Self-adjusting protection system for intelligent monitoring of sponge cutting

Abstract

The invention relates to the technical field of sponge cutting, and discloses a self-adjusting protection system for intelligent monitoring of sponge cutting, which realizes real-time monitoring and intelligent regulation of the cutting process, and greatly improves the cutting precision and safety. Physical parameters and image information in the cutting process are collected in real time through a monitoring system, and a control unit can quickly process data and predict possible abnormal behaviors, so that a control strategy is generated and adjusted in time; the execution mechanism accurately executes protection actions according to the strategies, so that the problems of fire risk caused by too high cutting temperature, sponge damage caused by cutting deviation and the like are effectively avoided; the sponge cutting efficiency and the product quality are improved, the production cost and the safety risk are reduced, and sponge, equipment and personnel are protected.

Description

Technical Field

[0001] The invention relates to the technical field of sponge cutting, in particular to a self-regulating protection system for intelligent monitoring of sponge cutting. Background Art

[0002] As a material widely used in daily life, industrial manufacturing and medical fields, the cutting process of sponge is crucial to the quality and performance of the product. The traditional sponge cutting method mainly relies on manual input parameters to enable the cutting equipment to cut the sponge, but this cutting method lacks real-time regulation and is difficult to cope with complex and changeable cutting needs. There is a lack of protection for cutting, such as the sponge catching fire due to excessive cutting temperature, and the sponge cutting is damaged due to offset during the cutting process. Therefore, the development of a sponge cutting system that can intelligently monitor the cutting process and automatically adjust the protection strategy has important practical significance and practical value. For this purpose, a self-adjusting protection system for intelligent monitoring of sponge cutting is proposed. Summary of the invention

[0003] In view of the deficiencies in the prior art, the present invention provides a self-adjusting protection system for intelligent monitoring of sponge cutting to solve the background technical problems.

[0004] To achieve the above-mentioned object, the present invention provides the following technical solutions: a self-adjusting protection system for intelligent monitoring of sponge cutting, comprising a monitoring system, a control unit and an actuator; The monitoring system is used to monitor the physical parameters and image information during the cutting process in real time; The control unit is used to process monitoring data, predict abnormal behavior and generate control strategies. The actuator is used to execute corresponding protection actions according to the instructions of the control unit.

[0005] Preferably, the monitoring system comprises a high-precision sensor array and a high-definition camera; The high-precision sensor array includes pressure sensors, temperature sensors, vibration sensors and displacement sensors, which are precisely arranged at key parts of the cutting machine to monitor the changes in physical parameters during the cutting process in real time and transmit them to the control unit for analysis; The high-definition camera is used to capture the image of the cutting area and transmit it to the control unit for image processing.

[0006] Preferably, the control unit includes a processor, a storage unit and a communication module; Preferably, abnormal behavior during the cutting process is derived and a control strategy is generated using a self-regulating protection mechanism; The storage unit is used to store historical data and model parameters; The communication module is used to realize data exchange between various components within the system.

[0007] Preferably, the actuator includes a solenoid valve and a stepper motor; The solenoid valve is used to control the spraying of coolant and water; The stepper motor is used to adjust the position and angle of the cutting tool.

[0008] Preferably, it also includes a human-computer interaction interface, which is used to display cutting progress, monitoring data, alarm information and cutting parameter adjustment.

[0009] Preferably, the self-regulating protection mechanism specifically includes: Dynamic adjustment of cutting parameters: According to the real-time feedback from the intelligent monitoring system, the cutting speed, pressure and angle parameters are dynamically adjusted to ensure a stable and efficient cutting process; ‌Emergency shutdown and protection‌: When potential danger or abnormal situation is detected, the emergency shutdown mechanism is immediately triggered and protective measures are initiated at the same time; The adaptive cutting strategy module automatically selects the most suitable cutting strategy according to the type, thickness and hardness of the sponge material.

