A nondestructive testing method and system for H-type wire clamp

Abstract

The present invention discloses a non-destructive testing method and system for an H-type wire clamp, which relates to the field of intelligent testing technology. The method comprises: dividing the H-type wire clamp by structural characteristics to obtain clamping, connection, and middle areas; performing non-destructive testing evaluation based on the importance coefficient of each area; and configuring a network to build a dual-channel non-destructive testing analysis for the wire clamp based on the importance coefficient of multi-area non-destructive testing; testing each area using ultrasonic and magnetic particle testing devices to obtain multi-area ultrasonic testing data and multi-area magnetic particle testing data, and performing interference correction; inputting the corrected data into the dual-channel non-destructive testing analysis for the wire clamp to generate a non-destructive testing report. The present invention solves the technical problem in the prior art that traditional non-destructive testing methods are difficult to fully discover potential defects and lack accurate detection of complex areas, thereby achieving the technical effect of improving the accuracy, comprehensiveness, and detection efficiency of non-destructive testing of H-type wire clamps.

Description

Technical Field

[0001] The present invention relates to the field of intelligent detection technology, and in particular to a non-destructive 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 key fixing components in power lines, have become increasingly important. Due to their long-term exposure to mechanical stress, temperature fluctuations, and environmental corrosion during power transmission, the service life and performance of H-type wire clamps are directly related to the stable operation of power systems. Therefore, timely and accurate testing and evaluation of the health of H-type wire clamps is key to ensuring the safety of power facilities and improving equipment operation and maintenance efficiency.

[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] The first aspect of the present application provides a non-destructive testing method for an H-type wire clamp, the method comprising: dividing the H-type wire clamp by structural characteristics 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 non-destructive testing importance evaluation based on the multi-level regions of the wire clamp to obtain a multi-region non-destructive testing importance coefficient; based on the multi-region non-destructive testing importance coefficient, performing non-destructive testing analysis record learning on the H-type wire clamp according to a flaw analysis learning configuration network, and building a wire clamp non-destructive testing analysis dual channel, wherein the wire clamp non-destructive testing analysis dual channel It includes an internal flaw detection analysis channel for the wire clamp and a surface flaw detection analysis channel for the wire clamp; based on the multi-region non-destructive testing importance coefficient, the multi-level regions of the wire clamp are detected 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; 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 non-destructive testing analysis dual channels to generate a wire clamp non-destructive testing report.

[0006] The second aspect of the present application provides a non-destructive testing system for an H-type wire clamp, 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 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; a non-destructive testing importance evaluation module, the non-destructive testing importance evaluation module is used to perform non-destructive testing importance evaluation based on the multi-level regions of the wire clamp to obtain a multi-region non-destructive testing importance coefficient; an analytical dual-channel construction module, the analytical dual-channel construction 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 according to the flaw analysis learning configuration network, and build a wire clamp non-destructive testing analysis dual channel, wherein the wire clamp non-destructive testing The dual-channel analysis includes 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 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; 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 testing analysis dual channel to generate a wire clamp non-destructive testing report.

[0007] One or more technical solutions provided in this application have at least the following technical effects or advantages:

[0008] The present application provides a non-destructive testing method and system for an H-type wire clamp, which relates to the field of intelligent detection technology. By structurally dividing the H-type wire clamp to obtain clamping, connection, and middle areas, non-destructive testing evaluation is performed based on the regional importance coefficient. A flaw detection analysis learning configuration network is used to build dual channels (internal and surface flaw detection analysis channels). Data is acquired through ultrasonic and magnetic particle detection devices, and a comprehensive non-destructive testing report is generated after interference correction. This method solves the technical problems in the existing technology that traditional non-destructive testing methods have difficulty in comprehensively discovering potential defects and lack accurate detection of complex areas, and achieves the technical effect of improving the accuracy, comprehensiveness, and detection efficiency of H-type wire clamp non-destructive testing. BRIEF DESCRIPTION OF THE DRAWINGS

[0009] 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.

[0010] 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.

[0011] Figure 2 A schematic structural diagram of a nondestructive testing system for an H-type wire clamp provided in an embodiment of the present application.

[0012] Explanation of the accompanying symbols: 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

[0013] 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.

