Local area network multi-person cooperation archive digital acquisition and processing system and method

Abstract

The invention particularly relates to a system and a method for digitally acquiring and processing files through multi-user cooperation in a local area network. The system comprises a client terminal and a server terminal, wherein the client terminal is connected with the server terminal; the client terminal is used for collecting archive images, performing image processing and uploading image data to the server terminal; and the server side is used for storing and managing the acquired file image data and allowing the client terminal to access and operate. The local area network multi-person cooperation archive digital acquisition and processing system and method are simple and flexible in operation, high in compatibility and high in automation degree, archive digital efficiency is improved, manual operation time is remarkably shortened, image quality is improved, safety of archive data in acquisition, transmission and storage processes is guaranteed, and the system and the method are suitable for popularization and application. And the traceable management of the archive data is realized, and the adaptability of the system to special situations is improved.

Description

Technical Field

[0001] The present invention relates to the technical field of file management, and particularly to a multi-person collaborative file digitization acquisition and processing system and method in a local area network. Background Art

[0002] Currently, the common technical means in the field of file digitization have the following limitations:

[0003] It is difficult to balance efficiency and quality: Traditional methods are inefficient, automated equipment is costly and has limited capabilities for processing complex files.

[0004] Data security and privacy issues: Cloud systems have data security and privacy problems, while local systems lack effective security protection measures.

[0005] Difficulties in management and traceability: There is a lack of a unified management system, making it difficult to effectively trace and manage file data.

[0006] Lack of flexibility: Existing systems and equipment lack sufficient flexibility and configurability when processing different types of files.

[0007] Based on the limitations of the above-mentioned existing technologies, the present invention proposes a multi-person collaborative acquisition and processing system for local area network files based on a client, aiming to improve the efficiency and quality of file digitization, ensure data security and traceability, and provide flexible management and processing functions. Summary of the Invention

[0008] In order to make up for the deficiencies of the existing technology, the present invention provides a simple and efficient multi-person collaborative file digitization acquisition and processing system and method in a local area network.

[0009] The present invention is realized through the following technical solutions:

[0010] A multi-person collaborative file digitization acquisition and processing system in a local area network, characterized in that: it includes a client terminal and a server terminal;

[0011] The client terminal is used to acquire file images, perform image processing, and upload the image data to the server terminal;

[0012] The client terminal is provided with an image acquisition module, an image processing module, an automatic recognition module, an artificial intervention module, and an upload and storage module;

[0013] The image acquisition module is used to control a scanning device to acquire file images;

[0014] The image processing module is used to preprocess the acquired images, including image denoising, contrast adjustment, edge enhancement, and color correction;

[0015] The automatic recognition module is used to achieve page number recognition and pre-disassembly processing;

[0016] The manual intervention module is used to help users achieve manual adjustment and classification of images;

[0017] The upload and storage module is used to upload the processed image data to the server side for storage and management;

[0018] The server side is used to store and manage the collected archival image data and provide access and operations for the client terminals;

[0019] The server side is provided with an AI recognition and security encryption module, a metadata management module, a data storage module, and a file import and export module;

[0020] The AI recognition and security encryption module is used for the AI intelligent recognition module to recognize the bibliographic information, and encrypt the stored image data and bibliographic information to prevent unauthorized access and tampering;

[0021] The metadata management module is used to manage the image data, preprocess the images according to a preset template, and record the information of the collection personnel for traceability;

[0022] The data storage module is used to store the image data collected by the client terminals;

[0023] The file import and export module is used to achieve the import of multi-format files and export the archival data into a custom unified format file.

[0024] The client terminals include scanning devices (such as scanners or high-resolution cameras) and computing devices, and the computing devices use computers or mobile terminals (such as tablets);

[0025] The operating system of the computing device is any one of Windows, macOS, Linux, or Android, and is used to run the image acquisition module, the image processing module, the automatic recognition module, the manual intervention module, and the upload and storage module.

