CN114324401A - Full-coverage type pipeline detection system based on annular multi-beam sonar - Google Patents
Full-coverage type pipeline detection system based on annular multi-beam sonar Download PDFInfo
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Abstract
The invention relates to the technical field of hydrologic vascular tubes, in particular to a full-coverage type pipeline detection system based on an annular multi-beam sonar. The device comprises a bionic machine unit, a video inspection unit, a sonar precision measurement unit and a detection application unit; the bionic machine unit is used for controlling and managing the bionic machine fish; the video checking unit is used for visually checking defects through graphic images; the sonar precision measurement unit is used for accurately detecting the pipeline defect through multi-beam scanning measurement; the detection application unit is used for accurately quantifying the defect condition and is applied to repair. The design of the invention realizes flexible operation in the underground pipeline by designing the highly bionic robot fish; the defect target visible to the naked eye can be visually inspected; by adopting the multi-beam unmanned detection system, qualitative and quantitative data aiming at the engineering defects can be acquired, the all-dimensional scanning detection of the pipeline is realized, the overall situation and the detail situation of the local defects of the pipeline are rapidly detected, the detection efficiency and the coverage rate are improved, and the guarantee is provided for the safe operation of the pipeline.
Description
Technical Field
The invention relates to the technical field of hydrologic vascular tubes, in particular to a full-coverage type pipeline detection system based on an annular multi-beam sonar.
Background
In order to relieve the current situation of water resource shortage, optimize water resource allocation and improve water supply guarantee capacity, a water transfer project is a great benefit for people, wherein detection of underground pipelines is an important technical means for finding and exploring internal defects and hidden dangers of the pipelines, and important basis can be provided for pipeline safety and state evaluation and defect repair treatment. When the pipeline is in a full water state, the hidden danger of the pipeline is more concealed, so that the internal defects need to be detected by means of professional nondestructive testing equipment. The traditional pipeline inspection mode is that a high-pixel camera is carried on an underwater robot to finish pipeline photographing and video recording tasks, and technicians confirm whether defects exist in the pipeline or not by checking the pictures. However, a large amount of image data is obtained by the underwater robot, the description of the pipeline defect is qualitative judgment, quantitative description is lacked aiming at the size and the size of the defect, and the quantitative indexes are important parameters for measuring the grade of the pipeline defect and also important basic data for making a defect repair plan. The multi-beam sounding technology brings possibility for obtaining high-precision three-dimensional data of the pipeline, and the high-precision defect data obtained by multi-beam scanning can be used for extracting and obtaining accurate coordinates of a target position of the pipeline, so that quantitative description of a defect part can be calculated. However, at present, there is no precise and complete full-coverage type pipeline detection system based on the annular multi-beam sonar.
Disclosure of Invention
The invention aims to provide a full-coverage type pipeline detection system based on an annular multi-beam sonar, so as to solve the problems in the background technology.
In order to solve the technical problems, the invention provides a full-coverage type pipeline detection system based on annular multi-beam sonar, which comprises
The device comprises a bionic machine unit, a video inspection unit, a sonar precision measurement unit and a detection application unit; the bionic machine unit, the video inspection unit, the sonar precision measurement unit and the detection application unit are sequentially connected through network communication; the bionic machine unit is used for controlling and managing the operation and the motion process of the bionic machine fish; the video inspection unit is used for visually inspecting the possible defect problem in the pipeline by acquiring clear graphic image data in the full-water pipeline; the sonar precision measurement unit is used for acquiring high-precision three-dimensional data inside the pipeline through multi-beam scanning measurement so as to accurately detect the defect of the pipeline; the detection application unit is used for combining the data detected by the video and the sonar to accurately quantify the defect condition and applying the defect condition to pipeline repairing work;
the bionic machine unit comprises a power supply management module, a motion management module, an autonomous cruise module and an automatic obstacle avoidance module;
the video checking unit comprises a high-definition shooting module, a return storage module, an abnormality detection module and a training optimization module;
the sonar precision measurement unit comprises a comprehensive scanning module, a data processing module, a point cloud registration module and a three-dimensional reconstruction module;
the detection application unit comprises a data fusion module, a defect analysis module, a safety evaluation module, a repair plan module and a comprehensive report module.
As a further improvement of the technical scheme, the power management module, the motion management module, the autonomous cruise module and the automatic obstacle avoidance module are sequentially connected through network communication; the power management module is used for managing and controlling power supply work and electric energy distribution in the bionic robot fish, monitoring the electric energy surplus of the robot fish in real time, and returning to a recovery point according to a preset program when the electric energy surplus is insufficient; the motion management module is used for carrying out centralized automatic management on the underwater motion process of the bionic robot fish through the controller and the various motion driving devices; the automatic cruising module is used for driving the bionic robot fish to carry out automatic cruising in the pipeline by combining an original design drawing of the pipeline and a satellite navigation/inertial navigation system through the processor and the controller, and can also be combined with the auxiliary function of the depth sensor to accurately position the position coordinate of the bionic robot fish in the pipeline in real time in the automatic cruising process; the automatic obstacle avoidance module is used for carrying a forward-looking sonar at the front end of the bionic robot fish so as to realize real-time automatic obstacle avoidance in the moving process of the robot fish and carry out rough scanning and measurement on the pipeline condition of the advancing direction of the robot fish so as to supplement a data source for precise measurement.
The motion state of the bionic robot fish comprises but is not limited to forward movement, backward movement, upward floating, sinking, steering and the like, and the motion driving device in the bionic robot fish comprises but is not limited to a power propulsion device, a buoyancy regulator and the like.
