Pipeline intelligent inspection system and method based on pipeline robot

Abstract

The application relates to the technical field of pipeline intelligent inspection, and discloses a pipeline intelligent inspection method and system based on a pipeline robot, which comprises the following steps: acquiring 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; acquiring historical maintenance data, automatically planning an optimal detection path through an improved hybrid A-star global path planning algorithm, and performing inspection; 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 key indexes with an intelligent pipe network system, and generating a new inspection scheme for inspection. The application can realize comprehensive and accurate data acquisition, efficient path planning, accurate local navigation and intelligent health management.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of pipeline intelligent inspection, in particular to a pipeline intelligent inspection system and method based on a pipeline robot. BACKGROUND

[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 environmental factors, medium corrosion, geological subsidence, and equipment aging, the safe operation of pipelines faces severe challenges. The traditional pipeline inspection method mainly relies on manual periodic inspection, which is not only inefficient and labor-intensive, but also difficult to detect the internal conditions of the pipeline comprehensively and accurately, and cannot timely detect potential safety hazards, which easily leads to pipeline leakage, rupture and other accidents, causing great losses to people's life and property safety 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 manual entry into the pipeline for detection to some extent, there are still many deficiencies in data acquisition and processing, path planning, and health assessment. For example, in data collection, most pipeline robots can only acquire a single type of data, making it difficult to fully reflect the actual condition of the pipeline; in path planning, traditional algorithms often cannot balance global optimality and local obstacle avoidance flexibility; for pipeline health assessment, there is a lack of systematicness and intelligence, and real-time data cannot be used to automatically generate effective resource allocation and inspection schemes. Moreover, most cable-equipped pipeline robots achieve energy and information transmission through cables, and due to the length of the cable and the resistance when pulling the cable, the inspection length is limited. Cable-free pipeline robots achieve self-power supply through internal power supply, and are limited by battery capacity, with insufficient endurance, requiring wireless power supply from the outside to achieve energy transmission; there is also a problem of insufficient environmental adaptability of pipeline robots, such as complex terrains such as mountainous areas and pipelines with a burial depth greater than 5m, which are difficult to achieve stable signal coverage, resulting in an increased rate of missed detection. Therefore, there is an urgent need for a more intelligent, efficient, and comprehensive pipeline inspection method to meet the growing demand for pipeline maintenance. SUMMARY

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

[0005] The present application provides a pipeline intelligent inspection method based on a pipeline robot, comprising:

[0006] The detection module is mounted on the pipeline robot, and the pipe wall thickness data, temperature field distribution data and three-dimensional deformation data of the pipe network are obtained through the detection module, and a three-dimensional mapping of the pipe network is constructed based on the laser slam sensing technology according to the pipe wall thickness data, temperature field distribution data and three-dimensional deformation data.

[0007] The historical maintenance data is obtained, the optimal detection path is automatically planned through the improved hybrid A-star global path planning algorithm, and the inspection is performed according to the optimal detection path.

[0008] The local map information is obtained, the detection data of the inertial measurement unit and the angle encoder are fused, the attitude angle data of the pipeline robot is obtained, the body attitude of the pipeline robot is constructed, and the local path planning is performed based on the artificial potential field method.

[0009] The pipeline health index evaluation system is constructed, the key indicators are connected with the intelligent pipe network system, the resource allocation scheme is automatically generated and optimized, the new inspection scheme is generated, and the pipeline robot continues to inspect, and the key indicators include the corrosion rate and the remaining life.

[0010] Preferably, the detection module comprises a pressure sensor, a humidity sensor, an inertial navigation unit and a laser radar.

[0011] Preferably, the improved hybrid A-star global path planning algorithm is as follows:

[0012] The current point position coordinates and the target point position coordinates of the pipeline robot are obtained according to the inertial navigation unit and the global satellite positioning system.

