An intelligent project evaluation method and system for a foreign investment attraction scene and a storage medium

Abstract

The application discloses a project intelligent evaluation method and system for a project investment attraction scene and a storage medium. The method comprises the following steps: receiving project data input of a project to be evaluated, performing text analysis and key field extraction on the project data, and forming a project information set; inputting the project information set and a preset project investment evaluation context into a preset language model after combination to perform information enhancement processing, and obtaining enhanced project structured description information; generating project evaluation dimension data based on the project structured description information and a preset project investment evaluation strategy; inputting the project evaluation dimension data into a preset evaluation calculation model to perform comprehensive calculation, and obtaining a project evaluation result; and generating and outputting a project evaluation report containing multi-dimensional scores, risk prompts and follow-up suggestions according to the project evaluation result. The application can improve the automation degree of project investment data processing, evaluation calculation and report generation, and support configuration and adjustment of evaluation dimensions, weights and scoring rules according to different regions, parks, industrial tracks or project investment strategies, so that the efficiency, adaptability and consistency of project evaluation are improved.

Description

Technical Field

[0001] This invention relates to the fields of artificial intelligence, natural language processing, and investment promotion project evaluation technology, and in particular to an intelligent project evaluation method, system, and storage medium for investment promotion scenarios. Background Technology

[0002] In the actual investment promotion process, investment promotion personnel usually need to conduct preliminary screening, comprehensive evaluation and follow-up on a large number of projects to be introduced, including judging whether the project is in line with local industrial planning, whether it is compatible with existing policy conditions, whether it is feasible to be implemented, and whether it can bring comprehensive value such as investment, taxation, employment and industrial chain synergy.

[0003] Current project evaluation methods primarily rely on manual review of documents such as company introductions, business plans, investment proposals, financial data, and due diligence materials, combined with the experience of investment promotion personnel. However, due to the complexity and variety of project materials—including text, PDFs, Word documents, company promotional materials, and project roadshow materials—manual extraction of key information is inefficient and prone to missing important fields.

[0004] Meanwhile, different investment promotion personnel have varying understandings of project value, industry matching, policy compatibility, and risk factors, leading to inconsistent evaluation standards and highly subjective evaluation results. Traditional evaluation forms or scoring tools can usually only perform simple scoring based on fixed fields, making it difficult to understand, complete, correct, and restructure the semantic information in project materials. They are unable to fully adapt to the actual situation in investment promotion business, such as incomplete materials, non-standard expressions, and significant industry differences.

[0005] Furthermore, in terms of project risk identification, traditional methods typically rely on manual queries of external data such as business registration, judicial records, and environmental protection documents, making it difficult to generate timely and unified risk warnings and mitigation suggestions. Therefore, it is necessary to provide an intelligent project evaluation method tailored to investment attraction scenarios. This method can improve the automation, standardization, and intelligence of investment project evaluation through text parsing, key field extraction, language model enhancement, evaluation dimension mapping, and comprehensive score calculation. Summary of the Invention

[0006] The purpose of this invention is to provide an intelligent project evaluation method, system, and storage medium for investment promotion scenarios, which solves the problems of low efficiency of manual processing, inconsistent evaluation standards, insufficient structuring of project data, inadequate risk identification, and low report generation efficiency in the existing investment promotion project evaluation process.

[0007] To achieve the above objectives, this invention provides an intelligent project evaluation method for investment promotion scenarios, comprising: receiving project data input from a project to be evaluated, wherein the project data includes at least one of project introduction text and enterprise documents; parsing the project data and extracting key fields to form a project information set; combining the project information set with a preset investment promotion evaluation context and inputting it into a preset language model for information enhancement processing to obtain enhanced project structured description information; based on the enhanced project structured description information, mapping and generating project evaluation dimension data, wherein the project evaluation dimension data includes at least one of industry matching degree, policy adaptability, feasibility of implementation, and investment contribution; inputting the project evaluation dimension data into a preset evaluation calculation model, wherein the model includes weighting coefficients, scoring metric, and fusion calculation logic for each evaluation dimension to perform comprehensive calculation and obtain project evaluation results; and generating and outputting a project evaluation report containing multi-dimensional scores, risk warnings, and follow-up suggestions based on the project evaluation results.

