A deformation detection method and system for underground pipeline

Abstract

The present invention provides a deformation detection system for underground pipelines, including a main pipeline and a ground receiving terminal, wherein a detection component is arranged inside the main pipeline, and the detection component includes a detection box, a rear cover, a lamp bead, a camera, a front cover plate, a positioning radar, a Raspberry Pi device, a wireless communication module, an underground pipeline gyroscope, a battery, an electric rod, a top box, a ball bearing and a micro motor, and the rear side wall of the detection box is fixedly connected with the rear cover, the lamp bead and the camera, and the rear cover is a transparent glass cover, and the number of the lamp beads is set to be multiple, and is evenly distributed in a ring shape on the rear side wall of the detection box. By arranging the detection box, the electric rod, the top box, the ball bearing and the micro motor, the detection device can be directly placed in the main pipeline, and can be moved along the inside of the main pipeline after starting, without the need for manual detection, thereby improving the detection efficiency and reducing the difficulty of work.

Description

Technical Field

[0001] The present invention relates to the field of underground pipeline detection, and in particular to a deformation detection method and system for underground pipelines. Background Art

[0002] Underground pipelines are an important part of urban infrastructure, including municipal utility pipelines such as water supply, rainwater, sewage, recycled water, natural gas, heat, electricity, and communications. They are important carriers of urban energy transmission, material transmission, and information transmission, and are crucial to the normal operation of the city. At present, there are mainly the following types of laying methods for underground pipelines: underground direct burial, underground trench laying, common trench laying, etc.

[0003] After underground pipelines are laid, geological subsidence will occur, which will cause local settlement of the pipelines, resulting in pipeline deformation, bending and damage. However, the existing deformation detection devices for underground pipelines are generally performed manually. Not only is the detection workload large and the efficiency low, but the detection function is single and cannot accurately determine the cause, degree and location of the deformation, which brings inconvenience to maintenance. Summary of the invention

[0004] 1. Technical issues to be resolved

[0005] In view of the shortcomings of the prior art, the present invention provides a deformation detection method and system for underground pipelines, which solves the problems of existing detection devices requiring manual inspection, large workload, low efficiency, single function, and inability to accurately determine the cause, degree and location of deformation.

[0006] (II) Technical solution

[0007] To achieve the above purpose, the present invention is implemented through the following technical solutions: a deformation detection system for underground pipelines, including a main pipeline and a ground receiving terminal, a detection component is arranged inside the main pipeline, and the detection component includes a detection box, a rear cover, lamp beads, a camera, a front cover, a positioning radar, a Raspberry Pi device, a wireless communication module, an underground pipeline gyroscope, a battery, an electric pole, a top box, a ball bearing and a micro motor, the rear side wall of the detection box is fixedly connected with the rear cover, the lamp beads and the camera, the rear cover is a transparent glass cover, the number of the lamp beads is set to be multiple, and they are evenly distributed in a ring shape on the rear side wall of the detection box, and the camera is located at the lamp cover. The detection box is in the middle of the bead, the lamp bead and the camera are both located inside the rear cover body, the front cover plate is clamped on the front side wall of the detection box, the front side wall of the front cover plate is fixedly connected with a positioning radar, the rear side wall of the front cover plate is fixedly connected with an underground pipeline gyroscope, the inner circumferential surface of the detection box is fixedly connected with a Raspberry Pi device and a wireless communication module, the Raspberry Pi device has a built-in Raspberry Pi system, the inner bottom surface of the detection box is fixedly connected with a battery, the outer circumferential surface of the detection box is fixedly connected with an electric rod, the top of the electric rod is fixedly connected with a top box, the inner rotatable connection of the top box is connected with a ball, and the right side wall of the top box is fixedly connected with a micro motor.

[0008] Preferably, a video storage module is fixedly connected to the inner rear side wall of the detection box, and the video storage module is electrically connected to the camera to store video information and improve data security.

[0009] Preferably, the ground receiving terminal includes a receiving module, an image processing module, a position information processing module and a deep learning intelligent inspection module, wherein the deep learning intelligent inspection module is mainly used to annotate the collected images, and the deep learning intelligent inspection module builds a deep learning neural network environment and uses the YOLO V8 algorithm for data training and deformation detection and recognition. The transmitted information is processed and intuitive data is output, which facilitates understanding of the deformation and subsequent targeted maintenance, thereby improving maintenance efficiency.