[0013] The four-dimensional state space (x, y, theta, phi) of the pipeline robot is defined, and the heuristic function is reconstructed and optimized, wherein theta represents the heading angle of the pipeline robot, and phi represents the rotation angle of the pipeline robot around the pipe circumference; node expansion is performed, and a motion library is customized according to the motion mode of the pipeline robot.

[0014] The three-dimensional coordinate values of the obstacles are obtained based on the laser slam sensing technology, collision detection optimization is performed, the obstacles are mapped to the column coordinate grid, and the probability occupancy grid update strategy is adopted.

[0015] Based on the special structure environment of the pipeline and the turning radius of the pipeline robot, the optimal detection path is determined to satisfy the minimum turning radius of the pipeline robot through the curvature constraint checker.

[0016] Preferably, the heuristic function is reconstructed and optimized, and the specific process is as follows:

[0017]

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

[0019] Preferably, the movement mode comprises: pure axial forward / backward movement, axial + clockwise rotation, axial + counterclockwise rotation, pure circumferential rotation, spiral movement and damage detection movement.

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

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

[0022] Preferably, the method further comprises: when a sudden leakage event occurs, adopting a gas diffusion inversion algorithm, combining a methane laser detection module, and positioning a leakage source.

[0023] The application further discloses a pipe intelligent inspection system based on a pipe robot, which is used for applying the pipe intelligent inspection method based on the pipe robot.

[0024] The detection module is configured to be carried on the pipe robot and acquire pipe wall thickness data, temperature field distribution data and three-dimensional deformation data of the pipe network.

[0025] The three-dimensional mapping module is configured to construct a three-dimensional mapping of the pipe network based on a laser slam sensing technology according to the pipe wall thickness data, the temperature field distribution data and the three-dimensional deformation data.

[0026] The path planning module is configured to acquire historical maintenance data, automatically plan an optimal detection path through an improved hybrid A-star global path planning algorithm, and perform inspection according to the optimal detection path; and is further configured to acquire local map information, fuse detection data of an inertial measurement unit and an angle encoder, acquire attitude angle data of the pipe robot, construct a body posture of the pipe robot, and perform local path planning based on an artificial potential field method.

[0027] The inspection updating module is configured to construct a pipe health index evaluation system, dock key indicators with an intelligent pipe network system, automatically generate and optimize a resource allocation scheme, generate a new inspection scheme, and control the pipe robot to continue inspection, wherein the key indicators comprise a corrosion rate and a remaining life.

[0028] Preferably, the pipeline robot comprises a body module and a driving module, the body module is arranged in a spaced manner with the driving module, the pipeline robot is arranged in a W shape or an M shape as a whole, the driving module is an omni-directional wheel, and the detection module is arranged on the body module close to the advancing direction.

[0029] Compared with the prior art, the pipeline robot has the beneficial effects that:

[0030] The pipeline robot can simultaneously obtain the pipe wall thickness data, temperature field distribution data and three-dimensional deformation data of the pipe network by carrying the detection module, compared with the traditional single data acquisition mode, the actual state of the pipeline is more comprehensively reflected, rich basic data is provided for subsequent analysis and decision-making, and various potential problems of the pipeline, such as local corrosion, temperature anomaly and structural deformation, can be more accurately found, so that the accuracy and reliability of pipeline inspection are improved.

[0031] The three-dimensional mapping of the pipe network is constructed by using the laser SLAM sensing technology, and the improved hybrid A-star global path planning algorithm is combined to automatically plan the optimal detection path, so that the best inspection route can be quickly found in the complex pipeline network, the blindness and inefficiency of manual planning are avoided, the inspection efficiency is greatly improved, the inspection time and cost are reduced, and the comprehensive coverage of the pipeline and the detailed detection of the key areas are ensured. After obtaining the local map information, the attitude angle data of the pipeline robot is obtained by fusing the detection data of the inertial measurement unit and the angle encoder, 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 can make the pipeline robot flexibly avoid obstacles in the pipeline, accurately travel along the predetermined path, effectively avoid collision with the inner wall of the pipeline or other obstacles, improve the safety and stability of the inspection, and is especially suitable for complex shape or obstacle existing pipeline environment.

