Large model-based compliance report automatic generation method

Abstract

The invention belongs to the technical field of compliance reports, and particularly relates to a compliance report automatic generation method based on a large model, which comprises the following steps of: 1, reading a report generation request; 2, a draft preparation stage is set, wherein a report generation request is responded, and a data packet is retrieved and extracted from a database; 3, a draft generation setting stage: inputting the data packet to generate a first report draft based on a report generation model; a fixed report template is preset, a blank window domain capable of being filled with data is arranged in the template, a report generation request is generated based on user operation, a data packet is extracted from a database, then the blank window domain is filled with the data packet, so that a first report draft is obtained, and manual auditing is performed once based on the first report draft, so that the report generation efficiency is improved. After manual auditing is completed, a second report draft is obtained, then the computer deletes a blank window domain which is not filled with the data packet in the second report draft, and a compliance report is obtained.

Description

Technical Field

[0001] The present invention belongs to the technical field of compliance reports, and in particular to a method for automatically generating compliance reports based on a large model. Background Art

[0002] Compliance means that the business activities of financial institutions must be consistent with laws, rules and regulations. That is, in the business activities of financial institutions, corresponding compliance reports need to be generated to supervise the financial institutions. In related technologies, the corresponding compliance reports are mainly generated manually. Although the corresponding compliance reports can be generated manually, the cost of manual methods is high, and the quality of the generated compliance reports is difficult to control.

[0003] Based on the above, methods for automatically generating compliance reports have emerged in the prior art, such as a compliance report generation method disclosed in application number 202410186630.6, which includes: collecting corresponding business data from multiple business systems, and distributing and storing the business data in a cloud database; in response to a compliance report generation request, obtaining business data from a cloud database, the compliance report generation request including compliance requirements and user needs; determining a compliance report template that meets the compliance requirements and user needs from a compliance report template library; the compliance report template library is determined in the following manner: obtaining an initial template for the compliance report; obtaining multiple preset compliance requirements and multiple preset user needs, and combining the multiple preset compliance requirements and the multiple preset user needs to generate multiple preset compliance report requirements; adjusting the initial template for the compliance report according to the multiple preset compliance report requirements to obtain compliance report templates corresponding to the multiple preset compliance report requirements;

[0004] However, in the above-mentioned prior art, since there are many types of compliance reports at this stage, such as internal compliance reports for enterprises, such as audit reports and risk management reports, and external compliance reports for regulatory authorities, such as financial reports, environmental impact assessment reports, etc., assuming that with the passage of time, compliance requirements and user needs increase, the number of template libraries will increase. Among them, when compliance requirements and user needs are not determined in advance, templates corresponding to the compliance requirements and user needs cannot be obtained. That is, the above-mentioned prior art is limited to the predetermined template library, and when a report type different from the existing template appears, it cannot adapt.

[0005] To this end, the present invention provides a method for automatically generating a compliance report based on a large model. Summary of the invention

[0006] In order to make up for the deficiencies of the prior art, at least one technical problem raised in the background technology is solved.

[0007] The technical solution adopted by the present invention to solve the technical problem is: a method for automatically generating a compliance report based on a large model according to the present invention comprises the following steps:

[0008] Step 1: Read the report generation request;

[0009] Step 2: Set up the draft preparation phase: Respond to the report generation request, retrieve and extract the data package from the database;

[0010] Step 3: Setting the draft generation stage: Based on the report generation model, inputting the data packet to generate the first report draft; the report generation model presets the report template, and the data packet is filled into the blank form field in the report template;

[0011] Step 4: Set up the manual review stage: Generate a second draft report based on the review and modification of the first draft report;

[0012] Step 5: Set up the final verification stage: Check the second draft report, remove the blank form fields of the unfilled data package, and obtain a compliant report that meets the report generation request.

[0013] Preferably, the report generation request in step 1 is in response to a compliance report generation operation generated by a user based on a computer operation, and input information corresponding to the compliance report generation operation is obtained, and a compliance report generation request corresponding to the input information is generated; the input information includes report type information and user information;

[0014] The data packet in step 2 is composed of multiple data sets, and the data set includes any one of data information and regulatory information; the data set is pre-distributedly stored in the database.

