Method for monitoring the grate plate status of pellet chain grate

Abstract

The present invention relates to the field of metallurgical technology, and discloses a method for monitoring the grate plate state of a pellet chain grate machine. The specific method includes: dividing the wear degree of the grate bars into four types, namely no wear, slight wear, moderate wear and severe wear, collecting a set of grate bar images containing the four wear degrees, using a convolutional neural network (CNN) model to build an image classification model, using the grate bar image set to train the image classification model, and when the chain grate machine is working, collecting the image of the grate plate, and the grate plate image is processed and divided into multiple grate bar images, and the grate bar images are respectively transmitted to the image classification model to determine the wear degree, and when moderate or severe wear occurs, an alarm is triggered. The convolutional neural network model is used to realize the automatic monitoring of the wear degree of the chain grate bar, which significantly improves the monitoring efficiency and reduces the labor cost. The image classification model can accurately identify the wear degree of the grate bar, effectively reducing the risk of missed detection, and further improving the accuracy and reliability of monitoring.

Description

Technical Field

[0001] The invention relates to the technical field of metallurgy, in particular to a method for monitoring the grate plate state of a pellet chain grate. Background Art

[0002] Chain grate is a thermal equipment working in high temperature environment, mainly used in pelletizing production line of steel enterprises. Grate plate is the key component of chain grate, which is used to carry the green balls made by disc pelletizing machine, evenly distribute the green balls on grate bed, and transport the balls forward during the operation of chain grate. Grate bar is the narrow and long hole on the grate plate, which allows hot air flow to pass through, and these hot air flow heats the green balls, realizing the heat treatment process of dehydration, drying, preheating and oxidation consolidation of green balls.

[0003] Due to quality defects in equipment manufacturing and wear under high-intensity operating conditions, the grate bars on the grate plate are often damaged, fall off or missing. This situation leads to serious ball leakage during the sintering process, resulting in a significant decrease in production capacity. At present, the status monitoring of chain grate bars mainly relies on manual monitoring. This method is easily affected by the intensity of work and the sense of responsibility of the staff, and there is a large risk of missed detection. Therefore, it is urgent to improve the monitoring mechanism to achieve more accurate and effective maintenance management. To this end, we propose a method for monitoring the grate plate status of a pellet chain grate. Summary of the invention

[0004] The object of the present invention is to provide a method for monitoring the grate plate state of a pellet chain grate to solve the problems raised in the above background technology.

[0005] To achieve the above object, the present invention provides the following technical solution: a method for monitoring the grate plate state of a pellet chain grate, the specific steps comprising:

[0006] S1. Classify the wear degree of the grate into four types, namely, no wear, slight wear, moderate wear and severe wear. Collect a set of grate images containing the four wear degrees, and annotate each image in the set with a label corresponding to the wear degree.

[0007] S2. Use the labeled grate image set to train an image classification model. The image classification model can identify the degree of grate wear based on the grate image. The specific steps for obtaining the image classification model are:

[0008] S201, performing one-hot encoding on the annotation labels in the castanets image set;

[0009] S202, dividing the one-hot encoded grating image set into a training set, a validation set and a test set;

[0010] S203, using the convolutional neural network model to build an image classification model, and inputting the training set into the convolutional neural network model for model training;

[0011] S204, evaluating the performance of the image classification model on the validation set, recording the performance indicators of each set of parameters by adjusting the parameters of the model, and comparing the performance indicators of each set of parameters to determine the parameters with the best model performance, wherein the performance indicators include the accuracy, recall rate and F1-Score value of the image classification results;

[0012] S205, evaluating the model performance after the final selected parameters on the test set to determine whether the performance of the image classification model can meet the requirements of production use;

[0013] S3, when the chain grate machine is working, continuously collect image 1 of the grate plate above the head of the chain grate machine, the width of image 1 is an integral multiple of the width of the grate plate, the height of image 1 is the same as the height of the grate plate, and multiple collection devices of image 1 are provided, which are evenly arranged above the head of the chain grate machine according to the width of the grate bed; collect image 2 of the brand of the small shaft head on one side of the head of the chain grate machine, the collection frequency of image 2 is the same as that of image 1, and the small shaft head in image 2 corresponds to the grate plate in image 1;

