Method and apparatus for measuring liquid level of slag conveyor, storage medium, and electronic device

Abstract

The present application relates to the technical field of data processing. Disclosed are a method and apparatus for measuring the liquid level of a slag conveyor, a storage medium, and an electronic device. The method comprises: acquiring image data of a liquid level scale in a slag tank of a slag conveyor acquired by an industrial camera; by means of a bilateral filter, pre-processing the image data to obtain recognizable image data; inputting the recognizable image data into a preset deep learning model for image recognition, so as to obtain point information of the liquid level scale, the preset deep learning model being used to recognize the point information of the liquid level scale contained in the image; and, on the basis of the point information, determining a liquid level reading of the liquid level scale. Compared with the prior art, the present application can use computer image recognition technology to process real-time images, which greatly enhances work safety and reduces pollution and corrosion of measurement components by the environment while improving the measurement accuracy, thereby reducing labor and apparatus costs and also improving the accuracy of the measured liquid level.

Description

A method, apparatus, storage medium, and electronic equipment for measuring the liquid level of a slag remover. Technical Field

[0001] This application relates to the field of data processing technology, and in particular to a method, device, storage medium and electronic equipment for measuring the liquid level of a slag remover. Background Technology

[0002] A slag removal machine is a mechanical device used to remove slag from a slag pit, typically found in thermal power plants, industrial boilers, and other similar facilities. The level of the slag removal machine refers to the horizontal height of the liquid (usually cooling water) in the slag pit. Monitoring the slag removal machine's level is crucial for ensuring the normal operation of the equipment and safe production.

[0003] Currently, the liquid level in slag removal machines is mainly measured using methods such as the tuning fork method, float level gauge, and radio frequency admittance method. However, the high temperature (generally between 50℃ and 70℃) and large slag content in the slag pool of the slag removal machine pose serious difficulties for liquid level measurement. For example, slag and mud can clog the gaps of the tuning fork level gauge, making it impossible to measure the liquid level and requiring a large amount of maintenance. The slag and water adhering to the float also have a significant corrosive effect, resulting in a short service life for the float level gauge. Similarly, the corrosiveness of the slag and water can damage the radio frequency admittance probe and probe rod, making it impossible to accurately measure the liquid level in the slag pool of the slag removal machine, and also increasing the measurement cost. Summary of the Invention

[0004] In view of this, this application provides a method, device, storage medium and electronic equipment for measuring the liquid level of a slag remover, the main purpose of which is to improve the technical problem that the existing technology cannot accurately measure the liquid level in the slag pool of the slag remover, and also leads to increased measurement costs.

[0005] In a first aspect, this application provides a method for measuring the liquid level of a slag removal machine, comprising:

[0006] Acquire image data of the liquid level gauge in the slag pool of the slag removal machine captured by an industrial camera;

[0007] The image data is preprocessed using a bilateral filter to obtain recognizable image data.

[0008] The identifiable image data is input into a preset deep learning model for image recognition to obtain the position information of the liquid level gauge. The preset deep learning model is used to identify the position information of the liquid level gauge contained in the image.

[0009] Based on the location information, the liquid level reading of the liquid level gauge is determined.

[0010] Optionally, the preprocessing of the image data based on a bilateral filter to obtain identifiable image data includes:

[0011] Identify the target region in the image data that meets the noise reduction conditions;

[0012] The target region is smoothed and denoised using a bilateral filter to obtain the denoised image data.

[0013] Optionally, the preprocessing of the image data based on a bilateral filter to obtain identifiable image data further includes:

[0014] The image data after noise reduction is divided into image boundaries to determine the boundaries of the image data after noise reduction.

[0015] The image data after removing the image data boundaries from the denoised image data is subjected to image interference removal processing to obtain interference-free image data.

[0016] Optionally, the preprocessing of the image data based on a bilateral filter to obtain identifiable image data further includes:

[0017] The image enhancement process is performed on the distorted image data using the single-scale Retinex algorithm;

[0018] The image after image enhancement is identified as the recognizable image.