[0015] Preferably, the training method of the report generation model in step 3 is:

[0016] Get a sample of data packets;

[0017] Input the data package sample into the initial report generation model, and generate a compliance report sample corresponding to the data package based on the report template;

[0018] According to the differences between the compliance report sample and the standard compliance report, the initial report generation model is trained to obtain an optimized report generation model;

[0019] The report template includes a plurality of blank form fields, and a label is set in each blank form field.

[0020] Preferably, the method for generating the first draft report in step 3 is:

[0021] Identify multiple data sets based on the data identification model and extract feature parameters;

[0022] Match and map the extracted feature parameters with the labels corresponding to each blank form field in the report template;

[0023] When the characteristic parameters and labels are matched successfully, the data set corresponding to the characteristic parameters is filled into the blank form field corresponding to the label;

[0024] Perform an integrity check on the data set. If there are no omissions, generate a first draft report. If there are omissions, generate an error message while generating the first draft report.

[0025] Preferably, the method of reviewing and modifying the first draft report and generating the second draft report in step 4 is:

[0026] Determine whether there is an error message:

[0027] When there is an error message, manually fill the missing data set into the corresponding blank form field;

[0028] When there is no error message, check whether the data set matches the blank form field:

[0029] If the data set matches the blank form field, a second draft report is generated;

[0030] If the data set does not match the blank form field, manually modify it and generate a second draft report.

[0031] Preferably, the method for deleting the unfilled blank form field in step 5 is:

[0032] The computer goes through each blank form field in the report template when:

[0033] If any blank form field is filled with data, it is considered a valid form field;

[0034] On the contrary, a blank form field that is not filled is considered an invalid form field;

[0035] Remove the blank form fields that are determined to be invalid form fields.

[0036] Preferably, the training method of the data recognition model comprises the following steps:

[0037] Collect data set samples and prepare report templates;

[0038] Based on the initial data recognition model, a deep learning algorithm is used to perform semantic and keyword recognition on the dataset samples;

[0039] According to the recognition results, the characteristic parameters of the data set samples are generated;

[0040] Match feature parameters with preset labels;

[0041] Repeatedly generate feature parameters to optimize the matching relationship between feature parameters and labels;

[0042] Set up a test set to verify the effect of the data recognition model;

[0043] Achieve the preset matching accuracy and obtain the data recognition model.

[0044] Preferably, the method further comprises step 6: creating a log record for each report and storing the log record in a database; the log record includes the generation time of the report, the source of the data package used, the editing history, and the review and modification information; the report includes the first draft report, the second draft report, and the compliance report;

[0045] A report recording module is established in the database for storing and managing reports; the report recording module creates a unique identifier for each report, and each version of the report shares the unique identifier; the various versions of the report are a first draft report, a second draft report, and a compliance report.

[0046] Preferably, the step 4 further comprises the following steps:

[0047] When there is an error message, the missing data set is displayed;

[0048] Manually match the missing datasets with the preset labels, and

[0049] Manually create feature parameters for missing datasets;

[0050] According to the created feature parameters and corresponding labels, the matching relationship between the feature parameters and labels in the data recognition model is updated.

[0051] Preferably, the data packet in step 2 is stored in a database after preprocessing, and the preprocessing method further comprises the following steps:

[0052] Obtaining the preprocessed original data packet, encrypting the original data packet, and generating an encrypted data packet;

[0053] Using a signature algorithm, multiple parameters constituting a data packet are sorted, concatenated, and calculated to generate a signature;

[0054] The generated signature is stored together with the encrypted data packet in a database;

[0055] In the step 2, extracting the data packet further comprises the following steps:

[0056] Use the same signature algorithm to verify the validity of the signature:

[0057] When the verification result is valid, the encrypted data packet is decrypted to restore the original data packet;

[0058] Otherwise, a warning message will be issued.

