Converter tuyere state monitoring method and related equipment

Abstract

The invention discloses a converter tuyere state monitoring method and related equipment, and relates to the technical field of image recognition, the method comprises the following steps: collecting target image data in real time, the target image data comprising image data of a current converter tuyere; inputting the target image data into a target monitoring model so as to recognize the current state of the converter tuyere in real time to obtain a state recognition result; the target monitoring model is obtained by training based on a converter tuyere image data set; and according to the state recognition result, a maintenance target or an adjustment measure of the current converter tuyere is formulated. According to the method, the state of the converter tuyere can be automatically and accurately identified, and the converter operation efficiency and safety are remarkably improved. In addition, the implementation of the invention promotes the intelligence of the maintenance work of the converter tuyere, reduces the need of manual intervention, and ensures the stability and continuity of the production process.

Description

Technical Field

[0001] The present application relates to the field of image recognition technology, and in particular to a converter tuyere status monitoring method and related equipment. Background Art

[0002] In the steel production process, converter blowing is one of the key steps. The status of the converter bottom blowing port directly affects the blowing efficiency and product quality. Traditionally, the inspection and maintenance of the seal relies on manual observation and experience, which is not only time-consuming and labor-intensive, but also difficult to ensure the accuracy and timeliness of the results.

[0003] Therefore, it is necessary to propose a converter tuyere status monitoring method to solve the above problems. Summary of the invention

[0004] A series of simplified concepts are introduced in the Summary of the Invention section, which will be further described in detail in the Detailed Description of the Invention section. The Summary of the Invention section of this application does not mean to attempt to define the key features and essential technical features of the claimed technical solution, nor does it mean to attempt to determine the scope of protection of the claimed technical solution.

[0005] This application specifically includes the following aspects:

[0006] In a first aspect, the present application proposes a method for monitoring converter tuyere status, comprising:

[0007] Real-time acquisition of target image data, wherein the target image data is image data including the current converter tuyere;

[0008] The target image data is input into a target monitoring model to identify the current state of the converter tuyere in real time to obtain a state recognition result; the target monitoring model is trained based on the converter tuyere image data set;

[0009] According to the state identification result, maintenance goals or adjustment measures for the current converter tuyere are formulated.

[0010] In a feasible implementation manner, the step of acquiring the converter tuyere image data set includes:

[0011] Collect image data of target converter tuyere;

[0012] The image data of the target converter tuyere is preprocessed to obtain the converter tuyere image data set; the preprocessing includes adjusting the image size, normalizing the image data and enhancing the image data.

[0013] In a feasible implementation manner, the collecting image data of the target converter tuyere includes:

[0014] By using the image acquisition device, when the inclination angle of the target converter reaches 90 degrees and the image acquisition device recognizes that there is no pedestrian blocking the current area, the image data of the tuyere of the target converter is acquired in real time.

[0015] In a feasible implementation manner, preprocessing the image data of the target converter tuyere to obtain the converter tuyere image data set further includes:

[0016] Identifying the state of the tuyere of the target converter in each image in the image data of the tuyere of the target converter;

[0017] The tuyere status is classified, and the image data of the target converter tuyere is marked with features according to different categories; the status classification includes normal state, blocked state, damaged state and eroded state.

[0018] In a feasible implementation manner, the target monitoring model is obtained by training based on the converter tuyere image dataset, including:

[0019] Build an initial monitoring model;

[0020] Training the initial monitoring model based on the converter tuyere image data set until the model reaches a convergence condition;

[0021] Input the test data set into the trained monitoring model and output the state recognition test results;

[0022] The state recognition test result is evaluated. If the state recognition test result does not reach the preset threshold, the initial monitoring model continues to be trained; if the state recognition test result reaches the preset threshold, the training is completed to obtain the target monitoring model.

