Intelligent file previewing method and system

Abstract

The invention discloses an intelligent file previewing method and system, and relates to a file previewing system which comprises a file management module, a snapshot module, a data management module, a personalized recommendation module, an analysis module and an upgrade association module. The file management module performs preprocessing, format detection, file integrity verification and the like on a file, acquires basic information of the file, transmits the basic information to the data management module, and transmits preview information generated by a system to the data management module; a unique analysis model is provided for each user through the personalized recommendation unit, model parameters are adjusted through the model optimization unit, the system can analyze the interaction history of the users for different files, previews conforming to user preferences are selected from multiple pieces of preview snapshot information, a simple display mode of a traditional file preview system is avoided, and the user experience is improved. And more intelligent display is provided according to requirements of different users, so that the user experience is improved.

Description

Technical Field

[0001] The invention relates to a file preview system, in particular to a file intelligent preview method and system. Background Art

[0002] The file preview system is an innovative tool designed to improve work efficiency and information access convenience. It allows users to easily preview and view the contents of multiple file types without installing additional software or specific applications. Whether it is office documents, PDF files, images, or metadata previews of audio and video files, it can provide a smooth and high-quality preview experience.

[0003] The current existing file preview system is mainly the file management function that comes with the Windows system. The built-in file management module only provides users with basic file preview functions. New file types and industry-specific file formats have emerged. These files have complex structures and specific contents. Files in specific formats cannot be previewed, which has limitations.

[0004] In view of the above technical defects, a solution is now proposed. Summary of the invention

[0005] In view of the deficiencies in the prior art, the present invention provides a file intelligent preview method and system.

[0006] To achieve the above objectives, the present invention is implemented through the following technical solutions: a file intelligent preview method and system, including: a file management module, a snapshot module, a data management module, a personalized recommendation module, a parsing module and an upgrade association module.

[0007] The file management module performs preprocessing, format detection, file integrity verification, etc. on the file and obtains the basic information of the file and transmits it to the data management module, and transmits the preview image information generated by the system to the data management module.

[0008] The snapshot module is responsible for retrieving whether the file has preview information when the user opens the file, obtaining the file reading progress, taking multiple snapshots after the reading is completed, storing them as snapshot information, and generating snapshot-related information to transmit to the data management module.

[0009] The data management module includes a snapshot unit and an association unit, which store snapshot information and snapshot association information respectively.

[0010] The personalized recommendation module provides an interactive page to the user, obtains the snapshot information selected by the user, and transmits the feature points and feature values ​​contained in the snapshot information selected by the user to the parsing module.

[0011] The parsing module optimizes the snapshot information through the filtering unit, crops the edges of the photos in the snapshot information, and preliminarily filters out blank and low-quality photos. The model analysis unit marks the feature points of the remaining snapshots in the snapshot information to obtain feature values, calculates the snapshot reference value F based on the feature points and feature values, sets the snapshot with the highest snapshot reference value F as the preview image information and transmits it to the data management module. The model optimization unit optimizes the weight coefficients in the model based on the preferred feature points and preferred feature values ​​preferred by the user.

[0012] The upgrade association module uses incremental learning to update the model, continuously collects user behavior and system feedback to fine-tune the model, identifies model performance, and adjusts the weight parameters of feature points based on feedback.

[0013] The file management module performs format detection and integrity verification on the file and obtains the basic information of the file, such as file name, size, type, time, etc., to generate relevant meta-information and transmit it to the data management module. For specific types of files such as PDF documents and image files, preview image information will be generated according to the Window system and transmitted to the data management module. The specific file is a file format that can be recognized by the preview function of the Windows system.

[0014] The snapshot module retrieves whether the file has preview image information. If not, the following steps are performed: during the file reading process, the snapshot module monitors the file reading progress, takes a screenshot when the file reading is completed to generate multiple snapshot information, generates file association information based on the file information, and packages the snapshot information and file association information and transmits them to the data management module.

