Intelligent cutting control system and method for aluminum alloy profiles

Abstract

The present application relates to the technical field of aluminum alloy profile cutting, and specifically discloses an intelligent cutting control system and method for aluminum alloy profiles. The system collects an image of the cutting surface state of the aluminum alloy profile and introduces an artificial intelligence-based image processing and analysis algorithm at the back end to analyze the image of the cutting surface state of the aluminum alloy profile, thereby capturing the semantic features of the cutting surface state of the aluminum alloy profile shown in the image. Based on the semantic representation of the cutting surface state of the aluminum alloy profile, the cutting surface state is identified to determine whether there is a burn, and corresponding cutting optimization control instructions are automatically generated based on the identification results, such as increasing the coolant flow rate, to prevent and reduce the occurrence of surface defects. In this way, the cooling system parameters during cutting can be dynamically optimized and controlled according to the actual cutting surface state of the aluminum alloy profile, thereby improving the intelligence level of the aluminum alloy profile cutting control process and further improving the reliability and stability of the cutting process.

Description

Technical Field

[0001] The present application relates to the technical field of aluminum alloy profile cutting, and more specifically, to an intelligent cutting control system and method for aluminum alloy profiles. Background Art

[0002] In modern industrial production, aluminum alloy profiles are widely used in various fields such as construction, transportation, and electronics due to their advantages such as light weight, high strength, good conductivity, and corrosion resistance. However, the processing of aluminum alloy profiles, especially the cutting process, can present a series of technical difficulties. The most notable of these is surface defects such as burns caused by overheating during the cutting process. These surface defects not only affect the product's appearance but can also reduce the material's mechanical properties, thereby limiting the product's scope of application.

[0003] Traditionally, to avoid overheating during the cutting process, people usually rely on fixed parameter control methods or rely on the operator's experience to adjust the cutting parameters and cooling system settings. Fixed parameter control methods cannot adapt to changes in different material properties, environmental conditions or cutting requirements. For example, for certain complex cutting tasks, fixed cooling parameters may not be sufficient to prevent overheating, thereby increasing the risk of surface defects. At the same time, setting overly conservative cooling parameters to avoid overheating may lead to excessive use of resources such as coolant. The manual control method of the operator is affected by the differences in experience and technical level of different operators, resulting in inconsistent product quality. In addition, when some surface defects occur, the operator may not be able to detect them immediately or react quickly enough, resulting in an increase in defective products.

[0004] Therefore, an intelligent cutting control solution for aluminum alloy profiles is desired. Summary of the Invention

[0005] The present application provides an intelligent cutting control system and method for aluminum alloy profiles, which can dynamically optimize and control the cooling system parameters during cutting according to the actual cutting surface state of the aluminum alloy profile, thereby improving the intelligence level of the aluminum alloy profile cutting control process and further improving the reliability and stability of the cutting process.

[0006] In a first aspect, a method for intelligent cutting control of aluminum alloy profiles is provided, comprising:

[0007] Acquire the state image of the cutting surface of the aluminum alloy profile captured by the camera;

[0008] Extracting features from the aluminum alloy profile cutting surface state image to obtain aluminum alloy profile cutting surface state features;

[0009] The aluminum alloy profile cutting surface state feature is subjected to pixel granularity-based explicit feature enhancement processing to obtain an aluminum alloy profile cutting surface state semantic enhancement feature, comprising: performing pixel granularity feature decoupling on the aluminum alloy profile cutting surface state feature to obtain a set of aluminum alloy profile cutting surface state pixel granularity decoupling features; calculating the significant feature weight of each aluminum alloy profile cutting surface state pixel granularity decoupling feature in the set of aluminum alloy profile cutting surface state pixel granularity decoupling features, and performing pixel granularity weighted enhancement on the aluminum alloy profile cutting surface state feature based on the significant feature weight of each aluminum alloy profile cutting surface state pixel granularity decoupling feature to obtain an aluminum alloy profile cutting surface state semantic enhancement feature;

[0010] Based on the semantic enhancement features of the cutting surface state of the aluminum alloy profile, the cutting surface state is identified to determine whether there is a burn. If the identification result shows that there is a burn, a cutting optimization control instruction is generated to indicate a cutting optimization control instruction for increasing the coolant flow rate.

[0011] In a second aspect, an intelligent cutting control system for aluminum alloy profiles is provided, comprising:

[0012] Aluminum alloy profile cutting image acquisition module, used to obtain the aluminum alloy profile cutting surface state image captured by the camera;

[0013] an aluminum alloy profile cutting surface state feature extraction module, configured to extract features from the aluminum alloy profile cutting surface state image to obtain aluminum alloy profile cutting surface state features;

[0014] An aluminum alloy profile cutting surface state feature enhancement processing module is used to perform pixel-granularity-based explicit feature enhancement processing on the aluminum alloy profile cutting surface state feature to obtain an aluminum alloy profile cutting surface state semantic enhancement feature, wherein the aluminum alloy profile cutting surface state feature enhancement processing module includes: a pixel-granularity feature decoupling processing unit, used to perform pixel-granularity feature decoupling on the aluminum alloy profile cutting surface state feature to obtain a set of aluminum alloy profile cutting surface state pixel-granularity decoupling features; a weighted enhancement processing unit, used to calculate the significant feature weight of each aluminum alloy profile cutting surface state pixel-granularity decoupling feature in the set of aluminum alloy profile cutting surface state pixel-granularity decoupling features, and perform pixel-granularity weighted enhancement on the aluminum alloy profile cutting surface state feature based on the significant feature weight of each aluminum alloy profile cutting surface state pixel-granularity decoupling feature to obtain an aluminum alloy profile cutting surface state semantic enhancement feature;

[0015] A cutting surface state recognition module is used to perform cutting surface state recognition based on the semantic enhancement features of the cutting surface state of the aluminum alloy profile to determine whether there is a burn. If the recognition result is that there is a burn, a cutting optimization control instruction is generated to indicate a cutting optimization control instruction for increasing the coolant flow rate.

[0016] The present application provides an intelligent cutting control system and method for aluminum alloy profiles. This system collects images of the aluminum alloy profile's cut surface state and analyzes them using an artificial intelligence-based image processing and analysis algorithm at the back end. This allows the system to capture the semantic features of the aluminum alloy profile's cut surface state as represented in the image. The system then identifies the cut surface state based on the semantic representation of the aluminum alloy profile's cut surface state to determine whether burns exist. Based on the identification results, the system automatically generates corresponding cutting optimization control instructions, such as increasing the coolant flow rate, to prevent and mitigate surface defects. This allows the system to dynamically optimize and control cooling system parameters during cutting based on the actual state of the aluminum alloy profile's cut surface, thereby enhancing the intelligence of the aluminum alloy profile cutting control process and further improving the reliability and stability of the cutting process. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] In order to more clearly illustrate the technical solutions of the embodiments of the present application, the following briefly introduces the drawings of the embodiments of the present application. Obviously, the drawings described below only relate to some embodiments of the present application, and are not intended to limit the present application.