[0014] The following will be combined with the accompanying drawings in the embodiments of this application to clearly and completely describe the technical solutions in the embodiments of this application. Obviously, the embodiments described are only some of the embodiments of this application, not all of them. Based on the embodiments in this application, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of this application.

[0015] 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 clearly listed, but may include other steps or modules that are not clearly listed or inherent to these processes, methods, products or devices.

[0016] Example 1, as Figure 1 As shown, the present application provides a non-destructive testing method for an H-type wire clamp, the method comprising:

[0017] P10: Structural characteristics of the H-type wire clamp are divided 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.

[0018] Specifically, before conducting nondestructive testing on an H-type wire clamp, it's necessary to first classify its structural features to ensure comprehensive and accurate testing. This structural classification aims to break the clamp down into multiple areas, each with distinct structural characteristics and functions, providing clear guidance and a basis for subsequent flaw detection. By classifying the different parts of the H-type wire clamp, appropriate nondestructive testing methods can be selected in a targeted manner, while also improving testing efficiency and accuracy.

[0019] Optionally, in this step, the multi-layer 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 stresses and working conditions, so the importance and method of inspection of each area are also different.

[0020] The clamping area is the portion of the H-type clamp that comes into direct contact with the conductor or cable, securing and stabilizing the conductor. This area is typically subject to significant mechanical stress and vibration, so its structural integrity requires special attention, inspecting for cracks, fatigue, deformation, or corrosion. Due to the high functional requirements of the clamping area, damage can cause the cable to loosen or disconnect, leading to serious power system failures. Therefore, this area requires high-precision inspection, especially for detecting surface cracks, for which magnetic particle inspection is a suitable option.

[0021] The connection area refers to the portion of the H-type clamp that connects to the supporting structure (such as a tower or pole). This area is responsible for withstanding external tensile and wind forces and is crucial to the overall stability of the clamp. The connection area is typically the part of the H-type clamp that experiences the most contact with the external environment, making corrosion, friction, and wear more common here. Nondestructive testing, particularly ultrasonic testing, can effectively detect cracks or metal fatigue within the connection area, ensuring the reliability of the connection.

[0022] The central area is typically located at the center of the H-type clamp. This area is relatively evenly stressed and, in most cases, does not come into direct contact with cables or other external components. While the probability of damage to the central area is low, its structural safety cannot be ignored. Nondestructive testing (NDT) of this area primarily checks for tiny internal cracks or structural defects that could affect the long-term service life of the clamp. Ultrasonic testing is particularly effective in this area, helping to detect internal defects and avoid potential problems that could lead to subsequent equipment failure.

[0023] This structural feature division process 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 detection results.

[0024] P20: Perform nondestructive testing importance evaluation on the multi-level regions of the wire clamp to obtain a multi-region nondestructive testing importance coefficient.

[0025] Furthermore, step P20 in this embodiment of the present application further includes:

[0026] 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 multi-area non-destructive testing importance coefficients.

[0027] 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.

[0028] First, the importance of each area of ​​the wire clamp is evaluated based on its function and operating characteristics. Each area's operating environment, stress conditions, and potential risks vary, determining its priority in NDT. For example, the clamping area, which directly bears the conductor's holding pressure and vibration, may receive a higher priority than other areas, while the connection area requires attention to the stability of its connection to the supporting structure. Through this evaluation, each area is assigned an importance factor, which reflects its criticality.

[0029] Next, historical data analysis is conducted using cable clamp usage logs. Combined with actual equipment usage and environmental factors, the damage probability of each area is predicted. Cable clamp usage logs typically include information such as the equipment's workload, environmental conditions, and maintenance history. By analyzing this data, the likelihood of damage occurring in each area over the next period of time can be estimated. Areas exposed to long-term extreme weather or high-frequency vibration are likely to have a higher probability of damage, while areas less exposed to stress or interference have a lower probability of damage.

[0030] After obtaining the importance coefficient and damage probability for each region, the next step is to normalize these two values ​​so that they can be compared under the same criteria. The normalization process typically adjusts the values ​​to a range of 0 to 1, ensuring that the scores for each region are on a consistent scale. The normalized data will form a regional evaluation matrix, in which each element corresponds to a comprehensive evaluation score for each region, reflecting the region's overall importance and damage risk.

