Large model-based file management method and device, electronic equipment and storage medium

Abstract

This disclosure discloses a file management method, apparatus, electronic device, and storage medium based on a large model, relating to the field of computer science, and particularly to artificial intelligence technologies such as large models, deep learning, and image processing. The method includes: acquiring descriptive information for file management; performing intent recognition on the descriptive information for file management using a large model to obtain a file management intent, wherein the file management intent includes the file to be managed on the terminal and the management action corresponding to the file to be managed; generating an executable file management script based on the file management intent; and executing the file management script to manage the file to be managed.

Description

Technical Field

[0001] This disclosure relates to the field of computer technology, and in particular to the fields of artificial intelligence technology such as large models, deep learning, and image processing, specifically to file management methods, devices, electronic devices, and storage media based on large models. Background Technology

[0002] In related technologies, when users organize files on their terminals, they often need to manually create folders and move the files to be managed from the terminals into the created folders to complete file management. This manual file management method is inefficient. Summary of the Invention

[0003] This disclosure provides a file management method, apparatus, electronic device, and storage medium based on a large model. The specific solution is as follows:

[0004] According to one aspect of this disclosure, a file management method based on a large model is provided, comprising: Retrieve description information for file management; The file management intent is obtained by performing intent recognition on the description information of the file management through a large model. The file management intent includes the file to be managed on the terminal and the management action corresponding to the file to be managed. Based on the stated file management intent, generate an executable file management script; The file management script is executed to manage the files to be managed.

[0005] According to another aspect of this disclosure, a file management device based on a large model is provided, comprising: The acquisition module is used to obtain descriptive information about file management. The intent recognition module is used to perform intent recognition on the description information of the file management through a large model to obtain the file management intent, wherein the file management intent includes the file to be managed on the terminal and the management action corresponding to the file to be managed; The script generation module is used to generate an executable file management script based on the file management intent. The script execution module is used to execute the file management script to manage the files to be managed.

[0006] According to another aspect of this disclosure, an electronic device is provided, comprising: At least one processor; and A memory communicatively connected to the at least one processor; wherein, The memory stores instructions that can be executed by the at least one processor, which, when executed by the at least one processor, enables the at least one processor to perform the method described in the above embodiments.

[0007] According to another aspect of this disclosure, a non-transitory computer-readable storage medium is provided storing computer instructions, wherein the computer instructions are used to cause the computer to perform the method described in the above embodiments.

[0008] According to another aspect of this disclosure, a computer program product is provided, including a computer program that, when executed by a processor, implements the steps of the method described in the above embodiments.

[0009] The file management method, apparatus, electronic device, and storage medium based on a large model disclosed herein have the following beneficial effects: In this embodiment, file management description information is first obtained. A large-scale model is then used to identify the intent behind the file management description information, resulting in the file management intent. Based on this intent, an executable file management script is generated, and the management of the files is achieved by executing the script. In this embodiment, the large-scale model upgrades manual management or command-based interaction to cognitive, automated file management. Users do not need to remember precise syntax or file paths; they only need to describe their management needs in natural language to autonomously complete the entire file management loop, from intent parsing and task planning to secure execution. This significantly lowers the operational threshold for file management and allows users to easily complete complex file management tasks, thereby achieving automated file management on the terminal and significantly improving the user experience.

[0010] It should be understood that the description in this section is not intended to identify key or essential features of the embodiments of this disclosure, nor is it intended to limit the scope of this disclosure. Other features of this disclosure will become readily apparent from the following description. Attached Figure Description

[0011] The accompanying drawings are provided to better understand this solution and do not constitute a limitation of this disclosure. Wherein: Figure 1 A flowchart illustrating a file management method based on a large model provided in an embodiment of this disclosure; Figure 2 A flowchart illustrating a file management method based on a large model, provided in another embodiment of this disclosure; Figure 3 A flowchart illustrating a file management method based on a large model, provided in another embodiment of this disclosure; Figure 4A flowchart illustrating a file management method based on a large model, provided in another embodiment of this disclosure; Figure 5 A schematic diagram of the structure of a file management device based on a large model provided in an embodiment of this disclosure; Figure 6 This is a block diagram of an electronic device used to implement the file management method based on a large model according to the embodiments of this disclosure. Detailed Implementation

[0012] The exemplary embodiments of this disclosure are described below with reference to the accompanying drawings, including various details of the embodiments to aid understanding, and should be considered merely exemplary. Therefore, those skilled in the art will recognize that various changes and modifications can be made to the embodiments described herein without departing from the scope and spirit of this disclosure. Similarly, for clarity and brevity, descriptions of well-known functions and structures are omitted in the following description.