[0018] Figure 1 This is a schematic flow chart of the intelligent cutting control method for aluminum alloy profiles according to an embodiment of the present application.

[0019] Figure 2 This is a data flow diagram of the intelligent cutting control method for aluminum alloy profiles according to an embodiment of the present application.

[0020] Figure 3 This is a schematic flow chart of step S3 in the intelligent cutting control method for aluminum alloy profiles according to an embodiment of the present application.

[0021] Figure 4 This is a schematic flowchart of step S32 in the intelligent cutting control method for aluminum alloy profiles according to an embodiment of the present application.

[0022] Figure 5 This is a schematic flow chart of step S4 in the intelligent cutting control method for aluminum alloy profiles according to an embodiment of the present application.

[0023] Figure 6 This is a schematic block diagram of an intelligent cutting control system for aluminum alloy profiles according to an embodiment of the present application. DETAILED DESCRIPTION

[0024] The following will clearly and completely describe the technical solutions in the embodiments of the present application with reference to the accompanying drawings. Obviously, the embodiments described are only some embodiments of the present application, not all embodiments. Based on the embodiments of the present application, all other embodiments obtained by ordinary technicians in this field without making creative efforts also fall within the scope of protection of this application.

[0025] With the development of computer vision technology and machine learning algorithms, intelligent control methods have begun to be applied in the field of metal processing to achieve more accurate and automated cutting process control. Based on this, in the technical solution of this application, an intelligent cutting control method for aluminum alloy profiles is proposed, which can monitor and analyze the cutting process in real time through a camera to determine whether there is burn, and dynamically adjust the cutting parameters according to the actual situation, thereby improving the reliability and stability of the aluminum alloy profile cutting process. In addition, this method reduces the dependence on manual intervention and improves the automation level of production control, thereby avoiding the defects brought about by traditional control methods.

[0026] Specifically, the technical concept of the present application is to collect an image of the cutting surface state of an aluminum alloy profile and introduce an artificial intelligence-based image processing and analysis algorithm at the back end to analyze the image of the cutting surface state of the aluminum alloy profile, so as to capture the semantic features of the cutting surface state of the aluminum alloy profile shown in the image, and then perform cutting surface state recognition based on the semantic representation of the cutting surface state of the aluminum alloy profile to determine whether there is a burn, and automatically generate corresponding cutting optimization control instructions based on the recognition results, such as increasing the coolant flow rate, to prevent and reduce the occurrence of surface defects. In this way, the cooling system parameters during cutting can be dynamically optimized and controlled according to the actual cutting surface state of the aluminum alloy profile, thereby improving the intelligence level of the aluminum alloy profile cutting control process and further improving the reliability and stability of the cutting process.

[0027] Based on this, Figure 1 and Figure 2 As shown, the intelligent cutting control method for aluminum alloy profiles includes: S1, obtaining an aluminum alloy profile cutting surface state image captured by a camera; S2, performing feature extraction on the aluminum alloy profile cutting surface state image to obtain aluminum alloy profile cutting surface state features; S3, performing explicit feature enhancement processing on the aluminum alloy profile cutting surface state features based on pixel granularity to obtain aluminum alloy profile cutting surface state semantic enhancement features; S4, performing cutting surface state recognition based on the aluminum alloy profile cutting surface state semantic enhancement features to determine whether there is a burn, and if the recognition result is that there is a burn, generating a cutting optimization control instruction for indicating a cutting optimization control instruction for increasing the coolant flow rate.

[0028] For example, in step S1, an image of the state of the cut surface of the aluminum alloy profile captured by the camera is obtained. It should be understood that the purpose of obtaining the image of the state of the cut surface of the aluminum alloy profile captured by the camera is to achieve intelligent monitoring and real-time feedback control of the cutting process. Specifically, this is to be able to dynamically analyze the state of the surface of the aluminum alloy profile during the cutting process, identify whether there are defects such as burns, and adjust the cutting parameters (such as coolant flow) in real time based on this information to prevent and reduce the occurrence of surface defects and ensure product quality. Traditional methods rely on fixed parameters or manual experience to adjust cutting conditions. This method is difficult to adapt to changing working environments and material properties, and is prone to product defects or waste of resources.

[0029] In one embodiment, obtaining an image of the cutting surface status of an aluminum alloy profile captured by a camera includes: installing one or more high-definition cameras at appropriate locations on the cutting equipment to ensure that the camera can clearly capture key areas on the cutting surface. The selection of the camera should take into account the working environment (such as whether there is high temperature, dust, etc.) and the required resolution and frame rate. In order to ensure the accuracy of the image data, the camera can be calibrated, including optical distortion correction, focal length adjustment, etc., so that the collected image can truly reflect the actual situation of the cutting surface. The camera is connected to the cutting control system through a programming interface or other means to ensure time synchronization between the two, that is, the camera can start recording the image at the same time when each cutting operation occurs.

[0030] For example, in step S2, feature extraction is performed on the aluminum alloy profile cutting surface state image to obtain aluminum alloy profile cutting surface state features. It should be understood that raw image data typically has very high dimensions (e.g., each pixel is a data point), and directly processing this high-dimensional data poses a significant challenge to computing resources and algorithm efficiency. Feature extraction can convert the raw image into a set of more compact and representative feature vectors or feature maps, significantly reducing the amount of data and complexity required for subsequent processing. Feature extraction is not just a process of reducing data volume; more importantly, it can capture high-level semantic information in the image that is relevant to a specific task. In this case, feature extraction is performed on the cutting surface state image to focus on important visual patterns that reflect cutting quality (such as the presence of burns) while ignoring insignificant background noise or other interfering factors. Different cutting tasks may have different requirements and challenges. Feature extraction allows the system to more flexibly respond to various changes because it can extract common yet differentiated features from different types of input images, making it applicable to a wider range of application scenarios.