[0008] In one optional implementation, during the text parsing and key field extraction process, key dimension fields missing in the project data are identified, supplementary information is generated, and supplementary content input by the user based on the supplementary information is obtained and merged into the project information set.

[0009] In one alternative implementation, the project information set includes at least one or more of the following: corporate background, core technology direction, proposed investment scale, implementation requirements, market positioning, and industry chain position.

[0010] In one optional implementation, the information enhancement process includes: using the language model to perform semantic completion, logical consistency correction, and structured restructuring oriented towards investment promotion business standards on the project information set.

[0011] In one alternative implementation, the investment contribution includes a projected tax contribution based on the industry to which the project belongs, the amount of investment, and the expected output value, as well as job creation analysis data.

[0012] In one optional implementation, the risk warning includes at least one of the following: equity dispute risk, technology dependence risk, operational abnormality risk, and environmental compliance risk identified through cross-validation of multi-source data, and outputs corresponding risk mitigation reference suggestions.

[0013] In one optional implementation, the preset evaluation calculation model supports configurable adjustments to the evaluation dimension weights, scoring thresholds, and industry judgment rules for different industry tracks.

[0014] In one alternative implementation, the project evaluation report can be exported as a structured Word document or a PDF document.

[0015] This invention also provides an intelligent project evaluation system for investment promotion scenarios, comprising: an input module configured to receive project data input from a project to be evaluated; a parsing module configured to perform text parsing and key field extraction on the project data to obtain a project information set; an enhancement module configured to combine the project information set with a preset investment promotion evaluation context, and perform information enhancement processing through a language model to obtain enhanced structured description information of the project; a mapping module configured to map and generate project evaluation dimension data based on the enhanced structured description information of the project; a calculation module configured to input the project evaluation dimension data into a preset evaluation calculation model, perform comprehensive scoring calculation, and obtain a project evaluation result; and an output module configured to generate and output a project evaluation report containing multi-dimensional scores, risk warnings, and follow-up suggestions based on the project evaluation result.

[0016] The present invention also provides a computer-readable storage medium having a computer program stored thereon, wherein the program, when executed by a processor, implements the steps of the above-described intelligent project evaluation method for investment attraction scenarios.

[0017] Compared with existing technologies, this invention has at least the following beneficial effects: It can automatically parse and extract key fields from investment promotion project materials, reducing the workload of investment promotion personnel in manually reading and organizing materials; by combining project information sets with a preset investment promotion evaluation context and using language models for information enhancement, it can semantically complete, logically correct, and structurally reorganize incomplete, non-standard, or unstructured content in project materials, improving the completeness and usability of project description information; based on the enhanced structured project description information, it generates project evaluation dimension data such as industry matching degree, policy adaptability, feasibility of implementation, and investment contribution, making project evaluation more aligned with investment promotion business scenarios; through a preset evaluation calculation model containing weighted coefficients, scoring metric, and fusion calculation logic for comprehensive scoring, it can improve the consistency and configurability of evaluation standards and reduce differences in subjective human judgment; and it can generate project evaluation reports containing multi-dimensional scores, risk warnings, and follow-up suggestions, facilitating investment promotion personnel, park managers, or government decision-makers to quickly understand project value and potential risks, improving the efficiency of investment promotion project assessment. Attached Figure Description

[0018] The following figures are used to illustrate embodiments of the present invention and do not constitute a limitation on the scope of protection of the present invention.

[0019] Figure 1 This is a flowchart illustrating an intelligent project evaluation method for investment promotion scenarios provided by an embodiment of the present invention.

