Underground pipeline repair method and device

Abstract

The present invention discloses an underground pipeline repair method and device, which relates to the technical field of underground pipeline repair. The method includes the following steps: the robot collects and transmits the internal image of the pipeline and the displacement information of the robot to the intelligent platform in real time; the intelligent platform processes the received internal image of the pipeline through an AI image algorithm and compares and analyzes it with the construction drawings to identify and mark the abnormal points in the pipeline; the robot receives and analyzes the abnormal points sent by the intelligent platform to determine the position and repair parameters of the target repair point; based on the constructed internal environment model of the pipeline and the position of the target repair point, the optimal path for the robot to move is determined; for the pipeline damage type, a corresponding hot melt repair parameter database is established through a hot melt control algorithm, and the hot melt parameters are optimized according to the repair effect information collected in real time. Through the communication and interconnection between the robot and the intelligent platform, image processing, path planning and hot melt control are completed, and the rapid and low-cost repair of pipeline damage points is realized.

Description

Technical Field

[0001] The present invention relates to the technical field of underground pipeline repair, and in particular, to a method and device for repairing underground pipelines. Background Art

[0002] For the already constructed underground pipelines, when there are situations such as the pipeline being bent by heavy machinery or the pipeline being damaged and leaking water, it is relatively common to adopt the method of excavating the ground to deal with the pipeline damage problem. The specific operation is that the construction workers roughly determine the position of the pipeline according to the pipeline laying drawings or through some simple detection equipment, and then use large excavation machinery to excavate the ground until the damaged pipeline part is found. However, this traditional method has many drawbacks.

[0003] First of all, for the situation where the underground pipeline orientation is not clear, the detection process may not be accurate, resulting in deviation of the excavation position, which not only wastes time, manpower and material resources, but also may damage other underground facilities in the vicinity.

[0004] Secondly, in a narrow construction site area, it is difficult to operate large excavation equipment, and the operation is restricted, which increases the construction difficulty and safety risks.

[0005] Furthermore, when the geological conditions are complex, such as encountering soft soil layers, rock layers or high groundwater levels, excavation is likely to cause safety accidents such as ground collapse and landslide, and at the same time, it will also greatly increase the repair cost.

[0006] In addition, in old urban areas, the underground pipe networks are dense and complex, and the pipeline paths conflict frequently with many underground facilities. Excavation and repair are extremely likely to damage other pipelines, affecting the normal water supply, electricity supply, gas supply, etc. of the surrounding residents. Moreover, the entire repair process is slow and cannot meet the demand for quickly restoring the normal operation of the pipeline.

[0007] Therefore, it is crucial to study a method for repairing underground pipelines with high safety, fast repair speed and low cost. Summary of the Invention

[0008] The technical problem to be solved by the present invention is how to use trenchless repair technology to accurately find the damage point and repair it under the condition that the underground pipeline orientation is not clear.

[0009] In the first aspect of the present invention, in order to solve the above technical problem, a method for repairing underground pipelines is provided. The method includes the following steps:

[0010] The robot collects and transmits the internal image of the pipeline and the displacement information of the robot to the intelligent platform in real time;

[0011] The intelligent platform processes the received internal pipeline images through AI image algorithms and compares and analyzes them with the construction drawings to identify and mark abnormal points in the pipeline;

[0012] The robot receives and analyzes the abnormal points sent by the intelligent platform to determine the location and repair parameters of the target repair points;

[0013] Based on the constructed internal pipeline environment model and the location of the target repair points, determine the optimal path for the robot to move;

[0014] For the pipeline damage type, establish a corresponding hot melt repair parameter database through the hot melt control algorithm, and optimize the hot melt parameters according to the real-time collected repair effect information.

[0015] Furthermore, the method further includes a secondary detection, and the secondary detection specifically includes:

[0016] After the hot melt repair of the target repair points is completed, the robot performs a secondary scan on the entire pipeline system under the pressurized state of the pipeline and transmits the data of the secondary scan to the intelligent platform;

[0017] The intelligent platform receives and analyzes the data of the secondary scan to evaluate whether the repaired target repair points meet the requirements.

[0018] Furthermore, the displacement information of the robot includes the position information and attitude information of the robot in the pipeline.

[0019] Furthermore, the calculation method of the position information of the robot in the pipeline includes:

[0020] Establish a three-dimensional coordinate system in the pipeline;

[0021] Calculate the axial position of the robot along the pipeline and the position in the pipeline cross-section respectively to obtain the current position information of the robot in the pipeline.

[0022] Furthermore:

[0023] The three-dimensional coordinate system includes:

[0024] Take the pipeline central axis as the Z-axis, any direction of the pipeline cross-section as the X-axis, and the direction perpendicular to the X-axis and Z-axis as the Y-axis; the origin of the coordinate system can be the initial position when the robot enters the pipeline;

[0025] The calculation steps of the axial position of the robot along the pipeline include:

[0026] Record the number of rotations of the robot wheels through the odometer built in the robot, then the axial position of the robot along the pipeline Satisfies the expression:

[0027]

[0028] Among them, is the circumference of the robot wheel, is the number of rotations of the robot wheel;

[0029] The calculation steps of the position of the robot in the pipeline cross-section include:

[0030] The robot is provided with at least three groups of laser range sensors at 120° intervals with the X-axis, and the distances from the three groups of laser range sensors to the inner wall of the pipeline are calculated respectively; the distance from the current robot to the central axis of the pipeline is calculated according to the Pythagorean theorem; the position coordinates of the robot in the pipeline cross-section are corrected by comparing the size of this distance with the radius of the pipeline.