[0059] The beneficial effects of the present invention are as follows:

[0060] 1. The method for automatically generating a compliance report based on a large model described in the present invention pre-sets a fixed report template, and sets a blank form field that can be filled with data in the template. Based on a report generation request generated by a user operation, a data packet is extracted from a database, and then the data packet is filled into the blank form field, thereby obtaining a first draft report. Based on the first draft report, it is manually reviewed again. After the manual review, a second draft report is obtained, and then the computer deletes the blank form field that is not filled with the data packet in the second draft report to obtain a compliance report;

[0061] 2. The method for automatically generating compliance reports based on a large model described in the present invention sets a label in a blank form field and uses a mapping match between the label and the characteristic parameters representing the data packet to complete the association between the data packet and the blank form field. Therefore, a deep learning algorithm is used to perform semantic and keyword recognition on the data sample, and then matching training is performed with the label based on the extracted characteristic parameters, thereby optimizing the extraction of the data packet characteristic parameters by the data recognition model, which is beneficial to matching with the label in the blank form field. BRIEF DESCRIPTION OF THE DRAWINGS

[0062] The present invention will be further described below in conjunction with the accompanying drawings.

[0063] Figure 1 It is a flow chart of the present invention. DETAILED DESCRIPTION

[0064] In order to make the technical means, creative features, objectives and effects achieved by the present invention easy to understand, the present invention is further explained below in conjunction with specific implementation methods.

[0065] like Figure 1 As shown, a method for automatically generating a compliance report based on a large model according to an embodiment of the present invention includes the following steps:

[0066] Step 1: Read the report generation request;

[0067] Step 2: Set up the draft preparation phase: Respond to the report generation request, retrieve and extract the data package from the database;

[0068] Step 3: Setting the draft generation stage: Based on the report generation model, inputting the data packet to generate the first report draft; the report generation model presets the report template, and the data packet is filled into the blank form field in the report template;

[0069] Step 4: Set up the manual review stage: Generate a second draft report based on the review and modification of the first draft report;

[0070] Step 5: Set up the final verification stage: Check the second draft report, remove the blank form fields of the unfilled data package, and obtain a compliant report that meets the report generation request.

[0071] In the above-mentioned prior art, since there are many types of compliance reports at this stage, such as internal compliance reports for enterprises, such as audit reports and risk management reports, and external compliance reports for regulatory authorities, such as financial reports, environmental impact assessment reports, etc., assuming that with the passage of time, compliance requirements and user needs increase, the number of template libraries will increase. Among them, when compliance requirements and user needs are not determined in advance, templates corresponding to compliance requirements and user needs cannot be obtained. That is, the above-mentioned prior art is limited to the predetermined template library, and when a report type different from the existing template appears, it cannot adapt.

[0072] In one embodiment of the present invention, a fixed report template is pre-set, and a blank form field that can be filled with data is set in the template. Based on the report generation request generated by the user operation, a data packet is extracted from the database, and then the data packet is filled into the blank form field, thereby obtaining a first draft report. Based on the first draft report, it is manually reviewed again. After the manual review, a second draft report is obtained, and then the blank form fields that are not filled with data packets in the second draft report are deleted by a computer to obtain a compliance report. It is worth noting that multiple chapter contents are pre-set in the report template, and blank form fields are set in each chapter content. However, due to different compliance report types, not every compliance report needs to use all chapters. Therefore, due to different compliance report types, there must be unfilled blank form fields in the report template. Therefore, after manual review, it is necessary to remove the blank form fields that are not filled with data packets based on the computer, and update the page number of the compliance report.

[0073] In one embodiment, the report generation request in step 1 is in response to a compliance report generation operation generated by a user based on a computer operation, input information corresponding to the compliance report generation operation is obtained, and a compliance report generation request corresponding to the input information is generated; the input information includes report type information and user information;

[0074] The data packet in step 2 is composed of multiple data sets, and the data set includes any one of data information and regulatory information; the data set is pre-distributedly stored in the database.

