Pipeline leakage point intelligent detection method and system

Abstract

The invention discloses a pipeline leakage point intelligent detection method and system, and the method comprises the steps: starting to check the operation conditions of all assemblies, and inputting the specification parameters of a to-be-detected pipeline; operating the intelligent control assembly to control the actuator to reach the area where the pipeline leakage point is located; performing comprehensive shooting on the leakage point area through a camera component to obtain the appearance of the leakage point area, and performing preliminary judgment and marking on the leakage point area and the leakage point position according to the real-time transmission picture; the intelligent control assembly is operated to control the sound wave detection assembly to detect leakage points in the marked area, and the specific positions of the leakage points are determined and marked; starting an ultrasonic detection assembly to measure pipe wall thickness and feed back data to an intelligent controller according to the marked leakage point position, and comparing the data with input pipeline specification parameters to preliminarily analyze reasons; the feedback data information is comprehensively processed and analyzed, the severity of the leakage points is evaluated, and an analysis report is generated. Through cooperative work of the intelligent controller and the actuator, rapid and accurate positioning detection, marking and analysis of pipeline leakage points are achieved.

Description

Technical Field

[0001] The present invention belongs to the technical field of engineering pipeline detection, and more specifically, relates to a method and system for intelligent detection of pipeline leakage points. Background Art

[0002] In industrial production and infrastructure maintenance, pipeline systems, as key transmission channels, are widely used in fields such as oil, natural gas, chemical industry, and water supply. The safety and integrity of pipelines are directly related to production efficiency and public safety. However, due to long-term exposure to harsh environments or the influence of factors such as high pressure and high temperature, pipelines are prone to leakage problems, which not only cause resource waste but may also lead to serious safety accidents. Therefore, early detection and accurate location of pipeline leakage points are crucial for ensuring the safe operation of facilities. There are many reasons for pipeline leakage, such as welding quality defects, external force damage, fluid impact, medium corrosion, coating peeling, and pipe wall corrosion. Some leaks occur in areas that are visible to the naked eye and easy to operate, and in this case, we will use some conventional means for repair and restoration. However, some leaks occur in areas that are difficult to detect or in high-altitude areas, and in this case, we need some special means to detect the leakage location. To effectively detect pipeline leakage points, the industry has developed a variety of technologies and solutions. However, there are some deficiencies in the existing technologies. Existing detection equipment lacks an integrated design. Some detection systems can only provide single detection data and cannot comprehensively analyze multiple detection data. Moreover, the detection method requires on-site manual operation and cannot provide real-time feedback of the detection images and results, which affects the accuracy of leakage point marking. Summary of the Invention

[0003] Aiming at the above defects or improvement requirements of the existing technology, the present invention provides a method and system for intelligent detection of pipeline leakage points, which realizes rapid and accurate location detection, marking, and analysis of pipeline leakage points through the collaborative work of an intelligent controller and an actuator.

[0004] To achieve the above object, according to one aspect of the present invention, a method for intelligent detection of pipeline leakage points is provided, including:

[0005] S100: Start and check the operation of each component to ensure that the predetermined detection task can be reliably executed, and ensure the operating condition of the actuator in the dangerous area. Input the pipeline specification parameters of the pipeline leakage point area to be detected into the intelligent controller.

[0006] S200: Operate the intelligent control component, turn on the camera component to observe the appearance of the pipeline along the way, and control the actuator to move on the pipeline to reach the general area where the pipeline leakage point is located.

[0007] S300: After reaching the designated leak point area, comprehensively photograph the leak point area through the camera component to obtain the appearance of the leak point area, observe the appearance of the detection area based on the real-time transmission screen, make a preliminary judgment on the leak point area and the leak point position, and mark the leak point or the leak point area according to the preliminary judgment result to reduce the exploration range, including: image preprocessing and candidate area extraction, comprehensive scoring of leak point probability; mark the area coordinates that meet the conditions;

[0008] The candidate area feature extraction is to extract the following features from the candidate area:

[0009] ① Area ratio: The ratio of the area of the leak point area to the area of the pipeline:

[0010]

[0011] ② Shape complexity: The ratio of the perimeter of the candidate area to the perimeter of the circular area:

[0012]

