Rich media intelligent information editing method and device

Abstract

The embodiment of the application provides a kind of rich media intelligent information editing method and device, method includes: receiving and storing the rich media resource file uploaded by user, the file identification is carried out to the rich media resource file, and according to the file type, file content feature and preset size threshold obtained by identification, the file is segmented by adaptive segmentation algorithm to the rich media resource file, obtains one or more file segments;The file segment is vectorized by preset rich media vectorization model to multi-modal content, and the corresponding semantic meaning array is extracted, the similarity of other rich media resource files and the current rich media resource file is determined according to the semantic meaning array by multidimensional weighted similarity algorithm, and the information of similar rich media resource file with the similarity of current rich media resource file exceeding preset threshold is shown in preset user file editing interface;The application can more flexibly process rich media information.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of artificial intelligence, in particular to a rich media intelligent information compilation and review method and device. BACKGROUND

[0002] In today's era of information explosion, the rapid growth of rich media content has brought great challenges to information compilation and review. Traditional information compilation and review methods have been difficult to meet the requirements of efficiency, accuracy and consistency in the face of massive and diversified rich media content. Existing technologies have many limitations and problems in processing rich media information compilation.

[0003] Firstly, the traditional information compilation and review method mainly relies on manual operation. Editors need to review and edit rich media files one by one, which not only consumes time and effort, but also is easily affected by personal subjective judgment, making it difficult to ensure the consistency of review standards. In the face of a large number of complex rich media content, the efficiency of manual methods is difficult to meet the demand, and is easy to cause work backlog and delay.

[0004] Secondly, some existing automatic compilation and review tools can only process single type of media files, such as supporting only text or image. However, the rich media content in reality usually contains mixed content of multiple forms such as text, image, audio and video. These tools are not competent when dealing with complex multi-modal content, and cannot fully grasp the overall semantic and content characteristics of rich media resources. SUMMARY

[0005] In view of the problems in the prior art, the present application provides a rich media intelligent information compilation and review method and device, which can more flexibly process rich media information.

[0006] In order to solve at least one of the above problems, the present application provides the following technical solutions:

[0007] In a first aspect, the present application provides a rich media intelligent information compilation and review method, comprising:

[0008] receiving and storing a rich media resource file uploaded by a user, performing file recognition on the rich media resource file, and performing file segmentation on the rich media resource file according to the file type, file content characteristics and preset size threshold obtained by recognition through an adaptive segmentation algorithm to obtain one or more file segments;

[0009] The file segment is subjected to multi-modal content vectorization by a preset rich media vectorization model, a corresponding semantic meaning array is extracted, the similarity of other rich media resource files to the current rich media resource file is determined according to the semantic meaning array by a multi-dimensional weighted similarity algorithm, and information of similar rich media resource files with a similarity to the current rich media resource file exceeding a preset threshold is displayed in a preset user file editing interface, wherein the multi-dimensional weighted similarity algorithm includes content similarity calculation, structure similarity calculation and semantic similarity calculation, and the similarity calculation weight of each dimension is determined by the file type;

[0010] A file editing instruction sent by the user is received, content integration of the corresponding current rich media resource file and the similar rich media resource file selected by the user is performed according to the file editing instruction, and conflict processing of similar file content is performed by a conflict resolution mechanism, and the file content after the content integration and the conflict processing is displayed in the preset user file editing interface.

[0011] Further, the file recognition of the rich media resource file includes:

[0012] The binary data stream of the rich media resource file is read, magic number information and a file extension of a file header are extracted, the extracted magic number information is matched with a preset file type magic number database, and a preliminary file type is determined;

[0013] The metadata structure of the file is parsed, key attribute information is extracted, a pre-trained deep learning model is used to extract a feature vector of the file content, a file content feature is obtained, the preliminary file type, the key attribute information and the content feature vector are comprehensively input into a decision tree classifier, and a final file type recognition result is obtained.

[0014] Further, the file is segmented into one or more file segments by an adaptive segmentation algorithm according to the identified file type, file content feature and preset size threshold, including:

[0015] The total size of the file is calculated according to the identified file type, a content feature extraction algorithm and a segmentation strategy are selected, and the total size is compared with a preset size threshold to determine an initial segmentation number;

[0016] The file content is analyzed by the selected feature extraction algorithm, a content feature sequence is generated, an adaptive segmentation algorithm based on dynamic programming is applied to calculate an optimal segmentation point, a file cutting operation is performed at the calculated segmentation point, one or more file segments are generated, and metadata containing original file information, a segmentation position and a segment number is created for each file segment.

[0017] Further, the multi-modal content vectorization of the file segment by the preset rich media vectorization model includes:

[0018] According to the type of the file segment, a corresponding pre-trained model is selected for format conversion, noise reduction and standardization preprocessing, and the preprocessed data is input into the selected pre-trained model to extract a high-dimensional feature vector representation;

[0019] The high-dimensional feature vector representation is input into a pre-trained semantic understanding model, and key semantic features in the high-dimensional feature vector are extracted by a multi-layer self-attention mechanism of the semantic understanding model. A hierarchical clustering algorithm is used to cluster the extracted semantic features to form different levels of semantic concepts. The clustering results are mapped to a predefined semantic ontology library to obtain standardized semantic labels and importance scores, and a structured semantic meaning array is constructed, wherein the elements in the semantic meaning array include semantic labels, importance scores and related metrics.

[0020] Further, the similarity between the current rich media resource file and other rich media resource files is determined according to the semantic meaning array by a multi-dimensional weighted similarity algorithm, including:

[0021] Each semantic label in the semantic meaning array is mapped to a predefined feature space, and each dimension is assigned a weight according to the importance score of the semantic label to generate a weighted feature vector as input for similarity calculation;

[0022] The similarity of the content, structure and semantic dimensions is calculated respectively, the weight of each dimension is determined according to the file type, and the weighted similarity results of the three dimensions are integrated to obtain the final similarity score.

[0023] Further, the file editing instruction sent by the user is received, and the content of the corresponding current rich media resource file and the similar rich media resource file selected by the user is integrated according to the file editing instruction, including:

[0024] The content of the file editing instruction sent by the user is parsed, the instruction type and operation parameters are identified, and the parsed instruction is converted into a system executable operation sequence;

[0025] The content of the current rich media resource file and the similar rich media resource file selected by the user is loaded, a corresponding content integration algorithm is selected according to the file type to perform a content integration operation, a new integrated rich media resource file is generated, and the version information of the original file is preserved, wherein the content integration operation includes content merging, replacement, deletion or insertion.

[0026] Further, the conflict processing of the conflict of similar file content by the conflict resolution mechanism comprises:

[0027] comparing the content structure of the current rich media resource file and the similar rich media resource file to identify repeated, contradictory or inconsistent content segments, marking the conflict position and type;

[0028] processing part of the conflict according to the conflict type and the preset resolution strategy, generating corresponding user options for the conflict that cannot be automatically resolved, receiving and executing the user's conflict processing selection, updating the content of the integrated rich media resource file, and eliminating the conflict.

[0029] In a second aspect, the present application provides a rich media intelligent information editing device, comprising:

[0030] a file segmentation module for receiving and storing a rich media resource file uploaded by a user, performing file identification on the rich media resource file, and performing file segmentation on the rich media resource file according to the file type, file content features and preset size threshold obtained by identification through an adaptive segmentation algorithm, to obtain one or more file segments;

[0031] a similar display module for performing multi-modal content vectorization on the file segments through a preset rich media vectorization model, extracting corresponding semantic meaning arrays, determining the similarity of other rich media resource files to the current rich media resource file according to the semantic meaning arrays through a multi-dimensional weighted similarity algorithm, and displaying information of similar rich media resource files with a similarity to the current rich media resource file exceeding a preset threshold in a preset user file editing interface, wherein the multi-dimensional weighted similarity algorithm comprises content similarity calculation, structure similarity calculation and semantic similarity calculation, and the similarity calculation weight of each dimension is determined by the file type;

[0032] a similar merging module for receiving a file editing instruction sent by the user, performing content integration on the corresponding current rich media resource file and the user-selected similar rich media resource file according to the file editing instruction, and performing conflict processing of the conflict of similar file content through a conflict resolution mechanism, and displaying the file content after the content integration and the conflict processing in the preset user file editing interface.

[0033] In a third aspect, the present application provides an electronic device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, wherein the processor executes the program to implement the steps of the rich media intelligent information editing method.

[0034] In a fourth aspect, the present application provides a computer readable storage medium, which stores a computer program, and the computer program is executed by a processor to implement the steps of the intelligent information editing method of rich media.

