Auxiliary device for detecting wall thickness of boiler pipeline

Abstract

The invention relates to the technical field of industrial detection, and discloses an auxiliary device for boiler pipeline wall thickness detection, which comprises a table board, a moving assembly, a boiler pipeline supporting assembly and a concave cantilever type scanning frame with a correlation laser scanning probe. The core of the system is that a linear scanning speed real-time regulation and control system is integrated, and the system comprehensively analyzes multi-dimensional parameters such as background light noise, pipeline surface state, environment vibration, pipeline temperature, wall thickness change rate and ovality in real time through an optical environment evaluation module, a surface anomaly evaluation module, an adaptation evaluation module and a geometric stability evaluation module. And the axial moving speed of the scanning frame is dynamically adjusted accordingly. According to the invention, automatic, full-coverage and high-precision detection of the wall thickness of the boiler pipeline in a complex industrial environment is realized, and the detection efficiency and the measurement precision are intelligently balanced.

Description

Technical Field

[0001] This invention belongs to the field of industrial testing technology, and in particular relates to an auxiliary device for detecting the wall thickness of boiler pipes. Background Technology

[0002] In the manufacturing process of boiler pipes, wall thickness, as a core quality indicator, directly relates to the product's pressure-bearing capacity, service life, and ultimate operational safety. Efficient, comprehensive, and accurate wall thickness testing on the production line is crucial for ensuring product quality, controlling production costs, and meeting increasingly stringent safety standards. However, current mainstream wall thickness testing methods on production lines primarily rely on contact or single-point non-contact measurements, such as manual sampling using ultrasonic thickness gauges or simple measuring fixtures at fixed points on the production line. These traditional methods have several inherent drawbacks: First, sampling cannot cover the entire pipe body, posing a risk of missed detections and making it difficult to identify uneven wall thickness, micro-cracks, or potential weak points. Second, testing efficiency is low, severely incompatible with the continuous, high-speed production pace of modern manufacturing, easily creating capacity bottlenecks. Third, test results are significantly affected by operator skill and subjective factors in the selection of measurement points, making repeatability and consistency difficult to guarantee, hindering accurate traceability and analysis of quality data.

[0003] With the increasing demand for automation, some laser- or vision-based scanning devices have been introduced into production lines. While these devices can achieve non-contact continuous measurement, they still face significant challenges in actual production environments. Production workshops are complex and variable, with unpredictable ambient light fluctuations, such as sunlight filtering through workshop windows or background light noise caused by the switching on and off of lighting equipment. These changes can interfere with the signal reception of laser probes. Mechanical vibrations caused by equipment operation, with their frequency and amplitude dynamically changing due to other processes, can lead to instability in the measurement reference. Electromagnetic interference from welding or other hot processing can disrupt electronic signal transmission. Furthermore, temperature gradients in pipelines caused by hot rolling processes result in uneven thermal expansion of materials, further affecting measurement accuracy. The combined effect of these environmental factors continuously interferes with the signal stability and measurement accuracy of precision optical probes, leading to large fluctuations and unreliable measurement results.

[0004] Furthermore, the surface of pipelines is not ideally smooth during production; it may contain oxide scale, fine scratches, residual coolant spots, or temporary marking ink. Existing technologies often fail to effectively distinguish these surface anomalies from actual wall thickness changes. For example, abrupt changes in reflectivity gradient caused by oxide scale may be misinterpreted as wall thinning, or abnormal contour curvature caused by scratches may be mistaken for geometric defects, leading to distorted point cloud data and misjudgments. The most prominent contradiction lies in the difficulty of balancing "detection efficiency" and "measurement accuracy." In actual production, high-speed scanning can meet accuracy requirements when pipeline wall thickness changes gradually; however, when encountering areas with rapid wall thickness changes (such as weld transition zones) or when the pipeline has geometric irregularities such as ellipticity, excessively high scanning speeds result in insufficient sampling point density, failing to accurately capture details of wall thickness changes and significantly reducing accuracy. Conversely, reducing scanning speed to ensure accuracy significantly prolongs detection time, slowing down the overall production pace. The existing system lacks an adaptive mechanism that can sense the pipeline's own geometric characteristics (such as wall thickness change rate and instantaneous ellipticity) and environmental conditions (such as vibration intensity and pipeline temperature) in real time and dynamically and intelligently adjust the scanning rate accordingly, resulting in unstable overall performance on complex and ever-changing production lines.

[0005] To address the aforementioned issues, existing technologies urgently need improvement. Summary of the Invention

[0006] The purpose of this invention is to provide an auxiliary device for detecting the wall thickness of boiler pipes, thereby solving the aforementioned problems.

[0007] This invention is implemented as follows: an auxiliary device for detecting the wall thickness of boiler pipes includes a platform, a moving component, a boiler pipe support component, and a concave cantilever scanning frame mounted on the platform. It further includes two opposing laser scanning probes mounted at both ends of the concave cantilever scanning frame. The boiler pipe support component supports the boiler pipe and drives its rotation. The moving component drives the concave cantilever scanning frame to move axially along the boiler pipe. A real-time linear scanning speed control system is electrically connected to the control terminal of the moving component. This system adjusts the moving speed of the cantilever scanning frame and includes an optical environment assessment module based on internal background light noise intensity, external background light noise intensity, and internal probe... The system calculates the optical environment coefficient based on the effective data rate of the point cloud and the effective data rate of the external probe point cloud; the surface anomaly assessment module calculates the surface anomaly coefficient based on the inner wall reflection intensity gradient, the outer wall reflection intensity gradient, the frequency of abrupt changes in the inner wall contour curvature, and the frequency of abrupt changes in the outer wall contour curvature; the adaptation assessment module calculates the adaptation degree based on the environmental vibration intensity and pipe temperature under the optical environment coefficient and the surface anomaly coefficient; the geometric stability assessment module calculates the geometric stability coefficient based on the wall thickness change rate and the instantaneous value of ellipticity; and the axial scan movement speed adjustment module calculates the target axial scan movement speed based on the adaptation degree, the geometric stability coefficient, and the current axial scan movement speed, and adjusts the current axial scan movement speed to the target axial scan movement speed.

[0008] A further technical solution is that the operation process of the axial scanning movement speed adjustment module is as follows: based on the current axial scanning movement speed, fit, geometric stability coefficient, and preset adjustment threshold, the adjustment factor is obtained by comparing the product of the fit and the geometric stability coefficient with the adjustment threshold; the current axial scanning movement speed is multiplied by the product of the adjustment factor and the speed adjustment sensitivity coefficient to obtain the speed adjustment amount; the current axial scanning movement speed is added to the speed adjustment amount to obtain the target axial scanning movement speed, wherein the speed adjustment direction is determined by the relative magnitude of the product and the adjustment threshold, and the speed adjustment amplitude is controlled by the speed adjustment sensitivity coefficient.

[0009] A further technical solution involves the following steps for the geometric stability assessment module: obtaining the current wall thickness change rate and instantaneous ellipticity value; comparing the current wall thickness change rate and instantaneous ellipticity value with the allowable maximum wall thickness change rate threshold and the allowable maximum ellipticity threshold, respectively, and then using a min function to limit the upper limit to 1 to obtain the wall thickness change rate index and the instantaneous ellipticity value index; weighting the two indices based on preset weights to obtain a comprehensive instability index; and mapping the comprehensive instability index to a geometric stability coefficient using a preset function.

[0010] A further technical solution is that the geometric stability coefficient is negatively correlated with the comprehensive instability index, and the mapping function guarantees that when the comprehensive instability index approaches 0, the geometric stability coefficient approaches 1, and when the comprehensive instability index increases, the geometric stability coefficient decreases.

[0011] A further technical solution involves the following operation flow of the adaptation evaluation module: obtaining the current environmental vibration intensity and pipeline temperature; processing the ratio of the current environmental vibration intensity to the maximum allowable vibration threshold of the system, and then using a min function to limit the upper limit to 1 to obtain the environmental vibration intensity index; performing maximum-minimum normalization on the pipeline temperature to obtain the pipeline temperature index; performing composite calculations on the environmental vibration intensity index, the pipeline temperature index, and the interaction term between the environmental vibration intensity index and the pipeline temperature index according to preset weights to obtain the environmental stress influence factor; multiplying the optical environment coefficient, the surface anomaly coefficient, and the environmental stress influence factor to obtain the adaptation degree. The calculation of the environmental stress influence factor comprehensively considers the individual effects of environmental vibration intensity and pipeline temperature, as well as their coupling effect, and the sum of the weights of each influencing factor is 1.

