Nondestructive testing method and system for H-shaped wire clamp

Abstract

The invention discloses a nondestructive testing method and system for an H-shaped wire clamp, and relates to the technical field of intelligent detection.The method comprises the steps that structural feature division is conducted on the H-shaped wire clamp, a clamping area, a connecting area and a middle area are obtained, nondestructive inspection evaluation is conducted according to importance coefficients of all the areas, and based on the multi-area nondestructive inspection importance coefficients, a nondestructive inspection result is obtained; configuring a network to establish two channels for nondestructive inspection analysis of the wire clamp; detecting each area through an ultrasonic and magnetic particle detection device to obtain multi-area ultrasonic detection data and multi-area magnetic particle detection data, and performing interference correction; and inputting the corrected data into the wire clamp nondestructive inspection analysis dual-channel to generate a nondestructive inspection report. The technical problems that in the prior art, a traditional nondestructive testing method is difficult to comprehensively find potential defects and lacks accurate detection on complex areas are solved, and the technical effect of improving the accuracy, comprehensiveness and detection efficiency of nondestructive testing of the H-shaped wire clamp is achieved.

Description

Technical Field

[0001] The present invention relates to the field of intelligent detection technology, and in particular to a nondestructive detection method and system for an H-type wire clamp. Background Art

[0002] With the continuous development of power engineering, the safety and reliability of H-type wire clamps, as important fixed components in power lines, have become particularly important. Since they are subjected to mechanical pressure, temperature changes, and environmental corrosion during power transmission, the service life and performance of H-type wire clamps are directly related to the stable operation of the power system. Therefore, timely and accurate detection and evaluation of the health status of H-type wire clamps is the key to ensuring the safety of power facilities and improving the efficiency of equipment operation and maintenance.

[0003] Traditional H-type wire clamp inspection methods usually rely on manual inspection or local flaw detection technology. These methods have certain limitations, making it difficult to fully discover potential defects and lacking accurate inspection of complex areas. Summary of the invention

[0004] The present application provides a non-destructive testing method and system for an H-type wire clamp, which is used to solve the technical problems in the prior art that traditional non-destructive testing methods are difficult to fully discover potential defects and lack accurate detection of complex areas.

[0005] In a first aspect of the present application, a nondestructive testing method for an H-type wire clamp is provided, the method comprising: dividing the H-type wire clamp by structural features to obtain multi-level regions of the wire clamp, wherein the multi-level regions of the wire clamp include a clamping region, a connection region and a middle region; performing nondestructive testing importance evaluation according to the multi-level regions of the wire clamp to obtain a multi-region nondestructive testing importance coefficient; based on the multi-region nondestructive testing importance coefficient, performing nondestructive testing analysis record learning on the H-type wire clamp according to a flaw analysis learning configuration network, and building a nondestructive testing analysis dual channel for the wire clamp, wherein the nondestructive testing analysis dual channel for the wire clamp It includes an internal flaw detection analysis channel for a wire clamp and a surface flaw detection analysis channel for a wire clamp; based on the multi-region nondestructive flaw detection importance coefficient, the multi-level regions of the wire clamp are detected according to an ultrasonic detection device and a magnetic particle detection device to obtain multi-region ultrasonic detection data and multi-region magnetic particle detection data; detection interference correction is performed on the multi-region ultrasonic detection data and the multi-region magnetic particle detection data to generate multi-region ultrasonic detection results and multi-region magnetic particle detection results; the multi-region ultrasonic detection results and the multi-region magnetic particle detection results are input into the wire clamp nondestructive flaw detection analysis dual channels to generate a wire clamp nondestructive detection report.

[0006] In a second aspect of the present application, a nondestructive testing system for an H-type wire clamp is provided, the system comprising: a structural feature division module, the structural feature division module is used to perform structural feature division on the H-type wire clamp to obtain a multi-level region of the wire clamp, wherein the multi-level region of the wire clamp includes a clamping region, a connection region and a middle region; a nondestructive testing importance evaluation module, the nondestructive testing importance evaluation module is used to perform nondestructive testing importance evaluation based on the multi-level region of the wire clamp to obtain a multi-region nondestructive testing importance coefficient; an analytical dual-channel construction module, the analytical dual-channel construction module is used to perform nondestructive testing analysis record learning on the H-type wire clamp based on the multi-region nondestructive testing importance coefficient according to the flaw analysis learning configuration network, and build a nondestructive testing analysis dual channel for the wire clamp, wherein the wire clamp nondestructive testing The dual analysis channels include a wire clamp internal flaw detection analysis channel and a wire clamp surface flaw detection analysis channel; a multi-region detection module, which is used to detect the multi-level regions of the wire clamp based on the multi-region non-destructive flaw detection importance coefficient according to the ultrasonic detection device and the magnetic particle detection device, and obtain multi-region ultrasonic detection data and multi-region magnetic particle detection data; a detection interference correction module, which is used to perform detection interference correction on the multi-region ultrasonic detection data and the multi-region magnetic particle detection data, and generate multi-region ultrasonic detection results and multi-region magnetic particle detection results; a detection report generation module, which is used to input the multi-region ultrasonic detection results and the multi-region magnetic particle detection results into the wire clamp non-destructive flaw detection analysis dual channel to generate a wire clamp non-destructive detection report.

[0007] One or more technical solutions provided in this application have at least the following technical effects or advantages: The present application provides a nondestructive testing method and system for an H-type wire clamp, which relates to the field of intelligent detection technology. The H-type wire clamp is structurally divided to obtain clamping, connection and middle areas, and nondestructive testing evaluation is performed based on the regional importance coefficient. A flaw detection analysis learning configuration network is used to build a dual channel (internal and surface flaw detection analysis channel), and data is obtained through ultrasonic and magnetic particle detection devices. After interference correction, a comprehensive nondestructive testing report is generated, which solves the technical problems in the prior art that traditional nondestructive testing methods are difficult to fully discover potential defects and lack accurate detection of complex areas, and achieves the technical effect of improving the accuracy, comprehensiveness and detection efficiency of nondestructive testing of H-type wire clamps. BRIEF DESCRIPTION OF THE DRAWINGS

[0008] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0009] Figure 1 A schematic flow chart of a nondestructive testing method for an H-type wire clamp provided in an embodiment of the present application.

[0010] Figure 2 A schematic diagram of the structure of a nondestructive testing system for an H-type wire clamp provided in an embodiment of the present application.

[0011] Explanation of the reference numerals: structural feature division module 11, non-destructive testing importance evaluation module 12, analytical dual-channel construction module 13, multi-region detection module 14, detection interference correction module 15, detection report generation module 16. DETAILED DESCRIPTION

[0012] The present application provides a non-destructive testing method and system for an H-type wire clamp, which is used to solve the technical problems in the prior art that traditional non-destructive testing methods are difficult to fully discover potential defects and lack accurate detection of complex areas.