[0031] In one embodiment, feature extraction is performed on the aluminum alloy profile cut surface state image to obtain aluminum alloy profile cut surface state features, including: inputting the aluminum alloy profile cut surface state image into a cut surface state feature extractor based on the Mobile-Former model to obtain an aluminum alloy profile cut surface state feature map as the aluminum alloy profile cut surface state features. Specifically, the aluminum alloy profile cut surface state image is input into the cut surface state feature extractor based on the Mobile-Former model for feature mining to extract implicit feature information of the aluminum alloy profile cut surface state, thereby obtaining the aluminum alloy profile cut surface state feature map. It should be understood that Mobile-Former is a lightweight deep learning model that maintains high accuracy while reducing computational complexity and memory usage, enabling real-time processing. This is crucial for industrial applications, as real-time performance is a key factor in ensuring a smooth cutting process and reducing defective products. Furthermore, Mobile-Former can effectively extract meaningful features from complex cut surface images, providing a solid foundation for subsequent defect detection. Based on this, the use of the cross-section state feature extractor based on the Mobile-Former model can provide powerful feature extraction capabilities while ensuring efficient calculation to capture the cross-section state feature information of aluminum alloy profiles in different cutting scenarios.

[0032] For example, in step S3, the state feature of the aluminum alloy profile cutting surface is enhanced by an explicit feature based on pixel granularity to obtain a semantic enhancement feature of the state of the aluminum alloy profile cutting surface. It should be understood that, considering that the aluminum alloy profile cutting surface state feature map contains the state representation information of the aluminum alloy profile cutting surface, however, when cutting the aluminum alloy profile, the initial burns are relatively mild, and even very subtle changes or defects, such as slight burn marks, may have a significant impact on the quality of the final product, so it is necessary to promptly discover and perform corresponding control optimization adjustments. In addition, considering that the defects generated during the cutting process are often not isolated, but are closely related to the surrounding local semantics, therefore, in order to focus on the fine-grained semantic feature information of the aluminum alloy profile cutting surface state related to the subsequent burn state recognition task, in the technical solution of the present application, the state feature of the aluminum alloy profile cutting surface is further enhanced by an explicit feature based on pixel granularity to obtain a semantic enhancement feature of the state of the aluminum alloy profile cutting surface. This improves the model's sensitivity to these subtle burn details, helping to more comprehensively and accurately distinguish between normal cutting marks and defects (such as burns), providing support for subsequent cutting optimization control. Furthermore, the pixel-level explicit feature enhancement process decouples unnecessary connections between pixels, allowing each pixel-level feature to be evaluated individually, thereby better capturing the local characteristic information of subtle burn defects that are easily overlooked but critical to product quality.

[0033] In one embodiment, Figure 3 As shown, in step S3, the aluminum alloy profile cutting surface state feature is subjected to pixel granularity-based explicit feature enhancement processing to obtain the aluminum alloy profile cutting surface state semantic enhancement feature, including: S31, pixel granularity feature decoupling of the aluminum alloy profile cutting surface state feature to obtain a set of aluminum alloy profile cutting surface state pixel granularity decoupling features; S32, calculating the significant feature weight of each aluminum alloy profile cutting surface state pixel granularity decoupling feature in the set of aluminum alloy profile cutting surface state pixel granularity decoupling features, and performing pixel granularity weighted enhancement on the aluminum alloy profile cutting surface state feature based on the significant feature weight of each aluminum alloy profile cutting surface state pixel granularity decoupling feature to obtain the aluminum alloy profile cutting surface state semantic enhancement feature.

[0034] In one embodiment, in step S31, pixel-granularity feature decoupling is performed on the aluminum alloy profile cutting surface state features to obtain a set of aluminum alloy profile cutting surface state pixel-granularity decoupling features, including: performing pixel-granularity feature decoupling on the aluminum alloy profile cutting surface state feature map to obtain a set of aluminum alloy profile cutting surface state pixel-granularity decoupling feature vectors as the set of aluminum alloy profile cutting surface state pixel-granularity decoupling features. Specifically, this process can be expressed as follows:

[0035] decouple(F i )={v1,v2,...,v i ,...,v n}

[0036] Among them, F i is the characteristic diagram of the cutting surface of the aluminum alloy profile, decouple(F i ) is for F i Perform pixel granularity feature decoupling, v1, v2, v i and v n They are respectively the 1st, 2nd, i-th and n-th aluminum alloy profile cutting surface state pixel granularity decoupling feature vectors in the set of aluminum alloy profile cutting surface state pixel granularity decoupling feature vectors.

[0037] As can be understood, by independently analyzing each pixel-level feature, it is possible to isolate the fine-grained semantic features relevant to the burn identification task while excluding irrelevant or redundant information, such as background noise or changes in non-critical areas. This processing approach enables more sensitive perception of easily overlooked local features that are critical to product quality, such as initial, subtle burn marks, which may be imperceptible at the macro level but exhibit unique texture or brightness differences at the micro level. By decoupling pixel-level features, each pixel-level feature can be evaluated individually. This not only helps better capture the semantic features closely related to burn status identification, but also strengthens the intrinsic connections between features, making the resulting feature map more semantically meaningful. This feature representation is crucial for distinguishing normal cutting marks from defects such as burns, supporting subsequent cutting optimization control. Furthermore, this approach allows for dynamic adjustment of focus based on actual material properties, ensuring accurate judgment of the cut surface condition even on complex or irregularly shaped profiles, while also flexibly addressing the various variables that may exist in the production environment, such as changes in temperature, humidity, and lighting conditions. The decoupled feature vector sets usually have better distribution characteristics, which means that they can provide higher-quality input data for the deep learning model in training, helping to speed up the model's learning process and improve the final performance.

[0038] In one embodiment, Figure 4 As shown, in step S32, the significant feature weight of each aluminum alloy profile cutting surface state pixel granularity decoupling feature in the set of aluminum alloy profile cutting surface state pixel granularity decoupling features is calculated, and the aluminum alloy profile cutting surface state feature is pixel granularity weighted enhanced based on the significant feature weight of each aluminum alloy profile cutting surface state pixel granularity decoupling feature to obtain the aluminum alloy profile cutting surface state semantic enhancement feature, including: S321, calculating the significant feature of the set of aluminum alloy profile cutting surface state pixel granularity decoupling feature vectors to obtain the aluminum alloy profile cutting surface state structural semantic enhancement feature. Saliency distribution matrix; S322, performing masking processing on each eigenvalue in the aluminum alloy profile cutting surface state structure semantic significance distribution matrix based on feature explicit layering to obtain an aluminum alloy profile cutting surface state structure semantic significance distribution weight matrix composed of multiple aluminum alloy profile cutting surface state structure semantic weights; S323, performing weighted optimization on the aluminum alloy profile cutting surface state feature map based on the aluminum alloy profile cutting surface state structure semantic significance distribution weight matrix to obtain an aluminum alloy profile cutting surface state semantic enhancement feature map as the aluminum alloy profile cutting surface state semantic enhancement feature.

