Tablet automatic cleaning method, device and computer system based on multi-stage disinfection

Abstract

The present application relates to the technical field of cleaning control, and especially relates to a tablet automatic cleaning method and device based on multi-stage disinfection and a computer system. The method comprises the following steps: surface detection is performed on tablet materials to be treated to obtain surface topography data, and micro-texture features and pore depth distribution parameters representing surface structures are extracted; structural characteristics of the tablet materials are analyzed based on the features to generate a matching disinfectant particle size scheme and a disinfectant concentration sequence varying with the degree of pollution adhesion; a multi-stage permeation treatment process is constructed for gradually acting on deep layers from the surface layer of the materials, and different particle sizes and concentrations of disinfectants are sequentially applied to complete stage-by-stage treatment; the surface state of the tablet materials is analyzed, and treatment parameters are dynamically adjusted according to the residual distribution and repeated execution until the preset cleaning standard is met. The present application solves the problems of insufficient disinfection permeability and unstable cleaning effect in the existing tablet cleaning process, and realizes adaptive and efficient cleaning of the multi-layer structure of the tablet materials.

Description

Technical Field

[0001] This invention relates to the field of cleaning control technology, and in particular to an automatic cleaning method, apparatus and computer system for film preparation based on multi-stage disinfection. Background Technology

[0002] In medical testing, biological experiments, pathological analysis, and related precision manufacturing fields, slides (such as pathological tissue sections, glass slides, biological sample slides, or other precision substrates) typically require cleaning and sterilization before or after use to remove residual contaminants, microorganisms, or chemical reagents from their surface and interior, thereby ensuring the accuracy and reliability of subsequent test results. Slides often possess characteristics such as fine surfaces, minute structures, and the presence of micropores or multilayered structures, which places high demands on cleaning and sterilization processes.

[0003] Existing slide cleaning methods mostly employ manual cleaning, immersion in a single disinfectant solution, spray cleaning, or ultrasonic cleaning. These methods typically rely on fixed cleaning parameters and a single disinfection level. While they can remove contaminants from the slide surface to some extent, they still have significant shortcomings in practical applications. On the one hand, due to the complex surface and internal structure of slides, disinfectant media of a single concentration or particle size cannot effectively penetrate the micropores or deep structures of the slides, easily leading to deep contaminant residue. On the other hand, different types of slides vary in material, pore distribution, and tolerance. Using uniform cleaning and disinfection parameters often makes it difficult to balance cleaning effectiveness with slide integrity, easily resulting in incomplete cleaning or damage to the slides. Furthermore, existing slide cleaning processes are mostly open-loop control methods with preset procedures, lacking real-time detection and feedback adjustment mechanisms for cleaning effectiveness. They cannot dynamically adjust the disinfection level and cleaning parameters according to the actual contamination of the slides, resulting in poor stability and consistency of cleaning effects, making it difficult to meet the application requirements of automation, high precision, and high reliability.

[0004] Therefore, there is an urgent need to provide a cleaning method that can adopt a multi-stage disinfection strategy and achieve automated control based on the structural characteristics and contamination status of the prepared film, so as to improve the disinfection penetration effect and cleaning quality, while reducing damage to the prepared film itself, thereby better meeting the needs of practical applications. Summary of the Invention

[0005] This invention provides an automated cleaning method, apparatus, and computer system for sheet preparation based on multi-stage disinfection. It achieves efficient and stable cleaning of sheet preparation materials through precise surface detection, dynamic adjustment of disinfectant parameters, and a step-by-step penetration process.

[0006] In a first aspect, the present invention provides an automated cleaning method for slide preparation based on multi-stage disinfection, the method comprising:

[0007] Step S1: Perform surface inspection on the sheet material to be processed, obtain the surface morphology data of the sheet material, and extract the micro-texture features and pore depth distribution parameters that characterize the surface structure of the sheet material based on the surface morphology data.

[0008] Step S2: Based on the micro-texture features and pore depth distribution parameters, analyze the structural characteristics of the sheet material surface and generate a corresponding disinfectant particle size matching scheme; based on the degree of contaminant adhesion on the sheet material surface, generate a corresponding disinfectant concentration change sequence;

[0009] Step S3: Configure the disinfectant particle size matching scheme and the disinfectant concentration change sequence according to a preset time sequence to construct a multi-level penetration treatment process that acts step by step from the surface of the sheet material to the deep layers;

[0010] Step S4: Apply disinfectant components of different particle sizes and concentrations according to the processing flow to complete the staged treatment; obtain surface state data of the treated sheet material, analyze the distribution of residues and determine whether it meets the preset cleaning standards;

[0011] Step S5: If the preset cleaning standard is not met, adjust the disinfectant particle size matching scheme and the concentration change sequence according to the residue distribution to generate a new treatment configuration and execute it.

[0012] As a preferred embodiment of the present invention, step S1 involves extracting the micro-texture features and pore depth distribution parameters from the surface morphology data, including:

[0013] High-resolution microscopic imaging technology is used to image the surface of the substrate material to be processed, acquiring surface morphology data containing surface texture information and pore structure information. The surface morphology data is preprocessed, and digital image processing methods are used to extract the microscopic texture features of the substrate material surface. Combined with multi-view imaging or stereo reconstruction results, depth analysis is performed on the surface of the substrate material and its internal pore structure to obtain the distribution of pores in the depth direction, and the pore depth distribution parameters are determined accordingly. The pore depth distribution parameters are used to characterize the depth levels of pores extending from the surface to the interior of the material.

[0014] As a preferred embodiment of the present invention, step S2, generating a disinfectant particle size matching scheme, includes:

[0015] Based on the microtexture features and the pore depth distribution parameters, geometric feature information of the pore structure is extracted. A hierarchical interval division method is used to determine the proportion distribution of different pore scales in the geometric feature information. Based on the proportion distribution of different pore scales, the curvature change inside the pores and the degree of micropore connectivity in the geometric feature information, a targeted disinfectant particle size matching scheme is generated. The disinfectant particle size matching scheme is mapped to the pore scale distribution to generate the adaptation relationship between particle size and pore structure. The geometric feature information includes at least pore size, curvature change inside the pores, and degree of micropore connectivity on the surface.

[0016] As a preferred embodiment of the present invention, step S2, generating the concentration change sequence, includes:

[0017] Based on the surface roughness peak-valley difference and the hardness of the surface deposits on the sheet material in the surface morphology data, the initial disinfection intensity requirement is calculated; the initial ratio of high-concentration disinfectant is determined by concentration gradation mapping technology; a gradually decreasing concentration change sequence is generated based on the distribution density of surface residues; the initial disinfection intensity requirement is correlated with the concentration change sequence to ensure that the concentration change matches the surface treatment requirements.

[0018] As a preferred embodiment of the present invention, step S3, forming a step-by-step penetration process, includes:

[0019] Based on the geometric characteristics of the surface to deep pores of the sheet material, the applicable range of each particle size level in the disinfectant particle size matching scheme is determined; the concentration change sequence is mapped to each particle size level to determine the working concentration and action time corresponding to each particle size level; based on the working concentration and action time, a step-by-step penetration process is formed to ensure that the disinfectant acts gradually from the surface to the deep layers.

[0020] As a preferred embodiment of the present invention, step S4 includes a phased processing procedure, comprising:

[0021] According to the step-by-step penetration process, disinfectant components with different particle sizes and concentrations are applied sequentially; the treatment parameters for each stage are adjusted according to the hardness of the surface deposits and the curvature of the pores; the treatment gradually transitions from surface cleaning to deep microporous sterilization, completing the staged treatment; the treatment data for each stage are recorded, including the usage of disinfectant components and the treatment effect.

