Digital monitoring platform for gas drainer based on image recognition

Abstract

The invention relates to the technical field of digital prediction management, and provides a gas drainer digital monitoring platform based on image recognition, an image recognition module collects image data and three-dimensional point cloud data of a gas drainer, an anti-interference sensing module carries out reflectivity correction, image intensity adjustment and data fusion on the image data, and the image data and the three-dimensional point cloud data are integrated. The dynamic analysis module extracts surface features by combining a migration learning technology with a lightweight network, generates a dynamic mask shielding reflective area through curvature analysis and generates a high-dimensional feature map, and the evolution decision-making module performs corrosion data calculation and time sequence modeling based on the high-dimensional feature map. The corrosion depth is quantified by comparing point cloud height changes, and the service life of equipment is predicted; accurate images are obtained through polaroid filtering, algorithm attenuation compensation and data fusion, false detection is avoided in combination with transfer learning and a dynamic mask, the trend is analyzed through corrosion quantification and a space-time algorithm, and the service life of equipment is monitored.

Description

Technical Field

[0001] The invention relates to the technical field of digital prediction management, in particular to a digital monitoring platform for gas drainers based on image recognition. Background Art

[0002] As a key device in the gas pipeline system, the core function of the gas drainer is to isolate the gas pressure and drain the condensed water through the 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 are disconnected to trigger the flash alarm. At the same time, the PLC system links the valve to adjust the pressure to avoid gas leakage.

[0003] In recent years, the introduction of Internet of Things technology has made remote monitoring possible. Some drain monitoring systems can upload pressure and water level data to the central control room through wireless transmission, reducing the frequency of manual inspections. However, such systems have obvious limitations: sensors can only monitor the conduction state or pressure threshold, and cannot capture complex problems such as gradual changes in water levels, internal blockages, or structural corrosion. For example, when a drain has tiny cracks due to long-term corrosion, traditional sensors may not respond until the cracks expand and cause the water seal to fail.

[0004] 1. The applicability of image recognition technology is insufficient. 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 and stains are difficult to distinguish. It lacks the ability to adapt to dynamic environments and cannot intelligently respond to interference factors such as lighting changes and mechanical vibrations. As a result, the image features collected at different times are significantly different, and manual recalibration of parameters is required.

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

[0006] In order to solve the above technical problems, the present invention provides a digital monitoring platform for gas drainers based on image recognition, which obtains accurate images through physical polarizer filtering, algorithm compensation attenuation and data fusion, combines transfer learning and dynamic masking to avoid false detection, and then uses corrosion quantification and spatiotemporal algorithms to analyze trends and predict failure time, thereby reducing the false detection rate in industrial environments, quantitatively evaluating equipment failures and providing a basis for preventive maintenance, so as to solve the problems in the prior art.

[0007] The technical solution of the present invention is as follows: A digital monitoring platform for gas drainage devices based on image recognition, comprising: An image recognition module, which includes an industrial camera and a lidar device installed on the gas drainage device, and is used for real-time acquisition of image data and three-dimensional point cloud data of the gas drainage device; An anti-interference perception module, which is data-connected to the image recognition module, performs reflectivity correction, image intensity adjustment and data fusion processing on the image data of the gas drainage device, compensates for steam attenuation in combination with the Beer-Lambert law, and eliminates environmental interference to ensure accurate and reliable data; A dynamic analysis module, which is data-connected to the anti-interference perception module, 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-making module, which is data-connected to the dynamic analysis module, calculates corrosion data and performs time series modeling based on the high-dimensional feature map, quantifies the corrosion depth by comparing the change in point cloud height, and predicts the equipment life; A monitoring and display module, which is data-connected to the evolutionary decision-making module, displays the equipment status through three-dimensional visualization and a data dashboard, and integrates historical data comparison and multi-form abnormal alarm functions.

[0008] Preferably, the industrial camera in the image recognition module includes a physical polarizer for filtering specular reflection light on the metal surface and retaining the original RGB image of the diffuse reflection light component ; At the same time, the image recognition module also obtains the historical image information and three-dimensional point cloud data of the gas drainage device based on the historical monitoring log of the gas drainage device.