[0031] Further, the calculation method of the attitude information of the robot in the pipeline includes:

[0032] Calculating the pitch angle and roll angle of the robot through the gravity acceleration components measured by the accelerometer built in the robot;

[0033] Calculating the yaw angle of the robot through the magnetic field components measured by the magnetometer built in the robot.

[0034] Further, the method for identifying and marking abnormal points in the pipeline includes:

[0035] Preprocessing the collected internal pipeline image by using the Gaussian filtering method;

[0036] Extracting the key features of the preprocessed internal pipeline image by using the image feature extraction algorithm;

[0037] Identifying the abnormal points in the pipeline according to the analysis results of the key features;

[0038] Integrating and marking the identified abnormal points.

[0039] Further, the key features include edge features and texture features.

[0040] Further, the extraction method of the edge features includes using the edge detection algorithm to determine the edge position by calculating the gradient of the pixel points of the internal pipeline image.

[0041] Further, the extraction method of the texture features includes using the gray-level co-occurrence matrix method to calculate the gray-level change statistical information of the internal pipeline image in different directions and distances to characterize the texture features.

[0042] Further, the abnormal points include a damaged area and / or a deformed area and / or a corroded area, where:

[0043] For the damaged area:

[0044] Use an image segmentation algorithm to separate the damaged area from the background, and determine the area of the damaged area by calculating the area and perimeter of the damaged area;

[0045] Use the edge detection algorithm to determine the shape of the damaged area;

[0046] Determine the position of the damaged area in the pipeline through the image coordinate system;

[0047] For the deformed area:

[0048] Analyze the change of the inner wall contour of the pipeline and calculate the degree of pipeline deformation;

[0049] For the corroded area:

[0050] Determine the scope and severity of the corroded part of the pipeline by analyzing the color change and material texture change of the internal pipeline image.

[0051] Further, the method for constructing the internal environment model of the pipeline includes:

[0052] Conduct a preliminary scan of the pipeline interior through an infrared camera, and based on the pipeline design drawings, establish the internal environment model of the pipeline; among them, the internal environment model of the pipeline can adopt the grid map representation method to divide the internal space of the pipeline into several grids with equal areas; each grid has one of the attributes of a passable area, an obstacle area, and an unknown area.

[0053] Further, the obstacle avoidance strategy for the robot to move in the pipeline includes:

[0054] Based on the obstacle avoidance algorithm of the artificial potential field method, denote the current position of the robot as and the position of the target repair point as , the position of the obstacle as , the gravitational coefficient as , the repulsive coefficient as , then the distance from the robot to the position of the target repair point

[0055]

[0056] The distance from the robot to the position of the obstacle

[0057]

[0058] Gravitational force Satisfies the expression:

[0059]

[0060] Repulsive force Satisfies the expression:

[0061]

[0062] Wherein, Is the safe distance between the robot and the obstacle position;

[0063] The resultant force received by the robot Satisfies the expression:

[0064]

[0065] The robot adjusts its moving direction according to the resultant force.

[0066] Furthermore, the optimal path selection strategy for the robot to move in the pipeline includes:

[0067] Using the evaluation function in the AI algorithm To select the node expansion order until the target repair point is found; wherein, the evaluation function Satisfies the expression:

[0068]

[0069] Wherein, Is the actual moving distance from the starting position to the node ; Is the heuristic estimated cost from the node To the target repair point.

[0070] Furthermore, using the evaluation function in the AI algorithm To select the node expansion order specifically includes:

[0071] The robot starts from the starting position and calculates the Value of the passable nodes around;

[0072] Select the passable node with the smallest Value as the next expansion node, while taking into account the obstacle avoidance strategy;

[0073] By continuously calculating and selecting, to find the optimal path from the starting position to the target repair point.

[0074] Further, the step of taking into account the obstacle avoidance strategy includes:

[0075] In the grid map, the robot moves from the current grid to an adjacent grid at a cost that satisfies the expression:

[0076]

[0077] where is the movement cost from to ;

[0078] Then the heuristic estimated cost satisfies the expression:

[0079]

[0080] where , , is the central coordinate of the grid .

[0081] Further, the types of pipeline damage at least include hole-shaped damage, crack-shaped damage, and area-shaped damage. Feature parameters corresponding to the types of pipeline damage are extracted through the internal image and sensor of the pipeline, where:

[0082] The feature parameters of the hole-shaped damage at least include the hole diameter and depth;

[0083] The feature parameters of the crack-shaped damage at least include the crack length, width, and orientation;

[0084] The feature parameters of the area-shaped damage at least include the damaged area and shape.

[0085] Further, the hot melt repair parameter database at least includes the optimal hot melt temperature, heating time, and pressure corresponding to each type of pipeline damage.

[0086] Further, the step of optimizing the hot melt parameters based on the repair effect information collected in real time includes:

[0087] Using the sensors carried by the hot melt terminal to monitor the temperature and pressure data in real time, and comparing the monitored data with the standard data in the hot melt repair parameter database;

[0088] According to the deviation between the monitored data and the standard data, the robot decides whether to adjust the working state of the hot melt terminal.

