A method for intelligent generation of analysis reports based on large models

Abstract

The present invention discloses a method for intelligent generation of analysis reports based on a large model, including: Module 1, creating an indicator system module, defining indicator semantics; Module 2, setting an analysis report theme module, defining the analysis data range, indicator system, and visualization method; Module 3, optimizing and training the large model, and continuously optimizing the accuracy of the intelligent analysis report semantic interaction conversion data query summary code to the visualization of the entire end-to-end. The present invention relates to the technical field of intelligent generation of analysis reports. The beneficial effect of the present invention is that the method and method for intelligent generation of analysis reports based on a large model can effectively realize the automatic generation of intelligent analysis reports, improve analysis efficiency and accuracy, and provide strong support for data analysis and decision-making in various fields.

Description

Technical Field

[0001] The present invention relates to the field of intelligent generation of analysis reports, and in particular to a method for intelligent generation of analysis reports based on a large model. Background Art

[0002] In today's era of information explosion, various fields such as finance, market, and scientific research have accumulated massive amounts of data. Effectively analyzing these data and generating valuable analysis reports are of great significance for corporate decision-making, academic research, etc. The traditional way of generating analysis reports usually relies on manual data collection, data organization, and analysis using statistical analysis methods, and then writing reports based on the analysis results. This method is not only time-consuming and labor-intensive, but also easily affected by human factors, resulting in uneven accuracy of analysis results and quality of reports. In addition, for complex data analysis tasks, manual analysis often finds it difficult to dig out deep information and potential laws in the data. With the development of artificial intelligence technology, especially the emergence of large models, new solutions are provided for the generation of intelligent analysis reports.

[0003] Therefore, we propose a method for intelligent generation of analysis reports based on large models to solve the aforementioned problems. Summary of the invention

[0004] The purpose of the present invention is to solve the above problems and to design a method for intelligently generating analysis reports based on a large model.

[0005] The technical solution of the present invention to achieve the above-mentioned purpose is a method for intelligently generating an analysis report based on a large model, comprising: Module 1: Create an indicator system module and define indicator semantics; Module 2: Set up the analysis report theme module, define the analysis data scope, indicator system, and visualization method; Module 3: Optimize the training of large models and continuously optimize the accuracy of the end-to-end conversion of intelligent analysis reports, data query summary codes and visualization; Module 4: By interacting with the big model; Indicator system module: users create indicator systems, define indicator semantics, and encapsulate the processing logic of business data into data indicators described by standard indicator semantics; The report theme module sets the analysis report theme, defines the analysis report theme and the corresponding data range, indicator system, and visualization method; in the report theme module, collects multi-source data related to the theme to be analyzed, and the multi-source data includes structured data, semi-structured data, and unstructured data; The intelligent analysis report large model tuning module optimizes the training of large models and continuously optimizes the accuracy of the intelligent analysis report semantic interaction, data query summary code, and visualization end-to-end; cleans, converts, and integrates the collected training data to convert it into a standard format suitable for large model analysis; The big model intelligent analysis module uses the pre-trained big model to perform intelligent analysis on the processed data. By interacting with the big model, it automatically generates an intelligent analysis report based on the preset report template and the analysis results. The report includes a text description, data chart display, and conclusion and suggestion section.