[0031] Finally, based on the regional evaluation matrix, a weighted calculation is performed on each region to generate the final multi-region NDT importance coefficient. The weighted calculation takes into account both the importance weight and the damage probability weight. The importance weight reflects the impact of the regional function on the overall equipment performance, while the damage probability weight reflects the risk level of regional damage. By combining these two weights, the importance of each region in NDT can be more accurately assessed, providing data support for the selection of NDT equipment and testing plans. The weighted multi-region NDT importance coefficient can provide a basis for subsequent testing processes. Regions with high importance coefficients will receive priority testing, while regions with low importance coefficients can have their testing frequency or intensity appropriately reduced, thereby optimizing resource allocation and improving the overall efficiency and accuracy of NDT.

[0032] P30: Based on the multi-region NDT importance coefficient, the NDT analysis record learning of the H-type wire clamp is performed according to the NDT analysis learning configuration network, and a dual-channel NDT analysis for the wire clamp is established, wherein the dual-channel NDT analysis for the wire clamp includes an internal NDT analysis channel for the wire clamp and a surface NDT analysis channel for the wire clamp.

[0033] Furthermore, step P30 in the embodiment of the present application further includes:

[0034] P31: Input 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; P32: 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; 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 Internal flaw detection analysis channel for the clamp; 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.

[0035] Optionally, based on the calculated multi-region NDT importance coefficients, a flaw analysis and learning configuration network is used to perform NDT analysis and record learning on H-type wire clamps, thereby establishing a dual-channel NDT analysis system for the wire clamps. This dual-channel system includes both internal and surface flaw analysis for the wire clamps. This dual-dimensional flaw analysis method comprehensively analyzes potential defects in different areas, ensuring efficient and accurate NDT.

[0036] First, the importance coefficients of the multi-region NDT are input into the NDT analysis learning configuration network. This network determines the learning configuration results for 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 NDT analysis is improved. For example, due to its higher importance coefficient, the clamping area will generate multiple learning configuration models to ensure higher NDT analysis accuracy; for regions with lower importance, the number of models or accuracy requirements may be reduced. Through the intelligent configuration network, inspection resources can be allocated to different regions more accurately, and learning configuration results for the clamping area, the connection area, and the central area can be obtained.

[0037] The clamping area will have a larger number of learned configuration models and higher resolution accuracy, ensuring accurate detection of potential cracks, corrosion, and other defects. The connection area is similar to the clamping area, but may have a slightly smaller number of configuration models and lower accuracy requirements, focusing on stability and wear at the connection. The central area is relatively stable, with fewer learned configuration models and a focus on detecting potential structural defects.

[0038] Next, based on the learning configuration results of each area, the internal flaw detection analysis characteristics of the H-type wire clamp are learned. By specifically analyzing the characteristics of different areas, the following internal flaw detection analysis branches are constructed, including: the internal flaw detection analysis branch of the clamping area, which mainly studies the potential damage patterns within 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 within the connection part, such as corrosion, welding defects, and material deformation. The internal flaw detection analysis branch of the central 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.

[0039] After learning the internal flaw analysis features for each region, the clamping region internal flaw analysis branch, the connection region internal flaw analysis branch, and the middle region internal flaw analysis branch are connected as parallel nodes. This parallelized structure generates the wire clamp internal flaw analysis channel. This channel can simultaneously process inspection data from different regions, providing more comprehensive internal flaw analysis results.

[0040] Furthermore, in addition to internal flaw detection, surface flaw detection is also a key component in evaluating the condition of wire clamps. At this stage, based on the learning configuration results for each area, the surface flaw detection characteristics of the H-type wire clamp are learned and constructed: a surface flaw detection analysis branch for the clamping area detects problems such as cracks, corrosion, and peeling that may occur on the surface of the clamping area. A surface flaw detection analysis branch for the connection area analyzes defects such as surface wear, cracks, or surface corrosion in the connection area. A surface flaw detection analysis branch for the central area 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 all areas of the H-type wire clamp.

[0041] 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.

[0042] 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 non-destructive 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 channels work together to handle the internal and surface defect detection tasks of the wire clamp, respectively. They can perform non-destructive testing of H-type wire clamps efficiently and comprehensively, ensuring that both internal and surface defects can be accurately identified and evaluated.

