A Text-Assisted Proofreading Method and System Based on a Large Language Model

Abstract

A text-assisted proofreading method and system based on a large language model is disclosed. The system includes: a text processing unit for learning text proofreading techniques based on the large language model, uploading, partitioning, and proofreading texts requiring proofreading, and outputting proofreading results; a data acquisition unit for collecting various data required by the system; a core processing unit for receiving the text proofreading results and various data, analyzing and processing them, and generating a text proofreading report based on the analysis and processing results; and a display and reminder unit for receiving the text proofreading report generated by the core processing unit, displaying it hierarchically according to the relevant content of the received report, and issuing warnings and reminders based on pre-set thresholds. This invention improves the efficiency of text proofreading.

Description

Technical Field

[0001] This invention belongs to the field of data processing technology, and specifically relates to a text-assisted review and proofreading method and system based on a large language model. Background Technology

[0002] With the development of internet technology and the increasing informatization of society, the volume of text data has exploded. Text data, with its rich information content and wide range of applications, has become an important form for people to acquire knowledge, express opinions, and transmit information. However, due to various reasons, such as individual language proficiency, cognitive abilities, and limitations of input devices, people may make various errors when writing and editing text, such as typos, misspellings, and grammatical errors. These errors not only affect the quality of the text but also hinder people's accurate understanding and effective use of textual information. Faced with a large amount of text containing errors, traditional manual proofreading methods cannot meet the ever-increasing proofreading needs, and are also extremely labor-intensive, time-consuming, and inefficient. Summary of the Invention

[0003] To address the shortcomings of existing technologies, this invention provides a text-assisted proofreading method and system based on a large language model, which solves the technical problems that traditional manual proofreading methods cannot meet the growing proofreading needs, and are also characterized by huge workload, time and labor consumption, and low efficiency.

[0004] To solve the above-mentioned technical problems, the present invention adopts the following technical solution.

[0005] This invention first discloses a text-assisted proofreading method based on a large language model, which includes the following steps:

[0006] It includes a text processing unit, a data acquisition unit, a network transmission unit, a core processing unit, and a display and notification unit.

[0007] The text processing unit is used to learn text review and proofreading based on a large language model, and to upload, partition, and review and proofread the text that needs to be reviewed and proofread, and output the text review and proofreading results.

[0008] The data acquisition unit is used to collect various data required by the text-assisted review and proofreading system, including data on the number of text errors, the number of text characters, the number of text punctuation marks, the size of text images, the size of text tables, and the duration of text review and proofreading.

[0009] The network transmission unit is used to transmit the text review and proofreading results output by the text processing unit and the various data required by the text auxiliary review and proofreading system collected by the data acquisition unit to the core processing unit, and to transmit the text review and proofreading report generated by the core processing unit to the display and reminder unit.

[0010] The core processing unit is used to receive the text review and proofreading results output by the text processing unit and the various data required by the text-assisted review and proofreading system collected by the data acquisition unit, and to analyze and process the text review and proofreading results and various data, and generate a text review and proofreading report based on the analysis and processing results.

[0011] The display and reminder unit is used to receive the text review and proofreading report generated by the core processing unit, and to display it in a hierarchical manner according to the relevant content of the received report, while issuing warnings and reminders based on a pre-set threshold.

[0012] The present invention further includes the following preferred embodiments:

[0013] The text processing unit includes a large language active learning module, a text upload module, a text partitioning module, a text review module, and a result output module. The large language active learning module is used to actively learn and improve the text review and proofreading based on a large language model. The text upload module is used to upload text requiring review and proofreading to the review and proofreading system for review and proofreading. The text partitioning module is used to divide the uploaded text requiring review and proofreading into multiple parts. The text review and proofreading module is used to review and proofread the partitioned text requiring review and proofreading. The result output module is used to output the review and proofreading results of the completed text.