[0013] The embodiments disclosed herein relate to the fields of artificial intelligence technologies such as large models, deep learning, and intelligent recommendation.

[0014] Artificial Intelligence (AI) is a new technological science that studies, develops, and applies theories, methods, technologies, and application systems to simulate, extend, and expand human intelligence.

[0015] Large models, also known as Foundation Models, are models that extract knowledge from hundreds of millions of corpora or images, learn, and then produce large models with hundreds of millions of parameters.

[0016] Deep learning (DL) learns the inherent patterns and hierarchical representations of sample data. The information gained during this learning process greatly aids in interpreting data such as text, images, and sound. The ultimate goal of deep learning is to enable machines to possess analytical and learning capabilities similar to humans, allowing them to recognize data such as text, images, and sound.

[0017] Image processing is the technology of using computer algorithms to analyze, process, and manipulate images to meet visual, psychological, or other needs. Simply put, it's the process of transforming one image into another modified image, or extracting useful information from an image.

[0018] It should be noted that the acquisition, storage, use, and processing of data in this disclosed technical solution comply with the relevant provisions of national laws and regulations and do not violate public order and good morals.

[0019] The following description, with reference to the accompanying drawings, outlines a file management method, apparatus, electronic device, and storage medium based on a large model, according to embodiments of the present disclosure.

[0020] Figure 1 This is a flowchart illustrating a file management method based on a large model, as provided in an embodiment of this disclosure.

[0021] It should be noted that the application scenarios of the file management method based on the large model proposed in this disclosure can be determined according to actual needs. For example, it can be applied to application scenarios involving multiple related scenarios such as digital photo management, document archiving, financial organization, and multimedia entertainment library management, and this disclosure does not limit it in this regard.

[0022] In some embodiments, the execution subject of the file management method based on large models proposed in this disclosure is a hardware device with large model processing capabilities and / or the necessary software to drive the hardware device to work, such as a mobile terminal, personal computer, server, etc.

[0023] The file management method based on a large model according to the present disclosure can be executed by the file management device based on a large model according to the present disclosure. The file management device based on a large model according to the present disclosure can be configured in any electronic device to execute the file management method based on a large model according to the present disclosure.

[0024] like Figure 1 As shown, this file management method based on a large model includes: S101, Obtain description information for file management.

[0025] In some embodiments, descriptive information about file management input by the user via a text box can be obtained. In some embodiments, descriptive information about file management input by the user via a microphone can be obtained.

[0026] In some embodiments, descriptive information about file management input by the user based on a voice assistant can be obtained.

[0027] In some embodiments, the descriptive information for file management may include information about the files that the user is trying to manage, as well as the type of management to be performed on these files, i.e., the management actions that the user is taking to manage these files.

[0028] In some embodiments, file information can be used to locate or query files to be managed.

[0029] In some embodiments, file information may be identification information of a file that the user is attempting to manage. File information may include, but is not limited to, file time information, file location information, file name information, etc.

[0030] In some embodiments, management actions may be used to instruct the method or type of management of the files to be managed.

[0031] In some embodiments, management actions may include, but are not limited to, classifying, moving, or deleting the files to be managed.

[0032] For example, the description information for file management could be "to manage files or images of type xx under folder A, according to..." "Processing in a specific way." For example, generating subfolders from all document types in a folder based on file content and month. Another example is organizing screenshot files in directory B and deleting them. Yet another example is organizing duplicate files in directory C and generating a dynamic page for confirmation before deletion.

[0033] For example, the description information for file management could be "Organize photos taken during the ## time period and organize them into corresponding subdirectories according to a given method." For instance, organize photos taken in 2025 and generate corresponding subdirectories based on the shooting location or month.

[0034] For example, the description information for file management could be "Convert photos taken during the ## time period to yy resolution or zz format". For instance, convert photos taken in 2025 to 1920×1080 resolution or JPEG format.

[0035] S102, the intent of file management is obtained by performing intent recognition on the descriptive information of file management through a large model.

[0036] In some embodiments, file management intent includes files to be managed on the terminal and management actions corresponding to those files.