[0023] In a feasible implementation manner, the initial monitoring model is trained based on the converter tuyere image data set until the model reaches a convergence condition, further comprising:

[0024] The initial monitoring model is trained using a cross entropy loss function and a back propagation algorithm;

[0025] The early stopping method, regularization method and learning rate adjustment method are used to optimize the initial monitoring model after training.

[0026] In a feasible implementation manner, the state recognition test result includes accuracy, recall rate and F1 score.

[0027] In a second aspect, the present application proposes a converter tuyere state monitoring device, which is applied to the converter tuyere state monitoring method described in any one of the above embodiments, comprising:

[0028] An image acquisition unit, used for acquiring target image data in real time, wherein the target image data is image data including the current converter tuyere;

[0029] An image recognition unit is used to input the target image data into a target monitoring model to identify the state of the current converter tuyere in real time and obtain a state recognition result; the target monitoring model is trained based on the converter tuyere image data set;

[0030] The result output unit is used to formulate the maintenance target or adjustment measures of the current converter tuyere according to the state identification result.

[0031] In a third aspect, an electronic device comprises: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor is configured to implement the steps of the converter tuyere status monitoring method of any one of the first aspects described above when executing the computer program stored in the memory.

[0032] In a fourth aspect, the present application further proposes a computer-readable storage medium having a computer program stored thereon, and when the computer program is executed by a processor, the steps of the converter tuyere status monitoring method of any one of the first aspects are implemented.

[0033] In summary, the converter tuyere state monitoring method proposed in this application can automatically and accurately identify the state of the converter tuyere by using a convolutional neural network for image recognition, significantly improving the efficiency and safety of converter operation. In addition, the implementation of this application promotes the intelligent maintenance of the converter tuyere, reduces the need for manual intervention, and ensures the stability and continuity of the production process.

[0034] The converter tuyere condition monitoring method proposed in the present application and other advantages, objectives and features of the present application will be partially reflected through the following description, and will also be partially understood by technical personnel in the field through research and practice of the present application. BRIEF DESCRIPTION OF THE DRAWINGS

[0035] Various other advantages and benefits will become apparent to those of ordinary skill in the art by reading the detailed description of the preferred embodiments below. The accompanying drawings are only for the purpose of illustrating the preferred embodiments and are not to be considered as limiting the present specification. Also, the same reference symbols are used throughout the accompanying drawings to represent the same components. In the accompanying drawings:

[0036] Figure 1 A schematic diagram of a converter tuyere status monitoring method provided in an embodiment of the present application;

[0037] Figure 2 A schematic diagram of the functional modules of a converter tuyere status monitoring device provided in an embodiment of the present application;

[0038] Figure 3 A schematic diagram of the structure of an electronic device for monitoring the status of a converter tuyere provided in an embodiment of the present application. DETAILED DESCRIPTION

[0039] In order to better understand the technical solutions provided by the embodiments of this specification, the technical solutions of the embodiments of this specification are described in detail below through the accompanying drawings and specific embodiments. It should be understood that the embodiments of this specification and the specific features in the embodiments are detailed descriptions of the technical solutions of the embodiments of this specification, rather than limitations on the technical solutions of this specification. In the absence of conflict, the embodiments of this specification and the technical features in the embodiments can be combined with each other.

[0040] 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 that there is 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 the process, method, article or equipment including a series of elements includes not only those elements, but also includes other elements not explicitly listed, or also includes elements inherent to such process, method, article or equipment. In the absence of more restrictions, the elements limited by the statement "comprise one..." do not exclude the existence of other identical elements in the process, method, article or equipment including the elements. The term "more than two" includes two or more than two situations.

[0041] The present application provides a method for monitoring the status of a converter tuyere, which automatically collects tuyere images by cooperating with a high-definition camera system, and uses image processing and machine learning technology to analyze and process the tuyere images, thereby achieving rapid and accurate identification of the tuyere status.

[0042] See also Figure 1 , which is a schematic diagram of a converter tuyere status monitoring method provided in an embodiment of the present application, which may specifically include:

[0043] S110, collecting target image data in real time, wherein the target image data is image data including the current converter tuyere.