[0089] In a second aspect of the present invention, there is provided an underground pipeline repair device, including a device body, where the device body includes:

[0090] A robot with moving wheels, the robot is provided with a main board, a camera, and a hot melt component;

[0091] An intelligent platform communicatively connected to the robot, the intelligent platform being capable of controlling the actions of the robot.

[0092] Further, the main board is integrated with a control module, a power module, and a communication module, where:

[0093] The control module is used to process various data and instructions;

[0094] The power module is used to provide power supply for the robot;

[0095] The robot performs data transmission with the intelligent platform through the communication module.

[0096] Further, the hot melt component includes a hot melt terminal and a robotic arm connected to each other, and both the hot melt terminal and the robotic arm are communicatively connected to the control module; where:

[0097] The robotic arm can extend, rotate, and move to a specified position;

[0098] The hot melt terminal melts the inner plastic lining material of the pipeline by heating, so that the inner plastic lining material melts and fills the target repair point; the hot melt terminal is equipped with sensors, and the sensors are used to monitor the temperature and pressure parameters during the hot melt process in real time.

[0099] Compared with the prior art, the embodiments of the present invention have the following beneficial effects:

[0100] In the present invention, the robot and the intelligent platform are communicatively interconnected to complete image processing, path planning, and hot melt control, so as to accurately find the damaged point when the underground pipeline orientation is unclear, avoid a series of problems caused by inaccurate detection, improve the accuracy of positioning, and do not require relying on large-scale excavation equipment. BRIEF DESCRIPTION OF THE DRAWINGS

[0101] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following will briefly introduce the drawings required for description in the embodiments. Obviously, the following drawings are only some embodiments of the present invention. For those of ordinary skill in the art, without creative efforts, other drawings can be obtained based on these drawings.

[0102] Figure 1 It is a flowchart disclosed in the embodiments of the present invention;

[0103] Figure 2 Schematic diagram of the device disclosed in the embodiments of the present invention.

[0104] In the figure:

[0105] 000, steel pipe; 001, plastic lining;

[0106] 110, camera; 120, main board; 131, robotic arm; 132, hot melt terminal. Specific embodiments

[0107] In order to enable those skilled in the art of the present technology to better understand the solutions of the present invention, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without making creative efforts shall fall within the protection scope of the present invention.

[0108] The present invention aims to provide an underground pipeline repair method and its device, and its purposes are as follows: on the one hand, it can accurately find the damaged point in the case where the underground pipeline route is not clear, avoiding a series of problems caused by inaccurate detection; on the other hand, it does not need to rely on large-scale excavation equipment to reduce the construction difficulty and safety risk; furthermore, it can reduce the damage to other pipelines.

[0109] First, the underground pipeline repair method disclosed in this embodiment will be described.

[0110] Please refer to Figure 1 , the method includes the following steps:

[0111] S1. The robot collects and transmits the internal image of the pipeline and the displacement information of the robot to the intelligent platform in real time.

[0112] Start the high-definition infrared camera on the micro-robot in the underground pipeline to conduct a comprehensive image collection of the pipeline interior. The camera 110 moves slowly in the pipeline with the robot and takes images of the inner wall of the pipeline at a certain speed and angle to ensure that the entire inner wall surface of the pipeline is covered, so as to obtain detailed information about the pipeline interior, including whether there are abnormalities such as damage, deformation, and corrosion.

[0113] The image data collected by the camera 110 is transmitted to the intelligent platform in real time through the communication module on the main board 120 of the robot; at the same time, the displacement information of the robot during the movement in the pipeline is also transmitted, so that the intelligent platform can accurately judge the specific position of the abnormal point in the pipeline.

[0114] The displacement information of the robot includes the position information and attitude information of the robot in the pipeline.

[0115] The calculation of the specific position information and attitude information is as follows.

[0116] S11. Calculation of the position information of the robot in the pipeline.

[0117] Establish a three-dimensional coordinate system in the pipeline.

[0118] Among them, the three-dimensional coordinate system includes taking the central axis of the pipeline as the Z-axis, any direction of the pipeline cross-section as the X-axis, and the direction perpendicular to the X-axis and Z-axis as the Y-axis; the origin of the coordinate system can be the initial position when the robot enters the pipeline.

[0119] Calculate the axial position of the robot along the pipeline and the position in the pipeline cross-section respectively to obtain the current position information of the robot in the pipeline.

[0120] Among them, the calculation steps of the axial position of the robot along the pipeline include:

[0121] Record the number of rotations of the robot's wheels through the odometer built in the robot, then the axial position of the robot along the pipeline Satisfies the expression:

[0122]

[0123] Among them, is the circumference of the robot's wheel, is the number of rotations of the robot's wheel.

[0124] Among them, the calculation steps of the position of the robot in the pipeline cross-section include:

[0125] The robot is provided with at least three groups of laser rangefinder sensors at intervals of 120° with the X-axis. That is, there are three sensors on the robot, which are respectively installed in the directions of 0°, 120°, and 240° with the X-axis. Calculate the distance from the laser rangefinder sensor to the inner wall of the pipeline, and calculate the distance from the current robot to the central axis of the pipeline according to the Pythagorean theorem; correct the position coordinates of the robot in the pipeline cross-section by comparing this distance with the radius of the pipeline to ensure the accuracy of the calculation.

