MES-based window decoration product production management system and method

Abstract

The invention relates to the field of intelligent management, and particularly discloses an MES-based window decoration product production management system and method, and the method comprises the steps: collecting a surface texture image of a to-be-detected window decoration product through a camera, and extracting a design texture pattern image from a background database; and performing texture feature extraction on the surface texture image of the to-be-detected window decoration product and the design texture pattern image by adopting an image analysis and processing algorithm based on an artificial intelligence technology, so as to intelligently judge whether the to-be-detected window decoration product is qualified or not according to differential semantic representation features of the surface texture image and the design texture pattern image on textures. In this way, the image of the product can be automatically collected and processed, the manual detection time is shortened, the labor intensity is reduced, and the production detection efficiency is remarkably improved. Meanwhile, an artificial intelligence algorithm is used for image analysis, deep fine texture features can be recognized and extracted more accurately, and the detection accuracy is improved.

Description

Technical Field

[0001] The present application relates to the field of intelligent management, and more specifically, to a window decoration product production management system and method based on MES. Background Art

[0002] Window products, such as curtains, blinds, roller blinds, etc., are important components of interior decoration and architectural elements. They not only provide privacy protection, light control and aesthetic decoration, but also have the functions of heat insulation and energy saving. High-quality window products are an important factor in winning consumer trust and market competitiveness. Production management requires a strict quality control system to ensure that each product meets the standards.

[0003] However, the traditional window decoration product inspection in the production management of window decoration products can only detect the quality problems on the surface or part of the product, and is often powerless to detect the internal cracks or defects, which makes it difficult to fully cover all possible defects. In addition, the traditional quality inspection has a low degree of intelligence and mainly relies on manual inspection, which is not only inefficient, but also easily affected by human judgment, making it difficult to ensure the consistency and accuracy of the inspection results.

[0004] Therefore, an optimized window decoration product production management system is desired. Summary of the invention

[0005] In order to solve the above-mentioned technical problems, the present application is proposed. The embodiment of the present application provides a window decoration product production management system and method based on MES, which collects the surface texture image of the window decoration product to be inspected through a camera, extracts the design texture pattern image from the background database, and uses an image analysis and processing algorithm based on artificial intelligence technology to extract the texture features of the surface texture image of the window decoration product to be inspected and the design texture pattern image, so as to intelligently judge whether the window decoration product to be inspected is qualified based on the differential semantic representation features of the surface texture image of the window decoration product to be inspected and the design texture pattern image in texture. In this way, the image of the product can be automatically collected and processed, which reduces the time and labor intensity of manual inspection and significantly improves the efficiency of production inspection. At the same time, the use of artificial intelligence algorithms for image analysis can more accurately identify and extract deep and fine texture features, thereby improving the accuracy of detection.

[0006] According to one aspect of the present application, a window decoration product production management system based on MES is provided, which includes:

[0007] A texture image acquisition module for the window decoration product to be detected, used to acquire the surface texture image of the window decoration product to be detected through a camera;

[0008] Design texture pattern image extraction module, used for extracting design texture pattern images from the background database;

[0009] A reference texture image feature extraction module to be detected, used for inputting the surface texture image of the window decoration product to be detected and the design texture pattern image into the texture twin detection network to obtain a surface texture feature map of the window decoration product to be detected and a reference feature map of the design texture pattern;

[0010] A texture feature enhancement module, used for performing feature enhancement processing on the surface texture feature map of the window decoration product to be detected and the reference feature map of the design texture pattern based on a compression suppression structure to obtain an enhanced surface texture feature map of the window decoration product to be detected and an enhanced reference feature map of the design texture pattern;

[0011] A texture difference semantic calculation module, used to calculate a texture difference semantic representation feature map between the enhanced surface texture feature map of the window decoration product to be detected and the enhanced design texture pattern reference feature map as a texture difference semantic representation feature;

[0012] The quality inspection result generating module is used to obtain the quality inspection result based on the texture difference semantic representation feature, and the quality inspection result is used to indicate whether the window decoration product to be inspected is qualified.

[0013] According to another aspect of the present application, a method for managing window decoration product production based on MES is provided, comprising:

[0014] The surface texture image of the window decoration product to be inspected is collected by a camera;

[0015] Extracting design texture pattern images from a background database;

[0016] Inputting the surface texture image of the window decoration product to be detected and the design texture pattern image into a texture twin detection network to obtain a surface texture feature map of the window decoration product to be detected and a design texture pattern reference feature map;

[0017] Performing feature enhancement processing based on a compression suppression structure on the surface texture feature map of the window decoration product to be detected and the reference feature map of the design texture pattern to obtain an enhanced surface texture feature map of the window decoration product to be detected and an enhanced reference feature map of the design texture pattern;

[0018] Calculating a texture difference semantic representation feature map between the enhanced surface texture feature map of the window decoration product to be inspected and the enhanced design texture pattern reference feature map as a texture difference semantic representation feature;

[0019] Based on the texture difference semantic representation feature, a quality inspection result is obtained, and the quality inspection result is used to indicate whether the window decoration product to be inspected is qualified.