[0012] A further technical solution involves the following operation flow of the optical environment assessment module: acquiring the current internal background light noise intensity, external background light noise intensity, effective data rate of the internal probe point cloud, and effective data rate of the external probe point cloud; processing the ratios of the current internal and external background light noise intensities to the maximum allowable background light noise intensity of the device, and using a min function to limit the upper limit to 1, taking the larger value after limiting as the background light noise intensity index; comprehensively processing the effective data rates of the internal and external probe point clouds to obtain a comprehensive data validity index; and inversely combining the comprehensive data validity index with the background light noise intensity index to obtain the optical environment coefficient, wherein the optical environment coefficient increases with the increase of the comprehensive data validity index and decreases with the increase of the background light noise intensity index.

[0013] A further technical solution is that the surface anomaly assessment module operates as follows: It acquires the current inner wall reflection intensity gradient, outer wall reflection intensity gradient, inner wall contour curvature abrupt change frequency, and outer wall contour curvature abrupt change frequency; it then ratios the larger of the current inner wall reflection intensity gradient and outer wall reflection intensity gradient with a reflection intensity gradient threshold, and applies a min function with an upper limit of 1 to acquire the reflection intensity gradient index; it then ratios the larger of the current inner wall contour curvature abrupt change frequency and outer wall contour curvature abrupt change frequency with a contour curvature abrupt change frequency threshold, and applies a min function with an upper limit of 1 to acquire the contour curvature abrupt change frequency index; it takes the larger of the reflection intensity gradient index and the contour curvature abrupt change frequency index as the surface anomaly degree index; and it converts the surface anomaly degree index into a surface anomaly coefficient using a preset decreasing mapping function, wherein the surface anomaly coefficient decreases as the surface anomaly degree index increases, and the surface anomaly coefficient decreases accordingly when any one of the inner wall reflection intensity gradient, outer wall reflection intensity gradient, inner wall contour curvature abrupt change frequency, or outer wall contour curvature abrupt change frequency is abnormal.

[0014] A further technical solution is that the boiler pipe support assembly includes a platform with a rotating shaft rotatably mounted on a bracket, and multiple support sleeves fixed on each of the two rotating shafts. A motor is fixed on the top of the platform, and the rotating end of the motor is connected to the rotating shaft.

[0015] In a further technical solution, the moving component includes a guide groove located on the table between two rotating shafts, a guide slider slidably connected in the guide groove, a concave cantilever scanning frame fixed on the guide slider, a lead screw rotatably connected in the guide groove, the lead screw passing through the guide slider and threadedly connected to the guide slider, and a second motor fixed on the table, the rotating end of the second motor being connected to the lead screw.

[0016] Compared with the prior art, the beneficial effects of the present invention are as follows:

[0017] The device provided by this invention achieves intelligent adaptive adjustment of scanning speed by introducing a real-time linear scanning speed control system. This system can sense and quantify the effects of multiple factors such as optical interference, surface defects, environmental stress, and changes in pipeline geometry in real time. When detection conditions are favorable, it automatically increases the scanning speed to improve efficiency; when the environment is harsh or the pipeline condition is complex, it automatically decreases the speed to ensure data accuracy, thus operating stably and reliably in complex and changing actual production environments. Compared to traditional sampling or fixed-speed scanning methods, this invention significantly improves detection coverage and efficiency, reduces the risk of missed detections, and enhances robustness against various interferences in industrial settings, providing an efficient and accurate solution for the quality control of boiler pipelines. Attached Figure Description

[0018] Figure 1This is a schematic diagram of the structure of an auxiliary device for detecting the wall thickness of boiler pipes provided by the present invention;

[0019] Figure 2 A flowchart of the real-time linear scanning speed control system provided by the present invention.

[0020] In the attached diagram: 1. Tabletop; 2. Rotating shaft; 3. Support sleeve; 4. Motor 1; 5. Guide groove; 6. Guide slider; 7. Lead screw; 8. Motor 2; 9. Concave cantilever scanning frame; 10. Laser scanning probe. Detailed Implementation

[0021] To make the objectives, technical solutions, and advantages of this invention clearer, the 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 merely illustrative and not intended to limit the invention.

[0022] During boiler pipe wall thickness inspection, environmental factors such as changing ambient light, vibrations caused by equipment operation, electromagnetic interference, and temperature differences within the pipe itself continuously interfere with the signal stability of the optical probe. Simultaneously, abnormal conditions on the pipe surface, such as oxide scale, minor scratches, or residual coolant, are easily misinterpreted as wall thickness changes by existing technologies, leading to distorted measurement data. Furthermore, there is an inherent contradiction between inspection efficiency and measurement accuracy. High-speed scanning results in insufficient sampling density in areas of drastic wall thickness changes or geometrically irregular locations, significantly reducing accuracy. Conversely, reducing scanning speed cannot match the cycle time requirements of continuous production lines, making the inspection process a bottleneck in production capacity. The repeatability and consistency of measurement data are difficult to guarantee, hindering accurate traceability and analysis of quality data.

[0023] For example, on a continuous production line for high-temperature and high-pressure boiler pipes, after the pipes have undergone welding, oxide scale adheres to their surface and coolant remains. Furthermore, the workshop lighting fluctuates due to equipment start-ups and shutdowns, and vibrations from the production line cause the optical probe's reference point to shift. At this point, the effective data rate of the laser scanning device's inner probe point cloud decreases significantly, and the gradient of the outer wall's reflection intensity increases abnormally. The system identifies the surface oxide layer as a thinning area, triggering a false alarm. Further, to avoid missing local weak points, operators are forced to reduce the scanning speed, leading to extended inspection time and disconnection from the high-speed production line. Some pipes accumulate due to incomplete full-circumference inspection, and areas with varying pipe ellipticity cannot accurately capture abrupt changes in wall thickness due to insufficient sampling.

[0024] If the above problems are not resolved, the reliability of wall thickness testing will not meet safety standards, and local wall thickness unevenness or microcracks may be missed, directly affecting the pressure-bearing capacity and service life of the pipeline; distortion of quality data will lead to failure of production process control, which is not conducive to the optimization and adjustment of process parameters; in the long run, the inefficiency and instability of the testing process will restrict the overall production efficiency and fail to meet the operating requirements of modern continuous production lines, thus posing a systemic risk to the product quality assurance system.

[0025] The specific implementation of the present invention will be described in detail below with reference to specific embodiments.

[0026] like Figure 1 and Figure 2 As shown in the illustration, an auxiliary device for detecting the wall thickness of boiler pipes according to an embodiment of the present invention includes a platform 1, a moving component, a boiler pipe support component, and a concave cantilever scanning frame 9 mounted on the platform. It also includes two opposing laser scanning probes 10 mounted at both ends of the concave cantilever scanning frame 9. The boiler pipe support component supports the boiler pipe and drives its rotation. The moving component drives the concave cantilever scanning frame 9 to move axially along the boiler pipe. By precisely controlling the displacement of the concave cantilever scanning frame 9, the moving component achieves scanning coverage of the entire length of the boiler pipe. During operation, the concave cantilever scanning frame 9 drives the two opposing laser scanning probes 10 to move axially along the boiler pipe, simultaneously detecting the inner and outer walls of the boiler pipe. The wall thickness of the boiler pipe can be obtained by subtracting the distance between the two laser scanning probes 10 from the distance between the inner and outer walls detected by the two laser scanning probes 10. After the inspection of one side of the boiler pipe is completed, the boiler pipe support assembly rotates the supported boiler pipe by a preset angle, such as 90 degrees, and then continues to inspect the other side of the boiler pipe until the wall thickness of the entire pipe is inspected.

[0027] The real-time linear scanning speed control system is electrically connected to the control terminal of the moving component. Specifically, the system is connected to the motor driver of the moving component via a simple cable, enabling it to send speed commands. The system is used to adjust the moving speed of the cantilever scanning frame. Its core function is to dynamically adjust the axial moving speed of the concave cantilever scanning frame 9 based on real-time detected environmental conditions and pipeline status. This system aims to optimize the balance between detection efficiency and measurement accuracy. The real-time linear scanning speed control system includes:

[0028] The optical environment assessment module calculates the optical environment coefficient based on the internal background light noise intensity, external background light noise intensity, effective data rate of the internal probe point cloud, and effective data rate of the external probe point cloud. This module can consist of a light sensor and a data processing unit. The light sensor monitors the internal and external background light noise intensities in real time, while the data processing unit calculates the optical environment coefficient using preset empirical formulas or lookup tables based on these intensity values ​​and the effective data rates of the internal and external probe point clouds.

[0029] The surface anomaly assessment module calculates surface anomaly coefficients based on the reflection intensity gradient of the inner wall, the reflection intensity gradient of the outer wall, the frequency of abrupt changes in the curvature of the inner wall, and the frequency of abrupt changes in the curvature of the outer wall. This module can consist of an image acquisition unit and an image processing unit. The image acquisition unit acquires images of the inner and outer walls of the pipe, while the image processing unit identifies surface anomalies by analyzing the reflection intensity gradient and the frequency of abrupt changes in the curvature of the images, and calculates the surface anomaly coefficients according to preset rules.

