A method for preparing a high-strength calcium carbonate facing panel

Abstract

The application discloses a preparation method of high-strength calcium carbonate veneer, and relates to the related field of building material preparation. The method comprises the following steps: obtaining a target decorative panel image, introducing a panel image saliency evaluation plan, performing saliency evaluation analysis on the target decorative panel image, and obtaining a target saliency block; recording the target saliency block as a first-level saliency, and obtaining a first-level particle size strategy corresponding to the first-level saliency; screening a target calcium carbonate raw material of the target decorative panel image based on the first-level particle size strategy, and obtaining a first-level particle size raw material; taking the first-level particle size raw material as a target optimization constraint, performing global optimization analysis on a surface modification database, and obtaining an optimal modification strategy; and performing surface modification treatment on the first-level particle size raw material according to the optimal modification strategy, and obtaining a target calcium carbonate veneer. The method solves the technical problems of lack of high efficiency and insufficient precision in the preparation of existing calcium carbonate veneer, and achieves the technical effect of improving the efficiency, precision and product process management capability of the preparation of the calcium carbonate veneer.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of building material preparation, and particularly relates to a preparation method of high-strength calcium carbonate decorative panel. BACKGROUND

[0002] In the field of building decoration materials, calcium carbonate decorative panels are favored due to their good physical properties, beautiful appearance, and environmental protection characteristics. The preparation process of traditional calcium carbonate decorative panels relies on manual experience and trial-and-error method. In the preparation process, the proportioning of raw materials, the selection of particle size, and the modification treatment and other links rely on experience judgment, and lack precise control means, which not only leads to low production efficiency, but also is difficult to ensure the consistency of product quality. Therefore, an efficient and precise preparation method is urgently needed, which can optimize the design and production of calcium carbonate decorative panels according to decoration requirements.

[0003] In the related art at present, there are technical problems of lack of efficiency and insufficient precision in the preparation of high-strength calcium carbonate decorative panels. SUMMARY

[0004] The present application provides a preparation method of high-strength calcium carbonate decorative panel, which adopts the technical means of obtaining a target decorative panel image, performing saliency evaluation analysis, obtaining a primary particle size strategy, screening primary particle size raw materials, obtaining an optimal modification strategy through global optimization analysis, and surface modification treatment, so as to improve the efficiency, precision and product process management ability of the preparation of calcium carbonate decorative panel.

[0005] The present application provides a preparation method of high-strength calcium carbonate decorative panel, which includes: obtaining a target decorative panel image, and introducing a panel image saliency evaluation plan to perform saliency evaluation analysis on the target decorative panel image to obtain a target saliency block; recording the target saliency block as a primary saliency, and obtaining a primary particle size strategy corresponding to the primary saliency; screening target calcium carbonate raw materials of the target decorative panel image based on the primary particle size strategy to obtain primary particle size raw materials; performing global optimization analysis on a surface modification database with the primary particle size raw materials as a target optimization constraint to obtain an optimal modification strategy; and performing surface modification treatment on the primary particle size raw materials according to the optimal modification strategy to obtain a target calcium carbonate decorative panel.

[0006] In a possible implementation, a target decoration panel image is acquired, a panel image saliency evaluation plan is introduced, and the target decoration panel image is subjected to saliency evaluation analysis to obtain a target saliency block. The following processing is performed: the target decoration panel image is subjected to block segmentation to obtain a segmentation result, where the segmentation result includes a plurality of blocks; a predetermined spatial dimension in the panel image saliency evaluation plan is extracted; spatial features of a first block in the plurality of blocks are collected based on the predetermined spatial dimension to obtain first spatial features; a predetermined center-surround difference quantization mechanism is read, and difference quantization analysis is performed on the first spatial features according to the predetermined center-surround difference quantization mechanism to obtain a first difference coefficient; the plurality of blocks are sorted in descending order according to the first difference coefficient to obtain a target difference coefficient; and a block corresponding to the target difference coefficient is recorded as the target saliency block.

[0007] In a possible implementation, the following processing is performed: the predetermined spatial dimension includes a color dimension, a brightness dimension, and a direction dimension, and the color dimension includes a red dimension, a green dimension, and a blue dimension.