[0013] The following will be combined with the drawings in the embodiments of the present application to clearly and completely describe the technical solutions in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, rather than all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of this application.

[0014] It should be noted that the terms "first", "second", etc. in the specification of the present application and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence. It should be understood that the data used in this way can be interchangeable where appropriate, so that the embodiments of the present application described herein can be implemented in an order other than those illustrated or described herein. In addition, the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusions, for example, a process, method, system, product, or server that includes a series of steps or units is not necessarily limited to those steps or units that are clearly listed, but may include other steps or modules that are not clearly listed or inherent to these processes, methods, products, or devices.

[0015] Embodiment 1, as Figure 1 As shown, the present application provides a non-destructive testing method for an H-type wire clamp, the method comprising: P10: Structural feature division of the H-type wire clamp is performed to obtain a multi-level region of the wire clamp, wherein the multi-level region of the wire clamp includes a clamping region, a connection region and a middle region.

[0016] Specifically, before conducting nondestructive testing of H-type wire clamps, the structural characteristics of the wire clamps must first be divided to ensure the comprehensiveness and accuracy of the test. The purpose of structural characteristic division is to decompose the wire clamp into multiple areas, each with different structural characteristics and functions, so as to provide clear guidance and basis for subsequent flaw detection work. By dividing the different parts of the H-type wire clamp, the appropriate nondestructive testing method can be selected in a targeted manner, while improving the efficiency and accuracy of the test.

[0017] Optionally, in this step, the multi-level area of ​​the wire clamp is divided into three main parts: the clamping area, the connection area and the middle area. These areas have different forces and working conditions, so the importance and method of detection of each area are also different.

[0018] The clamping area is the part of the H-type wire clamp that is in direct contact with the conductor or cable, and is responsible for fixing and stabilizing the position of the conductor. This area is usually subject to greater mechanical pressure and vibration, so it is necessary to focus on its structural integrity and check for problems such as cracks, fatigue, deformation or corrosion. Due to the high functional requirements of the clamping area, once damaged, it may cause the cable to loosen or disconnect, causing serious power system failures. Therefore, this part needs to be inspected with high precision, especially in the detection of surface cracks, and magnetic particle inspection will be a more appropriate choice.

[0019] The connection area refers to the part where the H-type wire clamp is connected to the supporting structure (such as a tower or a rod). This area is responsible for bearing external tension and wind force, and is crucial to the overall stability of the wire clamp. The connection area is usually the part of the H-type wire clamp that has the most contact with the external environment, so problems such as corrosion, friction and wear are more common in this part. Through non-destructive testing, especially ultrasonic testing, cracks or metal fatigue in the connection area can be effectively found to ensure the reliability of the connection part.

[0020] The middle area is usually located in the center of the H-type wire clamp. The force on this part is relatively uniform, and in most cases it does not directly contact the cable or other external components. Although the probability of damage to the middle area is low, its structural safety cannot be ignored. Non-destructive testing of this part is mainly to check whether there are tiny internal cracks or structural defects, which may affect the long-term service life of the wire clamp. Ultrasonic testing is particularly effective in this area, which can help detect internal defects and avoid later equipment failures due to potential problems.

[0021] This process of structural feature division not only improves the targetedness of detection, but also lays the foundation for subsequent flaw detection data processing and evaluation, and helps to perform differentiated processing according to the different importance of areas in subsequent steps, thereby improving the detection effect.

[0022] P20: Perform nondestructive testing importance evaluation on the multi-level regions of the wire clamp to obtain a multi-region nondestructive testing importance coefficient.

[0023] Furthermore, step P20 of the embodiment of the present application further includes: P21: Evaluate the importance of the multi-level areas of the wire clamp and obtain the importance coefficient of each area; P22: Predict the damage probability of the multi-level areas of the wire clamp based on the wire clamp usage log of the H-type wire clamp and determine the damage probability of each area; P23: Normalize the importance coefficients of each area and the damage probability of each area to establish a wire clamp area evaluation matrix; P24: Perform weighted calculation on the wire clamp area evaluation matrix based on the importance weights of the wire clamp areas and the damage probability weights of the wire clamp areas to generate the importance coefficients of the multi-area non-destructive testing.

[0024] It should be understood that by evaluating the importance of non-destructive testing of multi-level areas of the H-type wire clamp, the importance and priority of each area in non-destructive testing can be determined, thereby ensuring the rational allocation of testing resources and maximizing the efficiency and effectiveness of testing.

[0025] First, the importance of each area of ​​the wire clamp is evaluated based on its function and working characteristics. The working environment, stress conditions and potential risks of each area are different, which determines its priority in non-destructive testing. For example, the clamping area may have a higher priority than other areas because it directly bears the fixing pressure and vibration of the wire, while the connection area needs to pay attention to the stability of its connection with the supporting structure. Through such an evaluation, an importance coefficient can be assigned to each area, which reflects the criticality of the area.

[0026] Next, the cable clamp usage log is used to analyze historical data, and combined with the actual use of the equipment and environmental factors, the damage probability of each area is predicted. The cable clamp usage log usually includes information such as the workload, environmental conditions, and maintenance history of the equipment during operation. By analyzing these data, the possibility of damage in each area in the future can be estimated. For areas that are exposed to extreme climate or high-frequency vibration for a long time, the probability of damage may be higher, while the probability of damage in areas with less force or interference is lower.

[0027] After obtaining the importance coefficient and damage probability of each area, the next step is to normalize these two values ​​so that they can be compared under the same standard. The normalization process usually adjusts the value to a range of 0 to 1, which ensures that the score of each area is on a uniform scale. The normalized data will form a regional evaluation matrix, and each element in the matrix corresponds to the comprehensive evaluation score of each area, reflecting the comprehensive importance and damage risk of the area.

[0028] Finally, based on the regional evaluation matrix, each region is weighted and calculated to generate the final multi-region NDT importance coefficient. In the weighted calculation, the importance weight and the damage probability weight are considered. The importance weight reflects the degree of influence of the regional function on the overall equipment performance, while the damage probability weight reflects the risk level of regional damage. By weighting these two weights, the importance of each region in NDT can be evaluated more accurately, thereby providing data support for the selection of NDT equipment and testing schemes. The weighted calculated multi-region NDT importance coefficient can provide a basis for subsequent testing processes. Regions with high importance coefficients will be given priority for testing, while regions with low importance coefficients can appropriately reduce the frequency or intensity of testing, thereby optimizing resource allocation and improving the overall efficiency and accuracy of NDT.

