Text auxiliary auditing and proofreading method and system based on large language model

Abstract

The invention discloses a text auxiliary auditing and proofreading method and system based on a large language model. The system comprises a text processing unit which is used for performing text auditing and proofreading learning based on a large language model, performing uploading, partitioning and auditing and proofreading on a text needing to be audited and proofreading, and outputting a text auditing and proofreading result; the data acquisition unit is used for acquiring various data required by the system; the core processing unit is used for receiving the text checking and proofreading result and the various data, analyzing and processing the text checking and proofreading result and the various data, and generating a text checking and proofreading report according to an analysis and processing result; and the display reminding unit is used for receiving the text checking and proofreading report generated by the core processing unit, performing hierarchical display according to related contents of the received report, and performing warning reminding based on a preset threshold value. According to the scheme, the text checking and proofreading efficiency is improved.

Description

Technical Field

[0001] The present invention belongs to the technical field of data processing, and in particular relates to a text-assisted review and proofreading method and system based on a large language model. Background Art

[0002] With the development of Internet technology and the improvement of social informatization, the amount of text data has shown explosive growth. With its rich information content and wide application fields, text data has become an important form for people to acquire knowledge, express opinions and transmit information. However, due to various reasons, such as personal language level, cognitive ability, input device limitations, etc., people may make various errors when writing and editing texts, such as word fraud, word errors, grammatical errors, etc. These errors not only affect the quality of the text, but also hinder people's accurate understanding and effective use of text information. Faced with a large number of erroneous texts, traditional manual proofreading methods cannot meet the growing proofreading needs, and the workload is huge, time-consuming and labor-intensive, and inefficient. Summary of the invention

[0003] In order to address the deficiencies in the prior art, the present invention provides a text-assisted review and proofreading method and system based on a large language model to solve the technical problems that traditional manual proofreading methods cannot meet the growing proofreading needs, and have a huge workload, are time-consuming and labor-intensive, and have low efficiency.

[0004] In order to solve the above technical problems, the present invention adopts the following technical solutions.

[0005] The present invention first discloses a text-assisted review and proofreading method based on a large language model, the method comprising the following steps:

[0006] It includes text processing unit, data collection unit, network transmission unit, core processing unit and display reminder unit.

[0007] The text processing unit is used to learn text review and proofreading based on the large language model, 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 collection unit is used to collect various data required by the text-assisted review and proofreading system, including data on the number of text errors, data on the number of text characters, data on the number of text punctuation marks, data on the size of text images, data on the size of text tables, and data on 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 various data required by the text auxiliary review and proofreading system collected by the data collection unit to the core processing unit, and transmit the text review and proofreading report generated by the core processing unit to the display reminder unit;

[0010] The core processing unit is used to receive the text review and proofreading results output by the text processing unit and various data required by the text auxiliary review and proofreading system collected by the data collection unit, and analyze and process the text review and proofreading results and various data, and generate a text review and proofreading report according to the analysis and processing results;

[0011] 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 graded manner according to the relevant content of the received report, and to issue a warning reminder based on a preset threshold value.

[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 proofreading module and a result output module; the large language active learning module is used to actively learn and improve text review and proofreading based on the large language model; the text upload module is used to upload the text that needs to be reviewed and proofread to the review and proofreading system for review and proofreading; the text partitioning module is used to divide the uploaded text that needs to be reviewed and proofread into multiple parts; the text proofreading module is used to review and proofread the text that needs to be reviewed and proofread after the partitioning is completed; the result output module is used to output the review and proofreading results of the text that has been reviewed and proofread.

[0014] The data collection unit includes an error quantity collection module, a word quantity collection module, a punctuation quantity collection module, an image size collection module, a table size collection module and a review and proofreading time collection module; the error quantity collection module is used to collect various data on the number of text errors required by the text-assisted review and proofreading system; the word quantity collection module is used to collect various data on the number of text words required by the text-assisted review and proofreading system; the punctuation quantity collection module is used to collect various data on the number of text punctuation required by the text-assisted review and proofreading system; the image size collection module is used to collect various data on the size of text images required by the text-assisted review and proofreading system; the table size collection module is used to collect various data on the size of text tables required by the text-assisted review and proofreading system; the review and proofreading time collection module is used to collect the time data for text review and proofreading by the text-assisted review and proofreading system.

