Pressure-bearing equipment global wall thickness detection system and method

Abstract

The application relates to a kind of pressure equipment global wall thickness detection system and method, wherein, system includes: global scanning robot, for scanning the global wall thickness data of target pressure equipment;Modeling module, for constructing the three-dimensional model of target pressure equipment based on global wall thickness data;Early warning module, for tracking the data variation of global wall thickness data, to calculate the wall thickness change rate of target pressure equipment based on data variation, according to wall thickness change rate corresponding early warning action is matched;Display module, for optimizing the rendering effect of three-dimensional model based on early warning action, to highlight the area of target pressure equipment that does not meet the preset wall thickness condition. Thus, in the related art, the technical problems of insufficient detection coverage, response lag, difficulty in accurately obtaining defect location information when detecting defects, difficulty in timely remediation, great safety hazards and poor economy are solved.

Description

Technical Field

[0001] This application relates to the field of nondestructive testing technology for industrial equipment, and in particular to a system and method for detecting the full-area wall thickness of pressure equipment. Background Technology

[0002] In related technologies, thickness measurement and inspection of industrial equipment mainly relies on single-point thickness measurement using thickness gauges. This method measures the thickness of a specific point on the equipment while it is stopped, which has inherent limitations: insufficient coverage (manual ultrasonic thickness measurement only checks 5%-20% of the area, making it difficult to remedy significant safety hazards in unchecked areas); delayed response (offline inspection cycle >72 hours, leading to substantial downtime economic losses); and data silos (thickness data is stored in Excel spreadsheets, making it impossible to correlate with the spatial location of the equipment and accurately pinpoint defect locations), all of which urgently need improvement. Summary of the Invention

[0003] This application provides a system and method for detecting the full-area wall thickness of pressure equipment, in order to solve the technical problems in related technologies, such as insufficient detection coverage, delayed response, difficulty in accurately obtaining defect location information when defects are detected, difficulty in timely remediation, significant safety hazards, and poor economic efficiency.

[0004] The first aspect of this application provides a global wall thickness detection system for pressure-bearing equipment, comprising: a global scanning robot for scanning global wall thickness data of a target pressure-bearing equipment; a modeling module for constructing a three-dimensional model of the target pressure-bearing equipment based on the global wall thickness data; an early warning module for tracking the data change of the global wall thickness data, calculating the wall thickness change rate of the target pressure-bearing equipment based on the data change, and matching a corresponding early warning action according to the wall thickness change rate; and a display module for optimizing the rendering effect of the three-dimensional model based on the early warning action, so as to highlight the areas of the target pressure-bearing equipment that do not meet the preset wall thickness conditions.

[0005] Based on the aforementioned technical means, this application embodiment can use a global scanning robot to scan the global wall thickness data of the target pressure-bearing equipment, achieving global scanning, reducing safety hazards caused by spot checks, and using a modeling module to construct a 3D model of the target pressure-bearing equipment based on the global wall thickness data. An early warning module tracks the data changes in the global wall thickness data, calculates the wall thickness change rate of the target pressure-bearing equipment, and thus confirms the defect location to match the corresponding early warning action. Finally, the rendering effect of the 3D model is optimized based on the early warning action to highlight areas of the target pressure-bearing equipment that do not meet the preset wall thickness conditions, achieving defect location, facilitating timely remediation, eliminating the need for long-term downtime, and ensuring economic efficiency. Optionally, in one embodiment of this application, the global scanning robot includes: a laser scanner for scanning the surrounding environment using a laser to obtain the contour features around the global scanning robot, thereby determining the current position of the global scanning robot; an electromagnetic ultrasonic array sensor for calculating the local wall thickness data corresponding to the current position using electromagnetic ultrasonic waves, so as to obtain the global wall thickness data after the global scanning robot completes the global scan; and a magnetic adsorption moving platform for carrying the electromagnetic ultrasonic array sensor and the laser scanner, adsorbing them onto the surface of the target pressure-bearing device, and moving on the surface according to a preset detection command.

[0006] Based on the above-mentioned technical means, the embodiments of this application can realize automated, full-coverage, and high-precision data collection through a full-domain scanning robot, replacing the high-risk, low-efficiency, and easily missed manual high-altitude operations and "blind spot" spot checks, thus ensuring the integrity and objectivity of the data.

[0007] Optionally, in one embodiment of this application, it further includes: a plurality of static acquisition modules disposed at the target detection points of the target pressure-bearing equipment, for acquiring static wall thickness data of the target detection points at preset intervals; and a transmission module for transmitting the static wall thickness data and the corresponding location data of the target detection points to the early warning module.

[0008] Based on the above technical means, the embodiments of this application can construct a three-dimensional monitoring network through full-domain periodic inspection and fixed-point continuous monitoring to accurately capture rapid changes that may occur between two robot scans and increase the accuracy of judgment.

[0009] Optionally, in one embodiment of this application, the early warning module includes: an acquisition unit for acquiring the measured temperature and a preset reference temperature of the target pressure-bearing equipment; a first calculation unit for calculating the temperature difference between the measured temperature and the preset reference temperature; a correction unit for inputting the global wall thickness data, the static wall thickness data, and the temperature difference into a pre-built temperature compensation model to obtain temperature-corrected global wall thickness correction data and static wall thickness correction data; and a second calculation unit for calculating the wall thickness change rate using the global wall thickness correction data and the static wall thickness correction data.

