Pipeline dredging system and method, equipment and medium

Abstract

The invention relates to the technical field of pipeline dredging, and provides a pipeline dredging system and method, equipment and a medium. In the scheme, firstly, an image sensor deployed on the inner wall of a pipeline collects high-definition image data in the pipeline in real time, and precise sensing of the internal state of the pipeline and disease positioning are achieved; secondly, the intelligent control module performs automatic identification and analysis on the image data by using a blockage assessment model constructed based on deep learning, accurately judges the blockage type, degree and position, and forms a scientific and objective blockage assessment result; and finally, a high-pressure cleaning assembly in the mechanical execution module automatically performs lossless cleaning on the blockages in the pipeline according to the evaluation result. According to the pipeline dredging device, undamaged cleaning is carried out on blockages in the pipeline in an integrated mode of automatic sensing, decision making and execution, and the dredging efficiency and accuracy are improved.

Description

Technical Field

[0001] This application relates to the field of pipeline dredging technology, and in particular to a pipeline dredging system, method, equipment, and medium. Background Technology

[0002] In everyday life scenarios such as bathroom maintenance, renovation of aging drainage systems, and medical cleaning, extending to industrial precision pipeline dredging and smart city utility tunnel operation and maintenance, covering a wide range of applications from micro to macro, pipeline inspection and maintenance have traditionally relied on manual inspections and mechanical dredging. However, these manual methods suffer from low inspection efficiency and an inability to accurately locate internal defects, while mechanical dredging often relies on experience, making it difficult to completely remove stubborn blockages and potentially damaging the pipe walls. With the aging of pipeline infrastructure and increasingly stringent environmental requirements, the aforementioned extensive manual operation mode can no longer meet the needs of modern, refined management. Therefore, there is an urgent need for an automated technology that can deeply integrate detection and unblocking functions to achieve accurate perception of the internal condition of pipelines, precise location of defects, and efficient and non-destructive cleaning of blockages, thereby significantly improving operation and maintenance efficiency and reducing safety risks and overall costs. Summary of the Invention

[0003] This application provides a pipeline dredging system, method, equipment, and medium to solve the problems of low inspection efficiency and inability to accurately locate internal defects when using manual methods for pipeline inspection and dredging, and the fact that mechanical dredging often relies on experience, making it difficult to completely remove stubborn blockages and potentially damaging the pipe wall.

[0004] The first aspect of this application provides a pipe unblocking system, which includes: a visual perception module, including an image sensor disposed on the inner wall of the pipe for acquiring image data of the pipe; an intelligent control module, including a blockage assessment model constructed using a deep learning method for identifying blockages in the image data to obtain a blockage assessment result within the pipe; and a mechanical execution module, including the pipe and a high-pressure cleaning component disposed therein, the high-pressure cleaning component for unblocking the pipe according to the blockage assessment result.

[0005] In some embodiments of this application, the blockage assessment model includes: a feature extraction unit, a 3D reconstruction unit, a spatial coverage calculation unit, and / or a depth impact calculation unit, and / or a structural risk calculation unit, and / or a material risk calculation unit, and a blockage assessment calculation unit, wherein: the feature extraction unit is used to extract features from image data to obtain image features; the 3D reconstruction unit is used to perform 3D reconstruction of the pipeline based on the image features to obtain 3D reconstruction data of the pipeline; the spatial coverage calculation unit is used to calculate the cross-sectional blockage rate of the pipeline based on the 3D reconstruction data; the depth impact calculation unit is used to calculate the blockage length of the pipeline based on the 3D reconstruction data corresponding to multiple frames of image data; the structural risk calculation unit is used to calculate the eccentricity of the blockage area of ​​the pipeline based on the 3D reconstruction data; the material risk calculation unit is used to calculate the material hardness category of the pipeline based on the 3D reconstruction data; and the blockage assessment calculation unit is used to calculate the blockage assessment result within the pipeline based on the cross-sectional blockage rate, and / or blockage length, and / or blockage area eccentricity, and / or material hardness category.

[0006] In some embodiments of this application, the blockage assessment calculation unit is configured to calculate the blockage assessment result in the pipeline in the following manner:

[0007] in, Cross-sectional blockage rate, For the length of the blockage, The length of the pipe, The eccentricity of the congested area, Where is the diameter of the pipe. According to the material hardness category, , , , All of these are preset weight parameters.

[0008] In some embodiments of this application, the high-pressure cleaning assembly includes a high-pressure pipe, a high-pressure pump, and a high-pressure nozzle, wherein: the high-pressure pump is used to convert normal pressure water into high-pressure water; the high-pressure pipe is used to connect the high-pressure pump and the high-pressure nozzle to transmit the high-pressure water to the high-pressure nozzle for spraying; the high-pressure nozzle includes one or more, rotatably disposed on the inner wall of the pipe, for spraying high-pressure water to unclog the pipe.

[0009] In some embodiments of this application, the blockage assessment model includes: a pipe diameter monitoring unit and an edge density monitoring unit, wherein: the pipe diameter monitoring unit is used to calculate the pipe diameter based on image data; the edge density monitoring unit is used to calculate the edge density of the inner wall of the pipe based on image data; and the blockage assessment calculation unit is configured to obtain the blockage assessment result in the pipe based on the pipe diameter and the edge density of the inner wall of the pipe.

[0010] In some embodiments of this application, the mechanical actuation module further includes a chemical solvent cleaning component for spraying chemical solvents onto the pipes. The chemical solvents can dissolve organic blockages in the pipes to unclog them.

[0011] In some embodiments of this application, the system further includes: a dredging scheme generation module, used to generate a dredging scheme for the pipeline based on the blockage assessment results, wherein the dredging scheme includes a dredging mode of activating a high-pressure cleaning component and / or activating a chemical solvent cleaning component to dredge the pipeline.

[0012] The second aspect of this application provides a pipe dredging method, which includes: acquiring image data of the pipe; calculating a blockage assessment result in the pipe based on the image data; and dredging the pipe based on the blockage assessment result.

[0013] A third aspect of this application provides an electronic device, which includes a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, it implements the steps of the method described in the second aspect.

[0014] A fourth aspect of this application provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the method described in the second aspect of the above embodiments.

[0015] This application has the following beneficial effects: This application proposes a pipe dredging solution. First, image sensors deployed on the inner wall of the pipe collect high-definition image data of the pipe's interior in real time, enabling precise perception of the pipe's internal condition and location of blockages. Second, an intelligent control module uses a blockage assessment model built based on deep learning to automatically identify and analyze the image data, accurately determining the type, degree, and location of the blockage, forming a scientific and objective blockage assessment result. Finally, a high-pressure cleaning component in the mechanical execution module automatically and non-destructively cleans the blockage within the pipe based on the assessment result. This integrated automated perception, decision-making, and execution method for non-destructive cleaning of blockages improves dredging efficiency and accuracy. Attached Figure Description

[0016] The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with this application and, together with the specification, serve to explain the technical solutions of this application.