[0075] In one embodiment of the present invention, the report generation request includes report type information and user information, wherein the report type information is used to extract a data package from a database, such as a financial report, which requires extracting annual business data and corresponding account information from the database, and the user information is the reporting unit of the user's compliance report, which is generally used on the cover of the compliance report. The data package consists of multiple data sets, such as a financial report, which may require providing business data, account information and other related materials, and the above materials may be distributed and stored in different units in the database, so the data package generally consists of multiple data sets;

[0076] It is worth noting that when the computer retrieves an update to a pre-stored data set, it can actively download it to the database. Secondly, in order to distinguish the historical version from the current version of the data set, a timestamp is created for each type of data set to distinguish the version of the data set. When generating a compliance report, the corresponding data set is extracted according to the compliance report request to prevent errors. In addition, the data stored or downloaded to the database needs to be preprocessed, including data cleaning, data organization and data analysis, where data analysis includes analyzing the compliance and legality of the data source.

[0077] In one embodiment, the training method of the report generation model in step 3 is:

[0078] Get a sample of data packets;

[0079] Input the data package sample into the initial report generation model, and generate a compliance report sample corresponding to the data package based on the report template;

[0080] According to the differences between the compliance report sample and the standard compliance report, the initial report generation model is trained to obtain an optimized report generation model;

[0081] The report template includes a plurality of blank form fields, and a label is set in each blank form field.

[0082] In one embodiment of the present invention, the initial report generation model refers to an untrained model for generating compliance reports, which may be a deep learning model such as a recursive neural network (RNN), a convolutional neural network (CNN), etc. The compliance report sample corresponding to the data packet sample can be determined through the initial report generation model. The optimized report generation model is obtained by training the initial report generation model based on multiple data packet samples. It is worth noting that the report template is set in the report generation model. Unlike the prior art, in this application, there is only one report template, and multiple chapters are set to meet the needs of compliance reports of known types. At the same time, a blank form field is set in each chapter for filling in the data packet. Specifically, in order to facilitate the matching of the data packet with the blank form field, a label is also set in the blank form field, and the label is used to strengthen the association between the data packet and the blank form field, so that the data packet can be accurately filled in the blank form field.

[0083] In one embodiment, the method for generating the first draft report in step 3 is:

[0084] Identify multiple data sets based on the data identification model and extract feature parameters;

[0085] Match and map the extracted feature parameters with the labels corresponding to each blank form field in the report template;

[0086] When the characteristic parameters and labels are matched successfully, the data set corresponding to the characteristic parameters is filled into the blank form field corresponding to the label;

[0087] Perform an integrity check on the data set. If there are no omissions, generate a first draft report. If there are omissions, generate an error message while generating the first draft report.

[0088] In one embodiment of the present invention, a data set is identified through a data recognition model, and characteristic parameters representing the data set are extracted. The extracted characteristic parameters are used to map and match with the labels corresponding to each blank form field in the report template, so that after the characteristic parameters and the labels are successfully matched, the data set corresponding to the characteristic parameters can be filled into the corresponding blank form field. It is worth noting that in the process of filling the data set into the blank form field, there may be omissions of the data set, that is, there is a data set that is not filled into any blank form field. Therefore, it is also necessary to review whether there are omissions in the data set. If there are omissions, it is necessary to generate the first draft report and generate an error prompt message at the same time to facilitate quick resolution during manual review.

[0089] In one embodiment, the method of reviewing and modifying the first draft report to generate the second draft report in step 4 is:

[0090] Determine whether there is an error message:

[0091] When there is an error message, manually fill the missing data set into the corresponding blank form field;

[0092] When there is no error message, check whether the data set matches the blank form field:

[0093] If the data set matches the blank form field, a second draft report is generated;

[0094] If the data set does not match the blank form field, manually modify it and generate a second draft report.

[0095] Even though the existing technology can realize automatic generation of compliance reports based on data packages and report templates, manual review of the generated compliance reports is still required to prevent defects. In one embodiment of the present invention, after the first draft report is generated, manual review is required, which specifically includes checking whether the data set matches the blank form field. If so, a second draft report is generated. If not, manual modification is required to generate a second draft report. In addition, when the first draft report is generated, after an error prompt message appears, it is necessary to check which blank form field the missing data set matches, and after manual filling, generate a second draft report.