[0010] Based on the above-mentioned technical means, the embodiments of this application can detect the corrosion acceleration trend at an early stage through dynamic prediction, and then automatically trigger different levels of early warning to achieve predictive maintenance.

[0011] Optionally, in one embodiment of this application, the modeling module is further configured to perform non-uniform rational B-spline surface reconstruction using the wall thickness data, wherein the reconstruction expression is: , in, ( u ) indicates the first i 1, number of times p The B-spline basis functions in the parameters u The value at that location, No. j 1, number of times q The B-spline basis functions in the parameters v The value at that location, The parametric coordinates on the surface are The location of the point, Indicates the control point grid in u and v Maximum index in the direction.

[0012] Optionally, in one embodiment of this application, it further includes: a judgment module, used to judge whether the target pressure-bearing equipment is in a preset corrosive environment based on the gas environment data of the target pressure-bearing equipment; an enhancement module, used to predict the wall thickness risk of the target pressure-bearing equipment by combining a pre-built enhancement degradation model and the wall thickness change rate when the target pressure-bearing equipment is in the preset corrosive environment; and an alarm module, used to execute a corresponding alarm action based on the wall thickness risk.

[0013] Based on the above-mentioned technical means, the embodiments of this application realize intelligent diagnosis and early warning based on multi-source information fusion, thereby improving the safety management level of pressure equipment.

[0014] A second aspect of this application provides a method for detecting the global wall thickness of a pressure-bearing device, comprising the following steps: scanning global wall thickness data of a target pressure-bearing device; constructing a three-dimensional model of the target pressure-bearing device based on the global wall thickness data; tracking the data change amount of the global wall thickness data, calculating the wall thickness change rate of the target pressure-bearing device based on the data change amount, and matching a corresponding early warning action according to the wall thickness change rate; optimizing the rendering effect of the three-dimensional model based on the early warning action to highlight the areas of the target pressure-bearing device that do not meet the preset wall thickness conditions.

[0015] A third aspect of this application provides an electronic device, including: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the program to implement the pressure equipment full-area wall thickness detection method as described in the above embodiments.

[0016] A fourth aspect of this application provides a computer-readable storage medium storing computer instructions for causing the computer to perform the pressure equipment full-area wall thickness detection method as described in the above embodiments.

[0017] A fifth aspect of this application provides a computer program product, including a computer program, which, when executed, is used to implement the above-described method for detecting the full-area wall thickness of pressure equipment.

[0018] This application embodiment utilizes a global scanning robot to scan the global wall thickness data of a target pressure-bearing device, achieving global scanning and reducing safety hazards caused by spot checks. A modeling module constructs a 3D model of the target pressure-bearing device based on the global wall thickness data. An early warning module tracks changes in the global wall thickness data, calculates the wall thickness change rate of the target pressure-bearing device, and thus confirms the defect location to match corresponding early warning actions. Finally, the rendering effect of the 3D model is optimized based on the early warning actions to highlight areas of the target pressure-bearing device that do not meet preset wall thickness conditions, achieving defect localization and facilitating timely remediation without long-term downtime, ensuring economic efficiency. This solves the technical problems in related technologies, such as insufficient detection coverage, delayed response, difficulty in accurately obtaining defect location information when defects are detected, difficulty in timely remediation, significant safety hazards, and poor economic efficiency.

[0019] Additional aspects and advantages of this application will be set forth in part in the description which follows, and in part will be obvious from the description, or may be learned by practice of this application. Attached Figure Description

[0020] The above and / or additional aspects and advantages of this application will become apparent and readily understood from the following description of the embodiments taken in conjunction with the accompanying drawings, wherein: Figure 1 This is a schematic diagram of a pressure equipment full-area wall thickness detection system provided according to an embodiment of this application; Figure 2 This is a schematic diagram of a pressure equipment full-area wall thickness detection system according to an embodiment of this application; Figure 3 This is a schematic diagram of the structure of a pressure equipment full-area wall thickness detection system according to an embodiment of this application; Figure 4 This is a schematic diagram of data flow according to an embodiment of this application; Figure 5 This is a schematic diagram comparing economic benefits according to one embodiment of this application; Figure 6 This is a flowchart of a method for detecting the full-area wall thickness of pressure equipment according to an embodiment of this application; Figure 7 This is a schematic diagram of the structure of an electronic device provided according to an embodiment of this application. Detailed Implementation

[0021] The embodiments of this application are described in detail below. Examples of these embodiments are shown in the accompanying drawings, wherein the same or similar reference numerals denote the same or similar elements or elements having the same or similar functions throughout. The embodiments described below with reference to the accompanying drawings are exemplary and intended to explain this application, and should not be construed as limiting this application.

[0022] The following description, with reference to the accompanying drawings, describes a pressure equipment full-area wall thickness detection system and method according to embodiments of this application. Addressing the technical problems mentioned in the background art, such as insufficient detection coverage, delayed response, difficulty in accurately obtaining defect location information when defects are detected, difficulty in timely remediation, significant safety hazards, and poor economic efficiency, this application provides a pressure equipment full-area wall thickness detection method. In this system, a full-area scanning robot can scan the global wall thickness data of the target pressure equipment to achieve global scanning, reducing safety hazards caused by spot checks. A modeling module constructs a three-dimensional model of the target pressure equipment based on the global wall thickness data. An early warning module tracks the data changes in the global wall thickness data, calculates the wall thickness change rate of the target pressure equipment, and thus confirms the defect location to match the corresponding early warning action. Finally, the rendering effect of the three-dimensional model is optimized based on the early warning action to highlight areas of the target pressure equipment that do not meet the preset wall thickness conditions, achieving defect location and facilitating timely remediation without long-term downtime, thus ensuring economic efficiency. This solves the technical problems in related technologies, such as insufficient detection coverage, delayed response, difficulty in accurately obtaining defect location information when defects are detected, difficulty in timely remediation, significant safety hazards, and poor economic efficiency.

