Comparison analysis-based equipment waste and old material integrity management method and system

Abstract

The invention discloses an equipment type waste and old material integrity management method and system based on comparative analysis. The method comprises the steps of obtaining equipment information of waste and old materials to be warehoused and image information of each key part; determining a material image library of the to-be-warehoused waste and old materials according to the equipment information of the to-be-warehoused waste and old materials; for the image information of each key part of the waste and old materials to be warehoused, selecting an image most similar to the image information from a material image library of the waste and old materials to be warehoused, and taking the integrity information marked by the image as the integrity information of the key parts of the waste and old materials to be warehoused; and integrating the integrity information of all key parts of the to-be-warehoused waste and old materials to obtain the integrity information of the to-be-warehoused waste and old materials. And the accuracy and efficiency of integrity identification of the equipment type waste and old materials are improved.

Description

A Method and System for Integrity Management of Equipment-Related Scrap Materials Based on Comparative Analysis Technical Field

[0001] This invention relates to the field of materials management technology, and in particular to a method and system for managing the integrity of equipment-related scrap materials based on comparative analysis. Background Technology

[0002] The statements in this section are merely background information related to the present invention and do not necessarily constitute prior art.

[0003] Currently, we rely on the e-materials system to carry out mobile receiving and shipping of warehouse scrap materials. All scrap materials are managed at the material level. When equipment scrap materials are put into storage, it is necessary to confirm the integrity of the scrap materials.

[0004] The current system for managing the warehousing of scrap equipment requires manual verification of the completeness of the scrap equipment, which is inefficient and has a high error rate. Summary of the Invention

[0005] To address the aforementioned problems, this invention proposes a method and system for managing the integrity of equipment-related scrap materials based on comparative analysis, which enables automatic identification and assessment of the integrity of scrap materials upon entry into the warehouse.

[0006] To achieve the above objectives, the present invention adopts the following technical solution: Firstly, a method for managing the integrity of equipment-type scrap materials based on comparative analysis is proposed, comprising: acquiring equipment information and image information of each key component of the scrap materials to be stored; determining a material image library for the scrap materials to be stored based on the equipment information; for each key component of the scrap materials to be stored, selecting the image most similar to that image from the material image library, and using the integrity information marked on that image as the integrity information of the key component of the scrap materials to be stored; integrating the integrity information of all key components of the scrap materials to be stored to obtain the integrity information of the scrap materials to be stored.

[0007] Furthermore, the material image library for waste materials to be put into storage stores standard images of key parts of the waste materials to be put into storage and labeled images of different degrees of failure. Among them, the labeled images are marked with the integrity information of the key parts.

[0008] Furthermore, after identifying the image library of the scrap materials to be stored, information such as installation location, operating environment parameters, maintenance records, and component replacement details stored in the image library is also obtained.

[0009] Furthermore, for the image information of each key part of the waste materials to be put into storage, the similarity between the image information and each image in the material image library of the waste materials to be put into storage is calculated; the image with the highest similarity is selected as the image most similar to the image information.

[0010] Furthermore, the constructed material integrity recognition model is trained using a material image database. Once training is complete, a trained material integrity recognition model is obtained. This model is constructed using a deep learning model. Using the trained material integrity recognition model, the image information of key parts of the waste materials to be stored is identified to determine the integrity information of the key parts of the waste materials to be stored.

[0011] Furthermore, obtain the nameplate images of the scrap materials to be put into storage; based on the nameplate images, determine the equipment information of the scrap materials to be put into storage.

[0012] Secondly, a comparative analysis-based integrity management system for equipment-type scrap materials is proposed, comprising: an information acquisition unit for acquiring equipment information and image information of each key part of the scrap materials to be stored; a material image library determination unit for determining the material image library of the scrap materials to be stored based on the equipment information; an integrity information determination unit for selecting the image most similar to the image information of each key part of the scrap materials to be stored from the material image library of the scrap materials to be stored, and using the integrity information marked on the image as the integrity information of the key part of the scrap materials to be stored; and integrating the integrity information of all key parts of the scrap materials to be stored to obtain the integrity information of the scrap materials to be stored.