[0096] In one embodiment, the method for deleting the unfilled blank form field in step 5 is:

[0097] The computer goes through each blank form field in the report template when:

[0098] If any blank form field is filled with data, it is considered a valid form field;

[0099] On the contrary, a blank form field that is not filled is considered an invalid form field;

[0100] Remove the blank form fields that are determined to be invalid form fields.

[0101] Since the report template contains multiple blank form fields, and due to different compliance report types, some blank form fields may not be filled with data packets. In one embodiment of the present invention, after the second report draft is generated, a computer checks each blank form field in the report template. When a blank form field is filled with a data packet, it indicates that the blank form field is a valid form field, otherwise it is an invalid form field. The invalid form field is then automatically removed based on the computer to obtain a compliance report.

[0102] In one embodiment, the training method of the data recognition model comprises the following steps:

[0103] Collect data set samples and prepare report templates;

[0104] Based on the initial data recognition model, a deep learning algorithm is used to perform semantic and keyword recognition on the dataset samples;

[0105] According to the recognition results, the characteristic parameters of the data set samples are generated;

[0106] Match feature parameters with preset labels;

[0107] Repeatedly generate feature parameters to optimize the matching relationship between feature parameters and labels;

[0108] Set up a test set to verify the effect of the data recognition model;

[0109] Achieve the preset matching accuracy and obtain the data recognition model.

[0110] As mentioned above, the present application sets a label in the blank form field and uses the mapping matching between the label and the characteristic parameters representing the data packet to complete the association between the data packet and the blank form field. Therefore, a deep learning algorithm is used to perform semantic and keyword recognition on the data sample, and then matching training is performed with the label based on the extracted characteristic parameters, so as to optimize the extraction of the characteristic parameters of the data packet by the data recognition model, which is conducive to matching with the label in the blank form field;

[0111] It is worth noting that repeatedly generating feature parameters and optimizing the matching relationship between feature parameters and labels also include adaptively updating the feature parameter matching mechanism corresponding to the label; specifically, after obtaining the data recognition model, semantic and keyword recognition is performed based on the extracted data packet to obtain the feature parameters of the data packet. For continuously updated data packets, such as laws and regulations, which may change over time, when there is no report generation request, when the computer retrieves relevant legal and regulatory updates, it downloads the corresponding laws and regulations, and performs semantic and keyword recognition on the updated laws and regulations based on the deep learning algorithm to generate new feature parameters, and establish an association with the label, so that when a report generation request is subsequently generated, the feature parameters can be accurately generated and matched with the label.

[0112] In one embodiment, the step 6 is further included: creating a log record for each report and storing the log record in a database; the log record includes the generation time of the report, the source of the data package used, the editing history, and the review and modification information; the report includes the first draft report, the second draft report, and the compliance report;

[0113] A report recording module is established in the database for storing and managing reports; the report recording module creates a unique identifier for each report, and each version of the report shares the unique identifier; the various versions of the report are a first draft report, a second draft report, and a compliance report.

[0114] In one embodiment of the present invention, when a user operation generates a report generation request, a first draft report and a second draft report are necessary in the process of extracting a data packet based on a computer and generating a compliance report using a report template. In order to facilitate the establishment of a complete audit trail of the report generation process and ensure data integrity and compliance with relevant regulations, a detailed log record is created for each report, thereby enabling tracking and tracing of the report generation process. In addition, as the number of compliance reports generated increases, omissions may occur when retrieving compliance reports at a later stage. Therefore, in one embodiment of the present invention, a unique identifier is created for each report. The unique identifier allows users to quickly retrieve and compare different versions of reports, thereby achieving effective document management and control.

[0115] In one embodiment, the step 4 further includes the following steps:

[0116] When there is an error message, the missing data set is displayed;

[0117] Manually match the missing datasets with the preset labels, and

[0118] Manually create feature parameters for missing datasets;

[0119] According to the created feature parameters and corresponding labels, the matching relationship between the feature parameters and labels in the data recognition model is updated.