[0013] ③ Color contrast: The ratio of the average color vector of the candidate area to the average color vector of the pipeline background area:

[0014] C i =|μ region -μ background ||2

[0015] ④ Texture roughness: Based on the gray-level co-occurrence matrix GLCM:

[0016]

[0017] P(i,j) is the joint probability of gray levels i and j in the gray-level co-occurrence matrix GLCM;

[0018] Comprehensively score the leak point probability by linearly weighted fusion of multiple features:

[0019] Score(R i )=α·A i +β·S i +γ·C i +δ·T i

[0020] Among them, α, β, γ, δ are weight coefficients. If Score(R i )>T leak , it is determined as a leak point candidate area, and T leak is the leak point determination threshold;

[0021] S400: Based on the marked area, operate the intelligent control component to control the acoustic wave detection component to detect the leak point in the marked area, determine the specific position of the leak point, and send a command to the marking component for marking;

[0022] S500: According to the relative position of the leak point in the pipeline, start the ultrasonic detection component to measure the wall thickness of the pipeline and feedback the data to the intelligent controller, and compare it with the input pipeline specification parameters to preliminarily analyze the cause;

[0023] S600: Through the data storage and analysis module of the intelligent controller, comprehensively process and analyze the data information fed back in step S300, step S400, and step S500, evaluate the severity of the leakage point, and generate a detailed analysis report according to the analysis results.

[0024] Further, the image preprocessing in step S300 includes:

[0025] Edge intensity calculation:

[0026]

[0027] where I(x,y) is the pixel gray value, and significant edges are screened by setting a threshold T edge which needs to be adjusted according to the pipeline material and lighting conditions;

[0028] Color anomaly detection, perform color segmentation on the leak point area:

[0029]

[0030] H ∈ [H min , H max is the hue range for locating specific color anomalies, S ∈ [S min , S max is the saturation range to exclude low-saturation noise, and V ∈ [V min , V max is the value range to filter out overly dark or overly bright areas.

[0031] Further, step S400 includes:

[0032] S401: After reaching the specified leak point area, start the acoustic wave detection component on the actuator;

[0033] S402: The acoustic wave detection component uses the refraction principle of acoustic wave conduction to detect the leak point, and the detected acoustic wave signal is fed back to the intelligent control component of the intelligent controller;

[0034] S403: The intelligent control component transmits the received acoustic wave signal to the display component, and clearly displays the change of the acoustic wave on the display component;

[0035] S404: The operator determines the exact location of the leak point according to the change of the acoustic wave waveform diagram feedback on the display component.

[0036] Further, the step S500 includes:

[0037] When the location is at an elbow or an area with large impact, operate the intelligent control component to control the ultrasonic detection component to measure the wall thickness around the leak point mark, and extend the measurement to the straight pipe sections on both sides. Compare and analyze the wall thickness feedback to the display component with the pipeline specification parameters input to the intelligent control component;

[0038] When the location is on the straight pipe section, observe the coating problem and the corrosion condition around the leak point according to the picture transmitted back by the camera component. Operate the intelligent control component to control the ultrasonic detection component to measure the wall thickness around the leak point mark, and extend the measurement to the straight pipe sections on both sides. Compare and analyze the wall thickness feedback to the display component with the pipeline specification parameters input to the intelligent control component.

[0039] Further, when comparing the ultrasonic detection wall thickness with the pipeline specification parameters in step S500, the preliminary analysis reasons include:

[0040] If the error between the wall thickness data detected at a single point and the input pipeline specification parameters exceeds the set standard range, it is necessary to perform multi-point detection on the wall thickness of the pipeline;

[0041] Single-point thickness deviation calculation:

[0042]

[0043] where, T spec is the designed wall thickness of the pipeline, T meas,i is the actual wall thickness of the i-th measurement point, and ΔT i is the percentage of thickness loss;

[0044] Regional average thickness loss:

[0045]

[0046] where, N is the number of measurement points in the current partition where the leak point is located, and ΔT zone is the regional average thickness loss;

[0047] Based on the comparison and analysis of single-point and multi-point detection results, analyze the abnormal reasons:

[0048] ① Judgment of local pitting or mechanical impact wear damage:

[0049]

[0050] where, η localis the local damage threshold;

[0051] ② Uniform corrosion determination:

[0052]

[0053] wherein, σ zone is the standard deviation of the thickness loss within the area, η uniform is the uniform corrosion threshold, and σ th is the standard deviation threshold.