[0020] Compared with the prior art, the present application provides a window decoration product production management system and method based on MES, which collects the surface texture image of the window decoration product to be inspected through a camera, extracts the design texture pattern image from the background database, and uses an image analysis and processing algorithm based on artificial intelligence technology to extract the texture features of the surface texture image of the window decoration product to be inspected and the design texture pattern image, so as to intelligently judge whether the window decoration product to be inspected is qualified based on the differential semantic representation features of the surface texture image of the window decoration product to be inspected and the design texture pattern image in texture. In this way, the image of the product can be automatically collected and processed, reducing the time and labor intensity of manual inspection, and significantly improving the efficiency of production inspection. At the same time, the use of artificial intelligence algorithms for image analysis can more accurately identify and extract deep and fine texture features, thereby improving the accuracy of detection. BRIEF DESCRIPTION OF THE DRAWINGS

[0021] By describing the embodiments of the present application in more detail in conjunction with the accompanying drawings, the above and other purposes, features and advantages of the present application will become more apparent. The accompanying drawings are used 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 accompanying drawings, the same reference numerals generally represent the same components or steps.

[0022] Figure 1 It is a block diagram of a window decoration product production management system based on MES according to an embodiment of the present application.

[0023] Figure 2 It is a schematic diagram of the architecture of the window decoration product production management system based on MES according to an embodiment of the present application.

[0024] Figure 3 It is a block diagram of the texture feature enhancement module in the MES-based window decoration product production management system according to an embodiment of the present application.

[0025] Figure 4 It is a block diagram of a surface texture feature weight calculation unit in a window decoration product production management system based on MES according to an embodiment of the present application.

[0026] Figure 5 It is a flowchart of the window decoration product production management method based on MES according to an embodiment of the present application. DETAILED DESCRIPTION

[0027] The embodiments of the present disclosure will be described in more detail below with reference to the accompanying drawings. Although certain embodiments of the present disclosure are shown in the accompanying drawings, it should be understood that the present disclosure can be implemented in various forms and should not be construed as being limited to the embodiments described herein, but rather these embodiments are provided for a more thorough and complete understanding of the present disclosure. It should be understood that the drawings and embodiments of the present disclosure are only for exemplary purposes and are not intended to limit the scope of protection of the present disclosure.

[0028] It should be understood that the various steps described in the method embodiments of the present disclosure may be performed in different orders and / or in parallel. In addition, the method embodiments may include additional steps and / or omit the steps shown. The scope of the present disclosure is not limited in this respect.

[0029] In the description of the embodiments of the present disclosure, the term "including" and similar terms should be understood as open inclusion, that is, "including but not limited to". The term "based on" should be understood as "based at least in part on". The term "one embodiment" or "the embodiment" should be understood as "at least one embodiment". The terms "first", "second", etc. may refer to different or the same objects. Other explicit and implicit definitions may also be included below.

[0030] It should be noted that the modifications of "one" and "plurality" mentioned in the present disclosure are illustrative rather than restrictive, and those skilled in the art should understand that unless otherwise clearly indicated in the context, it should be understood as "one or more".

[0031] Window products, such as curtains, blinds, roller blinds, etc., are important components of interior decoration and architectural elements. They not only provide privacy protection, light control and aesthetic decoration, but also have the functions of heat insulation and energy saving. High-quality window products are an important factor in winning consumer trust and market competitiveness. Production management requires a strict quality control system to ensure that each product meets the standards.

[0032] However, the traditional window decoration product inspection in the production management of window decoration products can only detect the quality problems on the surface or part of the product, and is often powerless to detect the internal cracks or defects, which makes it difficult to fully cover all possible defects. In addition, the traditional quality inspection has a low degree of intelligence and mainly relies on manual inspection, which is not only inefficient, but also easily affected by human judgment, making it difficult to ensure the consistency and accuracy of the inspection results.

[0033] It is worth mentioning that MES (Manufacturing Execution System) is a software system specially designed for factory environment, which can coordinate, track, record and monitor the entire manufacturing process from raw materials to semi-finished products and finished products. In the production management of window decoration products, the MES system can formulate production plans and assign them to various production lines based on orders and inventory to ensure the smooth progress of the production process. In addition, the MES system can monitor product quality, promptly discover and correct quality problems in the production process, ensure that products meet quality standards, etc. Therefore, the MES system plays an important role in the production management of window decoration products, and can help enterprises realize the automation, informatization and intelligence of the production process, thereby improving production efficiency, reducing costs, improving product quality and meeting customer needs.