[0010] Preferably, the lamp beads, camera, positioning radar, Raspberry Pi device, wireless communication module, underground pipeline gyroscope, electric rod and micro motor are all electrically connected to the battery, the camera, positioning radar and underground pipeline gyroscope are electrically connected to the Raspberry Pi device, and the Raspberry Pi device is connected to the ground receiving terminal through the wireless communication module signal. The location information and image information are processed in time and transmitted to the outside.

[0011] Preferably, the number of the electric rod, top box, ball bearing and micro motor is multiple and evenly distributed around the detection box, and the output end of the micro motor is fixedly connected to the ball bearing, which is used to stabilize the detection component and enable the detection component to move inside the main pipeline, so as to facilitate automatic detection of the entire main pipeline, reduce work difficulty and improve detection efficiency.

[0012] A deformation detection method for underground pipelines includes the following steps:

[0013] S1. Place the detection assembly in the main pipeline, start the electric rod, make the ball close to the inner circumference of the main pipeline, and set the initial zero coordinates of the underground pipeline gyroscope and positioning radar;

[0014] S2, start the micro motor to rotate the ball, so that the detection component moves along the inside of the main pipeline. During the movement, the lamp beads provide lighting, the camera records the internal situation of the main pipeline, and stores the data into the video storage module and sends the data to the ground receiving terminal through the wireless communication module. At the same time, the underground pipeline gyroscope performs pipeline detection on the main pipeline as the detection component moves forward, and automatically generates three-dimensional coordinates, and sends the three-dimensional coordinates to the ground receiving terminal through the wireless communication module. At the same time, the positioning radar sends the real-time position of the detection component to the ground receiving terminal through the wireless communication module;

[0015] S3, after receiving the three-dimensional coordinates of the underground pipeline gyroscope, the position coordinates of the positioning radar and the image information of the camera, the ground receiving terminal processes the data information and then outputs it;

[0016] S4. After completing the detection task, the detection component returns to the starting position and the task is completed;

[0017] S5. The specific steps for the ground receiving terminal to process the data information are as follows:

[0018] S51, the ground receiving terminal receives the image data, the three-dimensional coordinates of the underground pipeline gyroscope and the position coordinates of the positioning radar through the receiving module, and loads them into the image processing module and the position information processing module respectively;

[0019] S52, the position information processing module comprehensively outputs the three-dimensional coordinates of the underground pipeline gyroscope and the position coordinates of the positioning radar, and builds a deep learning neural network environment in combination with the deep learning intelligent inspection module, uses the YOLO V8 algorithm for data training and deformation detection and recognition, identifies the deformation of the main pipeline, and distinguishes whether the deformation is settlement, fracture or bending, etc.;

[0020] S53, the image processing module processes the image data, and builds a deep learning neural network environment in combination with the deep learning intelligent inspection module. The YOLO V8 algorithm is used for data training and deformation detection and recognition, and the deformation of the main pipeline is identified to distinguish whether the defect is a crack, dent or peeling, etc.;

[0021] S54. Extract features of the identified deformation type, such as the position, length, width, area, severity, etc. of the deformation.

[0022] S55, then make corresponding selections in combination with other auxiliary information, such as pipeline mileage, deformation location, deformation type, whether it affects the normal use of the main pipeline, etc., to improve the disease information.

[0023] S56. Mark the deformation in combination with the auxiliary information.

[0024] S57, output the marked final information and store it in the storage system at the same time to classify the severity level, deformation cause, treatment measures, etc., so as to generate a test report.

[0025] (III) Beneficial effects

[0026] The present invention provides a deformation detection method and system for underground pipelines, which have the following beneficial effects:

[0027] 1. By setting up the detection box, electric rod, top box, ball and micro motor, the detection device can be directly placed in the main pipeline. After starting, it can move along the inside of the main pipeline. No manual detection is required, which improves the detection efficiency and reduces the difficulty of work.

[0028] 2. By setting up the detection box, rear cover, lamp beads, camera, front cover, positioning radar, Raspberry Pi device, wireless communication module and underground pipeline gyroscope, during the movement of the detection box, not only can the detection picture be known at any time, but also the deformation of the main pipeline can be accurately understood, and the cause, degree and position of the deformation of the main pipeline can be understood more intuitively, which is convenient for targeted maintenance and improves maintenance efficiency. BRIEF DESCRIPTION OF THE DRAWINGS

[0029] Figure 1 This is a front view of a deformation detection system for underground pipelines proposed by the present invention;

[0030] Figure 2 A cross-sectional structural diagram of a detection component of a deformation detection system for underground pipelines proposed by the present invention;

[0031] Figure 3 This is a rear view of a deformation detection system for underground pipelines proposed by the present invention;

[0032] Figure 4This is a data information processing flow chart of a deformation detection method for underground pipelines proposed by the present invention.