[0008] In a possible implementation, a predetermined center-surround difference quantization mechanism is read, and difference quantization analysis is performed on the first spatial features according to the predetermined center-surround difference quantization mechanism to obtain a first difference coefficient. The following processing is performed: a first set of surrounding blocks of the first block is acquired, the first set of surrounding blocks includes M surrounding blocks, and M is an integer greater than or equal to 2 and less than or equal to 4; a first surrounding block in the M surrounding blocks is extracted, and first surrounding spatial features of the first surrounding block are collected; and according to the predetermined center-surround difference quantization mechanism, a mean value of a first difference value obtained by normalizing and weighting a first difference feature between the first surrounding spatial features and the first spatial features is recorded as the first difference coefficient.

[0009] In a possible implementation, the following processing is further performed: an arbitrary block in the plurality of blocks is extracted; similarity calculation is performed on an arbitrary spatial feature of the arbitrary block and a target spatial feature of the target saliency block to obtain an arbitrary similarity; it is determined whether the arbitrary similarity is within a predetermined similarity threshold; if the arbitrary similarity is within the predetermined similarity threshold, the arbitrary block is recorded as a secondary saliency, and the target calcium carbonate raw material is screened according to a secondary particle size strategy corresponding to the secondary saliency to obtain a secondary particle size raw material; and the secondary particle size raw material is used as the target optimization constraint for global optimization.

[0010] In a possible implementation, the following processing is performed: if the arbitrary similarity is not at the predetermined similarity threshold, the arbitrary block is marked as a third-level saliency, and a third-level particle size strategy corresponding to the third-level saliency is used to screen the target calcium carbonate raw material to obtain a third-level particle size raw material, and the third-level particle size raw material is used as a target optimization constraint for global optimization.

[0011] In a possible implementation, the first-level particle size raw material is used as a target optimization constraint to perform global optimization analysis on a surface modification database to obtain an optimal modification strategy, and the following processing is performed: a first data group in the surface modification database is extracted, where the first data group includes a first calcium carbonate particle size; when the first calcium carbonate particle size meets the target optimization constraint, the first data group is added to a target optimization space; a modification fitness evaluation function is read and used as a target optimization evaluation index; and global optimization analysis is performed on the target optimization space based on the target optimization evaluation index to obtain the optimal modification strategy.

[0012] In a possible implementation, the following processing is performed: the expression of the modification fitness evaluation function is as follows:

[0013] ;

[0014] wherein, is a modification fitness, , , , and are, in sequence, a dispersity, an oil absorption value, a compatibility, a glossiness, and a transparency of the calcium carbonate raw material after modification, , , , and are all weight coefficients, and .

[0015] In a possible implementation, global optimization analysis is performed on the target optimization space based on the target optimization evaluation index to obtain the optimal modification strategy, and the following processing is performed: an arbitrary data group in the target optimization space is randomly obtained; an arbitrary modified feature in the arbitrary data group is calculated based on the target optimization evaluation index to obtain an arbitrary fitness; iterative optimization is performed with the maximum arbitrary fitness as a target to obtain a target data group corresponding to a target fitness; and a target modification strategy in the target data group is used as the optimal modification strategy.

[0016] The application provides a preparation method of a high-strength calcium carbonate decorative panel. First, a target decorative panel image is obtained, and a saliency evaluation plan is introduced to perform saliency evaluation analysis on the target decorative panel image to obtain a target saliency block. Then, the target saliency block is recorded as a first-level saliency, and a first-level particle size strategy corresponding to the first-level saliency is obtained. Next, the first-level particle size strategy is used to screen a target calcium carbonate raw material of the target decorative panel image to obtain a first-level particle size raw material. Then, the first-level particle size raw material is used as a target optimization constraint to perform global optimization analysis on a surface modification database to obtain an optimal modification strategy. Finally, the first-level particle size raw material is subjected to surface modification treatment according to the optimal modification strategy to obtain the target calcium carbonate decorative panel, so that the technical effect of improving the efficiency, precision and product process management capability of the preparation of the calcium carbonate decorative panel is achieved. BRIEF DESCRIPTION OF DRAWINGS

[0017] In order to more clearly illustrate the technical solutions of the embodiments of the application, the drawings of the embodiments of the application will be briefly introduced below. In the present application, a flowchart is used to illustrate the operations performed by the method according to the embodiments of the application. It should be understood that the foregoing or the following operations are not necessarily performed in sequence. On the contrary, various steps can be processed in reverse order or simultaneously as needed. Meanwhile, other operations can be added to these processes, or a step or several steps can be removed from these processes.

[0018] Figure 1 A flowchart of the preparation method of the high-strength calcium carbonate decorative panel provided by the embodiments of the application.