[0022] As a preferred embodiment of the present invention, step S4, analyzing the distribution of residues and determining whether it meets the preset cleaning standards, includes:

[0023] Surface condition data of the processed sheet material are acquired using multi-layer scanning imaging technology; based on the surface condition data, the particle size range of residues and the layer coverage of the detection area are analyzed; combined with the chemical composition analysis of residues and the adsorption capacity of residues in micropores, the cleanliness level of each area is determined; the cleanliness level is compared with the preset cleanliness standard to determine whether the cleanliness requirements are met.

[0024] As a preferred embodiment of the present invention, step S5, generating and executing a new processing configuration, includes:

[0025] If the detected image contrast threshold shows an anomaly in the spatial coordinates of the residue distribution, then the residue removal difficulty level and the parameter correction range after detection are obtained; based on the residue removal difficulty level and the correction range, the particle size distribution in the disinfectant particle size matching scheme is adjusted; based on the adjusted particle size distribution, the concentration change sequence is regenerated; the adjusted scheme and sequence are combined into a new processing configuration, and a new processing flow is executed.

[0026] Secondly, the present invention also provides an automatic film cleaning device based on multi-stage disinfection for implementing the above-mentioned method, the device comprising:

[0027] The surface inspection unit is used to perform surface inspection on the sheet material to be processed, acquire the surface morphology data of the sheet material, and extract the micro-texture features and pore depth distribution parameters characterizing the surface structure of the sheet material based on the surface morphology data.

[0028] The structural analysis unit is used to analyze the structural characteristics of the sheet material surface based on the micro-texture features and pore depth distribution parameters, and generate a corresponding disinfectant particle size matching scheme; and to generate a corresponding disinfectant concentration change sequence based on the degree of contaminant adhesion on the sheet material surface.

[0029] A multi-level control construction unit is used to configure the disinfectant particle size matching scheme and the disinfectant concentration change sequence according to a preset time sequence to construct a multi-level penetration control process that acts step by step from the surface of the sheet material to the deep layer.

[0030] The phased processing unit is used to complete phased processing according to the multi-level control process by using disinfectant components with different particle sizes and concentrations; to acquire surface state data of the processed sheet material, analyze the distribution of residues, and determine whether it meets the preset cleaning standards;

[0031] The effect evaluation unit is used to adjust the disinfectant particle size matching scheme and the concentration change sequence according to the residue distribution when the preset cleaning standard is not met, generate a new control configuration and execute it.

[0032] Thirdly, the present invention also provides an automated film cleaning computer system based on multi-stage disinfection, the computer system comprising: a memory and at least one processor, the memory storing instructions; the at least one processor calling the instructions in the memory to cause the automated film cleaning computer system based on multi-stage disinfection to perform the above-described method.

[0033] The beneficial effects of this invention are as follows:

[0034] This invention utilizes high-resolution microscopic imaging technology to comprehensively analyze the surface of the prepared material, acquiring precise surface morphology data, including microscopic texture features and pore depth distribution parameters. This accurately reflects the surface state of the material, providing support for generating matching disinfectant particle size and concentration. Based on the acquired surface data, the microstructural characteristics of the material are analyzed to generate corresponding disinfectant particle size matching schemes and concentration variation sequences. The disinfectant concentration can be adaptively adjusted according to the degree of contaminant adhesion on the surface of the prepared material, ensuring targeted cleaning effects on both the surface and deep layers. For materials with high surface roughness or strong pore connectivity, precise particle size selection and concentration adjustment avoid problems of uneven disinfectant penetration or overtreatment. A multi-stage penetration process configures the disinfectant particle size and concentration to different treatment stages, penetrating the prepared material layer by layer, allowing the disinfectant to gradually penetrate and achieve optimal results. The process involves penetrating from the surface to the depths, ensuring effective cleaning of pores at different levels. Through this step-by-step penetration method, the disinfectant can reach deep into the material's pores, removing stubborn contaminants while avoiding damage to the material surface from high-concentration disinfectants. Finally, scanning imaging technology is used to acquire surface state data of the cleaned material, analyze the distribution of residues, and determine whether it meets the preset cleaning standards. If the expected standards are not met, the particle size and concentration sequence of the disinfectant are adjusted based on the distribution of residues to ensure optimal treatment results at each cleaning stage. Through the synergy of these technical solutions, refined cleaning of the material is achieved, not only improving the stability of the disinfection effect but also avoiding the problems of insufficient cleaning or material damage commonly found in traditional single-disinfectant solutions. This provides an efficient and precise automated cleaning solution for scenarios with high-standard cleaning requirements, such as medical devices and microelectronics processing. Attached Figure Description

[0035] To more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings used in the description of the embodiments will be briefly introduced below. Obviously, the drawings described below are some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0036] Figure 1This is a flowchart of the automated cleaning method for slide preparation based on multi-stage disinfection, as shown in the embodiment.

[0037] Figure 2 This is a flowchart of the concentration change sequence generation method in the embodiment;

[0038] Figure 3 This is a histogram showing the distribution of particle size ranges of residue after slide preparation and cleaning in the embodiment;

[0039] Figure 4 This is a structural diagram of the automated film cleaning system based on multi-stage disinfection in the embodiment. Detailed Implementation

[0040] This invention provides an automated cleaning method, apparatus, and computer system for slide preparation based on multi-stage disinfection. The terms "first," "second," "third," "fourth," etc. (if applicable) in the specification, claims, and accompanying drawings are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments described herein can be implemented in a sequence other than that illustrated or described herein. Furthermore, the terms "comprising" or "having" and any variations thereof are intended to cover a non-exclusive inclusion; for example, a process, method, system, product, or apparatus that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus.

[0041] For ease of understanding, the specific process of the embodiments of the present invention will be described below, such as... Figure 1 As shown, the automated slide cleaning method based on multi-stage disinfection in this embodiment of the invention includes:

[0042] Step S1: Perform surface inspection on the sheet material to be processed, obtain surface morphology data of the sheet material, and extract microscopic texture features and pore depth distribution parameters characterizing the surface structure of the sheet material based on the surface morphology data; specifically including:

[0043] High-resolution microscopic imaging technology is used to image the surface of the substrate material to be processed, acquiring surface morphology data containing surface texture information and pore structure information. The surface morphology data is preprocessed, and digital image processing methods are used to extract the microscopic texture features of the substrate material surface. Combined with multi-view imaging or stereo reconstruction results, depth analysis is performed on the surface of the substrate material and its internal pore structure to obtain the distribution of pores in the depth direction, and the pore depth distribution parameters are determined accordingly. The pore depth distribution parameters are used to characterize the depth levels of pores extending from the surface to the interior of the material.

[0044] Specifically, sheet material refers to the raw materials used to prepare functional sheet-like medical device components. Sheet preparation refers to the process of coating, fixing, drying, and shaping. For example, a gene chip can be made by adding probes to a glass slide made from sheet material, or a biosensor sheet can be made by loading enzymes onto an electrode substrate made from sheet material. Before performing multi-level disinfection, the sheet material to be treated is subjected to precise surface testing to obtain surface morphology data that can truly reflect the surface structure of the sheet material. Based on this, microscopic texture features and pore depth distribution parameters for subsequent multi-level disinfection parameter setting are extracted, thereby providing reliable data support for the grading, matching, and dynamic adjustment of disinfection strategies.

[0045] High-resolution microscopic imaging technology is used to image and acquire surface image data with sufficient spatial resolution of the material to be processed. Scanning electron microscopy (SEM) is preferred for imaging the surface of the material. SEM uses a focused electron beam to scan the surface point by point and simultaneously acquires the secondary electron signals generated by the material surface, thus forming a surface morphology image with high contrast and high detail fidelity. This effectively avoids the problem of missing fine pores, microcracks, or rough structures due to insufficient resolution, making it particularly suitable for applications such as medical film materials where high surface cleanliness is required. After acquiring the surface image data, the raw image data is preprocessed to improve the accuracy and stability of subsequent feature extraction. This preprocessing includes grayscale conversion of the image data, transforming multi-channel data into single-channel grayscale data to reduce computational complexity. Simultaneously, filtering algorithms are used to suppress random noise in the image to eliminate interference signals introduced during electron imaging, ensuring the complete preservation of the true surface structure information in the image.

