Optical cable cross-connecting cabinet terminal state identification system based on picture partition shooting

Abstract

The invention provides an optical cable cross-connecting cabinet terminal state identification system based on picture partition shooting, and belongs to the field of operator transmission systems, and the system comprises an image acquisition module which is used for carrying out precise partition shooting on the interior of an optical cable cross-connecting cabinet; the partition control module is used for presetting a plurality of shooting areas according to the internal structure of the optical cable cross connecting cabinet and shooting one by one according to the preset areas; and the data storage and display module is used for storing the processed picture and the recognition result, and providing a visual interface for a user to view. The interior of the optical cable cross connecting cabinet is accurately partitioned and shot one by one, so that the recognition rate of the terminal state is remarkably improved, the misjudgment rate is reduced, and the maintenance efficiency is improved.

Description

Technical Field

[0001] The present invention relates to the field of operator transmission systems, and in particular to an optical cross-connect box terminal status recognition system based on image partition shooting. Background Art

[0002] The application of AI in identifying the status of optical cross-connection terminals in the communications field has brought significant advantages and efficiency improvements. The main benefits are:

[0003] 1: Improve verification efficiency:

[0004] The traditional method of checking the data of optical cross-connection boxes relies on manual checking one by one, which is not only time-consuming and labor-intensive, but also difficult to ensure data accuracy. AI recognition technology uses image recognition technology, such as the YOLOV5 target detection algorithm, to quickly identify the number, location and status of terminals in the optical cross-connection box, effectively shortening the verification time.

[0005] 2: Enhanced data accuracy:

[0006] Compared with manual verification, AI recognition technology has higher accuracy. Through deep learning and algorithm optimization, AI can accurately identify details in the optical cross-connect box, such as whether the terminal is idle, occupied, missing or damaged.

[0007] 3: Reduce operation and maintenance costs:

[0008] The application of AI recognition technology reduces the reliance on a large number of human resources and reduces operation and maintenance costs. At the same time, AI's real-time monitoring and rapid response capabilities help to promptly detect and deal with potential problems, further reducing fault handling and maintenance costs.

[0009] However, AI recognition currently relies on network operation and maintenance personnel to collect images of optical devices such as optical cross-connect boxes, and then the IT system uses AI recognition capabilities to identify the status of the terminals in the images. Therefore, the accuracy of the recognition results also depends on:

[0010] 1: The quality of the images collected by the operation and maintenance personnel;

[0011] 2: The internal status of the optical device. Summary of the invention

[0012] In order to solve the above technical problems, the present invention provides an optical cross-box terminal status recognition system based on partitioned picture shooting, which can reduce the coupling between the various parts of the optical cross-box and improve the recognition rate by realizing partitioned picture shooting of the optical cross-box.

[0013] The technical solution of the present invention is:

[0014] An optical cross-connection box terminal status recognition system based on image partition shooting, comprising:

[0015] Image acquisition module: used to accurately partition and shoot the interior of the light cross box;

[0016] Partition control module: according to the internal structure of the optical cross-box, several shooting areas are preset, and the preset areas are photographed one by one;

[0017] Data storage and display module: stores processed images and recognition results, and provides a visual interface for users to view.

[0018] Furthermore,

[0019] The optical cross-box equipment is photographed in partitions to reduce the coupling between modules.

[0020] Going further,

[0021] The image acquisition module includes a high-resolution camera and a focus-adjustable lens.

[0022] Going further,

[0023] A partitioning scheme is designed based on the internal structure of the optical cross-connect box to ensure that each terminal can be clearly photographed.

[0024] Going further,

[0025] The partition control module is used to control the camera to shoot one by one in the preset partition order; when shooting each partition, the camera's focal length and exposure parameters can be adjusted as needed to obtain the best shooting effect.

[0026] Going further,

[0027] The image processing module is used to pre-process the collected images to remove noise and enhance contrast to improve image quality.

[0028] Going further,

[0029] Image recognition algorithm is used to identify the terminal status of the preprocessed images.

[0030] Going further,

[0031] The recognition results are stored in the data storage and display module and displayed to the user through a visual interface; the user can quickly locate the problem terminal based on the recognition results and perform corresponding processing.

[0032] The present invention significantly improves the recognition rate of terminal status, reduces the misjudgment rate, and improves maintenance efficiency by accurately partitioning the interior of the optical cross-connect box and photographing it one by one.