[0044] Exemplarily, it is detected whether there is any pedestrian blocking the current scene, and when it is identified that there is no pedestrian blocking the scene, the image data of the current converter tuyere is automatically collected.

[0045] S120, inputting the target image data into a target monitoring model to identify the current state of the converter tuyere in real time to obtain a state recognition result; the target monitoring model is trained based on a converter tuyere image data set.

[0046] For example, deep learning and other machine learning techniques are used to analyze key features in the target image, compare them with standard tuyere data in the expert system, and accurately determine the state of the tuyere through the target monitoring model.

[0047] S130. Formulate maintenance goals or adjustment measures for the current converter tuyere according to the state identification result.

[0048] Exemplarily, in combination with the furnace condition expert system, maintenance and adjustment suggestions are made according to the status of the tuyere.

[0049] In some examples, the step of acquiring the converter tuyere image dataset includes:

[0050] Collect image data of target converter tuyere;

[0051] The image data of the target converter tuyere is preprocessed to obtain the converter tuyere image data set; the preprocessing includes adjusting the image size, normalizing the image data and enhancing the image data.

[0052] Exemplarily, the collected image data first undergoes a preprocessing stage, including operations such as image resizing, normalization, and enhancement, to improve the efficiency and accuracy of model training.

[0053] In some examples, collecting image data of a target converter tuyere includes:

[0054] By using the image acquisition device, when the inclination angle of the target converter reaches 90 degrees and the image acquisition device recognizes that there is no pedestrian blocking the current area, the tuyere image data of the target converter is acquired in real time.

[0055] Exemplarily, the image acquisition process is performed using a high-definition camera installed on the front wall of the converter. The camera is specifically used to capture image data of the converter tuyere. During the operation of the converter, the converter inclination is controlled to ensure that when the converter inclination reaches 90 degrees, the furnace mouth faces the camera. At this time, it is detected whether there are pedestrians blocking the scene to ensure that the image is automatically acquired without obstruction. This acquisition method not only improves the image acquisition efficiency, but also ensures the quality of the acquired images, providing high-quality raw data for subsequent image processing and analysis.

[0056] In some examples, preprocessing the image data of the target converter tuyere to obtain the converter tuyere image data set further includes:

[0057] Identifying the state of the tuyere of the target converter in each image in the image data of the tuyere of the target converter;

[0058] The tuyere status is classified, and the tuyere image data is feature-labeled according to different categories; the status classification includes normal state, blocked state, damaged state and eroded state.

[0059] Exemplarily, the corresponding vent state category is clearly identified for each image. This is because the model needs to learn the characteristics of different vent states in order to accurately classify new images. The labeling work may be done by professionals, who assign corresponding category labels to each image based on the actual vent state, such as "normal state", "abnormal state", etc. With the labeled image data, the model can adjust its own parameters according to these labels during the training process to minimize the error between the predicted results and the true labels, thereby improving the model's recognition accuracy of the vent state.

[0060] Furthermore, the normal state is characterized by unobstructed tuyere, smooth flow of gas or liquid medium, no blockage or obstruction. The refractory material around the tuyere is intact, without obvious erosion, damage or shedding. The tuyere has a good blowing effect and can evenly inject the required medium, such as oxygen, fuel, etc., into the furnace.

[0061] Abnormal conditions include: blocked state, that is, the tuyere is blocked by waste slag, impurities or solidified metal in the furnace, resulting in the inability of the medium to be ejected normally. Alternatively, there may be accumulated waste slag or other foreign matter around the tuyere, affecting the patency of the tuyere. When the blockage is severe, it may cause the tuyere pressure to increase, affecting the pressure balance of the entire converter system. The blocked state will reduce the production efficiency of the converter, because the blocked tuyere cannot spray the medium normally, affecting the speed of the reaction in the furnace. It may also cause uneven temperature in the furnace and affect product quality. If not handled in time, the blockage may become more and more serious, and even cause damage to the tuyere, requiring repair or replacement.

