Hierarchical picture file management system and method

Abstract

The invention discloses a system and a method for managing layered picture files, and aims to provide an efficient and extensible picture file management solution, the system comprises a plurality of modules, and through technical means such as layered file management, image processing, authority control, version management, retrieval and screening, data backup and recovery, log recording and analysis and the like, the picture files can be managed by the modules. The problems that in the prior art, picture file storage is disordered, access authority is not clear, version management is difficult, and retrieval efficiency is low are solved.

Description

Technical Field

[0001] The present invention relates to a system and method for managing hierarchical image files, and more specifically, to efficiently managing, storing, retrieving and restoring image files in large-scale image file management through hierarchical organization, image processing, permission control, version management and other technical means. The present invention can be widely used in multiple fields such as image storage management, digital media library, cloud storage service, online image library, etc., to improve the management efficiency and security of image files, and optimize file storage and access methods. Background Art

[0002] With the advent of the information age, digital image files have been widely used in various businesses, especially in the fields of the Internet, cloud storage and digital media. The number of image files continues to increase, and the complexity of file management has also increased. Traditional image file management systems mainly rely on simple file storage methods, lacking functions such as hierarchical organization of image files, image processing, permission control and version management, resulting in problems such as file confusion, storage space waste, low retrieval efficiency and poor data security in large-scale data management.

[0003] At present, there are some file management systems on the market that can provide functions such as file classification, search and backup, but they still have the following defects: Insufficient hierarchical management capabilities: Most systems only provide a flat storage structure and are unable to efficiently manage files based on their hierarchical relationships, resulting in confusion in file classification and organization, affecting subsequent retrieval and management efficiency.

[0004] Limited image processing capabilities: Traditional systems fail to perform effective format conversion, compression, resolution adjustment, and other processing on images, and are unable to adapt to a variety of file formats and storage requirements, resulting in low image quality and storage efficiency.

[0005] Inaccurate permission management: Most systems have relatively simple permission control and are unable to dynamically adjust permissions based on different user roles and operational requirements, resulting in insufficient file security and management flexibility.

[0006] Lack of version management functions: The existing system has incomplete version management and modification history tracking functions for files. When collaboratively editing files, version conflicts and data loss are prone to occur, affecting the traceability and historical records of the files. Summary of the invention

[0007] The present invention provides a hierarchical image file system and method for managing the hierarchical image file system. Through the collaboration of multiple modules, efficient hierarchical file management, format conversion, permission control, version management, retrieval and screening, data backup and recovery, and log recording and analysis are achieved in large-scale image file management. Through these technical means, the problems of low efficiency of image file management, poor data security, and difficulty in version tracing in the prior art are solved, and the intelligence and security of file management are improved.

[0008] The present invention specifically includes the following contents: 1. Hierarchical file management module: used to organize and classify files in layers according to the hierarchical relationship and management requirements of image files, mainly including: (1) File hierarchy construction function based on multi-level tree directory structure: Generate a multi-level storage structure for image files according to the directory tree structure. In the directory tree, each layer represents a file category or file group. The deeper the layer, the more specific the file classification. In order to optimize the hierarchical structure of file storage, the system uses a hierarchical clustering optimization algorithm: The image file set is , the hierarchical tree is , then the hierarchical objective function is:

[0009] in, is the file importance weight, is the similarity between the file and the hierarchy center, is the depth penalty factor, is the level depth of the level tree; (2) File classification and labeling function: allows image files to be classified and labeled for quick screening and categorization. Each file can be assigned one or more labels based on its attributes (file type, shooting location, shooting time). Labeling not only improves file management efficiency, but also allows the target file to be quickly found based on the label during retrieval.

[0010] (3) Dynamic adjustment of the relationship between file hierarchies: This function is used to dynamically adjust the position of files in the hierarchical tree. As the number of files and classification requirements change, the hierarchical relationship of files may need to be reconfigured. In order to achieve this flexibility, a hash mapping table is used to record the level of the file and its changes. When the level of a file changes, the hash mapping table can quickly locate and adjust the position of the file.

[0011] 2. Image processing module: used for format conversion, compression, resolution adjustment and color optimization of image files, mainly including: (1) Image batch format conversion function: Through a unified batch processing interface, a large number of images can be quickly and efficiently converted from one format to another (JPEG, PNG, BMP conversion), ensuring the compatibility of images in different devices and application scenarios.

[0012] (2) Image quality improvement function: Use deep learning models to intelligently optimize images and improve the visual quality of images through denoising, detail enhancement, and super-resolution.