[0023] Specifically, Figure 1 This is a schematic diagram of the structure of a pressure equipment full-area wall thickness detection system provided in an embodiment of this application.

[0024] like Figure 1 As shown, the pressure equipment full-area wall thickness detection system 10 includes: a full-area scanning robot 100, a modeling module 200, an early warning module 300, and a display module 400.

[0025] Specifically, the global scanning robot 100 is used to scan the global wall thickness data of the target pressure-bearing equipment.

[0026] Among them, pressure-bearing equipment can refer to equipment and containers that bear internal pressure (such as gas or liquid pressure); global wall thickness data can refer to a dense and continuous set of wall thickness measurements obtained after scanning a large area or the entire surface of the equipment.

[0027] In actual implementation, the embodiments of this application can utilize the global scanning robot 100 as a dynamic data acquisition core unit, which can autonomously move on the surface of the pressure-bearing equipment and perform scanning tasks.

[0028] Optionally, in one embodiment of this application, the global scanning robot 100 includes: a laser scanner, an electromagnetic ultrasonic array sensor, and a magnetic adsorption mobile platform.

[0029] Among them, the laser scanner is used to scan the surrounding environment with a laser to obtain the contour features of the area around the global scanning robot, so as to determine the current position of the global scanning robot 100.

[0030] An electromagnetic ultrasonic array sensor is used to calculate the local wall thickness data corresponding to the current location using electromagnetic ultrasonic waves, so as to obtain the global wall thickness data after the global scanning robot 100 completes the global scanning.

[0031] The magnetic adsorption mobile platform is used to carry electromagnetic ultrasonic array sensors and laser scanners, and to adsorb onto the surface of the target pressure equipment, and move on the surface according to preset detection instructions.

[0032] Among them, the laser scanner measures distance by emitting laser beams and receiving reflected light, serving as the "eyes" of the global scanning robot 100 for simultaneous localization and map building. It determines the robot's precise location (current position) by scanning the contours of the surrounding environment and provides geometric shape data for subsequent 3D modeling.

[0033] Electromagnetic ultrasonic array sensors are the core of non-destructive testing technology. When ultrasonic waves propagate in a material, they are reflected back at interfaces (such as the opposite surface or defects). By calculating the round-trip time of the sound waves, the wall thickness can be accurately measured. Electromagnetic ultrasound can directly generate ultrasonic waves in metallic materials through electromagnetic effects, realizing non-contact (or dry coupling) detection, suitable for automated scanning, and eliminating the need to clean surface coupling agents. Array sensors are composed of multiple ultrasonic crystals arranged in a certain pattern, enabling electronic scanning. They can cover a certain area without moving the sensor itself, greatly improving detection efficiency.

[0034] The magnetic adsorption mobile platform can be attached to the surface of pressure-bearing equipment made of steel (including the top and vertical walls) and move according to preset path instructions, carrying electromagnetic ultrasonic array sensors and laser scanners to complete full-area scanning.

[0035] Among them, when performing full-domain scanning with the robot 100, high-precision point cloud data and full-domain wall thickness data can be obtained.

[0036] High-precision point cloud data can be acquired by a laser scanner mounted on a magnetically adsorbed mobile platform, obtaining the precise geometric shape of the device surface, with a point cloud density of up to 5876 points / m². 2 (e.g., at the bend).

[0037] The global wall thickness data can be synchronously acquired by the 16-channel EMAT ultrasonic array mounted on the global scanning robot 100. Each thickness measurement point carries spatial position information (x_r, y_r, z_r) and thickness value δ in the robot coordinate system.

[0038] The global scanning robot 100 can use a greedy algorithm to achieve a full-coverage trajectory during scanning. The spiral path planning parameters involved may include: (Polar coordinate equation) Constraints: (Overlap rate) The greedy algorithm discretizes the surface to be scanned into a grid map. The global scanning robot 100 evaluates all candidate directions at each decision point and selects the direction that covers the largest unscanned area as the movement path. To achieve seamless data stitching and compensate for positioning errors, a 3.8% overlap rate is set for the path. This parameter has been experimentally verified to ensure 100% coverage while improving scanning efficiency by 36 times. The algorithm's operation flow is as follows: Step S1: Rasterize the scan area; Step S2: Starting from the starting point, generate candidate movement directions; Step S3: Calculate the profit value in each direction. ;in, To cover the new number of grid cells, For steering angle, These are the weighting coefficients.

[0039] Step S4: Select the direction with the highest profit and move accordingly; Step S5, repeat steps S3-S4 until full coverage.

[0040] The electromagnetic ultrasonic array sensor mounted on the global scanning robot 100, used for measuring wall thickness, can sample from a 16-channel EMAT array (5MHz center frequency) and a laser scanner (accuracy ±0.1mm). The sensor measurement model is as follows: Ultrasonic array: f EMAT =5MHz.

[0041] LiDAR: X min =0.1mm.

[0042] In this system equation, the material property M=Inconel718 is used as a constant parameter.