[0029] P30: Based on the multi-region nondestructive testing importance coefficient, the H-type wire clamp is subjected to nondestructive testing analysis record learning according to the flaw detection analysis learning configuration network, and a wire clamp nondestructive testing analysis dual channel is established, wherein the wire clamp nondestructive testing analysis dual channel includes a wire clamp internal flaw detection analysis channel and a wire clamp surface flaw detection analysis channel.

[0030] Furthermore, step P30 of the embodiment of the present application further includes: P31: Input the multi-region nondestructive testing importance coefficient into the flaw detection analysis learning configuration network to obtain the clamping region learning configuration results, the connection region learning configuration results and the middle region learning configuration results; P32: Based on the clamping region learning configuration results, the connection region learning configuration results and the middle region learning configuration results, the internal flaw detection analysis feature learning of the H-type wire clamp is performed to construct the clamping region internal flaw detection analysis branch, the connection region internal flaw detection analysis branch and the middle region internal flaw detection analysis branch; P33: Connect the clamping region internal flaw detection analysis branch, the connection region internal flaw detection analysis branch and the middle region internal flaw detection analysis branch as parallel nodes to generate the line P34: Based on the clamping area learning configuration result, the connection area learning configuration result and the middle area learning configuration result, the surface flaw detection analysis feature learning of the H-type wire clamp is performed, and the clamping area surface flaw detection analysis branch, the connection area surface flaw detection analysis branch and the middle area surface flaw detection analysis branch are constructed; P35: The clamping area surface flaw detection analysis branch, the connection area surface flaw detection analysis branch and the middle area surface flaw detection analysis branch are connected as parallel nodes to generate the wire clamp surface flaw detection analysis channel; P36: The wire clamp internal flaw detection analysis channel and the wire clamp surface flaw detection analysis channel are encapsulated into the wire clamp non-destructive flaw detection analysis dual channel.

[0031] Optionally, based on the calculated multi-region NDT importance coefficient, the NDT analysis record learning of the H-type wire clamp is performed through the NDT analysis learning configuration network, and then a dual channel of NDT analysis of the wire clamp is built. The dual channel includes the internal NDT analysis channel of the wire clamp and the surface NDT analysis channel of the wire clamp, which aims to comprehensively analyze the potential defects in different areas through dual-dimensional NDT methods to ensure efficient and accurate NDT.

[0032] First, the importance coefficients of the multi-region nondestructive testing are input into the flaw detection analysis learning configuration network. The network determines the learning configuration results of each region based on the importance coefficient of each region. For regions with larger importance coefficients, more learning models are configured and the accuracy of flaw detection analysis is improved. For example, due to its higher importance coefficient, the clamping area will generate multiple learning configuration models to ensure higher flaw detection analysis accuracy; while for regions with lower importance, the number of models or accuracy requirements may be reduced. Through the intelligent configuration network, detection resources can be allocated to different regions more accurately, and the learning configuration results of the clamping area, the connection area and the middle area can be obtained.

[0033] Among them, the clamping area will have more learning configuration models and higher analysis accuracy, ensuring accurate detection of possible cracks, corrosion and other defects. The connection area is similar to the clamping area, but in comparison, the number of configuration models may be slightly less, the accuracy requirements are slightly lower, and the focus is on the stability and wear of the connection parts. The middle area is relatively stable, with fewer learning configuration models, and the focus is on detecting potential structural defects.

[0034] Next, based on the learning configuration results of each area, the internal flaw detection analysis features of the H-type wire clamp are learned. By analyzing the characteristics of different areas in a targeted manner, the following internal flaw detection analysis branches are constructed respectively, including: the internal flaw detection analysis branch of the clamping area, which mainly studies the potential damage mode inside the clamping area and identifies problems such as material fatigue and crack propagation caused by long-term stress or environmental factors. The internal flaw detection analysis branch of the connection area focuses on possible structural problems inside the connection part, such as corrosion, welding defects, material deformation, etc. The internal flaw detection analysis branch of the middle area detects possible structural cracks, excessive wear and other problems. These branches will independently learn the characteristics of their respective areas to ensure that the flaw detection capabilities of each area are fully utilized.

[0035] After completing the internal flaw analysis feature learning of each area, the internal flaw analysis branch of the clamping area, the internal flaw analysis branch of the connection area, and the internal flaw analysis branch of the middle area are connected as parallel nodes. Through this parallelized structure, the wire clamp internal flaw analysis channel is generated. This channel can process the detection data from different areas at the same time and provide more comprehensive internal flaw analysis results.

[0036] Furthermore, in addition to internal flaw detection, surface flaw detection is also a key part of evaluating the status of the wire clamp. At this stage, based on the learning configuration results of each area, the surface flaw detection characteristics of the H-type wire clamp are learned and constructed: the clamping area surface flaw detection analysis branch detects cracks, corrosion, peeling and other problems that may occur on the surface of the clamping area. The connection area surface flaw detection analysis branch analyzes defects such as surface wear, cracks or surface corrosion in the connection area. The middle area surface flaw detection analysis branch focuses on surface defects such as scratches and corrosion marks. These surface flaw detection branches, together with the internal flaw detection channels, ensure comprehensive inspection of each area of ​​the H-type wire clamp.

[0037] The clamping area surface flaw detection analysis branch, the connection area surface flaw detection analysis branch and the middle area surface flaw detection analysis branch are connected as parallel nodes to generate the wire clamp surface flaw detection analysis channel, which means that the surface flaw detection branches of each area will run in parallel in the same network and cooperate with each other to more efficiently process surface detection data from different areas.

[0038] Finally, the wire clamp internal flaw detection analysis channel and the wire clamp surface flaw detection analysis channel are encapsulated in a unified framework to generate the wire clamp nondestructive flaw detection analysis dual channel. The dual channel includes the wire clamp internal flaw detection analysis channel and the wire clamp surface flaw detection analysis channel. The two work together to handle the internal and surface defect detection tasks of the wire clamp, respectively, and can efficiently and comprehensively perform nondestructive testing of the H-type wire clamp to ensure that its internal and surface defects can be accurately identified and evaluated.

[0039] Furthermore, step P32 of the embodiment of the present application also includes: P32-1: The clamping area learning configuration result includes multiple clamping area learning configuration models and clamping area flaw detection analysis accuracy; P32-2: Based on the clamping area, a global search is performed on the internal flaw detection analysis record to obtain a clamping area ultrasonic detection record set and a clamping area internal flaw detection analysis record set; P32-3: Based on the clamping area ultrasonic detection record set and the clamping area internal flaw detection analysis record set, supervised training is performed on the multiple clamping area learning configuration models respectively to generate multiple clamping area flaw detection analysis accuracy that meets the clamping area. A clamping area internal flaw detection parser; P32-4: using the output data sets of the multiple clamping area internal flaw detection parsers as input information and the clamping area internal flaw detection analysis record set as output information to train a clamping area internal flaw detection analysis fusion; P32-5: connecting the multiple clamping area internal flaw detection parsers to generate a clamping area internal flaw detection analysis layer; P32-6: merging the clamping area internal flaw detection analysis layer with the input layer of the clamping area internal flaw detection analysis fusion to generate the clamping area internal flaw detection analysis branch.

