Full-course intelligent detection method for composite material pipeline forming and application

Abstract

The invention relates to the technical field of pipeline detection, and discloses a full-process intelligent detection method for composite material pipeline forming and application, and the method comprises the following steps: S1, early-stage preparation: collecting a pipeline drawing, a historical detection report, geological data and operation data, then debugging equipment, evaluating environmental risks, and carrying out early-stage preparation; obtaining operation approval and deploying safety protection measures; s2, pipeline dredging is conducted, specifically, sludge and sundries in the pipeline are removed through environment-friendly dredging equipment, and the sludge is temporarily stored; and S3, robot deployment: calibrating a robot sensor, planning an advancing path, setting a communication and positioning system, and putting the dredging detection robot into the pipeline through the access hole. The desilting detection robot is deployed, and various sensors carried by the desilting detection robot are used for collecting data in real time, so that the problems of high safety risk and low efficiency caused by the fact that manual well descending observation is needed in most traditional pipeline detection, the internal condition of a pipeline is complicated, and the detection speed of workers is limited are solved.

Description

Technical Field

[0001] The present invention relates to the technical field of pipeline detection, and specifically to an intelligent full-process detection method and application for the forming of composite material pipelines. Background Art

[0002] As an important part of municipal facilities, urban drainage pipelines are the basic conditions for ensuring the normal and efficient operation of cities and the sustainable development of society, economy, and environment. However, most of the pipelines laid in the early stage have reached their service life and are gradually aging. Coupled with uneven ground settlement and the action of other external forces, serious structural defects such as breakage, hidden leakage, disconnection, misalignment, and deformation occur in the old pipelines, resulting in a decrease in the water passing capacity of the pipelines, frequent road surface waterlogging, and even serious floods such as urban waterlogging in severe cases.

[0003] Traditional pipeline detection mostly requires manual observation in the well. Due to the complex internal situation of the pipeline and the limited detection speed of workers, this will cause problems of high safety risks and low efficiency. Summary of the Invention

[0004] In view of the deficiencies of the prior art, the present invention provides an intelligent full-process detection method and application for the forming of composite material pipelines, which solves the problems that traditional pipeline detection mostly requires manual observation in the well. Due to the complex internal situation of the pipeline and the limited detection speed of workers, this will cause problems of high safety risks and low efficiency.

[0005] To achieve the above objectives, the present invention is realized through the following technical solutions: An intelligent full-process detection method and application for the forming of composite material pipelines, including the following steps: S1. Preliminary preparation: Collect pipeline drawings, historical detection reports, geological data, and operation data, then debug the equipment, evaluate environmental risks, obtain operation approvals, and deploy safety protection measures; S2. Pipeline dredging: Use environmental protection dredging equipment to remove the silt and debris in the pipeline and temporarily store the sludge; S3. Robot deployment: Calibrate the robot sensors, plan the travel path, set the communication and positioning system, and put the dredging and detection robot into the pipeline through the inspection port; S4. Data collection: The dredging and detection robot uses the robot sensors to collect data on the inner wall image, structure, and environmental parameters of the pipeline in real time; S5. Defect identification and analysis: Preprocess the collected images, and then use the convolutional neural network algorithm in deep learning and multi-modal data fusion to process the collected inner wall image data of the pipeline, identify pipeline cracks and corrosion defects, evaluate the severity, and generate a quantitative evaluation report and repair suggestions; S6. Waste treatment: Dewater and solidify the sludge, harmlessly treat harmful substances, and transport them to the designated disposal site for classification according to environmental protection standards.

[0006] Preferably, the equipment in S1 includes environmental protection dredging equipment, dredging detection robots, robot sensors, positioning system equipment, communication equipment, sludge treatment equipment, and transportation equipment.

[0007] Preferably, the environmental protection dredging equipment is used to remove silt in the pipeline, the dredging detection robot is used to carry detection equipment, the positioning system equipment is used to provide position information for the dredging detection robot, the communication equipment is used for communication between the dredging detection robot and the outside world, the sludge treatment equipment is used to dewater and solidify the sludge and treat harmful substances in the sludge, and the transportation equipment is used to transport the treated sludge to the designated disposal site.