[0030] The adaptation assessment module calculates the adaptation degree based on the environmental vibration intensity and pipeline temperature under the optical environment coefficient and surface anomaly coefficient. This module can consist of a vibration sensor, a temperature sensor, and a data fusion unit. The vibration sensor monitors the environmental vibration intensity in real time, the temperature sensor monitors the pipeline temperature in real time, and the data fusion unit combines the optical environment coefficient and surface anomaly coefficient with the environmental vibration intensity and pipeline temperature, calculating the adaptation degree through a preset weighted average or fuzzy logic algorithm.

[0031] The geometric stability assessment module calculates the geometric stability coefficient based on the wall thickness change rate and instantaneous ellipticity value. This module can consist of a wall thickness data acquisition unit and a geometric analysis unit. The wall thickness data acquisition unit acquires real-time wall thickness data, while the geometric analysis unit calculates the wall thickness change rate and instantaneous ellipticity value based on this data, and then calculates the geometric stability coefficient using a preset mathematical model.

[0032] The axial scanning speed adjustment module calculates and adjusts the target axial scanning speed to the target speed based on the fit, geometric stability coefficient, and current axial scanning speed. This module can consist of a central processing unit (CPU) that receives the fit, geometric stability coefficient, and current axial scanning speed as input. It then calculates the new target axial scanning speed using a preset proportional-integral-derivative (PID) controller or fuzzy control algorithm and sends the corresponding speed command to the motor driver of the moving component.

[0033] Compared to traditional contact or single-point non-contact inspection methods, the device in this embodiment achieves continuous and comprehensive scanning of boiler pipe wall thickness. In the above example, traditional methods may require manual measurement at multiple points, which is time-consuming and prone to missing local defects. However, this embodiment, through the cooperation of the concave cantilever scanning frame 9 and the laser scanning probe 10, can perform blind-angle inspection of the entire inner and outer walls of the pipe, significantly improving the inspection coverage and reliability.

[0034] More importantly, the real-time linear scanning speed control system introduced in this embodiment overcomes the limitations of existing automated scanning devices in terms of performance in complex production environments. In the example above, when there are changes in ambient lighting, equipment vibration, or oxide scale on the pipe surface, existing systems often experience decreased measurement accuracy due to signal interference or data distortion, or are forced to reduce scanning speed to ensure accuracy, affecting production efficiency. However, the control system in this embodiment, through the collaborative work of the optical environment assessment module, surface anomaly assessment module, adaptation assessment module, and geometric stability assessment module, can perceive and quantify the impact of these complex factors in real time. For example, when the optical environment deteriorates or anomalies appear on the pipe surface, the system can intelligently reduce the scanning speed to ensure high-quality measurement data is still acquired under adverse conditions; while when the environment is favorable and the pipe geometry is stable, the system can automatically increase the scanning speed, thereby maximizing detection efficiency while ensuring accuracy.

[0035] This adaptive speed control mechanism, based on multi-dimensional real-time evaluation, enables the device in this embodiment to intelligently balance the contradiction between detection efficiency and measurement accuracy, a feature not found in existing technologies. By dynamically adjusting the axial scanning speed, the device ensures sufficient sampling density in areas with drastic wall thickness variations or geometrically irregular regions such as ellipticity in the pipe, avoiding accuracy degradation due to excessive speed. Simultaneously, in areas with stable pipe geometry and good surface conditions, it can scan at a faster speed, avoiding unnecessary waste of detection time.

[0036] In summary, the auxiliary device of this embodiment, through its integrated design and intelligent real-time control capabilities, provides an efficient, accurate, and robust solution for boiler pipe wall thickness detection in complex industrial environments, significantly improving the manufacturing quality control level and production efficiency of boiler pipes.

[0037] This application further proposes the following operating procedure for the axial scanning moving speed adjustment module:

[0038] Based on the current axial scan movement speed, fit, geometric stability coefficient, and preset adjustment threshold, the adjustment factor is obtained by comparing the product of the fit and geometric stability coefficient with the adjustment threshold; the speed adjustment amount is obtained by multiplying the current axial scan movement speed by the product of the adjustment factor and the speed adjustment sensitivity coefficient; the target axial scan movement speed is obtained by adding the current axial scan movement speed and the speed adjustment amount, wherein the speed adjustment direction is determined by the relative magnitude of the product and the adjustment threshold, and the speed adjustment amplitude is controlled by the speed adjustment sensitivity coefficient; the specific calculation formula is as follows: in, The current axial scanning speed is the actual speed at which the concave cantilever scanning frame 9 moves along the boiler pipe axis at the current moment. It can be obtained in real time through encoder feedback of the moving component or linear displacement sensor. For adaptability, a dimensionless coefficient is calculated based on factors such as optical environment, surface condition, environmental vibration intensity and pipeline temperature. It is used to measure the system's adaptability and potential performance level in the current environment. The geometric stability coefficient is a dimensionless coefficient calculated by the geometric stability assessment module based on the wall thickness change rate and the instantaneous value of ellipticity. It is used to assess the stability and uniformity of the boiler pipe geometry. The threshold is used as a reference for judging the direction and magnitude of speed adjustment, and its value can be set according to actual application requirements and system performance. The speed adjustment sensitivity coefficient, ranging from 0 to 1, is used to control the aggressiveness of speed adjustment; a larger value indicates a higher sensitivity. A higher value implies a faster and more significant rate of change, while a smaller value indicates a lower rate of change. This value makes the speed adjustment more gradual; The target axial scanning speed is defined by this formula. This formula can be implemented as a software algorithm within a real-time linear scanning speed control system, running on a microcontroller or industrial computer. It receives output values ​​from each evaluation module, calculates a new target speed command, and then sends it to the motor driver of the moving component. Alternatively, this formula can be implemented using the mathematical operations of a programmable logic controller (PLC), taking sensor data and evaluation results as input and outputting the control speed of the moving component's motor.

[0039] This application's solution, by introducing the operating formula of the aforementioned axial scanning movement speed adjustment module, enables the linear scanning speed real-time control system to achieve precise, dynamic, and predictable scanning speed adjustment. The system continuously receives the fit degree output from the fit evaluation module. and the geometric stability coefficient output by the geometric stability evaluation module And combined with the current axial scanning speed Preset adjustment threshold and speed adjustment sensitivity coefficient The target axial scanning speed is calculated in real time through mathematical operations. .when The product is greater than When the current detection environment and pipeline geometry are both ideal, the system performance can be fully utilized. At this point, the adjustment term in the formula is positive, prompting... Higher than This increases scanning speed. Conversely, when The product is less than When this indicates that environmental or pipeline conditions are challenging and may affect detection accuracy, the adjustment term is negative, prompting... Below This reduces scanning speed to ensure data quality. If the product equals... If the value is zero, the adjustment term is zero, and the scanning speed remains unchanged. Speed ​​adjustment sensitivity coefficient. This further refines the responsiveness of speed adjustments. This feedback adjustment mechanism based on a quantization model ensures that the moving speed of the concave cantilever scanning frame 9 always matches the real-time changing detection conditions, thereby maximizing detection efficiency while ensuring detection accuracy.

[0040] As a specific implementation method, it is assumed that during the boiler pipe wall thickness detection process, the linear scanning speed real-time control system monitors the current axial scanning movement speed. The speed is 15 mm / s. At this point, the adaptation evaluation module calculates the adaptation degree based on a comprehensive evaluation of factors such as the optical environment, surface condition, environmental vibration intensity, and pipe temperature. The value is 0.9. Simultaneously, the geometric stability assessment module calculates the geometric stability coefficient based on the wall thickness variation rate and the instantaneous value of ellipticity. The value is 0.85. This is the system's preset adjustment threshold. The speed adjustment sensitivity coefficient is 0.7. The value is 0.6. The axial scanning movement speed adjustment module substitutes these parameters into the formula to calculate and obtains 15.8352. At this time, because... (0.765) is greater than (0.7) The system determines that the current conditions allow for an increase in scanning speed, therefore adjusting the target speed to approximately 15.84 mm / s. Upon receiving this target speed, the control terminal of the moving component adjusts the rotational speed of motor 8 accordingly, thereby changing the rotational speed of the lead screw 7, and consequently adjusting the moving speed of the guide slider 6 and the concave cantilever scanning frame 9. Conversely, in another detection area, the fit... The geometric stability coefficient decreased to 0.6. If it drops to 0.7, then It is 0.42, which is less than At this point, the calculation yields... The target velocity was adjusted to approximately 11.4 mm / s to adapt to poor detection conditions and ensure data acquisition quality.

