A method and system for automatically generating bond project implementation plans

Abstract

The present invention provides a method and system for automatically generating a bond project implementation plan, comprising: obtaining key information of the bond project input by a user terminal; performing data matching on the key information of the bond project using a pre-built bond business model based on the key information of the bond project, and outputting an initial project plan corresponding to the key information of the bond project; extracting business data information from the initial project plan, and performing data identification based on the business data information using a matching algorithm to obtain a data table and business description corresponding to the business data information; generating a project implementation plan corresponding to the key information of the bond project based on the data table and the business description; the present application significantly improves the accuracy of information extraction and the accuracy of plan matching through automated acquisition of key information of the bond project, intelligent matching and data identification, and can quickly generate standardized project implementation plans, thereby reducing manual intervention, reducing errors and improving efficiency.

Description

Technical Field

[0001] The present invention relates to the field of data processing technology, and in particular to a method and system for automatically generating a bond project implementation plan. Background Art

[0002] At present, in the existing bond project management and review process, the key information collection, data matching and business data identification of bond projects still mainly rely on manual operations or simple rule-based algorithms. These traditional methods have significant shortcomings in information extraction accuracy, data matching accuracy and the efficiency of generating project implementation plans.

[0003] Specifically, there are multiple problems with the preparation of traditional special bond project implementation plans. First, the workload of compiling implementation plans is very large. The content involved in the project is complicated, including a large amount of financial data, market information, risk assessment, etc., and there are complex logical and computational relationships between these contents. Because these plans are complex and lengthy, they usually need to be written manually, resulting in extremely low work efficiency. For example, the plan may contain hundreds of pages of content. When writing, it is necessary to pay attention to the consistency of data in different parts at all times to ensure the accuracy and logical correctness of various types of information. This huge manual writing process is not only time-consuming, but also prone to errors. Especially during the modification process, errors and deviations in logical relationships are difficult to avoid, which seriously affects the quality and efficiency of the plan.

[0004] Secondly, while existing bond business models can provide some rules and templates, they lack intelligent data matching and analysis, resulting in limited flexibility and adaptability, making them unable to efficiently address the needs of complex bond projects. The initial plan generation process for bond projects often relies on manual adjustments and modifications. Frequent manual operations not only increase workload but also lead to inefficient plans and high error rates. This is particularly true in areas such as business data extraction, table generation, and the accuracy of business descriptions, which still lack effective automated support.

[0005] Furthermore, the current special bond project plan revision process lacks effective verification and verification methods, making it prone to inconsistencies and logical errors in the content and data of implementation plans. Manually compiled implementation plans lack a unified review mechanism, making it difficult to ensure their quality, impacting both project application and actual implementation.

[0006] In summary, the degree of automation in bond project management and review in existing technologies is low, the information processing accuracy is insufficient, the plan matching is inaccurate, and the generated project implementation plans often have deviations. Summary of the Invention

[0007] To address the problems in the prior art of key information collection, data matching, and business data identification for bond projects, which are characterized by a low degree of automation and often rely on manual intervention, resulting in inaccurate information extraction, inaccurate matching of business data and solutions, deviations in generated project implementation plans, low efficiency, and prone to errors, the present invention proposes a method for automatically generating bond project implementation plans, comprising:

[0008] Obtain key information of bond projects input by the user;

[0009] Based on the key information of the bond project, use the pre-built bond business model to perform data matching on the key information of the bond project, and output the initial project plan corresponding to the key information of the bond project;

[0010] Extracting business data information from the initial project plan, and performing data identification using a matching algorithm based on the business data information to obtain a data table and business description corresponding to the business data information;

[0011] Generate a project implementation plan corresponding to the key information of the bond project based on the data table and the business description;

[0012] The bond business macro model is obtained based on data extraction and text parsing of a preset bond model library.

[0013] Optionally, the bond business model includes the following construction process:

[0014] Using OCR technology, extract data from each project in the preset bond model library to obtain the project content information corresponding to each project;

[0015] Performing text analysis on the project content information corresponding to each project to obtain semantic analysis information of each project;

[0016] Based on the semantic analysis information of each project, the LLM model is trained to obtain the bond business model.

[0017] Optionally, performing data matching on the key information of the bond project using a pre-built bond business model based on the key information of the bond project, and outputting an initial project plan corresponding to the key information of the bond project, includes:

[0018] Performing data matching on the bond project key information and the semantic parsing information of each project in the bond business macro model to obtain the project in the bond business macro model corresponding to the bond project key information;

[0019] According to the project corresponding to the key information of the bond project in the bond business macro model, a project plan corresponding to the project is output as an initial project plan corresponding to the key information of the bond project.

[0020] Optionally, extracting business data information from the initial project plan includes:

[0021] Classify data according to the business content in the initial project plan to obtain business content corresponding to different business categories in the initial project plan;

[0022] According to the business content corresponding to the different business categories, a preset information extraction model is used to extract data to obtain business data information corresponding to the initial project plan;

[0023] The business data information at least includes: business content category and business data elements.

[0024] Optionally, the performing data identification based on the business data information using a matching algorithm to obtain a data table and business description corresponding to the business data information includes:

[0025] Performing full word matching, word vector similarity matching, and unit feature matching on the business data information to obtain an operation formula operator corresponding to the business data information;

[0026] According to the operation formula operator, the business data information is matched with an operator to obtain an operator corresponding to the business data information;

[0027] Generating a construction formula corresponding to the business data information according to the operator;

[0028] Generate a data table corresponding to the business data information according to the operation relationship in the construction formula corresponding to the business data information and the business logic sequence in the business data information;

[0029] Extracting target business indicators from a data table corresponding to the business data information, and using the target business indicators as new business content in the initial project plan according to a pre-set indicator code;

[0030] Generate a corresponding business description according to the business data information and a preset data format;

[0031] The data tables include one or more of the following: a project annual investment plan table, a project financing status table, a project income calculation table, a project cost calculation table, a project principal and interest payment table, a project capital balance table, and a project stress test table;

[0032] The target business indicators include one or more of the following: the total investment in the investment estimate table, the start and completion time in the basic project information, and the amounts of various types of financing funds in the project financing information;

[0033] The business description includes one or more of the following: the construction content of the project, the economic and social benefits of the project, and the construction objectives of the project.

[0034] Optionally, generating a construction formula corresponding to the business data information according to the operator includes:

[0035] Selecting a formula matching algorithm corresponding to the business data information according to the operator;

[0036] Generate a construction formula corresponding to the business data information according to a formula matching algorithm corresponding to the business data information;

[0037] The formula matching algorithm includes at least one or more of the following: an operator level priority algorithm, a formula matching priority algorithm, and a formula matching exclusion algorithm.