[0037] In some embodiments, descriptive information about file management is input into the large model. Since this descriptive information can include information about the files the user is trying to manage, and the type of management performed on these files—that is, the management actions the user is attempting to take—the large model can then use its language understanding capabilities to semantically analyze the descriptive information, extracting key elements such as the file to be managed on the terminal (i.e., the target file) and the management actions. Optionally, the file to be managed can be an exact filename or a vague description based on time, content, or topic; optionally, the management actions can be instructions such as "delete," "categorize," "move," or "organize."

[0038] For example, the description information for file management could be "generate sub-files for all documents in folder A according to month". The larger model can use semantic parsing to determine that the target file the user is trying to manage is "all documents in folder A", and the management action is "generate sub-files according to month".

[0039] For example, the description information for file management can be "organize the screenshot files in directory B and delete these screenshot files". The large model can use semantic parsing to determine that the target file that the user is trying to manage is "the screenshot files in directory B" and the management action is "delete".

[0040] S103, Generate an executable file management script based on the file management intent.

[0041] In some embodiments, after parsing the file to be managed and the corresponding management action in the file management intent, the large model can perform task planning and instruction generation based on thought chain technology to obtain an executable file management script. It is understood that the file management script can carry the file path, operation type, and necessary file management parameters (such as the new filename and target directory), thereby converting natural language instructions into a machine-executable file management script.

[0042] In some embodiments, executable file management scripts generated by large models can be securely executed by an external scheduler via structured JSON function call requests.

[0043] In some embodiments, the executable file management scripts generated by the large model can be Bash / PowerShell underlying scripts, suitable for complex file management.

[0044] S104, execute the file management script to manage the files to be managed.

[0045] In some embodiments, after the file management script is determined, the large model can be fed back to the front end of the terminal. Further, the script is parsed on the front end to obtain file management commands. Further, an automation tool is called to execute the file management commands and perform file management on the files to be managed.

[0046] In this embodiment, file management description information is first obtained. A large-scale model is then used to identify the intent behind the file management description information, resulting in the file management intent. Based on this intent, an executable file management script is generated, and the management of the files is achieved by executing the script. In this embodiment, the large-scale model upgrades manual management or command-based interaction to cognitive, automated file management. Users do not need to remember precise syntax or file paths; they only need to describe their management needs in natural language to autonomously complete the entire file management loop, from intent parsing and task planning to secure execution. This significantly lowers the operational threshold for file management and allows users to easily complete complex file management tasks, thereby achieving automated file management on the terminal and significantly improving the user experience.

[0047] Figure 2 This is a flowchart illustrating another file management method based on a large model provided in an embodiment of this application. Figure 2 As shown, this file management method based on a large model may include, but is not limited to, the following steps: S201, Obtain description information for file management.

[0048] For details on the specific implementation of step S201, please refer to the implementation method described in any embodiment of this application, which will not be repeated here.

[0049] S202, through large model, perform intent recognition on the descriptive information of file management to obtain the file management intent.

[0050] In some embodiments, file management intent includes files to be managed on the terminal and management actions corresponding to those files.

[0051] In some embodiments, semantic parsing of the file management description information is performed using a large model to identify the file indication information and action information carried in the description information. Based on the file indication information, the file to be managed on the terminal is determined, and based on the action information, the management action for the file to be managed is determined.

[0052] In this embodiment of the disclosure, the requirements can be described using everyday language, and the large model can autonomously complete the conversion from fuzzy semantics to precise operations, which can reduce the technical threshold of file management. Further intent recognition is the starting point for building automated file management, which can provide accurate input for subsequent task planning, script generation and secure execution, and ultimately realize the upgrade from manual file management to intelligent file management.

[0053] S203, decompose the file management intent into tasks to obtain at least one subtask, and determine the dependencies between the subtasks.

[0054] S204. Arrange subtasks based on dependencies to obtain a task sequence.

[0055] In some embodiments, the large model can be based on a thought chain to break down the file management intent into multiple atomic subtasks, which is the set of subtasks corresponding to the file management intent. For example, the file management intent of "organizing the files in folder A" can be broken down into a series of atomic subtasks such as searching, categorizing, and moving.

[0056] For example, the subtasks can include, but are not limited to, finding the corresponding file or image, moving the corresponding file or image, deleting the corresponding file or image, and performing operations on the file or image, such as format conversion, file compression, and size adjustment.