[0009] Preferably, the anti-interference perception module includes a laser-assisted imaging unit and a multi-modal 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, compensates for the attenuation of laser propagation in the steam environment in combination with the laser point cloud data, eliminates the influence of steam interference factors on the image and point cloud data, and obtains calibrated laser point cloud data; The multi-modal data fusion unit performs projection and matching, rigid body transformation solution and data fusion based on the filtered image and the calibrated laser point cloud data, accurately matches the visible light texture with the three-dimensional geometry, and outputs aligned fusion data.

[0010] Preferably, the laser-assisted imaging unit inputs the original RGB image , performs reflectivity correction, and calculates the reflectivity of the metal surface to light at different incident angles according to the Fresnel formula ; Image intensity correction, for each pixel of the original image the brightness value is attenuated according to the formula: ; to obtain the filtered image , then, combined with the laser point cloud data , according to the Beer-Lambert law, compensate for the attenuation of the laser propagation in the steam environment; Let the attenuation coefficient of the steam medium calibrated through experiments be denoted as , and the path length of the laser penetrating the steam layer be denoted as , then the calculation formula for the corrected point cloud data is: ; Calculate each three-dimensional coordinate in the laser point cloud data set to restore the calibrated laser point cloud data set under steam interference .

[0011] Preferably, the multi-modal data fusion unit performs multi-modal data fusion, inputs the filtered image and the calibrated laser point cloud data set for projection and matching; Project the 3D point cloud feature points onto the 2D image plane through the camera projection function, and the camera projection function is expressed as: ; where, u and v represent the pixel coordinates of the three-dimensional point P in the calibrated laser point cloud data set projected onto the image plane, represents the camera focal length, is the camera optical center coordinate, which is determined by the camera parameters, and then through the KD tree nearest neighbor search, establish the 2D-3D feature point correspondence: ; where, represents the set of matching pairs, is the matching threshold, set to 1 pixel, and then solve the rigid body transformation, construct the least squares optimization problem to solve the rotation matrix S and the translation vector L: ; where, represents the camera projection function, that is, the above projection formula, and uses the Levenberg-Marquardt algorithm to iterate and solve to obtain the optimal and ; Data fusion generation, map the calibrated laser point cloud data to the image coordinate system: ; Fuse the RGB filtered image with the three-dimensional coordinates of the laser point cloud: ; Output the aligned and fused data , realizing the precise matching of visible light texture and three-dimensional geometry.

[0012] Preferably, the dynamic analysis module includes an analysis unit and a confidence calculation unit; The analysis unit adopts transfer learning technology and pre-establishes a lightweight ResNet-18 network pre-trained based on an industrial corrosion data set, which is used to extract the texture features of metal surface oxidation and cracks; Then input the aligned and fused data , and the lightweight ResNet-18 network automatically extracts a 64-channel high-dimensional feature map. Using the calibrated laser point cloud data set , calculate the surface curvature of each local area, generate a dynamic mask, mark the reflective interference pixels, and in the 64-channel feature map, force the activation values of the areas covered by the mask to zero and shield them to generate a shielded 64-channel high-dimensional feature map; The confidence calculation unit flattens the shielded 64-channel feature map into a feature vector, maps it to the defect category space through a fully connected layer, and outputs a confidence report based on the classification result.

[0013] Preferably, the evolutionary decision module includes a corrosion quantification unit, a time-series corrosion rate modeling unit, and a life prediction unit; The corrosion quantification unit analyzes and calculates the shielded texture feature map and the calibrated laser point cloud data to obtain the corrosion depth and corrosion area of the gas drainer, and quantifies the corrosion degree of the equipment; The time-series corrosion rate modeling unit analyzes and processes the corrosion data at different time points, establishes a mathematical model, analyzes the variation law of the corrosion rate with time, and predicts the future corrosion development trend; The life prediction unit combines the corrosion quantification results, the change trend of the corrosion rate, and the equipment design parameter factors, and according to the current corrosion depth and rate, combines the material safety threshold set based on the material characteristics to output the most probable failure time point.