[0019] Optionally, determining the liquid level reading of the liquid level gauge based on the location information includes:

[0020] The point information is modeled according to a predetermined transformation standard to obtain the modeling data corresponding to the point information;

[0021] Based on the modeling data corresponding to the location information, the liquid level reading of the liquid level gauge is determined.

[0022] Optional, also includes:

[0023] Based on the predetermined mapping relationship between the liquid level reading and the output current of the slag remover, the output current corresponding to the liquid level reading of the liquid level gauge is determined.

[0024] The output current is connected to a display system, and the liquid level reading is displayed through the display system.

[0025] Secondly, this application provides a device for measuring the liquid level of a slag removal machine, comprising:

[0026] The acquisition module is configured to acquire image data of the liquid level gauge in the slag pool of the slag removal machine captured by an industrial camera;

[0027] The processing module is configured to preprocess the image data based on a bilateral filter to obtain recognizable image data;

[0028] The recognition module is configured to input the recognizable image data into a preset deep learning model for image recognition to obtain the position information of the liquid level gauge. The preset deep learning model is used to identify the position information of the liquid level gauge contained in the image.

[0029] The determining module is configured to determine the liquid level reading of the liquid level gauge based on the location information. Thirdly, this application provides a computer-readable storage medium storing a computer program thereon, which, when executed by a processor, implements the liquid level measurement method for the slag remover described in the first aspect.

[0030] Fourthly, this application provides an electronic device, including a storage medium, a processor, and a computer program stored on the storage medium and executable on the processor, wherein the processor executes the computer program to implement the method for measuring the liquid level of the slag remover described in the first aspect.

[0031] Fifthly, this application provides a computer program product, the computer program product comprising a computer program, characterized in that, when the computer program is executed by a processor, it implements the method for measuring the liquid level of the slag remover described in the first aspect.

[0032] By employing the above technical solution, this application provides a method, device, storage medium, and electronic equipment for measuring the liquid level of a slag removal machine. Compared with existing technologies, this application acquires image data of the liquid level gauge in the slag pool of the slag removal machine captured by an industrial camera; preprocesses the image data based on a bilateral filter to obtain identifiable image data; inputs the identifiable image data into a preset deep learning model for image recognition to obtain the position information of the liquid level gauge; the preset deep learning model is used to identify the position information of the liquid level gauge contained in the image; and determines the liquid level reading of the liquid level gauge based on the position information. Real-time images can be processed using computer image recognition technology. Compared with directly measuring the liquid level in the slag pool of the slag removal machine located in a hazardous environment, this method significantly improves measurement accuracy and operational safety, greatly reducing the environmental pollution and corrosion of measuring components. It not only reduces labor and equipment costs but also improves the accuracy of the measured liquid level. Attached Figure Description

[0033] The accompanying drawings, which are incorporated in and form part of this specification, illustrate embodiments consistent with this application and, together with the description, serve to explain the principles of this application.

[0034] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, for those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0035] Figure 1 shows a flowchart illustrating a method for measuring the liquid level of a slag remover according to an embodiment of this application;

[0036] Figure 2 shows a flowchart illustrating a method for measuring the liquid level of a slag remover according to an embodiment of this application;

[0037] Figure 3 shows a schematic diagram of the structure of a liquid level measuring device for a slag remover provided in an embodiment of this application;

[0038] Figure 4 shows a schematic diagram of the structure of an electronic device provided in an embodiment of this application. Detailed Implementation

[0039] The embodiments of this application will now be described in more detail with reference to the accompanying drawings. It should be noted that, unless otherwise specified, the embodiments and features described herein can be combined with each other.