[0015] The core processing unit includes: a data receiving module, an analysis and processing module and a report generating 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 auxiliary review and proofreading system collected by the data collecting unit; the analysis and processing module is used to analyze and process the various data received by the data receiving module; the report generating module is used to generate a report on the text review and proofreading according to the analysis and processing results of 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 pictures contained in the text, the total size of tables contained in the text, and the length of time the review and proofreading system takes to review and proofread the text, and analyze and process the above data to obtain the review and proofreading level evaluation index EI of the text-assisted review and proofreading system:

[0017]

[0018] Among them, 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 the images contained in the text, TS is the total size of the tables contained in the text, and t is the time it takes for the review and proofreading system to review and proofread the text;

[0019] The review and proofreading level of the text review and proofreading system is evaluated according to the review and proofreading level evaluation index.

[0020] The analysis and processing module is further used to obtain, through the data acquisition unit, the total number of words in each part, the total number of punctuation marks in each part, the total size of each picture, the total size of each table, the number of erroneous words in each place, the number of words between two adjacent word errors, the number of erroneous punctuation marks in each place, the size of each erroneous picture and the size of each erroneous table, and obtain the review and proofreading attention index AP for each error based on the above-obtained data analysis and processing:

[0021]

[0022] ZW is the total number of words in each part, ZP is the total number of punctuation marks in each part, ZI is the total size of each picture, ZT is the total size of each table, CW is the total size of each picture, i is the number of each error text, n is the total number of error texts, LW ais 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 in each error, CI is the size of each error picture, and CT is the size of each error table;

[0023] The review and proofreading attention level index of each error is displayed in a graded manner through a graded display module.

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

[0025] The present invention also discloses a text-assisted review and proofreading method based on a large language model using the aforementioned text-assisted review and proofreading system based on a large language model, comprising:

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

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

[0028] Step 3: The text review and proofreading results output by the text processing unit and various data required by the text auxiliary review and proofreading system collected by the data collection unit are transmitted to the core processing unit through the network transmission unit;

[0029] Step 4: receiving the text review and proofreading results output by the text processing unit and various data required by the text auxiliary review and proofreading system collected by the data collection unit, and analyzing and processing the text review and proofreading results and various data, and generating a text review and proofreading report according to 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 graded manner according to the relevant content of the received report, and issue warning reminders based on pre-set thresholds.

[0031] Accordingly, the present 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 used to operate according to the instructions to execute the steps of the aforementioned text-assisted review and proofreading method based on a large language model.

[0034] Correspondingly, the present application also discloses a computer-readable storage medium on which a computer program is stored. When the program is executed by a processor, the steps of the aforementioned text-assisted review and proofreading method based on a large language model are implemented.

[0035] The beneficial effect of the present invention lies in that, compared with the prior art, the present invention provides a text-assisted review and proofreading method and system based on a large language model, which improves the problems of huge workload and low review and proofreading efficiency caused by traditional manual proofreading, and can 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 pictures contained in the text, the total size of tables contained in the text, and the review and proofreading time of the text by the review and proofreading system, and analyze and process the above data to obtain the review and proofreading level evaluation index of the text-assisted review and proofreading system, and evaluate the review and proofreading level of the text review and proofreading system according to the review and proofreading level evaluation index of the text-assisted review and proofreading system. Get the total number of words in each part, the total number of punctuation marks in each part, the total size of each picture, the total size of each table, the number of erroneous words in each place, the number of words between two adjacent text errors, the number of erroneous punctuation marks in each place, the size of each erroneous picture and the size of each erroneous table, analyze and process to get the review and proofreading attention index of each error, and display them in a graded manner according to the review and proofreading attention index of each error through a graded display module. BRIEF DESCRIPTION OF THE DRAWINGS

[0036] Figure 1 It is a structural diagram of the text-assisted review and proofreading system based on a large language model in the present invention. DETAILED DESCRIPTION

[0037] In order to make the purpose, technical solution and advantages of the present invention more clear, the technical solution of the present invention will be clearly and completely described below in conjunction with the accompanying drawings in the embodiments of the present invention.

[0038] The embodiments described in this application are only some embodiments of the present invention, not all embodiments. Based on the spirit of the present invention, other embodiments obtained by ordinary technicians in this field without creative work are all within the protection scope of the present invention.