[0035] In a fifth aspect, the present application provides a computer program product, which comprises computer programs / instructions, and the computer programs / instructions are executed by a processor to implement the steps of the intelligent information editing method of rich media.

[0036] From the above technical solution, the present application provides an intelligent information editing method and device of rich media, which receives and stores a rich media resource file uploaded by a user, performs file identification on the rich media resource file, and performs file segmentation on the rich media resource file by an adaptive segmentation algorithm according to a file type obtained by identification, a file content feature, and a preset size threshold, to obtain one or more file segments; performs multi-modal content vectorization on the file segments by a preset rich media vectorization model, extracts a corresponding semantic meaning array, determines a similarity between other rich media resource files and the current rich media resource file by a multi-dimensional weighted similarity algorithm according to the semantic meaning array, and displays information of similar rich media resource files with a similarity to the current rich media resource file exceeding a preset threshold in a preset user file editing interface, so that the rich media information can be processed more flexibly. BRIEF DESCRIPTION OF DRAWINGS

[0037] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the prior art, the drawings needed to be used in the embodiments or prior art description will be briefly introduced. Obviously, the drawings in the following description are some embodiments of the present application, and other drawings can be obtained by those skilled in the art without creative labor.

[0038] Figure 1 FIG. 1 is a flowchart of an intelligent information editing method of rich media in an embodiment of the present application;

[0039] Figure 2 FIG. 2 is a flowchart of an intelligent information editing method of rich media in another embodiment of the present application;

[0040] Figure 3 FIG. 3 is a flowchart of an intelligent information editing method of rich media in another embodiment of the present application;

[0041] Figure 4 FIG. 4 is a flowchart of an intelligent information editing method of rich media in another embodiment of the present application;

[0042] Figure 5 FIG. 5 is a flowchart of an intelligent information editing method of rich media in another embodiment of the present application;

[0043] Figure 6 Fig. 6 is a flowchart of a method for compiling and reviewing intelligent information of rich media according to an embodiment of the present application;

[0044] Figure 7 Fig. 7 is a flowchart of a method for compiling and reviewing intelligent information of rich media according to an embodiment of the present application;

[0045] Figure 8 Fig. 8 is a structural diagram of a device for compiling and reviewing intelligent information of rich media according to an embodiment of the present application;

[0046] Figure 9 Fig. 9 is a structural diagram of an electronic device according to an embodiment of the present application.

[0047] Reference Signs:

[0048] Electronic device 9600, central processor 9100, memory 9140, communication module 9110, input unit 9120, audio processor 9130, display 9160, power supply 9170, buffer memory 9141, application / function storage unit 9142, data storage unit 9143, driver storage unit 9144, antenna 9111, speaker 9131, microphone 9132. DETAILED DESCRIPTION

[0049] In order to make the objects, technical solutions and advantages of the embodiments of the present application clearer, the technical solutions of the embodiments of the present application will be described clearly and completely below with reference to the drawings in the embodiments of the present application. Obviously, the described embodiments are some but not all of the embodiments of the present application. Based on the embodiments in the present application, all other embodiments obtained by those of ordinary skill in the art without creative work fall within the scope of the present application.

[0050] The acquisition, storage, use, processing and the like of data in the technical solutions of the present application comply with relevant provisions of national laws and regulations.

[0051] In view of the problems in the prior art, the present application provides a rich media intelligent information compiling method and device, which receives and stores a rich media resource file uploaded by a user, identifies the rich media resource file, and performs file segmentation on the rich media resource file according to a file type, a file content feature, and a preset size threshold obtained through an adaptive segmentation algorithm to obtain one or more file segments; performs multi-modal content vectorization on the file segments through a preset rich media vectorization model to extract a corresponding semantic meaning array, determines a similarity between other rich media resource files and the current rich media resource file through a multi-dimensional weighted similarity algorithm according to the semantic meaning array, and displays information with a similarity to the current rich media resource file exceeding a preset threshold in a preset user file editing interface, so that the rich media information can be processed more flexibly.

[0052] In order to be able to more flexibly process rich media information, the present application provides an embodiment of a rich media intelligent information compiling method, as shown in Figure 1 , which specifically includes the following content:

[0053] Step S101: receiving and storing a rich media resource file uploaded by a user, identifying the rich media resource file, and performing file segmentation on the rich media resource file according to a file type, a file content feature, and a preset size threshold obtained through an adaptive segmentation algorithm to obtain one or more file segments;

[0054] Optionally, in the present embodiment, step S101 is the first key step of the rich media intelligent information compiling method, mainly involving the technical processes of file receiving, storage, identification, and segmentation. The implementation of this step not only lays a foundation for subsequent content analysis and processing, but also solves the efficiency problem of processing large rich media files.

[0055] In the specific implementation process, the system first needs to establish a stable file uploading and storage mechanism. This may involve the use of a distributed file system or a cloud storage solution to ensure that a large number of concurrent upload requests can be efficiently processed and various types of rich media files can be safely and reliably stored. For example, the system may use an object storage service to store uploaded files, which can automatically perform data redundancy and backup to ensure the security and availability of files.

[0056] After receiving the file, the system will perform file recognition on the uploaded rich media resource file. This process involves multiple techniques, including file header analysis, content feature extraction, and machine learning classification. The system first checks the magic number and MIME type of the file, which can usually quickly determine the basic type of the file. For more complex files, such as composite documents, the system may need to further analyze the file structure and content features.

[0057] For example, for a video file, the system not only needs to identify it as a video file, but also needs to determine its encoding format, resolution, frame rate, and other features. These information is crucial for subsequent processing, because different types and characteristics of files may require different segmentation and analysis strategies.

[0058] After file recognition is complete, the system will use an adaptive segmentation algorithm to segment the file according to the recognition results and preset size thresholds. This adaptive segmentation algorithm is one of the core technologies in this step, which can dynamically adjust the segmentation strategy according to the file type and content features.

[0059] For text files, the segmentation algorithm may consider paragraph structure, chapter division, or semantic integrity. For example, a long document may be segmented by chapters or themes, rather than simply divided by fixed word count. For image files, the algorithm may consider the content area of the image, such as the boundary of important objects, rather than mechanically cutting the image into equal-sized blocks. For video files, segmentation may be based on scene changes, audio features, or semantic boundaries of content.

[0060] This adaptive segmentation method solves the problem of content integrity and continuity caused by traditional fixed-size segmentation. It can better preserve the semantic structure of the file, which is beneficial for subsequent content analysis and processing. At the same time, through reasonable segmentation, the system can more effectively utilize computing resources, improving the efficiency of processing large files.

[0061] The main technical problems solved by this step include: how to efficiently process large rich media files, how to accurately identify different types of media files, and how to effectively segment files while maintaining content integrity. By implementing these technologies, this step achieves the technical effects of improving file processing efficiency, enhancing content analysis accuracy, and improving user experience.

[0062] Specifically, the adaptive segmentation algorithm enables the system to handle various types and sizes of rich media files more intelligently, avoiding the content fragmentation problems that may arise from traditional fixed-size segmentation methods. This not only improves the accuracy of subsequent content analysis but also provides users with a better editing and viewing experience. For example, when users need to edit specific parts of video content, they can directly manipulate the relevant video segments without having to deal with the entire large file, greatly improving editing efficiency.

[0063] In addition, this step also lays the foundation for subsequent parallel processing. Since the file is reasonably segmented into multiple segments, the system can process these segments simultaneously, making full use of distributed computing resources and significantly improving processing speed. This is of great significance for application scenarios that require real-time response, such as online video editing or real-time content analysis.

[0064] Overall, step S101, through intelligent file processing and segmentation strategies, not only improves the efficiency and flexibility of the system in processing rich media files but also creates favorable conditions for subsequent content analysis and editing operations, serving as an important foundation for the entire rich media intelligent information editing method.

[0065] Step S102: Through a pre-set rich media vectorization model, the file segments are subjected to multi-modal content vectorization, and corresponding semantic meaning arrays are extracted. Based on the semantic meaning arrays, a multi-dimensional weighted similarity algorithm is used to determine the similarity of other rich media resource files to the current rich media resource file, and in a pre-set user file editing interface, information of similar rich media resource files with a similarity exceeding a pre-set threshold to the current rich media resource file is displayed. The multi-dimensional weighted similarity algorithm includes content similarity calculation, structure similarity calculation, and semantic similarity calculation, and the weight of each dimension of similarity calculation is determined by the file type.