[0014] The data acquisition unit includes an error count acquisition module, a text count acquisition module, a punctuation mark acquisition module, an image size acquisition module, a table size acquisition module, and a proofreading time acquisition module. The error count acquisition module collects data on the number of text errors required by the text-assisted proofreading system. The text count acquisition module collects data on the number of text characters required by the text-assisted proofreading system. The punctuation mark acquisition module collects data on the number of punctuation marks required by the text-assisted proofreading system. The image size acquisition module collects data on the size of text images required by the text-assisted proofreading system. The table size acquisition module collects data on the size of text tables required by the text-assisted proofreading system. The proofreading time acquisition module collects data on the time taken for the text to be proofread by the text-assisted proofreading system.

[0015] The core processing unit includes a data receiving module, an analysis and processing module, and a report generation module. The data receiving module receives the text review and proofreading results output by the text processing unit and the various data transmissions required by the text-assisted review and proofreading system collected by the data acquisition unit. The analysis and processing module analyzes and processes the data received by the data receiving module. The report generation module generates a report on text review and proofreading based on the analysis and processing results from the analysis and processing module.

[0016] The analysis and processing module is further used to obtain the number of errors correctly identified by the review and proofreading system, the total number of errors identified by the review and proofreading system, the total number of errors actually existing in the text, the total number of characters contained in the text, the total number of punctuation marks contained in the text, the total size of images contained in the text, the total size of tables contained in the text, and the review and proofreading time of the review and proofreading system. Based on the analysis and processing of the above data, the review and proofreading level evaluation index EI of the text-assisted review and proofreading system is derived.

[0017]

[0018] Where RW is the number of errors correctly identified by the review and proofreading system, XZ is the total number of errors identified by the review and proofreading system, SZ is the total number of errors actually existing in the text, NW is the total number of characters contained in the text, NP is the total number of punctuation marks contained in the text, IS is the total size of images contained in the text, TS is the total size of tables contained in the text, and t is the review and proofreading time of the review and proofreading system.

[0019] The review and proofreading level of the text review and proofreading system is evaluated based on the review and proofreading level assessment index.

[0020] The analysis and processing module is further used to obtain, through the data acquisition unit, the total number of texts in each section, the total number of punctuation marks in each section, the total size of images in each section, the total size of tables in each section, the number of erroneous texts at each location, the number of texts between two adjacent text errors, the number of erroneous punctuation marks at each location, the size of each erroneous image, and the size of each erroneous table. Based on the data obtained above, the module analyzes and processes the data to obtain the review and proofreading attention index (AP) for each error.

[0021]

[0022] ZW represents the total number of text characters in each section, ZP represents the total number of punctuation marks in each section, ZI represents the total size of images in each section, ZT represents the total size of tables in each section, and CW represents the total size of tables in each section. i LW represents the number of errors per instance, where n is the total number of errors. a, where m is the number of characters between two adjacent text errors, CP is the number of punctuation marks per error, CI is the size of the image for each error, and CT is the size of the table for each error.

[0023] The level of attention paid to each error during review and proofreading is displayed in a tiered manner through a tiered display module.

[0024] The display and reminder unit includes: a report receiving module, a hierarchical display module, a threshold setting module, and a warning and reminder module; the report receiving module is used to receive text review and proofreading reports generated by the core processing unit; the hierarchical display module is used to display errors found after text review and proofreading in a hierarchical manner according to the relevant content of the reports received by the report receiving module; the threshold setting module is used to set the threshold for warning and reminder for the text undergoing review and proofreading; the warning and reminder module is used to issue warnings and reminders when the relevant results in the received reports reach the set threshold.

[0025] This invention also discloses a text-based proofreading method using the aforementioned large language model-based text-assisted proofreading system, comprising:

[0026] Step 1: Use the text processing unit to upload, partition, and review the text that needs to be reviewed and proofread, and output the text review and proofreading results;

[0027] Step 2: Collect the various data required by the text-assisted review and proofreading system, including data on the number of text errors, the number of text characters, the number of text punctuation marks, the size of text images, the size of text tables, and the duration of text review and proofreading;

[0028] Step 3: Transmit the text review and proofreading results output by the text processing unit and the data required by the text auxiliary review and proofreading system collected by the data acquisition unit to the core processing unit via the network transmission unit;

[0029] Step 4: Receive the text review and proofreading results output by the text processing unit and the various data required by the text-assisted review and proofreading system collected by the data acquisition unit, analyze and process the text review and proofreading results and various data, and generate a text review and proofreading report based on the analysis and processing results;

[0030] Step 5: Receive the text review and proofreading report generated by the core processing unit, and display it in a hierarchical manner according to the relevant content of the received report, while issuing warnings and reminders based on pre-set thresholds.

