File processing methods, electronic device, storage medium and computer program product

Abstract

The present disclosure relates to the fields of large model technology and text processing. Disclosed are file processing methods, an electronic device, a storage medium and a computer program product. A method comprises: in response to an input instruction acting on an operation interface, displaying on the operation interface a file to be processed, said file containing text to be processed of at least one modality; and, in response to a processing instruction acting on the operation interface, displaying a processing result on the operation interface, the processing result being used for representing that said file has been successfully named and stored on the basis of a target processing rule, the target processing rule being a processing rule corresponding to a target type of said file, the target type being determined by comparing said text with preset text contained in a plurality of preset files, and the types of different preset files being different. The present disclosure solves the technical problem in the prior art of relatively low file processing accuracy.

Description

File processing method, electronic device, storage medium and computer program product TECHNICAL FIELD

[0001] The present disclosure relates to the technical field of large model technology and text processing, in particular to a file processing method, an electronic device, a storage medium and a computer program product. BACKGROUND

[0002] At present, efficient and accurate processing of text materials is the key to improving text processing speed. Traditional text processing can include manual processing, rule-based automation and traditional machine learning methods, but each has its limitations. Manual processing is costly, inefficient and prone to error; rule-based methods have poor flexibility and are difficult to adapt to the diversity of text types and changing needs, resulting in low accuracy in text processing. SUMMARY

[0003] The embodiments of the present disclosure provide a file processing method, an electronic device, a storage medium and a computer program product to at least solve the technical problem of low file processing accuracy in related technologies.

[0004] According to an aspect of an embodiment of the present disclosure, a file processing method is provided, including: displaying a to-be-processed file on an operation interface in response to an input instruction acting on the operation interface, wherein the to-be-processed file contains to-be-processed text of at least one modality; displaying a processing result on the operation interface in response to a processing instruction acting on the operation interface, wherein the processing result is used to represent that the to-be-processed file is successfully named and stored based on a target processing rule, the target processing rule is a processing rule corresponding to a target type of the to-be-processed file, the target type is determined by comparing the to-be-processed text with preset text contained in a plurality of preset files, and the types of different preset files are different.

[0005] According to another aspect of an embodiment of the present disclosure, a file processing method is provided, including: obtaining a to-be-processed file; analyzing to-be-processed text to obtain to-be-processed text contained in the to-be-processed file; comparing the to-be-processed text with preset text contained in a plurality of preset files to determine a target type of the to-be-processed file, wherein the types of different preset files are different; naming and storing the to-be-processed file based on a processing rule corresponding to the target type.

[0006] According to another aspect of the embodiments of the present disclosure, a file processing method is provided, including: receiving a judicial dossier file sent by a client; parsing the judicial dossier file to obtain a judicial dossier text contained in the judicial dossier file; comparing the judicial dossier text with preset dossier texts contained in a plurality of preset dossier files to determine a target type of the judicial dossier file, wherein the types of different preset dossier files are different; naming and storing the judicial dossier file based on a processing rule corresponding to the target type to obtain a processing result; and sending the processing result to the client.

[0007] According to another aspect of the embodiments of the present disclosure, a file processing method is provided, including: obtaining a to-be-processed file by calling a first interface, wherein the first interface includes a first parameter, and a parameter value of the first parameter includes the to-be-processed file; parsing the to-be-processed text to obtain a to-be-processed text contained in the to-be-processed file; comparing the to-be-processed text with preset texts contained in a plurality of preset files to determine a target type of the to-be-processed file, wherein the types of different preset files are different; naming and storing the to-be-processed file based on a processing rule corresponding to the target type to obtain a processing result; and outputting the processing result by calling a second interface, wherein the second interface includes a second parameter, and a parameter value of the second parameter includes the processing result.

[0008] According to another aspect of the embodiments of the present disclosure, an electronic device is provided, including: a memory storing an executable program; and a processor configured to run the program, wherein the program runs to execute the method in any one of the above embodiments.

[0009] According to another aspect of the embodiments of the present disclosure, a computer readable storage medium is provided, including a stored executable program, wherein the executable program runs to control a device where the computer readable storage medium is located to execute the method in any one of the above embodiments.

[0010] According to another aspect of the embodiments of the present disclosure, a computer program product is provided, including a computer program, which, when executed by a processor, executes the method in any one of the above embodiments.

[0011] According to another aspect of the embodiments of the present disclosure, a computer terminal is also provided, including: a memory storing an executable program; and a processor configured to run the program, wherein the program runs to execute the method in the embodiments of the present disclosure.

[0012] According to another aspect of the embodiments of the present disclosure, a computer readable storage medium is also provided, including a stored executable program, wherein the executable program runs to control a device where the computer readable storage medium is located to execute the method in the embodiments of the present disclosure.

[0013] According to a further aspect of the embodiments of the present disclosure, a computer program product is also provided, including a computer program which, when executed by a processor, implements the method in any of the embodiments of the present disclosure.

[0014] According to a further aspect of the embodiments of the present disclosure, a computer program product is also provided, including a non-volatile computer readable storage medium storing a computer program which, when executed by a processor, implements the method in any of the embodiments of the present disclosure.

[0015] According to a further aspect of the embodiments of the present disclosure, a computer program is also provided which, when executed by a processor, implements the method in any of the embodiments of the present disclosure.

[0016] In the embodiments of the present disclosure, in response to an input instruction acting on the operation interface, a to-be-processed file is displayed on the operation interface, wherein the to-be-processed file contains to-be-processed text of at least one modality; in response to a processing instruction acting on the operation interface, a processing result is displayed on the operation interface, wherein the processing result is used to represent that the to-be-processed file is successfully named and stored based on a target processing rule, the target processing rule is a processing rule corresponding to a target type of the to-be-processed file, the target type is determined by comparing the to-be-processed text with preset texts contained in a plurality of preset files, the types of different preset files are different, and the purpose of improving the file processing accuracy is achieved. It is easy to note that for the to-be-processed text containing at least one modality, the target type of the to-be-processed text can be determined by comparing the to-be-processed text with the preset texts contained in the plurality of preset files according to the type of the preset text similar to the to-be-processed text, so that the file is automatically named and stored appropriately based on the processing rule corresponding to the target type of the to-be-processed file, the manual intervention is reduced, the processing speed and accuracy are improved, and thus the technical problem of low file processing accuracy in the related art is solved.

[0017] It is easy to note that the general description and the following detailed description are only for exemplifying and explaining the present disclosure, and do not constitute a limitation on the present disclosure. BRIEF DESCRIPTION OF DRAWINGS

[0018] The drawings described herein are used to provide further understanding of the present disclosure, form a part of the present disclosure, and the illustrative embodiments of the present disclosure and the description thereof are used to explain the present disclosure, and do not constitute an improper limitation on the present disclosure. In the drawings:

[0019] FIG. 1 is a schematic diagram of an application scenario of a file processing method according to an embodiment of the present disclosure;

[0020] FIG. 2 is a flowchart of a file processing method according to an embodiment of the present disclosure;

[0021] FIG. 3 is a schematic diagram of a cataloging function of sending an address confirmation letter according to an embodiment of the present disclosure;

[0022] FIG. 4 is a schematic diagram of a classification and storage manner of sending an address confirmation letter in a court document management system according to an embodiment of the present disclosure;

[0023] FIG. 5 is a flowchart of a document processing method according to an embodiment of the present disclosure;

[0024] FIG. 6 is a flowchart of a document processing method according to an embodiment of the present disclosure;

[0025] FIG. 7 is a flowchart of a document processing method according to an embodiment of the present disclosure;

[0026] FIG. 8 is a schematic diagram of a document processing apparatus according to an embodiment of the present disclosure;

[0027] FIG. 9 is a schematic diagram of a document processing apparatus according to an embodiment of the present disclosure;

[0028] FIG. 10 is a schematic diagram of a document processing apparatus according to an embodiment of the present disclosure;

[0029] FIG. 11 is a schematic diagram of a document processing apparatus according to an embodiment of the present disclosure;

[0030] FIG. 12 is a structural block diagram of an electronic device according to an embodiment of the present disclosure. DETAILED DESCRIPTION

[0031] In order to make the person skilled in the art better understand the present disclosure scheme, the technical solutions in the embodiments of the present disclosure will be clearly and completely described below in conjunction with the drawings in the embodiments of the present disclosure. Obviously, the described embodiments are only a part of the embodiments of the present disclosure, not all the embodiments. Based on the embodiments in the present disclosure, all other embodiments obtained by those skilled in the art without creative labor should be within the scope of protection of the present disclosure.

[0032] It should be noted that the terms "first", "second" and the like in the specification and claims of the present disclosure and the above-described drawings are used to distinguish similar objects, and do not necessarily indicate a specific order or a chronological sequence. It should be understood that the data thus used can be interchanged under appropriate circumstances, so that the embodiments of the present disclosure described herein can be implemented in an order other than those illustrated or described herein. In addition, the terms "include" and "have" and any variations thereof are intended to cover non-exclusive inclusion, for example, a process, method, system, product or device including a series of steps or units does not have to be limited to those steps or units clearly listed, but can include other steps or units not clearly listed or inherent to these processes, methods, products or devices.

[0033] The technical solutions provided by the present disclosure are mainly implemented by using large model technology. Here, the large model refers to a deep learning model with a large number of model parameters, which can generally include hundreds of millions, billions, tens of billions, hundreds of billions or even more than ten trillion model parameters. The large model can also be referred to as a foundation model. A large-scale unlabeled corpus is used to pre-train the large model, and a pre-training model with more than one hundred million parameters is output. Such a model can adapt to a wide range of downstream tasks, and the model has good generalization ability. For example, a large-scale language model (LLM) and a multi-modal pre-training model.

[0034] It should be noted that in actual application, the pre-training model can be fine-tuned by a small number of samples, so that the large model can be applied to different tasks. For example, the large model can be widely applied to the fields of natural language processing (NLP), computer vision, speech processing, etc. Specifically, it can be applied to computer vision field tasks such as visual question answering (VQA), image description (IC), image generation, etc. It can also be widely applied to natural language processing field tasks such as text-based sentiment classification, text summary generation, machine translation, etc. Therefore, the main application scenarios of the large model include but are not limited to digital assistants, intelligent robots, search, online education, office software, e-commerce, intelligent design, etc. In the embodiments of the present disclosure, the data processing by the text processing model in the text processing scenario is taken as an example for explanation and description.