[0013] Thirdly, a computer device is proposed, comprising: a processor adapted to execute a computer program; and a computer-readable storage medium storing a computer program, wherein when the computer program is executed by the processor, it implements the equipment-type waste material integrity management method based on comparative analysis proposed in the first aspect.

[0014] Fourthly, a computer-readable storage medium is proposed, which stores a computer program adapted to be loaded and executed by a processor using the equipment-type waste material integrity management method based on comparative analysis proposed in the first aspect.

[0015] Fifthly, a computer program product is proposed, which includes a computer program. When the computer program is executed by a processor, it implements the equipment-type waste material integrity management method based on comparative analysis proposed in the first aspect.

[0016] Compared with existing technologies, the beneficial effects of this invention are as follows: The method and system for managing the integrity of equipment-type scrap materials proposed in this invention, based on comparative analysis, involves the following steps: When scrap materials are put into storage, the method collects equipment information and image information of each key component of the scrap materials. Using the equipment information, a database of material images to be compared is determined. Then, images most similar to the key component images are extracted from the database of images of the scrap materials to be put into storage, and the integrity information of these images is used as the integrity information of the key components of the scrap materials to be put into storage. By individually identifying the integrity information of each key component, the accuracy of identifying the integrity information of each key component is improved. Finally, the integrity information of all key components of the scrap materials to be put into storage is integrated to obtain the complete integrity information of the scrap materials to be put into storage. This achieves accurate and comprehensive acquisition of the integrity information of the key components of the scrap materials to be put into storage, improving the accuracy and efficiency of obtaining integrity information when scrap materials are put into storage.

[0017] Advantages of additional aspects of the invention 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 the invention. Attached Figure Description

[0018] The accompanying drawings, which form part of this application, are used to provide a further understanding of this application. The illustrative embodiments of this application and their descriptions are used to explain this application and do not constitute an undue limitation of this application.

[0019] Figure 1 is a flowchart of the equipment waste material integrity management method based on comparative analysis proposed in this invention; Figure 2 is a business architecture diagram of the application system of the equipment waste material integrity management method based on comparative analysis proposed in this invention; Figure 3 is a technical architecture diagram of the application system of the equipment waste material integrity management method based on comparative analysis proposed in this invention. Detailed Implementation

[0020] The present invention will be further described below with reference to the accompanying drawings and embodiments.

[0021] It should be noted that the following detailed descriptions are illustrative and intended to provide further explanation of this application. Unless otherwise specified, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application pertains.

[0022] It should be noted that the terminology used herein is for the purpose of describing particular embodiments only and is not intended to limit the exemplary embodiments according to this application. As used herein, the singular form is intended to include the plural form as well, unless the context clearly indicates otherwise. Furthermore, it should be understood that when the terms "comprising" and / or "including" are used in this specification, they indicate the presence of features, steps, operations, devices, components, and / or combinations thereof.

[0023] Where there is no conflict, the embodiments and features in the embodiments of the present invention can be combined with each other.

[0024] First, the application background of the equipment waste material integrity management method based on comparative analysis proposed in the embodiments of the present invention will be explained.

[0025] The equipment-type scrap material integrity management method based on comparative analysis proposed in this invention is applied to the application scenario of equipment-type scrap material warehousing management.

[0026] Currently, the mobile receiving and shipping of warehouse scrap materials relies on the e-Materials system. Scrap materials are managed at the material level, and the integrity of critical components in equipment scrap materials requires manual verification, resulting in low efficiency and a high error rate. On-site personnel and system functionality lack the capacity to analyze and verify the integrity of equipment scrap materials. Furthermore, current data is only used to support system operations and has not formed a data model library for comparing the integrity of equipment scrap materials. This leads to difficulties in real-time and accurate assessment of material integrity during the management of equipment scrap material warehousing, compromised data quality, and deficiencies in the value extraction and reuse of scrap materials. There is an urgent need to research an AI-based comparative analysis technology for managing the integrity of equipment scrap materials, enabling online and unified management of scrap material integrity.