[0006] The indicator system module is characterized by the indicator semantic definition module and indicator calculation description; Define the semantics of indicators, explain and modify the scope and restriction conditions of indicators with clear semantics. Two different types of indicators, atomic indicators and composite indicators, are defined in the indicator system. The calculation of atomic indicators is based on the attributes in the data model. When defining atomic indicators, first specify the source layer or integration layer data model on which it depends, then define the processing logic of atomic indicators for one or more data model attributes in the form of function expressions, and finally define the data filtering conditions of atomic indicators; the calculation of composite indicators is based on atomic indicators. When defining composite indicators, first define the processing logic of composite indicators for one or more atomic indicators in the form of function expressions, and then define the time window for composite indicator calculation, which is specifically divided into three types: the time window from the starting point of the data flow to the current time point, the time window from the cumulative statistics of indicator data of the data flow within the specified time length to the current time point, the instant statistics of indicator data flowing through the data at the current time point, the composite indicators are summarized according to the same time window, and the composite indicators with the same time window are used as data model attributes to create an indicator summary data model, that is, the composite indicators with the same time window are aggregated together in the form of a data model; Corresponding to different indicators, a clearer calculation description is given, usually using the database SQL language or other code languages. When describing the calculation, first determine the data source, use different calculation description methods according to different data sources, and then give the corresponding calculation code description based on the semantic definition of the indicator. Different languages ​​are adapted to different scenarios and can better cope with various situations. When actually connecting to csv and excel data files, using python language can make data calculation more convenient.

[0007] The report subject module is characterized by the report subject description and report data collection; To describe the report theme, first define the theme area of ​​the analysis report, then explain the indicator system and indicator content involved in the area. In the indicator theme area, semantic description is given for time and regional scope restrictions. Then, according to different indicators, semantic description is given for the visualization method of each indicator or multiple indicators. The visualization methods include bar charts, line charts, pie charts, and radar charts. Specific data visualization is displayed through month-on-month, year-on-year, and trend analysis methods. Report data collection includes report source management, which is used to configure and manage the source channels for data collection, and supports real-time data collection and historical data import. Report data collection obtains high-quality report data with clear structure, clear theme, and visual analysis through preliminary processing. High-quality and low-quality report data are scored through scoring, providing better support for the tuning of the subsequent report intelligent generation large model.

[0008] The large model in the large model optimization training module adopts a neural network architecture based on deep learning and has the ability to continuously learn and optimize. It continuously optimizes the accuracy of the intelligent analysis report semantic interaction, data query summary code, and visualization end-to-end, and updates the model according to new data and task requirements. Training data preprocessing: The data cleaning methods used include removing duplicate data, correcting erroneous data, processing missing data, and filtering noise data. The collected training data is cleaned, converted, and integrated to convert it into a standard format suitable for large model analysis. The analysis report data used to train the model needs to evaluate the quality of the analysis report. Training the model with high-quality analysis reports can produce better model parameters. Intelligent analysis report large model training and tuning, the input data is the overall analysis report, and the local indicator visualization data; the output is the large model parameters; the model basic framework adopts Transformer, the attention mechanism deep learning architecture, which performs well in natural language processing tasks; it consists of multiple encoder and decoder layers to process complex language structures and semantic information; Text encoding uses word embedding technology, including Word2Vec, GloVe, and BERT pre-trained language models to convert each word in the input text into a vector representation and adopt a hierarchical encoding method to capture semantic information at different levels; Structured data processing: For tabular data, it is converted into text descriptions and then processed using text encoding technology. In addition, a specialized recurrent neural network is used to process the tabular data. Chart data processing: For chart data, we use image recognition technology to extract the features of the chart, and then convert it into text description for processing. For analytical charts, there are two processing methods: one is direct image processing, and the other is conversion into structured data description. We use a special chart analysis algorithm to process the chart data. Analysis and reasoning: Use multi-layer neural networks for analysis and reasoning to extract key information and patterns from input data, adopt attention mechanisms to focus on important parts of input data to improve the accuracy of analysis, and use reinforcement learning methods to continuously optimize the quality of analysis reports based on user feedback.

[0009] The large model intelligent analysis module is characterized by: report generation and report template; Use the pre-trained big model to perform intelligent analysis on the processed data. By interacting with the big model, an intelligent analysis report is automatically generated according to the preset report template and the analysis results, and an analysis report text in natural language is generated; based on the analysis report text, post-processing optimization is performed to generate a part including text description, data chart display, and conclusion and suggestion; The report template is customized according to user needs and industry characteristics, and can automatically adapt to different analysis results to ensure the integrity and professionalism of the report; the generated report is post-processed.