[0043] To ensure uninterrupted scanning by the full-area scanning robot 100, its outer shell can be made of Inconel 718 alloy (350℃ operating condition). This ensures that the full-area scanning robot 100 can operate stably at 350℃. The Inconel 718 shell acts as a high-temperature protective cover, creating a normal working environment for the interior, which is the basis for realizing online detection and avoiding long-term downtime.

[0044] Modeling module 200 is used to construct a three-dimensional model of the target pressure-bearing equipment based on global wall thickness data.

[0045] The modeling module 200 can combine the massive, discrete wall thickness data points collected by the full-domain scanning robot 100 with the geometric shape of the equipment to construct a three-dimensional digital model with wall thickness attributes.

[0046] Optionally, in one embodiment of this application, the modeling module 200 is further configured to perform non-uniform rational B-spline surface reconstruction using wall thickness data, wherein the reconstruction expression is: , The meaning of the above parameters may include: The parametric coordinates on the surface are The position (3D coordinates) of the point; Represents surface parameters, typically taken from... , used to locate a point on a curved surface; Indicates the control point grid in and Maximum index in direction (number of control points) ); Indicates the first i 1, number of times p The B-spline basis functions in the parameters u The value at; Indicates the first j 1, number of times q The B-spline basis functions in the parameters v The value at; Represents the first control point in the grid. The coordinates of each control point (usually a 3D point, which can be bound to a thickness value).

[0047] Wherein, basis functions : Composed of node vectors and exponents The decision is used to determine the impact of weighted control points; control points To form the "skeleton" of the surface, the shape of the surface can be changed by adjusting the control points; number of iterations The polynomial degree of the B-spline basis function is indicated; the higher the degree, the smoother the surface. Non-uniform means that the node spacing in the node vector can be non-uniform, which is suitable for complex geometric shapes. Rational means that if the control points are weighted, it is called a rational B-spline (NURBS), which can accurately represent conic sections, etc.

[0048] In the embodiments of this application: control point It also binds real-time thickness values ​​for color mapping in 3D visualization.

[0049] The 3D dynamic modeling process in this embodiment is not a simple offline reconstruction, but a continuously updated dynamic process based on data fusion of "mobile scanning + fixed sensing". Its core lies in utilizing the processing power of an edge computing gateway to unify multi-source heterogeneous data in the same 3D space and generate a lightweight digital twin model that can be bound to real-time monitoring data. The entire process includes the following steps: Step S1: Multi-source data collection and uploading.

[0050] Step S2, edge-side data fusion and registration (executed in the edge computing gateway) to preprocess the data to facilitate the construction of an accurate 3D model.

[0051] Coordinate System 1 (Point Cloud-Thickness Data Registration): Point cloud coordinates P collected by the robot cloud Coordinates of thickness measurement point P thickness Although data is acquired synchronously, it is in different sensor coordinate systems. The edge computing gateway uses the ICP (Iterative Closest Point) algorithm to solve for the optimal rotation matrix R and translation vector t, minimizing the following objective function: .

[0052] in, Belongs to P thickness , It is P cloud The nearest point in the model is determined. Through iterative calculation, all thickness measurement points are precisely mapped onto a high-precision point cloud model, achieving accurate binding between "thickness value" and "spatial location".

[0053] Fusion with fixed sensor data: The installation location of the fixed sensor is known on the device (it can be imported from CAD drawings or marked in the initial scan). The gateway associates the sensor's time-series data δs(t) with its fixed location node in the 3D model, assigning dynamically changing attributes to that node.

[0054] Step S3, NURBS surface reconstruction and lightweighting (performed in the edge computing gateway).

[0055] To facilitate network transmission and real-time rendering on the web, massive point cloud data needs to be converted into lightweight surface models.

[0056] This application embodiment can use the Non-Uniform Rational B-Spline (NURBS) method to perform surface fitting on the registered point cloud (with thickness values ​​already bound) for surface reconstruction. Its expression is: .

[0057] Among them, control points It not only includes three-dimensional coordinate information but also thickness attributes. This means that the reconstructed surface model has an inherently unified geometry and wall thickness.

[0058] The embodiments of this application can convert the reconstructed NURBS surface model into a Web-friendly 3D format such as glTF or Draco compression format, which greatly reduces the amount of model data, achieves model lightweighting, and lays the foundation for real-time visualization.

[0059] Step S4, Cloud Dynamic Rendering and Update (executed in the cloud early warning platform).

[0060] The lightweight model is uploaded to the cloud-based early warning platform for final dynamic presentation.

[0061] The cloud-based early warning platform can load and display the device's 3D model using a WebGL-based 3D rendering engine (such as Three.js). The platform can receive real-time data streams from edge gateways and fixed sensors. Based on pre-defined rules, the model surface is dynamically colored.

[0062] Step S5: Visual interaction and early warning.

[0063] Users can freely rotate, scale, and section the 3D model on the platform interface. For example, by hovering the mouse over any location, users can view the real-time thickness value, historical variation curve, and predicted remaining lifespan of that point.

[0064] After receiving the aforementioned warning signals, the cloud-based early warning platform drives the dynamic rendering of the 3D model, mapping different equipment status signals to different color indicators (e.g., safe - green, warning - yellow, danger - red and flashing). This visualization method allows operators to intuitively and quickly perceive and understand the spatial distribution differences and degradation trends of equipment wall thickness, significantly improving monitoring efficiency and serving as a key human-computer interaction element in achieving the technical effect of "3D visualization early warning." When the system determines that the thinning of a certain area exceeds the limit based on the corrosion rate model, that area will be highlighted in red and flashing on the 3D model, and the platform will simultaneously trigger multi-level alarm mechanisms such as sound and light, SMS, and work orders.