[0008] Preferably, the environmental protection dredging equipment in S2 is a high-pressure water jet device. The working pressure of the high-pressure water jet device is controlled at 100 - 450 bar, the flow rate is controlled at 10 L / min - 100 L / min, the jet angle is controlled at 0° - 360°, the traveling speed is controlled at 0.1 m / min - 1 m / min, and the nozzle diameter of the high-pressure water jet device is 0.5 - 5 mm.

[0009] Preferably, the robot sensors in S3 include camera sensors, lidar sensors, ultrasonic phased array radar sensors, and gas sensors.

[0010] Preferably, the positioning system equipment adopts inertial navigation and UWB positioning technology. The positioning error of the inertial navigation and UWB positioning technology is ≤ 0.5 m, and the communication equipment adopts Bluetooth and wireless radio frequency communication.

[0011] Preferably, the frame rate of the camera sensor is controlled at 25 - 60 fps, the focal length is controlled at 6 - 12 mm, the field of view angle is controlled at 60° - 120°, and the sensitivity is controlled at 100 - 1600; the horizontal viewing angle of the lidar sensor is 360°, the vertical viewing angle is controlled at 30° - 90°, the ranging range is controlled at 0.1 m - 20 m, the ranging accuracy is controlled at ± 2 cm, and the scanning frequency is controlled at 10 Hz - 20 Hz; the gas sensor can detect methane, carbon monoxide, hydrogen sulfide, and oxygen. The detection range of methane is 0 - 100% LEL, the detection range of carbon monoxide is 0 - 1000 ppm, the detection range of hydrogen sulfide is 0 - 100 ppm, the accuracy of the gas sensor is controlled at ± 5% FS, the response time is controlled at 10 - 60 seconds, and the recovery time is controlled at 30 - 120 seconds.

[0012] Preferably, the waterproof level of the dredging detection robot is IP68, and the obstacle crossing height ≥ 50 mm.

[0013] Preferably, the preprocessing in S5 includes image data processing, structure data processing, and environmental parameter data processing.

[0014] Application of a full-process intelligent detection method for composite material pipeline forming in trenchless pipeline detection.

[0015] The present invention provides a full-process intelligent detection method and application for composite material pipeline forming. It has the following beneficial effects: 1. By deploying a dredging detection robot, the present invention uses various sensors carried by it to collect data in real time, and analyzes and processes the data with the help of deep learning algorithms and multi-modal data fusion technology, thus avoiding manual entry into the well, and solving the problem that traditional pipeline detection mostly requires manual entry into the well for observation. Due to the complex internal situation of the pipeline and the limited detection speed of workers, this will cause great safety risks and low efficiency.

[0016] 2. Through inertial navigation and UWB positioning technology, the positioning error of the present invention is ≤ 0.5 m. At the same time, Bluetooth and radio frequency communication are used as communication devices, providing relatively accurate position information and stable communication for the dredging detection robot, ensuring that the robot can travel along the planned path and transmit data in real time.

[0017] 3. Through various sensors, including camera sensors, lidar sensors, ultrasonic phased array radar sensors, and gas sensors, the present invention can collect multi-source data such as images, structures, and environmental parameters of the pipeline inner wall in real time, and uses multi-modal data fusion technology for processing in S5, which can more comprehensively and accurately identify defects such as pipeline cracks and corrosion. Description of the Drawings

[0018] Figure 1 It is a flowchart of the method of the present invention. Detailed Embodiments

[0019] The following will clearly and completely describe the technical solutions of the present invention with reference to the drawings of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the protection scope of the present invention.

[0020] Please refer to the attached Figure 1 , the embodiment of the present invention provides a full-process intelligent detection method for composite material pipeline forming, including the following steps: S1. Preliminary Preparation: Collect pipeline drawings, historical inspection reports, geological data, and operation data. Then, commission the equipment, assess environmental risks, obtain operation approvals, and deploy safety protection measures. S2. Pipeline Dredging: Use environmental protection dredging equipment to remove silt and debris in the pipeline and temporarily store the sludge. S3. Robot Deployment: Calibrate the robot sensors, plan the travel path, set up the communication and positioning system, and lower the dredging and inspection robot into the pipeline through the inspection opening. S4. Data Acquisition: The dredging and inspection robot uses robot sensors to collect data on the inner wall image, structure, and environmental parameters of the pipeline in real time. S5. Defect Identification and Analysis: Preprocess the collected images, and then use the convolutional neural network algorithm in deep learning and multi-modal data fusion to process the collected inner wall image data of the pipeline, identify pipeline cracks and corrosion defects, evaluate the severity, and generate a quantitative evaluation report and repair suggestions. S6. Waste Disposal: Dewater and solidify the sludge, harmlessly treat harmful substances, and transport them to the designated disposal site according to environmental protection standards for classification.

