Power plant design thematic report generation method and system based on generative artificial intelligence technology

Abstract

The invention provides a power plant design thematic report generation method based on a generative artificial intelligence technology, and the method is characterized in that the method comprises the following steps: (1) data collection and processing; (2) natural language processing and information extraction; (3) training a generative artificial intelligence model; (4) report generation and content organization; (5) outputting and customizing a report; and (6) quality evaluation and optimization. Compared with the prior art, the method has the advantages that the report can be automatically generated, the time and energy of a designer for compiling the report are saved, the working efficiency is greatly improved, manual errors are reduced, the consistency of the report is ensured, and comprehensive and accurate information such as technical parameters and economic evaluation is provided.

Description

Technical Field

[0001] The present invention relates to artificial intelligence technology, and specifically to a method and system for generating a power plant design special report based on generative artificial intelligence technology. Background Art

[0002] During the power plant design process, designers are required to prepare detailed reports to convey key information such as design solutions, technical parameters, and economic assessments. However, preparing reports often requires significant manpower and time, and is prone to tedious, repetitive work and errors.

[0003] Designers need to collect extensive data, analyze technical documentation, and compile it into a comprehensive report. This process consumes considerable time and effort, limiting their time to focus on other important tasks. Furthermore, the report's complexity and tediousness can lead to omissions and errors, further increasing the workload of revisions and proofreading.

[0004] Different designers may have different styles and preferences, resulting in a lack of consistency in the special report. In addition, due to human factors, the special report may contain errors, omissions or inconsistent information, which reduces the accuracy and reliability of the special report. Summary of the Invention

[0005] The purpose of the present invention is to provide a method and system for generating special reports on power plant design. The method is based on a pre-trained natural language model of generative artificial intelligence, supplemented by query and calculation modules. The method quickly generates special reports based on key information such as the special report outline compiled by designers, thereby forming efficient and professional productivity.

[0006] This application discloses a method for generating a power plant design special report based on generative artificial intelligence technology, the method comprising the following steps:

[0007] (1) Data collection and processing:

[0008] Collect various data required for power plant design and clean, organize and standardize the collected data for subsequent analysis and processing. The data includes: natural language data, calculation data and basic data;

[0009] (2) Natural language processing and information extraction:

[0010] Leveraging natural language processing technology to conduct semantic analysis and information extraction on technical documents, identify key information, establish calculation rules and data sources, and provide computing services, thereby building a relational database that is highly relevant to regions, owners, and individuals.

[0011] (3) Generative AI model training:

[0012] For the natural language data, based on generative artificial intelligence technology, a deep learning model is used for model training, so that it can understand and learn the semantics and structure related to the design;

[0013] For the calculation categories and basic data, a query and calculation module is constructed using generative artificial intelligence technology. The query and calculation module can interact with the existing database and perform query and calculation operations. It can also retrieve relevant data from the database based on keywords or query conditions specified by the designer. The query and calculation module will filter or calculate and integrate the data retrieved from the database to select accurate data relevant to the report.

[0014] (4) Report generation and content organization:

[0015] For the natural language data, the trained generative artificial intelligence model is used to automatically generate the content of the power plant design report;

[0016] For the aforementioned calculations and basic data, a generative AI model is used to populate the corresponding locations in the overall report with accurate data from the query. The model can automatically generate appropriate sentences or paragraphs based on the structure and semantic understanding of the report, and seamlessly integrate the query data into the report.

[0017] Consider the structure and semantic coherence of the report to ensure that the generated report is of high quality and consistency;

[0018] (5) Report output and customization:

[0019] Output the generated report into an editable document format for subsequent modification and customization, and configure a customization module that allows designers to adjust the report according to their needs

[0020] In a preferred embodiment, the method further comprises step (6):

[0021] (6) Quality assessment and optimization:

[0022] The quality assessment module is used to evaluate the quality of the generated reports, including checks for grammatical errors, logical consistency, information accuracy, etc.