[0065] Specifically, in this embodiment of the application, the wall thickness ratio ratio = δ can be calculated by an edge computing gateway based on real-time monitoring data and the wall thickness designed for the device. current / δ design Based on the following technical criteria, it automatically generates equipment status warning signals of different levels: When ratio ≥ 0.9, a safe operating status signal is generated; When 0.7 ≤ ratio < 0.9, an early warning monitoring status signal is generated; When ratio < 0.7, a danger alarm status signal is generated and the system's automatic alarm mechanism is triggered.

[0066] After each periodic scan, updated model differential data is generated. The platform only updates the changed areas, enabling model version iteration. Sensor data is pushed to the platform at a higher frequency (e.g., every minute), driving real-time changes in the color of the corresponding nodes, as if the data is "breathing" on the 3D model, intuitively displaying the corrosion process.

[0067] The early warning module 300 is used to track the data change of global wall thickness data, calculate the wall thickness change rate of the target pressure-bearing equipment based on the data change, and match the corresponding early warning action according to the wall thickness change rate.

[0068] The early warning module 300 can analyze data trends and provide predictive early warnings. It calculates the wall thickness change rate through the wall thickness degradation rate model and provides corresponding early warnings.

[0069] The wall thickness degradation rate model can be:

[0070] in, ρ For the density of the medium, V For flow rate, k This is a material constant (calibrated through corrosion testing).

[0071] Display module 400 is used to optimize the rendering effect of the 3D model based on the early warning action, so as to highlight the area of ​​the target pressure equipment that does not meet the preset wall thickness conditions.

[0072] The display module 400, serving as an interactive module, can intuitively present the analysis results and enable rapid location of abnormal areas. Through color mapping (e.g., green for safety, yellow for warning, and red for danger), operators can instantly identify problem areas on complex equipment models, significantly shortening analysis time and preventing omissions.

[0073] Optionally, in one embodiment of this application, the pressure equipment full-area wall thickness detection system 10 further includes: a static acquisition module and a transmission module.

[0074] Among them, multiple static acquisition modules are set at the target detection points of the target pressure-bearing equipment to collect static wall thickness data of the target detection points at preset intervals.

[0075] The transmission module is used to transmit static wall thickness data and the corresponding target detection point location data to the early warning module.

[0076] In addition to dynamic data acquisition, this application embodiment can also achieve static data acquisition by setting a static acquisition module, such as an implantable MEMS sensor. The structure of the implantable MEMS sensor can be as shown in Table 1.

[0077] Table 1

[0078] During static data acquisition, fixed-point time-series thickness data can be collected by an embedded MEMS sensor array. Long-term thickness data δs(t) at specific key points are provided for calculating corrosion rates. Environmental data, including temperature, can be monitored simultaneously by sensors for compensation. The data stream can then be wirelessly transmitted via LoRa to an edge computing gateway in the field.

[0079] The embodiments of this application can install fixed sensors at some key or high-risk locations for long-term uninterrupted monitoring. By combining with the global scanning robot 100, a three-dimensional monitoring network of global periodic inspection and fixed-point continuous monitoring is formed.

[0080] Optionally, in one embodiment of this application, the early warning module 300 includes: an acquisition unit, a first calculation unit, a correction unit, and a second calculation unit.

[0081] The acquisition unit is used to acquire the measured temperature and preset reference temperature of the target pressure equipment.

[0082] The first calculation unit is used to calculate the temperature difference between the measured temperature and the preset reference temperature.

[0083] The correction unit is used to input global wall thickness data, static wall thickness data and temperature difference into a pre-built temperature compensation model to obtain temperature-corrected global wall thickness correction data and static wall thickness correction data.

[0084] The second calculation unit is used to calculate the wall thickness change rate using global wall thickness correction data and static wall thickness correction data.

[0085] Furthermore, based on the data collected by the global scanning robot 100 and the static acquisition module, this embodiment of the application can use a temperature compensation model to correct the data in order to obtain more accurate wall thickness data.

[0086] The temperature compensation model can be:

[0087] in, =2.3 10 5 (Temperature coefficient of 316L steel) = -25℃.

[0088] Optionally, in one embodiment of this application, the pressure equipment full-area wall thickness detection system 10 further includes: a judgment module, an enhancement module, and an alarm module.

[0089] The judgment module is used to determine whether the target pressure equipment is in a preset corrosive environment based on the gas environment data of the target pressure equipment.

[0090] The enhancement module is used to predict the wall thickness risk of target pressure equipment under preset corrosive environments by combining a pre-built enhancement degradation model and wall thickness change rate.

[0091] The alarm module is used to execute corresponding alarm actions based on wall thickness risk.

[0092] Among them, the gas environment data is provided by additional gas sensors, which are the chemical composition of the environment around the equipment (such as H2S, CO2 concentration, humidity, etc.), which are key factors leading to corrosion.

[0093] This application embodiment can determine whether the target pressure equipment is in a corrosive environment based on the gas environment. Then, in a corrosive environment, combined with an enhanced degradation model, it can predict the future wall thickness degradation of the equipment in harsh environments. Based on the prediction results, this application embodiment can issue corresponding alarms, such as screen pop-ups, audible and visual alarms, and SMS / email notifications to maintenance personnel.