[0031] Accordingly, this application also discloses a terminal, including a processor and a storage medium;

[0032] The storage medium is used to store instructions;

[0033] The processor is configured to operate according to the instructions to perform the steps of the aforementioned text-assisted review and proofreading method based on a large language model.

[0034] Accordingly, this application also discloses a computer-readable storage medium storing a computer program that, when executed by a processor, implements the steps of the aforementioned text-assisted review and proofreading method based on a large language model.

[0035] The beneficial effects of this invention are that, compared with the prior art, this invention provides a text-assisted proofreading method and system based on a large language model, which improves the problems of huge workload and low efficiency of traditional manual proofreading. Furthermore, it can obtain the number of errors correctly identified by the proofreading system, the total number of errors identified by the proofreading system, the total number of actual errors in the text, the total number of characters contained in the text, the total number of punctuation marks contained in the text, the total size of images contained in the text, the total size of tables contained in the text, and the time taken by the proofreading system to review and proofread the text. By analyzing and processing the above data, a proofreading level evaluation index for the text-assisted proofreading system is obtained, and the proofreading level of the text-assisted proofreading system is evaluated based on the proofreading level evaluation index. The system obtains the total number of texts in each section, the total number of punctuation marks in each section, the total size of images in each section, the total size of tables in each section, the number of erroneous texts in each section, the number of texts between two adjacent text errors, the number of erroneous punctuation marks in each section, the size of images in each section, and the size of tables in each section. It then analyzes and processes these data to obtain the review and proofreading attention index for each error and displays them in a tiered manner based on this index using the tiered display module. Attached Figure Description

[0036] Figure 1 This is a schematic diagram of the structure of the text-assisted review and proofreading system based on a large language model in this invention. Detailed Implementation

[0037] To make the objectives, technical solutions, and advantages of the present invention clearer, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention.

[0038] The embodiments described in this application are merely some, not all, embodiments of the present invention. Based on the spirit of the present invention, other embodiments obtained by those skilled in the art without inventive effort are all within the protection scope of the present invention.

[0039] To address the shortcomings of existing technologies, this invention proposes a text-assisted proofreading method and system based on a large language model. The method learns from the large language model to perform text proofreading, uploads, partitions, performs proofreading, and outputs the proofreading results. It obtains the total number of characters in each section, the total number of punctuation marks in each section, the total size of each image in each section, the total size of each table in each section, the number of characters at each error, the number of characters between two adjacent errors, the number of punctuation marks at each error, the size of each error image, and the size of each error table. It analyzes and processes these data to obtain a proofreading attention index for each error, and then displays the text in a tiered manner based on this index using a tiered display module.

[0040] See Figure 1 As shown, the text-assisted review and proofreading system based on a large language model includes a text processing unit, a data acquisition unit, a network transmission unit, a core processing unit, and a display and reminder unit.

[0041] The text processing unit is used to learn text review and proofreading based on a large language model, and to upload, partition, and review the text that needs to be reviewed and proofread, and output the text review and proofreading results.

[0042] In a further embodiment, the text processing unit includes a large language active learning module, a text upload module, a text partitioning module, a text review module, and a result output module. The large language active learning module is used to actively learn and improve the text review and proofreading based on a large language model; the text upload module is used to upload the text requiring review and proofreading to the review and proofreading system for review and proofreading; the text partitioning module is used to divide the uploaded text requiring review and proofreading into multiple parts; the text review and proofreading module is used to review and proofread the partitioned text requiring review and proofreading; and the result output module is used to output the review and proofreading results of the completed text.