[0040] In a possible embodiment of the present application, in-depth internal flaw detection analysis feature learning is performed on the clamping area, and the accuracy and reliability of non-destructive testing in this area are improved through multi-level learning configuration and model training.

[0041] First, based on the learning configuration results of the clamping area, multiple machine learning models are constructed, such as decision trees and neural networks. These models are determined based on the previous nondestructive testing importance coefficients, and a higher model accuracy and quantity are set for the complexity and importance of the clamping area. Each model will focus on different types of defects (such as cracks, corrosion, wear, etc.) and different working environments during training to ensure that the detection results of the clamping area can be accurately analyzed. At the same time, the accuracy of the clamping area flaw detection analysis is set to ensure that each model can achieve the expected detection accuracy when processing actual data.

[0042] In order to make full use of historical data, a global search of internal flaw detection analysis records is performed. This search is not limited to the clamping area of ​​the current H-type wire clamp, but also includes the clamping area data of multiple H-type wire clamps of the same model. By obtaining more data, the model can learn a wider range of patterns and features, thereby improving its generalization ability. Through global search, the clamping area ultrasonic detection record set and the clamping area internal flaw detection analysis record set can be obtained. These data will serve as the basis for model training to ensure that the generated model has good adaptability and accuracy.

[0043] Next, supervised training is performed on the learning configuration model of multiple clamping areas based on the retrieved clamping area ultrasonic detection record set and internal flaw analysis record set. During the training process, the model optimizes itself by annotating data (such as the specific location and type of defects such as cracks and corrosion), and continuously adjusts parameters to improve the accuracy of the clamping area flaw analysis, ensuring that more possible defect types can be identified in practical applications. In this way, the generated model can meet the requirements of the clamping area flaw analysis accuracy, thereby providing an accurate decision-making basis for the subsequent flaw detection process.

[0044] Next, the output data sets of multiple clamping area internal flaw detection parsers are used as input information, and then compared with the clamping area internal flaw detection parsing record set to train the clamping area internal flaw detection parsing fusion. The function of this fusion is to effectively fuse the output results of multiple models to further improve the comprehensive accuracy of the flaw detection results. The fusion can automatically adjust the weights of different models so that the output results are more in line with the actual flaw detection needs, thereby reducing the misjudgment or omission that may be caused by a single model.

[0045] The trained internal flaw detection parsers of the clamping area will be connected together to form a clamping area internal flaw detection parsing layer. This parsing layer is responsible for processing the input data from multiple models and generating the final flaw detection results. The hierarchical processing method can not only improve the data processing efficiency, but also ensure that the detection of complex defects is not missed.

[0046] Finally, the internal flaw detection analysis layer of the clamping area is merged with the input layer of the internal flaw detection analysis fusion device of the clamping area to generate a complete internal flaw detection analysis branch of the clamping area. The function of this branch is to organically integrate the entire flaw detection process, and the analysis results of different models can be integrated to ensure that the final flaw detection conclusion has high reliability and accuracy. Through this branch, the system can efficiently perform internal flaw detection analysis in the clamping area when performing H-type wire clamp inspection and discover potential defects in a timely manner.

[0047] P40: Based on the multi-region nondestructive testing importance coefficient, the multi-level regions of the wire clamp are inspected using an ultrasonic inspection device and a magnetic particle inspection device to obtain multi-region ultrasonic inspection data and multi-region magnetic particle inspection data.

[0048] Specifically, based on the multi-region nondestructive testing importance coefficient, a comprehensive inspection is performed on multiple regions of the H-type wire clamp by using different nondestructive testing devices (ultrasonic testing device and magnetic particle testing device).

[0049] Among them, the ultrasonic detection device is used to detect potential defects inside the wire clamp, such as cracks, voids, delamination, etc. Compared with traditional detection methods, ultrasonic testing has a deeper detection capability, which can penetrate the metal surface and go deep into the material to find hidden defects that cannot be detected by the naked eye. In this step, the focus of ultrasonic testing is to conduct targeted testing of each area based on the importance coefficient of multi-area non-destructive testing. For areas with larger importance coefficients (such as clamping areas), the focus of ultrasonic testing will be clearer, and the frequency and accuracy requirements will be higher to ensure that possible defects can be accurately captured.

[0050] During implementation, the ultrasonic probe emits high-frequency sound waves, receives the reflected waveform, and analyzes the changes in the waveform to determine the internal state of the material. By collecting ultrasonic testing data from various areas, a preliminary judgment can be made as to whether there are internal defects in each area.

[0051] Unlike ultrasonic testing, magnetic particle testing is mainly used to detect defects on or near the surface of metals, and is particularly suitable for detecting cracks, corrosion or other surface damage. In this step, magnetic particle testing is mainly used to detect surface defects. This method forms a surface pattern by applying fine magnetic powder to the metal surface under the action of a magnetic field. If there are cracks or other defects on the metal surface, the magnetic powder will gather at the defect, forming an obvious magnetic field change, so that the defect can be found through visual observation or scanning equipment.

[0052] Depending on the importance of non-destructive testing, magnetic particle testing will use different inspection densities in different areas. For example, the clamping area may be more prone to surface cracks due to the greater mechanical stress and friction it is subjected to, so magnetic particle testing will particularly strengthen the inspection frequency and accuracy of this area.

[0053] After each area is tested by ultrasonic and magnetic particle testing, the multi-area ultrasonic test data and multi-area magnetic particle test data obtained will be used as basic data for subsequent processing. Ultrasonic testing mainly provides data support for internal defects, while magnetic particle testing focuses on the detection of surface defects. The combination of the two can achieve a comprehensive inspection of each area of ​​the H-type wire clamp to ensure reliability in different working environments.

[0054] P50: Perform detection interference correction on the multi-region ultrasonic detection data and the multi-region magnetic particle detection data to generate a multi-region ultrasonic detection result and a multi-region magnetic particle detection result.

[0055] Further, to generate multi-region ultrasonic detection results, step P50 of the embodiment of the present application further includes: P51: Collect ultrasonic detection scene parameters corresponding to the multi-region ultrasonic detection data to obtain ultrasonic detection scenes in each region; P52: Perform detection interference identification according to the ultrasonic detection scenes in each region to obtain ultrasonic detection interference identification results in each region; P53: Input the multi-region ultrasonic detection data and the ultrasonic detection interference identification results in each region into an ultrasonic detection interference correction model to obtain ultrasonic detection correction results in each region; P54: Output the ultrasonic detection correction results in each region as the multi-region ultrasonic detection results.