[0015] The data management module includes a snapshot unit and an association unit;

[0016] The snapshot unit stores multiple snapshot information of a file, and the snapshot information includes the key content and structure of the file. The association unit is responsible for saving the file association information, and the file association information includes the snapshot information ID, the basic information ID of the file, the file's creation time, modification time, size, permissions and other attributes, and other associated data of the file. The snapshot ID and file ID are used as key fields for association. Whenever a new snapshot is created, a unique snapshot ID is obtained and recorded in the association unit together with the corresponding file ID.

[0017] The personalized recommendation unit is responsible for pushing a variety of snapshot options to users. The snapshot options are selected based on the user's historical behavior and preferences. Snapshot information feature points are obtained based on the snapshot information selected by the user, and the obtained feature points and feature values ​​are marked as preferred feature points and preferred feature values ​​and transmitted to the model optimization unit.

[0018] The analysis module includes a filtering unit, a model analysis unit and a model optimization unit;

[0019] The filtering unit accurately crops the edges of the photos in the snapshot information and removes unnecessary blank areas in the image. The edge areas are usually software edges or software tool icons and are fixed. Then, by evaluating the image quality, low-quality snapshots are identified and eliminated, and clear snapshot information with complete content is retained.

[0020] The model in the model analysis unit features the photos in the snapshot information to extract feature point information and obtain feature values. For image files, the system will extract edge feature values ​​M, object recognition values ​​N and other information as feature points. For text files, the system will extract keyword values ​​C, structured information values ​​O and other information as feature points, calculate the snapshot reference value F based on the feature values, set the snapshot information with the highest snapshot reference value F as preview image information and transmit it to the data management module.

[0021] The model analysis unit calculates the snapshot reference value F of the snapshot information according to a formula, and the specific formula is as follows:

[0022] F=M×X1+N×X2+C×X3+O×X4

[0023] Among them, X1 represents the weight coefficient of the edge feature value, X2 represents the weight coefficient of the object recognition value, X3 represents the weight coefficient of the keyword value, and X4 represents the weight coefficient of the structured information value.

[0024] The model optimization unit intermittently obtains preferred feature points and preferred feature values ​​from the personalized recommendation unit. The model optimization unit uses an optimization algorithm to iteratively adjust the weight coefficients in the model. The optimization algorithm comprehensively considers the influence of the preferred feature points on the recommendation results and the strength of user preferences reflected by the preferred feature values. The optimization processing of the model optimization unit is not completed in one go, but is a continuous iterative process, and the weight coefficients are constantly fine-tuned.

[0025] The upgrade association module adopts incremental learning to update the existing model, collects local user behavior data and system feedback, which include user clicks, browsing, dwell time and other interactive information, as well as user feedback on the system, and fine-tunes and updates the existing model based on the above data. The upgrade association module starts a complete performance monitoring and evaluation mechanism after each update.

[0026] The specific method of intelligent preview of files is as follows:

[0027] S1. The file management module detects the file and obtains the basic information and preview image information of the file and transmits it to the data management module.

[0028] S2. The snapshot module retrieves the preview information of the file. When the file is opened, the file content is screenshotted to generate snapshot information and file association information, and transmits the snapshot information and file association information to the data management module.

[0029] S3. The personalized recommendation module pushes multiple snapshot options to the user, obtains the preferred feature points and preferred feature values ​​in the snapshot information according to the user's selection, and transmits them to the model optimization module.

[0030] S4. The model optimization unit adjusts the weight coefficient in the snapshot reference value F formula according to the preferred feature points and the preferred feature values, thereby optimizing the model in the model analysis unit.

[0031] S5. The model analysis unit uses the model to analyze the feature points of the snapshot information in the data management module and obtains the snapshot reference value F. When the user clicks on the file, the file management module displays the snapshot information with the highest snapshot reference value F to the user.