[0013] (3) Image compression function: It provides two algorithms: lossless compression (no information is lost during the compression process, and the image quality remains unchanged, which is suitable for scenes with extremely high quality requirements) and lossy compression (some information is lost during the compression process, which can usually achieve a higher compression ratio, which is suitable for scenes with high file size requirements (web page loading, scenes with limited storage space)). The compression method is adaptively selected according to actual needs. The image compression formula is: Pixel partitioning , define the entropy value of each zone as , then the maximum image compression ratio is:

[0014] in, is the total pixels of the image, is the number of pixels in the partition.

[0015] 3. Permission control module: As one of the core components in the system, it is mainly used to set the access, editing, and sharing permissions of image files based on user roles and security requirements, mainly including: (1) User authority level division function: supports three user role configurations: Administrator: has the highest authority and can perform system configuration, authority allocation, user management and other operations, and can access almost all functions in the system; Editor: has certain permissions and can modify content, edit files, update data, etc., but cannot modify system settings or assign permissions; Viewer: has the lowest permissions and can only view files or data but cannot edit or configure them. (2) Dynamic permission adjustment function: permissions are automatically updated according to user behavior and time range. By analyzing user behavior patterns and access history, the system can intelligently recommend permission adjustments to ensure the dynamic and real-time nature of permission configuration. Specifically, a dynamic permission recommendation algorithm based on Bayesian reasoning is used. The user behavior sequence is: , the permission level is , then the probability that a user belongs to a certain permission level is:

[0016] (3) Data security audit function: ensure the recording and tracking of permission allocation, change and use process. This function will record each operation in detail and analyze these records to discover potential security threats and violations, mainly including: Permission allocation and change records: Whenever the system's permission configuration changes, a record will be generated, detailing the time of the permission change, the person who executed it, the content of the change, and other information; Operation behavior tracking: record each user's operations on files, data or systems, including access, modification, and deletion. By recording the user, time, and content of the operation, the specific operation can be traced back; Security risk analysis: By analyzing user operation records, the system can identify abnormal behaviors, such as excessive privilege escalation, illegal access, frequent privilege changes, etc., issue alerts in a timely manner, and notify administrators to handle the situation.

[0017] 4. Version management module: used to record and track the modification history of image files, support version backtracking and multi-version storage, mainly including: (1) Automatic saving of modification records: The automatic saving of modification records function will automatically generate an independent version number each time an image file is modified, and record the specific content of the modification, including: File size: The size of the modified file can reflect the amount of change in the file content; Modification time: records the timestamp of file modification, used to trace the history; Modifier: Record the user who performed the modification operation for later review and tracing.

[0018] These modification records will be stored in a dedicated version database to form a complete modification history chain. Each time a modification is made, the system will automatically save a copy of the modification and generate a unique version number. The version number can be generated using an incremental numbering or a timestamp-based strategy. In this way, users can easily trace back to the historical status of the file, view the evolution of the file content, and ensure that any changes can be traced.

[0019] (2) Version comparison and conflict resolution function: When multiple people collaborate to modify the same file, it may happen that multiple users modify the same file at the same time. In order to avoid file content conflicts, the version management module provides version comparison and conflict resolution functions. The version comparison function automatically identifies which parts have changed by comparing the differences between different versions. The system analyzes the differences in text, data or images between each version and generates a difference report. The report includes the modified parts (marking the changes between the two versions for easy identification by users) and the time and person who modified the file (showing who modified the file and when). For conflicting versions, the system provides three options: latest version first (retain the changes of the latest version and ignore the changes of the previous version), merge changes of different versions (merge changes of different versions together, usually requiring manual intervention, and users decide how to merge based on the specific content), and manual confirmation (if the system cannot automatically resolve the conflict, users can manually select which version of the changes should be retained), allowing users to choose how to merge these versions.

[0020] (3) Timeline backtracking function: allows users to view all historical versions of a file through the timeline. Each version will be displayed on the timeline, and users can select a version at any time point for recovery. The timeline displays three contents: version number (each version has a unique number to facilitate identification of different versions), modification time (records the creation time of each version to help users quickly locate a specific version), and a brief description of the modification content (the modification content of each version will be briefly described so that users can understand the changes in the file).

[0021] 5. Retrieval and screening module, used to quickly search and screen according to the name, label, attribute and content of the image file, mainly including: (1) Multi-condition search function: supports users to search based on multiple conditions and provides flexible search methods. The search conditions include: File name: Perform exact or fuzzy matching based on the file name. Users can search for related files by entering some characters in the file name. Tags: Users can search based on tags attached to files (such as "people" and "landscape"); Creation time and modification time: Users can specify a time range to search for files. For example, search for files created or modified on a specific date or date range; File size: supports filtering based on file size. Users can set an upper or lower limit for file size to find files that meet the size criteria.