[0057] Furthermore, leveraging knowledge from a pre-trained knowledge base, the causal and sequential relationships between subtasks are derived. The large model then performs topological sorting of dependencies, orchestrating multiple subtasks into a linearly executed task sequence. For example, before moving a file, it must be ensured that the file exists and the target path has been created. Understandably, all preconditions for any subtask in the task sequence must be satisfied before the subtask is triggered. The task sequence can include independent branches that can be executed in parallel; the task sequence is a structured task blueprint containing sequential control flow and exception rollback paths.

[0058] In this embodiment of the disclosure, users can break down file management tasks through intelligent interaction with the large model, and through the logical dependency analysis mechanism, they can fundamentally avoid common problems in manual management such as accidental deletion and path errors, thus ensuring the accurate completion of file management.

[0059] S205 generates an executable file management script based on the task sequence.

[0060] In some embodiments, after obtaining the task sequence, a script can be written to the task sequence to obtain an executable file management script. Optionally, the large model can dynamically match the most suitable function or script implementation from a predefined toolset to write a script for the task sequence to obtain an executable file management script.

[0061] In some embodiments, the management complexity of the files to be managed is determined based on the task sequence of the files to be managed, and a file management script is generated based on the management complexity and the task sequence.

[0062] In some embodiments, based on management complexity, a target script implementation is selected from candidate script implementations, and a script is written based on the target script implementation to obtain a file management script. Optionally, for simple task sequences, such as moving files, lightweight inline instructions, such as JSON function calls, can be used to reduce overhead and improve response speed; while for complex task sequences involving multiple dependencies, such as batch classification and archiving, structured native scripts can be generated to ensure the accuracy of file management.

[0063] In some embodiments, the file type of the file to be managed can be determined, the management complexity of the file to be managed can be determined based on the file type, and further, a file management script can be generated based on the management complexity and the task sequence.

[0064] In some embodiments, the large model has multiple candidate script implementations, each with pre-defined script generation consumption information. After obtaining the management complexity, the script generation consumption information can be predicted based on the management complexity. Furthermore, the predicted script generation consumption information is matched with the pre-defined script generation consumption information of each candidate script implementation to obtain a target script implementation suitable for the management complexity. Based on the target script implementation, the task sequence is scripted to obtain the file management script.

[0065] In this embodiment of the disclosure, different script writing methods are dynamically adapted according to the management complexity during script writing, which can achieve the optimal balance between resource efficiency and execution reliability. This adaptive strategy avoids the redundant overhead of generating heavy code for trivial tasks, while ensuring logical expressiveness and execution robustness in complex task scenarios.

[0066] S206, execute the file management script to manage the files to be managed.

[0067] For details on the specific implementation of step S206, please refer to the implementation method described in any embodiment of this application, which will not be repeated here.

[0068] In this embodiment, a large model can upgrade manual management or command-based interaction to cognitive, automated file management. Users do not need to remember precise syntax or file paths; they only need to describe their management needs in natural language to autonomously complete the entire file management loop, from intent parsing and task planning to secure execution. This not only significantly lowers the operational threshold for file management but also enables users to easily complete complex file management tasks, thereby achieving automated management of files on the terminal and improving the user experience.

[0069] Figure 3 This is a flowchart illustrating another file management method based on a large model provided in an embodiment of this application. Figure 3 As shown, this file management method based on a large model may include, but is not limited to, the following steps: S301, Obtain description information for file management.

[0070] For details on the specific implementation of step S301, please refer to the implementation method described in any embodiment of this application, which will not be repeated here.

[0071] S302, through large model, perform intent recognition on the descriptive information of file management to obtain the file management intent.

[0072] For details on the specific implementation of step S302, please refer to the implementation method described in any embodiment of this application, which will not be repeated here.

[0073] S303, decompose the file management intent into tasks to obtain at least one subtask, and determine the dependencies between the subtasks.

[0074] For details on the specific implementation of step S303, please refer to the implementation method described in any embodiment of this application, which will not be repeated here.

[0075] S304, Arrange subtasks based on dependencies to obtain a task sequence.

[0076] For details on the specific implementation of step S304, please refer to the implementation method described in any embodiment of this application, which will not be repeated here.

[0077] S305 generates an executable file management script based on the task sequence.

[0078] For details on the specific implementation of step S305, please refer to the implementation method described in any embodiment of this application, which will not be repeated here.