[0027] In related technologies, there are also identification methods that determine the integrity of waste materials by collecting images of the waste materials and then identifying the images. However, this method is suitable for waste materials with small volume and simple structure. For equipment waste materials, which contain multiple key parts and these key parts are not on the same side, taking switch cabinets as an example, the key parts on the switch cabinet include the cabinet body, copper conductors, copper busbars, aluminum conductors, circuit breakers, transformers, relays, surge arresters, capacitors, reactors, fuses, etc. The integrity of the cabinet body needs to be judged by the appearance of the cabinet body. When only obtaining images of the waste materials from one direction for waste material integrity identification, there is a problem of missing the integrity information of key parts on other sides.

[0028] To achieve comprehensive and accurate automatic identification of the integrity of equipment-type scrap materials upon entry into the warehouse, this invention proposes a method for managing the integrity of equipment-type scrap materials based on comparative analysis. By individually identifying the integrity of each key part of the scrap materials to be entered into the warehouse, the comprehensiveness and accuracy of the identification of the integrity of key parts are ensured.

[0029] As shown in Figure 2, the equipment-type waste material integrity management method based on comparison analysis proposed in this embodiment of the invention is applied to the business architecture of the warehouse management system, specifically to the inbound management system of this business architecture.

[0030] As shown in Figure 3, the overall technical architecture of the equipment waste material integrity management method application system based on comparison analysis proposed in this embodiment of the invention is based on Spring Cloud microservice architecture, and the system is split according to business modules and functional independence.

[0031] The mobile platform uses the Vue.js progressive framework, which is built on standard HTML, CSS, and JavaScript. It provides a declarative, component-based programming model and uses virtual DOM technology to maintain a virtual DOM tree in the background. It calculates changes and then updates the actual DOM only to a minimum, improving rendering performance. Vue is equipped with a powerful ecosystem, enabling flexible development, high efficiency, page splitting, and reusable components that can be easily combined and nested. This shortens the development cycle, saves development costs, and ensures early completion within the development cycle. It also allows for white-box and black-box testing to ensure that the functionality is complete and stable.

[0032] The backend adopts Spring Cloud's design philosophy, focusing on supporting microservice architecture. It uses modular design to build distributed systems and covers all the business functionalities required for disaster recovery in different locations. The system foundation includes service discovery, configuration management, load balancing, caching, and national cryptographic security components. It has achieved advantages such as service module decoupling, flexible geographically distributed deployment, independent deployment of single modules, and seamless upgrades, minimizing the impact of natural disasters on the system.

[0033] Based on the above application scenarios and systems, the method for managing the integrity of equipment-type scrap materials based on comparative analysis proposed in this embodiment of the invention will be described in detail.

[0034] The equipment-based waste material integrity management method proposed in this invention embodiment, as shown in Figure 1, includes: acquiring equipment information and image information of each key part of the waste material to be put into storage; determining the material image library of the waste material to be put into storage based on the equipment information; for the image information of each key part of the waste material to be put into storage, selecting the image most similar to the image information from the material image library of the waste material to be put into storage, and using the integrity information marked on the image as the integrity information of the key part of the waste material to be put into storage; integrating the integrity information of all key parts of the waste material to be put into storage to obtain the integrity information of the waste material to be put into storage.

[0035] The image library for waste materials awaiting warehousing stores standard images of key parts of the waste materials awaiting warehousing and labeled images of different degrees of failure. The labeled images include the integrity information of the key parts.

[0036] This invention proposes a method for managing the integrity of equipment-related scrap materials based on comparative analysis. Through advanced AI technology, it achieves accurate identification, efficient management, and integrity assessment of scrap materials during the warehousing process. This method utilizes AI comparative analysis technology for intelligent management of equipment-related scrap materials. By collecting image information and equipment information of key parts of the scrap materials, and comparing it with a pre-set AI comparison image library during the warehousing process, it achieves accurate identification and assessment of the integrity of the equipment-related scrap materials upon warehousing.

