A rare earth reflective heat-insulating coating and its preparation method

Abstract

The present application relates to the field of rare earth coating technology, specifically disclosing a rare earth reflective thermal insulation coating and a preparation method thereof. During the viscosity adjustment process of the rare earth reflective thermal insulation coating, a target state image of the mixture is first generated based on a target viscosity value input by a user. Then, image feature extraction and feature enhancement processing are performed on the target state image and the actual surface state image of the mixture using deep learning-based image processing technology to extract surface state features of both materials. Based on the feature differences between the actual surface state of the mixture and the target state, an intelligent recommendation for the amount of thickener to be applied is then made. This allows for optimized control of the amount of thickener applied, improving the production efficiency and quality stability of the coating.

Description

Technical Field

[0001] The present application relates to the technical field of rare earth coatings, and more specifically, to a rare earth reflective heat-insulating coating and a preparation method thereof. Background Art

[0002] With the increasing demand for building energy efficiency, thermal insulation coatings are attracting widespread attention due to their excellent heat-reflecting properties. Especially in high summer temperatures, thermal insulation coatings can effectively reduce internal building temperatures, reduce air conditioning energy consumption, and improve living comfort. However, traditional thermal insulation coatings suffer from low reflectivity and a single color, making them difficult to meet the diverse market demands.

[0003] In this regard, the invention patent with publication number CN115558356A proposes a rare earth reflective thermal insulation coating. This coating solves the problem of low reflectivity of existing colored coatings by adding specific rare earth pigments and fillers as well as other auxiliary materials such as dispersants, wetting agents, defoaming agents, water, film-forming agents, thickeners, etc., and meets the demand for color diversity. At the same time, patent CN115558356A also proposes a preparation method of rare earth reflective thermal insulation coating, which includes: step 1: adding solvent, dispersant and defoaming agent to a dispersion tank, and dispersing at low speed until uniform; step 2: continuing to add rare earth pigments and precipitated barium sulfate to the dispersion tank, and dispersing at high speed until uniform to obtain a first mixture; step 3: grinding the first mixture to obtain a first mixture powder; step 4: mixing the first mixture powder, pure acrylic emulsion, wetting agent and film-forming aid and stirring evenly to obtain a second mixture; step 5: adding a thickener to adjust the viscosity of the second mixture to obtain a third mixture; step 6: adding a fungicide to the third mixture and stirring until uniform to obtain a rare earth reflective thermal insulation coating.

[0004] However, in the actual production process of rare earth reflective thermal insulation coatings, viscosity control is a crucial step. Specifically, the appropriate viscosity of the coating directly affects the coating's application performance, drying speed, and final coating quality. For example, if the viscosity is too high, the coating will easily sag during spraying or brushing; if the viscosity is too low, the coating may not form a uniform coating, affecting the thermal insulation effect.

[0005] Currently, viscosity adjustment relies primarily on operator experience and trial and error. This method is inefficient and prone to errors, making it difficult to ensure consistent and stable coating quality. Precise viscosity control is particularly challenging in rare earth reflective thermal insulation coatings, which have complex formulations and a wide variety of pigments and fillers.

[0006] Therefore, an optimized rare earth reflective thermal insulation coating and a preparation method thereof are expected. Summary of the Invention

[0007] In order to solve the above-mentioned technical problems, the present application is proposed. The embodiments of the present application provide a new type of rare earth reflective thermal insulation coating and its preparation method. During the viscosity adjustment process of the rare earth reflective thermal insulation coating, the corresponding target state image of the mixture is first generated according to the target viscosity value input by the user, and then the image processing technology based on deep learning is used to extract and enhance the image features of the target state image of the mixture and the actual surface state image of the mixture to extract the surface state features of the two materials. Then, based on the feature difference between the actual surface state of the mixture and the target state, the amount of thickener to be applied is intelligently recommended. In this way, the amount of thickener applied can be optimized and controlled, thereby improving the production efficiency and quality stability of the coating.

[0008] Accordingly, according to one aspect of the present application, a method for preparing a rare earth reflective thermal insulation coating is provided, comprising: step 1: adding a solvent, a dispersant and a defoaming agent to a dispersion tank, and dispersing at a low speed until uniform, wherein the solvent is toluene; step 2: continuing to add rare earth pigments and precipitated barium sulfate to the dispersion tank, and dispersing at a high speed until uniform to obtain a first mixture; step 3: grinding the first mixture to obtain a first mixture powder; step 4: mixing the first mixture powder, pure acrylic resin, a wetting agent and a film-forming aid and stirring evenly to obtain a second mixture; step 5: adding a thickener to adjust the viscosity of the second mixture to obtain a third mixture; step 6: adding a bactericide to the third mixture and stirring until uniform to obtain a rare earth reflective thermal insulation coating.

[0009] In the above-mentioned method for preparing rare earth reflective thermal insulation coating, step 5 also includes: receiving a target viscosity value input by a user, and simultaneously obtaining the second mixture material surface state image captured by an industrial camera; generating a second mixture material target state generation image based on the target viscosity value; extracting the material surface state features of the second mixture material target state generation image and the second mixture material surface state image respectively to obtain a second mixture material target state image semantic coding feature map and a second mixture material surface state image semantic coding feature map; performing pixel-level semantic association space enhancement on the second mixture material target state image semantic coding feature map and the second mixture material surface state image semantic coding feature map to obtain a second mixture material target state image semantic space enhanced coding feature map and a second mixture material surface state image semantic space enhanced coding feature map; generating a recommended value for the amount of thickener to be applied based on the feature difference between the second mixture material target state image semantic space enhanced coding feature map and the second mixture material surface state image semantic space enhanced coding feature map.