[0023] In a preferred example, the deep learning model includes: a recurrent neural network (RNN) or a transformer model (Transformer).

[0024] In a preferred embodiment, the deep learning in step (3) further includes using a large-scale power plant design data set for model training.

[0025] In a preferred example, the natural language data includes: technical specifications, solution descriptions; the calculation data includes: design parameters, economic data; the basic data includes hydrological and meteorological conditions, application standards and owner company information.

[0026] In a preferred embodiment, the accurate data in step (3) includes: technical documents, specifications and equipment parameters to meet all aspects of information required for the report.

[0027] In a preferred embodiment, the generated report has different chapters and paragraphs, including:

[0028] Description of the design scheme, introduction of technical parameters and economic evaluation results.

[0029] In a preferred example, the system includes: a data processing module, a database, a computing module, a query module and a generative artificial intelligence module, wherein the computing module and the query module assist the generative artificial intelligence module, the intelligent evaluation and optimization module is connected to the output of the generative artificial intelligence module, and the database is connected to the query module.

[0030] In a preferred example, the data processing module is configured to classify the original materials of the power plant special report into calculation data, natural language data and basic data.

[0031] In a preferred example, the generation system further includes a quality assessment and optimization module, which is connected to the output end of the generative artificial intelligence module.

[0032] The advantages of the present invention are:

[0033] 1) This invention uses generative artificial intelligence technology to automatically generate reports, saving designers time and energy in compiling reports and greatly improving work efficiency;

[0034] 2) Manually compiled reports may be subject to subjective bias and information errors. Generative AI models, on the other hand, can generate report content based on a large amount of training data and accurate query data, avoiding errors caused by human factors. The generated reports are consistent and accurate, improving the quality and reliability of the reports.

[0035] 3) The present invention provides customization options, allowing designers to adjust the format, style, and content of reports according to their needs. By setting parameters and selecting options, the generated report can be flexibly adjusted to meet the requirements of specific projects. This customization capability enhances the personalization of reports and meets the diversity of different projects and needs;

[0036] 4) Maintaining report consistency through the use of generative AI technology. Generated reports are highly consistent in terms of structure, semantic coherence, and information presentation. This eliminates differences between different personnel and inconsistencies in style, ensuring uniformity of reports.

[0037] 5) The generated power plant design report provides comprehensive information on technical parameters, economic assessments, and other aspects. These accurate and comprehensive reports provide crucial support for investors, financing institutions, and decision-makers. Decisions based on data and information help drive project implementation and improve the accuracy and reliability of decisions. BRIEF DESCRIPTION OF THE DRAWINGS

[0038] Figure 1 The present invention is a flowchart of the process of generating a power plant design special report. DETAILED DESCRIPTION

[0039] The inventors of the present invention have invented a method for generating a power plant design special report based on generative artificial intelligence technology. Compared with the existing technology, the method automatically generates reports by utilizing generative artificial intelligence technology, saving designers the time and energy of compiling reports, greatly improving work efficiency, reducing human errors and ensuring the consistency of reports, and providing comprehensive and accurate technical parameters, economic evaluation and other information.

[0040] The present invention is described in detail below with reference to the various embodiments shown in the accompanying drawings, but it should be noted that these embodiments are not limitations of the present invention, and any equivalent transformations or substitutions in functions, methods, or structures made by ordinary technicians in this field based on these embodiments are all within the scope of protection of the present invention.

[0041] The specific implementation process of the present invention is described below through examples.

[0042] Example:

[0043] One embodiment of the present invention is Figure 1As shown, it shows the generation process of the power plant design special report of the generative artificial intelligence technology described in the present invention. It forms three types of computing, basic, and natural language data through data collection and processing based on original data such as various power plant special reports. Then, computing resources and services, and basic relational databases are established for semantic analysis and information extraction of natural language data. The generative artificial intelligence model is trained and query and calculation modules are created to query and obtain computing services from basic databases and computing resources. Designers generate relevant special reports by inputting relevant keywords or outline documents. Designers further improve relevant special reports in the report output and customization module. In general, the ability of generative artificial intelligence in natural language processing is combined with traditional computing and relational databases to help designers prepare efficient and professional special reports. Specifically, it includes the following steps:

[0044] (1) Data collection and processing:

[0045] Based on various raw materials, various types of data required for power plant design are collected and the collected data are cleaned, organized and standardized for subsequent analysis and processing. The data include: natural language data, calculation data and basic data. Among them, natural language data includes technical specifications, solution descriptions, etc.