[0066] Optionally, in the present embodiment, step S102 is the core link in the rich media intelligent information editing method, mainly involving content vectorization, semantic analysis, and similarity calculation. The technical process and principles of this step aim to convert different types of rich media content into comparable mathematical representations and conduct intelligent similarity analysis based on these representations.

[0067] First, the system uses a pre-set rich media vectorization model to vectorize the file segments in multiple modalities. The technical principle of this process is based on deep learning and representation learning theory, aiming to convert data of different modalities (such as text, images, audio and video) into a unified vector space representation. For text content, the system may use pre-trained language models based on Transformer architecture, such as BERT or GPT; for image content, convolutional neural networks (CNN) or visual Transformer models may be used; for audio content, waveform-to-spectrum conversion combined with deep neural networks may be used; and for video content, time series modeling techniques such as long short-term memory networks (LSTM) or 3D convolutional networks may be used.

[0068] This multi-modal vectorization technology solves the technical problem of how to uniformly process different types of media content. By mapping various types of content to the same vector space, the system can represent and compare information of different modalities in a consistent manner, laying the foundation for subsequent similarity calculation.

[0069] After vectorization, the system extracts the corresponding semantic meaning array. This process is actually a further semantic parsing and abstraction of the vector representation. The technical principle may involve attention mechanisms, semantic segmentation or topic modeling techniques. For example, for text content, the system may extract keywords, topics and sentiment tendencies; for image content, it may identify main objects, scenes and visual styles; for audio content, it may analyze timbre, rhythm and emotional characteristics; and for video content, it may analyze visual and auditory features and extract temporal semantic information.

[0070] The extraction of this semantic meaning array solves the technical problem of how to extract high-level semantic information from low-level features, enabling the system to more accurately understand and compare the actual meaning of rich media content, rather than just surface features.

[0071] Next, the system uses a multi-dimensional weighted similarity algorithm to determine the similarity of other rich media resource files to the current file. This algorithm considers three dimensions of content similarity, structure similarity and semantic similarity. Content similarity mainly focuses on the original features of the media, such as the word frequency distribution of text, color and texture features of images, spectral features of audio, etc.; structure similarity focuses on the organization of media content, such as the chapter structure of documents, spatial layout of images, temporal structure of videos, etc.; semantic similarity focuses on the high-level meaning and theme relevance of the content.

[0072] One key innovation of the algorithm is that it dynamically adjusts the calculation weights of each dimension based on the file type. This adaptive weight mechanism can better adapt to the characteristics of different types of media content. For example, for text files, semantic similarity may be given a higher weight; for image files, content similarity may be more important; for video files, a balance between the three dimensions may be needed.

[0073] This multi-dimensional weighted similarity algorithm solves the technical problem of how to comprehensively and accurately evaluate the similarity of different types of rich media content. It not only considers the surface features of the content, but also pays attention to the deep structure and semantic information, so it can more accurately identify truly relevant content.

[0074] Finally, the system displays the rich media resource file information with a similarity score exceeding the preset threshold in the preset user file editing interface. This process involves user interface design and information visualization technology. The system may sort the results according to the similarity score and display key information of similar files in the form of thumbnails, summaries or other intuitive forms.

[0075] This intelligent similar content recommendation mechanism solves the technical problem of how to help users quickly find relevant resources. It can greatly improve user efficiency and reduce manual search and screening time.

[0076] Overall, step S102 achieves multiple important technical effects. First, it realizes the unified representation and processing of different types of rich media content, providing a technical foundation for cross-modal content analysis and comparison. Second, through deep semantic analysis and multi-dimensional similarity calculation, the system can more accurately understand and match content, greatly improving the accuracy of similar content identification. Third, the dynamic weight adjustment mechanism enables the system to more flexibly adapt to different types of media content, improving the algorithm's versatility and robustness. Finally, through intelligent similar content recommendation, the system significantly improves user efficiency and experience.

[0077] Step S103: Receive the file editing instructions sent by the user, integrate the content of the corresponding current rich media resource file and the user-selected similar rich media resource file according to the file editing instructions, and handle the conflicts of similar file content through a conflict resolution mechanism. Display the file content after the content integration and conflict handling in the preset user file editing interface.

[0078] Optionally, in this embodiment, step S103 is a key link in the rich media intelligent information editing and reviewing method, mainly involving file editing, content integration, and conflict resolution. The core goal of this step is to provide an intelligent file editing environment for users, effectively integrating the contents of multiple similar rich media resource files, and resolving conflicts that may arise during integration, ultimately presenting an optimized file content.

[0079] Firstly, the system receives the file editing instructions sent by the user. This process involves human-computer interaction and user intention understanding technology. The system needs to accurately analyze the user's editing intentions, which may include content merging, partial replacement, structure reorganization, etc. To achieve this goal, the system may use natural language processing technology to understand the user's text instructions, or capture the user's editing intentions through graphical user interface (GUI) operations. This intelligent parsing technology can greatly improve the user's operation efficiency and reduce tedious manual editing steps.

[0080] Next, the system integrates the contents of the current rich media resource file and the similar rich media resource file selected by the user according to the parsed editing instructions. The technical principle of this process is based on document structure analysis, content matching, and intelligent merging algorithm. The system first needs to understand the structure and content of each file, which may involve document parsing technology, semantic analysis, and topic modeling. Then, the system will determine the content parts that need to be integrated according to the user's editing instructions, and use intelligent merging algorithms to fuse these contents.

[0081] This content integration technology solves the technical problem of how to effectively combine valuable information from multiple similar files. It can help users quickly extract and combine the required information from multiple related resources, greatly improving the efficiency of content creation and knowledge integration. For example, when writing a research report, the system can intelligently integrate the contents of multiple related literature, providing users with a comprehensive and refined information summary.

[0082] However, during the content integration process, conflicts inevitably arise, such as content duplication, information inconsistency, or structural conflicts. To solve these problems, the system adopts a conflict resolution mechanism. The technical principle of this mechanism may include version control algorithms, difference comparison techniques, and intelligent decision systems.

[0083] The conflict resolution mechanism first identifies and classifies different types of conflicts. For content duplication, the system may use text similarity algorithms to detect and merge duplicate content; for information inconsistency, the system may use rule-based logical reasoning or machine learning models to determine which information is more reliable or updated; for structural conflicts, the system may analyze the logical structure of the document and use heuristic algorithms to determine the most reasonable organization method.

[0084] In handling these conflicts, the system may automatically make decisions based on preset rules, or it may ask the user's opinion at key points. This way of human-machine collaboration ensures both efficiency and the final result meeting the user's expectations. The application of the conflict resolution mechanism solves the technical problem of how to maintain consistency and logic in the complex content integration process, greatly improving the quality and usability of the integration result.

[0085] After content integration and conflict processing, the system will display the processed file content in the preset user file editing interface. This display process involves information visualization and user interface design techniques. The system may use highlights, annotations, or other visual effects to mark the integrated and processed parts, allowing users to clearly understand the changed content. At the same time, the system may also provide options for undo, modification, or further editing, allowing users to fine-tune or correct.

[0086] This intelligent content display and interaction solves the technical problem of how to let users intuitively understand and control complex editing results. It not only improves user efficiency, but also enhances user control over the editing process, thereby improving the overall user experience.

[0087] Overall, step S103 achieves several important technical effects. First, it realizes intelligent file editing and content integration, greatly improving user efficiency in handling complex rich media resources. Users can quickly extract valuable information from multiple similar files and integrate it into the current work, which has wide application value in research, writing, and content creation.

[0088] Second, through the conflict resolution mechanism, the system can intelligently handle various conflicts in the content integration process, ensuring consistency and reasonableness of the final result. This not only reduces the user's workload in manually resolving conflicts, but also improves the quality and reliability of the integration result.

[0089] Third, this step provides an intuitive and interactive editing environment for users, making the complex content integration process more transparent and controllable. Users can clearly see the integration result and make further adjustments as needed, greatly enhancing user operation experience and satisfaction.

[0090] From the above description, the rich media intelligent information editing method provided by the embodiments of the present application can receive and store the rich media resource file uploaded by the user, perform file identification on the rich media resource file, and perform file segmentation on the rich media resource file according to the file type, file content features and preset size threshold obtained through the adaptive segmentation algorithm to obtain one or more file segments; the file segments are subjected to multi-modal content vectorization through a preset rich media vectorization model to extract corresponding semantic meaning arrays, the similarity of other rich media resource files to the current rich media resource file is determined through a multi-dimensional weighted similarity algorithm according to the semantic meaning arrays, and information with a similarity to the current rich media resource file exceeding a preset threshold is displayed in a preset user file editing interface, so that the rich media information is more flexibly processed.

