Support hanger defect detection method, device, equipment, medium and product

Abstract

The application relates to the technical field of industrial equipment support and hanger detection and intelligent operation and maintenance, in particular to a support and hanger defect detection method, device, equipment, medium and product, which comprises the following steps: collecting image data of a current support and hanger to be detected; identifying the image data to obtain an identification result, and determining a defect type and a defect degree level of the current support and hanger to be detected based on a preset support and hanger standard library in the case that the identification result meets a preset abnormal state; and generating a support and hanger defect detection report according to the image data, the identification result, the defect type and the defect degree level. Thus, the problems that manual inspection depends on the experience of professional personnel, is low in efficiency, is easy to miss, is not unified in judgment standard and is difficult to manage due to scattered data are solved, automatic detection, standardized grading and report generation of support and hanger defects are realized, and thus personnel threshold is reduced, inspection efficiency and data traceability are improved.

Description

Technical Field

[0001] This application relates to the field of industrial equipment support and hanger inspection and intelligent operation and maintenance technology, and in particular to a method, device, equipment, medium and product for detecting defects in supports and hangers. Background Technology

[0002] In existing large-scale industrial plants such as thermal power, chemical, and nuclear power, various types of support and hanger structures are widely used for load bearing, limiting, or guiding. These supports and hangers are not only used in pipeline systems, but also widely used for supporting and fixing boilers, pressure vessels, heat exchangers, fans, pumps, flues, and other industrial equipment. Their operating status is directly related to the safety and stability of the equipment and systems, and regular inspections and condition assessments are required.

[0003] Currently, the inspection of equipment and pipeline supports mainly relies on on-site visual inspection, experience-based judgment, and manual recording by professional technicians. This method is highly dependent on the professional skills of the inspectors and has low inspection efficiency. Due to the diverse types, complex structures, large numbers, and dispersed distribution of supports, defects exhibit a variety of forms, making it difficult for non-professionals to accurately identify abnormal conditions. Furthermore, the complex on-site environment and the reliance on manual recording methods can easily lead to information omissions, non-standard descriptions, and inconsistent defect judgment criteria, affecting the accuracy of subsequent maintenance decisions.

[0004] Furthermore, traditional inspections are mostly recorded in paper form or simple spreadsheets, with image data and text records stored separately. This lack of systematic and standardized management methods makes it difficult to achieve efficient utilization, rapid retrieval, and sharing of inspection data. Existing image-assisted inspection solutions are largely limited to image acquisition and simple recording functions, failing to achieve intelligent identification, automatic classification, and grading of support and hanger defects. Inspection results still require manual compilation and summarization, making it difficult to meet the standardized, intelligent, and digital management needs of modern industrial installations for support and hanger inspections.

[0005] Therefore, there is an urgent need for an intelligent inspection and defect identification system suitable for equipment and pipeline supports to reduce the difficulty of inspection for non-professionals, improve the work efficiency of professionals, and ensure the safe operation of various supports and the equipment they support. Summary of the Invention

[0006] This application provides a method, apparatus, equipment, medium, and product for detecting defects in supports and hangers, which solves the problems of manual inspection relying on the experience of professional personnel, low efficiency, easy omissions, inconsistent judgment standards, and scattered and difficult-to-manage data. It realizes the automated detection, standardized classification, and report generation of defects in supports and hangers, thereby reducing the personnel threshold, improving inspection efficiency, and data traceability.

[0007] The first aspect of this application provides a method for detecting defects in supports and hangers, including the following steps: Collect image data of the support or hanger to be inspected; The image data is identified to obtain identification results. If the identification results meet the preset abnormal state, the defect type and defect severity level of the current support to be detected are determined based on the preset support standard library. A support / hanger defect detection report is generated based on the image data, the identification results, the defect type, and the defect severity level.

[0008] According to one embodiment of this application, the acquisition of image data of the current support to be detected includes: Obtain the identification information of the support and hanger to be inspected, and retrieve the corresponding structural type and load-bearing parameters from the preset support and hanger parameter database based on the identification information; Based on the structural type and load-bearing parameters, the shooting angle and focal length are determined, and the current support to be inspected is photographed from multiple angles to obtain the image data.

[0009] According to one embodiment of this application, the step of identifying the image data to obtain an identification result includes: Feature extraction is performed on the image data to obtain a defect feature map; The similarity comparison is performed between the defect feature map and the reference features of each type of defect in the preset support and hanger defect type library to obtain the similarity comparison result. Based on the similarity comparison results, at least one candidate defect type and a confidence level corresponding to each candidate defect type are output, and the identification result is obtained based on the confidence level corresponding to each candidate defect type.