[0037] Before identifying the scrap materials to be stored, this invention first constructs an image library containing various types of equipment scrap materials, such as low-voltage switchgear, high-voltage switchgear, ring main units, and box-type switchgear. Each material image library is labeled and distinguished according to the equipment information of the material. Each material image library stores the equipment information of the material, standard images of each key part, and labeled images of different fault degrees. The standard image is the image when the key part is not faulty. The labeled images are marked with the integrity information of the key part, such as whether the component is missing, the location of the fault, and the degree of the fault. The degree of fault includes minor, poisoning, and severe. Each material image library also stores the installation location, operating environment parameters, maintenance records, and component replacement information of the corresponding material.

[0038] The equipment information for scrap materials includes equipment name, model, specifications, major category description, intermediate category description, and minor category description.

[0039] This invention collects images of key parts of various types of scrap equipment, then annotates and cleans the collected data to obtain a material image library for each material.

[0040] The process of collecting images of key components from various types of scrap equipment involves: acquiring images and standard images of key components from different equipment types and manufacturers. Key components include critical parts and the overall appearance of the equipment. Taking switchgear as an example, the appearance of the switchgear and its key components are photographed from all angles with high precision. The overall appearance of the switchgear is captured from different angles, including the cabinet doors, sides, and top, with a focus on various key components such as circuit breakers, disconnect switches, instrument transformers, and insulators. Samples of switchgear of different models and degrees of aging and damage are included.

[0041] We collect and analyze equipment archives to extract detailed textual information for each piece of equipment. In addition to equipment name, model, specifications, service life, and cause of failure, we also obtain installation location, operating environment parameters, maintenance records, and component replacement information. Storing this information in the corresponding material image database will provide crucial metadata support for subsequent data annotation and model training, helping the model understand the characteristic changes of equipment under different operating conditions.

[0042] Collect publicly available equipment datasets from within the power grid, especially data related to the identification and classification of scrap materials. This data can compensate for the shortcomings of internal power grid data in certain specific scenarios or rare fault types, enriching the diversity and breadth of the data. By integrating internal and external data, the constructed material integrity identification model can learn more comprehensive and generalized characteristics of equipment scrap materials.

[0043] The process of data annotation for key parts of various types of equipment scrap materials is as follows: A team composed of power equipment experts and data annotation professionals is formed to jointly develop annotation standards specifically for power grid equipment scrap materials. For image data, the specific model of the equipment is accurately labeled, the category of various components is identified, and detailed records are made of whether the components are missing, the damaged parts, and the degree of damage (minor, moderate, severe).

[0044] The data cleaning process for labeled images involves using advanced image recognition algorithms and image processing techniques (based on Transformer object detection algorithms, Mask R-CNN, and SOLOv2 model algorithms, etc.) to clean the images of key parts of the acquired equipment. This automatically identifies and removes blurry, noisy, or images where key components are unrecognizable due to shooting angle issues, as well as duplicate images. Image enhancement techniques, such as histogram equalization, contrast-limited adaptive histogram equalization, and edge enhancement, are used to improve image clarity and the recognizability of key features. Furthermore, data augmentation techniques, such as rotation, flipping, scaling, and cropping, are employed to increase the diversity of image data, expand the dataset size, and improve the model's generalization ability.

[0045] In some embodiments, the nameplate image of the scrap material to be put into storage is obtained; based on the nameplate image, the equipment information of the scrap material to be put into storage is determined.

[0046] Based on the equipment information, a material image library for the waste materials to be put into storage is determined, and this image library is used as the material image library for subsequent use.

[0047] In this embodiment of the invention, after determining the material image library of the scrap materials to be put into storage, the installation location, operating environment parameters, maintenance records and component replacement information stored in the material image library are also obtained. This information is used as the status information of the scrap materials to be put into storage for reference by the warehouse manager.

[0048] In this embodiment of the invention, after determining the material image library corresponding to the information of the scrap materials and equipment to be put into storage, the similarity between the image information of each key part of the scrap materials to be put into storage and each image in the material image library of the scrap materials to be put into storage is calculated; the image with the highest similarity is selected as the image most similar to the image information, and the integrity information marked on the image is used as the integrity information of the key parts of the scrap materials to be put into storage.

