Intelligent pipeline inspection system and method based on pipeline robot

Abstract

The invention relates to the technical field of intelligent pipeline inspection, and discloses an intelligent pipeline inspection method and system based on a pipeline robot, and the method comprises the steps: obtaining the pipe wall thickness data, temperature field distribution data and three-dimensional deformation data of a pipe network through a detection module of the pipeline robot, and constructing a three-dimensional mapping of the pipe network; historical maintenance data are obtained, an optimal detection path is automatically planned through an improved mixed A star global path planning algorithm, and routing inspection is carried out; acquiring local map information and attitude angle data of the pipeline robot, constructing a body attitude of the pipeline robot, and performing local path planning based on an artificial potential field method; and constructing a pipeline health index evaluation system, connecting the key indexes with an intelligent pipe network system, and generating a new inspection scheme for inspection. According to the invention, comprehensive and accurate data acquisition, efficient path planning, accurate local navigation and intelligent health management can be realized.

Description

Technical Field

[0001] The present invention relates to the technical field of intelligent pipeline inspection, and particularly to an intelligent pipeline inspection system and method based on a pipeline robot. Background Art

[0002] With the continuous development of urban infrastructure and the increasing demand for energy, various pipeline systems have been widely used in many fields such as oil, natural gas, water supply, and drainage. However, due to the influence of various complex factors such as environmental factors, medium corrosion, geological settlement, and equipment aging, the safe operation of pipelines faces severe challenges. The traditional pipeline inspection method mainly relies on manual regular inspections, which is not only inefficient and labor-intensive, but also difficult to comprehensively and accurately detect the internal conditions of pipelines, unable to timely discover potential safety hazards, and prone to accidents such as pipeline leakage and rupture, causing huge losses to people's lives and property and the ecological environment.

[0003] In recent years, pipeline robot technology has gradually emerged, providing a new solution for pipeline inspection. Although existing pipeline robots can replace humans to a certain extent to enter the pipeline for inspection, there are still many deficiencies in data acquisition and processing, path planning, and health assessment. For example, in terms of data collection, most pipeline robots can only obtain single-type data and are difficult to comprehensively reflect the actual conditions of pipelines; in path planning, traditional algorithms often cannot balance global optimality and local obstacle avoidance flexibility; the assessment of pipeline health status also lacks systematicness and intelligence, and cannot automatically generate effective resource allocation and inspection plans based on real-time data. And most of the cable-connected pipeline robots transmit energy and information through cables. Due to the cable length and the resistance when dragging the cable, the inspection length is limited. While the cable-free pipeline robots achieve self-power supply through internal power sources. Limited by the battery capacity, the endurance mileage is insufficient, and energy transmission needs to be achieved through wireless power supply with the outside; there are also problems with the insufficient environmental adaptability of pipeline robots. In complex terrains such as mountains and pipelines with a burial depth greater than 5m, it is difficult to achieve stable signal coverage, resulting in an increase in the missed inspection rate. Therefore, there is an urgent need for a more intelligent, efficient, and comprehensive pipeline inspection method to meet the growing pipeline maintenance needs. Summary of the Invention

[0004] The purpose of the present invention is to provide an intelligent pipeline inspection system and method based on a pipeline robot, aiming to solve the above problems.

[0005] The present invention provides an intelligent pipeline inspection method based on a pipeline robot, including:

[0006] Install a detection module on the pipeline robot, obtain the pipe wall thickness data, temperature field distribution data and three-dimensional deformation data of the pipe network through the detection module, and construct a three-dimensional map of the pipe network based on the laser slam perception technology according to the pipe wall thickness data, temperature field distribution data and three-dimensional deformation data;

[0007] Obtain historical maintenance data, automatically plan the optimal detection path through an improved hybrid A* global path planning algorithm, and conduct inspections according to the optimal detection path;

[0008] Obtain local map information, fuse the detection data of the inertial measurement unit and the angle encoder, obtain the attitude angle data of the pipeline robot, construct the body attitude of the pipeline robot, and conduct local path planning based on the artificial potential field method;

[0009] Construct a pipeline health index evaluation system, connect the key indicators with the intelligent pipe network system, automatically generate and optimize the resource allocation plan, generate a new inspection plan, and control the pipeline robot to continue the inspection. The key indicators include the corrosion rate and the remaining life.