[0017] Figure 1 This is a schematic diagram of the framework of the first embodiment of the pipe dredging system provided in this application; Figure 2This is a schematic diagram of the framework of the first embodiment of the congestion assessment model provided in this application; Figure 3 This is a schematic diagram of the framework of the second embodiment of the congestion assessment model provided in this application; Figure 4 This is a schematic diagram of the framework of the third embodiment of the congestion assessment model provided in this application; Figure 5 This is a flowchart illustrating an embodiment of the pipeline blockage determination scheme provided in this application; Figure 6 This is a schematic diagram of the framework of the second embodiment of the pipe dredging system provided in this application; Figure 7 This is a schematic diagram of the framework of the third embodiment of the pipe dredging system provided in this application; Figure 8 This is a flowchart illustrating an embodiment of the pipeline dredging strategy provided in this application; Figure 9 This is a schematic flowchart of the first embodiment of the pipe dredging method provided in this application; Figure 10 This is a schematic diagram of an example of the UI operation interface of the pipe dredging system provided in this application; Figure 11 This is an example schematic diagram of the metal protective sleeve for endoscopes provided in this application; Figure 12 This is an example schematic diagram of the metal clip for the endoscope and the high-pressure nozzle before the installation of the protective cover provided in this application; Figure 13 This is an example schematic diagram of the metal clip for the endoscope and the high-pressure nozzle after the protective cover is installed, as provided in this application; Figure 14 This is an example schematic diagram of a camera being placed into a standard conduit, as provided in this application; Figure 15 This is an example schematic diagram of the UI operation interface of the pipeline dredging system provided in this application displaying an uncalibrated error message; Figure 16 This is an example schematic diagram of the UI operation interface of the pipeline dredging system provided in this application displaying a calibration success message; Figure 17 This is a schematic diagram of the framework of an embodiment of the electronic device provided in this application; Figure 18 This is a schematic diagram of a framework of an embodiment of the computer-readable storage medium provided in this application. Detailed Implementation

[0018] The embodiments of this application will now be described in detail with reference to the accompanying drawings.

[0019] In the following description, specific details such as particular system architectures, interfaces, and technologies are presented for illustrative purposes rather than for limiting purposes, in order to provide a thorough understanding of this application.

[0020] In this document, the term "and / or" is merely a description of the relationship between related objects, indicating that three relationships can exist. For example, A and / or B can represent: A existing alone, A and B existing simultaneously, and B existing alone. Additionally, the character " / " generally indicates that the preceding and following related objects have an "or" relationship. Furthermore, "many" in this document means two or more. Moreover, the term "at least one" in this document means any combination of at least two of any one or more of a plurality of objects. For example, including at least one of A, B, and C can mean including any one or more elements selected from the set consisting of A, B, and C.

[0021] As described in the background section, existing pipeline inspection and dredging methods rely on manual methods, which result in low inspection efficiency, inability to accurately locate internal defects, and mechanical dredging often relies on experience, making it difficult to completely remove stubborn blockages and potentially damaging the pipe wall.

[0022] To address the aforementioned issues, this application proposes an automated pipe dredging solution. In this solution, the blockage in the pipe is cleaned non-destructively through an integrated automated sensing, decision-making, and execution process, thereby improving dredging efficiency and accuracy.

[0023] This application will now be described in detail with reference to the accompanying drawings and specific embodiments.

[0024] According to one embodiment of this application, a pipe unblocking system is proposed, such as... Figure 1 As shown, the system includes: a visual perception module, including an image sensor installed on the inner wall of the pipe for collecting image data of the pipe; an intelligent control module, including a blockage assessment model built using a deep learning method for identifying blockages in the image data to obtain a blockage assessment result within the pipe; and a mechanical execution module, including the pipe and a high-pressure cleaning component installed inside it, which is used to unclog the pipe according to the blockage assessment result.

[0025] Therefore, in the above embodiments, firstly, image sensors deployed on the inner wall of the pipe collect high-definition image data of the pipe's interior in real time, achieving accurate perception of the pipe's internal condition and location of defects; secondly, the intelligent control module uses a blockage assessment model built based on deep learning to automatically identify and analyze the image data, accurately determining the type, degree, and location of the blockage, forming a scientific and objective blockage assessment result; finally, the high-pressure cleaning component in the mechanical execution module automatically performs non-destructive cleaning of the blockage in the pipe based on the assessment result. This integrated automated perception, decision-making, and execution method for non-destructive cleaning of blockages in pipes improves both dredging efficiency and accuracy.

[0026] It should be noted that the automated pipe dredging solution of this application is applicable to pipe dredging of any size. The following explanation will elaborate on each module using small diameter pipes of 15mm to 110mm as an example.

[0027] I. Visual Perception Module This application addresses the blockage problem in 15mm-110mm diameter pipes in urban underground pipe networks. The visual perception unit employs fiber optic bundle coupling technology to perform optical-to-electrical-to-optical conversion between a front-end 4K@120fps CMOS image sensor and a back-end FPGA image processing platform. This ensures image transmission quality down to 50m while achieving a crack width measurement accuracy of ±0.1mm. A miniature CMOS camera is integrated into the pipe as the image sensor. The camera is a 5.5mm diameter 360° panoramic waterproof camera (1280×720 resolution@30fps).

[0028] In this embodiment, the parameters of the endoscope camera used are as follows: camera lens diameter: 5.5mm; camera focal length: 80mm; camera resolution: 4K high definition (pixels: ); Camera angle: 70°.

[0029] It should be noted that multiple image sensors can be installed inside the pipeline as needed to ensure that images of the pipeline's interior can be captured.

[0030] Therefore, the above embodiments of this application achieve high-quality optical-electrical-optical conversion by connecting the front-end high-resolution (4K@120fps) CMOS image sensor and the intelligent control module using fiber optic bundle coupling technology. This design ensures the stability and clarity of the image signal even at a transmission distance of up to 50m, and achieves a high-precision crack measurement level of ±0.1 mm. At the same time, the miniature 360° panoramic waterproof camera (only 5.5mm in diameter) integrated inside the pipe, with its compact structure and panoramic field of view, can capture the condition of the inner wall of the pipe without blind spots, providing reliable dual visual protection for accurate identification and assessment of pipe blockage and damage, and significantly improving the accuracy, reliability and applicability of the detection.

[0031] In this embodiment, given a camera focal length u = 80mm and a camera viewing angle θ = 70°, the horizontal field of view diameter of a single frame image is:

[0032] Considering the projection distortion of a circular pipe, the actual imaging range is:

[0033] in, The diameter of the pipe. The physical outer diameter of the miniature camera is given. The calculation results clearly demonstrate that a single frame of image data can cover the entire cross-section of pipes up to 110mm in diameter without any blind spots.