[0049] In some embodiments, the constructed material integrity recognition model is trained using a material image library. Once training is complete, a trained material integrity recognition model is obtained. The material integrity recognition model is constructed using a deep learning model. Using the trained material integrity recognition model, the image information of key parts of the waste materials to be stored is identified to determine the integrity information of the key parts of the waste materials to be stored.

[0050] The preferred deep learning model is the Mask R-CNN model, which is trained using the material image database of all materials constructed in this embodiment of the invention. Through training, the model parameters can be continuously adjusted, thereby improving the model's accuracy and generalization ability.

[0051] The Mask R-CNN model extracts image features from the input image information, calculates the similarity between the image features and the features of the training data images, and selects the annotation information of the image to which the image feature with the highest similarity belongs as the annotation information of the key parts of the waste materials to be put into storage.

[0052] After the material integrity identification model is trained, cross-validation and other methods are used to evaluate the model and analyze its performance metrics, such as accuracy. Based on the evaluation results, the model is optimized, including adjusting the model structure and adding data augmentation.

[0053] In this embodiment of the invention, the material number, equipment name, equipment nameplate image, and images of key parts of the equipment are filled in the warehouse management system. After layer-by-layer annotation and approval, the annotation information is saved to the database and the image file on the OSS server. A scheduled function that runs monthly or is manually executed extracts the images and annotation information, calls the PyTorch API, and sends it to the model computing node. Using the two-stage model Mask R-CNN (which simultaneously performs object detection and instance segmentation), after multi-step calculations to complete deep feature extraction and learning, the trained material integrity recognition model and the original model file are exported. The original file is used for the next accuracy stacking. The application will then incrementally save the trained material integrity recognition model to the cloud platform, which OpenCV can then directly call. The trained material integrity recognition model corresponding to the equipment achieves accurate recognition, with a correct recognition rate of over 95% even with partial object occlusion or different viewing angles.

[0054] Based on the calculated and categorized images and detailed photos, different devices generate their own trained material integrity recognition models. Users can use the app's photo-taking function for automatic recognition and select the corresponding trained material integrity recognition model based on device information. This allows users to easily view standard images and related standard data information from the devices, minimizing the probability of machine misjudgment and improving the recognition accuracy of various material components.

[0055] The user then takes a photo, which is then identified using a trained material integrity recognition model. Any unidentified equipment parts are returned and displayed on the app page. If all parts are present, the scrap warehousing process is initiated.

[0056] The equipment integrity management method based on comparative analysis proposed in this invention obtains equipment information and images of key parts of each piece of equipment, annotates each image to form training data, trains the constructed material integrity identification model, and uses the trained material integrity identification model to identify the integrity of key parts of the waste materials to be put into storage, thereby improving the accuracy and efficiency of equipment integrity identification.

[0057] This invention adds an AI-powered image capture function to the waste material warehousing function of the e-materials system. When waste materials are warehoused, the AI ​​image capture function acquires images of the nameplate, overall image information, and images of key components of the waste materials to be warehoused. Based on the nameplate image, the equipment information of the waste materials to be warehoused is determined. Based on the equipment information, the material image library of the waste materials to be warehoused is determined. By comparing the overall image with standard images in the material image library of the waste materials to be warehoused, it is determined whether there are any missing parts. When a missing part is determined, a real-time alarm is issued for the missing part, improving the efficiency of integrity verification for complex equipment.

[0058] Among them, the material integrity identification model is integrated into the waste material warehousing system; achieving seamless integration between the AI ​​model library and the existing waste material warehousing system.

[0059] The mobile app acquires images of nameplates and key components of scrap materials to be stored. By calling the model library interface, the mobile app can take and upload images of nameplates and equipment from multiple angles on-site. The model recognition results are fed back to the business system in real time through the interface, enabling rapid identification and processing of scrap materials. The integrity of the scrap materials is then assessed based on the model recognition results.