[0032] A pipeline health index evaluation system is constructed, key indicators such as corrosion rate and remaining life are connected with the intelligent pipe network system, and real-time monitoring and intelligent evaluation of the pipeline health condition are realized. According to the evaluation result, a resource allocation scheme and a new inspection scheme are automatically generated and optimized, the pipeline can be maintained and managed in a targeted manner, the occurrence of pipeline accidents can be prevented in advance, the service life of the pipeline is prolonged, the maintenance cost is reduced, the operation efficiency and safety of the entire pipe network system are improved, the pipeline is changed from passive maintenance to active preventive maintenance, and remarkable economic benefits and social benefits are obtained.

[0033] The pipeline robot wheel is an omni-directional wheel structure, the joint angle of the robot is adjusted through position control to realize the posture adjustment of the robot, and it is ensured that the robot can pass through straight pipes, variable-diameter pipes, elbow pipes, three-way pipes and other complex working conditions. BRIEF DESCRIPTION OF DRAWINGS

[0034] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the prior art, the accompanying drawings needed to be used in the description of the embodiments or the prior art will be briefly introduced. Obviously, the accompanying drawings in the following description only only the embodiments of the present application, and for those skilled in the art, other drawings can also be obtained without creative labor on the basis of the provided drawings.

[0035] Figure 1 is a flowchart of a pipeline intelligent inspection method based on a pipeline robot of the present application;

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

[0037] Figure 3 is a schematic diagram of the pipeline robot when it is inspecting in the pipeline;

[0038] Figure 4 is a front view of the pipeline robot in the present application;

[0039] Figure 5 is a top view of the pipeline robot in the present application;

[0040] Figure 6 is an isometric view of the pipeline robot in the present application.

[0041] In the figure, 1, body module; 11, first section of trunk; 12, second section of trunk; 13, third section of trunk; 14, fourth section of trunk; 15, connecting shaft; 16, wheel shaft; 2, driving module; 21, omni-directional wheel; 30, detection module. DETAILED DESCRIPTION

[0042] The technical solutions in the embodiments of the present application will be described clearly and completely below with reference to the accompanying drawings in the embodiments of the present application. Obviously, the described embodiments are only some of the embodiments of the present application, not all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative labor fall within the scope of protection of the present application.

[0043] As shown in Figure 1 , Figure 3 , the present application provides a pipeline intelligent inspection method based on a pipeline robot, comprising:

[0044] A detection module is mounted on the pipeline robot, and the detection module is used to acquire pipe wall thickness data, temperature field distribution data and three-dimensional deformation data of the pipe network, a three-dimensional mapping of the pipe network is constructed based on laser slam sensing technology according to the pipe wall thickness data, the temperature field distribution data and the three-dimensional deformation data, and the detection module comprises a pressure sensor, a humidity sensor, an inertial navigation unit and a laser radar.

[0045] Historical maintenance data is acquired, an optimal detection path is automatically planned by using an improved hybrid A-star global path planning algorithm, and inspection is performed according to the optimal detection path.

[0046] Local map information is acquired, detection data of an inertial measurement unit and an angle encoder are fused, attitude angle data of the pipeline robot is acquired, a body attitude of the pipeline robot is constructed, and local path planning is performed based on an artificial potential field method to complete autonomous obstacle avoidance in a complex pipe network topology environment; the attitude angle data comprises a pitch angle, a roll angle and a heading angle.

[0047] A pipeline health index evaluation system is constructed, key indicators are connected to the intelligent pipe network system, a resource allocation scheme is automatically generated and optimized, a new inspection scheme is generated, and the pipeline robot continues to perform inspection, and the key indicators comprise a corrosion rate and a remaining life.