[0091] In an embodiment of the rich media intelligent information editing method of the present application, referring to Figure 2 , the following content can also be specifically included:

[0092] Step S201: reading a binary data stream of the rich media resource file, extracting magic number information and a file extension of a file header, matching the extracted magic number information with a preset file type magic number database to determine a preliminary file type;

[0093] Step S202: analyzing a metadata structure of the file, extracting key attribute information, and using a pre-trained deep learning model to extract a feature vector of the file content to obtain file content features, and comprehensively inputting the preliminary file type, the key attribute information and the content feature vector into a decision tree classifier to obtain a final file type identification result.

[0094] Optionally, in the present embodiment, steps S201 and S202 constitute an efficient and accurate rich media resource file type identification process, which combines traditional file structure analysis and modern machine learning technology. This process aims to solve the technical problem of accurately identifying various complex rich media file types, especially in the case where the file extension may be tampered with or lost.

[0095] In step S201, the system first reads the binary data stream of the file, which is a bottom-layer file access method that can obtain the original information of the file. From this data stream, the system extracts the magic number information and the file extension of the file header. The magic number is a specific byte sequence at the beginning of the file, which is usually used to quickly identify the file type. The system matches the extracted magic number information with a preset file type magic number database, which contains standard magic numbers of various file types. Through this matching, the system can preliminarily determine the file type. The advantage of this method is fast and reliable, and it can still correctly identify the file type even if the file extension is changed.

[0096] Step S202 further deepens the file type recognition process. First, the system parses the file's metadata structure, extracting key attribute information. These attributes can include file creation time, author, version, and other information that can provide additional clues for file type determination. Next, the system uses a pre-trained deep learning model to extract feature vectors from the file content. This process involves complex machine learning algorithms such as convolutional neural networks (CNN) or recurrent neural networks (RNN), which can extract high-dimensional abstract features from file content.

[0097] Finally, the system inputs the preliminary file type, key attribute information, and content feature vectors into a decision tree classifier. The decision tree classifier is a machine learning model that can handle multiple input features, and it makes a final classification result of the file type through a series of judgment nodes. This comprehensive analysis method can fully utilize the various feature information of the file, greatly improving the accuracy of recognition.

[0098] This two-step file type recognition process solves the technical problem of how to accurately identify complex rich media file types. It can effectively handle cases where file extensions may be tampered with or missing, while also identifying new or non-standard file formats. The technical effects of this method mainly include the following aspects: First, it improves the accuracy of file type recognition and reduces the possibility of misjudgment. Second, it enhances the system's processing capability for various complex rich media files, laying a foundation for subsequent file processing and management. Third, this method has good scalability, and can adapt to new file types by updating the magic number database and retraining the machine learning model. Finally, this comprehensive analysis method improves the security of the system, effectively identifying disguised or maliciously altered files, which is of great significance for preventing security threats.

[0099] In an embodiment of the rich media intelligent information editing method of the present application, referring to Figure 3 It can also specifically include the following content:

[0100] Step S301: According to the identified file type, select the corresponding content feature extraction algorithm and segmentation strategy to calculate the total size of the file, and compare it with the preset size threshold to determine the initial segmentation number;

[0101] Step S302: Analyze the file content through the selected feature extraction algorithm to generate a content feature sequence, and apply an adaptive segmentation algorithm based on dynamic programming to calculate the optimal segmentation point. Perform file cutting operation at the calculated segmentation point to generate one or more file segments, and create metadata for each file segment containing original file information, segmentation position and segment number.

[0102] Optionally, in this embodiment, steps S301 and S302 constitute an intelligent file segmentation process that combines file type recognition, content feature extraction, and adaptive segmentation algorithms to address the efficient processing and storage of large rich media files. This approach not only adapts to the characteristics of different file types but also intelligently segments based on the semantic structure of the file content, thereby improving the efficiency of subsequent processing and storage.

[0103] In step S301, the system first selects the appropriate content feature extraction algorithm and segmentation strategy based on the previously identified file type. This file type-based algorithm selection is crucial because different types of files (such as text, images, audio, or video) have different content structures and feature distributions. For example, for a text file, the system may choose a natural language processing-based feature extraction algorithm, while for an image file, it may choose a computer vision-based algorithm. Next, the system calculates the total size of the file and compares it with a pre-set size threshold to determine the initial number of segments. The purpose of this step is to provide a preliminary reference framework for subsequent fine segmentation, while also ensuring that the sizes of the segmented file segments are suitable for storage and transmission.

[0104] Step S302 is the core of the entire segmentation process, involving complex content analysis and optimization algorithms. First, the system uses the selected feature extraction algorithm to analyze the file content and generate a content feature sequence. This process may involve deep learning models such as convolutional neural networks or recurrent neural networks to capture the semantic features and structural information of the file content. Next, the system applies an adaptive segmentation algorithm based on dynamic programming to calculate the optimal segmentation points. The core idea of this algorithm is to find the best segmentation location while maintaining the semantic integrity of the content, so that the segmented file segments not only meet the size requirements but also maintain the coherence of the content.

[0105] The dynamic programming algorithm iteratively calculates various possible segmentation schemes and selects the optimal solution based on a pre-defined objective function (such as minimizing content disruption and balancing segment size). This approach effectively handles large-scale files and achieves a good balance between computational efficiency and segmentation quality. After determining the optimal segmentation points, the system performs the actual file cutting operation to generate one or more file segments.

[0106] Finally, the system creates metadata for each file segment containing information about the original file, segmentation location, and segment number. These metadata are crucial for subsequent file recombination and management, as they not only record the position and order of each segment in the original file but also preserve key information from the original file, ensuring the integrity and traceability of the file.

[0107] This two-step file segmentation process solves the technical problem of how to efficiently process and store large rich media files. Its main technical effects include: first, by intelligently selecting feature extraction algorithms and segmentation strategies, the adaptability of the segmentation process to different file types is improved. Second, adaptive segmentation based on content features ensures the semantic coherence of the segmented file segments, maximizing the preservation of the structure and meaning of the original file. Third, the application of dynamic programming algorithm improves the efficiency and quality of segmentation, enabling the system to quickly process large-scale files. Finally, detailed metadata creation facilitates subsequent file management, retrieval and recombination.

[0108] In an embodiment of the rich media intelligent information editing method of the present application, referring to Figure 4 It can also specifically include the following content:

[0109] Step S401: according to the type of the file segment, select the corresponding pre-trained model for format conversion, noise reduction and standardization preprocessing, input the preprocessed data into the selected pre-trained model, and extract high-dimensional feature vector representation;

[0110] Step S402: input the high-dimensional feature vector representation into the pre-trained semantic understanding model, extract the key semantic features in the high-dimensional feature vector through the multi-layer self-attention mechanism of the semantic understanding model, use hierarchical clustering algorithm to cluster the extracted semantic features, form different levels of semantic concepts, map the clustering results to the pre-defined semantic ontology library, get the standardized semantic labels and sort them according to the importance, and construct a structured semantic meaning array, wherein the elements in the semantic meaning array include semantic labels, importance scores and related metrics.

[0111] Optionally, in the embodiment, steps S401 and S402 constitute a complex and powerful file content semantic analysis process, which combines advanced machine learning techniques and semantic understanding methods, aiming to extract deep semantic information from file segments and convert it into structured, easy-to-understand and process form. This method can not only process various types of file content, but also capture the semantic level and importance of the content, providing a strong foundation for subsequent content analysis, retrieval and management.

[0112] In step S401, the system first selects a corresponding pre-trained model based on the type of file segment to perform a series of preprocessing operations. These operations include format conversion, noise reduction, and standardization. Format conversion ensures that different types of file content can be uniformly processed; the noise reduction process removes noise information that may affect semantic extraction; and standardization makes data from different sources comparable. The technical principles of these preprocessing steps involve technologies in multiple fields such as signal processing, image processing, and natural language processing. The preprocessed data is input into the selected pre-trained model, which may be a deep neural network such as a convolutional neural network (CNN) or a Transformer model. These models can extract high-dimensional feature vector representations from input data, which contain rich semantic information of file content.

[0113] Step S402 is the core of the entire process, involving complex semantic understanding and knowledge organization processes. First, the system inputs the high-dimensional feature vector into the pre-trained semantic understanding model. This model may be based on advanced natural language processing models such as BERT or GPT. Through the model's multi-layer self-attention mechanism, the system can capture key semantic features in the feature vector. The self-attention mechanism allows the model to focus on different parts of the input and understand their relationships, which is crucial for understanding complex semantic structures.