[0043] The data acquisition unit is used to collect various data required by the text-assisted review and proofreading system, including data on the number of text errors, the number of text characters, the number of text punctuation marks, the size of text images, the size of text tables, and the duration of text review and proofreading.

[0044] In a further embodiment, the data acquisition unit includes an error count acquisition module, a text count acquisition module, a punctuation count acquisition module, an image size acquisition module, a table size acquisition module, and a proofreading time acquisition module. The error count acquisition module is used to collect data on the number of text errors required by the text-assisted proofreading system. The text count acquisition module is used to collect data on the number of text characters required by the text-assisted proofreading system. The punctuation count acquisition module is used to collect data on the number of punctuation marks required by the text-assisted proofreading system. The image size acquisition module is used to collect data on the size of text images required by the text-assisted proofreading system. The table size acquisition module is used to collect data on the size of text tables required by the text-assisted proofreading system. The proofreading time acquisition module is used to collect data on the time taken for the text to be proofread by the text-assisted proofreading system.

[0045] The network transmission unit is used to transmit the text review and proofreading results output by the text processing unit and the various data required by the text auxiliary review and proofreading system collected by the data acquisition unit to the core processing unit, and to transmit the text review and proofreading report generated by the core processing unit to the display and reminder unit.

[0046] The core processing unit is used to receive the text review and proofreading results output by the text processing unit and the various data required by the text-assisted review and proofreading system collected by the data acquisition unit, and to analyze and process the text review and proofreading results and various data, and generate a text review and proofreading report based on the analysis and processing results.

[0047] In a further embodiment, the core processing unit includes: a data receiving module, an analysis and processing module, and a report generation module; the data receiving module is used to receive the text review and proofreading results output by the text processing unit and the various data transmissions required by the text-assisted review and proofreading system collected by the data acquisition unit; the analysis and processing module is used to analyze and process the various data received by the data receiving module; the report generation module is used to generate a report on text review and proofreading based on the analysis and processing results of the analysis and processing module.

[0048] In a further embodiment, the analysis and processing module, during analysis and processing, obtains the number of errors correctly identified by the review and proofreading system, the total number of errors identified by the review and proofreading system, the total number of actual errors in the text, the total number of characters contained in the text, the total number of punctuation marks contained in the text, the total size of images contained in the text, the total size of tables contained in the text, and the time taken for the review and proofreading system to review and proofread the text. Based on the analysis and processing of the above data, it derives the text-assisted review and proofreading level evaluation index EI:

[0049]

[0050] Where RW represents the number of errors correctly identified by the review and proofreading system, XZ represents the total number of errors identified by the review and proofreading system, SZ represents the total number of errors actually existing in the text, NW represents the total number of characters contained in the text, NP represents the total number of punctuation marks contained in the text, IS represents the total size of images contained in the text, TS represents the total size of tables contained in the text, and t represents the review and proofreading time of the review and proofreading system.

[0051] The proofreading and review level of the text-assisted review and review system is evaluated based on its performance assessment index. A higher performance assessment index indicates a higher level of proofreading and review, while a lower index indicates a lower level. When the performance assessment index reaches the threshold set by the threshold setting module, an alert is issued via the warning module, and the system is upgraded and optimized to improve its overall performance.

[0052] In a further embodiment, the analysis and processing module, during analysis and processing, obtains the total number of texts in each part, the total number of punctuation marks in each part, the total size of images in each part, the total size of tables in each part, the number of erroneous texts at each error, the number of texts between two adjacent text errors, the number of erroneous punctuation marks at each error, the size of images at each error, and the size of tables at each error through the data acquisition unit. Based on the data obtained above, the module analyzes and processes the data to obtain the review and proofreading attention index (AP) for each error.

[0053]

[0054] ZW represents the total number of text characters in each section, ZP represents the total number of punctuation marks in each section, ZI represents the total size of images in each section, ZT represents the total size of tables in each section, and CW represents the total size of tables in each section. i LW represents the number of errors per instance, where n is the total number of errors. a is the number of characters between two adjacent text errors, m is the total number of groups of two adjacent text errors, CP is the number of punctuation marks per error, CI is the size of the image per error, and CT is the size of the table for each error.