[0019] Figure 2 A flowchart of the saliency evaluation analysis on the target decorative panel image in the preparation method of the high-strength calcium carbonate decorative panel provided by the embodiments of the application. DETAILED DESCRIPTION

[0020] The above description is only a summary of the technical solutions of the application. In order to more clearly understand the technical means of the application, the application can be implemented according to the content of the specification, and in order to make the above and other purposes, characteristics and advantages of the application more obvious and easy to understand, the following specific embodiments of the application are described.

[0021] In order to make the purposes, technical solutions and advantages of the application more clear, the application will be further described in detail below with reference to the drawings. The described embodiments should not be regarded as limiting the application. All other embodiments obtained by those skilled in the art without making creative efforts fall within the scope of protection of the application.

[0022] In the following description, "some embodiments" are referred, which describe a subset of all possible embodiments, but it can be understood that "some embodiments" can be the same subset or different subset of all possible embodiments, and can be combined with each other without conflict, the term "first\second" referred to only distinguishes similar objects, and does not represent a specific order for the objects. The terms "include" and "have" and any variations thereof, are intended to cover non-exclusive inclusion, for example, a process, method, system, product or server including a series of steps or units does not have to be limited to those steps or units clearly listed, but can include other steps or modules not clearly listed or inherent to these processes, methods, products or devices. Unless otherwise defined, all technical and scientific terms used herein have the same meaning as understood by those skilled in the art to which the present application belongs. The terms used herein are only for the purpose of describing the embodiments of the present application.

[0023] The embodiments of the present application provide a preparation method of a high-strength calcium carbonate decorative panel, as shown in the following formula (I): Figure 1 The method comprises the following steps:

[0024] In step S100, a target decorative panel image is obtained, and a panel image saliency evaluation plan is introduced to perform saliency evaluation analysis on the target decorative panel image to obtain a target saliency block.

[0025] Specifically, an image file containing a desired decoration effect is received from a customer or a designer. This image file can be a high-definition photo, a vector graph or any format that can clearly show the decoration intention. The panel image saliency evaluation plan is introduced, and the plan is a set of predefined rules or algorithms for evaluating salient regions in the image. These rules can be based on color contrast, texture complexity, spatial frequency and other factors. For example, the saliency evaluation method can be based on a visual attention model, which calculates the saliency value of each pixel in the image, and then determines which regions are salient according to these values. The target decorative panel image is evaluated using the plan to determine the most eye-catching block in the image, which specifically includes dividing the image into multiple small blocks, calculating the saliency value of each block, and then sorting according to these values. Finally, the block with the highest saliency value is selected as the target saliency block.

[0026] As shown in the following formula (II): Figure 2In one possible implementation, as shown, the target decorative panel image is acquired, and a panel image saliency evaluation plan is introduced to perform saliency evaluation on the target decorative panel image to obtain a target salient block. Step S100 further includes step S110 of performing block segmentation on the target decorative panel image to obtain a segmentation result, where the segmentation result includes a plurality of blocks. Specifically, the target decorative panel image is segmented into blocks so that each block can be subsequently evaluated for saliency. Block segmentation can be performed using various methods, such as adaptive segmentation based on image content, fixed-size grid segmentation, etc. For example, an adaptive image segmentation algorithm can be used to automatically determine block boundaries based on features such as color, texture, etc. in the image, thereby obtaining a plurality of blocks of different sizes and shapes.

[0027] Step S120 involves extracting predetermined spatial dimensions from the panel image saliency evaluation plan. Specifically, the panel image saliency evaluation plan defines some spatial dimensions for evaluating image saliency, such as color contrast, texture complexity, directionality, etc. These predetermined spatial dimensions are extracted from the plan and used to collect spatial features of the blocks.

[0028] Step S130 involves collecting spatial features of a first block in the plurality of blocks based on the predetermined spatial dimensions to obtain first spatial features. Specifically, spatial features of each block (the first block) obtained by segmentation are collected, and feature values of the block in each predetermined spatial dimension are calculated. For example, if the predetermined spatial dimensions include color contrast and texture complexity, feature values of the first block in these two dimensions need to be calculated.

[0029] Step S140 involves reading a predetermined center-surround difference quantification mechanism and performing difference quantification analysis on the first spatial features based on the predetermined center-surround difference quantification mechanism to obtain a first difference coefficient. Specifically, a predetermined center-surround difference quantification mechanism is read, which is used to quantify the difference between a block and its surrounding environment. Difference quantification analysis includes calculating the difference values of the block and surrounding blocks in terms of color, texture, etc., and obtaining a difference coefficient based on the difference values. For example, a statistical-based difference quantification method can be used to accurately quantify the difference between a block and its surrounding environment.