[0034] Based on this, the present application proposes a window decoration product production management system based on MES, which first formally places a production order according to the production plan or customer demand. Then, the orders after the order is placed are arranged to arrange the production sequence. Then, the arranged orders are handed over to the workshop for production. The workshop performs track cutting, marking and fabric cutting on the window decoration products to be produced in the order, and then assembles the materials after marking and fabric cutting to obtain the window decoration products to be tested. After that, the assembled window decoration products to be tested are tested for quality, and the qualified window decoration products are inner packaged and outer packaged in turn. Finally, the packaged window decoration products are arranged for shipment for transportation and distribution of the products to ensure that they are delivered to customers on time. In particular, the MES-based window decoration product production management system can place various personalized orders, and the operators of each step can scan the code to query the order content through the system to perform operations, and record each operator to facilitate the traceability of defective products.

[0035] Correspondingly, in the above-mentioned quality inspection of the assembled window decoration products to be inspected, the surface texture image of the window decoration product to be inspected is collected by a camera, and the design texture pattern image is extracted from the background database, and the image analysis and processing algorithm based on artificial intelligence technology is used to extract the texture features of the surface texture image of the window decoration product to be inspected and the design texture pattern image, so as to intelligently judge whether the window decoration product to be inspected is qualified based on the differential semantic representation features of the surface texture image of the window decoration product to be inspected and the design texture pattern image in texture. In this way, the image of the product can be automatically collected and processed, which reduces the time and labor intensity of manual inspection and significantly improves the efficiency of production inspection. At the same time, the use of artificial intelligence algorithms for image analysis can more accurately identify and extract deep and fine texture features, thereby improving the accuracy of detection.

[0036] Figure 1It is a block diagram of a window decoration product production management system based on MES according to an embodiment of the present application. Figure 2 FIG. 1 is a schematic diagram of the structure of a window decoration product production management system based on MES according to an embodiment of the present application. Figure 1 and Figure 2 As shown, according to the MES-based window decoration product production management system 100 of the embodiment of the present application, it includes: a texture image acquisition module 110 for the window decoration product to be detected, which is used to collect the surface texture image of the window decoration product to be detected through a camera; a design texture pattern image extraction module 120, which is used to extract the design texture pattern image from the background database; a reference texture image feature extraction module 130 to be detected, which is used to input the surface texture image of the window decoration product to be detected and the design texture pattern image into the texture twin detection network to obtain the surface texture feature map of the window decoration product to be detected and the design texture pattern reference feature map; a texture feature enhancement module 140, which is used to The surface texture feature map of the window decoration product to be inspected and the reference feature map of the design texture pattern are subjected to feature enhancement processing based on a compression suppression structure to obtain an enhanced surface texture feature map of the window decoration product to be inspected and an enhanced reference feature map of the design texture pattern; a texture differential semantic calculation module 150 is used to calculate a texture differential semantic representation feature map between the enhanced surface texture feature map of the window decoration product to be inspected and the enhanced reference feature map of the design texture pattern as a texture differential semantic representation feature; a quality inspection result generation module 160 is used to obtain a quality inspection result based on the texture differential semantic representation feature, and the quality inspection result is used to indicate whether the window decoration product to be inspected is qualified.

[0037] In an embodiment of the present application, the texture image acquisition module 110 of the window decoration product to be detected is used to collect the surface texture image of the window decoration product to be detected through a camera. The design texture pattern image extraction module 120 is used to extract the design texture pattern image from the background database. It should be understood that considering that the surface texture image of the window decoration product to be detected is obtained by directly shooting with a camera, the texture and pattern of the surface of the actual window decoration product are displayed. The design texture pattern image extracted from the background database is pre-stored in the background database, representing the original texture and pattern of the window decoration product design, usually an ideal texture image generated by the design software, which can be used as a standard reference for product quality inspection. Based on this, in the technical solution of the present application, the surface texture image of the window decoration product to be detected is collected by a camera, and the design texture pattern image is extracted from the background database, and analyzed and processed, so that the difference between the actual product texture image and the design texture pattern image can be automatically compared, so as to detect whether the product is manufactured according to the design specifications, whether there are defects or deviations, so as to ensure that the product quality meets the design and manufacturing standards.

[0038] In an embodiment of the present application, the reference texture image feature extraction module 130 to be detected is used to input the surface texture image of the window decoration product to be detected and the design texture pattern image into a texture twin detection network to obtain a surface texture feature map of the window decoration product to be detected and a reference feature map of the design texture pattern. Specifically, in an embodiment of the present application, the reference texture image feature extraction module to be detected is used to: input the surface texture image of the window decoration product to be detected and the design texture pattern image into a texture twin detection network including a first image encoder and a second image encoder to obtain a surface texture feature map of the window decoration product to be detected and a reference feature map of the design texture pattern, wherein the first image encoder and the second image encoder have the same network structure. Accordingly, considering that both the surface texture image of the window decoration product to be detected and the design texture pattern image contain feature information about the surface texture of the window decoration product, these surface texture information plays a vital role in the subsequent judgment of whether the quality of the window decoration to be detected is qualified. Based on this, in the technical solution of the present application, the surface texture image of the window decoration product to be detected and the design texture pattern image are input into a texture twin detection network including a first image encoder and a second image encoder to capture and mine the surface texture feature information of the product image and the design image, respectively, thereby obtaining a surface texture feature map of the window decoration product to be detected and a reference feature map of the design texture pattern. In particular, the first image encoder and the second image encoder described herein have the same network structure.