[0014] S4, after collecting image 1 each time, judge whether image 1 includes complete castor plate, if image 1 does not include complete castor plate, then image 1 is discarded and continues to collect; if image 1 includes complete castor plate, then image 1 is pre-processed to obtain high-definition castor plate image, further image 1 is segmented to obtain image 3 of each castor bar in the castor plate, and image 2 collected at the same time is saved;

[0015] S5. Input the image 3 obtained in step S4 into the image classification model trained in step S2 respectively, and judge the degree of wear of the grate bar according to the output of the image classification model. If there is moderate wear or severe wear in the output result of the image classification model, an alarm is triggered, and image 1 and image 2 are recorded. The maintenance personnel can find the worn grate plate in image 1 according to the small shaft head brand in image 2.

[0016] Preferably, in step S203, the specific steps of constructing the convolutional neural network model are:

[0017] Step Ⅰ: Import the deep learning framework TensorFlow and use the high-order neural network library Keras provided by TensorFlow to create a sequential model object;

[0018] Step II: Add several convolutional layers to the sequential model object, and add a pooling layer after each convolutional layer. Use the convolution kernel to slide on the image to extract image features, and use the pooling layer to reduce the spatial size of the feature map and the number of parameters.

[0019] Step III: Add a flattening layer to the sequential model object to flatten the multi-dimensional image features output by the pooling layer into a one-dimensional vector;

[0020] Step IV: Add a fully connected layer to the sequential model object to achieve nonlinear mapping between image features and neural network nodes through the fully connected layer;

[0021] Step V: Add a classification output layer to the sequential model object and use the activation function softmax for classification output;

[0022] Step VI: Define the loss function, parameter optimizer, and performance evaluation metric for model training in the sequential model object.

[0023] Step VII: Use the sequential model object to load the training set separated in step S202 and start model training.

[0024] Preferably, in step S4, the implementation method of determining whether the image 1 contains a complete castor plate is based on a binary classification model for training image recognition. The output of whether the image 1 contains a complete castor plate can be obtained by inputting the binary classification model. The specific steps for obtaining the binary classification model are:

[0025] S401, collecting a large number of images 1 as training data;

[0026] S402, feature extraction and data marking are performed on the training data, wherein the extracted features include whether the edge of the grate plate fits around the image, and whether the upper and lower edges of the grate plate are symmetrical in the image; the types of data marking include 0 and 1, 0 represents not including a complete grate plate, and 1 represents including a complete grate plate;

[0027] S403, using the support vector machine (SVM) algorithm to build a binary classification model for image recognition;

[0028] S404: Use the training data to train the binary classification model, and judge the model performance by the accuracy of the model output until the model performance meets the requirements for production use.

[0029] Preferably, in step S4, the image 1 is segmented according to the size of the grate board. If the width of the image 1 is L times the grate board, and the grate board contains M rows and N columns of grate bars, L*M*N grate bar images can be obtained after segmentation.

[0030] Preferably, the pixel size of the segmented grating image is consistent with the image in the grating image set.

[0031] Preferably, the preprocessing of the image 1 in step S4 includes noise reduction and geometric correction.

[0032] Preferably, the image 1 is subjected to noise reduction processing by using a median filtering method, and the value of each pixel point in the image 1 is replaced by the median value of its neighborhood pixel values ​​to eliminate isolated noise points:

[0033] ;

[0034] Where f(x, y) is the pixel value of the original image at point (x, y), g(x, y) is the pixel value of the image at point (x, y) after median filtering, med{} represents the median operation, W is the neighborhood window, and (k, l) is the element in window W, indicating the offset of x and y.

[0035] Preferably, the perspective transformation model is used to perform geometric correction on the image 1, and the specific implementation steps are as follows:

[0036] 1) Select four control points at the four corners of the original image and the corrected image respectively;

[0037] 2) Using the coordinates of the four control points selected in the previous step, solve the perspective transformation matrix through linear algebra methods, and use the perspective transformation matrix to describe the coordinate mapping relationship between the original image and the corrected image;

[0038] 3) Apply the perspective transformation matrix to the original image and use the interpolation algorithm to process the pixel values ​​of non-integer coordinates after the transformation to achieve the perspective transformation of the image.