[0033] Among them, 1. Main pipeline; 2. Detection component; 201. Detection box; 202. Rear cover; 203. Lamp beads; 204. Camera; 205. Front cover; 206. Positioning radar; 207. Raspberry Pi device; 208. Wireless communication module; 209. Underground pipeline gyroscope; 210. Battery; 211. Electric pole; 212. Top box; 213. Ball; 214. Micro motor. DETAILED DESCRIPTION

[0034] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0035] like Figure 1-4As shown, an embodiment of the present invention provides a deformation detection system for underground pipelines, including a main pipeline 1 and a ground receiving terminal. A detection component 2 is arranged inside the main pipeline 1. The detection component 2 includes a detection box 201, a rear cover 202, a lamp bead 203, a camera 204, a front cover plate 205, a positioning radar 206, a Raspberry Pi device 207, a wireless communication module 208, an underground pipeline gyroscope 209, a battery 210, an electric rod 211, a top box 212, a ball bearing 213 and a micro motor 214. The rear side wall of the detection box 201 is fixedly connected to There are a rear cover body 202, lamp beads 203 and a camera 204. The rear cover body 202 is a transparent glass cover. The number of lamp beads 203 is set to be multiple and evenly distributed in a ring shape on the rear side wall of the detection box 201. The camera 204 is located in the middle of the lamp beads 203. The lamp beads 203 and the camera 204 are both located inside the rear cover body 202. A front cover plate 205 is clamped on the front side wall of the detection box 201. A positioning radar 206 is fixedly connected to the front side wall of the front cover plate 205 for locating the position of the detection component 2, which is convenient for locating the deformation position. The front cover plate 205 An underground pipeline gyroscope 209 is fixedly connected to the rear side wall of the detection box 201 for detecting the deformation of the main pipeline 1. A Raspberry Pi device 207 and a wireless communication module 208 are fixedly connected to the inner circumferential surface of the detection box 201. The Raspberry Pi device 207 has a built-in Raspberry Pi system for processing various data information. A battery 210 is fixedly connected to the inner bottom surface of the detection box 201 for providing power to various components. An electric rod 211 is fixedly connected to the outer circumferential surface of the detection box 201. A top box 212 is fixedly connected to the top of the electric rod 211. The inner The upper part is rotatably connected with a ball 213, and the right side wall of the top box 212 is fixedly connected with a micro motor 214. There are multiple electric rods 211, top boxes 212, balls 213 and micro motors 214, which are evenly distributed around the detection box 201. The output end of the micro motor 214 is fixedly connected with the ball 213. When the detection component 2 is placed in the main pipeline 1, the electric rod 211 is started to make the ball 213 close to the inner circumference of the main pipeline 1, and then the micro motor 204 is started to move the detection component 2 along the inside of the main pipeline 1.

[0036] A video storage module is fixedly connected to the inner rear side wall of the detection box 201, and the video storage module is electrically connected to the camera 204 for storing videos to improve data security. The lamp bead 203, camera 204, positioning radar 206, Raspberry Pi device 207, wireless communication module 208, underground pipeline gyroscope 209, electric pole 211 and micro motor 214 are all electrically connected to the battery 210, the camera 204, positioning radar 206 and underground pipeline gyroscope 209 are electrically connected to the Raspberry Pi device 207, and the Raspberry Pi device 207 is connected to the ground receiving terminal through the wireless communication module 208 signal.

[0037] The ground receiving terminal includes a receiving module, an image processing module, a position information processing module and a deep learning intelligent inspection module. The deep learning intelligent inspection module is mainly used to annotate the collected images. The deep learning intelligent inspection module builds a deep learning neural network environment and uses the YOLO V8 algorithm for data training and deformation detection and recognition.