[0056] Optionally, in order to ensure the accuracy and validity of the test data, the multi-region data obtained from ultrasonic testing and magnetic particle testing must be corrected for test interference to eliminate interference caused by external factors, equipment errors or improper operation, thereby generating more accurate ultrasonic test results and magnetic particle test results.

[0057] First, collect the ultrasonic detection scene parameters corresponding to each area. These scene parameters include information such as the equipment configuration used in the ultrasonic detection process, the probe angle, frequency, the distance between the probe and the detection surface, and environmental conditions. These parameters provide an important basis for subsequent interference identification and correction, because different parameter combinations may affect the propagation of the ultrasonic beam and the reception of the echo signal, thereby introducing interference or errors. Therefore, accurately recording and analyzing these parameters is the key to ensuring the correction effect.

[0058] After collecting the ultrasonic detection scene parameters for each area, detection interference identification is required. Interference may come from multiple sources, such as environmental noise, equipment failure, improper probe installation, etc. At this stage, by analyzing the relationship between the ultrasonic detection data and the scene parameters of each area, interference factors that may affect the accuracy of the data are identified. For example, if the probe angle is set improperly or there are irregular shapes on the surface, the echo signal may be distorted or lost, which will affect the judgment of defects. The purpose of interference identification is to find these problems and determine what specific corrections need to be made.

[0059] After the interference is identified, the multi-region ultrasonic detection data and the ultrasonic detection interference identification results are input into the ultrasonic detection interference correction model. The correction model automatically corrects the identified interference based on historical data and algorithms. The correction method may include adjusting the signal amplitude, frequency, time delay and other parameters, or removing the noise introduced by the interference through algorithms. This process can significantly improve the reliability of the detection data and make the final detection results more accurate.

[0060] After the correction is completed, the ultrasonic detection correction results of each area are output as the final results to generate multi-area ultrasonic detection results. These corrected results will be used for further defect assessment and decision support to ensure that the ultrasonic detection data of each area can accurately reflect the actual state of the material without being affected by interference. Through this series of interference correction steps, the accuracy of ultrasonic detection can be effectively improved, the errors caused by equipment, environment or operation can be reduced, and reliable basic data can be provided for subsequent defect analysis and evaluation.

[0061] Further, to generate multi-region magnetic particle inspection results, step P50 of the embodiment of the present application further includes: P55: Collect the magnetic particle detection scene parameters corresponding to the multi-region magnetic particle detection data to obtain the magnetic particle detection scenes of each region; P56: Perform detection interference identification according to the magnetic particle detection scenes of each region to obtain the magnetic particle detection interference identification results of each region; P57: Input the multi-region magnetic particle detection data and the magnetic particle detection interference identification results of each region into the magnetic particle detection interference correction model to obtain the magnetic particle detection correction results of each region; P58: Output the magnetic particle detection correction results of each region as the multi-region magnetic particle detection results.

[0062] Optionally, in addition to interference correction of ultrasonic detection data, similar detection interference correction is also required for multi-region detection data obtained from the magnetic particle detection device to ensure that the results obtained from the magnetic particle detection are more accurate and avoid detection errors caused by environmental factors or operating errors.

[0063] Similar to ultrasonic testing, when performing magnetic particle testing, it is necessary to collect the magnetic particle testing scene parameters corresponding to each area. These scene parameters include the type of magnetic powder, magnetic field strength, magnetic powder application method, equipment used in the flaw detection process, and probe configuration. Scene parameters may also involve the distribution of magnetic powder on the metal surface, as well as the surrounding environmental conditions (such as temperature, humidity, etc.). These parameters are crucial for subsequent interference identification and correction, because improper magnetic field application or uneven magnetic powder distribution may cause distortion of the test results.

[0064] After obtaining the magnetic particle inspection scene parameters of each area, it is necessary to identify interferences in the magnetic particle inspection data of each area. The interference of magnetic particle inspection mainly comes from factors such as surface unevenness, magnetic field instability, and irregular operation during operation. For example, if the magnetic field strength is too high or too low, or the magnetic powder is unevenly coated, the magnetic powder may not accumulate correctly on cracks or defects, affecting the visibility of defects. Through in-depth analysis of the inspection scene parameters and data, these potential interferences can be identified and the corresponding interference identification results can be recorded.

[0065] Next, the magnetic particle inspection data after interference identification and the magnetic particle inspection interference identification results are input into the magnetic particle inspection interference correction model. The role of this model is to correct the errors in the inspection data by compensating and correcting the identified interference. Based on historical data and machine learning technology, the correction model can automatically adjust the interference effects and provide more accurate inspection results. For example, if the uneven application of the magnetic field causes incomplete magnetic particle distribution in certain areas, the model can automatically adjust the inspection results in these areas to eliminate the uneven effects.

[0066] Finally, the corrected magnetic particle inspection results of each area are output to form the final multi-area magnetic particle inspection results. These results will serve as the basis for subsequent analysis to help identify potential defects or safety hazards in each area. Through interference correction, the inspection results will be more accurate and reliable, which can effectively improve the safety and service life of the H-type wire clamp.

[0067] P60: Input the multi-region ultrasonic test results and the multi-region magnetic particle test results into the double channels of the wire clamp non-destructive testing analysis to generate a wire clamp non-destructive testing report.

[0068] Furthermore, step P60 of the embodiment of the present application further includes: P61: Input the multi-area ultrasonic detection results into the wire clamp internal flaw detection analysis channel to obtain the clamping area internal flaw detection analysis results, the connecting area internal flaw detection analysis results and the middle area internal flaw detection analysis results; P62: Input the multi-area magnetic particle detection results into the wire clamp surface flaw detection analysis channel to obtain the clamping area surface flaw detection analysis results, the connecting area surface flaw detection analysis results and the middle area surface flaw detection analysis results; P63: Arrange the clamping area internal flaw detection analysis results, the connecting area internal flaw detection analysis results, the middle area internal flaw detection analysis results, the clamping area surface flaw detection analysis results, the connecting area surface flaw detection analysis results and the middle area surface flaw detection analysis results to obtain the wire clamp non-destructive testing report.

[0069] It should be understood that after interference correction, the multi-region ultrasonic test results and the multi-region magnetic particle test results are input into the wire clamp non-destructive testing analysis dual channels to generate a final wire clamp non-destructive testing report.