[0048] The present application improves the accuracy and efficiency of pipeline inspection, reduces the cost and risk of manual inspection, and realizes real-time monitoring of various parameters of the pipe network by mounting various sensors, provides comprehensive data support for the health status evaluation of the pipeline, and at the same time, uses advanced navigation and path planning technology to realize autonomous inspection of the pipeline robot, improves the intelligent level of inspection, and further constructs a pipeline health index evaluation system to timely find potential hazards of the pipeline and provide a scientific basis for the maintenance and management of the pipeline.

[0049] Specifically, through the detection module combined with the all-weather inspection capability of the unmanned aerial vehicle platform, pipe wall thickness, temperature field distribution and three-dimensional deformation data can be collected in real time in complex scenes such as buried pipelines and high-altitude pipe galleries.

[0050] The local path planning based on the artificial potential field method comprises:

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

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

[0053] The resultant force determines the motion direction: the motion direction and speed of the pipeline robot are determined by calculating the resultant force of the attractive force and the repulsive force. The pipeline robot moves along the direction of the resultant force, tends to the target position and avoids obstacles.

[0054] In some embodiments of the present application, an improved hybrid A-star global path planning algorithm is provided, specifically: obtaining the current point position coordinates and target point position coordinates of the pipeline robot according to the inertial navigation unit and the global satellite positioning system; defining the four-dimensional state space (x, y, θ, φ) of the pipeline robot, and reconstructing and optimizing the heuristic function, wherein 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; node expansion is performed, and a motion library is customized according to the motion mode of the pipeline robot; three-dimensional coordinate values of obstacles are obtained based on laser slam sensing technology, collision detection optimization is performed, the obstacles are mapped to a cylindrical coordinate grid, and a probability occupancy grid update strategy is adopted; based on the special structure environment of the pipeline and the turning radius of the pipeline robot, the optimal detection path is determined to satisfy the minimum turning radius of the pipeline robot through a curvature constraint checker.

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

[0056] It can be understood that by comprehensively considering the motion characteristics of the pipeline robot and the special structure environment of the pipeline, the accuracy and feasibility of 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, the probability occupancy grid update strategy and the curvature constraint checker make the optimal detection path more consistent with the actual motion ability of the pipeline robot, improving the efficiency and safety of the inspection.

[0057] In some embodiments of the present application, the heuristic function is reconstructed and optimized, specifically:

[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 radius of the pipeline, and Δφ represents the rotation angle of the pipeline robot around the circumference of the pipeline.

[0060] It can be understood that through in-depth analysis and optimization of the heuristic function, the path planning algorithm is more intelligent and efficient in complex pipeline environment. Among them, h(n) as the core of the heuristic function, the calculation of its value fully considers 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 calculation amount of path planning, so that the pipeline robot can complete the global path planning in a shorter time and improve the inspection efficiency.

[0061] In addition, the patent technical solution further enhances the safety and feasibility of path planning through the application of probability occupancy grid update strategy and curvature constraint checker. The probability occupancy grid update strategy can update the position and state of obstacles in the pipeline in real time, providing accurate obstacle information for the pipeline robot to avoid collision risks. The curvature constraint checker checks the curvature constraint of the path according to the turning radius of the pipeline robot and the special structure environment of the pipeline, ensuring the feasibility of the path and avoiding the problem of difficult movement or inability to reach the target point of the pipeline robot due to too winding path.

[0062] In some embodiments of the present application, the motion mode includes pure axial forward / backward, axial + clockwise rotation, axial + counterclockwise rotation, pure circumferential rotation, spiral motion and damage detection motion.

[0063] In this embodiment, the motion library is designed according to the motion method of the robot, which contains all possible motion modes and action combinations of the robot. The construction of the motion library needs to fully consider the motion ability and work demand of the robot, to ensure that the robot can move and work flexibly and efficiently in the pipeline.

