Image recognition-based coal gas drainer digital monitoring platform

Abstract

The present application relates to the technical fields of digital prediction management, and provides a coal gas drainer digital monitoring platform based on image recognition, wherein an image recognition module collects image data and three-dimensional point cloud data of the coal gas drainer, an anti-interference sensing module performs reflectivity correction, image intensity adjustment and data fusion on the image data, combines with the Beer-Lambert law to compensate for steam attenuation, eliminates environmental interference, a dynamic analysis module extracts surface features through a transfer learning technology combined with a lightweight network, generates a dynamic mask to shield a light reflection area through curvature analysis, generates a high-dimensional feature map, an evolution decision module performs corrosion data calculation and time series modeling based on the high-dimensional feature map, quantifies corrosion depth by comparing point cloud height variation, and predicts equipment life; the present application acquires accurate images through polaroid filtering, algorithm compensation attenuation and data fusion, avoids false detection by combining transfer learning and dynamic masks, and further analyzes trends and monitors equipment life by using corrosion quantification and space-time algorithm.

Description

Technical Field

[0001] This invention relates to the field of digital predictive management technology, specifically to a digital monitoring platform for gas drainers based on image recognition. Background Technology

[0002] As a key piece of equipment in gas pipeline systems, the gas drainer's core function is to isolate gas pressure and drain condensate through a water seal structure. Traditional monitoring methods mainly rely on sensors such as electrical contacts and pressure transmitters. When the water seal is broken by high pressure, the electrical contacts disconnect, triggering a flashing alarm. At the same time, the PLC system links the valves to regulate the pressure and prevent gas leakage.

[0003] In recent years, the introduction of IoT technology has made remote monitoring possible. Some drain monitoring systems can now wirelessly transmit pressure and water level data to the central control room, reducing the frequency of manual inspections. However, these systems have significant limitations: sensors can only monitor conductivity or pressure thresholds and cannot detect complex issues such as gradual water level changes, internal blockages, or structural corrosion. For example, when a drain develops tiny cracks due to long-term corrosion, traditional sensors may not detect them until the cracks widen and cause the water seal to fail.

[0004] 1. The applicability of image recognition technology is insufficient. It is affected by industrial environments such as metal reflection and steam interference. The general training model has a high false detection rate. For example, water vapor is easily misjudged as leaking white smoke, and rust spots are difficult to distinguish from dirt. It lacks the ability to adapt to dynamic environments and cannot intelligently cope with interference factors such as changes in light and mechanical vibration. This results in significant differences in the features of images collected at different times, requiring manual recalibration of parameters.

[0005] 2. Digital monitoring based on image recognition results lacks tracking and prediction of state evolution. It lacks the ability to quantitatively assess progressive failures, such as internal wall corrosion, and cannot calculate corrosion rate or predict remaining service life, resulting in insufficient basis for preventive maintenance. For example, internal wall corrosion of drainers is usually a progressive process, but current algorithms can only identify the rust area of ​​a single frame image and cannot quantify the corrosion rate or predict the failure time. Summary of the Invention

[0006] To address the aforementioned technical problems, this invention provides a digital monitoring platform for gas drainers based on image recognition. It acquires accurate images through physical polarizer filtering, algorithmic attenuation compensation, and data fusion. Combined with transfer learning and dynamic masking, it avoids false detections. Furthermore, it utilizes corrosion quantification and spatiotemporal algorithms to analyze trends and predict failure times. This reduces the false detection rate in industrial environments, quantitatively assesses equipment failures, and provides a basis for preventative maintenance, thus solving the problems in existing technologies.

[0007] The technical solution of this invention is as follows:

[0008] A digital monitoring platform for gas drainers based on image recognition, comprising:

[0009] An image recognition module, comprising an industrial camera and a lidar device installed on the gas drainer, for real-time acquisition of image data and three-dimensional point cloud data of the gas drainer;

[0010] An anti-interference sensing module is connected to the image recognition module to perform reflectivity correction, image intensity adjustment and data fusion processing on the image data of the gas drainer. It also compensates for steam attenuation by combining Beer-Lambert's law and eliminates environmental interference to ensure accurate and reliable data.