[0039] Preferably, the method of triggering the early warning includes sound and light alarm of the equipment, SMS notification and automatic sending of emails to maintenance personnel.

[0040] Compared with the prior art, the present invention has the following beneficial effects:

[0041] The present invention realizes the automatic monitoring of the wear degree of the chain grate bars by adopting the convolutional neural network (CNN) model, which significantly improves the monitoring efficiency and reduces the labor cost. Since the monitoring process is automated, it is no longer affected by the responsibility and fatigue of the maintenance personnel and can operate continuously and stably. When the bars are damaged, detached or missing, the maintenance personnel can obtain the alarm information in time, thereby avoiding losses to the enterprise's production process. In addition, the image classification model can accurately identify the wear degree of the bars, effectively reducing the risk of missed detection, and further improving the accuracy and reliability of monitoring. BRIEF DESCRIPTION OF THE DRAWINGS

[0042] Figure 1 This is a working principle diagram of the present invention for monitoring the grate plate;

[0043] Figure 2 Training flowchart for convolutional neural network model;

[0044] Figure 3 Diagram of the steps to build a binary classification model for grate images. DETAILED DESCRIPTION

[0045] 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 field without creative work are within the scope of protection of the present invention.

[0046] See also Figure 1-3 The present invention provides a technical solution: a method for monitoring the grate plate state of a pellet chain grate, the specific steps of which include:

[0047] S1. The wear degree of the grate bars is divided into four types, namely, no wear, slight wear, moderate wear and severe wear. A set of grate bar images containing the four wear degrees is collected, and each image in the image set is labeled with a label corresponding to the wear degree. The grate bar image set is collected by collecting the discarded grate plates that have been used in the past, obtaining the discarded grate plate images by photographing, processing the discarded grate plate images, obtaining the grate bar images with wear marks, and the maintenance personnel manually label them according to the wear degree.

[0048] S2. Use the labeled grate image set to train an image classification model. The image classification model can identify the degree of grate wear based on the grate image. The specific steps for obtaining the image classification model are:

[0049] S201, performing one-hot encoding on the annotated labels in the comb image set; the one-hot encodings corresponding to the four labels of no wear, slight wear, moderate wear and severe wear are: [1,0,0,0], [0,1,0,0], [0,0,1,0], [0,0,0,1] respectively;

[0050] S202, dividing the one-hot encoded grating image set into a training set, a validation set and a test set;

[0051] S203, using a convolutional neural network model (CNN) to build an image classification model, and input the training set into the convolutional neural network model for model training; CNN can automatically learn and extract features from images without manually designing feature extractors, making the monitoring process more automated and accurate;

[0052] S204. Evaluate the performance of the image classification model on the validation set. Adjust the parameters of the model and record the performance indicators of each set of parameters. Compare the performance indicators of each set of parameters to determine the parameters with the best model performance. The performance indicators include the accuracy, recall and F1-Score value of the image classification results. In the monitoring of chain grate plates, high accuracy means that the model can reliably identify the wear state of the grate plates, whether normal or defective. High recall means that the model can detect almost all worn grate bars, reducing the risk of missed detection, which is very important for preventing failures and planning maintenance. F1-Score provides an evaluation indicator that comprehensively considers false positives (FP) and missed detections (FN), helping to determine whether the model has achieved a good balance between reducing false positives and missed detections.