[0038] A deformation detection method for underground pipelines includes the following steps:

[0039] S1, place the detection assembly 2 in the main pipeline 1, start the electric rod 211, make the ball 213 close to the inner circumference of the main pipeline 1, and set the initial zero coordinates of the underground pipeline gyroscope 209 and the positioning radar 206;

[0040] S2, start the micro motor 214 to rotate the ball 213, so that the detection component 2 moves along the inside of the main pipeline 1. During the movement, the lamp bead 203 provides lighting, and the camera 204 records the internal situation of the main pipeline 1, and stores the data into the video storage module and sends the data to the ground receiving terminal through the wireless communication module 208. At the same time, the underground pipeline gyroscope 209 performs pipeline detection on the main pipeline 1 as the detection component 2 moves forward, and automatically generates three-dimensional coordinates, and sends the three-dimensional coordinates to the ground receiving terminal through the wireless communication module 208. At the same time, the positioning radar 206 sends the real-time position of the detection component 2 to the ground receiving terminal through the wireless communication module 208;

[0041] S3, after receiving the three-dimensional coordinates of the underground pipeline gyroscope 209, the position coordinates of the positioning radar 206 and the image information of the camera 204, the ground receiving terminal processes the data information and then outputs it;

[0042] S4, after completing the detection task, the detection component 2 returns to the starting position and the task is completed;

[0043] S5. The specific steps for the ground receiving terminal to process the data information are as follows:

[0044] S51, the ground receiving terminal receives the image data, the three-dimensional coordinates of the underground pipeline gyroscope 209 and the position coordinates of the positioning radar 206 through the receiving module, and loads them into the image processing module and the position information processing module respectively;

[0045] S52, the position information processing module comprehensively outputs the three-dimensional coordinates of the underground pipeline gyroscope 209 and the position coordinates of the positioning radar 206, and builds a deep learning neural network environment in combination with the deep learning intelligent inspection module, uses the YOLO V8 algorithm for data training and deformation detection and recognition, identifies the deformation of the main pipeline 1, and distinguishes whether the deformation is settlement, fracture or bending, etc.;

[0046] S53, the image processing module processes the image data, and builds a deep learning neural network environment in combination with the deep learning intelligent inspection module, uses the YOLO V8 algorithm for data training and deformation detection and recognition, identifies the deformation of the main pipeline 1, and distinguishes whether the defect is a crack, a dent, or peeling, etc.;

[0047] S54. Extract features of the identified deformation type, such as the position, length, width, area, severity, etc. of the deformation.

[0048] S55, then make corresponding selections in combination with other auxiliary information, such as pipeline mileage, deformation location, deformation type, whether it affects the normal use of the main pipeline, etc., to improve the disease information.

[0049] S56. Mark the deformation in combination with the auxiliary information.

[0050] S57, output the marked final information and store it in the storage system at the same time to classify the severity level, deformation cause, treatment measures, etc., so as to generate a test report.

[0051] Although embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions and variations may be made to the embodiments without departing from the principles and spirit of the present invention, and that the scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. A deformation detection system for underground pipelines, comprising a main pipeline (1) and a ground receiving terminal, characterized in that: A detection assembly (2) is arranged inside the main pipeline (1), and the detection assembly (2) comprises a detection box (201), a rear cover (202), lamp beads (203), a camera (204), a front cover plate (205), a positioning radar (206), a Raspberry Pi device (207), a wireless communication module (208), an underground pipeline gyroscope (209), a storage battery (210), an electric rod (211), a top box (212), a ball bearing (213) and a micro motor (214). The rear side wall of the detection box (201) is fixedly connected with the rear cover (202), the lamp beads (203) and the camera (204). The rear cover (202) is a transparent glass cover. A plurality of lamp beads (203) are arranged and are evenly distributed in a ring shape on the rear side wall of the detection box (201). The camera (204) is located in the middle of the lamp beads (203). The lamp beads (203) and the camera (204) are arranged in a ring shape. 4) are located inside the rear cover body (202), a front cover plate (205) is clamped on the front side wall of the detection box (201), a positioning radar (206) is fixedly connected to the front side wall of the front cover plate (205), an underground pipeline gyroscope (209) is fixedly connected to the rear side wall of the front cover plate (205), a Raspberry Pi device (207) and a wireless communication module (208) are fixedly connected to the inner circumferential surface of the detection box (201), and the Raspberry Pi device (207) and the wireless communication module (208) are fixedly connected to the inner circumferential surface of the detection box (201). The Pi device (207) has a built-in Raspberry Pi system, a battery (210) is fixedly connected to the inner bottom surface of the detection box (201), an electric rod (211) is fixedly connected to the outer circumferential surface of the detection box (201), a top box (212) is fixedly connected to the top of the electric rod (211), a ball bearing (213) is rotatably connected inside the top box (212), and a micro motor (214) is fixedly connected to the right side wall of the top box (212).