[0010] Preferably, the detection module includes a pressure sensor, a humidity sensor, an inertial navigation unit and a lidar.

[0011] Preferably, the improved hybrid A* global path planning algorithm is specifically:

[0012] Obtain the current point position coordinates and the target point position coordinates of the pipeline robot according to the inertial navigation unit and the global positioning system;

[0013] Define the four-dimensional state space (x, y, θ, φ) of the pipeline robot, and reconstruct and optimize the heuristic function. Among them, θ represents the heading angle of the pipeline robot, and φ represents the rotation angle of the pipeline robot around the circumference of the pipeline; perform node expansion and customize the motion library according to the motion mode of the pipeline robot;

[0014] Obtain the three-dimensional coordinate values of the obstacles based on the laser slam perception technology, conduct collision detection optimization, map the obstacles to the cylindrical coordinate grid, and adopt the probability occupancy grid update strategy;

[0015] Based on the special structure environment of the pipeline and the turning radius of the pipeline robot, determine that the optimal detection path meets the minimum turning radius of the pipeline robot through a curvature constraint checker.

[0016] Preferably, the reconstruction and optimization of the heuristic function is specifically:

[0017]

[0018] In the formula, h(n) represents the heuristic function, s goal represents the axial coordinate of the target point position, sn The axial coordinate of the current point position is represented by, the pipe radius is represented by R, and the rotation angle of the pipeline robot around the pipe circumference is represented by Δφ.

[0019] Preferably, the motion modes include: pure axial forward / backward, axial + clockwise rotation, axial + counterclockwise rotation, pure circumferential rotation, helical motion, and damage detection motion.

[0020] Preferably, the attitude angle data of the pipeline robot include: pitch angle, roll angle, and heading angle.

[0021] Preferably, the method further includes: a pipeline aging prediction model based on a deep spatio-temporal network, which warns of pipeline weak points by fusing historical maintenance records, real-time stress data, and environmental corrosion parameters.

[0022] Preferably, the method further includes: when a sudden leakage event occurs, using a gas diffusion inversion algorithm and combining it with a methane laser detection module to locate the leakage source.

[0023] The present invention also discloses a pipeline intelligent inspection system based on a pipeline robot, which is used to apply the above-mentioned pipeline intelligent inspection method based on a pipeline robot, and includes:

[0024] A detection module, configured to be carried on the pipeline robot and obtain the pipe wall thickness data, temperature field distribution data, and three-dimensional deformation data of the pipe network;

[0025] A three-dimensional mapping module, configured to construct a three-dimensional map of the pipe network based on the laser slam perception technology according to the pipe wall thickness data, temperature field distribution data, and three-dimensional deformation data;

[0026] A path planning module, configured to obtain historical maintenance data, automatically plan an optimal detection path through an improved hybrid A* global path planning algorithm, and perform inspections according to the optimal detection path; it is also configured to obtain local map information, fuse the detection data of the inertial measurement unit and the angle encoder, obtain the attitude angle data of the pipeline robot, construct the body attitude of the pipeline robot, and perform local path planning based on the artificial potential field method;

[0027] An inspection update module, configured to construct a pipeline health index evaluation system, connect key indicators with the intelligent pipe network system, automatically generate and optimize a resource allocation plan, generate a new inspection plan, and control the pipeline robot to continue inspections. The key indicators include corrosion rate and remaining life.

[0028] Preferably, the pipeline robot includes a body module and a driving module. The body module and the driving module are arranged at intervals. The overall shape of the pipeline robot is in a W shape or an M shape. The driving module is an omnidirectional wheel, and the detection module is arranged on the body module close to the advancing direction.