[0046] Based on the preprocessed surface image data, digital image processing techniques are used to extract the surface micro-texture features and pore depth distribution range. Specifically, edge detection algorithms are used to identify texture boundaries in the surface image. These edge detection algorithms locate texture change regions by calculating the gray-level gradients of image pixels in different directions. For example, the Sobel operator is used to calculate the gradient values ​​in the horizontal and vertical directions respectively, and the two are combined into a gradient magnitude map, thereby highlighting surface texture features and suppressing interference from flat areas. After completing texture edge recognition, statistical analysis is performed on the identified texture regions to calculate the number of edge points per unit area and the texture depth distribution. The directional distribution is used as a characterizing feature density and directionality of the microstructure of the film material surface. Simultaneously, to obtain the true spatial scale information of the pores on the film material surface, a stereo reconstruction method is used to estimate the pore depth. Specifically, multiple images of the film material surface are acquired from different perspectives, and feature points are matched between images from each perspective. The disparity relationship between corresponding feature points is calculated, and then the pore depth distribution is deduced based on the imaging geometry model, thus obtaining the pore depth distribution range. By combining the texture feature extraction results with the pore depth estimation results, the surface of the film material can be comprehensively reflected in the planar direction. Regarding the microstructural characteristics in the depth direction, the construction process of the aforementioned imaging geometric model includes: using a scanning electron microscope or optical microscopy to perform multi-view or multi-layer scanning acquisition on the surface of the prepared material to obtain three-dimensional tomographic image data containing the surface layer to the depth of the micropores; subsequently, the aforementioned multi-view image data is preprocessed to eliminate noise and unify grayscale expression; through feature point extraction and matching, the correspondence between images from different viewpoints is established to obtain an imaging geometric model used to describe the transformation relationship between the imaging coordinate system, the depth coordinate system, and the spatial coordinate system of the prepared material, enabling subsequent pore depth estimation, layer coverage statistics, and abnormal residual spatial coordinate location. The process is completed within a unified geometric framework. In specific applications, when the substrate material is porous ceramic, the texture features extracted by the Sobel operator can effectively distinguish between rough and relatively smooth areas on the surface, while the 3D reconstruction method can accurately estimate the pore depth in the porous structure. The two work together to provide a reliable basis for the subsequent disinfectant penetration scheme, thereby avoiding insufficient or excessive disinfectant penetration. Through the above technical solution, not only is high-precision detection of the surface morphology of the substrate material achieved, but a complete processing link from raw image data to structural parameter data is also established, providing a data foundation for automated substrate cleaning.

[0047] Step S2: Based on the micro-texture features and pore depth distribution parameters, analyze the structural characteristics of the sheet material surface and generate a corresponding disinfectant particle size matching scheme; based on the degree of contaminant adhesion on the sheet material surface, generate a corresponding disinfectant concentration change sequence;

[0048] In step S2, generating a disinfectant particle size matching scheme includes:

[0049] Based on the microtexture features and the pore depth distribution parameters, geometric feature information of the pore structure is extracted. A hierarchical interval division method is used to determine the proportion distribution of different pore scales in the geometric feature information. Based on the proportion distribution of different pore scales, the curvature change inside the pores and the degree of micropore connectivity in the geometric feature information, a targeted disinfectant particle size matching scheme is generated. The disinfectant particle size matching scheme is mapped to the pore scale distribution to generate the adaptation relationship between particle size and pore structure. The geometric feature information includes at least different pore scales, changes in curvature inside the pores, and the degree of micropore connectivity on the surface.

[0050] Specifically, to ensure that the disinfectant particles applied during the multi-stage disinfection process can effectively penetrate the surface and deep pore structures of the substrate material, and to avoid insufficient penetration or surface blockage due to particle size mismatch, after collecting surface morphology data of the substrate material and extracting micro-texture features and pore depth distribution range, the changes in pore curvature and the degree of surface micropore connectivity are analyzed based on the aforementioned micro-texture features and pore depth distribution parameters to identify key geometric resistance factors in the disinfectant penetration process. Specifically, the change in pore curvature characterizes the degree of tortuosity of the pore wall from the opening to the depth, which directly affects the disinfectant particles. The movement path and diffusion efficiency in pore channels are specifically analyzed by modeling the geometric changes of the pore wall as it extends from the opening to the depth of the pore, thereby calculating the curvature changes inside the pore. The curvature changes are used to characterize the trend of the degree of bending of the pore wall. Gaussian curvature is preferably used as a quantitative index. That is, the curvature value is obtained by calculating the product of the two principal curvatures at the pore wall to reflect the geometric nonlinear characteristics of the internal structure of the pore. The larger the curvature value, the more complex the internal path of the pore, and the easier it is for disinfectant particles to be trapped or blocked during the penetration process. Therefore, smaller particle sizes are needed to adapt to the curved channels.

[0051] Meanwhile, to further characterize the connectivity between pores, the connectivity of surface micropores is evaluated based on curvature variation data. This connectivity is measured by calculating the effective permeation path length and the number of branches between adjacent pores. For example, Euclidean distance is used to measure the path length between pores. When the path length is less than a preset length and the number of branches is less than a set value, it is determined to be a highly connected structure. This connectivity result is used to reflect the ease of diffusion of disinfectant in the pore network. That is, when the connectivity is high, particles can enter the deep region along a simplified path, thus allowing the use of relatively large particles to improve surface cleaning efficiency. When the connectivity is low and the curvature is high, small particles need to be used first to ensure permeability. After obtaining the evaluation results of the curvature changes and micropore connectivity within the pores, a hierarchical interval division method was adopted to determine the proportion distribution of different pore sizes in order to further establish the statistical distribution of pore size. Specifically, the pore depth distribution range was divided into multiple scale intervals, such as 0–5 micrometers, 5–10 micrometers, etc., and the proportion of the number of pores in each interval to the total number of pores was statistically analyzed to form a pore size proportion vector. This proportion vector is used to reflect the dominant scale characteristics of the pore structure of the sheet material. That is, when the proportion of shallow pores is significantly higher than the set proportion, the size of the disinfectant particles should take into account both uniform coverage and rapid surface sterilization; while when the proportion of deep pores is higher, it is necessary to enhance the penetration ratio of small-diameter particles to improve the deep sterilization capability.

[0052] Furthermore, based on the aforementioned proportions of different pore sizes, the changes in pore curvature, and the degree of micropore connectivity, a targeted disinfectant particle size matching scheme is generated. Specifically, the pore size range with the highest proportion is used as the primary matching target. The pore size is further divided into stratified ranges based on pore depth distribution parameters, and the proportion of each pore size range within the overall structure of the sheet material is statistically analyzed to identify the primary pore ranges (those with a proportion greater than a set ratio) and the secondary pore ranges (those with a proportion less than the set ratio). Subsequently, the changes in pore curvature and the degree of micropore connectivity within each pore range are further analyzed to characterize the tortuous complexity of the pore structure and the potential permeation channels of the disinfectant. Specifically, in the particle size matching process, for ranges with a high proportion of pore sizes and large changes in pore curvature (i.e., a change rate greater than a set value), smaller disinfectant particles are preferentially matched. To adapt to curved paths and enhance penetration into deep pores, for regions with a high proportion of pore size and small changes in internal curvature (i.e., a rate of change less than the aforementioned set value), larger-diameter disinfectant particles are matched to improve the coverage efficiency and removal capacity of the disinfectant within these regions. For regions with a low proportion of pore size but large changes in internal curvature, smaller-diameter disinfectant particles are configured as auxiliary matching objects to prevent residual retention in high-curvature structures, but these regions are not used as the primary particle size configuration regions to avoid excessive consumption of disinfection resources. For regions with a low proportion of pore size and small changes in internal curvature, their weight in the particle size matching scheme is reduced, and coverage is primarily achieved through the penetration of adjacent pore regions. The disinfectant particles are then fine-tuned within a set range based on the connectivity of the micropores; when the connectivity is high, the disinfectant particles are increased, and when the connectivity is low, the disinfectant particles are decreased.