[0126] S12. Calculation of the attitude information of the robot in the pipeline.

[0127] For the pitch angle: Use the accelerometer built in the robot to measure.

[0128] The accelerometer can measure the components of the gravitational acceleration on the three axes of the robot coordinate system, and calculate the pitch angle through the arctangent function.

[0129] For the yaw angle: Measure the angle of the earth's magnetic field direction in the robot coordinate system through the magnetometer built in the robot to obtain the yaw angle.

[0130] For the roll angle: Similarly, the component of gravitational acceleration is measured by the accelerometer for calculation.

[0131] S2. The intelligent platform processes the received internal pipeline images through AI image algorithms and compares and analyzes them with the construction drawings to identify and mark the abnormal points inside the pipeline.

[0132] After the intelligent platform receives the image data from the robot, its data processing module compares and analyzes the image data with the pre-stored normal pipeline inner wall image template.

[0133] The image data is processed through AI algorithms to identify the abnormal points inside the pipeline, such as the size, shape, position, etc. of the damaged points, and the distribution of these abnormal points in the pipeline system is integrated and marked.

[0134] After confirming that all the abnormal points in all pipeline segments have been scanned and the data has been accurately transmitted, proceed to the next step.

[0135] Specifically, the method for identifying and marking the abnormal points inside the pipeline includes:

[0136] S21. Preprocess the collected internal pipeline images using the Gaussian filtering method.

[0137] The normal pipeline inner wall image template is a standard image collected after the pipeline construction is completed or under the known condition of no damage, and contains the feature information such as the texture and color of the pipeline inner wall.

[0138] Preprocessing the collected images using the Gaussian filtering method includes grayscale processing and image filtering. Among them, grayscale processing is used to convert the color image into a grayscale image to reduce the data volume and highlight the texture features of the image; image filtering is used to remove noise interference to improve the clarity of the image.

[0139] S22. Use the image feature extraction algorithm to extract the key features of the preprocessed internal pipeline images.

[0140] Use the image feature extraction algorithm to extract the key features in the pipeline inner wall image.

[0141] Among them, the key features include edge features and texture features.

[0142] The extraction method of edge features includes using the edge detection algorithm to determine the edge position by calculating the gradient of the pixel points in the internal pipeline image.

[0143] The extraction method of texture features includes using the gray-level co-occurrence matrix method to calculate the gray-level change statistical information of the internal pipeline image in different directions and distances to characterize the texture features.

[0144] Analyze the extracted key features and compare the differences with the features of the inner wall of a normal pipeline.

[0145] For example, regarding the edge features, the edges of the inner wall of a normal pipeline should be continuous and smooth. If there are damages, the edges will show interruptions, irregularities, etc.

[0146] Regarding the texture features, the texture in the damaged area may change, such as texture disorder, absence, etc.

[0147] S23. Identify the abnormal points in the pipeline according to the analysis results of the key features.

[0148] Identify the abnormal points in the pipeline according to the analysis results of the key features. Among them, the abnormal points include the damaged area and / or the deformed area and / or the corroded area.

[0149] For the damaged area: Use the image segmentation algorithm to separate the damaged area from the background, and determine the area of the damaged area by calculating the area and perimeter of the damaged area. Use the edge detection algorithm to determine the shape of the damaged area; such as circular, rectangular, irregular shape, etc. Determine the position of the damaged area in the pipeline through the image coordinate system.

[0150] For the deformed area: Analyze the change of the inner wall contour of the pipeline and calculate the degree of pipeline deformation; such as parameters such as the depth and length of the depression or protrusion.

[0151] For the corroded area: Determine the range and severity of the corroded part of the pipeline by analyzing the color change and material texture change of the internal image of the pipeline, and calculate the corresponding parameters.

[0152] S24. Integrate and mark the identified abnormal points.

[0153] Integrate the information of the identified abnormal points, including the type of abnormal points (damage, deformation, corrosion), position (axial position and cross-sectional position in the pipeline), size, shape, etc.

[0154] Mark the abnormal points on the 3D model or 2D drawing of the pipeline system so as to visually display the distribution of the abnormal points in the entire pipeline system.

[0155] Among them, the marking method can use different colors and shapes to distinguish different types of abnormal points. For example, mark the damaged area with a red circle, mark the deformed area with a green rectangle, mark the corroded area with a yellow triangle, etc. At the same time, mark the detailed information of the abnormal points, such as size, severity, etc., next to the mark for convenient subsequent viewing and analysis.

[0156] S3. The robot receives and analyzes the abnormal points sent by the intelligent platform to determine the position of the target repair point and the repair parameters.

[0157] The intelligent platform sends the processed data that needs to be repaired to the robot terminal through the wireless communication module. After the main board 120 of the robot receives this data, it analyzes the instructions to determine the specific points that need to be repaired and the corresponding repair parameters, such as the hot melt temperature, time, etc.

[0158] S4. Determine the optimal path for the robot to move based on the constructed internal environment model of the pipeline and the position of the target repair point.

[0159] S41. Construction of the internal environment model of the pipeline

[0160] Before entering the pipeline, the robot conducts a preliminary scan of the inside of the pipeline through a high-definition infrared camera to obtain the general outline of the inside of the pipeline and information about possible obstacles.