[0039] In view of the deficiencies of the prior art, the present invention proposes a text-assisted review and proofreading method and system based on a large language model, which learns text review and proofreading based on the large language model, and uploads, partitions, reviews and proofreads the text that needs to be reviewed and proofread, and outputs the review and proofreading results. The total number of words in each part, the total number of punctuation marks in each part, the total size of each picture, the total size of each table, the number of erroneous words in each place, the number of words between two adjacent text errors, the number of erroneous punctuation marks in each place, the size of each erroneous picture and the size of each erroneous table are obtained, and the review and proofreading attention index of each error is obtained through analysis and processing, and the review and proofreading attention index of each error is graded and displayed through a graded display module.

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

[0041] The text processing unit is used to learn text review and proofreading based on a large language model, upload, partition and review and proofread 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 proofreading 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 the large language model; the text upload module is used to upload the text that needs to be reviewed and proofread to the review and proofreading system for review and proofreading; the text partitioning module is used to divide the uploaded text that needs to be reviewed and proofread into multiple parts; the text proofreading module is used to review and proofread the text that needs to be reviewed and proofread after the partitioning is completed; the result output module is used to output the review and proofreading results of the text that has been reviewed and proofread.

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

[0044] In a further embodiment, the data collection unit includes an error number collection module, a word number collection module, a punctuation number collection module, an image size collection module, a table size collection module and a review and proofreading time collection module; the error number collection module is used to collect various data on the number of text errors required by the text-assisted review and proofreading system; the word number collection module is used to collect various data on the number of text words required by the text-assisted review and proofreading system; the punctuation number collection module is used to collect various data on the number of text punctuation required by the text-assisted review and proofreading system; the image size collection module is used to collect various data on the size of text images required by the text-assisted review and proofreading system; the table size collection module is used to collect various data on the size of text tables required by the text-assisted review and proofreading system; the review and proofreading time collection module is used to collect the time data for text review and proofreading by the text-assisted review and proofreading system.

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

[0046] The core processing unit is used to receive the text review and proofreading results output by the text processing unit and various data required by the text auxiliary review and proofreading system collected by the data collection unit, and 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 generating 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 auxiliary 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 generating 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 obtains the number of errors correctly identified by the audit and proofreading system, the total number of errors identified by the audit 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 pictures contained in the text, the total size of tables contained in the text, and the duration of the audit and proofreading of the text by the audit and proofreading system during the analysis and processing, and analyzes and processes the above data to obtain the audit and proofreading level evaluation index EI of the text-assisted audit and proofreading system:

[0049]

[0050] Among them, 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 pictures contained in the text, TS is the total size of tables contained in the text, and t is the time it takes for the review and proofreading system to review and proofread the text.

[0051] According to the audit and proofreading level evaluation index of the text-assisted audit and proofreading system, the audit and proofreading level of the text-assisted audit and proofreading system is evaluated. The higher the performance evaluation index of the text-assisted audit and proofreading system, the higher the audit and proofreading level of the text-assisted audit and proofreading system. Conversely, the lower the audit and proofreading level of the text-assisted audit and proofreading system. When the audit and proofreading level evaluation index of the text-assisted audit and proofreading system reaches the threshold value set by the threshold setting module, the warning and reminder module is used to issue a warning and reminder, and the text-assisted audit and proofreading system is upgraded and optimized to improve the audit and proofreading level of the text-assisted audit and proofreading system.

[0052] In a further embodiment, the analysis and processing module obtains the total number of words in each part, the total number of punctuation marks in each part, the total size of each picture, the total size of each table, the number of erroneous words in each place, the number of words between two adjacent word errors, the number of erroneous punctuation marks in each place, the size of each erroneous picture and the size of each erroneous table through the data acquisition unit during the analysis and processing, and obtains the review and proofreading attention index AP for each error based on the above acquired data analysis and processing:

[0053]

[0054] ZW is the total number of words in each part, ZP is the total number of punctuation marks in each part, ZI is the total size of each picture, ZT is the total size of each table, CW is the total size of each picture, i is the number of each error text, n is the total number of error texts, LW 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 in each error, CI is the size of each error picture, and CT is the size of each error table.