[0054] Furthermore, the pipeline specification parameters include but are not limited to the wall thickness and material type of the pipeline.

[0055] Furthermore, the data information in step S600 includes acoustic detection data, ultrasonic detection data, and the images captured by the camera component, and the report content includes the leak point location, the deviation of the pipe wall thickness at the leak point, the severity of the leak point, and the cause of the leak point formation.

[0056] According to another aspect of the present invention, the present invention provides 8. A pipeline leak intelligent detection system for implementing the steps of the above-mentioned pipeline leak intelligent detection method, characterized in that it includes: an intelligent controller and an actuator,

[0057] The intelligent controller is connected to the actuator through wireless communication to achieve data exchange and instruction transmission;

[0058] The actuator is the core execution unit of the entire system, and under the instruction of the intelligent controller, it completes the integrated operation of accurately positioning the pipeline leak point, collecting and imaging multi-point detection data, and physical marking.

[0059] Furthermore, the actuator includes

[0060] an acoustic detection component for determining the exact position of the pipeline leak point according to the acoustic wave change;

[0061] an ultrasonic detection component for measuring the thickness of the pipeline in the leak point area;

[0062] a camera component for real-time transmitting a high-definition video stream of the pipeline detection area, providing visual feedback required for the intelligent controller to navigate the actuator, and performing refined imaging on the appearance of the leak point area;

[0063] a marking component for marking the leak point position or the leak point area position.

[0064] Furthermore, the intelligent controller includes:

[0065] an intelligent control component for controlling the movement path and operation tasks of the actuator and sending instructions to the actuator;

[0066] A display component that provides a user interface for visualizing the data information fed back by the actuator, and displays the real-time state of the picture during the detection process, acoustic detection data, and ultrasonic detection data.

[0067] A data storage and analysis module that stores the input pipeline parameters and the feedback information of the actuator, and processes and analyzes the data to evaluate the severity of the leakage point and gives an analysis report.

[0068] Generally speaking, compared with the prior art by the above technical solutions conceived by the present invention, the following beneficial effects can be achieved:

[0069] 1. The actuator of the intelligent pipeline leakage detection method and system of the present invention integrates an acoustic detection component, an ultrasonic detection component, a camera component and a marking component. The components cooperate with each other to solve the problem that traditional single-point detection cannot comprehensively detect, and realize the comprehensive detection of the pipeline leakage area and the pipeline wall thickness. The intelligent controller can store, analyze and display the information and parameters fed back by the actuator in the picture by adopting an intelligent module, which integrates a control component, a display component and a data storage and analysis module, solves the problem of single traditional feedback information, and can better systematically analyze the feedback information. Through the collaborative work of the intelligent controller and the actuator, the rapid and accurate positioning detection, marking and analysis of the pipeline leakage point are realized.

[0070] 2. The intelligent pipeline leakage detection method and system of the present invention solve the problems of traditional manual on-site detection or semi-intelligent detection through the information transmission between the intelligent controller and the actuator, and the visualization of the feedback information on the display component, greatly reducing the operation of manual on-site detection. And the operator can have a clear visual impression of the pipeline appearance through the real-time feedback picture, which provides convenience for subsequent operations. The waveform diagrams during the operation of the acoustic detection component and the ultrasonic detection component can be clearly displayed in the picture, and the leakage point can be accurately marked through the picture, achieving the effect of reducing labor costs and reducing the safety risks of construction personnel.