[0161] At the same time, combined with the pre-stored normal pipeline inner wall image template (including information such as pipe diameter, orientation, and bend position), an internal environment model of the pipeline is established.

[0162] Among them, the internal environment model of the pipeline can adopt the grid map representation method, dividing the internal space of the pipeline into multiple grids of equal size, and each grid has different attributes, such as passable, obstacle, unknown, etc.

[0163] According to the internal pipeline image collected by the camera 110 and the normal pipeline inner wall image template, the pipeline wall, known obstacles, etc. are marked as non-passable areas, that is, obstacle areas, and the remaining spaces are marked as passable areas, and the areas that have not been detected are marked as unknown areas.

[0164] S42. Obstacle avoidance strategy for the robot to move in the pipeline

[0165] During the movement of the robot in the pipeline, it continuously monitors the surrounding environment using a high-definition infrared camera and a laser range finder sensor. When an obstacle is detected ahead, corresponding obstacle avoidance measures are taken according to the distance information and the position information of the obstacle feedback by the sensor.

[0166] Among them, the obstacle avoidance algorithm is an obstacle avoidance algorithm based on the artificial potential field method. Its principle is: the robot is regarded as moving in a virtual force field, the target point generates an attractive force on the robot, causing the robot to move towards the target point; while the obstacle generates a repulsive force on the robot, preventing the robot from approaching the obstacle. The robot adjusts its movement direction according to the resultant force direction of the attractive force and the repulsive force, so as to achieve obstacle avoidance.

[0167] Denote the current position of the robot as , and the position of the target repair point as , the obstacle position is , the gravitational coefficient is , the repulsive coefficient is , then the distance from the robot to the position of the target repair point satisfies the expression:

[0168]

[0169] The distance from the robot to the obstacle position satisfies the expression:

[0170]

[0171] Gravity satisfies the expression:

[0172]

[0173] Repulsion satisfies the expression:

[0174]

[0175] Among them, is the safe distance between the robot and the obstacle position;

[0176] The resultant force received by the robot satisfies the expression:

[0177]

[0178] The robot adjusts its moving direction according to the resultant force.

[0179] S43. The optimal path selection strategy for the robot to move in the pipeline.

[0180] Given the pipeline environment model and the position of the target repair point, based on the AI algorithm, the robot needs to select an optimal path to reach the target point.

[0181] Use the evaluation function in the AI algorithm to select the node expansion order until the target repair point is found.

[0182] Among them, the evaluation function satisfies the expression:

[0183]

[0184] Among them, is the actual moving distance from the starting position to the node ; is the heuristic estimated cost from the node to the target repair point.

[0185] The robot calculates the values of the passable nodes around it starting from the starting position. Value.

[0186] Select The passable node with the smallest value is used as the next expansion node, taking into account the obstacle avoidance strategy.

[0187] By continuously calculating and selecting, an optimal path from the starting position to the target repair point is found.

[0188] Among them, during the process of expanding nodes, the obstacle avoidance strategy is taken into account to avoid selecting paths passing through obstacles. Specifically:

[0189] In the grid map, the robot moves from the current grid to the adjacent grid The cost Satisfies the expression:

[0190]

[0191] Among them, Is the movement cost from To The movement cost;

[0192] Then the heuristic estimated cost Satisfies the expression:

[0193]

[0194] Among them, , , Is the center coordinate of the grid The center coordinate.

[0195] By continuously calculating and selecting, an optimal path from the starting position to the target repair point is found.

[0196] When the robot reaches the target position through the optimal path, it moves to the repair point position by driving the telescopic rotating robotic arm 131.

[0197] After the robotic arm 131 reaches the specified position, the robotic arm 131 adjusts the position and angle of the hot melt terminal 132 to align it with the damaged point.

[0198] The hot melt terminal 132 heats the inner plastic 001 material to the corresponding precise temperature according to parameters such as the size, shape, and position of the repair point, and starts the hot melt repair operation.

[0199] During the patching process, the sensors attached to the hot melt terminal 132 monitor parameters such as temperature and pressure in real time, and feed the data back to the main board 120. The main board 120 adjusts the heating power and time according to the preset process requirements to ensure the patching quality.

[0200] The sensor data types are synchronized with the acquisition frequency. The sensors attached to the hot melt terminal 132 include a temperature sensor and a pressure sensor.

[0201] Among them, the temperature sensor is used to monitor the temperature of the inner plastic 001 material during the hot melt process. The pressure sensor is used to monitor the pressure change during the hot melt process.

[0202] To ensure that the acquisition frequencies of the temperature sensor and the pressure sensor are the same, it is set to collect data every 0.1 s, so that the temperature and pressure information at the same moment can be corresponding during data fusion.

[0203] Before data fusion, the data collected by the temperature sensor and the pressure sensor are calibrated.

[0204] Among them, for the temperature sensor, by calibrating in a known temperature environment, the relationship between the temperature measurement value and the actual temperature value is obtained, so as to correct the collected temperature data. Preprocessing of the data includes operations such as removing outliers and filtering. Among them:

[0205] Outliers may be incorrect data caused by sensor failures or interference, and can be judged and removed by setting reasonable thresholds.