[0094] Combination Figures 2 to 4 As shown, the working principle of the pressure equipment full-area wall thickness detection system of this application embodiment is described in detail with an example.

[0095] For example, the device parameters in this application embodiment may be: material: SA-210C carbon steel, design wall thickness: 4.5mm, and operating temperature: 280℃.

[0096] like Figure 2 , Figure 3 and Figure 4 As shown, embodiments of this application may include the following steps: Step S1, scanning stage: In this embodiment, a magnetic adsorption moving platform can be set up, equipped with an electromagnetic ultrasonic array sensor and a laser scanner. The magnetic adsorption moving platform uses an Inconel 718 alloy shell, is equipped with a SiC cooling module (204), and has an operating temperature range of -20℃ to 350℃. The magnetic adsorption force is ≥150N, and it is equipped with a permanent magnet wheel (0.5mm gap) and a brushless motor (2.5N·m torque). The moving platform path planning uses a greedy algorithm to achieve a full-coverage scanning path with an overlap rate of 3.8%. It is equipped with an inertial navigation unit (302), integrating IMU and visual SLAM technology, with a positioning error ≤0.15mm.

[0097] The implantable MEMS sensor assembly includes a piezoelectric sensing unit and a LoRa / BLE dual-mode wireless transmission module. The implantable MEMS sensor assembly has a multi-layer structure, including: an Al2O3 ceramic protective layer (0.2 mm thick, corrosion resistant to pH 1-14), a PZT-5H piezoelectric sensing layer (50 μm thick, 4×4 matrix distribution), and a 316L stainless steel substrate layer (Φ5 mm × 0.8 mm).

[0098] The velocity is 0.5 m / s, and the SH wave frequency is 10 kHz.

[0099] Step S2, edge modeling performance: In this embodiment of the application, point cloud-thickness data registration can be performed to generate a three-dimensional dynamic model, as shown in Table 2. The model surface is dynamically rendered according to the ratio of real-time thickness to design thickness.

[0100] Table 2

[0101] The dynamic rendering rules are as follows: green is displayed when the thickness ratio is ≥0.9; yellow is displayed when the thickness ratio is 0.7≤thickness ratio<0.9; and red is displayed and an alarm is triggered when the thickness ratio is <0.7.

[0102] Step S3: This embodiment of the application can realize dynamic rendering of the 3D model and alarm record management. For example, early warning events: 2025-03-15 14:23: Thinning rate of 0.12mm / month detected; 2025-03-15 14:25: Triggering a level 3 alarm (SMS + pop-up); 2025-03-16 09:10: Inspection confirms actual thinning of 1.8mm. As another example, when v>0.05mm / month, a level 3 alarm mechanism is activated: Level 1 alarm: Platform pop-up warning; Level 2 alarm: SMS notification to the responsible person; Level 3 alarm: Automatic generation of a maintenance work order.

[0103] For nuclear power plant steam generators, the embodiments of this application can collect wall thickness data in both dynamic and static ways, and then correct the measured values ​​based on a compensation model to calculate the wall thickness reduction rate.

[0104] In this embodiment, temperature correction can be performed as shown in Table 3.

[0105] Table 3

[0106] For chemical reactors, the embodiments of this application can use sampling enhancement degradation models to predict wall thickness in corrosive environments.

[0107] In particular, the embodiments of this application can provide corrosion early warning and enhance the corrosion model:

[0108] ( Chloride ion concentration, ppm).

[0109] It should be noted that the embodiments of this application can be configured with explosion-proof structures in chemical application scenarios. During data acquisition, the embodiments of this application can also add a temperature compensation circuit to the sensor group used for data acquisition, so that the accuracy is maintained at ±0.1mm in an environment of 300℃.

[0110] Based on the above process, as shown in Table 4, the embodiments of this application can achieve improved technical performance.

[0111] Table 4

[0112] The resulting economic benefits can be as follows Figure 5 As shown.

[0113] The pressure equipment full-area wall thickness detection system proposed in this application can scan the global wall thickness data of the target pressure equipment using a full-area scanning robot, achieving global scanning and reducing safety hazards caused by spot checks. A modeling module constructs a 3D model of the target pressure equipment based on the global wall thickness data. An early warning module tracks the data changes in the global wall thickness, calculates the wall thickness change rate of the target pressure equipment, and thus confirms the defect location to match the corresponding early warning action. Finally, the rendering effect of the 3D model is optimized based on the early warning action to highlight areas of the target pressure equipment that do not meet the preset wall thickness conditions, achieving defect location and facilitating timely remediation without long-term downtime, thus ensuring economic efficiency. This solves the technical problems in related technologies, such as insufficient detection coverage, delayed response, difficulty in accurately obtaining defect location information when defects are detected, difficulty in timely remediation, significant safety hazards, and poor economic efficiency.

[0114] Next, referring to the accompanying drawings, a pressure equipment full-area wall thickness detection device according to an embodiment of this application is described.

[0115] Figure 6This is a flowchart of the method for detecting the full-area wall thickness of pressure equipment according to an embodiment of this application.

[0116] like Figure 6 As shown, the method for detecting the full-area wall thickness of this pressure-bearing equipment includes the following steps: In step S601, the global wall thickness data of the target pressure-bearing equipment is scanned.

[0117] In step S602, a three-dimensional model of the target pressure-bearing device is constructed based on the global wall thickness data.