[0038] Optionally, after generating a project implementation plan corresponding to the key information of the bond project based on the data table and the business description, the method further includes:

[0039] Conducting a project evaluation on the project implementation plan based on the business data information in the project implementation plan to determine whether the project implementation plan needs to be adjusted;

[0040] If yes, the key information of the bond project input by the user is retrieved, and the data in the project implementation plan is adjusted according to the retrieved key information of the bond project to obtain a new project implementation plan.

[0041] Optionally, the key information of the bond project includes one or more of the following: project name, project region, project construction period, time to be put into operation, project type field, project investment field, user region, project investment information, project construction content and income description, special bond issuance period, income type and total project investment amount.

[0042] Based on the same inventive concept, the present invention also provides a system for automatically generating a bond project implementation plan, comprising:

[0043] The information acquisition module is used to obtain key information of the bond project input by the user;

[0044] An initial plan generating module is used to perform data matching on the key information of the bond project using a pre-built bond business model, and output an initial project plan corresponding to the key information of the bond project;

[0045] A data identification module is used to extract business data information from the initial project plan, and based on the business data information, perform data identification using a matching algorithm to obtain a data table and business description corresponding to the business data information;

[0046] A target solution generation module, configured to generate a project implementation plan corresponding to the key information of the bond project based on the data table and the business description;

[0047] The bond business macro model is obtained based on data extraction and text parsing of a preset bond model library.

[0048] Optionally, the generation system further includes: a model building module, configured to:

[0049] Using OCR technology, extract data from each project in the preset bond model library to obtain the project content information corresponding to each project;

[0050] Performing text analysis on the project content information corresponding to each project to obtain semantic analysis information of each project;

[0051] Based on the semantic analysis information of each project, the LLM model is trained to obtain the bond business model.

[0052] Optionally, the initial solution generation module includes:

[0053] a semantic parsing submodule, configured to perform data matching between the bond project key information and the semantic parsing information of each project in the bond business macromodel, and obtain the project in the bond business macromodel corresponding to the bond project key information;

[0054] The project matching submodule is used to output a project plan corresponding to the project in the bond business model as an initial project plan corresponding to the key information of the bond project.

[0055] Optionally, the data identification module includes:

[0056] A business classification submodule is used to classify data according to the business content in the initial project plan to obtain the business content corresponding to different business categories in the initial project plan;

[0057] A data extraction submodule is used to extract data based on the business content corresponding to the different business categories using a preset information extraction model to obtain business data information corresponding to the initial project plan;

[0058] The business data information at least includes: business content category and business data elements.

[0059] Optionally, the data identification module further includes:

[0060] An operator matching submodule, configured to perform full word matching, word vector similarity matching, and unit feature matching on the business data information to obtain an operation formula operator corresponding to the business data information;

[0061] An operator matching submodule, configured to perform operator matching on the business data information according to the operation formula operator to obtain the operator corresponding to the business data information;

[0062] A formula generation submodule, configured to generate a construction formula corresponding to the business data information according to the operator;

[0063] A table output submodule, configured to generate a data table corresponding to the business data information according to the operation relationship in the construction formula corresponding to the business data information and the business logic sequence in the business data information;

[0064] A business content replacement submodule is used to extract target business indicators based on the data table corresponding to the business data information, and use the target business indicators as new business content in the initial project plan according to a pre-set indicator code;

[0065] A business description output submodule is used to generate a corresponding business description according to the business data information and a preset data format;

[0066] The data tables include one or more of the following: a project annual investment plan table, a project financing status table, a project income calculation table, a project cost calculation table, a project principal and interest payment table, a project capital balance table, and a project stress test table;

[0067] The target business indicators include one or more of the following: the total investment in the investment estimate table, the start and completion time in the basic project information, and the amounts of various types of financing funds in the project financing information;

[0068] The business description includes one or more of the following: the construction content of the project, the economic and social benefits of the project, and the construction objectives of the project.

[0069] Optionally, the formula generation submodule includes:

[0070] a priority matching unit, configured to select a formula matching algorithm corresponding to the business data information according to the operator;

[0071] A formula output unit, configured to generate a construction formula corresponding to the business data information according to a formula matching algorithm corresponding to the business data information;

[0072] The formula matching algorithm includes at least one or more of the following: an operator level priority algorithm, a formula matching priority algorithm, and a formula matching exclusion algorithm.

[0073] Optionally, the generation system further includes: a scheme adjustment module, configured to:

[0074] Conducting a project evaluation on the project implementation plan based on the business data information in the project implementation plan to determine whether the project implementation plan needs to be adjusted;

[0075] If yes, the key information of the bond project input by the user is retrieved, and the data in the project implementation plan is adjusted according to the retrieved key information of the bond project to obtain a new project implementation plan.

[0076] Optionally, the key information of the bond project includes one or more of the following: project name, project region, project construction period, time to be put into operation, project type field, project investment field, user region, project investment information, project construction content and income description, special bond issuance period, income type and total project investment amount.

[0077] In yet another aspect, the present invention further provides a computer device comprising: one or more processors;

[0078] a memory for storing one or more programs;

[0079] When the one or more programs are executed by the one or more processors, the aforementioned method for automatically generating a bond project implementation plan is implemented.

[0080] On the other hand, the present invention also provides a computer-readable storage medium having a computer program stored thereon. When the computer program is executed, the method for automatically generating a bond project implementation plan as described above is implemented.

[0081] Compared with the prior art, the present invention has the following beneficial effects:

[0082] The present invention provides a method and system for automatically generating a bond project implementation plan, comprising: obtaining key information of the bond project input by a user terminal; performing data matching on the key information of the bond project using a pre-built bond business model according to the key information of the bond project, and outputting an initial project plan corresponding to the key information of the bond project; extracting business data information from the initial project plan, and performing data identification using a matching algorithm based on the business data information to obtain a data table and business description corresponding to the business data information; generating a project implementation plan corresponding to the key information of the bond project according to the data table and the business description; the present application extracts data from the key information of the bond project in an automated manner and performs intelligent matching and identification, thereby significantly improving the generation efficiency and accuracy of the bond project implementation plan, and utilizing the pre-built bond business model and matching algorithm to quickly and accurately generate a preliminary plan, and automatically extract key business data and descriptions, thereby reducing manual intervention and errors, and ultimately, through the combination of data tables and business descriptions, generates high-quality, standardized project implementation plans, thereby improving the intelligence and automation level of bond project management. BRIEF DESCRIPTION OF THE DRAWINGS

[0083] Figure 1 A schematic flow chart of a method for automatically generating a bond project implementation plan provided by the present invention;

[0084] Figure 2 A flowchart for automatically generating a project implementation plan in a method for automatically generating a bond project implementation plan provided by the present invention;

[0085] Figure 3 The calculation formula in the automatic generation method of the bond project implementation plan provided by the present invention automatically generates an overall flow chart;

[0086] Figure 4 A schematic diagram of a formula tree structure in a method for automatically generating a bond project implementation plan provided by the present invention;

[0087] Figure 5 A schematic diagram of an implementation plan adjustment process for a method for automatically generating a bond project implementation plan provided by the present invention;

[0088] Figure 6 A schematic diagram of operator matching for a method for automatically generating a bond project implementation plan provided by the present invention;

[0089] Figure 7 This is a schematic diagram of the structural composition of a bond project implementation plan automatic generation system provided by the present invention. DETAILED DESCRIPTION

[0090] The present invention proposes a method, system, device and medium for automatically generating a bond project implementation plan. The specific implementation methods of the present invention are further described in detail below with reference to the accompanying drawings.