[0014] Preferably, the corrosion quantification unit calculates the corrosion depth and corrosion area; Let the shielded texture feature map be H, and input the calibrated laser point cloud data ; Calculation of rust area: Extract the maximum value of the channel dimension from the texture feature map H as the activation area, and convert the actual physical area according to the ratio of the pixels to the area in the feature map, and output the proportion of the rust-covered area. Calculate the proportion of rust pixels: Align the calibrated laser point cloud data with the masked texture feature map H based on spatial coordinate mapping, compare the current point cloud height with the historical reference height, calculate the average depression depth, and output the average corrosion depth.

[0015] Preferably, the specific modeling process of the time-series corrosion rate modeling unit is as follows: Multi-scale feature fusion modeling, using an improved spatio-temporal attention LSTM network, simultaneously extract the short-term fluctuation features and long-term trend features of corrosion data based on historical data results, automatically allocate weights to features of different time scales through the attention mechanism, establish a normal corrosion rate distribution space during the model training stage, and calculate the deviation degree of real-time data through the Mahalanobis distance during online monitoring; Model dynamic update strategy, based on the recursive least squares algorithm with forgetting factor, dynamically adjust the weights of historical data according to data timeliness, so that the model adapts to the corrosion law changes brought by the aging of the drainer material.

[0016] Preferably, the monitoring and display module includes a 3D visualization interface, which displays the real-time state of the gas drainer in the form of a 3D model, and the display includes the appearance of the device, the positions of key components, and visual markers of the corrosion condition; At the same time, it also includes a data dashboard for real-time display of corrosion depth, corrosion area, and corrosion rate parameters, and provides a historical data query function; in addition, it supports multi-terminal adaptation, and operators can view monitoring information anytime and anywhere through computer, tablet, and mobile phone devices, and also has an abnormal state alarm function. When the device state is detected to be abnormal, the monitoring personnel will be notified through pop-up windows, sounds, and text messages.

[0017] Compared with the prior art, the present invention has the following beneficial effects: 1. The present invention uses a physical polarizer for light filtering, modifies the reflectivity by the Fresnel formula, compensates for steam attenuation by the Beer-Lambert law, combines laser-assisted imaging with multi-modal data fusion to obtain accurate image data; then uses transfer learning and dynamic masking technology to extract stable texture features, avoid false detection, effectively reduce the false detection rate in complex industrial environments, improve the system's adaptability, and reduce manual calibration. 2. The present invention calculates the corrosion depth and area by using the corrosion quantification unit, analyzes the change trend of the corrosion rate through the spatio-temporal attention LSTM network and the recursive least squares algorithm, combines the device parameters, predicts the failure time, and realizes the quantitative evaluation of progressive faults, providing a reliable basis for preventive maintenance. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] Figure 1 is a schematic connection diagram between the modules of the present invention; Figure 2 It is a schematic diagram of the calculation process of the anti-interference perception module of the present invention. Specific embodiments

[0019] The following further describes the embodiments of the present invention in detail with reference to the accompanying drawings and examples. The following examples are used to illustrate the present invention, but cannot be used to limit the scope of the present invention.

[0020] The present invention provides a digital monitoring platform for gas drainers based on image recognition, including: An image recognition module, which includes an industrial camera and a lidar device installed on the gas drainer, and is used to collect image data and three-dimensional point cloud data of the gas drainer in real time; An anti-interference perception module, which is data-connected to the image recognition module, performs reflectivity correction, image intensity adjustment and data fusion processing on the image data of the gas drainer, compensates for steam attenuation in combination with the Beer-Lambert law, and eliminates environmental interference to ensure the accuracy and reliability of the data; A dynamic analysis module, which is data-connected to the anti-interference perception module, 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-making module, which is data-connected to the dynamic analysis module, performs corrosion data calculation and time series modeling based on the high-dimensional feature map, quantifies the corrosion depth by comparing the change in point cloud height, and predicts the equipment life; A monitoring and display module, which is data-connected to the evolutionary decision-making module, displays the equipment status through three-dimensional visualization and a data dashboard, and integrates historical data comparison and multi-form abnormal alarm functions. Example 1:

[0021] As Figures 1-2 shown, in this embodiment, in the gas transmission pipeline network of a large factory, there are multiple gas drainers for discharging condensate water in the gas pipeline. The drainers are in an environment of high temperature, humidity and steam diffusion for a long time. The metal surface is extremely easy to rust due to the erosion of water vapor. At the same time, the vibration generated during the operation of the equipment and the change of light at different times make it extremely difficult to collect images and monitor the status. In the past, manual regular inspections and traditional monitoring equipment were used, which had problems such as low detection efficiency, high misjudgment rate, and inability to predict equipment failures in advance. Therefore, the present invention is introduced.