[0118] In step S603, the data change amount of the global wall thickness data is tracked, and the wall thickness change rate of the target pressure-bearing equipment is calculated based on the data change amount. The corresponding early warning action is matched according to the wall thickness change rate.

[0119] In step S604, the rendering effect of the three-dimensional model is optimized based on the warning action to highlight the area where the target pressure-bearing equipment does not meet the preset wall thickness condition.

[0120] It should be noted that the foregoing explanation of the embodiment of the pressure equipment full-area wall thickness detection system also applies to the pressure equipment full-area wall thickness detection method of this embodiment, and will not be repeated here.

[0121] The pressure equipment full-area wall thickness detection method proposed in this application can scan the global wall thickness data of the target pressure equipment using a full-area scanning robot, achieving global scanning and reducing safety hazards caused by spot checks. A modeling module constructs a 3D model of the target pressure equipment based on the global wall thickness data. An early warning module tracks the data changes in the global wall thickness, calculates the wall thickness change rate of the target pressure equipment, and thus confirms the defect location to match the corresponding early warning action. Finally, the rendering effect of the 3D model is optimized based on the early warning action to highlight areas of the target pressure equipment that do not meet the preset wall thickness conditions, achieving defect location and facilitating timely remediation without long-term downtime, thus ensuring economic efficiency. This solves the technical problems in related technologies, such as insufficient detection coverage, delayed response, difficulty in accurately obtaining defect location information when defects are detected, difficulty in timely remediation, significant safety hazards, and poor economic efficiency.

[0122] Figure 7 A schematic diagram of the structure of an electronic device provided in an embodiment of this application. The electronic device may include: The memory 701, the processor 702, and the computer program stored on the memory 701 and executable on the processor 702.

[0123] When the processor 702 executes the program, it implements the pressure equipment full-area wall thickness detection method provided in the above embodiments.

[0124] Furthermore, electronic devices also include: Communication interface 703 is used for communication between memory 701 and processor 702.

[0125] The memory 701 is used to store computer programs that can run on the processor 702.

[0126] The memory 701 may include high-speed RAM memory, and may also include non-volatile memory, such as at least one disk storage device.

[0127] If the memory 701, processor 702, and communication interface 703 are implemented independently, then the communication interface 703, memory 701, and processor 702 can be interconnected via a bus to complete communication between them. The bus can be an Industry Standard Architecture (ISA) bus, a Peripheral Component Interconnect (PCI) bus, or an Extended Industry Standard Architecture (EISA) bus, etc. Buses can be categorized as address buses, data buses, control buses, etc. For ease of representation, Figure 7 The bus is represented by a single thick line, but this does not mean that there is only one bus or one type of bus.

[0128] Optionally, in a specific implementation, if the memory 701, processor 702, and communication interface 703 are integrated on a single chip, then the memory 701, processor 702, and communication interface 703 can communicate with each other through an internal interface.

[0129] The processor 702 may be a central processing unit (CPU), an application specific integrated circuit (ASIC), or one or more integrated circuits configured to implement the embodiments of this application.

[0130] This embodiment also provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the above-described method for detecting the full-area wall thickness of pressure equipment.

[0131] This application also provides a computer program product, including a computer program that, when executed by a processor, implements the pressure equipment full-area wall thickness detection method provided in this embodiment of the invention.

[0132] In the description of this specification, the references to terms such as "one embodiment," "some embodiments," "example," "specific example," or "some examples," etc., indicate that a specific feature, structure, material, or characteristic described in connection with that embodiment or example is included in at least one embodiment or example of this application. In this specification, the illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples. Moreover, without contradiction, those skilled in the art can combine and integrate the different embodiments or examples described in this specification, as well as the features of different embodiments or examples.

[0133] Furthermore, the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include at least one of that feature. In the description of this application, "N" means at least two, such as two, three, etc., unless otherwise explicitly specified.

[0134] Any process or method described in the flowchart or otherwise herein can be understood as representing a module, segment, or portion of code comprising one or N executable instructions for implementing custom logic functions or processes, and the scope of the preferred embodiments of this application includes additional implementations in which functions may be performed not in the order shown or discussed, including substantially simultaneously or in reverse order depending on the functions involved, as should be understood by those skilled in the art to which embodiments of this application pertain.

[0135] The logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered as a sequenced list of executable instructions for implementing logical functions, and can be embodied in any computer-readable medium for use by, or in conjunction with, an instruction execution system, apparatus, or device (such as a computer-based system, a processor-included system, or other system that can fetch and execute instructions from, an instruction execution system, apparatus, or device). For the purposes of this specification, "computer-readable medium" can be any means that can contain, store, communicate, propagate, or transmit programs for use by, or in conjunction with, an instruction execution system, apparatus, or device. More specific examples (a non-exhaustive list) of computer-readable media include: an electrical connection having one or more wires (electronic device), a portable computer disk drive (magnetic device), random access memory (RAM), read-only memory (ROM), erasable and editable read-only memory (EPROM or flash memory), fiber optic devices, and portable optical disc read-only memory (CDROM). Alternatively, the computer-readable medium may be paper or other suitable media on which the program can be printed, since the program can be obtained electronically by optically scanning the paper or other medium, followed by editing, interpreting, or otherwise processing as necessary, and then stored in a computer memory.