[0091] Example 1:

[0092] The present invention provides a method for automatically generating a bond project implementation plan, the flow chart of which is as follows: Figure 1 Shown, including:

[0093] Step 1: Obtain key information of the bond project input by the user;

[0094] Step 2: Based on the key information of the bond project, use the pre-built bond business model to perform data matching on the key information of the bond project and output the initial project plan corresponding to the key information of the bond project;

[0095] Step 3: Extract business data information from the initial project plan, and use matching algorithms to identify data based on the business data information to obtain data tables and business descriptions corresponding to the business data information;

[0096] Step 4: Generate a project implementation plan corresponding to the key information of the bond project based on the data table and business description;

[0097] Among them, the bond business model is obtained based on data extraction and text analysis of the preset bond model library.

[0098] For example, the key information of the bond project in the above step 1 may include one or more of the following: project name, project region, project construction period, time to be put into operation, project type field, project investment field, user region, project investment information, project construction content and income description, special bond issuance period, income type and total project investment amount.

[0099] In one implementation, Figure 2 As shown, the process of performing data matching on the key information of the bond project using the pre-built bond business model and outputting the initial project plan corresponding to the key information of the bond project in step 2 may include:

[0100] Match the key information of the bond project with the semantic parsing information of each project in the bond business model to obtain the project in the bond business model that corresponds to the key information of the bond project;

[0101] According to the project corresponding to the key information of the bond project in the bond business model, the project plan corresponding to the project is output as the initial project plan corresponding to the key information of the bond project;

[0102] In this implementation, users interact with the system through interactive information entry. The system leverages semantic recognition and information extraction technologies to automatically extract key information from user input, such as project name, location, construction period, operational time, and project type. The system then accurately matches this key information with semantically parsed information from a pre-built bond business macromodel to automatically generate an initial bond project proposal. This process not only significantly improves the efficiency of bond project proposal generation but also significantly reduces the need for manual intervention and errors caused by manual input. Through semantic parsing and key information matching, core project-related elements can be efficiently extracted from complex project data, ensuring that the generated initial proposal is highly aligned with project requirements and accurate. Furthermore, the macromodel's matching capabilities make the proposal generation process more intelligent and flexible, enabling rapid response to the needs of diverse bond project types, improving overall processing efficiency and proposal accuracy. Ultimately, this implementation not only optimizes the bond project implementation process but also effectively improves work quality, reduces deviations caused by human error, and ensures the high quality and consistency of bond project proposals.

[0103] In one implementation, the aforementioned bond business model may include the following construction process:

[0104] Using OCR technology, we extract data from each project in the preset bond model library to obtain the corresponding project content information;

[0105] Perform text analysis on the project content information corresponding to each project to obtain semantic analysis information of each project;

[0106] Based on the semantic analysis information of each project, the LLM model is trained to obtain the bond business model;

[0107] In this implementation, by combining OCR technology with the LLM large model, the construction of the bond business large model was successfully realized, which greatly improved the automated processing and intelligent analysis capabilities of bond project data. Specifically, the system first uses OCR technology (such as Baidu Paddle's Paddle-OCR4 model) to extract data from projects in the preset bond model library, and can automatically extract printed text and table information from files of various formats (such as PDF). The Paddle-OCR4 model performs particularly well in Chinese text parsing and table content recognition. It can accurately identify the position, content and text coordinates of the table. The parsing accuracy meets high standards, and with GPU acceleration, it has high processing efficiency. On the basis of data extraction, the content information of each project is deeply parsed, and the LLM large model (such as Mass Spectrum Qingyan's Chat-GLM4 model) is used for semantic analysis and key information extraction. By collecting and analyzing a large number of historically publicized special bond project implementation plans and other disclosure documents, it is possible to train an efficient implementation plan model for different bond project scenarios and investment areas. This model can not only extract the core content of the bond project, but also self-learn and optimize based on new implementation plans and user feedback, gradually improving the accuracy and adaptability of the model. Compared with traditional manual processing methods, this implementation method achieves efficient extraction and intelligent matching of bond project data through the synergy of OCR and LLM large models, greatly reducing manual intervention and improving the accuracy and efficiency of information processing. In addition, the self-learning ability of the model enables the system to continuously optimize in the ever-changing bond project needs, ensuring the high accuracy and reliability of the project implementation plan. This implementation method not only improves the processing efficiency of bond projects, but also significantly reduces the error rate in manual operations, ensuring the high quality and consistency of bond project implementation plans.

[0108] In one implementation, the process of extracting business data information from the initial project plan in step 3 may include:

[0109] Classify data according to the business content in the initial project plan to obtain the business content corresponding to different business categories in the initial project plan;

[0110] According to the business content corresponding to different business categories, use the preset information extraction model to extract data and obtain the business data information corresponding to the initial project plan;

[0111] The business data information may include at least: business content categories and business data elements;

[0112] This implementation significantly enhances the automation and intelligence of data processing by accurately classifying and extracting business content from initial bond project proposals. By categorizing the content within the initial project proposals into different business categories, the structure and characteristics of each type of business data can be automatically identified, laying a solid foundation for subsequent data extraction. Utilizing a pre-defined information extraction model, this business content is further analyzed in depth, automatically extracting business data information, including business content categories and key data elements. The efficient use of this information extraction model enables the rapid and accurate extraction of complex business data, ensuring data integrity and consistency. Compared to traditional manual extraction methods, this intelligent model-based data classification and extraction approach not only significantly improves work efficiency but also reduces errors and biases caused by human intervention. Especially for large-scale and complex bond project data, it can quickly and accurately parse and extract massive amounts of information, significantly improving the accuracy and intelligence of data processing. This approach enables efficient and structured processing of various business information involved in bond projects, providing reliable data support for subsequent project analysis, risk assessment, and solution optimization. In addition, automated information extraction has strong adaptability and can be flexibly adjusted according to the characteristics and needs of different projects, thereby ensuring the efficient processing of various bond projects and the accurate generation of implementation plans.