[0114] Next, the system uses a hierarchical clustering algorithm to cluster the extracted semantic features. Hierarchical clustering can discover hierarchical relationships between features, forming semantic concepts at different levels. The advantage of this method is that it can capture the multi-level structure of semantics, thus more comprehensively understanding the meaning of the content. The clustering results are then mapped to a pre-defined semantic ontology library. The semantic ontology library is a knowledge base containing standardized semantic concepts and their relationships, and through this mapping, the system can convert the extracted semantic features into standardized semantic labels.

[0115] Finally, the system sorts the semantic labels according to their importance and constructs a structured semantic meaning array. Each element in this array contains a semantic label, an importance score, and related metrics. This structured representation not only preserves the key semantic information of the original content, but also provides importance and relevance assessments of this information, providing a rich semantic context for subsequent applications.

[0116] This two-step semantic analysis process addresses the technical problem of extracting deep semantic information from complex rich media files and transforming it into a structured, quantifiable form. Its main technical effects include: First, through pre-processing and the application of pre-trained models, the processing capacity for various types of file content and the accuracy of feature extraction are improved. Second, the use of multi-layer self-attention mechanisms enables the system to understand complex semantic relationships and contextual information. Third, hierarchical clustering and semantic ontology mapping achieve the standardization and structuring of semantic information, facilitating subsequent retrieval and analysis. Finally, the structured semantic meaning array provides strong support for intelligent understanding and management of content.

[0117] In an embodiment of the rich media intelligent information editing method of the present application, referring to Figure 5 , it can also specifically include the following content:

[0118] Step S501: Map each semantic label in the semantic meaning array to a pre-defined feature space, assign weights to each dimension according to the importance score of the semantic label, generate a weighted feature vector as input for similarity calculation;

[0119] Step S502: Calculate the similarity of content, structure and semantic dimensions respectively, determine the weight of each dimension according to the file type, and integrate the weighted similarity results of the three dimensions to obtain the final similarity score.

[0120] Optionally, in the present embodiment, steps S501 and S502 constitute a comprehensive and detailed file similarity calculation process. This process integrates semantic understanding, feature mapping and multi-dimensional similarity analysis techniques, aiming to solve the technical problem of complex rich media file similarity evaluation. This method not only considers the surface features of the file, but also deeply analyzes its internal semantic structure, thus providing a more accurate and meaningful similarity measure.

[0121] In step S501, the system first maps each semantic label in the semantic meaning array to a pre-defined feature space. This feature space is a multi-dimensional vector space, where each dimension represents a specific semantic concept or attribute. The mapping process actually converts abstract semantic concepts into quantifiable numerical features. The technical principle of this mapping is based on distributed semantic representation theories, such as word2vec or GloVe models. These models can encode semantic concepts into dense vector representations, preserving the semantic relationships between concepts.

[0122] Next, the system assigns weights to the respective dimensions in the feature space based on the importance scores of each semantic tag. The purpose of this step is to highlight important semantic features while reducing the impact of less relevant or less important features. The process of weight assignment may involve normalization to ensure comparability of weight distribution across different files. Through this weighting process, the system generates a weighted feature vector that not only contains the original semantic information but also reflects the relative importance of each semantic concept.

[0123] Step S502 is the core of similarity calculation, which adopts a multi-dimensional analysis method. The system calculates the similarity of content, structure and semantics respectively. Content similarity may involve text matching, image feature comparison and other technologies; structure similarity may consider the organization method and format characteristics of the file; and semantic similarity is calculated based on the weighted feature vector generated earlier, using cosine similarity or other vector similarity measurement methods.

[0124] When calculating the similarity of the three dimensions, the system determines the weight of each dimension according to the file type. This is because the importance of the three dimensions may be different for different types of files. For example, for text files, semantic similarity may be more important; while for image files, content similarity may occupy a greater weight. Through this dynamic weight assignment, the system can flexibly adapt to the characteristics of different types of files, providing more accurate similarity evaluation.

[0125] Finally, the system integrates the weighted similarity results of the three dimensions to obtain the final similarity score. This integration process may involve simple linear combination, or more complex nonlinear models such as support vector machine (SVM) or neural network to capture the complex interaction between dimensions.

[0126] This two-step similarity calculation process solves the technical problem of how to comprehensively and accurately evaluate the similarity of complex rich media files. Its main technical effects include: first, by mapping semantic tags to feature space and weighting, it realizes the quantification and importance differentiation of semantic information. Second, multi-dimensional similarity calculation considers the content, structure and semantic features of the file, providing a more comprehensive similarity evaluation. Third, the method of dynamically adjusting the weight of each dimension according to the file type improves the flexibility and adaptability of similarity calculation. Finally, the integration of multiple dimension similarity results results in a comprehensive and accurate final similarity score.

[0127] In an embodiment of the rich media intelligent information editing method of the present application, see Figure 6 It can also specifically include the following content:

[0128] Step S601: Analyze the content of the file editing instruction sent by the user, identify the instruction type and operation parameters, and convert the parsed instruction into a system executable operation sequence;

[0129] Step S602: Load the content of the current rich media resource file and the user-selected similar rich media resource file, select the corresponding content integration algorithm according to the file type to perform content integration operations, generate the integrated new rich media resource file, and retain the version information of the original file.

[0130] Optionally, in the embodiment, steps S601 and S602 constitute an intelligent rich media resource file editing and integration process, which combines natural language processing, command analysis and content integration technology, aiming to solve the problem of efficient editing and intelligent integration of complex rich media files. This method not only understands and executes the user's editing intention, but also selects the most suitable integration strategy according to the file type, thereby improving editing efficiency and integration quality.

[0131] In step S601, the system first analyzes the content of the file editing instruction sent by the user. This process involves natural language processing and command analysis technology. The system needs to identify the type of instruction (such as merge, replace, delete or insert) and related operation parameters (such as target position, content range, etc.). This parsing process may use rule-based methods, or use more advanced machine learning models such as sequence labeling models or intent recognition models. These models can extract structured operation information from natural language instructions.

[0132] After parsing, the system converts these instructions into a system executable operation sequence. This conversion process involves command mapping and operation optimization. The system needs to convert high-level editing instructions into low-level file operations, while considering the order and dependency of operations to ensure the correctness and efficiency of execution. The technical principle of this step is based on the design theory of command interpreter in the operating system.

[0133] Step S602 is the core of the whole process, which involves a complex file content integration process. First, the system loads the content of the current rich media resource file and the user-selected similar rich media resource file. This loading process needs to consider the characteristics of different types of rich media files, which may involve file decoding, content parsing and other technologies.

[0134] Next, the system selects a corresponding content integration algorithm based on the file type. This file type-based algorithm selection is crucial because different types of files (such as text, images, audio, or video) require different integration strategies. For example, for text files, the system may use a semantic understanding-based paragraph merging algorithm; for image files, a computer vision-based image fusion technique may be used; and for audio-video files, complex multimedia editing algorithms may be involved.

[0135] The system performs content integration operations, including content merging, replacement, deletion, or insertion. The specific implementation of these operations depends on the selected integration algorithm. For example, content merging may involve intelligent splicing of text paragraphs, seamless fusion of images, or smooth transition of audio-video segments. Replacement operations may need to consider contextual consistency to ensure that the replaced content is coordinated with the surrounding environment. Deletion operations may need to reorganize the remaining content to maintain the coherence of the file. Insertion operations need to consider how to naturally integrate new content into the existing structure.

[0136] After the integration operation is completed, the system generates a new integrated rich media resource file. At the same time, the system preserves the version information of the original file, which is very important for file management, version control, and possible rollback operations. The preservation of version information may use incremental storage or snapshot technology to save storage space and support efficient version comparison and recovery.

[0137] This two-step file editing and integration process solves the technical problem of how to efficiently process and integrate complex rich media files. Its main technical effects include: first, by intelligently parsing user instructions, it improves the accuracy and efficiency of editing operations. Second, the integration algorithm selection based on file type ensures the adaptability of the integration process to different types of files. Third, complex content integration operations achieve high-quality file merging and editing. Finally, the preservation of version information provides support for file management and version control.

[0138] In an embodiment of the rich media intelligent information editing and reviewing method of the present application, referring to Figure 7 It can also specifically include the following content:

[0139] Step S701: Compare the content structure of the current rich media resource file and the similar rich media resource file to identify duplicate, contradictory, or inconsistent content segments, mark the conflict position and type;

[0140] Step S702: Process part of the conflict according to the conflict type and the preset resolution strategy, generate corresponding user options for conflicts that cannot be automatically resolved, receive and execute the user's conflict processing selection, update the content of the integrated rich media resource file, and eliminate the conflict.