[0011] The dynamic analysis module is connected to the anti-interference perception module. It extracts surface features by using transfer learning technology combined with a lightweight network, generates a dynamic mask to shield the reflective area through curvature analysis, and generates a high-dimensional feature map.

[0012] An evolutionary decision module is connected to the dynamic analysis module. It performs corrosion data calculation and time series modeling based on high-dimensional feature maps, quantifies corrosion depth by comparing point cloud height changes, and predicts equipment lifespan.

[0013] The monitoring and display module is connected to the evolutionary decision-making module. It displays the equipment status through three-dimensional visualization and data dashboard, and integrates historical data comparison and multi-form anomaly alarm functions.

[0014] Preferably, the industrial camera in the image recognition module includes a physical polarizer to filter out specular reflections from the metal surface and retain the diffuse reflection component of the original RGB image. ;

[0015] Meanwhile, the image recognition module also obtains historical image information and 3D point cloud data of the gas drainer based on the historical monitoring logs of the gas drainer.

[0016] Preferably, the anti-interference sensing module includes a laser-assisted imaging unit and a multimodal data fusion unit;

[0017] The laser-assisted imaging unit performs reflectivity correction and image intensity correction on the original RGB image to obtain a filtered image. It then combines the laser point cloud data to compensate for the laser propagation attenuation in the steam environment, eliminates the influence of steam interference on the image and point cloud data, and obtains calibrated laser point cloud data.

[0018] The multimodal data fusion unit performs projection and matching, rigid body transformation solution and data fusion based on the filtered image and calibrated laser point cloud data, accurately matching the visible light texture with the three-dimensional geometry and outputting aligned fused data.

[0019] Preferably, the laser-assisted imaging unit inputs a raw RGB image. Reflectivity correction is performed, and the reflectivity of the metal surface is calculated for different incident angles according to the Fresnel formula. The reflectivity of light ;

[0020] Image intensity correction, each pixel of the original image The brightness value is attenuated according to the formula:

[0021] ;

[0022] Obtain the filtered image Then, combined with laser point cloud data According to the Beer-Lambert law, compensation is made for the attenuation of laser propagation in a steam environment;

[0023] Let the attenuation coefficient of the steam medium, calibrated experimentally, be denoted as... The path length of the laser penetrating the vapor layer is denoted as . The corrected point cloud data calculation formula is:

[0024] ;

[0025] Calculate the laser point cloud dataset using the point cloud data calculation formula. For each three-dimensional coordinate in the dataset, reconstruct the calibration laser point cloud dataset under steam interference. .

[0026] Preferably, the multimodal data fusion unit performs multimodal data fusion and inputs the filtered image. and calibration laser point cloud dataset Perform projection and matching;

[0027] The camera projection function projects 3D point cloud feature points onto a 2D image plane. The camera projection function is expressed as follows:

[0028] ;

[0029] Where u and v represent the calibration laser point cloud dataset. The pixel coordinates of a 3D point P projected onto the image plane. Indicates the camera's focal length. The camera's optical center coordinates are determined using camera parameters, and then a 2D-3D feature point correspondence is established using a KD-tree nearest neighbor search.

[0030] ;

[0031] in, Represents a set of matching pairs. To match the threshold, set to 1 pixel, a rigid body transformation is then performed to solve for the rotation matrix S and translation vector L using a least-squares optimization problem.

[0032] ;

[0033] in, The camera projection function, i.e., the projection formula above, is solved iteratively using the Levenberg-Marquardt algorithm to obtain the optimal solution. and ;

[0034] Data fusion generation maps the calibrated laser point cloud data to the image coordinate system:

[0035] ;

[0036] Fusion of RGB filtered image and laser point cloud 3D coordinates:

[0037] ;

[0038] Output aligned and blended data This enables precise matching between visible light textures and three-dimensional geometry.