[0043] Furthermore, step P32 of the embodiment of the present application further includes:

[0044] 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 the clamping area ultrasonic detection record set and the 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, the multiple clamping area learning configuration models are supervised and trained respectively to generate multiple models that meet the clamping area flaw detection analysis accuracy. clamping area internal flaw detection parsers; 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, train the 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.

[0045] 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.

[0046] First, based on the clamping area learning configuration results, multiple machine learning models, such as decision trees and neural networks, are constructed. These models are determined based on the importance coefficients of previous nondestructive testing, with high model accuracy and quantity set to account for the complexity and importance of the clamping area. Each model is trained with a focus on different types of defects (such as cracks, corrosion, and wear) and different operating environments to ensure accurate analysis of the clamping area's flaw detection results. Furthermore, the clamping area flaw analysis accuracy is set to ensure that each model achieves the expected detection accuracy when processing real-world data.

[0047] To fully utilize historical data, a global search of internal flaw detection and analysis records is performed. This search is not limited to the clamping area of ​​the current H-type wire clamp, but also includes data from the clamping areas of multiple H-type wire clamps of the same model. By acquiring more data, the model can learn a wider range of patterns and features, thereby improving its generalization ability. This global search obtains a set of ultrasonic inspection records for the clamping area, as well as a set of internal flaw detection and analysis records for the clamping area. This data serves as the foundation for model training, ensuring the generated model has good adaptability and accuracy.

[0048] Next, supervised training of the learning configuration model for multiple clamping areas is performed based on the retrieved clamping area ultrasonic inspection records and internal flaw analysis records. During training, the model self-optimizes by using annotated data (such as the specific location and type of defects such as cracks and corrosion), continuously adjusting parameters to improve the accuracy of clamping area flaw analysis and ensure that more possible defect types can be identified in practical applications. This generates a model that meets the required accuracy for clamping area flaw analysis, providing a precise basis for decision-making in subsequent flaw detection processes.

[0049] Next, the output datasets from multiple internal clamping region flaw detection parsers were used as input and compared with the internal clamping region flaw detection parsing record set to train the internal clamping region flaw detection parser. This fusion model effectively fuses the output results of multiple models to further improve the overall accuracy of flaw detection results. The fusion model automatically adjusts the weights of different models to ensure that the output results are more consistent with actual flaw detection requirements, thereby reducing the potential for misjudgments or omissions caused by a single model.

[0050] The trained internal clamping region flaw detection parsers are connected together to form a single internal clamping region flaw detection parsing layer. This parsing layer processes input data from multiple models and generates the final flaw detection results. This layered approach not only improves data processing efficiency but also ensures that even complex defects are detected.

[0051] Finally, the clamping area internal flaw detection analysis layer is merged with the input layer of the clamping area internal flaw detection analysis fusion unit to generate a complete clamping area internal flaw detection analysis branch. This branch organically integrates the entire flaw detection process, fusing the analysis results of different models to ensure the final flaw detection conclusions are highly reliable and accurate. This branch enables the system to efficiently perform internal flaw detection analysis in the clamping area during H-type wire clamp inspections, promptly identifying potential defects.

[0052] 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.

[0053] Specifically, based on the multi-region nondestructive testing importance coefficient, comprehensive testing is performed on multiple regions of the H-type wire clamp using different nondestructive testing devices (ultrasonic testing device and magnetic particle testing device).

[0054] Ultrasonic testing is used to detect potential defects within the wire clamp, such as cracks, voids, and delamination. Compared to traditional testing methods, ultrasonic testing offers deeper detection capabilities, penetrating the metal surface and penetrating deep into the material, revealing hidden defects that are invisible to the naked eye. In this step, ultrasonic testing focuses on targeted inspection of each area based on the importance factor of multi-area nondestructive testing. For areas with higher importance factors (such as the clamping area), ultrasonic testing is more focused, requiring higher frequency and accuracy to ensure accurate detection of potential defects.

[0055] During implementation, the ultrasonic probe emits high-frequency sound waves and receives the reflected waveform, analyzing the changes in the waveform to determine the internal condition of the material. By collecting ultrasonic test data from various areas, a preliminary judgment can be made as to whether each area has internal defects.