[0064] When customizing the motion library according to the motion mode of the pipeline robot, first, the basic motion method of the robot is determined according to the pipeline environment and work demand. This is the premise and basis of customizing the motion library. Second, the motion library is designed based on the selected motion method. The motion library should contain all possible actions and action combinations of the robot under this motion method, to meet the needs of different work scenarios. Finally, the effectiveness and feasibility of the motion library are verified through experiments and simulations. The motion library is adjusted and optimized according to the actual situation, to ensure that the robot can move and work stably and efficiently in the pipeline.

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

[0066] It can be understood that the prediction model can comprehensively consider various factors such as the historical maintenance of the pipeline, the current stress state and environmental factors, so as to realize accurate prediction of the aging condition of the pipeline. Through early warning of the weak points of the pipeline, potential safety hazards can be found in time, providing strong support for the maintenance and management of the pipeline. This not only helps to prolong 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 comprises: when a sudden leakage event occurs, using a gas diffusion inversion algorithm in combination with a methane laser detection module to locate the leakage source.

[0068] It can be understood that the leakage source positioning method combines the gas diffusion inversion algorithm and 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, which helps to quickly respond to leakage events, reduce environmental pollution and property losses. At the same time, the application of this scheme can also provide important reference for subsequent repair work, shorten the repair time and ensure the rapid recovery of the pipeline system.

[0069] As shown in Figure 2 , Figure 4 , Figure 5 The present application also discloses a pipeline intelligent inspection system based on a pipeline robot, which is used for applying the pipeline intelligent inspection method based on the pipeline robot, and comprises a detection module configured to be carried on the pipeline robot and configured to acquire pipe wall thickness data, temperature field distribution data and three-dimensional deformation data of the pipe network.

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

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

[0072] An inspection updating module is configured to construct a pipeline health index evaluation system, dock key indicators with an intelligent pipe network system, automatically generate and optimize a resource allocation scheme, generate a new inspection scheme, and control the pipeline robot to continue inspection, wherein the key indicators include a corrosion rate and a remaining life.

[0073] The application improves the efficiency and accuracy of pipeline inspection, reduces the cost and risk of manual inspection. The multi-dimensional data obtained by the detection module can comprehensively reflect the running state of the pipeline, providing strong data support for subsequent pipeline maintenance and management. The three-dimensional mapping module uses advanced laser slam sensing technology to construct a precise pipe network three-dimensional model, providing a reliable foundation for path planning. The path planning module realizes the automatic planning of the optimal detection path through the improved hybrid A-star global path planning algorithm, and combines local path planning to ensure the stable inspection of the pipeline robot in complex environments. The inspection update module realizes the automatic generation and optimization of the inspection scheme through the construction of the pipeline health index evaluation system, further improving the intelligent level of the inspection.

[0074] In some embodiments of the application, the pipeline robot comprises a body module 1 and a driving module 2, the body module 1 is spaced apart from the driving module 2, the pipeline robot is arranged in a W shape or an M shape as a whole, the driving module 2 is an omni-directional wheel, and the detection module is arranged on the body module 1 close to the advancing direction.

[0075] Figure 6 For a preferred embodiment of the application, the trunk module comprises 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, omni-directional wheels 21 are arranged on both sides of the connecting shaft 15 of the first trunk 11 and the second trunk 12, the connection mode between the second trunk 12 and the third trunk 13 and the connection mode between the third trunk 13 and the fourth trunk 14 are the same as the connection mode of the first trunk 11 and the second trunk 12, the adjacent two trunks form a V shape, the pipeline robot is connected together by a plurality of groups of V shapes, the first trunk 11 head and the fourth trunk 14 tail are both provided with an axle 16, and omni-directional wheels 21 are arranged on both sides of the axle, the pipeline robot realizes omnidirectional free movement in the pipeline by the mutual rotation between the trunks and the multidirectional movement of the omni-directional wheels. The detection module 30 is arranged at the head of the first trunk, i.e. the advancing direction of the pipeline robot, and the detection module detects the pipeline in real time when the pipeline robot advances, facilitating the inspection of the inside of the pipeline.