[0040] To address the serious difficulties in measuring the liquid level in the slag pool of a slag remover due to the high temperature (generally 50℃ to 70℃) and large slag content, existing technologies suffer from several problems. For example, slag and sludge can clog the tuning fork level gauge, making level measurement impossible and requiring extensive maintenance. Furthermore, the slag and sludge adhering to the float have a significant corrosive effect, shortening the lifespan of the float level gauge. Similarly, the corrosiveness of the slag and sludge can damage the radio frequency admittance probe and probe rod, leading to inaccurate liquid level measurement in the slag pool. This embodiment provides a method for measuring the liquid level in a slag remover, as shown in Figure 1. The method includes:

[0041] Step 101: Obtain image data of the liquid level gauge in the slag pool of the slag removal machine captured by the industrial camera.

[0042] In this application embodiment, the industrial camera is a high-performance camera specifically designed for industrial automation and machine vision applications. These cameras feature high resolution, high speed, and high precision, and are widely used in manufacturing, logistics, medical, and scientific research fields. The industrial camera is designed to meet the specific image acquisition needs of industrial environments, such as high-speed shooting, high-precision measurement, and stability in harsh environments.

[0043] In some examples, a slag removal machine is a mechanical device specifically designed to remove industrial slag, typically used in facilities such as thermal power plants and industrial boilers. The purpose of a slag removal machine is to remove slag produced during combustion from the furnace or ash hopper and transport it to a designated location for processing or disposal.

[0044] Specifically, slag removers are typically installed at the bottom of the furnace or in the ash hopper to collect slag produced during combustion. The slag enters the slag remover by gravity or a conveyor. The slag remover is internally equipped with a conveyor system, such as a chain or scraper, to transport the slag from the collection point to the processing point. The conveyor is usually motor-driven and its speed can be adjusted as needed. To prevent damage to the equipment from the high-temperature slag, the slag remover is usually equipped with a cooling system, which can use water cooling or air cooling to reduce the temperature of the slag. After cooling, the slag can be transported to a designated location for further processing, including but not limited to composting, landfilling, and recycling.

[0045] In this embodiment, the level gauge (or level indicator) in the slag pool is a device used to monitor the liquid level (usually cooling water) in the slag pool. This monitoring is crucial to ensuring the normal operation and safety of the equipment. Excessively high or low liquid levels in the slag pool can lead to a series of problems; therefore, accurate monitoring and control of the liquid level is essential.

[0046] In this embodiment of the application, the image data is the image data of the liquid level gauge in the slag pool of the slag removal machine captured by an industrial camera.

[0047] Step 102: Preprocess the image data based on a bilateral filter to obtain recognizable image data.

[0048] In some examples, the bilateral filter is an advanced filtering technique used in image processing that can smooth an image while preserving edge details. The bilateral filter combines spatial proximity and pixel value similarity, making it well-suited for removing noise without losing edge information in the image.

[0049] The bilateral filter calculates the new value of each pixel using two Gaussian functions: a spatial Gaussian function to control the spatial proximity of pixels, typically using the standard deviation to control the range of influence of spatial proximity; and a value Gaussian function to control the similarity of pixel values, typically using the standard deviation to control the range of influence of pixel value similarity.

[0050] Specifically, bilateral filters can effectively preserve edge information while smoothing images. They can limit the weight of edge pixels by using a Gaussian function, thus avoiding edge blurring. Bilateral filters can effectively remove random noise in images, and they have a good removal effect on salt-and-pepper noise and Gaussian noise.

[0051] In this embodiment of the application, the identifiable image data is image data that can be identified by the following preset deep learning model.

[0052] Step 103: Input the recognizable image data into the preset deep learning model for image recognition to obtain the position information of the liquid level gauge.

[0053] The preset deep learning model is used to identify the position information of the liquid level gauge contained in the image.

[0054] In this embodiment, the preset deep learning model can be a Mask Region-based Convolutional Neural Network (Mask R-CNN). Specifically, Mask R-CNN is an advanced deep learning model mainly used for instance segmentation. It is an improvement on Faster R-CNN and can not only perform object detection, but also generate pixel-level segmentation masks for each detected object, thereby distinguishing different object instances in the image.