[0041] Through the above technical solution, this application provides an axial scanning movement speed adjustment method based on a quantization model, overcoming the arbitrariness and inaccuracy that may exist in traditional adjustment methods. This method can adjust the axial scanning movement speed according to the real-time changes in the adaptation. and geometric stability coefficient The system dynamically and intelligently adjusts the moving speed of the concave cantilever scanning frame 9. This allows the device to automatically optimize the balance between scanning efficiency and detection accuracy based on the actual condition of the boiler pipes and environmental conditions during the inspection process. When conditions permit, the system can increase the scanning speed to shorten the inspection cycle; when conditions are unfavorable, it can promptly reduce the speed to ensure that the laser scanning probe 10 acquires high-quality inspection data, thereby significantly improving the overall performance and reliability of boiler pipe wall thickness detection.

[0042] This application further proposes the following operational flow for the geometric stability assessment module:

[0043] The current wall thickness change rate and instantaneous ellipticity value are obtained. The wall thickness change rate refers to the degree of change in wall thickness between adjacent measurement points during axial or circumferential scanning of the pipeline, aiming to quantify the uniformity or non-uniformity of the pipeline wall thickness in a local area. This can be achieved by performing differential calculations on continuously acquired wall thickness data, such as calculating the ratio of the wall thickness difference between adjacent measurement points to the distance, or calculating the standard deviation of the wall thickness values ​​over a certain length range. Another approach is to measure the wall thickness at multiple preset points along the scanning path and then calculate the ratio of the maximum difference in wall thickness values ​​between these measurement points to the average wall thickness value. The instantaneous ellipticity value refers to the degree to which the shape of a cross-section of the pipeline deviates from an ideal circle, aiming to assess the deformation of the pipeline cross-section. This can be achieved by using the inner and outer wall contour data of the pipeline acquired by the laser scanning probe 10 to fit the maximum and minimum diameters of the current cross-section, and then calculating (maximum diameter - minimum diameter) / (maximum diameter + minimum diameter) as the ellipticity. Another approach is to use multiple sensors or multi-angle scanning data to directly calculate the ellipticity parameters of the current cross-section, such as the ratio of the major axis to the minor axis, through geometric algorithms.

[0044] The current wall thickness change rate and instantaneous ellipticity value are compared with the allowable maximum wall thickness change rate threshold and the allowable maximum ellipticity threshold, respectively. After applying a min function with an upper limit of 1, the wall thickness change rate exponent and the instantaneous ellipticity exponent are obtained. This step aims to transform the original physical quantities into dimensionless, standardized exponents for subsequent unified calculation and evaluation. The ratio processing reflects the degree of deviation of the current value from the allowable maximum value. The purpose of limiting the min function to 1 is to ensure that the exponent value does not exceed 1; that is, when the actual value exceeds the threshold, the exponent is at most 1, indicating that the term has reached or exceeded the acceptable limit, avoiding excessive influence of extreme values ​​on subsequent calculations. This can be implemented by having a processor execute the logic. Another implementation method is to preset these thresholds through hardware or firmware modules during the data acquisition or preprocessing stage, and then compare and normalize them in real time, directly outputting the results in exponential form.

[0045] The two indices are weighted and combined based on preset weights to obtain a comprehensive instability index. The comprehensive instability index is then mapped to a geometric stability coefficient using a preset function. The geometric stability coefficient is negatively correlated with the comprehensive instability index, and the mapping function ensures that the geometric stability coefficient approaches 1 when the comprehensive instability index approaches 0, and decreases when the comprehensive instability index increases. Specifically, the calculation method can be as follows: The wall thickness change rate index and the instantaneous ellipticity index are imported into the formula. Obtain the geometric stability coefficient , Used to evaluate the stability of pipe geometry. The value ranges from 0 to 1, where 1 indicates that the geometry is very stable and 0 indicates that it is extremely unstable. This part refers to... The definition and explanation clarify its physical meaning and value range. It is a standardized indicator that allows for a unified comparison of geometric stability under different pipelines or different testing scenarios. This is the wall thickness variation rate index. The instantaneous value exponent of ellipticity. The weights for the wall thickness change rate are in the range of 0-1. This allows users or the system to adjust the importance of wall thickness variation rate in geometric stability assessment based on practical experience or specific pipe type. This step uses a composite formula to fuse two independent geometric characteristic indices into a single geometric stability coefficient. This coefficient visually quantifies the overall stability of the pipe geometry, providing crucial input for subsequent scan rate adjustments. The square root operation in the formula helps smooth the data, while the weighting factors... This allows for adjustment of the relative impact of wall thickness variation rate and ellipticity on geometric stability according to actual application requirements. This can be achieved by executing the mathematical operation using a digital signal processor (DSP) or microcontroller. For example, the formula can be directly calculated in programming languages ​​such as C++ or Python. and It is the index obtained in the previous step. It is a preset floating-point number between 0 and 1. Another implementation method is to implement the hardware logic of this formula in an FPGA (Field-Programmable Gate Array) to achieve high-speed parallel computing and real-time output. . Used to evaluate the stability of pipe geometry, its value ranges from 0 to 1, where 1 indicates that the geometry is very stable and 0 indicates that it is extremely unstable.

[0046] The geometric stability assessment module of this application, within the aforementioned auxiliary device for boiler pipe wall thickness detection, provides crucial input to the real-time linear scanning speed control system by precisely quantifying the geometric stability of the boiler pipe. This module first acquires the instantaneous values ​​of the current boiler pipe wall thickness change rate and ellipticity in real time; these parameters directly reflect the degree of local geometric defects and overall deformation of the pipe. Subsequently, the system compares these raw measurements with a preset maximum allowable threshold and applies amplitude limiting, transforming them into a dimensionless wall thickness change rate index. and ellipticity instantaneous value index This standardization process ensures the comparability of different physical quantities in subsequent calculations and avoids the excessive influence of extreme values ​​on the evaluation results. Finally, the geometric stability evaluation module imports these two indices into a composite mathematical formula and, combined with a preset weight α for the wall thickness change rate, calculates the geometric stability coefficient. This coefficient ranges from 0 to 1, intuitively representing the stability level of the pipeline geometry, where 1 represents excellent stability and 0 represents an extremely unstable state. In this way, the geometric stability assessment module can provide the axial scan movement speed adjustment module with a quantitative, real-time indicator of the pipeline's geometric stability. A low value indicates a significant anomaly in the pipe geometry. The axial scanning speed adjustment module can then reduce the scanning speed to ensure the laser scanning probe 10 has sufficient time and accuracy to capture details and avoid missed detections. Conversely, a high value indicates a significant anomaly in the pipe geometry. When the value is high, the scanning speed can be appropriately increased, thereby improving detection efficiency while ensuring detection quality. This real-time feedback mechanism based on pipeline geometric stability enables the entire linear scanning speed real-time control system to work more intelligently and adaptively, significantly improving the accuracy and efficiency of boiler pipeline wall thickness detection.

[0047] In one specific implementation, the geometric stability evaluation module can be integrated into an embedded controller, such as a high-performance ARM Cortex-M series microprocessor. This microprocessor receives wall thickness and profile data from the laser scanning probe 10 via a high-speed data bus. When acquiring the current wall thickness change rate and instantaneous ellipticity values, the microprocessor can run a real-time data processing algorithm. For example, for the wall thickness change rate, the average wall thickness can be calculated every 1 mm along the axial distance, and then the ratio of the difference between two adjacent average values ​​to that distance can be calculated. For the instantaneous ellipticity value, the microprocessor can use the acquired inner and outer wall point cloud data to fit the elliptic parameters of the current cross-section using the least squares method, and then calculate the ellipticity. When performing ratio processing and min function limiting, the microprocessor's internal memory presets the maximum allowable wall thickness change rate threshold and the maximum allowable ellipticity threshold; for example, the maximum wall thickness change rate threshold can be set to 0.05 mm / mm, and the maximum ellipticity threshold can be set to 0.1. The microprocessor compares the instantaneously calculated wall thickness change rate and ellipticity values ​​with these thresholds, applies a min function for amplitude limiting, and generates a wall thickness change rate index. and ellipticity instantaneous value index Subsequently, the microprocessor combines these two indices with a preset weighted wall thickness change rate. (For example, (It can be set to 0.6, indicating that the wall thickness change rate has a slightly larger weight in the evaluation.) Substitute this into the formula. Calculations are performed to obtain the geometric stability coefficient. .this The value is then sent to the axial scan movement speed adjustment module, serving as an important basis for adjusting the scan speed.