[0039] In one embodiment, in step S321, the significant features of the set of the aluminum alloy profile cutting surface state pixel granularity decoupling feature vectors are calculated to obtain the aluminum alloy profile cutting surface state structural semantic significance distribution matrix, including: inputting each aluminum alloy profile cutting surface state pixel granularity decoupling feature vector in the set of the aluminum alloy profile cutting surface state pixel granularity decoupling feature vectors into a hyperbolic space mapper to obtain a set of aluminum alloy profile cutting surface state hyperbolic space modulation pixel granularity decoupling feature vectors; calculating the fine-grained structural semantic significance measurement coefficient of each aluminum alloy profile cutting surface state hyperbolic space modulation pixel granularity decoupling feature vector in the set of the aluminum alloy profile cutting surface state hyperbolic space modulation pixel granularity decoupling feature vectors to obtain multiple aluminum alloy profile cutting surface state fine-grained structural semantic significance measurement coefficients; arranging the multiple aluminum alloy profile cutting surface state fine-grained structural semantic significance measurement coefficients into a matrix to obtain the aluminum alloy profile cutting surface state structural semantic significance distribution matrix. Specifically, the process can be expressed as follows:

[0040] h i =W1v i W2

[0041]

[0042] Wherein, v is the i-th aluminum alloy profile cutting surface state pixel granularity decoupling feature vector in the set of aluminum alloy profile cutting surface state pixel granularity decoupling feature vectors, W1 and W2 are the first weight matrix and the second weight matrix respectively, hj v i The corresponding hyperbolic spatial modulation pixel granularity decoupling eigenvector of the aluminum alloy profile cutting surface state, ||·|| 2 To calculate the square of the vector norm, s i is the semantic saliency distribution matrix of the aluminum alloy profile cutting surface state structure h i The corresponding semantic saliency coefficient of the fine-grained structure of the cutting surface state of aluminum alloy profiles.

[0043] As can be understood, hyperbolic space, with its natural hierarchical structure and exponential expansion, is well-suited for representing similarities and distances between data points with complex relationships. For cut surface state analysis, this mapping enhances fine-grained structural semantic information that may be imperceptible in the original image space but is crucial for burn identification. When the decoupled feature vectors are mapped to hyperbolic space, they are transformed into so-called "hyperbolic space modulated pixel-level decoupled feature vectors." This new representation amplifies or emphasizes subtle textures and brightness variations inherent in the cut surface, particularly those localized anomalies associated with burns. Next, a fine-grained structural semantic saliency metric is calculated for these hyperbolic space feature vectors. This metric reflects the importance of the feature at each pixel location—that is, whether the feature at that location is likely to be part of a burn mark, as well as its relative importance within the entire image. By quantifying all correlation coefficients, a series of values is obtained, which together form a distribution matrix describing the semantic saliency of the entire cut surface state structure. This saliency distribution matrix is essentially a two-dimensional array, where each element corresponds to a pixel in the original image and carries information about whether that pixel and the surrounding area are potentially susceptible to burns. High values indicate a more pronounced burn feature at that location, while low values suggest a normal cut or other less critical surface detail. This matrix not only condenses the vast amount of information extracted from the image but also presents it in a structured manner, making it easily usable in subsequent processing steps.

[0044] In one embodiment, in step S322, each eigenvalue in the aluminum alloy profile cutting surface state structure semantic saliency distribution matrix is masked based on feature explicit hierarchical processing to obtain an aluminum alloy profile cutting surface state structure semantic saliency distribution weight matrix composed of multiple aluminum alloy profile cutting surface state structure semantic weights, including: inputting the aluminum alloy profile cutting surface state structure semantic saliency distribution matrix into a feature explicit hierarchical precipitation module based on a multi-layer masking function to obtain the aluminum alloy profile cutting surface state structure semantic saliency distribution weight matrix. Specifically, this process can be expressed as follows:

[0045]

[0046] Among them, s i is the semantic saliency distribution matrix of the aluminum alloy profile cutting surface state structure h i The corresponding semantic saliency coefficient of the fine-grained structure of the aluminum alloy profile cutting surface state, θ is the preset threshold, mask is the masking operation, s i ' is the semantic significance distribution weight matrix of the aluminum alloy profile cutting surface state structure h i The corresponding semantic significance distribution weight value of the aluminum alloy profile cutting surface state structure.

[0047] It should be understood that the feature explicit hierarchical precipitation module based on a multi-layer masking function can selectively amplify or suppress features at different levels, thereby more accurately capturing the fine-grained semantic features most relevant to the burn identification task and reducing the influence of noise and other irrelevant factors. The generated semantic saliency distribution weight matrix for the aluminum alloy profile cut surface state structure not only retains the spatial layout of the original saliency distribution matrix but also adds a deeper level of understanding and interpretation. Each element now represents not only the saliency of the feature at that location but also an assessment of its relative importance within the overall cut surface state. A high weight value indicates that the location is likely to contain obvious burn marks or other defects requiring attention; conversely, a low weight value indicates that the cut surface is likely normal or contains unimportant background information. This weight matrix provides guidance for subsequent weighted optimization, enabling the decision-making process to prioritize more valuable features while ignoring less relevant information.

[0048] In one embodiment, in step S323, the aluminum alloy profile cutting surface state feature map is weighted optimized based on the aluminum alloy profile cutting surface state structure semantic saliency distribution weight matrix to obtain an aluminum alloy profile cutting surface state semantic enhancement feature map as the aluminum alloy profile cutting surface state semantic enhancement feature, including: calculating the position point multiplication between the aluminum alloy profile cutting surface state structure semantic saliency distribution weight matrix and each feature matrix along the channel dimension in the aluminum alloy profile cutting surface state feature map to obtain the aluminum alloy profile cutting surface state semantic enhancement feature map. Specifically, this process can be expressed as follows:

[0049]

[0050] Among them, F i is the aluminum alloy profile cutting surface state feature map, S is the aluminum alloy profile cutting surface state structure semantic significance distribution weight matrix, In order to use the matrix to perform weighted multiplication on each feature matrix along the channel dimension of the feature map, F fIt is a semantically enhanced feature map of the cutting surface state of the aluminum alloy profile.