[0053] S205, evaluating the performance of the model after the final selected parameters on the test set to determine whether the performance of the image classification model can meet the requirements of production use; the test set contains data that the model has not seen during the training process. By evaluating the model performance on the test set, the model's ability to recognize new data can be verified, thereby ensuring that the model has good generalization ability;

[0054] S3. When the chain grate machine is working, the image 1 of the grate plate is continuously collected above the head of the chain grate machine. The width of the image 1 is an integer multiple of the width of the grate plate, and the height of the image 1 is the same as the height of the grate plate, so as to ensure that the state of the entire grate plate can be fully captured. There are multiple acquisition devices for image 1, which are evenly arranged above the head of the chain grate machine according to the width of the grate bed. The multiple acquisition devices are evenly arranged according to the width of the grate bed, which can provide a wider viewing angle and more detailed grate plate image data; an image 2 of the small shaft head brand is collected on one side of the chain grate machine head. The acquisition frequency of image 2 is the same as that of image 1. The small shaft head in image 2 corresponds to the grate plate in image 1, which helps to establish a connection between image 1 and the actual position of the grate plate in the monitoring system; the small shaft head brand in image 2 provides a unique identifier for each grate plate, which helps to quickly locate the specific grate plate when a problem is detected. The wear state of the grate plate can be monitored through image 1, and image 2 provides the position information of the worn grate plate. The two work together to improve the accuracy and response speed of monitoring;

[0055] The acquisition frequency of image 2 is the same as that of image 1, which ensures the synchronization in time and makes the monitoring results more accurate;

[0056] S4, after each acquisition of image 1, determine whether image 1 contains a complete grate plate, if image 1 does not contain a complete grate plate, discard image 1 and continue to acquire; if image 1 contains a complete grate plate, pre-process image 1 to obtain a high-definition grate plate image, further segment image 1 to obtain image 3 of each grate bar in the grate plate, and save image 2 acquired at the same time; first, by ensuring that image 1 contains a complete grate plate, misjudgment caused by incomplete image is avoided; secondly, pre-process image 1 to improve image clarity, which is helpful for subsequent feature extraction and wear analysis;

[0057] S5. Input the image 3 obtained in step S4 into the image classification model trained in step S2 respectively, and judge the degree of wear of the grate bar according to the output of the image classification model. If there is moderate wear or severe wear in the output result of the image classification model, an alarm is triggered, and image 1 and image 2 are recorded. The maintenance personnel can find the worn grate plate in image 1 according to the small shaft head brand in image 2.

[0058] The image classification model is used to determine the degree of wear of the chain grate bars in real time. When moderate or severe wear is detected, an alarm is triggered immediately, ensuring timely fault response and reducing unexpected downtime in the production process. By accurately recording Image 1 and Image 2, maintenance personnel can quickly locate and take action, thereby significantly reducing maintenance costs and improving production efficiency.

[0059] The present invention will be further described below in conjunction with Examples 1 to 3:

[0060] Embodiment 1: In step S203, the specific steps of constructing a convolutional neural network model are:

[0061] Step Ⅰ: Import the deep learning framework TensorFlow, and use the high-order neural network library Keras provided by TensorFlow to create a sequential model object; initialize a model framework to provide a sequential container for building the network structure. The sequential model allows the network structure to be stacked layer by layer, making the model building process clear and easy to manage;

[0062] Step II: Add several convolutional layers to the sequential model object, and add a pooling layer after each convolutional layer. Use the convolution kernel to slide on the image to extract image features, and use the pooling layer to reduce the spatial size of the feature map and the number of parameters. The convolutional layer extracts local features of the image through the convolution kernel, while the pooling layer is responsible for reducing the spatial dimension of the features, reducing the number of parameters and calculation amount of the subsequent network, and improving the translation invariance of the model.

[0063] Step III: Add a flattening layer to the sequential model object to flatten the multi-dimensional image features output by the pooling layer into a one-dimensional vector to facilitate processing in subsequent steps;

[0064] Step IV: Add a fully connected layer to the sequential model object. The fully connected layer (Dense Layer) is used to realize the complex relationship mapping between features and outputs, integrate the information of the previous layer through nonlinear transformation, and perform higher-level feature extraction;

[0065] Step V. Add a classification output layer to the sequential model object and use the activation function softmax for classification output; softmax converts the network output into a probability distribution for each classification;

[0066] Step VI: Define the loss function, parameter optimizer, and performance evaluation index for model training in the sequential model object; the loss function defines the difference between the model prediction value and the true value, the optimizer is used to adjust the network parameters during the training process to minimize the loss function, and the performance evaluation index is used to monitor the training effect;