[0039] Preferably, the dynamic parsing module includes a parsing unit and a confidence calculation unit;

[0040] The parsing unit employs transfer learning technology, pre-establishing a lightweight ResNet-18 network based on an industrial corrosion dataset for extracting oxidation and crack texture features from metal surfaces.

[0041] Then input the alignment and blending data. A lightweight ResNet-18 network automatically extracts 64-channel high-dimensional feature maps, utilizing a calibrated laser point cloud dataset. The surface curvature of each local region is calculated, a dynamic mask is generated, and reflective interference pixels are marked. In the 64-channel feature map, the activation values ​​of the mask-covered area are forced to zero to mask the area, and a masked 64-channel high-dimensional feature map is generated.

[0042] The confidence calculation unit flattens the masked 64-channel feature map into feature vectors, maps them to the defect category space through a fully connected layer, and outputs a confidence report based on the classification results.

[0043] Preferably, the evolution decision module includes a corrosion quantification unit, a time-series corrosion rate modeling unit, and a lifetime prediction unit;

[0044] The corrosion quantification unit analyzes and calculates the texture feature map after shielding and the calibration laser point cloud data to obtain the corrosion depth and corrosion area of ​​the gas drainer, and quantifies the degree of equipment corrosion.

[0045] The time-series corrosion rate modeling unit analyzes and processes corrosion data at different time points, establishes a mathematical model, analyzes the variation law of corrosion rate over time, and predicts future corrosion development trends.

[0046] The life prediction unit combines corrosion quantification results, corrosion rate change trends, and equipment design parameters to output the time point with the highest probability of failure based on the current corrosion depth and rate, combined with the material safety threshold set based on material properties.

[0047] Preferably, the corrosion quantification unit calculates the corrosion depth and corrosion area;

[0048] Let the masked texture feature map be H, and input the calibration laser point cloud data. ;

[0049] Rust spot area calculation:

[0050] Extract the channel dimension from the texture feature map H, take the maximum value as the activation region, and calculate the actual physical area based on the ratio of pixels to area in the feature map, and output the percentage of rust coverage area.

[0051] Calculate the percentage of rust pixels:

[0052] Calibrate laser point cloud data The texture feature map after masking is aligned with H based on spatial coordinate mapping. The current point cloud height is compared with the historical baseline height, the average depth of the depression is calculated, and the average erosion depth is output.

[0053] Preferably, the specific modeling process of the time-series corrosion rate modeling unit is as follows:

[0054] Multi-scale feature fusion modeling is adopted, using an improved spatiotemporal attention LSTM network. Based on historical data results, short-term fluctuation features and long-term trend features of corrosion data are extracted simultaneously. The weights of features at different time scales are automatically assigned through the attention mechanism. A normal corrosion rate distribution space is established during the model training phase, and the deviation of real-time data is calculated through Mahalanobis distance during online monitoring.

[0055] The model's dynamic update strategy, based on the recursive least squares algorithm with a forgetting factor, dynamically adjusts the weights of historical data according to the real-time data, enabling the model to adapt to the changes in corrosion patterns caused by the aging of the drainer material.

[0056] Preferably, the monitoring and display module includes a three-dimensional visualization interface that displays the real-time status of the gas drainer in the form of a three-dimensional model, showing the appearance of the equipment, the location of key components, and visual markers indicating corrosion status.

[0057] It also includes a data dashboard for real-time display of corrosion depth, corrosion area, and corrosion rate parameters, and provides historical data query function; in addition, it supports multi-terminal adaptation, allowing operators to view monitoring information anytime and anywhere via computers, tablets, and mobile devices, and also has an abnormal status alarm function, which notifies monitoring personnel via pop-up windows, sound, and SMS when an abnormal equipment status is detected.