[0010] Preferably, the self-adjusting protection mechanism also includes a cutting path correction algorithm adjustment. When a cutting offset is detected, the deviation between the actual cutting path captured by a high-definition camera and the preset path is used to instantly calculate and adjust the motion trajectory of the stepper motor through an image processing algorithm to achieve automatic correction of the cutting path and ensure cutting accuracy.

[0011] Preferably, the machine learning model further comprises: Cutting quality prediction model: Combined with historical cutting data, a cutting quality prediction model is established using machine learning algorithms. Based on real-time image data and physical parameter change data during the cutting process, the type and degree of defects that may occur under the current cutting conditions are predicted, and cutting parameters are adjusted in advance to avoid quality problems. Abnormal behavior pattern library: Build a pattern library containing a variety of known abnormal cutting behaviors. By comparing real-time monitoring data with the pattern library, potential anomalies can be quickly identified and warned, thereby improving the system's abnormal response speed and accuracy.

[0012] Preferably, the process of executing the image processing algorithm further comprises the following steps: Image preprocessing: Preprocess the cutting area images captured by the high-definition camera, including denoising, contrast enhancement and grayscale operations, to improve image quality and provide clear and accurate image data for subsequent processing.

[0013] Feature extraction: Use image processing algorithms to extract features from the preprocessed image, including edge detection, contour extraction, and texture analysis to identify key information such as the shape, size, and location of the cutting area.

[0014] Target recognition and positioning: Based on the results of feature extraction, a deep learning algorithm is used to identify and locate the cutting target to ensure that the cutting tool can accurately align with the target area to avoid miscutting or missing cutting.

[0015] Cutting path planning: Based on the results of target recognition and positioning, combined with the cutting requirements, the optimal cutting path planning is automatically generated, including the parameters of cutting sequence, cutting speed and cutting depth, to achieve efficient and accurate cutting.

[0016] Real-time monitoring and feedback: During the cutting process, the cutting area image is continuously monitored in real time, and the cutting progress, cutting quality and potential abnormal behavior are detected through image processing algorithms. If cutting deviation or cutting speed abnormality occurs, the monitoring results are fed back to the control unit in real time to adjust the cutting parameters or trigger the protection mechanism in time.

[0017] Compared with the prior art, the present invention has the following beneficial effects: The present invention realizes real-time monitoring and intelligent control of the cutting process, greatly improving the accuracy and safety of cutting; the monitoring system collects physical parameters and image information in the cutting process in real time, and the control unit can quickly process data and predict possible abnormal behaviors, so as to generate and adjust control strategies in time; the actuator accurately executes protection actions according to these strategies, effectively avoiding the risk of fire caused by excessive cutting temperature, and sponge damage caused by cutting offset; it not only improves the efficiency of sponge cutting and product quality, but also reduces production costs and safety risks, and realizes the protection of sponges, equipment and personnel.

[0018] Other features and advantages of the present invention will be described in the following description, and partly become apparent from the description, or understood by practicing the present invention. The purpose and other advantages of the present invention can be realized and obtained by the structures pointed out in the description, claims and drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] Figure 1 The block diagram of the self-adjusting protection system for intelligent monitoring of sponge cutting of the present invention; Figure 2 This is a flow chart of the self-adjusting protection system for intelligent monitoring of sponge cutting of the present invention; Figure 3 It is a block diagram of the self-adjusting protection mechanism of the present invention. DETAILED DESCRIPTION

[0020] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this technical field without creative work are within the scope of protection of the present invention.

[0021] See also Figure 1-3 The present embodiment provides a self-adjusting protection system for intelligent monitoring of sponge cutting, which specifically includes a monitoring system, a control unit, an actuator and a human-computer interaction interface, which is used to perform intelligent monitoring during sponge cutting and implement automatic adjustment protection based on the monitoring results.