[0026] The server side includes a high-performance server or workstation and is used to store and manage large-scale archival image data;

[0027] The operating system of the server side is Windows Server, Linux, or other server operating systems, and is used to run the AI recognition and security encryption module, the metadata management module, the data storage module, and the file import and export module.

[0028] A method for digital acquisition and processing of archival materials in a local area network with multi-person collaboration includes the following steps:

[0029] Step S1, Image Acquisition and Processing

[0030] The client terminal controls the scanning device to acquire archive images through the image acquisition module, and the acquired images are preprocessed by the image processing module;

[0031] In the said step S1, the preprocessing steps are as follows:

[0032] Step S1.1, Image Denoising: Use a filtering algorithm to remove noise points in the image;

[0033] Step S1.2, Contrast Adjustment: Customize and adjust the contrast according to the brightness distribution of the image to make the image clearer;

[0034] Step S1.3, Edge Enhancement: Use an edge detection algorithm to enhance the edge features in the image;

[0035] Step S1.4, Color Correction: Correct the color of the image to restore the original color of the image.

[0036] Step S2, Automatic Recognition and Pre-disassembly Processing

[0037] The automatic recognition module uses optical character recognition (OCR) technology to recognize the page numbers and disassembly information in the image, and performs pre-disassembly processing according to the recognition results;

[0038] In the said step S2, the automatic recognition module automatically classifies the recognition results according to the image content and custom classification criteria.

[0039] Step S3, Manual Intervention and Classification

[0040] The user, according to actual needs, performs custom manual adjustment and classification on the image through the manual intervention module; uploads the processed image data to the server through the upload and storage module for storage and management.

[0041] In the said step S3, the user, according to actual needs, views, edits and / or deletes the acquired images through the manual intervention module, and customizes and adds tags and remarks.

[0042] Step S4, AI Recognition and Encryption of Bibliographic Information

[0043] The AI recognition and security encryption module uses machine learning algorithms to recognize the bibliographic information in the image, and encrypts the recognized bibliographic information and pre-fills it into the system for storage to prevent tampering and unauthorized access;

[0044] Step S5, Metadata Management and Preprocessing Template

[0045] To improve the processing efficiency, different templates are pre-configured through the metadata management module, and the images are preprocessed according to the preset templates;

[0046] In step S5, the metadata management module configures an OCR recognition template, a page number recognition template, and a disassembly information extraction template for the preprocessing step.

[0047] Step S6, Import and export of multi-format files

[0048] The file import and export module supports the import of files in multiple formats such as tif, pdf, and ofd, customizes the export format, and exports the imported files through a unified export format (such as ofd), facilitating the unified management and use of archives.

[0049] A local area network multi-person collaboration file digitization acquisition and processing device, characterized in that it includes a memory and a processor; the memory is used to store a computer program, and the processor is used to implement the above method steps when executing the computer program.

[0050] A readable storage medium, characterized in that a computer program is stored on the readable storage medium, and the computer program implements the above method steps when executed by a processor.

[0051] The beneficial effects of the present invention are as follows: The local area network multi-person collaboration file digitization acquisition and processing system and method are simple and flexible to operate, have strong compatibility, high automation, improve the file digitization efficiency, significantly reduce the manual operation time, not only improve the image quality, ensure the security of file data during the acquisition, transmission, and storage processes, but also realize the traceable management of file data and improve the system's adaptability to special situations. Brief Description of the Drawings

[0052] In order to more clearly illustrate the technical solutions in the embodiments of the present invention or in the prior art, the following will briefly introduce the drawings required for use in the description of the embodiments or the prior art. Obviously, the following drawings are some embodiments of the present invention. For those of ordinary skill in the art, other drawings can be obtained based on these drawings without creative efforts.

[0053] Attached Figure 1 It is a schematic diagram of the architecture of the local area network multi-person collaboration file digitization acquisition and processing system of the present invention.