[0022] The combined retrieval method also supports exact matching, fuzzy matching and regular expression search, and can cope with a variety of complex search requirements.

[0023] (2) Tag filtering function: Files can be classified according to tag information, thereby improving the flexibility and convenience of retrieval. Files can be assigned multiple tags (theme, color, shooting location, etc.), which can be easily filtered using these tags.

[0024] (3) Image content retrieval function: Image recognition technology is used to perform similarity retrieval based on the content features of the image (shape, color distribution, texture, etc.). The image content retrieval function can not only search the file name or tag of the image, but also search based on the characteristics of the image itself.

[0025] (4) Intelligent sorting and priority screening: Based on machine learning algorithms, the system analyzes users' historical search behaviors, predicts and intelligently adjusts the priority of file display. The system automatically optimizes the sorting of search results based on users' past searches and selections, thereby providing more personalized and relevant search results.

[0026] 6. Data backup and recovery module, used to periodically back up the hierarchical data of image files and provide data recovery function after system failure, mainly including: (1) Incremental backup function: When a file changes, only the changed part is backed up instead of the entire file. By recording the hash value of the file (a hash value is generated for each file, and the hash value of the file is checked regularly to see if it has changed. Only files with changed hash values ​​are considered to have changed and are backed up) or using timestamps to identify the changed content (each time a file changes, the system records the timestamp of the file. During the backup, the system compares the timestamps. If the timestamp of a file is updated, it is considered that the file has changed and is backed up). This achieves efficient data backup and saves storage space and bandwidth.

[0027] (2) Regular backup and manual backup functions: Users can set up periodic backups, such as daily or weekly automatic backups. This ensures that users' files are backed up within a certain period of time without manual operation, making it easier to keep data up to date. The system also allows users to manually initiate backup operations. Users can press the backup button at the time of need, select the backup content and storage location, and perform an instant backup. Regardless of whether it is a regular backup or a manual backup, the backup can be stored in a local storage device or in the cloud.

[0028] 7. Log recording and analysis module, used to record user operation behavior and system operation status, and generate analysis reports to optimize system performance, mainly including: (1) User operation log recording function: The system records all operation behaviors of each user, including file upload, download, modification, deletion, and viewing. Each operation records the operation time, user ID, operation type, and file information of the operation object; (2) Data analysis and optimization suggestions: Through big data analysis technology, user behavior logs and system operation data are analyzed to explore potential needs for system optimization, such as loading speed bottlenecks, files with the highest access frequency, etc. Based on the analysis results, the system automatically generates optimization suggestions, such as caching strategies, file access path optimization, etc., to improve overall performance.

[0029] A method for managing a layered image file system includes the following steps: Step 1: Based on the hierarchical relationship and management requirements of image files, the system dynamically generates image file storage hierarchies through a multi-level tree directory structure, and uses a hierarchical clustering optimization algorithm to optimize the file organization structure. Files are quickly retrieved and screened through labeling. When files are migrated or reorganized, a hash mapping table is used to record hierarchical changes, supporting cross-level batch migration and data synchronization.

[0030] Step 2: The image files are processed by format conversion, compression, resolution adjustment, and color optimization. Batch conversion of images into different formats is supported. Image quality is intelligently optimized through a deep learning model to improve detail expression and clarity. Image compression uses lossless compression and lossy compression algorithms. The lossless compression optimization algorithm based on dynamic partitioning adjusts the image size to balance file size and quality.

[0031] Step 3. Based on user roles and security requirements, the system sets access, editing and sharing permissions for image files. The system configures permissions for different roles, dynamically adjusts permissions based on user behavior analysis and time range, and uses a dynamic permission recommendation algorithm based on Bayesian reasoning to predict and adjust user permissions. The system records and tracks user operation behavior and permission usage to ensure transparency and traceability of operations.

[0032] Step 4. Record and track the modification history of image files, support version backtracking and multi-version storage. Each time a file is modified, the system automatically saves the modification record, including file size, modification time and modifier information, and generates a new version number. The system compares the contents of different versions of files, detects conflicts and prompts users to select a conflict resolution strategy. Users can view all version histories of files through the timeline and restore the file version to a certain point in time.