[0053] The aforementioned disinfectant particle size matching scheme uses the pore size distribution as the primary factor and dynamically corrects the particle size configuration by combining the changes in curvature within the pores and the micropore connectivity. This ultimately generates a matching scheme that includes multiple particle size levels, and maps this matching scheme to the pore size distribution results, establishing a matching relationship between particle size and pore structure. Through this technical solution, a complete data processing chain is achieved, from analyzing surface microtexture features and pore depth distribution parameters to analyzing curvature changes, size distribution, and surface micropore connectivity, and finally to particle size mapping and matching. This enables the disinfectant particle size matching scheme to accurately reflect the pore structure characteristics of the film-making material, thus providing a quantifiable, executable, and targeted key process foundation for automated film cleaning methods based on multi-level disinfection.

[0054] Further, in step S2, a concentration change sequence is generated, such as... Figure 2 As shown, it includes:

[0055] Based on the surface roughness peak-valley difference and the hardness of the surface deposits on the sheet material in the surface morphology data, the initial disinfection intensity requirement is calculated; the initial ratio of high-concentration disinfectant is determined by concentration gradation mapping technology; a gradually decreasing concentration change sequence is generated based on the distribution density of surface residues; the initial disinfection intensity requirement is correlated with the concentration change sequence to ensure that the concentration change matches the surface treatment requirements.

[0056] Specifically, in order to enable the film preparation materials to achieve precise disinfection and residue removal for different surface complexities during the automatic cleaning process, a control method that generates concentration change sequences is used to uniformly quantify surface morphology parameters, mechanical properties of adhering substances, and the distribution state of residues. Furthermore, a gradually decreasing disinfectant concentration configuration is generated through data mapping and sequence association algorithms, thereby forming a multi-level disinfection scheme that dynamically matches the needs of film preparation surface treatment.

[0057] At the beginning of the automated cleaning process, the initial disinfection intensity requirement is calculated based on surface morphology data. This surface morphology data includes at least the peak-to-valley difference of the surface roughness of the sheet material and the hardness parameter of the surface deposits. The peak-to-valley difference is preferably obtained using high-resolution microscopic imaging technology, and the surface microstructure undulation amplitude is obtained through image reconstruction and height difference extraction algorithms. Simultaneously, the hardness parameter of the deposits can be evaluated based on a preset material parameter library of the surface deposits, thereby achieving a quantitative characterization of the density and removal difficulty of the deposits. Further, the peak-to-valley difference and deposit hardness are normalized, assigned corresponding weight coefficients, and then weighted and summed to obtain the initial disinfection intensity requirement value. This requirement value reflects the comprehensive level of the microporous structure complexity of the sheet surface and the resistance to deposit removal, ensuring that the subsequent disinfectant concentration is matched to the initial contamination state of the material. After determining the required strength, the initial concentration ratio of the disinfectant is further determined using a concentration grading mapping technique. This technique is not simply based on empirical settings, but rather divides the peak-to-valley difference of surface roughness into multiple concentration level ranges according to its correspondence with concentration. The mapping parameters are then dynamically corrected based on the hardness of the deposits, forming a multi-parameter joint mapping relationship. Specifically, the peak-to-valley difference is input into a preset mapping table or mapping function to determine its corresponding difference range and output the corresponding concentration ratio range. Simultaneously, the range is adjusted based on the hardness of the deposits. For example, when the hardness is higher than the preset hardness, the initial concentration ratio is automatically increased to enhance the dissolution ability of highly dense deposits. The aforementioned mapping table is preferably obtained through statistical analysis of historical cleaning data and can cover the surface characteristics of different sheet materials such as metal, glass, and plastic. This avoids insufficient sterilization due to excessive dilution of the disinfectant or material damage due to excessively high concentration, thus achieving adaptive configuration of the initial concentration.

[0058] After determining the initial ratio, a progressively decreasing concentration change sequence is generated based on the surface residue distribution density. This residue distribution density characterizes the spatial distribution of contaminant particles on the surface of the prepared film. Specifically, digital image processing algorithms are used to identify and count particles in the surface scan image, calculating the number of residual particles per unit area to obtain the residue distribution density value. This density value is then used as the control input for sequence generation. Subsequently, a linear decreasing function or a piecewise decreasing strategy is employed to generate the concentration change sequence based on the density. In high-density scenarios, the concentration starts from a higher initial ratio and decreases gradually with smaller step sizes, allowing the disinfectant to quickly dissolve surface deposits and gradually penetrate deep into the micropores. In low-density scenarios, the decreasing interval is shortened and the intensity of action is reduced to decrease chemical load and improve material safety. The resulting concentration change sequence not only reflects the concentration decreasing trend but also maintains logical consistency with the decreasing residue density between each concentration level, ensuring that the disinfection effect decreases synchronously as contamination decreases.

[0059] After generating the concentration change sequence, to ensure that the concentration changes match the surface treatment requirements, the initial disinfection intensity requirement is further correlated with the concentration change sequence. Specifically, the initial intensity requirement is mapped to the start of the sequence, so that a higher intensity requirement corresponds to a higher initial concentration, and a lower intensity requirement corresponds to a lower initial concentration, thus establishing an initial correspondence between the intensity requirement and the concentration sequence. Subsequently, during the sequence decrease, the normalized intensity requirement is used as a weighting factor to proportionally adjust the concentration values ​​at each level, ensuring that the concentration decay rate is consistent with the decay trend of the intensity requirement. Through the above correlation processing, the final concentration change sequence is output and implemented during the automatic cleaning process. As a control parameter for multi-stage disinfection spraying or immersion, a continuous transition from high-intensity initial disinfection to low-intensity fine cleaning is achieved on the surface of the film preparation material. The above technical solution quantifies the peak-to-valley difference of surface roughness, the hardness of the adhering substances, and the density of residue distribution in a unified manner, and generates a gradually decreasing concentration change sequence through concentration gradation mapping and sequence association algorithms. This allows the multi-stage disinfection process to dynamically adjust the disinfectant concentration configuration according to the surface condition of the film preparation material, thereby improving the adaptability of automatic cleaning, disinfection efficiency, and material protection effect. It avoids the problems of incomplete cleaning or material damage caused by fixed disinfection parameters in the prior art, and is suitable for specific implementation scenarios of automatic cleaning methods for film preparation based on multi-stage disinfection.

[0060] Step S3: Configure the disinfectant particle size matching scheme and the disinfectant concentration change sequence according to a preset time sequence to construct a multi-stage penetration treatment process that acts step by step from the surface of the sheet material to the deeper layers; specifically including:

[0061] Based on the geometric characteristics of the surface to deep pores of the sheet material, the applicable range of each particle size level in the disinfectant particle size matching scheme is determined; the concentration change sequence is mapped to each particle size level to determine the working concentration and action time corresponding to each particle size level; based on the working concentration and action time, a step-by-step penetration process is formed to ensure that the disinfectant acts gradually from the surface to the deep layers.

[0062] Specifically, in one embodiment, to address the problems of complex pore structure, significant differences in surface and internal contaminant distribution, and uneven disinfectant penetration in the automatic cleaning and disinfection process of sheet materials, a disinfectant particle size matching scheme and a disinfectant concentration change sequence are configured according to a preset time sequence to construct a multi-level penetration treatment process that acts step by step from the surface to the depth of the sheet material. This process is based on the microstructure data of the sheet material and achieves synergistic matching between particle size, disinfectant concentration, and action time through multi-dimensional parameter analysis and data mapping, thereby ensuring that the disinfection process is targeted, gradual, and stable.