[0010] According to one embodiment of this application, determining the defect type and defect severity level of the current support to be inspected based on a preset support and hanger standard library includes: Retrieve the defect judgment rule corresponding to the current support or hanger to be inspected from the support and hanger standard library. The defect judgment rule includes the severity grading standard corresponding to different defect types. Obtain the measured parameters of the current support and hanger to be detected in the image data. The measured parameters include at least one of displacement, gap size, corrosion area, or fastener loosening angle. The measured parameters are matched with the defect judgment rules to determine the defect severity level of the current support to be inspected. The defect severity level includes a first defect level, a second defect level, and a third defect level. Wherein, the second defect level is higher than the first defect level, and the third defect level is higher than the second defect level.

[0011] According to one embodiment of this application, the method for detecting defects in supports and hangers further includes: Obtain the three-dimensional spatial coordinates of the current support to be inspected, and map the defect type and the defect severity level to the pre-constructed three-dimensional digital model of the support to obtain the defect visualization and location results; The defect visualization and location results are added to the support and hanger defect detection report.

[0012] According to the support and hanger defect detection method provided in this application embodiment, image data of the support and hanger to be inspected is collected; the image data is identified to obtain the identification result; and if the identification result meets the preset abnormal state, the defect type and defect severity level of the support and hanger to be inspected are determined based on a preset support and hanger standard library; and a support and hanger defect detection report is generated based on the image data, identification result, defect type, and defect severity level. This solves the problems of manual inspection relying on professional experience, low efficiency, easy omissions, inconsistent judgment standards, and scattered and difficult-to-manage data, realizing automated detection, standardized grading, and report generation of support and hanger defects, thereby reducing the personnel threshold, improving inspection efficiency, and data traceability.

[0013] A second aspect of this application provides a support and hanger defect detection device, comprising: The acquisition module is used to acquire image data of the support and hanger to be inspected. The detection module is used to identify the image data, obtain the identification result, and, if the identification result meets the preset abnormal state, determine the defect type and defect severity level of the current support to be detected based on the preset support standard library; The generation module is used to generate a support defect detection report based on the image data, the recognition result, the defect type, and the defect severity level.

[0014] According to one embodiment of this application, the acquisition module is used for: Obtain the identification information of the support and hanger to be inspected, and retrieve the corresponding structural type and load-bearing parameters from the preset support and hanger parameter database based on the identification information; Based on the structural type and load-bearing parameters, the shooting angle and focal length are determined, and the current support to be inspected is photographed from multiple angles to obtain the image data.

[0015] According to one embodiment of this application, the detection module is used for: Feature extraction is performed on the image data to obtain a defect feature map; The similarity comparison is performed between the defect feature map and the reference features of each type of defect in the preset support and hanger defect type library to obtain the similarity comparison result. Based on the similarity comparison results, at least one candidate defect type and a confidence level corresponding to each candidate defect type are output, and the identification result is obtained based on the confidence level corresponding to each candidate defect type.

[0016] According to one embodiment of this application, the detection module is used for: Retrieve the defect judgment rule corresponding to the current support or hanger to be inspected from the support and hanger standard library. The defect judgment rule includes the severity grading standard corresponding to different defect types. Obtain the measured parameters of the current support and hanger to be detected in the image data. The measured parameters include at least one of displacement, gap size, corrosion area, or fastener loosening angle. The measured parameters are matched with the defect judgment rules to determine the defect severity level of the current support to be inspected. The defect severity level includes a first defect level, a second defect level, and a third defect level. Wherein, the second defect level is higher than the first defect level, and the third defect level is higher than the second defect level.

[0017] According to one embodiment of this application, the support and hanger defect detection device is further used for: Obtain the three-dimensional spatial coordinates of the current support to be inspected, and map the defect type and the defect severity level to the pre-constructed three-dimensional digital model of the support to obtain the defect visualization and location results; The defect visualization and location results are added to the support and hanger defect detection report.

[0018] According to the support and hanger defect detection device provided in this application embodiment, image data of the support and hanger to be inspected is collected; the image data is identified to obtain the identification result; and if the identification result meets the preset abnormal state, the defect type and defect severity level of the support and hanger to be inspected are determined based on the preset support and hanger standard library; and a support and hanger defect detection report is generated based on the image data, identification result, defect type, and defect severity level. This solves the problems of manual inspection relying on professional personnel experience, low efficiency, easy omissions, inconsistent judgment standards, and scattered and difficult-to-manage data, realizing automated detection, standardized grading, and report generation of support and hanger defects, thereby reducing the personnel threshold, improving inspection efficiency, and data traceability.