[0033] Step 5. Image files are quickly retrieved and filtered based on name, tag, attribute and content. Users can search based on file name, tag, creation time, modification time, file size and other conditions. It supports exact and fuzzy matching and regular expression retrieval. Image files are classified by tags, and users can filter files based on multiple tag combination conditions. The system also uses image recognition technology to perform content similarity retrieval based on content features, analyzes user historical search behavior through machine learning algorithms, and intelligently adjusts the sorting of retrieval results to improve search accuracy and efficiency.

[0034] Step 6. The system provides periodic backup and recovery functions for files to ensure data integrity and security. When a file changes, the system only backs up the changed part instead of the entire file. The changed part is marked with a hash value or timestamp. The system supports regular automatic backup and allows users to manually initiate backup operations.

[0035] Step 7. The system records user operations and system operating status in real time, and generates data analysis reports to optimize system performance. The system records all operation behaviors of each user and saves information such as operation time, user ID, and operation type. The system uses big data analysis technology to analyze user behavior and system operation data, explore potential needs for system optimization, and automatically generate optimization suggestions based on the analysis results to improve overall performance.

[0036] A layered image file system and method for managing the file system has the following beneficial effects: Improve the efficiency and accuracy of system file management By adopting a multi-level tree-like directory structure and hierarchical clustering optimization algorithm, the system can efficiently and accurately classify and store image files. The hierarchical construction of files not only ensures the clarity and order of the storage structure, but also reduces the time overhead of file retrieval and improves file management efficiency through optimization based on file importance weights and similarities. The functions of dynamically adjusting file hierarchical relationships and supporting cross-level batch migration further enhance the flexibility and adaptability of the system.

[0037] Improving the intelligence of image processing The system improves image quality by introducing a deep learning model, which can automatically optimize image quality and reduce the need for manual intervention. In addition, the lossless compression optimization algorithm based on dynamic partitioning can adaptively select the appropriate compression strategy to maximize the compression ratio while ensuring image quality, saving storage space and bandwidth for users, and is particularly suitable for the management of massive image files.

[0038] Enhanced data security and traceability Through the log recording and analysis module, the system comprehensively records user operations and supports tracking operations such as file upload, download, modification, deletion, and viewing. This function provides a detailed audit trail for the system, which can effectively monitor and prevent improper operations. At the same time, the data analysis function can evaluate user behavior and system operation status through big data analysis technology, and promptly discover potential security risks or performance bottlenecks.

[0039] Improve the flexibility of file recovery and version management The system provides users with an efficient file backup and recovery mechanism through incremental backup, regular backup, and recovery point creation functions. Incremental backup only backs up the changed parts of the file, saving storage space, and through the historical version recovery function, users can flexibly restore the file to any historical state. The version management module also provides version comparison and conflict resolution functions, supports version merging when multiple users modify in parallel, and ensures the integrity and consistency of file data. BRIEF DESCRIPTION OF THE DRAWINGS

[0040] Figure 1 It is a flowchart of the hierarchical file management module; Figure 2 It is the flow chart of the image processing module; Figure 3 It is the flow chart of the permission control module; Figure 4 It is the flow chart of the version management module; Figure 5 It is the flow chart of the retrieval and screening module; Figure 6 It is the flow chart of data backup and recovery module; Figure 7 It is the flow chart of the log recording and analysis module; Figure 8 It is a flow chart of the method steps. DETAILED DESCRIPTION

[0041] The technical solutions in the embodiments of the present invention will be described clearly and completely below in conjunction with the accompanying drawings in the embodiments of the present invention: 1. Hierarchical file management module: used to organize and classify files in layers according to the hierarchical relationship and management requirements of image files, mainly including: (1) File hierarchy construction function based on multi-level tree directory structure: Generate a multi-level storage structure for image files according to the directory tree structure. In the directory tree, each layer represents a file category or file group. The deeper the layer, the more specific the file classification. In order to optimize the hierarchical structure of file storage, the system uses a hierarchical clustering optimization algorithm: The image file set is , the hierarchical tree is , then the hierarchical objective function is:

[0042] in, is the file importance weight, is the similarity between the file and the hierarchy center, is the depth penalty factor, is the level depth of the level tree; (2) File classification and labeling function: allows image files to be classified and labeled for quick screening and categorization. Each file can be assigned one or more labels based on its attributes (file type, shooting location, shooting time). Labeling not only improves file management efficiency, but also allows the target file to be quickly found based on the label during retrieval.

[0043] (3) Dynamic adjustment of the relationship between file hierarchies: This function is used to dynamically adjust the position of files in the hierarchical tree. As the number of files and classification requirements change, the hierarchical relationship of files may need to be reconfigured. In order to achieve this flexibility, a hash mapping table is used to record the level of the file and its changes. When the level of a file changes, the hash mapping table can quickly locate and adjust the position of the file.