[0054] Attached Figure 2 It is a schematic diagram of the local area network multi-person collaboration file digitization acquisition and processing method of the present invention. Detailed Embodiments

[0055] To enable those skilled in the art to better understand the technical solutions in the present invention, the following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all of the embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts shall fall within the scope of protection of the present invention.

[0056] The local area network multi-person collaborative file digital acquisition and processing system includes a client terminal and a server end;

[0057] The client terminal is used to collect file images, perform image processing, and upload the image data to the server end;

[0058] The client terminal is provided with an image acquisition module, an image processing module, an automatic recognition module, a manual intervention module, and an upload and storage module;

[0059] The image acquisition module is used to control the scanning device to collect file images;

[0060] The image processing module is used to preprocess the collected images, including image denoising, contrast adjustment, edge enhancement, and color correction;

[0061] The automatic recognition module is used to realize page number recognition and pre-disassembly processing;

[0062] The manual intervention module is used to help users manually adjust and classify images;

[0063] The upload and storage module is used to upload the processed image data to the server end for storage and management;

[0064] The server end is used to store and manage the collected file image data and provide access and operations for the client terminal;

[0065] The server end is provided with an AI recognition and security encryption module, a metadata management module, a data storage module, and a file import and export module;

[0066] The AI recognition and security encryption module is used for the AI intelligent recognition module to recognize the bibliographic information and encrypt the stored image data and bibliographic information to prevent unauthorized access and tampering;

[0067] The metadata management module is used to manage the image data, preprocess the images according to a preset template, and record the information of the collection personnel for traceability;

[0068] The data storage module is used to store the image data collected by the client terminal;

[0069] The file import and export module is used to implement the import of multi-format files and export the archive data as a custom unified format file.

[0070] The client terminal includes a scanning device (such as a scanner or a high-resolution camera) and a computing device, and the computing device uses a computer or a mobile terminal (such as a tablet computer);

[0071] The operating system of the computing device is any one of Windows, macOS, Linux, or Android, and is used to run the image acquisition module, the image processing module, the automatic recognition module, the manual intervention module, and the upload and storage module.

[0072] The server side includes a high-performance server or a workstation, which is used to store and manage a large amount of archive image data;

[0073] The operating system of the server side is Windows Server, Linux, or other server operating systems, and is used to run the AI recognition and security encryption module, the metadata management module, the data storage module, and the file import and export module.

[0074] The method for digitizing and processing archive collection with multi-person collaboration in a local area network includes the following steps:

[0075] Step S1, Image acquisition and processing

[0076] The client terminal controls the scanning device to acquire archive images through the image acquisition module, and the acquired images are preprocessed by the image processing module;

[0077] In the step S1, the preprocessing steps are as follows:

[0078] Step S1.1, Image denoising: Use a filtering algorithm to remove noise in the image;

[0079] Step S1.2, Contrast adjustment: Customize the contrast adjustment according to the brightness distribution of the image to make the image clearer;

[0080] Step S1.3, Edge enhancement: Use an edge detection algorithm to enhance the edge features in the image;

[0081] Step S1.4, Color correction: Correct the color of the image to restore the original color of the image.

[0082] Step S2, Automatic recognition and pre-disassembly processing

[0083] The automatic recognition module uses optical character recognition (OCR) technology to recognize the page numbers and disassembly information in the image, and performs pre-disassembly processing according to the recognition results;

[0084] In step S2, the automatic recognition module automatically classifies the recognition results according to the image content and custom classification criteria.

[0085] Step S3, Manual Intervention and Classification

[0086] According to actual needs, the user makes custom manual adjustments and classifications to the images through the manual intervention module; and uploads the processed image data to the server side through the upload and storage module for storage and management.

[0087] In step S3, according to actual needs, the user views, edits, and / or deletes the collected images through the manual intervention module, and custom adds tags and remarks.