[0029] Compared with the prior art, the beneficial effects of the present invention are as follows:

[0030] By mounting a detection module on the pipeline robot, the present invention can simultaneously obtain the wall thickness data, temperature field distribution data, and three-dimensional deformation data of the pipeline network. Compared with the traditional single data acquisition method, it more comprehensively reflects the actual state of the pipeline, provides rich basic data for subsequent analysis and decision-making, helps to more accurately discover various potential problems existing in the pipeline, such as local corrosion, temperature anomalies, structural deformation, etc., thereby improving the accuracy and reliability of pipeline inspection.

[0031] Using the laser SLAM perception technology to construct a three-dimensional map of the pipeline network, and combining with an improved hybrid A* global path planning algorithm to automatically plan the optimal detection path, it can quickly find the best inspection route in a complex pipeline network, avoiding the blindness and inefficiency of manual planning, greatly improving the inspection efficiency, reducing the inspection time and cost, and at the same time ensuring the comprehensive coverage of the pipeline and the detailed detection of key areas. After obtaining the local map information, the detection data of the inertial measurement unit and the angle encoder are fused to obtain the attitude angle data of the pipeline robot, and then the body attitude is constructed, and the local path planning is carried out based on the artificial potential field method. This local path planning method enables the pipeline robot to flexibly avoid obstacles inside the pipeline, accurately travel along the predetermined path, effectively avoiding collisions with the inner wall of the pipeline or other obstacles, and improving the safety and stability of the inspection, especially suitable for pipeline environments with complex shapes or obstacles.

[0032] Construct a pipeline health index evaluation system, connect key indicators such as corrosion rate and remaining life with the intelligent pipeline network system, and realize the real-time monitoring and intelligent evaluation of the pipeline health status. Automatically generate and optimize the resource allocation plan and a new inspection plan according to the evaluation results, which can targetedly maintain and manage the pipeline, prevent the occurrence of pipeline accidents in advance, extend the service life of the pipeline, reduce the maintenance cost, improve the operation efficiency and safety of the entire pipeline network system, and realize the transformation of the pipeline from passive maintenance to active preventive maintenance, with significant economic and social benefits.

[0033] The wheels of the pipeline robot are of an omnidirectional wheel structure. The robot joint angles are adjusted through position control to realize the robot attitude adjustment, ensuring that the robot can pass through complex working conditions such as straight pipes, reduced-diameter pipes, bent pipes, and tees. Brief Description of the Drawings

[0034] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the following will briefly introduce the accompanying drawings required in the description of the embodiments or the prior art. Obviously, the accompanying drawings in the following description are only the embodiments of the present invention. For those of ordinary skill in the art, other accompanying drawings can be obtained based on the provided drawings without creative efforts.

[0035] Figure 1 is a schematic flow chart of a pipeline intelligent inspection method based on a pipeline robot according to the present invention;

[0036] Figure 2 is a functional block diagram of a pipeline intelligent inspection system based on a pipeline robot according to the present invention;

[0037] Figure 3 is a schematic diagram of a pipeline robot during inspection in a pipeline;

[0038] Figure 4 is a front view of the pipeline robot according to the present invention;

[0039] Figure 5 is a top view of the pipeline robot according to the present invention;

[0040] Figure 6 is an isometric view of the pipeline robot according to the present invention.

[0041] In the figure, 1 is a body module; 11 is the first torso; 12 is the second torso; 13 is the third torso; 14 is the fourth torso; 15 is a connecting shaft; 16 is a wheel shaft; 2 is a driving module; 21 is an omnidirectional wheel; 30 is a detection module. Detailed implementation manners

[0042] The following will clearly and completely describe the technical solutions in the embodiments of the present application with reference to the accompanying drawings in the embodiments of the present application. Obviously, the described embodiments are only a part of the embodiments of the present application, rather than all of the embodiments. Based on the embodiments of the present application, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the scope of protection of the present application.