[0034] Therefore, the above embodiments of this application, through precise optical calculations (focal length 80mm, viewing angle 70°), show that the theoretical horizontal field of view diameter of a single frame image within a 110mm pipe is approximately 112mm. Further, after correcting for projection distortion by incorporating the camera's own diameter, the effective imaging range still reaches 111.2mm. This calculation result demonstrates that, under the current system configuration, a single shot from a single camera can achieve complete cross-sectional coverage of pipes with diameters of 110mm and below without any blind spots. This provides a crucial and comprehensive first-hand data foundation for subsequent 3D reconstruction and precise quantitative analysis, fundamentally ensuring the comprehensiveness and accuracy of the blockage assessment results.

[0035] In this embodiment, based on the camera resolution: A pixel can be calculated as follows:

[0036] Minimum recognizable feature size:

[0037] Near-end resolution:

[0038] At a maximum detection distance of 150mm, the far-end resolution is:

[0039] Therefore, the above embodiments of this application, by combining the high resolution (3840×2160) of the camera with the calculated field of view, quantify the imaging capability to the pixel level, resulting in a near-end resolution of approximately 29.17 micrometers / pixel. This enables the pipe unblocking system to clearly identify blockage features as small as 58.34 micrometers. More importantly, by using optical principles, it is calculated that the far-end resolution at the maximum detection distance of 150mm can still reach approximately 19 micrometers / pixel. This ensures the richness and consistency of image details throughout the detection range, providing extremely high accuracy for subsequent feature extraction and 3D reconstruction algorithms to identify minute initial blockages, accurately calculate the blockage rate, and analyze the texture and morphology of blockages. This enables early and precise quantitative diagnosis of the health status of the pipes.

[0040] In this embodiment, the image sensor or camera is configured to scan spirally inside the pipe. Rotation step angle:

[0041] Axial movement step size:

[0042] Therefore, the embodiments of this application, by precisely setting the rotation step angle to approximately 0.021° and the axial movement step size to 22.2 mm, construct a precise spiral scanning strategy. This strategy utilizes an extremely small rotation angle to ensure pixel-level continuous coverage between adjacent images in the circumferential direction, while controlling the axial step size to achieve a large-scale overlap of up to 80% between adjacent frames. This high-overlap, gapless scanning method not only completely eliminates detection blind spots, but more importantly, provides an extremely rich and reliable data foundation for subsequent high-precision image registration and 3D point cloud reconstruction, thereby ensuring the integrity and accuracy of the geometric details of the final generated 3D model of the pipe's interior.

[0043] II. Intelligent Control Module According to one embodiment of this application, such as Figure 2As shown, the blockage assessment model includes: a feature extraction unit, a 3D reconstruction unit, a spatial coverage calculation unit, and / or a depth impact calculation unit, and / or a structural risk calculation unit, and / or a material risk calculation unit, and a blockage assessment calculation unit. Specifically: the feature extraction unit extracts features from image data to obtain image features; the 3D reconstruction unit performs 3D reconstruction of the pipeline based on the image features to obtain 3D reconstruction data of the pipeline; the spatial coverage calculation unit calculates the cross-sectional blockage rate of the pipeline based on the 3D reconstruction data; the depth impact calculation unit calculates the blockage length of the pipeline based on multi-frame image data; the structural risk calculation unit calculates the eccentricity of the blockage area of ​​the pipeline based on the 3D reconstruction data; the material risk calculation unit calculates the material hardness category of the pipeline based on the 3D reconstruction data; and the blockage assessment calculation unit calculates the blockage assessment result within the pipeline based on the cross-sectional blockage rate, and / or blockage length, and / or blockage area eccentricity, and / or material hardness category.

[0044] Therefore, the above embodiments of this application, by constructing a blockage assessment model that includes feature extraction, three-dimensional reconstruction, and multi-dimensional quantitative analysis units, can accurately reconstruct the three-dimensional structure of the pipeline from image features, and on this basis, calculate accurate cross-sectional blockage rate, continuous blockage length, blockage location eccentricity, and pipe wall material hardness category, etc. This multi-parameter fusion assessment mechanism makes the final blockage assessment result no longer a single empirical judgment, but a scientific decision that integrates spatial coverage, depth of influence, structural imbalance risk, and material damage risk, thereby achieving a comprehensive, accurate, and quantitative assessment of the pipeline blockage state from geometric morphology to physical properties.

[0045] In addition, such as Figure 3 As shown, the blockage assessment model of this application also includes an image preprocessing unit for performing distortion correction and image enhancement processing on the image data.

[0046] Therefore, the blockage assessment model in the above embodiments of this application integrates an image preprocessing unit to perform image enhancement processing on the original image data, ensuring that the image data input to the subsequent analysis algorithm has higher geometric accuracy and visual quality, thereby directly improving the accuracy and robustness of blockage identification and quantitative analysis.

[0047] The following section details the functions performed by some units of the congestion assessment model.

[0048] (a) Image preprocessing unit According to one embodiment of this application, the image data includes the horizontal and vertical coordinate data of pixels, and the image preprocessing unit is configured to perform distortion correction and image enhancement processing on the image data in the following manner:

[0049]

[0050] in, The x-coordinate data of the pixels. The ordinate data of the pixels. The distance from a pixel to the center of the image. The corrected x-coordinate data of the pixels. The vertical coordinate data of the corrected pixel is given, and k1 and k2 are preset radial distortion coefficients.

[0051] Therefore, the above embodiments of this application utilize a radial distortion correction model to correct the image, effectively eliminating image distortion caused by the inherent characteristics of the camera's optical lens, restoring the image to a perspective relationship that conforms to the true geometric proportions, and laying a reliable geometric foundation for subsequent accurate size measurement and three-dimensional reconstruction.

[0052] According to one embodiment of this application, the image preprocessing unit is configured to perform motion blur compensation image enhancement processing on the image data in the following manner:

[0053] in, This refers to the camera's frame rate.

[0054] Therefore, the above embodiments of this application can effectively evaluate the maximum tolerable object movement speed of the camera at a specific frame rate and spatial resolution. By controlling the actual movement speed within this threshold, or by adjusting the camera exposure time, frame rate and imaging parameters accordingly, motion blur caused by rapid object movement can be significantly reduced, thereby improving image clarity and detail retention.

[0055] (ii) Spatial Coverage Calculation Unit According to one embodiment of this application, the spatial coverage calculation unit is configured to process three-dimensional reconstruction data using an image segmentation method to obtain the blocked area of ​​the pipe, and calculate the cross-sectional blockage rate in the following manner:

[0056]

[0057] in, The area of ​​the congested region. This represents the cross-sectional area of ​​the pipe.

[0058] Therefore, the above embodiments of this application accurately identify and extract the blocked areas in the three-dimensional reconstruction data through image segmentation technology, and then use the intuitive and quantitative indicator of cross-sectional blockage rate for evaluation, transforming the complex spatial blockage situation into an accurate cross-sectional area ratio, realizing an objective and quantitative measurement of the severity of blockage, and effectively avoiding the error of subjective human evaluation.