[0206] The filtering operation adopts the moving average filtering method to smooth the continuously collected data, reduce data fluctuations, and improve the stability and reliability of the data.

[0207] S5. For the pipeline damage type, a corresponding hot melt patching parameter database is established through the hot melt control algorithm, and the hot melt parameters are optimized based on the real-time collected patching effect information.

[0208] S51. Data fusion

[0209] According to the influence degrees of temperature and pressure on the hot melt patching quality, different weights are assigned to the temperature data and the pressure data. Assume that the influence weight of temperature on the patching quality is , and the influence weight of pressure on the patching quality is , and + = 1. Let the data sequence collected by the temperature sensor be , and the data sequence collected by the pressure sensor be ( is the number of collected data). After weighted average fusion, the data satisfies the expression:

[0210]

[0211] According to the actual hot-melt repair process requirements, determine the optimal weight values of temperature and pressure.

[0212] In the current hot-melt repair process, the influence of temperature on the repair quality is more critical. It can be set that , . Through this data fusion method, considering the temperature and pressure information comprehensively, it provides a more accurate basis for the motherboard 120 to adjust the working state of the hot-melt terminal 132.

[0213] S52. Hot-melt control

[0214] The types of pipeline damage at least include hole-shaped damage, crack-shaped damage, and area-shaped damage. Extract the characteristic parameters corresponding to the types of pipeline damage through the internal pipeline image and sensors, where:

[0215] The characteristic parameters of hole-shaped damage at least include the hole diameter and depth.

[0216] The characteristic parameters of crack-shaped damage at least include the crack length, width, and direction.

[0217] The characteristic parameters of area-shaped damage at least include the damaged area and shape.

[0218] Establish a hot-melt repair parameter database. Based on actual engineering experience, establish a corresponding hot-melt repair parameter database for different types of pipeline damage.

[0219] The hot-melt repair parameter database at least includes parameters such as the optimal hot-melt temperature, heating time, and pressure corresponding to each type of the pipeline damage. For example:

[0220] For small hole-shaped damage with a diameter less than , the experimental results show that the optimal hot-melt temperature is , the heating time is , and the pressure is .

[0221] For crack damage with a length between , the experimental results show that the optimal hot-melt temperature is , the heating time is , and the pressure is .

[0222] In a further solution, use the sensors carried by the hot-melt terminal 132 to monitor the temperature and pressure data in real time, and compare the monitored data with the standard data in the hot-melt repair parameter database.

[0223] Based on the deviation between the monitoring data and the standard data, the robot decides whether to adjust the working state of the hot melt terminal 132. For example:

[0224] If the temperature is lower than the standard value, increase the heating power.

[0225] If the pressure is too high, appropriately reduce the heating speed.

[0226] Meanwhile, the repair effect is observed in real time through a high-definition infrared camera, and the hot melt parameters are further optimized according to the repair situation to ensure accurate repair for various different types of damage.

[0227] S6. Secondary detection

[0228] After the hot melt repair of the target repair point is completed, the entire pipeline is flushed to remove possible residues and debris inside the pipeline, and then a pressure test is carried out to simulate the pressure conditions during the normal operation of the pipeline.

[0229] When the pipeline is under pressure, the robot starts the high-definition infrared camera again to perform a secondary scan of the entire pipeline system, focusing on checking whether the repair point is firm and whether there are problems such as leakage, and transmits the data of the secondary scan to the intelligent platform.

[0230] The intelligent platform receives and analyzes the data of the secondary scan to evaluate whether the target repair point after repair meets the requirements. If the repair point does not meet the requirements, re-repair or other measures are taken according to the situation until the pipeline system resumes normal operation.

[0231] Please refer to Figure 2 , the present invention also discloses an underground pipeline repair device, including a device body, and the device body includes: a robot with moving wheels and an intelligent platform communicatively connected to the robot, wherein the intelligent platform can control the actions of the robot. The robot is provided with a main board 120, a camera 110 and a hot melt component. The main board 120 is integrated with a control module, a power module and a communication module.

[0232] The camera 110 selects a high-definition infrared camera for image acquisition of the internal environment of the pipeline. Its high-definition imaging function can clearly capture the details inside the pipeline, and its infrared function enables it to work effectively in a dim or complex pipeline environment, providing accurate visual information for subsequent analysis and repair operations.

[0233] The power module provides stable power supply for the entire device to ensure the normal operation of each component, and can adopt forms such as rechargeable batteries to meet the needs of working in the underground pipeline for a long time.

[0234] The control module is an integrated chip responsible for processing various data and instructions, such as processing the image data collected by the camera 110, generating control instructions for the robotic arm 131 and the hot melt terminal 132, etc.

[0235] The communication module realizes data transmission between the device and external devices (such as intelligent platforms), including uploading the image data collected by the camera 110 and the status information of the robot itself, and receiving control instructions and repair data from the intelligent platform, etc.

[0236] The hot melt component includes a hot melt terminal 132 and a robotic arm 131 which are connected to each other. Both the hot melt terminal 132 and the robotic arm 131 are communicatively connected to the control module; wherein:

[0237] The robotic arm 131 has telescopic and rotational functions and can move flexibly within the pipeline to reach the designated position for operation. The design of the robotic arm 131 enables it to adapt to the requirements of different pipe diameters and pipe shapes, and accurately locate the damaged point under intelligent control, providing accurate position adjustment for the hot melt repair operation.