[0113] In this implementation, if Figure 3 As shown, the above process of performing data identification based on the business data information using a matching algorithm to obtain a data table and business description corresponding to the business data information may include:

[0114] Perform full word matching, word vector similarity matching, and unit feature matching on the business data information to obtain the operation formula operator corresponding to the business data information;

[0115] According to the operation formula operator, the business data information is matched with the operator to obtain the operator corresponding to the business data information;

[0116] Generate construction formulas corresponding to business data information based on operators;

[0117] Generate a data table corresponding to the business data information based on the operation relationship in the construction formula corresponding to the business data information and the business logic sequence in the business data information;

[0118] Extract target business indicators from the data table corresponding to the business data information, and use the target business indicators as new business content in the initial project plan according to the pre-set indicator coding;

[0119] Generate corresponding business description according to business data information and preset data format;

[0120] The data tables include one or more of the following: project annual investment plan table, project financing status table, project income calculation table, project cost calculation table, project principal and interest payment table, project capital balance table, and project stress test table;

[0121] Target business indicators include one or more of the following: total investment in the investment estimate table, commencement and completion dates in the project basic information table, and various financing amounts in the project financing table;

[0122] The business description includes one or more of the following: the project's construction content, the project's economic and social benefits, and the project's construction objectives.

[0123] In this implementation, the process of generating a construction formula corresponding to the business data information based on the operator may include:

[0124] Based on the operator, select the formula matching algorithm corresponding to the business data information;

[0125] Generate a construction formula corresponding to the business data information based on the formula matching algorithm corresponding to the business data information;

[0126] The formula matching algorithm includes at least one or more of the following: an operator level priority algorithm, a formula matching priority algorithm, and a formula matching exclusion algorithm.

[0127] This implementation involves three steps: first, extracting data and identifying business data elements; second, generating formulas; and third, generating data tables and business descriptions, and then generating implementation plans. The details are as follows:

[0128] (1) Extract data and identify business data elements

[0129] You can choose to use Baidu PaddlePaddle's ERNIE model to classify the user's completed business content and mark it as a business category. Through the pre-trained Baidu PaddlePaddle UIE model, business data information is extracted from the classified business content, different business data elements are identified, and marked as different business data elements.

[0130] (2) Generate formula

[0131] Based on the accumulated data of special bond projects over the years, the general large model is fine-tuned and trained to form a large model for proprietary business fields, and the business data extraction effect in the generation of implementation plans is enhanced. Figure 3The business data elements are intelligently matched in a described manner to automatically generate the required formulas. Specifically, based on the obtained formula template, the operators in the business formula template are matched with the business data elements extracted in step 2 through three methods: full word matching, word vector similarity matching, and unit of measurement feature matching. The matching methods and usage order of the three matching methods are shown in Table 1:

[0132] Table 1. Operator and business data information matching method and sequence diagram

[0133]

[0134] For those that the model fails to automatically identify, the matching results can be improved through interactive information entry;

[0135] Operator matching is performed based on the business characteristic rules of the calculation formula operator. Common operators are shown in Table 2:

[0136] Table 2 Schematic diagram of common operators

[0137]

[0138]

[0139] When the system recursively generates a complete calculation formula, it uses the algorithm shown in Table 3 to implement recursive construction of the calculation formula, ensuring the accuracy and efficiency of formula generation.

[0140] Table 3 Formula generation algorithm

[0141]

[0142] The process of recursively generating a formula tree is as follows:

[0143] 1) According to the above steps, a preliminary calculation formula is formed from the formula template.

[0144] 2) Starting from the top level, determine whether an operator has a corresponding value or formula, as well as a corresponding operator. If so, it is used as an operator in the formula. If no corresponding numerical formula exists, the historical project calculation formula knowledge base and domain model are used to determine whether it can be combined with other operators. This process is continued downward until all formulas are determined to be fixed operators.

[0145] 3) According to different levels, the formula is composed to form the final calculation formula. The formula tree is shown as follows: Figure 4 shown.

[0146] Generating data tables and business descriptions, and simultaneously generating implementation plans, is mainly divided into three steps:

[0147] The first is to update the data tables in the implementation plan: according to the business logic sequence and the operation relationship in the calculation formula, data tables are automatically generated. The tables automatically generated by the system mainly include project investment estimation table, project financing status table, project income calculation table, project cost calculation table, project principal and interest payment table, project capital balance table, project stress test table, etc. After generating the relevant tables, the system automatically updates the data tables in the implementation plan through table synchronization tools (such as poi-tl), displays the data according to the table style set by the model, and automatically adjusts the column width, row height, etc. according to the data.

[0148] Second, adjust the business content of the implementation plan based on the data in the data table: extract the required business indicators from the generated data table and replace the corresponding indicator content in the implementation plan model according to the indicator codes pre-set in the implementation plan model. Key business indicators include the total investment in the investment estimate table, the start and completion dates in the project basic information table, and the various financing amounts in the project financing status table. This ensures that the table data and business content descriptions are consistent.

[0149] Third, adjust the business description information in the implementation plan based on the project's specific circumstances. The business domain model generates description information in a fixed format based on project data retrieved from the system, as well as various information obtained from the internet or historical project databases. The generated business description primarily includes the project's construction content, economic and social benefits, and construction objectives. Based on the generated content, the corresponding business description code in the implementation plan model is replaced to ensure consistency between the table data and the business content description.

[0150] Through the implementation of the above steps, the business content and description of the implementation plan are automatically generated and pushed based on the matching implementation plan model, comprehensively enhancing the automated processing and intelligent analysis capabilities of bond project business data. First, the business description and data are indexed based on the project's region, and the corresponding business content is divided and identified through index coding. This process utilizes a multi-layered matching algorithm and operator recognition mechanism to accurately identify and parse complex business data, ensuring that each data element and its associated operational logic are correctly reflected. Through methods such as full-word matching, word vector similarity matching, and unit-of-measure feature matching, key operational formula operators are extracted from the project's business data, generating corresponding structured data tables and detailed business descriptions. Next, a comprehensive search is conducted based on user-entered information such as the project's investment sector and region, combined with a database of historical projects. Based on multiple dimensions such as the publication date of historical implementation plans, region, and project investment sector, a recommendation algorithm is used to generate specific business content, including project investment details, construction content, and key revenue descriptions. This business content is then pushed to users. This intelligent recommendation not only improves the accuracy of business content, but also ensures that the implementation plan can be dynamically adjusted and customized according to the specific needs of the project. Finally, the user further improves the business description and data based on the pushed business content, such as modifying the total investment amount of the project, adjusting the income type, and increasing the number of special bond issuance periods. Through the automated calculation and generation process of intelligent algorithms, the system can quickly adapt to the needs of different projects and generate business forms and description content that meet actual needs. This adaptive capability and efficient processing method not only greatly improves the processing efficiency of bond projects, but also reduces manual intervention and errors, ensuring the accuracy of the data and the high quality of the plan. Overall, this technical solution, by combining intelligent recommendation with data matching algorithms, not only optimizes the implementation plan preparation process for bond projects and improves work efficiency, but also provides more efficient and intelligent data support for bond project management, and promotes project management to develop in the direction of higher accuracy and adaptive capabilities.