As a further improvement of the technical solution, a signal output end of the high-definition shooting and recording module is connected with a signal input end of the return storage module, a signal output end of the return storage module is connected with a signal input end of the abnormality detection module, and a signal output end of the abnormality detection module is connected with a signal input end of the training optimization module; the high-definition shooting module is used for shooting and recording an image video in the pipeline in real time in the autonomous cruising process of the robot fish by carrying high-definition underwater shooting equipment on the bionic robot fish; the return storage module is used for locally caching the real-time video data shot by the front-end video manager, and timely returning and storing the video data to the data processing layer of the central machine room through a wireless transmission technology; the abnormity detection module is used for carrying out manual and intelligent viewing and identification on the video data so as to detect visual and visible pipeline defect conditions as comprehensively as possible; the training optimization module is used for training and optimizing the intelligent image recognition model through mature algorithms such as neural networks and machine learning so as to continuously improve the recognition accuracy of the intelligent image recognition model.
As a further improvement of the technical scheme, the abnormality detection module comprises an identification marking module, an image processing module, an intelligent identification module and a comparison and verification module; the signal output end of the identification and labeling module is connected with the signal input end of the image processing module, the signal output end of the image processing module is connected with the signal input end of the intelligent identification module, and the signal output end of the intelligent identification module is connected with the signal input end of the comparison and verification module; the identification and marking module is used for identifying and marking the defects which can be judged by naked eyes in the pictures or videos in the pipeline by technicians in a manual mode; the image processing module is used for carrying out preprocessing such as dimension reduction, mean value filtering, color binarization and the like on the video or the image so as to enable the image data to be suitable for a format which can be clearly identified by an intelligent image identification technology; the intelligent identification module is used for intelligently identifying the preprocessed image through an artificial intelligent image identification technology and framing an abnormal part which is possibly judged as a defect; the comparison and verification module is used for comparing the abnormal part marked by artificial identification with the target position identified by artificial intelligence, judging the effects of two identification modes through consistency verification, realizing omission and gap detection of video inspection and improving the comprehensive integrity of video inspection.
As a further improvement of the technical solution, in the comparison and verification module, a consistency verification algorithm uses a consistency ratio index CRThe test is carried out, and the formula is as follows:
firstly, the consistency index C is calculatedI：
Wherein λ ismaxThe maximum eigenvalue of the matrix is approximated by: multiplying the comparison matrix by the priority scale vector to obtain a new vector, dividing the 1 st number of the new vector by the 1 st number of the priority vector, dividing the 2 nd number by the 2 nd number of the priority vector, … …, adding the results, and dividing by the number n of factors to obtain λmax；
Then determining corresponding average random consistency index R by table look-upI；
Then, the consistency ratio C is calculatedRAnd judging:
when C is presentRWhen < 0.1, it is considered acceptable to judge the consistency of the matrix, when CRAnd when the judgment matrix is more than 0.1, the judgment matrix is considered not to meet the consistency requirement, and the judgment matrix needs to be revised again.
As a further improvement of the technical solution, a signal output end of the comprehensive scanning module is connected with a signal input end of the data processing module, a signal output end of the data processing module is connected with a signal input end of the point cloud registration module, and a signal output end of the point cloud registration module is connected with a signal input end of the three-dimensional reconstruction module; the comprehensive scanning module is used for scanning the pipeline condition within the 360-degree coverage range of the vertical course of the bionic robot fish through a multi-beam detection sonar carried by the bionic robot fish, and realizing comprehensive automatic unmanned scanning by combining rough measurement data of a forward-looking sonar; the data processing module is used for preprocessing the original data acquired through the sonar, and removing interference noise in the data to acquire clear underwater echo intensity information so as to facilitate subsequent data application; the point cloud registration module is used for splicing three-dimensional sonar point clouds according to position information and time information of the bionic robotic fish for real-time positioning, and performing registration processing on the three-dimensional sonar point clouds to enable the overlapped parts of the two point clouds to be aligned as much as possible, so that the measurement error of sonar equipment or the visual difference caused by the influence of external interference when the sonar equipment works is reduced, and the registration accuracy is ensured; the three-dimensional reconstruction module is used for importing comprehensive sonar data into drawing software and reconstructing a three-dimensional image model in the pipeline by combining a curved surface reconstruction technology.
As a further improvement of the technical scheme, the data processing module comprises a signal transmission module, a data analysis module, a filtering and denoising module and an inclined distance correction module; the signal output end of the signal transmission module is connected with the signal input end of the data analysis module, the signal output end of the data analysis module is connected with the signal input end of the filtering and denoising module, and the signal output end of the filtering and denoising module is connected with the signal input end of the slant distance correction module; the signal transmission module is used for transmitting sonar data acquired by sonar scanning measurement to a data processing layer of a central machine room for storage and processing in a signal form of CW pulse square waves; the data analysis module is used for analyzing and converting original sonar data into a format which can be identified and processed by a computer; the filtering and denoising module is used for removing the interference noise such as the environmental noise, the self-noise machine reverberation and the like in the original sonar data through a filtering means; the slope distance correction module is used for converting slope distance calculation between two points which are not at the same height and are measured by sonar into actual flat distance so as to accurately measure the shape and the size of the defect part.
As a further improvement of the present technical solution, a calculation expression of the skew distance correction module is as follows:
Lx＝Lp÷cosα
Lp＝Lx·cosα
wherein; l isxThe linear distance (equivalent to the hypotenuse of a right triangle) between the bionic robot fish and the measured object, LpThe minimum linear distance between the measured target and the bionic robot fish in the vertical direction is shown, cos alpha is the cosine of the depression angle when the bionic robot fish scans the position of the target, and alpha is the minimum included angle between the bionic robot fish and the measured target.