[0043] As Figure 1 、 Figure 3 shown, the present invention provides a pipeline intelligent inspection method based on a pipeline robot, including:

[0044] Install a detection module on the pipeline robot. Obtain the wall thickness data, temperature field distribution data, and three-dimensional deformation data of the pipeline network through the detection module. Based on the laser slam perception technology, construct a three-dimensional map of the pipeline network according to the wall thickness data, temperature field distribution data, and three-dimensional deformation data. The detection module includes a pressure sensor, a humidity sensor, an inertial navigation unit, and a lidar.

[0045] Obtain historical maintenance data, automatically plan the optimal detection path through an improved hybrid A* global path planning algorithm, and conduct inspections according to the optimal detection path.

[0046] Obtain local map information, fuse the detection data of the inertial measurement unit and the angle encoder, obtain the attitude angle data of the pipeline robot, construct the body attitude of the pipeline robot, and conduct local path planning based on the artificial potential field method to complete autonomous obstacle avoidance in a complex pipeline network topological environment; the attitude angle data includes: pitch angle, roll angle, and heading angle.

[0047] Construct a pipeline health index evaluation system, connect key indicators with the intelligent pipeline network system, automatically generate and optimize the resource allocation plan, generate a new inspection plan, and control the pipeline robot to continue the inspection. The key indicators include corrosion rate and remaining life.

[0048] The present invention improves the accuracy and efficiency of pipeline inspection, reduces the cost and risk of manual inspection. By installing a variety of sensors, real-time monitoring of various parameters of the pipeline network is achieved, providing comprehensive data support for the health status assessment of the pipeline. At the same time, using advanced navigation and path planning technologies, autonomous inspection of the pipeline robot is realized, improving the intelligent level of inspection. In addition, by constructing a pipeline health index evaluation system, potential hidden dangers of the pipeline can be discovered in a timely manner, providing a scientific basis for the maintenance and management of the pipeline.

[0049] Specifically, through the combination of the detection module and the all-weather inspection ability of the drone platform, the wall thickness, temperature field distribution, and three-dimensional deformation data can be collected in real time in complex scenarios such as buried pipelines and elevated pipe galleries.

[0050] Conduct local path planning based on the artificial potential field method, including: constructing a gravitational field and a repulsive field through the artificial potential field method:

[0051] Gravitational field: The target point (such as the inspection end point or charging station) generates an attractive force on the pipeline robot. The magnitude of the attractive force is proportional to the distance between the pipeline robot and the target, and the direction points to the target.

[0052] Repulsive field: The inner wall of the pipeline and obstacles (such as sediments or elbows) generate a repulsive force on the pipeline robot. The magnitude of the repulsive force increases as the distance decreases, and the direction is perpendicular to the surface of the obstacle.

[0053] Determining the movement direction by the resultant force: The movement direction and speed of the pipeline robot are determined by calculating the resultant force of the gravitational force and the repulsive force. The pipeline robot moves along the direction of the resultant force, tending towards the target position and avoiding obstacles.

[0054] In some embodiments of the present application, the improved hybrid A* global path planning algorithm is specifically as follows: Obtain the current point position coordinates and the target point position coordinates of the pipeline robot according to the inertial navigation unit and the global positioning system; Define the four-dimensional state space (x, y, θ, φ) of the pipeline robot, and reconstruct and optimize the heuristic function, where θ represents the heading angle of the pipeline robot, and φ represents the rotation angle of the pipeline robot around the pipeline circumference; Perform node expansion, and customize the motion library according to the movement mode of the pipeline robot; Based on the laser slam perception technology, obtain the three-dimensional coordinate values of the obstacles, perform collision detection optimization, map the obstacles to the cylindrical coordinate grid, and adopt the probability occupancy grid update strategy; Based on the special structure of the pipeline environment and the turning radius of the pipeline robot, determine the optimal detection path that satisfies the minimum turning radius of the pipeline robot through the curvature constraint checker.

[0055] In this embodiment, considering the particularity of the pipeline environment, the pipeline is a cylindrical closed space, and the movement of the robot needs to meet the circumferential attitude constraint (such as avoiding collision with the pipe wall) and the axial movement continuity requirement. The three-dimensional state space (x, y, θ) of the traditional hybrid A* algorithm needs to be expanded to a four-dimensional state space.