[0022] First, the image recognition module includes an industrial camera and a lidar device installed on the gas drainer, which collect image data and three-dimensional point cloud data of the gas drainer in real time; Among them, the industrial camera includes a physical polarizer for filtering out specular reflection light on the metal surface and retaining the original RGB image of the diffuse reflection light component ; Meanwhile, the image recognition module also obtains the historical image information and 3D point cloud data of the gas drainer based on the historical monitoring log of the gas drainer, and transmits them to the anti-interference perception module.

[0023] The anti-interference perception module includes a laser-assisted imaging unit and a multi-modal data fusion unit; The laser-assisted imaging unit inputs the original RGB image , performs reflectivity correction, and calculates the reflectivity of the metal surface to light with different incident angles according to the Fresnel formula ; ; Image intensity correction, the brightness value of each pixel point of the original image is attenuated according to the formula: ; The filtered image is obtained , and then, combined with the laser point cloud data , according to the Beer-Lambert law, compensates for the attenuation of the laser propagation in the steam environment; Let the attenuation coefficient of the steam medium calibrated through experiments be denoted as , and the path length of the laser penetrating the steam layer be denoted as , then the calculation formula for the corrected point cloud data is: ; Calculate each three-dimensional coordinate in the laser point cloud data set through the point cloud data calculation formula, and restore the calibrated laser point cloud data set under steam interference .

[0024] The multi-modal data fusion unit performs multi-modal data fusion, inputs the filtered image and the calibrated laser point cloud data set for projection and matching; Project the 3D point cloud feature points to the 2D image plane through the camera projection function, and the camera projection function is expressed as: ; where, u and v represent the pixel coordinates of the three-dimensional point P in the calibrated laser point cloud data set projected onto the image plane, represents the camera focal length, is the camera optical center coordinate, which is determined by the camera parameters, and then through the KD tree nearest neighbor search, establish the 2D-3D feature point correspondence: ; where, represents the set of matching pairs, Let \(\theta\) be the matching threshold, set to 1 pixel. Then, rigid body transformation is solved, and a least squares optimization problem is constructed to solve for the rotation matrix \(S\) and the translation vector \(L\): ; where, represents the camera projection function, i.e., the above projection formula. The Levenberg - Marquardt algorithm is used for iterative solution to obtain the optimal and ; Data fusion is generated by mapping the calibrated laser point cloud data to the image coordinate system: ; Fuse the RGB filtered image and the three - dimensional coordinates of the laser point cloud: ; Output the aligned and fused data , achieving the precise matching of visible light texture and three - dimensional geometry.

[0025] Most of the specular reflected light is first filtered out through the physical polarizer of the industrial camera to obtain a relatively clear original RGB image. The laser - assisted imaging unit of the anti - interference perception 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; Then, combined with the laser point cloud data, the attenuation of the laser propagation in the steam environment is compensated using the Beer - Lambert law to restore the calibrated laser point cloud data set. The multi - modal data fusion unit realizes the precise matching of visible light texture and three - dimensional geometry for the filtered image and the calibrated laser point cloud data through the camera projection function and the KD - tree nearest neighbor search operation, and outputs clear and minimally - disturbed aligned and fused data. Compared with the prior art, the present invention effectively eliminates steam and reflection interference through multi - technology collaborative processing, and the obtained image data is clear and accurate, providing a reliable basis for subsequent analysis. It greatly improves the accuracy and reliability of image recognition. Embodiment Two:

[0026] As Figures 1-2 shown, in this embodiment, with the expansion of the factory scale in Embodiment One, the gas consumption surges, and the gas pressure and flow rate in the pipeline fluctuate frequently, further increasing the working load of the drainer. The inner wall of the drainer is corroded by acidic substances in the gas for a long time, and the corrosion process shows a non - linear acceleration trend.