[0032] The present invention provides a file intelligent preview method and system. Compared with the prior art, it has the following advantages:

[0033] Beneficial effects:

[0034] The present invention provides a unique analysis model for each user through a personalized recommendation unit and uses a model optimization unit to adjust model parameters. The system can analyze the user's interaction history with different files and select preview images that meet the user's preferences from multiple preview snapshot information, avoiding the simple display mode of traditional file preview systems and providing smarter display according to the needs of different users, thereby improving the user experience.

[0035] The present invention takes screenshots of files in special formats through a snapshot module, filters out low-quality snapshots, and displays preview images to users by screening and associating information, thereby ensuring that users can browse preview images of various complex files and saving users time in searching for target files from a large number of files. For file organization in daily office work or document retrieval in large projects, file management and processing become more efficient. BRIEF DESCRIPTION OF THE DRAWINGS

[0036] Figure 1 It is a schematic diagram of the principle framework of the present invention. DETAILED DESCRIPTION

[0037] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only 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 creative work are within the scope of protection of the present invention.

[0038] Embodiment 1:

[0039] See also Figure 1 The present application provides a file intelligent preview method and system, including: a file management module, a snapshot module, a data management module, a personalized recommendation module, a parsing module and an upgrade association module.

[0040] The file management module performs preprocessing, format detection, file integrity verification, etc. on the file and obtains the basic information of the file and transmits it to the data management module, and transmits the preview image information generated by the system to the data management module.

[0041] The snapshot module is responsible for retrieving whether the file has preview information when the user opens the file, obtaining the file reading progress, taking multiple snapshots after the reading is completed, storing them as snapshot information, and generating snapshot-related information to transmit to the data management module.

[0042] The data management module includes a snapshot unit and an association unit, which store snapshot information and snapshot association information respectively.

[0043] The personalized recommendation module provides an interactive page to the user, obtains the snapshot information selected by the user, and transmits the feature points and feature values ​​contained in the snapshot information selected by the user to the parsing module.

[0044] The parsing module optimizes the snapshot information through the filtering unit, crops the edges of the photos in the snapshot information, and preliminarily filters out blank and low-quality photos. The model analysis unit marks the feature points of the remaining snapshots in the snapshot information to obtain feature values, and calculates the snapshot reference value F based on the feature points and feature values. The snapshot with the highest snapshot reference value F is set as the preview image information and transmitted to the data management module. The model optimization unit optimizes the weight coefficients in the model according to the preferred feature points and preferred feature values ​​preferred by the user.

[0045] The upgrade association module uses incremental learning to update the model, continuously collects user behavior and system feedback to fine-tune the model, identifies model performance, and adjusts the weight parameters of feature points based on feedback.

[0046] The file management module performs format detection and integrity verification on the file and obtains the basic information of the file, such as file name, size, type, time, etc., to generate relevant meta-information and transmit it to the data management module. For specific types of files such as PDF documents and image files, preview image information will be generated according to the Window system and transmitted to the data management module. Specific files are in a file format that can be recognized by the preview function that comes with the Windows system.

[0047] The system can generate thumbnails for certain types of files (such as PDF documents, image files, Office documents, etc.). These files are in formats that can be recognized by the preview function built into the Windows system, such as pictures (.jpg, .png), PDF files, Word documents, etc. The generated thumbnails speed up the file preview process.

[0048] The personalized recommendation unit is responsible for pushing a variety of snapshot options to users. The snapshot options are selected based on the user's historical behavior and preferences. The snapshot information feature points are obtained according to the snapshot information selected by the user, and the obtained feature points and feature values ​​are marked as preferred feature points and preferred feature values ​​and transmitted to the model optimization unit;

[0049] When a user opens a file in the preview system, the system will display multiple snapshots for the user to choose from. The user can select the one that best meets the needs from the snapshots as the preliminary preview image.

[0050] The parsing module includes a filtering unit, a model analysis unit and a model optimization unit;

[0051] The filtering unit accurately crops the edges of the photos in the snapshot information and removes unnecessary blank areas in the image. The edge areas are usually software edges or software tool icons and are fixed. Then, by evaluating the image quality, low-quality snapshots are identified and eliminated, and clear, complete snapshot information is retained.

