location and contour data of water sample spots located on the water sample plate to be processed. The first processing unit is used to perform zoned pressure adaptation processing on the pressurized frame with pressing ribs of the water sample plate to be processed according to the water sample spot position contour data, and obtain zoned pressure configuration data. The second processing unit is used to control the pressurizing frame to pressurize and fix the water sample to be processed according to the partition pressure configuration data, so as to obtain the partition pressurized and fixed water sample. The third processing unit is used to perform multi-dimensional flatness redundancy detection processing on the partitioned pressurized water sample plate to obtain multi-dimensional flatness detection data. The fourth processing unit is used to adjust the partitioned pressure-fixed water sample plate according to the multi-dimensional flatness detection data to obtain the target flatness-fixed water sample plate.

[0006] A third aspect of this application provides a terminal including a processor, an input device, an output device, and a memory, wherein the processor, input device, output device, and memory are interconnected, wherein the memory is used to store a computer program, the computer program including program instructions, and the processor is configured to invoke the program instructions to execute the step instructions as described in the first aspect of this application.

[0007] A fourth aspect of this application provides a computer-readable storage medium storing a computer program for electronic data interchange, wherein the computer program causes a computer to perform some or all of the steps described in the first aspect of this application.

[0008] A fifth aspect of this application provides a computer program product, wherein the computer program product includes a non-transitory computer-readable storage medium storing a computer program operable to cause a computer to perform some or all of the steps described in the first aspect of this application. The computer program product may be a software installation package.

[0009] Implementing the embodiments of this application has the following beneficial effects: By acquiring the location contour data of water sample spots on the water sample plate to be treated, the pressure of the pressurized frame with compression ribs on the water sample plate to be treated can be adapted to different zones based on the location contour data. This yields zone pressure configuration data, which allows the pressurized frame to apply pressure and fix the water sample plate according to the zone pressure configuration data, resulting in a zone-pressure-fixed water sample plate. Furthermore, the zone-pressure-fixed water sample plate undergoes multi-dimensional flatness redundancy detection processing to obtain multi-dimensional flatness detection data. This data can then be used to adjust the zone-pressure-fixed water sample plate to obtain the target flatness-fixed water sample plate. This improves the accuracy and effectiveness of flattening and fixing water sample plates with compression ribs. Attached Figure Description

[0010] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0011] Figure 1A This application provides a schematic diagram of the structure of a laser liquid analysis system. Figure 1B This application provides a schematic diagram of a pressure frame with compression ribs not being fully compressed in an embodiment of the present application; Figure 1C This application provides a schematic diagram of the state of a pressure frame with pressing ribs being compressed, as shown in the embodiment of the present application. Figure 2 This application provides a flowchart illustrating a method for flattening and fixing a pressure frame-type water sample plate with compression ribs. Figure 3 This is a schematic diagram of the structure of a terminal provided in an embodiment of this application; Figure 4 This application provides a structural schematic diagram of a pressure frame type water sample plate leveling and fixing device with pressing ribs. Detailed Implementation

[0012] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0013] The terms "first," "second," etc., in the specification, claims, and accompanying drawings of this application are used to distinguish different objects, not to describe a specific order. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover non-exclusive inclusion. For example, a process, method, system, product, or apparatus that includes a series of steps or units is not limited to the listed steps or units, but may optionally include steps or units not listed, or may optionally include other steps or units inherent to these processes, methods, products, or apparatuses.

[0014] In this application, the reference to "embodiment" means that a specific feature, structure, or characteristic described in connection with an embodiment may be included in at least one embodiment of this application. The appearance of this phrase in various places throughout the specification does not necessarily refer to the same embodiment, nor is it a mutually exclusive, independent, or alternative embodiment. It will be explicitly and implicitly understood by those skilled in the art that the embodiments described in this application can be combined with other embodiments.

[0015] To better understand the method for leveling and fixing a pressure frame-type water sample plate with pressing ribs provided in this application embodiment, the existing leveling and fixing methods are briefly introduced below. Existing methods for leveling and fixing flexible membranes that support spots have significant shortcomings. On the one hand, traditional leveling and fixing methods often rely on manual operation or simple mechanical clamping, making it difficult to accurately control the flatness of the flexible membrane. This easily leads to problems such as local wrinkles and displacement of the flexible membrane, causing the spot morphology to shift during laser bombardment and severely affecting the accuracy of spectral detection. On the other hand, existing fixing methods lack quantitative detection of the flatness of the flexible membrane, failing to ensure that the flexible membrane meets the high-precision requirements of laser bombardment after each fixing, thereby reducing the overall detection reliability and stability of the laser liquid analysis system.

[0016] To address the aforementioned issues, this application provides a method for flattening and fixing a pressure frame-type water sample plate with pressure ribs. Through innovative pressure frame structural design and a multi-dimensional detection mechanism, it achieves precise flatness detection and stable fixing of the flexible membrane bearing the spots. This effectively solves the technical problems in existing flattening and fixing methods, such as abnormal laser bombardment spot morphology and spectral detection data deviation caused by flexible membrane wrinkles and displacement, thereby improving the accuracy and efficiency of laser water quality analysis.

[0017] The laser liquid analysis system may include a control platform and a laser water quality analyzer. The control platform is communicatively connected to at least one laser water quality analyzer. The laser water quality analyzer may include a water sample drying and spot formation device (which may include a laser bombardment unit, a water sample preparation unit, and a detection unit) and a pressure frame-type water sample plate leveling and fixing device with pressing ribs. Liquids that can be analyzed by the laser water quality analyzer include, but are not limited to, water, oil, and pharmaceutical solutions. The control platform performs data backup and subsequent application processing based on the liquid analysis results from the laser water quality analyzer.

[0018] Please see Figure 1A , Figure 1A A schematic diagram of a laser liquid analysis system is shown. Figure 1A The diagram shows a partial structural representation of a laser liquid analysis system. This system includes a laser bombardment unit 11, a water sample preparation unit 12, and a detection unit (not shown). The laser output from the laser bombardment unit 11 passes through its end 111 and bombards the spots on the water sample preparation area 121 of the water sample preparation unit 12. The detection unit uses a spectrometer. The laser water quality analyzer primarily detects heavy metals and non-metals in the spots formed after the liquid has been dried.

[0019] Understandably, a predetermined volume (e.g., 10 μL) of liquid is placed on the water sample preparation area 121 of the flexible membrane in the water sample preparation unit 12, and the liquid in the water sample preparation area 121 is dried to form spots. Before drying the liquid in the water sample preparation area 121 to form spots, a pressure frame-type water sample plate leveling and fixing device with pressure ribs (not shown in the figure) performs an initial flatness test and fixation on the flexible membrane (i.e., the flexible membrane where the water sample preparation area 121 is located) to ensure the water sample plate remains level, thus promoting more uniform spots after drying. After drying the liquid in the water sample preparation area 121 to form spots, the pressure frame-type water sample plate leveling and fixing device with pressure ribs performs a secondary flatness test and fixation on the flexible membrane carrying the water sample spots to ensure the flexible membrane is flat and stable, preventing spot morphology shift or flexible membrane displacement during subsequent laser bombardment by the laser bombardment unit 11, thereby ensuring accurate spectral detection data.

[0020] The laser generated by the laser in laser bombardment unit 11 contacts the dried residue of the liquid (i.e., the water sample spot mentioned later). The water sample spot forms plasma at high temperature, achieving a transition from a low-energy state to a high-energy state. However, the high-energy state is unstable and immediately returns to the ground state (i.e., the original state), at which point the energy is emitted in the form of light. The light emitted by each element is different. The spectrometer of the detection module monitors the light emitted by the laser bombardment unit 11 after bombarding the spot. The liquid results can be analyzed based on the data detected by the detection unit. In other words, the laser water quality analyzer can quickly detect multiple elements without consuming chemical reagents.

[0021] Optionally, the water sample preparation unit 12 includes a support platform, an unwinding mechanism, a winding structure, and a flexible membrane. The flexible membrane is unwound from the unwinding mechanism, passes over the support platform, and a predetermined volume (e.g., 10 μL) of liquid is placed in the water sample preparation area 121 on the support platform. Before the next test, the unwinding mechanism and the winding structure work together to wind the discarded flexible membrane onto the winding structure, and the unused flexible membrane is placed on the support platform so that a predetermined volume (e.g., 10 μL) of liquid can be placed in the water sample preparation area 121 on the support platform. The implementation of the unwinding mechanism and the winding structure can be selected from existing technologies and will not be described in detail here. Optionally, the flexible membrane is a zinc membrane.