[0058] Compared with the prior art, the present invention has the following beneficial effects:

[0059] 1. This invention obtains accurate image data by using physical polarizer filtering, Fresnel formula to correct reflectivity, and Beer-Lambert law to compensate for vapor attenuation, combined with laser-assisted imaging and multimodal data fusion; then, by using transfer learning and dynamic masking technology, stable texture features are extracted to avoid false detections, effectively reducing the false detection rate in complex industrial environments, improving the system's adaptive capability, and reducing manual calibration.

[0060] 2. This invention utilizes corrosion quantification units to calculate corrosion depth and area, and spatiotemporal attention LSTM networks and recursive least squares algorithms to analyze corrosion rate trends. Combined with equipment parameters, it predicts failure time, thereby achieving quantitative assessment of progressive failures and providing a reliable basis for preventive maintenance. Attached Figure Description

[0061] Figure 1 This is a schematic diagram showing the connections between the various modules of the present invention;

[0062] Figure 2 This is a schematic diagram of the calculation process of the anti-interference sensing module of the present invention. Detailed Implementation

[0063] The embodiments of the present invention will be described in further detail below with reference to the accompanying drawings and examples. The following examples are for illustrative purposes only and should not be construed as limiting the scope of the invention.

[0064] This invention provides a digital monitoring platform for gas drainers based on image recognition, comprising:

[0065] An image recognition module, comprising an industrial camera and a lidar device installed on the gas drainer, for real-time acquisition of image data and three-dimensional point cloud data of the gas drainer;

[0066] An anti-interference sensing module is connected to the image recognition module to perform reflectivity correction, image intensity adjustment and data fusion processing on the image data of the gas drainer. It also compensates for steam attenuation by combining Beer-Lambert's law and eliminates environmental interference to ensure accurate and reliable data.

[0067] The dynamic analysis module is connected to the anti-interference perception module. It extracts surface features by using transfer learning technology combined with a lightweight network, generates a dynamic mask to shield the reflective area through curvature analysis, and generates a high-dimensional feature map.

[0068] An evolutionary decision module is connected to the dynamic analysis module. It performs corrosion data calculation and time series modeling based on high-dimensional feature maps, quantifies corrosion depth by comparing point cloud height changes, and predicts equipment lifespan.

[0069] The monitoring and display module is connected to the evolutionary decision-making module. It displays the equipment status through three-dimensional visualization and data dashboard, and integrates historical data comparison and multi-form anomaly alarm functions. Example 1:

[0070] like Figures 1-2 As shown in this embodiment, multiple gas drainers are installed in the gas transmission pipeline network of a large factory to remove condensate from the gas pipelines. The drainers are constantly exposed to a high-temperature, humid environment with abundant steam, making their metal surfaces highly susceptible to rust due to moisture corrosion. Furthermore, the vibrations generated during equipment operation and variations in lighting conditions at different times of day make image acquisition and status monitoring extremely difficult. Previous methods using manual periodic inspections and traditional monitoring equipment suffer from low detection efficiency, high false alarm rates, and the inability to predict equipment failures in advance. Therefore, this invention is introduced.

[0071] First, the image recognition module includes an industrial camera and a lidar device installed on the gas drainer to collect image data and 3D point cloud data of the gas drainer in real time.

[0072] The industrial camera includes a physical polarizer to filter out specular reflections from metal surfaces, preserving the diffuse light component of the original RGB image. ;

[0073] Meanwhile, the image recognition module also obtains historical image information and 3D point cloud data of the gas drainer based on the historical monitoring logs of the gas drainer, and transmits them to the anti-interference perception module.

[0074] The anti-interference sensing module includes a laser-assisted imaging unit and a multimodal data fusion unit;

[0075] The laser-assisted imaging unit inputs a raw RGB image. Reflectivity correction is performed, and the reflectivity of the metal surface is calculated for different incident angles according to the Fresnel formula. The reflectivity of light ;

[0076] Image intensity correction, each pixel of the original image The brightness value is attenuated according to the formula:

[0077] ;

[0078] Obtain the filtered image Then, combined with laser point cloud data According to the Beer-Lambert law, compensation is made for the attenuation of laser propagation in a steam environment;

[0079] Let the attenuation coefficient of the steam medium, calibrated experimentally, be denoted as... The path length of the laser penetrating the vapor layer is denoted as . The corrected point cloud data calculation formula is:

[0080] ;

[0081] Calculate the laser point cloud dataset using the point cloud data calculation formula. For each three-dimensional coordinate in the dataset, reconstruct the calibration laser point cloud dataset under steam interference. .