[0055] In some examples, Mask R-CNN can simultaneously perform object detection and pixel-level segmentation. Detection determines the location and category of objects, while segmentation generates precise pixel-level masks for each object. Mask R-CNN inherits the Region Proposal Network (RPN) from Faster R-CNN, improving both detection speed and accuracy. The Regions of Interest (RoIs) generated by the RPN are used for object classification and segmentation. Mask R-CNN can use different base network architectures (such as ResNet, VGG, etc.). The model can be adapted to different application scenarios by adjusting the network structure and training parameters. Through pixel-level segmentation, Mask R-CNN can accurately identify object boundaries, making it suitable for fine-grained object segmentation tasks.

[0056] In this embodiment, the Mask R-CNN model can be used to identify the identifiable image and obtain the position information of the liquid level gauge.

[0057] Specifically, liquid level gauges usually have several points (or scales, measurement points) that help monitor whether the liquid level is within a safe range.

[0058] Step 104: Determine the liquid level reading on the liquid level gauge based on the location information.

[0059] In this embodiment of the application, the liquid level reading on the liquid level gauge can determine whether the liquid level in the slag pool of the slag remover is within a safe range.

[0060] Compared with existing technologies, this embodiment acquires image data of the liquid level gauge in the slag pool of a slag removal machine from an industrial camera; preprocesses the image data using a bilateral filter to obtain identifiable image data; inputs the identifiable image data into a preset deep learning model for image recognition to obtain the position information of the liquid level gauge; the preset deep learning model is used to identify the position information of the liquid level gauge contained in the image; and determines the liquid level reading of the liquid level gauge based on the position information. Computer image recognition technology can be used to process real-time images. Compared with directly measuring the liquid level in the slag pool of a slag removal machine located in a hazardous environment, this method significantly improves measurement accuracy and operational safety, greatly reducing the environmental pollution and corrosion of measuring components. This not only reduces labor and equipment costs but also improves the accuracy of the measured liquid level.

[0061] As a refinement and extension of the above embodiments, when performing preprocessing of the image data based on a bilateral filter to obtain identifiable image data, the following method can be used, but is not limited to, as shown in Figure 2. This method includes:

[0062] Step 201: Determine the target region in the image data that meets the noise reduction conditions.

[0063] In some examples, image denoising refers to the process of removing noise from an image in digital image processing to improve image quality. Noise in an image can originate from various factors during the imaging process, such as sensor noise, transmission errors, and lighting conditions. The goal of denoising is to restore the true content of the image, making it clearer and more usable.

[0064] Specifically, the noise reduction conditions in this application can be set according to the actual situation, and the specific content of the noise reduction conditions is not limited here.

[0065] Furthermore, the target region is the area in the image that meets the noise reduction conditions.

[0066] For example, if the image data are image A, image B and image C, then the target regions A, B and C that meet the noise reduction conditions in image A, image B and image C can be determined respectively.

[0067] Step 202: Perform regional smoothing and noise reduction on the target region based on a bilateral filter to obtain the noise-reduced image data.

[0068] In this application embodiment, Regional Smoothing Denoising is an image processing technique that removes noise by applying smoothing operations to local regions of an image. This method is based on the assumption that within a certain region, the differences between adjacent pixels are small, while noise is random and discontinuous. Smoothing can reduce the impact of these random variations, thereby achieving noise reduction.

[0069] Specifically, the core idea of ​​region-based smoothing denoising is to apply smoothing operations to local regions of an image. Common smoothing operations include mean filtering and median filtering. These methods reduce the impact of noise by changing the value of each pixel, while preserving as many image edges and other important features as possible.