[0052] The model in the model analysis unit features the photos in the snapshot information to extract feature point information and obtain feature values. For image files, the system will extract edge feature values ​​M, object recognition values ​​N and other information as feature points. For text files, the system will extract keyword values ​​C, structured information values ​​O and other information as feature points, calculate the snapshot reference value F based on the feature values, set the snapshot information with the highest snapshot reference value F as preview image information and transmit it to the data management module.

[0053] The model analysis unit calculates the snapshot reference value F of the snapshot information according to the formula. The specific formula is as follows:

[0054] F=M×X1+N×X2+C×X3+O×X4

[0055] Among them, X1 represents the weight coefficient of the edge feature value, X2 represents the weight coefficient of the object recognition value, X3 represents the weight coefficient of the keyword value, and X4 represents the weight coefficient of the structured information value.

[0056] The model optimization unit intermittently obtains preferred feature points and preferred feature values ​​from the personalized recommendation unit. The model optimization unit uses the optimization algorithm to iteratively adjust the weight coefficients in the model. The optimization algorithm comprehensively considers the influence of the preferred feature points on the recommendation results and the strength of user preferences reflected by the preferred feature values. The optimization processing of the model optimization unit is not completed in one go, but is a continuous iterative process, which continuously fine-tunes the weight coefficients.

[0057] Specific workflow:

[0058] When a user opens a file without a preview image, the snapshot module will obtain the file reading progress, take a screenshot after the file is fully opened and save it as snapshot information, and generate file-related information. In the personalized settings interface, the personalized recommendation module will provide multiple snapshots for the user to choose from. The model optimization unit adjusts the model in the analysis module and the weight coefficient in the snapshot reference value F formula based on the snapshot information selected by the user. After multiple adjustment iterations, when the user clicks on a file to view the preview image, the snapshot information with the largest snapshot reference value F is retrieved from the data management unit and displayed to the user.

[0059] Furthermore, the present invention provides a unique analysis model for each user through a personalized recommendation unit, and uses a model optimization unit to adjust model parameters. The system can analyze the user's interaction history with different files and select preview images that meet the user's preferences from multiple preview snapshot information, avoiding the simple display mode of traditional file preview systems, and providing a smarter display according to the needs of different users, thereby improving the user experience.

[0060] Embodiment 2:

[0061] The snapshot module searches whether the file has preview information. If not, the following steps are performed: during the file reading process, the snapshot module monitors the file reading progress. When the file reading is completed, a screenshot is taken to generate multiple snapshot information. File association information is generated based on the file information, and the snapshot information and file association information are packaged and transmitted to the data management module.

[0062] The data management module includes a snapshot unit and a correlation unit;

[0063] The snapshot unit stores multiple snapshot information of a file. The snapshot information contains the key content and structure of the file. The association unit is responsible for saving the file association information. The file association information package includes the snapshot information ID, the basic information ID of the file, the file's creation time, modification time, size, permissions and other attributes, as well as other associated data of the file. The snapshot ID and file ID are used as the key fields for association. Whenever a new snapshot is created, a unique snapshot ID is obtained and recorded in the association unit together with the corresponding file ID.

[0064] The upgrade association module uses incremental learning to update the existing model and collects local user behavior data and system feedback. These data include user clicks, browsing, dwell time and other interactive information, as well as user feedback on the system. The existing model is fine-tuned and updated based on the above data. The upgrade association module launches a complete performance monitoring and evaluation mechanism after each update.

[0065] The specific method of file intelligent preview is as follows:

[0066] S1. The file management module detects the file and obtains the basic information and preview image information of the file and transmits it to the data management module.

[0067] S2. The snapshot module retrieves the preview information of the file. When the file is opened, the file content is screenshotted to generate snapshot information and file association information, and transmits the snapshot information and file association information to the data management module.

[0068] S3. The personalized recommendation module pushes multiple snapshot options to the user, obtains the preferred feature points and preferred feature values ​​in the snapshot information according to the user's selection, and transmits them to the model optimization module.