[0019] A third aspect of this application provides an electronic device, including: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the program to implement the support and hanger defect detection method as described in the above embodiments.

[0020] A fourth aspect of this application provides a computer-readable storage medium storing computer instructions for causing the computer to perform the support and hanger defect detection method as described in the above embodiments.

[0021] A fifth aspect of this application provides a computer program product, including a computer program that, when executed by a processor, implements the support and hanger defect detection method as described in the above embodiments.

[0022] Additional aspects and advantages of this application will be set forth in part in the description which follows, and in part will be obvious from the description, or may be learned by practice of this application. Attached Figure Description

[0023] The above and / or additional aspects and advantages of this application will become apparent and readily understood from the following description of the embodiments taken in conjunction with the accompanying drawings, wherein: Figure 1 This is a flowchart of a method for detecting defects in supports and hangers according to an embodiment of this application; Figure 2 This is a block diagram of a support and hanger defect detection device according to an embodiment of this application; Figure 3 This is a schematic diagram of the structure of an electronic device provided in an embodiment of this application. Detailed Implementation

[0024] The embodiments of this application are described in detail below. Examples of these embodiments are shown in the accompanying drawings, wherein the same or similar reference numerals denote the same or similar elements or elements having the same or similar functions throughout. The embodiments described below with reference to the accompanying drawings are exemplary and intended to explain this application, and should not be construed as limiting this application.

[0025] The following description, with reference to the accompanying drawings, describes a method, apparatus, equipment, medium, and product for detecting defects in supports and hangers according to embodiments of this application. Addressing the problems mentioned in the background art, such as reliance on professional experience, low efficiency, susceptibility to omissions, inconsistent judgment standards, and fragmented and difficult-to-manage data in manual inspection, this application provides a method for detecting defects in supports and hangers. In this method, image data of the support or hanger to be inspected is collected; the image data is identified to obtain an identification result; and if the identification result meets a preset abnormal state, the defect type and defect severity level of the support or hanger to be inspected are determined based on a preset support and hanger standard library; a support and hanger defect detection report is generated based on the image data, identification result, defect type, and defect severity level. This solves the problems of reliance on professional experience, low efficiency, susceptibility to omissions, inconsistent judgment standards, and fragmented and difficult-to-manage data in manual inspection, achieving automated detection, standardized grading, and report generation of support and hanger defects, thereby reducing the personnel threshold, improving inspection efficiency, and enhancing data traceability.

[0026] Before introducing the support and hanger defect detection method of this application, let me first introduce the support and hanger defect detection system involved in the support and hanger defect detection method of this application. This system realizes intelligent identification, automatic classification and categorization of various support and hanger defects and automatic generation of inspection reports by constructing a support and hanger sample database, defect identification model and standardized judgment system, thereby improving the efficiency, accuracy and consistency of inspection work.

[0027] Specifically, the support and hanger defect detection system of this application includes: an image acquisition module, a support and hanger parameter database module, a defect identification and classification module, a defect judgment and grading module, a structured recording and editing module, a multimodal data binding module, an automatic report generation module, a wireless communication and data synchronization module, and a manual feedback and system optimization module. The system adopts a modular architecture design, and each functional module can be enabled, configured, or expanded according to the actual needs of different inspection units, equipment types, or system structures.

[0028] The image acquisition module is used to acquire image or video information of on-site equipment supports or pipe supports, forming individual sample data of supports. The image acquisition module includes a high-definition camera and / or a video camera, supports static photography and dynamic recording, and has functions such as automatic focus and low light compensation.

[0029] The structured recording and editing module is used to associate the corresponding support and hanger number and defect type after image acquisition is completed, automatically generate structured data entries, and support on-site supplementary text notes, voice descriptions and measurement data to achieve standardized recording of inspection information.

[0030] The support and hanger parameter database module is used to store basic information about supports and hangers, including support and hanger number, installation location, structural type, load-bearing parameters, allowable displacement and applicable equipment type, etc., to provide data support for defect identification and judgment.