[0063] During implementation, the applicable range of each particle size level in the disinfectant particle size matching scheme is determined based on the pore geometry characteristics of the sheet material. To this end, structural analysis is performed on the pore geometry characteristics from the surface to the interior of the sheet material, and the applicable pore range for different disinfectant particle sizes corresponding to the pores from the surface to the interior of the sheet material is obtained. Furthermore, the disinfectant concentration change sequence is mapped to each particle size level to determine the specific working concentration and duration of each particle size in the multi-stage permeation treatment process. Specifically, by mapping the preset disinfectant concentration change sequence to the particle size classification structure, different particle sizes of disinfectant particles... Different concentration ranges are corresponding to different particle sizes, and corresponding action times are configured for each size. For example, high-concentration disinfectant is preferentially matched with small-diameter particles to enhance its initial cleaning and sterilization capabilities on the surface of the sheet material and in areas with high curvature micropores. As the particle size increases, the disinfectant concentration decreases accordingly, while the action time gradually increases, thereby adapting to the kinetic characteristics of the disinfectant's diffusion into the deeper pores. Through this correspondence between concentration and particle size, the chemical action intensity and physical penetration ability of the disinfectant in different structural levels are kept in harmony, avoiding local overtreatment or under-disinfection caused by a single change in concentration or particle size.

[0064] After configuring the particle size, disinfectant concentration, and action time, a step-by-step penetration process is constructed based on the parameters at each level. Specifically, a time-series process is built, arranging disinfectants of different particle sizes in ascending order and descending concentration, and applying them sequentially to the surface of the sheet material, allowing the disinfectant's action path to gradually extend from the surface to the deeper layers. Simultaneously, the coverage effect of each treatment stage is evaluated through simulation calculations of the penetration depth. The disinfectant particle size, concentration, and action time are input into the penetration depth calculation model to obtain the corresponding penetration depth, reflecting the cumulative effect of the disinfectant on the sheet material structure per unit time. This penetration depth calculation model is a regression model that fits the historical relationship between disinfectant particle size, concentration, action time, and historical penetration depth. When the simulation results show that the coverage of deep pores does not reach the preset threshold, the action time or concentration parameters for the corresponding particle size level can be adjusted to ensure a continuous and stable disinfection gradient between the surface and deep layers throughout the entire treatment process.

[0065] Furthermore, the multi-stage permeation treatment process can be adaptively optimized according to material characteristics under different sheet material types or operating conditions. For example, in ceramic sheet materials or high-temperature processing scenarios, a temperature correction factor can be introduced during the micropore connectivity assessment to ensure that the disinfectant particle matching scheme still has good permeation effect under different thermal environments. At the same time, for cases where the surface deposits of the sheet material have high hardness, the concentration and duration of disinfectant corresponding to small-diameter particles can be appropriately increased in the initial stage of the treatment process to enhance the removal ability of stubborn pollutants. In low-hardness or fine-structured materials, standard parameters are maintained to avoid unnecessary damage to the material itself.

[0066] Through the above technical solutions, the multi-stage permeation treatment process achieves an organic combination of disinfectant particle size, concentration change sequence and action time, enabling the disinfection process to be dynamically matched and adjusted according to the microstructural characteristics of the film material. This allows for a step-by-step disinfection effect from surface removal of adhering substances to deep sterilization in automated film cleaning scenarios, significantly improving the compliance rate of cleaning standards and treatment consistency, and providing a reliable data basis for feedback and adjustment of subsequent cleaning effects.

[0067] Step S4: Apply disinfectant components of different particle sizes and concentrations according to the processing flow to complete the staged treatment; obtain surface state data of the treated sheet material, analyze the distribution of residues and determine whether it meets the preset cleaning standards;

[0068] Step S4, the phased processing procedure, includes:

[0069] According to the step-by-step penetration process, disinfectant components with different particle sizes and concentrations are applied sequentially; the treatment parameters for each stage are adjusted based on the hardness of the surface deposits and the changes in the curvature of the pores; the process gradually transitions from surface cleaning to deep microporous sterilization, completing the staged treatment; and the treatment data for each stage are recorded, including the usage of disinfectant components and the treatment effect.

[0070] Specifically, to ensure that the particle size, concentration, and mode of action of the disinfectant are progressively matched to the pore structure of the sheet material, thereby achieving a continuous transition from surface cleaning to deep micropore sterilization, disinfectant components of different particle sizes and concentrations are sequentially configured according to a preset time sequence based on the aforementioned disinfectant particle size matching scheme and disinfectant concentration change sequence. During the configuration process, the particle size is correlated with the pore depth distribution range, so that the disinfectant particle size changes progressively along the treatment stage to adapt to the scale changes of the pore channels. Subsequently, each level of disinfectant components is applied to the surface of the sheet material to be treated by spraying or wetting, so that each level of component can enter its matched pore scale region and complete the disinfection reaction within the corresponding action time. The spraying method is used to improve the uniformity of surface coverage, while the wetting method is used to enhance the continuous penetration ability inside the pores, thereby ensuring the accessibility of the disinfectant in different pore structures.

[0071] During the application of disinfectant components, to avoid overtreatment or insufficient deep penetration due to fixed parameters, the treatment parameters for each stage are adaptively adjusted based on the hardness of the surface deposits and the changes in the curvature inside the pores. Specifically, the hardness data of the surface deposits is obtained and used together with the peak-to-valley difference of surface roughness to determine the initial disinfection intensity requirement. The hardness of the deposits reflects the shear resistance and adhesion strength of the residues and can be quantified through nanoindentation testing, for example, by using Vickers hardness (HV). Simultaneously, the changes in the curvature inside the pores are quantitatively analyzed. Based on the distribution of different pore sizes, a layered interval division method is used to divide the pore structure into layers, and the pore depth distribution is further analyzed. Point cloud data of pore walls are fitted to obtain local curvature values. Then, the rate of change of curvature radius from the surface to the depth is calculated to characterize the trend of pore wall curvature. Since the greater the curvature change, the higher the flow resistance and retention risk of disinfectant inside the pores, the treatment parameters are jointly corrected by combining the hardness of the attached material and the rate of curvature change. When the hardness of the attached material is higher than the preset hardness value, the action time is increased first to enhance the removal ability. When the rate of curvature change is large, i.e., greater than the preset rate of curvature change, the particle size is reduced first or the applied pressure is increased to improve permeability and reduce the probability of clogging. This generates an adjusted parameter sequence, which is then updated in conjunction with the concentration grading mapping technology to keep the disinfection intensity consistent with the complexity of the pore structure.

[0072] After configuring and adjusting the above parameters, a phased transition from surface cleaning to deep microporous sterilization is achieved. This involves applying a relatively high concentration of disinfectant with large particle size in the initial stage using the adjusted parameter sequence to quickly break down and peel off surface deposits on the sheet material. In subsequent stages, the concentration of the disinfectant gradually decreases, allowing it to diffuse along the pore pathways and penetrate deep into the tortuous micropores, achieving a step-by-step sterilization effect. To ensure continuity and controllability between stages, the surface condition after the previous stage is monitored and evaluated during stage transitions. For example, the residue distribution density is calculated immediately after surface cleaning, and this density result is used as the basis for correcting the parameters of the next deep sterilization stage, ensuring a smooth transition between stages driven by data. To avoid localized residue buildup or insufficient deep sterilization due to gaps between stages, the processing data for each stage is recorded to support subsequent cleaning standard determination and parameter correction. This data includes the dosage, concentration, particle size, applied pressure, and duration of each disinfectant component, as well as a comparison of surface condition data before and after treatment. This data is stored in log form. When subsequent detection reveals abnormal image contrast thresholds or residue distribution that does not meet preset standards, the log for that stage can be retrieved to trace parameter configuration and execution, thus providing a verifiable data basis for subsequent adaptive correction. Through this staged processing method, a continuous sterilization chain from surface to depth can be formed in multi-level disinfection and cleaning scenarios for medical device sheet materials, improving deep penetration efficiency and enhancing the consistency of overall cleaning results.