[0020] Figure 2 This is a schematic diagram of the structure of an intelligent project evaluation system provided in an embodiment of the present invention.

[0021] Figure 3 This is a schematic diagram of the processing flow of a project evaluation calculation model provided in an embodiment of the present invention. Detailed Implementation

[0022] The technical solution of the present invention will be further described below with reference to the accompanying drawings and embodiments. It should be understood that the described embodiments are only used to explain the present invention and are not intended to limit the scope of protection of the present invention.

[0023] like Figure 1 As shown in the figure, this embodiment provides a project intelligent evaluation method for investment promotion scenarios. This method can be applied to investment promotion management platforms, park project management systems, industrial investment promotion auxiliary decision-making systems, or government digital platforms.

[0024] In step S101, the system receives the project data input for the project to be evaluated. The project data may include project introduction text, documents submitted by the company, business plan, project investment plan, company promotional materials, project roadshow materials, financial statements, technical specifications, etc.

[0025] In practice, project data can be obtained through text input boxes, file upload interfaces, database read interfaces, or third-party system interfaces. File formats can include Word documents, PDF documents, plain text files, spreadsheet files, or text content derived from image recognition.

[0026] In step S102, the system performs text parsing on the project data, identifies the main text, title content, table content, and attachment content in the project data, and extracts key fields from them to form a project information set.

[0027] The project information set may include one or more of the following: company background, company name, establishment time, registered capital, main business, core technology direction, planned investment scale, planned land area, planned construction content, implementation requirements, market positioning, target customers, industry chain position, upstream and downstream collaborative relationships, expected output value, expected tax revenue, and expected number of employees.

[0028] In one implementation, the system can identify key fields in project data through keyword matching, named entity recognition, rule templates, semantic classification models, or language model extraction.

[0029] In another implementation, the system identifies missing key dimension fields during the field extraction process. For example, when project data lacks fields such as planned investment scale, expected output value, land use requirements, or implementation needs, the system generates a supplementary information prompt. After the user inputs the supplementary information according to the prompt, the system merges the supplementary information into the project information set.

[0030] In step S103, the system combines the project information set with the preset investment promotion evaluation context to form language model input content, and inputs it into the preset language model for information enhancement processing.

[0031] The preset investment promotion evaluation context may include regional industrial planning, key industrial tracks, investment promotion policies, industrial chain map, project access conditions, evaluation rules, industry classification standards, park resource conditions, land conditions, energy consumption and environmental protection requirements, etc.

[0032] The information enhancement processing of the language model can include semantic completion, logical consistency correction, and structured restructuring. Semantic completion refers to reasonably supplementing or summarizing incomplete information in project materials based on existing project information and the context of investment promotion evaluation. Logical consistency correction refers to identifying obvious contradictions between information such as investment amount, construction period, expected output value, and number of employees in project materials, and generating correction suggestions or anomaly markers. Structured restructuring refers to reorganizing the project content into a unified structure according to the business scope of investment promotion, such as company overview, project overview, technology and products, market and customers, investment and output, implementation needs, policy matching, and risk factors.

[0033] After the above processing, the system obtains enhanced structured description information of the project.

[0034] In step S104, the system generates project evaluation dimension data based on the enhanced project structured description information. The project evaluation dimension data may include at least one of industry matching degree, policy adaptability, implementation feasibility, and investment contribution.

[0035] Among them, industry matching degree can be used to indicate the degree of matching between the project's industry, technology direction, product type, and position in the industrial chain and the target region's industrial planning, key industrial tracks, or industrial park positioning.

[0036] Policy compatibility can be used to indicate whether a project meets local industrial support policies, tax incentives, talent policies, science and technology innovation policies, land use policies, environmental protection policies, and other conditions.

[0037] Feasibility can be used to indicate the degree to which a project is feasible in terms of land, factory buildings, energy consumption, environmental protection, safe production, capital investment, construction period, talent supply, and supply chain support.