[0031] The defect identification and classification module is based on image recognition algorithms. It analyzes the collected image data, automatically identifies common defects of various supports and hangers, and has a preset library of support and hanger defect types. The defect types include at least loose nuts, spring failure, component corrosion, jamming, and abnormal displacement. Each type of defect is accompanied by a text description and reference image. For defects that the system cannot accurately identify, it supports manual selection and supplementary annotation by inspectors.

[0032] The defect identification and classification module uses a pre-set support and hanger standard library and judgment rules to determine the nature and severity of identified defects, which is used to guide subsequent risk assessment and handling decisions.

[0033] The automatic report generation module is used to automatically format inspection data and generate support and hanger inspection reports according to preset templates. The reports support multiple output formats such as PDF, Word, and Excel, and can be exported in batches.

[0034] The wireless communication and data synchronization module is used to support the offline operation of the system and, when the network is available, to synchronize local inspection data to the cloud database or operation and maintenance management platform to achieve centralized data management and sharing.

[0035] The manual feedback and system optimization module is used to manually review and correct the defect identification and judgment results, and to update the review results back to the system database and identification model in order to continuously improve the system's identification accuracy and judgment reliability.

[0036] In addition, the system also includes a 3D digital modeling and defect visualization and location module as a custom function module, which is used to create equipment-level, system-level or area-level 3D digital models of supports and hangers according to inspection requirements, and map the identified defect information to the corresponding model position to realize the spatial location and visualization of defects.

[0037] The following details the method for detecting defects in supports and hangers as described in this application.

[0038] Specifically, Figure 1 This is a flowchart illustrating a method for detecting defects in supports and hangers provided in an embodiment of this application.

[0039] like Figure 1 As shown, the defect detection method for this support and hanger includes the following steps: In step S101, image data of the current support to be detected is acquired.

[0040] Furthermore, in some embodiments, acquiring image data of the current support to be inspected includes: obtaining the identification information of the current support to be inspected, and retrieving the corresponding structural type and load-bearing parameters from a preset support parameter database based on the identification information; taking multi-angle shots of the current support to be inspected based on the shooting angle and focal length determined by the structural type and load-bearing parameters to obtain image data.

[0041] Specifically, prior to the inspection, technicians bring a portable intelligent image acquisition and recording device to the site. The device is pre-loaded with basic parameter information of the equipment under inspection and its supports, including but not limited to the support number, model, structural form, installation location, affiliated equipment or system, design load, allowable displacement range, and applicable operating conditions. Simultaneously, the device is pre-loaded with common support defect types and corresponding technical descriptions. After starting the device, the system performs self-checks on the camera, storage unit, display, and input interfaces to confirm that the equipment is in normal working order.

[0042] Furthermore, the image acquisition module is used for on-site image acquisition. Technicians use the device's built-in camera to capture images or record videos of the supports and hangers being inspected. The image acquisition module supports multi-angle and multi-distance shooting, and automatically records time, shooting angle, and environmental information (such as lighting conditions) during the acquisition process. It supports continuous acquisition of multiple photos and videos to meet the needs of different inspection angles. The acquired images or videos serve as individual sample data for the supports and hangers, providing a data foundation for subsequent defect identification.

[0043] In step S102, the image data is identified to obtain the identification result. If the identification result meets the preset abnormal state, the defect type and defect severity level of the current support to be inspected are determined based on the preset support standard library.

[0044] Furthermore, in some embodiments, the identification of image data to obtain identification results includes: extracting features from the image data to obtain a defect feature map; comparing the defect feature map with the reference features of each type of defect in a preset support and hanger defect type library to obtain a similarity comparison result; outputting at least one candidate defect type and the confidence level corresponding to each candidate defect type based on the similarity comparison result; and obtaining the identification result based on the confidence level corresponding to each candidate defect type.

[0045] Furthermore, in some embodiments, based on a preset support and hanger standard library, the defect type and defect severity level of the current support and hanger to be inspected are determined, including: retrieving the defect judgment rule corresponding to the current support and hanger to be inspected from the support and hanger standard library, the defect judgment rule including severity grading standards corresponding to different defect types; acquiring the measured parameters of the current support and hanger to be inspected in the image data, the measured parameters including at least one of displacement, gap size, corrosion area or fastener loosening angle; matching the measured parameters with the defect judgment rule to determine the defect severity level of the current support and hanger to be inspected, the defect severity level including a first defect level, a second defect level and a third defect level; wherein, the second defect level is higher than the first defect level, and the third defect level is higher than the second defect level.