[0070] In some examples, the process of smoothing and denoising a target region in this application may include: determining the target region, wherein the target region can be a window of appropriate size that covers a local area in the image. The choice of window size depends on the characteristics of the noise and the content of the image. Calculating statistical measures of pixel values ​​within the target region, such as the mean, median, etc., using the calculated statistics. Updating the value of the center pixel of the target region using the calculated statistics. Repeating this step until the entire image has been traversed. During the smoothing process, an appropriate method (such as bilateral filtering) is used to preserve the edge information of the image, thereby obtaining the denoised image data.

[0071] Step 203: Perform image boundary segmentation on the denoised image data to determine the boundaries of the denoised image data.

[0072] In this embodiment, the image boundary is divided into the denoised image data. Specifically, the boundary of the denoised image data can be determined to effectively process the interference in the image without confusing the image boundary.

[0073] Step 204: Perform image interference removal processing on the image data after removing the image data boundaries from the denoised image data to obtain the interference-free image data.

[0074] For example, boundary preservation is a key objective in image processing because boundaries often represent important features in an image, such as the contours of objects. Embodiments of this application can employ bilateral filtering to address this issue. Specifically, bilateral filtering is a non-linear filtering technique that effectively preserves edges while smoothing the image. Bilateral filtering uses two Gaussian functions to calculate a new value for each pixel: one for spatial proximity and the other for pixel value similarity. Using bilateral filtering for denoising can remove noise while preserving edge information.

[0075] For example, in the embodiments of this application, bilateral filtering can be performed on the denoised data to effectively process the interference in the image without obscuring the image boundaries, thereby obtaining the interference-free image data.

[0076] Step 205: Perform image enhancement processing on the distorted image data using the single-scale Retinex algorithm.

[0077] In some examples, Single Scale Retinex (SSR) is an image enhancement technique designed to mimic how the human visual system processes images. Retinex theory posits that the human visual system can adjust its perceived brightness according to changes in lighting conditions. This algorithm is particularly effective at enhancing image contrast, improving image performance under varying lighting conditions, and effectively handling interference in images without obscuring boundaries.

[0078] Specifically, the single-scale Retinex algorithm assumes that the reflectance and illumination components of an image are separable. An image I(x, y) can be represented as the product of the reflectance component R(x, y) and the illumination component L(x, y); the goal of the single-scale Retinex algorithm is to estimate the reflectance component R(x, y) from the image, thereby enhancing the image's contrast. The algorithm achieves this by calculating the brightness ratio of each pixel to its surrounding pixels.

[0079] In this embodiment, since the image is affected by various light waves and lighting pollution in the environment, the image is enhanced using a single-scale Retinex algorithm. Specifically, the process of enhancing the image data after noise removal using the single-scale Retinex algorithm may include: loading the noise-removed image data using an image processing library (such as OpenCV), filtering the image using a Gaussian kernel to obtain the illumination component, calculating the reflection component using the original image and the illumination component, and normalizing the calculated reflection component so that it displays the enhanced image within the range of [0, 255]. The enhanced image is the recognizable image in this application.

[0080] Step 206: Determine the image after image enhancement as a recognizable image.

[0081] Furthermore, when determining the liquid level reading on the level gauge based on the location information, the following methods can be used, but are not limited to:

[0082] Step 11: Perform reading modeling on the point information according to the predetermined transformation standard to obtain the modeling data corresponding to the point information.

[0083] In this embodiment, the predetermined transformation standard can be the Hough Transform. Specifically, the Hough Transform is a classic algorithm used in image processing and computer vision, mainly for detecting regular shapes such as lines, circles, and ellipses in images. The Hough Transform is particularly effective in detecting lines in images and can handle noise and incomplete line segments.

[0084] In some examples, the basic idea of ​​the Hough transform is to detect specific shapes in an image through a voting mechanism in parameter space. For line detection, the Hough transform uses parameters in polar coordinates to represent lines and finds the parameter combinations that are most likely to represent lines through cumulative voting.

[0085] Step 12: Determine the liquid level reading on the liquid level gauge based on the modeling data corresponding to the location information.