As a further improvement of the technical solution, the data fusion module, the defect analysis module, the security evaluation module, the repair plan module and the comprehensive report module are sequentially connected through network communication; the data fusion module is used for combining original design data of the pipeline, analysis data of video inspection and accurate data of multi-beam sonar scanning and measuring, and superposing the data to accurately analyze a target part possibly with defects; the defect analysis module is used for comprehensively analyzing the defect condition from the aspects of the type, the size, the positioning and the like of a defect target; the safety evaluation module is used for judging the defect degree of the defect part according to a preset standard rule and evaluating the overall safety condition of the pipeline according to the detected defect condition; the repair plan module is used for customizing an accurate repair plan and an implementation scheme aiming at each defect part according to the detected accurate defect data and in combination with the safety requirement of pipeline design, and tracking a repair flow so as to feed back the repair condition; the comprehensive report module is used for collecting the work records and all data of the once detected and repaired whole process and arranging the work records and all data into a comprehensive detection report so as to archive and report the comprehensive detection report.
As a further improvement of the technical scheme, the defect analysis module comprises a type qualitative module, a size quantitative module, an accurate positioning module and a repair calculation module; the type qualitative module, the size quantitative module, the accurate positioning module and the repair calculation module are sequentially connected through network communication; the type qualitative module is used for qualitatively determining the problem type of the defect part according to the image and the three-dimensional image structure; the size quantifying module is used for accurately measuring the size parameters of the defect part; the accurate positioning module is used for combining the original pipeline design image and the position information of the defect part in the three-dimensional reconstruction model to accurately position the position coordinate of the defect; and the repair calculation module is used for calculating a change value to be repaired according to the comparison result of the size parameter of the defect part and the original design data.
Wherein the defect types include, but are not limited to, pipe cracking, surface damage, fouling of impurities, pipe corrosion, pipe wall fouling, and the like.
Wherein, the size parameters of the defect part include but are not limited to the length/width/shape of the crack of the pipeline, the shape/area/depth of the breakage, the thickness of the siltation, the corrosion degree, the thickness of the fouling and the like.
The invention also aims to provide an operation method of the full-coverage type pipeline detection system based on the annular multi-beam sonar, which comprises the following steps:
firstly, placing the bionic robot fish in a full-water pipeline, driving the bionic robot fish to realize autonomous cruising motion through a controller under the combined action of a forward-looking sonar and a satellite/inertial navigation system, shooting a pipeline image in a visual field range in real time by a high-definition camera in the cruising process and transmitting the pipeline image back to a central machine room, checking a video image and manually marking a defective part by a technician, automatically identifying the part possibly abnormal in the image through an artificial intelligence technology by a processor, carrying out comparative analysis on the manually marked part and the intelligently identified part, improving the accuracy of intelligent identification through training optimization, scanning the interior of the pipeline in real time through an annular multi-beam sonar in the cruising process of the bionic robot fish, reconstructing a real-time three-dimensional image in the pipeline after processing sonar data, comprehensively and accurately analyzing the condition of a defective target by combining all data, and customizing an optimal repair plan according to an analysis result, and all data of the whole detection process are integrated to generate a comprehensive structural report so as to be convenient for archiving and reporting.
The invention also provides an operating device of the full-coverage type pipeline detection system based on the annular multi-beam sonar, which comprises a processor, a memory and a computer program stored in the memory and operated on the processor, wherein the processor is used for realizing any one of the full-coverage type pipeline detection systems based on the annular multi-beam sonar when executing the computer program.
It is a fourth object of the present invention to provide a computer-readable storage medium storing a computer program which, when executed by a processor, implements any of the above-described full coverage pipe probing systems based on annular multi-beam sonar.
Compared with the prior art, the invention has the beneficial effects that:
1. the full-coverage type pipeline detection system based on the annular multi-beam sonar designs the highly bionic intelligent bionic robot fish, has the advantages of low noise, high speed, high flexibility, strong maneuverability, small turning radius, excellent environmental compatibility and the like, can realize flexible turning, free and free swimming in the pipeline and no space constraint, and realizes flexible operation in the underground pipeline;
2. the full-coverage type pipeline detection system based on the annular multi-beam sonar stores and transmits the pictures captured by the underwater camera back to the display screen of the operation table, so that macroscopic defect targets can be visually detected;
3. this full-coverage formula pipeline detection system based on annular multi-beam sonar adopts intelligent bionic machine fish to carry on multi-beam detection sonar's unmanned detection system, carry out the joint work inspection to underground piping, realize surveying a strip regional measurement from the point, improve measuring coverage at every turn by a wide margin, improve detection efficiency on the one hand, the other party realizes that continuous nothing is lost and is leaked and carries out full-coverage to the geminate transistors wall and survey, can acquire the data to engineering defect qualitative and quantification, realize that the all-round scanning of pipeline detects, not only can detect the whole condition of pipeline fast, and can carefully inspect local defect's detail and deposit condition, improve full water pipeline inside detection efficiency and coverage, provide the guarantee for the safe operation of pipeline.
Drawings
FIG. 1 is an exemplary product architecture diagram of the present invention;
FIG. 2 is a block diagram of the overall system apparatus of the present invention;
FIG. 3 is a diagram of one embodiment of a local system device architecture;
FIG. 4 is a second block diagram of a local system apparatus according to the present invention;
FIG. 5 is a third block diagram of a local system apparatus according to the present invention;
FIG. 6 is a fourth embodiment of the present invention;
FIG. 7 is a fifth embodiment of the present invention;
FIG. 8 is a sixth embodiment of the present invention;
FIG. 9 is a seventh embodiment of the present invention;
FIG. 10 is a block diagram of an exemplary electronic computer platform assembly in accordance with the present invention.