[0071] 3. The intelligent pipeline leakage detection method and system of the present invention visualize the picture real-time feedback by the camera component on the display component. The operator can make a preliminary analysis and judgment on the pipeline appearance, and can analyze the specific location of the leakage point according to the acoustic wave diagram fed back by the acoustic detection component. Through the data analysis and calculation of the ultrasonic detection component, the specific thickness of the pipe wall is obtained and compared with the input pipeline specification parameters for analysis. Through the information fed back from multiple aspects, a preliminary analysis report is formed. It solves the problem of the single function of traditional detection devices, realizes the integration of the system, the diversity of detection methods, and can better analyze problems in an intuitive way, improves the speed of problem handling, and also enables a better understanding of the extended problems caused by a pipeline leakage point. Brief Description of the Drawings

[0072] Figure 1 FIG. is a schematic flow chart of an intelligent detection method for pipeline leakage points according to an embodiment of the present invention;

[0073] Figure 2 FIG. is a schematic diagram of an intelligent detection system for pipeline leakage points according to an embodiment of the present invention. Detailed Embodiments

[0074] In order to make the objectives, technical solutions and advantages of the present invention more clear and understandable, the present invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not used to limit the present invention. In addition, the technical features involved in the various embodiments of the present invention described below can be combined with each other as long as they do not conflict with each other.

[0075] It should be noted that the technical solutions between the various embodiments of the present invention can be combined with each other, but it must be based on the fact that those skilled in the art can implement them. When the combination of technical solutions conflicts with each other or cannot be implemented, it should be considered that such a combination of technical solutions does not exist and is not within the scope of protection required by the present invention.

[0076] In the present invention, the terms "include", "comprise" or any other variant thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements not only includes those elements but also includes other elements not expressly listed, or further includes elements inherent to such process, method, article or device. Without further limitation, the elements defined by the statement "including..." do not exclude the presence of additional identical elements in the process, method, article or device including the said elements.

[0077] As Figure 2 shown, in an embodiment of the present invention, the present application provides an intelligent detection system for pipeline leakage points, including an intelligent controller and actuator data. Among them,

[0078] The intelligent controller is connected to the actuator through wireless communication to achieve data exchange and instruction transmission, and it includes an intelligent control component, a display component and a data storage and analysis module.

[0079] The intelligent control component is used to control the moving path and operation tasks of the actuator, send instructions to the actuator, and pipeline parameters can also be input into the intelligent control component.

[0080] The display component provides a user interface, which is a visualization of the actuator feedback signal received by the intelligent control component. It shows the real-time state of the detection process, acoustic detection data, and ultrasonic detection data, and can more clearly reflect the pipeline leakage point and more information about the pipeline, so as to more accurately determine the leakage point area and better analyze the cause of the pipeline leakage point.

[0081] The data storage and analysis module stores the pipeline parameters input in the intelligent control component and the video, image, and data information obtained from the actuator, and processes and analyzes the data to evaluate the severity of the leakage point and generate an analysis report.

[0082] The actuator integrates functions such as acoustic detection, ultrasonic thickness measurement, visual imaging, and intelligent marking, and is the execution unit of the entire system. Under the instruction of the intelligent controller, it completes the integrated operation of accurately positioning the pipeline leakage point, collecting and imaging multi-dimensional detection data, and physical marking. It includes an acoustic detection component, an ultrasonic detection component, a camera component, and a marking component.

[0083] The acoustic detection component uses the refraction principle of acoustic wave conduction to detect the position of the leakage point. When the acoustic detection component is activated, the change of the acoustic wave can be clearly seen on the display component of the intelligent controller, and the precise position of the pipeline leakage point is determined according to the change of the feedback acoustic wave.

[0084] The ultrasonic detection component uses ultrasonic waves for thickness measurement. The principle is that when the ultrasonic pulse emitted by the probe passes through the measured object and reaches the material interface, the pulse is reflected back to the probe, and the thickness of the measured material is determined by accurately measuring the time of the ultrasonic wave in the material.

[0085] The camera component is used to transmit the high-definition video stream of the pipeline detection area in real time, provide the visual feedback required for the actuator navigation of the intelligent controller, and can better operate the intelligent control component to successfully complete the detection of the leakage point position; and track the acoustic detection component and the ultrasonic detection component in real time, perform refined imaging on the appearance of the leakage point area, and feedback the determined pipeline leakage point position to the display component.

[0086] After being analyzed by the data storage and analysis module, the marking component marks the leakage point position or the leakage point area position through the intelligent control component.

