A petrochemical pipeline phased array ultrasonic nondestructive testing method, system and device

Abstract

The application discloses a petrochemical pipeline phased array ultrasonic nondestructive testing method, system and equipment, comprising the following steps: acquiring three-dimensional point cloud data of a petrochemical pipeline; identifying defect probability and defect position information of a ring weld to be detected to generate a ring weld defect heat map; acquiring an initial detection route with the lowest total value of a cost function according to the ring weld defect heat map; acquiring an actual segment value of a wall-climbing robot moving from a current detection position of the initial detection route to a next detection position; the wall-climbing robot is provided with a phased array detection probe; judging whether the actual segment value is greater than a corresponding theoretical segment value; if yes, updating the ring weld defect heat map based on the current detection position, and reacquiring a calibrated detection route with the lowest total value of the cost function according to the updated ring weld defect heat map; if not, continuing to execute the initial detection route, and the application has the advantages of being applicable to detection route planning of complex multi-pipelines.

Description

Technical Field

[0001] This application relates to the field of pipeline inspection technology, and in particular to a phased array ultrasonic non-destructive testing method, system and equipment for petrochemical pipelines. Background Technology

[0002] According to the current regulations and standards of my country's petrochemical industry, welds formed during the installation and repair of petrochemical pipelines need to be subjected to non-destructive testing to ensure the quality of pipeline welding. Currently, phased array ultrasonic non-destructive testing technology can be used. This technology uses multiple crystals in a single probe assembly to deflect, focus, and scan the sound beam. By utilizing the sound beam deflection, commonly known as fan-shaped scanning, a mapped image of the workpiece under test can be generated at an appropriate angle, which greatly simplifies the process of inspecting workpieces with complex geometries.

[0003] When performing ultrasonic testing on multiple pipelines with complex spatial arrangements, a wall-climbing robot equipped with a phased array detection system can be used to move and detect different defects (such as corrosion and cracks) in the welds of the pipeline according to a predetermined detection route, thereby collecting detection data for analysis. However, the existing pipeline detection route planning methods are based on relatively single references and are only suitable for simple single pipeline inspections. For complex multi-pipe systems, it is difficult to find an optimal detection route, which increases the detection cost and reduces the detection efficiency. Summary of the Invention

[0004] The main objective of this application is to provide a phased array ultrasonic non-destructive testing method, system, and equipment for petrochemical pipelines, aiming to solve the technical problem that existing pipeline inspection route planning methods are unable to find optimal inspection routes for complex multi-pipeline applications.

[0005] To achieve the above objectives, this application provides a phased array ultrasonic non-destructive testing method for petrochemical pipelines, comprising the following steps:

[0006] Acquire 3D point cloud data of petrochemical pipelines;

[0007] Based on the 3D point cloud data, the defect probability and defect location information of the circumferential weld to be detected are identified to generate a heat map of the circumferential weld defect.

[0008] Based on the heat map of circumferential weld defects and the preset cost function, the initial detection route with the lowest total cost value is obtained; where the total cost value is the sum of the theoretical segment cost values ​​between two adjacent detection positions in the initial detection route, and each pair of adjacent detection positions is a segment.

[0009] Based on the cost function, the actual segment cost of the wall-climbing robot moving from the current detection position on the initial detection route to the next detection position is obtained; wherein, the wall-climbing robot is equipped with a phased array detection probe;

[0010] Determine whether the actual segment value is greater than the corresponding theoretical segment value;

[0011] If so, based on the current detection position, the defect probability and defect location information of the remaining circumferential welds to be detected are re-identified to update the circumferential weld defect heat map, and the calibration detection route with the lowest total value is obtained again based on the updated circumferential weld defect heat map and cost function.

[0012] If not, continue executing the initial detection route and return to the point where the actual segment cost of the wall-climbing robot moving from the current detection position on the initial detection route to the next detection position is obtained according to the cost function.

[0013] Optionally, the cost function can be expressed as:

[0014] Q=k1·E+k2·h+k3·|θ-θ'|+k4·g;

[0015] In the formula, Q is the cost, E is the energy cost, h is the moving distance, θ is the normal angle of the phased array detection probe, θ' is the reference angle perpendicular to the petrochemical pipeline wall, g is the cost of the vacuum adsorption force of the wall-climbing robot on the petrochemical pipeline, k1 is the first adjustment coefficient, k2 is the second adjustment coefficient, k3 is the third adjustment coefficient, and k4 is the fourth adjustment coefficient.

[0016] Optionally, the expression for the cost g of vacuum adsorption force is:

[0017] ,

[0018] In the formula, F is the actual value of the vacuum suction force of the wall-climbing robot. min This represents the minimum vacuum suction force required by the wall-climbing robot in the corresponding segment.