[0038] Investment contribution can be used to represent the expected contribution of a project to the regional economy, including investment amount, expected output value, predicted tax contribution, employment analysis data, and industrial chain driving effect.

[0039] In step S105, the system inputs the project evaluation dimension data into a preset evaluation calculation model, performs comprehensive score calculation, and obtains the project evaluation result. The preset evaluation calculation model includes the weighting coefficients corresponding to each evaluation dimension, the scoring rubric, and the fusion calculation logic of the comprehensive score.

[0040] For example, the comprehensive score can be calculated as follows: Comprehensive score = Industry matching score × First weight + Policy adaptability score × Second weight + Feasibility of implementation score × Third weight + Investment contribution score × Fourth weight - Risk deduction score.

[0041] The weights for each indicator can be configured according to different industry sectors, regional investment promotion strategies, or project types. For example, for advanced manufacturing projects, the weights of indicators such as investment scale, output value contribution, and land use efficiency can be increased; for technological innovation projects, the weights of indicators such as core technology, R&D capabilities, talent teams, and intellectual property rights can be increased; and for headquarters economy projects, the weights of indicators such as tax contribution, brand influence, and industrial driving capacity can be increased.

[0042] The scoring rubric can include score ranges, threshold conditions, scoring rules, deduction rules, and risk level rules. For example, when a project belongs to an industry that is a key development industry in the region, the industry matching score is increased; when a project has risks such as operational abnormalities, major litigation, or environmental penalties, the comprehensive score is reduced or a risk label is generated.

[0043] In step S106, the system generates a project evaluation report based on the project evaluation results. The project evaluation report may include basic project information, company background, project overview, industry matching analysis, policy compatibility analysis, feasibility analysis, investment contribution analysis, multi-dimensional scoring results, comprehensive score, project level, risk warnings, and follow-up suggestions.

[0044] The risk warning may include at least one of the following risks identified through cross-validation of multi-source data: equity dispute risk, technology dependence risk, operational abnormality risk, and environmental compliance risk, and corresponding risk mitigation reference suggestions will be provided.

[0045] For example, when the system identifies situations such as frozen equity, major litigation, or frequent changes in actual controllers of a company, it can generate equity dispute risk warnings and suggest that investment promotion personnel further verify the company's equity structure and control stability.

[0046] When the system identifies that a project's core technology relies on external licensing, key equipment relies on imports, or the ownership of intellectual property rights is unclear, it can generate a technology dependence risk warning and suggest further verification of the technology source, patent ownership, and supply chain stability.

[0047] Project evaluation reports can be output in formats such as web pages, structured Word documents, or PDF documents, facilitating internal circulation, archiving, and decision-making by investment promotion personnel.

[0048] like Figure 2 As shown in the figure, this embodiment also provides a project intelligent evaluation system for investment promotion scenarios, including an input module, a parsing module, an enhancement module, a mapping module, a calculation module, and an output module.

[0049] The input module is used to receive project data input from the project to be evaluated.

[0050] The parsing module is used to parse the text of project data and extract key fields to obtain a project information set. The parsing module can also be used to identify missing fields, generate supplementary information prompts, and merge the user-submitted content into the project information set.

[0051] The enhancement module combines the project information set with the preset investment evaluation context, performs information enhancement processing through a language model, and obtains enhanced structured description information of the project.

[0052] The mapping module is used to generate project evaluation dimension data based on the enhanced project structured description information.

[0053] The calculation module is used to input project evaluation dimension data into a preset evaluation calculation model, perform comprehensive scoring calculation, and obtain project evaluation results.

[0054] The output module is used to generate and output a project evaluation report that includes multi-dimensional scores, risk warnings, and follow-up suggestions based on the project evaluation results.

[0055] The modules described above can be implemented through software programs or through a combination of software and hardware. They can be deployed on the same server or distributed across multiple servers or cloud services.

[0056] This embodiment also provides a computer-readable storage medium on which a computer program is stored. When the computer program is executed by a processor, it implements the above-described intelligent project evaluation method for investment promotion scenarios.