[0073] Further, in step S4, the distribution of residues is analyzed and it is determined whether the preset cleaning standards are met, including:

[0074] Surface condition data of the processed sheet material are acquired using multi-layer scanning imaging technology; based on the surface condition data, the particle size range of residues and the layer coverage of the detection area are analyzed; combined with the chemical composition analysis results of the residues and the adsorption capacity of the residues in the micropores, the cleanliness of each area is determined; the cleanliness is compared with the preset cleanliness standard to determine whether the cleanliness requirements are met.

[0075] Specifically, to achieve an objective evaluation of the multi-stage disinfection and cleaning effect and to provide data for subsequent adaptive parameter adjustment, multi-layer scanning imaging technology is used to acquire surface state data of the treated sheet material. This multi-layer scanning imaging technology is used to scan the sheet material layer by layer from the surface to the depth of the micropores in the same coordinate system to form two-dimensional or three-dimensional image data covering the entire structural depth range. Preferably, the multi-layer scanning imaging device can be implemented by using an optical microscope or an electron microscope combined with tomographic imaging, that is, by controlling the scanning focal plane or the tomographic section depth, images are acquired layer by layer to obtain continuous images of surface texture, internal pore structure, and residues deep in the pores. Based on the scanning results, a surface state dataset is further generated. The dataset includes at least the image pixel values ​​of each scanning layer and the depth coordinate information corresponding to each pixel, so that subsequent residue identification and layered statistics can maintain spatial consistency and traceability.

[0076] After obtaining the surface condition data, the size range of residual particles and the layered coverage of the detection area are analyzed based on the surface condition data. Specifically, the edge contours of residual particles are extracted from each layer of scanned images using an image segmentation algorithm, and the residual area is separated from the background area, thereby obtaining the boundary point set and equivalent diameter parameter of each residual particle. Figure 3 As shown, the particle size range of residues is statistically formed. To reflect the distribution gradient of residues in the pore depth direction, the detection area is divided into layers according to depth ranges, and the proportion of residue pixels to the total number of pixels in each layer is statistically analyzed to obtain the layer coverage rate. By combining the particle size range and the layer coverage rate, the scale characteristics and spatial distribution characteristics of residues can be reflected simultaneously. For example, when the pores are deep, the particle size range of residues can cover 0.1 micrometers to 5 micrometers, and the layer coverage rate shows a gradient distribution of high at the surface and low at the depth, thereby revealing areas of insufficient deep penetration that may exist in the multi-stage disinfection process, providing a clear direction for subsequent process optimization.

[0077] Based on the above spatial distribution analysis of residues, the cleanliness level of each area is further determined by combining the chemical composition analysis of residues with the adsorption capacity assessment of residues within micropores. Specifically, chemical composition analysis is performed on the residues to identify their molecular structure and type. This chemical composition analysis is preferably achieved using spectroscopic methods, such as infrared spectroscopy or mass spectrometry, to scan the spectral lines of the residue samples and match the obtained spectral lines with the characteristic peaks of known compounds in a database, thereby determining whether the residue components belong to organic matter, inorganic salts, or biological residues. Subsequently, the adsorption capacity of residues within micropores is assessed. This adsorption capacity assessment uses a surface energy calculation model to quantify the adsorption intensity. This surface energy calculation model is based on the energy superposition principle of van der Waals interactions and electrostatic interactions, and introduces a pore curvature correction factor to reflect the influence of the microporous environment on adsorption behavior. By calculating the binding energy between the residue and the pore wall, the adsorption capacity of residues within micropores is quantitatively assessed. For example, the expression of the surface energy calculation model is:

[0078] ,

[0079] The binding energy between the residue and the micropore wall is calculated using a formula based on the binding energy of van der Waals forces and electrostatic interactions. The strength of the adsorption force is then characterized by comparing this binding energy with a preset threshold. Furthermore, the cleanliness score for each area is calculated by fusing the chemical composition analysis results with the corresponding adsorption force assessment values. This cleanliness score is preferably obtained through a weighted summation method, specifically by normalizing and weighting the hazard level quantification of the residue components and the adsorption force magnitude. The weight of the hazard level is greater than the weight of the adsorption force magnitude, ensuring that residues with higher biological risks or stronger adsorption characteristics have a higher influence proportion in the score. This method allows for the simultaneous inclusion of both the type risk and the retention risk of the residue. By incorporating a cleanliness evaluation system, the comprehensiveness and reliability of the cleanliness assessment of medical device film materials can be improved. For example, when the residue on the film material is determined to be protein residue by chemical composition analysis and the adsorption force is assessed as medium strength, a cleanliness score can be calculated as the first score. Based on this score, if the score is lower than the preset standard, reprocessing is required to reduce the residual risk of medical device film materials in reuse scenarios. In an optional embodiment, when the residue is identified as stubborn organic matter, an iterative calculation mechanism can be introduced into the adsorption force assessment. By simulating the adsorption process multiple times and gradually introducing the influence of micropore curvature on binding energy until the calculation converges, a more accurate binding energy value can be obtained, thereby improving the accuracy and consistency of cleanliness assessment in high-porosity material scenarios.

[0080] Finally, the cleanliness level is compared with the preset cleanliness standard to determine whether the cleanliness requirements are met. Specifically, the cleanliness score is compared with the preset score. When the cleanliness score is higher than the preset score, the material is deemed to meet the cleanliness requirements and the processing procedure is completed. When the cleanliness score is lower than the preset score, it is deemed not to meet the standard and the subsequent processing parameter adjustment and re-execution mechanism is triggered. For example, when the cleanliness score is the second score and higher than the preset score, the cleaning and disinfection results are confirmed to meet the requirements, thereby improving the efficiency of the processing cycle. Conversely, when the cleanliness score is the third score and lower than the preset score, a residual risk is determined and a closed-loop correction process is initiated to ensure the safety and reusability of the material in medical device application scenarios.

[0081] Step S5: If the preset cleaning standard is not met, adjust the disinfectant particle size matching scheme and the concentration change sequence according to the residue distribution to generate a new treatment configuration and execute it; specifically including:

[0082] The surface state data of the processed sheet material is acquired, the image contrast value of the detection area is analyzed and calculated, and compared with a preset contrast threshold. When the image contrast value is abnormal, the spatial coordinates of the abnormal residue distribution area are marked, and the residue removal difficulty level and the parameter correction range after detection are determined. According to the residue removal difficulty level and the correction range, the particle size distribution in the disinfectant particle size matching scheme is adjusted. According to the adjusted particle size distribution, the concentration change sequence is regenerated. The adjusted particle size distribution scheme and the concentration change sequence are combined to form a new processing configuration, and a new processing flow is executed.

[0083] Specifically, in order to achieve closed-loop optimization of the automatic cleaning method for slides based on multi-level disinfection, when the cleaning judgment result shows that the preset cleaning standard is not met, based on the surface state data of the treated slide material, a new processing configuration is formed and the processing process is re-executed through abnormal area location, quantification of the difficulty of residue removal, calculation of correction range, and linkage reconstruction of particle size and concentration sequence, thereby improving the cleaning standard compliance stability of slide materials with complex porous structure.