[0019] Optionally, F min The expression is:

[0020] F min =η(G+F1+F2);

[0021] In the formula, η is the safety factor, G is the weight of the wall-climbing robot, F1 is the centrifugal force of the wall-climbing robot when it moves on the petrochemical pipeline, and F2 is the wind resistance.

[0022] Alternatively, the expression for energy cost E is:

[0023] E = P1·t1 + P2·t2;

[0024] In the formula, P1 is the power required for the wall-climbing robot to generate the corresponding vacuum suction force on the petrochemical pipe, t1 is the time required to supply the wall-climbing robot to generate the vacuum suction force, P2 is the power required to drive the wall-climbing robot to crawl on the petrochemical pipe, and t2 is the crawling time of the wall-climbing robot on the petrochemical pipe.

[0025] Optionally, it also includes:

[0026] Acquire the real-time pose information of the phased array detection probe to obtain the pose deviation Δx;

[0027] Based on the pose deviation Δx, obtain the angle correction Δθ;

[0028] Based on the angle correction Δθ, adjust the normal angle θ of the phased array detection probe to reduce the value of |θ-θ'|.

[0029] Optionally, the expression for the pose deviation Δx is:

[0030] In the formula, α is the curvature compensation coefficient, and r is the radius of the petrochemical pipeline. Let θ be the deflection angle. max This is the maximum tolerance angle.

[0031] Optionally, after obtaining the real-time pose information of the phased array detection probe to obtain the pose deviation Δx, the method further includes: determining whether the pose deviation Δx is greater than a preset deviation threshold.

[0032] If so, the third adjustment coefficient k3 will be increased accordingly.

[0033] To achieve the above objectives, this application also provides a phased array ultrasonic non-destructive testing system for petrochemical pipelines, comprising:

[0034] The point cloud data acquisition module is used to acquire the three-dimensional point cloud data of petrochemical pipelines;

[0035] The defect information acquisition module is used to identify the defect probability and defect location information of the circumferential weld to be inspected based on the three-dimensional point cloud data, so as to generate a heat map of the circumferential weld defect.

[0036] The initial route generation module is used to obtain the initial detection route with the lowest total cost based on the heat map of the circumferential weld defect and the preset cost function; wherein, the total cost value is the sum of the theoretical segment cost values ​​between two adjacent detection positions in the initial detection route, and each pair of adjacent detection positions is a segment;

[0037] The cost calculation module is used to obtain the actual segment cost of the wall-climbing robot moving from the current detection position to the next detection position from the initial detection route, based on the cost function; wherein, the wall-climbing robot is equipped with a phased array detection probe;

[0038] The judgment module is used to determine whether the actual segment value is greater than the corresponding theoretical segment value.

[0039] The route calibration module is used to, if so, re-identify the defect probability and defect location information of the remaining circumferential welds to be inspected based on the current detection position, so as to update the circumferential weld defect heat map, and re-obtain the calibration detection route with the lowest total value based on the updated circumferential weld defect heat map and cost function.

[0040] The iteration module is used to continue executing the initial detection route if not, and return to the point where the actual segment cost of the wall-climbing robot moving from the current detection position to the next detection position according to the cost function is obtained.

[0041] To achieve the above objectives, this application also provides a computer device, which includes a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to implement the above-described method.

[0042] To achieve the above objectives, this application also provides a computer-readable storage medium storing a computer program, on which a processor executes the computer program to implement the above-described method.

[0043] The beneficial effects that this application can achieve are as follows:

[0044] This application targets complex multi-pipe structures with intricate spatial arrangements. By acquiring 3D point cloud data of the pipes, the structure and relative position information of each pipe in three-dimensional space can be intuitively displayed. Then, the defect probability and defect location information of the circumferential welds to be inspected in each pipe can be identified, thereby generating a heat map of circumferential weld defects, ensuring the accuracy of the acquired data. At this point, based on the circumferential weld defect heat map and a preset cost function, the cost factors of the preliminary assessment can be input into the cost function to calculate the corresponding cost value. An initial inspection route with the lowest total cost value can be planned. When the wall-climbing robot equipped with a phased array detection probe completes the inspection from the first inspection position according to the initial inspection route, it moves along the pipe to the second inspection position. At this time, the actual data of each cost factor during the movement of the wall-climbing robot in this segment can be collected. This allows for the accurate calculation of the actual segment cost value. Considering that the theoretical segment cost value calculated earlier may contain calculation errors or inaccurate cost factor data evaluation, it is also determined whether the actual segment cost value is greater than the corresponding theoretical segment cost value. If so, based on the current detection position, the defect probability and defect location information of the remaining circumferential weld to be detected are re-identified to update the circumferential weld defect heat map. Based on the updated circumferential weld defect heat map and cost function, the calibration detection route with the lowest total cost value is re-obtained. This process is repeated. During the detection process, the wall-climbing robot verifies the matching of the theoretical and actual cost values ​​for each segment it moves, dynamically updating the detection route and improving its rationality. This method is applicable to ultrasonic non-destructive testing of multi-pipe systems with complex spatial arrangements. Attached Figure Description

[0045] To more clearly illustrate the technical solutions in the specific embodiments of this application or the prior art, the drawings used in the description of the specific embodiments or the prior art will be briefly introduced below. In all the drawings, similar elements or parts are generally identified by similar reference numerals. In the drawings, the elements or parts are not necessarily drawn to scale.