[0035] First, some nouns or terms that appear in the description of the embodiments of the present disclosure are applicable to the following explanations:

[0036] Retrieval-Augmented Generation (RAG) technology: RAG technology is a technology that combines retrieval and generation. It not only uses generative models to answer questions, but also retrieves relevant information from external databases to enhance the accuracy and richness of the generated content. This means that when generating an answer, it not only relies on its pre-training knowledge, but also retrieves relevant resources in real time to ensure that the answer is more comprehensive and reliable.

[0037] Large model: A large model refers to an artificial intelligence model that is trained with a large amount of data and computing resources, and has high complexity and wide adaptability. For example, the generative pre-trained model (Generative Pre-trained Transformer 4, GPT-4 for short) is a typical large model that can understand and generate natural language text and perform well in various tasks. Large models usually have parameters in the order of hundreds of millions or even larger, and can handle complex semantic relationships and diverse task requirements.

[0038] Few-shot learning: Few-shot learning is a machine learning method that can perform well even with only a small amount of training samples. Unlike traditional methods that require a large amount of labeled data, few-shot learning quickly adapts and learns new tasks with only a small amount of new samples by utilizing existing knowledge and experience. This technology is particularly suitable for scenarios where data is scarce and it is difficult to obtain labeled data, effectively reducing the cost of data labeling and acquisition.

[0039] Currently, the automatic arrangement (classification, extraction, naming) of judicial files is an important requirement. With the increasing number of files that need to be processed every day, manual arrangement has become unable to meet the demand. Therefore, large model technology needs to be used to automatically arrange these judicial files.

[0040] Large model technology can solve the pain points of automatic arrangement of judicial files. Large models are trained on a large amount of data, enabling them to handle various types and forms of materials, whether they are procedural legal documents, transcripts, or evidence materials. Through few-shot learning and fine-tuning, the training sample data is reduced to a lower level, significantly reducing the cost of additional data preparation and preprocessing, and reducing the dependence on a large amount of labeled data. Whether it is a new material type, a change in coding rules, or a change in processing procedures, by configuring a case library and a coding rule library to meet new task requirements, the workload of redeveloping and training in traditional methods is reduced.

[0041] According to the embodiments of the present disclosure, a file processing method is provided. It should be noted that the steps shown in the flowchart of the accompanying drawings can be executed in a computer system such as a set of computer executable instructions, and although the logical order is shown in the flowchart, in some cases, the steps shown or described can be executed in an order different from that shown here.

[0042] In consideration of the large amount of model parameters of the large model and the limited computing resources of the mobile terminal, the file processing method provided in the embodiments of the present disclosure can be applied to the application scenario shown in FIG. 1, but is not limited thereto. FIG. 1 is a schematic diagram of an application scenario of a file processing method according to an embodiment of the present disclosure. In the application scenario shown in FIG. 1, the large model is deployed in a server 10, and the server 10 can be connected to one or more client devices 20 through a local area network connection, a wide area network connection, an Internet connection, or other types of data networks. The client device 20 can include, but is not limited to, a smartphone, a tablet computer, a notebook computer, a palm computer, a personal computer, a smart home device, a vehicle-mounted device, and the like. The client device 20 can interact with the user through a graphical user interface to call the large model and implement the method provided in the embodiments of the present disclosure.

[0043] In the embodiments of the present disclosure, the system composed of the client device and the server can perform the following steps: the client device performs the step of uploading the to-be-processed file. The server performs the steps of displaying the to-be-processed file on the operation interface in response to an input instruction acting on the operation interface, wherein the to-be-processed file contains to-be-processed text of at least one modality; and displaying the processing result on the operation interface in response to a processing instruction acting on the operation interface, wherein the processing result is used to represent the result of naming and storing the to-be-processed file based on the processing rule corresponding to the target type of the to-be-processed file, and the target type is determined by comparing the to-be-processed text with the preset text contained in the plurality of preset files. It should be noted that, in the case that the running resources of the client device can meet the deployment and running conditions of the large model, the embodiments of the present disclosure can be performed in the client device.

[0044] In the above running environment, the present disclosure provides a file processing method as shown in FIG. 2. FIG. 2 is a flowchart of a file processing method according to an embodiment of the present disclosure. As shown in FIG. 2, the method can include the following steps:

[0045] Step S202, displaying the to-be-processed file on the operation interface in response to an input instruction acting on the operation interface.

[0046] The to-be-processed file contains to-be-processed text of at least one modality.

[0047] The operation interface described above is a front-end interface for user interaction with the system. The user can operate the related controls on the operation interface, for example, after inputting the to-be-processed file on the operation interface, an input instruction can be generated, and the to-be-processed file can be displayed on the operation interface according to the input instruction. The user can upload and process the document through this interface.

[0048] The to-be-processed file described above can be a document uploaded by a user to an operation interface for processing. The to-be-processed file can include, but is not limited to, a document file (Word), a portable document format (PDF), and a joint photographic experts group (JPG). The to-be-processed file includes to-be-processed text of at least one modality. The to-be-processed file can be a judicial file, a patent review opinion, etc., which is not limited here and can be determined according to the application scenario.

[0049] The at least one modality described above can include, but is not limited to, a text modality, an image modality, a table modality, an audio modality, a video modality, and a metadata modality. The to-be-processed text of the at least one modality can also be pure text, a combination of text and image, a combination of text and table, or any other form of combination, which is not limited to the to-be-processed text of the at least one modality.

[0050] The text modality can refer to pure text information in a file, such as text in a Word document or text content in a PDF. The file of the image modality can include images, charts, photos, or scanned copies, etc., which need to be processed and understood through image recognition technology, such as optical character recognition for extracting text from pictures. The file of the table modality can include table data, which needs to be read and understood through special table parsing technology. The audio modality in court records, conference records, etc., can be audio information, which needs to be converted into text through speech recognition technology and then processed. The video modality can include video materials, which need to be processed using video recognition and analysis technology, possibly involving video-to-text or video-to-image conversion. The metadata modality can be metadata of a file, such as creation date, author, file type, etc.

[0051] In an optional embodiment, a user uploads or selects a to-be-processed file, such as a judicial file or a patent review opinion, through an operation interface, such as a file upload interface of a computer. The input instruction in this process can be the user's action of uploading a file in an input box of the interactive interface. After the system recognizes this action, the user-uploaded to-be-processed file is displayed on the operation interface. After the user uploads or selects the to-be-processed file, the system displays the to-be-processed file on the operation interface. The to-be-processed file includes to-be-processed text of at least one modality, i.e., the file can be in a Word document, PDF, JPG picture, etc.

[0052] Step S204, in response to a processing instruction acting on the operation interface, displaying a processing result on the operation interface.

[0053] The processing result is used to represent that the target processing rule is successfully used to name and store the to-be-processed file based on the target processing rule corresponding to the target type of the to-be-processed file, and the target type is determined by comparing the to-be-processed text with the preset texts contained in the plurality of preset files.

[0054] The plurality of preset files can be files that have been classified in advance. The target type of the to-be-processed text can be determined by comparing the similarity between the to-be-processed text and the preset texts contained in the plurality of preset files, so as to determine the preset text with a greater similarity to the to-be-processed text, and then determine the target type of the to-be-processed text according to the type of the preset file in which the preset text is located. In the judicial field, the preset file refers to an example file stored in a case example library, which is used for comparison with the to-be-processed file. These examples cover different types of judicial documents, and the preset file can be an example stored in the case example library.

[0055] The preset text refers to the text content in the preset file, which is used for semantic comparison and matching with the to-be-processed text in the to-be-processed file.

[0056] The target type can be a file category identified and determined according to the content of the to-be-processed file. In the judicial field, the target type can be a service confirmation letter, a judgment, etc.

[0057] The processing rule can be a standard and process for classifying, naming and storing the to-be-processed file. In the judicial field, the processing rule can be stored in a case cataloging rule library. The case cataloging rule library is mainly responsible for storing and defining the rules and standards of case material classification, naming and storage. These rules are usually formulated according to judicial practice and legal requirements, aiming to ensure that case materials can be accurately and consistently classified and named, so as to facilitate subsequent management and retrieval.

[0058] The processing rule in the case cataloging rule library can include but is not limited to classification rules, naming rules, storage rules and retrieval rules. The classification rules are used to define which category different types of judicial materials should be classified into, for example, “indictment”, “judgment”, “evidence list” and the like are classified in different directories. The naming rules are used to specify the naming format of different materials, including key elements such as case number, file type, date, party name, etc. The storage rules are used to determine the storage location and method of materials in the system, including the physical path, file structure and storage format of electronic files, etc. The retrieval rules are used to define how to retrieve specific case materials according to keywords, case information, etc.

[0059] The similarity comparison between the to-be-processed text and the preset texts contained in the plurality of preset files can be a semantic similarity comparison or a structural similarity comparison. The type of the similarity comparison is not limited and can be determined according to actual needs.

[0060] In an optional embodiment, the user clicks a processing button on the operation interface, and the system starts processing the uploaded file after receiving the processing instruction. The processing instruction is triggered by the user in the interactive interface, and the to-be-processed file is analyzed and archived according to the processing rule corresponding to the target type. The to-be-processed file can be named and stored according to the target type of the to-be-processed file and the processing rule stored in the volume cataloging rule library. The target type is determined by comparing the to-be-processed text with the preset texts of the plurality of preset files stored in the volume sample library. The preset files can be legal files of different types, such as delivery confirmation and judgment. They are used as references in the sample library to help the system identify and classify the to-be-processed file. The category of the file can be determined by comparing the similarity between the to-be-processed text and the preset text.

[0061] After inputting the to-be-processed file, the to-be-processed file can be provided to an intelligent document processing system (IDP). The document structure of the to-be-processed file can be parsed by the document processing system, the key information can be extracted, the text meaning can be recognized, the document can be classified and searched, and the efficiency and accuracy of document processing are greatly improved.

[0062] The comparison between the to-be-processed text and the preset texts contained in the plurality of preset files can be realized by a large model. The generalization ability and processing ability of different modal information of the large model ensure that the file can be accurately classified even in the case of insufficient samples.

[0063] In the field of judicial volume arrangement, the user uploads a document named “A address confirmation”. The document is a JPG format picture. In step S202, after the user uploads the file, the operation interface displays the picture, which is ready for processing. In step S204, the user clicks the processing button to extract the category, title, abstract, and keywords of the document. Then, the most similar case to these information can be found in the volume sample library to determine the accurate type of the file. According to the rules in the volume cataloging rule library, the document can be automatically renamed and archived in the correct directory. This process is automated and does not require manual intervention, greatly improving the efficiency and accuracy of processing judicial volumes. Through this process, the system can not only quickly and accurately identify and classify documents, but also rename and archive them according to custom rules.