[0039] In an embodiment of the present application, the texture feature enhancement module 140 is used to perform feature enhancement processing based on a compression suppression structure on the surface texture feature map of the window decoration product to be detected and the reference feature map of the design texture pattern to obtain an enhanced surface texture feature map of the window decoration product to be detected and an enhanced reference feature map of the design texture pattern. Specifically, in an embodiment of the present application, the texture feature enhancement module is used to: input the surface texture feature map of the window decoration product to be detected and the reference feature map of the design texture pattern into a feature attention selection enhancer under a compression suppression structure respectively to obtain the enhanced surface texture feature map of the window decoration product to be detected and the enhanced reference feature map of the design texture pattern. It should be understood that, considering that the surface texture feature map of the window decoration product to be detected and the reference feature map of the design texture pattern contain subtle key texture features that are crucial to the qualified rate judgment of the window decoration product to be detected, however, these two feature maps are also mixed with some non-surface texture key background features, and this information is not necessary for the texture recognition process and may even cause interference. Therefore, in order to pay special attention to and extract those tiny texture semantic features that play a decisive role, while excluding those irrelevant background features, so as to ensure that the quality judgment of the window decoration product to be detected is both accurate and efficient, in the technical solution of the present application, the surface texture feature map of the window decoration product to be detected and the reference feature map of the design texture pattern are respectively input into the feature attention selection enhancer under the compression suppression structure to obtain the enhanced surface texture feature map of the window decoration product to be detected and the enhanced reference feature map of the design texture pattern. Specifically, firstly, the surface texture feature map of the window decoration product to be detected is processed by global average pooling to compress and extract the most core surface texture semantic features, thereby forming a texture feature compression information representation vector. Then, the texture feature compression information representation vector is deeply analyzed using one-dimensional convolutional coding to explore and reveal the internal semantic connection about the surface texture in the vector. Subsequently, these features processed by convolutional coding are integrated with the texture feature compression information representation vector to enhance the model's recognition and understanding ability of the subtle semantic features of the local texture on the surface of the window decoration product to be detected, so as to obtain a multi-scale representation vector of the surface texture feature compression information of the window decoration product to be detected. Next, by constructing a multi-layer perceptron network containing two fully connected layers and SiLU activation function, the feature fine-grained semantic information of the multi-scale representation vector of the surface texture feature compression information of the window decoration product to be detected obtained through the cascade operation is further deepened and refined. Furthermore, the Sigmoid activation function is used to normalize the deepened and refined multi-scale correlation feature vector to obtain the relative importance weight of each feature channel. Finally, the surface texture feature map of the window decoration product to be detected is weighted and adjusted according to these weights to enhance the significance of the tiny surface texture semantic features and reduce the influence of non-critical features, thereby obtaining an enhanced surface texture feature map of the window decoration product to be detected.In particular, the processing method of the reference feature map of the design texture pattern is consistent with the processing method of the surface texture feature map of the window decoration product to be tested.

[0040] Figure 3 is a block diagram of a texture feature enhancement module in a window decoration product production management system based on MES according to an embodiment of the present application. Specifically, in the embodiment of the present application, Figure 3 As shown, the texture feature enhancement module 140 includes: a surface texture feature compression unit 141 for detecting the window decoration product surface texture feature map, which is used to perform channel compression on the surface texture feature map of the window decoration product surface texture feature map to obtain a surface texture feature compression information representation vector of the window decoration product surface texture feature map to be detected; a surface texture feature compression association unit 142 for detecting the window decoration product surface texture feature map, which is used to perform one-dimensional convolution encoding on the surface texture feature compression information representation vector of the window decoration product surface texture feature map to obtain a surface texture compression information representation feature vector of the window decoration product surface texture; a surface texture multi-scale feature fusion unit 143 for merging the surface texture of the window decoration product surface texture map to be detected. The feature compression information representation vector and the correlation representation feature vector between the surface texture compression information of the window decoration product to be detected are cascaded to obtain a multi-scale representation vector of the surface texture compression information of the window decoration product to be detected; a texture feature multi-scale association unit 144 is used to input the multi-scale representation vector of the surface texture compression information of the window decoration product to be detected into a compression information feature extraction module to obtain a multi-scale correlation feature vector of the surface texture compression information of the window decoration product to be detected, wherein the compression information feature extraction module is a multi-layer perceptron including two fully connected layers and a SiLU activation function; a surface texture feature weight calculation unit 145 is used to use a Sigmoid function to normalize the multi-scale correlation feature vector of the surface texture compression information of the window decoration product to be detected to obtain a surface texture weight feature vector of the window decoration product to be detected; a texture feature amplification and suppression unit 146 is used to perform feature amplification and suppression operations on the surface texture feature map of the window decoration product to be detected based on the surface texture weight feature vector of the window decoration product to be detected to obtain the enhanced surface texture feature map of the window decoration product to be detected.