[0238] The hot melt terminal 132 heats the inner plastic-lined 001 material of the galvanized steel pipe 000 to melt the inner plastic-lined 001 material and fill it into the target repair point.

[0239] The hot melt terminal 132 is equipped with sensors for real-time monitoring of temperature and pressure parameters during the hot melt process; ensuring the safety and quality of the repair process. The sensors feed back the data to the main board 120, and the main board 120 adjusts the working state of the hot melt terminal 132 according to these data to ensure that the repair operation meets the process requirements.

[0240] By heating the inner plastic-lined 001 material to the corresponding temperature to melt it and fill it into the damaged part, the repair of the pipeline is realized. The heating temperature and time can be accurately controlled according to the size, shape and position of different repair points to achieve the best repair effect.

[0241] Although the embodiments of the present invention have been shown and described, for those of ordinary skill in the art, it can be understood that various changes, modifications, substitutions and variations can be made to these embodiments without departing from the principles and spirit of the present invention. The scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. A method for repairing an underground pipeline, characterized in that: The method comprises the following steps: The robot collects and transmits the internal image of the pipeline and the displacement information of the robot to the intelligent platform in real time; The intelligent platform processes the received internal image of the pipeline through an AI image algorithm and compares and analyzes it with the construction drawings to identify and mark abnormal points in the pipeline; The robot receives and analyzes the abnormal point sent by the intelligent platform to determine the location and repair parameters of the target repair point; Determining an optimal path for the robot to move based on the constructed pipeline internal environment model and the position of the target repair point; According to the type of pipeline damage, a corresponding hot-melt repair parameter database is established through the hot-melt control algorithm, and the hot-melt parameters are optimized according to the real-time collected repair effect information; different weights are assigned to temperature data and pressure data according to the degree of influence of temperature and pressure on the quality of hot-melt repair; characteristic parameters of the corresponding pipeline damage type are extracted through internal images and sensors of the pipeline; corresponding hot-melt repair parameter databases are established for different types of pipeline damage; according to the deviation between the monitoring data and the standard data, the robot decides whether to adjust the working status of the hot-melt terminal.

2. The underground pipeline repair method according to claim 1, characterized in that: The method further includes a secondary detection, which specifically includes: After the hot-melt repair of the target repair point is completed, the robot performs a secondary scan of the entire pipeline system under the pipeline pressurization state, and transmits the data of the secondary scan to the intelligent platform; The intelligent platform receives and analyzes the data of the secondary scan to evaluate whether the target repair point after repair meets the requirements.

3. The underground pipeline repair method according to claim 1, characterized in that: The displacement information of the robot includes the position information and posture information of the robot in the pipeline.

4. The underground pipeline repair method according to claim 3, characterized in that: The method for calculating the position information of the robot in the pipeline includes: Establish a three-dimensional coordinate system in the pipeline; The axial position of the robot along the pipeline and the position in the cross section of the pipeline are calculated respectively to obtain the current position information of the robot in the pipeline.

5. The underground pipeline repair method according to claim 4, characterized in that: The three-dimensional coordinate system includes: The central axis of the pipeline is the Z axis, any direction of the pipeline cross section is the X axis, and the direction perpendicular to the X axis and the Z axis is the Y axis; the origin of the coordinate system may be the initial position of the robot when it enters the pipeline; The step of calculating the axial position of the robot along the pipeline includes: The robot's built-in odometer records the number of revolutions of the robot's wheels, and the robot's axial position along the pipeline is Satisfies the expression: in, is the circumference of the robot wheel, is the number of rotations of the robot's wheels; The step of calculating the position of the robot within the pipe cross section comprises: The robot is provided with at least three groups of laser ranging sensors at intervals of 120° from the X-axis, and the distances from the three groups of laser ranging sensors to the inner wall of the pipeline are calculated respectively; the current distance from the robot to the central axis of the pipeline is calculated according to the Pythagorean theorem; and the position coordinates of the robot in the cross section of the pipeline are corrected by comparing the distance with the radius of the pipeline.

6. The underground pipeline repair method according to claim 3, characterized in that: The method for calculating the posture information of the robot in the pipeline includes: Calculating the pitch angle and roll angle of the robot by using the gravity acceleration component measured by the built-in accelerometer of the robot; The yaw angle of the robot is calculated by measuring the magnetic field component of the robot's built-in magnetometer.

7. The underground pipeline repair method according to claim 1, characterized in that: The method for identifying and marking abnormal points in a pipeline comprises: Preprocessing the collected internal image of the pipeline by using a Gaussian filtering method; Using an image feature extraction algorithm to extract key features of the preprocessed image inside the pipeline; According to the analysis results of the key features, identifying the abnormal points in the pipeline; The identified abnormal points are integrated and marked.

8. The underground pipeline repair method according to claim 7, characterized in that: The key features include edge features and texture features.

9. The underground pipeline repair method according to claim 8, characterized in that: The edge feature extraction method includes using an edge detection algorithm to determine the edge position by calculating the gradient of the pixel points of the internal image of the pipeline.

10. The underground pipeline repair method according to claim 8, characterized in that: The texture feature extraction method includes using a gray level co-occurrence matrix method to calculate gray level change statistics of the pipeline internal image in different directions and distances to characterize the texture feature.