[0021] Specifically, through S1, collecting drawings, reports, geological, and operation data provides basic information for inspection, assisting in understanding the pipeline background and current situation. Through S4, using various sensors to collect data on the inner wall image, structure, and environmental parameters in real time, covering all aspects of the pipeline condition, lays a data foundation for accurate inspection. After preprocessing the collected images through S5, using the convolutional neural network algorithm and multi-modal data fusion to accurately identify defects such as cracks and corrosion, precisely evaluate the severity, and generate a quantitative report and repair suggestions, ensuring the accuracy and reliability of the inspection results. Through the close cooperation of links such as S1 equipment commissioning, S2 pipeline dredging, and S3 robot deployment, the waiting time for operations is reduced, and the overall inspection efficiency is improved. Dredging creates good conditions for robot operation and data collection, and each step is advanced in an orderly manner to ensure the efficient development of the inspection work. Through S5, using deep learning algorithms to quickly process a large amount of data greatly shortens the inspection cycle compared with manual analysis and provides a basis for pipeline maintenance decisions in a timely manner. By assessing environmental risks and deploying protection measures in S1, setting up the communication and positioning system in S3, and real-time monitoring of the robot's position and status, the safety of operators and equipment is ensured, and the probability of safety accidents is reduced. By temporarily storing the sludge in S2, dewatering and solidifying the sludge, harmlessly treating it, and transporting and disposing of it according to standards in S6, pollution to the surrounding environment is avoided, meeting environmental protection requirements.

[0022] The equipment in S1 includes environmental protection dredging equipment, dredging and inspection robots, robot sensors, positioning system equipment, communication equipment, sludge treatment equipment, and transportation equipment.

[0023] Specifically, through the collaborative work of various devices, the environmental protection dredging equipment starts the obstacle removal link before detection, creating conditions for the subsequent operation of the equipment; the dredging detection robot, as the core carrier, is equipped with sensors to complete data collection; the positioning system equipment and communication equipment ensure the operation of the robot and data transmission; the sludge treatment equipment and transportation equipment process the waste generated by the detection, enabling the entire detection process to proceed orderly from preparation, implementation to conclusion, ensuring the integrity and coherence of the detection work.

[0024] The environmental protection dredging equipment is used to remove the silt in the pipeline. The dredging detection robot is used to carry the detection equipment. The positioning system equipment is used to provide position information for the dredging detection robot. The communication equipment is used for the communication between the dredging detection robot and the outside world. The sludge treatment equipment is used to dehydrate and solidify the sludge and treat the harmful substances in the sludge. The transportation equipment is used to transport the treated sludge to the designated disposal site.

[0025] Specifically, through the environmental protection dredging equipment, the silt in the pipeline is effectively removed, preventing the silt from obstructing the detection equipment and ensuring that the dredging detection robot can move smoothly in the pipeline, laying a foundation for the subsequent detection link and enabling the detection work to start smoothly; through the dredging detection robot carrying the detection equipment, it penetrates deep into the pipeline and uses a variety of sensors carried to comprehensively collect data on the inner wall image, structure and environmental parameters of the pipeline, providing a key data source for accurately evaluating the pipeline condition; through the positioning system equipment, accurate position information is provided for the dredging detection robot, enabling the collected data to be accurately corresponding to the actual position of the pipeline. When analyzing pipeline defects, the problem area can be quickly located based on the accurate position, improving the accuracy and efficiency of the detection and contributing to the subsequent targeted repair work; through the communication equipment, it is ensured that the dredging detection robot communicates with the outside world in real time, and a large amount of collected data is transmitted to the operator in a timely manner. The operator can monitor the detection process in real time, adjust the detection strategy in a timely manner according to the data feedback, and can also issue instructions to the robot to ensure the efficient operation of the detection work; through the sludge treatment equipment, the sludge generated by the dredging is dehydrated and solidified, and the harmful substances in it are treated to meet the environmental protection standards. The transportation equipment transports the treated sludge to the designated disposal site to avoid polluting the surrounding environment and realizing the environmental protection requirements during the detection process.