[0070] First, the multi-area ultrasonic test results are input into the internal flaw detection analysis channel of the wire clamp. This channel is specially used to process data related to the internal structure of the wire clamp, especially for the internal defect analysis of the clamping area, the connection area and the middle area. According to the characteristics of each area, ultrasonic testing mainly focuses on detecting deep defects such as cracks, holes, and delamination. By inputting these results into the internal flaw detection analysis channel, the internal flaw detection analysis results of each area can be obtained, including the internal flaw detection analysis results of the clamping area, which can detect whether there are structural defects such as cracks and pores inside. The internal flaw detection analysis results of the connection area can analyze whether there is internal damage caused by stress or environmental influences at the connection part. The internal flaw detection analysis results of the middle area evaluate the stability of the middle area and check whether there are potential fatigue cracks or corrosion phenomena.

[0071] Next, the multi-area magnetic particle inspection results are input into the wire clamp surface flaw detection analysis channel. Magnetic particle inspection is mainly used to analyze surface or near-surface defects, such as cracks, scratches, corrosion, etc. Unlike internal flaw detection, magnetic particle inspection focuses on the surface state. After processing through this channel, the surface flaw detection analysis results of each area are obtained, including: the surface flaw detection analysis results of the clamping area, to detect whether there are cracks, corrosion and other problems on the surface of the clamping area, which may affect the stability of the wire fixation. The surface flaw detection analysis results of the connection area focus on whether there are cracks or wear on the surface of the connection area, especially in places with greater force and vibration. The surface flaw detection analysis results of the middle area analyze whether there is surface damage in the middle area due to environmental changes or long-term use.

[0072] Finally, the flaw detection analysis results of all areas are sorted out, including: internal flaw detection analysis results and surface flaw detection analysis results of the clamping area, internal flaw detection analysis results and surface flaw detection analysis results of the connection area, and internal flaw detection analysis results and surface flaw detection analysis results of the middle area. These results are merged and summarized to finally generate a complete non-destructive testing report for the wire clamp. The report will list the test results of each area, the potential defects found, and their possible impacts in detail. It can not only provide test data for each area, but also provide a basis for subsequent repairs, maintenance and risk assessment, which helps to extend the service life of the equipment and improve the stability of the power system.

[0073] In summary, the embodiments of the present application have at least the following technical effects: This application divides the H-type wire clamp into structural parts to obtain clamping, connection and middle areas, performs non-destructive testing evaluation based on the regional importance coefficient, uses the flaw detection analysis learning configuration network to build dual channels (internal and surface flaw detection analysis channels), obtains data through ultrasonic and magnetic particle detection devices, and generates a comprehensive non-destructive testing report after interference correction.

[0074] The technical effect of improving the accuracy, comprehensiveness and detection efficiency of H-type wire clamp non-destructive testing is achieved.

[0075] Embodiment 2, based on the same inventive concept as the nondestructive testing method of an H-type wire clamp in the above embodiment, Figure 2 As shown, the present application provides a non-destructive testing system for an H-type wire clamp, and the system and method embodiments in the present application are based on the same inventive concept. The system includes: The structural feature division module 11 is used to divide the structural features of the H-type wire clamp to obtain a multi-level region of the wire clamp, wherein the multi-level region of the wire clamp includes a clamping region, a connection region and a middle region.

[0076] The non-destructive testing importance evaluation module 12 is used to perform non-destructive testing importance evaluation according to the multi-level areas of the wire clamp to obtain multi-area non-destructive testing importance coefficients.

[0077] The dual-channel analysis building module 13 is used to perform non-destructive testing analysis record learning on the H-type wire clamp based on the multi-region non-destructive testing importance coefficient and configure the network according to the flaw detection analysis learning, and build a dual-channel non-destructive testing analysis for the wire clamp, wherein the dual-channel non-destructive testing analysis for the wire clamp includes a wire clamp internal flaw detection analysis channel and a wire clamp surface flaw detection analysis channel.

[0078] The multi-region detection module 14 is used to detect the multi-level regions of the wire clamp based on the multi-region non-destructive testing importance coefficient according to the ultrasonic detection device and the magnetic particle detection device to obtain multi-region ultrasonic detection data and multi-region magnetic particle detection data.

[0079] The detection interference correction module 15 is used to perform detection interference correction on the multi-region ultrasonic detection data and the multi-region magnetic particle detection data to generate a multi-region ultrasonic detection result and a multi-region magnetic particle detection result.

[0080] The test report generation module 16 is used to input the multi-region ultrasonic test results and the multi-region magnetic particle test results into the wire clamp non-destructive testing analysis dual channel to generate a wire clamp non-destructive testing report.

[0081] Furthermore, the nondestructive testing importance evaluation module 12 is also used to perform the following steps: Importance evaluation is performed on the multi-level areas of the wire clamp to obtain the importance coefficient of each area; damage probability prediction is performed on the multi-level areas of the wire clamp based on the wire clamp usage log of the H-type wire clamp to determine the damage probability of each area; normalization is performed based on the importance coefficients of each area and the damage probability of each area to establish a wire clamp area evaluation matrix; weighted calculation is performed on the wire clamp area evaluation matrix based on the importance weights of the wire clamp areas and the damage probability weights of the wire clamp areas to generate the importance coefficients of the multi-area non-destructive testing.

[0082] Furthermore, the dual-channel analysis building module 13 is also used to perform the following steps: The importance coefficient of the multi-region nondestructive testing is input into the flaw detection analysis learning configuration network to obtain the clamping region learning configuration result, the connection region learning configuration result and the middle region learning configuration result; based on the clamping region learning configuration result, the connection region learning configuration result and the middle region learning configuration result, the internal flaw detection analysis feature learning of the H-type wire clamp is performed to construct the clamping region internal flaw detection analysis branch, the connection region internal flaw detection analysis branch and the middle region internal flaw detection analysis branch; the clamping region internal flaw detection analysis branch, the connection region internal flaw detection analysis branch and the middle region internal flaw detection analysis branch are connected as parallel nodes to generate the wire The method comprises the following steps: generating an internal flaw detection analysis channel for the H-type wire clamp; performing surface flaw detection analysis feature learning on the H-type wire clamp based on the clamping area learning configuration result, the connection area learning configuration result and the middle area learning configuration result, and constructing a clamping area surface flaw detection analysis branch, a connection area surface flaw detection analysis branch and a middle area surface flaw detection analysis branch; connecting the clamping area surface flaw detection analysis branch, the connection area surface flaw detection analysis branch and the middle area surface flaw detection analysis branch as parallel nodes to generate the wire clamp surface flaw detection analysis channel; encapsulating the wire clamp internal flaw detection analysis channel and the wire clamp surface flaw detection analysis channel into the wire clamp non-destructive flaw detection analysis dual channel.