[0120] Based on the above, after obtaining the data recognition model, the matching relationship between the preset feature parameters and the label can be obtained based on the data samples used for training. However, in actual application, the data recognition model may not be able to recognize updated laws and regulations, resulting in the inability to generate feature parameters that can match the labels, resulting in individual data sets being omitted. In the manual review stage, it is necessary to manually establish a matching association between the missing data sets and the preset labels, and then create feature parameters based on the missing data sets, and update the matching relationship between the created feature parameters and the corresponding labels, so as to achieve the effect of real-time optimization of the data recognition model. It is worth noting that in the data recognition model, the preset label may correspond to multiple feature parameters, that is, multiple feature parameters all point to the same label.

[0121] In one embodiment, the data packet in step 2 is stored in a database after being preprocessed, and the preprocessing method further includes the following steps:

[0122] Obtaining the preprocessed original data packet, encrypting the original data packet, and generating an encrypted data packet;

[0123] Using a signature algorithm, multiple parameters constituting a data packet are sorted, concatenated, and calculated to generate a signature;

[0124] The generated signature is stored together with the encrypted data packet in a database;

[0125] In the step 2, extracting the data packet further comprises the following steps:

[0126] Use the same signature algorithm to verify the validity of the signature:

[0127] When the verification result is valid, the encrypted data packet is decrypted to restore the original data packet;

[0128] Otherwise, a warning message will be issued.

[0129] With the development of network technology, data faces security threats such as being intercepted and tampered with during transmission. For compliance reports generated by enterprises, if the original data packet is tampered with or deleted, the data packet will be incomplete or lack data, which will lead to the generation of non-compliant reports and affect the enterprise. Based on the above, in one embodiment of the present invention, the original data packet is encrypted and a signature is generated at the same time, and the signature and the encrypted data packet are stored together in a database. When a compliance report generation request occurs, the validity of the encrypted data packet is verified, that is, the accuracy of the signature is verified by the same signature algorithm. If the signature changes, it means that the encrypted data packet has been tampered with, and the integrity of the data packet needs to be checked, otherwise it will affect the enterprise, that is, a warning prompt message is generated to inform the user. On the contrary, if the signature has not changed, that is, the signature is valid, the encrypted data packet is decrypted and the original data packet is restored to generate a compliance report.

[0130] Working principle: a fixed report template is set in advance, and blank form fields that can be filled with data are set in the template. Based on the report generation request generated by the user operation, the data packet is extracted from the database, and then the data packet is filled into the blank form field, so as to obtain the first draft report. Based on the first draft report, it is manually reviewed again. After the manual review, the second draft report is obtained, and then the computer deletes the blank form fields that are not filled with data packets in the second draft report to obtain a compliance report. It is worth noting that multiple chapter contents are pre-set in the report template, and blank form fields are set in each chapter content. However, due to the different types of compliance reports, not every compliance report needs to use all chapters. Therefore, due to the different types of compliance reports, there must be unfilled blank form fields in the report template. Therefore, after manual review, it is necessary to remove the blank form fields that are not filled with data packets based on the computer, and update the page number of the compliance report.

[0131] The above shows and describes the basic principles, main features and advantages of the present invention. It should be understood by those skilled in the art that the present invention is not limited to the above embodiments. The above embodiments and descriptions are only for explaining the principles of the present invention. Without departing from the spirit and scope of the present invention, the present invention may have various changes and improvements, which fall within the scope of the present invention. The scope of protection of the present invention is defined by the attached claims and their equivalents.

Claims

1. A method for automatically generating compliance reports based on a large model, characterized in that: The following steps are involved: Step 1: Read the report generation request; Step 2: Set up the draft preparation phase: Respond to the report generation request, retrieve and extract the data package from the database; Step 3: Setting the draft generation stage: Based on the report generation model, inputting the data packet to generate the first report draft; the report generation model presets the report template, and the data packet is filled into the blank form field in the report template; Step 4: Set up the manual review stage: Generate a second draft report based on the review and modification of the first draft report; Step 5: Set up the final verification stage: Check the second draft report, remove the blank form fields of the unfilled data package, and obtain a compliant report that meets the report generation request.