[0010] In the preparation method of the above-mentioned rare earth reflective thermal insulation coating, a second mixture material target state generation image is generated based on the target viscosity value, including: performing one-hot encoding on the target viscosity value to obtain a target viscosity one-hot encoding vector; inputting the target viscosity one-hot encoding vector into a second mixture material target state image generator based on a diffusion model to obtain the second mixture material target state generation image.

[0011] In the above-mentioned preparation method of rare earth reflective thermal insulation coating, the material surface state features of the second mixture material target state generation image and the second mixture material surface state image are extracted respectively to obtain the second mixture material target state image semantic coding feature map and the second mixture material surface state image semantic coding feature map, including: inputting the second mixture material target state generation image and the second mixture material surface state image into a surface state feature extractor based on a deep separable convolutional neural network model to obtain the second mixture material target state image semantic coding feature map and the second mixture material surface state image semantic coding feature map.

[0012] In the preparation method of the above-mentioned rare earth reflective thermal insulation coating, the second mixture material target state image semantic coding feature map and the second mixture material surface state image semantic coding feature map are subjected to pixel-level semantic association spatial enhancement to obtain the second mixture material target state image semantic space enhancement coding feature map and the second mixture material surface state image semantic space enhancement coding feature map, including: calculating the pixel-level semantic association of the second mixture material target state image semantic coding feature map to obtain the second mixture material target state image pixel granularity semantic association topological matrix; based on the pixel spatial position information of the second mixture material target state image semantic coding feature map, performing spatial attention attenuation modulation on the second mixture material target state image pixel granularity semantic association topological matrix to obtain the second mixture material target state semantic association space soft constraint topological feature matrix; based on the second mixture material target state semantic association space soft constraint topological feature matrix, performing pixel granularity spatial enhancement processing on the second mixture material target state image semantic coding feature map to obtain the second mixture material target state image semantic space enhancement coding feature map.

[0013] In the preparation method of the above-mentioned rare earth reflective thermal insulation coating, the pixel-level semantic association of the semantic coding feature map of the second mixture material target state image is calculated to obtain the second mixture material target state image pixel granularity semantic association topological matrix, including: feature discretization of the second mixture material target state image semantic coding feature map along the channel dimension to obtain a set of second mixture material target state image pixel granularity feature vectors; calculating the semantic association score between any two second mixture material target state image pixel granularity feature vectors in the set of second mixture material target state image pixel granularity feature vectors to obtain the second mixture material target state image pixel granularity semantic association topological matrix.

[0014] In the above-mentioned method for preparing the rare earth reflective thermal insulation coating, based on the pixel spatial position information of the second mixture material target state image semantic coding feature map, spatial attention attenuation modulation is performed on the second mixture material target state image pixel granularity semantic association topology matrix to obtain a second mixture material target state semantic association spatial soft constraint topology feature matrix, including: using the natural exponential function value of the Euclidean distance between any two second mixture material target state image pixel granularity feature vectors in the set of the second mixture material target state image pixel granularity feature vectors as a spatial distance factor to obtain the second mixture material target state image pixel granularity spatial distance topology matrix; multiplying the second mixture material target state image pixel granularity spatial distance topology matrix by a preset attenuation modulation constant to obtain a spatial attention attenuation modulation matrix; and calculating the position-wise division of the second mixture material target state image pixel granularity semantic association topology matrix and the spatial attention attenuation modulation matrix to obtain a second mixture material target state semantic association spatial soft constraint topology matrix. Dilated convolution coding is performed on the second mixture material target state semantic association spatial soft constraint topology matrix to obtain the second mixture material target state semantic association spatial soft constraint topology feature matrix.

[0015] In the preparation method of the above-mentioned rare earth reflective thermal insulation coating, based on the second mixture material target state semantic association space soft constraint topological feature matrix, the second mixture material target state image semantic coding feature map is subjected to pixel granularity space enhancement processing to obtain the second mixture material target state image semantic space enhanced coding feature map, including: inputting the set of the second mixture material target state image pixel granularity feature vectors and the second mixture material target state semantic association space soft constraint topological feature matrix into the graph convolution coding module to obtain a set of context semantic association enhanced second mixture material target state pixel granularity feature vectors; and performing feature shape reshaping on the set of context semantic association enhanced second mixture material target state pixel granularity feature vectors to obtain the second mixture material target state image semantic space enhanced coding feature map.

[0016] In the preparation method of the above-mentioned rare earth reflective thermal insulation coating, a recommended value of the thickener application amount is generated based on the feature difference between the semantic space enhancement coding feature map of the target state image of the second mixture material and the semantic space enhancement coding feature map of the surface state image of the second mixture material, including: calculating the positional difference between the semantic space enhancement coding feature map of the target state image of the second mixture material and the semantic space enhancement coding feature map of the surface state image of the second mixture material to obtain a state difference image semantic coding feature map; inputting the state difference image semantic coding feature map into a decoder-based thickener application recommendation module to obtain the recommended value of the thickener application amount.

[0017] According to another aspect of the present application, a rare earth reflective thermal insulation coating is provided, wherein the rare earth reflective thermal insulation coating is prepared by the preparation method of the rare earth reflective thermal insulation coating as described above.