11. The underground pipeline repair method according to claim 9, characterized in that: The abnormal points include damaged areas and / or deformed areas and / or corroded areas, wherein: For the damaged area: Separating the damaged area from the background by using an image segmentation algorithm, and determining the area of ​​the damaged area by calculating the area and perimeter of the damaged area; Determining the shape of the damaged area using the edge detection algorithm; Determining the position of the damaged area in the pipeline by an image coordinate system; For the deformation area: Analyze the changes in the inner wall profile of the pipeline and calculate the degree of pipeline deformation; For the corrosion area: The scope and severity of the pipeline corrosion site are determined by analyzing the color changes and material texture changes of the pipeline internal image.

12. The underground pipeline repair method according to claim 1, characterized in that: The method for constructing the pipeline internal environment model comprises: The interior of the pipeline is preliminarily scanned by an infrared camera, and a pipeline internal environment model is established based on the pipeline design drawings; wherein the pipeline internal environment model can use a grid map representation method to divide the internal space of the pipeline into a number of grids of equal area; a single grid has any one of the attributes of a passable area, an obstacle area, and an unknown area.

13. The underground pipeline repair method according to claim 12, characterized in that: The obstacle avoidance strategy of the robot moving in the pipeline includes: Obstacle avoidance algorithm based on artificial potential field method, the current position of the robot is recorded as , the position of the target patch point is , the obstacle position is , the gravitational coefficient is , the repulsion coefficient is , then the distance from the robot to the target repair point Satisfies the expression: The distance from the robot to the obstacle Satisfies the expression: gravitational Satisfies the expression: Repulsion Satisfies the expression: in, is the safe distance between the robot and the obstacle; The total force on the robot Satisfies the expression: The robot adjusts its moving direction according to the combined force.

14. The underground pipeline repair method according to claim 13, characterized in that: The optimal path selection strategy for the robot to move in the pipeline includes: Using the evaluation function in AI algorithm To select the node expansion order until the target patch point is found; wherein the evaluation function Satisfies the expression: in, From the starting position to the node The actual moving distance; is from the node A heuristic estimate of the cost to the target patch point.

15. The underground pipeline repair method according to claim 14, characterized in that: The evaluation function in the AI ​​algorithm is used To select the node expansion order, specifically: The robot starts to calculate the surrounding traversable nodes from the starting position. value; Select the The traversable node with the smallest value is used as the next expansion node, while taking into account the obstacle avoidance strategy; Through continuous calculation and selection, the optimal path from the starting position to the target repair point is found.

16. The underground pipeline repair method according to claim 15, characterized in that: The steps of taking into account the obstacle avoidance strategy include: In the grid map, the robot moves from the current grid Move to adjacent grid The cost Satisfies the expression: in, For arrive The cost of movement; Then the heuristic estimated cost Satisfies the expression: in, , , For Grid The center coordinates of .

17. The underground pipeline repair method according to claim 1, characterized in that: The pipeline damage types include at least hole-shaped damage, crack-shaped damage and area-shaped damage. The characteristic parameters corresponding to the pipeline damage types are extracted through the pipeline internal image and the sensor, wherein: The characteristic parameters of the hole-shaped damage include at least the hole diameter and depth; The characteristic parameters of the crack-shaped damage include at least the length, width and direction of the crack; The characteristic parameters of the area damage at least include the area and shape of the damaged region.

18. The underground pipeline repair method according to claim 1, characterized in that: The hot melt repair parameter database at least includes the optimal hot melt temperature, heating time and pressure corresponding to each type of pipeline damage.

19. The underground pipeline repair method according to claim 1, characterized in that: The step of optimizing the hot melt parameters based on the repair effect information collected in real time includes: Using the sensor carried by the hot melt terminal to monitor the temperature and pressure data in real time, and comparing the monitoring data with the standard data in the hot melt repair parameter database; According to the deviation between the monitoring data and the standard data, the robot decides whether to adjust the working state of the hot melt terminal.

20. An underground pipeline repair device, characterized in that: The device comprises a device body, wherein the device body comprises: A robot with moving wheels, the robot is provided with a mainboard, a camera and a hot melt component, the hot melt component includes a hot melt terminal and a mechanical arm connected to each other, the hot melt terminal carries a sensor, and the sensor is used to monitor the temperature and pressure parameters in the hot melt process in real time; An intelligent platform in communication with the robot, wherein the intelligent platform is capable of controlling the actions of the robot; A hot melt repair parameter database compares the monitoring data of the sensor with the standard data in the hot melt repair parameter database; according to the deviation between the monitoring data and the standard data, the robot decides whether to adjust the working state of the hot melt terminal.

21. The underground pipeline repair device according to claim 20, characterized in that: The mainboard integrates a control module, a power module and a communication module, wherein: The control module is used to process various data and instructions; The power module is used to provide power supply for the robot; The robot transmits data with the intelligent platform via the communication module.

22. The underground pipeline repair device according to claim 21, characterized in that: The hot melt terminal and the mechanical arm are both in communication connection with the control module; wherein: The mechanical arm can be extended, rotated and moved to a specified position; The hot melt terminal heats the inner lining plastic material of the pipe to melt the inner lining plastic material and fill the target repair point.

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