[0056] Unlike ultrasonic testing, magnetic particle testing is primarily 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 primarily used to detect surface defects. This method involves applying fine magnetic powder to the metal surface under the influence of a magnetic field, creating a surface pattern. If cracks or other defects are present on the metal surface, the magnetic powder will accumulate at the defect, creating a noticeable change in the magnetic field. This allows the defect to be detected visually or with scanning equipment.

[0057] Depending on the importance of non-destructive testing, magnetic particle testing will use different inspection intensities in different areas. For example, the clamping area may be more prone to surface cracks due to the high mechanical stress and friction it bears. Therefore, magnetic particle testing will increase the inspection frequency and accuracy of this area.

[0058] After ultrasonic and magnetic particle testing are performed on each area, the resulting multi-area ultrasonic and magnetic particle testing data serve as the foundation for subsequent processing. Ultrasonic testing primarily provides data support for internal defects, while magnetic particle testing focuses on detecting surface defects. Combining these two methods enables comprehensive inspection of all areas of the H-type wire clamp, ensuring reliability in various operating environments.

[0059] 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.

[0060] Furthermore, to generate multi-region ultrasonic detection results, step P50 of the embodiment of the present application further includes:

[0061] P51: Collect the ultrasonic detection scene parameters corresponding to the multi-region ultrasonic detection data to obtain the ultrasonic detection scenes of each region; P52: Perform detection interference identification based on the ultrasonic detection scenes of each region to obtain the ultrasonic detection interference identification results of each region; P53: Input the multi-region ultrasonic detection data and the ultrasonic detection interference identification results of each region into the ultrasonic detection interference correction model to obtain the ultrasonic detection correction results of each region; P54: Output the ultrasonic detection correction results of each region as the multi-region ultrasonic detection results.

[0062] Optionally, 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 and magnetic particle test results.

[0063] First, collect the ultrasonic testing scenario parameters for each area. These scenario parameters include information such as the equipment configuration used during ultrasonic testing, probe angle, frequency, distance between the probe and the test surface, and environmental conditions. These parameters provide a crucial basis for subsequent interference identification and correction, as different parameter combinations can affect ultrasonic beam propagation and echo signal reception, introducing interference or errors. Therefore, accurately recording and analyzing these parameters is key to ensuring effective correction.

[0064] After collecting the ultrasonic inspection scene parameters for each area, interference identification is required. Interference can come from a variety of sources, such as environmental noise, equipment failure, and improper probe installation. During this phase, the relationship between the ultrasonic inspection data for each area and the scene parameters is analyzed to identify interference factors that may affect data accuracy. For example, improper probe angle settings or surface irregularities can cause echo signal distortion or loss, affecting the identification of defects. The purpose of interference identification is to identify these issues and determine the specific corrections that need to be made.

[0065] After interference is identified, the multi-region ultrasonic inspection data and ultrasonic interference identification results are input into the ultrasonic interference correction model. Based on historical data and algorithms, this correction model automatically corrects the identified interference. Correction methods may include adjusting signal parameters such as amplitude, frequency, and time delay, or using algorithms to remove noise introduced by the interference. This process significantly improves the reliability of the inspection data and results in more accurate final inspection results.

[0066] After calibration is complete, the ultrasonic test calibration results for each region are output as the final results, generating multi-region ultrasonic test results. These corrected results are used for further defect assessment and decision support, ensuring that the ultrasonic test data for each region accurately reflects the actual material condition and is unaffected by interference. This series of interference correction steps effectively improves the accuracy of ultrasonic testing, reduces errors caused by equipment, environment, or operation, and provides reliable basic data for subsequent defect analysis and assessment.

[0067] Furthermore, to generate multi-region magnetic particle inspection results, step P50 of the embodiment of the present application further includes:

[0068] P55: Collect magnetic particle inspection scene parameters corresponding to the multi-region magnetic particle inspection data to obtain magnetic particle inspection scenes in each region; P56: Perform detection interference identification based on the magnetic particle inspection scenes in each region to obtain magnetic particle inspection interference identification results in each region; P57: Input the multi-region magnetic particle inspection data and the magnetic particle inspection interference identification results in each region into a magnetic particle inspection interference correction model to obtain magnetic particle inspection correction results in each region; P58: Output the magnetic particle inspection correction results in each region as the multi-region magnetic particle inspection result.