[0018] Compared to existing technologies, the rare earth reflective thermal insulation coating and its preparation method described in this application first generates a target state image of the mixture based on the target viscosity value input by the user during the viscosity adjustment process. Deep learning-based image processing technology is then used to extract and enhance image features from the target state image and the actual surface state image of the mixture to extract surface state characteristics of both materials. Based on the characteristic differences between the actual surface state of the mixture and the target state, the amount of thickener to be applied is intelligently recommended. This allows for optimal control of the thickener application amount, improving coating production efficiency and quality stability. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] The above and other purposes, features, and advantages of the present application will become more apparent through a more detailed description of the embodiments of the present application in conjunction with the accompanying drawings. The accompanying drawings are intended to provide a further understanding of the embodiments of the present application and constitute a part of the specification. Together with the embodiments of the present application, they are used to explain the present application and do not constitute a limitation of the present application. In the drawings, the same reference numerals generally represent the same components or steps.

[0020] Figure 1 This is a flow chart of adding a thickener to adjust the viscosity of the second mixed material to obtain a third mixed material in the preparation method of the rare earth reflective thermal insulation coating according to an embodiment of the present application.

[0021] Figure 2 This is a flow chart of step S52 in the method for preparing the rare earth reflective thermal insulation coating according to an embodiment of the present application.

[0022] Figure 3 This is a flow chart of step S54 in the method for preparing the rare earth reflective thermal insulation coating according to an embodiment of the present application.

[0023] Figure 4 This is a flow chart of step S55 in the method for preparing the rare earth reflective thermal insulation coating according to an embodiment of the present application. DETAILED DESCRIPTION

[0024] Below, the exemplary embodiments according to the present application will be described in detail with reference to the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present application, rather than all the embodiments of the present application, and it should be understood that the present application is not limited to the exemplary embodiments described herein.

[0025] In the technical solution of this application, to change the rare earth reflective thermal insulation coating from water-based to oil-based, water is replaced with toluene (that is, the solvent water is replaced with toluene). Simultaneously, the pure acrylic emulsion is replaced with pure acrylic resin. Other materials and processes are fine-tuned based on existing technologies.

[0026] In a specific example, the formula composition of the rare earth reflective thermal insulation coating described in this application is shown in Table 1:

[0027] Table 1 Formula composition of rare earth reflective thermal insulation coating

[0028]

[0029] After testing, the performance of the rare earth reflective thermal insulation coating is shown in Table 2:

[0030] Table 2 Performance indicators of rare earth reflective thermal insulation coatings

[0031]

[0032] It can be seen from the data in Table 2 that the rare earth reflective thermal insulation coating described in the embodiment of the present application has excellent temperature resistance, the color remains stable, and it also performs well in performance indicators such as waterproofness, adhesion, aging resistance, and salt spray resistance. It has a short drying time and uniform thickness, meeting all the requirements of high-performance thermal insulation coatings.

[0033] In particular, in the technical solution of the present application, the rare earth reflective thermal insulation coating is an oily reflective thermal insulation coating, which has the advantages of durability, acid and alkali resistance, and aging resistance. In specific applications, it can be sprayed or used for pre-roll coating. In particular, the rare earth reflective thermal insulation coating is a rare earth composite material with high reflectivity (reflectance greater than 92%), a reflection range of 200nm-2500nm, and high radiation capacity (hemispherical emissivity greater than 86%), radiating in the 2-15μm band. It is worth mentioning that it has excellent thermal insulation properties. Due to the oily paint and ultra-thin level (film thickness controlled within 200μm), pre-roll coated metal sheets can be produced, which have friendly processing characteristics such as stamping and bending.

[0034] It's also worth noting that the rare earth reflective thermal insulation coating achieves temperature reduction by efficiently reflecting sunlight and radiant heat while also blocking and insulating the surface. By combining the reflective properties of rare earth compounds against infrared sunlight and the refractive reflective properties of spherical microspheres, the coating achieves high reflection across the entire wavelength range of 200nm to 2500nm for both visible and infrared light. This coating utilizes a unique microscopically convex and concave high-emissivity surface, increasing its surface area severalfold. This allows it to radiate unreflected solar heat within the 2-15μm wavelength range, further reducing surface temperatures and achieving effective heat dissipation and cooling control.

[0035] In practical applications, the appropriate viscosity of rare earth reflective thermal insulation coatings directly impacts the coating's application performance, drying speed, and final coating quality. For example, if the viscosity is too high, the coating will easily sag during spraying or brushing; if the viscosity is too low, the coating may not form a uniform coating, affecting the thermal insulation effect. Therefore, precise control of the coating's viscosity is essential during the actual production of rare earth reflective thermal insulation coatings.

[0036] However, the current regulation of coating viscosity mainly relies on the operator's manual experience and repeated trials. This method is not only inefficient, but also prone to errors, making it difficult to ensure the consistency and stability of the coating quality. In particular, in rare earth reflective thermal insulation coatings with complex formula ingredients and a wide variety of pigments and fillers, precise control of viscosity has become a technical difficulty. In response to the above technical problems, the present application further introduces intelligent industrial control technology to adjust the amount of thickener applied. Specifically, in the viscosity adjustment process of rare earth reflective thermal insulation coatings, the corresponding target state image of the mixture is first generated according to the target viscosity value input by the user, and then the image processing technology based on deep learning is used to extract image features and enhance features of the target state image of the mixture and the actual surface state image of the mixture to extract the surface state features of the two materials, and then based on the feature differences between the actual surface state of the mixture and the target state, the amount of thickener applied is intelligently recommended. In this way, the optimized control of the amount of thickener applied can be achieved, thereby improving the production efficiency and quality stability of the coating.