[0064] The automatic processing and archiving of judicial files can be realized by the file processing method, the processing efficiency is improved, the operation cost is reduced, and the consistency and accuracy of archiving are ensured. Meanwhile, since the few-sample learning technology, i.e., the comparison of the to-be-processed text with the preset texts contained in the plurality of preset files to determine the target type, and the retrieval generation technology are adopted, the system can flexibly cope with new types of files and changes in rules, and has high adaptability and expansibility.

[0065] Through the above steps, in response to an input instruction acting on the operation interface, a to-be-processed file is displayed on the operation interface, wherein the to-be-processed file contains at least one modality of to-be-processed text; in response to a processing instruction acting on the operation interface, a processing result is displayed on the operation interface, wherein the processing result is used to represent that the to-be-processed file is successfully named and stored based on a target processing rule, the target processing rule is a processing rule corresponding to a target type of the to-be-processed file, the target type is determined by comparing the to-be-processed text with preset texts contained in a plurality of preset files, the types of different preset files are different, and the purpose of improving the file processing accuracy is achieved. It is easy to note that for the to-be-processed text containing at least one modality, the target type of the to-be-processed text can be determined by comparing the to-be-processed text with the preset texts contained in the plurality of preset files according to the type of the preset text similar to the to-be-processed text, so that the file is automatically named and stored based on the processing rule corresponding to the target type of the to-be-processed file, the manual intervention is reduced, the processing speed and accuracy are improved, and thus the technical problem of low file processing accuracy in the related art is solved.

[0066] In the above embodiments of the present disclosure, in response to a processing instruction acting on the operation interface, the method further includes: displaying target multi-dimensional information of the to-be-processed file on the operation interface, wherein the target multi-dimensional information is information obtained by extracting the to-be-processed text by using a text processing model; in response to a confirmation instruction acting on the target multi-dimensional information, displaying a target file matched with the to-be-processed file in the plurality of preset files on the operation interface, wherein the target file is a file determined from the plurality of preset files by comparing the target multi-dimensional information with preset multi-dimensional information of the plurality of preset files; in response to a confirmation instruction acting on the target file, displaying a target type of the to-be-processed file on the operation interface, wherein the target type is a type of the target file; and in response to a confirmation instruction acting on the target type, displaying the processing result on the operation interface.

[0067] The above target multi-dimensional information refers to information of a plurality of dimensions extracted from the to-be-processed file, including but not limited to the category, title, abstract and category keywords of the file. These information are refined representations of the file content, and are used for subsequent file matching and type confirmation processes.

[0068] The text processing model described above is an artificial intelligence model for understanding and processing text data. The text processing model can be a deep learning-based model, such as a large language model (LLM), for extracting semantic features and constructing multi-dimensional information representations from text. In the field of judicial case arrangement, the text processing model based on large model technology can efficiently process and understand text information in judicial cases, classify and catalog materials. The text processing model described above can also be an intelligent document processing system (IDP). The IDP can analyze multi-modal materials, extract and process the content of the materials, and deliver the results to the case index library and the case cataloging rule library.

[0069] The preset multi-dimensional information described above can be multi-dimensional information associated with a plurality of preset files, including file categories, titles, abstracts, and category keywords, etc., for comparison with the target multi-dimensional information of the file to be processed.

[0070] The target file described above is a file with a higher matching degree with the multi-dimensional information of the file to be processed among the plurality of preset files. The target file can be used to determine the classification and archiving rules of the file to be processed.

[0071] The processing result described above refers to the final result of the system automatically arranging the file to be processed, including the classification, renaming, and archiving location of the file in the system.

[0072] In an optional embodiment, the target multi-dimensional information of the file to be processed can be extracted using the text processing model, including file categories, titles, abstracts, and keywords, and these information can be displayed on the operation interface. The user can check the accuracy of the information through the target multi-dimensional information displayed on the operation interface. The target multi-dimensional information can be compared with the preset multi-dimensional information of the plurality of preset files to find the target file most similar to the file to be processed, and the matching results can be displayed on the operation interface. After the user confirms the target file, the type of the target file can be automatically identified and displayed as the target type of the file to be processed, to further confirm the accuracy of the classification. According to the target type, the file to be processed can be automatically archived to the correct directory, and a standard file name can be generated. The user can view the automatic arrangement results on the operation interface.

[0073] In the field of judicial document arrangement, users can automatically arrange judicial documents. Users can upload a document material, i.e., a JPG picture of "B Address Confirmation Letter". The system first extracts the category "Address Confirmation Letter", the title "B Address Confirmation Letter", the abstract, and the keywords from the picture using a text processing model. These multi-dimensional information is displayed on the operation interface for user confirmation. After the user confirms that the target multi-dimensional information is correct, the matching process is entered, and the extracted target multi-dimensional information is compared with the preset file library. In the preset file library, the system finds a similar case to the current file, such as a previously correctly classified "C Address Confirmation Letter" file, and displays it as the "target file" on the operation interface. After confirming the target file, the "target type" of the file, i.e., "Address Confirmation Letter", can be automatically identified and displayed, further confirming the correct classification. Finally, according to the preset archiving rules and element extraction (such as region, date, etc.), the file can be automatically archived under the "Address Confirmation Letter" directory of the "General Catalogue" and renamed as "B Address Confirmation Letter". The processing result is presented in real time on the operation interface, and the user can check the accuracy of the archiving, significantly improving the efficiency and standardization of judicial document arrangement.

[0074] Through the above process, the system not only automatically completes the classification and naming of the document material, but also ensures that the material can be accurately archived under the corresponding directory, providing convenience for subsequent case processing and material retrieval, and achieving the improvement of judicial efficiency and the standardization of data processing. By using large models combined with RAG technology, the efficiency, accuracy, and consistency of judicial document automatic arrangement are improved. Through few-shot learning, the dependence on data annotation is reduced, and the operating cost is reduced.

[0075] In the above embodiments of the present disclosure, in response to the modification instruction acting on the target multi-dimensional information, the method further comprises: in response to the modification instruction acting on the target multi-dimensional information, displaying the modified multi-dimensional information on the operation interface; in response to the confirmation instruction acting on the modified multi-dimensional information, displaying a new file on the operation interface, and adjusting the text processing model using the modified multi-dimensional information, wherein the new file is a file determined from the plurality of preset files by comparing the modified multi-dimensional information with the preset multi-dimensional information of the plurality of preset files.

[0076] The above modified multi-dimensional information can be multi-dimensional information obtained by adjusting or replacing the target multi-dimensional information displayed on the operation interface after the user views the target multi-dimensional information. The modified multi-dimensional information can include file type, title, abstract, keywords, date, signatory, etc.

[0077] In the operation interface, the modification instruction generally refers to an operation triggered by the user to change the system display content or data state. By touching the relevant control in the operation interface, the above-mentioned modification instruction can be generated. In the judicial case file arrangement system, the user may need to modify the multi-dimensional information automatically extracted by the large model to more accurately reflect the true content of the material or meet the specific cataloging rules.

[0078] The above-mentioned confirmation instruction is the user's approval and acceptance operation on the system display or suggested content, which is usually used to finally determine the change of a certain operation or data state. The confirmation instruction involves the user's approval of the modified multi-dimensional information and the confirmation that these information is correctly applied to the cataloging and classification of the target file.

[0079] In an optional embodiment, when the target multi-dimensional information extracted by the large model needs to be modified, the user can directly modify it on the operation interface. After the modification is completed, the user confirms the instruction, and the system not only displays the modified file information, but also uses these information to adjust the text processing model. This adjustment process is based on user feedback, allowing the model to learn more accurate cataloging rules and standards, so as to perform more accurately and efficiently in future cataloging tasks.

[0080] By allowing the user to directly modify and confirm the multi-dimensional information on the operation interface, the system can immediately feedback the user's correction of the cataloging result, which not only improves the accuracy of the information, but also enhances the user's sense of participation and control. The execution of the confirmation instruction not only updates the display on the operation interface, but also triggers the model adjustment based on the user's modification, so that the model can gradually learn more rich cataloging rules and more accurate classification standards, reducing the dependence on a large amount of labeled data, and also improving the generalization ability and adaptability of the model. This model adjustment mechanism based on user feedback significantly improves the accuracy, consistency and efficiency of the automatic arrangement of judicial case files, reduces the operating cost, enhances the flexibility and scalability of the system.

[0081] In the above-mentioned embodiments of the present disclosure, the method further comprises: in response to the modification instruction acting on the target file, displaying the modified target file on the operation interface; and in response to the confirmation instruction acting on the modified target file, displaying a new type on the operation interface, wherein the new type is the type of the modified target file.

[0082] The above-mentioned target file can be matched with the to-be-processed file. In the field of judicial case file arrangement, the target file can be a judicial case file material that has been previously confirmed, such as a delivery address confirmation, a delivery summary detail, and other certificates and various files.

[0083] The modification instruction mentioned above is an operation command issued by the user to edit or modify the target file, such as correcting information in the file, adjusting the file format or content layout, etc.

[0084] The confirmation instruction mentioned above is an operation command issued by the user to confirm the modification result after the system completes the modification. After confirmation, the system will perform further operations, such as displaying the file type, saving the modification result, etc.

[0085] The new type mentioned above refers to the classification to which the target file belongs after modification, such as a delivery address confirmation letter or a delivery summary detail. This new type reflects the archiving and retrieval categories of the file in the field of judicial case arrangement.

[0086] In an optional embodiment, when the user modifies the target file (such as adjusting the material category or modifying the elements in the file name), the system displays a preview of the modified file on the operation interface after responding to the modification instruction. After the user confirms that the modification is correct, the system further displays the "target type" of the file, which is intelligently classified based on the content and elements of the modified file, ensuring that the modified file can be accurately cataloged and archived.

[0087] In the field of judicial case arrangement, the target file covers various judicial files, including judgment letters, indictments, and evidence photos in various formats and types. The modification instruction and confirmation instruction are key links for user interaction with the system. The former is used to instruct the system to modify specific parts of the file, and the latter is used to confirm whether the modified content meets the requirements. The target type is the result of automatic identification and classification by the system based on file content and cataloging rules, used to guide file storage and retrieval. By utilizing the intelligent processing capabilities of large models and the efficient retrieval and generation capabilities of RAG technology, the speed of file cataloging is greatly improved, and the accuracy and consistency of the modification and confirmation process are ensured. Through the few-shot learning technology, the dependence on a large amount of labeled data is reduced, and the system operation cost is reduced.