[0010] A method for intelligently generating an analysis report based on a large model, the steps comprising: S1: Use the data collection part in the indicator system module and the report theme module to collect and analyze multi-source data related to the report theme and analysis indicators; S2: Clean, convert and integrate the collected data through the data preprocessing module in the large model intelligent analysis module; S3: Use the large model optimization training module to perform intelligent analysis and large model optimization training on the processed data to obtain the model tuning results; S4: Based on the preset templates and analysis results in the large model intelligent analysis module, intelligent analysis reports are efficiently generated through interaction with customers.

[0011] S5: Based on the definition modules of indicators and analysis reports, the business and calculation definitions of analysis reports and indicators are continuously improved and modified through interactive feedback from customers through intelligently generated analysis reports, providing support for data collection, large model tuning, and post-processing of report generation.

[0012] In step S1, the data acquisition module automatically acquires data from various data sources according to the acquisition parameters and data source configuration set by the user, and preliminarily organizes and classifies the data.

[0013] In step S2, the data preprocessing module adopts different cleaning strategies for different types of data, performs field verification and data standardization on structured data, parses and extracts key information from semi-structured data, and performs text extraction and classification on unstructured data.

[0014] In step S3, the large model intelligent analysis module inputs the preprocessed data into the pre-trained large model, and outputs multi-dimensional analysis results including data feature analysis, trend prediction, and correlation judgment through the model's reasoning calculation.

[0015] In step S4, the report generation module automatically fills in the corresponding text description and data chart in the report template according to the type and importance of the analysis results, and generates the final intelligent analysis report. The report can be exported to multiple formats, including PDF and WORD; In step S5, the analysis report definition module sets business-oriented and professional specifications for indicators and analysis reports based on business analysis and user feedback, effectively ensuring the professionalism and standardization of analysis reports and indicators, and improving the efficiency and accuracy of analysis report generation. The analysis report definition is defined from both qualitative and quantitative dimensions, and is defined and described using natural language and code language.

[0016] A method for intelligently generating analysis reports based on a large model, which is prepared using the technical solution of the present invention, effectively realizes the automatic generation of intelligent analysis reports, improves analysis efficiency and accuracy, and provides strong support for data analysis and decision-making in various fields. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] Figure 1 This is a diagram of the architecture of the method for intelligently generating analysis reports based on a large model according to the present invention.

[0018] exist Figure 1 The connection relationship and data flow between the indicator system setting module, analysis report setting module, large model intelligent analysis training module and intelligent analysis report generation module are demonstrated.

[0019] Figure 2 The present invention is a flow chart of the intelligent generation method of analysis reports based on large models.

[0020] exist Figure 2 In this flow chart, the five main steps from data acquisition to report generation are described in detail. DETAILED DESCRIPTION

[0021] The following is a clear and complete description of the scheme of the embodiment of the present application in conjunction with the drawings in the embodiment of the present application. It should be noted that the implementation methods described below are not limitations of the present invention. Based on the embodiments of the present application, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of the present invention.

[0022] See also Figure 1 and Figure 2 ,The present invention provides a method for intelligently generating analysis reports based on a large model. Figure 1 The brief structure of the device part in the embodiment of the present application is described. The device is structurally divided into four modules: an indicator setting module, which builds an indicator system, defines indicator semantics, and collects indicators and indicator system data that can be used for large model training; an analysis report setting module, which sets the analysis report theme, defines the analysis data range, indicator system, and visualization method, and collects analysis report data that can be used for large model training; an analysis report large model optimization training module, which continuously optimizes the accuracy of the intelligent analysis report semantic interaction to the data query summary code to the visualization of the entire end-to-end; an analysis report large model intelligent generation module, which interacts with the large model to realize the executable computing task from dialogue to intelligent generation of analysis reports, and optimizes the content and display method of the generated report through post-processing methods.