[0069] S4. The model optimization unit adjusts the weight coefficient in the snapshot reference value F formula according to the preferred feature points and the preferred feature values, thereby optimizing the model in the model analysis unit.

[0070] S5. The model analysis unit uses the model to analyze the feature points of the snapshot information in the data management module and obtains the snapshot reference value F. When the user clicks on the file, the file management module displays the snapshot information with the highest snapshot reference value F to the user.

[0071] Specific workflow:

[0072] When a user opens a file without a preview image, the snapshot module obtains the file reading progress, takes a screenshot after the file is fully opened and saves it as snapshot information, and generates file-related information. For files with preview image information generated by the Window system, the file management module transmits the preview image information to the data management unit, and the parsing module obtains the snapshot information in the data management module, calculates the snapshot reference value F through a calculation formula and marks it. When a user clicks on a file to view the preview image, the snapshot information with the largest snapshot reference value F is retrieved from the data management unit according to the file-related information and displayed to the user.

[0073] Furthermore, the present invention takes screenshots of files in special formats through a snapshot module, filters out low-quality snapshots, and displays preview images to users by screening and associating information, thereby ensuring that users can browse preview images of various complex files and saving users time in searching for target files from a large number of files. Whether it is file organization in daily office work or document retrieval in large projects, file management and processing become more efficient.

[0074] Some of the data in the above formulas are dimensionless and numerically calculated. Meanwhile, the contents not described in detail in this specification belong to the prior art known to those skilled in the art.

[0075] The above embodiments are only used to illustrate the technical method of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical method of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical method of the present invention.

Claims

1. A file intelligent preview system, characterized in that: include: File management module, snapshot module, data management module, personalized recommendation module, analysis module and upgrade association module. The file management module performs preprocessing, format detection, file integrity verification, etc. on the file and obtains the basic information of the file and transmits it to the data management module, and transmits the preview image information generated by the system to the data management module. The snapshot module is responsible for retrieving whether the file has preview information when the user opens the file, obtaining the file reading progress, taking multiple snapshots after the reading is completed, storing them as snapshot information, and generating snapshot-related information to transmit to the data management module. The data management module includes a snapshot unit and an association unit, which store snapshot information and snapshot association information respectively. The personalized recommendation module provides an interactive page to the user, obtains the snapshot information selected by the user, and transmits the feature points and feature values ​​contained in the snapshot information selected by the user to the parsing module. The parsing module optimizes the snapshot information through the filtering unit, crops the edges of the photos in the snapshot information, and preliminarily filters out blank and low-quality photos. The model analysis unit marks the feature points of the remaining snapshots in the snapshot information to obtain feature values, calculates the snapshot reference value F based on the feature points and feature values, sets the snapshot with the highest snapshot reference value F as the preview image information and transmits it to the data management module. The model optimization unit optimizes the weight coefficients in the model based on the preferred feature points and preferred feature values ​​preferred by the user. The upgraded association module updates the model with incremental learning, continuously collects user behavior and system feedback to fine-tune the model, identifies model performance, and adjusts the weight parameters of feature points based on feedback.

2. A file intelligent preview system according to claim 1, characterized in that: The file management module performs format detection and integrity verification on the file and obtains the basic information of the file, such as file name, size, type, time, etc., to generate relevant meta-information and transmit it to the data management module. For specific types of files such as PDF documents and image files, preview image information will be generated according to the Window system and transmitted to the data management module. The specific file is a file format that can be recognized by the preview function of the Windows system.

3. The file intelligent preview system according to claim 1, characterized in that: The snapshot module retrieves whether the file has preview image information. If not, the following steps are performed: during the file reading process, the snapshot module monitors the file reading progress, takes a screenshot when the file reading is completed to generate multiple snapshot information, generates file association information based on the file information, and packages the snapshot information and file association information and transmits them to the data management module.