[0067] Step VII: Use the sequential model object to load the training set separated in step S202 and start model training. First, initialize the network weights, and then adjust the model parameters by repeatedly forward propagation, error calculation, back propagation and updating weights to minimize the loss function and improve the prediction performance of the model. The following is an example of building a convolutional neural network model using Python as the programming language:

[0068]

[0069] Embodiment 2: In step S4, the implementation method of judging whether the image 1 contains a complete castor plate is based on a binary classification model for training image recognition. The output of whether the image 1 contains a complete castor plate can be obtained by inputting the binary classification model. The specific steps for obtaining the binary classification model are:

[0070] S401, collecting a large number of images 1 as training data, where the images should contain grate plates in various situations, whether complete or incomplete, to ensure the diversity and comprehensiveness of the training data;

[0071] S402, feature extraction and data marking are performed on the training data, wherein the extracted features include whether the edge of the grate plate fits around the image, and whether the upper and lower edges of the grate plate are symmetrical in the image; the types of data marking include 0 and 1, 0 represents not including a complete grate plate, and 1 represents including a complete grate plate;

[0072] S403. Use the support vector machine (SVM) algorithm to build a binary classification model for image recognition. SVM is a supervised learning algorithm that maximizes the sample boundary by finding the optimal decision boundary (hyperplane);

[0073] S404, use the training data to train the binary classification model, and judge the model performance by the accuracy of the model output until the model performance meets the requirements of production use. Use the labeled training data set to train the SVM model. During the training process, SVM will try to adjust the model parameters to find the optimal decision boundary, and evaluate the performance of the model through the cross-validation method, focusing on the accuracy of the model, that is, the ability of the model to correctly classify images; judge the model performance according to the accuracy of the model output. If the model performance does not meet the preset requirements, return to step S403, adjust the model parameters or feature extraction method, and then retrain; once the model performance meets the requirements of production use, the training is completed, and a binary classification model that can determine whether image 1 contains a complete grate is obtained.

[0074] In step S4, the image 1 is segmented according to the size of the grate board. If the width of the image 1 is L times the grate board, and the grate board contains M rows and N columns of grate bars, L*M*N grate bar images can be obtained after segmentation.

[0075] The pixel size of the segmented grating image is consistent with the image in the grating image set, so that the training data of the image classification model is consistent with the monitoring data during operation, thereby improving the recognition accuracy of the image classification model.

[0076] Embodiment 3: The preprocessing of the image 1 in step S4 includes noise reduction and geometric correction.

[0077] The median filtering method is used to reduce the noise of Image 1. The value of each pixel in Image 1 is replaced by the median value of its neighborhood pixel values ​​to eliminate isolated noise points:

[0078] ;

[0079] Where f(x, y) is the pixel value of the original image at point (x, y), g(x, y) is the pixel value of the image at point (x, y) after median filtering, med{} represents the median operation, W is the neighborhood window, and (k, l) is the element in window W, indicating the offset of x and y.

[0080] For each pixel (x, y) in the image, select a neighborhood window W of a specific size (such as 3x3, 5x5, etc.); read the grayscale values ​​of all pixels in the neighborhood window W, recorded as a set S; sort the pixel values ​​in the set S by size to generate a monotonically increasing (or decreasing) sequence; if the size of the neighborhood window W is an odd number, take the middle value of the sorted sequence as the new value of the pixel (x, y); if the size of the neighborhood window W is an even number, take the average of the two middle values ​​of the sorted sequence as the new value of the pixel (x, y).

[0081] By replacing the value of each pixel with the median of its neighboring pixel values, the salt and pepper noise in the image is effectively removed while retaining the edge information of the image, thereby improving the image quality. This not only enhances the extraction of image features, but also reduces misjudgments caused by noise, and improves the accuracy of the monitoring system.