[0057] The computer program can run on a server, cloud server, edge computing device, personal computer, government platform server, or park management system server.

[0058] The computer-readable storage medium may include a read-only memory, a random access memory, a magnetic disk, an optical disk, a flash memory, a portable storage device, or other media capable of storing program code.

[0059] The above description is merely a preferred embodiment of the present invention and is not intended to limit the scope of protection of the present invention. Any modifications, equivalent substitutions, improvements, or combinations made within the spirit and principles of the present invention should be included within the scope of protection of the present invention.

Claims

1. A project intelligent evaluation method for investment promotion scenarios, characterized in that, include: Receive project data input for the project to be evaluated, wherein the project data includes at least one of the following: project introduction text and company documents; The project data is parsed and key fields are extracted to form a project information set; The project information set is combined with a preset investment evaluation context and then input into a preset language model for information enhancement processing to obtain enhanced project structured description information. Based on the enhanced project structured description information, project evaluation dimension data is generated. The project evaluation dimension data includes target evaluation dimension data determined according to the investment promotion evaluation strategy. The target evaluation dimension data includes at least one of the following: industry matching degree, policy adaptability, implementation feasibility, investment contribution, employment creation, industrial chain synergy, resource consumption, and risk compliance. The project evaluation dimension data are input into a preset evaluation calculation model. The model includes the weighting coefficients, scoring rubrics, and fusion calculation logic of the comprehensive score corresponding to each evaluation dimension, so as to perform comprehensive calculation and obtain the project evaluation result. Based on the project evaluation results, a project evaluation report is generated and output, which includes multi-dimensional scores, risk warnings, and follow-up suggestions.

2. The method according to claim 1, characterized in that, During the text parsing and key field extraction process, the missing key dimension fields in the project data are identified, supplementary information is generated, and the supplementary content input by the user based on the supplementary information is obtained. The supplementary content is then merged into the project information set.

3. The method according to claim 1, characterized in that, The project information set shall include at least one or more of the following: corporate background, core technology direction, proposed investment scale, implementation needs, market positioning, and position in the industry chain.

4. The method according to claim 1, characterized in that, The information enhancement processing includes: using the language model to perform semantic completion, logical consistency correction, and structured restructuring oriented towards investment promotion business standards on the project information set.

5. The method according to claim 1, characterized in that, The investment contribution includes the projected tax contribution based on the industry to which the project belongs, the amount of investment, and the expected output value, as well as employment-driving analysis data.

6. The method according to claim 1, characterized in that, The risk warning includes at least one of the following risks identified through cross-validation of multi-source data: equity dispute risk, technology dependence risk, operational abnormality risk, and environmental compliance risk, and provides corresponding risk mitigation reference suggestions.

7. The method according to claim 1, characterized in that, The preset evaluation calculation model supports configurable adjustments to the evaluation dimension weights, scoring thresholds, and industry judgment rules for different industry tracks.

8. The method according to claim 1, characterized in that, The project evaluation report can be exported in structured Word or PDF document format.

9. A project intelligent evaluation system for investment promotion scenarios, characterized in that, Include: The input module is configured to receive project data input from the project to be evaluated. The parsing module is configured to perform text parsing and key field extraction on the project data to obtain a project information set. The enhancement module is configured to combine the project information set with a preset investment evaluation context, and perform information enhancement processing through a language model to obtain enhanced project structured description information; The mapping module is configured to generate project evaluation dimension data based on the enhanced project structured description information. The calculation module is configured to input the project evaluation dimension data into a preset evaluation calculation model, perform comprehensive scoring calculation, and obtain the project evaluation result; The output module is configured to generate and output a project evaluation report containing multi-dimensional scores, risk warnings, and follow-up suggestions based on the project evaluation results.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the program is executed by a processor, it implements the steps of the method as described in any one of claims 1 to 8.

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