[0055] The level of attention paid to each error during the review and proofreading process is displayed in a tiered manner through the tiered display module. The higher the level of attention paid to each error during the review and proofreading process, the higher the level of display for the error discovered during the review and proofreading process, and vice versa.

[0056] The display and reminder unit is used to receive the text review and proofreading report generated by the core processing unit, and to display it in a hierarchical manner according to the relevant content of the received report, while issuing warnings and reminders based on a pre-set threshold.

[0057] In a further embodiment, the display and reminder unit includes: a report receiving module, a hierarchical display module, a threshold setting module, and a warning and reminder module. The report receiving module is used to receive text review and proofreading reports generated by the core processing unit; the hierarchical display module is used to hierarchically display errors found after text review and proofreading based on the relevant content of the reports received by the report receiving module; the threshold setting module is used to set thresholds for warning and reminders for the text undergoing review and proofreading; and the warning and reminder module is used to issue warnings and reminders when relevant results in the received reports reach the set thresholds.

[0058] According to another aspect of the present invention, a text-assisted proofreading method based on a large language model is provided. This method is implemented based on the aforementioned text-assisted proofreading system based on a large language model, and specifically includes the following steps:

[0059] Step 1: Use the text processing unit to upload, partition, and review the text that needs to be reviewed and proofread, and output the text review and proofreading results.

[0060] Specifically, the text upload module of the text processing unit uploads the text that needs to be reviewed and proofread, and then the text partitioning module, text review module and result output module perform partitioning, review and proofreading, and output the text review and proofreading results.

[0061] Step 2: Collect the various data required by the text-assisted review and proofreading system, including data on the number of text errors, the number of text characters, the number of text punctuation marks, the size of text images, the size of text tables, and the duration of text review and proofreading;

[0062] Specifically, the error count collection module, text count collection module, punctuation count collection module, image size collection module, table size collection module, and proofreading time collection module of the data collection unit collect various data required by the proofreading system during the text review and proofreading process, including the number of text errors, the number of text characters, the number of text punctuation marks, the size of text images, the size of text tables, and the time for text review and proofreading.

[0063] Step 3: Transmit the text review and proofreading results output by the text processing unit and the data required by the text auxiliary review and proofreading system collected by the data acquisition unit to the core processing unit via the network transmission unit;

[0064] Step 4: Receive the text review and proofreading results output by the text processing unit and the various data required by the text-assisted review and proofreading system collected by the data acquisition unit, analyze and process the text review and proofreading results and various data, and generate a text review and proofreading report based on the analysis and processing results;

[0065] Specifically, the data receiving module of the core processing unit receives the transmitted review and proofreading results and the various data required by the review and proofreading system. The analysis and processing module then analyzes and processes the data received by the data receiving module. During analysis, the analysis and processing module obtains the number of errors correctly identified by the review and proofreading system, the total number of errors identified by the review and proofreading system, the total number of actual errors in the text, the total number of characters contained in the text, the total number of punctuation marks contained in the text, the total size of images contained in the text, the total size of tables contained in the text, and the review and proofreading time of the review and proofreading system. Based on the analysis and processing of these data, a review and proofreading level evaluation index for the text-assisted review and proofreading system is derived. The review and proofreading level of the text-assisted review and proofreading system is evaluated based on this evaluation index. A higher performance evaluation index indicates a higher review and proofreading level, and vice versa.

[0066] Simultaneously, the analysis and processing module can acquire data from the data acquisition unit during analysis and processing, including the total number of texts in each section, the total number of punctuation marks in each section, the total size of images in each section, the total size of tables in each section, the number of erroneous texts, the number of texts between two adjacent text errors, the number of erroneous punctuation marks, the size of each erroneous image, and the size of each erroneous table. Based on this data analysis and processing, it obtains the review and proofreading attention index for each error. This index is then used to display each error in a tiered manner through the hierarchical display module. A higher review and proofreading attention index indicates a higher level of error display during text review and proofreading, and vice versa. Finally, the report generation module generates a text review and proofreading report based on the analysis and processing results from the analysis and processing module.