[0082] The perspective transformation model is used to perform geometric correction on the image 1. The perspective transformation is a geometric transformation that maps a plane area in the image to another plane and is used to correct the perspective distortion in the image. In the grate plate state monitoring of the chain grate, the perspective transformation can be used to correct the image distortion caused by the viewing angle or the shooting position, so that the image of the grate plate is closer to its real plane geometric shape, which is convenient for the subsequent feature extraction and state analysis. The specific implementation steps are as follows:

[0083] 1) Select four control points at the four corners of the original image and the rectified image respectively;

[0084] 2) Using the coordinates of the four control points selected in the previous step, solve the perspective transformation matrix through linear algebra methods, and use the perspective transformation matrix to describe the coordinate mapping relationship between the original image and the corrected image;

[0085] 3) Apply the perspective transformation matrix to the original image and use the interpolation algorithm to process the pixel values ​​of non-integer coordinates after the transformation to achieve the perspective transformation of the image.

[0086] The formula for perspective transformation is:

[0087] ;

[0088] Where (x, y) is the coordinate in the original image (homogeneous coordinate), (x', y') is the coordinate in the transformed image (after perspective transformation), and w' is the third component of the homogeneous coordinate of each point in the transformed image. arrive It is an element of the perspective transformation matrix. Homogeneous coordinates are used to represent points on a two-dimensional plane. Homogeneous coordinates can easily represent the projection relationship in perspective transformation. Homogeneous coordinates are a three-dimensional vector, but when used on a two-dimensional plane, only the first two components need to be considered, and the third component (weight) is used to represent the projection relationship.

[0089] The triggering methods of early warning include sound and light alarm of the equipment, SMS notification and automatic email to maintenance personnel. Through various early warning methods such as sound and light alarm, SMS notification and automatic email to maintenance personnel, the timeliness of the grate plate status monitoring of the chain grate can be ensured, the reliability and safety of the equipment can be improved, the failure rate and downtime can be reduced, and greater economic benefits can be brought to the enterprise.

[0090] It should be noted that, in this article, relational terms such as first and second, etc. are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the terms "include", "comprise" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device.

[0091] Although embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions and variations may be made to the embodiments without departing from the principles and spirit of the present invention, and that the scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. A method for monitoring the grate plate state of a pellet chain grate, characterized in that: The specific steps include: S1. Classify the wear degree of the grate into four types, namely, no wear, slight wear, moderate wear and severe wear. Collect a set of grate images containing the four wear degrees, and annotate each image in the set with a label corresponding to the wear degree. S2. Use the labeled grate image set to train an image classification model. The image classification model can identify the degree of grate wear based on the grate image. The specific steps for obtaining the image classification model are: S201, performing one-hot encoding on the annotation labels in the castanets image set; S202, dividing the one-hot encoded grating image set into a training set, a validation set and a test set; S203, using the convolutional neural network model to build an image classification model, and inputting the training set into the convolutional neural network model for model training; S204, evaluating the performance of the image classification model on the validation set, recording the performance indicators of each set of parameters by adjusting the parameters of the model, and comparing the performance indicators of each set of parameters to determine the parameters with the best model performance, the performance indicators include the accuracy, recall rate and F1-Score value of the image classification result, and the F1-Score represents an evaluation indicator that comprehensively considers false positives FP and missed detections FN; S205, evaluating the model performance after the final selected parameters on the test set to determine whether the performance of the image classification model can meet the requirements of production use; S3, when the chain grate machine is working, continuously collect image 1 of the grate plate above the head of the chain grate machine, the width of image 1 is an integral multiple of the width of the grate plate, the height of image 1 is the same as the height of the grate plate, and multiple collection devices of image 1 are provided, which are evenly arranged above the head of the chain grate machine according to the width of the grate bed; collect image 2 of the brand of the small shaft head on one side of the head of the chain grate machine, the collection frequency of image 2 is the same as that of image 1, and the small shaft head in image 2 corresponds to the grate plate in image 1; S4, after collecting image 1 each time, judge whether image 1 includes complete castor plate, if image 1 does not include complete castor plate, then image 1 is discarded and continues to collect; if image 1 includes complete castor plate, then image 1 is pre-processed to obtain high-definition castor plate image, further image 1 is segmented to obtain image 3 of each castor bar in the castor plate, and image 2 collected at the same time is saved; S5. Input the image 3 obtained in step S4 into the image classification model trained in step S2 respectively, and judge the degree of wear of the grate bar according to the output of the image classification model. If there is moderate wear or severe wear in the output result of the image classification model, an alarm is triggered, and image 1 and image 2 are recorded. The maintenance personnel can find the worn grate plate in image 1 according to the small shaft head brand in image 2.