[0048] Through the above technical solution, the geometric stability assessment module of this application can quantify the geometric stability of boiler pipelines in real time and accurately. This module comprehensively considers the wall thickness change rate and the instantaneous value of ellipticity, standardizes them into an exponent, and then calculates the geometric stability coefficient through weighted fusion. This provides a comprehensive and intuitive indicator for evaluating the pipeline's geometric condition. This allows the real-time linear scanning speed control system to dynamically adjust the axial movement speed of the concave cantilever scanning frame 9 based on the actual geometric stability of the pipeline. For example, when the pipeline geometry is unstable (… When the value is low, the system can automatically reduce the scanning speed to ensure that the laser scanning probe 10 has sufficient time and resolution to accurately capture local defects and deformations in the pipeline, avoiding data loss or decreased detection accuracy due to excessively fast scanning speed. Conversely, when the pipeline geometry is stable ( When the value is high, the system can appropriately increase the scanning speed, thereby significantly improving the detection efficiency without sacrificing the detection quality. This adaptive scanning speed adjustment mechanism effectively solves the problem of insufficient detection accuracy or low efficiency that may be caused by traditional fixed-speed scanning under complex pipeline geometry conditions, greatly improving the reliability and practicality of boiler pipeline wall thickness detection.

[0049] This application further proposes the following operating procedure for the adaptation evaluation module:

[0050] The test acquires the current environmental vibration intensity and pipeline temperature. Environmental vibration intensity refers to the level of mechanical vibration experienced by the equipment or pipeline at the testing site, and its function is to quantify the impact of external mechanical interference on measurement stability. It can be obtained in various ways. For example, an accelerometer can be installed near the device or pipeline to monitor and output the root mean square or peak value of the vibration signal in real time; alternatively, a laser Doppler vibration meter can be used to non-contactly measure the vibration velocity on the pipeline surface, and then calculate the vibration intensity. Pipeline temperature refers to the real-time temperature of the boiler pipeline under test, and its function is to reflect the thermal expansion of the pipeline, changes in material properties, and its impact on the working stability of the laser probe. Acquisition methods can include: using a contact temperature sensor directly attached to the pipeline surface for measurement; or using a non-contact infrared thermometer to scan the pipeline surface and obtain its radiant temperature.

[0051] The environmental vibration intensity is obtained by comparing the current environmental vibration intensity with the system's maximum permissible vibration threshold, and then applying a min function with an upper limit of 1. This step aims to standardize the actual measured environmental vibration intensity into a dimensionless exponent for subsequent comprehensive evaluation. The ratio reflects the proportional relationship between the current vibration level and the system's tolerable limit. The upper limit of 1 for the min function ensures that the exponent will not exceed 1; that is, when the actual vibration intensity exceeds the maximum permissible threshold, the exponent will be at most 1, indicating that the vibration has reached or exceeded the system's tolerable limit. This can be achieved by preset a maximum vibration threshold in the control unit, then dividing the real-time acquired environmental vibration intensity value by this threshold to obtain a ratio. This ratio is then compared with 1, and the smaller value is taken as the environmental vibration intensity exponent.

[0052] The pipeline temperature is subjected to maximum and minimum normalization to obtain the pipeline temperature index. The purpose of this step is to convert pipeline temperature data with different dimensions and ranges into a unified dimensionless index between 0 and 1, facilitating calculations that integrate with other evaluation parameters. Maximum and minimum normalization eliminates the influence of dimensions while maintaining the relative relationships of the original data. This can be achieved by pre-setting a minimum and maximum allowable value for the pipeline temperature in the control unit. Then, the real-time acquired pipeline temperature is substituted into the normalization formula for calculation.

[0053] The environmental vibration intensity index, pipeline temperature index, and the interaction term between the environmental vibration intensity index and pipeline temperature index are compositely calculated according to preset weights to obtain the environmental stress influence factor. The optical environment coefficient and surface anomaly coefficient are multiplied by the environmental stress influence factor to obtain the fit. The calculation of the environmental stress influence factor comprehensively considers the individual effects of environmental vibration intensity and pipeline temperature, as well as their coupling effect, and the sum of the weights of all influencing factors is 1. Specifically, the calculation method can be as follows: [The text abruptly ends here, likely due to an incomplete sentence or missing information.] Surface anomaly coefficient The environmental vibration intensity index and the pipeline temperature index are imported into the formula. Get compatibility , This indicates the overall performance level that the system can achieve under the current specific optical conditions, surface condition, and combined environmental stress. The value ranges from 0 to 1, where 1 indicates a perfect fit (100% performance) and 0 indicates a complete misfit (testing should be paused). The environmental vibration intensity index. This refers to the pipe temperature index. , and All are influence weights in the output range of 0-1, and Influence weights are parameters used to adjust the importance of different factors in decision-making, and can be assigned values ​​through preset strategies or dynamic algorithms. This step is the core of the adaptation evaluation module, which comprehensively considers multiple influencing factors through a composite formula to calculate the overall adaptability of the system. This formula weights and fuses four key factors—optical conditions, surface condition, environmental vibration, and pipe temperature—to quantify the comprehensive impact of the current detection environment on system performance. This is achieved by processing the calculated optical environment coefficients in the processor of the control unit. Surface anomaly coefficient And the environmental vibration intensity index calculated in this module. and pipeline temperature index As input. Simultaneously, preset influence weights. , and Then, the processor executes the above formula to calculate and output the final fitness score. .

[0054] In boiler pipe wall thickness inspection, to ensure accuracy and efficiency, the real-time linear scanning speed control system needs to accurately assess the suitability of the current environment. The adaptation evaluation module achieves this by comprehensively considering multiple key factors. First, the module acquires the ambient vibration intensity and the temperature of the pipe under test in real time. Ambient vibration intensity reflects the impact of external mechanical interference on measurement stability, while pipe temperature is related to the thermal expansion of the pipe material, the probe's working state, and the stability of laser measurement. To convert these physical quantities into comparable indicators, the system ratios the ambient vibration intensity with a preset maximum allowable vibration threshold and limits the amplitude, thus obtaining an ambient vibration intensity index. This index intuitively represents the potential impact of the current vibration level on the inspection. Simultaneously, the pipe temperature is converted into a pipe temperature index through maximum-minimum normalization, reflecting the suitability of the temperature for inspection within the range of 0 to 1. Subsequently, the adaptation evaluation module compares these environmental indices with the optical environment coefficient provided by the optical environment evaluation module. and the surface anomaly coefficients provided by the surface anomaly assessment module. The process involves fusion. A composite mathematical formula is used to weight and calculate the four key parameters, ultimately yielding the fit. This level of compatibility It is a comprehensive indicator that quantifies the overall performance level that the entire detection system can achieve under specific optical conditions, surface conditions, and combined environmental stresses. Adaptability The calculation results directly influence the axial scanning speed adjustment module's decision on the scanning speed, enabling the system to dynamically adjust the scanning strategy according to actual environmental conditions. This ensures high-quality detection data is obtained even in complex and changing environments, and optimizes detection efficiency. This comprehensive evaluation mechanism allows the real-time linear scanning speed control system to adapt to various detection scenarios more intelligently and robustly, effectively solving the problem that single-factor evaluation is insufficient to cope with complex environmental challenges.

[0055] As a specific implementation, the adaptation evaluation module operates as follows: First, the device collects environmental vibration data in real time using a piezoelectric accelerometer mounted on a concave cantilever scanning frame 9 or a platform 1, and the signal processing unit calculates the root mean square value of the vibration intensity. Simultaneously, the surface temperature of the boiler pipes is measured non-contactly using an infrared thermometer. Assuming the system's preset maximum allowable vibration threshold is 10 m / s², when the measured environmental vibration intensity is 8 m / s², the environmental vibration intensity index is calculated to be 0.8. If the measured vibration intensity is 12 m / s², the index is limited to 1. For pipe temperature, assuming the preset minimum allowable temperature is 0℃ and the maximum allowable temperature is 200℃, when the measured pipe temperature is 100℃, the pipe temperature index is calculated to be 0.5. Next, the adaptation evaluation module receives the optical environment coefficient from the optical environment evaluation module. (e.g., 0.9), Surface anomaly coefficients are received from the surface anomaly assessment module. (e.g., 0.8). Preset influence weight. , and The values ​​are 0.4, 0.3, and 0.3 respectively. These parameters are then substituted into the fitness formula for calculation. This calculation process can be completed in the device's central processing unit, which outputs the fitness score based on the calculation results. This is for further use by the axial scanning movement speed adjustment module.

[0056] Through the above technical solution, the adaptability evaluation module of this application can comprehensively and dynamically evaluate the suitability of the current detection environment. By comprehensively quantifying and calculating the adaptability of multiple key factors such as optical environment conditions, pipe surface condition, environmental vibration intensity, and pipe temperature, the linear scanning speed real-time control system can obtain a more accurate and comprehensive environmental adaptability index. This avoids the limitations of single-factor evaluation and ensures that the system can more intelligently adjust the axial scanning speed under complex and changing environmental conditions. When environmental conditions are harsh, the adaptability decreases, and the system can correspondingly slow down the scanning speed to ensure the quality and accuracy of data acquisition; when environmental conditions are favorable, the adaptability increases, and the system can speed up the scanning speed, thereby improving detection efficiency. This refined environmental adaptability evaluation significantly improves the robustness and reliability of boiler pipe wall thickness detection, effectively solving the technical problem of balancing detection accuracy and efficiency under complex working conditions.