[0037] Figure 1 This is a flow chart of adding a thickener to adjust the viscosity of the second mixed material to obtain a third mixed material in the preparation method of the rare earth reflective heat-insulating coating according to an embodiment of the present application. Figure 1As shown, the step 5 also includes: S51, receiving the target viscosity value input by the user, and simultaneously acquiring the second mixture material surface state image captured by the industrial camera; S52, generating the second mixture material target state generation image based on the target viscosity value; S53, respectively extracting the material surface state features of the second mixture material target state generation image and the second mixture material surface state image to obtain the second mixture material target state image semantic coding feature map and the second mixture material surface state image semantic coding feature map; S54, performing pixel-level semantic association space enhancement on the second mixture material target state image semantic coding feature map and the second mixture material surface state image semantic coding feature map to obtain the second mixture material target state image semantic space enhanced coding feature map and the second mixture material surface state image semantic space enhanced coding feature map; S55, generating a recommended value for the amount of thickener to be applied based on the feature difference between the second mixture material target state image semantic space enhanced coding feature map and the second mixture material surface state image semantic space enhanced coding feature map.

[0038] In the above-mentioned method for preparing the rare earth reflective thermal insulation coating, the step S51 receives the target viscosity value input by the user and simultaneously obtains the surface state image of the second mixed material captured by the industrial camera. It should be understood that by capturing the surface state image of the mixed material by the industrial camera, the current material viscosity information is provided, so that the amount of thickener to be applied can be recommended based on the current material state. At the same time, different application scenarios may require coatings of different viscosities. For example, in some coating processes, a higher viscosity helps prevent sagging; in other cases, a lower viscosity can improve spraying efficiency. Therefore, by receiving the target viscosity value input by the user, various specific application requirements can be better adapted.

[0039] In the above-mentioned method for preparing rare earth reflective thermal insulation coating, the step S52 generates a second mixed material target state image based on the target viscosity value. Figure 2 FIG. 1 is a flow chart of step S52 in the method for preparing the rare earth reflective thermal insulation coating according to an embodiment of the present application. Figure 2 As shown, the step S52 includes: S521, performing one-hot encoding on the target viscosity value to obtain a target viscosity one-hot encoding vector; S522, inputting the target viscosity one-hot encoding vector into a second mixed material target state image generator based on a diffusion model to obtain the second mixed material target state generation image.

[0040] Specifically, in step S521, the target viscosity value is uniquely encoded to obtain a target viscosity uniquely encoded vector. It should be understood that, considering that the target viscosity value is a discrete numerical value, in the machine learning model, directly processing its raw data may be affected by the size of the numerical value itself, resulting in difficulty in model training. Therefore, the present application further adopts the method of uniquely encoding to convert the target viscosity value into a binary vector of fixed length, in which only one element is 1 and the remaining elements are 0, and each binary vector corresponds to a specific viscosity value. In this way, the numerical value is converted into category information, so that the machine learning model can better understand and utilize the target viscosity information, thereby improving the generalization ability of the model.

[0041] Specifically, in step S522, the target viscosity one-hot encoding vector is input into a second mixture target state image generator based on a diffusion model to obtain the second mixture target state generated image. That is, to provide a clear reference standard for the recommended control of the thickener application amount, the present application utilizes diffusion model-based image generation technology to process the target viscosity one-hot encoding vector. This utilizes the powerful image generation capability of the diffusion model to iteratively generate a mixture target state image corresponding to the target viscosity value. This allows for a more intuitive assessment of the required thickener application amount by comparing the target state image with the actual mixture surface state image, thereby improving the accuracy of the thickener application amount recommendation.

[0042] In the above-mentioned method for preparing the rare earth reflective thermal insulation coating, the step S53 extracts the material surface state features of the second mixture material target state generated image and the second mixture material surface state image respectively to obtain the second mixture material target state image semantic coding feature map and the second mixture material surface state image semantic coding feature map. It should be understood that in order to achieve a comparative analysis of the feature differences between the second mixture material target state generated image and the second mixture material surface state image, it is necessary to further use image processing technology to extract the material state feature representations in the two. In a specific example of the present application, the step S53 includes: inputting the second mixture material target state generated image and the second mixture material surface state image into a surface state feature extractor based on a depthwise separable convolutional neural network model to obtain the second mixture material target state image semantic coding feature map and the second mixture material surface state image semantic coding feature map. Among them, the deep separable convolutional neural network significantly reduces the amount of computation and the number of model parameters by decomposing the standard convolution layer into depthwise convolution and pointwise convolution, while maintaining a high feature extraction capability. It can effectively extract key surface state features from the image, including but not limited to texture, color, glossiness, and surface roughness, providing an accurate basis for subsequent recommendations on the amount of thickener to be applied.

[0043] In the preparation method of the above-mentioned rare earth reflective thermal insulation coating, the step S54 performs pixel-level semantic association space enhancement on the semantic coding feature map of the target state image of the second mixture material and the semantic coding feature map of the surface state image of the second mixture material to obtain the semantic space enhancement coding feature map of the target state image of the second mixture material and the semantic space enhancement coding feature map of the surface state image of the second mixture material. It should be understood that, considering that the above-mentioned convolution operation can usually only capture local image features, the understanding of the global context information of the image is limited. Therefore, in order to further enhance the global expression ability of image features, the present application proposes a feature enhancement method based on pixel granularity, which realizes an in-depth understanding of the global context information of the image by mining the pixel-level feature semantic associations in the image, so that the model can better identify and understand the surface state of the mixture.