[0076] Each trunk can be provided with a motor, a battery and a driving board as needed, the motor is powered by the battery, the omni-directional wheels rotate and advance under the control of the driving board, and the intelligent inspection of the pipeline robot is realized, and the battery in the embodiment can realize wireless charging, reducing the influence of the cable on the pipeline robot during operation, and realizing the self-circulation power supply of the pipeline robot.

[0077] It can be understood that the design of the pipeline robot enhances its adaptability and flexibility in the pipeline, especially in the pipeline with curved or complex structure. The interval arrangement of the body module and the driving module, and the overall W-shaped or M-shaped configuration make the pipeline robot better fit the inner wall of the pipeline, reducing the motion obstacles caused by the change of the pipeline shape. The adoption of the omni-directional wheels makes the pipeline robot more flexible in advancing, retreating, turning and other actions, improving the flexibility and efficiency of the inspection. In addition, the detection module is arranged on the body module close to the advancing direction, ensuring the real-time and accuracy of the detection data, and providing key information for the subsequent pipeline health assessment and maintenance.

[0078] Those skilled in the art will appreciate that embodiments of the application can be provided as methods, systems or computer program products. Accordingly, the application can be embodied in the form of an entirely hardware embodiment, an entirely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the application can be embodied in the form of a computer program product on one or more computer-usable storage media (including, but not limited to, disk memory, CD-ROM, optical memory and the like) embodying computer usable program code.

[0079] The application is described with reference to flowcharts and / or block diagrams of methods, apparatus (systems) and computer program products according to embodiments of the application. It should be understood that each flow and / or block in the flowcharts and / or block diagrams can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general purpose computer, a special purpose computer, an embedded processor or other programmable data processing device to produce a machine, so that the instructions, which are executed via the processor of the computer or other programmable data processing device, generate a means for implementing the functions specified in the flowcharts and / or block diagrams. Figure 1 one or more flows and / or blocks Figure 1 means for carrying out the functions specified in the flowchart

[0080] These computer program instructions can also be stored in a computer readable memory that can direct the computer or other programmable data processing device to work in a specific manner, so that the instructions stored in the computer readable memory produce a product including instruction means, which implement the functions specified in the flowcharts and / or block diagrams. Figure 1 one or more flows and / or blocks Figure 1 means for carrying out the functions specified in the flowchart

[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 performed on the computer or other programmable device to produce a computer implemented process, so that the instructions executed on the computer or other programmable device provide a means for implementing the functions specified in the flowcharts and / or block diagrams. Figure 1one or more processes and / or blocks Figure 1 the steps of the functions specified in the one or more blocks.

[0082] Finally, it should be noted that the above-mentioned embodiments are merely used to illustrate the technical solutions of the present application, but not to limit it. Although the present application has been described in detail with reference to the above embodiments, those skilled in the art should understand that the specific embodiments of the present application can be modified or equivalent replaced without departing from the spirit and scope of the present application, and any modification or equivalent replacement should be covered in the protection scope of the claims of the present application.