[0141] Optionally, in this embodiment, steps S701 and S702 constitute an intelligent rich media resource file conflict detection and resolution process, which combines content comparison, conflict identification, and intelligent decision-making technology, aiming to solve the content conflict problem in the complex rich media file integration process. This method not only accurately identifies various types of conflicts, but also flexibly handles these conflicts according to preset strategies and user intentions, thereby improving the quality and efficiency of file integration.

[0142] In step S701, the system first compares the content structure of the current rich media resource file and similar rich media resource files. This comparison process involves complex content analysis and structure matching technology. For text content, the system may use text similarity algorithms such as cosine similarity or Jaccard similarity; for image content, image feature extraction and matching technology may be used; for audio and video content, time series analysis and content fingerprint technology may be involved. The comprehensive application of these technologies enables the system to identify repeated, contradictory, or inconsistent parts in different types of rich media content.

[0143] In the identification process, the system needs to consider different levels of content units, from words and paragraphs to image regions and audio and video segments. The system determines the relationship between content units by setting thresholds or using machine learning models to identify potential conflicts. The technical principle of this process is based on information retrieval and pattern recognition theory, which can effectively filter out parts that need special attention from massive amounts of content.

[0144] Once the conflict is identified, the system will mark the location and type of the conflict. Conflict types may include content duplication, information contradiction, format inconsistency, etc. This marking not only includes the location information of the conflict, but also may contain the severity of the conflict, the impact range, etc. metadata, providing an important reference for subsequent conflict handling.

[0145] Step S702 is the core of conflict resolution, which adopts an intelligent processing method. First, the system handles part of the conflict according to the conflict type and the preset resolution strategy. These preset strategies may be based on statistical rules, domain knowledge, or previous user choices. For example, for simple repeated content, the system may automatically select to retain one version; for minor format inconsistencies, the system may automatically perform format unification.

[0146] However, for complex or high-risk conflicts, the system cannot automatically make decisions. In this case, the system will generate corresponding user options. These options need to be presented to the user in a clear and understandable way, which may include conflict content comparison, solution suggestions, etc. The system receives the user's choice and performs the corresponding conflict handling operation. This process embodies the idea of human-machine cooperation, combining the efficient processing capacity of the system with human judgment.

[0147] After performing the conflict resolution, the system updates the integrated rich media resource file content to eliminate the conflicts. This update process needs to consider the coherence and consistency of the content, which may involve content rearrangement, format adjustment, and other operations. The system also needs to ensure that these changes do not introduce new conflicts or problems.

[0148] This two-step conflict detection and resolution process addresses how to efficiently handle content conflicts in complex rich media file integration processes. Its main technical effects include: First, through intelligent comparison and conflict identification, the accuracy and comprehensiveness of conflict detection are improved. Second, the application of preset strategies enables the automatic processing of partial conflicts, improving efficiency. Third, the human-machine collaborative conflict resolution method ensures the accuracy of key decisions. Finally, intelligent conflict handling and content updating ensure the quality and consistency of the final file.

[0149] This method not only improves the quality and efficiency of rich media resource file integration, but also provides strong support for intelligent content management, version control, collaborative editing, and other applications. It has wide application prospects in document management systems, content creation platforms, knowledge base integration, and other fields, significantly improving the intelligent level of content processing and management, and providing users with more efficient and accurate content integration services.

[0150] In order to be able to more flexibly process rich media information, the present application provides an embodiment of a rich media intelligent information editing and reviewing device for implementing all or part of the content of the rich media intelligent information editing and reviewing method, see Figure 8 , which specifically includes the following content:

[0151] The file segmentation module 10 is used for receiving and storing the rich media resource file uploaded by the user, identifying the file, and performing file segmentation on the rich media resource file according to the file type, file content characteristics, and preset size threshold through an adaptive segmentation algorithm to obtain one or more file segments.

[0152] The similar display module 20 is used for performing multi-modal content vectorization on the file segment through a preset rich media vectorization model, extracting the corresponding semantic meaning array, determining the similarity between other rich media resource files and the current rich media resource file through a multi-dimensional weighted similarity algorithm according to the semantic meaning array, and displaying the information of similar rich media resource files with a similarity to the current rich media resource file exceeding a preset threshold in a preset user file editing interface.

[0153] The similarity merging module 30 is configured to receive the file editing instruction sent by the user, integrate the content of the current rich media resource file and the similar rich media resource file selected by the user according to the file editing instruction, and perform conflict processing on the conflicts of the similar file content through a conflict resolution mechanism, and display the file content after the content integration and the conflict processing in the preset user file editing interface.

[0154] As can be seen from the above description, the rich media intelligent information compiling device provided by the embodiments of the present application can receive and store the rich media resource file uploaded by the user, identify the file, and perform file segmentation on the rich media resource file through an adaptive segmentation algorithm according to the file type, file content features and preset size threshold, to obtain one or more file segments; perform multi-modal content vectorization on the file segments through a preset rich media vectorization model, extract the corresponding semantic meaning array, determine the similarity between other rich media resource files and the current rich media resource file through a multi-dimensional weighted similarity algorithm according to the semantic meaning array, and display information with a similarity to the current rich media resource file exceeding a preset threshold in a preset user file editing interface, thereby more flexibly processing rich media information.

[0155] From the hardware level, in order to more flexibly process rich media information, the present application provides an embodiment of an electronic device for implementing all or part of the content of the rich media intelligent information compiling method, which specifically includes the following content:

[0156] A processor, a memory, a communications interface and a bus; wherein the processor, the memory and the communications interface complete mutual communication through the bus; the communications interface is configured to realize information transmission between the rich media intelligent information compiling device and a core business system, a user terminal and a related database and other related devices; the logic controller can be a desktop computer, a tablet computer, a mobile terminal and the like, and the embodiments are not limited thereto. In the embodiments, the logic controller can be implemented by referring to the embodiments of the rich media intelligent information compiling method and the embodiments of the rich media intelligent information compiling device, the contents of which are incorporated herein, and repeated descriptions are omitted.

[0157] It can be understood that the user terminal can include a smart phone, a tablet electronic device, a network set-top box, a portable computer, a desktop computer, a personal digital assistant (PDA), a vehicle-mounted device, a smart wearable device and the like. The smart wearable device can include smart glasses, a smart watch, a smart bracelet and the like.

[0158] In practical applications, part of the rich media intelligent information editing method can be executed on the electronic device as described above, or all operations can be completed in the client device. Specifically, the processing capacity of the client device and the use scenario of the user can be selected. The present application does not limit this. If all operations are completed in the client device, the client device can also include a processor.

[0159] The client device described above can have a communication module (i.e., a communication unit) that can be connected to a remote server to realize data transmission with the server. The server can include a server on the task scheduling center side, and other implementation scenarios can also include a server on an intermediate platform, such as a server on a third-party server platform that is communicatively linked to the task scheduling center server. The server can include a single computer device, a server cluster composed of multiple servers, or a distributed server structure.

[0160] Figure 9 A schematic block diagram of the system configuration of the electronic device 9600 of the embodiment of the present application is shown in FIG. 9. As shown in the figure, the electronic device 9600 can include a central processor 9100 and a memory 9140; the memory 9140 is coupled to the central processor 9100. It is worth noting that the structure shown in the figure is exemplary; other types of structures can also be used to supplement or replace the structure to realize telecommunication functions or other functions. Figure 9 Figure 9 The structure shown in the figure is exemplary; other types of structures can also be used to supplement or replace the structure to realize telecommunication functions or other functions.

[0161] In an embodiment, the rich media intelligent information editing method function can be integrated into the central processor 9100. The central processor 9100 can be configured to control as follows:

[0162] Step S101: receiving and storing a rich media resource file uploaded by a user, performing file identification on the rich media resource file, and performing file segmentation on the rich media resource file according to the file type, file content features, and preset size threshold obtained by identification through an adaptive segmentation algorithm to obtain one or more file segments;

[0163] ​Step S102: The file fragment is vectorized into multimodal content using a preset rich media vectorization model to extract the corresponding semantic meaning array. Based on the semantic meaning array, the similarity between other rich media resource files and the current rich media resource file is determined using a multi-dimensional weighted similarity algorithm. Information on similar rich media resource files whose similarity to the current rich media resource file exceeds a preset threshold is displayed in a preset user file editing interface. The multi-dimensional weighted similarity algorithm includes content similarity calculation, structural similarity calculation, and semantic similarity calculation. The similarity calculation weight of each dimension is determined by the file type.