[0056] It can be understood that by comprehensively considering the movement characteristics of the pipeline robot and the special structure of the pipeline environment, the accuracy and feasibility of the global path planning are improved. The reconstruction and optimization of the heuristic function make the path planning more efficient, and the node expansion and collision detection optimization further ensure the safety of the path. At the same time, adopting the probability occupancy grid update strategy and the curvature constraint checker makes the optimal detection path more conform to the actual movement ability of the pipeline robot, improving the efficiency and safety of the inspection.

[0057] In some embodiments of the present application, the reconstruction and optimization of the heuristic function are specifically as follows:

[0058]

[0059] In the formula, h(n) represents the heuristic function, s goal represents the axial coordinate of the target point position, s n represents the axial coordinate of the current point position, R represents the pipeline radius, and Δφ represents the rotation angle of the pipeline robot around the pipeline circumference.

[0060] It can be understood that through in-depth analysis and optimization of the heuristic function, the path planning algorithm becomes more intelligent and efficient in complex pipeline environments. Among them, h(n), as the core of the heuristic function, calculates its value by fully considering the axial distance between the target point and the current point, the pipeline radius, and the rotation angle of the pipeline robot around the pipeline circumference, so as to more accurately evaluate the optimal path of the pipeline robot from the current point to the target point. This optimization strategy not only improves the accuracy of path planning but also greatly reduces the computational complexity of path planning, enabling the pipeline robot to complete global path planning in a shorter time and improving the inspection efficiency.

[0061] In addition, the patent technical solution further enhances the safety and feasibility of path planning through the application of the probabilistic occupancy grid update strategy and the curvature constraint checker. The probabilistic occupancy grid update strategy can update the position and status of obstacles in the pipeline in real time, providing accurate obstacle information for the pipeline robot to avoid collision risks. The curvature constraint checker, based on the turning radius of the pipeline robot and the special structural environment of the pipeline, conducts curvature constraint checks on the path to ensure the feasibility of the path and avoid problems such as difficult movement or inability to reach the target point of the pipeline robot due to overly tortuous paths.

[0062] In some embodiments of the present application, the motion modes include: pure axial forward / backward, axial + clockwise rotation, axial + counterclockwise rotation, pure circumferential rotation, helical motion, and damage detection motion.

[0063] In this embodiment, the motion library is designed according to the motion methods of the robot, and it contains all possible motion modes and action combinations of the robot. The construction of the motion library needs to fully consider the motion capabilities and operation requirements of the robot to ensure that the robot can move and operate flexibly and efficiently in the pipeline.

[0064] When customizing the motion library according to the motion modes of the pipeline robot, first, determine the basic motion methods of the robot according to the pipeline environment and operation requirements. This is the premise and foundation for customizing the motion library. Second, design the motion library based on the selected motion methods. The motion library should contain all possible actions and action combinations of the robot under this motion method to meet the needs of different operation scenarios. Finally, verify the effectiveness and feasibility of the motion library through experiments and simulations. Adjust and optimize the motion library according to the actual situation to ensure that the robot can move and operate stably and efficiently in the pipeline.

[0065] In some embodiments of the present application, the method further includes: a pipeline aging prediction model based on a deep spatio-temporal network, which warns of pipeline weak points by fusing historical maintenance records, real-time stress data, and environmental corrosion parameters.

[0066] It is understandable that the prediction model can comprehensively consider various factors, such as the historical maintenance situation of the pipeline, the current stress state, and environmental factors, so as to achieve accurate prediction of the pipeline aging situation. Through early warning of pipeline weak points, potential safety hazards can be detected in a timely manner, providing strong support for pipeline maintenance and management. This not only helps to extend the service life of the pipeline, but also effectively prevents safety accidents caused by pipeline aging, ensuring the safe and stable operation of the pipeline system.