[0069] Optionally, in addition to performing interference correction on the ultrasonic test data, similar test interference correction is also required for the multi-region test data obtained from the magnetic particle testing device to ensure that the results obtained from the magnetic particle testing are more accurate and to avoid test errors caused by environmental factors or operational errors.

[0070] Similar to ultrasonic testing, magnetic particle testing requires collecting scene parameters for each area. These include the type of magnetic powder, magnetic field strength, powder application method, equipment used during testing, and probe configuration. Scene parameters may also include the distribution of magnetic powder on the metal surface and surrounding environmental conditions (such as temperature and humidity). These parameters are crucial for subsequent interference identification and correction, as improper magnetic field application or uneven magnetic powder distribution can distort test results.

[0071] After obtaining the magnetic particle inspection scene parameters for each area, interference identification is required for each area's magnetic particle inspection data. Interference in magnetic particle inspection primarily arises from factors such as surface unevenness, magnetic field instability, and improper operation during operation. For example, excessively high or low magnetic field strength or uneven magnetic powder coating can prevent the powder from properly accumulating on cracks or defects, affecting defect visibility. Through in-depth analysis of inspection scene parameters and data, these potential interferences can be identified and the corresponding interference identification results recorded.

[0072] Next, the interference-identified magnetic particle inspection data and the interference identification results are fed into the magnetic particle inspection interference correction model. This model corrects errors in the inspection data by compensating for and correcting the identified interference. Based on historical data and machine learning techniques, the correction model automatically adjusts for the effects of interference and provides more accurate inspection results. For example, if uneven magnetic field application results in incomplete magnetic particle distribution in certain areas, the model can automatically adjust the inspection results in those areas to eliminate the effects of the unevenness.

[0073] Finally, the corrected magnetic particle inspection results for each area are output to form the final multi-area magnetic particle inspection results. These results serve as the basis for subsequent analysis, helping to identify potential defects or safety hazards in each area. Interference correction makes inspection results more accurate and reliable, effectively improving the safety and service life of the H-type wire clamp.

[0074] P60: Input the multi-region ultrasonic testing results and the multi-region magnetic particle testing results into the wire clamp non-destructive testing analysis dual channel to generate a wire clamp non-destructive testing report.

[0075] Furthermore, step P60 of the embodiment of the present application further includes:

[0076] 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 connection 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 connection 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 connection area internal flaw detection analysis results, the middle area internal flaw detection analysis results, the clamping area surface flaw detection analysis results, the connection area surface flaw detection analysis results and the middle area surface flaw detection analysis results to obtain the wire clamp non-destructive testing report.

[0077] It should be understood that after interference correction, the multi-region ultrasonic testing results and the multi-region magnetic particle testing results are input into the wire clamp non-destructive testing analysis dual channels to generate a final wire clamp non-destructive testing report.

[0078] First, the multi-zone ultrasonic testing results are input into the wire clamp internal flaw detection analysis channel. This channel is specifically designed to process data related to the wire clamp's internal structure, particularly for analyzing internal defects in the clamping, connection, and central regions. Based on the characteristics of each region, ultrasonic testing primarily focuses on detecting deep-seated defects such as cracks, holes, and delamination. By inputting these results into the internal flaw detection analysis channel, internal flaw detection analysis results for each region can be obtained. This includes internal flaw detection analysis results for the clamping region, which can detect the presence of internal structural defects such as cracks and pores. Internal flaw detection analysis results for the connection region can be used to analyze whether the connection has internal damage caused by stress or environmental influences. Internal flaw detection analysis results for the central region are used to assess the stability of the central region and check for potential fatigue cracks or corrosion.

[0079] 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 condition. 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, which detects 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 the middle area has surface damage caused by environmental changes or long-term use.

[0080] Finally, the flaw analysis results for all areas are collated, including internal and surface flaw analysis results for the clamping area, the connection area, and the central area. These results are combined and summarized to generate a complete nondestructive testing report for the cable clamp. This report details the test results for each area, the potential defects found, and their potential impact. This report not only provides inspection data for each area but also provides a basis for subsequent repairs, maintenance, and risk assessments, helping to extend the service life of the equipment and improve power system stability.

[0081] In summary, the embodiments of the present application have at least the following technical effects:

[0082] This application divides the H-type wire clamp into structural areas, obtains 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.