[0136] It should be understood that the various parts of this application can be implemented using hardware, software, firmware, or a combination thereof. In the above embodiments, the N steps or methods can be implemented using software or firmware stored in memory and executed by a suitable instruction execution system. For example, if implemented in hardware as in another embodiment, it can be implemented using any one or a combination of the following techniques known in the art: discrete logic circuits having logic gates for implementing logical functions on data signals, application-specific integrated circuits (ASICs) having suitable combinational logic gates, programmable gate arrays (PGAs), field-programmable gate arrays (FPGAs), etc.

[0137] Those skilled in the art will understand that all or part of the steps of the methods in the above embodiments can be implemented by a program instructing related hardware. The program can be stored in a computer-readable storage medium, and when executed, the program includes one or a combination of the steps of the method embodiments.

[0138] Furthermore, the functional units in the various embodiments of this application can be integrated into a processing module, or each unit can exist physically separately, or two or more units can be integrated into a module. The integrated module can be implemented in hardware or as a software functional module. If the integrated module is implemented as a software functional module and sold or used as an independent product, it can also be stored in a computer-readable storage medium.

[0139] The storage medium mentioned above can be a read-only memory, a disk, or an optical disk, etc. Although embodiments of this application have been shown and described above, it is understood that the above embodiments are exemplary and should not be construed as limiting this application. Those skilled in the art can make changes, modifications, substitutions, and variations to the above embodiments within the scope of this application.

Claims

1. A system for detecting the full-area wall thickness of pressure-bearing equipment, characterized in that, include: A global scanning robot is used to scan the global wall thickness data of target pressure-bearing equipment. The modeling module is used to construct a three-dimensional model of the target pressure-bearing device based on the global wall thickness data; The early warning module is used to track the data change of the global wall thickness data, calculate the wall thickness change rate of the target pressure-bearing equipment based on the data change, and match the corresponding early warning action according to the wall thickness change rate. The display module is used to optimize the rendering effect of the three-dimensional model based on the warning action, so as to highlight the area where the target pressure-bearing equipment does not meet the preset wall thickness conditions.

2. The system according to claim 1, characterized in that, The global scanning robot includes: A laser scanner is used to scan the surrounding environment with a laser to obtain the contour features around the global scanning robot, so as to determine the current position of the global scanning robot. An electromagnetic ultrasonic array sensor is used to calculate the local wall thickness data corresponding to the current location using electromagnetic ultrasonic waves, so as to obtain the global wall thickness data after the global scanning robot completes the global scanning. A magnetic adsorption moving platform is used to carry the electromagnetic ultrasonic array sensor and the laser scanner, and to adsorb onto the surface of the target pressure-bearing device, and to move on the surface according to a preset detection command.

3. The system according to claim 1, characterized in that, Also includes: Multiple static acquisition modules are installed at the target detection points of the target pressure-bearing equipment to collect static wall thickness data of the target detection points at preset time intervals. The transmission module is used to transmit the static wall thickness data and the corresponding target detection point location data to the early warning module.

4. The system according to claim 3, characterized in that, The early warning module includes: The acquisition unit is used to acquire the measured temperature and preset reference temperature of the target pressure-bearing equipment; The first calculation unit is used to calculate the temperature difference between the measured temperature and the preset reference temperature; The correction unit is used to input the global wall thickness data, the static wall thickness data and the temperature difference into a pre-built temperature compensation model to obtain temperature-corrected global wall thickness correction data and static wall thickness correction data. The second calculation unit is used to calculate the wall thickness change rate using the global wall thickness correction data and the static wall thickness correction data.

5. The system according to claim 1, characterized in that, The modeling module is further used to reconstruct a non-uniform rational B-spline surface using the wall thickness data, wherein the reconstruction expression is: ， in, ( u ) indicates the first i 1, number of p The B-spline basis functions in the parameters u The value at that location, Indicates the first j 1, number of q The B-spline basis functions in the parameters v The value at that location, The parametric coordinates on the surface are... The location of the point, , Indicates the control point grid in u and v Maximum index in the direction.

6. The system according to claim 1, characterized in that, Also includes: The judgment module is used to determine whether the target pressure equipment is in a preset corrosive environment based on the gas environment data of the target pressure equipment; An enhancement module is used to predict the wall thickness risk of the target pressure-bearing equipment under the preset corrosive environment by combining a pre-built enhancement degradation model and the wall thickness change rate. An alarm module is used to perform corresponding alarm actions based on the wall thickness risk.

7. A method for detecting the full-area wall thickness of pressure-bearing equipment, characterized in that, Includes the following steps: Scan the global wall thickness data of the target pressure-bearing equipment; A three-dimensional model of the target pressure-bearing equipment is constructed based on the global wall thickness data; Track the data change of the global wall thickness data, calculate the wall thickness change rate of the target pressure-bearing equipment based on the data change, and match the corresponding early warning action according to the wall thickness change rate; The rendering effect of the three-dimensional model is optimized based on the warning action to highlight the area where the target pressure-bearing equipment does not meet the preset wall thickness condition.

8. An electronic device, characterized in that, include: The device includes a memory, a processor, and a computer program stored in the memory and executable on the processor, the processor executing the program to implement the pressure equipment full-area wall thickness detection method as described in claim 7.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that, The program is executed by the processor to implement the pressure equipment full-area wall thickness detection method as described in claim 7.

10. A computer program product, comprising a computer program, characterized in that, When the computer program is executed, it is used to implement the method for detecting the full-area wall thickness of pressure equipment as described in claim 7.

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