[0022] 1. Monitoring system 1. High-precision sensor array: including pressure sensors, temperature sensors, vibration sensors, displacement sensors, etc. These sensors are precisely arranged at key parts of the cutting machine to monitor the changes in physical parameters during the cutting process in real time; for example, the pressure sensor monitors the pressure applied by the cutting tool to the sponge, the temperature sensor monitors the temperature of the cutting area, the vibration sensor monitors the vibration generated during the cutting process, and the displacement sensor monitors the displacement of the cutting tool. The number of these sensors is not limited to one, but can be used in combination or for monitoring different positions. These sensors transmit the real-time monitored data to the control unit for analysis.

[0023] 2. High-definition camera: used to capture images of the cutting area and transmit them to the control unit for image processing. The high-definition camera has high-resolution and high-speed capture capabilities, and moves with the cutting machine to capture the details of the cutting process in real time.

[0024] 2. Control unit: 1. Processor: Executes image processing algorithms and machine learning models to analyze the data sent back by the monitoring system, and uses a self-adjusting protection mechanism to generate a control strategy; 1) Image processing algorithm: (1) Image preprocessing: Denoising: The Gaussian filter algorithm is used to denoise the image. The expression formula is: The Gaussian filter algorithm is used to denoise the image. The mathematical formula is: ; Where G(x,y) is the image after Gaussian filtering, f(x,y) is the original image, and σ is the standard deviation; Contrast enhancement: The histogram equalization algorithm is used to enhance the image contrast. This algorithm redistributes the image grayscale values ​​to make the grayscale distribution more uniform. Grayscale: Convert a color image to a grayscale image. The mathematical formula is: ; Among them, Gray is the grayscale value, and R, G, and B are the pixel values ​​of the red, green, and blue channels respectively.

[0025] (2) Feature extraction: Edge detection: The Canny edge detection algorithm is used to extract image edges. The algorithm detects edges by calculating image gradients and setting high and low thresholds. Contour extraction: The Hough transform algorithm is used to extract the image contour. This algorithm extracts the contour by detecting specific shapes in the image (such as straight lines, circles, etc.); Texture analysis: The gray-level co-occurrence matrix (GLCM) algorithm is used to analyze image texture. This algorithm describes texture features by calculating the gray-level relationship between pairs of pixels in the image.

[0026] (3) Target recognition and positioning: A deep learning algorithm is used to identify and locate the cutting target. The algorithm is trained with a large amount of image data to accurately identify the target and output its position and size. For example, the YOLO algorithm is used to identify and locate the cutting target; The YOLO algorithm treats object detection as a regression problem, which directly predicts bounding boxes and category probabilities at multiple locations in the image; Bounding Box Prediction The YOLO algorithm divides the image into S×S grids, each of which is responsible for predicting the target whose center point falls within the grid; for each grid, the algorithm predicts B bounding boxes and their confidences. The bounding box is usually represented by the center point coordinates (x, y), width w, and height h; Center point coordinates (x, y): represent the position of the center of the bounding box relative to the grid cell, and are normalized to be between 0 and 1; Width w and height h: represent the ratio of the bounding box to the entire image, and are also normalized; In YOLOv1, these values ​​are predicted directly, but in subsequent versions (such as YOLOv3), these values ​​are usually obtained by predicting offsets relative to anchor boxes.

[0027] Confidence calculation Confidence indicates the probability of the existence of an object within the bounding box and the accuracy of the bounding box. It is usually calculated by the following formula: ; Among them, Pr(Object) represents the probability of the existence of an object in the bounding box. Represents the intersection-over-union ratio between the predicted bounding box and the true bounding box.

[0028] Class Probability For each grid, the algorithm also predicts C class probabilities, which represent the probability of an object belonging to each class existing in that grid.

[0029] Final Output Ultimately, the YOLO algorithm outputs a (B×5+C)-dimensional vector for each grid, where B×5 represents the coordinates, width, height, and confidence of B bounding boxes (the confidence can be calculated using the above formula, but is usually output as a separate output), and C represents the category probability.