[0044] 2. Image processing module: used for format conversion, compression, resolution adjustment and color optimization of image files, mainly including: (1) Image batch format conversion function: Through a unified batch processing interface, a large number of images can be quickly and efficiently converted from one format to another (JPEG, PNG, BMP conversion), ensuring the compatibility of images in different devices and application scenarios.

[0045] (2) Image quality improvement function: Use deep learning models to intelligently optimize images and improve the visual quality of images through denoising, detail enhancement, and super-resolution.

[0046] (3) Image compression function: It provides two algorithms: lossless compression (no information is lost during the compression process, and the image quality remains unchanged, which is suitable for scenes with extremely high quality requirements) and lossy compression (some information is lost during the compression process, which can usually achieve a higher compression ratio, which is suitable for scenes with high file size requirements (web page loading, scenes with limited storage space)). The compression method is adaptively selected according to actual needs. The image compression formula is: Pixel partitioning , define the entropy value of each zone as , then the maximum image compression ratio is:

[0047] in, is the total pixels of the image, is the number of pixels in the partition.

[0048] 3. Permission control module: As one of the core components in the system, it is mainly used to set the access, editing, and sharing permissions of image files based on user roles and security requirements, mainly including: (1) User authority level division function: supports three user role configurations: Administrator: has the highest authority and can perform system configuration, authority allocation, user management and other operations, and can access almost all functions in the system; Editor: has certain permissions and can modify content, edit files, update data, etc., but cannot modify system settings or assign permissions; Viewer: has the lowest permissions and can only view files or data but cannot edit or configure them. (2) Dynamic permission adjustment function: permissions are automatically updated according to user behavior and time range. By analyzing user behavior patterns and access history, the system can intelligently recommend permission adjustments to ensure the dynamic and real-time nature of permission configuration. Specifically, a dynamic permission recommendation algorithm based on Bayesian reasoning is used. The user behavior sequence is: , the permission level is , then the probability that a user belongs to a certain permission level is:

[0049] (3) Data security audit function: ensure the recording and tracking of permission allocation, change and use process. This function will record each operation in detail and analyze these records to discover potential security threats and violations, mainly including: Permission allocation and change records: Whenever the system's permission configuration changes, a record will be generated, detailing the time of the permission change, the person who executed it, the content of the change, and other information; Operation behavior tracking: record each user's operations on files, data or systems, including access, modification, and deletion. By recording the user, time, and content of the operation, the specific operation can be traced back; Security risk analysis: By analyzing user operation records, the system can identify abnormal behaviors, such as excessive privilege escalation, illegal access, frequent privilege changes, etc., issue alerts in a timely manner, and notify administrators to handle the situation.

[0050] 4. Version management module: used to record and track the modification history of image files, support version backtracking and multi-version storage, mainly including: (1) Automatic saving of modification records: The automatic saving of modification records function will automatically generate an independent version number each time an image file is modified, and record the specific content of the modification, including: File size: The size of the modified file can reflect the amount of change in the file content; Modification time: records the timestamp of file modification, used to trace the history; Modifier: Record the user who performed the modification operation for later review and tracing.

[0051] These modification records will be stored in a dedicated version database to form a complete modification history chain. Each time a modification is made, the system will automatically save a copy of the modification and generate a unique version number. The version number can be generated using an incremental numbering or a timestamp-based strategy. In this way, users can easily trace back to the historical status of the file, view the evolution of the file content, and ensure that any changes can be traced.

[0052] (2) Version comparison and conflict resolution function: When multiple people collaborate to modify the same file, it may happen that multiple users modify the same file at the same time. In order to avoid file content conflicts, the version management module provides version comparison and conflict resolution functions. The version comparison function automatically identifies which parts have changed by comparing the differences between different versions. The system analyzes the differences in text, data or images between each version and generates a difference report. The report includes the modified parts (marking the changes between the two versions for easy identification by users) and the time and person who modified the file (showing who modified the file and when). For conflicting versions, the system provides three options: latest version first (retain the changes of the latest version and ignore the changes of the previous version), merge changes of different versions (merge changes of different versions together, usually requiring manual intervention, and users decide how to merge based on the specific content), and manual confirmation (if the system cannot automatically resolve the conflict, users can manually select which version of the changes should be retained), allowing users to choose how to merge these versions.

[0053] (3) Timeline backtracking function: allows users to view all historical versions of a file through the timeline. Each version will be displayed on the timeline, and users can select a version at any time point for recovery. The timeline displays three contents: version number (each version has a unique number to facilitate identification of different versions), modification time (records the creation time of each version to help users quickly locate a specific version), and a brief description of the modification content (the modification content of each version will be briefly described so that users can understand the changes in the file).