[0041] More specifically, in an embodiment of the present application, the surface texture feature compression unit of the window decoration product to be detected is used to calculate the global mean of each feature matrix along the channel dimension of the surface texture feature map of the window decoration product to be detected to obtain the surface texture feature compression information representation vector of the window decoration product to be detected.

[0042] Figure 4 is a block diagram of a surface texture feature weight calculation unit in a window decoration product production management system based on MES according to an embodiment of the present application. More specifically, in the embodiment of the present application, Figure 4As shown, the surface texture feature weight calculation unit 145 includes: a surface texture compression information multi-scale exponential value calculation sub-unit 1451, which is used to use the negative number of the eigenvalue of each position in the multi-scale associated feature vector of the surface texture compression information of the window decoration product to be detected as the exponent of the natural constant to calculate the exponential function value based on the natural constant according to the position to obtain the multi-scale associated class support feature vector of the surface texture compression information of the window decoration product to be detected; a surface texture weight value calculation sub-unit 1452, which is used to calculate the reciprocal of the sum of the eigenvalue of each position in the multi-scale associated class support feature vector of the surface texture compression information of the window decoration product to be detected and the constant one to obtain the surface texture weight feature vector of the window decoration product to be detected.

[0043] More specifically, in an embodiment of the present application, the texture feature amplification and suppression unit is used to: multiply the eigenvalues ​​of each position in the surface texture weight feature vector of the window decoration product to be detected by position with each feature matrix along the channel dimension of the surface texture feature map of the window decoration product to be detected to obtain the enhanced surface texture feature map of the window decoration product to be detected.

[0044] In the embodiment of the present application, specifically, the texture feature enhancement module is used to: input the surface texture feature map of the window decoration product to be detected into the feature attention selection enhancer under the compression suppression structure, and process it with the following enhancement formula to obtain the enhanced surface texture feature map of the window decoration product to be detected; wherein the enhancement formula is:

[0045]

[0046] V s =concat(V s1 ,Conv 1d (V s1 ))

[0047]

[0048] Wherein, F is the surface texture feature map of the window decoration product to be tested, f xo (i o , j o ) represents the xth of the surface texture feature map F of the window decoration product to be detected o The coordinates in the channel are (i o , j o ), H and W are the height and width of the surface texture feature map of the window decoration product to be detected, V s1 is the surface texture feature compression information representation vector of the window decoration product to be detected, Conv 1d (·) is a one-dimensional convolutional code, concat(·,·) is a cascade process of vectors, V sis the multi-scale representation vector of the surface texture compression information of the window decoration product to be detected, MLP is a multi-layer perceptron, represents multiplication by position, and F' is the enhanced surface texture feature map of the window decoration product to be detected. In particular, the encoding method of the design texture pattern reference feature map is consistent with the encoding method of the surface texture feature map of the window decoration product to be detected.

[0049] In an embodiment of the present application, the texture differential semantic calculation module 150 is used to calculate the texture differential semantic representation feature map between the enhanced surface texture feature map of the window decoration product to be inspected and the enhanced design texture pattern reference feature map as a texture differential semantic representation feature. Accordingly, in order to further compare and analyze the differences between the enhanced surface texture feature map of the window decoration product to be inspected and the enhanced design texture pattern reference feature map, such as the lack of texture, misalignment or additional texture, so as to more accurately judge the qualified problems of the window decoration product to be inspected, in the technical solution of the present application, the texture differential semantic representation feature map between the enhanced surface texture feature map of the window decoration product to be inspected and the enhanced design texture pattern reference feature map is calculated. That is, the differential feature map provides a quantitative method to evaluate the quality of the product surface texture, which helps to highlight the tiny but critical texture features in the window decoration products to be inspected, thereby improving the accuracy of production inspection management.

[0050] In an embodiment of the present application, the quality inspection result generation module 160 is used to obtain a quality inspection result based on the texture differential semantic representation feature, and the quality inspection result is used to indicate whether the window decoration product to be inspected is qualified. Specifically, in an embodiment of the present application, the quality inspection result generation module is used to: input the texture differential semantic representation feature map into a quality inspection module based on a classifier to obtain the quality inspection result, and the quality inspection result is used to indicate whether the window decoration product to be inspected is qualified. That is, the texture differential semantic representation features between the enhanced surface texture feature map of the window decoration product to be inspected and the enhanced design texture pattern reference feature map are used for classification processing to intelligently determine whether the window decoration product to be inspected is qualified. In this way, the image of the product can be automatically collected and processed, which reduces the time and labor intensity of manual inspection and significantly improves the efficiency of production inspection. At the same time, the use of artificial intelligence algorithms for image analysis can more accurately identify and extract deep fine texture features and improve the accuracy of detection.