[0030] Step S150 involves ranking the plurality of blocks in descending order based on the first difference coefficient to obtain a target difference coefficient. Specifically, the difference coefficients of each block obtained in step S140 are ranked in descending order, i.e., the block with the largest difference coefficient is ranked first, and the block with the smallest difference coefficient is ranked last, and the target difference coefficient is the first difference coefficient in the ranking.

[0031] Step S160, mark the block corresponding to the target difference coefficient as the target salient block. Specifically, the block with the largest difference coefficient is the target salient block, and the target salient block is the part of the image that attracts the most attention and has the most visual impact. This implementation accurately quantifies the saliency of each block through block segmentation, spatial feature collection, difference quantization analysis, and other steps, ensuring that the final calcium carbonate facing plate can be consistent with the target decorative panel in terms of visual effect.

[0032] In a possible implementation, step S120 further includes step S121, the predetermined spatial dimensions include color dimension, brightness dimension, and direction dimension, and the color dimension includes red dimension, green dimension, and blue dimension.

[0033] Specifically, the RGB color space is used to represent the color, and the RGB color space is composed of three components: red (R), green (G), and blue (B), and the value range of each component is 0 to 255. The color dimension includes the red dimension, the green dimension, and the blue dimension, which correspond to the R, G, and B components in the RGB color space, respectively.

[0034] In the RGB color space, the brightness can be obtained by calculating the weighted sum of the R, G, and B components (for example, the common weighting coefficients are 0.299, 0.587, and 0.114, which correspond to the R, G, and B components, respectively). A certain color component (such as the green component G, because the human eye is most sensitive to green) or the average value of the R, G, and B components can also be directly used as a measure of brightness.

[0035] The direction dimension is used to describe the directionality of the texture or edge in the image, and can be obtained by calculating the image gradient (such as the Sobel operator, Prewitt operator, etc.). The gradient is a vector, and its size represents the rate of change of the image at that point, and the direction represents the direction of the fastest change.

[0036] The color dimension can capture the color features in the image, the brightness dimension can reflect the light and dark changes of the image, and the direction dimension can describe the directionality of the texture or edge in the image. This implementation improves the accuracy of image saliency evaluation by combining these predetermined spatial dimensions for spatial feature collection, and provides a reliable basis for subsequent particle size strategy selection and surface modification treatment.

[0037] In a possible implementation, the predetermined center-surround difference quantification mechanism is read, and a first difference coefficient is obtained by performing difference quantification analysis on the first spatial feature according to the predetermined center-surround difference quantification mechanism, step S140 further includes step S141, a first set of surrounding blocks of the first block is obtained, and the first set of surrounding blocks includes M surrounding blocks, and M is an integer greater than or equal to 2 and less than or equal to 4. Specifically, a set of surrounding blocks of the first block is determined, and these surrounding blocks are used for comparison with the first block to quantify the saliency. In a specific implementation, neighboring blocks above, below, left and right of the first block can be selected as the surrounding blocks according to the position of the first block in the image. The number M of the selected surrounding blocks can be determined according to actual needs, and the value range of M is 2 to 4, which can balance the calculation complexity and the accuracy of saliency evaluation. For example, if M = 4, four neighboring blocks above, below, left and right of the first block can be selected as the first set of surrounding blocks. If the image is two-dimensional and the blocks are arranged in a grid, the row and column indexes of the first block can be determined, and then the row and column indexes of the neighboring blocks are calculated to find these surrounding blocks.

[0038] In step S142, a first surrounding block in the M surrounding blocks is extracted, and a first surrounding spatial feature of the first surrounding block is collected. Specifically, spatial features of each first surrounding block are collected, and these features are consistent with the spatial features of the first block collected in step S130.

[0039] In step S143, according to the predetermined center-surround difference quantification mechanism, a mean value of a first difference value obtained by normalizing and weighting a difference between the first surrounding spatial feature and the first spatial feature is recorded as the first difference coefficient. Specifically, because the dimensions and value ranges of different features are different, normalization processing is needed before difference calculation to make the features have the same scale. The normalization method can be linear normalization, logarithmic normalization, etc. Different features have different influences on saliency, and the difference is reflected by weighting, and the weight is determined according to experimental results. After normalization and weighting, a difference value between the first block and each surrounding block thereof is calculated, and the difference value can be a Euclidean distance, a Manhattan distance, a cosine similarity or other measurement standards. Then, a mean value of the difference values is calculated as the first difference coefficient. This implementation compares the differences in spatial features between the first block and its surrounding blocks, considers not only the spatial features inside the block but also the relative relationship between the blocks, and thus accurately evaluates the saliency of the first block.