[0046] Figure 1 This is a schematic flowchart of a phased array ultrasonic non-destructive testing method for petrochemical pipelines, as described in an embodiment of this application.

[0047] Figure 2 This is a schematic diagram illustrating the detection route planning based on a complex spatial arrangement of multiple pipelines in an embodiment of this application.

[0048] Figure 3 This is a schematic diagram of the framework of a phased array ultrasonic non-destructive testing system for petrochemical pipelines, as described in an embodiment of this application.

[0049] The realization of the purpose, functional features and advantages of this application will be further explained in conjunction with the embodiments and with reference to the accompanying drawings. Detailed Implementation

[0050] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of this application, and not all of the embodiments. Based on the embodiments of this application, all other embodiments obtained by those of ordinary skill in the art without creative effort are within the scope of protection of this application.

[0051] It should be noted that if the embodiments of this application involve descriptions such as "first" or "second," these descriptions are for descriptive purposes only and should not be construed as indicating or implying their relative importance or implicitly specifying the number of technical features indicated. Therefore, features defined with "first" or "second" may explicitly or implicitly include at least one of those features. Furthermore, the technical solutions of the various embodiments can be combined with each other, but this must be based on the ability of those skilled in the art to implement them. When the combination of technical solutions is contradictory or impossible to implement, it should be considered that such a combination of technical solutions does not exist and is not within the scope of protection claimed in this application.

[0052] Example 1:

[0053] Reference Figures 1-3 This embodiment provides a phased array ultrasonic non-destructive testing method for petrochemical pipelines, including the following steps:

[0054] Step S10: Obtain the 3D point cloud data of the petrochemical pipeline;

[0055] In this step, when collecting 3D point cloud data, a drone equipped with a scanner (such as a laser scanner, camera scanner, or ultrasonic scanner) can be used to fly and scan the areas of each pipeline that need to be inspected (i.e., the circumferential weld area), thereby quickly completing the data acquisition work.

[0056] Step S20: Based on the three-dimensional point cloud data, identify the defect probability and defect location information of the circumferential weld to be detected, so as to generate a heat map of the circumferential weld defect.

[0057] In this step, the defect probability of the circumferential weld to be inspected is first calculated. The defect locations of the circumferential welds to be inspected that exceed the defect probability threshold are identified and marked. Finally, the identified multiple defect locations are arranged in a spatial layout to form a circumferential weld defect heat map. This circumferential weld defect heat map is the smallest three-dimensional space that can encompass all defect locations, which facilitates the subsequent planning of the inspection route and allows staff to intuitively view the spatial distribution of defect locations and pipelines, enabling necessary manual adjustments during route planning.

[0058] When identifying the probability of defects in a circumferential weld to be inspected, the following steps are included:

[0059] Fractal dimension features are used to enhance the separability of defects, and a hybrid feature vector X is constructed by combining wavelet packet energy features. The expression for the hybrid feature vector X is as follows:

[0060] X=[E WP D f ]+λ·R;

[0061] In the formula, E WP The wavelet packet energy entropy is used to quantify the frequency domain features of the signal, effectively identifying the high-frequency components of the crack; D f λ is the fractal dimension, used to describe the complexity of the defect contour; a larger value indicates more severe corrosion. λ is the weighting coefficient (experimentally optimized value is 0.8), used to balance the contribution of morphological features. R is the morphological feature. Experiments show that fractal features can improve feature separability by 6.3%, which can significantly improve the accuracy of circumferential weld defect identification.

[0062] The SVM classification decision function (i.e., the support vector machine classification model) incorporating the RBF kernel function is expressed as follows:

[0063] F(X) = sgn(∑a i y i exp( γ X X i 2 )+b);

[0064] In the formula, a i For support vector weights, y i X represents the sample label, γ represents the kernel parameter, and X represents the sample label. i For support vectors, b is the bias coefficient;

[0065] The correlation between the mixed feature vector X and the SVM classification decision function is as follows:

[0066] (1) The mixed feature vector is used as the input to the classification decision function.