[0088] In the above embodiments of the present disclosure, in response to the confirmation instruction acting on the target multi-dimensional information, the target file matching the file to be processed is displayed on the operation interface, including: displaying at least one candidate file on the operation interface, wherein the at least one candidate file is determined by recalling a plurality of preset files stored in a file database based on the target multi-dimensional information; in response to the determination instruction acting on the at least one candidate file, the target file is displayed on the operation interface, wherein the target file is determined from at least one first sorted file, and the at least one first sorted file is obtained by sorting the at least one candidate file using a text processing model.

[0089] The file database stores a large number of preset files, which can be historical processed judicial case materials, for system learning and recalling similar files.

[0090] The candidate files are files recalled from the file database by the system through target multi-dimensional information, and the candidates have similarities with the input files in some aspects and are candidates for further sorting.

[0091] The sorting refers to a process of arranging the candidate files in descending order of relevance to the target file according to the analysis results of the text processing model. It should be noted that the sorting process can be fine sorting, which refers to further refining the sorting results on the basis of the preliminary sorting of at least one candidate file by the text processing model to more accurately determine the category and relevance of the target file and reduce misjudgment and improve accuracy.

[0092] By fine sorting at least one candidate file by the text processing model, a case with a higher matching degree of the category of the current file to be processed can be accurately selected from the recalled similar case materials. The fine sorting process can be implemented by a fine sorting model. The fine sorting model can be a deep learning model that evaluates the relevance and similarity between the query document and the sample document by calculating and analyzing various text and vector similarity features, thereby determining whether they belong to the same category.

[0093] The fine sorting model is a key component for determining the target type of the text to be processed, which is based on large models and RAG technology and combines various text and vector similarity features to improve the accuracy of case material classification.

[0094] The feature part of the fine sorting model includes various similarity measures between the query document and the sample document, which can include but are not limited to text similarity features and vector similarity features.

[0095] Among them, the text similarity features can include text similarity scores of categories, titles, abstracts and keywords, as well as hybrid ranking scores calculated based on these similarity scores. Text similarity measures the closeness of two documents in terms of text content. Vector similarity features can include vector similarity scores of categories, titles, abstracts and keywords, as well as comprehensive vector similarity scores, providing another method for measuring document similarity from the perspective of semantic vector space. Vector similarity takes into account the semantic relationship of words and can capture semantic details that may be overlooked in text similarity features.

[0096] The network structure of the fine sorting model adopts a linear network of input layer and output layer. The input layer can receive the similarity scores calculated by the feature part, which form a fixed-size vector as the input of the model. The output layer can output the prediction value (logits) scores for prediction to determine the consistency of the input material with the category of the recall case.

[0097] The fine sorting model adopts a contrastive loss function (Circle Loss) as the prediction objective function, which is a loss function designed to improve the distance between similar cases and dissimilar cases, suitable for retrieval tasks. Through Circle Loss, the model can learn how to more accurately rank the recalled cases, ensuring that the cases consistent with the category of the input file to be processed are ranked in the front.

[0098] By integrating various text and vector similarity features, the fine sorting model can more comprehensively understand the content and context of the input text, thereby improving the accuracy of classification. Even in the field of judicial archives sorting, it can ensure that the materials are correctly classified and archived, even in the face of complex and diverse archives materials. The fine sorting model based on RAG technology relies heavily on the example library and rule library, rather than a large amount of labeled data, which reduces the dependence on manual labeling. The maintenance of the example library is simpler than the retraining of traditional machine learning models, saving a lot of manpower and time cost. Since the fine sorting model can be configured with the case library and cataloging rule library to cope with the changes of new material types or rules, it makes the system quickly adapt to changes in judicial project requirements, without the need for frequent model retraining, improving the flexibility and scalability of the system.

[0099] By using target multi-dimensional information for recall and fine sorting, the efficiency and accuracy of judicial archives sorting can be significantly improved. Compared with traditional manual sorting, this approach reduces a lot of labor cost and improves processing speed. At the same time, due to the generalization ability and flexibility of the large model, even in the face of new file types or changes in cataloging rules, it can quickly adapt through fine-tuning to maintain efficient operation.

[0100] In the above embodiments of the present disclosure, the method further includes: in response to a modification instruction acting on the at least one candidate file, displaying at least one modified candidate file on the operation interface; and in response to a confirmation instruction acting on the at least one modified candidate file, displaying a target file on the operation interface, wherein the target file is a file determined from at least one second sorted file, and the at least one second sorted file is a file determined by sorting the at least one modified candidate file using the text processing model.

[0101] The modification instruction can be a requirement of the user or the system for modification of the preliminarily identified or classified candidate files. When the user considers that the result is inaccurate or needs to be adjusted, the at least one candidate file can be modified according to the modification instruction to obtain at least one candidate file after modification.

[0102] The confirmation instruction can be an instruction generated after the user confirms that the at least one candidate file after modification is correct.

[0103] In an optional embodiment, when the user or the system has a modification requirement for the preliminary processing result, a modification instruction is issued. The system responds to these modification instructions, reprocesses the related candidate files, and displays the modified files on the operation interface for the user. After the user confirms that the modification is correct, the user informs the system through a confirmation instruction. At this time, the text processing model will determine the final sorting and classification of the confirmed files, generate target files, and display the target files on the operation interface for subsequent archiving and management. The user can directly modify the operation interface without complex operation processes, thereby improving the convenience of interaction.

[0104] In the field of judicial case file arrangement, the above-mentioned modification of the candidate files not only improves the accuracy of file classification, but also simplifies the operation of the user, ensures the standardization and consistency of the files in the judicial case file management system, and effectively improves the efficiency and quality of judicial work.

[0105] In the above-mentioned embodiments of the present disclosure, the target file is displayed on the operation interface in response to the determination instruction acting on the at least one candidate file, including: displaying at least one first sorting file on the operation interface, wherein the at least one first sorting file is obtained by sorting the at least one candidate file using the matching score of the at least one candidate file, and the matching score of the at least one candidate file is obtained by comparing the target multi-dimensional information and the candidate multi-dimensional information of the at least one candidate file using the text processing model. The matching score is used to represent the probability that the to-be-processed file and the corresponding candidate file are of the same category; and in response to the confirmation instruction acting on the at least one first sorting file, the target file is displayed on the operation interface, wherein the target file is the first file in the at least one first sorting file.

[0106] The first sorting file can be a file list sorted according to the similarity between the file and the examples in the case library after processing by the large model. After sorting, these files are more likely to be sorted in front of the files of the same category as the current to-be-processed file.

[0107] The matching score described above can be used to measure the quantitative indicator of the similarity between the to-be-processed file and the candidate file. In the judicial case file sorting system, the matching score is calculated by the large model according to the multi-dimensional information of the file (such as type, title, abstract, keywords, etc.), reflecting the consistency probability of the file category.

[0108] The candidate multi-dimensional information described above can be the feature information of the candidate file, including type, title, abstract, keywords, etc., for comparison with the multi-dimensional information of the to-be-processed file.

[0109] The candidate file described above can be a known classified file stored in the system, used as a reference sample to help identify the category of the to-be-processed file.

[0110] The user issues an instruction through the operation interface, and the instruction is used to select the candidate file for fine sorting comparison, and the confirmation instruction is used to confirm the sorting result, and the first item of the first sorting file is taken as the target file for subsequent operation.

[0111] In an optional embodiment, when the user selects a certain candidate file on the operation interface, the system responds to the determination instruction and starts the large model for more in-depth fine sorting comparison. The matching score of the to-be-processed file and the candidate file can be calculated by using the large model, the candidate files are sorted according to the score, and the result is displayed on the operation interface. The retrieved sample can be compared in depth with the to-be-processed file. After the user confirms the sorting result, the system responds to the confirmation instruction and automatically takes the first file in the sorting list as the target file, i.e., the file with a higher matching degree, for subsequent confirmation operation. The system displays the confirmed most matching file to the user as the basis for the next operation, such as information extraction, archiving, etc.

[0112] By inputting the target multi-dimensional information (such as the category, title, abstract, and keywords of the material) and the candidate multi-dimensional information of the candidate file into the text processing module, and using the pre-trained text processing model for comparison and matching, the matching score obtained can accurately reflect the probability that the to-be-processed file and the candidate file are of the same category. This is more accurate than traditional manual or rule-based classification methods, because the large model can understand complex semantic relationships and text patterns, thereby reducing classification errors and improving overall classification accuracy.

[0113] The candidate files can be sorted according to the matching score, with the file most likely to be the same as the to-be-processed file ranked first. When the user confirms the sorting result on the operation interface, the system will preferentially display the first file in the first sorting file, i.e., the file with a higher matching score. This design improves the efficiency of the user's viewing and confirming files, reduces the time and effort of the user's manual screening among a large number of files, and improves the user interaction experience of the judicial case file sorting.

[0114] The use of large models and RAG technology for file matching and sorting can significantly improve processing speed, even with massive amounts of judicial documents, and quickly provide classification results. This avoids the problems of traditional methods of manual comparison or large consumption of computing resources, enabling the system to efficiently process the increasing number of judicial documents. Through intelligent sorting and high-precision matching, the system reduces the need for manual classification and confirmation. Users only need to make simple confirmations on the operation interface to complete the classification and archiving of files, which greatly reduces the complexity and workload of manual operations. When new file types need to be processed in the judicial process, the system can quickly adapt by updating the case library and cataloging rule library without retraining the model, thus maintaining the scalability and flexibility of the system.

[0115] In the above embodiments of the present disclosure, the method further comprises: in response to a modification instruction acting on at least one first sorted file, displaying at least one modified sorted file on the operation interface; and in response to a confirmation instruction acting on at least one modified sorted file, adjusting the text processing model based on at least one modified sorted file, wherein the target file is the first file in at least one modified sorted file.

[0116] The above modification instruction can be an instruction for the user to reclassify or rename one or more files in the sorted file.

[0117] The above confirmation instruction can be an instruction for the user to approve the modified sorted file, confirming that the file classification and naming are correct. This is the step of the user's final review of the output of the automated system.