[0151] In one implementation, after generating the project implementation plan corresponding to the key information of the bond project based on the data table and business description, the following steps may also be included:

[0152] Conduct project evaluation based on the business data information in the project implementation plan to determine whether the project implementation plan needs to be adjusted;

[0153] If so, re-acquire the key information of the bond project input by the user, and adjust the data in the project implementation plan according to the re-acquired key information of the bond project to obtain a new project implementation plan;

[0154] In this implementation, the system automatically evaluates the business data in a project implementation plan to determine whether it meets project requirements and whether adjustments are necessary. Specifically, users can evaluate a pre-generated implementation plan to confirm its accuracy and feasibility. If they determine that no modifications are necessary, they can directly download the generated implementation plan to complete the plan creation process. However, if the data or content in the plan does not meet the actual project requirements, users can make further adjustments through interactive data entry. The system determines the modification scenario based on the user's input, including adding model content or adjusting model values. Once the determination is complete, the system automatically jumps to the corresponding step to re-adjust module content or values, regenerates the data table based on the adjusted data, and updates the project implementation plan. This process ensures the flexibility and intelligence of the project implementation plan, allowing for timely adjustments based on actual needs. This automated adjustment and optimization mechanism not only improves the efficiency of plan generation and modification, but also ensures the accuracy and consistency of the plan. Users can quickly modify the implementation plan through interactive data entry, avoiding plan deviations caused by manual operation or input errors. At the same time, through dynamic evaluation and adjustment, the system achieves a highly adaptive project implementation plan, enabling continuous optimization based on user needs and feedback, ultimately generating an implementation plan that aligns with the project's actual circumstances. Overall, this intelligent evaluation and adjustment mechanism significantly improves the efficiency of bond project implementation plan compilation, reduces manual intervention, and enhances the quality and adaptability of the plans, promoting the intelligent development of bond project management.

[0155] In summary, the present invention aims to solve the problems in the prior art of key information collection, data matching and business data identification of bond projects, which are low in automation and often rely on manual intervention, resulting in inaccurate information extraction, inaccurate matching of business data and solutions, deviations in generated project implementation plans, low efficiency and error-proneness. By integrating big data, artificial intelligence and business domain models, the present invention provides an automated and efficient method for generating bond project implementation plans. Figure 5As shown, the user first provides key information about the bond project through interactive data entry. Based on this information, the pre-built bond business model performs data matching to automatically generate an initial project plan. Based on data extraction and text parsing from a pre-built bond model library, the bond business model accurately identifies project-related business elements, providing a reliable foundation for subsequent plan generation. After generating the initial plan, the system extracts business data information and uses a matching algorithm to identify data, generating detailed data tables and business descriptions related to the project. This process not only improves automated data processing capabilities but also ensures data accuracy and structure, avoiding errors and inefficiencies associated with manual processing. Subsequently, the system automatically generates a complete project implementation plan based on the generated data tables and business descriptions. The system also supports user-defined modifications to the business data and descriptions. Through intelligent data recognition and calculation formula generation, the system automatically generates project-related business data tables and simultaneously updates the implementation plan, ensuring consistency and accuracy of the business descriptions and data content. Ultimately, users can optimize the project implementation plan based on their modifications, improving both the quality and feasibility of the plan.

[0156] Example 2:

[0157] A specific example is used to illustrate a method for automatically generating a bond project implementation plan provided by the present invention, comprising:

[0158] Step S1 matches the project implementation plan model with the business domain model;

[0159] 1.1 Forming a model library through large models:

[0160] We collected information on the implementation plans of special bond projects published in previous years, extracted the implementation plan content using OCR technology, and used the LLM large model to train the implementation plan model. The final implementation plan model structure is shown in Table 4:

[0161] Table 4 Example of a special bond project implementation plan model

[0162]

[0163] 1.2 Model Matching

[0164] When a user enters "I want to build a parking project," the system generates the following information recommendations based on historical data and the user's location and time. The user then completes the key information based on the proposed project to form the basic project information corresponding to Table 5.

[0165] Table 5 Example of basic project information description

[0166] Key information types Information content Project Name XX District Urban Management Parking Integration Project (Phase I) Project Area xx District, xx City Project construction period February 2023-December 2024 Project investment areas City parking lot

[0167] The system identifies the user's registration area as xx District, xx City. Based on the investment area, it identifies the project as an urban parking project. Based on other basic information descriptions, it automatically matches in the system model library and finds the special bond project implementation plan model that best suits the project description, which is the urban parking model.

[0168] Step S2: Interactively improve business description and business data

[0169] Based on the determined implementation plan and the project's investment areas, the system recommends the project implementation plan content, including static investment, self-raised funds, revenue, and costs, and automatically generates business plan recommendations for each business module. The system pushes business plan content as shown in Table 6.

[0170] Table 6 Example of recommended content for business solutions

[0171]

[0172] Based on the pushed business plan content, the user adjusts the description of the plan, such as modifying the total investment and income descriptions. See Table 7 for modification examples.

[0173] Table 7 Example of business content classification

[0174]

[0175]

[0176] The adjusted business content description is directly transmitted to the system in its entirety through interactive information entry.

[0177] Step S3 identifies business data elements, generates data tables and business descriptions, and generates implementation plans:

[0178] 3.1 Extract data and identify business data elements

[0179] Baidu PaddlePaddle's ERNIE model is used to classify the user's completed business content and mark the various business content categories. Baidu PaddlePaddle's UIE model is then used to extract business data information from the classified business content, identifying business data elements of seven types of business content, including project annual investment plans, project financing status, and project revenue estimates. The specific content identified is shown in Table 8.

[0180] Table 8 Schematic diagram of project business data extraction

[0181]

[0182] 3.2 Generating formula

[0183] 3.2.1 Using the Business Domain Big Model to Identify Applicable Formula Templates

[0184] By using a large model in a professional field and intelligently matching business data elements, the formulas required for common business data elements are automatically generated based on various business data elements.

[0185] For example:

[0186] Annual investment plan for the project:

[0187]

Formula: Total investment = static total investment + construction period interest

[0188] [Formula: Static total investment = project cost + other construction costs + reserve]

[0189] [Formula: Construction period interest = Issue amount 1 * Issue interest rate 1 * Issue construction period 1 + ...]

[0190] Project income estimation:

[0191] [Formula: Total income = income 1 + income 2 + ...]

[0192] [Formula: Revenue 1 = Unit Price * Quantity * …]

[0193] [Formula: Revenue 1 = Price * Quantity * Monthly Subscription Rate * …]

[0194] 3.2.2 Operational formula operator matching

[0195] According to the obtained formula template, the operators in the business formula template are matched with the business data elements extracted in step S2 through three methods: full word matching, word vector similarity matching, and unit of measurement feature matching. The matching diagram is shown in the figure below. Figure 6 .