[0046] Specifically, the system first utilizes a structured recording and editing module to edit and bind on-site inspection information. After image acquisition, technicians select the corresponding support / hanger number on the device interface, and the system automatically matches the support / hanger parameter information. Technicians can input inspection information in the following ways: either the system automatically identifies and recommends defect types, or they manually select the defect category and add a description. The system supports multiple input methods, including text input, voice-to-text, and preset defect tags, and binds the inspection description with the corresponding image or video to generate structured inspection data entries.

[0047] Furthermore, defect identification, judgment, and classification are performed. The defect identification and classification module analyzes the collected image data, automatically identifies abnormal states of the supports and hangers, classifies defect types, and judges the nature and severity of the identified defects based on the built-in support and hanger standard library and judgment rules.

[0048] In step S103, a support defect detection report is generated based on the image data, recognition results, defect type, and defect severity level.

[0049] Furthermore, in some embodiments, the support and hanger defect detection method further includes: obtaining the three-dimensional spatial coordinates of the support and hanger to be detected, mapping the defect type and defect severity level to a pre-constructed three-dimensional digital model of the support and hanger, and obtaining the defect visualization location result; adding the defect visualization location result to the support and hanger defect detection report.

[0050] Specifically, when the 3D digital modeling module is enabled, the system creates 3D digital models of supports and hangers at the equipment, system, or regional levels according to inspection requirements. The defect judgment results are bound to the spatial location information of the corresponding supports and hangers and mapped into the 3D model, enabling the visual location and intuitive display of defects, facilitating a quick understanding of defect distribution.

[0051] In addition, all image data, inspection records, defect judgment results, and model association information are stored in the device's local storage module in real time. The system automatically generates a unique identifier for each inspection record and supports on-site previewing, editing, and deletion of entered data, ensuring data integrity and traceability.

[0052] Furthermore, the system summarizes inspection records and generates automatic reports. Upon completion of the inspection, the system automatically summarizes all support and hanger inspection data for the inspection period and generates an inspection report according to a preset template. The report includes: basic information about the inspected equipment and supports; inspection results and defect descriptions; defect classification; relevant images or 3D positioning screenshots. The inspection report can be exported in PDF, Word, Excel, and other formats.

[0053] Furthermore, embodiments of this application can also upload inspection data to an enterprise server or cloud platform for backup and centralized management via wired or wireless networks. Defect correction information after manual review can be fed back to the system management terminal for updating the defect database and identification model, enabling continuous system optimization and improved identification accuracy. Pre-set parameter libraries and problem classification libraries can be updated remotely or locally to ensure timely and effective information. The software system supports upgrades to enhance user experience and expand functionality.

[0054] The beneficial effects of adopting the technical solution involved in this invention are as follows: (1) Significantly improve on-site inspection efficiency and continuous operation capability: This system integrates image acquisition, parameter calling and inspection information editing into the same terminal device. Inspectors can complete the synchronous operation of collecting the status of supports and hangers and recording problems on-site, avoiding the inefficient process of taking photos, recording and sorting multiple times in the traditional method, thereby significantly shortening the inspection time of a single support and hanger and improving on-site inspection efficiency.

[0055] (2) Improve the integrity and consistency of inspection data and reduce human error: By using a pre-set support and hanger basic parameter library, a standardized defect type library and an automatic image binding mechanism, the structured recording of inspection information is realized, avoiding the problems of information loss, inconsistent description or data omission caused by free filling, effectively reducing human input error and improving the objectivity and consistency of inspection results.

[0056] (3) Reduce reliance on the professional experience of inspectors and improve system applicability: The system enables non-professional or inexperienced personnel to complete the inspection of supports and hangers according to unified standards through defect type preset, intelligent identification and judgment rules. At the same time, it provides professional personnel with quick judgment and verification methods, reduces the technical threshold of inspection work, and improves the applicability of the system in different units and different application scenarios.

[0057] (4) Realize intelligent management and rapid summary analysis of inspection data: This system can automatically collect and manage the collected images, videos, parameter information and defect judgment results, and automatically generate inspection records and reports based on preset templates, reducing the workload of manual sorting and significantly improving the efficiency of inspection data summary, analysis and archiving.

[0058] (5) Enhance the traceability of inspection results and process transparency: The system automatically generates a unique identifier for each inspection record and binds it with time information, inspection object and operator information, and associates it with corresponding image or video data, so that the inspection process and results have a complete data chain, realize the traceable management of the support status, and facilitate subsequent review, comparative analysis and responsibility determination.