[0083] The technical effect of improving the accuracy, comprehensiveness and efficiency of non-destructive testing of H-type wire clamps is achieved.

[0084] Embodiment 2 is 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 H-type wire clamps. The system and method embodiments in the present application are based on the same inventive concept. The system includes:

[0085] The structural feature division module 11 is used to divide the structural features of the H-type wire clamp 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.

[0086] The non-destructive testing importance evaluation module 12 is used to perform non-destructive testing importance evaluation based on the multi-level areas of the wire clamp to obtain multi-area non-destructive testing importance coefficients.

[0087] The dual-channel analysis construction 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 the flaw detection analysis learning configuration network, 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 an internal flaw detection analysis channel for the wire clamp and a surface flaw detection analysis channel for the wire clamp.

[0088] 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 using an ultrasonic detection device and a magnetic particle detection device to obtain multi-region ultrasonic detection data and multi-region magnetic particle detection data.

[0089] 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 multi-region ultrasonic detection results and multi-region magnetic particle detection results.

[0090] The test report generating 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.

[0091] Furthermore, the non-destructive testing importance evaluation module 12 is further configured to perform the following steps:

[0092] 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 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 according to the importance weights of the wire clamp areas and the damage probability weights of the wire clamp areas to generate the multi-area non-destructive testing importance coefficients.

[0093] Furthermore, the dual-channel analysis building module 13 is further configured to perform the following steps:

[0094] 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 line The invention provides an internal flaw detection analysis channel for the 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 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; 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 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.

[0095] Furthermore, the dual-channel analysis building module 13 is further configured to perform the following steps:

[0096] 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 based on 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 based on 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.

[0097] Furthermore, the interference detection and correction module 15 is further configured to perform the following steps:

[0098] 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 based on 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; and output the ultrasonic detection correction results in each region as the multi-region ultrasonic detection results.

[0099] Furthermore, the interference detection and correction module 15 is further configured to perform the following steps:

[0100] Magnetic particle inspection scene parameters corresponding to the multi-region magnetic particle inspection data are collected to obtain magnetic particle inspection scenes in each region; detection interference identification is performed based on the magnetic particle inspection scenes in each region to obtain magnetic particle inspection interference identification results in each region; the multi-region magnetic particle inspection data and the magnetic particle inspection interference identification results in each region are input into a magnetic particle inspection interference correction model to obtain magnetic particle inspection correction results in each region; and the magnetic particle inspection correction results in each region are output as the multi-region magnetic particle inspection result.

[0101] Furthermore, the test report generating module 16 is further configured to perform the following steps:

[0102] The multi-region ultrasonic detection results are input into the wire clamp internal flaw detection analysis channel to obtain the clamping area internal flaw detection analysis results, the connection area internal flaw detection analysis results and the middle area internal flaw detection analysis results; the multi-region magnetic particle detection results are input into the wire clamp surface flaw detection analysis channel to obtain the clamping area surface flaw detection analysis results, the connection area surface flaw detection analysis results and the middle area surface flaw detection analysis results; the clamping area internal flaw detection analysis results, the connection area internal flaw detection analysis results, the middle area internal flaw detection analysis results, the clamping area surface flaw detection analysis results, the connection area surface flaw detection analysis results and the middle area surface flaw detection analysis results are sorted to obtain the wire clamp non-destructive testing report.

[0103] It should be noted that the order in which the embodiments of the present application are presented is for illustrative purposes only and does not necessarily represent the superiority or inferiority of the embodiments. Furthermore, the foregoing descriptions of specific embodiments of this specification are provided. Furthermore, the processes depicted in the accompanying drawings do not necessarily require the specific order or sequential sequence shown to achieve the desired results. In certain embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0104] The above description is only a preferred embodiment of the present application and is not intended to limit the present application. Any modifications, equivalent replacements, improvements, etc. made within the spirit and principles of the present application should be included in the scope of protection of the present application.

[0105] This specification and drawings are merely illustrative of the present application and are intended to cover any and all modifications, variations, combinations, or equivalents within the scope of this application. Obviously, those skilled in the art may make various modifications and variations to this application without departing from the scope of this application. Thus, this application is intended to include such modifications and variations as fall within the scope of this application and its equivalents.