[0030] To obtain the final position and size of the object, the algorithm performs the following steps: Sort the B bounding boxes of each grid by confidence; Use the non-maximum suppression (NMS) algorithm to remove overlapping bounding boxes and only keep the bounding box with the highest confidence; According to the coordinates, width and height of the retained bounding box, the final position and size of the target can be obtained.

[0031] (4) Cutting path planning: Based on the results of target recognition and positioning and the cutting requirements, the A algorithm or the Dijkstra algorithm is used to automatically generate the optimal cutting path planning; the A algorithm selects the optimal path by evaluating the cost and heuristic function of the nodes, and the Dijkstra algorithm generates the cutting path by calculating the shortest path between nodes.

[0032] (5) Real-time monitoring and feedback: During the cutting process, the cutting area image is continuously monitored in real time. The cutting progress, cutting quality and potential abnormal behavior are detected through image processing algorithms (such as shape matching, color recognition, etc.), and the monitoring results are fed back to the control unit.

[0033] 2) Machine Learning Model: (1) Cutting quality prediction model: Combined with historical cutting data, a cutting quality prediction model is established using machine learning algorithms (such as support vector machine (SVM) or random forest (RF)). The model input is real-time cutting image data and physical parameter change data during the cutting process (such as temperature, pressure, etc.), and the output is the predicted defect type and degree. The specific steps include: a. Data preparation Input data: Real-time segmentation of image data (which can be preprocessed into a series of feature vectors, such as features extracted by image recognition techniques); Data on changes in physical parameters during the cutting process (such as temperature, pressure, etc.).

[0034] Output data: The predicted defect type and severity (can be a classification label or a regression value).

[0035] b. Feature extraction For image data, features can be extracted using convolutional neural networks (CNN) or traditional image processing techniques (such as SIFT, HOG, etc.); For physical parameter data, it can be directly input as a feature; Assuming that N features are extracted, the feature vector of each sample is expressed as , the corresponding labels (defect type and degree) are .

[0036] c. Support Vector Machine (SVM) The basic idea of ​​SVM is to find a hyperplane , so that the two types of samples (or multiple types of samples) are separated as much as possible. In the binary classification problem, SVM tries to maximize the interval (margin) between the two types of samples; for multi-classification problems, a "one-vs-all" or "one-vs-one" strategy is used; For non-linearly separable problems, the kernel trick is used to map the input data into a high-dimensional feature space where the data becomes linearly separable.

[0037] d. Objective function The objective function of SVM can be expressed as: ; in: ∥w∥2 is a regularization term used to control the complexity of the model; C is a regularization parameter used to balance the complexity of the model and the training error; is a slack variable used to handle nonlinearly separable and noisy data.

[0038] The constraints are: ; For nonlinear SVM, the kernel function K( , ) replaces the inner product ,Commonly used kernel functions include Gaussian kernel (RBF kernel), polynomial kernel, etc.

[0039] e. Model training Using the training dataset ( , ) Train the SVM model and obtain the optimal hyperplane parameters w and b.

[0040] f. Model prediction For new samples , use the trained SVM model for prediction: ; in is a function that maps input data to a high-dimensional feature space (for linear SVM, (x)=x), Y is the set of all possible labels.

[0041] g. Model running formula During model training and prediction, the specific formulas include: Linear SVM decision function: ;

[0042] For multi-classification problems, when using the "one-vs-all" strategy, each class has such a decision function; Nonlinear SVM (with kernel function) decision function:

[0043] in is the Lagrange multiplier, is the kernel function.

[0044] Prediction output: For classification problems, the output is the predicted defect type (label); for regression problems (if the degree of defect needs to be quantified), support vector regression (SVR) can be used, and the output is the predicted defect degree value.