[0079] S306 simulates a test environment for file management scripts and tests the file management scripts in that environment.

[0080] In some embodiments, after the large model has written the file management script, it can simulate a test environment for the script based on its type. In this test environment, the script is tested to determine if its logic is correct, preventing malfunctions during actual execution. Furthermore, if the file management script fails the test, feedback can be provided to the large model, which can then optimize the script based on the failure information to obtain a final, testable file management script.

[0081] In some embodiments, static syntax validation can be performed on the file management script before testing it in a test environment to catch potential logical errors in advance, such as paths not existing.

[0082] In this embodiment of the disclosure, by testing the file management script, potential logical errors can be identified in advance to avoid data corruption problems caused during actual execution. Moreover, the testing process can verify the script execution results to ensure that they match the user's original file management intentions. Figure 1 Furthermore, the script generation strategy for large models can be iteratively optimized based on test feedback results to improve the task processing performance of large models.

[0083] S307, in response to the successful test of the file management script, the file management script is executed to manage the files to be managed.

[0084] For details on the specific implementation of step S307, please refer to the implementation method described in any embodiment of this application, which will not be repeated here.

[0085] In some embodiments, a confirmation page for file management can be generated based on the file management intent. In response to the confirmation control on the confirmation page being triggered, a file management script is executed to manage the files to be managed. This dynamic page includes the files to be managed and prompts on how to manage them, as well as confirmation and rejection components. After the user clicks the confirmation component on the dynamic page, the file management script can be executed to manage the files to be managed.

[0086] In this embodiment of the disclosure, a confirmation page can be generated before managing the file to further confirm the management with the user, which can make file management more accurate and avoid misoperation.

[0087] In this embodiment, a large model can upgrade manual management or command-based interaction to cognitive, automated file management. Users do not need to remember precise syntax or file paths; they only need to describe their management needs in natural language to autonomously complete the entire file management loop, from intent parsing and task planning to secure execution. This not only significantly lowers the operational threshold for file management but also enables users to easily complete complex file management tasks, thereby achieving automated management of files on the terminal and improving the user experience.

[0088] Figure 4 This is a flowchart illustrating another file management method based on a large model provided in an embodiment of this application. Figure 4 As shown, this file management method based on a large model may include, but is not limited to, the following steps: S401, initiates the large model for file management.

[0089] In some embodiments, users can install a large model for file management on the terminal. Optionally, the terminal may include, but is not limited to, mobile terminals, personal computers, in-vehicle terminals, smart home devices, etc.

[0090] S402, Input file management description information into the large model.

[0091] S403 uses a large model to identify the intent of the file management description information to obtain the file management intent.

[0092] In some embodiments, file management intent includes files to be managed on the terminal and management actions corresponding to those files.

[0093] S404 generates an executable file management script based on the file management intent.

[0094] S405, the terminal parses the file management script to obtain file management commands.

[0095] S406, file management is performed on files to be managed based on file management commands.

[0096] In some embodiments, during the file management process of the files to be managed, the dynamic effects of file management are displayed on the front-end interface of the terminal.

[0097] In some embodiments, during the file management process of the file to be managed, the file management process of the file to be managed is monitored, and the management process information of the file to be managed is displayed on the front-end interface.

[0098] In this embodiment of the disclosure, the information displayed on the front-end interface allows users to intuitively understand the file management process and progress, increasing interactivity.

[0099] S407: Determine the folder change information after file management, and update the original first file directory according to the folder change information to obtain the second file directory.

[0100] In some embodiments, after file management is completed, folder changes are often caused. The folder change information can be recorded. Furthermore, the original first file directory can be updated based on the folder change information to obtain a second file directory. This ensures that after files are moved, renamed, or deleted, the directory tree structure remains consistent with the actual storage state, avoiding subsequent read / write operations pointing to the wrong path or triggering file not found exceptions due to outdated cache. Moreover, by keeping the directory tree structure consistent with the actual storage state, the efficiency of subsequent file operations can be guaranteed.

[0101] For example, the description of file management is "Organize duplicate files in my directory A, generate a dynamic page for me to confirm deletion." This description is input into a large-scale model, which performs semantic parsing to determine the user's file management intent, such as deleting duplicate files and generating a confirmation page. Further, a file management script is generated based on this intent. Before executing the script, a dynamic page can be generated, including information about the files to be managed and how to manage them, as well as confirmation and rejection components. After the user clicks the confirmation component on the dynamic page, the file management script is executed to manage the files.