[0087] During use, the intelligent controller and the actuator communicate instructions through a wireless communication system. When the actuator is located on the pipeline, the intelligent control component can be operated to make it reach the pipeline leak area (non-weld), and the camera component on the actuator will transmit the real-time images on the path to the display component for convenient operation. After reaching the designated location, the acoustic detection component is activated to perform acoustic detection in the leak area, and the exact location of the leak is determined through the acoustic waveform diagram feedback on the display component. Then, the intelligent control component is operated to operate the marking component to mark the determined pipeline leak. The ultrasonic detection component is activated to detect the pipe wall around the leak. The data storage and analysis module of the intelligent controller stores the data information fed back by the actuator, and analyzes and compares the fed-back data information with the pipeline parameters input in the intelligent control component. If the error exceeds the standard value, multi-point detection should be carried out on the pipe wall thickness of the pipeline. If most of the detected data have an error exceeding the standard value, there is a problem with the pipeline material. If the error of the multi-point detected data does not exceed the standard, only the area around the leak is problematic, then the leak may be at a position with a relatively large impact force, and the wall thickness of the pipeline at this point needs to be strengthened. For the leak at the weld, only the leak needs to be detected by the acoustic detection component and marked.

[0088] As Figure 1 shown, in another embodiment of the present invention, based on the above system, the present application provides an intelligent detection method for pipeline leaks, including:

[0089] S100: Start and check the operation status of each component to ensure that the predetermined detection tasks can be reliably executed, and ensure the operation status of the actuator in the dangerous area. Input the pipeline specification parameters of the area to be detected into the intelligent controller. The pipeline specification parameters generally include but are not limited to pipe wall thickness, material type, diameter, use environment, etc. These parameters will be used as the reference basis for subsequent data analysis.

[0090] S200: Operate the intelligent control component to turn on the camera component to observe the appearance of the pipeline along the way, control the actuator to move on the pipeline, and make it reach the general area where the pipeline leak is located. During the movement, the camera component on the actuator transmits the images on the path to the display component of the intelligent controller in real time. The operator observes the images on the display component to ensure that the actuator moves along the correct path and avoids obstacles on the pipeline, etc.;

[0091] S300: After reaching the designated leak area, take a comprehensive picture of the leak area through the camera component to obtain the appearance of the leak area, observe the appearance of the detection area according to the real-time transmitted images, and make a preliminary judgment on the leak area and the leak location, and mark the leak or the leak area according to the preliminary judgment result to reduce the exploration range. Specifically, the preliminary judgment and marking are based on the real-time transmitted pipeline appearance images by the camera component, including:

[0092] (1) Image preprocessing and candidate region extraction

[0093] Edge intensity calculation:

[0094]

[0095] where I(x, y) is the pixel gray value, and by setting the threshold T edge Screening significant edges (such as rust cracks, damage contours) needs to be adjusted according to the pipeline material and lighting conditions.

[0096] Color anomaly detection (HSV space), color segmentation of leakage areas (such as rust, stains):

[0097]

[0098] H ∈ [H min , H max is the hue range for locating specific color anomalies, S ∈ [S min , S max is the saturation range to exclude low-saturation noise, V ∈ [V min , V max is the value range of lightness to filter out too dark or too bright areas.

[0099] Candidate region feature extraction, for the segmented connected region R i Extract the following features:

[0100] ① Area ratio: The ratio of the area of the leakage area to the area of the pipeline (the basis for distinguishing large-scale corrosion or small-hole leakage)

[0101]

[0102] ② Shape complexity: The ratio of the perimeter of the candidate region to the perimeter of the circular region. The larger the value, the more complex the shape (crack S i > 1.5, circular hole S i ≈ 1.0)

[0103]

[0104] ③ Color contrast: The ratio of the average color vector of the candidate region to the average color vector of the pipeline background region, quantifying the color difference

[0105] C i = ||μ region - μ background ||2

[0106] ④ Texture roughness (based on gray-level co-occurrence matrix GLCM): The larger the value, the rougher the texture (the texture of the corrosion area is chaotic, and the surface of the normal pipeline is smooth)

[0107]

[0108] P(i, j): The joint probability of gray levels i and j in the gray-level co-occurrence matrix (GLCM).

[0109] (2) Comprehensive scoring of leakage point probability

[0110] By linearly weighted fusion of multiple features:

[0111] Score(R i ) = α·A i + β·S i + γ·C i + δ·T i

[0112] where α, β, γ, δ are weight coefficients. If Score(R i ) > T leak , then it is determined as a leakage point candidate region, and T leak is the leakage point determination threshold.