[0045] (2) Abnormal behavior pattern library: Build a pattern library containing a variety of known abnormal cutting behaviors. By comparing real-time monitoring data with the pattern library, quickly identify and warn potential anomalies. The comparison algorithm can use algorithms such as Euclidean distance, cosine similarity or Manhattan distance; For example, the Euclidean distance algorithm is used for comparison. The specific comparison steps are: Step 1: Data Preprocessing Normalize the real-time monitoring data and the data in the pattern library to ensure that the data in each dimension are at the same level in terms of value, so as to avoid excessive impact of some dimensional data being too large or too small on the distance calculation; Set a reasonable threshold to determine whether the similarity between the real-time monitoring point and the known abnormal behavior meets the warning standard.

[0046] Step 2: Calculate the Euclidean distance For each cutting behavior data point monitored in real time (recorded as vector x), and each known abnormal behavior data point in the pattern library (recorded as vector , i=1,2,...,n), calculate the Euclidean distance between them; The calculation formula of Euclidean distance is: ; Among them, m represents the dimension of the data (that is, the number of features of the cutting behavior data), and They represent the values ​​of the real-time monitoring points and known abnormal behavior points in the jth dimension respectively.

[0047] Step 3: Judge and warn For each calculated Euclidean distance, compare it with the preset threshold; If a certain distance is less than or equal to the threshold, it is considered that the real-time monitoring point has a high similarity with the corresponding known abnormal behavior, and abnormal behavior may exist, and the early warning mechanism is triggered; If all distances are greater than the threshold, it is considered that the real-time monitoring point is significantly different from the known abnormal behaviors in the pattern library, and the current cutting behavior is normal.

[0048] 3) Self-regulating protection mechanism: a. Dynamically adjust cutting parameters: According to the real-time feedback from the intelligent monitoring system, dynamically adjust the cutting speed, pressure and angle parameters; the adjustment algorithm can adopt PID control algorithm or fuzzy control algorithm to fine-tune the cutting parameters according to the real-time monitoring data.

[0049] PID control algorithm:

[0050] Where u(t) is the control output, e(t) is the error signal, , , are the proportional, integral and differential coefficients respectively; Fuzzy control algorithm: By defining fuzzy sets and fuzzy rules, real-time monitoring data is mapped to control outputs; this algorithm can handle complex problems such as nonlinearity, uncertainty, and time-varying properties.

[0051] b. Emergency stop and protection: When potential dangers or abnormal conditions are detected (such as overtemperature, cutting deviation, etc.), the emergency stop mechanism is immediately triggered and protective measures are initiated at the same time (such as spraying coolant, turning off the power of the cutting tool, etc.).

[0052] c. Adaptive cutting strategy module: Automatically select the most suitable cutting strategy based on the type, thickness and hardness of the sponge material. The strategy selection algorithm can be optimized based on the physical properties of the material and historical cutting data.

[0053] d. Cutting path correction algorithm adjustment: When the cutting offset is detected, the deviation between the actual cutting path captured by the high-definition camera and the preset path is used to instantly calculate and adjust the motion trajectory of the stepper motor through an image processing algorithm (such as the least squares method or the Kalman filter algorithm).

[0054] Least squares method: Find the optimal solution by minimizing the sum of squared errors. The mathematical formula is:

[0055] in is the actual observed value, is the predicted value.

[0056] Kalman filter algorithm: solves linear filtering and prediction problems recursively. The mathematical formula includes state equation and observation equation, as well as the corresponding covariance matrix update formula.

[0057] 2. The storage unit is used to store historical data and model parameters, and the processor calls the model parameters.

[0058] 3. The communication module is used to realize data exchange between various components within the system. For example, the data monitored by the high-precision sensor array and the high-definition camera are sent to the storage unit and the processor through the communication module. For example, the formulated control strategy is sent to the execution unit through the communication module to execute the strategy.