[0059] (III) Calculation Unit for In-Depth Impact According to one embodiment of this application, the depth impact calculation unit is configured to calculate the blockage length of the pipe in the following manner:

[0060] in, L is the step size of the camera when capturing images (80mm in this application).

[0061] Therefore, the above embodiments of this application use the known movement step length of the camera in the pipeline as a spatial scale and the continuous frame image matching technology to calculate the longitudinal displacement of the blockage, transforming one-dimensional linear movement into a precise measurement of the actual length of the blockage in three-dimensional space, thus realizing a quantitative assessment of the longitudinal extension range of the blockage area. At the same time, this also makes up for the deficiency that the blockage rate of a single cross-section cannot reflect the overall scale of the blockage, and can distinguish between local point blockage and linear blockage with a wider impact, thereby providing a basis for judging the severity of the blockage, estimating the amount of dredging work, and formulating accurate maintenance plans.

[0062] (iv) Structural Risk Calculation Unit According to one embodiment of this application, the structural risk calculation unit is configured to calculate the eccentricity of the blockage area in the following manner: calculate the distance between the center of the pipeline and the centroid of the blockage area, and use this distance as the eccentricity of the blockage area.

[0063] Therefore, the above embodiments of this application quantify the eccentricity by directly calculating the spatial distance between the geometric center of the pipeline and the centroid of the blockage area, simplifying the complex spatial distribution problem into an intuitive and clear geometric metric. This enables the evaluation standard to shift from qualitative description to quantitative analysis. This value can accurately reflect the degree of deviation of the blockage within the pipeline cross-section, which is crucial for determining whether the blockage may lead to flow deviation, local scouring, or structural eccentric wear. Thus, it provides key data support for predicting pipeline life and assessing blockage, effectively improving the scientificity and accuracy of maintenance decisions.

[0064] (v) Material Risk Calculation Unit According to one embodiment of this application, the material risk calculation unit is configured to: extract the texture features of the pipeline based on the three-dimensional reconstruction data, and calculate the material hardness category based on the texture features using a pre-trained material hardness classification model.

[0065] Therefore, the above embodiments of this application extract the texture features of the inner wall of the pipe through three-dimensional reconstruction data and use a pre-trained model to identify the material hardness, thereby achieving non-contact, refined, and quantitative assessment of the aging and wear state of the pipe material, so as to achieve non-destructive unblocking of the pipe.

[0066] (vi) Congestion Assessment Calculation Unit According to one embodiment of this application, the blockage assessment calculation unit is configured to calculate the blockage assessment result in the pipeline in the following manner:

[0067] in, Cross-sectional blockage rate, For the length of the blockage, The length of the pipe, The eccentricity of the congested area, The diameter of the pipe. According to the material hardness category, , , , All of these are preset weight parameters. The four weight parameters are set to values ​​of 0.4, 0.3, 0.2, and 0.1, respectively.

[0068] Therefore, the above embodiments of this application collect multi-dimensional data in real time for intelligent analysis. This multi-parameter fusion evaluation mechanism makes the final blockage assessment result no longer a single experience judgment, but a scientific decision that integrates spatial coverage, depth of influence, structural imbalance risk and material damage risk. This achieves a comprehensive, accurate and quantitative assessment of the pipe blockage status from geometric shape to physical properties, generating accurate and reliable pipe unblocking solutions for operators.

[0069] In this embodiment, as Figure 4 As shown, the blockage assessment model of this application includes: a pipe diameter monitoring unit for calculating the pipe diameter based on image data; an edge density monitoring unit for calculating the edge density of the inner wall of the pipe based on image data; and a blockage assessment calculation unit configured to obtain the blockage assessment result in the pipe based on the pipe diameter and the edge density of the inner wall of the pipe.

[0070] (vii) Pipeline diameter monitoring unit The pipe diameter parameter is set for real-time monitoring by the camera. The actual inner diameter of the pipe is d. Because the system needs to be calibrated using standard components before measurement, there is... inner diameter change rate .when At this time, the system determines that the pipe is slightly blocked, at which point there is localized sediment deposited in the pipe, and the water flow resistance increases slightly; when When the system determines that the pipeline is moderately blocked, the pipeline wall narrows significantly and the pressure difference increases by 30% to 100%; At this point, the system determines that the pipeline is severely blocked, almost completely blocked, causing a sharp increase in pressure differential and a sudden drop in flow rate.

[0071] (viii) Edge density monitoring unit Let the density at the edge of the pipe's inner wall be... The total number of pixels in the image data is If the edge pixel is P, then: .

[0072] when When the system detects a severe blockage, high density indicates a large amount of deposits adhering to the inner wall of the pipe; when At that time, the system determined it to be a moderate blockage; when At that time, the system determined it to be a minor blockage.

[0073] In this embodiment, as Figure 5 As shown, after calculating the pipe diameter, the system determines whether the pipe diameter is less than 50mm. If so, the blockage assessment result is severe blockage; otherwise, it determines whether the pipe diameter is less than 80mm. If so, it determines whether the edge density is greater than 0.25. If so, the blockage assessment result is moderate blockage; otherwise, it is mild blockage. Alternatively, it determines whether the edge density is greater than 0.4. If so, the blockage assessment result is severe blockage; otherwise, it determines whether the edge density is greater than 0.25. If so, the blockage assessment result is moderate blockage; otherwise, it is mild blockage.

[0074] Therefore, this application constructs an intelligent blockage assessment model based on multi-dimensional visual parameters by integrating pipe diameter monitoring and inner wall edge density analysis. This model not only directly quantifies the physical narrowing of the pipe through real-time pipe diameter measurement, but also introduces edge density analysis to accurately capture the adhesion and distribution characteristics of deposits on the inner wall. This dual-parameter fusion judgment mechanism overcomes the limitation that relying solely on pipe diameter changes may ignore early soft adhesion blockages, and achieves accurate, early identification and classification (mild, moderate, severe) of the entire spectrum of blockage states, from local adhesion to severe narrowing. This provides reliable data support for the accurate formulation of subsequent dredging strategies (such as high-pressure water pressure selection and chemical solvent activation decisions), greatly improving the intelligence level and processing efficiency of dredging operations.

[0075] In summary, this application constructs a multi-level, multi-parameter blockage assessment model. The system not only achieves rapid screening of blockage status through basic pipe diameter measurement and edge density analysis, but also introduces a spatial structure analysis dimension based on three-dimensional reconstruction. By accurately calculating key indicators such as cross-sectional blockage rate, blockage depth length, blockage eccentricity, and pipe material hardness, it achieves in-depth quantitative assessment of the spatial distribution, volume scale, structural stability, and potential mechanical risks to the pipeline caused by the blockage. This comprehensive diagnostic system, which goes from the surface to the core, from two-dimensional to three-dimensional, and from geometric morphology to material properties, greatly improves the comprehensiveness, accuracy, and predictability of blockage assessment. It can provide a more scientific and reliable decision-making basis for the selection of dredging operation strategies (such as mechanical force, construction sequence, and safety warning), thereby significantly enhancing the intelligence level and engineering safety of the entire pipeline dredging system.