4. The intelligent file preview system according to claim 1, characterized in that: The data management module includes a snapshot unit and an association unit; The snapshot unit stores multiple snapshot information of a file, and the snapshot information includes the key content and structure of the file. The association unit is responsible for saving the file association information, and the file association information includes the snapshot information ID, the basic information ID of the file, the file's creation time, modification time, size, permissions and other attributes, and other associated data of the file. The snapshot ID and file ID are used as key fields for association. Whenever a new snapshot is created, a unique snapshot ID is obtained and recorded in the association unit together with the corresponding file ID.

5. The intelligent file preview system according to claim 1, characterized in that: The personalized recommendation unit is responsible for pushing a variety of snapshot options to users. The snapshot options are selected based on the user's historical behavior and preferences. Snapshot information feature points are obtained based on the snapshot information selected by the user, and the obtained feature points and feature values ​​are marked as preferred feature points and preferred feature values ​​and transmitted to the model optimization unit.

6. The intelligent file preview system according to claim 1, characterized in that: The analysis module includes a filtering unit, a model analysis unit and a model optimization unit; The filtering unit accurately crops the edges of the photos in the snapshot information and removes unnecessary blank areas in the image. The edge areas are usually software edges or software tool icons and are fixed. Then, by evaluating the image quality, low-quality snapshots are identified and eliminated, and clear snapshot information with complete content is retained. The model in the model analysis unit features the photos in the snapshot information to extract feature point information and obtain feature values. For image files, the system will extract edge feature values ​​M, object recognition values ​​N and other information as feature points. For text files, the system will extract keyword values ​​C, structured information values ​​O and other information as feature points, calculate the snapshot reference value F based on the feature values, set the snapshot information with the highest snapshot reference value F as preview image information and transmit it to the data management module.

7. The intelligent file preview system according to claim 1, characterized in that: The model analysis unit calculates the snapshot reference value F of the snapshot information according to a formula, and the specific formula is as follows: F=M×X1+N×X2+C×X3+O×X4 Among them, X1 represents the weight coefficient of the edge feature value, X2 represents the weight coefficient of the object recognition value, X3 represents the weight coefficient of the keyword value, and X4 represents the weight coefficient of the structured information value. The model optimization unit intermittently obtains preferred feature points and preferred feature values ​​from the personalized recommendation unit. The model optimization unit uses an optimization algorithm to iteratively adjust the weight coefficients in the model. The optimization algorithm comprehensively considers the influence of the preferred feature points on the recommendation results and the strength of user preferences reflected by the preferred feature values. The optimization processing of the model optimization unit is not completed in one go, but is a continuous iterative process, and the weight coefficients are constantly fine-tuned.

8. The file intelligent preview system according to claim 1, characterized in that: The upgrade association module adopts incremental learning to update the existing model, collects local user behavior data and system feedback, which include user clicks, browsing, dwell time and other interactive information, as well as user feedback on the system, and fine-tunes and updates the existing model based on the above data. The upgrade association module starts a complete performance monitoring and evaluation mechanism after each update.

9. A file intelligent preview method, based on the file intelligent preview system according to any one of claims 1 to 8, characterized in that: The specific method of intelligent preview of files is as follows: S1. The file management module detects the file and obtains the basic information and preview image information of the file and transmits it to the data management module. S2. The snapshot module retrieves the preview image information of the file. When the file is opened, the file content is screenshoted to generate snapshot information and file association information, and then transmitted to the data management module. S3. The personalized recommendation module pushes multiple snapshot options to the user, obtains the preferred feature points and preferred feature values ​​in the snapshot information according to the user's selection, and transmits them to the model optimization module. S4. The model optimization unit adjusts the weight coefficient in the snapshot reference value F formula according to the preferred feature points and the preferred feature values, thereby optimizing the model in the model analysis unit. S5. The model analysis unit uses the model to analyze the feature points of the snapshot information in the data management module and obtains the snapshot reference value F. When the user clicks on the file, the file management module displays the snapshot information with the highest snapshot reference value F to the user.

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