[0062] The damaged state means that the refractory material of the tuyere is cracked, damaged or falls off, which destroys the structural integrity of the tuyere. A damaged tuyere may cause medium leakage, affecting the safe operation of the converter. The furnace shell around the tuyere may be corroded by high-temperature gas or liquid, further aggravating the damage to the tuyere. The damaged state will affect the production safety of the converter, because the damaged tuyere may cause high-temperature gas or liquid leakage, causing a safety accident. It also reduces the production efficiency of the converter, requiring maintenance or replacement of the tuyere, resulting in production interruptions. This will increase production costs because repairing or replacing the tuyere takes time and resources.

[0063] The erosion state, that is, the tuyere is eroded by high-temperature gas, liquid and slag for a long time, causing the size of the tuyere to gradually become smaller and the shape to change. When the erosion is severe, the wall thickness of the tuyere may become thinner, affecting the strength and durability of the tuyere. The refractory materials around the tuyere may also be eroded and need to be maintained and replaced regularly. The erosion state will reduce the service life of the tuyere, requiring frequent replacement of the tuyere, increasing production costs. And affect the production efficiency of the converter, because the eroded tuyere may affect the injection effect of the medium, resulting in uneven reaction in the furnace. If the erosion is severe, it may cause damage to the tuyere and affect the production safety of the converter.

[0064] In some examples, the target monitoring model is obtained by training based on the converter tuyere image dataset, including:

[0065] Build an initial monitoring model;

[0066] Training the initial monitoring model based on the converter tuyere image data set until the model reaches a convergence condition;

[0067] Input the test data set into the trained monitoring model and output the state recognition test results;

[0068] The state recognition test result is evaluated. If the state recognition test result does not reach the preset threshold, the initial monitoring model continues to be trained; if the state recognition test result reaches the preset threshold, the training is completed to obtain the target monitoring model.

[0069] For example, the test data set is a data set that has not been used in the model training process. It can objectively evaluate the performance of the model when facing new data, and avoid the model only remembering the training data and failing to generalize to other situations. If the training data is used to evaluate the model performance, its actual ability may be overestimated due to the overfitting of the model to the training data. The test data set can more truly reflect the accuracy and reliability of the model in actual applications.

[0070] By comparing the state recognition test results obtained by the model predicting the air outlet state in the test data set with the actual air outlet state, we can understand the performance of the model in different situations and find out the shortcomings of the model so as to make targeted adjustments and optimizations. For example, if the model performs poorly on certain types of air outlet states, we can further analyze the reasons and improve the model.

[0071] In some examples, the state recognition test results include precision, recall, and F1 score.

[0072] For example, the model performance is measured using indicators such as precision, recall, and F1 score.

[0073] Accuracy refers to the ratio of the number of samples correctly predicted by the model to the total number of samples. In converter tuyere state recognition, accuracy refers to the ratio of the number of tuyere states (whether normal or abnormal) correctly identified by the model to the total number of samples. Accuracy reflects the overall accuracy of the model, but may have limitations in some cases. For example, if the number of samples of different categories in the data set is unbalanced, the accuracy may be dominated by the categories with a larger proportion, while ignoring the recognition ability of the minority category.

[0074] The recall rate, also known as the recall rate, refers to the proportion of samples correctly predicted as positive by the model among all samples that are actually positive. For converter tuyere status recognition, the recall rate represents the ratio of the number of samples that the model correctly identifies as a specific tuyere status (such as abnormal status) to the total number of samples in actual abnormal status among all samples that are actually in a specific tuyere status (such as abnormal status). The recall rate focuses on the model's ability to identify positive samples, ensuring that as many truly abnormal tuyere status tuyere as possible are discovered to avoid missed detections. In some scenarios with high safety requirements, the recall rate is particularly important, as missed detections may lead to serious consequences.