[0026] The environmental protection dredging equipment in S2 is a high-pressure water jet device. The high-pressure water jet device controls the working pressure at 100 - 450 bar, the flow rate at 10 L / minute - 100 L / minute, the spraying angle at 0° - 360°, the traveling speed at 0.1 m / minute - 1 m / minute, and the nozzle diameter of the high-pressure water jet device at 0.5 - 5 mm.

[0027] Specifically, with a working pressure of 100 - 450 bar, it can generate a powerful impact force, effectively shattering and flushing away various kinds of silt, dirt, and adhered debris on the inner wall of the pipeline, ensuring thorough cleaning of the pipeline; through the flow rate control of 10 L / min - 100 L / min, the water flow rate can be precisely adjusted according to the actual situation such as the accumulation amount and viscosity of the silt in the pipeline. For areas with more and thicker silt, the flow rate can be increased to improve the dredging efficiency. In areas with less silt, reducing the flow rate can also ensure the dredging effect while saving water resources; through the adjustment of the jet angle from 0° to 360°, the high-pressure water jet can scour the inner wall of the pipeline in all directions. Whether it is the top, bottom, or side of the pipeline, as well as various complex bends and corners, dredging operations can be achieved to ensure that the entire inner wall of the pipeline can be effectively cleaned; with a nozzle diameter of 0.5 - 5 mm, the appropriate nozzle can be selected according to the diameter of the pipeline and the dredging requirements. Small-diameter nozzles are suitable for parts with smaller pipe diameters or where fine dredging is required, which can concentrate the water flow force and enhance the local scouring effect. Large-diameter nozzles can be used for large-diameter pipelines, which can cover a larger area per unit time and improve the overall dredging efficiency; through the traveling speed control of 0.1 m / min - 1 m / min, the operator can control the moving speed of the high-pressure water jet device according to the distribution of the silt in the pipeline and the dredging difficulty. In areas with thick silt and difficult cleaning, the traveling speed is reduced to allow the high-pressure water jet to act on the silt for more time to ensure thorough cleaning. In areas with thinner silt, the traveling speed is increased to improve the overall dredging progress and achieve efficient dredging operations.

[0028] The robot sensors in S3 include camera sensors, lidar sensors, ultrasonic phased array radar sensors, and gas sensors.

[0029] Specifically, the high-definition images of the inner wall of the pipeline can be directly collected by the camera sensor, providing intuitive visual information for the operator; the lidar sensor can create a three-dimensional point cloud model of the inside of the pipeline by emitting laser beams and measuring the time of the reflected light, accurately obtaining the spatial structure information of the pipeline, including the diameter change, bending degree, position and shape of internal obstacles, etc. At the same time, it can measure the distance between the robot and the inner wall of the pipeline and obstacles with high precision, providing accurate data support for the path planning and obstacle avoidance of the robot, ensuring the safe and stable progress of the robot in the pipeline, and can also detect the minor deformation of the pipeline. By comparing the three-dimensional data collected at different time points, it can timely detect whether there is a deformation trend in the pipeline, providing an important basis for the safety assessment of the pipeline; the ultrasonic phased array radar sensor uses the reflection principle of ultrasonic waves to penetrate the pipeline wall and detect hidden defects inside the pipeline, such as delamination, cavities, inclusions, etc. in the pipe wall. By analyzing the propagation time and reflection signal of ultrasonic waves in the pipe wall, the thickness data of different positions of the pipe wall can be obtained, which helps to evaluate the corrosion degree and remaining life of the pipeline. For pipelines with complex shapes and structures, such as elbows, tees and other parts, the ultrasonic phased array radar sensor can achieve omnidirectional detection by adjusting the beam direction and angle, further improving the defect detection; the gas sensor can detect the gas composition and concentration in the pipeline in real time, timely discover the presence of harmful gases such as methane, hydrogen sulfide, carbon monoxide, etc., ensuring the safety of the robot and the subsequent personnel entering the pipeline for operation. By monitoring the change of gas concentration, early warning of the environmental risk in the pipeline can be carried out. For example, when the concentration of combustible gas reaches a certain threshold, an alarm is issued in time to prevent the occurrence of dangerous accidents such as explosion. It can also evaluate the ventilation condition in the pipeline. According to the detected gas distribution and flow situation, it can judge whether the ventilation system is operating normally, providing a reference for the maintenance and management of the pipeline.