[0051] It should be understood that in the technical solution of the present application, the surface texture feature map of the window decoration product to be detected and the reference feature map of the design texture pattern represent the image semantic features of the design texture pattern image and the surface texture image of the window decoration product to be detected based on the texture twin detection network, respectively. Considering that in addition to the difference in image source domain content between the design texture pattern image and the surface texture image of the window decoration product to be detected, the detection interference introduced in the image acquisition process such as image noise and image distortion will be introduced when acquiring the surface texture image of the window decoration product to be detected. This makes the surface texture feature map of the window decoration product to be detected generated by the texture twin detection network encoding have many class out-of-distribution noises relative to the reference feature map of the design texture pattern. After feature attention selection enhancement and feature map difference processing under the compression suppression structure, the obtained texture difference semantic representation feature map will have a local causal association missing in the posterior class probability relative to the image source domain difference representation between the surface texture image of the window decoration product to be detected and the design texture pattern image, thereby affecting the accuracy of the classification result.

[0052] Based on this, in a preferred embodiment of the present application, the texture difference semantic representation feature map is input into a quality inspection module based on a classifier to obtain the quality inspection result, including: performing a probability activation function based on each feature value of the texture difference semantic representation feature map, such as s The method comprises the following steps: performing probabilistic processing of the igmoid function and the softmax function to obtain a probabilistic texture differential semantic representation feature map; obtaining a qualified probability value by inputting the texture differential semantic representation feature map into the quality inspection module based on the classifier; determining a class recognition symbol value based on a comparison between each eigenvalue of the probabilistic texture differential semantic representation feature map and the qualified probability value, wherein the class recognition symbol value is equal to one, zero and negative one respectively in response to the eigenvalue of the probabilistic texture differential semantic representation feature map being greater than, equal to and less than the qualified probability value; calculating the mean of all eigenvalues ​​of the probabilistic texture differential semantic representation feature map to obtain a class overall phase shift value; multiplying each eigenvalue of the probabilistic texture differential semantic representation feature map by the class recognition symbol value and the class overall phase shift value, performing weighted difference calculation, and taking the absolute value to obtain the optimized eigenvalue of the probabilistic texture differential semantic representation feature map; inputting the optimized texture differential semantic representation feature map composed of the optimized eigenvalues ​​into the quality inspection module based on the classifier to obtain the quality inspection result.

[0053] Specifically, in this preferred embodiment, the probabilistic texture difference semantic representation feature map is optimized to obtain an optimized texture difference semantic representation feature map, and the process formula is expressed as follows:

[0054]

[0055] Among them, f i,j,k is the feature value of each position in the probabilistic texture difference semantic representation feature map, p is the qualified probability value, sgn is the sign function, α and β are weight hyperparameters, is the overall phase shift value of the class, f' i,j,k It is the feature value of each position in the optimized texture differential semantic representation feature map.

[0056] Accordingly, in the above-mentioned optimization process, the class cognitive phase shift response of the texture differential semantic representation feature map is obtained by comparing the probabilistic amplitude of the eigenvalue of the texture differential semantic representation feature map with the class probability, and the phase shift response of the eigenvalue of the texture differential semantic representation feature map relative to the class probability representation of the feature map as a whole is subjected to a feature distribution sequence invariance transformation based on the class differential distribution expansion, so as to realize the causal constraint of the posterior class probability of the texture differential semantic representation feature map on its prior feature distribution representation, so as to improve the accuracy of the quality inspection results obtained by the texture differential semantic representation feature map input into the quality inspection module based on the classifier. In this way, the image of the product can be automatically collected and processed, the time and labor intensity of manual inspection can be reduced, and the efficiency of production inspection can be significantly improved. At the same time, the use of artificial intelligence algorithms for image analysis can more accurately identify and extract deep fine texture features and improve the accuracy of detection.

[0057] In summary, the MES-based window decoration product production management system 100 based on the embodiment of the present application is explained, which collects the surface texture image of the window decoration product to be inspected through a camera, extracts the design texture pattern image from the background database, and uses an image analysis and processing algorithm based on artificial intelligence technology to extract texture features of the surface texture image of the window decoration product to be inspected and the design texture pattern image, so as to intelligently judge whether the window decoration product to be inspected is qualified based on the differential semantic representation features of the surface texture image of the window decoration product to be inspected and the design texture pattern image in texture. In this way, the image of the product can be automatically collected and processed, which reduces the time and labor intensity of manual inspection and significantly improves the efficiency of production inspection. At the same time, the use of artificial intelligence algorithms for image analysis can more accurately identify and extract deep and fine texture features, thereby improving the accuracy of detection.