[0196] 3.2.3 Operator Matching

[0197] The system matches operators based on the business characteristics of the calculation formula operators by selecting the operator at the corresponding position in the operator matching formula. The operator results are generated as follows.

[0198] Annual investment plan for the project:

[0199] [Formula: Static total investment = project cost + other construction costs + reserve]

[0200] Total investment: RMB 182.8175 million

[0201] Project cost: 80 million yuan - Addition operator

[0202] Other construction costs: RMB 40 million

[0203] Reserve Fund: RMB 72.8175 million - Addition operator

[0204] Project income estimation:

[0205] [Formula: Total income = income 1 + income 2 + ...]

[0206] [Formula: Revenue 1 = Unit Price * Quantity * …]

[0207] [Formula: Revenue 1 = Price * Quantity * Monthly Subscription Rate * …]

[0208] Parking Lot Revenue: - Addition Operator

[0209] 2000 parking spaces - multiplication operator

[0210] Monthly fee: 3,000 yuan / month — Multiplication operator

[0211] Monthly subscription rate 35% - multiplication operator

[0212] Fast charging pile service fee - addition operator

[0213] 800 fast charging piles — multiplication operator

[0214] 350 days of operation throughout the year - multiplication operator

[0215] The charge is RMB 0.60 per kWh - multiplication operator

[0216] Average charging 80 degrees - multiplication operator

[0217] 3.2.4 Calculation formula construction

[0218] Through the algorithm, the composition of operators and operators is identified, and recursion is performed level by level to finally form a complete formula tree.

[0219] Annual investment plan for the project:

[0220] [Formula 1: Static total investment = project cost + other construction costs + reserve]

[0221] [Formula 1: Total investment = static total investment + issuance amount * bond term * interest rate]

[0222] Project income estimation:

[0223] [Formula 1: Operating income = parking lot income + fast charging pile income]

[0224] Formula 2: Parking Lot Revenue = Number of Parking Spaces * Monthly Fee (3,000 yuan / month) * 12 (months) * Monthly Rate

[0225] Formula 3: Fast charging pile revenue = Fast charging pile * Annual operating time * Charging standard * Average charging

[0226] 3.3 Generate data tables and business descriptions, and simultaneously generate implementation plans

[0227] The generated calculation formula generates corresponding data tables. The system automatically generates tables that primarily include annual project investment plans, project financing status tables, project revenue estimates, project cost estimates, project principal and interest repayment tables, project capital balance tables, and project stress testing tables. As shown in the figure, based on the XX City implementation plan model, schematic diagrams of the investment plan and project operating income forecast tables are generated, as shown in Tables 9 and 10.

[0228] Table 9 Project annual investment plan Unit: 10,000 yuan

[0229] Serial number project 2023 2024 total 1 Construction and installation projects 8,000.00 0.00 8,000.00 2 Equipment investment 4,000.00 4,000.00 3 reserve fund 7,281.75 7,281.75 4 Other construction project costs 0.00 0.00 one Static total investment 8,000.00 11,281.75 19,281.75 two Construction period interest 160.00 520.00 680.00 three Bond issuance fees 8.00 6.00 14.00 Four Total investment 8,168.00 11,807.75 19,975.75

[0230] Table 10 Project operating income forecast Unit: 10,000 yuan

[0231] Serial number Income Category unit 2025 2026 2027 2028 2029 2030 1 Parking lot revenue Ten thousand yuan 2,520.00 2,520.00 2,520.00 2,520.00 2,520.00 2,520.00 Off-street parking indivual 2,000.00 2,000,00 2,000.00 2,000.00 2,000.00 2,000.00 TOLL Yuan / month 3,000.00 3,000.00 3,000.00 3,000.00 3,000.00 3,000.00 Monthly subscription rate ％ 35.00 35.00 35.00 35.00 35.00 35.00 Month moon 12.00 12.00 12.00 12.00 12.00 12.00 2 Fast charging pile service fee Ten thousand yuan 3,810.00 3.84 3,810.00 3,840.00 3,840.00 3,840.00 Fast charging pile indivual 80.00 800.00 80.00 80.00 80.00 80.00 Charging Standards Yuan / degree 0.60 0.60 0.60 0.60 0.60 0.60 Average daily charging degrees / day 80.00 80.00 80.00 80.00 80.00 80.00 Total income Ten thousand yuan 2,520.38 2,523.84 2,520.38 2,520.38 2,520.38 2,520.38

[0232] Based on the generated data tables, the system automatically generates numerical indicators for the business content in the implementation plan. Key business indicators include the total investment in the investment estimate table, the start and completion time in the project basic situation, and the various financing amounts in the project financing situation.

[0233] Replace the corresponding content according to the parameter indicators in the parking lot model.

[0234] The following are some adjustment examples:

[0235] Before adjustment:

[0236] Project Name: {{0001}}

[0237] Project Unit: {{0002}}

[0238] Project zoning: {{0003}}

[0239] After adjustment:

[0240] Project Name: XX District Urban Management Parking Integration Project (Phase I)

[0241] Project Unit: Urban Management Committee of xx District, xx City

[0242] Project Division: xx District, xx City

[0243] Based on historical special bond project data, a business description knowledge base system has been formed. By providing basic information and a description of the basic project situation, the large model can generate the required business description information. The business description to be generated mainly includes the construction content of the project, the economic and social benefits of the project, and the construction goals of the project.

[0244] The following are some adjustment examples:

[0245] Before adjustment:

[0246] Project size: {{1001}}

[0247] After adjustment:

[0248] Project Scale: This project includes six subprojects, including the construction of a district-wide smart parking command center with a total construction area of ​​1,685 square meters, the intelligent transformation of roadside parking on 99 road sections within the district, the installation of 3,000 roadside parking spaces, and the construction of 800 vehicle charging stations.

[0249] Step S4: Improve the implementation plan

[0250] Users can evaluate the pre-generated implementation plan to determine whether further adjustments to the business data and content are necessary. If necessary, they can continue to adjust the data through interactive information entry. The system will automatically jump to step S2 for re-identification and regeneration. If no modifications are required, the generated implementation plan can be downloaded to complete the implementation plan generation process.

[0251] The user enters "The unit price of parking lot revenue is uniformly changed to 3,500 yuan / month, and the monthly subscription rate is changed to 40%." The system determines the content that the user needs to adjust based on the semantics of the user input, updates the original business description, and generates new business description information, as shown in Table 11.

[0252] Table 11 Project business description update

[0253]

[0254]

[0255] After the update, the system automatically jumps to step S3, continues the corresponding analysis and generation, and finally updates to the implementation plan, as shown in Table 12.