[0067] The mixed feature vector X is the direct input to the SVM classification decision function, and the extracted features (i.e., wavelet packet energy entropy E) are... wp Enhanced crack high-frequency response, fractal dimension D f The quantized erosion complexity provides SVM with a highly separable data representation. This optimized feature space makes data points easier to separate by hyperplanes, for example, the fractal dimension D. f It can improve feature separability by 6.3%, thereby significantly reducing the computational complexity of SVM in high-dimensional mapping.

[0068] (2) RBF kernel function dependent feature space metric

[0069] RBF kernel function exp( γ X X i 2 The core of this is to calculate the mixed feature vector X and the support vector X' of the input. i Euclidean distance X X i In the mixed features, the morphological weighting of λ·R adjusts the distance metric, making similar defective samples more clustered in the kernel space, thereby improving the classification confidence of the classification decision function F(X).

[0070] (3) Collaboratively improve classification accuracy and generalization

[0071] Enhanced separability: Hybrid feature vector optimization makes the decision boundary clearer. SVM separates defect categories by maximizing the margin hyperplane, and the support vector position is directly determined by the feature distribution.

[0072] Kernel parameter adaptation: γ controls the kernel width (typical value 0.1-1). A smaller γ smooths the decision boundary, adapting to the global distribution of mixed features; a larger γ focuses on local feature details (such as D). f (The revealed microscopic defect morphology).

[0073] In summary, the hybrid feature vector reduces X X i The computational dimension, SVM dynamically adjusts the weights a i The method, using a bias coefficient b, achieves a 41% efficiency improvement over traditional methods while also exhibiting anti-overfitting capabilities. This is achieved through joint parameter tuning of λ and γ (e.g., γ=0.5 to balance boundary curvature), which avoids the model's sensitivity to noise features. This correlation mechanism enables a defect identification accuracy of 97%, thus providing a reliable data foundation for accurate subsequent detection route planning.

[0074] Step S30: Based on the heat map of the circumferential weld defect and the preset cost function, obtain the initial detection route with the lowest total cost value; wherein, the total cost value is the sum of the theoretical segment cost values ​​between two adjacent detection positions in the initial detection route, and each pair of adjacent detection positions is a segment;

[0075] In this step, based on the heat map of defects in the circumferential weld, multiple alternative inspection routes can be generated. By inputting the cost factors (such as energy consumption, distance, environmental parameters, etc.) of the preliminary assessment into the cost function, the total cost value corresponding to each alternative inspection route can be calculated, thereby selecting the alternative inspection route with the lowest total cost value as the initial inspection route.

[0076] It should be noted that in the initial inspection route, each segment consists of every two adjacent inspection locations (i.e., defect location points), such as... Figure 2 As shown, the straight line segment corresponding to the segment does not represent the actual movement path of the wall-climbing robot. This straight line segment represents the direction of movement of the wall-climbing robot from the previous defect location point to the next defect location point. The movement path of the wall-climbing robot between segments needs to be planned according to the positional relationship between the pipe and the defect location point. For example, if the two defect location points of a segment are both on the same pipe, it can move in a straight line along the pipe wall. If they are on different pipes, it needs to first move vertically from the current pipe to the other pipe, and then move along the pipe wall to the corresponding defect location point. In addition, when planning the inspection route, when two inspection locations that are close to each other are on different pipes, it is necessary to consider whether the wall-climbing robot has the ability to change the climbing pipe (generally based on the vertical distance between the two pipes). If it does not have this capability, an optimal inspection route needs to be found.

[0077] Step S40: Based on the cost function, obtain the actual segment cost of the wall-climbing robot moving from the current detection position on the initial detection route to the next detection position; wherein, the wall-climbing robot is equipped with a phased array detection probe;

[0078] Step S50: Determine whether the actual segment cost is greater than the corresponding theoretical segment cost. If so, based on the current detection position, re-identify the defect probability and defect location information of the remaining circumferential weld to be detected, update the circumferential weld defect heat map, and re-obtain the calibration detection route with the lowest total cost based on the updated circumferential weld defect heat map and cost function. If not, continue to execute the initial detection route and return to the point where the actual segment cost of the wall-climbing robot moving from the current detection position of the initial detection route to the next detection position is obtained based on the cost function.