[0030] The positioning system equipment adopts inertial navigation and UWB positioning technology, and the positioning error of inertial navigation and UWB positioning technology is ≤0.5m. The communication equipment adopts Bluetooth and radio frequency communication.

[0031] Specifically, by using inertial sensors such as accelerometers and gyroscopes through inertial navigation, the acceleration and angular velocity of the robot are measured in real time. Through integral operations, the position and attitude changes of the robot are deduced. Even in the absence of external signals, relatively accurate position information can be continuously provided, enabling the robot to accurately travel along the preset path. Through UWB positioning technology, positioning with centimeter-level accuracy can be achieved. Combined with inertial navigation, the accuracy and reliability of positioning are further improved, effectively controlling the positioning error within the range of ≤0.5m, and the specific position of the robot in the pipeline can be accurately determined, facilitating the operator to grasp its whereabouts in real time. Through Bluetooth communication, it can be used for communication between the robot and nearby handheld devices or local control terminals, facilitating operations such as parameter setting, instruction issuing, and data viewing for the operator at the scene. Through radio frequency communication, long-distance communication can be achieved, enabling the robot to transmit the collected data to a monitoring center or other remote devices far from the pipeline, and at the same time receive instructions from the remote control terminal to ensure real-time and stable communication between the robot and the external system.

[0032] The frame rate of the camera sensor is controlled at 25 - 60fps, the focal length is controlled at 6 - 12mm, the field of view angle is controlled at 60° - 120°, and the sensitivity is controlled at 100 - 1600. The horizontal viewing angle of the lidar sensor is 360°, the vertical viewing angle is controlled at 30° - 90°, the ranging range is controlled at 0.1m - 20m, the ranging accuracy is controlled at ±2cm, and the scanning frequency is controlled at 10Hz - 20Hz. The gas sensor can detect methane, carbon monoxide, hydrogen sulfide, and oxygen. The detection range of methane is 0 - 100%LEL, the detection range of carbon monoxide is 0 - 1000ppm, the detection range of hydrogen sulfide is 0 - 100ppm. The accuracy of the gas sensor is controlled at ±5%FS, the response time is controlled at 10 - 60 seconds, and the recovery time is controlled at 30 - 120 seconds.

[0033] Specifically, by controlling the frame rate of the camera sensor at 25 - 60 fps, it can ensure that the captured video footage is smooth without obvious stuttering or ghosting, clearly recording the real-time conditions inside the pipeline. With a focal length of 6 - 12 mm and a field of view angle of 60° - 120°, the shooting range and the focusing degree on objects can be flexibly adjusted according to different pipeline diameters and detection requirements, enabling both the overall condition of the pipeline to be obtained and local details to be captured. With an ISO value of 100 - 1600, the camera can automatically adjust the ISO in different lighting conditions inside the pipeline, ensuring the image brightness and clarity, and high-quality images can also be obtained in dimly lit environments. Through the large-range scanning of the lidar sensor with a horizontal viewing angle of 360° and a vertical viewing angle of 30° - 90°, the three-dimensional spatial information inside the pipeline can be comprehensively obtained, constructing an accurate three-dimensional model of the pipeline, clearly presenting the shape, size, orientation of the pipeline, as well as the position and shape of internal obstacles. With a ranging range of 0.1 m - 20 m and an accuracy of ±2 cm, the distance between the robot and the pipeline wall and obstacles can be accurately measured, providing accurate data support for the path planning and obstacle avoidance of the robot, ensuring the safe and accurate movement of the robot inside the pipeline. With a scanning frequency of 10 Hz - 20 Hz, the environmental data can be updated quickly, and dynamic changes inside the pipeline can be detected in a timely manner. Through the gas sensor, various gases such as methane, carbon monoxide, hydrogen sulfide, and oxygen can be detected, comprehensively monitoring the gas composition inside the pipeline, promptly detecting possible flammable and explosive gases and toxic and harmful gases, providing guarantees for the dredging operation and personnel safety. By controlling the detection range of methane at 0 - 100% LEL, carbon monoxide at 0 - 1000 ppm, and hydrogen sulfide at 0 - 100 ppm, the gas detection requirements under different risk levels can be met. With an accuracy control of ±5% FS, the accuracy of the measurement results can be ensured. With a response time of 10 - 60 seconds and a recovery time of 30 - 120 seconds, the change in gas concentration can be quickly sensed and the detection results can be output in a timely manner, and it can also quickly return to the normal detection state after the gas environment changes, realizing the real-time and continuous monitoring of the gas conditions inside the pipeline.