[0102] To implement the above embodiments, this disclosure also proposes a file management device based on a large model.

[0103] Figure 5 This is a schematic diagram of the structure of a file management device based on a large model provided in an embodiment of this disclosure. Figure 5 As shown, the file management device 500 includes: an acquisition module 501, an intent recognition module 502, a script generation module 503, and a script execution module 504.

[0104] The acquisition module 501 is used to acquire description information for file management; The intent recognition module 502 is used to perform intent recognition on the description information of the file management through a large model to obtain the file management intent, wherein the file management intent includes the file to be managed on the terminal and the management action corresponding to the file to be managed; The script generation module 503 is used to generate an executable file management script based on the file management intent. The script execution module 504 is used to execute the file management script to manage the file to be managed.

[0105] In some embodiments, the script generation module 503 is further configured to: The file management intent is broken down into tasks to obtain at least one subtask, and the dependencies between the subtasks are determined. The subtasks are arranged based on the dependencies to obtain a task sequence; The executable file management script is generated based on the task sequence.

[0106] In some embodiments, the script generation module 503 is further configured to: The management complexity of the files to be managed is determined based on the task sequence of the files to be managed. The file management script is generated based on the management complexity and the task sequence.

[0107] In some embodiments, the script generation module 503 is further configured to: Based on the management complexity, the target script implementation method is selected from the candidate script implementation methods; Based on the target script implementation method, the task sequence is scripted to obtain the file management script.

[0108] In some embodiments, the script execution module 504 is further configured to: Simulate the test environment for the file management script and test the file management script in the test environment; Upon successful testing of the file management script, the file management script is executed to manage the file to be managed.

[0109] In some embodiments, the intent recognition module 502 is further configured to: The descriptive information of the file management is semantically parsed using a large model to identify the file indication information and action information carried by the descriptive information; Based on the file instruction information, determine the file to be managed on the terminal; Based on the action information, determine the management action for the file to be managed.

[0110] In some embodiments, the script execution module 504 is further configured to: Based on the stated file management intent, a confirmation page for file management is generated; In response to the confirmation control being triggered on the confirmation page, the file management script is executed to manage the file to be managed.

[0111] In some embodiments, the script execution module 504 is further configured to: After executing the file management script to manage the files to be managed, determine the folder change information after file management; The original first file directory is updated based on the folder change information to obtain the second file directory.

[0112] In some embodiments, the script execution module 504 is further configured to display dynamic effects of file management on the front-end interface during the file management process of the file to be managed.

[0113] In some embodiments, the script execution module 504 is further configured to monitor the file management process of the file to be managed and display the management process information of the file to be managed on the front-end interface.

[0114] In this embodiment, file management description information is first obtained. A large-scale model is then used to identify the intent behind the file management description information, resulting in the file management intent. Based on this intent, an executable file management script is generated, and the management of the files is achieved by executing the script. In this embodiment, the large-scale model upgrades manual management or command-based interaction to cognitive, automated file management. Users do not need to remember precise syntax or file paths; they only need to describe their management needs in natural language to autonomously complete the entire file management loop, from intent parsing and task planning to secure execution. This significantly lowers the operational threshold for file management and allows users to easily complete complex file management tasks, thereby achieving automated file management on the terminal and significantly improving the user experience.

[0115] According to embodiments of this disclosure, this disclosure also provides an electronic device, a readable storage medium, and a computer program product.

[0116] Figure 6 A schematic block diagram of an example electronic device 600 that can be used to implement embodiments of the present disclosure is shown. The electronic device is intended to represent various forms of digital computers, such as laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The electronic device may also represent various forms of mobile devices, such as personal digital processors, cellular phones, smartphones, wearable devices, and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely illustrative and are not intended to limit the implementation of the present disclosure described and / or claimed herein.

[0117] like Figure 6 As shown, device 600 includes a computing unit 601, which can perform various appropriate actions and processes based on a computer program stored in ROM (Read-Only Memory) 602 or a computer program loaded from storage unit 606 into RAM (Random Access Memory) 603. RAM 603 may also store various programs and data required for the operation of device 600. The computing unit 601, ROM 602, and RAM 603 are interconnected via bus 604. I / O (Input / Output) interface 605 is also connected to bus 604.