Claims

1. A nondestructive testing method for H-type wire clamps, 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 NDT importance coefficient, the NDT analysis record learning of the H-type wire clamp is performed according to the NDT analysis learning configuration network, and a NDT analysis dual channel for the wire clamp is established, wherein the NDT analysis dual channel for the wire clamp includes an internal NDT analysis channel for the wire clamp and a surface NDT analysis channel for the wire clamp; Based on the multi-region non-destructive testing importance coefficient, the multi-layered 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; 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; Inputting the multi-region ultrasonic testing results and the multi-region magnetic particle testing results into the wire clamp non-destructive testing analysis dual channel to generate a wire clamp non-destructive testing report; 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 an ultrasonic detection correction result for each region; Outputting the ultrasonic detection correction results of each region as the multi-region ultrasonic detection results; Based on the multi-region NDT importance coefficient, the NDT analysis record learning of the H-type wire clamp is performed according to the NDT analysis learning configuration network, and a NDT analysis dual channel for the wire clamp is established. The NDT analysis dual channel for the wire clamp includes a wire clamp internal NDT analysis channel and a wire clamp surface NDT 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 results, the connection area learning configuration results, and the middle area learning configuration results, 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 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 connected as parallel nodes to generate the wire clamp internal flaw detection analysis channel; Based on the clamping area learning configuration results, the connection area learning configuration results, and the middle area learning configuration results, 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 wire clamp internal flaw detection and analysis channel and the wire clamp surface flaw detection and analysis channel are packaged into the wire clamp non-destructive flaw detection and analysis dual channel.

2. The method according to claim 1, wherein According to the importance evaluation of nondestructive testing of the multi-level areas of the wire clamp, the importance coefficient of nondestructive testing of multiple areas is obtained, including: Performing importance evaluation on the multi-level regions of the wire clamp to obtain the importance coefficient of each region; According to the H-type wire clamp usage log, 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 based on the importance coefficient of each area and the damage probability of each area to establish 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 multi-area non-destructive testing importance coefficient.

3. The method according to claim 1, wherein Based on the clamping area learning configuration results, the connection area learning configuration results, and the middle area learning configuration results, 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, 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; Based on the clamping area ultrasonic detection record set and the clamping area internal flaw detection analysis record set, the multiple clamping area learning configuration models are respectively supervised trained to generate multiple 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 parsers as input information and the clamping area internal flaw detection parsing record set as output information, training a clamping area internal flaw detection parsing 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.

4. The method according to claim 1, wherein 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 inspection scene parameters corresponding to the multi-region magnetic particle inspection data to obtain magnetic particle inspection 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 for each region; The correction results of the magnetic particle inspection of each region are output as the multi-region magnetic particle inspection result.

5. The method according to claim 1, wherein 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, including: Inputting 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; Inputting the multi-region magnetic particle inspection 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 out to obtain the non-destructive testing report of the wire clamp.

6. A non-destructive testing system for H-type wire clamps, characterized in that: The system comprises: A structural feature division module, wherein the structural feature division module is used to divide the H-type wire clamp 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; A nondestructive testing importance evaluation module, wherein the nondestructive testing importance evaluation module is used to perform nondestructive testing importance evaluation based on the multi-level areas of the wire clamp to obtain a multi-area nondestructive testing importance coefficient; A dual-channel analysis module is configured 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 a flaw analysis learning configuration network, thereby building 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 analysis channel and a wire clamp surface flaw 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 using an ultrasonic detection device and a magnetic particle detection device to obtain multi-region ultrasonic detection data and multi-region magnetic particle detection data; a detection interference correction module, the detection interference correction module being used to perform 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; a test report generation module, the test report generation module being configured to input the multi-region ultrasonic test results and the multi-region magnetic particle test results into the wire clamp nondestructive testing analysis dual channel to generate a wire clamp nondestructive testing report; The interference detection and correction module further performs the following steps: Collecting ultrasonic detection scene parameters corresponding to the multi-region ultrasonic detection data to obtain ultrasonic detection scenes in each region; performing detection interference identification based on the ultrasonic detection scenes in each region to obtain ultrasonic detection interference identification results in each region; inputting 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; and outputting the ultrasonic detection correction results in each region as the multi-region ultrasonic detection results; The analysis dual-channel building block is further configured 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 line The invention provides an internal flaw detection analysis channel for the 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 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; 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 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.

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