[0057] This application further proposes the following operating procedure for the optical environment assessment module:

[0058] The system acquires the current internal background light noise intensity, external background light noise intensity, effective data rate of the internal probe point cloud, and effective data rate of the external probe point cloud. Internal and external background light noise intensity refer to the intensity of all stray light other than the laser signal emitted by the laser scanning probe 10 in the boiler pipe environment, both inside and outside the pipe. These stray lights may originate from ambient lighting, reflected light inside the pipe, or other light sources, which can interfere with the effective signal received by the laser scanning probe 10 and reduce measurement accuracy. These noise intensities can be monitored in real time by independent photoelectric sensors (such as photodiodes or ambient light sensors) integrated inside or near the laser scanning probe 10, or by analyzing the ambient light signals received by the laser scanning probe 10 during non-measurement periods or measurement intervals. The effective data rate of the internal and external probe point clouds refers to the ratio of the amount of point cloud data successfully acquired and identified as effective measurement points by the internal and external laser scanning probes 10 per unit time to the total number of emitted laser pulses or scans. Effective data rate is a key indicator for measuring the data quality and reliability of the laser scanning probe 10. It reflects the degree of attenuation, scattering, and noise interference experienced by the laser signal during transmission and reception. This data rate is typically calculated in real time by the internal processing unit of the laser scanning probe 10, or obtained by post-processing the raw point cloud data (e.g., filtering out invalid points, outliers, or low-confidence points).

[0059] The current internal and external background light noise intensities are compared to the maximum allowable background light noise intensity of the equipment. After applying a min function with an upper limit of 1, the larger of these values ​​is taken as the background light noise intensity index. This aims to standardize the original background light noise intensity data into a dimensionless index for subsequent comprehensive evaluation. The ratio processing compares the actual noise intensity with the system's tolerable limits, thus quantifying the relative impact of the noise. The upper limit of 1 for the min function ensures that the index value will not exceed 1; even if the noise intensity exceeds the maximum allowable value, the index remains at 1, indicating that the noise has reached its maximum impact. Taking the larger value after limiting reflects the "barrel effect," that is, using the poorest optical conditions in the internal and external environments as a representative of the overall background light noise, ensuring the conservatism and safety of the evaluation.

[0060] The effective data rates of the point clouds from the internal and external test heads are combined to obtain a comprehensive data validity index. This comprehensive data validity index is then inversely correlated with the background light noise intensity index to obtain an optical environment coefficient. The optical environment coefficient increases with the comprehensive data validity index and decreases with the background light noise intensity index. Specifically, the calculation method involves importing the point cloud effective data rate and the background light noise intensity index into a formula... Obtain the optical environment coefficient , Used for comprehensive evaluation of the advantages and disadvantages of optical measurement environments. The value ranges from 0 to 1, where 1 indicates an excellent optical environment, allowing the laser scanning probe 10 to operate with maximum efficiency and accuracy; 0 indicates an extremely poor optical environment, making effective measurement impossible, in which case the detection strategy should be paused or adjusted. For the effective data rate of the internal test head point cloud, For the effective data rate of the external test head point cloud, This is the background light noise intensity index. The formula comprehensively considers the data quality and environmental noise effects of the laser scanning probe 10. By calculating the geometric mean of the effective data rates of the point clouds from the inner and outer probes, the overall data acquisition capability of both probes can be fairly reflected, avoiding the excessive influence of extreme values ​​in the performance of a single probe on the overall evaluation. Simultaneously, this average is multiplied by... The item visually demonstrates the negative impact of background light noise on measurement performance; the higher the noise figure, the worse the measurement performance. The smaller the value, the better the optical environment factor. The lower the value, the better.

[0061] This application's solution acquires and quantifies the intensity of internal and external background light noise and the effective data rate of the point cloud from the laser scanning probe 10 in real time, transforming these key parameters into standardized indices. Specifically, the background light noise intensity index is obtained by comparing the intensity of internal and external background light noise with a preset maximum allowable value and taking the larger value, thus objectively reflecting the severity of environmental noise. Simultaneously, the effective data rate of the internal and external point clouds provided by the laser scanning probe 10 is directly used to evaluate the quality of data acquisition. Subsequently, these indices are substituted into a specific mathematical model, i.e., the formula... Calculate the comprehensive optical environment coefficient This coefficient, ranging from 0 to 1, intuitively represents the quality of the current optical measurement environment. When the optical environment coefficient... A value close to 1 indicates a good optical environment, allowing the laser scanning probe 10 to operate stably and efficiently; when the optical environment coefficient is close to 1, it indicates a good optical environment. A value close to 0 indicates a harsh optical environment, which may lead to unreliable measurement data or even make measurement impossible. In this way, the optical environment assessment module can provide a quantitative, real-time feedback on the optical environment status, providing accurate input for the subsequent adaptation assessment module and axial scanning movement speed adjustment module. This ensures that the movement speed of the concave cantilever scanning frame 9 can be reasonably adjusted under different optical conditions, thereby guaranteeing the accuracy and reliability of wall thickness detection.

[0062] In one specific implementation, the optical environment assessment module can be implemented using an embedded controller (e.g., based on an ARM Cortex-M series microprocessor). This controller connects to an ambient light sensor (e.g., a photoresistor or photodiode) mounted on a concave cantilever scanning gantry 9 via an analog-to-digital converter (ADC) interface to acquire the internal and external background light noise intensities in real time. The laser scanning probe 10 (e.g., a laser displacement sensor employing triangulation) transmits the real-time calculated effective data rates of the internal and external probe point clouds to the embedded controller via a high-speed serial communication interface (e.g., SPI or UART). Upon receiving this data, the embedded controller first compares the internal and external background light noise intensities with the maximum allowed background light noise intensity (e.g., set to a Lux value through laboratory calibration or experience) pre-stored in memory, applies a min function to limit the result to between 0 and 1, and then takes the larger value as the background light noise intensity index. Next, the controller substitutes the acquired effective data rates of the internal and external probe point clouds and the background light noise intensity index into the formula. Calculations were performed to obtain the optical environment coefficient. The optical environment factor It is then sent via the internal bus to the adaptation evaluation module as an important input parameter for calculating the adaptation degree.

[0063] Through the above technical solution, this application can evaluate the optical environment conditions during boiler pipe wall thickness detection in real time and accurately. This quantitative evaluation allows the system to dynamically adjust subsequent scanning strategies and parameters according to actual changes in the optical environment. For example, by adapting the evaluation module to influence the axial scanning speed adjustment module, the moving speed of the concave cantilever scanning frame 9 can be optimized. This not only improves the reliability and accuracy of data acquired by the laser scanning probe 10, effectively avoiding invalid or low-quality measurements under harsh optical conditions, but also ensures the stability and efficiency of the entire wall thickness detection process, significantly improving the accuracy of the detection results.

[0064] This application further proposes the following operating procedure for the surface anomaly assessment module:

[0065] The system acquires the current inner wall reflection intensity gradient, outer wall reflection intensity gradient, inner wall profile curvature abrupt change frequency, and outer wall profile curvature abrupt change frequency; these parameters are key indicators for quantifying pipe surface anomalies. The reflection intensity gradient reflects the uniformity of surface materials or coatings; for example, defects such as rust, oil stains, or coating peeling on the pipe surface can cause drastic changes in laser reflection intensity. The profile curvature abrupt change frequency indicates sudden changes in surface geometry, such as pits, protrusions, weld slag, or cracks. Acquiring this raw data provides a foundation for subsequent quantification of the degree of surface anomalies. This data can be acquired by the laser scanning probe 10 simultaneously recording the intensity information of laser reflection during scanning and performing differential or gradient calculations on the reflection intensity values ​​of continuous scanning points or regions to obtain the reflection intensity gradient. Simultaneously, the inner and outer wall profiles of the pipe are reconstructed using the point cloud data acquired by the laser scanning probe 10, and the profile curvature abrupt change frequency is obtained by calculating the curvature of these profile data and identifying regions where the curvature values ​​change significantly and frequently.

[0066] The larger of the current inner and outer wall reflection intensity gradients is compared with a reflection intensity gradient threshold, and then a min function is used to limit the amplitude to an upper limit of 1 to obtain the reflection intensity gradient exponent. This step aims to standardize the raw reflection intensity gradient data into a dimensionless exponent, making it comparable and calculable with other evaluation indicators. By comparing it with a preset threshold, the deviation of the current reflection intensity gradient from the acceptable range can be quantified. The upper limit of the min function to 1 ensures that the exponent will not exceed 1, representing the most severe anomaly. For example, an empirical reflection intensity gradient threshold can be preset, representing the maximum allowable variation in reflection intensity under normal pipe surface conditions. The larger of the actually measured inner and outer wall reflection intensity gradients is divided by this threshold to obtain a ratio. Then, the min(ratio, 1) function is used to limit the amplitude, ensuring that the exponent is between 0 and 1. Alternatively, the threshold can be dynamically adjusted based on historical data or the characteristics of specific materials. After ratio processing, a nonlinear function is used to map the output value to a range between 0 and 1.