[0164] Step S103: Receive the file editing instruction sent by the user, integrate the content of the corresponding current rich media resource file and the similar rich media resource file selected by the user according to the file editing instruction, and handle the conflict of similar file content through the conflict resolution mechanism, and display the file content after the content integration and conflict handling in the preset user file editing interface.

[0165] As described above, the electronic device provided in this application embodiment receives and stores rich media resource files uploaded by users, performs file identification on the rich media resource files, and segments the rich media resource files using an adaptive segmentation algorithm based on the identified file type, file content features, and a preset size threshold to obtain one or more file fragments; performs multimodal content vectorization on the file fragments using a preset rich media vectorization model to extract the corresponding semantic meaning array, determines the similarity between other rich media resource files and the current rich media resource file using a multi-dimensional weighted similarity algorithm based on the semantic meaning array, and displays information in a preset user file editing interface that has a similarity exceeding a preset threshold with the current rich media resource file, thereby processing rich media information more flexibly.

[0166] In another embodiment, the rich media intelligent information editing and reviewing device can be configured separately from the central processing unit 9100. For example, the rich media intelligent information editing and reviewing device can be configured as a chip connected to the central processing unit 9100, and the rich media intelligent information editing and reviewing method function can be realized through the control of the central processing unit.

[0167] like Figure 9 As shown, the electronic device 9600 may further include: a communication module 9110, an input unit 9120, an audio processor 9130, a display 9160, and a power supply 9170. It is worth noting that the electronic device 9600 does not necessarily need to include these components. Figure 9 All components shown; in addition, the electronic device 9600 may also include Figure 9 For components not shown, please refer to existing technologies.

[0168] As shown in FIG. 9, the central processing unit 9100, which is sometimes referred to as a controller or operation control, can include a microprocessor or other processor device and / or logic device, which receives input and controls the operation of the various components of the electronic device 9600. Figure 9

[0169] The memory 9140, for example, can be one or more of a buffer, a flash memory, a hard drive, a removable media, a volatile memory, a non-volatile memory, or other suitable device. Information relating to failures can be stored, in addition to programs for executing the information. The central processing unit 9100 can execute the programs stored in the memory 9140 to achieve information storage or processing, etc.

[0170] The input unit 9120 provides input to the central processing unit 9100. The input unit 9120 is, for example, a key or touch input device. The power supply 9170 is used to provide power to the electronic device 9600. The display 9160 is used to display display objects such as images and text. The display can be, for example, an LCD display, but is not limited thereto.

[0171] The memory 9140 can be a solid state memory such as a read only memory (ROM), a random access memory (RAM), a SIM card, etc. It can also be a memory that retains information even when power is off, can be selectively erased, and is provided with more data, examples of which are sometimes referred to as EPROM, etc. The memory 9140 can also be some other type of device. The memory 9140 includes a buffer memory 9141 (sometimes referred to as a buffer). The memory 9140 can include an application / function storage section 9142 for storing application programs and function programs or for executing the flow of the operation of the electronic device 9600 by the central processing unit 9100.

[0172] The memory 9140 can also include a data storage section 9143 for storing data such as contacts, digital data, pictures, sounds, and / or any other data used by the electronic device. A driver program storage section 9144 of the memory 9140 can include various driver programs of the electronic device for communication functions and / or for performing other functions of the electronic device such as a messaging application, an address book application, etc.

[0173] The communication module 9110 is a transmitter / receiver that transmits and receives signals via an antenna 9111. The communication module 9110 (transmitter / receiver) is coupled to the central processing unit 9100 to provide input signals and receive output signals, which can be the same as in the case of a conventional mobile communication terminal. ​

[0174] Based on different communication technologies, multiple communication modules 9110, such as a cellular network module, a Bluetooth module, and / or a wireless local area network module, etc., can be provided in the same electronic device. The communication module 9110 (transmitter / receiver) is also coupled to a speaker 9131 and a microphone 9132 via an audio processor 9130 to provide audio output via the speaker 9131 and to receive audio input from the microphone 9132, thereby enabling the usual telecommunication functions. The audio processor 9130 can include any suitable buffers, decoders, amplifiers, etc. In addition, the audio processor 9130 is also coupled to the central processor 9100, thereby enabling the recording of sounds on the local device via the microphone 9132 and enabling the playing of stored sounds on the local device via the speaker 9131.

[0175] The embodiments of the present application further provide a computer readable storage medium capable of implementing all steps of the rich media intelligent information editing method in the above-mentioned embodiments in which the execution subject is a server or a client. The computer readable storage medium stores a computer program which, when executed by a processor, implements all steps of the rich media intelligent information editing method in the above-mentioned embodiments in which the execution subject is a server or a client. For example, when the processor executes the computer program, the following steps are implemented:

[0176] Step S101: receiving and storing a rich media resource file uploaded by a user, performing file identification on the rich media resource file, and performing file segmentation on the rich media resource file by an adaptive segmentation algorithm according to a file type, file content features, and a preset size threshold obtained by the identification, to obtain one or more file segments;

[0177] Step S102: performing multi-modal content vectorization on the file segments by a preset rich media vectorization model, extracting a corresponding semantic meaning array, determining the similarity of other rich media resource files to the current rich media resource file by a multi-dimensional weighted similarity algorithm according to the semantic meaning array, and displaying information of similar rich media resource files that have a similarity to the current rich media resource file exceeding a preset threshold in a preset user file editing interface, wherein the multi-dimensional weighted similarity algorithm includes content similarity calculation, structure similarity calculation, and semantic similarity calculation, and the similarity calculation weight of each dimension is determined by the file type;

[0178] Step S103: receiving a file editing instruction sent by the user, performing content integration on the corresponding current rich media resource file and the similar rich media resource file selected by the user according to the file editing instruction, performing conflict processing on the conflict of similar file content by a conflict resolution mechanism, and displaying the file content after the content integration and the conflict processing in the preset user file editing interface.

[0179] From the above description, the computer readable storage medium provided by the embodiments of the application receives and stores the rich media resource file uploaded by the user, performs file identification on the rich media resource file, and performs file segmentation on the rich media resource file according to the file type, file content features and preset size threshold obtained by identification through an adaptive segmentation algorithm to obtain one or more file segments; the file segments are subjected to multi-modal content vectorization through a preset rich media vectorization model, and corresponding semantic meaning arrays are extracted; the similarity between other rich media resource files and the current rich media resource file is determined through a multi-dimensional weighted similarity algorithm according to the semantic meaning arrays, and information about the similar rich media resource files with a similarity to the current rich media resource file exceeding a preset threshold is displayed in a preset user file editing interface, so that the rich media information is more flexibly processed.

[0180] The embodiments of the application also provide a computer program product capable of implementing all steps of the rich media intelligent information editing method in which the execution subject is a server or a client. The computer program / instruction is executed by a processor to implement the steps of the rich media intelligent information editing method, for example, the computer program / instruction implements the following steps:

[0181] Step S101: receiving and storing a rich media resource file uploaded by a user, performing file identification on the rich media resource file, and performing file segmentation on the rich media resource file according to the file type, file content features and preset size threshold obtained by identification through an adaptive segmentation algorithm to obtain one or more file segments;

[0182] Step S102: performing multi-modal content vectorization on the file segments through a preset rich media vectorization model, extracting corresponding semantic meaning arrays, determining the similarity between other rich media resource files and the current rich media resource file through a multi-dimensional weighted similarity algorithm according to the semantic meaning arrays, and displaying information about similar rich media resource files with a similarity to the current rich media resource file exceeding a preset threshold in a preset user file editing interface, wherein the multi-dimensional weighted similarity algorithm includes content similarity calculation, structure similarity calculation and semantic similarity calculation, and the similarity calculation weight of each dimension is determined by the file type;

[0183] Step S103: receiving a file editing instruction sent by the user, performing content integration on the corresponding current rich media resource file and the similar rich media resource file selected by the user according to the file editing instruction, performing conflict processing on the conflict of similar file content through a conflict resolution mechanism, and displaying the file content after the content integration and the conflict processing in the preset user file editing interface.

[0184] From the above description, the computer program product provided by the embodiment of the present application receives and stores a rich media resource file uploaded by a user, performs file identification on the rich media resource file, and performs file segmentation on the rich media resource file according to a file type, file content features, and a preset size threshold obtained through identification by using an adaptive segmentation algorithm to obtain one or more file segments; performs multi-modal content vectorization on the file segments by using a preset rich media vectorization model to extract a corresponding semantic meaning array, determines the similarity of other rich media resource files to the current rich media resource file by using a multi-dimensional weighted similarity algorithm according to the semantic meaning array, and displays information about rich media resource files that have a similarity to the current rich media resource file exceeding a preset threshold in a preset user file editing interface, thereby more flexibly processing rich media information.