The various reference numbers in the figures mean:
1. simulating a robotic fish; 2. a satellite ground base station; 3. a central machine room; 31. a processor; 32. a display terminal; 4. a cloud database; 5. a water conservancy bureau data management platform;
100. a biomimetic machine unit; 101. a power management module; 102. a motion management module; 103. an autonomous cruise module; 104. an automatic obstacle avoidance module;
200. a video inspection unit; 201. a high-definition shooting module; 202. a pass-back storage module; 203. an abnormality detection module; 2031. identifying a labeling module; 2032. an image processing module; 2033. an intelligent identification module; 2034. a comparison and verification module; 204. a training optimization module;
300. a sonar precision measurement unit; 301. a full scan module; 302. a data processing module; 3021. a signal transmission module; 3022. a data analysis module; 3023. a filtering and denoising module; 3024. a skew correction module; 303. a point cloud registration module; 304. a three-dimensional reconstruction module;
400. a probe application unit; 401. a data fusion module; 402. a defect analysis module; 4021. a type qualitative module; 4022. a size quantification module; 4023. a precise positioning module; 4024. a repair calculation module; 403. a security evaluation module; 404. a repair plan module; 405. and a comprehensive report module.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
Example 1
As shown in fig. 1-10, the present embodiment provides a full coverage type pipe detecting system based on annular multi-beam sonar, which comprises
A bionic machine unit 100, a video inspection unit 200, a sonar precision measurement unit 300 and a detection application unit 400; the bionic machine unit 100, the video inspection unit 200, the sonar precision measurement unit 300 and the detection application unit 400 are sequentially connected through network communication; the bionic machine unit 100 is used for controlling and managing the operation and the motion process of the bionic machine fish; the video inspection unit 200 is used for visually inspecting the possible defect problem in the pipeline by acquiring clear graphic image data in the full-water pipeline; the sonar precision measurement unit 300 is used for acquiring high-precision three-dimensional data inside the pipeline through multi-beam scanning measurement so as to accurately detect the defect of the pipeline; the detection application unit 400 is used for combining the data detected by the video and sonar to accurately quantify the defect condition and applying the defect condition to pipeline repairing work;
the bionic machine unit 100 comprises a power management module 101, a motion management module 102, an autonomous cruise module 103 and an automatic obstacle avoidance module 104;
the video checking unit 200 comprises a high-definition shooting module 201, a return storage module 202, an anomaly detection module 203 and a training optimization module 204;
the sonar precision measurement unit 300 comprises a comprehensive scanning module 301, a data processing module 302, a point cloud registration module 303 and a three-dimensional reconstruction module 304;
the detection application unit 400 includes a data fusion module 401, a defect analysis module 402, a security evaluation module 403, a repair planning module 404, and a comprehensive reporting module 405.
In this embodiment, the power management module 101, the motion management module 102, the autonomous cruise module 103, and the automatic obstacle avoidance module 104 are sequentially connected through network communication; the power management module 101 is used for managing and controlling power supply work and electric energy distribution in the bionic robot fish, monitoring the electric energy surplus of the robot fish in real time, and returning to a recovery point according to a preset program when the electric energy surplus is insufficient; the motion management module 102 is used for performing centralized automatic management on the underwater motion process of the bionic robot fish through a controller and a plurality of motion driving devices; the autonomous cruising module 103 is used for driving the bionic robot fish to autonomously cruise in the pipeline by combining an original design drawing of the pipeline and a satellite navigation/inertial navigation system through a processor and a controller, and can accurately position the position coordinates of the bionic robot fish in the pipeline in real time by combining the auxiliary function of a depth sensor in the autonomous cruising process; the automatic obstacle avoidance module 104 is used for carrying a forward-looking sonar at the front end of the bionic robot fish so as to realize real-time automatic obstacle avoidance in the moving process of the robot fish and carry out rough scanning and measurement on the pipeline condition of the advancing direction of the robot fish so as to supplement a data source for precise measurement.
The motion state of the bionic robot fish comprises but is not limited to forward movement, backward movement, upward floating, sinking, steering and the like, and the motion driving device in the bionic robot fish comprises but is not limited to a power propulsion device, a buoyancy regulator and the like.
In this embodiment, the signal output end of the high definition shooting module 201 is connected to the signal input end of the return storage module 202, the signal output end of the return storage module 202 is connected to the signal input end of the abnormality detection module 203, and the signal output end of the abnormality detection module 203 is connected to the signal input end of the training optimization module 204; the high-definition shooting module 201 is used for shooting and recording an image video inside the pipeline in real time in the autonomous cruising process of the robot fish by carrying high-definition underwater shooting equipment on the bionic robot fish; the return storage module 202 is configured to locally cache the real-time recorded video data through the front-end video manager, and timely return the video data to the data processing layer of the central machine room through a wireless transmission technology and store the video data; the anomaly detection module 203 is used for carrying out artificial and intelligent viewing and identification on the video data so as to detect visual and visible pipeline defect conditions as comprehensively as possible; the training optimization module 204 is used for training and optimizing the model of intelligent image recognition through mature algorithms such as neural network and machine learning so as to continuously improve the recognition accuracy of the model.