[0084] During implementation, surface state data of the processed sheet material is acquired, and anomalies in the distribution of residues in the detection area are identified. Preferably, the surface state data is obtained through multi-layer scanning imaging technology, that is, the sheet material is scanned layer by layer from the surface to the depth of the micropores to form tomographic image data with depth coordinate information. Based on the tomographic image, the distribution image of residue particles in the detection area is extracted, and the corresponding image contrast value is calculated. The image contrast value is obtained by gray-level histogram analysis, that is, the distribution range of pixel gray-level values ​​in the detection area is statistically analyzed and the degree of gray-level difference is calculated. The above contrast value is then compared with a preset contrast threshold. When an abnormal contrast value is detected, that is, when the deviation is greater than the set deviation range, it is determined that there is an abnormal distribution of residues, and the spatial coordinates of the corresponding abnormal area are marked in a unified spatial coordinate system to generate an abnormal distribution map. In order to avoid the material texture from interfering with the anomaly identification, the contrast threshold is set differently according to the surface characteristics of the sheet material. For example, the deviation range is appropriately widened for materials with high roughness to reduce the probability of false judgment, while the sensitivity of the deviation range is increased for smooth surface materials to capture the abnormal distribution of small residues.

[0085] After obtaining the abnormal distribution map, multidimensional quantitative analysis was performed on the residues within the abnormal area to determine the difficulty level of residue removal. Specifically, the size range and distribution density of residue particles within the abnormal area were analyzed, and a comprehensive judgment was made by combining the results of residue chemical composition detection and adsorption capacity assessment. The residue chemical composition was used to distinguish between organic, inorganic salt, and biological residues, while the adsorption capacity assessment characterized the stability of the residue's binding to the micropore walls. The removal difficulty level was obtained by weighted calculation after normalizing particle size, distribution density, and adsorption capacity. Larger particles with higher distribution density and stronger adsorption capacity were classified as high-difficulty, while smaller particles with sparse distribution were classified as low-difficulty. This weighted calculation method was used to determine the difficulty level. Factors are used to generate the final difficulty level value using a weighted average method, which avoids misjudgment caused by a single indicator, thereby improving the targeting of subsequent parameter corrections and reducing the number of repeated treatments. After the removal difficulty level is determined, the parameter correction magnitude after detection is calculated based on the removal difficulty level and the coverage of the anomaly distribution map. Specifically, the surface state data before and after treatment are compared to calculate the residue reduction rate. The difference between the above reduction rate and the target reduction rate corresponding to the expected cleaning standard is analyzed to obtain the percentage correction magnitude reflecting the degree of treatment inadequacy. This correction magnitude is used to quantify the gap between the current treatment configuration and the expected cleaning effect, and serves as the control input for subsequent particle size distribution adjustment and concentration sequence reconstruction, transforming parameter correction from empirical adjustment to data-driven controllable adjustment.

[0086] After obtaining the removal difficulty level and correction range, the particle size distribution in the disinfectant particle size matching scheme is adjusted according to the removal difficulty level and correction range. Specifically, the abnormal area is divided into multiple treatment priority intervals, and the adjustment direction of particle size distribution is determined according to the priority. Specifically, for high difficulty priority intervals, the proportion of larger particle sizes is increased to enhance physical removal ability; for low difficulty priority intervals, the proportion of small or medium particles is increased to enhance penetration efficiency and reduce clogging risk. At the same time, the correction range is introduced into the dynamic adjustment process of particle size ratio. When the correction range indicates that the residue reduction rate is significantly lower than expected, the proportion of large particle sizes is further increased to improve removal power; when the reduction rate is close to expected but local abnormalities still exist, the proportion of medium particle sizes is increased through fine-tuning to achieve a balance between removal effect and penetration depth. This particle size distribution adjustment process is also differentiated in combination with the pore depth distribution range of the tablet material. For example, when the pore depth is deep, a larger particle size range is preferentially configured in the high difficulty interval to enhance deep removal, while when the pore depth is shallow, the large particle size range is limited to avoid surface wear.

[0087] After adjusting the particle size distribution, a new concentration change sequence is generated based on the adjusted particle size distribution to maintain the synergistic relationship between particle size and concentration gradient. Specifically, the target removal area corresponding to each particle size range is analyzed, and the initial disinfectant concentration is determined by combining the residue distribution density and adsorption capacity assessment results within that area. Subsequently, a gradually decreasing concentration change sequence is generated according to the particle size from largest to smallest, so that larger particle sizes correspond to higher concentrations to enhance removal ability, while smaller particle sizes correspond to lower concentrations to reduce the chemical load on the material surface and improve the safety of deep treatment. Furthermore, the above concentration change sequence can be optimized according to material tolerance. For materials with low tolerance, the concentration deceleration rate is increased to reduce the high-concentration action time, while for materials with high tolerance, the high-concentration stage is extended to improve the removal effect of stubborn residues.

[0088] Finally, the adjusted particle size distribution scheme is integrated with the regenerated concentration change sequence to form a new treatment configuration. Based on this new configuration, the step-by-step penetration treatment process is re-executed. The particle size range, concentration value, and duration of action at each stage in the new configuration are recorded. Disinfectant components with different particle sizes and concentrations are applied sequentially according to this configuration to complete the staged treatment. This allows the treatment process to achieve a stronger cleaning effect on abnormal areas and more thorough penetration into deep pores. The above technical solution, through a closed-loop correction mechanism, can achieve an adaptive optimization link of anomaly location, difficulty quantification, parameter correction, and re-execution in the disinfection and cleaning of porous ceramic, metal alloy, and other complex porous materials, thereby significantly improving the stability and efficiency of achieving cleaning standards.

[0089] This invention provides an automated slide cleaning device based on multi-stage disinfection, used to implement the above-mentioned method, such as... Figure 4 As shown, the system includes:

[0090] The surface inspection unit is used to perform surface inspection on the sheet material to be processed, acquire the surface morphology data of the sheet material, and extract the micro-texture features and pore depth distribution parameters characterizing the surface structure of the sheet material based on the surface morphology data.

[0091] The structural analysis unit is used to analyze the structural characteristics of the sheet material surface based on the micro-texture features and pore depth distribution parameters, and generate a corresponding disinfectant particle size matching scheme; and to generate a corresponding disinfectant concentration change sequence based on the degree of contaminant adhesion on the sheet material surface.

[0092] A multi-level control unit is used to configure the disinfectant particle size matching scheme and the disinfectant concentration change sequence according to a preset time sequence, and to construct a multi-level penetration control process that acts step by step from the surface of the sheet material to the deep layer.

[0093] The phased processing unit is used to complete phased processing according to the multi-level control process by using disinfectant components with different particle sizes and concentrations; to acquire surface state data of the processed sheet material, analyze the distribution of residues, and determine whether it meets the preset cleaning standards;

[0094] The effect evaluation unit is used to adjust the disinfectant particle size matching scheme and the concentration change sequence according to the residue distribution when the preset cleaning standard is not met, generate a new control configuration and execute it.

[0095] The present invention also provides an automated cleaning computer system for film preparation based on multi-stage disinfection, the computer system comprising: a memory and at least one processor, wherein the memory stores instructions;

[0096] The at least one processor invokes the instructions in the memory to cause the multi-stage disinfection-based automated film cleaning computer system to perform the above-described method.

[0097] In summary, this invention utilizes high-resolution microscopic imaging technology to comprehensively analyze the surface of the prepared material, obtaining precise surface morphology data, including microscopic texture features and pore depth distribution parameters. This accurately reflects the surface state of the material, providing support for generating matching disinfectant particle sizes and concentrations. Based on the acquired surface data, the microstructural characteristics of the material are analyzed, generating corresponding disinfectant particle size matching schemes and concentration variation sequences. The disinfectant concentration can be adaptively adjusted according to the degree of contaminant adhesion on the surface of the prepared material, ensuring targeted cleaning effects on both the surface and deep layers. For materials with high surface roughness or strong pore connectivity, precise particle size selection and concentration adjustment avoid problems of uneven disinfectant penetration or overtreatment. A multi-stage penetration process configures the disinfectant particle size and concentration to different treatment stages, penetrating the prepared material layer by layer, thus achieving effective disinfection. The disinfectant gradually penetrates from the surface to the depths, ensuring that pores at different levels are effectively cleaned. Through this step-by-step penetration method, the disinfectant can penetrate deep into the pores of the material, removing stubborn contaminants while avoiding damage to the material surface caused by high-concentration disinfectant. Finally, scanning imaging technology is used to acquire surface state data of the cleaned material, analyze the distribution of residues, and determine whether it meets the preset cleaning standards. If the expected standards are not met, the particle size and concentration change sequence of the disinfectant are adjusted according to the distribution of residues to ensure that the treatment effect of each cleaning stage is optimal. Through the synergy of the above technical solutions, refined cleaning of the material is achieved, which not only improves the stability of the disinfection effect but also avoids the problems of insufficient cleaning or material damage commonly found in traditional single disinfectant solutions. This provides an efficient and precise automated cleaning solution for scenarios with high-standard cleaning requirements, such as medical devices and microelectronics processing.