[0086] For example, this application can use algorithms such as Hough transform to model readings and correctly read the liquid level readings of the slag remover's liquid level gauge.

[0087] After determining the liquid level reading on the level gauge based on the location information, the following methods can be used, but are not limited to:

[0088] Step 21: Based on the predetermined mapping relationship between the liquid level reading and the output current of the slag remover, determine the output current corresponding to the liquid level reading on the liquid level gauge.

[0089] Step 22: Connect the output current to the display system and display the liquid level reading through the display system.

[0090] For example, this application can establish a mapping table between the reading and the output current 4-20mA in the computer, complete the connection of the computer output signal to the 4-20mA circuit board, and connect the standard current signal 4-20mA to the DCS system or other meters to display the liquid level of the slag remover in real time.

[0091] Compared with existing technologies, this embodiment acquires image data of the liquid level gauge in the slag pool of a slag removal machine from an industrial camera; preprocesses the image data using a bilateral filter to obtain identifiable image data; inputs the identifiable image data into a preset deep learning model for image recognition to obtain the position information of the liquid level gauge; the preset deep learning model is used to identify the position information of the liquid level gauge contained in the image; and determines the liquid level reading of the liquid level gauge based on the position information. Computer image recognition technology can be used to process real-time images. Compared with directly measuring the liquid level in the slag pool of a slag removal machine located in a hazardous environment, this method significantly improves measurement accuracy and operational safety, greatly reducing the environmental pollution and corrosion of measuring components. This not only reduces labor and equipment costs but also improves the accuracy of the measured liquid level.

[0092] Furthermore, as a specific implementation of the methods shown in Figures 1 and 2, this embodiment provides a measuring device for the liquid level of a slag removal machine, as shown in Figure 3. The device includes: an acquisition module 31, a processing module 32, an identification module 33, and a determination module 34.

[0093] The acquisition module 31 is configured to acquire image data of the liquid level gauge in the slag pool of the slag removal machine collected by the industrial camera;

[0094] Processing module 32 is configured to preprocess the image data based on a bilateral filter to obtain recognizable image data;

[0095] The recognition module 33 is configured to input the recognizable image data into a preset deep learning model for image recognition to obtain the position information of the liquid level gauge. The preset deep learning model is used to identify the position information of the liquid level gauge contained in the image.

[0096] The determining module 34 is configured to determine the liquid level reading of the liquid level gauge based on the location information. In some examples of this embodiment, the processing module 32 is specifically configured to determine the target region in the image data that meets the noise reduction conditions; and to perform regional smoothing noise reduction on the target region based on a bilateral filter to obtain the noise-reduced image data.

[0097] In some examples of this embodiment, the processing module 32 is further configured to perform image boundary division on the denoised image data to determine the boundary of the denoised image data; and to perform image interference removal processing on the image data after removing the image data boundary from the denoised image data to obtain interference-free image data.

[0098] In some examples of this embodiment, the processing module 32 is further configured to perform image enhancement processing on the identifiable image data using a single-scale Retinex algorithm; and to determine the image after image enhancement processing as the identifiable image.

[0099] In some examples of this embodiment, the determining module 34 is specifically configured to perform reading modeling on the point information according to a predetermined transformation standard to obtain modeling data corresponding to the point information; and determine the liquid level reading of the liquid level gauge based on the modeling data corresponding to the point information.

[0100] In some examples of this embodiment, the determining module 34 is further configured to determine the output current corresponding to the liquid level reading of the liquid level gauge based on a predetermined mapping relationship between the liquid level reading and the output current of the slag remover; connect the output current to the display system, and display the liquid level reading through the display system.

[0101] It should be noted that other corresponding descriptions of the functional units involved in the liquid level measuring device of the slag remover provided in this embodiment can be found in the corresponding descriptions in Figures 1 and 2, and will not be repeated here.