[0022] It should be noted that in this application, the flexible membrane is the core carrier (such as a zinc membrane), and the water sample preparation area 121 is the area on the surface of the flexible membrane used for spot formation. The traditional concept of a water sample plate is replaced by the flexible membrane in this scenario, and the object of the leveling and fixing equipment changes from a water sample plate to a flexible membrane with spots. This design achieves automated continuous detection through the replacement of the flexible membrane's winding mechanism (unwinding and rewinding), while the leveling and fixing equipment ensures the morphological accuracy of the flexible membrane after each spot formation. In the embodiments described below, the term "water sample plate" is used, but its essence is still a flexible membrane with spots, and this does not constitute a limitation on this application.

[0023] Specifically, such as Figure 1B and Figure 1C As shown, Figure 1B An exemplary diagram illustrates a pressure frame with compression ribs that is not fully compressed. Figure 1CAn exemplary diagram illustrates the state of the pressurized frame with pressing ribs. It should be noted that in actual operation, after the unwinding mechanism of the water sample preparation unit lays the flexible zinc film onto the support platform, it can first be leveled and fixed using the pressurized frame with pressing ribs. This eliminates initial wrinkles and uneven tension caused during the zinc film laying process, ensuring the zinc film substrate is in a stable and flat state. Subsequently, water samples are dropped onto this leveled zinc film area and dried. Due to the heat during drying, the zinc film and surface spots may undergo slight morphological deformation. At this point, the pressurized frame leveling and fixing device with pressing ribs provided in this application can perform a second, precise leveling and fixing of the zinc film bearing the spots. That is, by acquiring the contour data of the spot area, parameter matching, region mapping, and pressure distribution are performed on the grid pressing ribs of the pressurized frame, and the zinc film and spots are applied pressure in a zoned manner to correct the local deformation caused by drying. Ultimately, this ensures the morphological accuracy of the zinc film and spots before laser bombardment, providing a stable detection carrier for subsequent laser bombardment and spectral detection.

[0024] In other words, the core improvement of this application focuses on the secondary flattening and fixing process, which can effectively solve the problem that the existing technology cannot cope with the deformation of the zinc film after drying by only one flattening, and can further improve the morphological stability of the carrier before laser detection.

[0025] Please see Figure 2 , Figure 2 This application provides a flowchart illustrating a method for leveling and fixing a pressure frame-type water sample plate with compression ribs, as described in an embodiment. Figure 2 As shown, the method for leveling and fixing the pressure frame type water sample plate with compression ribs includes: S10: Obtain the location and contour data of the water sample spots located on the water sample plate to be processed.

[0026] The water sample to be treated can be understood as the core carrier for subsequent leveling and fixing operations. Optionally, the water sample to be treated can refer to a substrate covered with a zinc film. Specifically, the surface of the zinc film can be a water sample spot that has been formed after water sample dripping and drying, ready for subsequent testing. The water sample spot can be a solid area formed on the zinc film after the water sample has been uniformly dried by microporous airflow. It is commonly a standardized circle with a diameter that can usually be controlled within 15mm ± 1mm. It may also have an irregular shape due to dripping method or environmental factors. It is the core target for further laser detection.

[0027] The water sample spot location contour data can be a dataset describing the location and edge morphology of the water sample spot on the zinc film. Optionally, the water sample spot location contour data for circular water sample spots may include data such as center coordinates and standard diameter, while the water sample spot location contour data for irregular water sample spots may include a series of continuous contour boundary coordinates, etc. This application does not impose any limitations on this.

[0028] Specifically, for standardized sample preparation processes, no additional scanning is required. The device can directly receive the center coordinates of the water sample spots transmitted by the overall control system and call the pre-stored standard diameter data of the water sample spots to quickly combine and obtain the aforementioned water sample spot position contour data. For irregular water sample spots, the thickness difference between the water sample spot and the zinc film can be identified by a laser thickness gauge, or the grayscale difference between the two can be captured by a miniature camera. After quickly scanning the zinc film surface, a complete set of water sample spot contour boundary coordinates can be generated, thereby ensuring that the water sample spot position contour data can accurately match the actual shape of the water sample spot. This application does not impose any limitations on this.

[0029] S20: Based on the water sample spot position contour data, perform zoned pressure adaptation processing on the pressure frame with pressing ribs of the water sample plate to be treated to obtain zoned pressure configuration data.

[0030] The pressure frame with ribs is a key component for fixing the zinc film. Optionally, the pressure frame is typically made of 316 stainless steel, and the inner frame size is usually 140mm × 140mm. The inner side of the pressure frame with ribs may have integrally formed mesh-like fine strips, i.e., pressure ribs, which are mostly made of 304 stainless steel, a material that combines hardness and stability. The width of a single pressure rib is typically 0.2mm ± 0.02mm, and the height is typically 0.3mm ± 0.03mm. The mesh is commonly distributed in a 10×10 or 20×20 pattern, which is not limited in this application. It should be noted that 316 stainless steel is used for the pressure frame to improve corrosion resistance, while 304 stainless steel is used for the pressure ribs to balance hardness and cost; this is not limited in this application either.

[0031] The zoned pressure adaptation process can be understood as dividing the grid pressing ribs of the pressurizing frame into different regions based on the position and contour of the water sample spot, and matching corresponding pressure parameters to different regions. The core of this zoned pressure adaptation process is distinguishing between regions requiring pressurization and regions requiring only contact (i.e., no pressurization). The zoned pressure configuration data can be the final result of the above-mentioned zoned pressure adaptation process, that is, recording the specific pressure value of each pressing rib grid unit in a correspondence format. Optionally, multiple independently controlled pneumatic cylinders can be integrated on the pressurizing frame, and each grid pressing rib can correspond to a pressure valve with a pressure accuracy of ±0.01 MPa; this application does not impose any limitations on this.

[0032] Specifically, the complete grid parameters of the pressurization frame can be input, including the number of rows and columns of the grid, the size of a single pressing rib, and the four corner coordinates of each grid unit (also known as a network pressing rib). These coordinates should be consistent with the platform coordinates of the water sample plate to be treated. Furthermore, the water sample spot position contour data obtained in the aforementioned steps should be accurately matched with the coordinates of the grid units. This allows grid units covered by water sample spots to be marked as non-pressurized units, and uncovered grid units to be marked as pressurized units. Pressure parameters can then be assigned to the two types of grid units. Pressurized grid units can be assigned a fixed pressure of 0.8MPa±0.05MPa, while non-pressurized grid units can retain only the minimum pressure required for adhesion, typically ≤0.01MPa. After integrating the above parameters, the above-mentioned zoned pressure configuration data can be obtained. This application does not impose any restrictions on this.

[0033] It should be noted that, based on the material strength and toughness characteristics of flexible membranes (such as zinc films), and the need to protect the dried and formed spots from damage during pressure application, multiple comparative experiments were conducted to verify the smoothness repair effect and spot integrity of the flexible membrane under different pressure values. The optimal pressure value was determined to be 0.8 MPa. This pressure value can accurately correct local deformation of the flexible membrane after drying and effectively prevent spot damage and morphological displacement, ensuring the effectiveness of secondary smoothing and fixing and the integrity of the detection carrier. Optionally, a pressure value of 0.8 MPa does not constitute a limitation on this application.

[0034] S30: Control the pressurizing frame to apply pressure and fix the water sample to be treated according to the partition pressure configuration data, so as to obtain a partition pressurized and fixed water sample.

[0035] Pressure fixing can be understood as a combined action of lowering and attaching the pressure frame while applying targeted force. It's important to note that this pressure fixing operation must ensure the zinc film adheres tightly to the sample stage to guarantee overall stability, while strictly avoiding any squeezing damage to the water sample spots. Partial pressure fixing of the water sample plate can be understood as the intermediate state of the water sample plate after the above pressure fixing actions are completed. At this point, the blank areas of the zinc film are firmly pressed down by the pressure ribs, while the spot areas are only lightly supported by the pressure ribs, achieving a preliminary flatness.