[0113] (3) Mark the coordinates of the regions that meet the conditions:

[0114] Marked_Regions = {(x i , y i ) | Score(R i ) > T leak}

[0115] Through the above model, initially judge and identify the leakage point regions. For leakage points caused by obvious external force collisions and damages; for leakage points with no appearance damage, obvious leakage and being in the process of leakage; for regions with no appearance damage, being in the process of leakage during production and the leakage points are not obvious, initially detect and mark them to narrow the search scope for accurately finding the leakage points in the follow-up; for different pipelines, the parameters in the above model can also be dynamically adjusted to adapt to the detection requirements of different materials, environments and leakage types.

[0116] S400: According to the marked regions, operate the intelligent control component to control the acoustic wave detection component to detect the leakage points in the marked regions, determine the specific positions of the leakage points, and send commands to the marking component for marking.

[0117] Specifically, it includes:

[0118] S401: After reaching the specified leakage point region, start the acoustic wave detection component on the actuator.

[0119] S402: The acoustic wave detection component uses the refraction principle of acoustic wave conduction to detect leakage points, and the detected acoustic wave signals are fed back to the intelligent control component of the intelligent controller.

[0120] S403: The intelligent control component transmits the received acoustic wave signal to the display component, and clearly displays the changes of the acoustic wave on the display component, such as the acoustic wave waveform diagram, etc.

[0121] S404: The operator determines the exact position of the leak point according to the change of the acoustic wave waveform diagram fed back on the display component. For example, when the acoustic wave waveform shows abnormal reflected waves, attenuation changes and other characteristics, it can be judged that this position is the leak point.

[0122] S500: According to the relative position of the leak point in the pipeline, start the ultrasonic detection component to measure the wall thickness and feedback the data to the intelligent controller, and compare it with the input pipeline specification parameters to preliminarily analyze the cause. Including:

[0123] (1) If the position is at the elbow or the area with large impact, operate the intelligent control component to control the ultrasonic detection component to measure the wall thickness around the leak point mark, and extend the measurement to the straight pipe sections on both sides. Compare and analyze the wall thickness fed back to the display component with the pipeline specification parameters input to the intelligent control component, and analyze the influence degree of the wall thickness and impact wear on the leak point according to the comparison result;

[0124] (2) If the position is on the straight pipe section, observe the coating problem and the corrosion condition around the leak point according to the picture transmitted back by the camera component. Operate the intelligent control component to control the ultrasonic detection component to measure the wall thickness around the leak point mark, and extend the measurement to the straight pipe sections on both sides. Compare and analyze the wall thickness fed back to the display component with the pipeline specification parameters input to the intelligent control component, and analyze the influence degree of the wall thickness and corrosion on the leak point according to the comparison result.

[0125] The comparison and analysis judgment of the ultrasonic detection of the wall thickness includes:

[0126] If the error between the wall thickness data detected at a single point and the input pipeline specification parameters exceeds the set standard range, it is necessary to perform multi-point detection on the wall thickness of the pipeline.

[0127] Calculation of single-point thickness deviation:

[0128]

[0129] where, T spec is the designed wall thickness of the pipeline (specification parameters input from S100, unit: mm), T meas,i is the actual wall thickness of the i-th measurement point (ultrasonic detection value, unit: mm), and ΔT i is the percentage of thickness loss.

[0130] Regional average thickness loss:

[0131]

[0132] N is the number of measurement points in the current leakage point area, and ΔT zone is the regional average thickness loss.

[0133] Based on the comparison and analysis of single-point and multi-point detection results, analyze the abnormal reasons:

[0134] ① Judgment of local pitting corrosion or mechanical impact wear damage:

[0135]

[0136] Among them, η local is the local damage threshold (usually ≥ 30%).

[0137] This situation indicates that there is a problem with the pipe wall thickness only around the leakage point. It may be that the leakage point is at a position with a relatively large impact force, resulting in local thinning of the pipe wall. It is necessary to strengthen the pipe wall thickness at this place, such as adding a protective layer, making local repairs, etc.