[0046] Calculation data, such as design parameters, economic data, etc.

[0047] Basic data, such as hydrological and meteorological conditions, application standards, and owner company information.

[0048] (2) Natural language processing and information extraction:

[0049] Natural language processing technology is used to perform semantic analysis and information extraction on technical documents, identifying key information and establishing calculation rules and data sources. This provides computing services, thereby building a relational database that is highly relevant to regions, owners, and individuals. Optionally, in one embodiment, the key information includes design requirements, equipment configuration, system architecture, etc.

[0050] (3) Generative AI model training:

[0051] For the natural language data, a deep learning model is used to train the model based on generative artificial intelligence technology, so that it can understand and learn the semantics and structure related to the design. Optionally, in one embodiment, the deep learning model includes: a recurrent neural network (RNN) and a transformer model.

[0052] For the calculation categories and basic data, generative artificial intelligence technology is used to build a query and calculation module, which can interact with the existing database and perform query and calculation operations, and can retrieve relevant data from the database according to the keywords or query conditions specified by the designer; the query and calculation module will filter or calculate and integrate the data retrieved from the database, and select accurate data related to the report. These data may include technical documents, specifications and standards, equipment parameters, etc., to meet all aspects of information required for the report.

[0053] (4) Report generation and content organization:

[0054] For the natural language data, the trained generative artificial intelligence model is used to automatically generate the content of the power plant design report;

[0055] For the aforementioned calculations and basic data, a generative AI model is used to populate the corresponding locations in the overall report with accurate data from the query. The model can automatically generate appropriate sentences or paragraphs based on the structure and semantic understanding of the report, and seamlessly integrate the query data into the report.

[0056] Consider the structure and semantic coherence of the report to ensure that the generated report is of high quality and consistency;

[0057] (5) Report output and customization:

[0058] The generated report is output to an editable document format for subsequent modification and customization. The document mode is Word or PDF, and a customization module is configured. The customization module allows designers to adjust the format, style and content of the report as needed to meet the requirements of specific projects.

[0059] Optionally, in one embodiment, the method further comprises step (6):

[0060] (6) Quality assessment and optimization:

[0061] The quality assessment module is used to evaluate the quality of the generated reports, including checks for grammatical errors, logical consistency, information accuracy, etc.

[0062] Optionally, in one embodiment, the deep learning in step (3) further includes using a large-scale power plant design dataset for model training.

[0063] This embodiment also provides a power plant design report generation system, characterized by comprising: a data processing module, a database, a calculation module, a query module, and a generative artificial intelligence module. The calculation module and the query module assist the generative artificial intelligence module, the intelligent evaluation and optimization module is connected to the output of the generative artificial intelligence module, and the database is connected to the query module. The data processing module is configured to classify the raw materials of the power plant report into calculation data, natural language data, and basic data.

[0064] Optionally, in one embodiment, the generation system further includes a quality assessment and optimization module, and a customization module. The quality assessment and optimization module is connected to the output end of the generative artificial intelligence module.

[0065] The series of detailed descriptions listed above are only specific descriptions of feasible implementation methods of the present invention. They are not intended to limit the scope of protection of the present invention. Any equivalent implementation methods or changes that do not deviate from the technical spirit of the present invention should be included in the scope of protection of the present invention.