[0067] The ratio of the larger of the current inner and outer wall profile curvature abrupt change frequencies to a profile curvature abrupt change frequency threshold is processed, and then the upper limit of the min function is set to 1 to obtain the profile curvature abrupt change frequency index. This step serves the same purpose as obtaining the reflection intensity gradient index: to standardize the original profile curvature abrupt change frequency data into a dimensionless index for subsequent comprehensive evaluation. By comparing it with a preset threshold, the severity of surface geometric anomalies can be quantified. The upper limit of the min function set to 1 also ensures a reasonable range for the index. For example, a profile curvature abrupt change frequency threshold can be preset, representing the maximum allowable curvature abrupt change frequency under normal pipe surface geometry. The larger of the actually measured inner and outer wall profile curvature abrupt change frequencies is divided by this threshold to obtain a ratio. Then, the min(ratio, 1) function is used for amplitude limiting. Alternatively, this threshold can be set according to the pipe's nominal diameter, wall thickness tolerance, and expected defect size. After ratio processing, a piecewise linear function or lookup table can be used to map the ratio to an exponential range of 0 to 1.

[0068] The larger value between the reflection intensity gradient index and the contour curvature abrupt change frequency index is taken as the surface anomaly degree index. The surface anomaly degree index is converted into a surface anomaly coefficient using a preset decreasing mapping function. The surface anomaly coefficient decreases as the surface anomaly degree index increases, and the surface anomaly coefficient decreases accordingly when any one of the following is abnormal: the inner wall reflection intensity gradient, the outer wall reflection intensity gradient, the inner wall contour curvature abrupt change frequency, or the outer wall contour curvature abrupt change frequency. Specifically, the calculation method can be as follows: Import the reflection intensity gradient index and the contour curvature abrupt change frequency index into the formula: Obtain the surface anomaly coefficient , Used to assess whether there are abnormalities on the pipe surface (such as rust, welding slag, pits, etc.). The reflection intensity gradient exponent, This is the contour curvature abrupt change frequency index. The formula combines two key surface anomaly indicators to generate a single surface anomaly coefficient. By taking the larger of the two and subtracting it from 1, it ensures that if either anomaly indicator is high, the final surface anomaly coefficient will be low, thus accurately reflecting the severity of surface anomalies. The value range is 0-1, where 1 indicates a normal surface and 0 indicates a severe anomaly. This allows the coefficient to be directly used in subsequent fit calculations, guiding the adjustment of the scanning speed. This formula can be directly implemented in the calculation unit of the control system, receiving... and As input, and return the calculated result. .

[0069] This application's solution provides crucial input for a real-time linear scanning speed control system by finely evaluating pipe surface anomalies, thereby optimizing the efficiency and accuracy of boiler pipe wall thickness detection. The solution first acquires the reflection intensity gradient and profile curvature abrupt change frequency of the inner and outer walls of the pipe in real time using a laser scanning probe 10. These raw data directly reflect key information about pipe surface defects (such as rust, weld slag, pits, etc.), as these defects directly lead to uneven reflection intensity or abrupt geometric changes. Subsequently, the system compares these raw measurements with a preset threshold and uses a min function for amplitude limiting, thus standardizing the complex physical quantities into a dimensionless reflection intensity gradient exponent. and the frequency index of contour curvature abrupt change This standardization process allows for the quantification and comparison of different types of surface anomalies on a uniform scale, and by taking the larger of the inner and outer wall measurements, it ensures that the most severe anomalies are adequately reflected. Finally, these two standardized indices are substituted into the formula. In the calculation, the surface anomaly coefficient is obtained. The ingenious design of this formula lies in its use of the maximum value of two outlier indicators as the dominant factor, which is then subtracted from 1, resulting in... The value can intuitively represent the degree of "normality" of the surface: the closer the value is to 1, the more normal the surface is; the closer the value is to 0, the more severe the surface abnormality is.

[0070] This precisely calculated surface anomaly coefficient It is then passed to the adaptation evaluation module. In the adaptation evaluation module, With optical environment factor The overall fit is calculated by combining the environmental vibration intensity index and the pipeline temperature index. When the surface anomaly coefficient A low level indicates severe defects on the pipe surface, which will directly affect the fit. The degree of decrease. Adaptability The reduction will further prompt the axial scan movement speed adjustment module to calculate an even lower target axial scan movement speed based on its operating formula. The system adjusts the current axial scanning speed accordingly. This linkage mechanism ensures that in areas with complex pipe surface conditions or defects, the concave cantilever scanning frame 9 drives the laser scanning probe 10 to scan at a slower speed, providing more time for the laser scanning probe 10 to acquire high-quality point cloud data and effectively avoiding missed detections or data distortion caused by excessive scanning speed. Conversely, in areas with good surface conditions, the system can maintain or increase the scanning speed, thereby significantly improving the overall detection efficiency. In this way, the solution of this application realizes intelligent and adaptive control of the boiler pipe wall thickness detection process, greatly improving the accuracy and reliability of the detection results while optimizing detection efficiency.

[0071] The following is a specific example to illustrate this. Suppose that when inspecting the wall thickness of a section of boiler pipe, the laser scanning probe 10 detects obvious corrosion and pitting on the outer wall of the pipe in a specific area. Specifically, the system obtains that the reflection intensity gradient of the inner wall in this area is 0.1, and the reflection intensity gradient of the outer wall is 0.8; the frequency of abrupt changes in the curvature of the inner wall profile is 0.05, and the frequency of abrupt changes in the curvature of the outer wall profile is 0.7.

[0072] To calculate the surface anomaly coefficient First, an exponentialization process is performed. Assuming a preset reflection intensity gradient threshold of 0.5, the larger of the reflection intensity gradients of the inner and outer walls is 0.8. This value is then compared to the threshold and limited to obtain the reflection intensity gradient exponent. The value is 1.

[0073] Next, assuming a preset threshold for the frequency of abrupt changes in contour curvature is 0.6, the larger of the frequencies of abrupt changes in contour curvature for the inner and outer walls is 0.7. After comparing this to the threshold and limiting the amplitude, the contour curvature abrupt change frequency index is obtained. The value is 1.

[0074] Finally, substitute these two exponents into the formula. The surface anomaly coefficient was calculated. It is 0.

[0075] This calculation result indicates that there are very serious anomalies on the pipe surface in this area. When this... When the value (0) is passed to the fit evaluation module, it will result in a fit score. The speed of the axial scanning movement is significantly reduced, which in turn prompts the axial scanning movement speed adjustment module to greatly reduce the movement speed of the concave cantilever scanning frame 9, and may even pause the scanning, in order to ensure that more detailed observation or other detection measures can be taken in the severely abnormal area, thereby avoiding data acquisition failure or misjudgment due to surface defects.

[0076] Through the above technical solution, this application can accurately quantify the degree of anomalies on the surface of boiler pipes. By comprehensively considering the reflection intensity gradient of the inner and outer walls and the frequency of abrupt changes in contour curvature, and converting them into a unified surface anomaly coefficient, the system can objectively and accurately assess defects such as rust, welding slag, and pits on the pipe surface. This refined surface anomaly assessment result serves as a key input to the adaptive assessment module, enabling the real-time linear scanning speed control system to more intelligently and precisely adjust the axial movement speed of the concave cantilever scanning frame 9. In areas with surface anomalies, the system can automatically reduce the scanning speed, ensuring that the laser scanning probe 10 has sufficient time to acquire high-quality data, effectively avoiding measurement errors or data loss caused by surface defects, and significantly improving the reliability and accuracy of the detection. At the same time, in areas with good surface conditions, the system can maintain or increase the scanning speed, thereby maximizing the overall detection efficiency while ensuring detection quality. This adaptive scanning speed adjustment mechanism effectively solves the problem of difficulty in balancing detection accuracy and efficiency caused by complex surface conditions in traditional detection methods.

[0077] like Figure 1 As shown, in a preferred embodiment of the present invention, the boiler pipe support assembly includes a platform 1 and a rotating shaft 2 rotatably mounted on a bracket. Multiple support sleeves 3 are fixed on both rotating shafts 2. A motor 4 is fixed on the top of the platform 1, and the rotating end of the motor 4 is connected to the rotating shaft 2.

[0078] In this embodiment of the invention, multiple support sleeves 3 are fixed on each rotating shaft 2, allowing the boiler pipe to be supported at multiple points, effectively distributing the weight of the pipe and enhancing the stability of the support. A motor 4 is connected to one or two rotating shafts 2 via its rotating end, providing precise rotational power. When the motor 4 starts, it drives the rotating shaft 2 to rotate, which in turn drives the boiler pipe to rotate smoothly and controllably through the support sleeves 3. This dual-rotating-shaft, multi-support-sleeve structure ensures that the boiler pipe remains in a preset position during axial movement and rotational scanning, avoiding swaying or eccentricity caused by unstable support, thus providing a stable measurement environment for the laser scanning probe 10 on the concave cantilever scanning frame 9.