[0067] Step 5: Receive the text review and proofreading report generated by the core processing unit, and display it in a hierarchical manner according to the relevant content of the received report, while issuing warnings and reminders based on pre-set thresholds.

[0068] Specifically, the text review and proofreading report is transmitted to the display and reminder unit via the network transmission unit. The report receiving module of the display and reminder unit receives the transmitted report, and then the hierarchical display module displays the errors found after the text review and proofreading in a hierarchical manner. At the same time, when the results in the text review and proofreading report reach the threshold set by the threshold setting module, the warning and reminder module issues the corresponding warning and reminder to upgrade and optimize the text-assisted review and proofreading system to improve the review and proofreading level of the text-assisted review and proofreading system.

[0069] The beneficial effects of this invention are that, compared with the prior art, this invention provides a text-assisted proofreading method and system based on a large language model, which can improve the problems of huge workload and low efficiency of traditional manual proofreading. Furthermore, it can obtain the number of errors correctly identified by the proofreading system, the total number of errors identified by the proofreading system, the total number of actual errors in the text, the total number of characters contained in the text, the total number of punctuation marks contained in the text, the total size of images contained in the text, the total size of tables contained in the text, and the time taken by the proofreading system to review and proofread the text. By analyzing and processing the above data, a proofreading level evaluation index for the text-assisted proofreading system is obtained, and the proofreading level of the text-assisted proofreading system is evaluated based on the proofreading level evaluation index. The system obtains the total number of texts in each section, the total number of punctuation marks in each section, the total size of images in each section, the total size of tables in each section, the number of erroneous texts in each section, the number of texts between two adjacent text errors, the number of erroneous punctuation marks in each section, the size of images in each section, and the size of tables in each section. It then analyzes and processes these data to obtain the review and proofreading attention index for each error and displays them in a tiered manner based on this index using the tiered display module.

[0070] Based on the spirit of this invention, those skilled in the art will readily conceive of a computer program product derived from the aforementioned text-assisted proofreading method based on a large language model. The computer program product may include a computer-readable storage medium on which computer-readable program instructions are loaded to enable a processor to implement various aspects of this disclosure. That is, this application also includes a terminal comprising a processor and a storage medium; the storage medium is used to store instructions; the processor is used to operate according to the instructions to perform the steps of the aforementioned text-assisted proofreading method based on a large language model.

[0071] Computer-readable storage media can be tangible devices capable of holding and storing instructions for use by an instruction execution device. Computer-readable storage media can be, for example, but not limited to, electrical storage devices, magnetic storage devices, optical storage devices, electromagnetic storage devices, semiconductor storage devices, or any suitable combination of the foregoing. More specific examples (a non-exhaustive list) of computer-readable storage media include: portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), static random access memory (SRAM), portable compact disc read-only memory (CD-ROM), digital multifunction disc (DVD), memory sticks, floppy disks, mechanical encoding devices, such as punch cards or recessed protrusions storing instructions thereon, and any suitable combination of the foregoing. The computer-readable storage media used herein are not to be construed as transient signals themselves, such as radio waves or other freely propagating electromagnetic waves, electromagnetic waves propagating through waveguides or other transmission media (e.g., light pulses through fiber optic cables), or electrical signals transmitted through wires.

[0072] The computer-readable program instructions described herein can be downloaded from computer-readable storage media to various computing / processing devices, or downloaded via a network, such as the Internet, local area network, wide area network, and / or wireless network, to an external computer or external storage device. The network may include copper transmission cables, fiber optic transmission, wireless transmission, routers, firewalls, switches, gateway computers, and / or edge servers. A network adapter card or network interface in each computing / processing device receives the computer-readable program instructions from the network and forwards them to the computer-readable storage media in the respective computing / processing device.