[0060] This invention employs the Mask R-CNN model to identify image information of key components. While traditional image comparison methods, such as feature point matching (SIFT, SURF, ORB, etc.), can also extract image features, these features are generated through manually designed algorithms, and traditional image processing methods cannot capture sufficiently rich image features. In contrast, Mask R-CNN uses a deep learning model that can automatically learn low-level and high-level image features through multi-layer nonlinear transformations. This allows it to better capture the semantic information of the image, possessing higher expressive power and thus improving the accuracy of the determined integrity information of the waste materials to be stored.

[0061] This invention not only ensures accurate identification of the integrity of waste materials but also improves sorting efficiency; it can effectively reduce manual inspection costs, with an estimated annual saving of 20% in labor costs. Simultaneously, by promptly identifying missing key components, it enhances resource recycling rates and increases revenue streams from component regeneration, with an estimated annual increase in recycling rates of 10%, creating more waste material recycling revenue for enterprises.

[0062] By applying AI models and AI visual analysis technology, we can enhance the management of the integrity of waste materials entering the warehouse, achieve refined control over the last link of the entire equipment life cycle, improve the efficiency of material integrity verification by about 50%, reduce human error, improve management transparency and efficiency, and effectively reduce potential risks caused by incomplete equipment.

[0063] By recycling waste materials, we can promote the development of a circular economy, reduce the negative environmental impact of waste, and expect to reduce resource waste by 10%-15% annually. At the same time, by optimizing resource allocation, we can help conserve natural resources, promote the development of green industries, and reduce carbon emissions.

[0064] The AI ​​model library constructed in this embodiment of the invention uses AI visual analysis technology to accurately compare scrap materials with standard models, provide real-time feedback on the integrity of key parts, and issue real-time alarms for missing parts, thereby improving the efficiency of integrity verification of complex equipment. The integrity of equipment scrap materials entering the warehouse is 100%, ensuring that equipment materials are "completely removed from the warehouse".

[0065] This invention also proposes an equipment-based waste material integrity management system based on comparative analysis, comprising: an information acquisition unit for acquiring equipment information and image information of each key part of the waste material to be stored; a material image library determination unit for determining the material image library of the waste material to be stored based on the equipment information; an integrity information determination unit for selecting the image most similar to the image information of each key part of the waste material to be stored from the material image library of the waste material to be stored, and using the integrity information marked on the image as the integrity information of the key part of the waste material to be stored; and integrating the integrity information of all key parts of the waste material to be stored to obtain the integrity information of the waste material to be stored.

[0066] It should be noted that the equipment-type scrap material integrity management system based on comparison analysis provided in the above embodiments is only illustrated by the division of the above functional modules when managing the integrity of equipment-type scrap materials. In actual applications, the above functions can be assigned to different functional modules as needed, that is, the internal structure of the equipment can be divided into different functional modules to complete all or part of the functions described above. In addition, the equipment-type scrap material integrity management system based on comparison analysis provided in the above embodiments and the equipment-type scrap material integrity management method embodiments based on comparison analysis belong to the same concept, and the specific implementation process is detailed in the method embodiments, which will not be repeated here.

[0067] The present invention also discloses a computer device, comprising: a processor adapted to execute a computer program; and a computer-readable storage medium storing a computer program, wherein when the computer program is executed by the processor, it implements the equipment-type waste material integrity management method based on comparison analysis disclosed in the embodiments of the present invention.

[0068] The present invention also discloses a computer-readable storage medium storing a computer program adapted for loading and execution by a processor of the equipment-type waste material integrity management method based on comparison analysis disclosed in the embodiments of the present invention.

[0069] The present invention also discloses a computer program product, which includes a computer program. When the computer program is executed by a processor, it implements the equipment-type waste material integrity management method based on comparison analysis disclosed in the embodiments of the present invention.

[0070] The method disclosed in this invention can be directly implemented by a hardware processor, or implemented by a combination of hardware and software modules within the processor. The software modules can reside in readily available storage media in the art, such as random access memory, flash memory, read-only memory, programmable read-only memory, electrically erasable programmable memory, or registers. This storage medium is located in memory, and the processor reads information from the memory and, in conjunction with its hardware, completes the steps of the above method. To avoid repetition, detailed descriptions are omitted here.