[0044] Figure 3 FIG. 5 is a flow chart of step S54 in the method for preparing the rare earth reflective thermal insulation coating according to an embodiment of the present application. Figure 3As shown, the step S54 includes: S541, calculating the pixel-level semantic association of the second mixture material target state image semantic coding feature map to obtain the second mixture material target state image pixel granularity semantic association topological matrix; S542, based on the pixel spatial position information of the second mixture material target state image semantic coding feature map, performing spatial attention attenuation modulation on the second mixture material target state image pixel granularity semantic association topological matrix to obtain the second mixture material target state semantic association space soft constraint topological feature matrix; S543, based on the second mixture material target state semantic association space soft constraint topological feature matrix, performing pixel granularity space enhancement processing on the second mixture material target state image semantic coding feature map to obtain the second mixture material target state image semantic space enhanced coding feature map.

[0045] Specifically, the step S541 includes: performing feature discretization on the semantic encoding feature map of the second mixture material target state image along the channel dimension to obtain a set of pixel granularity feature vectors of the second mixture material target state image; calculating the semantic association score between any two second mixture material target state image pixel granularity feature vectors in the set of the second mixture material target state image pixel granularity feature vectors to obtain the second mixture material target state image pixel granularity semantic association topological matrix.

[0046] In a specific example of the present application, the semantic association score between any two second mixture material target state image pixel granularity feature vectors is calculated, including: performing nonlinear mapping on each second mixture material target state image pixel granularity feature vector in the set of the second mixture material target state image pixel granularity feature vectors to obtain a set of mapped second mixture material target state image pixel granularity feature vectors; calculating the Poincare distance between any two mapped second mixture material target state image pixel granularity feature vectors in the set of mapped second mixture material target state image pixel granularity feature vectors as the semantic association score to obtain the second mixture material target state image pixel granularity semantic association topological matrix.

[0047] Here, taking the semantic coding feature map of the target state image of the second mixed material as an example, the features are first dispersed along the channel dimension to split it into a set of pixel-granular feature vectors. Then, by calculating the semantic association between any two pixel-granular feature vectors in the set, the pixel-granular semantic association topological matrix of the target state image of the second mixed material is constructed, thereby revealing the intrinsic semantic association of the image. In particular, in this process, considering that the hyperbolic space can more effectively capture the nonlinear relationship between data compared with the Euclidean space, the present application first maps each pixel-granular feature vector from the Euclidean space to the Poincare space, so that it adapts to the geometric characteristics of the hyperbolic space while maintaining the original semantic information, and calculates the Poincare distance between any two mapped pixel-granular feature vectors as the semantic association score between the two, so as to utilize the geometric characteristics of the Poincare space to enhance the nonlinear association between features and improve the model's ability to distinguish features of different categories, thereby more accurately simulating the intrinsic semantic association structure of the image.

[0048] That is, in the above specific example, the implementation process of step S541 can be expressed as follows:

[0049]

[0050]

[0051]

[0052] in, is the semantic coding feature map of the target state image of the second mixed material, 、 and represent the height, width and number of channels of the semantic coding feature map of the target state image of the second mixture respectively, represents the feature shape reshaping, represents the set of pixel granularity feature vectors of the target state image of the second mixed material, and Respectively represent the first and The second mixed material target state image pixel granularity feature vector, The value of is the number of feature vectors in the set of pixel particle size feature vectors of the second mixed material target state image, and , The value of is the length of the pixel granularity feature vector of the target state image of the second mixed material, is the weight transformation matrix, is the weight transformation vector, and Respectively represent the first and The pixel granularity feature vector of the second mixed material target state image after mapping, represents the inverse hyperbolic cosine function in hyperbolic space, represents the norm of the eigenvector, Indicates the the pixel granularity feature vector of the target state image of the second mixed material after mapping and the The Poincare distance between the pixel granularity feature vectors of the second mixed material target state image after mapping, The pixel-granular semantic association topology matrix of the target state image of the second mixed material is represented by The semantic relevance score value of the location.

[0053] Specifically, the step S542 includes: using the natural exponential function value of the Euclidean distance between any two second mixture material target state image pixel granularity feature vectors in the set of the second mixture material target state image pixel granularity feature vectors as the spatial distance factor to obtain the second mixture material target state image pixel granularity spatial distance topology matrix; using a preset attenuation modulation constant to multiply the second mixture material target state image pixel granularity spatial distance topology matrix to obtain a spatial attention attenuation modulation matrix, and calculating the position point division of the second mixture material target state image pixel granularity semantic association topology matrix and the spatial attention attenuation modulation matrix to obtain the second mixture material target state semantic association space soft constraint topology matrix; and performing void convolution coding on the second mixture material target state semantic association space soft constraint topology matrix to obtain the second mixture material target state semantic association space soft constraint topology feature matrix.

[0054] In this specific example, the above process can be expressed as:

[0055]

[0056]

[0057] in, The pixel-granular semantic association topology matrix of the target state image of the second mixed material is represented by The semantic relevance score of the position, is the preset attenuation modulation constant, Indicates the The second mixed material target state image pixel granularity feature vector and the first The Euclidean distance between the pixel size feature vectors of the second mixed material target state image, is the soft constraint topology matrix of the second mixture material target state semantic association space, The second mixture material target state semantic association space soft constraint topology matrix The eigenvalues ​​of the position, represents dilated convolutional coding, It is the soft constraint topological feature matrix of the second mixture material target state semantic association space.

[0058] That is, considering that in practical applications, adjacent image areas often have stronger correlations than distant areas, this application further performs spatial attention attenuation modulation on the pixel granularity semantic association topological matrix of the second mixed material target state image based on the spatial distance between any two pixel granularity feature vectors to simulate the correlation attenuation characteristics between local image regions, so that the model pays more attention to the feature associations of adjacent regions. Furthermore, the topological matrix adjusted by spatial soft constraints is subjected to dilated convolution encoding using dilated convolution technology to capture the semantic association topological features between each pixel granularity feature, thereby obtaining the second mixed material target state semantic association spatial soft constraint topological feature matrix.