[0040] In step S200, the target salient block is recorded as a primary salient, and a primary particle size strategy corresponding to the primary salient is obtained.

[0041] Specifically, the target salient patches are labeled as first-level saliency, which means they are identified as the highest level of salient patches in the saliency evaluation. A first-level particle size strategy is obtained, which is a pre-set rule or dataset that defines the optimal particle size distribution of the calcium carbonate raw material corresponding to the first-level salient patches. This strategy is derived based on experimental data. For example, if the first-level salient patches contain a large amount of fine details, then smaller particle sizes need to be selected to preserve these details.

[0042] Step S300, based on the first-level particle size strategy, the target calcium carbonate raw material of the target decorative panel drawing is screened to obtain a first-level particle size raw material.

[0043] Specifically, a batch of calcium carbonate raw materials are prepared as candidates for screening, i.e. target calcium carbonate raw materials, which can come from different suppliers, batches or production processes. According to the obtained first-level particle size strategy, the target calcium carbonate raw materials are screened, including measuring the size distribution, shape and uniformity of the raw material particles, and comparing them with the optimal particle size distribution defined in the first-level particle size strategy. Only the raw materials that meet or approach the optimal particle size distribution will be selected as the first-level particle size raw materials.

[0044] Step S400, with the first-level particle size raw material as the target optimization constraint, the surface modification database is globally optimized and analyzed to obtain the optimal modification strategy.

[0045] Specifically, the obtained first-level particle size raw material is used as a constraint condition for global optimization analysis, i.e. when searching for the optimal modification strategy, the characteristics of these raw materials, such as particle size distribution, chemical composition, etc. must be considered. A database containing various modification methods and conditions is prepared, including chemical reactions, physical treatments or a combination of both, to improve the performance of calcium carbonate raw materials, such as increasing strength, weather resistance or processability. Global optimization algorithms (such as genetic algorithms, particle swarm optimization, etc.) are used to search the surface modification database to find the optimal modification strategy that meets the constraints of the first-level particle size raw material. This process includes multiple iterations and evaluations until the modification strategy that meets all conditions and has the best performance is found.

[0046] In a possible implementation, the first particle size raw material is targeted for optimization constraint, and global optimization analysis is performed on the surface modification database to obtain the optimal modification strategy. Step S400 further includes step S410 of extracting a first data set in the surface modification database, wherein the first data set includes a first calcium carbonate particle size. Specifically, data is extracted from a pre-established surface modification database, which contains data of various calcium carbonate particle sizes and corresponding modification strategies and their effects. The first data set is a subset of the database, which at least contains the key information of the first calcium carbonate particle size. In addition, it also contains detailed information such as the type and amount of modifier, modification conditions related to the particle size.

[0047] Step S420, when the first calcium carbonate particle size meets the target optimization constraint, the first data set is added to the target optimization space. Specifically, it is judged whether the calcium carbonate particle size in the extracted first data set is consistent with the particle size of the primary particle size raw material obtained in step S300. If it is consistent, the data set is added to the target optimization space. The target optimization space is a space or set for storing data sets that meet the constraint conditions for global optimization analysis.

[0048] Step S430, read the modification fitness evaluation function and take the modification fitness evaluation function as the target optimization evaluation index. Specifically, a pre-defined modification fitness evaluation function is read, which is used to evaluate the performance of calcium carbonate veneer under different modification strategies. The modification fitness evaluation function can be based on multiple factors such as the strength, wear resistance and weather resistance of the calcium carbonate veneer.

[0049] Step S440, based on the target optimization evaluation index, the target optimization space is globally optimized and analyzed to obtain the optimal modification strategy. Specifically, global optimization analysis is performed on the data in the target optimization space, that is, a global optimization algorithm is used to compare and evaluate multiple modification strategies, and according to the results of the modification fitness evaluation function, the optimal modification strategy is found through iteration and search. This implementation can quickly find the optimal modification strategy through global optimization analysis, thereby shortening the preparation time and improving the production efficiency. The optimal modification strategy can make the performance of the calcium carbonate veneer optimal, thereby meeting the user's requirements for product quality.