[0118] Multiple components in device 600 are connected to I / O interface 605, including: input unit 606, such as keyboard, mouse, etc.; output unit 606, such as various types of displays, speakers, etc.; storage unit 606, such as disk, optical disk, etc.; and communication unit 609, such as network card, modem, wireless transceiver, etc. Communication unit 609 allows device 600 to exchange information / data with other devices through computer networks such as the Internet and / or various telecommunications networks.

[0119] The computing unit 601 can be a variety of general-purpose and / or special-purpose processing components with processing and computing capabilities. Some examples of the computing unit 601 include, but are not limited to, CPUs (Central Processing Units), GPUs (Graphics Processing Units), various special-purpose AI (Artificial Intelligence) computing chips, various computing units running machine learning model algorithms, DSPs (Digital Signal Processors), and any suitable processor, controller, microcontroller, etc. The computing unit 601 performs the various methods and processes described above, such as file management methods. For example, in some embodiments, the file management method may be implemented as a computer software program tangibly contained in a machine-readable medium, such as storage unit 606. In some embodiments, part or all of the computer program may be loaded and / or installed on device 600 via ROM 602 and / or communication unit 609. When the computer program is loaded into RAM 603 and executed by the computing unit 601, one or more steps of the file management method described above may be performed. Alternatively, in other embodiments, the computing unit 601 may be configured to execute a file management method by any other suitable means (e.g., by means of firmware).

[0120] Various implementations of the systems and techniques described above herein can be implemented in digital electronic circuit systems, integrated circuit systems, FPGAs (Field Programmable Gate Arrays), ASICs (Application-Specific Integrated Circuits), ASSPs (Application-Specific Standard Products), SOCs (System-on-Chips), CPLDs (Complex Programmable Logic Devices), computer hardware, firmware, software, and / or combinations thereof. These various implementations may include implementations in one or more computer programs that can be executed and / or interpreted on a programmable system including at least one programmable processor, which may be a dedicated or general-purpose programmable processor, capable of receiving data and instructions from a storage system, at least one input device, and at least one output device, and transmitting data and instructions to the storage system, the at least one input device, and the at least one output device.

[0121] The program code used to implement the methods of this disclosure may be written in any combination of one or more programming languages. This program code may be provided to a processor or controller of a general-purpose computer, special-purpose computer, or other programmable data processing apparatus, such that when executed by the processor or controller, the program code causes the functions / operations specified in the flowcharts and / or block diagrams to be implemented. The program code may be executed entirely on a machine, partially on a machine, as a standalone software package partially on a machine and partially on a remote machine, or entirely on a remote machine or server.

[0122] In the context of this disclosure, a machine-readable medium can be a tangible medium that may contain or store a program for use by or in conjunction with an instruction execution system, apparatus, or device. A machine-readable medium can be a machine-readable signal medium or a machine-readable storage medium. A machine-readable medium can be, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, apparatus, or devices, or any suitable combination of the foregoing. More specific examples of machine-readable storage media include electrical connections based on one or more wires, portable computer disks, hard disks, RAM, ROM, EPROM (Electrically Programmable Read-Only Memory) or flash memory, optical fiber, CD-ROM (Compact Disc Read-Only Memory), optical storage devices, magnetic storage devices, or any suitable combination of the foregoing.

[0123] To provide interaction with a user, the systems and techniques described herein can be implemented on a computer having: a display device for displaying information to the user (e.g., a CRT (Cathode-Ray Tube) or LCD (Liquid Crystal Display) monitor); and a keyboard and pointing device (e.g., a mouse or trackball) through which the user provides input to the computer. Other types of devices can also be used to provide interaction with the user; for example, feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user can be received in any form (including sound input, voice input, or tactile input).

[0124] The systems and technologies described herein can be implemented in computing systems that include backend components (e.g., as data servers), or middleware components (e.g., application servers), or frontend components (e.g., user computers with graphical user interfaces or web browsers through which users can interact with implementations of the systems and technologies described herein), or any combination of such backend, middleware, or frontend components. The components of the system can be interconnected via digital data communication of any form or medium (e.g., communication networks). Examples of communication networks include LANs (Local Area Networks), WANs (Wide Area Networks), the Internet, and blockchain networks.