Further, the anomaly detection module 203 includes an identification labeling module 2031, an image processing module 2032, an intelligent identification module 2033 and a comparison and verification module 2034; the signal output end of the identification labeling module 2031 is connected to the signal input end of the image processing module 2032, the signal output end of the image processing module 2032 is connected to the signal input end of the intelligent identification module 2033, and the signal output end of the intelligent identification module 2033 is connected to the signal input end of the comparison checking module 2034; the identification and labeling module 2031 is used for identifying and labeling defects which can be judged by naked eyes in pictures or videos inside the pipeline by technicians in a manual mode; the image processing module 2032 is configured to perform preprocessing such as dimension reduction, mean value filtering, color binarization, and the like on the video or image, so that the image data is suitable for a format that can be clearly identified by an intelligent image identification technology; the intelligent recognition module 2033 is configured to perform intelligent recognition on the preprocessed image through an artificial intelligence image recognition technology, and frame an abnormal portion that may be determined as a defect; the comparison and verification module 2034 is used for comparing the abnormal part marked by artificial identification with the target position identified by artificial intelligence, judging the effects of two identification modes through consistency verification, realizing omission and gap detection of video inspection and improving the comprehensive integrity of video inspection.
Specifically, in the comparison and verification module 2034, the consistency verification algorithm uses the consistency ratio index CRIs examinedThe experiment shows that the formula is as follows:
firstly, the consistency index C is calculatedI：
Wherein λ ismaxThe maximum eigenvalue of the matrix is approximated by: multiplying the comparison matrix by the priority scale vector to obtain a new vector, dividing the 1 st number of the new vector by the 1 st number of the priority vector, dividing the 2 nd number by the 2 nd number of the priority vector, … …, adding the results, and dividing by the number n of factors to obtain λmax；
Then determining corresponding average random consistency index R by table look-upI；
Then, the consistency ratio C is calculatedRAnd judging:
when C is presentRWhen < 0.1, it is considered acceptable to judge the consistency of the matrix, when CRAnd when the judgment matrix is more than 0.1, the judgment matrix is considered not to meet the consistency requirement, and the judgment matrix needs to be revised again.
In this embodiment, the signal output end of the comprehensive scanning module 301 is connected to the signal input end of the data processing module 302, the signal output end of the data processing module 302 is connected to the signal input end of the point cloud registration module 303, and the signal output end of the point cloud registration module 303 is connected to the signal input end of the three-dimensional reconstruction module 304; the comprehensive scanning module 301 is used for scanning the pipeline condition within the 360-degree coverage range of the vertical course of the bionic robot fish through a multi-beam detection sonar carried by the bionic robot fish, and realizing comprehensive automatic unmanned scanning by combining rough measurement data of a forward-looking sonar; the data processing module 302 is configured to pre-process raw data acquired through sonar, and remove interference noise in the data to acquire clear water bottom echo intensity information, so as to facilitate subsequent data application; the point cloud registration module 303 is used for splicing three-dimensional sonar point clouds according to position information and time information of the bionic robotic fish for real-time positioning, and performing registration processing on the three-dimensional sonar point clouds to enable overlapping parts of the two point clouds to be aligned as much as possible, so that a sonar equipment measurement error or a visual difference caused by external interference when the sonar equipment works is reduced, and registration accuracy is ensured; the three-dimensional reconstruction module 304 is used for importing comprehensive sonar data into drawing software and reconstructing a three-dimensional image model inside the pipeline by combining a curved surface reconstruction technology.
Further, the data processing module 302 includes a signal transmission module 3021, a data parsing module 3022, a filtering and denoising module 3023, and a slant range correction module 3024; a signal output end of the signal transmission module 3021 is connected to a signal input end of the data analysis module 3022, a signal output end of the data analysis module 3022 is connected to a signal input end of the filtering and denoising module 3023, and a signal output end of the filtering and denoising module 3023 is connected to a signal input end of the slant range correction module 3024; the signal transmission module 3021 is configured to transmit sonar data acquired by sonar scanning measurement to a data processing layer of the central machine room in a CW pulse square wave signal form for storage and processing; the data analysis module 3022 is configured to analyze and convert the original sonar data into a format that can be recognized and processed by a computer; the filtering and denoising module 3023 is configured to remove interference noise such as environmental noise and self-noise machine reverberation in the original sonar data by a filtering means; the slope distance correction module 3024 is used to convert the slope distance calculation between two points at different heights measured by sonar into an actual flat distance so as to accurately measure the shape and size of the defect site.
Specifically, the calculation expression of the skew correction module 3024 is as follows:
Lx＝Lp÷cosα
Lp＝Lx·cosα
wherein; l isxThe linear distance (equivalent to the hypotenuse of a right triangle) between the bionic robot fish and the measured object, LpIs the minimum straight line distance between the measured target and the bionic robot fish in the vertical direction, cos alpha is the cosine of the depression angle when the bionic robot fish scans the target position, alpha is the distance between the bionic robot fish and the measured targetThe smallest included angle therebetween.
In this embodiment, the data fusion module 401, the defect analysis module 402, the security evaluation module 403, the repair plan module 404, and the comprehensive report module 405 are sequentially connected through network communication; the data fusion module 401 is used for combining original design data of the pipeline, analysis data of video inspection and accurate data of multi-beam sonar scanning and measuring, and superposing the data to accurately analyze a target part possibly with defects; the defect analysis module 402 is used for comprehensively analyzing the defect condition from the aspects of the type, size, positioning and the like of the defect target; the safety evaluation module 403 is configured to determine a defect degree of a defective portion according to a preset standard rule, and evaluate an overall safety condition of the pipeline according to a detected defect condition; the repair plan module 404 is configured to customize an accurate repair plan and an accurate repair implementation scheme for each defect part according to the detected accurate defect data and in combination with the safety requirements of pipeline design, and track a repair process so as to feed back the repair situation; the comprehensive report module 405 is used to collect the work records and all data of the entire process from detection and repair at a time, and collate the work records and all data into a comprehensive detection report so as to archive and report the comprehensive detection report.