[0034] The waterproof grade of the dredging detection robot is IP68, and the obstacle crossing height ≥ 50 mm.

[0035] Specifically, with the waterproof grade of IP68, it can completely prevent dust from entering, ensuring that the electronic components, mechanical parts, etc. inside the robot will not be affected in performance or damaged due to dust accumulation. At the same time, it can effectively avoid damages such as short circuits and corrosion of the internal circuit of the robot by water, reducing the risk of failures caused by water ingress and extending the service life of the robot. As a result, the robot can be applied to various wet or underwater pipeline scenarios, including sewage pipelines, drainage pipelines, submarine pipelines, etc., without being restricted by water, greatly expanding the applicable fields of the robot. By ensuring the obstacle crossing height ≥ 50 mm, the movement of the robot is unobstructed, and it can complete the dredging detection task according to the predetermined path, improving the passing ability of the robot in complex pipeline environments.

[0036] The preprocessing in S5 includes image data processing, structural data processing, and environmental parameter data processing.

[0037] Specifically, through image data processing, operations such as denoising, enhancing contrast, and correcting colors are used to reduce noise interference in the image, making the image clearer, with more prominent details, facilitating subsequent observation and analysis of the internal condition of the pipeline. By unifying the conversion of image data from different sources, resolutions, and formats into a standard format and size, it is convenient for the computer to store, transmit, and process, improving the efficiency and accuracy of image processing algorithms. Technologies such as edge detection and contour extraction are applied to highlight the key features in the image, which helps subsequent target recognition and analysis, providing a more accurate basis for judging abnormal situations in the pipeline. Through structural data processing, relevant data describing the pipeline structure, etc., can be sorted out and converted, facilitating spatial analysis and modeling, and can more intuitively display the layout and connection relationship of the pipeline. The coordinate system, unit, etc., of the structural data are unified and standardized to eliminate differences caused by different data acquisition devices and methods, ensuring the consistency and comparability of the data, enabling accurate docking and fusion of different parts of the structural data, and providing a reliable basis for overall structural analysis. Through the analysis and processing of structural data, abnormal points or discontinuities in the pipeline structure, such as pipeline deformation, dislocation, etc., can be discovered, timely warning of potential safety hazards, and providing important references for pipeline maintenance and repair. Through environmental parameter data processing, temperature, humidity, gas concentration, etc., can be calibrated to ensure the accuracy and reliability of the data, eliminating the influence of factors such as sensor errors on the data. At the same time, different types of environmental parameter data can be associated and fused, comprehensively analyzing the mutual relationship between environmental factors, and the change trend of environmental parameters can also be discovered, predicting possible future environmental changes and taking corresponding measures in advance.

[0038] Application of a full-process intelligent detection method for the forming of composite material pipelines in trenchless pipeline detection.