[0059] 3. Execution Agency: Control strategy for executing control unit output 1. Solenoid valve: Through control strategy, control the coolant to cool the cutting machine's tool. If a fire occurs due to excessive temperature, control the water spraying to extinguish the fire.

[0060] 2. Stepper motor: The stepper motor is controlled by the control strategy. As an actuator, the stepper motor is responsible for adjusting the position and angle of the cutting tool, realizing automatic correction of the cutting path and dynamic adjustment of the cutting parameters, or directly stopping working.

[0061] 4. Human-computer interaction interface: It is used to display cutting progress, monitoring data (such as temperature, pressure, cutting speed, etc.), alarm information and cutting parameter adjustment options; users can set cutting parameters, start and stop cutting tasks and monitor system status through the interface. Its interface composition can be: Interface layout and functional division Main interface: Top status bar: displays the current system time, logged-in user information, and system operation status (such as online / offline, busy / idle, etc.); Menu bar: provides quick access to common functions such as file management, system settings, and help center; Working area: occupies most of the interface space, used to display cutting progress, real-time monitoring data and alarm information; Bottom toolbar: Contains key operation buttons such as start / stop cutting, emergency stop, parameter adjustment, etc.

[0062] Cutting progress display area: A dynamic progress bar is used to intuitively display the completion status of the current cutting task, including the cut length, remaining time, etc. You can click anywhere on the progress bar to quickly jump to the corresponding time point or cutting position.

[0063] Monitoring data display area: Real-time display of key monitoring data such as temperature, pressure, cutting speed, etc. in the form of charts and numerical values; Provides data history query function, users can view the data change trend over the past period of time; When the data is abnormal, the alarm mechanism is automatically triggered and the abnormal data is highlighted in red on the interface.

[0064] Alarm information prompt area: Centrally display all alarm information, including alarm type, occurrence time, impact range, etc.; Provides confirmation and clearing functions for alarm information to ensure that users will not miss any important information; For serious alarms, the emergency stop mechanism can be automatically triggered to protect the safety of equipment and personnel; Cutting parameter adjustment area: Provide adjustment options for cutting speed, cutting depth, cutting angle and other parameters; Users can set parameters through sliders, input boxes, etc., and the interface will provide real-time feedback on the impact of parameter changes on cutting results; Provides parameter preset function, users can save commonly used parameter combinations for quick and easy call.

[0065] Interactive experience optimization Touch operation: Supports touch screen operation, users can complete interface navigation and function selection by sliding, clicking and other actions with their fingers.

[0066] Obviously, the above embodiments of the present invention are merely examples for clearly illustrating the present invention, and are not intended to limit the embodiments of the present invention. For those skilled in the art, other different forms of changes or modifications can be made based on the above description. It is not necessary and impossible to list all the embodiments here. Any modifications, equivalent substitutions and improvements made within the spirit and principles of the present invention should be included in the protection scope of the claims of the present invention.

Claims

1. A self-adjusting protection system for intelligent monitoring of sponge cutting, characterized in that: Includes monitoring system, control unit and actuator; The monitoring system is used to monitor the physical parameters and image information during the cutting process in real time; The control unit is used to process monitoring data, predict abnormal behavior and generate control strategies. The actuator is used to execute corresponding protection actions according to the instructions of the control unit.

2. The self-adjusting protection system for intelligent monitoring of sponge cutting according to claim 1 is characterized in that: The monitoring system includes a high-precision sensor array and a high-definition camera; The high-precision sensor array includes pressure sensors, temperature sensors, vibration sensors and displacement sensors, which are precisely arranged at key parts of the cutting machine to monitor the changes in physical parameters during the cutting process in real time and transmit them to the control unit for analysis; The high-definition camera is used to capture the image of the cutting area and transmit it to the control unit for image processing.