[0076] III. Mechanical Actuation Module Furthermore, the inventors discovered through research that there is a technological gap and industry gap in intelligent equipment for small-diameter (≤110mm) pipe dredging. The industry still relies on subjective experience for assessment, lacks quantitative standards, and is prone to damaging the pipe wall. Big data statistics are shown in Table 1 below. Traditional dredging tools expose multiple technical defects in small-diameter pipe operations: First, the mechanical compatibility between rigid dredging rods and flexible pipe walls is insufficient. A typical case shows that in dredging operations of Φ80mm cast iron pipes, when the lateral amplitude of the dredging head exceeds 15% of the pipe diameter, it causes the inner anti-corrosion layer to peel off. Second, the dredging process lacks quantitative monitoring parameters. Data from municipal pipeline maintenance in 2019 shows that the proportion of structural damage in dredging operations of pipes with diameters ≤110mm is as high as 23.7%, 18.2 percentage points higher than that of medium and large-diameter pipes. Third, existing equipment struggles to integrate miniature image sensors. Industry research indicates that the market penetration rate of pipe inspection cameras with diameters below 90mm is less than 7.8%, mainly limited by technical bottlenecks such as the size of the power supply module and the stability of image transmission. This technological lag directly leads to operators relying on experience to judge the effectiveness of dredging. In the statistics of pipeline maintenance accidents from 2018 to 2022 by the Ministry of Housing and Urban-Rural Development, 41.3% of pipeline rupture accidents were caused by non-visual dredging operation errors.

[0077] Table 1. Technical Defects of Traditional Pipeline Cleaning Tools for Small Diameters

[0078] Therefore, according to one embodiment of this application, such as Figure 6 As shown, the high-pressure cleaning assembly includes a high-pressure pipe, a high-pressure pump, and a high-pressure nozzle. The high-pressure pump is used to convert normal-pressure water into high-pressure water. The high-pressure pipe is used to connect the high-pressure pump and the high-pressure nozzle to transmit the high-pressure water to the high-pressure nozzle for spraying. The high-pressure nozzle includes one or more nozzles, which are rotatably disposed on the inner wall of the pipe and are used to spray high-pressure water to unclog the pipe.

[0079] Therefore, the above embodiments of this application, by adopting a rotatable high-pressure nozzle design, address the technical bottleneck of unblocking small-diameter pipes by replacing traditional mechanical unblocking methods with non-contact high-pressure water jets. This fundamentally avoids direct scraping and structural damage to the pipe wall by rigid tools. The rotating nozzle can achieve 360-degree uniform flushing, effectively removing various blockages while ensuring the uniformity of force distribution, preventing damage to the anti-corrosion layer or pipe wall caused by local stress concentration.

[0080] According to one embodiment of this application, the high-pressure cleaning assembly can also be used to connect chemical solvents to spray chemical solvents into the pipeline.

[0081] According to one embodiment of this application, the mechanical actuation module further includes a chemical solvent cleaning component for spraying chemical solvents onto the pipes. The chemical solvents can dissolve organic blockages in the pipes to unclog them.

[0082] Therefore, the above embodiments of this application construct a dual-mode unblocking mechanism of "physical high-pressure water flushing + chemical solvent dissolution". For stubborn organic blockages (such as grease and biological slime) that are difficult to effectively deal with by traditional single mechanical unblocking methods, chemical solvents can achieve targeted decomposition, significantly improving unblocking efficiency and thoroughness. This synergistic effect avoids the potential damage to the pipe wall caused by pure mechanical force and overcomes the limitations of chemical methods on inorganic hard blockages. It realizes adaptive cleaning of different types of blockages, thereby expanding the scope of unblocking application and effectively reducing the risk of secondary damage to pipelines. It provides a safer and more efficient comprehensive solution for pipeline maintenance under complex working conditions.

[0083] Among them, such as Figure 6 As shown, the mechanical actuation module of this application also includes a diesel engine for providing a power source for the operation of other components of the mechanical actuation module.

[0084] In this embodiment, the diesel engine is a single-cylinder or twin-cylinder air-cooled diesel engine with a power range of 10HP-78HP; the pressure range of the high-pressure pump is 0-25MPa, supporting stepless pressure adjustment to adapt to different blockages, and the flow rate is 40L / min-160L / min; the high-pressure output section includes a high-pressure pipe and a high-pressure nozzle, the high-pressure pipe has a pressure resistance value of ≥25MPa, and is equipped with an automatic coil locking device for easy deployment and retraction; the high-pressure nozzle has designs such as a rat-head nozzle, a rear six-hole nozzle, or a front six-hole nozzle with a rear one-hole nozzle, suitable for different blockages (such as grease, tree roots, and cement blocks).

[0085] IV. Dredging Plan Generation Module According to one embodiment of this application, such as Figure 7As shown, the system of this application also includes: a dredging scheme generation module, used to generate a dredging scheme for the pipeline based on the blockage assessment results, wherein the dredging scheme includes a dredging mode of activating a high-pressure cleaning component and / or activating a chemical solvent cleaning component to dredge the pipeline.

[0086] Therefore, the intelligent control module uses real-time image transmission technology (delay < 0.3s) to perform preliminary measurement of the pipe's inner diameter. Based on the pipe material (PVC / cast iron / concrete) and the blockage situation, the unblocking solution generation module automatically provides matching prompts for unblocking parameters, ensuring zero secondary damage to the pipe during construction.

[0087] In this embodiment, the dredging plan includes a recommended pressure threshold, operation time, and dredging mode. Operators can select the optimal dredging strategy based on the pipeline dredging system recommendations of this application and the actual working conditions. After a preset time period N, the system initiates a secondary diagnostic mode to dynamically optimize the combination of dredging parameters (including pressure regulation coefficients and timing control schemes). This iterative optimization process continues until the preset dredging standard is reached.

[0088] Therefore, the embodiments of this application comprehensively utilize multiple unblocking mechanisms to adapt to unblocking needs under different pipe diameters and complex working conditions. In the mechanical execution module and the unblocking scheme generation module, a high-pressure pump is used to construct an industrial-grade fluid power system. Through precisely controlled water hammer effect, a dynamic impact load of ≥15MPa is generated, which can quickly disintegrate short-term physical blockages in the pipe, meeting immediate unblocking needs. The chemical solvent cleaning component uses a multi-component alkaline hydrolysis system. A specific concentration of organic solvent is delivered to the core blockage area through targeted injection of chemical solvent. In a strongly alkaline environment with pH ≥12, the accumulated oil and esters undergo saponification, and the peptide bonds of keratin substances are broken, effectively solving the problem of stubborn organic blockages that are difficult to remove with traditional mechanical unblocking methods.