[0059] Specifically, the step S543 includes: inputting the set of pixel granularity feature vectors of the second mixed material target state image and the second mixed material target state semantic association space soft constraint topological feature matrix into the graph convolutional coding module to obtain a set of contextual semantic association enhanced second mixed material target state pixel granularity feature vectors; and performing feature reshaping on the set of contextual semantic association enhanced second mixed material target state pixel granularity feature vectors to obtain the second mixed material target state image semantic space enhanced coding feature map.

[0060] That is, the set of pixel granularity feature vectors of the second mixed material target state image and the soft-constraint topological feature matrix of the second mixed material target state semantic association space are further processed through graph convolution coding technology. The characteristics of graph convolution are utilized to update the feature representation of each pixel granularity of the image based on the semantic association information between pixels. By learning the intrinsic semantic association structure of the image, the global semantic context association feature expression of the image pixel granularity features is enhanced. Finally, by reshaping the feature shape of each pixel granularity feature after the context semantic association is strengthened, its original feature structure is restored to obtain the semantic space enhanced coding feature map of the second mixed material target state image, thereby achieving the refined enhancement of the local detail features of the image and the optimized expression of the global semantic information.

[0061] In this specific example, the execution process of this step can be expressed as:

[0062]

[0063]

[0064] in, is the soft constraint topological feature matrix of the second mixture material target state semantic association space, represents graph convolution processing, Represents the set of pixel granularity feature vectors of the second mixed material target state enhanced by contextual semantic association, Representing the semantic space enhanced encoding feature map of the target state image of the second mixture material.

[0065] In the above-mentioned method for preparing rare earth reflective thermal insulation coating, the step S55 generates a recommended value for the amount of thickener to be applied based on the feature difference between the semantic space enhanced coding feature map of the target state image of the second mixture material and the semantic space enhanced coding feature map of the surface state image of the second mixture material. Figure 4 FIG. 1 is a flow chart of step S55 in the method for preparing the rare earth reflective thermal insulation coating according to an embodiment of the present application. Figure 4 As shown, the step S55 includes: S551, calculating the position difference between the semantic space enhancement coding feature map of the target state image of the second mixture material and the semantic space enhancement coding feature map of the surface state image of the second mixture material to obtain a state difference image semantic coding feature map; S552, inputting the state difference image semantic coding feature map into the decoder-based thickener application recommendation module to obtain a recommended value of the thickener application amount.

[0066] Specifically, step S551 calculates the positional difference between the semantic space enhanced coding feature map of the second mixture target state image and the semantic space enhanced coding feature map of the second mixture surface state image to obtain a state difference image semantic coding feature map. That is, in order to reveal the characteristic difference between the mixture target state image and the actual surface state image, the present application further calculates the positional difference between the semantic space enhanced coding feature map of the second mixture target state image and the semantic space enhanced coding feature map of the second mixture surface state image to quantify the specific difference between the target state image and the actual surface state image, thereby providing a direct characteristic basis for the precise application of the thickener.

[0067] Specifically, step S552 involves inputting the semantically encoded feature map of the state difference image into a decoder-based thickener application recommendation module to obtain a recommended value for the thickener application amount. In the technical solution of the present application, the decoder is based on a multi-layer neural network architecture. By performing a series of nonlinear transformations on the semantically encoded feature map of the state difference image, it can fully understand the degree of difference between the mixed material and the target state, thereby accurately predicting the required thickener application amount and providing intuitive guidance to the operator.

[0068] In particular, the state-differential image semantic coding feature map is used to represent the image semantic difference coding features between the image semantic coding features of the second mixture material surface state image and the image semantic coding features of the second mixture material target state generated image generated by the target viscosity one-hot coding vector via the diffusion model. However, considering that image features are not position-by-position as the minimum feature unit, the process of calculating the position-by-position difference between the second mixture material target state image semantic space enhancement coding feature map and the second mixture material surface state image semantic space enhancement coding feature map results in the state-differential image semantic coding feature map having fine-grained image semantic difference coding diversity, affecting its decoding accuracy when input into the decoder-based thickener application recommendation module for decoding.

[0069] Based on this, in the technical solution of the present application, before the state difference image semantic coding feature map is input into the decoder-based thickener application recommendation module, the state difference image semantic coding feature map is subjected to feature distribution optimization, and the feature distribution optimization process includes:

[0070] Determine the feature mean and feature variance of the state difference image semantic coding feature map, and divide the feature mean by the feature variance to obtain a state difference image semantic coding probability statistic value:

[0071]

[0072] in, and Respectively represent the feature mean and feature variance of the semantic encoding feature map of the state difference image, Represents the probability statistics of the semantic encoding of the state difference image;

[0073] Perform a dot multiplication of the state difference image semantic coding feature map and the inverse of the maximum eigenvalue in the state difference image semantic coding feature map to obtain a state difference image semantic coding probability constraint map:

[0074]

[0075] in, represents the semantic encoding feature map of the state difference image, represents the maximum eigenvalue in the semantic encoding feature map of the state difference image, Indicates point multiplication by position, Represents the probability constraint graph of semantic encoding of state difference image;

[0076] After performing dot-wise addition of the state difference image semantic coding probability constraint graph and the state difference image semantic coding probability statistical value, a logarithmic value with base 2 is calculated to obtain a state difference image semantic coding information interaction graph:

[0077]

[0078] in, represents the probability constraint graph of semantic encoding of state difference image, Represents the probability statistics of the semantic encoding of the state difference image, Indicates adding by position point, Represents the semantic encoding information interaction graph of state difference image;

[0079] After performing point-wise subtraction on the state difference image semantic coding probability constraint graph using the state difference image semantic coding probability statistics, the inverse of each eigenvalue is calculated to obtain the state difference image semantic coding sequence constraint graph:

[0080]

[0081] in, represents the probability constraint graph of semantic encoding of state difference image, Represents the probability statistics of the semantic encoding of the state difference image, Indicates point-by-point subtraction. Represents the semantic encoding sequence constraint graph of state difference image;

[0082] The state difference image semantic coding information interaction graph and the state difference image semantic coding sequence constraint graph are interpolated to obtain an optimized state difference image semantic coding feature graph.

[0083] Furthermore, the optimized state difference image semantic coding feature map is passed through a decoder-based thickener application recommendation module to obtain a recommended value of the thickener application amount.

[0084] Accordingly, in this embodiment, the probabilistic statistical characteristics of the state differential image semantic coding feature map are used to simulate the mesoscale interaction structure under probability constraints between the eigenvalue scale and the feature map scale of the state differential image semantic coding feature map, so as to construct a bidirectional latent variable motif based on the mesoscale short sequence relative to the probability statistical value to perform mesoscale bidirectional migration, and to perform posterior recovery based on the sequence constraints on the interactive information, thereby improving the convergence effect in the probability density domain and improving the accuracy of the recommended value of the thickener application amount obtained by the decoder-based thickener application recommendation module of the state differential image semantic coding feature map.

[0085] In summary, according to the embodiments of the present application, the rare earth reflective thermal insulation coating and its preparation method are explained. During the viscosity adjustment process of the rare earth reflective thermal insulation coating, a corresponding target state image of the mixture is first generated based on the target viscosity value input by the user. Then, deep learning-based image processing technology is used to extract and enhance the image features of the target state image and the actual surface state image of the mixture to extract the surface state characteristics of both materials. Based on the characteristic differences between the actual surface state of the mixture and the target state, the amount of thickener to be applied is intelligently recommended. In this way, the amount of thickener applied can be optimized and controlled, improving the production efficiency and quality stability of the coating.

[0086] Finally, it should be noted that the embodiments described above are only some of the embodiments of the present invention, not all of them. The detailed description of the embodiments of the present invention is not intended to limit the scope of the invention as claimed, but rather merely represents selected embodiments of the present invention. All other embodiments derived by persons of ordinary skill in the art based on the embodiments of the present invention without inventive effort are also within the scope of protection of the present invention.

Claims

1. A method for preparing a rare earth reflective thermal insulation coating, comprising: Step 1: Add a solvent, a dispersant and a defoaming agent to a dispersion tank, and disperse at a low speed until uniform, wherein the solvent is toluene; Step 2: Continue to add rare earth pigments and precipitated barium sulfate to the dispersion tank, and disperse at a high speed until uniform to obtain a first mixture; Step 3: Grind the first mixture to obtain a first mixture powder; Step 4: Mix the first mixture powder, pure acrylic resin, a wetting agent and a film-forming aid and stir to obtain a second mixture; Step 5: Add a thickener to adjust the viscosity of the second mixture to obtain a third mixture; Step 6: Add a fungicide to the third mixture and stir until uniform to obtain a rare earth reflective thermal insulation coating, characterized in that step 5 comprises: receiving a target viscosity value input by a user, and simultaneously acquiring an image of a surface state of the second mixed material captured by an industrial camera; generating a second mixed material target state generation image based on the target viscosity value; Extracting material surface state features of the second mixed material target state generation image and the second mixed material surface state image respectively to obtain a second mixed material target state image semantic coding feature map and a second mixed material surface state image semantic coding feature map; performing pixel-level semantic association spatial enhancement on the second mixed material target state image semantic coding feature map and the second mixed material surface state image semantic coding feature map to obtain the second mixed material target state image semantic space enhanced coding feature map and the second mixed material surface state image semantic space enhanced coding feature map; generating a recommended value for the amount of thickener to be applied based on a feature difference between the semantic space enhanced coding feature map of the target state image of the second mixed material and the semantic space enhanced coding feature map of the surface state image of the second mixed material; Before inputting the state difference image semantic coding feature map into the decoder-based thickener application recommendation module, feature distribution optimization is performed on the state difference image semantic coding feature map, and the feature distribution optimization process includes: Determining a feature mean and a feature variance of the state difference image semantic coding feature map, and dividing the feature mean by the feature variance to obtain a state difference image semantic coding probability statistic value; Performing a dot multiplication on the state difference image semantic coding feature map and the inverse of the maximum eigenvalue in the state difference image semantic coding feature map to obtain a state difference image semantic coding probability constraint map; After performing a dot-wise addition of the state difference image semantic coding probability constraint graph and the state difference image semantic coding probability statistical value, a logarithmic value with base 2 is calculated to obtain a state difference image semantic coding information interaction graph; After performing point-wise subtraction on the state difference image semantic coding probability constraint map using the state difference image semantic coding probability statistics, the inverse of each eigenvalue is calculated to obtain a state difference image semantic coding sequence constraint map; Performing intersection analysis on the state difference image semantic coding information interaction graph and the state difference image semantic coding sequence constraint graph to obtain an optimized state difference image semantic coding feature graph; Passing the optimized state difference image semantic coding feature map through a decoder-based thickener application recommendation module to obtain a recommended value of the thickener application amount; The state difference image semantic coding feature map is obtained by calculating the position difference between the second mixture material target state image semantic space enhanced coding feature map and the second mixture material surface state image semantic space enhanced coding feature map.