2. The deformation detection system for underground pipelines according to claim 1, characterized in that: The number of the electric rod (211), the top box (212), the ball (213) and the micro motor (214) is plural and evenly distributed around the detection box (201), and the output end of the micro motor (214) is fixedly connected to the ball (213).

3. The deformation detection system for underground pipelines according to claim 1, characterized in that: A video storage module is fixedly connected to the inner rear side wall of the detection box (201), and the video storage module is electrically connected to the camera (204).

4. The deformation detection system for underground pipelines according to claim 1, characterized in that: The lamp bead (203), camera (204), positioning radar (206), Raspberry Pi device (207), wireless communication module (208), underground pipeline gyroscope (209), electric rod (211) and micro motor (214) are all electrically connected to the battery (210); the camera (204), positioning radar (206) and underground pipeline gyroscope (209) are electrically connected to the Raspberry Pi device (207); and the Raspberry Pi device (207) is connected to a ground receiving terminal via a wireless communication module (208) signal.

5. The deformation detection system for underground pipelines according to claim 1, characterized in that: The ground receiving terminal includes a receiving module, an image processing module, a position information processing module and a deep learning intelligent inspection module. The deep learning intelligent inspection module is mainly used to annotate the collected images. The deep learning intelligent inspection module builds a deep learning neural network environment and uses the YOLO V8 algorithm for data training and deformation detection and recognition.

6. A deformation detection method for underground pipelines, based on the deformation detection system for underground pipelines according to any one of claims 1 to 5, characterized in that: The steps include: S1, placing the detection assembly (2) in the main pipeline (1), starting the electric rod (211), making the ball (213) close to the inner circumference of the main pipeline (1), and setting the initial zero coordinates of the underground pipeline gyroscope (209) and the positioning radar (206); S2, starting the micro motor (214) to rotate the ball (213), thereby causing the detection component (2) to move along the inside of the main pipeline (1). During the movement, the lamp bead (203) provides lighting, the camera (204) records the internal situation of the main pipeline (1), stores the data in the video storage module, and sends the data to the ground receiving terminal through the wireless communication module (208). At the same time, the underground pipeline gyroscope (209) performs pipeline detection on the main pipeline (1) as the detection component (2) moves forward, and automatically generates three-dimensional coordinates, and sends the three-dimensional coordinates to the ground receiving terminal through the wireless communication module (208). At the same time, the positioning radar (206) sends the real-time position of the detection component (2) to the ground receiving terminal through the wireless communication module (208); S3, after receiving the three-dimensional coordinates of the underground pipeline gyroscope (209), the position coordinates of the positioning radar (206) and the image information of the camera (204), the ground receiving terminal processes the data information and then outputs it; S4. After completing the detection task, the detection component (2) returns to the starting position, and the task is completed; S5. The specific steps for the ground receiving terminal to process the data information are as follows: S51, the ground receiving terminal receives the image data, the three-dimensional coordinates of the underground pipeline gyroscope (209) and the position coordinates of the positioning radar (206) through the receiving module, and loads them into the image processing module and the position information processing module respectively; S52, the position information processing module comprehensively outputs the three-dimensional coordinates of the underground pipeline gyroscope (209) and the position coordinates of the positioning radar (206), and builds a deep learning neural network environment in combination with the deep learning intelligent inspection module, uses the YOLO V8 algorithm to perform data training and deformation detection and recognition, identifies the deformation of the main pipeline (1), and distinguishes whether the deformation is settlement, fracture or bending, etc.; S53, the image processing module processes the image data, and builds a deep learning neural network environment in combination with the deep learning intelligent inspection module, uses the YOLO V8 algorithm for data training and deformation detection and recognition, identifies the deformation of the main pipeline (1), and distinguishes whether the defect is a crack, a dent, or peeling, etc.; S54. Extract features of the identified deformation type, such as the position, length, width, area, severity, etc. of the deformation. S55, then make corresponding selections in combination with other auxiliary information, such as pipeline mileage, deformation location, deformation type, whether it affects the normal use of the main pipeline, etc., to improve the disease information. S56. Mark the deformation in combination with the auxiliary information. S57, output the marked final information and store it in the storage system at the same time to classify the severity level, deformation cause, treatment measures, etc., so as to generate a test report.

Images