[0036] Specifically, the main controller can send execution commands to the pneumatic cylinders and zoned pressure valves of the pressurization frame. For example, it can first control the overall smooth descent of the pressurization frame, so that all the grid pressing ribs can simultaneously and lightly adhere to the zinc film surface, ensuring that the entire area of ​​the zinc film is supported; then, it can start precise force application according to the zoned pressure configuration data, that is, only apply the preset pressure to the pressing ribs marked as pressure application units, and keep the pressing ribs marked as non-pressure application units in a light-adhering state without applying additional pressure; after the pressure stabilizes, the zinc film can be firmly fixed to the sample stage, thus obtaining the above-mentioned zoned pressure-fixed water sample plate.

[0037] S40: Perform multi-dimensional flatness redundancy detection processing on the partitioned pressure fixed water sample plate to obtain multi-dimensional flatness detection data.

[0038] Multi-dimensional flatness redundancy detection processing can be understood as a process of cross-validation through detection in two different dimensions to determine the flatness of a water sample. The core processing of this multi-dimensional flatness redundancy detection processing can include zinc film levelness detection and water sample spot thickness distribution detection. This dual detection method avoids the blind spots inherent in single-dimensional detection. Multi-dimensional flatness detection data can be comprehensive data integrating the results of the two types of detections. This multi-dimensional flatness detection data can include core indicators reflecting the flatness status and can also retain the original detection data, thus providing a complete basis for subsequent flatness judgment and adjustment.

[0039] Specifically, for the detection of the levelness of zinc film, an electronic level with an accuracy of 0.01° can be used to collect the tilt angle data of multiple sets of reference points on the water sample plate (each set may include reference points in the four corner areas and reference points in the center area), to calculate the average tilt angle of each set of reference points, and then calculate the maximum tilt angle deviation by the difference between the tilt angle of each reference point and the average tilt angle. Finally, the maximum tilt angle deviation is converted into a flatness error in μm / m². This application does not impose any restrictions on this.

[0040] Specifically, for detecting the thickness distribution of water sample spots, a laser thickness gauge can be used to perform a grid-like scan of the entire water sample spot area at a density of 15×15 detection points to collect the thickness value of each detection point. Subsequently, the average thickness and standard deviation of all detection points can be calculated, and the relative standard deviation (RSD) of the water sample spot thickness distribution can be further calculated. Optionally, to improve the anti-interference capability of the detection results, multiple sets of data can be collected, i.e., the above grid-like scanning process can be repeated three or more times to collect the thickness value of each detection point in each set, and then the average thickness, standard deviation, and RSD can be calculated multiple times. The average or maximum value of the multiple calculation results can be taken as the final data. This application does not impose any restrictions on this. Furthermore, integrating the detection data from the above two dimensions (i.e., zinc film leveling detection and water sample spot thickness distribution detection) and core indicators can form complete multi-dimensional flatness detection data.

[0041] S50: Adjust the partitioned pressure-fixed water sample plate according to the multi-dimensional flatness detection data to obtain the target flatness-fixed water sample plate.

[0042] The adjustment process can be understood as the process of correcting the unevenness of the water sample plate based on the aforementioned multi-dimensional flatness detection data. It should be noted that the focus of the adjustment process can be on the horizontal tilt angle of the zinc film and the height of local areas, thereby compensating for minor deviations after initial pressure fixing. The target flat and fixed water sample plate can be a water sample plate that fully meets the standards after the adjustment process. It is understood that the flatness of the target flat and fixed water sample plate must meet the core requirements of laser bombardment. Specifically, the target flat and fixed water sample plate can exhibit a flatness error ≤ 5 μm / m² and an RSD of the water sample spot thickness distribution ≤ 5%, but this application does not impose any limitations on this.

[0043] Specifically, thresholds can be preset, such as a flatness error threshold and a water sample spot thickness distribution RSD threshold. If the multi-dimensional flatness test data simultaneously meet the preset threshold standards, the aforementioned zoned pressure-fixed water sample plate can be directly used as the target flatness-fixed water sample plate. If the multi-dimensional flatness test data does not meet the standards, further precise fine-tuning can be performed (e.g., using a piezoelectric actuator). For example, if the flatness error exceeds the preset flatness error threshold, the tilt angle of the sample stage can be corrected in a fine-tuning step of 0.1 μm / step; if the RSD of the water sample spot thickness distribution exceeds the water sample spot thickness distribution RSD threshold, the platform height of the corresponding area can be compensated in a step of 0.05 μm / step. This application does not limit this. Optionally, after adjustment, multi-dimensional flatness redundancy testing can be performed on the water sample plate again, repeatedly looping until the test data meets the standards, ultimately obtaining the aforementioned target flatness-fixed water sample plate. This application does not limit this.

[0044] In this embodiment, the partitioned pressure adaptation design ensures that the spot area is only lightly supported by the pressure ribs, precisely protecting the water sample spots and avoiding detection errors. This completely eliminates the spot deformation and damage problems easily caused by traditional overall pressure methods. The complete spot morphology is the foundation of laser spectral detection, and this design significantly improves the accuracy of subsequent detection results. Macroscopic leveling detection can solve the problem of overall tilting of the water sample plate, while microscopic spot thickness distribution detection can identify localized minute deformations that are difficult for a level to detect. Dual detection further ensures flatness accuracy and meets high-precision requirements. The redundant verification formed by both methods ensures that the flatness accuracy of the water sample plate remains stable within a high standard range, meeting the requirement of uniform energy absorption during laser bombardment. Core requirements: This application is compatible with rapid parameter matching for standardized circular spots and can also adapt to irregular spots by acquiring data through scanning, without replacing core components such as the pressure frame. It can meet the testing needs of different water qualities and sample preparation scenarios. From spot positioning and pressure adaptation to pressure application and fixing, and testing adjustment, no manual intervention is required throughout the entire process. The fine-tuning logic is simple and can quickly correct deviations, avoiding repeated rework due to flatness issues and effectively improving the overall testing efficiency. The pressure rib adopts an integrated grid design, eliminating the need for complex independent drive components. Common equipment such as laser thickness gauges and electronic levels can be reused in the testing process, eliminating the need to purchase additional expensive hardware. It can be implemented on the basis of existing equipment, making it easy to implement and conducive to large-scale promotion.

[0045] In this embodiment, by acquiring the position contour data of water sample spots on the water sample plate to be treated, the pressure of the pressure frame with pressing ribs on the water sample plate to be treated can be adapted to different zones based on the water sample spot position contour data to obtain zone pressure configuration data. This allows the pressure frame to be controlled to apply pressure and fix the water sample plate to be treated according to the zone pressure configuration data, resulting in a zone-pressure-fixed water sample plate. Furthermore, the zone-pressure-fixed water sample plate is subjected to multi-dimensional flatness redundancy detection processing to obtain multi-dimensional flatness detection data. This data can then be used to adjust the zone-pressure-fixed water sample plate to obtain a target flatness-fixed water sample plate. This improves the accuracy and effectiveness of flattening and fixing water sample plates with pressing ribs.

[0046] In one possible implementation, the partitioned pressure adaptation process can be performed by progressively acquiring parameters, establishing mappings, marking attributes, and allocating pressure. For example, first, the parameter data of the grid pressure ribs of the pressurizing frame with pressure ribs is acquired; then, the correspondence between the pressure ribs and the spot regions is obtained through region mapping; finally, the pressure attributes of each grid pressure rib are marked, thereby allocating pressure values ​​to obtain partitioned pressure configuration data. Specifically, a method for performing partitioned pressure adaptation processing on the pressurizing frame with pressure ribs of the water sample plate to be processed based on the water sample spot position contour data to obtain partitioned pressure configuration data may include: A1. Obtain the grid pressing rib parameter data of the pressure frame with pressing ribs; A2. Perform region mapping processing based on the grid pressing rib parameter data and the water sample spot location contour data to obtain the pressing rib-spot region correspondence data; A3. Based on the corresponding relationship data between the pressing rib and the spot area, mark the pressure attribute of each grid pressing rib of the pressing frame with pressing ribs to obtain the grid pressing rib pressure attribute mark data; A4. Assign a pressure value to each of the mesh pressing ribs according to the pressure attribute marking data of the mesh pressing ribs to obtain the zonal pressure configuration data.