[0075] The F1 score is the harmonic mean of the precision and recall, which comprehensively considers the accuracy and completeness of the model. The calculation formula of the F1 score is: F1 = 2 × (accuracy × recall) / (accuracy + recall). The F1 score can balance the relationship between accuracy and recall, avoiding focusing on only one indicator and ignoring the other. In the recognition of the converter tuyere state, the F1 score can help find a model that strikes a good balance between accuracy and recall, which can accurately identify the tuyere state and detect as many abnormalities as possible.

[0076] Compare the performance of the model on different indicators to determine the strengths and weaknesses of the model. For example, if the accuracy is high but the recall is low, it may mean that the model is too conservative and is prone to misclassifying some positive samples as negative samples; conversely, if the recall is high but the accuracy is low, it may mean that the model is too sensitive and prone to false positives. Analyze the performance differences of the model on different types of outlet states. Some models may have better results in identifying outlets in certain specific states, but perform poorly in other states. By analyzing these differences, the model can be improved in a targeted manner. Evaluate based on the state recognition test results and try to adjust the model's hyperparameters, such as learning rate, regularization parameters, etc., to optimize the model's performance. You can use methods such as grid search and random search to find the best parameter combination. If the model performs poorly in some cases, add more training data, especially data for outlet states that the model has difficulty correctly identifying. Data augmentation techniques can also be used to expand the data set and improve the generalization ability of the model. Based on the state recognition test results and analysis, the structure of the model may need to be adjusted. For example, you can increase or decrease the number of layers in the model, change the number of neurons or the way they are connected, etc. It can also be combined with other machine learning or deep learning methods, such as ensemble learning, transfer learning, etc., to improve the performance of the model.

[0077] By evaluating the model performance using indicators such as accuracy, recall, and F1 score on an independent test dataset and making necessary adjustments and optimizations based on the test results, the model's recognition accuracy and reliability of the converter tuyere status can be continuously improved to better meet the requirements.

[0078] In some examples, the initial monitoring model is trained based on the converter tuyere image dataset until the model reaches a convergence condition, further comprising:

[0079] The initial monitoring model is trained using a cross entropy loss function and a back propagation algorithm;

[0080] The early stopping method, regularization method and learning rate adjustment method are used to optimize the initial monitoring model after training.

[0081] Exemplarily, a cross entropy loss function and a back propagation algorithm are used to train and optimize the model.

[0082] Cross entropy is a metric used to measure the difference between two probability distributions. In classification problems, the cross entropy loss function is used to calculate the gap between the probability distribution predicted by the model and the probability distribution of the true label. For the converter tuyere state recognition problem, the cross entropy loss function can make the probability distribution of the model output as close to the true category distribution as possible. For example, if the tuyere state corresponding to an image is normal, then the model should output a high probability value corresponding to the normal state category, while the probability values ​​of other categories are lower.

[0083] The back propagation algorithm is an efficient algorithm for training neural networks. It calculates the gradient of the loss function for each parameter of the model, and then updates the parameters according to the principle of gradient descent to reduce the value of the loss function. In the forward propagation process, the input image is calculated through each layer of the CNN model to obtain the prediction result. Then, the loss function is calculated based on the prediction result and the true label. In the back propagation process, starting from the output layer, the gradient of the loss function for each layer parameter is calculated in turn, and the gradient is back-propagated to the previous layer. Finally, the parameters of the model are updated according to the gradient.

[0084] Furthermore, techniques such as early stopping, regularization, and learning rate adjustment are applied to prevent overfitting and improve the generalization ability of the model. During the model training process, as the number of training rounds increases, the performance of the model on the training set may continue to improve, but at a certain moment, the performance of the model on the validation set may stop improving or even decrease. The early stopping method is to stop training when the performance of the model on the validation set begins to decrease to prevent the model from overfitting the training data. The data set is divided into a training set, a validation set, and a test set. During the training process, the performance of the model is regularly evaluated on the validation set. When it is found that the performance on the validation set has not improved for several consecutive rounds, stop training.