[0118] In an optional embodiment, when the user modifies the case materials automatically sorted by the large model on the operation interface, the system displays the updated sorted file list. After the user confirms the modification result, the system automatically displays the detailed content of the first file selected by the user (target file), and at the same time, the system adjusts the text processing model based on the user's modification feedback on the sorted file. The purpose of adjusting the model is to make it better adapt to the user's classification rules and needs, improving the accuracy and efficiency of future file processing. The user can intuitively see the modification result and confirm whether the classification and naming meet the requirements, which is crucial for improving the accuracy and standardization of case sorting. In addition, adjusting the model based on user feedback reduces the dependence on a large amount of labeled data, reduces operating costs, and improves the model's generalization ability, enabling it to better handle future additions of material types or changes in cataloging rules.

[0119] In the above embodiments of the present disclosure, the processing rule includes a naming rule and a storage rule. In response to a processing instruction acting on the operation interface, the method further includes: displaying, on the operation interface, naming element information in the to-be-processed file that matches the naming rule, and displaying a target directory corresponding to the to-be-processed file, wherein the naming element information is information extracted from the to-be-processed file by using a text processing model, and the target directory is a directory determined based on the storage rule; and in response to a confirmation instruction acting on the naming element information and the target directory, displaying, on the operation interface, a processing result, wherein the processing result is used to represent that the to-be-processed file is successfully named based on the naming element information and the naming rule, and is successfully stored in the target directory.

[0120] In file management, the above processing rule can be a specific criterion for guiding file naming and storage, including a naming rule and a storage rule. The naming rule can be used to define how a file is named, and the storage rule can be used to determine the directory structure and location of file storage.

[0121] The above naming element information can be key information used to generate a file name, which is extracted from a file by a text processing model based on a naming rule, such as a case number, a date, a party name, and the like.

[0122] The above target directory can be a storage location determined by the system for a file according to a storage rule. The selection of the directory is based on the type of the file, the extracted naming element information, and the like.

[0123] The above processing result can be a final state in which the file is given a new name that conforms to the naming rule and is stored in the target directory after the system performs the naming and storage operations.

[0124] In an optional embodiment, a to-be-processed file can be parsed according to a processing rule. On an operation interface, the system displays naming element information that matches a naming rule, and a target directory determined according to a storage rule. A user can confirm whether the information is accurate on the operation interface. Once the user confirms, the system automatically generates a new name for the to-be-processed file based on the confirmed naming element information and the naming rule, and stores it in the corresponding target directory according to the storage rule. Finally, the operation interface displays the processing result, i.e., the new name of the file and the storage location, for the user to check and confirm.

[0125] In the scenario of judicial file arrangement, the processing rules (naming rules and storage rules) are the core of the automatic arrangement system based on large model technology. The operation interface is the interface for interacting and controlling the file arrangement process. The text processing model in this scenario specifically refers to an intelligent model based on large model and few-shot learning technology, which is used to analyze and understand the semantics of the file materials and extract key naming element information. The determination of the target directory and the naming element information is to ensure that the file materials are classified and stored according to the standardized process. The processing result reflects the automatic arrangement capability of the system, ensuring efficient and accurate management of file materials.

[0126] In the above embodiments of the present disclosure, the method further includes: in response to a modification instruction acting on the naming element information and the target directory, displaying the modified element information on the operation interface; in response to a confirmation instruction acting on the modified element information, displaying the processing result on the operation interface, and adjusting the text processing model based on the modified element information, wherein the processing result is used to represent that the naming of the to-be-processed file is successfully performed based on the modified element information and the naming rule, and the to-be-processed file is successfully stored in the target directory based on the storage rule.

[0127] The naming element information described above can be key information or features used to determine the components of the file name, such as the category of the file, the date, the case number, etc.

[0128] The modification instruction described above can be an instruction issued by the user or the system to change the naming element information.

[0129] The confirmation instruction described above can be an instruction for the user to confirm the validity of the modified element information. After confirmation, the system will perform file naming and storage based on these element information.

[0130] The processing result described above is the result obtained after naming and archiving the to-be-processed file based on the modified element information and the defined naming rule and storage rule.

[0131] The target directory described above can be a storage location specified by the system for the file according to the file type and the element, so as to realize the effective classification and management of the file.

[0132] In an alternative embodiment, after the large model extracts the naming element information from the content in the file to be processed, the user or system administrator can check and modify it through the operation interface. Once the element information is modified, the operation interface will display the modified information in real time for the user to confirm. After the user confirms the modification, the system will generate a new file name based on the updated element information and in combination with the preset naming rules, and store the file in the corresponding target directory according to the storage rules. In addition, the user's modification of the element information can be recorded and used to adjust the text processing model, so that the model can more accurately identify and extract this information in the future, improving the accuracy and efficiency of automatic cataloging.

[0133] In the embodiments of the application, the user can directly modify the naming element information, which not only enhances the intuitiveness and convenience of operation, but also ensures the accuracy and standardization of the file cataloging. For example, the user can immediately correct the misidentified date, case number, etc. The system will respond immediately and display the modified information, and after the user confirms that there is no error, the system will automatically complete the renaming and correct archiving of the file according to the modified information and the preset rules. This dynamic adjustment mechanism based on user feedback not only effectively avoids human errors, but also continuously improves the text processing model, making it more intelligent and reducing the need for future human intervention. Ultimately, this process not only improves the efficiency of judicial file arrangement, but also reduces the error rate, ensuring the rigor and consistency of file management.

[0134] In the above-mentioned embodiments of the present disclosure, in response to the modification instruction acting on the target directory, the method further comprises: displaying the modified directory on the operation interface; in response to the confirmation instruction acting on the modified directory, displaying the processing result on the operation interface, and adjusting the storage rule based on the modified directory, wherein the processing result is obtained by naming the file to be processed based on the naming element information and the naming rule, and storing the file to be processed under the modified directory based on the storage rule.

[0135] The above-mentioned modification instruction can be an instruction issued by the user or system operator through the operation interface, aiming to change the target directory or cataloging rule. The modification instruction can be an adjustment of the directory structure, a reassignment of the material classification, etc.

[0136] The above-mentioned confirmation instruction can be the user's operation to confirm the modified directory, which indicates that the user approves the new directory assignment or cataloging rule adjustment, and the system will perform subsequent file naming and archiving operations according to the confirmation instruction.

[0137] In an alternative embodiment, the user or system operator can issue modification instructions to the preliminarily formed directory structure or cataloging rules. After the system receives these modification instructions, the modified directory structure is immediately reflected on the operation interface, so that the user can intuitively view the changes. If the user confirms the modification results, the operation interface displays the processing results, including the renaming and archiving of the files. The system adjusts the storage rules according to the confirmed modification instructions to ensure that subsequent files are stored according to the new directory structure and naming rules.

[0138] Through direct interaction between the user and the system, this scheme significantly improves the flexibility and user satisfaction of judicial case arrangement. Specifically, the user can modify the case directory and cataloging rules according to actual needs, which means that the case arrangement can be more accurately matched to the specific needs in judicial practice, improving the retrieval efficiency and classification accuracy of materials. Once the user confirms the modification, the system automatically adjusts the storage rules to ensure that all pending files can be renamed based on the latest naming element information and naming rules and stored under the modified directory. This process not only avoids errors in manual operations but also significantly improves the system's response speed and adaptability to changing needs, reduces the operating costs of case arrangement, and improves the efficiency and standardization of judicial work.

[0139] Figure 3 is a schematic diagram of a cataloging function for sending an address confirmation letter according to an embodiment of the present disclosure. As shown in Figure 3, the left part can be a pending file, which can be a delivery address confirmation letter image containing multiple fill-in items, including recipient information, sending address, contact information, etc. The last part shows the date and signature column. The right part can be the result of cataloging, which is divided into three points: first-level directory, second-level directory, and material name.

[0140] Among them, the first-level directory can be a delivery address confirmation letter, a delivery receipt or other delivery evidence. The second-level directory can be a delivery address confirmation letter, and the material name can be xxx delivery address confirmation letter.

[0141] FIG. 4 is a schematic diagram of the classification and storage of a sending address confirmation letter in a court document management system according to an embodiment of the present disclosure. The pending document can be a case file, the text processing model can be an intelligent document processing system, the target multi-dimensional information can be the category, title, abstract, and category keywords of the case file, and the target document can be a case file example. As shown in FIG. 4, the case file can be input into the intelligent document processing system, and the content of the case file can be parsed by the intelligent document processing system. The extracted content can be transmitted to the case index library and the case cataloging rule library to recall similar case file examples. When recalling similar case file examples, the case file can be refined by a large model to generate the category, title, abstract, and category keywords of the material. Similar case file examples can be found in the case file example library based on the refined information. The recalled cases can be sorted by a large model, and the target type of the case file can be determined according to the case file examples at the top of the ranking. The processing rules corresponding to the target type can be recalled from the cataloging rule library according to the target type. The processing rules can include naming rules and storage rules. The naming element information can be extracted from the case file by a text processing model, and the target directory of the case file can be determined based on the storage rules. The case file can be classified under the target directory according to the naming element information and the target directory, and the case file can be renamed. It should be noted that new examples can be manually added to the case file example library, and rules can be manually defined in the cataloging rule library. Through the schematic diagram, the entire cataloging technical process from inputting various formats of document materials to intelligent parsing, classification indexing, and finally finding and archiving can be clearly understood. The entire process fully utilizes the multi-modal material parsing capability of IDP and the content refinement and similar case file recall function of LLM, ensuring accurate cataloging, classification, and effective storage of materials.

[0142] In the field of justice, judicial cataloging has important project value, which is reflected in the following aspects:

[0143] Improving judicial efficiency: By introducing large model technology for judicial cataloging, the classification, archiving, and retrieval speed of legal documents, evidence materials, and other materials can be significantly improved, thereby greatly reducing manual operation time and improving judicial work efficiency. Case handling personnel can quickly retrieve the required materials to speed up the case processing process.

[0144] Improving accuracy and consistency: Large models have strong semantic understanding and generalization capabilities, which can accurately identify and classify various types of legal materials, even in the face of different court cataloging specification differences in naming, and can maintain high consistency and accuracy, avoiding human annotation errors, and ensuring the rigor and standardization of judicial data.

[0145] Reducing operating costs: By using few-shot learning and fine-tuning techniques, the dependence on large amounts of labeled data is greatly reduced, thereby reducing the cost of data acquisition and processing. A large amount of human resources and time is saved, and the overall operation efficiency is improved.