[0051] The positional dot product between the semantic saliency distribution weight matrix of the aluminum alloy profile cut surface state structure and each feature matrix along the channel dimension in the cut surface state feature map is calculated. This process aims to reapply the feature importance information (i.e., weights) obtained after processing through the multi-layer mask function back to the original feature map. Doing so can further enhance those areas that are critical to burn identification while suppressing or ignoring less relevant areas, thereby generating a semantically enhanced feature map that is more focused on key features.

[0052] It should be understood that when these weight values are dot-multiplied with the corresponding positions in the original feature map, the importance of the original features is actually adjusted: for those areas that are considered very important, their features will be amplified, making them more prominent in subsequent analysis; while for less important areas, the influence of the features is weakened, reducing their interference with the final result. The semantically enhanced feature map generated in this way not only retains the spatial structure and texture information of the original image, but also incorporates the feature importance evaluation after careful screening. This enhanced feature map can more accurately capture subtle burn marks and other potential defects on the cutting surface, greatly improving the system's sensitivity to these subtle changes. For example, in the actual cutting process, even extremely slight overheating can cause surface quality to deteriorate, but traditional detection methods may find it difficult to detect such early signs. However, with the help of semantically enhanced feature maps, intelligent control systems can quickly identify these risks and take timely measures (such as increasing coolant flow) to prevent problems from worsening and ensure product quality.

[0053] In summary, the pixel-level explicit feature enhancement process utilizes explicit modeling and hierarchical mask modulation of fine-grained pixel features of the aluminum alloy profile's cut surface state in the aluminum alloy profile cut surface state feature map to enhance features, effectively improving the quality of the cut surface state feature map. Specifically, after performing a pixel-level feature decoupling operation, the pixel-level explicit feature enhancement process further performs hyperbolic space mapping and calculates a fine-grained structure semantic saliency coefficient. This quantifies the importance of each pixel-level fine-grained feature in the cut surface state feature map, providing a scientific basis for subsequent feature selection. This facilitates more accurate judgment of the cut surface state, especially for complex or irregularly shaped profiles. A multi-layer masking function is then used to selectively amplify features at different levels while suppressing noisy or irrelevant features. This process, similar to an attention mechanism, allows for dynamic adjustment of the focus on different pixel-level semantic features in the cut surface state image. This semantic understanding is crucial for distinguishing normal cutting marks from burn defects, and helps improve the quality of the final feature representation. Finally, element-by-element multiplication is used to generate the final semantic enhancement feature map of the cutting surface state of the aluminum alloy profile as the semantic enhancement feature of the cutting surface state of the aluminum alloy profile, ensuring that the output semantic enhancement feature can highlight the most representative and discriminative pixel-level granularity semantic feature information of the cutting surface state of the aluminum alloy profile. These semantic features can focus on the subtle burn defect characteristics of the cutting surface state of the aluminum alloy profile and the feature parts that are crucial for classification or recognition tasks, thereby enabling the model to have higher sensitivity, accuracy and stronger generalization ability when detecting and evaluating the cutting surface state of the aluminum alloy profile, providing a solid foundation for subsequent cutting surface state recognition and burn defect detection.

[0054] In one embodiment, Figure 5As shown, in step S4, the state of the aluminum alloy profile cutting surface is identified based on the semantically enhanced features of the cutting surface state to determine whether burns are present. If the identification result indicates burns are present, a cutting optimization control instruction is generated to increase the coolant flow rate. The process includes: S41, inputting the semantically enhanced feature map of the aluminum alloy profile cutting surface state into a cutting surface state identifier based on a classifier to obtain a recognition result, wherein the recognition result indicates whether burns are present; S42, in response to the recognition result indicating burns are present, generating the cutting optimization control instruction, wherein the cutting optimization control instruction indicates increasing the coolant flow rate. In other words, the classification process is performed using the enhanced semantics of the aluminum alloy profile cutting surface state to identify the cutting surface state and determine whether burns are present. Furthermore, in response to the recognition result indicating burns are present, a cutting optimization control instruction is generated to prevent and reduce the occurrence of surface defects, wherein the cutting optimization control instruction indicates increasing the coolant flow rate. In this way, the cooling system parameters during cutting can be dynamically optimized based on the actual state of the aluminum alloy profile cutting surface, thereby improving the intelligence of the aluminum alloy profile cutting control process and further enhancing the reliability and stability of the cutting process.

[0055] In one specific embodiment, the classifier-based cut surface state identifier utilizes an SVM classifier trained on a large dataset of labeled aluminum alloy profile cut surface images, some of which represent normal cut surfaces, while others contain examples of burns of varying degrees. Through this training process, the SVM learns how to distinguish between normal cuts and burns based on input features. When a new semantically enhanced feature map enters the SVM, the classifier maps it into a high-dimensional space, where it searches for an optimal hyperplane that maximizes the separation between different classes. For binary classification problems, such as "burned" and "not burned" in this example, the SVM attempts to find a decision boundary that separates the two classes as closely as possible. Once this boundary is determined, the SVM determines which class a new test sample (i.e., the current semantically enhanced feature map) belongs to based on its relative position with respect to the decision boundary. When the intelligent control system uses the SVM classifier to identify signs of slight overheating (i.e., a potential burn risk) on the cut surface of an aluminum alloy profile, the system immediately responds to this recognition result and automatically generates a clear cutting optimization control instruction. Based on the SVM's recognition results, the intelligent control system immediately generates control instructions containing specific parameters and sends them to the PLC (programmable logic controller) responsible for regulating the cooling system via industrial communication protocols such as Modbus, Profibus, or EtherCAT. For example, this instruction calls for increasing the coolant pumping rate from the current 20 liters / minute to 30 liters / minute and activating the backup cooling nozzles to ensure even distribution of coolant across the entire cutting area. The system also instructs the system to maintain this enhanced cooling setting for at least 10 seconds to ensure effective temperature control before reassessing whether to restore normal flow. Upon receiving the instruction, the PLC interprets the specific parameters and prepares to execute the corresponding actions. It signals the coolant pump to increase the operating frequency or pressure, raising the coolant flow rate to 30 liters / minute. Simultaneously, the PLC triggers the solenoid valves of the backup cooling nozzles, opening them to provide additional cooling support. During this time, the system continues to monitor the cutting surface in real time and collects data from sensors, such as temperature readings, to verify cooling effectiveness. If the temperature drops successfully and there are no further signs of overheating, the system will gradually restore normal coolant flow after 10 seconds; if the problem persists, the system will continue to maintain or further increase cooling measures and may notify the operator for manual intervention.