[0050] In a possible implementation, step S430 further includes step S431, and the expression of the modification fitness evaluation function is as follows:

[0051] ;

[0052] wherein, is the modification fitness, , 、 、 and are the dispersion, oil absorption value, compatibility, glossiness, and transparency of the modified calcium carbonate raw material, respectively, 、 、 、 and are weight coefficients, and .

[0053] Specifically, the modified fitness evaluation function is used to quantitatively evaluate the performance of calcium carbonate raw materials under different modification strategies. The modified fitness is an index of comprehensive evaluation. The dispersion refers to the uniformity of the dispersion of calcium carbonate raw materials in the medium, which affects the uniformity and stability of the final product. The oil absorption value represents the ability of calcium carbonate raw materials to absorb oil, which is related to the particle size, shape, and surface properties of the raw materials. The compatibility refers to the compatibility of calcium carbonate raw materials when mixed with other materials (such as resins, plastics, etc.), which affects the physical and chemical properties of the final product. The glossiness refers to the light reflecting ability of the surface of the calcium carbonate veneer, which affects the appearance quality of the product. The transparency refers to the light transmission ability of the calcium carbonate veneer, which affects the transparency and visual effect of the product. The weight coefficients are used to adjust the relative importance of each performance index in the comprehensive evaluation, which is set according to the actual application requirements and professional knowledge. In execution, for calcium carbonate raw materials under each modification strategy, the dispersion, oil absorption value, compatibility, glossiness, and transparency are measured, and these values are substituted into the above evaluation function to calculate the corresponding modified fitness . Then, according to these modified fitness values, the performance of different modification strategies is compared to select the optimal modification strategy. This implementation method defines a modified fitness evaluation function containing multiple performance indicators, which can comprehensively and objectively evaluate the performance of calcium carbonate raw materials under different modification strategies, avoiding the one-sidedness of single indicator evaluation.

[0054] In one possible implementation, the target optimization space is globally optimized based on the target optimization evaluation index, and the optimal modification strategy is obtained. Step S440 further includes step S441 of randomly obtaining any data set in the target optimization space. Specifically, the target optimization space contains all possible modification strategy data sets that satisfy the primary particle size constraint. A random number generator is used to randomly select a data set in the target optimization space through programming. This data set contains all relevant information about the modification strategy, such as the type and amount of modifier, modification temperature, and modification time.

[0055] Step S442, calculate any modified features in any data set according to the target optimization evaluation index to obtain any fitness. Specifically, according to a pre-defined modification fitness evaluation function, the modification strategy in the selected data set is calculated to obtain the corresponding fitness value, and the function comprehensively considers multiple modified features and the relative importance between them.

[0056] Step S443, iteratively optimize the target fitness to obtain the target data set corresponding to the target fitness. Specifically, in the programming implementation, a loop is set, and each loop tries to find a data set with higher fitness. This can be achieved through operations such as mutation, crossover, selection, etc. When the loop reaches a pre-set iteration number or the fitness converges to a certain threshold, it is considered that the optimal or approximately optimal solution is found.

[0057] Step S444, take the target modification strategy in the target data set as the optimal modification strategy. Specifically, after the iterative optimization process is completed, a data set with the highest fitness is obtained, and the modification strategy in this data set is the optimal modification strategy. The optimal modification strategy is saved for surface modification treatment of the primary particle size raw material to obtain a high-strength calcium carbonate veneer. This implementation method randomly selects a data set as a starting point and searches in the entire target optimization space, ensuring the opportunity to find a global optimal solution. Even in the worst case, an approximately optimal solution can be found.

[0058] Step S500, surface modification treatment of the primary particle size raw material according to the optimal modification strategy to obtain a target calcium carbonate veneer.

[0059] Specifically, according to the obtained optimal modification strategy, the primary particle size raw material screened is subjected to surface modification treatment, for example, the raw material is exposed to specific chemical reagents, temperature, pressure or radiation conditions to change its surface properties. The modified raw material is used to prepare the final calcium carbonate veneer, including a series of processing processes such as mixing, molding, curing, etc. The embodiments of the present application use the technical means of obtaining a target decorative panel image, performing saliency evaluation and analysis, obtaining a primary particle size strategy, screening a primary particle size raw material, globally optimizing to obtain an optimal modification strategy, and surface modification treatment, etc. to improve the efficiency, precision and product process management capability of the preparation of calcium carbonate veneer.