[0083] Furthermore, the dual-channel analysis building module 13 is also used to perform the following steps: The clamping area learning configuration result includes multiple clamping area learning configuration models and clamping area flaw detection analysis accuracy; a global search of internal flaw detection analysis records is performed according to the clamping area to obtain a clamping area ultrasonic detection record set and a clamping area internal flaw detection analysis record set; supervised training is performed on the multiple clamping area learning configuration models according to the clamping area ultrasonic detection record set and the clamping area internal flaw detection analysis record set to generate multiple clamping area internal flaw detection parsers that meet the clamping area flaw detection analysis accuracy; a clamping area internal flaw detection analysis fusion device is trained using the output data sets of the multiple clamping area internal flaw detection parsers as input information and the clamping area internal flaw detection analysis record set as output information; the multiple clamping area internal flaw detection parsers are connected to generate a clamping area internal flaw detection analysis layer; the clamping area internal flaw detection analysis layer is merged with the input layer of the clamping area internal flaw detection analysis fusion device to generate the clamping area internal flaw detection analysis branch.

[0084] Furthermore, the interference detection and correction module 15 is further configured to perform the following steps: Collect ultrasonic detection scene parameters corresponding to the multi-region ultrasonic detection data to obtain ultrasonic detection scenes in each region; perform detection interference identification according to the ultrasonic detection scenes in each region to obtain ultrasonic detection interference identification results in each region; input the multi-region ultrasonic detection data and the ultrasonic detection interference identification results in each region into an ultrasonic detection interference correction model to obtain ultrasonic detection correction results in each region; output the ultrasonic detection correction results in each region as the multi-region ultrasonic detection results.

[0085] Furthermore, the interference detection and correction module 15 is further configured to perform the following steps: The magnetic particle detection scene parameters corresponding to the multi-region magnetic particle detection data are collected to obtain the magnetic particle detection scenes of each region; detection interference identification is performed according to the magnetic particle detection scenes of each region to obtain the magnetic particle detection interference identification results of each region; the multi-region magnetic particle detection data and the magnetic particle detection interference identification results of each region are input into a magnetic particle detection interference correction model to obtain the magnetic particle detection correction results of each region; and the magnetic particle detection correction results of each region are output as the multi-region magnetic particle detection results.

[0086] Furthermore, the test report generating module 16 is further configured to perform the following steps: Input the multi-region ultrasonic detection results into the wire clamp internal flaw detection analysis channel to obtain the clamping area internal flaw detection analysis results, the connecting area internal flaw detection analysis results and the middle area internal flaw detection analysis results; input the multi-region magnetic particle detection results into the wire clamp surface flaw detection analysis channel to obtain the clamping area surface flaw detection analysis results, the connecting area surface flaw detection analysis results and the middle area surface flaw detection analysis results; collate the clamping area internal flaw detection analysis results, the connecting area internal flaw detection analysis results, the middle area internal flaw detection analysis results, the clamping area surface flaw detection analysis results, the connecting area surface flaw detection analysis results and the middle area surface flaw detection analysis results to obtain the wire clamp nondestructive testing report.

[0087] It should be noted that the above-mentioned sequence of the embodiments of the present application is only for description and does not represent the advantages and disadvantages of the embodiments. And the above-mentioned specific embodiments of this specification are described. In addition, the processes depicted in the accompanying drawings do not necessarily require the specific order or continuous order shown to achieve the desired results. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0088] The above description is only a preferred embodiment of the present application and is not intended to limit the present application. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present application should be included in the protection scope of the present application.

[0089] This specification and the drawings are merely exemplary illustrations of the present application and are deemed to cover any and all modifications, variations, combinations or equivalents within the scope of the present application. Obviously, a person skilled in the art may make various modifications and variations to the present application without departing from the scope of the present application. Thus, if these modifications and variations of the present application fall within the scope of the present application and its equivalents, the present application intends to include these modifications and variations.

Claims

1. A nondestructive testing method for an H-type wire clamp, characterized in that: The method comprises: The H-type wire clamp is divided into structural features to obtain a multi-level region of the wire clamp, wherein the multi-level region of the wire clamp includes a clamping region, a connecting region and a middle region; Performing nondestructive testing importance evaluation on the multi-level regions of the wire clamp to obtain a multi-region nondestructive testing importance coefficient; Based on the multi-region nondestructive testing importance coefficient, the nondestructive testing analysis record learning of the H-type wire clamp is performed according to the flaw detection analysis learning configuration network, and a wire clamp nondestructive testing analysis dual channel is built, wherein the wire clamp nondestructive testing analysis dual channel includes a wire clamp internal flaw detection analysis channel and a wire clamp surface flaw detection analysis channel; Based on the multi-region nondestructive testing importance coefficient, the multi-level regions of the wire clamp are tested using an ultrasonic testing device and a magnetic particle testing device to obtain multi-region ultrasonic testing data and multi-region magnetic particle testing data; Performing detection interference correction on the multi-region ultrasonic detection data and the multi-region magnetic particle detection data to generate a multi-region ultrasonic detection result and a multi-region magnetic particle detection result; The multi-region ultrasonic test results and the multi-region magnetic particle test results are input into the wire clamp non-destructive testing analysis dual channels to generate a wire clamp non-destructive testing report.

2. The method according to claim 1, characterized in that According to the importance evaluation of non-destructive testing of the multi-level areas of the wire clamp, the importance coefficient of multi-area non-destructive testing is obtained, including: Perform importance evaluation according to the multi-level regions of the wire clamp to obtain the importance coefficient of each region; According to the wire clamp usage log of the H-type wire clamp, damage probability prediction is performed on the multi-level areas of the wire clamp to determine the damage probability of each area; Performing normalization processing according to the importance coefficient of each area and the damage probability of each area, and establishing a wire clamp area evaluation matrix; The wire clamp area evaluation matrix is ​​weightedly calculated according to the importance weight of the wire clamp area and the damage probability weight of the wire clamp area to generate the importance coefficient of the multi-area non-destructive testing.