[0027] In the past, manual regular inspections and traditional monitoring equipment were used, which had problems such as low detection efficiency, high false - positive rate, inability to predict equipment failures in advance, etc., and could not adapt to complex and changeable working conditions, making it difficult to effectively monitor the progressive corrosion of the drainer.

[0028] The dynamic parsing module includes a parsing unit and a confidence calculation unit, and receives the aligned and fused data from the anti-interference perception module. ; The parsing unit uses transfer learning technology to pre-establish a lightweight ResNet-18 network pre-trained based on an industrial corrosion dataset, which is used to extract the features of metal surface oxidation and crack textures. Then, the aligned and fused data is input. , and the lightweight ResNet-18 network automatically extracts a 64-channel high-dimensional feature map. Using the calibrated laser point cloud dataset , calculate the surface curvature of each local area, generate a dynamic mask, mark the reflective interference pixels, and in the 64-channel feature map, force the activation values in the area covered by the mask to zero and shield them to generate a shielded 64-channel high-dimensional feature map. The confidence calculation unit flattens the shielded 64-channel feature map into a feature vector, maps it to the defect category space through a fully connected layer, and outputs a confidence report based on the classification result.

[0029] The evolutionary decision-making module includes a corrosion quantification unit, a temporal corrosion rate modeling unit, and a life prediction unit. The corrosion quantification unit analyzes and calculates the shielded texture feature map and the calibrated laser point cloud data to obtain the corrosion depth and corrosion area of the gas drainer, and quantifies the corrosion degree of the equipment. The temporal corrosion rate modeling unit analyzes and processes the corrosion data at different time points, establishes a mathematical model, analyzes the variation law of the corrosion rate with time, and predicts the future corrosion development trend. The life prediction unit combines the corrosion quantification results, the corrosion rate change trend, and the equipment design parameter factors, and based on the current corrosion depth and rate, combines with the material safety threshold set based on the material properties to output the most probable failure time point.

[0030] The corrosion quantification unit calculates the corrosion depth and corrosion area. Let the shielded texture feature map be H, and input the calibrated laser point cloud data ; Calculation of rust area: Extract the maximum value in the channel dimension from the texture feature map H as the activation area, and convert the actual physical area according to the ratio of pixels to area in the feature map, and output the proportion of the rust-covered area. Calculation of the proportion of rust pixels: Align the calibrated laser point cloud data with the shielded texture feature map H based on spatial coordinate mapping, compare the current point cloud height with the historical reference height, calculate the average value of the depression depth, and output the average corrosion depth.

[0031] The specific modeling process of the time-series corrosion rate modeling unit is as follows: Multi-scale feature fusion modeling: An improved spatio-temporal attention LSTM network is used to simultaneously extract the short-term fluctuation features and long-term trend features of corrosion data based on historical data results. The attention mechanism is used to automatically assign weights to features at different time scales, and a normal corrosion rate distribution space is established during the model training stage. During online monitoring, the Mahalanobis distance is used to calculate the deviation degree of real-time data; Model dynamic update strategy: Based on the recursive least squares algorithm with forgetting factor, the weights of historical data are dynamically adjusted according to data timeliness, enabling the model to adapt to the corrosion law changes caused by the aging of the drainer material.

[0032] The monitoring and display module uses a 3D visualization interface to display the real-time state of the gas drainer in the form of a 3D model. The display includes the appearance of the device, the positions of key components, and the visualization of the corrosion condition; At the same time, the monitoring and display module also uses a data dashboard to display the corrosion depth, corrosion area, and corrosion rate parameters in real time, and provides a historical data query function; in addition, it supports multi-terminal adaptation. Operators can view the monitoring information anytime and anywhere through computer, tablet, and mobile phone devices, and it also has an abnormal state alarm function. When an abnormal device state is detected, the monitor is notified through pop-up windows, sounds, and text messages.

[0033] By using the corrosion quantification unit to calculate the corrosion depth and area, and the spatio-temporal attention LSTM network and the recursive least squares algorithm to analyze the corrosion rate change trend, combined with device parameters, the failure time is predicted to achieve a quantitative evaluation of progressive faults and provide a reliable basis for preventive maintenance.