[0085] Regularization: L1 and L2 regularization: By adding a penalty term for model parameters in the loss function, the size of model parameters is limited to prevent model overfitting. L1 regularization will make the model parameters sparse, that is, some parameters become zero; L2 regularization will make the model parameters close to zero but not zero. During the training process, the outputs of some neurons in the neural network are randomly set to zero, which is equivalent to using a different sub-network each time training. This can reduce the co-adaptation between neurons and prevent overfitting.

[0086] Learning rate adjustment: The learning rate determines the step size of each update of the model parameters. If the learning rate is too large, the model may oscillate near the optimal solution or even fail to converge; if the learning rate is too small, the model training speed will be very slow. Common learning rate adjustment methods include learning rate decay and adaptive learning rate algorithms. Learning rate decay is to gradually reduce the learning rate as the number of training rounds increases; adaptive learning rate algorithms such as Adam and Adagrad can automatically adjust the learning rate according to the training situation of the model.

[0087] Through the comprehensive application of the above technologies, we can effectively prevent the model from overfitting and improve the generalization ability of the model, so that it can accurately identify the tuyere status when faced with new converter tuyere image data.

[0088] In summary, the present application provides a method for monitoring the status of converter tuyere. Compared with the traditional manual method, the present application can more accurately identify the status of the tuyere and reduce human errors. The automated image acquisition and recognition process greatly shortens the time of tuyere detection and improves production efficiency. Combined with the maintenance suggestions provided by the expert system, precise adjustments can be made according to actual conditions to improve the scientific nature of furnace maintenance.

[0089] The technical solution of the present application is further described in detail below through specific embodiments.

[0090] Step 1: A high-definition camera installed on the wall in front of the converter automatically collects tuyere images when the converter inclination reaches 90 degrees and no pedestrians are blocking it. These images are then used as data for training and testing the convolutional neural network model (i.e., the monitoring model).

[0091] Step 2: The collected image data first undergoes a preprocessing phase, including image resizing, normalization, and enhancement, to improve the efficiency and accuracy of model training. In addition, the images are labeled to identify the air outlet state category corresponding to each image.

[0092] Step 3: Select the convolutional neural network as the classification model. The model structure includes multiple convolutional layers, pooling layers, and fully connected layers. The convolutional layer is used to extract features from the image, the pooling layer is used to reduce the dimension and reduce the amount of calculation, and the fully connected layer is used for the final classification decision.

[0093] Step 4: Use the preprocessed image dataset to train the CNN model. The cross entropy loss function and back propagation algorithm are used to train and optimize the model. At the same time, early stopping, regularization, and learning rate adjustment techniques are applied to prevent overfitting and improve the generalization ability of the model.

[0094] Step 5: Evaluate the performance of the model on an independent test data set, using indicators such as accuracy, recall, and F1 score to measure the accuracy and reliability of the model in identifying the converter tuyere status. Make necessary adjustments and optimizations to the model based on the test results.

[0095] Step 6: Deploy the trained and optimized CNN model to the actual converter tuyere monitoring system. The system automatically collects tuyere images, identifies the status of the tuyere in real time through the CNN model, and proposes maintenance suggestions or adjustment measures based on the identification results.

[0096] In summary, the above implementation steps provide a specific technical route and steps for developing and deploying a CNN-based converter tuyere condition monitoring method to monitor and maintain the status of the converter tuyere.

[0097] It should be noted that the above embodiments are only the best examples and are not limitations on the implementation methods of the present application.