[0082] The multimodal data fusion unit performs multimodal data fusion, inputting the filtered image. and calibration laser point cloud dataset Perform projection and matching;

[0083] The camera projection function projects 3D point cloud feature points onto a 2D image plane. The camera projection function is expressed as follows:

[0084] ;

[0085] Where u and v represent the calibration laser point cloud dataset. The pixel coordinates of a 3D point P projected onto the image plane. Indicates the camera's focal length. The camera's optical center coordinates are determined using camera parameters, and then a 2D-3D feature point correspondence is established using a KD-tree nearest neighbor search.

[0086] ;

[0087] in, Represents a set of matching pairs. To match the threshold, set to 1 pixel, a rigid body transformation is then performed to solve for the rotation matrix S and translation vector L using a least-squares optimization problem.

[0088] ;

[0089] in, The camera projection function, i.e., the projection formula above, is solved iteratively using the Levenberg-Marquardt algorithm to obtain the optimal solution. and ;

[0090] Data fusion generation maps the calibrated laser point cloud data to the image coordinate system:

[0091] ;

[0092] Fusion of RGB filtered image and laser point cloud 3D coordinates:

[0093] ;

[0094] Output aligned and blended data This enables precise matching between visible light textures and three-dimensional geometry.

[0095] The industrial camera's physical polarizer first filters out most of the specular reflection light, obtaining a relatively clear original RGB image. The laser-assisted imaging unit of the anti-interference sensing module corrects the reflectivity of the original RGB image according to the Fresnel formula and attenuates the image intensity according to a specific formula to obtain a filtered image.

[0096] By combining the laser point cloud data with Beer-Lambert's law to compensate for the attenuation of laser propagation in the steam environment, the calibrated laser point cloud dataset is restored. The multimodal data fusion unit uses a camera projection function and a KD tree nearest neighbor search operation to achieve precise matching between visible light texture and 3D geometry, outputting clear and minimally distorted aligned fused data.

[0097] Compared with existing technologies, this invention effectively eliminates steam and reflection interference through multi-technology collaborative processing, resulting in clear and accurate image data that provides a reliable foundation for subsequent analysis. This significantly improves the accuracy and reliability of image recognition. Example 2:

[0098] like Figures 1-2 As shown in this embodiment, with the expansion of the factory scale in Example 1, the gas consumption surged, and the gas pressure and flow rate in the pipeline fluctuated frequently, further exacerbating the workload of the drainer. The inner wall of the drainer was subjected to long-term corrosion by acidic substances in the gas, and the corrosion process exhibited a non-linear accelerating trend.

[0099] In the past, manual periodic inspections and traditional monitoring equipment were used, which had problems such as low detection efficiency, high misjudgment rate, inability to predict equipment failure in advance, and inability to adapt to complex and ever-changing working conditions, making it difficult to effectively monitor the progressive corrosion of drainers.

[0100] The dynamic parsing module includes a parsing unit and a confidence calculation unit, and receives aligned and fused data from the anti-interference sensing module. ;

[0101] The parsing unit employs transfer learning techniques, pre-establishing a lightweight ResNet-18 network based on an industrial corrosion dataset to extract features of metal surface oxidation and crack texture.

[0102] Then input the alignment and blending data. A lightweight ResNet-18 network automatically extracts 64-channel high-dimensional feature maps, utilizing a calibrated laser point cloud dataset. The surface curvature of each local region is calculated, a dynamic mask is generated, and reflective interference pixels are marked. In the 64-channel feature map, the activation values ​​of the mask-covered area are forced to zero to mask the area, and a masked 64-channel high-dimensional feature map is generated.