[0066] It will be apparent to those skilled in the art that the present invention is not limited to the details of the exemplary embodiments described above and that the invention can be embodied in other specific forms without departing from the spirit or essential characteristics of the invention. Therefore, the embodiments should be considered in all respects as illustrative and non-restrictive, and the scope of the invention is defined by the appended claims, not the foregoing description, and all variations within the meaning and range of equivalents of the claims are intended to be included therein. Any reference sign in a claim should not be construed as limiting the claim to which it relates.

[0067] In addition, it should be understood that although this specification is described in terms of implementation methods, not every implementation method contains only one independent technical solution. This narrative method of the specification is only for the sake of clarity. Those skilled in the art should regard the specification as a whole. The technical solutions in each embodiment can also be appropriately combined to form other implementation methods that can be understood by those skilled in the art.

Claims

1. A method for generating a power plant design report based on generative artificial intelligence technology, characterized in that: The method comprises the following steps: (1) Data collection and processing: Collect various data required for power plant design and clean, organize and standardize the collected data for subsequent analysis and processing. The data includes: natural language data, calculation data and basic data; (2) Natural language processing and information extraction: Leveraging natural language processing technology to conduct semantic analysis and information extraction on technical documents, identify key information, establish calculation rules and data sources, and provide computing services, thereby building a relational database that is highly relevant to regions, owners, and individuals. (3) Generative AI model training: For the natural language data, based on generative artificial intelligence technology, a deep learning model is used for model training, so that it can understand and learn the semantics and structure related to the design; For the calculation categories and basic data, a query and calculation module is constructed using generative artificial intelligence technology. The query and calculation module can interact with the existing database and perform query and calculation operations. It can also retrieve relevant data from the database based on keywords or query conditions specified by the designer. The query and calculation module will filter or calculate and integrate the data retrieved from the database to select accurate data relevant to the report. (4) Report generation and content organization: For the natural language data, the trained generative artificial intelligence model is used to automatically generate the content of the power plant design report; For the aforementioned calculations and basic data, a generative AI model is used to populate the corresponding locations in the overall report with accurate data from the query. The model can automatically generate appropriate sentences or paragraphs based on the structure and semantic understanding of the report, and seamlessly integrate the query data into the report. Consider the structure and semantic coherence of the report to ensure that the generated report is of high quality and consistency; (5) Report output and customization: The generated report is output to an editable document format for subsequent modification and customization, and a customization module is configured that allows designers to adjust the format, style and content of the report as needed to meet the requirements of specific projects.

2. The report generation method according to claim 1, wherein: The method further comprises step (6): (6) Quality assessment and optimization: The quality assessment module is used to evaluate the quality of the generated reports, including checks for grammatical errors, logical consistency, information accuracy, etc.

3. The report generation method according to claim 1, wherein: The deep learning model includes: a recurrent neural network (RNN) or a transformer model (Transformer).

4. The report generation method according to claim 1, wherein: The deep learning in step (3) also includes using a large-scale power plant design data set for model training.

5. The report generation method according to claim 1, wherein: The natural language data includes: technical specifications, scheme descriptions; the calculation data includes: design parameters, economic data; the basic data includes hydrological and meteorological conditions, application standards and owner company information.

6. The report generating method according to claim 5, wherein: The accurate data in step (3) includes: technical documents, specifications and equipment parameters to meet all aspects of information required for the report.

7. The report generation method according to claim 1, wherein: The generated report has different chapters and paragraphs, including: design solution description, technical parameter introduction and economic evaluation results.

8. A power plant design report generation system applicable to the method according to any one of claims 1 to 7, characterized in that: The system includes: a data processing module, a database, a computing module, a query module and a generative artificial intelligence module, wherein the computing module and the query module assist the generative artificial intelligence module, the intelligent evaluation and optimization module is connected to the output of the generative artificial intelligence module, and the database is connected to the query module.

9. The generation system according to claim 8, characterized in that The data processing module is configured to classify the original materials of the power plant special report into calculation data, natural language data and basic data.

10. The generation system according to claim 8, characterized in that The generation system also includes a quality assessment and optimization module, which is connected to the output end of the generative artificial intelligence module.

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