[0055] The review and proofreading attention index of each error is graded and displayed through a graded display module. The greater the review and proofreading attention index of each error, the higher the graded display level of the errors found by the text review and proofreading. Conversely, the lower the graded display level of the errors found by the text review and proofreading.

[0056] 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 graded manner according to the relevant content of the received report, and to issue a warning reminder based on a preset threshold value.

[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 reminder module. The report receiving module is used to receive the text review and proofreading report generated by the core processing unit; the hierarchical display module is used to hierarchically display the errors found after the text review and proofreading according to the relevant content of the report received by the report receiving module; the threshold setting module is used to set the threshold for the text to be reviewed and proofread to be warned; the warning reminder module is used to warn when the relevant results in the received report reach the set threshold.

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

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

[0060] Specifically, the text that needs to be reviewed and proofread is uploaded through the text upload module of the text processing unit, and is partitioned, reviewed and proofread through the text partitioning module, the text review module and the result output module, and the text review and proofreading results are output.

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

[0062] Specifically, the data collection unit's error quantity collection module, text quantity collection module, punctuation quantity collection module, picture size collection module, table size collection module and review and proofreading time collection module collect various data on the number of text errors, various data on the number of text words, various data on the number of text punctuation marks, various data on the size of text pictures, various data on the size of text tables and the time for text review and proofreading required by the review and proofreading system during the text review and proofreading process.

[0063] Step 3: The text review and proofreading results output by the text processing unit and various data required by the text auxiliary review and proofreading system collected by the data collection unit are transmitted to the core processing unit through the network transmission unit;

[0064] Step 4: receiving the text review and proofreading results output by the text processing unit and various data required by the text auxiliary review and proofreading system collected by the data collection unit, and analyzing and processing the text review and proofreading results and various data, and generating a text review and proofreading report according to the analysis and processing results;

[0065] Specifically, the data receiving module of the core processing unit receives the transmitted audit and proofreading results and the collected data required by the audit and proofreading systems, and analyzes and processes the various data received by the data receiving module through the analysis and processing module. During the analysis and processing, the analysis and processing module can obtain the number of errors correctly identified by the audit and proofreading system, the total number of errors identified by the audit 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 the pictures contained in the text, the total size of the tables contained in the text, and the duration of the audit and proofreading of the text by the audit and proofreading system, and analyzes and processes the above numbers to obtain the audit and proofreading level evaluation index of the text-assisted audit and proofreading system; the audit and proofreading level of the text-assisted audit and proofreading system is evaluated according to the audit and proofreading level evaluation index of the text-assisted audit and proofreading system. The higher the performance evaluation index of the text-assisted audit and proofreading system, the higher the audit and proofreading level of the text-assisted audit and proofreading system; conversely, the lower the audit and proofreading level of the text-assisted audit and proofreading system;

[0066] At the same time, the analysis and processing module can obtain the total number of words in each part, the total number of punctuation marks in each part, the total size of each picture, the total size of each table, the number of erroneous words in each place, the number of words between two adjacent text errors, the number of erroneous punctuation marks in each place, the size of each erroneous picture and the size of each erroneous table through the data acquisition unit during analysis and processing, and obtain the audit and proofreading attention index of each error according to the above acquired data analysis and processing. Thus, the audit and proofreading attention index of each error is graded and displayed through the graded display module. The larger the audit and proofreading attention index of each error, the higher the graded display level of the errors found by the text audit and proofreading. Conversely, the lower the graded display level of the errors found by the text audit and proofreading, and the report generation module generates a text audit and proofreading report according to the analysis and processing results of 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 graded manner according to the relevant content of the received report, and issue warning reminders based on pre-set thresholds.