2. The method for monitoring the grate plate state of a pellet chain grate according to claim 1, characterized in that: In step S203, the specific steps of constructing the convolutional neural network model are: Step Ⅰ: Import the deep learning framework TensorFlow and use the high-order neural network library Keras provided by TensorFlow to create a sequential model object; Step II: Add several convolutional layers to the sequential model object, and add a pooling layer after each convolutional layer. Use the convolution kernel to slide on the image to extract image features, and use the pooling layer to reduce the spatial size of the feature map and the number of parameters. Step III: Add a flattening layer to the sequential model object to flatten the multi-dimensional image features output by the pooling layer into a one-dimensional vector; Step IV: Add a fully connected layer to the sequential model object to achieve nonlinear mapping between image features and neural network nodes through the fully connected layer; Step V: Add a classification output layer to the sequential model object and use the activation function softmax for classification output; Step VI: Define the loss function, parameter optimizer, and performance evaluation metric for model training in the sequential model object. Step VII: Use the sequential model object to load the training set separated in step S202 and start model training.

3. The method for monitoring the grate plate state of a pellet chain grate according to claim 2, characterized in that: In step S4, the implementation method of judging whether the image 1 contains a complete castor plate is based on a binary classification model for training image recognition. The output of whether the image 1 contains a complete castor plate can be obtained by inputting the binary classification model. The specific steps of obtaining the binary classification model are: S401, collecting a large number of images 1 as training data; S402, feature extraction and data marking are performed on the training data, wherein the extracted features include whether the edge of the grate plate fits around the image, and whether the upper and lower edges of the grate plate are symmetrical in the image; the types of data marking include 0 and 1, 0 represents not including a complete grate plate, and 1 represents including a complete grate plate; S403, using the support vector machine (SVM) algorithm to build a binary classification model for image recognition; S404: Use the training data to train the binary classification model, and judge the model performance by the accuracy of the model output until the model performance meets the requirements for production use.

4. The method for monitoring the grate plate state of a pellet chain grate according to claim 3, characterized in that: In step S4, the image 1 is segmented according to the size of the grate board. If the width of the image 1 is L times the grate board, and the grate board contains M rows and N columns of grate bars, L*M*N grate bar images can be obtained after segmentation.

5. The method for monitoring the grate plate state of a pellet chain grate according to claim 4, characterized in that: The pixel size of the segmented striation image is consistent with that of the images in the striation image set.

6. The method for monitoring the grate plate state of a pellet chain grate according to claim 5, characterized in that: The preprocessing of the image 1 in step S4 includes noise reduction and geometric correction.

7. The method for monitoring the grate plate state of a pellet chain grate according to claim 6, characterized in that: The median filtering method is used to reduce the noise of Image 1. The value of each pixel in Image 1 is replaced by the median value of its neighborhood pixel values ​​to eliminate isolated noise points: ； Where f(x, y) is the pixel value of the original image at point (x, y), g(x, y) is the pixel value of the image at point (x, y) after median filtering, med{} represents the median operation, W is the neighborhood window, and (k, l) is the element in window W, indicating the offset of x and y.

8. The method for monitoring the grate plate state of a pellet chain grate according to claim 7, characterized in that: The perspective transformation model is used to perform geometric correction on image 1. The specific implementation steps are as follows: 1) Select four control points at the four corners of the original image and the corrected image respectively; 2) Using the coordinates of the four control points selected in the previous step, solve the perspective transformation matrix through linear algebra methods, and use the perspective transformation matrix to describe the coordinate mapping relationship between the original image and the corrected image; 3) Apply the perspective transformation matrix to the original image and use the interpolation algorithm to process the pixel values ​​of non-integer coordinates after the transformation to achieve the perspective transformation of the image.

9. The method for monitoring the grate plate state of a pellet chain grate according to claim 8, characterized in that: The warning triggering methods include the equipment's sound and light alarm, SMS notification, and automatic email to maintenance personnel.

Images