[0089] For example, such as Figure 8 As shown, the process of the pipe dredging scheme in this application is as follows: First, turn on the camera and collect image data inside the pipe; Second, calculate the current pipe diameter based on the image data and use the blockage assessment model to assess the blockage status inside the pipe; Third, determine the pipe dredging method based on the assessment results, which are physical dredging (high-pressure water spraying) and chemical dredging (spraying chemical solutions).

[0090] In addition, this application proposes a pipe dredging method, which includes: acquiring image data of the pipe; calculating a blockage assessment result in the pipe based on the image data; and dredging the pipe based on the blockage assessment result.

[0091] In one embodiment of this application, such as Figure 9As shown, the pipe dredging method of this application further includes: performing distortion correction and motion blur compensation processing on the image data; extracting features from the processed image data; reconstructing the pipe in three dimensions based on the extracted features; evaluating pipe blockage using the three-dimensional reconstructed image; and providing a dredging solution based on the evaluation results.

[0092] Therefore, this application embodiment relies on real-time image data of the inside of the pipeline collected by high-definition industrial endoscope camera equipment to construct a multi-dimensional pipeline blockage assessment model. This model constructs a multi-dimensional quantitative index system from geometric coverage, resolution analysis, depth positioning, and blockage degree. At the same time, through a closed-loop process of physical resolution calculation → image enhancement and quantification → dynamic three-dimensional reconstruction → intelligent classification → risk assessment, it achieves accurate assessment of small-diameter pipe blockage.

[0093] This application achieves a breakthrough upgrade of traditional operating modes through multi-dimensional technology, with its core features reflected in three aspects: First, it adopts a flexible articulated mechanical structure, using distributed pressure sensors to monitor the contact stress between the rod and the pipe wall in real time, controlling the lateral amplitude to within 5% of the pipe diameter (67% lower than traditional tools), effectively solving the problem of corrosion layer damage in Φ80-110mm pipes; Second, it integrates a miniature high-frame-rate industrial endoscope (only 5.5mm in diameter) with an AI image recognition module, which can automatically calibrate key parameters such as pipe wall cracks and deposit thickness, and actual tests have shown that the accuracy of structural damage identification can be improved to 98.6%; Finally, it constructs a human-machine collaborative intelligent operating system, equipped with a dual-mode display terminal (AR glasses + handheld Pad), and realizes three-dimensional modeling of the operation trajectory and risk warning through an IoT platform, reducing unplanned downtime by 42% in a pilot application in 2024.

[0094] To verify the effectiveness of the above-described embodiments of this application, the inventors conducted the following experiments: I. Small-diameter visual pipe cleaning machine system software (I) Introduction to the main functions of the system program The system program has an automatic calibration function upon startup, used to calibrate measurement data standards; the ability to capture on-site images and record videos at any time; the ability to predict pipeline blockage status; the ability to predict the effective diameter of the pipeline in real time; and other optional functions.

[0095] (ii) Automatic calibration function upon power-on The core control system program is developed using Python, deeply integrating pipeline dredging control algorithms and image processing software architecture. This system utilizes an industrial-grade high-definition pipeline endoscope camera as the video acquisition terminal. Through intelligent analysis of real-time captured images of the pipeline's inner wall, it achieves dynamic prediction of pipe diameter parameters. During system initialization, a mandatory calibration procedure is required, using a 10mm standard pipe as the benchmark test unit to complete the camera parameter calibration. If the standard pipe calibration procedure is not followed, the system will automatically terminate the pipe diameter prediction function and issue a warning. If the system calibration procedure is not strictly performed using a 10mm standard pipe diameter during initial operation, the key parameter acquisition system will be unable to accurately characterize the pipeline blockage status. Based on laboratory multi-condition testing and verification, after standardized calibration, the overall error band for pipes with diameters of 110mm and below can be stably controlled within the ±2% threshold range.

[0096] (iii) Real-time capture / video recording function After starting the test program, the system can monitor the internal state of the pipeline in real time using the camera, such as... Figure 10 As shown, the UI interface of the pipe dredging system in this application autonomously collects images, and the static images or dynamic video data captured by the high-precision sensor will be automatically stored in the specified directory "pipe_records".

[0097] (iv) Real-time pipeline status analysis and early warning function During real-time monitoring, the system dynamically captures pipe cross-sectional data using cameras, accurately measures the current inner diameter of the pipe through image processing technology, and performs real-time analysis of pipe diameter parameters based on intelligent algorithms. Simultaneously, it combines this with the Canny edge detection algorithm to analyze the roughness of the pipe's inner wall, comprehensively and accurately predicting the pipe blockage risk level. The system employs a three-level early warning mechanism, scientifically classifying the degree of blockage into three levels: "mild blockage," "moderate blockage," and "severe blockage."

[0098] (v) Other functions Based on the analysis of on-site operating parameters and the information provided by the intelligent system, the system operator can independently decide on and select the appropriate pipe dredging solution. Physical dredging technology is based on a comprehensive analysis of the material and morphology of the pipe blockage. It uses high-pressure water pressure to perform targeted clearing operations, offering the technical advantage of rapidly breaking down solid blockages. Chemical dredging technology, on the other hand, relies on the potent reactive properties of specialized chemical agents and is particularly suitable for scenarios involving organic pollutant blockages. When heavy grease buildup is confirmed in the pipe, the operator can prioritize physical dredging to remove the surface blockage, followed by precise application of a loosening grease-decomposing agent, creating a synergistic physical and chemical dredging effect.

[0099] II. Hardware Assembly of Small-Diameter Visual Pipe Dredging Machine (I) Key Hardware Components The system's core components consist of three main parts: a mechanical actuation module, a visual perception module, and an intelligent control module. The mechanical actuation module is equipped with a twin-cylinder, air-cooled diesel engine with a power range of 10HP-78HP; the high-pressure pump set has a pressure regulation accuracy of ±0.2MPa and supports stepless pressure adjustment to adapt to different blockages; the visual perception module integrates a 2-megapixel CMOS sensor, supporting real-time transmission of H.265 encoded 4K images. The visual perception module uses the USB 3.0 high-speed communication protocol to build a data transmission channel, enabling real-time information interaction with PC terminals and mobile devices. Its integrated intelligent control module can automatically generate intelligent predictive reports on pipeline health status through multi-dimensional data analysis.

[0100] (II) System Assembly Considering that high-pressure nozzles may experience uncontrollable vibrations or unavoidable collisions with the pipe wall during water spraying operations inside pipelines, to protect the physical structure and critical components of the high-definition endoscope lens, such as... Figure 11 As shown, a metal protective cover was added to the exterior of the endoscope during this assembly. The cover provides metal protection. Figure 12 and 13 As shown, the endoscope with the protective cover installed is secured to the high-pressure nozzle with metal clips.

[0101] (III) Testing To ensure the system can perform relatively accurate data prediction tests on the pipelines to be cleared, a "calibration procedure" is specifically set up before the system officially starts operating. Specifically, a standard pipe with a diameter of 10mm is selected as the original data reference for system calibration. After calibration, on-site testing is conducted. To ensure the stability of the test data, the system requires calibration every time it is started.