[0067] In some embodiments of the present application, the method further includes: when a sudden leakage event occurs, using a gas diffusion inversion algorithm and combining with a methane laser detection module to locate the leakage source.

[0068] It is understandable that this leakage source location method combines a gas diffusion inversion algorithm with high-precision methane laser detection technology, and can accurately lock the leakage position in a short time. Compared with traditional leakage detection methods, this method not only improves the detection efficiency, but also greatly enhances the positioning accuracy, helps to quickly respond to leakage events, reduce environmental pollution and property losses. At the same time, the application of this solution can also provide important references for subsequent repair work, shorten the repair time, and ensure the rapid restoration of the pipeline system operation.

[0069] As Figure 2 , Figures 4 - 5 shown, the present invention also discloses a pipeline intelligent inspection system based on a pipeline robot for applying the above-mentioned pipeline intelligent inspection method based on a pipeline robot, including: a detection module configured to be carried on the pipeline robot and obtain the pipe wall thickness data, temperature field distribution data, and three-dimensional deformation data of the pipe network.

[0070] A three-dimensional mapping module configured to construct a three-dimensional map of the pipe network based on the laser slam perception technology according to the pipe wall thickness data, temperature field distribution data, and three-dimensional deformation data.

[0071] A path planning module configured to obtain historical maintenance data, automatically plan an optimal detection path through an improved hybrid A* global path planning algorithm, and perform inspections according to the optimal detection path; also configured to obtain local map information, fuse the detection data of an inertial measurement unit and an angle encoder, obtain the attitude angle data of the pipeline robot, construct the body attitude of the pipeline robot, and perform local path planning based on the artificial potential field method.

[0072] An inspection update module configured to construct a pipeline health index evaluation system, connect key indicators with the intelligent pipe network system, automatically generate and optimize a resource allocation plan, generate a new inspection plan, and control the pipeline robot to continue inspections, where the key indicators include corrosion rate and remaining life.

[0073] The present invention improves the efficiency and accuracy of pipeline inspection, and reduces the cost and risk of manual inspection. The multi-dimensional data obtained by the detection module can comprehensively reflect the operating state of the pipeline, providing strong data support for subsequent pipeline maintenance and management. The three-dimensional mapping module uses advanced laser slam perception technology to construct an accurate three-dimensional model of the pipeline network, providing a reliable basis for path planning. The path planning module realizes the automatic planning of the optimal detection path through an improved hybrid A* global path planning algorithm, and combines local path planning to ensure the stable inspection of the pipeline robot in a complex environment. The inspection update module realizes the automatic generation and optimization of the inspection plan by constructing a pipeline health index evaluation system, further improving the intelligent level of inspection.

[0074] In some embodiments of the present application, the pipeline robot includes a body module 1 and a driving module 2. The body module 1 and the driving module 2 are arranged at intervals. The overall shape of the pipeline robot is in a W shape or an M shape. The driving module 2 is an omnidirectional wheel, and the detection module is arranged on the body module 1 close to the forward direction.

[0075] Figure 6 As a structural example of a preferred embodiment of the present application, the trunk module includes a first trunk 11, a second trunk 12, a third trunk 13, and a fourth trunk 14. The first trunk 11 and the second trunk 12 are rotatably connected to each other. Omnidirectional wheels 21 are arranged on both sides of the connecting shaft 15 between the first trunk 11 and the second trunk 12. The connection modes between the second trunk 12 and the third trunk 13 and between the third trunk 13 and the fourth trunk 14 are the same as the connection mode between the first trunk 11 and the second trunk 12. Adjacent two trunks form a V shape. The pipeline robot is formed by connecting several groups of Vs together. Wheel shafts 16 are arranged at the head of the first trunk 11 and the tail of the fourth trunk 14, and omnidirectional wheels 21 are arranged on both sides of the wheel shafts. The pipeline robot realizes omnidirectional free movement during pipeline inspection through the mutual rotation between the trunks and the multi-directional movement of the omnidirectional wheels. The detection module 30 is arranged at the head of the first trunk, that is, in the forward direction of the pipeline robot. When the pipeline robot moves forward, the detection module detects the pipeline in real time, facilitating the realization of the inspection of the pipeline interior.