[0056] Preferably, inputting the semantically enhanced feature map of the aluminum alloy profile cutting surface state into a classifier-based cutting surface state identifier to obtain a recognition result comprises:

[0057] The absolute value sum and the square root of the square sum of all eigenvalues of the semantic enhancement feature graph of the aluminum alloy profile cutting surface state are calculated to obtain the first aluminum alloy profile cutting surface state semantic enhancement spatial structure value and the second aluminum alloy profile cutting surface state semantic enhancement spatial structure value, that is:

[0058] w1=∑ i |f i |

[0059]

[0060] Among them, f i represents the i-th eigenvalue of the semantic enhancement feature map of the aluminum alloy profile cutting surface state, w1 represents the semantic enhancement spatial structure value of the first aluminum alloy profile cutting surface state, and w2 represents the semantic enhancement spatial structure value of the second aluminum alloy profile cutting surface state;

[0061] Determine the total number n of eigenvalues of all eigenvalues of the set of semantically enhanced feature graphs of the aluminum alloy profile cutting surface state;

[0062] For each eigenvalue of the semantic enhancement feature graph of the aluminum alloy profile cutting surface state, the first aluminum alloy profile cutting surface state semantic enhancement spatial structure value minus the product of the eigenvalue and the total number of eigenvalues is calculated to obtain the first aluminum alloy profile cutting surface state semantic enhancement long-range dependency value x i =w1-f i ×n, where w1 represents the semantically enhanced spatial structure value of the cutting surface state of the first aluminum alloy profile, f j represents the i-th eigenvalue of the semantic enhancement feature graph of the aluminum alloy profile cutting surface state, n represents the number of eigenvalues of all eigenvalues of the semantic enhancement feature graph of the aluminum alloy profile cutting surface state, x i Represents the semantically enhanced long-range dependency value of the cutting surface state of the first aluminum alloy profile; and calculates the second aluminum alloy profile cutting surface state semantically enhanced long-range dependency value obtained by multiplying the square root of the total number of eigenvalues by the product of the eigenvalues minus the second aluminum alloy profile cutting surface state semantically enhanced spatial structure value Among them, w2 represents the semantic enhancement spatial structure value of the cutting surface state of the second aluminum alloy profile, f i represents the i-th eigenvalue of the semantic enhancement feature graph of the aluminum alloy profile cutting surface state, n represents the number of eigenvalues of all eigenvalues of the semantic enhancement feature graph of the aluminum alloy profile cutting surface state, y i Indicates the semantically enhanced long-range dependency value of the cutting surface state of the second aluminum alloy profile;

[0063] The optimized eigenvalue is obtained by weighting the index value calculated by taking the semantically enhanced long-range dependency value of the cutting surface state of the first aluminum alloy profile as the exponent of the natural constant and the inverse of the semantically enhanced long-range dependency value of the cutting surface state of the second aluminum alloy profile. Among them, x i represents the semantically enhanced long-range dependency value of the cutting surface state of the first aluminum alloy profile, y i represents the semantically enhanced long-range dependency value of the cutting surface state of the second aluminum alloy profile, e represents a natural constant, α and β represent weighted hyperparameters, and f i represents the i-th optimized eigenvalue;

[0064] The optimized semantic enhancement feature map of the aluminum alloy profile cutting surface state composed of the optimized feature values is input into a cutting surface state identifier based on a classifier to obtain a recognition result.

[0065] Here, in the case where the aluminum alloy profile cutting surface state feature map represents the image semantic features of the aluminum alloy profile cutting surface, after performing feature enhancement based on feature explicit hierarchical precipitation, the aluminum alloy profile cutting surface state semantic enhancement feature map will also have significant feature explicit spatial structure differences due to the differences in image semantic feature hierarchical precipitation, affecting the convergence consistency of the classifier and thus affecting the consistency of the recognition results.

[0066] Based on this, in order to address the potential lack of spatial structural consistency in the feature set of the semantically enhanced feature graph of the cutting surface state of aluminum alloy profiles in high-dimensional space, which leads to the weight matrix of the classifier implicitly inferring spatial structural information based on features, resulting in inconsistent convergence, a long-range feature dependency relationship is established based on the spatial structure representation of the semantically enhanced feature graph of the cutting surface state of aluminum alloy profiles, thereby establishing the local connectivity of the features of the semantically enhanced feature graph of the cutting surface state of aluminum alloy profiles, and capturing the spatial ambiguity information of the object feature value through the prediction of the unstructured feature value points of the semantically enhanced feature graph of the cutting surface state of aluminum alloy profiles, thereby improving the spatial inductive bias perception ability of the feature set of the semantically enhanced feature graph of the cutting surface state of aluminum alloy profiles, improving the convergence consistency of the classifier, and improving the consistency of the recognition results obtained by the cutting surface state identifier based on the classifier. In this way, the cooling system parameters during cutting can be dynamically optimized according to the actual cutting surface state of aluminum alloy profiles, thereby improving the intelligence level of the aluminum alloy profile cutting control process and further improving the reliability and stability of the cutting process.

[0067] In summary, the intelligent cutting control method for aluminum alloy profiles according to the embodiment of the present application is explained. It collects the state image of the cutting surface of the aluminum alloy profile and introduces an image processing and analysis algorithm based on artificial intelligence at the back end to analyze the state image of the cutting surface of the aluminum alloy profile, so as to capture the semantic features of the state of the cutting surface of the aluminum alloy profile shown in the image, and then performs cutting surface state recognition based on the semantic representation of the state of the cutting surface of the aluminum alloy profile to determine whether there is a burn, and automatically generates corresponding cutting optimization control instructions based on the recognition results, such as increasing the flow rate of coolant, so as to prevent and reduce the occurrence of surface defects. In this way, the cooling system parameters during cutting can be dynamically optimized and controlled according to the actual state of the cutting surface of the aluminum alloy profile, thereby improving the intelligence level of the aluminum alloy profile cutting control process and further improving the reliability and stability of the cutting process.

[0068] Figure 6 This is a schematic block diagram of the intelligent cutting control system for aluminum alloy profiles according to an embodiment of the present application. Figure 6 As shown, the intelligent cutting control system 1 of the aluminum alloy profile includes: an aluminum alloy profile cutting image acquisition module 10, which is used to obtain the aluminum alloy profile cutting surface state image collected by the camera; an aluminum alloy profile cutting surface state feature extraction module 20, which is used to extract features from the aluminum alloy profile cutting surface state image to obtain the aluminum alloy profile cutting surface state feature; an aluminum alloy profile cutting surface state feature enhancement processing module 30, which is used to perform explicit feature enhancement processing on the aluminum alloy profile cutting surface state feature based on pixel granularity to obtain the aluminum alloy profile cutting surface state semantic enhancement feature; a cutting surface state recognition module 40, which is used to perform cutting surface state recognition based on the aluminum alloy profile cutting surface state semantic enhancement feature to determine whether there is a burn, and if the recognition result is that there is a burn, generate a cutting optimization control instruction for indicating a cutting optimization control instruction for increasing the coolant flow rate.