[0059] (6) Support remote data synchronization and centralized management to improve the level of operation and maintenance management: Through the wireless communication and data synchronization module, the inspection data can be automatically uploaded to the server or cloud platform when the network conditions permit, realizing centralized storage and unified management of data, which not only ensures data security, but also provides technical support for cross-regional and multi-device support and maintenance management.

[0060] (7) It helps to identify potential risks in advance and promote safe operation of equipment: Through timely identification, classification and judgment of defects in supports and hangers and comparative analysis of historical data, the system can help maintenance personnel discover abnormal development trends as early as possible, reduce the risks of abnormal equipment stress and limited operation caused by support and hanger failure, and thus effectively improve the operational safety and reliability of industrial equipment and its supports and hangers.

[0061] According to the support and hanger defect detection method proposed in this application, image data of the support and hanger to be inspected is collected; the image data is identified to obtain the identification result; and if the identification result meets the preset abnormal state, the defect type and defect severity level of the support and hanger to be inspected are determined based on a preset support and hanger standard library; and a support and hanger defect detection report is generated based on the image data, identification result, defect type, and defect severity level. This solves the problems of manual inspection relying on professional experience, low efficiency, easy omissions, inconsistent judgment standards, and scattered and difficult-to-manage data. It achieves automated detection, standardized grading, and report generation of support and hanger defects, thereby reducing the personnel threshold, improving inspection efficiency, and enhancing data traceability.

[0062] Next, the support and hanger defect detection device according to the embodiments of this application is described with reference to the accompanying drawings.

[0063] Figure 2 This is a block diagram of a support and hanger defect detection device according to an embodiment of this application.

[0064] like Figure 2As shown, the bracket defect detection device 10 includes: a data acquisition module 100, a detection module 200, and a generation module 300.

[0065] The acquisition module 100 is used to acquire image data of the support and hanger to be inspected; the detection module 200 is used to identify the image data, obtain the identification result, and determine the defect type and defect severity level of the support and hanger to be inspected based on the preset support and hanger standard library when the identification result meets the preset abnormal state; the generation module 300 is used to generate a support and hanger defect detection report based on the image data, identification result, defect type and defect severity level.

[0066] Furthermore, in some embodiments, the acquisition module 100 is used to: acquire the identification information of the current support to be inspected, and retrieve the corresponding structural type and load-bearing parameters from the preset support parameter database according to the identification information; and take multi-angle pictures of the current support to be inspected based on the shooting angle and focal length determined by the structural type and load-bearing parameters to obtain image data.

[0067] Furthermore, in some embodiments, the detection module 200 is used to: extract features from the image data to obtain a defect feature map; compare the defect feature map with the reference features of each type of defect in a preset support and hanger defect type library to obtain a similarity comparison result; output at least one candidate defect type and the confidence level corresponding to each candidate defect type based on the similarity comparison result, and obtain an identification result based on the confidence level corresponding to each candidate defect type.

[0068] Furthermore, in some embodiments, the detection module 200 is configured to: retrieve defect judgment rules corresponding to the current support to be inspected from the support standard library, the defect judgment rules including severity grading standards corresponding to different defect types; acquire measured parameters of the current support to be inspected from the image data, the measured parameters including at least one of displacement, gap size, corrosion area, or fastener loosening angle; match the measured parameters with the defect judgment rules to determine the defect severity level of the current support to be inspected, the defect severity level including a first defect level, a second defect level, and a third defect level; wherein the second defect level is higher than the first defect level, and the third defect level is higher than the second defect level.

[0069] Furthermore, in some embodiments, the support and hanger defect detection device 10 is also used to: obtain the three-dimensional spatial coordinates of the support and hanger to be detected, and map the defect type and defect severity level to the pre-constructed three-dimensional digital model of the support and hanger to obtain the defect visualization location result; and add the defect visualization location result to the support and hanger defect detection report.

[0070] It should be noted that the foregoing explanation of the embodiment of the support and hanger defect detection method also applies to the support and hanger defect detection device of this embodiment, and will not be repeated here.

[0071] The support and hanger defect detection device proposed in this application collects image data of the support and hanger to be inspected; identifies the image data to obtain identification results; and, if the identification results meet preset abnormal conditions, determines the defect type and defect severity level of the support and hanger to be inspected based on a preset support and hanger standard library; and generates a support and hanger defect detection report based on the image data, identification results, defect type, and defect severity level. This solves the problems of manual inspection relying on professional experience, low efficiency, easy omissions, inconsistent judgment standards, and scattered and difficult-to-manage data. It achieves automated detection, standardized grading, and report generation of support and hanger defects, thereby reducing personnel barriers, improving inspection efficiency, and enhancing data traceability.