[0071] Those skilled in the art will recognize that the units and algorithm steps described in conjunction with the embodiments herein can be implemented in electronic hardware or a combination of computer software and electronic hardware. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this application.

[0072] While the specific embodiments of the present invention have been described above in conjunction with the accompanying drawings, this is not intended to limit the scope of protection of the present invention. Those skilled in the art should understand that various modifications or variations that can be made by those skilled in the art without creative effort based on the technical solutions of the present invention are still within the scope of protection of the present invention.

Claims

1. A method for managing the integrity of equipment-related scrap materials based on comparative analysis, characterized in that, include: Obtain equipment information and image information of each key part of the scrap materials to be put into the warehouse; Based on the equipment information of the scrap materials to be put into storage, determine the material image library of the scrap materials to be put into storage; For each key part of the scrap materials to be put into storage, the image that is most similar to the image information is selected from the material image library of the scrap materials to be put into storage, and the integrity information marked on the image is used as the integrity information of the key parts of the scrap materials to be put into storage. Integrate the complete information of all key parts of the waste materials to be put into storage to obtain complete information on the waste materials to be put into storage.

2. The method for managing the integrity of equipment-type scrap materials based on comparative analysis as described in claim 1, characterized in that, The material image library for waste materials to be put into storage stores standard images of key parts of the waste materials to be put into storage and labeled images of different degrees of failure. Among them, the labeled images are marked with the integrity information of the key parts.

3. The method for managing the integrity of equipment-type scrap materials based on comparative analysis as described in claim 1, characterized in that, After identifying the image library of the scrap materials to be put into storage, the system also obtains information stored in the image library, such as installation location, operating environment parameters, maintenance records, and component replacement information.

4. The method for managing the integrity of equipment-type scrap materials based on comparative analysis as described in claim 1, characterized in that, For each key part of the scrap materials to be put into storage, calculate the similarity between the image information and each image in the material image library of the scrap materials to be put into storage; select the image with the highest similarity as the image most similar to the image information.

5. The method for managing the integrity of equipment-type scrap materials based on comparative analysis as described in claim 1, characterized in that, The material integrity recognition model is trained using a material image database. Once training is complete, a well-trained material integrity recognition model is obtained. This model is constructed using a deep learning model. The well-trained material integrity recognition model is then used to identify the image information of key parts of the waste materials to be stored, thereby determining the integrity information of these key parts.

6. The method for managing the integrity of equipment-type scrap materials based on comparative analysis as described in claim 1, characterized in that, Obtain the nameplate image of the scrap materials to be put into storage; determine the equipment information of the scrap materials to be put into storage based on the nameplate image.

7. A system for managing the integrity of equipment-related scrap materials based on comparative analysis, characterized in that: include: The information acquisition unit is used to acquire equipment information and image information of each key part of the scrap materials to be put into the warehouse; The material image library determination unit is used to determine the material image library of the scrap materials to be put into storage based on the equipment information of the scrap materials to be put into storage. The integrity information determination unit is used to select the image that is most similar to the image information of each key part of the waste material to be put into storage from the material image library of the waste material to be put into storage, and use the integrity information marked on the image as the integrity information of the key part of the waste material to be put into storage. Integrate the complete information of all key parts of the waste materials to be put into storage to obtain complete information on the waste materials to be put into storage.

8. An electronic device, characterized in that, The device includes: a processor adapted to execute a computer program; and a computer-readable storage medium storing a computer program, wherein when the computer program is executed by the processor, it implements the device-type waste material integrity management method based on comparison analysis as described in any one of claims 1-6.

9. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a computer program adapted to be loaded by a processor and executed by the equipment-type waste material integrity management method based on comparison analysis as described in any one of claims 1-6.

10. A computer program product, characterized in that, The computer program product includes a computer program, which, when executed by a processor, implements the equipment-type waste material integrity management method based on comparative analysis as described in any one of claims 1-6.

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