[0185] Those skilled in the art will understand that the embodiments of the present application can be provided as methods, apparatuses, or computer program products. Therefore, the present application can take the form of an entirely hardware embodiment, an entirely software embodiment, or an embodiment combining software and hardware aspects. Moreover, the present application can take the form of a computer program product implemented on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROMs, optical storage, etc.) containing computer-usable program code.

[0186] The present application is described with reference to the flowcharts and / or block diagrams of the methods, apparatuses (devices), and computer program products according to the embodiments of the present application. It should be understood that each flow and / or block in the flowcharts and / or block diagrams, and the combination of flows and / or blocks in the flowcharts and / or block diagrams can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing apparatus to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing apparatus produce a device that implements the functions specified in the flowcharts and / or block diagrams. Figure 1 one or more flows and / or blocks Figure 1 an apparatus that performs the functions specified in one or more flows and / or blocks.

[0187] These computer program instructions can also be stored in a computer-readable memory that can direct the computer or other programmable data processing apparatus to work in a specific manner, so that the instructions stored in the computer-readable memory produce a manufactured product including instruction apparatuses that implement the functions specified in the flowcharts and / or block diagrams. Figure 1 one or more flows and / or blocks Figure 1 an apparatus that performs the functions specified in one or more flows and / or blocks.

[0188] These computer program instructions can also be loaded into a computer or other programmable data processing devices, so that a series of operational steps are generated to realize the computer-implemented processes, and the instructions executed on the computer or other programmable devices provide a process for implementing the functions specified in the flowchart Figure 1 one flow or multiple flows and / or the functions specified in the block Figure 1 Figure 1 one flow or multiple flows and / or the functions specified in the block

[0189] The principles and implementation manners of the present application are described in the specific embodiments. The above description of the embodiments is only used to help understand the method and core idea of the present application; meanwhile, for those skilled in the art, according to the idea of the present application, the specific implementation manners and application ranges can be changed, and the above description of the present application should not be understood as a limitation.

Claims

1. A rich media intelligent information editing and review method, characterized in that, The method includes: Receive and store rich media resource files uploaded by users, perform file identification on the rich media resource files, and perform file segmentation on the rich media resource files according to the identified file type, file content characteristics and preset size threshold through an adaptive segmentation algorithm to obtain one or more file segments; The file fragment is vectorized into multimodal content using a preset rich media vectorization model, and the corresponding semantic meaning array is extracted. Based on the semantic meaning array, the similarity between other rich media resource files and the current rich media resource file is determined using a multi-dimensional weighted similarity algorithm. Information on similar rich media resource files whose similarity to the current rich media resource file exceeds a preset threshold is displayed in a preset user file editing interface. The multi-dimensional weighted similarity algorithm includes content similarity calculation, structural similarity calculation, and semantic similarity calculation, and the similarity calculation weight of each dimension is determined by the file type. The system receives file editing instructions sent by the user, integrates the content of the current rich media resource file and the similar rich media resource file selected by the user according to the file editing instructions, and handles the conflicts of similar file content through a conflict resolution mechanism. The file content after the content integration and conflict handling is displayed in the preset user file editing interface.

2. The rich media intelligent information editing and review method according to claim 1, characterized in that, The process of identifying the rich media resource file includes: Read the binary data stream of the rich media resource file, extract the magic number information and file extension from the file header, and match the extracted magic number information with a preset file type magic number database to determine the preliminary file type; The file's metadata structure is parsed to extract key attribute information. A pre-trained deep learning model is used to extract feature vectors from the file content to obtain file content features. The preliminary file type, the key attribute information, and the content feature vectors are then input into a decision tree classifier to obtain the final file type identification result.

3. The rich media intelligent information editing and review method according to claim 1, characterized in that, The rich media resource file is segmented using an adaptive segmentation algorithm based on the identified file type, file content features, and a preset size threshold to obtain one or more file segments, including: Based on the identified file type, select the appropriate content feature extraction algorithm and segmentation strategy to calculate the total size of the file, and compare it with a preset size threshold to determine the initial number of segments; The file content is analyzed by a selected feature extraction algorithm to generate a content feature sequence. An adaptive segmentation algorithm based on dynamic programming is applied to calculate the optimal segmentation point. The file is then split at the calculated segmentation point to generate one or more file segments. Metadata containing the original file information, segmentation position, and segment number is created for each file segment.

4. The rich media intelligent information editing and review method according to claim 1, characterized in that, The step involves performing multimodal content vectorization on the file fragment using a preset rich media vectorization model to extract the corresponding semantic meaning array, including: Based on the type of the file fragment, select the appropriate pre-trained model for format conversion, noise reduction, and standardization preprocessing. Input the preprocessed data into the selected pre-trained model to extract high-dimensional feature vector representations. The high-dimensional feature vector representation is input into a pre-trained semantic understanding model. The key semantic features in the high-dimensional feature vector are extracted through the multi-layer self-attention mechanism of the semantic understanding model. The extracted semantic features are clustered using a hierarchical clustering algorithm to form semantic concepts at different levels. The clustering results are mapped to a predefined semantic ontology library to obtain standardized semantic labels, which are then sorted according to importance. A structured semantic meaning array is constructed, wherein the elements in the semantic meaning array include semantic labels, importance scores, and relevance metrics.

5. The rich media intelligent information editing and review method according to claim 1, characterized in that, The step of determining the similarity between other rich media resource files and the current rich media resource file using a multi-dimensional weighted similarity algorithm based on the semantic meaning array includes: Each semantic label in the semantic meaning array is mapped to a predefined feature space, and weights are assigned to each dimension according to the importance score of the semantic label to generate a weighted feature vector, which is used as the input for similarity calculation. The similarity is calculated separately for the three dimensions of content, structure and semantics. The weight of each dimension is determined according to the file type. The weighted similarity results of the three dimensions are combined to obtain the final similarity score.

6. The rich media intelligent information editing and review method according to claim 1, characterized in that, The step of receiving the file editing instructions sent by the user and integrating the content of the corresponding current rich media resource file and the similar rich media resource file selected by the user according to the file editing instructions includes: Parse the content of file editing commands sent by the user, identify the command type and operation parameters, and convert the parsed commands into a system-executable operation sequence; Load the content of the current rich media resource file and similar rich media resource files selected by the user, select the corresponding content integration algorithm according to the file type to perform content integration operation, generate a new integrated rich media resource file, and retain the version information of the original file. The content integration operation includes content merging, replacement, deletion or insertion.

7. The rich media intelligent information editing and review method according to claim 1, characterized in that, The conflict resolution mechanism for handling conflicts in similar file content includes: Compare the content structure of current rich media resource files with similar rich media resource files to identify duplicate, contradictory, or inconsistent content segments, and mark conflict locations and types; Based on the conflict type and preset resolution strategy, some conflicts are handled. For conflicts that cannot be resolved automatically, corresponding user options are generated, the user's conflict handling selections are received and executed, the content of the integrated rich media resource files is updated, and the conflicts are eliminated.

8. A rich media intelligent information editing and reviewing device, characterized in that, The device includes: The file segmentation module is used to receive and store rich media resource files uploaded by users, perform file identification on the rich media resource files, and perform file segmentation on the rich media resource files according to the identified file type, file content characteristics and preset size threshold through an adaptive segmentation algorithm to obtain one or more file segments. The similarity display module is used to perform multimodal content vectorization on the file fragments through a preset rich media vectorization model, extract the corresponding semantic meaning array, determine the similarity between other rich media resource files and the current rich media resource file through a multi-dimensional weighted similarity algorithm based on the semantic meaning array, and display information of similar rich media resource files whose similarity to the current rich media resource file exceeds a preset threshold in a preset user file editing interface. The multi-dimensional weighted similarity algorithm includes content similarity calculation, structural similarity calculation, and semantic similarity calculation, and the similarity calculation weight of each dimension is determined by the file type. The similarity merging module is used to receive file editing instructions sent by the user, integrate the content of the corresponding current rich media resource file and the similar rich media resource file selected by the user according to the file editing instructions, handle the conflict of similar file content through a conflict resolution mechanism, and display the file content after the content integration and conflict handling in the preset user file editing interface.

9. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the program, it implements the steps of the rich media intelligent information editing and review method according to any one of claims 1 to 7.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that, When executed by a processor, the computer program implements the steps of the rich media intelligent information editing and review method according to any one of claims 1 to 7.

Images