[0047] The pressure frame with compression ribs can be a core component for fixing the water sample to be treated. Its inner side has grid-like protruding compression ribs, enabling precise application of force in designated areas. The grid compression rib parameter data can be basic information describing the grid-like compression ribs on the pressure frame. Optionally, this grid compression rib parameter data may include the number of rows and columns of the grid, the width and height of a single compression rib, the coordinates of the four corners of each grid cell, etc., which are not limited in this application. It is understood that the coordinate system of the grid cell can be unified with the platform coordinate system of the water sample to facilitate subsequent calculations and processing.

[0048] Region mapping can be understood as the process of matching the location contour data of water sample spots with the coordinates of the grid suppressor ribs to clarify the correspondence between the two. The suppressor rib-spot region correspondence data can be the associated data recording whether each grid suppressor rib covers a water sample spot. In essence, this suppressor rib-spot region correspondence data reflects the positional relationship between the grid cells and the water sample spots.

[0049] Specifically, in this embodiment, when performing region mapping processing, the acquired water sample spot location contour data can be compared one by one with the coordinate data of the grid pressing ribs to determine whether each grid pressing rib unit falls completely or mainly within the water sample spot area, thereby generating pressing rib-spot area correspondence data to clearly define the grid units that are associated with and not associated with the spots.

[0050] The pressure attribute labeling can be understood as the operation of marking each mesh compression reinforcement with the "pressured" or "non-pressured" attribute. The mesh compression reinforcement pressure attribute labeling data can be the data that clearly divides the pressure attributes of each mesh after the pressure attribute labeling is completed.

[0051] Specifically, in this embodiment, pressure attribute marking can be carried out based on the correspondence data between the pressing ribs and the spot areas. For example, the grid pressing ribs associated with the water sample spot are marked as "non-pressurized" to avoid damaging the spot with pressure; the grid pressing ribs corresponding to the blank areas not associated with the water sample spot are marked as "pressurized" to ensure that the water sample plate can be firmly fixed, thereby obtaining the grid pressing rib pressure attribute marking data.

[0052] Furthermore, after assigning specific pressure values ​​to each grid pressing rib based on the grid pressing rib pressure attribute marking data, partition pressure configuration data can be formed, which can serve as the basis for applying force to the pressurization frame.

[0053] Specifically, in this embodiment, a pressure value is assigned to each grid pressing rib according to the pressure attribute marking result. Typically, a fixed pressure of 0.8MPa±0.05MPa can be assigned to grid cells with the "pressure applied" attribute, and a bonding pressure of ≤0.01MPa can be assigned to grid cells with the "non-pressure applied" attribute. After further integrating the above pressure allocation information, the partition pressure configuration data can be obtained. It should be noted that the core purpose of setting an extremely small bonding pressure of ≤0.01MPa for grid cells with the "non-pressure applied" attribute is to ensure that the flexible membrane (such as zinc membrane) is tightly bonded to the support platform as a whole, avoiding local suspension, curling, etc., and at the same time, it will not cause additional pressure damage to the spots. This application does not limit this.

[0054] Optionally, machine learning models can be used to dynamically predict partitioned pressure configuration data, such as optimizing parameters based on historical testing data. Specifically, a training dataset can be constructed and a machine learning prediction model trained by collecting material parameters, post-drying deformation characteristics, spot location contour data, and corresponding optimal partitioned pressure configuration results of multiple sets of flexible membranes (such as zinc membranes). When actually performing partitioned pressure allocation, the currently detected flexible membrane material information, spot region contour data, drying process parameters, etc., can be input into the trained model, and the model can dynamically output suitable mesh pressing rib pressure allocation suggestions. Furthermore, the system can record the actual flatness effect of this pressure configuration in real time (such as flatness error and spot integrity) and feed this data back to the model for iterative optimization, continuously improving the accuracy and adaptability of partitioned pressure configuration and reducing manual debugging costs.

[0055] Optionally, the pressure value and detection frequency can be dynamically adjusted according to ambient temperature and humidity. Since changes in ambient temperature affect the toughness of the flexible membrane material (e.g., the membrane becomes brittle at low temperatures and softer at high temperatures), and changes in humidity may cause micro-deformation on the membrane surface, both of which affect the flatness and fixation effect; therefore, the system can integrate temperature and humidity sensors to collect environmental data in real time. When the temperature exceeds the preset range (e.g., 20℃±5℃) or the humidity deviates from the standard range (e.g., 40%RH±10%RH), the system can automatically fine-tune the pressure value of the "pressure" attribute grid unit (e.g., for every 5℃ increase in temperature, the pressure value is reduced by 0.02MPa to ensure no damage to the spots), and further appropriately increase the flatness detection frequency (e.g., from once per batch to twice per batch, performed before and after pressure application). By dynamically adapting to environmental changes, the system ensures the stability of flatness and fixation and the reliability of detection data under different working conditions.

[0056] In this embodiment, the partitioned pressure adaptation process can improve the accuracy of water sample plate flatness and fixation from the root of pressurization, effectively avoiding the drawbacks of the traditional overall pressurization mode. For example, by obtaining the parameter data of the grid pressing ribs of the pressurization frame with pressing ribs, the basic information of the pressurization structure can be clearly defined. By mapping the parameter data with the water sample spot position contour data, the correspondence between the pressing ribs and the spot area can be clearly identified, thereby avoiding misalignment between the pressurization position and the spot area. By marking the pressure attribute of each grid pressing rib, the blank area to be pressurized and the spot area to be avoided can be accurately distinguished. Based on this, the pressure value can be allocated to obtain the partitioned pressure configuration data. This can not only prevent the overall pressurization from squeezing and deforming the water sample spots, ensuring the original shape of the spots on the water sample plate, but also allow the pressurization force to be accurately applied to the effective fixation area, avoiding insufficient local pressure leading to loose fixation or excessive pressure causing deformation of the water sample plate, providing a stable basic sample for subsequent flatness detection.

[0057] In one possible implementation, when performing multi-dimensional flatness redundancy detection processing, flatness redundancy detection can be achieved through dual-dimensional detection and data fusion processing. For example, an electronic level can be used to detect the levelness of the water sample and calculate the level deviation, and a laser thickness gauge can be used to detect the thickness distribution of the water sample spots and obtain the thickness distribution deviation. Then, the data from the above different dimensions can be fused to obtain multi-dimensional flatness detection data. The dual-dimensional detection can include water sample level detection and water sample spot thickness distribution detection, which is not limited in this application. Specifically, a method for performing multi-dimensional flatness redundancy detection processing on the partitioned pressure-fixed water sample to obtain multi-dimensional flatness detection data can include: B1. Based on an electronic level, the horizontal state of the water sample plate fixed by the partition pressure is detected and processed to obtain a set of water sample plate horizontal detection data; B2. Based on the set of water sample level detection data, calculate the horizontal deviation of the zoned pressure-fixed water sample to obtain the water sample level deviation data; B3. Based on a laser thickness gauge, the thickness distribution of water sample spots is detected and processed on the partitioned pressure-fixed water sample plate to obtain a set of water sample spot thickness detection data. B4. Based on the set of water sample spot thickness detection data, calculate the thickness distribution deviation of the water sample spots to obtain the water sample spot thickness distribution deviation data; B5. The horizontal deviation data of the water sample plate and the thickness distribution deviation data of the water sample spots are fused to obtain multi-dimensional flatness detection data.

[0058] The multi-dimensional flatness redundancy detection process can be understood as a process of cross-verifying the flatness of a water sample through multiple independent detection dimensions. Optionally, this application uses the example of multi-dimensional flatness redundancy detection process specifically including horizontal state detection and water sample spot thickness distribution detection, which does not constitute a limitation on this application. Optionally, the multi-dimensional flatness redundancy detection process of this application may also include other detections that help to flatten and fix the water sample, and this application does not impose any restrictions on this.

[0059] An electronic level can be a high-precision device for detecting the horizontal tilt angle of a water sample, typically with an accuracy of 0.01°, capturing the macroscopic tilt state of the water sample. The water sample level detection dataset can include one or more water sample level detection data sets, which can be raw detection data collected by the electronic level to describe the tilt state of the water sample. Optionally, the water sample level detection data can include the original tilt angle values ​​and corresponding coordinates of multiple reference points, etc., which is not limited in this application.