[0102] 1. Calibration Test like Figure 14 As shown, a miniature high-definition camera with a diameter of 5.5mm is placed inside a standard straight pipe with an inner diameter of 10mm, ensuring that the camera maintains a certain angle with the inner wall of the pipe. Then, the camera's supplementary lighting settings are manually adjusted, and the other end of the pipe is sealed. The camera is connected to a PC, and the "Small Diameter Pipe Cleaning Machine" system software is launched. When the system interface displays "Calibration Successful," the system is ready for use. Figure 15 As shown, during the calibration process, if the camera is not correctly placed into the standard component for calibration according to the specifications, or if the camera is not placed into the calibration standard component at the specified angle, the system will automatically pop up an error alarm. Figure 16 As shown, after placing the camera into the standard part at the specified angle, click the "Recalibrate" button, and the system will recalibrate. If the calibration is successful, it will display "System calibration successful".

[0103] 2. Debugging After the system was calibrated, the camera was coupled with the high-pressure cleaning and chemical solvent cleaning components, and field tests were conducted. Field verification confirmed that the system fully met the design requirements.

[0104] Laboratory simulation tests of the prototype showed that in 30mm cast iron pipes, the average unblocking efficiency for typical blockages such as grease buildup (thickness ≤15mm), tree root intrusion (diameter ≤8mm), and construction waste reached 92.7%, which is 41% higher than traditional unblocking equipment.

[0105] In summary, the miniature diameter visual pipe cleaning system described in the above embodiments of this application integrates a miniature high-definition camera, enabling real-time display of the pipe's internal condition and effectively solving the problem of blind operation during traditional cleaning processes. The device combines deep learning algorithms to automatically identify the type of blockage (such as grease, tree roots, foreign objects, etc.) and its specific location, thereby assisting in formulating precise cleaning strategies. Furthermore, the integration of dynamic monitoring via video sensors and AI algorithms significantly improves the device's adaptability to complex environments. The system integrates multiple functions such as high-pressure flushing and chemical dissolution (using environmentally friendly reagents), avoiding secondary damage that may be caused by traditional rigid tools. It is suitable for pipe cleaning in narrow scenarios such as household drain pipes and air conditioner condensate pipes, as well as in places with high cleanliness requirements such as hospitals and laboratories.

[0106] Based on the inventive concept of the above embodiments, this application also provides a smartwatch, including a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, it implements the steps of the method described in the above embodiments. The following is in conjunction with... Figure 17 Please provide a detailed explanation.

[0107] like Figure 17 As shown, it illustrates the electronic device 100 of this application, which may specifically include a processor 110 and a memory 120. The memory 120 is coupled to the processor 110.

[0108] Processor 110 is used to control the operation of electronic devices. Processor 110 may also be referred to as a CPU (Central Processing Unit). Processor 110 may be an integrated circuit chip with signal processing capabilities. Processor 110 may also be a general-purpose processor, digital signal processor (DSP), application-specific integrated circuit (ASIC), field-programmable gate array (FPGA), or other programmable logic device, discrete gate or transistor logic device, or discrete hardware component. The general-purpose processor may be a microprocessor, or processor 110 may be any conventional processor.

[0109] The memory 120 is used to store computer programs and may be RAM, ROM, or other types of storage terminals. Specifically, the memory 120 may include one or more computer-readable storage media, which may be non-transitory or transient. The memory 120 may also include high-speed random access memory and non-volatile memory, such as one or more disk storage terminals or flash memory terminals. In some embodiments, the non-transitory computer-readable storage media in the memory 120 is used to store at least one line of program code.

[0110] The processor 110 is used to execute computer programs stored in the memory 120 to implement the methods described in the various method embodiments of this application.

[0111] In some embodiments, the electronic device may further include a peripheral device interface 130 and at least one peripheral terminal. The processor 110, memory 120, and peripheral device interface 130 may be connected via a bus or signal line. Each peripheral terminal may be connected to the peripheral device interface 130 via a bus, signal line, or circuit board. Specifically, the peripheral terminal includes at least one of a radio frequency circuit 140, a display screen 150, an audio circuit 160, and a power supply 170.

[0112] Peripheral interface 130 can be used to connect at least one I / O (Input / output) related peripheral terminal to processor 110 and memory 120. In some embodiments, processor 110, memory 120 and peripheral interface 130 are integrated on the same chip or circuit board; in some other embodiments, any one or two of processor 110, memory 120 and peripheral interface 130 can be implemented on separate chips or circuit boards, which is not limited in this embodiment.

[0113] The radio frequency (RF) circuit 140 is used to receive and transmit RF (Radio Frequency) signals, also known as electromagnetic signals. The RF circuit 140 communicates with communication networks and other IoT devices via electromagnetic signals; it is the communication circuit of the electronic device. The RF circuit 140 converts electrical signals into electromagnetic signals for transmission, or converts received electromagnetic signals back into electrical signals. Optionally, the RF circuit 140 includes: an antenna system, an RF transceiver, one or more amplifiers, a tuner, an oscillator, a digital signal processor, a codec chipset, an operator identification module card, etc. The RF circuit 140 can communicate with other terminals through at least one wireless communication protocol. This wireless communication protocol includes, but is not limited to: the World Wide Web, metropolitan area networks, intranets, various generations of mobile communication networks (2G, 3G, 4G, and 5G), wireless local area networks, and / or WiFi (Wireless Fidelity) networks. In some embodiments, the RF circuit 140 may also include circuitry related to NFC (Near Field Communication), which is not limited in this application.

[0114] Display screen 150 is used to display a UI (User Interface). This UI may include graphics, text, icons, videos, and any combination thereof. When display screen 150 is a touch display screen, it also has the ability to collect touch signals on or above its surface. These touch signals can be input as control signals to processor 110 for processing. In this case, display screen 150 can also be used to provide virtual buttons and / or a virtual keyboard, also known as soft buttons and / or a soft keyboard. In some embodiments, there may be one display screen 150, located on the front panel of the electronic device; in other embodiments, there may be at least two display screens, located on different surfaces of the electronic device or in a folded design; in still other embodiments, display screen 150 may be a flexible display screen, located on a curved or folded surface of the electronic device. Furthermore, display screen 150 may be configured as a non-rectangular, irregular shape, i.e., a non-rectangular screen. Display screen 150 may be made of materials such as LCD (Liquid Crystal Display) or OLED (Organic Light-Emitting Diode).

[0115] The audio circuit 160 may include a microphone and a speaker. The microphone is used to collect sound waves from the operator and the environment, converting the sound waves into electrical signals that are input to the processor 110 for processing, or input to the radio frequency circuit 140 for voice communication. For stereo sound acquisition or noise reduction purposes, multiple microphones may be used, each positioned in a different part of the electronic device. The microphone may also be an array microphone or an omnidirectional microphone. The speaker is used to convert electrical signals from the processor 110 or the radio frequency circuit 140 into sound waves. The speaker may be a conventional film speaker or a piezoelectric ceramic speaker. When the speaker is a piezoelectric ceramic speaker, it can convert electrical signals not only into audible sound waves but also into inaudible sound waves for purposes such as distance measurement. In some embodiments, the audio circuit 160 may also include a headphone jack.