[0073] Computer program instructions used to perform the operations of this disclosure may be assembly instructions, instruction set architecture (ISA) instructions, machine instructions, machine-dependent instructions, microcode, firmware instructions, status setting data, or source code or object code written in any combination of one or more programming languages, including object-oriented programming languages ​​such as Smalltalk, C++, etc., and conventional procedural programming languages ​​such as the "C" language or similar programming languages. The computer-readable program instructions may execute entirely on the user's computer, partially on the user's computer, as a standalone software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In cases involving a remote computer, the remote computer may be connected to the user's computer via any type of network—including a local area network (LAN) or a wide area network (WAN)—or may be connected to an external computer (e.g., via the Internet using an Internet service provider). In some embodiments, electronic circuitry, such as programmable logic circuitry, field-programmable gate arrays (FPGAs), or programmable logic arrays (PLAs), is personalized by utilizing the status information of the computer-readable program instructions to implement various aspects of this disclosure.

[0074] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and not to limit it. Although the present invention has been described in detail with reference to the above embodiments, those skilled in the art should understand that modifications or equivalent substitutions can still be made to the specific implementation of the present invention. Any modifications or equivalent substitutions that do not depart from the spirit and scope of the present invention should be covered within the protection scope of the claims of the present invention.

Claims

1. A text-assisted proofreading system based on a large language model, characterized in that, It includes a text processing unit, a data acquisition unit, a network transmission unit, a core processing unit, and a display and notification unit. The text processing unit is used to learn text review and proofreading based on a large language model, and to upload, partition, and review and proofread the text that needs to be reviewed and proofread, and output the text review and proofreading results. The data acquisition unit is used to collect various data required by the text-assisted review and proofreading system, including data on the number of text errors, the number of text characters, the number of text punctuation marks, the size of text images, the size of text tables, and the duration of text review and proofreading. The network transmission unit is used to transmit the text review and proofreading results output by the text processing unit and the various data required by the text auxiliary review and proofreading system collected by the data acquisition unit to the core processing unit, and to transmit the text review and proofreading report generated by the core processing unit to the display and reminder unit. The core processing unit is used to receive the text review and proofreading results output by the text processing unit and the various data required by the text-assisted review and proofreading system collected by the data acquisition unit, and to analyze and process the text review and proofreading results and various data, and generate a text review and proofreading report based on the analysis and processing results. The display reminder unit is used to receive the text review and proofreading report generated by the core processing unit, and to display it in a hierarchical manner according to the relevant content of the received report, while also issuing warning reminders based on a pre-set threshold. The core processing unit includes a data receiving module, an analysis and processing module, and a report generation module. The data receiving module receives the text review and proofreading results output by the text processing unit and the various data transmissions required by the text-assisted review and proofreading system collected by the data acquisition unit. The analysis and processing module analyzes and processes the various data received by the data receiving module. The report generation module generates a report on text review and proofreading based on the analysis and processing results from the analysis and processing module. The analysis and processing module is further used to obtain, through the data acquisition unit, the total number of texts in each section, the total number of punctuation marks in each section, the total size of images in each section, the total size of tables in each section, the number of erroneous texts at each location, the number of texts between two adjacent text errors, the number of erroneous punctuation marks at each location, the size of each erroneous image, and the size of each erroneous table. Based on the data obtained above, the module analyzes and processes the data to obtain the review and proofreading attention index (AP) for each error. ZW represents the total number of text characters in each section, ZP represents the total number of punctuation marks in each section, ZI represents the total size of images in each section, ZT represents the total size of tables in each section, and CW represents the total size of tables in each section. i LW represents the number of errors per instance, where n is the total number of errors. a , where m is the number of characters between two adjacent text errors, CP is the number of punctuation marks per error, CI is the size of the image for each error, and CT is the size of the table for each error. The level of attention paid to each error during review and proofreading is displayed in a tiered manner through a tiered display module.

2. The text-assisted proofreading system based on a large language model according to claim 1, characterized in that, The text processing unit includes a large language active learning module, a text upload module, a text partitioning module, a text review module, and a result output module. The large language active learning module is used to actively learn and improve the text review and proofreading based on a large language model. The text upload module is used to upload text requiring review and proofreading to the review and proofreading system for review and proofreading. The text partitioning module is used to divide the uploaded text requiring review and proofreading into multiple parts. The text review and proofreading module is used to review and proofread the partitioned text requiring review and proofreading. The result output module is used to output the review and proofreading results of the completed text.