[0088] Step S4, AI Recognition and Encryption of Bibliographic Information

[0089] The AI recognition and security encryption module uses machine learning algorithms to recognize the bibliographic information in the images, and encrypts the recognized bibliographic information and pre-fills it into the system for storage to prevent tampering and unauthorized access;

[0090] Step S5, Metadata Management and Preprocessing Template

[0091] To improve processing efficiency, different templates are pre-configured through the metadata management module, and the images are pre-processed according to the preset templates;

[0092] In step S5, the metadata management module configures OCR recognition templates, page number recognition templates, and disassembly information extraction templates for the preprocessing steps.

[0093] Step S6, Import and Export of Multi-format Files

[0094] The file import and export module supports the import of multiple format files such as tif, pdf, and ofd, customizes the export format, and exports the imported files through a unified export format (such as ofd) for the unified management and use of archives.

[0095] Compared with the prior art, the local area network multi-person collaboration file digitization acquisition and processing system and method have the following characteristics:

[0096] (1) The system integrates efficient image acquisition and processing modules, supports batch scanning and high-resolution image acquisition, significantly reduces the manual operation time, and improves the file digitization efficiency.

[0097] (2) Adopting OCR technology, it can automatically recognize the page numbers and disassembly information in the images, reducing the workload of manual classification and processing.

[0098] (3) The image processing module adopts advanced image processing technologies, including image denoising, contrast adjustment, edge enhancement, and color correction, which can ensure consistent and clear quality of the captured images; the improvement of image quality makes subsequent file management and use more convenient and accurate.

[0099] (4) The system guarantees the security of file data during the processes of collection, transmission, and storage through data encryption and permission management, and can prevent data tampering and unauthorized access; encrypting and storing the descriptive information further safeguards the confidentiality and integrity of file data.

[0100] (5) The metadata management module records the collection time, collection personnel, and processing logs of each image, realizes traceable management of file data, and ensures the transparency and standardization of the file processing process; this traceability ability helps with quality control and responsibility assignment in file management.

[0101] (6) The manual intervention module allows users to manually view, edit, and classify images, ensuring accurate processing and classification of complex file content, and improving the flexibility and accuracy of file digitization; users can intervene and adjust at any time, enhancing the system's adaptability to special situations.

[0102] (7) The file import / export module supports the import and export of multiple file formats such as tif, pdf, ofd, etc., enhancing the system's compatibility and flexibility to meet the needs of different file types. The system can uniformly export file data as ofd format files for easy long-term storage and unified management.

[0103] (8) Through preset templates, the metadata management module enables the system to automatically identify and classify different types of files, perform OCR recognition, page number recognition, and extraction of disassembly information, improving the efficiency and accuracy of file management; this optimized management process reduces manual operation steps and improves the overall efficiency of file processing.

[0104] (9) The system can process large-scale file data, supports multi-user collaborative work, and is applicable to file digitization projects of various archives, libraries, enterprises, institutions, and government agencies; the efficient batch processing ability enables the system to meet the needs of large-scale file digitization and significantly improves work efficiency.

[0105] The above-described embodiments are only one of the specific implementation manners of the present invention, and ordinary changes and substitutions made by those skilled in the art within the scope of the technical solution of the present invention should be included in the protection scope of the present invention.

Claims

1. A digital acquisition and processing system for multi - person collaborative files in a local area network, characterized in that: It includes a client terminal and a server side; The client terminal is used to collect archive images, perform image processing, and upload the image data to the server side; The client terminal is provided with an image acquisition module, an image processing module, an automatic recognition module, a manual intervention module, and an upload and storage module; The image acquisition module is used to control the scanning device to collect archive images; The image processing module is used to preprocess the collected images, including image denoising, contrast adjustment, edge enhancement, and color correction; The automatic recognition module is used to implement page number recognition and pre-disassembly processing; The manual intervention module is used to help users manually adjust and classify images; The upload and storage module is used to upload the processed image data to the server side for storage and management; The server side is used to store and manage the collected archive image data and provide access and operations for the client terminal; The server side is provided with an AI recognition and security encryption module, a metadata management module, a data storage module, and a file import / export module; The AI recognition and security encryption module is used for the AI intelligent recognition module to recognize the descriptive information, and encrypt the stored image data and descriptive information to prevent unauthorized access and tampering; The metadata management module is used to manage the image data, preprocess the images according to a preset template, and record the information of the collection personnel for traceability; The data storage module is used to store the image data collected by the client terminal; The file import / export module is used to implement file import and export the archive data into a custom unified format file.