[0054] 5. Retrieval and screening module, used to quickly search and screen according to the name, label, attribute and content of the image file, mainly including: (1) Multi-condition search function: supports users to search based on multiple conditions and provides flexible search methods. The search conditions include: File name: Perform exact or fuzzy matching based on the file name. Users can search for related files by entering some characters in the file name. Tags: Users can search based on tags attached to files (such as "people" and "landscape"); Creation time and modification time: Users can specify a time range to search for files. For example, search for files created or modified on a specific date or date range; File size: supports filtering based on file size. Users can set an upper or lower limit for file size to find files that meet the size criteria.

[0055] The combined retrieval method also supports exact matching, fuzzy matching and regular expression search, and can cope with a variety of complex search requirements.

[0056] (2) Tag filtering function: Files can be classified according to tag information, thereby improving the flexibility and convenience of retrieval. Files can be assigned multiple tags (theme, color, shooting location, etc.), which can be easily filtered using these tags.

[0057] (3) Image content retrieval function: Image recognition technology is used to perform similarity retrieval based on the content features of the image (shape, color distribution, texture, etc.). The image content retrieval function can not only search the file name or tag of the image, but also search based on the characteristics of the image itself.

[0058] (4) Intelligent sorting and priority screening: Based on machine learning algorithms, the system analyzes users' historical search behaviors, predicts and intelligently adjusts the priority of file display. The system automatically optimizes the sorting of search results based on users' past searches and selections, thereby providing more personalized and relevant search results.

[0059] 6. Data backup and recovery module, used to periodically back up the hierarchical data of image files and provide data recovery function after system failure, mainly including: (1) Incremental backup function: When a file changes, only the changed part is backed up instead of the entire file. By recording the hash value of the file (a hash value is generated for each file, and the hash value of the file is checked regularly to see if it has changed. Only files with changed hash values ​​are considered to have changed and are backed up) or using timestamps to identify the changed content (each time a file changes, the system records the timestamp of the file. During the backup, the system compares the timestamps. If the timestamp of a file is updated, it is considered that the file has changed and is backed up). This achieves efficient data backup and saves storage space and bandwidth.

[0060] (2) Regular backup and manual backup functions: Users can set up periodic backups, such as daily or weekly automatic backups. This ensures that users' files are backed up within a certain period of time without manual operation, making it easier to keep data up to date. The system also allows users to manually initiate backup operations. Users can press the backup button at the time of need, select the backup content and storage location, and perform an instant backup. Regardless of whether it is a regular backup or a manual backup, the backup can be stored in a local storage device or in the cloud.

[0061] 7. Log recording and analysis module, used to record user operation behavior and system operation status, and generate analysis reports to optimize system performance, mainly including: (1) User operation log recording function: The system records all operation behaviors of each user, including file upload, download, modification, deletion, and viewing. Each operation records the operation time, user ID, operation type, and file information of the operation object; (2) Data analysis and optimization suggestions: Through big data analysis technology, user behavior logs and system operation data are analyzed to explore potential needs for system optimization, such as loading speed bottlenecks, files with the highest access frequency, etc. Based on the analysis results, the system automatically generates optimization suggestions, such as caching strategies, file access path optimization, etc., to improve overall performance.

[0062] A method for managing a layered image file system includes the following steps: Step 1: Based on the hierarchical relationship and management requirements of image files, the system dynamically generates image file storage hierarchies through a multi-level tree directory structure, and uses a hierarchical clustering optimization algorithm to optimize the file organization structure. Files are quickly retrieved and screened through labeling. When files are migrated or reorganized, a hash mapping table is used to record hierarchical changes, supporting cross-level batch migration and data synchronization.

[0063] Step 2: The image files are processed by format conversion, compression, resolution adjustment, and color optimization. Batch conversion of images into different formats is supported. Image quality is intelligently optimized through a deep learning model to improve detail expression and clarity. Image compression uses lossless compression and lossy compression algorithms. The lossless compression optimization algorithm based on dynamic partitioning adjusts the image size to balance file size and quality.

[0064] Step 3. Based on user roles and security requirements, the system sets access, editing and sharing permissions for image files. The system configures permissions for different roles, dynamically adjusts permissions based on user behavior analysis and time range, and uses a dynamic permission recommendation algorithm based on Bayesian reasoning to predict and adjust user permissions. The system records and tracks user operation behavior and permission usage to ensure transparency and traceability of operations.