3. The method according to claim 1, characterized in that Based on the multi-region nondestructive testing importance coefficient, the H-type wire clamp is subjected to nondestructive testing analysis record learning according to the flaw detection analysis learning configuration network, and a wire clamp nondestructive testing analysis dual channel is built, wherein the wire clamp nondestructive testing analysis dual channel includes a wire clamp internal flaw detection analysis channel and a wire clamp surface flaw detection analysis channel, including: Inputting the multi-region nondestructive testing importance coefficient into the flaw detection analysis learning configuration network to obtain the clamping region learning configuration result, the connection region learning configuration result and the middle region learning configuration result; Based on the clamping area learning configuration result, the connection area learning configuration result and the middle area learning configuration result, the internal flaw detection analysis feature learning of the H-type wire clamp is performed, and a clamping area internal flaw detection analysis branch, a connection area internal flaw detection analysis branch and a middle area internal flaw detection analysis branch are constructed; The internal flaw detection analysis branch of the clamping area, the internal flaw detection analysis branch of the connection area and the internal flaw detection analysis branch of the middle area are connected as parallel nodes to generate the internal flaw detection analysis channel of the wire clamp; Based on the clamping area learning configuration result, the connection area learning configuration result and the middle area learning configuration result, the surface flaw detection analysis feature learning of the H-type wire clamp is performed, and a clamping area surface flaw detection analysis branch, a connection area surface flaw detection analysis branch and a middle area surface flaw detection analysis branch are constructed; The clamping area surface flaw detection analysis branch, the connection area surface flaw detection analysis branch and the middle area surface flaw detection analysis branch are connected as parallel nodes to generate the wire clamp surface flaw detection analysis channel; The internal flaw detection and analysis channel of the wire clamp and the surface flaw detection and analysis channel of the wire clamp are packaged into the non-destructive flaw detection and analysis dual channel of the wire clamp.

4. The method according to claim 3, characterized in that Based on the clamping area learning configuration result, the connection area learning configuration result and the middle area learning configuration result, the internal flaw detection analysis feature learning of the H-type wire clamp is performed, and the clamping area internal flaw detection analysis branch, the connection area internal flaw detection analysis branch and the middle area internal flaw detection analysis branch are constructed, including: The clamping area learning configuration result includes a plurality of clamping area learning configuration models and the clamping area flaw detection analysis accuracy; Perform a global search of internal flaw detection analysis records according to the clamping area to obtain a clamping area ultrasonic detection record set and a clamping area internal flaw detection analysis record set; According to the clamping area ultrasonic detection record set and the clamping area internal flaw detection analysis record set, respectively, the plurality of clamping area learning configuration models are supervisedly trained to generate a plurality of clamping area internal flaw detection analyzers that meet the clamping area flaw detection analysis accuracy; Using the output data sets of the plurality of clamping area internal flaw detection analyzers as input information and the clamping area internal flaw detection analysis record set as output information, training a clamping area internal flaw detection analysis fusion device; connecting the plurality of clamping area internal flaw detection analyzers to generate a clamping area internal flaw detection analysis layer; The clamping region internal flaw detection analysis layer is merged with the input layer of the clamping region internal flaw detection analysis fusion device to generate the clamping region internal flaw detection analysis branch.

5. The method according to claim 1, characterized in that Performing detection interference correction on the multi-region ultrasonic detection data and the multi-region magnetic particle detection data to generate multi-region ultrasonic detection results and multi-region magnetic particle detection results, including: Collecting ultrasonic detection scene parameters corresponding to the multi-region ultrasonic detection data to obtain ultrasonic detection scenes in each region; Perform detection interference identification according to the ultrasonic detection scenarios in each area, and obtain ultrasonic detection interference identification results in each area; Inputting the multi-region ultrasonic detection data and the ultrasonic detection interference identification results of each region into an ultrasonic detection interference correction model to obtain ultrasonic detection correction results of each region; The ultrasonic detection correction results of each region are output as the multi-region ultrasonic detection results.

6. The method according to claim 1, characterized in that Performing detection interference correction on the multi-region ultrasonic detection data and the multi-region magnetic particle detection data to generate multi-region ultrasonic detection results and multi-region magnetic particle detection results, including: Collecting magnetic particle detection scene parameters corresponding to the multi-region magnetic particle detection data to obtain magnetic particle detection scenes in each region; Perform detection interference identification according to the magnetic particle detection scenarios in each area, and obtain magnetic particle detection interference identification results in each area; Inputting the multi-region magnetic particle inspection data and the magnetic particle inspection interference identification results of each region into a magnetic particle inspection interference correction model to obtain a magnetic particle inspection correction result of each region; The magnetic particle inspection correction results of each region are output as the multi-region magnetic particle inspection results.

7. The method according to claim 1, characterized in that The multi-region ultrasonic test results and the multi-region magnetic particle test results are input into the double channels of the non-destructive testing analysis of the wire clamp to generate a non-destructive testing report of the wire clamp, including: Input the multi-region ultrasonic detection results into the wire clamp internal flaw detection analysis channel to obtain the clamping region internal flaw detection analysis results, the connection region internal flaw detection analysis results and the middle region internal flaw detection analysis results; Input the multi-region magnetic particle detection results into the wire clamp surface flaw detection analysis channel to obtain the clamping region surface flaw detection analysis results, the connection region surface flaw detection analysis results and the middle region surface flaw detection analysis results; The internal flaw detection analysis results of the clamping area, the internal flaw detection analysis results of the connection area, the internal flaw detection analysis results of the middle area, the surface flaw detection analysis results of the clamping area, the surface flaw detection analysis results of the connection area and the surface flaw detection analysis results of the middle area are sorted to obtain the non-destructive testing report of the wire clamp.

8. A nondestructive testing system for an H-type wire clamp, characterized in that: The system comprises: A structural feature division module, wherein the structural feature division module is used to divide the structural features of the H-type wire clamp to obtain a multi-level region of the wire clamp, wherein the multi-level region of the wire clamp includes a clamping region, a connection region and a middle region; A nondestructive testing importance evaluation module, wherein the nondestructive testing importance evaluation module is used to perform nondestructive testing importance evaluation according to the multi-level regions of the wire clamp to obtain a multi-region nondestructive testing importance coefficient; A dual-channel analysis building module, wherein the dual-channel analysis building module is used to perform non-destructive testing analysis record learning on the H-type wire clamp based on the multi-region non-destructive testing importance coefficient and according to the flaw detection analysis learning configuration network, and to build a dual-channel non-destructive testing analysis for the wire clamp, wherein the dual-channel non-destructive testing analysis for the wire clamp includes a wire clamp internal flaw detection analysis channel and a wire clamp surface flaw detection analysis channel; A multi-region detection module, the multi-region detection module is used to detect the multi-level regions of the wire clamp based on the multi-region non-destructive testing importance coefficient according to the ultrasonic detection device and the magnetic particle detection device, and obtain multi-region ultrasonic detection data and multi-region magnetic particle detection data; A detection interference correction module, the detection interference correction module is used to perform detection interference correction on the multi-region ultrasonic detection data and the multi-region magnetic particle detection data to generate a multi-region ultrasonic detection result and a multi-region magnetic particle detection result; The test report generation module is used to input the multi-region ultrasonic test results and the multi-region magnetic particle test results into the wire clamp non-destructive flaw detection analysis dual channel to generate a wire clamp non-destructive test report.

Images