[0256] Table 12 Adjusted Project Operating Income Forecast Unit: Ten Thousand Yuan

[0257] Serial number Income Category unit 2025 2026 2027 2028 2029 2030 1 Parking lot revenue Ten thousand yuan 3,360.00 3,360.00 3,360.00 3,360.00 3,360.00 3,360.00 Off-street parking indivual 2,000.00 2,000.00 2,000.00 2,000.00 2,000.00 2,000.00 TOLL Yuan / month 3,500.00 3,500.00 3,500.00 3,500.00 3,500.00 3,500.00 Monthly subscription rate ％ 40.00 40.00 40.00 40.00 40.00 40.00 Month moon 12.00 12.00 12.00 12.00 12.00 12.00 2 Fast charging pile service fee Ten thousand yuan 3,840.00 3.84 3,840.00 3,840.00 3,840.00 3,840.00 Fast charging pile indivual 80.00 800.00 80.00 80.00 80.00 80.00 Charging Standards Yuan / degree 0.60 0.60 0.60 0.60 0.60 0.60 Average daily charging degrees / day 80.00 80.00 80.00 80.00 80.00 80.00 Total income Ten thousand yuan 3,360.38 3,363.84 3,360.38 3,360.38 3,360.38 3,360,38

[0258] This example illustrates the technical advantages of the method for automatically generating a bond project implementation plan provided by the present invention in the process of writing a bond project implementation plan. During the specific implementation process, the system's automated calculation and data processing not only improves the efficiency of plan writing and reduces manual intervention, but also ensures that the generated implementation plan has high accuracy and logical consistency. Ultimately, this intelligent method makes the writing, modification, and adjustment of bond project implementation plans more efficient and accurate, greatly optimizing the problems of low efficiency and frequent errors in traditional manual operations. Therefore, the technical effect of the present invention in practical applications not only optimizes the plan writing process, but also improves the quality and executability of the plan.

[0259] Example 3:

[0260] The present invention based on the same inventive concept also provides a bond project implementation plan automatic generation system, the structural composition diagram is as follows Figure 7 Shown, including:

[0261] The information acquisition module is used to obtain key information of the bond project input by the user;

[0262] The initial plan generation module is used to match the key information of the bond project with the pre-built bond business model and output the initial project plan corresponding to the key information of the bond project;

[0263] The data identification module is used to extract business data information from the initial project plan and perform data identification based on the business data information using a matching algorithm to obtain the data table and business description corresponding to the business data information;

[0264] The target plan generation module is used to generate a project implementation plan corresponding to the key information of the bond project based on the data table and business description;

[0265] Among them, the bond business model is obtained based on data extraction and text analysis of the preset bond model library.

[0266] For example, the key information of the above-mentioned bond project may include one or more of the following: project name, project region, project construction period, time to be put into operation, project type field, project investment field, user region, project investment information, project construction content and income description, special bond issuance period, income type and total project investment amount.

[0267] In one implementation, the initial solution generation module may include:

[0268] The semantic parsing submodule is used to match the key information of the bond project with the semantic parsing information of each project in the bond business model, and obtain the project in the bond business model that corresponds to the key information of the bond project;

[0269] The project matching submodule is used to output the project plan corresponding to the project as the initial project plan corresponding to the key information of the bond project based on the project corresponding to the key information of the bond project in the bond business model.

[0270] In one implementation, the generation system may further include: a model building module, specifically configured to:

[0271] Using OCR technology, we extract data from each project in the preset bond model library to obtain the corresponding project content information;

[0272] Perform text analysis on the project content information corresponding to each project to obtain semantic analysis information of each project;

[0273] Based on the semantic analysis information of each project, the LLM model is trained to obtain the bond business model.

[0274] In one implementation, the data identification module may include:

[0275] The business classification submodule is used to classify data according to the business content in the initial project plan and obtain the business content corresponding to different business categories in the initial project plan;

[0276] The data extraction submodule is used to extract data based on the business content corresponding to different business categories using a preset information extraction model to obtain the business data information corresponding to the initial project plan;

[0277] The business data information includes at least: business content categories and business data elements.

[0278] In one implementation, the data identification module may further include:

[0279] The operator matching submodule is used to perform full word matching, word vector similarity matching, and unit feature matching on the business data information to obtain the operation formula operator corresponding to the business data information;

[0280] The operator matching submodule is used to match the business data information with the operator according to the operation formula operator to obtain the operator corresponding to the business data information;

[0281] The formula generation submodule is used to generate the construction formula corresponding to the business data information based on the operator;

[0282] The table output submodule is used to generate a data table corresponding to the business data information according to the operation relationship in the construction formula corresponding to the business data information and the business logic sequence in the business data information;

[0283] The business content replacement submodule is used to extract the target business indicators based on the data table corresponding to the business data information, and use the target business indicators as the new business content in the initial project plan according to the pre-set indicator code;

[0284] The business description output submodule is used to generate corresponding business descriptions according to the business data information and the preset data format;

[0285] The data tables include one or more of the following: project annual investment plan table, project financing status table, project income calculation table, project cost calculation table, project principal and interest payment table, project capital balance table, and project stress test table;

[0286] Target business indicators include one or more of the following: total investment in the investment estimate table, commencement and completion dates in the project basic information table, and various financing amounts in the project financing table;

[0287] The business description includes one or more of the following: the project's construction content, the project's economic and social benefits, and the project's construction objectives.

[0288] In one implementation, the formula generation submodule may include:

[0289] A priority matching unit, used to select a formula matching algorithm corresponding to the business data information based on an operator;

[0290] A formula output unit, configured to generate a construction formula corresponding to the business data information according to a formula matching algorithm corresponding to the business data information;

[0291] The formula matching algorithm includes at least one or more of the following: an operator level priority algorithm, a formula matching priority algorithm, and a formula matching exclusion algorithm.

[0292] In one implementation, the above-mentioned generation system may further include: a scheme adjustment module, specifically configured to:

[0293] Conduct project evaluation based on the business data information in the project implementation plan to determine whether the project implementation plan needs to be adjusted;

[0294] If so, the key information of the bond project input by the user is retrieved, and the data in the project implementation plan is adjusted according to the retrieved key information of the bond project to obtain a new project implementation plan.

[0295] Example 4:

[0296] Based on the same inventive concept, the present invention also provides a computer device, which includes a processor and a memory, the memory is used to store a computer program, the computer program includes program instructions, and the processor is used to execute the program instructions stored in a computer storage medium. The processor may be a central processing unit (CPU), or other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA) or other programmable logic devices, discrete gates or transistor logic devices, discrete hardware components, etc. It is the computing core and control core of the terminal, which is suitable for implementing one or more instructions, specifically suitable for loading and executing one or more instructions in a computer storage medium to implement the corresponding method flow or corresponding function, so as to implement the steps of the automatic generation method of a bond project implementation plan in the above embodiment.