[0058] As described above, the MES-based window decoration product production management system 100 according to the embodiment of the present application can be implemented in various wireless terminals, such as a server with an MES-based window decoration product production management algorithm. In one possible implementation, the MES-based window decoration product production management system 100 according to the embodiment of the present application can be integrated into the wireless terminal as a software module and / or a hardware module. For example, the MES-based window decoration product production management system 100 can be a software module in the operating system of the wireless terminal, or it can be an application developed for the wireless terminal; of course, the MES-based window decoration product production management system 100 can also be one of the many hardware modules of the wireless terminal.

[0059] Alternatively, in another example, the MES-based window decoration product production management system 100 and the wireless terminal may also be separate devices, and the MES-based window decoration product production management system 100 may be connected to the wireless terminal via a wired and / or wireless network and transmit interactive information in accordance with an agreed data format.

[0060] Figure 5 FIG. 1 is a flow chart of a method for managing window decoration product production based on MES according to an embodiment of the present application. Figure 5 As shown, according to the embodiment of the present application, the production management method of window decoration products based on MES includes: S110, collecting the surface texture image of the window decoration product to be detected through a camera; S120, extracting the design texture pattern image from the background database; S130, inputting the surface texture image of the window decoration product to be detected and the design texture pattern image into the texture twin detection network to obtain the surface texture feature map of the window decoration product to be detected and the design texture pattern reference feature map; S140, performing feature enhancement processing based on the compression suppression structure on the surface texture feature map of the window decoration product to be detected and the design texture pattern reference feature map to obtain the enhanced surface texture feature map of the window decoration product to be detected and the enhanced design texture pattern reference feature map; S150, calculating the texture difference semantic representation feature map between the enhanced surface texture feature map of the window decoration product to be detected and the enhanced design texture pattern reference feature map as the texture difference semantic representation feature; S160, based on the texture difference semantic representation feature, obtaining the quality inspection result, and the quality inspection result is used to indicate whether the window decoration product to be detected is qualified.

[0061] Here, those skilled in the art can understand that the specific operations of each step in the above-mentioned MES-based window decoration product production management method have been referred to above. Figures 1 to 4 The description of the MES-based window decoration product production management system has been introduced in detail, and therefore, its repeated description will be omitted.

[0062] The above descriptions of various implementations of the present disclosure are exemplary and not exhaustive. The disclosure is not limited to the disclosed implementations, and many modifications and variations are obvious to those of ordinary skill in the art without departing from the scope and spirit of the described implementations. The terms used herein are selected to best explain the principles of the implementations, practical applications, or improvements to the technology in the market, or to enable other persons of ordinary skill in the art to understand the various practical methods disclosed herein.

Claims

1. A window decoration product production management system based on MES, characterized in that: include: A texture image acquisition module for the window decoration product to be detected, used to acquire the surface texture image of the window decoration product to be detected through a camera; Design texture pattern image extraction module, used for extracting design texture pattern images from the background database; A reference texture image feature extraction module to be detected, used for inputting the surface texture image of the window decoration product to be detected and the design texture pattern image into the texture twin detection network to obtain a surface texture feature map of the window decoration product to be detected and a reference feature map of the design texture pattern; A texture feature enhancement module, used for performing feature enhancement processing on the surface texture feature map of the window decoration product to be detected and the reference feature map of the design texture pattern based on a compression suppression structure to obtain an enhanced surface texture feature map of the window decoration product to be detected and an enhanced reference feature map of the design texture pattern; A texture difference semantic calculation module, used to calculate a texture difference semantic representation feature map between the enhanced surface texture feature map of the window decoration product to be detected and the enhanced design texture pattern reference feature map as a texture difference semantic representation feature; The quality inspection result generating module is used to obtain the quality inspection result based on the texture difference semantic representation feature, and the quality inspection result is used to indicate whether the window decoration product to be inspected is qualified.

2. The MES-based window decoration product production management system according to claim 1 is characterized in that: The reference texture image feature extraction module to be detected is used to: input the surface texture image of the window decoration product to be detected and the design texture pattern image into a texture twin detection network including a first image encoder and a second image encoder to obtain a surface texture feature map of the window decoration product to be detected and a reference feature map of the design texture pattern, wherein the first image encoder and the second image encoder have the same network structure.

3. The MES-based window decoration product production management system according to claim 2 is characterized in that: The texture feature enhancement module is used to: input the surface texture feature map of the window decoration product to be detected and the reference feature map of the design texture pattern into the feature attention selection enhancer under the compression suppression structure respectively to obtain the enhanced surface texture feature map of the window decoration product to be detected and the enhanced reference feature map of the design texture pattern.