[0098] Furthermore, the present application also proposes a converter tuyere state monitoring device, which is applied to any of the above converter tuyere state monitoring methods, specifically as follows: Figure 2 FIG. 1 is a functional module diagram of a converter tuyere status monitoring device proposed in this application, including:

[0099] An image acquisition unit 21 is used to acquire target image data in real time, wherein the target image data is image data including the current converter tuyere;

[0100] The image recognition unit 22 is used to input the target image data into the target monitoring model to identify the state of the current converter tuyere in real time and obtain a state recognition result; the target monitoring model is trained based on the converter tuyere image data set;

[0101] The result output unit 23 is used to formulate the maintenance target or adjustment measures of the current converter tuyere according to the state identification result.

[0102] like Figure 3 As shown, an embodiment of the present application also provides an electronic device 300, including a processor 310, a memory 320, and a computer program 321 stored in the memory 320 and executable on the processor, and when the processor 310 executes the computer program 321, the steps of any of the above-mentioned methods for monitoring the status of the converter tuyere are implemented.

[0103] Since the electronic device introduced in this embodiment is a device used to implement a method for monitoring the status of a converter tuyere in the embodiment of the present application, based on the method introduced in the embodiment of the present application, technical personnel in this field can understand the specific implementation mode of the electronic device of this embodiment and its various variations. Therefore, how the electronic device implements the method in the embodiment of the present application will not be described in detail here. As long as the equipment used by technical personnel in this field to implement the method in the embodiment of the present application is within the scope of protection of this application.

[0104] In the specific implementation process, when the computer program 321 is executed by the processor, it can achieve Figure 1 Any implementation manner in the corresponding embodiments.

[0105] It should be noted that in the above embodiments, the description of each embodiment has its own emphasis, and for parts that are not described in detail in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0106] Those skilled in the art will appreciate that the embodiments of the present application may be provided as methods, systems, or computer program products. Therefore, the present application may adopt the form of a complete hardware embodiment, a complete software embodiment, or an embodiment in combination with software and hardware. Moreover, the present application may adopt the form of a computer program product implemented in one or more computer-readable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) that contain computer-readable program code.

[0107] The present application is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems), and computer program products according to the embodiments of the present application. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of the processes and / or boxes in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded computer, or other programmable data processing device to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0108] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0109] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 A step that specifies a function in one or more boxes.

[0110] An embodiment of the present application also provides a computer program product, which includes computer software instructions. When the computer software instructions are executed on a processing device, the processing device executes the process of the converter tuyere status monitoring method.

[0111] The computer program product includes one or more computer instructions. When the computer program instructions are loaded and executed on the computer, the process or function according to the embodiment of the present application is generated in whole or in part. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable devices. The computer instructions can be stored in a computer-readable storage medium, or transmitted from one computer-readable storage medium to another computer-readable storage medium. For example, the computer instructions can be transmitted from a website site, a computer, a server or a data center by wired (e.g., coaxial cable, optical fiber, digital subscriber line (digital subscriber line, DSL)) or wireless (e.g., infrared, wireless, microwave, etc.) mode to another website site, computer, server or data center. The computer-readable storage medium can be any available medium that a computer can store or a data storage device such as a server or a data center that includes one or more available media integration. The available medium can be a magnetic medium, (e.g., a floppy disk, a hard disk, a tape), an optical medium (e.g., a DVD), or a semiconductor medium (e.g., a solid state drive (SSD)), etc.

[0112] Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working processes of the systems, devices and units described above can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0113] In the several embodiments provided in the present application, it should be understood that the disclosed devices, apparatuses and methods can be implemented in other ways. For example, the device embodiments described above are only schematic, for example, the division of units is only a logical function division, and there may be other division methods in actual implementation, 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 an indirect coupling or communication connection through some interfaces, devices or units, which can be electrical, mechanical or other forms.

[0114] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0115] 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. The above-mentioned integrated unit may be implemented in the form of hardware or in the form of software functional units.

[0116] If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present application is essentially or the part that contributes to the prior art or all or part of the technical solution can be embodied in the form of a software product, and the computer software product is stored in a storage medium, including a number of instructions to enable a computer device (which can be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the various embodiments of the present application. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (Read-Only Memory, ROM), random access memory (Random Access Memory, RAM), disk or optical disk and other media that can store program codes.