Claims

1. A method for intelligent inspection of a pipeline based on a pipeline robot, characterized in that, The method comprises the following steps: A detection module is mounted on a pipeline robot, and the detection module is used to acquire pipe wall thickness data, temperature field distribution data and three-dimensional deformation data of a pipe network; a three-dimensional mapping of the pipe network is constructed based on a laser SLAM sensing technology according to the pipe wall thickness data, the temperature field distribution data and the three-dimensional deformation data; Historical maintenance data is acquired, an optimal detection path is automatically planned by using an improved hybrid A-star global path planning algorithm, and inspection is performed according to the optimal detection path; Local map information is acquired, detection data of an inertial measurement unit and an angle encoder are fused, attitude angle data of the pipeline robot is acquired, a body attitude of the pipeline robot is constructed, and local path planning is performed based on an artificial potential field method; A pipeline health index evaluation system is constructed, key indicators are connected to an intelligent pipe network system, a resource allocation scheme is automatically generated and optimized, a new inspection scheme is generated, and the pipeline robot continues to perform inspection, the key indicators including a corrosion rate and a remaining life; The improved hybrid A-star global path planning algorithm comprises the following steps: Current point position coordinates and target point position coordinates of the pipeline robot are acquired according to an inertial navigation unit and a global satellite positioning system; A four-dimensional state space (x, y, θ, φ) of the pipeline robot is defined, and a heuristic function is reconstructed and optimized, wherein θ represents a heading angle of the pipeline robot, and φ represents a rotation angle of the pipeline robot around a pipe circumference; Node expansion is performed, and a motion library is customized according to a motion mode of the pipeline robot; Three-dimensional coordinate values of obstacles are acquired based on the laser SLAM sensing technology, collision detection optimization is performed, the obstacles are mapped to a column coordinate grid, and a probability occupancy grid update strategy is adopted; Based on a special structure environment of the pipeline and a turning radius of the pipeline robot, an optimal detection path is determined to satisfy a minimum turning radius of the pipeline robot by using a curvature constraint checker. The heuristic function is reconstructed and optimized, and the reconstruction and optimization of the heuristic function comprises the following steps: where h(n) represents a heuristic function, s goal represents an axial coordinate of a target point position, s n represents an axial coordinate of a current point position, R represents a pipe radius, and Δφ represents a rotation angle of the pipe robot around a pipe circumference.

2. The method according to claim 1, wherein, The detection module comprises a pressure sensor, a humidity sensor, an inertial navigation unit and a laser radar.

3. The method according to claim 1, wherein, The motion mode comprises the following modes: pure axial forward / backward movement, axial + clockwise rotation, axial + counterclockwise rotation, pure circumferential rotation, spiral movement and damage detection movement.

4. The method according to claim 1, wherein, The attitude angle data of the pipeline robot comprises a pitch angle, a roll angle and a heading angle.

5. The method according to claim 1, wherein, The method further comprises the following steps: based on a pipeline aging prediction model of a deep spatiotemporal network, historical maintenance records, real-time stress data and environmental corrosion parameters are fused to perform early warning on weak points of the pipeline.

6. The method according to claim 1, wherein, When a sudden leakage event occurs, a gas diffusion inversion algorithm is adopted in combination with a methane laser detection module to perform leakage source positioning.

7. A pipeline intelligent inspection system based on a pipeline robot, for applying the pipeline intelligent inspection method based on a pipeline robot according to any one of claims 1-6, characterized in that, The method comprises the following steps: A detection module is mounted on a pipeline robot, and the detection module is used to acquire pipe wall thickness data, temperature field distribution data and three-dimensional deformation data of a pipe network; A three-dimensional mapping module is configured to construct a three-dimensional mapping of the pipe network based on a laser SLAM sensing technology according to the pipe wall thickness data, the temperature field distribution data and the three-dimensional deformation data. The path planning module is configured to acquire historical maintenance data, automatically plan an optimal detection path through an improved hybrid A-star global path planning algorithm, and perform inspection according to the optimal detection path; and is further configured to acquire local map information, fuse detection data of an inertial measurement unit and an angle encoder, acquire attitude angle data of the pipeline robot, construct a body attitude of the pipeline robot, and perform local path planning based on an artificial potential field method; The inspection updating module is configured to construct a pipeline health index evaluation system, dock key indicators with the intelligent pipe network system, automatically generate and optimize a resource allocation scheme, generate a new inspection scheme, and control the pipeline robot to continue inspection, wherein the key indicators include a corrosion rate and a remaining life.

8. The pipeline intelligent inspection system based on the pipeline robot according to claim 7, characterized in that, The pipeline robot comprises a body module and a driving module, the body module is arranged at intervals from the driving module, the pipeline robot is arranged in a whole W shape or M shape, the driving module is an omni-directional wheel, and the detection module is arranged on the body module close to the advancing direction.

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