[0060] In one possible implementation, the method further includes step S600 of extracting any block in the plurality of blocks. Specifically, the block set generated by step S110 is traversed using image processing software or algorithms. Through programming logic (such as loop statements), these blocks are extracted one by one.

[0061] Step S700, a similarity calculation is performed between the arbitrary spatial features of the arbitrary block and the target spatial features of the target salient block, obtaining an arbitrary similarity. Specifically, the same feature extraction process in step S130 is performed on the arbitrary block extracted in step S600. The similarity between the arbitrary spatial features and the target spatial features is calculated using a similarity measure such as cosine similarity, Euclidean distance, etc.

[0062] Step S800, it is determined whether the arbitrary similarity is within a predetermined similarity threshold. Specifically, the calculated arbitrary similarity is compared with the predetermined similarity threshold, which is set according to experimental data, to determine when the similarity of two blocks is high enough to be considered similar.

[0063] Step S900, if the arbitrary similarity is within the predetermined similarity threshold, the arbitrary block is marked as a secondary salient, and the target calcium carbonate raw material is screened according to the secondary particle size strategy corresponding to the secondary salient, obtaining a secondary particle size raw material. Specifically, if the similarity is above the predetermined threshold as determined in step S800, the current block is marked as a secondary salient. Then, the secondary particle size strategy associated with this secondary salient block is found and used to screen the target calcium carbonate raw material.

[0064] Step S1000, the secondary particle size raw material is used as the target optimization constraint for global optimization. Specifically, a global optimization algorithm is used to search for the optimal surface modification strategy, and this search process is constrained by the characteristics of the secondary particle size raw material. The algorithm tries different modification strategy combinations until it finds the optimal strategy that meets the specific performance requirements. This implementation can more finely control the particle size and modification strategy in different regions by introducing block similarity analysis and secondary salient blocks, ensuring that each block is appropriately treated according to its importance, improving the overall quality and consistency of the decorative panel.

[0065] In one possible implementation, the method further includes step S1100, if the arbitrary similarity is not within the predetermined similarity threshold, the arbitrary block is marked as a tertiary salient, and the target calcium carbonate raw material is screened according to the tertiary particle size strategy corresponding to the tertiary salient, obtaining a tertiary particle size raw material, and the tertiary particle size raw material is used as the target optimization constraint for global optimization.

[0066] Specifically, by programming logic, such as using conditional statements (e.g. if-else), those blocks with similarity not reaching the predetermined threshold are marked as third-level significant blocks according to the judgment result of step S800. Then, a corresponding particle size strategy is selected for each third-level significant block, and the target calcium carbonate raw material is screened using the selected third-level particle size strategy to obtain a raw material suitable for these third-level significant blocks. Finally, these third-level particle size raw materials are used as target optimization constraints to perform global optimization analysis on the surface modification database to find the best surface modification strategy to optimize the performance of these third-level significant blocks, such as improving strength, wear resistance or stain resistance, etc. This implementation mode can better meet the functional and visual needs of the veneer in different areas by subdividing the significant levels of the blocks and selecting appropriate particle size strategies and surface modification strategies for each level. For those blocks with lower importance (such as third-level significant blocks), lower-cost and moderately-performing raw materials and modification strategies can be selected, thereby reducing costs without sacrificing overall performance.

[0067] The above detailed description does not constitute a limitation on the protection scope of the present application. Those skilled in the art should understand that various modifications, combinations and substitutions can be made according to design requirements and other factors. Any modifications, equivalent substitutions and improvements made within the spirit and principles of the present application should be included in the protection scope of the present application. In some cases, the actions or steps described in the present application can be performed in an order different from that in the embodiments and still achieve the desired results. In addition, the processes depicted in the drawings do not necessarily require the specific order or continuous order shown to achieve the desired results. In some embodiments, multi-task processing and parallel processing are possible or can be advantageous.