[0034] The embodiments of the present invention are given for the purposes of illustration and description. Although the embodiments of the present invention have been shown and described above, it can be understood that the above embodiments are exemplary and should not be construed as limiting the present invention. Those of ordinary skill 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, Including: An image recognition module, which includes an industrial camera and a lidar device installed on the gas drainer, and is used to collect image data and three-dimensional point cloud data of the gas drainer in real time; An anti-interference perception module, which is data-connected to the image recognition module, performs reflectivity correction, image intensity adjustment and data fusion processing on the image data of the gas drainer, compensates for steam attenuation in combination with the Beer-Lambert law, and eliminates environmental interference to ensure the accuracy and reliability of the data; A dynamic analysis module, which is data-connected to the anti-interference perception module, 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-making module, which is data-connected to the dynamic analysis module, performs corrosion data calculation and time-series modeling based on the high-dimensional feature map, quantifies the corrosion depth by comparing the change in point cloud height, and predicts the equipment life; A monitoring and display module, which is data-connected to the evolutionary decision-making module, displays the equipment status through 3D visualization and a data dashboard, and integrates historical data comparison and multi-form abnormal alarm functions.

2. The digital monitoring platform for gas drainers based on image recognition according to claim 1, characterized in that: The industrial camera in the image recognition module includes a physical polarizer for filtering specular reflection light on the metal surface and retaining the original RGB image of the diffuse reflection light component ; At the same time, the image recognition module also obtains the historical image information and three-dimensional point cloud data of the gas drainer based on the historical monitoring log of the gas drainer.

3. The digital monitoring platform for gas drainers based on image recognition according to claim 2, wherein: The anti-interference perception module includes a laser-assisted imaging unit and a multi-modal data fusion unit; The laser-assisted imaging unit corrects the reflectivity and image intensity of the original RGB image to obtain a filtered image, compensates for the attenuation of laser propagation in the steam environment in combination with the laser point cloud data, eliminates the influence of steam interference factors on the image and point cloud data, and obtains calibrated laser point cloud data; The multi-modal data fusion unit performs projection and matching, rigid body transformation solution and data fusion based on the filtered image and the calibrated laser point cloud data, precisely matches the visible light texture with the three-dimensional geometry, and outputs aligned fusion data.

4. The digital monitoring platform for gas drainers based on image recognition according to claim 3, wherein: The laser-assisted imaging unit inputs the original RGB image , performs reflectivity correction, and calculates the reflectivity of the metal surface for light rays at different incident angles according to the Fresnel formula ; ; Image intensity correction, for each pixel of the original image the brightness value is attenuated according to the formula: ； Obtain a filtered image , and then, combine the laser point cloud data , and according to the Beer-Lambert law, compensate for the attenuation of the laser propagation in the steam environment; Let the attenuation coefficient of the steam medium calibrated through experiments be denoted as , and let the path length of the laser penetrating the steam layer be denoted as . Then the calculation formula for the corrected point cloud data is as follows: ； Calculate each three-dimensional coordinate in the laser point cloud dataset through the point cloud data calculation formula, and restore the calibrated laser point cloud dataset under steam interference in it, and restore the calibrated laser point cloud dataset under steam interference .

5. The digital monitoring platform for gas drainers based on image recognition according to claim 4, wherein: The multimodal data fusion unit performs multimodal data fusion, and inputs the filtered images and the calibrated laser point cloud data set for projection and matching; Project the 3D point cloud feature points onto the 2D image plane through the camera projection function, and the camera projection function is expressed as: ； where u and v represent the pixel coordinates of the three-dimensional point P in the calibrated laser point cloud dataset projected onto the image plane, represents the camera focal length, is the camera optical center coordinate, which is determined by camera parameters, and then the 2D-3D feature point correspondence is established through KD-tree nearest neighbor search: ； Among them, represents the set of matching pairs, is the matching threshold, set to 1 pixel. Then, the rigid body transformation is solved, and the least squares optimization problem is constructed to solve the rotation matrix S and the translation vector L: ； Among them, represents the camera projection function, that is, the above projection formula, and is iteratively solved using the Levenberg-Marquardt algorithm to obtain the optimal and ; Data fusion generation, mapping the calibrated laser point cloud data to the image coordinate system: ； Fusing the RGB filtered image and the three-dimensional coordinates of the laser point cloud: ； Output aligned and fused data , to achieve an exact match between visible light texture and three-dimensional geometry.