[0060] Horizontal deviation refers to the degree of deviation between the actual and ideal horizontal state of a water sample, which can be calculated using tilt angle-related data. The horizontal deviation data for a water sample can be structured data that integrates core indicators such as average tilt angle, maximum tilt angle deviation, and flatness error.

[0061] Specifically, an electronic level can be used to test the levelness of a water sample plate fixed under pressure in designated areas. Typically, five reference points are selected: the four corners and the center of the water sample plate. The inclination angle data at each reference point is collected, and these raw data are integrated to form a water sample plate level test data set. Furthermore, based on this water sample plate level test data set, the level deviation can be calculated, and the water sample plate level deviation data, including core indicators such as flatness error, can also be calculated. For details, please refer to the detailed description in the following embodiments; this application will not repeat them here.

[0062] A laser thickness gauge can be used to accurately measure the thickness of water sample spots, with an accuracy of ±0.1 μm, and can capture the microscopic thickness differences of water sample spots. The water sample spot thickness detection dataset can include one or more water sample spot thickness detection data points. This data can be the raw data collected after the laser thickness gauge scans the water sample spot, used to describe the microscopic thickness of the water sample spot. This water sample spot thickness detection data can include the thickness value and corresponding coordinates of each detection point, etc., and this application does not impose any limitations on this.

[0063] Thickness distribution deviation can be considered as the dispersion of thickness in different regions of a water sample spot, reflecting the uniformity of the spot's thickness. Water sample spot thickness distribution deviation data can be structured data that integrates indicators such as average thickness, standard deviation, and relative standard deviation.

[0064] Specifically, a laser thickness gauge can be activated to perform a grid-like scan of the entire water sample spot at a preset detection point density (e.g., 15×15 points) to collect the thickness value of each detection point. All raw thickness data can be aggregated to form the aforementioned water sample spot thickness detection data set. Further, based on this water sample spot thickness detection data set, the thickness distribution deviation can be calculated, and through statistical calculations, water sample spot thickness distribution deviation data including indicators such as relative standard deviation can be obtained. For details, please refer to the detailed description in the following embodiments; this application will not repeat them here.

[0065] Fusion processing can be the operation of integrating two types of data, horizontal inspection and thickness inspection, into a unified dataset. Multi-dimensional flatness inspection data can be the comprehensive data formed after the above fusion processing. This multi-dimensional flatness inspection data can cover the core indicators and key raw data of the two types of inspections, providing a complete basis for subsequent adjustments.

[0066] Specifically, during the fusion process, the horizontal deviation data of the water sample and the thickness distribution deviation data of the water sample spots can be correlated and integrated, such as by adding information like the detection timestamp and water sample identification, so as to form complete multi-dimensional flatness detection data to fully reflect the flatness of the water sample.

[0067] In this embodiment, multi-dimensional flatness redundancy detection solves the problem that a single detection dimension is insufficient to comprehensively evaluate the flatness of a water sample. This significantly improves the completeness and reliability of the flatness detection results. For example, by using an electronic level to collect water sample level detection data and calculate the level deviation, the overall macroscopic tilt state of the water sample can be controlled, thus enabling timely detection of overall non-level placement issues. By using a laser thickness gauge to obtain water sample spot thickness detection data and calculate the thickness distribution deviation, microscopic unevenness defects such as small protrusions or depressions in the water sample can be accurately captured. These defects are often difficult to detect and identify using a level. By fusing the two types of data to obtain multi-dimensional flatness detection data, both the overall macroscopic flatness of the water sample and the local microscopic flatness details can be covered, forming detection redundancy. This avoids possible misjudgments and omissions that may occur with a single detection dimension, providing a comprehensive and rigorous basis for judging whether the flatness and fixation of the water sample meet the standards.

[0068] In one possible implementation, when calculating the horizontal deviation of the partitioned pressure-fixed water sample plate, core horizontal detection indicators can be calculated step by step around the original tilt angle data. For example, multiple sets of benchmark point detection data subsets can be extracted from the water sample plate horizontal detection data set. After calculating the average tilt angle, maximum tilt angle deviation, and flatness error, the relevant data of these multiple sets of benchmark point detection data subsets can be integrated to determine the water sample plate horizontal deviation data. Specifically, a method for calculating the horizontal deviation of the partitioned pressure-fixed water sample plate based on the water sample plate horizontal detection data set to obtain the water sample plate horizontal deviation data may include: C1. Obtain a subset of the first benchmark point detection data from the water sample horizontal detection data set; C2. Calculate the average dip angle based on the dip angle data of each first reference point in the first reference point detection data subset to obtain the first average dip angle; C3. Calculate the maximum tilt angle deviation based on the first average tilt angle and the tilt angle data of each first reference point to obtain the first maximum tilt angle deviation data; C4. Perform flatness error conversion processing based on the first maximum tilt angle deviation data to obtain the first flatness error data; C5. Determine the horizontal deviation data of the water sample based on the first flatness error data, the first maximum tilt angle deviation data, the first average tilt angle, and the tilt angle data of each first reference point.

[0069] The first subset of benchmark point detection data can be a selection of benchmark point data used for calculation from the aforementioned set. This first subset typically includes a representative set of benchmark points, such as benchmark point detection data within the four corner areas and benchmark point detection data within the central area; this application does not impose any limitations on this. Optionally, this application uses the first subset of benchmark point detection data from the water sample level detection data set as an example for illustration, which does not constitute a limitation on this application. Optionally, a second subset of benchmark point detection data, a third subset of benchmark point detection data, etc., can also be obtained from the water sample level detection data set for subsequent calculation processing; this application does not impose any limitations on this.

[0070] The first reference point can be a reference detection point in the subset of first reference point detection data, and the tilt angle data of the first reference point can be the specific tilt angle value collected by an electronic level at that point. It is understood that the subset of first reference point detection data can include one or more first reference points, that is, it can include one or more reference detection points; this application does not limit this. The first average tilt angle can be the arithmetic mean obtained by calculating the tilt angle data of all first reference points in the subset of first reference point detection data, and can reflect the overall tilt trend of the water sample.

[0071] The first maximum tilt deviation can be the maximum value selected after taking the absolute value of the difference between the tilt angle data of each first reference point and the first average tilt angle. It can reflect the maximum degree of deviation of a single reference point from the overall tilt trend.

[0072] The flatness error conversion process can be understood as converting the first maximum tilt angle deviation into an industry-standard linear height difference index. The first flatness error data, after conversion, is a quantitative index in μm / m, which can intuitively reflect the macroscopic flatness of the water sample.

[0073] Optionally, taking a subset of the first benchmark point detection data containing five benchmark points as an example, the process of converting the flatness error based on the first maximum tilt angle deviation data to obtain the first flatness error data can be seen in the following formula: δ=max{|α1-(α1+α2+α3+α4+α5) / 5|,|α2-(α1+α2+α3+α4+α5) / 5|,..., |α5-(α1+α2+α3+α4+α5) / 5|}×K Where δ represents the first flatness error data, which can be the deviation per unit length, i.e., the deviation in micrometers within a 1m span, used to visually quantify the macroscopic flatness of the water sample; max{} represents a function that takes the maximum value, i.e., selects the largest value from multiple calculation results within the parentheses, used here to calculate the first maximum tilt angle deviation data, which is to extract the largest deviation value from the absolute values ​​of the deviations of the five reference points relative to the average tilt angle (i.e., (α1+α2+α3+α4+α5) / 5); || represents the operation of taking the absolute value, the purpose of which is to eliminate the influence of the positive and negative signs of the deviation; α1 can represent the tilt angle data of the first reference point in the first reference point detection data subset, such as called the first tilt angle data; α2 can represent the tilt angle of the second reference point in the first reference point detection data subset. The data, such as the second dip angle data; α3 can represent the dip angle data of the third reference point in the first reference point detection data subset, such as the third dip angle data; α4 can represent the dip angle data of the fourth reference point in the first reference point detection data subset, such as the fourth dip angle data; α5 can represent the dip angle data of the fifth reference point in the first reference point detection data subset, such as the fifth dip angle data; (α1+α2+α3+α4+α5) / 5 can represent the first average dip angle (i.e., α_avg); K can represent the unit conversion factor (which can be taken as 1000 after experimental calibration). Combining the small angle approximation principle (tanθ≈θ, after θ is converted to radians), the first maximum dip angle deviation (°) is converted into a linear deviation in μm / m units to ensure that the conversion result meets the engineering testing standards.