[0116] Power supply 170 is used to supply power to various components in an electronic device. Power supply 170 can be alternating current, direct current, a disposable battery, or a rechargeable battery. When power supply 170 includes a rechargeable battery, the rechargeable battery can be a wired rechargeable battery or a wireless rechargeable battery. A wired rechargeable battery is a battery that is charged via a wired line, while a wireless rechargeable battery is a battery that is charged via a wireless coil. The rechargeable battery can also be used to support fast charging technology.

[0117] For a detailed description of the functions and execution processes of each functional module or component in the smartwatch embodiments of this application, please refer to the descriptions in the above-mentioned method embodiments of this application, which will not be repeated here.

[0118] In the embodiments provided in this application, it should be understood that the disclosed electronic devices and methods can be implemented in other ways. For example, the embodiments of the electronic devices described above are merely illustrative. For instance, the division of modules or units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some data may be ignored or not executed. Furthermore, the mutual coupling or direct coupling or communication connection shown or discussed may be through some interfaces; the indirect coupling or communication connection of systems or units may be electrical, mechanical, or other forms.

[0119] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment, depending on actual needs.

[0120] Furthermore, the functional units in the various embodiments of this application can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit.

[0121] Based on the inventive concept of the above embodiments, this application also provides a computer-readable storage medium storing a computer program, wherein the computer program, when executed by a processor, performs the steps of the method described in any of the above embodiments. The following is in conjunction with... Figure 18 This describes the execution process of the above embodiments on a computer-readable storage medium.

[0122] like Figure 18 As shown, it illustrates the computer-readable storage medium of this application. The integrated units described above, if implemented as software functional units and sold or used as independent products, can be stored in the computer-readable storage medium 200. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions / computer programs to cause an Internet of Things device (which may be a personal computer, server, or network terminal, etc.) or processor to execute all or part of the steps of the methods of various embodiments of this application. The aforementioned storage medium includes various media such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks, as well as electronic terminals such as computers, mobile phones, laptops, tablets, and cameras that have the aforementioned storage media.

[0123] The execution process of program data in a computer-readable storage medium can be described with reference to the above-described method embodiments of this application, and will not be repeated here.

[0124] The above description is merely an embodiment of this application and does not limit the patent scope of this application. Any equivalent structural or procedural transformations made using the content of this application's specification and drawings, or direct or indirect applications in other related technical fields, are similarly included within the patent protection scope of this application.

[0125] Those skilled in the art will understand that, in the above-described method of the specific implementation, the order in which each step is written does not imply a strict execution order and does not constitute any limitation on the implementation process. The specific execution order of each step should be determined by its function and possible internal logic.

Claims

1. A pipe dredging system, characterized in that, The system includes: A visual perception module includes an image sensor, which is disposed on the inner wall of the pipe and is used to acquire image data of the pipe; The intelligent control module includes a congestion assessment model built using a deep learning method, which is used to identify congestion in the image data to obtain a congestion assessment result in the pipeline. The mechanical actuation module includes the pipe and a high-pressure cleaning component disposed therein, the high-pressure cleaning component being used to unclog the pipe based on the blockage assessment results.

2. The pipe dredging system according to claim 1, characterized in that, The blockage assessment model includes: a feature extraction unit, a 3D reconstruction unit, a spatial coverage calculation unit, and / or a depth impact calculation unit, and / or a structural risk calculation unit, and / or a material risk calculation unit, and a blockage assessment calculation unit, wherein: The feature extraction unit is used to extract features from the image data to obtain image features; The three-dimensional reconstruction unit is used to perform three-dimensional reconstruction of the pipeline based on the image features to obtain the three-dimensional reconstruction data of the pipeline; The spatial coverage calculation unit is used to calculate the cross-sectional blockage rate of the pipeline based on the three-dimensional reconstruction data; The depth impact calculation unit is used to calculate the blockage length of the pipeline based on multiple frames of image data; The structural risk calculation unit is used to calculate the eccentricity of the blockage area of ​​the pipeline based on the three-dimensional reconstruction data; The material risk calculation unit is used to calculate the material hardness category of the pipe based on the three-dimensional reconstruction data; The blockage assessment calculation unit is used to calculate the blockage assessment result in the pipeline based on the cross-sectional blockage rate, and / or the blockage length, and / or the blockage area eccentricity, and / or the material hardness category.

3. The pipe dredging system according to claim 2, characterized in that, The blockage assessment calculation unit is configured to calculate the blockage assessment result in the pipeline in the following manner: in, The cross-sectional blockage rate is... The length of the blockage. The length of the pipe, The eccentricity of the blockage area is... Where is the diameter of the pipe. The material hardness category, , , , All of these are preset weight parameters.

4. The pipe dredging system according to claim 2, characterized in that, The blockage assessment model includes: a pipe diameter monitoring unit and an edge density monitoring unit, wherein: The pipe diameter monitoring unit is used to calculate the pipe diameter based on image data; The edge density monitoring unit is used to calculate the edge density of the inner wall of the pipe based on image data; The blockage assessment calculation unit is configured to obtain the blockage assessment result inside the pipe based on the pipe diameter and the density of the inner wall edge of the pipe.

5. The pipe dredging system according to claim 1, characterized in that, The high-pressure cleaning assembly includes a high-pressure hose, a high-pressure pump, and a high-pressure nozzle, wherein: The high-pressure pump is used to convert atmospheric pressure water into high-pressure water; The high-pressure pipe is used to connect the high-pressure pump and the high-pressure nozzle to transmit the high-pressure water to the high-pressure nozzle for spraying. The high-pressure nozzle includes one or more, disposed on the inner wall of the pipe, and is used to spray high-pressure water to unclog the pipe.

6. The pipe dredging system according to claim 5, characterized in that, The mechanical actuation module also includes a chemical solvent cleaning component for spraying chemical solvents onto the pipes. The chemical solvents can dissolve organic blockages in the pipes to unclog them.

7. The pipe dredging system according to claim 6, characterized in that, The system further includes a dredging scheme generation module, used to generate a dredging scheme for the pipeline based on the blockage assessment results, wherein the dredging scheme includes: activating the dredging mode of the high-pressure cleaning component and / or activating the chemical solvent cleaning component to dredge the pipeline.

8. A pipe dredging method based on the pipe dredging system as described in claim 1, characterized in that, The method includes: Acquire image data of the pipeline; Based on the image data, the blockage assessment result inside the pipeline is calculated; The pipe is cleared based on the blockage assessment results.

9. An electronic device, characterized in that, The smartwatch includes a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the steps of the method as described in claim 8.

10. A computer-readable storage medium storing a computer program, characterized in that, When the computer program is executed by a processor, it implements the steps of the method of claim 8.

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