[0068] Specifically, the text review and proofreading report is transmitted to the display and reminder unit through the network transmission unit, and the transmitted report is received by the report receiving module of the display and reminder unit. Then, the errors found after the text review and proofreading are graded and displayed through the graded display module. At the same time, according to the threshold set by the threshold setting module, when the result in the text review and proofreading report reaches the set threshold, the warning reminder module will issue a corresponding warning 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 effect of the present invention is that, compared with the prior art, the present invention provides a text-assisted review and proofreading method and system based on a large language model, which can improve the problems of huge workload and low review and proofreading efficiency caused by traditional manual proofreading, and can 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 pictures contained in the text, the total size of tables contained in the text, and the review and proofreading time of the text by the review and proofreading system, and analyze and process the above data to obtain the review and proofreading level evaluation index of the text-assisted review and proofreading system, and evaluate the review and proofreading level of the text review and proofreading system according to the review and proofreading level evaluation index of the text-assisted review and proofreading system. Get the total number of words in each part, the total number of punctuation marks in each part, the total size of each picture, the total size of each table, the number of erroneous words in each place, the number of words between two adjacent text errors, the number of erroneous punctuation marks in each place, the size of each erroneous picture and the size of each erroneous table, analyze and process to get the review and proofreading attention index of each error, and display them in a graded manner according to the review and proofreading attention index of each error through a graded display module.

[0070] Based on the spirit of the present invention, those skilled in the art can easily think that a computer program product can be obtained based on the aforementioned text-assisted review and proofreading method based on a large language model. The computer program product may include a computer-readable storage medium, which carries computer-readable program instructions for enabling a processor to implement various aspects of the present disclosure. That is, the present application also includes a terminal, including 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 execute the steps of the aforementioned text-assisted review and proofreading method based on a large language model.

[0071] Computer readable storage medium can be a tangible device that can keep and store the instructions used by the instruction execution device. Computer readable storage medium can be, for example, - but not limited to - electrical storage device, magnetic storage device, optical storage device, electromagnetic storage device, semiconductor storage device or any suitable combination of the above. More specific examples (non-exhaustive list) of computer readable storage medium include: portable computer disk, hard disk, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), static random access memory (SRAM), portable compact disk read-only memory (CD-ROM), digital versatile disk (DVD), memory stick, floppy disk, mechanical encoding device, for example, punch card or groove protrusion structure with instructions stored thereon and any suitable combination of the above. Computer readable storage medium used here is not interpreted as instantaneous signal itself, such as radio wave or other free propagating electromagnetic wave, electromagnetic wave propagated by waveguide or other transmission medium (for example, light pulse by optical fiber cable) or electrical signal transmitted by wire.

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

[0073] The computer program instructions for performing the operation of the present disclosure may be assembly instructions, instruction set architecture (ISA) instructions, machine instructions, machine-related instructions, microcode, firmware instructions, state 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 "C" language or similar programming languages. Computer-readable program instructions may be executed completely on a user's computer, partially on a user's computer, as an independent software package, partially on a user's computer, partially on a remote computer, or completely on a remote computer or server. In the case of 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., using an Internet service provider to connect via the Internet). In some embodiments, an electronic circuit, such as a programmable logic circuit, a field programmable gate array (FPGA), or a programmable logic array (PLA) may be personalized by utilizing the state information of a computer-readable program instruction, and the electronic circuit may execute a computer-readable program instruction, thereby realizing various aspects of the present disclosure.

[0074] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the above embodiments, ordinary technicians in the relevant field should understand that the specific implementation methods of the present invention can still be modified or replaced by equivalents, and any modifications or equivalent replacements that do not depart from the spirit and scope of the present invention should be covered within the scope of protection of the claims of the present invention.

Claims

1. A text-assisted review and proofreading system based on a large language model, characterized in that: It includes text processing unit, data collection unit, network transmission unit, core processing unit and display reminder unit. The text processing unit is used to learn text review and proofreading based on the large language model, 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 collection unit is used to collect various data required by the text-assisted review and proofreading system, including data on the number of text errors, data on the number of text characters, data on the number of text punctuation marks, data on the size of text images, data on the size of text tables, and data on 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 various data required by the text auxiliary review and proofreading system collected by the data collection unit to the core processing unit, and transmit the text review and proofreading report generated by the core processing unit to the display reminder unit; The core processing unit is used to receive the text review and proofreading results output by the text processing unit and various data required by the text auxiliary review and proofreading system collected by the data collection unit, and analyze and process the text review and proofreading results and various data, and generate a text review and proofreading report according to 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 graded manner according to the relevant content of the received report, and to issue a warning reminder based on a preset threshold value.