[0074] Furthermore, the water sample level deviation data can be understood as structured data formed by integrating core data such as the first flatness error data, the first maximum tilt angle deviation data, and the first average tilt angle, as well as the original tilt angle data (i.e., the tilt angle data of each first reference point).

[0075] In this embodiment, by extracting a subset of first benchmark point detection data from the horizontal detection data set, key detection points can be focused on in each calculation, reducing redundant data interference. By calculating the first average tilt angle to establish a horizontal evaluation benchmark, and then comparing the tilt angles of each benchmark point with the average tilt angle to obtain the first maximum tilt angle deviation data, the extreme degree of horizontal tilt can be intuitively reflected. Converting the maximum tilt angle deviation into flatness error data can transform the tilt angle, an angular indicator, into a quantitative error indicator that is easier to use for flatness adjustment. Further integrating various data to determine the horizontal deviation data of the water sample not only transforms the assessment of the horizontal state from a vague judgment to a precise quantification, but also clearly locates the area with the most severe tilt, facilitating subsequent targeted adjustments to the pressurization position and intensity, and significantly improving the efficiency and accuracy of flatness correction.

[0076] In one possible implementation, when calculating the thickness distribution deviation of the water sample spot, multiple subsets of detection data from the water sample spot thickness detection data set can be extracted to further calculate the average thickness, standard deviation, and relative standard deviation. Then, the relevant data from each subset of first detection data are integrated to determine the water sample spot thickness distribution deviation data. Specifically, a method for calculating the thickness distribution deviation of the water sample spot based on the water sample spot thickness detection data set to obtain water sample spot thickness distribution deviation data may include: D1. Obtain a subset of detection data for the first detection point from the set of water sample spot thickness detection data; D2. Calculate the average thickness based on the thickness value of each first detection point in the first detection point data subset to obtain the first average thickness; D3. Calculate the standard deviation based on the thickness value of each first detection point in the subset of the first detection point detection data to obtain the first standard deviation; D4. Calculate the relative standard deviation based on the first average thickness and the first standard deviation to obtain the first relative standard deviation; D5. Determine the water sample spot thickness distribution deviation data based on the first relative standard deviation, the first standard deviation, the first average thickness, and the thickness value of each first detection point.

[0077] The first subset of detection data can be understood as a portion of the detection data selected from the aforementioned set for calculation. This first subset of detection data is typically a set of grid-like detection data covering the water sample spot. It is understood that this application uses the first subset of detection data from the water sample spot thickness detection data set as an example for illustration and does not constitute a limitation on this application. Optionally, a second subset of detection data, a third subset of detection data, etc., can also be obtained from the water sample spot thickness detection data set for subsequent calculations; this application does not impose any restrictions on this.

[0078] The first detection point can be a single thickness detection point within the subset of data collected by the laser thickness gauge at that detection point. The thickness value of the first detection point can be the specific thickness of the water sample spot acquired by the laser thickness gauge at that detection point. It is understood that the subset of data collected by the first detection point can include one or more first detection points, i.e., it can include one or more thickness detection points, and this application does not impose any limitations on this.

[0079] The first average thickness can be the arithmetic mean of the thickness values ​​of all the first detection points in the subset of the first detection point's detection data, reflecting the overall thickness level of the spot. The first standard deviation can be a statistical measure of the degree of deviation of the thickness value of each detection point from the first average thickness, reflecting the dispersion of the thickness data.

[0080] The calculation of relative standard deviation can be understood as the process of converting the ratio of the first standard deviation to the first average thickness into a percentage index. By calculating the relative standard deviation, the influence of the average thickness can be eliminated, and the calculated result can accurately reflect the uniformity of the thickness distribution. The first relative standard deviation can be the relative standard deviation of the set of test data (i.e., the subset of test data from the first test point) calculated based on the first standard deviation and the first average thickness. The water sample spot thickness distribution deviation data can be understood as structured data formed after integrating the above data, which can be used to evaluate the uniformity of spot thickness.

[0081] Optionally, the process of calculating the relative standard deviation based on the first average thickness and the first standard deviation to obtain the first relative standard deviation can be found in the following formula: RSD1=[√(Σ1 n (t j -(t1+t2+…+t n ) / n)² / (n-1))] / [(t1+t2+…+t n ) / n]×100% RSD1 represents the first relative standard deviation, which can be expressed as a percentage (%) and can be used to quantify the uniformity of the thickness distribution of water sample spots; t j t1 can represent the measured thickness value of the j-th detection point in the subset of detection data of the first detection point, in μm; t2 can represent the measured thickness value of the first detection point in the subset of detection data of the first detection point, in μm; t3 can represent the measured thickness value of the second detection point in the subset of detection data of the first detection point; t4 can represent the measured thickness value of the first detection point in the subset of detection data of the first detection point. n This can represent the measured thickness value of the nth detection point in the subset of detection data from the first detection point. For example, if n=225, then the subset of detection data from the first detection point includes 15×15 detection points; (t1+t2+…+t n ) / n can represent the first average thickness (i.e., t_avg1), which is the arithmetic mean of the thickness values ​​of n detection points; Σ1 n (t j -t_avg1)² can represent calculating the sum of squares of the differences between the thickness value at each detection point and the first average thickness; √ can represent performing the square root operation, which is the key operation for calculating the first standard deviation (σ1); to fully calculate σ1, the thickness value t at each detection point must first be calculated. jThe difference between the average thickness and the first average thickness is squared and summed to obtain the sum of squares of the differences. Then, the sum of squares is divided by (n-1), and finally the square root of the result is taken. Optionally, the standard deviation of the sample is calculated by dividing by (n-1). This method is more in line with the statistical logic of the detection data and can improve the representativeness of the data to the overall spot thickness distribution. It does not constitute a limitation on this application. σ1 / t_avg1×100% can represent the process of converting the ratio of the standard deviation to the average thickness into a percentage, that is, obtaining the first relative standard deviation. This can eliminate the influence of the average thickness on the degree of dispersion and intuitively reflect the thickness uniformity.

[0082] For example, the pressure frame-type leveling and fixing device with pressing ribs of this application can be made of 316 stainless steel for the frame. The inner frame size can be designed to be 140×140mm, and 304 stainless steel pressing ribs are embedded in the frame. The cross-sectional dimensions of the pressing ribs can be 0.2mm (width) × 0.3mm (height), forming a uniform 10×10 grid structure, which can achieve precise zonal pressure application to the zinc film. The pressure frame-type leveling and fixing device with pressing ribs can be driven by a pneumatic cylinder to apply a downward pressure of 0.8MPa±0.05MPa to the grid cells with the "pressing" attribute. At the same time, it can be used in conjunction with the lifting mechanism of the support platform to form a bidirectional clamping force to tightly press the zinc film. To ensure the uniformity of pressure transmission, the contact surface between the pressing ribs and the zinc film can be precision polished, and the surface roughness can be controlled within Ra0.2μm, so that the pressure distribution uniformity of the pressure application area exceeds 95%. In addition, the pressure frame-type leveling and fixing equipment with pressing ribs integrates a high-precision electronic level (measurement accuracy up to 0.01°) to monitor the horizontal status of the zinc film in real time. When the tilt exceeds the tolerance, the tilt angle of the support platform can be automatically finely adjusted by the piezoelectric actuator to control the flatness error of the zinc film within 5μm / m. This effectively corrects the local deformation of the zinc film after drying, ensures the stability of the spot morphology during laser bombardment, and provides a reliable guarantee for the accuracy of subsequent spectral detection.

[0083] In this embodiment, the core detection data is locked by extracting a subset of the detection data from the first detection point. The baseline value of the spot thickness can be determined by calculating the first average thickness. The first standard deviation is calculated by combining the thickness values ​​of each detection point, which can initially reflect the dispersion of the thickness data. The first relative standard deviation calculated by the average thickness and standard deviation can eliminate the influence of the thickness baseline value on the evaluation of dispersion, and more objectively reflect the uniformity of the spot thickness distribution. By integrating various data to determine the thickness distribution deviation data, the original thickness information of each detection point can be completely preserved. At the same time, the thickness distribution can be presented in a progressive manner through multiple quantitative indicators. This can accurately determine whether the spot has local thickness anomalies due to improper fixed pressure, and can also provide accurate data support for subsequent fine-tuning of the zone pressure to optimize the flatness, ensuring that the local flatness of the water sample meets the detection requirements.