[0146] Enhancing flexibility and scalability: The large model has high flexibility and scalability, and can flexibly cope with the addition of new material types and changes in cataloging rules, reducing the frequent re-development and re-training work in traditional methods, and quickly adapting to changes in judicial project requirements, improving the long-term use value of the system.

[0147] Standardizing processes and improving transparency: Through intelligent cataloging systems, standardized trial processes, and improved transparency and standardization of judicial work, the case handling process is more scientific and transparent, which helps to improve the public's trust in the judicial system.

[0148] In the present disclosure, by combining the retrieval process and the generation process, the large model has the ability to handle various types and forms of materials, and can cope with complex requirements of more than multiple different types of materials. Through the few-shot learning technique, the training sample data can be reduced to less data, thereby greatly reducing the cost of data acquisition and labeling. By configuring the case library and the cataloging rule library, new project requirements such as the addition of new material types and changes in cataloging rules can be flexibly coped with, reducing the workload of re-development and training.

[0149] It should be noted that the user information (including but not limited to user equipment information, user personal information, etc.) and data (including but not limited to data for analysis, stored data, displayed data, etc.) involved in the present disclosure are all information and data authorized by the user or authorized by all parties, and the collection, use and processing of related data need to comply with relevant laws, regulations and standards of relevant countries and regions, and provide corresponding operation portal for user to choose authorization or refusal.

[0150] It should be noted that for the foregoing method embodiments, in order to simply describe, they are all expressed as a series of action combinations, but those skilled in the art should know that the present disclosure is not limited by the order of the described actions, because according to the present disclosure, certain steps can be performed in other orders or simultaneously. Secondly, those skilled in the art should know that the embodiments described in the specification are all preferred embodiments, and the actions and modules involved are not necessarily necessary for the present disclosure.

[0151] Those skilled in the art can clearly understand that the method according to the above-mentioned embodiments can be realized by means of software and necessary general hardware platforms, and of course can also be realized by hardware. Based on such understanding, the technical solutions of the present disclosure can be embodied in the form of a software product, which is stored in a storage medium (such as a ROM / RAM, a magnetic disk, or an optical disk) and includes a plurality of instructions for causing a terminal device (which can be a mobile phone, a computer, a server, or a network device) to execute the methods of various embodiments of the present disclosure.

[0152] According to the embodiments of the present disclosure, a file processing method is also provided. It should be noted that the steps shown in the flowchart of the accompanying drawings can be executed in a computer system such as a set of computer executable instructions, and although the logical order is shown in the flowchart, in some cases, the steps shown or described can be executed in an order different from that shown here. FIG. 5 is a flowchart of a file processing method according to an embodiment of the present disclosure. As shown in FIG. 5, the method comprises:

[0153] In step S502, a file to be processed is obtained.

[0154] In an optional embodiment, the file to be processed can be received from an input source or manually uploaded. The file can be in Word, PDF, JPG, etc. format, and the source can be an electronic file system, submitted materials, or files from other judicial authorities. At this stage, the system does not perform any analysis on the file content, but simply obtains the file for subsequent processing.

[0155] In step S504, the text to be processed is parsed to obtain the text to be processed contained in the file to be processed.

[0156] After receiving the text to be processed, the text to be processed can be parsed to extract the text content therein. The parsing process is not limited to pure text, but can also parse non-text information such as charts, tables, and signatures, and convert these information into processable text form.

[0157] In step S506, the text to be processed is compared with the preset text contained in a plurality of preset files to determine the target type of the file to be processed.

[0158] Among them, the types of different preset files are different.

[0159] The parsed text is input into a large model, which determines the category of the file to be processed by comparing the text to be processed with the sample texts in the preset sample library. The preset files are files that have been defined and labeled in the system, and they constitute the sample library for training and verifying the model. The model finds the most similar preset file type to the text to be processed by calculating the text similarity, keyword matching, and other methods, thereby determining the target type of the file to be processed.

[0160] In step S508, the file to be processed is named and stored based on the processing rules corresponding to the target type.

[0161] After determining the target type of the file to be processed, the system will name and archive the file according to the rules stored in the volume cataloging rule library. The rule library defines how each file type should be named and where it should be stored.

[0162] In the field of judicial file arrangement, the entire process utilizes the powerful semantic understanding and generalization ability of the large model, achieving automatic classification and archiving of judicial files, significantly improving processing speed, reducing error rate, reducing dependence on human intervention, and enhancing the flexibility and scalability of the system.

[0163] Through the above steps, the file to be processed is obtained; the text to be processed is parsed to obtain the text to be processed contained in the file to be processed; the text to be processed is compared with the preset texts contained in the plurality of preset files to determine the target type of the file to be processed, wherein the types of different preset files are different; the file to be processed is named and stored based on the processing rules corresponding to the target type, achieving the purpose of improving file processing accuracy; it is easy to note that for the text to be processed containing at least one modality, the target type of the text to be processed can be determined by comparing the text to be processed with the preset texts contained in the plurality of preset files, according to the type of the preset text similar to the text to be processed, thereby effectively automatically naming and storing the file based on the processing rules corresponding to the target type of the file to be processed, reducing human intervention, improving processing speed and accuracy, and thereby solving the technical problem of low file processing accuracy in related technologies.

[0164] According to the embodiments of the present disclosure, a file processing method is also provided. It should be noted that the steps shown in the flowchart can be executed in a computer system such as a set of computer executable instructions, and although the logical order is shown in the flowchart, in some cases, the steps shown or described herein can be executed in a different order. FIG. 6 is a flowchart of a file processing method according to an embodiment of the present disclosure. As shown in FIG. 6, the method includes:

[0165] Step S602, receiving the judicial dossier file sent by the client.

[0166] In an optional embodiment, the judicial dossier file can be received from the client first, which may include but is not limited to legal documents, evidence materials, etc., usually in the format of Word, PDF or JPG, etc.

[0167] Step S604, parsing the judicial dossier file to obtain the judicial dossier text contained in the judicial dossier file.

[0168] After receiving the judicial dossier text, the file can be parsed using intelligent document processing technology to extract the text information therein. The IDP technology can process multi-modal materials, and even if the file contains images or tables, the text content therein can be accurately extracted.

[0169] Step S606, comparing the judicial dossier text with the preset dossier text contained in a plurality of preset dossier files to determine the target type of the judicial dossier file.

[0170] Among them, the types of different preset dossier files are different.

[0171] The extracted text information can be passed to the large model, which compares the text in the preset dossier file (sample library) to determine which type the current file belongs to. The key here is that the large model combines RAG technology to find the most similar file type from the sample library using the retrieval and generation capabilities, ensuring the accuracy and consistency of classification.

[0172] Step S608, naming and storing the judicial dossier file based on the processing rule corresponding to the target type, to obtain the processing result.

[0173] After determining the target type of the judicial dossier file, the processing rule corresponding to the target type can be read from the dossier cataloging rule library, and the judicial dossier file can be renamed and archived according to the processing rule. The processing rule defines how the file should be named, archived under which directory, and which key elements may need to be extracted.

[0174] Step S610, sending the processing result to the client.

[0175] The processing result (including the classification, naming and storage location of the file) can be sent back to the client to complete the entire automatic arrangement of the judicial dossier.

[0176] The above method of the present disclosure can improve the efficiency, accuracy and consistency of the arrangement of the judicial dossier, while reducing the dependence on a large amount of labeled data, reducing the operating cost, improving the flexibility and scalability of the system, and is an innovative solution in the field of automatic arrangement of judicial dossier.

[0177] By the above steps, the judicial file sent by the client is received; the judicial file is parsed to obtain the judicial file text contained in the judicial file; the judicial file text is compared with the preset file text contained in a plurality of preset files to determine the target type of the judicial file, wherein the types of different preset files are different; the judicial file is named and stored based on the processing rule corresponding to the target type to obtain a processing result; and the processing result is sent to the client, thereby achieving the purpose of improving the file processing accuracy. It is easy to note that for the to-be-processed text containing at least one modality, the to-be-processed text can be compared with the preset text contained in a plurality of preset files, and the target type of the to-be-processed text is determined according to the type of the preset text similar to the to-be-processed text, so that the to-be-processed file is automatically named and stored based on the processing rule corresponding to the target type of the to-be-processed file, manual intervention is reduced, the processing speed and accuracy are improved, and the technical problem of low file processing accuracy in the related art is solved.

[0178] According to the embodiments of the present disclosure, a file processing method is also provided. It should be noted that the steps shown in the flowchart can be executed in a computer system such as a set of computer executable instructions, and although the logical order is shown in the flowchart, in some cases, the steps shown or described herein can be executed in an order different from that shown herein. FIG. 7 is a flowchart of a file processing method according to an embodiment of the present disclosure. As shown in FIG. 7, the method comprises:

[0179] In step S702, the to-be-processed file is obtained by calling a first interface.

[0180] The first interface includes a first parameter, and the parameter value of the first parameter includes the to-be-processed file.

[0181] The first interface described above can be an interface for data interaction between a cloud server and a client. The to-be-processed file can be transmitted into an interface function as a first parameter of the interface function, so as to achieve the purpose of uploading the to-be-processed file to the cloud server.

[0182] In step S704, the to-be-processed text is parsed to obtain the to-be-processed text contained in the to-be-processed file.

[0183] In step S706, the to-be-processed text is compared with the preset text contained in a plurality of preset files to determine the target type of the to-be-processed file.

[0184] The types of different preset files are different.

[0185] In step S708, the to-be-processed file is named and stored based on the processing rule corresponding to the target type to obtain a processing result.

[0186] Step S710, output the processing result by calling the second interface.

[0187] The second interface includes a second parameter, and a parameter value of the second parameter includes the processing result.

[0188] The second interface described above can be an interface for data interaction between the cloud server and the client. The cloud server can pass the processing result into the interface function as the second parameter of the interface function to achieve the purpose of issuing the processing result to the client.

[0189] Through the above steps, the to-be-processed file is obtained by calling the first interface, wherein the first interface includes a first parameter, and a parameter value of the first parameter includes the to-be-processed file; the to-be-processed text contained in the to-be-processed file is obtained by analyzing the to-be-processed text; the target type of the to-be-processed file is determined by comparing the to-be-processed text with the preset text contained in the plurality of preset files, wherein the types of different preset files are different; the to-be-processed file is named and stored based on the processing rule corresponding to the target type, and the processing result is obtained; the processing result is output by calling the second interface, wherein the second interface includes a second parameter, and a parameter value of the second parameter includes the processing result, thereby achieving the purpose of improving the file processing accuracy. It is easy to note that for the to-be-processed text containing at least one modality, the target type of the to-be-processed text can be determined by comparing the to-be-processed text with the preset text contained in the plurality of preset files according to the type of the preset text similar to the to-be-processed text, so that the file is automatically named and stored appropriately based on the processing rule corresponding to the target type of the to-be-processed file, thereby reducing manual intervention, improving processing speed and accuracy, and further solving the technical problem of low file processing accuracy in the related art.