[0076] Each trunk can be provided with a motor, a battery, and a drive board as needed. The battery provides electrical energy for the motor, and the omnidirectional wheel rotates and moves forward under the control of the drive board to realize the intelligent inspection of the pipeline robot. The battery in this embodiment can be wirelessly charged, reducing the influence of cables on the operation of the pipeline robot and realizing the self-circulation power supply of the pipeline robot.

[0077] It can be understood that the design of this pipeline robot enhances its adaptability and flexibility within the pipeline, especially in pipelines with curved or complex structures. The spaced arrangement of the body module and the drive module, as well as the overall W-shaped or M-shaped configuration, enable the pipeline robot to better conform to the inner wall of the pipeline, reducing the movement obstacles caused by changes in the pipeline shape. The adoption of omnidirectional wheels makes the pipeline robot move more freely in forward, backward, turning and other actions, improving the flexibility and efficiency of inspection. In addition, setting the detection module on the body module close to the forward direction ensures the real-time and accuracy of the detection data, providing key information for subsequent pipeline health assessment and maintenance.

[0078] Those skilled in the art should understand that the embodiments of the present application can be provided as methods, systems, or computer program products. Therefore, the present application can take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware aspects. Moreover, the present application can take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0079] The present application is described with reference to the flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to the embodiments of the present application. It should be understood that each process and / or block in the flowchart and / or block diagram can be implemented by computer program instructions, and the combination of the processes and / or blocks in the flowchart and / or block diagram can also be implemented by computer program instructions. These computer program instructions can be provided to the processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing devices to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing devices generate a device for realizing the functions specified in Figure 1 one process or multiple processes and / or blocks Figure 1 one block or multiple blocks.

[0080] These computer program instructions can also be stored in a computer-readable memory that can direct a computer or other programmable data processing device to work in a specific manner, so that the instructions stored in the computer-readable memory generate a manufactured article including an instruction device, and the instruction device realizes the functions specified in Figure 1 one process or multiple processes and / or blocks Figure 1 one block or multiple blocks.

[0081] These computer program instructions can also be loaded onto a computer or other programmable data processing device, so that a series of operation steps are executed on the computer or other programmable device to generate a computer-implemented process, and thus the instructions executed on the computer or other programmable device provide for realizing the functions in Figure 1One process or multiple processes and / or boxes Figure 1 Steps of the functions specified in one box or multiple boxes.

[0082] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit them. Although the present invention has been described in detail with reference to the above embodiments, those of ordinary skill in the art should understand that: modifications or equivalent replacements can still be made to the specific implementation manners of the present invention, and any modification or equivalent replacement that does not depart from the spirit and scope of the present invention should be covered within the protection scope of the claims of the present invention.

Claims

1. The pipeline intelligent inspection method based on pipeline robot is characterized by: include: A detection module is installed on the pipeline robot to obtain pipe wall thickness data, temperature field distribution data and three-dimensional deformation data of the pipe network through the detection module, and a three-dimensional map of the pipe network is constructed based on the pipe wall thickness data, temperature field distribution data and three-dimensional deformation data based on the laser slam sensing technology; Obtain historical maintenance data, automatically plan the optimal inspection path through the improved hybrid A-star global path planning algorithm, and conduct inspections based on the optimal inspection path; Obtain local map information, integrate the detection data of the inertial measurement unit and the angle encoder, obtain the attitude angle data of the pipeline robot, construct the body attitude of the pipeline robot, and perform local path planning based on the artificial potential field method; Build a pipeline health index evaluation system, connect key indicators with the intelligent pipeline network system, automatically generate and optimize resource allocation plans, generate new inspection plans, and control pipeline robots to continue inspections. The key indicators include corrosion rate and remaining life.