[0103] The confidence calculation unit flattens the masked 64-channel feature map into feature vectors, maps them to the defect category space through a fully connected layer, and outputs a confidence report based on the classification results.

[0104] The evolutionary decision module includes a corrosion quantification unit, a time-series corrosion rate modeling unit, and a lifetime prediction unit;

[0105] The corrosion quantification unit analyzes and calculates the texture feature map after shielding and the calibration laser point cloud data to obtain the corrosion depth and corrosion area of ​​the gas drainer, thus quantifying the degree of equipment corrosion.

[0106] The time-series corrosion rate modeling unit analyzes and processes corrosion data at different time points, establishes mathematical models, analyzes the variation law of corrosion rate over time, and predicts future corrosion development trends.

[0107] The life prediction unit combines corrosion quantification results, corrosion rate change trends, and equipment design parameters to output the time point with the highest probability of failure based on the current corrosion depth and rate, combined with the material safety threshold set based on material properties.

[0108] The corrosion quantification unit calculates the corrosion depth and corrosion area;

[0109] Let the masked texture feature map be H, and input the calibration laser point cloud data. ;

[0110] Rust spot area calculation:

[0111] Extract the channel dimension from the texture feature map H, take the maximum value as the activation region, and calculate the actual physical area based on the ratio of pixels to area in the feature map, and output the percentage of rust coverage area.

[0112] Calculate the percentage of rust pixels:

[0113] Calibrate laser point cloud data The texture feature map after masking is aligned with H based on spatial coordinate mapping. The current point cloud height is compared with the historical baseline height, the average depth of the depression is calculated, and the average erosion depth is output.

[0114] The specific modeling process for the time-series corrosion rate modeling unit is as follows:

[0115] Multi-scale feature fusion modeling is adopted, using an improved spatiotemporal attention LSTM network. Based on historical data results, short-term fluctuation features and long-term trend features of corrosion data are extracted simultaneously. The weights of features at different time scales are automatically assigned through the attention mechanism. A normal corrosion rate distribution space is established during the model training phase, and the deviation of real-time data is calculated through Mahalanobis distance during online monitoring.

[0116] The model's dynamic update strategy, based on the recursive least squares algorithm with a forgetting factor, dynamically adjusts the weights of historical data according to the real-time data, enabling the model to adapt to the changes in corrosion patterns caused by the aging of the drainer material.

[0117] The monitoring and display module uses a 3D visualization interface to display the real-time status of the gas drainer in the form of a 3D model, showing the appearance of the equipment, the location of key components, and the corrosion status.

[0118] Meanwhile, the monitoring and display module also displays corrosion depth, corrosion area, and corrosion rate parameters in real time through a data dashboard, and provides historical data query function. In addition, it supports multi-terminal adaptation, allowing operators to view monitoring information anytime and anywhere via computers, tablets, and mobile devices. It also has an abnormal status alarm function, which notifies monitoring personnel via pop-up windows, sound, and SMS when an abnormal equipment status is detected.

[0119] By using corrosion quantification units to calculate corrosion depth and area, and analyzing corrosion rate trends using spatiotemporal attention LSTM networks and recursive least squares algorithms, combined with equipment parameters, failure time can be predicted, thereby achieving a quantitative assessment of progressive failures and providing a reliable basis for preventive maintenance.

[0120] The embodiments of the present invention are given for the purposes of illustration and description. Although embodiments of the present invention have been shown and described above, it is understood that the above embodiments are exemplary and should not be construed as limiting the present invention. Those skilled in the art can make changes, modifications, substitutions and variations to the above embodiments within the scope of the present invention.