[0297] Example 5:

[0298] Based on the same inventive concept, the present invention also provides a storage medium, specifically a computer-readable storage medium (Memory), which is a memory device in a computer device for storing programs and data. It can be understood that the computer-readable storage medium here can include both built-in storage media in the computer device and, of course, extended storage media supported by the computer device. The computer-readable storage medium provides a storage space, which stores the operating system of the terminal. In addition, one or more instructions suitable for being loaded and executed by the processor are also stored in the storage space. These instructions can be one or more computer programs (including program codes). It should be noted that the computer-readable storage medium here can be a high-speed RAM memory or a non-volatile memory, such as at least one disk memory. The processor can load and execute one or more instructions stored in the computer-readable storage medium to implement the steps of the automatic generation method of a bond project implementation plan in the above embodiment.

[0299] It will be understood by those skilled in the art that embodiments of the present invention may be provided as methods, systems, or computer program products. Thus, the present invention may take the form of an entirely hardware embodiment, an entirely software embodiment, or an embodiment combining software and hardware. Furthermore, the present invention may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to magnetic disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0300] The present invention is described with reference to flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to embodiments of the present invention. It should be understood that each process and / or block in the flowcharts and / or block diagrams, as well as combinations of processes and / or blocks in the flowcharts and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowcharts and / or block diagrams. Figure 1 a process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0301] These computer program instructions may also be stored in a computer readable memory that can direct a computer or other programmable data processing device to work in a specific manner, so that the instructions stored in the computer readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 a process or multiple processes and / or boxes Figure 1 The function specified in one or more boxes.

[0302] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operational steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing the instructions executed on the computer or other programmable device for implementing the process. Figure 1 a process or multiple processes and / or boxes Figure 1 The steps for the function specified in one or more boxes.

[0303] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit its scope of protection. Although the present invention has been described in detail with reference to the above embodiments, ordinary technicians in the field should understand that after reading the present invention, those skilled in the art may still make various changes, modifications or equivalent substitutions to the specific implementation methods of the application, but these changes, modifications or equivalent substitutions are all within the scope of protection of the pending claims.

Claims

1. A method for automatically generating a bond project implementation plan, characterized in that: include: Obtain key information of bond projects input by the user; Based on the key information of the bond project, use the pre-built bond business model to perform data matching on the key information of the bond project, and output the initial project plan corresponding to the key information of the bond project; Extracting business data information from the initial project plan, and performing data identification using a matching algorithm based on the business data information to obtain a data table and business description corresponding to the business data information; Generate a project implementation plan corresponding to the key information of the bond project based on the data table and the business description; Conducting a project evaluation on the project implementation plan based on the business data information in the project implementation plan to determine whether the project implementation plan needs to be adjusted; If yes, re-acquire the key information of the bond project input by the user, and adjust the data in the project implementation plan according to the re-acquired key information of the bond project to obtain a new project implementation plan; The bond business model is obtained by extracting data and parsing text from a preset bond model library.

2. The method according to claim 1, wherein The bond business model includes the following construction process: Using OCR technology, extract data from each project in the preset bond model library to obtain the project content information corresponding to each project; Performing text analysis on the project content information corresponding to each project to obtain semantic analysis information of each project; Based on the semantic analysis information of each project, the LLM model is trained to obtain the bond business model.

3. The method according to claim 2, wherein According to the key information of the bond project, data matching is performed on the key information of the bond project using a pre-built bond business model, and an initial project plan corresponding to the key information of the bond project is output, including: Performing data matching on the bond project key information and the semantic parsing information of each project in the bond business macro model to obtain the project in the bond business macro model corresponding to the bond project key information; According to the project corresponding to the key information of the bond project in the bond business macro model, a project plan corresponding to the project is output as an initial project plan corresponding to the key information of the bond project.

4. The method according to claim 1, wherein The extracting of business data information from the initial project plan includes: Classify data according to the business content in the initial project plan to obtain business content corresponding to different business categories in the initial project plan; According to the business content corresponding to the different business categories, a preset information extraction model is used to extract data to obtain business data information corresponding to the initial project plan; The business data information at least includes: business content category and business data elements.

5. The method according to claim 4, wherein The method of performing data identification based on the business data information using a matching algorithm to obtain a data table and business description corresponding to the business data information includes: Performing full word matching, word vector similarity matching, and unit feature matching on the business data information to obtain an operation formula operator corresponding to the business data information; According to the operation formula operator, the business data information is matched with an operator to obtain an operator corresponding to the business data information; Generating a construction formula corresponding to the business data information according to the operator; Generate a data table corresponding to the business data information according to the operation relationship in the construction formula corresponding to the business data information and the business logic sequence in the business data information; Extracting target business indicators from a data table corresponding to the business data information, and using the target business indicators as new business content in the initial project plan according to a pre-set indicator code; Generate a corresponding business description according to the business data information and a preset data format; The data tables include one or more of the following: project annual investment plan table, project financing status table, project income calculation table, project cost calculation table, project principal and interest payment table, project capital balance table, and project stress test table; The target business indicators include one or more of the following: the total investment in the investment estimate table, the start and completion time in the basic project information, and the amounts of various types of financing funds in the project financing information; The business description includes one or more of the following: the construction content of the project, the economic and social benefits of the project, and the construction objectives of the project.

6. The method according to claim 5, wherein Generating a construction formula corresponding to the business data information according to the operator includes: Selecting a formula matching algorithm corresponding to the business data information according to the operator; Generate a construction formula corresponding to the business data information according to a formula matching algorithm corresponding to the business data information; The formula matching algorithm includes at least one or more of the following: an operator level priority algorithm, a formula matching priority algorithm, and a formula matching exclusion algorithm.

7. The method according to claim 1, wherein The key information of the bond project includes one or more of the following: project name, project region, project construction period, time to be put into operation, project type field, project investment field, user region, project investment information, project construction content and income description, special bond issuance period, income type and total project investment amount.

8. A bond project implementation plan automatic generation system, characterized by: include: The information acquisition module is used to obtain key information of the bond project input by the user; An initial plan generating module is used to perform data matching on the key information of the bond project using a pre-built bond business model, and output an initial project plan corresponding to the key information of the bond project; A data identification module is used to extract business data information from the initial project plan, and based on the business data information, perform data identification using a matching algorithm to obtain a data table and business description corresponding to the business data information; A target solution generation module, configured to generate a project implementation plan corresponding to the key information of the bond project based on the data table and the business description; A plan adjustment module is used to evaluate the project implementation plan based on the business data information in the project implementation plan and determine whether the project implementation plan needs to be adjusted; If yes, re-acquire the key information of the bond project input by the user, and adjust the data in the project implementation plan according to the re-acquired key information of the bond project to obtain a new project implementation plan; The bond business macro model is obtained based on data extraction and text parsing of a preset bond model library.

9. The system according to claim 8, wherein The generation system further includes a model building module, configured to: Using OCR technology, extract data from each project in the preset bond model library to obtain the project content information corresponding to each project; Performing text analysis on the project content information corresponding to each project to obtain semantic analysis information of each project; Based on the semantic analysis information of each project, the LLM model is trained to obtain the bond business model.

Images