[0039] Although the embodiments of the present invention have been shown and described, for those of ordinary skill in the art, it can be understood that various changes, modifications, substitutions, and variations can be made to these embodiments without departing from the principles and spirit of the present invention. The scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. A full-process intelligent detection method for composite material pipeline forming, characterized by: The following steps are involved: S1. Preliminary preparation: Collect pipeline drawings, historical inspection reports, geological data and operation data, then debug the equipment, assess environmental risks, obtain operation approval and deploy safety protection measures; S2. Pipeline desilting: Use environmentally friendly desilting equipment to remove sludge and debris in the pipeline and temporarily store the sludge; S3, Robot deployment: calibrate the robot sensors, plan the travel path, set up the communication and positioning system, and place the dredging inspection robot into the pipeline through the inspection port; S4, data collection: The dredging detection robot collects the pipeline inner wall image, structure and environmental parameter data in real time through the robot sensor; S5. Defect identification and analysis: Pre-process the collected images, and then use the convolutional neural network algorithm in deep learning and multimodal data fusion to process the collected pipeline inner wall image data, identify pipeline cracks and corrosion defects, evaluate the severity, and generate quantitative assessment reports and repair suggestions; S6. Waste treatment: Dehydrate and solidify the sludge, treat harmful substances harmlessly, and transport them to designated disposal sites according to environmental protection standards.

2. The whole process intelligent detection method of composite material pipeline forming according to claim 1 is characterized in that: The equipment in S1 includes environmental dredging equipment, dredging detection robots, robot sensors, positioning system equipment, communication equipment, sludge treatment equipment and transportation equipment.

3. The whole process intelligent detection method of composite material pipeline forming according to claim 2 is characterized in that: The environmental protection dredging equipment is used to remove sludge in the pipeline, the dredging detection robot is used to carry the detection equipment, the positioning system equipment is used to provide location information for the dredging detection robot, the communication equipment is used for the dredging detection robot to communicate with the outside world, the sludge treatment equipment is used to dehydrate and solidify the sludge and treat harmful substances in the sludge, and the transportation equipment is used to transport the treated sludge to a designated disposal site.

4. The whole process intelligent detection method of composite material pipeline forming according to claim 1 is characterized in that: The environmental dredging equipment in S2 is a high-pressure water jet device, which controls the working pressure at 100-450 bar, controls the flow rate at 10L / min-100L / min, controls the spray angle at 0°-360°, controls the travel speed at 0.1m / min-1m / min, and the nozzle diameter of the high-pressure water jet device is 0.5-5mm.

5. The whole process intelligent detection method of composite material pipeline forming according to claim 1 is characterized in that: The robot sensors in S3 include camera sensors, lidar sensors, ultrasonic phased array radar sensors and gas sensors.

6. The whole process intelligent detection method of composite material pipeline forming according to claim 2 is characterized in that: The positioning system equipment adopts inertial navigation and UWB positioning technology, the positioning error of the inertial navigation and UWB positioning technology is ≤0.5m, and the communication equipment adopts Bluetooth and wireless radio frequency communication.

7. The whole process intelligent detection method of composite material pipeline forming according to claim 5 is characterized in that: The frame rate of the camera sensor is controlled at 25-60fps, the focal length is controlled at 6-12mm, the field of view angle is controlled at 60°-120°, and the sensitivity is controlled at 100-1600; The horizontal viewing angle of the laser radar sensor is 360°, the vertical viewing angle is controlled at 30°-90°, the ranging range is controlled at 0.1m-20m, the ranging accuracy is controlled at ±2cm, and the scanning frequency is controlled at 10Hz-20Hz; The gas sensor can detect methane, carbon monoxide, hydrogen sulfide and oxygen. The detection range of methane is 0-100%LEL, the detection range of carbon monoxide is 0-1000ppm, and the detection range of hydrogen sulfide is 0-100ppm. The accuracy of the gas sensor is controlled at ±5%FS, the response time is controlled at 10-60 seconds, and the recovery time is controlled at 30-120 seconds.

8. The whole process intelligent detection method of composite material pipeline forming according to claim 3 is characterized in that: The waterproof grade of the dredging detection robot is IP68, and the obstacle crossing height is ≥50mm.

9. The whole process intelligent detection method of composite material pipeline forming according to claim 5 is characterized in that: The preprocessing in S5 includes image data processing, structure data processing and environmental parameter data processing.

10. Application of the full-process intelligent detection method for composite material pipeline molding according to any one of claims 1 to 9 in trenchless pipeline detection.

Images