2. The method for automatically generating compliance reports based on a large model according to claim 1, characterized in that: The report generation request in step 1 is in response to a compliance report generation operation generated by a user based on a computer operation, obtaining input information corresponding to the compliance report generation operation, and generating a compliance report generation request corresponding to the input information; the input information includes report type information and user information; The data packet in step 2 is composed of multiple data sets, and the data set includes any one of data information and regulatory information; the data set is pre-distributedly stored in the database.

3. The method for automatically generating compliance reports based on a large model according to claim 1, characterized in that: The training method of the report generation model in step 3 is: Get a sample of data packets; Input the data package sample into the initial report generation model, and generate a compliance report sample corresponding to the data package based on the report template; According to the differences between the compliance report sample and the standard compliance report, the initial report generation model is trained to obtain an optimized report generation model; The report template includes a plurality of blank form fields, and a label is set in each blank form field.

4. The method for automatically generating compliance reports based on a large model according to claim 1, characterized in that: The method for generating the first draft report in step 3 is: Identify multiple data sets based on the data identification model and extract feature parameters; Match and map the extracted feature parameters with the labels corresponding to each blank form field in the report template; When the characteristic parameters and labels are matched successfully, the data set corresponding to the characteristic parameters is filled into the blank form field corresponding to the label; Perform an integrity check on the data set. If there are no omissions, generate a first draft report. If there are omissions, generate an error message while generating the first draft report.

5. The method for automatically generating compliance reports based on a large model according to claim 1, characterized in that: The method for reviewing and modifying the first draft report and generating the second draft report in step 4 is: Determine whether there is an error message: When there is an error message, manually fill the missing data set into the corresponding blank form field; When there is no error message, check whether the data set matches the blank form field: If the data set matches the blank form field, a second draft report is generated; If the data set does not match the blank form field, manually modify it and generate a second draft report.

6. The method for automatically generating compliance reports based on a large model according to claim 1, characterized in that: The method for deleting the unfilled blank form fields in step 5 is as follows: The computer goes through each blank form field in the report template when: If any blank form field is filled with data, it is considered a valid form field; On the contrary, a blank form field that is not filled is considered an invalid form field; Remove the blank form fields that are determined to be invalid form fields.

7. The method for automatically generating compliance reports based on a large model according to claim 4 is characterized in that: The training method of the data recognition model comprises the following steps: Collect data set samples and prepare report templates; Based on the initial data recognition model, a deep learning algorithm is used to perform semantic and keyword recognition on the dataset samples; According to the recognition results, the characteristic parameters of the data set samples are generated; Match feature parameters with preset labels; Repeatedly generate feature parameters to optimize the matching relationship between feature parameters and labels; Set up a test set to verify the effect of the data recognition model; Achieve the preset matching accuracy and obtain the data recognition model.

8. The method for automatically generating compliance reports based on a large model according to claim 1, characterized in that: The method further includes step 6: creating a log record for each report and storing the log record in a database; the log record includes the generation time of the report, the source of the data package used, the editing history, and the review and modification information; the report includes a first draft report, a second draft report, and a compliance report; A report recording module is established in the database for storing and managing reports; the report recording module creates a unique identifier for each report, and each version of the report shares the unique identifier; the various versions of the report are a first draft report, a second draft report, and a compliance report.

9. The method for automatically generating compliance reports based on a large model according to claim 5 is characterized in that: The step 4 also includes the following steps: When there is an error message, the missing data set is displayed; Manually match the missing datasets with the preset labels, and Manually create feature parameters for missing datasets; According to the created feature parameters and corresponding labels, the matching relationship between the feature parameters and labels in the data recognition model is updated.

10. The method for automatically generating compliance reports based on a large model according to claim 2, characterized in that: The data packets in step 2 are stored in the database after preprocessing, and the preprocessing method further includes the following steps: Obtaining the preprocessed original data packet, encrypting the original data packet, and generating an encrypted data packet; Using a signature algorithm, multiple parameters constituting a data packet are sorted, concatenated, and calculated to generate a signature; The generated signature is stored together with the encrypted data packet in a database; In the step 2, extracting the data packet further comprises the following steps: Use the same signature algorithm to verify the validity of the signature: When the verification result is valid, the encrypted data packet is decrypted to restore the original data packet; Otherwise, a warning message will be issued.

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