[0102] Based on the methods shown in Figures 1 and 2, this embodiment also provides a computer-readable storage medium storing a computer program thereon, which, when executed by a processor, implements the methods shown in Figures 1 and 2.

[0103] Based on this understanding, the technical solution of this application can be embodied in the form of a software product, which can be stored in a non-volatile storage medium (such as CD-ROM, USB flash drive, mobile hard drive, etc.) and includes several instructions to cause a computer device (such as personal computer, server, or network device, etc.) to execute the methods of various implementation scenarios of this application.

[0104] Figure 4 shows a schematic diagram of the hardware structure of an electronic device according to the present invention, including:

[0105] At least one processor 401; and,

[0106] A memory 402 is communicatively connected to at least one of the processors 401; wherein,

[0107] The memory 402 stores instructions that can be executed by at least one of the processors to enable the at least one processor to perform the method for measuring the liquid level of the slag remover as described above.

[0108] Figure 4 uses a processor 401 as an example.

[0109] The electronic device may also include an input device 403 and a display device 404.

[0110] The processor 401, memory 402, input device 403 and display device 404 can be connected by a bus or other means. Figure 4 shows an example of connection by bus.

[0111] The memory 402, as a non-volatile computer-readable storage medium, can be used to store non-volatile software programs, non-volatile computer-executable programs, and modules, such as the program instructions / modules corresponding to the slag remover liquid level measurement method in the embodiments of this application, for example, the method flow shown in Figures 1 and 2. The processor 401 executes various functional applications and data processing by running the non-volatile software programs, instructions, and modules stored in the memory 402, thereby realizing the slag remover liquid level measurement method in the above embodiments.

[0112] Memory 402 may include a program storage area and a data storage area. The program storage area may store the operating system and application programs required for at least one function. The data storage area may store data created based on the use of the method for measuring the liquid level of the slag remover. Furthermore, memory 402 may include high-speed random access memory and may also include non-volatile memory, such as at least one disk storage device, flash memory device, or other non-volatile solid-state storage device. In some embodiments, memory 402 may optionally include memory remotely located relative to processor 401, and this remote memory may be connected via a network to the apparatus performing the method for measuring the liquid level of the slag remover. Examples of such networks include, but are not limited to, the Internet, intranets, local area networks, mobile communication networks, and combinations thereof.

[0113] The input device 403 can receive user clicks and generate signal inputs related to user settings and function control of the slag remover's liquid level measurement method. The display device 404 may include a display screen or other display equipment.

[0114] When one or more modules are stored in the memory 402 and are run by one or more processors 401, the method for measuring the liquid level of the slag remover in any of the above method embodiments is executed.

[0115] Optionally, the aforementioned physical devices may also include a user interface, a network interface, a camera, radio frequency (RF) circuitry, sensors, audio circuitry, a Wi-Fi module, etc. The user interface may include a display screen, input units such as a keyboard, etc., and optional user interfaces may also include USB interfaces, card reader interfaces, etc. The network interface may optionally include standard wired interfaces, wireless interfaces (such as Wi-Fi interfaces), etc.

[0116] Those skilled in the art will understand that the physical device structure provided in this embodiment does not constitute a limitation on the physical device, and may include more or fewer components, or combine certain components, or have different component arrangements.

[0117] The storage medium may also include an operating system and a network communication module. The operating system is a program that manages the hardware and software resources of the aforementioned physical device, supporting the operation of information processing programs and other software and / or programs. The network communication module is used to enable communication between the various components within the storage medium, as well as communication with other hardware and software in the information processing physical device.