4. The MES-based window decoration product production management system according to claim 3 is characterized in that: The texture feature enhancement module comprises: A surface texture feature compression unit for the window decoration product to be detected, used for performing channel compression on the surface texture feature map of the window decoration product to be detected to obtain a surface texture feature compression information representation vector of the window decoration product to be detected; A surface texture feature compression and association unit for the window decoration product to be detected is used to perform one-dimensional convolution coding on the surface texture feature compression information representation vector of the window decoration product to be detected to obtain a correlation representation feature vector between the surface texture compression information of the window decoration product to be detected; A multi-scale feature fusion unit for the surface texture of the window decoration to be detected, used for cascading the representation vector of the surface texture feature compression information of the window decoration product to be detected and the feature vector representing the correlation between the surface texture compression information of the window decoration product to be detected to obtain a multi-scale representation vector of the surface texture compression information of the window decoration product to be detected; A texture feature multi-scale association unit, used for inputting the multi-scale representation vector of the surface texture compression information of the window decoration product to be detected into a compression information feature extraction module to obtain a multi-scale association feature vector of the surface texture compression information of the window decoration product to be detected, wherein the compression information feature extraction module is a multi-layer perceptron including two fully connected layers and a SiLU activation function; A surface texture feature weight calculation unit, used for using a Sigmoid function to perform a normalization operation on the multi-scale associated feature vector of the surface texture compression information of the window decoration product to be detected to obtain a surface texture weight feature vector of the window decoration product to be detected; The texture feature amplification and suppression unit is used to perform feature amplification and suppression operations on the surface texture feature map of the window decoration product to be detected based on the surface texture weight feature vector of the window decoration product to be detected to obtain the enhanced surface texture feature map of the window decoration product to be detected.

5. The MES-based window decoration product production management system according to claim 4 is characterized in that: The surface texture feature compression unit of the window decoration product to be detected is used to calculate the global mean of each feature matrix along the channel dimension of the surface texture feature map of the window decoration product to be detected to obtain the surface texture feature compression information representation vector of the window decoration product to be detected.

6. The MES-based window decoration product production management system according to claim 5 is characterized in that: The surface texture feature weight calculation unit comprises: A surface texture compression information multi-scale exponential value calculation subunit, used to use the negative number of the eigenvalue of each position in the multi-scale association eigenvector of the surface texture compression information of the window decoration product to be detected as the exponent of the natural constant to calculate the exponential function value based on the position with the natural constant as the base to obtain the multi-scale association class support eigenvector of the surface texture compression information of the window decoration product to be detected; The surface texture weight value calculation subunit is used to calculate the reciprocal of the sum of the eigenvalue of each position in the multi-scale association class support feature vector of the surface texture compression information of the window decoration product to be detected and the constant one to obtain the surface texture weight feature vector of the window decoration product to be detected.

7. The MES-based window decoration product production management system according to claim 6 is characterized in that: The texture feature amplification and suppression unit is used to: multiply the eigenvalues ​​of each position in the surface texture weight feature vector of the window decoration product to be detected by each feature matrix along the channel dimension of the surface texture feature map of the window decoration product to be detected by position to obtain the enhanced surface texture feature map of the window decoration product to be detected.

8. The MES-based window decoration product production management system according to claim 7 is characterized in that: The quality inspection result generating module is used to: input the texture difference semantic representation feature map into the classifier-based quality inspection module to obtain the quality inspection result, and the quality inspection result is used to indicate whether the window decoration product to be inspected is qualified.

9. A method for production management of window decoration products based on MES, characterized in that: include: The surface texture image of the window decoration product to be inspected is collected by a camera; Extracting design texture pattern images from a background database; Inputting the surface texture image of the window decoration product to be detected and the design texture pattern image into a texture twin detection network to obtain a surface texture feature map of the window decoration product to be detected and a design texture pattern reference feature map; Performing feature enhancement processing based on a compression suppression structure on the surface texture feature map of the window decoration product to be detected and the reference feature map of the design texture pattern to obtain an enhanced surface texture feature map of the window decoration product to be detected and an enhanced reference feature map of the design texture pattern; Calculating a texture difference semantic representation feature map between the enhanced surface texture feature map of the window decoration product to be inspected and the enhanced design texture pattern reference feature map as a texture difference semantic representation feature; Based on the texture difference semantic representation feature, a quality inspection result is obtained, and the quality inspection result is used to indicate whether the window decoration product to be inspected is qualified.

10. The MES-based window decoration product production management method according to claim 9, characterized in that: The surface texture image of the window decoration product to be detected and the design texture pattern image are input into a texture twin detection network to obtain a surface texture feature map of the window decoration product to be detected and a reference feature map of the design texture pattern, including: inputting the surface texture image of the window decoration product to be detected and the design texture pattern image into a texture twin detection network including a first image encoder and a second image encoder to obtain the surface texture feature map of the window decoration product to be detected and the reference feature map of the design texture pattern, wherein the first image encoder and the second image encoder have the same network structure.

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