2. The pipeline intelligent inspection method based on pipeline robot according to claim 1 is characterized in that: The detection module includes a pressure sensor, a humidity sensor, an inertial navigation unit and a laser radar.

3. The pipeline intelligent inspection method based on pipeline robot according to claim 1 is characterized in that: The improved hybrid A-star global path planning algorithm is as follows: Obtain the current position coordinates and target position coordinates of the pipeline robot according to the inertial navigation unit and the global satellite positioning system; Define the four-dimensional state space (x, y, θ, φ) of the pipeline robot, and reconstruct and optimize the heuristic function, where θ represents the heading angle of the pipeline robot and φ represents the rotation angle of the pipeline robot around the circumference of the pipeline; Expand nodes and customize the motion library according to the motion mode of the pipeline robot; Obtain the three-dimensional coordinates of obstacles based on laser slam perception technology, optimize collision detection, map obstacles to cylindrical coordinate grids, and adopt a probabilistic occupancy grid update strategy; Based on the special structural environment of the pipeline and the turning radius of the pipeline robot, the curvature constraint checker is used to determine the optimal detection path that meets the minimum turning radius of the pipeline robot.

4. The pipeline intelligent inspection method based on pipeline robot according to claim 3 is characterized in that: Reconstruct and optimize the heuristic function, specifically: In the formula, h(n) represents the heuristic function, s goal Indicates the axial coordinate of the target point position, s n represents the axial coordinate of the current point position, R represents the radius of the pipeline, and Δφ represents the rotation angle of the pipeline robot around the circumference of the pipeline.

5. The pipeline intelligent inspection method based on pipeline robot according to claim 3 is characterized in that: The movement modes include: pure axial advance / retract, axial + clockwise rotation, axial + counterclockwise rotation, pure circumferential rotation, spiral movement and damage detection movement.

6. The pipeline intelligent inspection method based on pipeline robot according to claim 1 is characterized in that: The attitude angle data of the pipeline robot include: pitch angle, roll angle and heading angle.

7. The pipeline intelligent inspection method based on pipeline robot according to claim 1 is characterized in that: The method also includes: a pipeline aging prediction model based on a deep spatiotemporal network, which provides early warning of pipeline weaknesses by integrating historical maintenance records, real-time stress data, and environmental corrosion parameters.

8. The pipeline intelligent inspection method based on pipeline robot according to claim 1 is characterized in that: The method further comprises: when a sudden leakage event occurs, a gas diffusion inversion algorithm is used in combination with a methane laser detection module to locate the leakage source.

9. A pipeline intelligent inspection system based on a pipeline robot, used for applying the pipeline intelligent inspection method based on a pipeline robot as described in any one of claims 1 to 8, characterized in that: include: The detection module is configured to be mounted on the pipeline robot and obtain pipe wall thickness data, temperature field distribution data and three-dimensional deformation data of the pipe network; A three-dimensional mapping module is configured to construct a three-dimensional map of the pipe network based on the pipe wall thickness data, the temperature field distribution data and the three-dimensional deformation data based on the laser slam sensing technology; The path planning module is configured to obtain historical maintenance data, automatically plan the optimal detection path through the improved hybrid A-star global path planning algorithm, and perform inspections according to the optimal detection path; it is also configured to obtain local map information, integrate the detection data of the inertial measurement unit and the angle encoder, obtain the attitude angle data of the pipeline robot, construct the body attitude of the pipeline robot, and perform local path planning based on the artificial potential field method; The inspection update module is configured to build a pipeline health index evaluation system, connect key indicators with the intelligent pipeline network system, automatically generate and optimize resource allocation plans, generate new inspection plans, and control the pipeline robot to continue inspections. The key indicators include corrosion rate and remaining life.

10. The pipeline intelligent inspection system based on pipeline robot according to claim 9 is characterized in that: The pipeline robot includes a body module and a driving module. The body module and the driving module are spaced apart. The pipeline robot is arranged in a W shape or an M shape as a whole. The driving module is an omnidirectional wheel. The detection module is arranged on the body module close to the forward direction.

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