[0065] Step 4. Record and track the modification history of image files, support version backtracking and multi-version storage. Each time a file is modified, the system automatically saves the modification record, including file size, modification time and modifier information, and generates a new version number. The system compares the contents of different versions of files, detects conflicts and prompts users to select a conflict resolution strategy. Users can view all version histories of files through the timeline and restore the file version to a certain point in time.

[0066] Step 5. Image files are quickly retrieved and filtered based on name, tag, attribute and content. Users can search based on file name, tag, creation time, modification time, file size and other conditions. It supports exact and fuzzy matching and regular expression retrieval. Image files are classified by tags, and users can filter files based on multiple tag combination conditions. The system also uses image recognition technology to perform content similarity retrieval based on content features, analyzes user historical search behavior through machine learning algorithms, and intelligently adjusts the sorting of retrieval results to improve search accuracy and efficiency.

[0067] Step 6. The system provides periodic backup and recovery functions for files to ensure data integrity and security. When a file changes, the system only backs up the changed part instead of the entire file. The changed part is marked with a hash value or timestamp. The system supports regular automatic backup and allows users to manually initiate backup operations.

[0068] Step 7. The system records user operations and system operating status in real time, and generates data analysis reports to optimize system performance. The system records all operation behaviors of each user and saves information such as operation time, user ID, and operation type. The system uses big data analysis technology to analyze user behavior and system operation data, explore potential needs for system optimization, and automatically generate optimization suggestions based on the analysis results to improve overall performance.

Claims

1. A file system for managing hierarchical images, characterized in that: Includes the following modules: The hierarchical file management module is used to organize and classify files according to the hierarchical relationship and management requirements of image files. The hierarchical organization is based on a multi-level tree directory structure and uses a hierarchical clustering optimization algorithm to optimize file organization. Files can be classified according to tags, time, size, and content features, and the hierarchical changes are recorded through a hash mapping table to support cross-level batch migration and data synchronization; Image processing module, used for format conversion, compression, resolution adjustment and color optimization of image files; The permission control module is used to set the access, editing, and sharing permissions of image files based on user roles and security requirements; The version management module is used to record and track the modification history of image files, supporting version backtracking and multi-version storage; The retrieval and screening module is used to quickly search and screen image files based on their names, tags, attributes and contents; Data backup and recovery module, used to periodically back up the hierarchical data of image files and provide data recovery function after system failure; The log recording and analysis module is used to record user operation behavior and system operation status, and generate analysis reports to optimize system performance.

2. The system according to claim 1, characterized in that The hierarchical file management module also includes: (1) The file hierarchy construction function based on the multi-level tree directory structure is used to dynamically generate a multi-level image file storage structure according to the directory tree, which involves a hierarchical clustering optimization algorithm: The image file set is , the hierarchical tree is , then the hierarchical objective function is: ； in, is the file importance weight, is the similarity between the file and the hierarchy center, is the depth penalty factor, is the level depth of the level tree; (2) File classification and annotation function, which enables rapid screening and classification through labeling; (3) Dynamic adjustment of the relationship between file levels, using a hash mapping table to record level changes, supporting cross-level batch migration and data synchronization.

3. The system according to claim 1, characterized in that The image processing module also includes: (1) Batch image format conversion function, supporting conversion between multiple formats such as JPEG, PNG, BMP, etc. (2) Image quality improvement function, using deep learning models for intelligent optimization; (3) Image compression function, through the adaptive selection of lossless compression and lossy compression algorithms, taking into account both file size and quality. The image compression function is based on lossless compression optimization based on dynamic partitioning, pixel partitioning , define the entropy value of each zone as , then the maximum image compression ratio is: ； in, is the total pixels of the image, is the number of pixels in the partition.

4. The system according to claim 1, characterized in that The authority control module also includes: (1) User permission level division function, supporting three role configurations: administrator, editor, and viewer; (2) Dynamic permission adjustment function, automatically updating permissions based on user behavior and time range, Using the dynamic permission recommendation algorithm based on Bayesian reasoning, the user behavior sequence is , the permission level is , then the probability that a user belongs to a certain permission level is: ； (3) Data security audit function, tracking permission allocation and usage through operation records.

5. The system according to claim 1, characterized in that The version management module also includes: (1) Automatic saving of modification records: Each modification of the image file is automatically saved. Each modification generates an independent version number and records the specific content of the modification. The modification record will be stored in a special version database for easy traceability and comparison later. (2) Version comparison and conflict resolution function. When multiple users modify the same file in parallel, this module will automatically compare the differences between versions and determine whether there is a conflict based on the content conflict detection algorithm. For conflicting versions, the system will prompt the user to manually confirm or select an appropriate merge strategy. (3) The timeline backtracking function supports users to view all versions of a file through the timeline and select a file version at any time point for recovery. The timeline display can show the modification history of each version, including the version number, modification time and a brief description of the modification content. Users can easily backtrack to a certain version and restore the historical status.