2. The method for preparing the rare earth reflective thermal insulation coating according to claim 1, characterized in that: Generating a second mixed material target state generation image based on the target viscosity value includes: One-hot encoding the target viscosity value to obtain a target viscosity one-hot encoding vector; The target viscosity one-hot encoding vector is input into a second mixed material target state image generator based on a diffusion model to obtain the second mixed material target state generation image.

3. The method for preparing the rare earth reflective thermal insulation coating according to claim 2, characterized in that: Extracting material surface state features of the second mixed material target state generation image and the second mixed material surface state image respectively to obtain a second mixed material target state image semantic coding feature map and a second mixed material surface state image semantic coding feature map, including: The second mixture material target state generation image and the second mixture material surface state image are input into a surface state feature extractor based on a depthwise separable convolutional neural network model to obtain a semantic coding feature map of the second mixture material target state image and a semantic coding feature map of the second mixture material surface state image.

4. The method for preparing the rare earth reflective thermal insulation coating according to claim 3, characterized in that: Performing pixel-level semantic association space enhancement on the second mixed material target state image semantic coding feature map and the second mixed material surface state image semantic coding feature map to obtain the second mixed material target state image semantic space enhanced coding feature map and the second mixed material surface state image semantic space enhanced coding feature map, including: Calculating pixel-level semantic association of the semantic encoding feature map of the second mixed material target state image to obtain a pixel-granularity semantic association topology matrix of the second mixed material target state image; Based on the pixel spatial position information of the semantic encoding feature map of the second mixed material target state image, performing spatial attention attenuation modulation on the pixel granularity semantic association topological matrix of the second mixed material target state image to obtain a second mixed material target state semantic association spatial soft constraint topological feature matrix; Based on the soft constraint topological feature matrix of the second mixture material target state semantic association space, pixel granularity space enhancement processing is performed on the second mixture material target state image semantic coding feature map to obtain the second mixture material target state image semantic space enhanced coding feature map.

5. The method for preparing the rare earth reflective thermal insulation coating according to claim 4, characterized in that: Calculating pixel-level semantic association of the semantic encoding feature map of the second mixed material target state image to obtain a pixel-level semantic association topology matrix of the second mixed material target state image includes: Performing feature dispersion on the semantic encoding feature map of the second mixed material target state image along the channel dimension to obtain a set of pixel granularity feature vectors of the second mixed material target state image; A semantic association score between any two second mixed material target state image pixel granularity feature vectors in the set of the second mixed material target state image pixel granularity feature vectors is calculated to obtain the second mixed material target state image pixel granularity semantic association topology matrix.

6. The method for preparing the rare earth reflective thermal insulation coating according to claim 5, characterized in that: Based on the pixel spatial position information of the semantic encoding feature map of the second mixed material target state image, spatial attention attenuation modulation is performed on the pixel granularity semantic association topological matrix of the second mixed material target state image to obtain a second mixed material target state semantic association spatial soft constraint topological feature matrix, including: Using the natural exponential function value of the Euclidean distance between any two second mixed material target state image pixel particle size feature vectors in the set of the second mixed material target state image pixel particle size feature vectors as a spatial distance factor to obtain a second mixed material target state image pixel particle size spatial distance topological matrix; Multiplying the second mixed material target state image pixel granularity spatial distance topological matrix by a preset attenuation modulation constant to obtain a spatial attention attenuation modulation matrix, and calculating the position point-wise division of the second mixed material target state image pixel granularity semantic association topological matrix and the spatial attention attenuation modulation matrix to obtain a second mixed material target state semantic association spatial soft constraint topological matrix; The second mixture material target state semantic association space soft constraint topology matrix is ​​subjected to dilated convolution coding to obtain the second mixture material target state semantic association space soft constraint topology feature matrix.

7. The method for preparing the rare earth reflective thermal insulation coating according to claim 6, characterized in that: Based on the second mixed material target state semantic association space soft constraint topological feature matrix, performing pixel granularity space enhancement processing on the second mixed material target state image semantic coding feature map to obtain the second mixed material target state image semantic space enhanced coding feature map, including: Inputting the set of pixel granularity feature vectors of the second mixed material target state image and the second mixed material target state semantic association space soft constraint topological feature matrix into a graph convolutional coding module to obtain a set of contextual semantic association enhanced second mixed material target state pixel granularity feature vectors; The set of the contextual semantic association enhanced second mixed material target state pixel granularity feature vectors is reshaped to obtain the second mixed material target state image semantic space enhanced coding feature map.

8. The method for preparing the rare earth reflective thermal insulation coating according to claim 7, characterized in that: Generating a recommended value for the amount of thickener to be applied based on a feature difference between the semantic space enhanced coding feature map of the target state image of the second mixed material and the semantic space enhanced coding feature map of the surface state image of the second mixed material, including: Calculating a position difference between the semantic space enhanced coding feature map of the second mixed material target state image and the semantic space enhanced coding feature map of the second mixed material surface state image to obtain a state difference image semantic coding feature map; The state difference image semantic coding feature map is input into a decoder-based thickener application recommendation module to obtain a recommended value of the thickener application amount.

9. A rare earth reflective thermal insulation coating, characterized in that: The rare earth reflective thermal insulation coating is prepared by the preparation method of the rare earth reflective thermal insulation coating according to any one of claims 1 to 8.

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