[0079] like Figure 1As shown, in a preferred embodiment of the present invention, the moving component includes a guide groove 5 located on the table 1 between two rotating shafts 2. A guide slider 6 is slidably connected within the guide groove 5. The guide groove 5 is a structure for guiding the guide slider 6 to perform linear motion, and its function is to provide a stable moving trajectory for the concave cantilever scanning frame 9. The concave cantilever scanning frame 9 is fixed on the guide slider 6. The function of the guide slider 6 is to transfer the weight and motion load of the concave cantilever scanning frame 9 to the guide groove 5 and ensure that the scanning frame moves smoothly along a predetermined path. A lead screw 7 is rotatably connected within the guide groove 5. The lead screw 7 passes through the guide slider 6 and is threadedly connected to the guide slider 6. A second motor 8 is fixed on the table 1. The rotating end of the second motor 8 is connected to the lead screw 7. When the lead screw 7 rotates, the guide slider 6 will move along the axial direction of the lead screw 7 due to the threaded engagement. The guide slider 6 may have an integrated internal thread structure, or it may be threadedly engaged with the lead screw 7 by installing a nut (e.g., a ball nut or trapezoidal nut).

[0080] In this embodiment of the invention, motor 8 drives lead screw 7. Under the guidance of guide groove 5, lead screw 7 drives guide slider 6 to move through thread transmission. Guide slider 6 drives concave cantilever scanning frame 9 to move. Concave cantilever scanning frame 9 drives laser scanning probe 1 to move linearly.

[0081] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. An auxiliary device for detecting the wall thickness of boiler pipes, comprising a platform, a movable component mounted on the platform, a boiler pipe support component, and a concave cantilever scanning frame, characterized in that, Also includes: Two opposing laser scanning probes are installed at both ends of the concave cantilever scanning frame. The boiler pipe support assembly is used to support the boiler pipe and drive the boiler pipe to rotate. The moving assembly is used to drive the concave cantilever scanning frame to move along the axial direction of the boiler pipe. A real-time linear scan speed control system is electrically connected to the control terminal of the moving component. This system is used to adjust the moving speed of the cantilever scanning gantry. The real-time linear scan speed control system includes: The optical environment assessment module calculates and obtains the optical environment coefficient based on the internal background light noise intensity, the external background light noise intensity, the effective data rate of the internal probe point cloud, and the effective data rate of the external probe point cloud. The surface anomaly assessment module calculates and obtains the surface anomaly coefficient based on the inner wall reflection intensity gradient, the outer wall reflection intensity gradient, the inner wall profile curvature abrupt change frequency, and the outer wall profile curvature abrupt change frequency. The adaptation evaluation module calculates the adaptation degree based on the environmental vibration intensity and pipeline temperature under the optical environment coefficient and surface anomaly coefficient. The geometric stability assessment module calculates and obtains the geometric stability coefficient based on the wall thickness change rate and the instantaneous value of ellipticity; The axial scanning speed adjustment module calculates and adjusts the target axial scanning speed to the target axial scanning speed based on the fit, geometric stability coefficient, and current axial scanning speed.

2. The auxiliary device for boiler pipe wall thickness detection according to claim 1, characterized in that, The operation process of the axial scanning moving speed adjustment module is as follows: Based on the current axial scanning speed, fit, geometric stability coefficient, and preset adjustment threshold, the adjustment factor is obtained by comparing the product of fit and geometric stability coefficient with the adjustment threshold. The speed adjustment amount is obtained by multiplying the current axial scanning movement speed by the product of the adjustment factor and the speed adjustment sensitivity coefficient; The target axial scanning speed is obtained by adding the current axial scanning speed to the speed adjustment amount. The direction of speed adjustment is determined by the relative magnitude of the product and the adjustment threshold, and the speed adjustment range is controlled by the speed adjustment sensitivity coefficient.

3. The auxiliary device for boiler pipe wall thickness detection according to claim 2, characterized in that, The operation flow of the geometric stability assessment module is as follows: Obtain the current wall thickness change rate and instantaneous ellipticity values; The current wall thickness change rate and instantaneous ellipticity value are compared with the maximum allowable wall thickness change rate threshold and the maximum allowable ellipticity threshold, respectively. After using the min function to limit the upper limit to 1, the wall thickness change rate index and instantaneous ellipticity value index are obtained. The two indices are weighted and combined based on preset weights to obtain a comprehensive instability index. The comprehensive instability index is mapped to the geometric stability coefficient using a preset function.

4. The auxiliary device for boiler pipe wall thickness detection according to claim 3, characterized in that, The geometric stability coefficient is negatively correlated with the comprehensive instability index, and the mapping function guarantees that the geometric stability coefficient approaches 1 when the comprehensive instability index approaches 0, and decreases when the comprehensive instability index increases.

5. The auxiliary device for boiler pipe wall thickness detection according to claim 2, characterized in that, The operation process of the adaptation evaluation module is as follows: Obtain the current environmental vibration intensity and pipeline temperature; The environmental vibration intensity index is obtained by comparing the current environmental vibration intensity with the maximum allowable vibration threshold of the system and then using the min function to limit the amplitude to 1. The pipe temperature is normalized to its maximum and minimum values ​​to obtain the pipe temperature index. The environmental stress influence factor is obtained by performing a composite calculation on the environmental vibration intensity index, the pipeline temperature index, and the interaction term between the environmental vibration intensity index and the pipeline temperature index according to preset weights. The fit is obtained by multiplying the optical environment coefficient, surface anomaly coefficient and environmental stress influence factor. The calculation of the environmental stress influence factor takes into account the individual effects of environmental vibration intensity and pipeline temperature as well as their coupling effect, and the sum of the weights of each influencing factor is 1.

6. The auxiliary device for boiler pipe wall thickness detection according to claim 5, characterized in that, The operation process of the optical environment assessment module is as follows: Acquire the current internal background light noise intensity, external background light noise intensity, effective data rate of internal probe point cloud, and effective data rate of external probe point cloud; The current internal background light noise intensity and external background light noise intensity are respectively compared with the maximum allowable background light noise intensity of the device. After using the min function to limit the upper limit to 1, the larger value after the limit is taken as the background light noise intensity index. The effective data rates of the internal and external head point clouds are processed together to obtain a comprehensive data validity index. The comprehensive data validity index is then inversely correlated with the background light noise intensity index to obtain an optical environment coefficient. The optical environment coefficient increases with the improvement of the comprehensive data validity index and decreases with the improvement of the background light noise intensity index.

7. The auxiliary device for boiler pipe wall thickness detection according to claim 5, characterized in that, The operation process of the surface anomaly assessment module is as follows: Obtain the current inner wall reflection intensity gradient, outer wall reflection intensity gradient, inner wall profile curvature abrupt change frequency, and outer wall profile curvature abrupt change frequency; The larger of the current inner wall reflection intensity gradient and outer wall reflection intensity gradient is compared with the reflection intensity gradient threshold, and the upper limit of the amplitude is limited to 1 by the min function to obtain the reflection intensity gradient exponent. The larger of the current inner wall profile curvature change frequency and the outer wall profile curvature change frequency is compared with the profile curvature change frequency threshold. After using the min function to limit the upper limit to 1, the profile curvature change frequency index is obtained. The larger value between the reflection intensity gradient index and the contour curvature abrupt change frequency index is taken as the surface anomaly index. The surface anomaly index is converted into a surface anomaly coefficient by a preset decreasing mapping function. The surface anomaly coefficient decreases as the surface anomaly index increases, and the surface anomaly coefficient decreases accordingly when any one of the following is abnormal: the inner wall reflection intensity gradient, the outer wall reflection intensity gradient, the inner wall profile curvature abrupt change frequency, or the outer wall profile curvature abrupt change frequency.

8. The auxiliary device for boiler pipe wall thickness detection according to claim 1, characterized in that, The boiler pipe support assembly includes a platform with a rotating shaft rotatably mounted on a bracket. Multiple support sleeves are fixed on both rotating shafts. A motor is fixed on the top of the platform, and the rotating end of the motor is connected to the rotating shaft.

9. The auxiliary device for boiler pipe wall thickness detection according to claim 8, characterized in that, The moving component includes a guide groove located on the table between two rotating shafts, a guide slider slidably connected in the guide groove, a concave cantilever scanning frame fixed on the guide slider, a lead screw rotatably connected in the guide groove, the lead screw passing through the guide slider and threadedly connected to the guide slider, and a second motor fixed on the table, the rotating end of the second motor being connected to the lead screw.

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