[0079] In summary, in this embodiment, for multi-pipe structures with complex spatial arrangements, the structure and relative position information of each pipe in three-dimensional space can be intuitively displayed by collecting three-dimensional point cloud data of the pipes. Then, the defect probability and defect location information of the circumferential welds to be inspected in each pipe can be identified, thereby generating a circumferential weld defect heat map, ensuring the accuracy of the collected data. At this time, based on the circumferential weld defect heat map and the preset cost function, the cost factors of the preliminary assessment can be input into the cost function to calculate the corresponding cost value, and an initial detection route with the lowest total cost value can be planned. When the wall-climbing robot equipped with the phased array detection probe completes the detection from the first detection position according to the initial detection route, it moves along the pipe to the second detection position. At this time, the cost factors of the wall-climbing robot during the movement of this segment can be collected. The actual segment cost is accurately calculated using real data. Considering that the theoretical segment cost may have calculation errors or inaccurate cost factor data evaluation in the previous calculation, it is also determined whether the actual segment cost is greater than the corresponding theoretical segment cost. If so, based on the current detection position, the defect probability and defect location information of the remaining circumferential weld to be detected are re-identified to update the circumferential weld defect heat map. Based on the updated circumferential weld defect heat map and cost function, the calibration detection route with the lowest total cost is re-obtained. This process is repeated. During the detection process, the wall-climbing robot verifies the matching of the theoretical and actual cost values ​​for each segment it moves to dynamically update the detection route, improving the rationality of the detection route. This method is applicable to ultrasonic non-destructive testing of multi-pipe systems with complex spatial layouts.

[0080] As an optional implementation, the cost function is expressed as follows:

[0081] Q=k1·E+k2·h+k3·|θ-θ'|+k4·g;

[0082] In the formula, Q is the cost, E is the energy cost, h is the moving distance, θ is the normal angle of the phased array detection probe, θ' is the reference angle perpendicular to the petrochemical pipeline wall, g is the cost of the vacuum adsorption force of the wall-climbing robot on the petrochemical pipeline, k1 is the first adjustment coefficient, k2 is the second adjustment coefficient, k3 is the third adjustment coefficient, and k4 is the fourth adjustment coefficient.

[0083] In this embodiment, when planning the detection route, the cost value Q is comprehensively evaluated using multiple cost factors, including energy consumption cost E, movement distance h, normal angle θ of the phased array detection probe, and vacuum adsorption force cost g. Energy consumption cost E characterizes the energy required to drive the wall-climbing robot and maintain vacuum adsorption on the pipe wall. Movement distance h directly represents the length of the detection route, i.e., the detection efficiency; the shorter the route, the higher the efficiency. The normal angle θ of the phased array detection probe is a measure of the perpendicular incidence of ultrasonic waves, and the difference |θ-θ'| represents the detection accuracy; the smaller the difference, the higher the accuracy. Vacuum adsorption force cost g measures the adhesion stability of the wall-climbing robot on the pipe wall, thus characterizing the risk of the robot slipping off the pipe; the larger g is, the higher the risk of slipping. Since the four cost factors have different parameter attributes, they are converted using different adjustment coefficients, allowing for the same quantification and superposition of the four cost factors. Each adjustment coefficient can also be assigned a corresponding weight value to characterize the degree of influence of different cost factors on the cost value Q.

[0084] As an optional implementation method, the expression for the cost g of vacuum adsorption force is:

[0085] ,

[0086] In the formula, F is the actual value of the vacuum suction force of the wall-climbing robot. min This represents the minimum vacuum suction force required by the wall-climbing robot in the corresponding segment.

[0087] In this embodiment, when calculating the cost g of the vacuum adsorption force, a minimum vacuum adsorption force F required by the wall-climbing robot in the current segment can be set. min When the actual value F of the vacuum suction force of the wall-climbing robot is greater than or equal to the minimum value F of the vacuum suction force, min When the vacuum suction force value g is high, it indicates that the risk of the robot slipping is very low, and the output value g is 0. Conversely, when the vacuum suction force value g is high, it indicates that there is a greater risk of slipping, and the output value g = (F... min -F) 2 This increases the cost value Q, thus enabling risk warning.

[0088] As an optional implementation method, F min The expression is:

[0089] F min =η(G+F1+F2);

[0090] In the formula, η is the safety factor, G is the weight of the wall-climbing robot, F1 is the centrifugal force of the wall-climbing robot when it moves on the petrochemical pipeline, and F2 is the wind resistance.

[0091] In this embodiment, the minimum value of the vacuum adsorption force F is calculated.min First, we need to consider the weight G (G=mg) of the wall-climbing robot and its requirements on the vacuum suction force. We also need to consider that when the wall-climbing robot moves from one detection position to another, its movement path is not necessarily a straight line, but rather a curve formed along the pipe wall. In this case, due to the influence of the moving speed, a certain centrifugal force F1 (F1=mω) will be generated. 2 (where r and ω are angular velocities, and r is the radius of the petrochemical pipe) This also places higher demands on the vacuum suction force. If the movement is linear, then F1=0. The influence of wind resistance F2 (which can be measured by a wind speed sensor mounted on the wall-climbing robot) is also considered. Therefore, the minimum vacuum suction force F is comprehensively evaluated by combining the weight G, centrifugal force F1, and wind resistance F2. min Furthermore, by calibrating with a safety factor η (e.g., η=1.5), the calculation is reliable and accurate, providing a reliable data reference basis for accurately assessing the cost value g of vacuum adsorption force.