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

3. The text-assisted review and proofreading system based on a large language model according to claim 2 is characterized in that: The data collection unit includes an error quantity collection module, a text quantity collection module, a punctuation quantity collection module, an image size collection module, a table size collection module, and a review and proofreading time collection module; the error quantity collection module is used to collect various data on the number of text errors required by the text auxiliary review and proofreading system; The character quantity collection module is used to collect various data on the number of text characters required by the text auxiliary review and proofreading system; The punctuation number collection module is used to collect various data on the number of text punctuation marks required by the text auxiliary review and proofreading system; The image size acquisition module is used to collect various data about the text image size required by the text assisted review and proofreading system; the table size acquisition module is used to collect various data about the text table size required by the text assisted review and proofreading system; the review and proofreading time acquisition module is used to collect the time data for text review and proofreading by the text assisted review and proofreading system.

4. The text-assisted review and proofreading system based on a large language model according to claim 3 is characterized in that: The core processing unit includes: a data receiving module, an analysis and processing module and a report generating 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 auxiliary review and proofreading system collected by the data collecting unit; the analysis and processing module is used to analyze and process the various data received by the data receiving module; the report generating module is used to generate a report on the text review and proofreading according to the analysis and processing results of the analysis and processing module.

5. The text-assisted review and proofreading system based on a large language model according to claim 4 is 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 pictures contained in the text, the total size of tables contained in the text, and the length of time the review and proofreading system takes to review and proofread the text, and analyze and process the above data to obtain the review and proofreading level evaluation index EI of the text-assisted review and proofreading system: Among them, 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 the images contained in the text, TS is the total size of the tables contained in the text, and t is the time it takes for the review and proofreading system to review and proofread the text; The review and proofreading level of the text review and proofreading system is evaluated according to the review and proofreading level evaluation index.

6. The text-assisted review and proofreading system based on a large language model according to claim 5 is characterized in that: The analysis and processing module is further used to obtain, through the data acquisition unit, the total number of words in each part, the total number of punctuation marks in each part, the total size of each picture, the total size of each table, the number of erroneous words in each place, the number of words between two adjacent word errors, the number of erroneous punctuation marks in each place, the size of each erroneous picture and the size of each erroneous table, and obtain the review and proofreading attention index AP for each error based on the above-obtained data analysis and processing: ZW is the total number of words in each part, ZP is the total number of punctuation marks in each part, ZI is the total size of each picture, ZT is the total size of each table, CW is the total size of each picture, i is the number of erroneous words in each place, n is the total number of erroneous words, LW 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 in each error, CI is the size of each error image, and CT is the size of each error table; The review and proofreading attention level index of each error is displayed in a graded manner through a graded display module.

7. The text-assisted review and proofreading system based on a large language model according to claim 6 is characterized in that: The display reminder unit includes: a report receiving module, a hierarchical display module, a threshold setting module and a warning reminder module; the report receiving module is used to receive the text review and proofreading report generated by the core processing unit; the hierarchical display module is used to hierarchically display the errors found after the text review and proofreading according to the relevant content of the report received by the report receiving module; the threshold setting module is used to set the threshold for warning reminders for the text to be reviewed and proofread; the warning reminder module is used to issue a warning reminder when the relevant results in the received report reach the set threshold.

8. A text-assisted review and proofreading method based on a large language model, characterized in that: include: Step 1: Use the text processing unit to upload, partition, and review and proofread the text that needs to be reviewed and proofread, and output the text review and proofreading results; Step 2: Collect various data required by the text-assisted review and proofreading system, including data on the number of text errors, data on the number of text characters, data on the number of text punctuation marks, data on the size of text images, data on the size of text tables, and data on the duration of text review and proofreading; Step 3: The text review and proofreading results output by the text processing unit and various data required by the text auxiliary review and proofreading system collected by the data collection unit are transmitted to the core processing unit through the network transmission unit; Step 4: receiving the text review and proofreading results output by the text processing unit and various data required by the text auxiliary review and proofreading system collected by the data collection unit, and analyzing and processing the text review and proofreading results and various data, and generating a text review and proofreading report according to 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 graded manner according to the relevant content of the received report, and issue warning reminders based on pre-set thresholds.

9. A terminal comprising a processor and a storage medium; characterized in that: The storage medium is used to store instructions; The processor is used to operate according to the instructions to execute the steps of the text-assisted review and proofreading method based on a large language model according to any one of claims 1-7.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the program is executed by a processor, the steps of the text-assisted review and proofreading method based on a large language model described in any one of claims 1 to 7 are implemented.

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