[0138] ② Judgment of uniform corrosion:

[0139]

[0140] Among them, σ zone is the standard deviation of the thickness loss in the area, η uniform is the uniform corrosion threshold (usually ≥ 15%), and σ th is the standard deviation threshold (usually ≤ 5%).

[0141] This situation indicates that there may be a quality problem with the pipe material, and it is necessary to further inspect and evaluate the entire pipe.

[0142] S600: Through the data storage and analysis module of the intelligent controller, comprehensively process and analyze the data information fed back in step S300, step S400, and step S500, evaluate the severity of the leakage point, and generate a detailed analysis report according to the analysis results. The data information includes acoustic detection data, ultrasonic detection data, the pictures taken by the camera component, etc. The report content includes the leakage point location, the deviation of the pipe wall thickness at the leakage point, the severity of the leakage point, the cause of the leakage point formation, etc., providing a basis for subsequent maintenance and repair.

[0143] Those skilled in the art can easily understand that the above is only a preferred embodiment of the present invention and is not used to limit the present invention. Any modifications, equivalent replacements, and improvements made within the spirit and principle of the present invention shall be included within the protection scope of the present invention.

Claims

1. A pipeline leakage intelligent detection method, characterized in that: include: S100: Start and check the operation of each component to ensure that the predetermined detection task can be reliably performed and the actuator can operate in the hazardous area, and input the pipeline specification parameters of the pipeline leakage area to be detected into the intelligent controller; S200: operate the intelligent control component, turn on the camera component to observe the appearance of the pipeline along the way, and control the actuator to move on the pipeline to reach the approximate area where the pipeline leak is located; S300: After arriving at the designated leakage point area, the leakage point area is fully photographed by the camera assembly to obtain the appearance of the leakage point area, the appearance of the detection area is observed according to the real-time transmission picture, and the leakage point area and the leakage point position are preliminarily judged, and the leakage point or leakage point area is marked according to the preliminary judgment result to reduce the scope of exploration, including: image preprocessing and candidate area extraction, leakage point probability comprehensive scoring; marking the coordinates of the area that meets the conditions; The candidate region feature extraction is to extract the following features from the candidate region: ① Area ratio: The ratio of the leakage area to the pipeline area: ② Shape complexity: the ratio of the circumference of the candidate area to the circumference of the circular area: ③ Color contrast: The ratio of the average color vector of the candidate area to the average color vector of the pipeline background area: C i =|μ region -m background ||2 ④Texture roughness: Based on gray level co-occurrence matrix GLCM: P(i,j) is the joint probability of gray levels i and j in the gray level co-occurrence matrix GLCM; Comprehensive scoring of leakage probability is performed by linearly weighted fusion of multiple features: Score(R i )=α·A i +β·S i +γ·C i +δ·T i Among them, α, β, γ, δ are weight coefficients. If Score(R i )>T leak , then it is determined as a candidate area for leakage, T leak is the leakage point determination threshold; S400: According to the marked area, the intelligent control component is operated to control the acoustic wave detection component to detect the leakage point in the marked area, determine the specific location of the leakage point, and send a command to the marking component to mark it; S500: According to the relative position of the leak point in the pipeline, the ultrasonic detection component is started to measure the pipe wall thickness and feedback the data to the intelligent controller, which is compared with the input pipeline specification parameters to preliminarily analyze the cause; S600: The data storage and analysis module of the intelligent controller performs comprehensive processing and analysis based on the data information fed back in step S300, step S400, and step S500, evaluates the severity of the leakage point, and generates a detailed analysis report based on the analysis results.

2. According to claim 1, a pipeline leakage intelligent detection method is characterized in that: The image preprocessing in step S300 includes: Edge strength calculation: Where I(x,y) is the pixel gray value, and by setting the threshold T edge Screening for significant edges; Color anomaly detection, color segmentation of leak area: H∈[H min ,H max ] is the hue range, which is used to locate specific color anomalies, S∈[S min ,S max ] is the saturation range, excluding low saturation noise, V∈[V min ,V max ] is the brightness range, filtering out areas that are too dark or too bright.