[0092] It should be noted that when the segment the wall-climbing robot moves to is located at the top of the horizontally arranged petrochemical pipe, since the petrochemical pipe acts as the support for the wall-climbing robot, the weight G mentioned above can be output as 0 to improve the accuracy of the data. Otherwise, the weight G should be considered for the vacuum suction force, whether the petrochemical pipe is vertical or inclined.

[0093] As an optional implementation method, the energy cost E is expressed as follows:

[0094] E = P1·t1 + P2·t2;

[0095] In the formula, P1 is the power required for the wall-climbing robot to generate the corresponding vacuum suction force on the petrochemical pipe, t1 is the time required to supply the wall-climbing robot to generate the vacuum suction force, P2 is the power required to drive the wall-climbing robot to crawl on the petrochemical pipe, and t2 is the crawling time of the wall-climbing robot on the petrochemical pipe.

[0096] In this embodiment, the energy consumption cost E is calculated mainly from the energy consumption required for vacuum suction force and the energy consumption required to drive the wall-climbing robot. Therefore, based on the above formula, when calculating the corresponding actual data during the initial planning of the detection route evaluation or the detection process, the above parameters can be obtained and substituted into the above formula to accurately calculate the corresponding energy consumption cost E. Since the wall-climbing robot is in a stationary state when it reaches a detection position to perform detection work, only vacuum suction force needs to be generated at this time. Therefore, the time t1 and t2 are calculated separately to improve the accuracy of the data.

[0097] As an optional implementation, it also includes:

[0098] Acquire the real-time pose information of the phased array detection probe to obtain the pose deviation Δx;

[0099] Based on the pose deviation Δx, obtain the angle correction Δθ;

[0100] Based on the angle correction Δθ, adjust the normal angle θ of the phased array detection probe to reduce the value of |θ-θ'|.

[0101] In this embodiment, since the wall-climbing robot may experience pose deviations due to various environmental factors after moving along the detection path, the pose of the phased array detection probe will also deviate accordingly. Therefore, after each segment of movement, the real-time pose information of the phased array detection probe can be obtained through the pose sensor mounted on the wall-climbing robot, thereby obtaining the pose deviation Δx of the probe. Then, the angle correction Δθ is obtained by solving it through rotation matrix or quaternion operations. Here, Δθ can be set to Δx / K, where K is the conversion coefficient, thereby adjusting and correcting the normal angle θ of the phased array detection probe to ensure that the phased array detection probe is always perpendicular to the curved surface of the pipe wall, meeting the adsorption force requirements, improving the adsorption force stability of the wall-climbing robot, and avoiding slippage. After adjustment, the value of |θ-θ'| can be reduced. At the same time, the influence of this penalty term can be reduced when calculating the cost Q of the next segment. Thus, through the parameter linkage and closed-loop control of the pose deviation compensation and path planning cost function, the path deviation problem caused by insufficient stiffness of the wall-climbing robot is solved.

[0102] As an optional implementation, the expression for the pose deviation Δx is:

[0103] In the formula, α is the curvature compensation coefficient, and r is the radius of the petrochemical pipeline. Let θ be the deflection angle. max This is the maximum tolerance angle.

[0104] In this embodiment, based on the above formula, the curvature compensation coefficient α can be taken as 0.8-1.2, the radius r of the petrochemical pipeline is obtained from actual pipeline measurement data, and the maximum tolerance angle θ max This is set to 15° to represent the deflection angle. The maximum deflection angle should not exceed 15°. The position deviation Δx can be accurately calculated by substituting it into the above formula. This allows the robot to continuously correct its position during the detection process to ensure its fit within the pipeline path. Through feature fusion and multi-objective optimization, a balance between detection efficiency and accuracy is achieved.

[0105] It should be noted that when the deflection angle is measured... Greater than the maximum tolerance angle θ max If this occurs, an emergency brake is triggered, and the robot will continue to move only after it has completed its pose correction in place, preventing it from slipping during the movement.

[0106] As an optional implementation, after acquiring the real-time pose information of the phased array detection probe to obtain the pose deviation Δx, the method further includes:

[0107] Determine whether the pose deviation Δx is greater than the preset deviation threshold;

[0108] If so, the third adjustment coefficient k3 will be increased accordingly.