[0190] According to the embodiments of the present disclosure, a file processing apparatus for implementing the above-mentioned file processing method is also provided. FIG. 8 is a schematic diagram of a file processing apparatus according to an embodiment of the present disclosure. As shown in FIG. 8, the apparatus 800 includes a first display device 802 and a second display device 804.

[0191] The first display device is configured to display the to-be-processed file on the operation interface in response to an input instruction acting on the operation interface, wherein the to-be-processed file contains to-be-processed text of at least one modality; and the second display device is configured to display the processing result on the operation interface in response to a processing instruction acting on the operation interface, wherein the processing result is used to represent that the to-be-processed file is successfully named and stored based on the target processing rule, the target processing rule is a processing rule corresponding to a target type of the to-be-processed file, and the target type is determined by comparing the to-be-processed text with preset text contained in a plurality of preset files, wherein the types of different preset files are different.

[0192] It should be noted that the first display device 802 and the second display device 804 correspond to steps S202 to S204 in the above embodiment, and the two modules have the same examples and application scenarios as the corresponding steps, but are not limited to the above embodiment.

[0193] It should be noted that the preferred embodiments involved in the above embodiment of the present disclosure have the same scheme, application scenario and implementation process as the above embodiment, but are not limited to the above embodiment.

[0194] According to the embodiments of the present disclosure, a file processing device for implementing the above file processing method is also provided. FIG. 9 is a schematic diagram of a file processing device according to an embodiment of the present disclosure. As shown in FIG. 9, the device 900 includes an acquisition module 902, an analysis module 904, a comparison module 906, and a processing module 908.

[0195] The acquisition module is configured to acquire a to-be-processed file. The analysis module is configured to analyze the to-be-processed text to obtain a to-be-processed text contained in the to-be-processed file. The comparison module is configured to compare the to-be-processed text with preset texts contained in a plurality of preset files to determine a target type of the to-be-processed file, wherein the types of the different preset files are different. The processing module is configured to name and store the to-be-processed file based on a processing rule corresponding to the target type.

[0196] It should be noted that the acquisition module 902, the analysis module 904, the comparison module 906, and the processing module 908 correspond to steps S502 to S508 in the above embodiment, and the four modules have the same examples and application scenarios as the corresponding steps, but are not limited to the above embodiment.

[0197] According to the embodiments of the present disclosure, a file processing device for implementing the above file processing method is also provided. FIG. 10 is a schematic diagram of a file processing device according to an embodiment of the present disclosure. As shown in FIG. 10, the device 1000 includes a receiving module 1002, an analysis module 1004, a comparison module 1006, a processing module 1008, and a sending module 1010.

[0198] The receiving module is configured to receive a judicial file sent by a client; the analyzing module is configured to analyze the judicial file to obtain a judicial file text contained in the judicial file; the comparing module is configured to compare the judicial file text with preset file texts contained in a plurality of preset files to determine a target type of the judicial file, wherein the types of the different preset files are different; the processing module is configured to name and store the judicial file based on a processing rule corresponding to the target type to obtain a processing result; and the sending module is configured to send the processing result to the client.

[0199] It should be noted that the receiving module 1002, the analyzing module 1004, the comparing module 1006, the processing module 1008, and the sending module 1010 correspond to steps S602 to S610 in the above embodiment, and the five modules have the same instances and application scenarios as the corresponding steps, but are not limited to the content disclosed in the above embodiment. It should be noted that the above modules or units can be hardware components or software components stored in a memory and processed by one or more processors, and the above modules can also be run in the server 10 provided in the above embodiment as part of the device.

[0200] According to the embodiments of the present disclosure, a file processing device for implementing the above file processing method is also provided. FIG. 11 is a schematic diagram of a file processing device according to an embodiment of the present disclosure. As shown in FIG. 11, the device 1100 includes an obtaining module 1102, an analyzing module 1104, a comparing module 1106, a processing module 1108, and an output module 1110.

[0201] The obtaining module is configured to obtain a to-be-processed file by calling a first interface, wherein the first interface includes a first parameter, and a parameter value of the first parameter includes the to-be-processed file; the analyzing module is configured to analyze the to-be-processed text to obtain a to-be-processed text contained in the to-be-processed file; the comparing module is configured to compare the to-be-processed text with preset texts contained in a plurality of preset files to determine a target type of the to-be-processed file, wherein the types of the different preset files are different; the processing module is configured to name and store the to-be-processed file based on a processing rule corresponding to the target type to obtain a processing result; and the output module is configured to output the processing result by calling a second interface, wherein the second interface includes a second parameter, and a parameter value of the second parameter includes the processing result.

[0202] It should be noted that the above obtaining module 1102, the parsing module 1104, the comparison module 1106, the processing module 1108, and the output module 1110 correspond to steps S702 to S710 in the above embodiment, and the five modules have the same instances and application scenarios as the corresponding steps, but are not limited to the above embodiment. It should be noted that the above modules or units can be hardware components or software components stored in the memory and processed by one or more processors, and the above modules can also be run in the server 10 provided in the above embodiment as part of the device.

[0203] Embodiments of the present disclosure can provide an electronic device, which can be any one of the electronic devices in the electronic device group. Alternatively, in the present embodiment, the electronic device can also be replaced by a terminal device such as a mobile terminal.

[0204] Alternatively, in the present embodiment, the electronic device can be located in at least one of the network devices in the computer network.

[0205] In the present embodiment, the computer terminal can execute the program code in the method.

[0206] Alternatively, FIG. 12 is a structural block diagram of an electronic device according to an embodiment of the present disclosure. As shown in FIG. 12, the electronic device A can include one or more (only one is shown in FIG. 12) processors 102, a memory 104, a storage controller, and a peripheral interface, wherein the peripheral interface is connected with a radio frequency module, an audio module, and a display.

[0207] The memory can be used to store software programs and modules, such as program instructions / modules corresponding to the method and device in the embodiments of the present disclosure. The processor executes various functional applications and data processing by running the software programs and modules stored in the memory, that is, implements the method in the above embodiment. The memory can include a high-speed random access memory, and can also include a non-volatile memory, such as one or more magnetic storage devices, flash memories, or other non-volatile solid-state memories. In some examples, the memory can further include a memory remotely arranged with respect to the processor, which can be connected to the terminal A through a network. Examples of the above network include but are not limited to the Internet, an intranet, a local area network, a mobile communication network, and a combination thereof.

[0208] The processor can call information and application programs stored in the memory through the transmission device to perform the following steps: displaying a to-be-processed file on the operation interface in response to an input instruction acting on the operation interface, wherein the to-be-processed file contains to-be-processed text of at least one mode; and displaying a processing result on the operation interface in response to a processing instruction acting on the operation interface, wherein the processing result is used to represent a processing rule corresponding to a target type of the to-be-processed file, and the target type is determined by comparing the to-be-processed text with preset text contained in a plurality of preset files, and the types of different preset files are different.

[0209] Those skilled in the art can understand that the structure shown in FIG. 12 is only schematic, and the electronic device can also be a terminal device such as a smart phone (such as an Android phone, an iOS phone, etc.), a tablet computer, a palm computer, a Mobile Internet Device (MID), a PAD, etc. FIG. 12 does not limit the structure of the above-mentioned electronic device. For example, the electronic device A can further include more or less components (such as a network interface, a display device, etc.) than those shown in FIG. 12, or have a different configuration from that shown in FIG. 12.

[0210] Those skilled in the art can understand that all or part of the steps in the various methods of the above-mentioned embodiments can be completed by instructing the relevant hardware of the terminal device through a program, and the program can be stored in a computer readable storage medium, which can include a flash disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), a magnetic disk or an optical disk, etc.

[0211] The embodiments of the present disclosure further provide a computer readable storage medium. Optionally, in the present embodiment, the above-mentioned computer readable storage medium can be used to save the program code executed by the method provided by the above-mentioned embodiments.

[0212] Optionally, in the present embodiment, the above-mentioned storage medium can be located in any one of the electronic devices in the group of electronic devices in the computer network, or in any one of the mobile terminals in the group of mobile terminals.

[0213] Optionally, in the embodiment, the computer readable storage medium is configured to store program code for performing the following steps: in response to an input instruction acting on the operation interface, displaying a to-be-processed file on the operation interface, wherein the to-be-processed file contains to-be-processed text of at least one modality; and in response to a processing instruction acting on the operation interface, displaying a processing result on the operation interface, wherein the processing result is used to represent a processing rule corresponding to a target type of the to-be-processed file, and the processing result is used to name and store the obtained result, and the target type is determined by comparing the to-be-processed text with preset text contained in a plurality of preset files, and the types of different preset files are different.

[0214] The embodiment of the present disclosure further provides a computer program product. Optionally, the computer program product can include a computer program, and the computer program is used to implement the method provided by the above embodiment when executed by a processor.

[0215] The embodiment of the present disclosure further provides a computer program product. Optionally, the computer program product can include a non-volatile computer readable storage medium, and the non-volatile computer readable storage medium can be used to store a computer program, and the computer program is used to implement the method provided by the above embodiment when executed by a processor.

[0216] The embodiment of the present disclosure further provides a computer program. Optionally, the computer program is used to implement the method provided by the above embodiment when executed by a processor.

[0217] In the above embodiments of the present disclosure, the description of each embodiment has its own focus, and the parts not described in detail in a certain embodiment can be referred to the relevant description of other embodiments.

[0218] In the several embodiments provided by the present disclosure, it should be understood that the disclosed technology can be implemented in other ways. Of course, the embodiment described above is only illustrative, and the division of units is only a logical function division, and there can be another division manner in actual implementation, for example, a plurality of units or components can be combined or integrated into another system, or some features can be ignored or not executed. In addition, the coupling or direct coupling or communication connection between the units shown or discussed can be indirect coupling or communication connection through some interfaces, units or modules, and can be electrical or other forms.