[0072] Figure 3 A schematic diagram of the structure of an electronic device provided in an embodiment of this application. The electronic device may include: The memory 301, the processor 302, and the computer program stored on the memory 301 and capable of running on the processor 302.

[0073] When the processor 302 executes the program, it implements the support and hanger defect detection method provided in the above embodiments.

[0074] Furthermore, electronic devices also include: Communication interface 303 is used for communication between memory 301 and processor 302.

[0075] The memory 301 is used to store computer programs that can run on the processor 302.

[0076] The memory 301 may include high-speed RAM memory, and may also include non-volatile memory, such as at least one disk storage device.

[0077] If the memory 301, processor 302, and communication interface 303 are implemented independently, then the communication interface 303, memory 301, and processor 302 can be interconnected via a bus to complete communication between them. The bus can be an Industry Standard Architecture (ISA) bus, a Peripheral Component Interconnect (PCI) bus, or an Extended Industry Standard Architecture (EISA) bus, etc. Buses can be categorized as address buses, data buses, control buses, etc. For ease of representation, Figure 3 The bus is represented by a single thick line, but this does not mean that there is only one bus or one type of bus.

[0078] Optionally, in a specific implementation, if the memory 301, processor 302, and communication interface 303 are integrated on a single chip, then the memory 301, processor 302, and communication interface 303 can communicate with each other through an internal interface.

[0079] Processor 302 may be a central processing unit (CPU), an application specific integrated circuit (ASIC), or one or more integrated circuits configured to implement the embodiments of this application.

[0080] This application also provides a computer-readable storage medium storing a computer program thereon, which, when executed by a processor, implements the above-described method for detecting defects in supports and hangers.

[0081] This application also provides a computer program product, including a computer program that, when executed by a processor, implements the above-described method for detecting defects in supports and hangers.

[0082] In the description of this specification, the references to terms such as "one embodiment," "some embodiments," "example," "specific example," or "some examples," etc., indicate that a specific feature, structure, material, or characteristic described in connection with that embodiment or example is included in at least one embodiment or example of this application. In this specification, the illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples. Moreover, without contradiction, those skilled in the art can combine and integrate the different embodiments or examples described in this specification, as well as the features of different embodiments or examples.

[0083] Furthermore, the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include at least one of that feature. In the description of this application, "N" means at least two, such as two, three, etc., unless otherwise explicitly specified.

[0084] Any process or method described in the flowchart or otherwise herein can be understood as representing a module, segment, or portion of code comprising one or more N executable instructions for implementing custom logic functions or processes, and the scope of the preferred embodiments of this application includes additional implementations in which functions may be performed not in the order shown or discussed, including substantially simultaneously or in reverse order depending on the functions involved, as should be understood by those skilled in the art to which embodiments of this application pertain.

[0085] The logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered as a sequenced list of executable instructions for implementing logical functions, and can be embodied in any computer-readable medium for use by, or in conjunction with, an instruction execution system, apparatus, or device (such as a computer-based system, a processor-included system, or other system that can fetch and execute instructions from, an instruction execution system, apparatus, or device). For the purposes of this specification, "computer-readable medium" can be any means that can contain, store, communicate, propagate, or transmit programs for use by, or in conjunction with, an instruction execution system, apparatus, or device. More specific examples (a non-exhaustive list) of computer-readable media include: an electrical connection having one or more wires (electronic device), a portable computer disk drive (magnetic device), random access memory (RAM), read-only memory (ROM), erasable and editable read-only memory (EPROM or flash memory), fiber optic devices, and portable optical disc read-only memory (CDROM). Alternatively, the computer-readable medium may be paper or other suitable media on which the program can be printed, since the program can be obtained electronically, for example, by optically scanning the paper or other medium, followed by editing, interpreting, or otherwise processing as necessary, and then stored in a computer memory.

[0086] It should be understood that the various parts of this application can be implemented using hardware, software, firmware, or a combination thereof. In the above embodiments, the N steps or methods can be implemented using software or firmware stored in memory and executed by a suitable instruction execution system. For example, if implemented in hardware as in another embodiment, it can be implemented using any one or a combination of the following techniques known in the art: discrete logic circuits having logic gates for implementing logical functions on data signals, application-specific integrated circuits (ASICs) having suitable combinational logic gates, programmable gate arrays (PGAs), field-programmable gate arrays (FPGAs), etc.