3. The self-adjusting protection system for intelligent monitoring of sponge cutting according to claim 2 is characterized in that: The control unit includes a processor, a storage unit and a communication module; The processor executes the image processing algorithm and the machine learning model to analyze the data sent back by the monitoring system, derives the abnormal behavior in the cutting process, and generates a control strategy using a self-adjusting protection mechanism; The storage unit is used to store historical data and model parameters; The communication module is used to realize data exchange between various components within the system.

4. The self-adjusting protection system for intelligent monitoring of sponge cutting according to claim 3 is characterized in that: The actuator includes a solenoid valve and a stepper motor; The solenoid valve is used to control the spraying of coolant and water; The stepper motor is used to adjust the position and angle of the cutting tool.

5. A self-adjusting protection system for intelligent monitoring of sponge cutting according to claim 4, characterized in that: It also includes a human-computer interaction interface, which is used to display cutting progress, monitoring data, alarm information and cutting parameter adjustment.

6. The self-adjusting protection system for intelligent monitoring of sponge cutting according to claim 3 is characterized in that: The self-regulating protection mechanism specifically includes: Dynamic adjustment of cutting parameters: According to the real-time feedback from the intelligent monitoring system, the cutting speed, pressure and angle parameters are dynamically adjusted to ensure a stable and efficient cutting process; ‌Emergency shutdown and protection‌: When potential danger or abnormal situation is detected, the emergency shutdown mechanism is immediately triggered and protective measures are initiated at the same time; The adaptive cutting strategy module automatically selects the most suitable cutting strategy according to the type, thickness and hardness of the sponge material.

7. A self-adjusting protection system for intelligent monitoring of sponge cutting according to claim 6, characterized in that: The self-adjustment protection mechanism also includes cutting path correction algorithm adjustment. When cutting deviation is detected, the deviation between the actual cutting path captured by the high-definition camera and the preset path is used to instantly calculate and adjust the motion trajectory of the stepper motor through the image processing algorithm to achieve automatic correction of the cutting path and ensure cutting accuracy.

8. The self-adjusting protection system for intelligent monitoring of sponge cutting according to claim 3 is characterized in that: The machine learning model further comprises: Cutting quality prediction model: Combined with historical cutting data, a cutting quality prediction model is established using machine learning algorithms. Based on real-time image data and physical parameter change data during the cutting process, the type and degree of defects that may occur under the current cutting conditions are predicted, and cutting parameters are adjusted in advance to avoid quality problems. Abnormal behavior pattern library: Build a pattern library containing a variety of known abnormal cutting behaviors. By comparing real-time monitoring data with the pattern library, potential anomalies can be quickly identified and warned, thereby improving the system's abnormal response speed and accuracy.

9. The self-adjusting protection system for intelligent monitoring of sponge cutting according to claim 3 is characterized in that: The process of executing the image processing algorithm also includes the following steps: Image preprocessing: Preprocess the cutting area images captured by the high-definition camera, including denoising, contrast enhancement and grayscale operations to improve image quality and provide clear and accurate image data for subsequent processing; Feature extraction: Use image processing algorithms to extract features from pre-processed images, including edge detection, contour extraction, and texture analysis, to identify key information such as the shape, size, and location of the cutting area; Target recognition and positioning: Based on the results of feature extraction, a deep learning algorithm is used to identify and locate the cutting target, ensuring that the cutting tool can accurately align with the target area to avoid miscutting or missing cutting; Cutting path planning: Based on the results of target recognition and positioning, combined with cutting requirements, the optimal cutting path planning is automatically generated, including parameters of cutting sequence, cutting speed and cutting depth, to achieve efficient and accurate cutting; Real-time monitoring and feedback: During the cutting process, the cutting area image is continuously monitored in real time, and the cutting progress, cutting quality and potential abnormal behavior are detected through image processing algorithms. If cutting deviation or cutting speed abnormality occurs, the monitoring results are fed back to the control unit in real time to adjust the cutting parameters or trigger the protection mechanism in time.

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