3. The intelligent pipeline leakage detection method according to claim 1 is characterized in that: The step S400 includes: S401: After reaching the designated leak point area, start the acoustic wave detection component on the actuator; S402: The acoustic wave detection component uses the refraction principle of acoustic wave conduction to detect leaks, and the detected acoustic wave signal is fed back to the intelligent control component of the intelligent controller; S403: the intelligent control component transmits the received sound wave signal to the display component, and the change of the sound wave is clearly displayed on the display component; S404: The operator determines the exact location of the leak according to the change of the acoustic wave waveform graph fed back on the display component.

4. The pipeline leakage intelligent detection method according to claim 1 is characterized in that: The step S500 includes: If the location is at an elbow or an area with large impact, operate the intelligent control component to control the ultrasonic detection component to measure the pipe wall thickness around the leak mark, and extend the measurement to the straight pipe sections on both sides. Compare and analyze the pipe wall thickness fed back to the display component with the pipe specification parameters input to the intelligent control component; The position is located in the straight pipe section. According to the images sent back by the camera component, the coating problems and corrosion conditions around the leak are observed. The intelligent control component is operated to control the ultrasonic detection component to measure the pipe wall thickness around the leak mark, and the measurement is extended to the straight pipe sections on both sides. The pipe wall thickness fed back to the display component is compared and analyzed with the pipeline specification parameters input to the intelligent control component.

5. The pipeline leakage intelligent detection method according to claim 4 is characterized in that: Step S500: Compare the ultrasonic testing pipe wall thickness with the pipe specification parameters. The preliminary analysis of the reasons includes: If the error between the pipe wall thickness data detected at a single point and the input pipe specification parameters exceeds the set standard range, it is necessary to conduct multi-point detection of the pipe wall thickness; Single point thickness deviation calculation: Among them, T spec is the pipe design wall thickness, T meas,i is the actual wall thickness at the i-th measuring point, ΔT i is the thickness loss percentage; Average thickness loss by area: Where N is the number of measurement points in the partition where the current leak point is located, ΔT zone is the average thickness loss of the region; Comparison and analysis of abnormal causes based on single-point and multi-point detection results: ① Determination of local pitting or mechanical impact wear damage: Among them, η local is the local damage threshold; ② Uniform corrosion determination: Among them, σ zone is the standard deviation of thickness loss in the region, η uniform is the uniform corrosion threshold, σ th is the standard deviation threshold.

6. A pipeline leakage intelligent detection method according to any one of claims 1 to 5, characterized in that: The pipeline specification parameters include but are not limited to the wall thickness and material type of the pipeline.

7. A pipeline leakage intelligent detection method according to any one of claims 1 to 5, characterized in that: The data information in step S600 includes sonic detection data, ultrasonic detection data, and images taken by the camera assembly, and the report content includes the leak location, the deviation of the pipe wall thickness at the leak, the severity of the leak, and the cause of the leak.

8. A pipeline leakage intelligent detection system, used to implement the pipeline leakage intelligent detection method according to any one of claims 1 to 7, characterized in that: include: Intelligent controllers and actuators; The intelligent controller is connected to the actuator via wireless communication to achieve data exchange and command transmission; The actuator is the core execution unit of the entire system, and completes the integrated operations of precise positioning of pipeline leakage points, multi-point detection data acquisition, imaging recording and physical marking under the instructions of the intelligent controller.

9. The intelligent pipeline leakage detection system according to claim 8, characterized in that: The actuator comprises: The acoustic wave detection component is used to determine the precise location of the pipeline leak based on the changes in the acoustic waves; Ultrasonic detection component, used to measure the thickness of the pipe in the leak area; The camera component is used to transmit high-definition video streams of the pipeline inspection area in real time, provide the intelligent controller with visual feedback required for actuator navigation, and perform detailed imaging of the appearance of the leak area; A marking component is used to mark the leak point location or the leak area location.

10. The intelligent pipeline leakage detection system according to claim 9, characterized in that: The intelligent controller comprises: An intelligent control component controls the movement path and operation task of the actuator and sends instructions to the actuator; A display component provides a user interface for visualizing the data information received from the actuator and displaying the real-time status of the image during the detection process, the acoustic wave detection data, and the ultrasonic wave detection data; The data storage and analysis module stores the input pipeline parameters and the actuator feedback information, and processes and analyzes the data to evaluate the severity of the leakage point and provide an analysis report.

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