Claims

1. A digital monitoring platform for gas drainers based on image recognition, characterized in that, include: An image recognition module, comprising an industrial camera and a lidar device installed on the gas drainer, for real-time acquisition of image data and three-dimensional point cloud data of the gas drainer; An anti-interference sensing module is connected to the image recognition module to perform reflectivity correction, image intensity adjustment and data fusion processing on the image data of the gas drainer. It also compensates for steam attenuation by combining Beer-Lambert's law and eliminates environmental interference to ensure accurate and reliable data. The dynamic analysis module is connected to the anti-interference perception module. It extracts surface features by using transfer learning technology combined with a lightweight network, generates a dynamic mask to shield the reflective area through curvature analysis, and generates a high-dimensional feature map. An evolutionary decision module is connected to the dynamic analysis module. It performs corrosion data calculation and time series modeling based on high-dimensional feature maps, quantifies corrosion depth by comparing point cloud height changes, and predicts equipment lifespan. The monitoring and display module is connected to the evolutionary decision-making module. It displays the equipment status through three-dimensional visualization and data dashboard, and integrates historical data comparison and multi-form anomaly alarm functions.

2. The digital monitoring platform for gas drainers based on image recognition as described in claim 1, characterized in that: The industrial camera in the image recognition module includes a physical polarizer to filter out specular reflections from the metal surface and retain the diffuse reflection component of the original RGB image. ; Meanwhile, the image recognition module also obtains historical image information and 3D point cloud data of the gas drainer based on the historical monitoring logs of the gas drainer.

3. The digital monitoring platform for gas drainers based on image recognition as described in claim 2, characterized in that: The anti-interference sensing module includes a laser-assisted imaging unit and a multimodal data fusion unit; The laser-assisted imaging unit performs reflectivity correction and image intensity correction on the original RGB image to obtain a filtered image. It then combines the laser point cloud data to compensate for the laser propagation attenuation in the steam environment, eliminates the influence of steam interference on the image and point cloud data, and obtains calibrated laser point cloud data. The multimodal data fusion unit performs projection and matching, rigid body transformation solution and data fusion based on the filtered image and calibrated laser point cloud data, accurately matching the visible light texture with the three-dimensional geometry and outputting aligned fused data.

4. The digital monitoring platform for gas drainers based on image recognition as described in claim 3, characterized in that: The laser-assisted imaging unit inputs a raw RGB image. Reflectivity correction is performed, and the reflectivity of the metal surface is calculated for different incident angles according to the Fresnel formula. The reflectivity of light ; Image intensity correction, each pixel of the original image The brightness value is attenuated according to the formula: ; Obtain the filtered image Then, combined with laser point cloud data According to the Beer-Lambert law, compensation is made for the attenuation of laser propagation in a steam environment; Let the attenuation coefficient of the steam medium, calibrated experimentally, be denoted as... The path length of the laser penetrating the vapor layer is denoted as . The corrected point cloud data calculation formula is: ; Calculate the laser point cloud dataset using the point cloud data calculation formula. For each three-dimensional coordinate in the dataset, reconstruct the calibration laser point cloud dataset under steam interference. .

5. The digital monitoring platform for gas drainers based on image recognition as described in claim 4, characterized in that: The multimodal data fusion unit performs multimodal data fusion and inputs the filtered image. and calibration laser point cloud dataset Perform projection and matching; The camera projection function projects 3D point cloud feature points onto a 2D image plane. The camera projection function is expressed as follows: ； Where u and v represent the calibration laser point cloud dataset. The pixel coordinates of a 3D point P projected onto the image plane. Indicates the camera's focal length. The camera's optical center coordinates are determined using camera parameters, and then a 2D-3D feature point correspondence is established using a KD-tree nearest neighbor search. ； in, Represents a set of matching pairs. To match the threshold, set to 1 pixel, a rigid body transformation is then performed to solve for the rotation matrix S and translation vector L using a least-squares optimization problem. ； in, The camera projection function, i.e., the projection function mentioned above, is solved iteratively using the Levenberg-Marquardt algorithm to obtain the optimal solution. and ; Data fusion generation maps the calibrated laser point cloud data to the image coordinate system: ； Fusion of RGB filtered image and laser point cloud 3D coordinates: ； Output aligned and blended data This enables precise matching between visible light textures and three-dimensional geometry.