[0069] In one embodiment, the aluminum alloy profile cutting surface state feature extraction module is used to: input the aluminum alloy profile cutting surface state image into a cutting surface state feature extractor based on the Mobile-Former model to obtain an aluminum alloy profile cutting surface state feature map as the aluminum alloy profile cutting surface state feature.

[0070] In one embodiment, the aluminum alloy profile cutting surface state feature enhancement processing module includes: a pixel granularity feature decoupling processing unit, which is used to perform pixel granularity feature decoupling on the aluminum alloy profile cutting surface state feature to obtain a set of aluminum alloy profile cutting surface state pixel granularity decoupling features; a weighted enhancement processing unit, which is used to calculate the significance feature weight of each aluminum alloy profile cutting surface state pixel granularity decoupling feature in the set of aluminum alloy profile cutting surface state pixel granularity decoupling features, and perform pixel granularity weighted enhancement on the aluminum alloy profile cutting surface state feature based on the significance feature weight of each aluminum alloy profile cutting surface state pixel granularity decoupling feature to obtain an aluminum alloy profile cutting surface state semantic enhancement feature.

[0071] Here, those skilled in the art will appreciate that the specific operations of the various modules and units in the intelligent cutting control system for aluminum alloy profiles have been described in detail above. Figures 1 to 5 The description of the intelligent cutting control method of aluminum alloy profiles has been introduced in detail, and therefore, its repeated description will be omitted.

[0072] Those skilled in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this application.

[0073] It should be understood that the specific examples in this article are only intended to help those skilled in the art better understand the embodiments of the present application, and are not intended to limit the scope of the embodiments of the present application.

[0074] It should also be understood that in the various embodiments of the present application, the size of the serial number of each process does not mean the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present application.

[0075] It should also be understood that the various implementation methods described in this specification can be implemented individually or in combination, and the embodiments of the present application are not limited to this.

[0076] Unless otherwise indicated, all technical and scientific terms used in the embodiments of the present application have the same meaning as those generally understood by those skilled in the art in the technical field of the present application. The terms used in this application are only for the purpose of describing specific embodiments and are not intended to limit the scope of this application. The term "and / or" used in this application includes any and all combinations of one or more related listed items. The singular forms of "a", "above" and "the" used in the embodiments of the present application and the appended claims are also intended to include plural forms, unless the context clearly indicates other meanings. In addition, the terms "first", "second" etc. are only used for descriptive purposes and are not to be understood as indicating or suggesting relative importance.

[0077] In the several embodiments provided in this application, it should be understood that the disclosed systems, devices and methods can be implemented in other ways. For example, the device embodiments described above are merely schematic. For example, the division of the units is merely a logical function division. In actual implementation, there may be other division methods, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of devices or units, which can be electrical, mechanical or other forms.

[0078] The units described as separate components may or may not be physically separate, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed across multiple network units. Some or all of these units may be selected to achieve the purpose of this embodiment according to actual needs.

[0079] In addition, each functional unit in each embodiment of the present application may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit.

[0080] If the functions are implemented in the form of software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present application, or the part that contributes to the prior art, or the part of the technical solution, can be embodied in the form of a software product. The computer software product is stored in a storage medium and includes several instructions for enabling a computer device (which can be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the method described in each embodiment of the present application. The aforementioned storage medium includes various media that can store program codes, such as a USB flash drive, a mobile hard disk, a read-only memory (ROM), a random access memory (RAM), a magnetic disk, or an optical disk.

[0081] The above description is merely a specific embodiment of the present application, but the scope of protection of the present application is not limited thereto. Any changes or substitutions that can be easily conceived by a person skilled in the art within the technical scope disclosed in this application should be included in the scope of protection of this application. Therefore, the scope of protection of this application should be based on the scope of protection of the claims.

Claims

1. An intelligent cutting control method for aluminum alloy profiles, characterized in that: include: Acquire the state image of the cutting surface of the aluminum alloy profile captured by the camera; Extracting features from the aluminum alloy profile cutting surface state image to obtain aluminum alloy profile cutting surface state features; The aluminum alloy profile cutting surface state feature is subjected to pixel granularity-based explicit feature enhancement processing to obtain an aluminum alloy profile cutting surface state semantic enhancement feature, comprising: performing pixel granularity feature decoupling on the aluminum alloy profile cutting surface state feature to obtain a set of aluminum alloy profile cutting surface state pixel granularity decoupling features; calculating the significant feature weight of each aluminum alloy profile cutting surface state pixel granularity decoupling feature in the set of aluminum alloy profile cutting surface state pixel granularity decoupling features, and performing pixel granularity weighted enhancement on the aluminum alloy profile cutting surface state feature based on the significant feature weight of each aluminum alloy profile cutting surface state pixel granularity decoupling feature to obtain an aluminum alloy profile cutting surface state semantic enhancement feature; Based on the semantic enhancement features of the cutting surface state of the aluminum alloy profile, the cutting surface state is identified to determine whether there is a burn. If the identification result shows that there is a burn, a cutting optimization control instruction is generated to indicate a cutting optimization control instruction for increasing the coolant flow rate.

2. The intelligent cutting control method for aluminum alloy profiles according to claim 1, characterized in that: Feature extraction is performed on the aluminum alloy profile cutting surface state image to obtain the aluminum alloy profile cutting surface state feature, including: inputting the aluminum alloy profile cutting surface state image into a cutting surface state feature extractor based on a Mobile-Former model to obtain an aluminum alloy profile cutting surface state feature map as the aluminum alloy profile cutting surface state feature.

3. The intelligent cutting control method for aluminum alloy profiles according to claim 2, characterized in that: The method comprises performing pixel granularity feature decoupling on the aluminum alloy profile cutting surface state feature to obtain a set of aluminum alloy profile cutting surface state pixel granularity decoupling features, comprising: performing pixel granularity feature decoupling on the aluminum alloy profile cutting surface state feature map to obtain a set of aluminum alloy profile cutting surface state pixel granularity decoupling feature vectors as the set of aluminum alloy profile cutting surface state pixel granularity decoupling features.