Claims

1. A method for preparing a high-strength calcium carbonate decorative panel, characterized in that: include: Obtain a target decorative panel image, and introduce a panel image saliency assessment plan to perform saliency assessment analysis on the target decorative panel image to obtain a target salient block; Recording the target significant block as first-level significant, and obtaining a first-level particle size strategy corresponding to the first-level significant; Screening the target calcium carbonate raw material of the target decorative panel diagram based on the primary particle size strategy to obtain a primary particle size raw material; Taking the primary particle size raw material as the target optimization constraint, a global optimization analysis is performed on the surface modification database to obtain the optimal modification strategy; Performing surface modification on the primary particle size raw material according to the optimal modification strategy to obtain a target calcium carbonate decorative panel; The method of performing global optimization analysis on the surface modification database with the primary particle size raw material as the target optimization constraint to obtain the optimal modification strategy includes: Retrieving a first data set from the surface modification database, wherein the first data set includes a first calcium carbonate particle size; When the first calcium carbonate particle size meets the target optimization constraint, adding the first data set to the target optimization space; Reading a modified fitness evaluation function, and using the modified fitness evaluation function as a target optimization evaluation index; Performing a global optimization analysis on the target optimization space based on the target optimization evaluation index to obtain the optimal modification strategy; The expression of the modified fitness evaluation function is as follows: ； in, refers to the modified fitness, 、 、 、 and It refers to the dispersion, oil absorption value, compatibility, gloss and transparency of the modified calcium carbonate raw materials. 、 、 、 and are weight coefficients, and ; The performing of a global optimization analysis on the target optimization space based on the target optimization evaluation index to obtain the optimal modification strategy includes: Randomly obtain any data group in the target optimization space; Calculating any modified feature in the arbitrary data set according to the target optimization evaluation index to obtain any fitness; Iterative optimization is performed with the maximum arbitrary fitness as the goal, to obtain a target data group corresponding to the target fitness; The target modification strategy in the target data set is used as the optimal modification strategy.

2. The method for preparing a high-strength calcium carbonate decorative panel according to claim 1, wherein: Obtain a target decorative panel image, and introduce a panel image saliency assessment plan to perform saliency assessment analysis on the target decorative panel image to obtain target saliency blocks, including: Performing block segmentation on the target decorative panel image to obtain a segmentation result, wherein the segmentation result includes a plurality of blocks; Extracting predetermined spatial dimensions in the panel chart's significant assessment plan; Collecting spatial features of a first block among the multiple blocks based on the predetermined spatial dimension to obtain a first spatial feature; Reading a predetermined center-surrounding difference quantization mechanism, and performing a difference quantization analysis on the first spatial feature according to the predetermined center-surrounding difference quantization mechanism to obtain a first difference coefficient; sorting the multiple blocks in descending order based on the first difference coefficient to obtain a target difference coefficient; The block corresponding to the target difference coefficient is recorded as the target significant block.

3. The method for preparing a high-strength calcium carbonate decorative panel according to claim 2, wherein: The predetermined spatial dimensions include a color dimension, a brightness dimension, and a direction dimension, and the color dimension includes a red dimension, a green dimension, and a blue dimension.

4. The method for preparing a high-strength calcium carbonate decorative panel according to claim 2, wherein: Reading a predetermined center-surrounding difference quantization mechanism, and performing a difference quantization analysis on the first spatial feature according to the predetermined center-surrounding difference quantization mechanism to obtain a first difference coefficient, including: Acquire a first surrounding block set of the first block, where the first surrounding block set includes M surrounding blocks, where M is an integer greater than or equal to 2 and less than or equal to 4; Extracting a first surrounding block from the M surrounding blocks, and collecting first surrounding spatial features of the first surrounding block; According to the predetermined center-surrounding difference quantization mechanism, an average of first difference values ​​obtained by normalizing and weighting the first difference feature between the first surrounding spatial feature and the first spatial feature is recorded as the first difference coefficient.

5. The method for preparing a high-strength calcium carbonate decorative panel according to claim 2, wherein: Also includes: Extracting any block from the plurality of blocks; Performing similarity calculation on any spatial feature of the arbitrary block and the target spatial feature of the target salient block to obtain any similarity; Determining whether any similarity is within a predetermined similarity threshold; If any similarity is within the predetermined similarity threshold, the arbitrary block is recorded as secondary significance, and the target calcium carbonate raw material is screened according to the secondary particle size strategy corresponding to the secondary significance to obtain a secondary particle size raw material; The secondary particle size raw material is used as the target optimization constraint for global optimization.

6. The method for preparing a high-strength calcium carbonate decorative panel according to claim 5, characterized in that: If any similarity is not within the predetermined similarity threshold, the arbitrary block is recorded as level 3 significance, and the target calcium carbonate raw material is screened according to the level 3 particle size strategy corresponding to the level 3 significance to obtain level 3 particle size raw material, and the level 3 particle size raw material is used as the target optimization constraint for global optimization.

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