6. The digital monitoring platform for gas drainers based on image recognition as described in claim 5, characterized in that: The dynamic parsing module includes a parsing unit and a confidence calculation unit; The parsing unit employs transfer learning technology, pre-establishing a lightweight ResNet-18 network based on an industrial corrosion dataset for extracting oxidation and crack texture features from metal surfaces. Then input the alignment and blending data. A lightweight ResNet-18 network automatically extracts 64-channel high-dimensional feature maps using a calibrated laser point cloud dataset. The surface curvature of each local region is calculated, a dynamic mask is generated, and reflective interference pixels are marked. In the 64-channel feature map, the activation values ​​of the mask-covered area are forced to zero to mask the area, and a masked 64-channel high-dimensional feature map is generated. The confidence calculation unit flattens the masked 64-channel feature map into feature vectors, maps them to the defect category space through a fully connected layer, and outputs a confidence report based on the classification results.

7. The digital monitoring platform for gas drainers based on image recognition as described in claim 1, characterized in that: The evolutionary decision module includes a corrosion quantification unit, a time-series corrosion rate modeling unit, and a lifetime prediction unit; The corrosion quantification unit analyzes and calculates the texture feature map after shielding and the calibration laser point cloud data to obtain the corrosion depth and corrosion area of ​​the gas drainer, and quantifies the degree of equipment corrosion. The time-series corrosion rate modeling unit analyzes and processes corrosion data at different time points, establishes a mathematical model, analyzes the variation law of corrosion rate over time, and predicts future corrosion development trends. The life prediction unit combines corrosion quantification results, corrosion rate change trends, and equipment design parameters to output the time point with the highest probability of failure based on the current corrosion depth and rate, combined with the material safety threshold set based on material properties.

8. The digital monitoring platform for gas drainers based on image recognition as described in claim 7, characterized in that: The corrosion quantification unit calculates the corrosion depth and corrosion area; Let the masked texture feature map be H, and input the calibration laser point cloud data. ; Rust spot area calculation: Extract the channel dimension from the texture feature map H, take the maximum value as the activation region, and calculate the actual physical area based on the ratio of pixels to area in the feature map, and output the percentage of rust coverage area. Calculate the percentage of rust pixels: Calibrate laser point cloud data The texture feature map after masking is aligned with H based on spatial coordinate mapping. The current point cloud height is compared with the historical baseline height, the average depth of the depression is calculated, and the average erosion depth is output.

9. The digital monitoring platform for gas drainers based on image recognition as described in claim 7, characterized in that: The specific modeling process of the time-series corrosion rate modeling unit is as follows: Multi-scale feature fusion modeling is adopted, using an improved spatiotemporal attention LSTM network. Based on historical data results, short-term fluctuation features and long-term trend features of corrosion data are extracted simultaneously. The weights of features at different time scales are automatically assigned through the attention mechanism. A normal corrosion rate distribution space is established during the model training phase, and the deviation of real-time data is calculated through Mahalanobis distance during online monitoring. The model's dynamic update strategy, based on the recursive least squares algorithm with a forgetting factor, dynamically adjusts the weights of historical data according to the real-time data, enabling the model to adapt to the changes in corrosion patterns caused by the aging of the drainer material.

10. The digital monitoring platform for gas drainers based on image recognition as described in claim 1, characterized in that: The monitoring and display module includes a three-dimensional visualization interface that displays the real-time status of the gas drainer in the form of a three-dimensional model, showing the appearance of the equipment, the location of key components, and visual markers indicating corrosion status. It also includes a data dashboard for real-time display of corrosion depth, corrosion area, and corrosion rate parameters, and provides historical data query function; in addition, it supports multi-terminal adaptation, allowing operators to view monitoring information anytime and anywhere via computers, tablets, and mobile devices, and also has an abnormal status alarm function, which notifies monitoring personnel via pop-up windows, sound, and SMS when an abnormal equipment status is detected.

Images