[0098] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the specific working processes of the systems and units described above can be referred to the corresponding processes in the foregoing method embodiments, and will not be repeated here.

[0099] If the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, 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 storage medium 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 the present invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0100] The above-described embodiments are only used to illustrate the technical solutions of the present invention, and are not intended to limit it. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. An automated cleaning method for slide preparation based on multi-stage disinfection, characterized in that, The method includes: Step S1: Perform surface inspection on the sheet material to be processed, obtain the surface morphology data of the sheet material, and extract the micro-texture features and pore depth distribution parameters that characterize the surface structure of the sheet material based on the surface morphology data; Step S2: Based on the micro-texture features and pore depth distribution parameters, analyze the structural characteristics of the sheet material surface and generate a corresponding disinfectant particle size matching scheme; based on the degree of adhesion of pollutants on the sheet material surface, generate a corresponding disinfectant concentration change sequence; Step S3: Configure the disinfectant particle size matching scheme and the disinfectant concentration change sequence according to a preset time sequence to construct a multi-level penetration treatment process that acts step by step from the surface of the sheet material to the deep layers; Step S4: Apply disinfectant components of different particle sizes and concentrations according to the multi-stage penetration treatment process to complete the staged treatment; obtain surface state data of the treated sheet material, analyze the distribution of residues, and determine whether it meets the preset cleaning standards; Step S5: If the preset cleaning standard is not met, adjust the disinfectant particle size matching scheme and the disinfectant concentration change sequence according to the residue distribution to generate a new treatment configuration and execute it.

2. The method according to claim 1, characterized in that, In step S1, the micro-texture features and pore depth distribution parameters in the surface morphology data are extracted, including: High-resolution microscopic imaging technology is used to image the surface of the substrate material to be processed, acquiring surface morphology data containing surface texture information and pore structure information. The surface morphology data is preprocessed, and digital image processing methods are used to extract the microscopic texture features of the substrate material surface. Combined with multi-view imaging or stereo reconstruction results, depth analysis is performed on the surface of the substrate material and its internal pore structure to obtain the distribution of pores in the depth direction, and the pore depth distribution parameters are determined accordingly. The pore depth distribution parameters are used to characterize the depth levels of pores extending from the surface to the interior of the material.

3. The method according to claim 1, characterized in that, In step S2, a disinfectant particle size matching scheme is generated, including: Based on the microtexture features and the pore depth distribution parameters, geometric feature information of the pore structure is extracted. A hierarchical interval division method is used to determine the proportion distribution of different pore scales in the geometric feature information. Based on the proportion distribution of different pore scales, the curvature change inside the pores and the degree of micropore connectivity in the geometric feature information, a targeted disinfectant particle size matching scheme is generated. The disinfectant particle size matching scheme is mapped to the pore scale distribution to generate the adaptation relationship between particle size and pore structure. The geometric feature information includes at least pore size, curvature change inside the pores, and degree of micropore connectivity on the surface.

4. The method according to claim 3, characterized in that, In step S2, a concentration change sequence is generated, including: Based on the surface roughness peak-valley difference and the hardness of the surface deposits on the sheet material in the surface morphology data, the initial disinfection intensity requirement is calculated; the initial ratio of high-concentration disinfectant is determined by concentration gradation mapping technology; a gradually decreasing concentration change sequence is generated based on the distribution density of surface residues; the initial disinfection intensity requirement is correlated with the concentration change sequence to ensure that the concentration change matches the surface treatment requirements.

5. The method according to claim 1, characterized in that, Step S3 establishes a step-by-step penetration process, including: Based on the geometric characteristics of the surface to deep pores of the sheet material, the applicable range of each particle size level in the disinfectant particle size matching scheme is determined; the concentration change sequence is mapped to each particle size level to determine the working concentration and action time corresponding to each particle size level; based on the working concentration and action time, a step-by-step penetration process is formed to ensure that the disinfectant acts gradually from the surface to the deep layers.

6. The method according to claim 1, characterized in that, Step S4 involves a phased processing procedure, including: According to the step-by-step penetration process, disinfectant components with different particle sizes and concentrations are applied sequentially; the treatment parameters for each stage are adjusted according to the hardness of the surface deposits and the curvature of the pores; the treatment gradually transitions from surface cleaning to deep microporous sterilization, completing the staged treatment; the treatment data for each stage are recorded, including the usage of disinfectant components and the treatment effect.

7. The method according to claim 6, characterized in that, In step S4, the distribution of residues is analyzed and it is determined whether the preset cleaning standards are met, including: Surface condition data of the processed sheet material are acquired using multi-layer scanning imaging technology; based on the surface condition data, the particle size range of residues and the layer coverage of the detection area are analyzed; combined with the chemical composition analysis of residues and the adsorption capacity of residues in micropores, the cleanliness level of each area is determined; the cleanliness level is compared with the preset cleanliness standard to determine whether the cleanliness requirements are met.

8. The method according to claim 1, characterized in that, In step S5, a new processing configuration is generated and executed, including: If the detected image contrast threshold shows an anomaly in the spatial coordinates of the residue distribution, then the residue removal difficulty level and the parameter correction range after detection are obtained; based on the residue removal difficulty level and the parameter correction range, the particle size distribution in the disinfectant particle size matching scheme is adjusted; based on the adjusted particle size distribution, the concentration change sequence is regenerated; the adjusted disinfectant particle size matching scheme and the disinfectant concentration change sequence are combined to form a new processing configuration, and a new processing flow is executed.

9. An automatic film cleaning device based on multi-stage disinfection, used to implement the method as described in any one of claims 1-8, characterized in that, The device includes: The surface inspection unit is used to perform surface inspection on the sheet material to be processed, acquire the surface morphology data of the sheet material, and extract the micro-texture features and pore depth distribution parameters characterizing the surface structure of the sheet material based on the surface morphology data. The structural analysis unit is used to analyze the structural characteristics of the sheet material surface based on the micro-texture features and pore depth distribution parameters, and generate a corresponding disinfectant particle size matching scheme; and to generate a corresponding disinfectant concentration change sequence based on the degree of contaminant adhesion on the sheet material surface. A multi-level control construction unit is used to configure the disinfectant particle size matching scheme and the disinfectant concentration change sequence according to a preset time sequence to construct a multi-level penetration control process that acts step by step from the surface of the sheet material to the deep layer. The phased processing unit is used to complete phased processing according to the multi-level penetration control process by using disinfectant components of different particle sizes and concentrations; to acquire surface state data of the processed sheet material, analyze the distribution of residues, and determine whether it meets the preset cleaning standards; The effect evaluation unit is used to adjust the disinfectant particle size matching scheme and the concentration change sequence according to the residue distribution when the preset cleaning standard is not met, generate a new control configuration and execute it.

10. An automated film cleaning computer system based on multi-stage disinfection, characterized in that, The computer system includes: a memory and at least one processor, the memory storing instructions; the at least one processor invokes the instructions in the memory to cause the multi-stage disinfection-based automated film cleaning computer system to perform the method as described in any one of claims 1-8.

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