[0087] Those skilled in the art will understand that all or part of the steps of the methods in the above embodiments can be implemented by a program instructing related hardware. The program can be stored in a computer-readable storage medium, and when executed, the program includes one or a combination of the steps of the method embodiments.

[0088] Furthermore, the functional units in the various embodiments of this application can be integrated into a processing module, or each unit can exist physically separately, or two or more units can be integrated into a module. The integrated module can be implemented in hardware or as a software functional module. If the integrated module is implemented as a software functional module and sold or used as an independent product, it can also be stored in a computer-readable storage medium.

[0089] The storage medium mentioned above can be a read-only memory, a disk, or an optical disk, etc. Although embodiments of this application have been shown and described above, it is understood that the above embodiments are exemplary and should not be construed as limiting this application. Those skilled in the art can make changes, modifications, substitutions, and variations to the above embodiments within the scope of this application.

Claims

1. A method for detecting defects in supports and hangers, characterized in that, Includes the following steps: Collect image data of the support or hanger to be inspected; The image data is identified to obtain identification results. If the identification results meet the preset abnormal state, the defect type and defect severity level of the current support to be detected are determined based on the preset support standard library. A support / hanger defect detection report is generated based on the image data, the identification results, the defect type, and the defect severity level.

2. The method according to claim 1, characterized in that, The acquisition of image data of the current support bracket to be inspected includes: Obtain the identification information of the support and hanger to be inspected, and retrieve the corresponding structural type and load-bearing parameters from the preset support and hanger parameter database based on the identification information; Based on the structural type and load-bearing parameters, the shooting angle and focal length are determined, and the current support to be inspected is photographed from multiple angles to obtain the image data.

3. The method according to claim 1, characterized in that, The process of identifying the image data to obtain the identification result includes: Feature extraction is performed on the image data to obtain a defect feature map; The similarity comparison is performed between the defect feature map and the reference features of each type of defect in the preset support and hanger defect type library to obtain the similarity comparison result. Based on the similarity comparison results, at least one candidate defect type and a confidence level corresponding to each candidate defect type are output, and the identification result is obtained based on the confidence level corresponding to each candidate defect type.

4. The method according to claim 1, characterized in that, The method, based on a pre-defined standard library of supports and hangers, determines the defect type and severity level of the current support or hanger to be inspected, including: Retrieve the defect judgment rule corresponding to the current support or hanger to be inspected from the support and hanger standard library. The defect judgment rule includes the severity grading standard corresponding to different defect types. Obtain the measured parameters of the current support and hanger to be detected in the image data. The measured parameters include at least one of displacement, gap size, corrosion area, or fastener loosening angle. The measured parameters are matched with the defect judgment rules to determine the defect severity level of the current support to be inspected. The defect severity level includes a first defect level, a second defect level, and a third defect level. Wherein, the second defect level is higher than the first defect level, and the third defect level is higher than the second defect level.

5. The method according to claim 1, characterized in that, Also includes: Obtain the three-dimensional spatial coordinates of the current support to be inspected, and map the defect type and the defect severity level to the pre-constructed three-dimensional digital model of the support to obtain the defect visualization and location results; The defect visualization and location results are added to the support and hanger defect detection report.

6. A device for detecting defects in supports and hangers, characterized in that, include: The acquisition module is used to acquire image data of the support and hanger to be inspected. The detection module is used to identify the image data, obtain the identification result, and, if the identification result meets the preset abnormal state, determine the defect type and defect severity level of the current support to be detected based on the preset support standard library; The generation module is used to generate a support defect detection report based on the image data, the recognition result, the defect type, and the defect severity level.

7. The apparatus according to claim 6, characterized in that, The acquisition module is used for: Obtain the identification information of the support and hanger to be inspected, and retrieve the corresponding structural type and load-bearing parameters from the preset support and hanger parameter database based on the identification information; Based on the structural type and load-bearing parameters, the shooting angle and focal length are determined, and the current support to be inspected is photographed from multiple angles to obtain the image data.

8. An electronic device, characterized in that, include: The memory, the processor, and the computer program stored in the memory and capable of running on the processor, wherein the processor executes the computer program to implement the support defect detection method as described in any one of claims 1-5.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that, The computer program is executed by a processor to implement the support and hanger defect detection method as described in any one of claims 1-5.

10. A computer program product, comprising a computer program, characterized in that, When the computer program is executed by the processor, it implements the support and hanger defect detection method as described in any one of claims 1-5.

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