Further, the defect analysis module 402 includes a type qualitative module 4021, a size quantitative module 4022, a precise positioning module 4023, and a repair calculation module 4024; the type qualitative module 4021, the size quantitative module 4022, the accurate positioning module 4023 and the repair calculation module 4024 are sequentially connected through network communication; the type qualitative module 4021 is used for determining the problem type of the defect part according to the image and the three-dimensional image structure; the size quantitative module 4022 is used for accurately measuring the size parameters of the defect part; the accurate positioning module 4023 is used for accurately positioning the position coordinates of the defect by combining the original pipeline design image and the position information of the defect part in the three-dimensional reconstruction model; the repair calculation module 4024 is configured to calculate a change value to be repaired according to a comparison result between the size parameter of the defect portion and the original design data.
Wherein the defect types include, but are not limited to, pipe cracking, surface damage, fouling of impurities, pipe corrosion, pipe wall fouling, and the like.
Wherein, the size parameters of the defect part include but are not limited to the length/width/shape of the crack of the pipeline, the shape/area/depth of the breakage, the thickness of the siltation, the corrosion degree, the thickness of the fouling and the like.
The embodiment also provides an operation method of the full-coverage type pipeline detection system based on the annular multi-beam sonar, which comprises the following steps:
firstly, placing the bionic robot fish in a full-water pipeline, driving the bionic robot fish to realize autonomous cruising motion through a controller under the combined action of a forward-looking sonar and a satellite/inertial navigation system, shooting a pipeline image in a visual field range in real time by a high-definition camera in the cruising process and transmitting the pipeline image back to a central machine room, checking a video image and manually marking a defective part by a technician, automatically identifying the part possibly abnormal in the image through an artificial intelligence technology by a processor, carrying out comparative analysis on the manually marked part and the intelligently identified part, improving the accuracy of intelligent identification through training optimization, scanning the interior of the pipeline in real time through an annular multi-beam sonar in the cruising process of the bionic robot fish, reconstructing a real-time three-dimensional image in the pipeline after processing sonar data, comprehensively and accurately analyzing the condition of a defective target by combining all data, and customizing an optimal repair plan according to an analysis result, and all data of the whole detection process are integrated to generate a comprehensive structural report so as to be convenient for archiving and reporting.
As shown in fig. 1, this embodiment still provides an exemplary product architecture of full-coverage formula pipeline detection system based on annular multi-beam sonar, including bionical machine fish 1, bionical machine fish 1 moves in full water pipe, the pipeline is equipped with satellite ground basic station 2 outward for assist the navigation of bionical machine fish 1 autonomous cruise in-process, still be equipped with center computer lab 3 outward from the pipeline, including treater 31 and supporting display terminal 32 in the center computer lab 3, the outer communication connection of treater 31 has cloud end database 4, cloud end database 4 is connected with water conservancy office data management platform 5, be used for obtaining the original design data of pipeline.
As shown in fig. 10, the present embodiment also provides an operating apparatus of the full coverage type pipe detecting system based on the annular multi-beam sonar, which includes a processor, a memory, and a computer program stored in the memory and operated on the processor.
The processor comprises one or more than one processing core, the processor is connected with the memory through the bus, the memory is used for storing program instructions, and the processor realizes the full-coverage type pipeline detection system based on the annular multi-beam sonar when executing the program instructions in the memory.
Alternatively, the memory may be implemented by any type or combination of volatile or non-volatile memory devices, such as Static Random Access Memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic memory, flash memory, magnetic or optical disks.
In addition, the invention also provides a computer readable storage medium, which stores a computer program, and the computer program is executed by a processor to realize the full-coverage type pipeline detection system based on the annular multi-beam sonar.
Optionally, the present invention also provides a computer program product containing instructions which, when run on a computer, cause the computer to perform the above aspects of the full coverage pipe-probing system based on annular multi-beam sonar.
It will be understood by those skilled in the art that all or part of the steps for implementing the above embodiments may be implemented by hardware, or may be implemented by a program instructing relevant hardware, and the program may be stored in a computer-readable storage medium, where the above-mentioned storage medium may be a read-only memory, a magnetic disk or an optical disk, etc.
The foregoing shows and describes the general principles, essential features, and advantages of the invention. It will be understood by those skilled in the art that the present invention is not limited to the embodiments described above, and the preferred embodiments of the present invention are described in the above embodiments and the description, and are not intended to limit the present invention. The scope of the invention is defined by the appended claims and equivalents thereof.