[0109] In this embodiment, when the calculated pose deviation Δx is greater than the preset deviation threshold, it indicates that the segment corresponding to the detection route on the pipe wall will have a significant impact on the pose of the phased array detection probe, causing the normal angle θ of the phased array detection probe to be affected as well. Therefore, the third adjustment coefficient k3 is dynamically increased, for example, by 5%. The greater the difference between the pose deviation Δx and the deviation threshold, the greater the increase in k3 should be, so as to increase the impact of this penalty term on the cost value Q, thereby forming a closed-loop control, improving the calculation accuracy of the cost value Q, and ultimately improving the accuracy of the detection route planning.

[0110] Example 2:

[0111] Based on the same inventive concept as the foregoing embodiments, this embodiment also provides a phased array ultrasonic nondestructive testing system for petrochemical pipelines, comprising:

[0112] The point cloud data acquisition module is used to acquire the three-dimensional point cloud data of petrochemical pipelines;

[0113] The defect information acquisition module is used to identify the defect probability and defect location information of the circumferential weld to be inspected based on the three-dimensional point cloud data, so as to generate a heat map of the circumferential weld defect.

[0114] The initial route generation module is used to obtain the initial detection route with the lowest total cost based on the heat map of the circumferential weld defect and the preset cost function; wherein, the total cost value is the sum of the theoretical segment cost values ​​between two adjacent detection positions in the initial detection route, and each pair of adjacent detection positions is a segment;

[0115] The cost calculation module is used to obtain the actual segment cost of the wall-climbing robot moving from the current detection position to the next detection position from the initial detection route, based on the cost function; wherein, the wall-climbing robot is equipped with a phased array detection probe;

[0116] The judgment module is used to determine whether the actual segment value is greater than the corresponding theoretical segment value.

[0117] The route calibration module is used to, if so, re-identify the defect probability and defect location information of the remaining circumferential welds to be inspected based on the current detection position, so as to update the circumferential weld defect heat map, and re-obtain the calibration detection route with the lowest total value based on the updated circumferential weld defect heat map and cost function.

[0118] The iteration module is used to continue executing the initial detection route if not, and return to the point where the actual segment cost of the wall-climbing robot moving from the current detection position to the next detection position according to the cost function is obtained.

[0119] The explanations and examples of the modules in this embodiment can be found in the methods of the foregoing embodiments, and will not be repeated here.

[0120] Example 3:

[0121] Based on the same inventive concept as the foregoing embodiments, this embodiment provides a computer device, which includes a memory and a processor. The memory stores a computer program, and the processor executes the computer program to implement the above-described method.

[0122] Example 4:

[0123] Based on the same inventive concept as the foregoing embodiments, this embodiment provides a computer-readable storage medium storing a computer program, and a processor executes the computer program to implement the above-described method.

[0124] The above are merely preferred embodiments of this application and do not limit the patent scope of this application. Any equivalent structural or procedural transformations made using the content of this application's specification and drawings, or direct or indirect applications in other related technical fields, are similarly included within the patent protection scope of this application.

Claims

1. A phased array ultrasonic non-destructive testing method for petrochemical pipelines, characterized in that, Includes the following steps: Acquire 3D point cloud data of petrochemical pipelines; Based on the 3D point cloud data, the defect probability and defect location information of the circumferential weld to be detected are identified to generate a heat map of the circumferential weld defect. Based on the heat map of circumferential weld defects and the preset cost function, the initial inspection route with the lowest total cost is obtained. The total cost is the sum of the theoretical segment costs between two adjacent inspection positions in the initial inspection route, with each pair of adjacent inspection positions constituting one segment. The expression for the cost function is: Q=k1·E+k2·h+k3·|θ-θ'|+k4·g; In the formula, Q is the cost, E is the energy cost, h is the moving distance, θ is the normal angle of the phased array detection probe, θ' is the reference angle perpendicular to the petrochemical pipeline wall, g is the cost of the vacuum adsorption force of the wall-climbing robot on the petrochemical pipeline, k1 is the first adjustment coefficient, k2 is the second adjustment coefficient, k3 is the third adjustment coefficient, and k4 is the fourth adjustment coefficient. The cost factors E, h, θ, and g are transformed through k1-k4 respectively, so that the cost factors E, h, θ, and g can be superimposed in the same quantity; the real-time pose information of the phased array detection probe is obtained to obtain the pose deviation Δx; based on the pose deviation Δx, the angle correction Δθ is obtained; based on the angle correction Δθ, the normal angle θ of the phased array detection probe is adjusted to reduce the value of |θ-θ'|. Based on the cost function, the actual segment cost of the wall-climbing robot moving from the current detection position on the initial detection route to the next detection position is obtained; wherein, the wall-climbing robot is equipped with a phased array detection probe; Determine whether the actual segment value is greater than the corresponding theoretical segment value; If so, based on the current detection position, the defect probability and defect location information of the remaining circumferential welds to be detected are re-identified to update the circumferential weld defect heat map, and the calibration detection route with the lowest total value is obtained again based on the updated circumferential weld defect heat map and cost function. If not, continue executing the initial detection route and return to the point where the actual segment cost of the wall-climbing robot moving from the current detection position on the initial detection route to the next detection position is obtained according to the cost function.