[0084] For examples consistent with the above embodiments, please refer to... Figure 3 , Figure 3 This is a schematic diagram of the structure of a terminal provided in an embodiment of this application, such as... Figure 3 As shown, the system includes a processor, an input device, an output device, and a memory, which are interconnected. The memory stores a computer program, which includes program instructions. The processor is configured to invoke the program instructions. The program includes instructions for performing the following steps. Obtain the location and contour data of the water sample spots located on the water sample plate to be processed; Based on the water sample spot location contour data, the pressure frame with pressing ribs of the water sample plate to be treated is subjected to partition pressure adaptation processing to obtain partition pressure configuration data. The pressurizing frame is controlled to apply pressure and fix the water sample plate to be treated according to the partition pressure configuration data, so as to obtain a partition pressurized and fixed water sample plate; Multi-dimensional flatness redundancy detection processing was performed on the partitioned pressurized water sample plate to obtain multi-dimensional flatness detection data. The partitioned pressure-fixed water sample plate is adjusted based on the multi-dimensional flatness detection data to obtain the target flatness-fixed water sample plate.

[0085] The above mainly describes the solutions of the embodiments of this application from the perspective of the method execution process. It is understood that, in order to achieve the above functions, the terminal includes the corresponding hardware structure and / or software modules for executing each function. Those skilled in the art should readily recognize that, in conjunction with the units and algorithm steps of the various examples described in the embodiments provided herein, this application can be implemented in hardware or a combination of hardware and computer software. Whether a function is executed in hardware or by computer software driving hardware depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this application.

[0086] This application embodiment can divide the terminal into functional units according to the above method example. For example, each function can be divided into a separate functional unit, or two or more functions can be integrated into one processing unit. The integrated unit can be implemented in hardware or as a software functional unit. It should be noted that the unit division in this application embodiment is illustrative and only represents one logical functional division. In actual implementation, there may be other division methods.

[0087] For those consistent with the above, please refer to Figure 4 , Figure 4 This application provides a schematic diagram of the structure of a pressure frame-type water sample plate leveling and fixing device with pressing ribs, as described in an embodiment of the present application. Figure 4 As shown, the device includes: Acquisition unit 101 is used to acquire the location contour data of water sample spots located on the water sample plate to be processed; The first processing unit 102 is used to perform partition pressure adaptation processing on the pressure frame with pressing ribs of the water sample plate to be processed according to the water sample spot position contour data, so as to obtain partition pressure configuration data. The second processing unit 103 is used to control the pressurizing frame to apply pressure and fix the water sample plate to be processed according to the partition pressure configuration data, so as to obtain a partition pressurized and fixed water sample plate. The third processing unit 104 is used to perform multi-dimensional flatness redundancy detection processing on the partitioned pressurized water sample plate to obtain multi-dimensional flatness detection data. The fourth processing unit 105 is used to adjust the partitioned pressure-fixed water sample plate according to the multi-dimensional flatness detection data to obtain the target flatness-fixed water sample plate.

[0088] In one possible implementation, the first processing unit 102 is configured to perform zoned pressure adaptation processing on the pressure frame with pressing ribs of the water sample plate to be processed based on the water sample spot position contour data, to obtain zoned pressure configuration data, specifically for: Obtain the grid compression rib parameter data of the compression frame with compression ribs; Based on the grid pressing rib parameter data and the water sample spot location contour data, region mapping processing is performed to obtain the pressing rib-spot region correspondence data; Based on the correspondence data between the pressing rib and the spot area, the pressing attribute is marked for each grid pressing rib of the pressing frame with pressing rib, and the pressing attribute marking data of the grid pressing rib is obtained. Based on the pressure attribute marking data of the mesh pressing ribs, a pressure value is assigned to each mesh pressing rib to obtain the zonal pressure configuration data.

[0089] In one possible implementation, the third processing unit 104 is used to perform multi-dimensional flatness redundancy detection processing on the partitioned pressurized water sample plate to obtain multi-dimensional flatness detection data, specifically for: Based on an electronic level, the horizontal state of the water sample plate fixed by the partition pressure is detected and processed to obtain a set of water sample plate horizontal detection data; Based on the set of water sample horizontal detection data, the horizontal deviation of the zoned pressure-fixed water sample is calculated to obtain the water sample horizontal deviation data. Based on a laser thickness gauge, the thickness distribution of water sample spots is detected and processed on the partitioned pressure-fixed water sample plate to obtain a set of water sample spot thickness detection data. Based on the set of water sample spot thickness detection data, the thickness distribution deviation of the water sample spots is calculated to obtain water sample spot thickness distribution deviation data; The horizontal deviation data of the water sample plate and the thickness distribution deviation data of the water sample spots are fused to obtain multi-dimensional flatness detection data.

[0090] In one possible implementation, the third processing unit 104 is used to calculate the horizontal deviation of the zoned pressure-fixed water sample based on the water sample horizontal detection data set, and obtain water sample horizontal deviation data, specifically for: Obtain a subset of the first benchmark point detection data from the water sample horizontal detection data set; The average dip angle is calculated based on the dip angle data of each first reference point in the first reference point detection data subset to obtain the first average dip angle. The maximum tilt angle deviation is calculated based on the first average tilt angle and the tilt angle data of each first reference point to obtain the first maximum tilt angle deviation data. The first flatness error data is obtained by performing flatness error conversion processing based on the first maximum tilt angle deviation data. Based on the first flatness error data, the first maximum tilt angle deviation data, the first average tilt angle, and the tilt angle data of each first reference point, the horizontal deviation data of the water sample is determined.

[0091] In one possible implementation, the third processing unit 104 is used to calculate the thickness distribution deviation of the water sample spots based on the set of water sample spot thickness detection data, and obtain water sample spot thickness distribution deviation data, specifically for: Obtain a subset of detection data for the first detection point from the set of water sample spot thickness detection data; The average thickness is calculated based on the thickness value of each first detection point in the subset of the first detection point detection data to obtain the first average thickness. The standard deviation is calculated based on the thickness value of each first detection point in the subset of the first detection point detection data to obtain the first standard deviation. The first relative standard deviation is obtained by calculating the relative standard deviation based on the first average thickness and the first standard deviation; Based on the first relative standard deviation, the first standard deviation, the first average thickness, and the thickness value of each first detection point, the thickness distribution deviation data of the water sample spot is determined.

[0092] This application embodiment also provides a computer storage medium, wherein the computer storage medium stores a computer program for electronic data interchange, the computer program causing a computer to perform some or all of the steps of any of the pressure frame type water sample plate flattening and fixing methods with pressing ribs as described in the above method embodiments.

[0093] This application also provides a computer program product, which includes a non-transitory computer-readable storage medium storing a computer program that causes a computer to perform some or all of the steps of any of the pressing frame water sample plate leveling and fixing methods with pressing ribs as described in the above method embodiments.

[0094] It should be noted that, for the sake of simplicity, the foregoing method embodiments are all described as a series of actions. However, those skilled in the art should understand that this application is not limited to the described order of actions, as some steps may be performed in other orders or simultaneously according to this application. Furthermore, those skilled in the art should also understand that the embodiments described in the specification are preferred embodiments, and the actions and modules involved are not necessarily essential to this application.

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

[0096] In the several embodiments provided in this application, it should be understood that the disclosed apparatus can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative; for instance, the division of units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be through some interfaces; the indirect coupling or communication connection between devices or units may be electrical or other forms.

[0097] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0098] Furthermore, the functional units in the various embodiments of the application can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software program module.

[0099] If the integrated unit is implemented as a software program module and sold or used as an independent product, it can be stored in a computer-readable storage device (CMD). Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a memory and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of this application. The aforementioned memory includes various media capable of storing program code, such as USB flash drives, read-only memory (ROM), random access memory (RAM), portable hard drives, magnetic disks, or optical disks.

[0100] Those skilled in the art will understand that all or part of the steps in the various methods of the above embodiments can be implemented by a program instructing related hardware. The program can be stored in a computer-readable storage device, which may include: a flash drive, a read-only memory, a random access memory, a magnetic disk, or an optical disk, etc.