Claims (10)
1. Full overlay type pipeline detection system based on annular multi-beam sonar, its characterized in that: comprises that
The device comprises a bionic machine unit (100), a video inspection unit (200), a sonar precision measurement unit (300) and a detection application unit (400); the bionic machine unit (100), the video inspection unit (200), the sonar precision measurement unit (300) and the detection application unit (400) are sequentially connected through network communication; the bionic machine unit (100) is used for controlling and managing the operation and the motion process of the bionic machine fish; the video inspection unit (200) is used for visually inspecting the possible defect problem in the pipeline by acquiring clear graphic image data in the full-water pipeline; the sonar precision measurement unit (300) is used for acquiring high-precision three-dimensional data inside the pipeline through multi-beam scanning measurement so as to accurately detect the defect of the pipeline; the detection application unit (400) is used for combining the data detected by the video and the sonar to accurately quantify the defect condition and applying the defect condition to pipeline repairing work;
the bionic machine unit (100) comprises a power supply management module (101), a motion management module (102), an autonomous cruise module (103) and an automatic obstacle avoidance module (104);
the video checking unit (200) comprises a high-definition shooting module (201), a return storage module (202), an abnormality detection module (203) and a training optimization module (204);
the sonar precision measurement unit (300) comprises a comprehensive scanning module (301), a data processing module (302), a point cloud registration module (303) and a three-dimensional reconstruction module (304);
the detection application unit (400) comprises a data fusion module (401), a defect analysis module (402), a security evaluation module (403), a repair plan module (404) and a comprehensive report module (405);
the full-coverage type pipeline detection system based on the annular multi-beam sonar comprises a controller, a high-definition camera, a processor, a full-water pipeline, a front-view sonar and a satellite/inertial navigation system, wherein when the full-coverage type pipeline detection system operates, the bionic robot fish is placed in a full-water pipeline, under the combined action of the front-view sonar and the satellite/inertial navigation system, the autonomous cruising motion is realized through the driving of the controller, a pipeline image in a visual field range is shot and recorded in real time by the high-definition camera in the cruising process and is transmitted back to a central machine room, technicians check a video image and manually mark defective parts, the processor automatically identifies the parts possibly abnormal in the image through an artificial intelligence technology at the same time, compares and analyzes the manually marked and intelligently identified parts, improves the accuracy of intelligent identification through training optimization, the inside of the pipeline is scanned in real time through the annular multi-beam sonar in the cruising process, real-time three-dimensional images in the pipeline are reconstructed after sonar data are processed, and comprehensively and accurately analyzing the defect target condition by combining all the data, customizing an optimal repair plan according to the analysis result, integrating all the data of the whole detection process, and generating a comprehensive structural report for archiving and reporting.
2. The full coverage type pipeline detection system based on annular multi-beam sonar according to claim 1, characterized in that: the power management module (101), the motion management module (102), the autonomous cruise module (103) and the automatic obstacle avoidance module (104) are sequentially connected in a network communication mode; the power management module (101) is used for managing and controlling power supply work and electric energy distribution in the bionic robot fish, monitoring the electric energy surplus of the robot fish in real time, and returning to a recovery point according to a preset program when the electric energy surplus is insufficient; the motion management module (102) is used for carrying out centralized automatic management on the underwater motion process of the bionic robot fish through a controller and a plurality of motion driving devices; the autonomous cruising module (103) is used for driving the bionic robot fish to autonomously cruise in the pipeline by combining an original design drawing of the pipeline and a satellite navigation/inertial navigation system through the processor and the controller, and can accurately position the position coordinate of the bionic robot fish in the pipeline in real time by combining the auxiliary function of the depth sensor in the autonomous cruising process; the automatic obstacle avoidance module (104) is used for carrying a forward-looking sonar at the front end of the bionic robot fish so as to realize real-time automatic obstacle avoidance in the moving process of the robot fish and carry out rough scanning and measurement on the pipeline condition of the advancing direction of the robot fish so as to supplement a data source for precise measurement.
3. The full coverage type pipeline detection system based on annular multi-beam sonar according to claim 1, characterized in that: the signal output end of the high-definition shooting module (201) is connected with the signal input end of the return storage module (202), the signal output end of the return storage module (202) is connected with the signal input end of the abnormality detection module (203), and the signal output end of the abnormality detection module (203) is connected with the signal input end of the training optimization module (204); the high-definition shooting module (201) is used for shooting and recording an image video inside the pipeline in real time in the autonomous cruising process of the robot fish by carrying high-definition underwater shooting equipment on the bionic robot fish; the return storage module (202) is used for locally caching the real-time video data shot by the front-end video manager, and timely returning and storing the video data to a data processing layer of the central machine room through a wireless transmission technology; the abnormality detection module (203) is used for carrying out artificial and intelligent viewing and identification on the video data so as to detect visual and visible pipeline defect conditions as comprehensively as possible; the training optimization module (204) is used for training and optimizing the intelligent image recognition model through mature algorithms such as neural networks and machine learning so as to continuously improve the recognition accuracy of the intelligent image recognition model.
4. The full coverage type pipeline detection system based on annular multi-beam sonar according to claim 3, characterized in that: the abnormality detection module (203) comprises an identification marking module (2031), an image processing module (2032), an intelligent identification module (2033) and a comparison and verification module (2034); the signal output end of the identification and annotation module (2031) is connected with the signal input end of the image processing module (2032), the signal output end of the image processing module (2032) is connected with the signal input end of the intelligent identification module (2033), and the signal output end of the intelligent identification module (2033) is connected with the signal input end of the comparison and verification module (2034); the identification and marking module (2031) is used for identifying and marking the defects which can be judged by naked eyes in the pictures or videos inside the pipeline in a manual mode by technicians; the image processing module (2032) is used for preprocessing the video or the image such as dimensionality reduction, mean value filtering, color binarization and the like so as to enable the image data to be suitable for a format which can be clearly identified by an intelligent image identification technology; the intelligent identification module (2033) is used for intelligently identifying the preprocessed image through an artificial intelligent image identification technology and framing an abnormal part which is possibly judged as a defect; the comparison and verification module (2034) is used for comparing the abnormal part marked by artificial identification with the target position identified by artificial intelligence, judging the effects of two identification modes through consistency verification, realizing omission and gap detection of video inspection and improving the comprehensive integrity of video inspection.