[0125] Computer systems can include clients and servers. Clients and servers are generally geographically separated and typically interact via communication networks. The client-server relationship is established by computer programs running on the respective computers and having a client-server relationship with each other. A server can be a cloud server, also known as a cloud computing server or cloud host, a hosting product within the cloud computing service ecosystem, addressing the shortcomings of traditional physical hosts and VPS (Virtual Private Server) services, such as high management difficulty and weak business scalability. Servers can also be servers for distributed systems or servers incorporating blockchain technology.

[0126] According to embodiments of this disclosure, this disclosure also provides a computer program product that, when an instruction processor in the computer program product is executed, performs the file management method based on a large model proposed in the above embodiments of this disclosure.

[0127] It should be understood that the various forms of processes shown above can be used to rearrange, add, or delete steps. For example, the steps described in this disclosure can be executed in parallel, sequentially, or in different orders, as long as the desired result of the technical solution disclosed in this disclosure can be achieved, and this is not limited herein.

[0128] The specific embodiments described above do not constitute a limitation on the scope of protection of this disclosure. Those skilled in the art should understand that various modifications, combinations, sub-combinations, and substitutions can be made according to design requirements and other factors. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of this disclosure should be included within the scope of protection of this disclosure.

Claims

1. A file management method based on a large model, wherein, The method includes: Retrieve description information for file management; The file management intent is obtained by performing intent recognition on the description information of the file management through a large model. The file management intent includes the file to be managed on the terminal and the management action corresponding to the file to be managed. Based on the stated file management intent, generate an executable file management script; The file management script is executed to manage the files to be managed.

2. The method according to claim 1, wherein, The step of generating an executable file management script based on the file management intent includes: The file management intent is broken down into tasks to obtain at least one subtask, and the dependencies between the subtasks are determined. The subtasks are arranged based on the dependencies to obtain a task sequence; The executable file management script is generated based on the task sequence.

3. The method according to claim 2, wherein, The step of generating the executable file management script based on the task sequence includes: The management complexity of the files to be managed is determined based on the task sequence of the files to be managed. The file management script is generated based on the management complexity and the task sequence.

4. The method according to claim 3, wherein, The step of generating the file management script based on the management complexity and the task sequence includes: Based on the management complexity, the target script implementation method is selected from the candidate script implementation methods; Based on the target script implementation method, the task sequence is scripted to obtain the file management script.

5. The method according to any one of claims 1-4, wherein, The execution of the file management script to manage the file to be managed includes: Simulate the test environment for the file management script and test the file management script in the test environment; Upon successful testing of the file management script, the file management script is executed to manage the file to be managed.

6. The method according to any one of claims 1-4, wherein, The process of performing intent recognition on the descriptive information of the file management to obtain the file management intent includes: The descriptive information of the file management is semantically parsed using a large model to identify the file indication information and action information carried by the descriptive information; Based on the file instruction information, determine the file to be managed on the terminal; Based on the action information, determine the management action for the file to be managed.

7. The method according to any one of claims 1-4, wherein, The execution of the file management script to manage the file to be managed includes: Based on the stated file management intent, a confirmation page for file management is generated; In response to the confirmation control being triggered on the confirmation page, the file management script is executed to manage the file to be managed.

8. The method according to any one of claims 1-4, wherein, After executing the file management script to manage the file to be managed, the process further includes: Determine folder changes after file management; The original first file directory is updated based on the folder change information to obtain the second file directory.

9. The method according to any one of claims 1-4, wherein, The method also includes at least one of the following operations: During the file management process of the files to be managed, the dynamic effects of file management are displayed on the front-end interface; The file management process of the file to be managed is monitored, and the management process information of the file to be managed is displayed on the front-end interface.

10. A file management device based on a large model, comprising: The acquisition module is used to obtain descriptive information about file management. The intent recognition module is used to perform intent recognition on the description information of the file management through a large model to obtain the file management intent, wherein the file management intent includes the file to be managed on the terminal and the management action of the file to be managed; The script generation module is used to generate an executable file management script based on the file to be managed and the management actions of the file to be managed. The script execution module is used to execute the file management script to manage the files to be managed.

11. An electronic device, comprising: At least one processor; as well as A memory communicatively connected to the at least one processor; wherein, The memory stores instructions that can be executed by the at least one processor to enable the at least one processor to perform the method of any one of claims 1-9.

12. A non-transitory computer-readable storage medium storing computer instructions, wherein, The computer instructions are used to cause the computer to perform the method according to any one of claims 1-9.

13. A computer program product comprising a computer program that, when executed by a processor, implements the steps of the method according to any one of claims 1-9.

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