[0219] The units described as separate components may or may not be physically separate, and the components displayed as units may or may not be physical units, that is, may be located in one place, or may be distributed on multiple network units. Part or all of the units can be selected according to actual needs to achieve the purpose of the embodiment scheme.

[0220] In addition, each functional unit in various embodiments of the present disclosure can be integrated in one processing unit, or each unit can be physically present alone, or two or more units can be integrated in one unit. The integrated unit can be realized in the form of hardware or in the form of a software functional unit.

[0221] The integrated unit, if realized in the form of a software functional unit and sold or used as an independent product, can be stored in a computer readable storage medium. Based on this understanding, the technical solutions of the present disclosure, essentially or the part that contributes to the prior art, or all or part of the technical solutions can be embodied in the form of a software product. The computer software product is stored in a storage medium, including a number of instructions to make a computer device (which can be a personal computer, a server, or a network device, etc.) execute all or part of the steps of the methods described in various embodiments of the present disclosure. The foregoing storage medium includes: a U disk, a read-only memory (ROM, Read-Only Memory), a random access memory (RAM, Random Access Memory), a mobile hard disk, a magnetic disk or an optical disk, and various program code storage media.

[0222] The above is only the preferred embodiment of the present disclosure, and it should be pointed out that for those skilled in the art, without departing from the principles of the present disclosure, a number of improvements and refinements can be made, and these improvements and refinements should be considered as the protection scope of the present disclosure.

Claims

1. A method for processing a file, comprising: displaying a to-be-processed file on an operation interface in response to an input instruction acting on the operation interface, wherein the to-be-processed file contains to-be-processed text of at least one modality; displaying a processing result on the operation interface in response to a processing instruction acting on the operation interface, wherein the processing result is used to represent that the to-be-processed file is successfully named and stored based on a target processing rule corresponding to a target type of the to-be-processed file, and the target type is determined by comparing the to-be-processed text with preset texts contained in a plurality of preset files, and types of different preset files are different.

2. The method of claim 1, wherein, In response to the processing instruction acting on the operation interface, the method further comprises: displaying target multi-dimensional information of the to-be-processed file on the operation interface, wherein the target multi-dimensional information is information obtained by extracting the to-be-processed text by using a text processing model; displaying a target file matched with the to-be-processed file on the operation interface in response to a confirmation instruction acting on the target multi-dimensional information, wherein the target file is a file determined from the plurality of preset files by comparing the target multi-dimensional information with preset multi-dimensional information of the plurality of preset files; displaying a target type of the to-be-processed file on the operation interface in response to a confirmation instruction acting on the target file, wherein the target type is a type of the target file; displaying the processing result on the operation interface in response to a confirmation instruction acting on the target type.

3. The method of claim 2, wherein, The method further comprises: displaying modified multi-dimensional information on the operation interface in response to a modification instruction acting on the target multi-dimensional information; displaying a new file on the operation interface in response to a confirmation instruction acting on the modified multi-dimensional information, and adjusting the text processing model by using the modified multi-dimensional information, wherein the new file is a file determined from the plurality of preset files by comparing the modified multi-dimensional information with preset multi-dimensional information of the plurality of preset files.

4. The method of claim 2, wherein, The method further comprises: displaying a modified target file on the operation interface in response to a modification instruction acting on the target file; displaying a new type on the operation interface in response to a confirmation instruction acting on the modified target file, wherein the new type is a type of the modified target file.

5. The method of claim 2, wherein, The displaying of the target file matched with the to-be-processed file on the operation interface in response to the confirmation instruction acting on the target multi-dimensional information comprises: displaying at least one candidate file on the operation interface, wherein the at least one candidate file is a file determined by recalling the plurality of preset files stored in a file database based on the target multi-dimensional information. In response to a determination instruction acting on the at least one candidate file, the target file is displayed on the operation interface, wherein the target file is a file determined from at least one first sorted file, and the at least one first sorted file is obtained by sorting the at least one candidate file by using the text processing model.

6. The method of claim 5, wherein, The method further comprises: In response to a modification instruction acting on the at least one candidate file, at least one modified candidate file is displayed on the operation interface; In response to a confirmation instruction acting on the at least one modified candidate file, the target file is displayed on the operation interface, wherein the target file is a file determined from at least one second sorted file, and the at least one second sorted file is determined by sorting the at least one modified candidate file by using the text processing model.

7. The method of claim 5, wherein, The method further comprises: The at least one first sorted file is displayed on the operation interface, wherein the at least one first sorted file is a result obtained by sorting the at least one candidate file by using a matching score of the at least one candidate file, and the matching score of the at least one candidate file is obtained by comparing the target multi-dimensional information and candidate multi-dimensional information of the at least one candidate file by using the text processing model, and the matching score is used to represent a probability that the to-be-processed file and a corresponding candidate file belong to the same category; In response to a confirmation instruction acting on the at least one first sorted file, the target file is displayed on the operation interface, wherein the target file is a first file in the at least one first sorted file.

8. The method of claim 7, wherein, The method further comprises: In response to a modification instruction acting on the at least one first sorted file, at least one modified sorted file is displayed on the operation interface; In response to a confirmation instruction acting on the at least one modified sorted file, the text processing model is adjusted based on the at least one modified sorted file, wherein the target file is a first file in the at least one modified sorted file.

9. The method of claim 1, wherein, The processing rule comprises a naming rule and a storage rule, and in response to a processing instruction acting on the operation interface, the method further comprises: The naming element information matched with the naming rule in the to-be-processed file is displayed on the operation interface, and the target directory corresponding to the to-be-processed file is displayed, wherein the naming element information is information extracted from the to-be-processed file by using the text processing model, and the target directory is a directory determined based on the storage rule; In response to a confirmation instruction acting on the naming element information and the target directory, the processing result is displayed on the operation interface, wherein the processing result is used to represent that the to-be-processed file is successfully named based on the naming element information and the naming rule, and the to-be-processed file is successfully stored in the target directory.

10. The method of claim 9, wherein, The method further comprises: In response to a modification instruction acting on the naming element information and the modified element information, display the modified element information on the operation interface; In response to a confirmation instruction acting on the modified element information, display the processing result on the operation interface, and adjust the text processing model based on the modified element information, wherein the processing result is used to represent that the naming of the to-be-processed file is successfully performed based on the modified element information and the naming rule, and the storage of the to-be-processed file into the target directory is successfully performed based on the storage rule.

11. The method of claim 9, wherein, In response to a modification instruction acting on the target directory, the method further comprises: displaying the modified directory on the operation interface; In response to a confirmation instruction acting on the modified directory, display the processing result on the operation interface, and adjust the storage rule based on the modified directory, wherein the processing result is obtained by naming the to-be-processed file based on the naming element information and the naming rule, and storing the to-be-processed file into the modified directory based on the storage rule.

12. A file processing method, comprising: obtaining a to-be-processed file; parsing the to-be-processed text to obtain a to-be-processed text contained in the to-be-processed file; comparing the to-be-processed text with a plurality of preset texts contained in a plurality of preset files to determine a target type of the to-be-processed file, wherein the types of different preset files are different; naming and storing the to-be-processed file based on a processing rule corresponding to the target type.

13. A file processing method, comprising: receiving a judicial case file sent by a client; parsing the judicial case file to obtain a judicial case text contained in the judicial case file; comparing the judicial case text with a plurality of preset case texts contained in a plurality of preset case files to determine a target type of the judicial case file, wherein the types of different preset case files are different; naming and storing the judicial case file based on a processing rule corresponding to the target type to obtain a processing result; sending the processing result to the client.

14. A file processing method, comprising: obtaining a to-be-processed file by calling a first interface, wherein the first interface comprises a first parameter, and a parameter value of the first parameter comprises the to-be-processed file; parsing the to-be-processed text to obtain a to-be-processed text contained in the to-be-processed file; comparing the to-be-processed text with a plurality of preset texts contained in a plurality of preset files to determine a target type of the to-be-processed file, wherein the types of different preset files are different; naming and storing the to-be-processed file based on a processing rule corresponding to the target type to obtain a processing result; outputting the processing result by calling a second interface, wherein the second interface comprises a second parameter, and a parameter value of the second parameter comprises the processing result.

15. An electronic device, comprising: a memory storing an executable program; The processor is configured to run the program, and when the program is running, the following method is executed: in response to an input instruction acting on an operation interface, a to-be-processed file is displayed on the operation interface, wherein the to-be-processed file contains to-be-processed text of at least one modality; and in response to a processing instruction acting on the operation interface, a processing result is displayed on the operation interface, wherein the processing result is used to represent that the to-be-processed file is successfully named and stored based on a target processing rule, the target processing rule is a processing rule corresponding to a target type of the to-be-processed file, and the target type is determined by comparing the to-be-processed text with preset texts contained in a plurality of preset files, and types of different preset files are different.

16. The electronic device of claim 15, wherein, When the program is running, the following method is further executed: Target multi-dimensional information of the to-be-processed file is displayed on the operation interface, wherein the target multi-dimensional information is information obtained by extracting the to-be-processed text by using a text processing model; In response to a confirmation instruction acting on the target multi-dimensional information, a target file matched with the to-be-processed file is displayed on the operation interface, wherein the target file is a file determined from the plurality of preset files by comparing the target multi-dimensional information with preset multi-dimensional information of the plurality of preset files; In response to a confirmation instruction acting on the target file, a target type of the to-be-processed file is displayed on the operation interface, wherein the target type is a type of the target file; In response to a confirmation instruction acting on the target type, the processing result is displayed on the operation interface.

17. The electronic device of claim 16, wherein, When the program is running, the following method is further executed: In response to a modification instruction acting on the target multi-dimensional information, modified multi-dimensional information is displayed on the operation interface; In response to a confirmation instruction acting on the modified multi-dimensional information, a new file is displayed on the operation interface, and the text processing model is adjusted by using the modified multi-dimensional information, wherein the new file is a file determined from the plurality of preset files by comparing the modified multi-dimensional information with preset multi-dimensional information of the plurality of preset files.

18. The electronic device of claim 16, wherein, When the program is running, the following method is further executed: In response to a modification instruction acting on the target file, a modified target file is displayed on the operation interface; In response to a confirmation instruction acting on the modified target file, a new type is displayed on the operation interface, wherein the new type is a type of the modified target file.

19. A computer readable storage medium comprising a stored executable program, wherein, The executable program is run to control a device in which the computer readable storage medium is located to execute the method of any one of claims 1 to 14.

20. A computer program product comprising a computer program which, when executed by a processor, implements the method of any one of claims 1 to 14.

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