5. The full coverage type pipeline detection system based on annular multi-beam sonar according to claim 4, characterized in that: in the comparison and verification module (2034), a consistency verification algorithm adopts a consistency ratio index CRThe test is carried out, and the formula is as follows:
firstly, the consistency index C is calculatedI：
Wherein λ ismaxThe maximum eigenvalue of the matrix is approximated by: multiplying the comparison matrix by the priority scale vector to obtain a new vector, dividing the 1 st number of the new vector by the 1 st number of the priority vector, dividing the 2 nd number by the 2 nd number of the priority vector, … …, adding the results, and dividing by the number n of factors to obtain λmax；
Then determining corresponding average random consistency index R by table look-upI；
Then, the consistency ratio C is calculatedRAnd judging:
when C is presentRWhen less than 0.1, the judgment matrix is considered to be consistentSex is acceptable, when CRAnd when the judgment matrix is more than 0.1, the judgment matrix is considered not to meet the consistency requirement, and the judgment matrix needs to be revised again.
6. The full coverage type pipeline detection system based on annular multi-beam sonar according to claim 1, characterized in that: the signal output end of the comprehensive scanning module (301) is connected with the signal input end of the data processing module (302), the signal output end of the data processing module (302) is connected with the signal input end of the point cloud registration module (303), and the signal output end of the point cloud registration module (303) is connected with the signal input end of the three-dimensional reconstruction module (304); the comprehensive scanning module (301) is used for scanning the pipeline condition within the 360-degree coverage range of the vertical course of the bionic robot fish through a multi-beam detection sonar carried by the bionic robot fish, and realizes comprehensive automatic unmanned scanning by combining rough measurement data of a forward-looking sonar; the data processing module (302) is used for preprocessing the original data acquired through the sonar, and removing interference noise in the data to acquire clear underwater echo intensity information so as to facilitate subsequent data application; the point cloud registration module (303) is used for splicing three-dimensional sonar point clouds according to position information and time information of the bionic robotic fish for real-time positioning, and registering the three-dimensional sonar point clouds to enable the overlapped parts of the two point clouds to be aligned as much as possible, so that the measurement error of sonar equipment or the visual difference caused by the influence of external interference when the sonar equipment works is reduced, and the registration accuracy is ensured; the three-dimensional reconstruction module (304) is used for importing comprehensive sonar data into drawing software and reconstructing a three-dimensional image model in the pipeline by combining a curved surface reconstruction technology.
7. The full coverage type pipeline detection system based on annular multi-beam sonar according to claim 6, characterized in that: the data processing module (302) comprises a signal transmission module (3021), a data analysis module (3022), a filtering and denoising module (3023) and a slant range correction module (3024); the signal output end of the signal transmission module (3021) is connected to the signal input end of the data analysis module (3022), the signal output end of the data analysis module (3022) is connected to the signal input end of the filtering and denoising module (3023), and the signal output end of the filtering and denoising module (3023) is connected to the signal input end of the slant range correction module (3024); the signal transmission module (3021) is used for transmitting sonar data acquired by sonar scanning to a data processing layer of a central machine room for storage and processing in a signal form of CW pulse square waves; the data analysis module (3022) is used for analyzing and converting original sonar data into a format which can be identified and processed by a computer; the filtering and denoising module (3023) is used for removing the environmental noise, the self-noise machine reverberation and other interference noise in the original sonar data through a filtering means; the slope distance correction module (3024) is used for converting slope distance calculation between two points which are not at the same height and are measured by sonar into actual flat distance so as to accurately measure the shape and the size of the defect part.
8. The full coverage type pipeline detection system based on annular multi-beam sonar according to claim 7, characterized in that: the calculation expression of the slope correction module (3024) is as follows:
Lx＝Lp÷cosα
Lp＝Lx·cosα
wherein; l isxThe linear distance (equivalent to the hypotenuse of a right triangle) between the bionic robot fish and the measured object, LpThe minimum linear distance between the measured target and the bionic robot fish in the vertical direction is shown, cos alpha is the cosine of the depression angle when the bionic robot fish scans the position of the target, and alpha is the minimum included angle between the bionic robot fish and the measured target.
9. The full coverage type pipeline detection system based on annular multi-beam sonar according to claim 1, characterized in that: the data fusion module (401), the defect analysis module (402), the safety evaluation module (403), the repair plan module (404) and the comprehensive report module (405) are sequentially connected through network communication; the data fusion module (401) is used for combining original design data of the pipeline, analysis data of video inspection and accurate data of multi-beam sonar scanning measurement, and superposing the data to accurately analyze a target part possibly with defects; the defect analysis module (402) is used for comprehensively analyzing the defect condition from the aspects of the type, the size, the positioning and the like of a defect target; the safety evaluation module (403) is used for judging the defect degree of the defect part according to a preset standard rule and evaluating the overall safety condition of the pipeline according to the detected defect condition; the repair plan module (404) is used for customizing an accurate repair plan and an accurate implementation scheme for each defect part according to the detected accurate defect data and in combination with the safety requirement of pipeline design, and tracking a repair flow so as to feed back the repair condition; the comprehensive report module (405) is used for collecting the work records and all data of the once detected and repaired full process and arranging the work records and all data into a comprehensive detection report so as to archive and report the comprehensive detection report.
10. The full coverage type pipeline detection system based on annular multi-beam sonar according to claim 9, characterized in that: the defect analysis module (402) comprises a type qualitative module (4021), a size quantitative module (4022), a precise positioning module (4023) and a repair calculation module (4024); the type qualitative module (4021), the size quantitative module (4022), the accurate positioning module (4023) and the repair calculation module (4024) are sequentially connected through network communication; the type qualitative module (4021) is used for qualitatively determining the problem type of the defect part according to the image and the three-dimensional image structure; the size quantification module (4022) is used for accurately measuring the size parameters of the defect part; the accurate positioning module (4023) is used for accurately positioning the position coordinates of the defects by combining the original pipeline design image and the position information of the defect parts in the three-dimensional reconstruction model; the repair calculation module (4024) is used for calculating a change value to be repaired according to the comparison result of the size parameters of the defect part and the original design data.
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