2. The phased array ultrasonic non-destructive testing method for petrochemical pipelines as described in claim 1, characterized in that, The expression for the value g of vacuum adsorption force is: ； In the formula, F is the actual value of the vacuum suction force of the wall-climbing robot. min This represents the minimum vacuum suction force required by the wall-climbing robot in the corresponding segment.

3. The phased array ultrasonic non-destructive testing method for petrochemical pipelines as described in claim 2, characterized in that, F min The expression is: F min =η(G+F1+F2); In the formula, η is the safety factor, G is the weight of the wall-climbing robot, F1 is the centrifugal force of the wall-climbing robot when it moves on the petrochemical pipeline, and F2 is the wind resistance.

4. The phased array ultrasonic non-destructive testing method for petrochemical pipelines as described in claim 1, characterized in that, The expression for energy cost E is: E = P1·t1 + P2·t2; In the formula, P1 is the power required for the wall-climbing robot to generate the corresponding vacuum suction force on the petrochemical pipe, t1 is the time required to supply the wall-climbing robot to generate the vacuum suction force, P2 is the power required to drive the wall-climbing robot to crawl on the petrochemical pipe, and t2 is the crawling time of the wall-climbing robot on the petrochemical pipe.

5. The phased array ultrasonic non-destructive testing method for petrochemical pipelines as described in claim 1, characterized in that, The expression for the pose deviation Δx is: ； In the formula, α is the curvature compensation coefficient, and r is the radius of the petrochemical pipeline. Let θ be the deflection angle. max This is the maximum tolerance angle.

6. A phased array ultrasonic non-destructive testing method for petrochemical pipelines as described in claim 1 or 5, characterized in that, After acquiring the real-time pose information of the phased array detection probe to obtain the pose deviation Δx, the process also includes: Determine whether the pose deviation Δx is greater than the preset deviation threshold; If so, the third adjustment coefficient k3 will be increased accordingly.

7. A phased array ultrasonic non-destructive testing system for petrochemical pipelines, characterized in that, include: The point cloud data acquisition module is used to acquire the three-dimensional point cloud data of petrochemical pipelines; The defect information acquisition module is used to identify the defect probability and defect location information of the circumferential weld to be inspected based on the three-dimensional point cloud data, so as to generate a heat map of the circumferential weld defect. The initial route generation module is used to obtain the initial inspection route with the lowest total cost based on the heat map of circumferential weld defects and a preset cost function. The total cost value is the sum of the theoretical segment costs between two adjacent inspection positions in the initial inspection route, with each pair of adjacent inspection positions constituting one segment. The expression for the cost function is: Q=k1·E+k2·h+k3·|θ-θ'|+k4·g; In the formula, Q is the cost, E is the energy cost, h is the moving distance, θ is the normal angle of the phased array detection probe, θ' is the reference angle perpendicular to the petrochemical pipeline wall, g is the cost of the vacuum adsorption force of the wall-climbing robot on the petrochemical pipeline, k1 is the first adjustment coefficient, k2 is the second adjustment coefficient, k3 is the third adjustment coefficient, and k4 is the fourth adjustment coefficient. The cost factors E, h, θ, and g are transformed through k1-k4 respectively, so that the cost factors E, h, θ, and g can be superimposed in the same quantity; the real-time pose information of the phased array detection probe is obtained to obtain the pose deviation Δx; based on the pose deviation Δx, the angle correction Δθ is obtained; based on the angle correction Δθ, the normal angle θ of the phased array detection probe is adjusted to reduce the value of |θ-θ'|. The cost calculation module is used to obtain the actual segment cost of the wall-climbing robot moving from the current detection position to the next detection position from the initial detection route, based on the cost function; wherein, the wall-climbing robot is equipped with a phased array detection probe; The judgment module is used to determine whether the actual segment value is greater than the corresponding theoretical segment value. The route calibration module is used to, if so, re-identify the defect probability and defect location information of the remaining circumferential welds to be inspected based on the current detection position, so as to update the circumferential weld defect heat map, and re-obtain the calibration detection route with the lowest total value based on the updated circumferential weld defect heat map and cost function. The iteration module is used to continue executing the initial detection route if not, and return to the point where the actual segment cost of the wall-climbing robot moving from the current detection position to the next detection position according to the cost function is obtained.

8. A computer device, characterized in that, The computer device includes a memory and a processor, wherein the memory stores a computer program and the processor executes the computer program to implement the method as described in any one of claims 1-6.

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