6. The digital monitoring platform for gas drainers based on image recognition according to claim 5, characterized in that: The dynamic analysis module includes an analysis unit and a confidence calculation unit; The analysis unit uses transfer learning technology to pre-establish a lightweight ResNet-18 network pre-trained based on an industrial corrosion data set, and is used to extract the oxidation and crack texture features of the metal surface; Then input the aligned and fused data , the lightweight ResNet-18 network automatically extracts a 64-channel high-dimensional feature map, and uses the calibrated lidar point cloud dataset , calculates the surface curvature of each local area, generates a dynamic mask, marks the reflective interference pixels, and in the 64-channel feature map, forces the activation values of the areas covered by the mask to zero and shields them to generate a shielded 64-channel high-dimensional feature map; The confidence calculation unit flattens the masked 64-channel feature map into a feature vector, maps it to the defect category space through a fully connected layer, and outputs a confidence report based on the classification result.

7. The digital monitoring platform for gas drainers based on image recognition according to claim 1, characterized in that: The evolutionary decision-making module includes a corrosion quantification unit, a time-series corrosion rate modeling unit and a life prediction unit; The corrosion quantification unit analyzes and calculates the masked texture feature map and the calibrated laser point cloud data to obtain the corrosion depth and corrosion area of the gas drainer, and quantifies the corrosion degree of the equipment; The timing corrosion rate modeling unit analyzes and processes corrosion data at different time points, establishes a mathematical model, analyzes the variation law of the corrosion rate over time, and predicts the future corrosion development trend; The life prediction unit combines the corrosion quantification results, the corrosion rate variation trend, and the equipment design parameter factors, and outputs the most probable failure time point based on the current corrosion depth and rate, in combination with the material safety threshold set based on the material characteristics.

8. The digital monitoring platform for gas drainers based on image recognition according to claim 7, characterized in that: The corrosion quantification unit calculates the corrosion depth and the corrosion area; Let the texture feature map after shielding be H, and input the calibrated laser point cloud data ; Calculation of the rust area: Extract the maximum value in the channel dimension from the texture feature map H as the activation area, convert the actual physical area according to the ratio of pixels to area in the feature map, and output the proportion of the rust-covered area; Calculation of the proportion of rust pixels: Calibrate the laser point cloud data Align the calibrated laser point cloud data with the masked texture feature map H based on spatial coordinate mapping, compare the current point cloud height with the historical reference height, calculate the average depression depth, and output the average corrosion depth.

9. The digital monitoring platform for gas drainers based on image recognition according to claim 7, characterized in that: The specific modeling process of the timing corrosion rate modeling unit is as follows: Multi-scale feature fusion modeling, using an improved spatio-temporal attention LSTM network to simultaneously extract the short-term fluctuation features and long-term trend features of the corrosion data based on the historical data results, automatically allocate the weights of different time-scale features through the attention mechanism, establish a normal corrosion rate distribution space during the model training stage, and calculate the deviation degree of the real-time data through the Mahalanobis distance during online monitoring; Model dynamic update strategy, based on the recursive least squares algorithm with forgetting factor, dynamically adjusts the weights of historical data according to the real-time nature of the data, so that the model adapts to the change of the corrosion law caused by the aging of the material of the drainer.

10. The digital monitoring platform for gas drainers based on image recognition according to claim 1, characterized in that: The monitoring and display module includes a three-dimensional visualization interface, which displays the real-time state of the gas drainer in the form of a three-dimensional model, and the display includes the appearance of the equipment, the positions of key components, and the visual marks of the corrosion condition; At the same time, it also includes a data dashboard for real-time display of the corrosion depth, corrosion area, and corrosion rate parameters, and provides a historical data query function; in addition, it supports multi-terminal adaptation, and operators can view the monitoring information anytime and anywhere through computer, tablet, and mobile phone devices. It also has an abnormal state alarm function. When the equipment state is detected to be abnormal, it notifies the monitoring personnel through pop-up windows, sounds, and text messages.

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