[0118] Through the above description of the embodiments, those skilled in the art can clearly understand that this application can be implemented by means of software plus necessary general-purpose hardware platforms, or it can be implemented by hardware. By applying the solution of this embodiment, compared with the existing technology, this embodiment acquires image data of the liquid level gauge in the slag pool of the slag removal machine collected by an industrial camera; and preprocesses the image data based on a bilateral filter to obtain identifiable image data; inputs the identifiable image data into a preset deep learning model for image recognition to obtain the position information of the liquid level gauge, and the preset deep learning model is used to identify the position information of the liquid level gauge contained in the image; based on the position information, the liquid level reading of the liquid level gauge is determined. Computer image recognition technology can be used to process real-time images. Compared with directly measuring the liquid level of the slag pool of the slag removal machine in a hazardous environmental area, the measurement accuracy is improved while the work safety is greatly improved. The environmental pollution and corrosion of the measuring components are greatly reduced, which can not only reduce labor and equipment costs, but also improve the accuracy of the measured liquid level.

[0119] It should be noted that, in this document, relational terms such as "first" and "second" are used merely to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitations, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes said element.

[0120] The above description is merely a specific embodiment of this application, enabling those skilled in the art to understand or implement this application. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of this application. Therefore, this application is not to be limited to the embodiments described herein, but is to be accorded the widest scope consistent with the principles and novel features claimed herein.

Claims

1. A method for measuring the liquid level in a slag removal machine, characterized in that, include: Acquire image data of the liquid level gauge in the slag pool of the slag removal machine captured by an industrial camera; The image data is preprocessed using a bilateral filter to obtain recognizable image data. The identifiable image data is input into a preset deep learning model for image recognition to obtain the position information of the liquid level gauge. The preset deep learning model is used to identify the position information of the liquid level gauge contained in the image. Based on the location information, the liquid level reading of the liquid level gauge is determined.

2. The method according to claim 1, characterized in that, The preprocessing of the image data based on a bilateral filter to obtain identifiable image data includes: Identify the target region in the image data that meets the noise reduction conditions; The target region is smoothed and denoised using a bilateral filter to obtain the denoised image data.

3. The method according to claim 2, characterized in that, The preprocessing of the image data based on a bilateral filter to obtain recognizable image data further includes: The image data after noise reduction is divided into image boundaries to determine the boundaries of the image data after noise reduction. The image data after removing the image data boundaries from the denoised image data is subjected to image interference removal processing to obtain interference-free image data.

4. The method according to claim 3, characterized in that, The preprocessing of the image data based on a bilateral filter to obtain recognizable image data further includes: The image enhancement process is performed on the denoised image data using the single-scale retinal Retinex algorithm. The image after image enhancement is identified as the recognizable image.

5. The method according to claim 4, characterized in that, Determining the liquid level reading on the liquid level gauge based on the location information includes: The point information is modeled according to a predetermined transformation standard to obtain the modeling data corresponding to the point information; Based on the modeling data corresponding to the location information, the liquid level reading of the liquid level gauge is determined.

6. The method according to claim 5, characterized in that, The method further includes: Based on the predetermined mapping relationship between the liquid level reading and the output current of the slag remover, the output current corresponding to the liquid level reading of the liquid level gauge is determined. The output current is connected to a display system, and the liquid level reading is displayed through the display system.

7. A device for measuring the liquid level of a slag remover, characterized in that, include: The acquisition module is configured to acquire image data of the liquid level gauge in the slag pool of the slag removal machine captured by an industrial camera; The processing module is configured to preprocess the image data based on a bilateral filter to obtain recognizable image data; The recognition module is configured to input the recognizable image data into a preset deep learning model for image recognition to obtain the position information of the liquid level gauge. The preset deep learning model is used to identify the position information of the liquid level gauge contained in the image. The determination module is configured to determine the liquid level reading of the liquid level gauge based on the location information.

8. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by a processor, it implements the method of any one of claims 1 to 6.

9. An electronic device, comprising a storage medium, a processor, and a computer program stored on the storage medium and executable on the processor, characterized in that, When the processor executes the computer program, it implements the method of any one of claims 1 to 6.

10. A computer program product, the computer program product comprising a computer program, characterized in that, When the computer program is executed by a processor, it implements the method as described in any one of claims 1 to 6.

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