[0117] The above embodiments are only used to illustrate the technical solutions of the present application, rather than to limit them. Although the present application has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present application.

[0118] Although the preferred embodiments of this specification have been described, those skilled in the art may make additional changes and modifications to these embodiments once they have learned the basic creative concept. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments and all changes and modifications that fall within the scope of this specification.

[0119] Obviously, those skilled in the art can make various changes and modifications to this specification without departing from the spirit and scope of this specification. Thus, if these modifications and variations of this specification fall within the scope of the claims of this specification and their equivalents, this specification is also intended to include these modifications and variations.

Claims

1. A method for monitoring converter tuyere status, characterized in that: include: Real-time acquisition of target image data, wherein the target image data is image data including the current converter tuyere; The target image data is input into a target monitoring model to identify the current state of the converter tuyere in real time to obtain a state recognition result; the target monitoring model is trained based on the converter tuyere image data set; According to the state identification result, maintenance goals or adjustment measures for the current converter tuyere are formulated.

2. The converter tuyere status monitoring method according to claim 1, characterized in that: The step of acquiring the converter tuyere image data set comprises: Collect image data of target converter tuyere; The image data of the target converter tuyere is preprocessed to obtain the converter tuyere image data set; the preprocessing includes adjusting the image size, normalizing the image data and enhancing the image data.

3. The converter tuyere status monitoring method according to claim 2, characterized in that: The collecting of the image data of the target converter tuyere includes: By using the image acquisition device, when the inclination angle of the target converter reaches 90 degrees and the image acquisition device recognizes that there is no pedestrian blocking the current area, the image data of the tuyere of the target converter is acquired in real time.

4. The converter tuyere status monitoring method according to claim 2, characterized in that: Preprocessing the image data of the target converter tuyere to obtain the converter tuyere image data set also includes: Identifying the state of the tuyere of the target converter in each image in the image data of the tuyere of the target converter; The tuyere status is classified, and the image data of the target converter tuyere is marked with features according to different categories; the status classification includes normal state, blocked state, damaged state and eroded state.

5. The converter tuyere status monitoring method according to claim 2, characterized in that: The target monitoring model is obtained by training based on the converter tuyere image data set, including: Build an initial monitoring model; Training the initial monitoring model based on the converter tuyere image data set until the model reaches a convergence condition; Input the test data set into the trained monitoring model and output the state recognition test results; The state recognition test result is evaluated. If the state recognition test result does not reach the preset threshold, the initial monitoring model continues to be trained; if the state recognition test result reaches the preset threshold, the training is completed to obtain the target monitoring model.

6. The method for monitoring converter tuyere status according to claim 5, characterized in that: The initial monitoring model is trained based on the converter tuyere image data set until the model reaches a convergence condition, further comprising: The initial monitoring model is trained using a cross entropy loss function and a back propagation algorithm; The early stopping method, regularization method and learning rate adjustment method are used to optimize the initial monitoring model after training.

7. The method for monitoring converter tuyere status according to claim 5, characterized in that: The state recognition test results include accuracy, recall and F1 score.

8. A converter tuyere status monitoring device, applied to the converter tuyere status monitoring method according to any one of claims 1 to 7, characterized in that: include: An image acquisition unit, used for acquiring target image data in real time, wherein the target image data is image data including the current converter tuyere; An image recognition unit is used to input the target image data into a target monitoring model to identify the state of the current converter tuyere in real time and obtain a state recognition result; the target monitoring model is trained based on the converter tuyere image data set; The result output unit is used to formulate the maintenance target or adjustment measures of the current converter tuyere according to the state identification result.

9. An electronic device, comprising: A memory and a processor, wherein the processor is used to implement the steps of the converter tuyere status monitoring method as described in any one of claims 1 to 7 when executing the computer program stored in the memory.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the converter tuyere status monitoring method according to any one of claims 1 to 7 are implemented.

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