[0033] The beneficial effects of the present invention are

[0034] 1: Improve recognition rate: Through partition shooting and efficient image processing technology, the recognition rate of the optical cross-connect box terminal status is significantly improved and the misjudgment rate is reduced.

[0035] 2. Improve maintenance efficiency: quickly locate problem terminals, shorten troubleshooting time, and improve network maintenance efficiency.

[0036] 3: Reduce labor costs: Reduce the workload of manual inspection and identification, and reduce labor costs.

[0037] 4: Enhance network stability: timely discover and handle internal problems of the optical cross-connect box to ensure stable network operation. DETAILED DESCRIPTION

[0038] In order to make the purpose, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of the present invention.

[0039] The present invention provides an optical cross-box terminal status recognition system based on image partition shooting, which mainly solves the problem of low AI recognition success rate of optical cross-box terminal status at present, describes the method of using image partition shooting, and increases the success rate of AI recognition of optical cross-box terminal status (idle and occupied). It mainly includes the current background technology and existing problems, the contents of each module of partition shooting, and the implementation method of partition shooting.

[0040] include

[0041] Precise zoning technology: Based on the internal structural characteristics of the optical cross-connect box, a reasonable zoning scheme is designed to ensure that each terminal can be photographed independently and clearly.

[0042] Intelligent shooting control: realize automatic control of the camera through the partition control module, reduce manual intervention and improve shooting efficiency.

[0043] The system composition of the present invention comprises:

[0044] Image acquisition module: includes a high-resolution camera and a focus-adjustable lens, which is used to accurately capture the interior of the light exchange box.

[0045] Partition control module: According to the internal structure of the optical cross-box, multiple shooting areas are preset, and the camera is controlled to shoot one by one according to the preset areas.

[0046] Data storage and display module: stores processed images and recognition results, and provides a visual interface for users to view.

[0047] Partition shooting method:

[0048] Step 1: Design a reasonable partitioning scheme based on the internal structure of the optical cross-connect box to ensure that each terminal can be clearly photographed.

[0049] Step 2: Use the partition control module to control the camera to shoot one by one in the preset partition order. When shooting each partition, the camera's focus, exposure and other parameters can be adjusted as needed to obtain the best shooting effect.

[0050] Step 3: The image processing module pre-processes the collected images to remove noise, enhance contrast, etc. to improve image quality.

[0051] Step 4: Apply image recognition algorithm to identify the terminal status of the pre-processed image. The recognition algorithm can be based on deep learning technology and improve the recognition accuracy by training a large amount of sample data.

[0052] Step 5: Store the recognition results in the data storage module and display them to the user through a visual interface. The user can quickly locate the problem terminal based on the recognition results and take corresponding measures.

[0053] The above description is only a preferred embodiment of the present invention, which is only used to illustrate the technical solution of the present invention, and is not used to limit the protection scope of the present invention. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention are included in the protection scope of the present invention.

Claims

1. A system for identifying the status of optical cross-connection terminals based on image partitioning, characterized in that: include: Image acquisition module: used to accurately partition and shoot the interior of the light cross box; Partition control module: according to the internal structure of the optical cross-box, several shooting areas are preset, and the preset areas are photographed one by one; Data storage and display module: stores processed images and recognition results, and provides a visual interface for users to view.

2. The system according to claim 1, characterized in that The optical cross-box equipment is photographed in sections to reduce coupling between modules.

3. The system according to claim 1, characterized in that The image acquisition module includes a high-resolution camera and a focus-adjustable lens.

4. The system according to claim 3, characterized in that A partitioning scheme is designed based on the internal structure of the optical cross-connect box to ensure that each terminal can be clearly photographed.

5. The system according to claim 4, characterized in that The partition control module is used to control the camera to shoot one by one in the preset partition order; when shooting each partition, the camera's focal length and exposure parameters can be adjusted as needed to obtain the best shooting effect.

6. The system according to claim 5, characterized in that The image processing module is used to pre-process the collected images to remove noise and enhance contrast to improve image quality.

7. The system according to claim 6, characterized in that Apply image recognition algorithm to identify the terminal status of the preprocessed images.

8. The system according to claim 7, characterized in that The recognition results are stored in the data storage and display module and displayed to the user through a visual interface; the user can quickly locate the problem terminal based on the recognition results and perform corresponding processing.