[0101] The embodiments of this application have been described in detail above. Specific examples have been used to illustrate the principles and implementation methods of this application. The description of the above embodiments is only for the purpose of helping to understand the method and core ideas of this application. At the same time, for those skilled in the art, there will be changes in the specific implementation methods and application scope based on the ideas of this application. Therefore, the content of this specification should not be construed as a limitation of this application.

Claims

1. A method for flattening and fixing a pressurized frame-type water sample plate with a press rib, characterized by, The ribbed pressing frame flat fixing method comprises the following steps: Obtain water sample spot position contour data on a water sample plate to be processed; According to the water sample spot position contour data, the ribbed pressing frame of the water sample plate to be processed is subjected to partition pressure adaptation processing to obtain partition pressure configuration data; The pressing frame is controlled to press and fix the water sample plate to be processed according to the partition pressure configuration data, and a partition pressure fixed water sample plate is obtained; The partition pressure fixed water sample plate is subjected to multi-dimensional flatness redundancy detection processing to obtain multi-dimensional flatness detection data; According to the multi-dimensional flatness detection data, the partition pressure fixed water sample plate is subjected to adjustment processing to obtain a target flat fixed water sample plate.

2. The method of claim 1, wherein the pressurized frame-type water sample plate is pressed and fixed by using a press rib. According to the water sample spot position contour data, the ribbed pressing frame of the water sample plate to be processed is subjected to partition pressure adaptation processing to obtain partition pressure configuration data, which comprises the following steps: Obtain grid rib parameters of the ribbed pressing frame; According to the grid rib parameters and the water sample spot position contour data, region mapping processing is performed to obtain rib-spot region correspondence data; According to the rib-spot region correspondence data, each grid rib of the ribbed pressing frame is marked with a pressure attribute to obtain grid rib pressure attribute marking data; According to the grid rib pressure attribute marking data, each grid rib is assigned a pressure value to obtain partition pressure configuration data.

3. The method of claim 2, wherein the pressurized frame-type water sample plate is pressed and fixed by using a press rib. The partition pressure fixed water sample plate is subjected to multi-dimensional flatness redundancy detection processing to obtain multi-dimensional flatness detection data, which comprises the following steps: Based on an electronic level, the partition pressure fixed water sample plate is subjected to horizontal state detection processing to obtain a water sample plate horizontal detection data set; According to the water sample plate horizontal detection data set, the horizontal deviation of the partition pressure fixed water sample plate is calculated to obtain water sample plate horizontal deviation data; Based on a laser thickness gauge, the partition pressure fixed water sample plate is subjected to water sample spot thickness distribution detection processing to obtain a water sample spot thickness detection data set; the electronic level and the laser thickness gauge are in communication connection with a processor; According to the water sample spot thickness detection data set, the thickness distribution deviation of the water sample spot is calculated to obtain water sample spot thickness distribution deviation data; The water sample plate horizontal deviation data and the water sample spot thickness distribution deviation data are fused to obtain multi-dimensional flatness detection data.

4. The method of claim 3, wherein the pressurized frame-type water sample plate is pressed and fixed by using a press rib. The water sample plate horizontal detection data set is obtained from the water sample plate horizontal detection data set; According to the first average inclination and the inclination data of each first reference point, the maximum inclination deviation is calculated to obtain first maximum inclination deviation data; According to the first maximum inclination deviation data, flatness error conversion processing is performed to obtain first flatness error data; ​ ​ According to the first flatness error data, the first maximum inclination deviation data, the first average inclination and the inclination data of each first reference point, water sample plate horizontal deviation data is determined.

5. The method of claim 3, wherein the pressurized frame-type water sample plate is pressed and fixed by using a pressurized frame-type water sample plate leveling fixture. The water sample spot thickness distribution deviation data is obtained by calculating the thickness distribution deviation of the water sample spot according to the water sample spot thickness detection data set, including: A first detection point detection data subset is obtained from the water sample spot thickness detection data set; A first average thickness is calculated according to the thickness value of each first detection point in the first detection point detection data subset; A first standard deviation is calculated according to the thickness value of each first detection point in the first detection point detection data subset; A first relative standard deviation is calculated according to the first average thickness and the first standard deviation; The water sample spot thickness distribution deviation data is determined according to the first relative standard deviation, the first standard deviation, the first average thickness and the thickness value of each first detection point.

6. The method of claim 4, wherein the pressurized frame-type water sample plate is pressed and fixed by using a press rib. The first flatness error data is obtained by performing flatness error conversion processing according to the first maximum inclination deviation data, including: The process of obtaining the first flatness error data by performing flatness error conversion processing according to the first maximum inclination deviation data is realized by the following formula; wherein the first reference point detection data subset includes five reference points; δ=max{|α1-(α1+α2+α3+α4+α5) / 5|,|α2-(α1+α2+α3+α4+α5) / 5|,..., |α5-(α1+α2+α3+α4+α5) / 5|}×K Wherein δ represents the first flatness error data; max{} represents a function of taking the maximum value, which is used to calculate the first maximum inclination deviation data here; || represents the absolute value operation; α1 represents the inclination data of the first reference point in the first reference point detection data subset; α2 represents the inclination data of the second reference point in the first reference point detection data subset; α3 represents the inclination data of the third reference point in the first reference point detection data subset; α4 represents the inclination data of the fourth reference point in the first reference point detection data subset; α5 represents the inclination data of the fifth reference point in the first reference point detection data subset; (α1+α2+α3+α4+α5) / 5 represents the first average inclination; K can represent the unit conversion coefficient.

7. The method of claim 5, wherein the pressurized frame-type water sampler plate is pressed and fixed by using a pressurized frame-type water sampler plate leveling fixture. The first relative standard deviation is calculated according to the first average thickness and the first standard deviation, including: The process of obtaining the first relative standard deviation by calculating the relative standard deviation according to the first average thickness and the first standard deviation is realized by the following formula; RSD1=[√(Σ1 n (t j -(t1+t2+…+t n ) / n)² / (n-1))] / [(t1+t2+…+t n ) / n]×100% Wherein, RSD1 represents the first relative standard deviation; t j represents the measured thickness value of the jth detection point in the first detection point detection data subset; t1 represents the measured thickness value of the first detection point in the first detection point detection data subset; t2 represents the measured thickness value of the second detection point in the first detection point detection data subset; t n represents the measured thickness value of the nth detection point in the first detection point detection data subset; (t1+t2+…+t n ) / n can represent the first average thickness; Σ1 n (t j -(t1+t2+…+t n ) / n)² represents the square sum of the difference between each detection point thickness value and the first average thickness; √ can represent the square root operation.

8. A flat fixing device for a pressurized frame type water sample plate with pressed ribs, characterized in that, The device includes: An acquisition unit is configured to acquire water sample spot position contour data located on a water sample plate to be processed; A first processing unit is configured to perform partition pressure adaptation processing on a pressurizing frame with pressing ribs of the water sample plate to be processed according to the water sample spot position contour data, and obtain partition pressure configuration data; A second processing unit is configured to control the pressurizing frame to press and fix the water sample plate according to the partition pressure configuration data, to obtain a partition-pressing-fixed water sample plate; A third processing unit is configured to perform multi-dimensional flatness detection on the partition-pressing-fixed water sample plate, to obtain multi-dimensional flatness detection data; A fourth processing unit is configured to adjust the partition-pressing-fixed water sample plate according to the multi-dimensional flatness detection data, to obtain a target flatness-fixed water sample plate.

9. A laser water quality analyzer characterized by comprising: The laser water quality analyzer is configured to perform the method for flatness fixing of the pressurizing frame water sample plate with pressure-pressed ribs according to any one of claims 1-7, wherein the laser water quality analyzer comprises a water sample drying spot forming device and a flatness fixing device for the pressurizing frame water sample plate with pressure-pressed ribs, which is configured to detect the flatness of the flexible film carrying the water sample spot dried by the water sample drying spot forming device and to fix the water sample spot.

10. A computer-readable storage medium, characterized in that, The computer readable storage medium stores a computer program, and the computer program comprises program instructions, which, when executed by a processor, cause the processor to perform the method for flatness fixing of the pressurizing frame water sample plate with pressure-pressed ribs according to any one of claims 1-7.

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