2. The local area network multi-person collaborative file digitization acquisition and processing system according to claim 1, wherein: The client terminal includes a scanning device and a computing device, and the computing device uses a computer or a mobile terminal; The operating system of the computing device is any one of Windows, macOS, Linux, or Android, and is used to run the image acquisition module, the image processing module, the automatic recognition module, the manual intervention module, and the upload and storage module.

3. The local area network multi-person collaborative file digitization acquisition and processing system according to claim 1, wherein: The server side includes a high-performance server or a workstation, which is used to store and manage a large amount of archive image data; The operating system of the server side is Windows Server or Linux operating system, and is used to run the AI recognition and security encryption module, the metadata management module, the data storage module, and the file import / export module.

4. A method for digital acquisition and processing of files with multi-person collaboration in a local area network, characterized in that: It includes the following steps: Step S1, Image acquisition and processing The client terminal controls the scanning device to collect archive images through the image acquisition module, and preprocesses the collected images through the image processing module; Step S2, Automatic recognition and pre-disassembly processing The automatic recognition module uses optical character recognition (OCR) technology to recognize the page numbers and disassembly information in the images, and performs pre-disassembly processing according to the recognition results; Step S3, Manual intervention and classification According to actual needs, the user customizes and manually adjusts and classifies the images through the manual intervention module; uploads the processed image data to the server side through the upload and storage module for storage and management. Step S4, AI recognition and encryption of descriptive information The AI recognition and security encryption module uses machine learning algorithms to identify the bibliographic information in the image, and pre-fills and stores the identified bibliographic information in the system after encryption processing to prevent tampering and unauthorized access; Step S5, Metadata management and preprocessing template To improve processing efficiency, different templates are pre-configured through the metadata management module, and the images are pre-processed according to the preset templates; Step S6, Import and export of multi-format files The file import and export module supports the import of tif, pdf, and ofd format files, customizes the export format, and exports the imported files through a unified export format for easy unified management and use of the archives.

5. The method for digitizing and processing of multi-person collaborative archives in a local area network according to claim 4, characterized in that: In the said step S1, the preprocessing steps are as follows: Step S1.1, Image denoising: Use a filtering algorithm to remove noise in the image; Step S1.2, Contrast adjustment: Customize the contrast adjustment according to the brightness distribution of the image to make the image clearer; Step S1.3, Edge enhancement: Use an edge detection algorithm to enhance the edge features in the image; Step S1.4, Color correction: Correct the color of the image to restore the original color of the image.

6. The method for digital acquisition and processing of local area network multi-person collaborative files according to claim 4, wherein: In the said step S2, the automatic recognition module automatically classifies the recognition results according to the image content and custom classification criteria.

7. The method for digitizing and processing local area network multi-person collaborative files according to claim 4, characterized in that: In the said step S3, the user can view, edit, and / or delete the collected images through the manual intervention module according to actual needs, and customize the addition of tags and remarks.

8. The method for digitizing and processing archives through multi-person collaboration in a local area network according to claim 4, wherein: In the said step S5, the metadata management module configures an OCR recognition template, a page number recognition template, and a disassembly information extraction template for the preprocessing steps.

9. A digital acquisition and processing device for multi-person collaborative files in a local area network, characterized in that: It includes a memory and a processor; the memory is used to store a computer program, and the processor is used to implement the method steps described in any one of claims 4 to 8 when executing the computer program.

10. A readable storage medium, characterized in that: A computer program is stored on the readable storage medium, and when the computer program is executed by the processor, the method steps described in any one of claims 4 to 8 are implemented.

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