3. The text-assisted proofreading system based on a large language model according to claim 2, characterized in that, The data acquisition unit includes an error count acquisition module, a text count acquisition module, a punctuation mark count acquisition module, an image size acquisition module, a table size acquisition module, and a proofreading time acquisition module; the error count acquisition module is used to collect various data on the number of text errors required by the text-assisted proofreading system. The text quantity acquisition module is used to collect various data related to the text quantity required by the text-assisted review and proofreading system; The punctuation mark quantity acquisition module is used to collect various data related to the quantity of punctuation marks in the text required by the text-assisted review and proofreading system. The image size acquisition module is used to acquire various data related to the size of text images required by the text-assisted review and proofreading system; the table size acquisition module is used to acquire various data related to the size of text tables required by the text-assisted review and proofreading system; and the review and proofreading time acquisition module is used to acquire data on the time taken by the text-assisted review and proofreading system to review and proofread text.

4. The text-assisted proofreading system based on a large language model according to claim 3, characterized in that, The analysis and processing module is further used to obtain the number of errors correctly identified by the review and proofreading system, the total number of errors identified by the review and proofreading system, the total number of errors actually existing in the text, the total number of characters contained in the text, the total number of punctuation marks contained in the text, the total size of images contained in the text, the total size of tables contained in the text, and the review and proofreading time of the review and proofreading system. Based on the analysis and processing of the above data, the review and proofreading level evaluation index EI of the text-assisted review and proofreading system is derived. Where RW is the number of errors correctly identified by the review and proofreading system, XZ is the total number of errors identified by the review and proofreading system, SZ is the total number of errors actually existing in the text, NW is the total number of characters contained in the text, NP is the total number of punctuation marks contained in the text, IS is the total size of images contained in the text, TS is the total size of tables contained in the text, and t is the review and proofreading time of the review and proofreading system. The review and proofreading level of the text review and proofreading system is evaluated based on the review and proofreading level assessment index.

5. The text-assisted proofreading system based on a large language model according to claim 4, characterized in that, The display and reminder unit includes: a report receiving module, a hierarchical display module, a threshold setting module, and a warning and reminder module; the report receiving module is used to receive text review and proofreading reports generated by the core processing unit; the hierarchical display module is used to display errors found after text review and proofreading in a hierarchical manner according to the relevant content of the reports received by the report receiving module; the threshold setting module is used to set the threshold for warning and reminder for the text undergoing review and proofreading; the warning and reminder module is used to issue warnings and reminders when the relevant results in the received reports reach the set threshold.

6. A text-assisted proofreading method based on the large language model-based text-assisted proofreading system according to any one of claims 1-5, characterized in that, include: Step 1: Use the text processing unit to upload, partition, and review the text that needs to be reviewed and proofread, and output the text review and proofreading results; Step 2: Collect the various data required by the text-assisted review and proofreading system, including data on the number of text errors, the number of text characters, the number of text punctuation marks, the size of text images, the size of text tables, and the duration of text review and proofreading; Step 3: Transmit the text review and proofreading results output by the text processing unit and the data required by the text auxiliary review and proofreading system collected by the data acquisition unit to the core processing unit via the network transmission unit; Step 4: Receive the text review and proofreading results output by the text processing unit and the various data required by the text-assisted review and proofreading system collected by the data acquisition unit, analyze and process the text review and proofreading results and various data, and generate a text review and proofreading report based on the analysis and processing results; Step 5: Receive the text review and proofreading report generated by the core processing unit, and display it in a hierarchical manner according to the relevant content of the received report, while issuing warnings and reminders based on pre-set thresholds.

7. A terminal, comprising a processor and a storage medium; characterized in that: The storage medium is used to store instructions; The processor is configured to operate according to the instructions to perform the steps of the text-assisted review and proofreading method based on a large language model according to any one of claims 6.

8. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the program is executed by the processor, it implements the steps of the text-assisted review and proofreading method based on a large language model as described in any one of claims 6.

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