4. The intelligent cutting control method for aluminum alloy profiles according to claim 3, characterized in that: Calculating the significant feature weight of each aluminum alloy profile cutting surface state pixel granularity decoupling feature in the set of aluminum alloy profile cutting surface state pixel granularity decoupling features, and performing pixel granularity weighted enhancement on the aluminum alloy profile cutting surface state features based on the significant feature weight of each aluminum alloy profile cutting surface state pixel granularity decoupling feature to obtain an aluminum alloy profile cutting surface state semantic enhancement feature, including: Calculating the saliency features of the set of pixel granularity decoupling feature vectors of the aluminum alloy profile cutting surface state to obtain a semantic saliency distribution matrix of the aluminum alloy profile cutting surface state structure; performing a masking process based on feature explicit layering on each eigenvalue in the aluminum alloy profile cutting surface state structure semantic saliency distribution matrix to obtain an aluminum alloy profile cutting surface state structure semantic saliency distribution weight matrix composed of a plurality of aluminum alloy profile cutting surface state structure semantic weights; Based on the semantic significance distribution weight matrix of the aluminum alloy profile cutting surface state structure, the aluminum alloy profile cutting surface state feature map is weighted optimized to obtain the aluminum alloy profile cutting surface state semantic enhancement feature map as the aluminum alloy profile cutting surface state semantic enhancement feature.

5. The intelligent cutting control method for aluminum alloy profiles according to claim 4, characterized in that: Calculating the significant features of the set of pixel granularity decoupling feature vectors of the aluminum alloy profile cutting surface state to obtain the aluminum alloy profile cutting surface state structure semantic significance distribution matrix, including: Inputting each aluminum alloy profile cutting surface state pixel granularity decoupling feature vector in the set of aluminum alloy profile cutting surface state pixel granularity decoupling feature vectors into a hyperbolic space mapper to obtain a set of aluminum alloy profile cutting surface state hyperbolic space modulation pixel granularity decoupling feature vectors; Calculating the fine-grained structural semantic saliency coefficient of each aluminum alloy profile cutting surface state hyperbolic space modulation pixel granularity decoupling feature vector in the set of the aluminum alloy profile cutting surface state hyperbolic space modulation pixel granularity decoupling feature vectors to obtain a plurality of aluminum alloy profile cutting surface state fine-grained structural semantic saliency coefficients; The multiple aluminum alloy profile cutting surface state fine-grained structural semantic significance measurement coefficients are arranged into a matrix to obtain the aluminum alloy profile cutting surface state structural semantic significance distribution matrix.

6. The intelligent cutting control method for aluminum alloy profiles according to claim 5, characterized in that: Each eigenvalue in the aluminum alloy profile cutting surface state structure semantic significance distribution matrix is subjected to a masking process based on feature explicit hierarchical processing to obtain an aluminum alloy profile cutting surface state structure semantic significance distribution weight matrix composed of multiple aluminum alloy profile cutting surface state structure semantic weights, including: inputting the aluminum alloy profile cutting surface state structure semantic significance distribution matrix into a feature explicit hierarchical precipitation module based on a multi-layer masking function to obtain the aluminum alloy profile cutting surface state structure semantic significance distribution weight matrix.

7. The intelligent cutting control method for aluminum alloy profiles according to claim 6, characterized in that: Based on the semantic significance distribution weight matrix of the aluminum alloy profile cutting surface state structure, the aluminum alloy profile cutting surface state feature map is weighted optimized to obtain an aluminum alloy profile cutting surface state semantic enhancement feature map as the aluminum alloy profile cutting surface state semantic enhancement feature, including: calculating the position point multiplication between the aluminum alloy profile cutting surface state structure semantic significance distribution weight matrix and each feature matrix along the channel dimension in the aluminum alloy profile cutting surface state feature map to obtain the aluminum alloy profile cutting surface state semantic enhancement feature map.

8. The intelligent cutting control method for aluminum alloy profiles according to claim 7, characterized in that: Based on the semantic enhancement feature of the aluminum alloy profile cutting surface state, a cutting surface state recognition is performed to determine whether burns exist. If the recognition result indicates that burns exist, a cutting optimization control instruction is generated to indicate that a coolant flow rate is increased, including: Inputting the semantic enhancement feature map of the aluminum alloy profile cutting surface state into a cutting surface state identifier based on a classifier to obtain a recognition result, wherein the recognition result is used to indicate whether there is a burn; In response to the identification result that burns exist, the cutting optimization control instruction is generated, and the cutting optimization control instruction is used to indicate increasing the coolant flow rate.

9. An intelligent cutting control system for aluminum alloy profiles, characterized in that: include: Aluminum alloy profile cutting image acquisition module, used to obtain the aluminum alloy profile cutting surface state image captured by the camera; an aluminum alloy profile cutting surface state feature extraction module, configured to extract features from the aluminum alloy profile cutting surface state image to obtain aluminum alloy profile cutting surface state features; An aluminum alloy profile cutting surface state feature enhancement processing module is used to perform pixel-granularity-based explicit feature enhancement processing on the aluminum alloy profile cutting surface state feature to obtain an aluminum alloy profile cutting surface state semantic enhancement feature, wherein the aluminum alloy profile cutting surface state feature enhancement processing module includes: a pixel-granularity feature decoupling processing unit, used to perform pixel-granularity feature decoupling on the aluminum alloy profile cutting surface state feature to obtain a set of aluminum alloy profile cutting surface state pixel-granularity decoupling features; a weighted enhancement processing unit, used to calculate the significant feature weight of each aluminum alloy profile cutting surface state pixel-granularity decoupling feature in the set of aluminum alloy profile cutting surface state pixel-granularity decoupling features, and perform pixel-granularity weighted enhancement on the aluminum alloy profile cutting surface state feature based on the significant feature weight of each aluminum alloy profile cutting surface state pixel-granularity decoupling feature to obtain an aluminum alloy profile cutting surface state semantic enhancement feature; A cutting surface state recognition module is used to perform cutting surface state recognition based on the semantic enhancement features of the cutting surface state of the aluminum alloy profile to determine whether there is a burn. If the recognition result is that there is a burn, a cutting optimization control instruction is generated to indicate a cutting optimization control instruction for increasing the coolant flow rate.

10. The intelligent cutting control system for aluminum alloy profiles according to claim 9, characterized in that: The aluminum alloy profile cutting surface state feature extraction module is used to: input the aluminum alloy profile cutting surface state image into the cutting surface state feature extractor based on the Mobile-Former model to obtain an aluminum alloy profile cutting surface state feature map as the aluminum alloy profile cutting surface state feature.

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