6. The system according to claim 1, characterized in that The retrieval and screening module also includes: (1) Multi-condition search function: users can search based on file name, tag, creation time, modification time, file size and other conditions. It supports exact match, fuzzy match and regular expression search. (2) Tag filtering function: image files can be classified by adding tags, and users can filter files based on tag combination conditions; (3) Image content retrieval function, which uses image recognition technology to perform content similarity retrieval based on the content features of the image; (4) Intelligent sorting and priority screening: Through machine learning algorithms, we analyze users’ historical search behaviors, intelligently adjust the priority of file display, provide personalized search results, and improve search efficiency and accuracy.

7. The system according to claim 1, characterized in that The data backup and recovery module also includes: (1) Incremental backup function: When a file is changed, only the changed part is backed up instead of the entire file. By recording the hash value of the file or using a timestamp to identify the changed content, efficient data backup is achieved, saving storage space and bandwidth. (2) Regular backup and manual backup functions. The system supports regular automatic backup and also allows users to manually initiate backup operations. The backup content can be stored locally or in the cloud.

8. The system according to claim 1, characterized in that The log recording and analysis module also includes: (1) User operation log recording function. The system records all operation behaviors of each user, including file upload, download, modification, deletion, and viewing. Each operation records the operation time, user ID, operation type, and file information of the operation object; (2) Data analysis and optimization suggestions: Through big data analysis technology, user behavior logs and system operation data are analyzed to explore potential needs for system optimization, such as loading speed bottlenecks, files with the highest access frequency, etc. Based on the analysis results, the system automatically generates optimization suggestions, such as caching strategies, file access path optimization, etc., to improve overall performance.

9. A method for managing layered image files, characterized in that: The following steps are involved: Step 1: According to the hierarchical relationship and management requirements of image files, the system dynamically generates image file storage hierarchies through a multi-level tree directory structure, and uses a hierarchical clustering optimization algorithm to optimize the file organization structure. Files are quickly retrieved and screened through labeling. When files are migrated or reorganized, a hash mapping table is used to record hierarchical changes, supporting cross-level batch migration and data synchronization; Step 2: The image files are processed by format conversion, compression, resolution adjustment, and color optimization. The images can be converted into different formats in batches. The image quality is intelligently optimized through a deep learning model to improve detail performance and clarity. The image compression uses lossless compression and lossy compression algorithms. The image size is adjusted based on a lossless compression optimization algorithm based on dynamic partitioning to balance file size and quality. Step 3: Based on user roles and security requirements, the system sets access, editing and sharing permissions for image files. The system configures permissions for different roles, dynamically adjusts permissions based on user behavior analysis and time range, and uses a dynamic permission recommendation algorithm based on Bayesian reasoning to predict and adjust user permissions. The system records and tracks user operation behaviors and permission usage to ensure transparency and traceability of operations. Step 4: Record and track the modification history of the image file, support version backtracking and multi-version storage. Each time a file is modified, the system automatically saves the modification record, including the file size, modification time and modifier information, and generates a new version number. The system compares the contents of different versions of files, detects conflicts and prompts the user to select a conflict resolution strategy. The user can view all version histories of the file through the timeline and restore the file version to a certain point in time. Step 5: Image files are quickly retrieved and filtered based on name, tag, attribute and content. Users can search based on file name, tag, creation time, modification time, file size and other conditions. It supports exact, fuzzy matching and regular expression search. Image files are classified by tags. Users can filter files based on multiple tag combination conditions. The system also uses image recognition technology to perform content similarity search based on content features, analyzes user historical search behavior through machine learning algorithms, and intelligently adjusts the sorting of search results to improve search accuracy and efficiency. Step 6. The system provides periodic backup and recovery functions for files to ensure data integrity and security. When a file changes, the system only backs up the changed part instead of the entire file. The changed part is marked with a hash value or timestamp. The system supports regular automatic backup and allows users to manually initiate backup operations. Step 7. The system records user operations and system operating status in real time, and generates data analysis reports to optimize system performance. The system records all operation behaviors of each user and saves information such as operation time, user ID, and operation type. The system uses big data analysis technology to analyze user behavior and system operation data, explore potential needs for system optimization, and automatically generate optimization suggestions based on the analysis results to improve overall performance.

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