[0023] Figure 2 Describes the general steps of the method part of the embodiment of this application. Figure 2 As shown, the embodiment of the present application includes five steps: data collection, data preprocessing, optimization training of the analysis report model, intelligent generation of the analysis report model, and report and indicator setting, which gradually converts the business processing logic of the intelligent generation of the analysis report into an executable stream computing task. In the actual business application process, it is mainly completed by the intelligent generation module of the analysis report model.

[0024] In a specific embodiment of the present application, it is assumed that an operation analysis report is produced for a certain e-commerce website. The data sources of the indicator statistics in this assumed scenario include website access data stream, order data stream and payment data stream. Below, the data information related to the indicator statistics in the data stream is briefly described, and the information in the data stream that is not related to the indicator statistics is not described in detail.

[0025] The specific embodiments of the present invention are described in detail below with reference to the accompanying drawings: 1. Data acquisition module implementation: In practical applications, such as when conducting e-commerce operation analysis, the data collection module first determines the data source. Structured data such as product sales data and user evaluation data can be obtained from the database of the e-commerce platform, and semi-structured and unstructured data such as market dynamics and competitor information can be collected from e-commerce industry information websites through web crawlers. Use the data source management submodule to configure the collection parameters, such as the collection frequency is once a day, the collection range is the data of the past year, etc. Then start the data collection process, and the collected data is stored in the local temporary database, classified and stored according to the data type and source, which is convenient for subsequent processing.

[0026] 2. Data preprocessing module implementation: For the commodity sales data obtained from the e-commerce platform database, the data preprocessing module first checks the integrity of the data, such as whether there are missing fields such as sales quantity and price. For missing data, mean filling, median filling or model-based filling methods can be used for processing. Then the data is standardized, for example, the sales price is normalized to be within a specific numerical range. For user evaluation data, text cleaning is performed to remove stop words, emoticons, etc., and key information is extracted, such as user evaluation keywords on product quality, service attitude, etc. The processed commodity sales data and user evaluation data are integrated, and the two are associated through associated fields such as product ID to form a complete analysis data set, which is stored in the analysis database.

[0027] 3. Implementation of large model intelligent analysis module: The data in the analysis database is input into the pre-trained large model, and the large model based on the Transformer and multi-head attention mechanism (AttentionMechanism) architecture is used for analysis. The large model first extracts features from the product sales data and user evaluation data, and learns features such as product sales trends and user preferences. Then, correlation analysis is performed to find out the key factors that affect product sales, such as the correlation between the rate of favorable comments in user reviews and product sales. Trend prediction can also be performed based on historical sales data to predict the sales trend of products in the future. The analysis results of the large model are stored in the result database in a structured form, including feature vectors, correlation coefficients, prediction results and other information.

[0028] 4. Report generation module implementation: The report generation module generates reports based on the preset e-commerce operation analysis report template. The analysis results are read from the result database, and the product sales trend analysis results are displayed in the body of the report in the form of a line chart. The user evaluation and sales correlation analysis results are presented in the form of a table, and marketing strategy suggestions for e-commerce merchants are given in the conclusion part based on the analysis results, such as optimizing product quality and improving service levels. In addition, through the setting of specific analysis report topics, some conventional analysis indicators and analysis contents, such as the number of website visits, the number of independent visitors, the number of new visitors, the number of orders, the number of order users, the order amount, the number of paid orders, the number of instant payment orders, the average order value and the conversion rate, are integrated with the report generated by the large model. After integration and optimization, the generated report is finally exported to PDF or PPT format, etc., which is convenient for e-commerce enterprise management to view and make decisions.

[0029] 5. Implementation of indicator definition and analysis report definition module: Based on the actual needs of users, from the beginning of the work, the semantic definition of indicators and the definition of calculation logic are continuously refined iteratively, and the subject segmentation, analysis methods, combination splitting of analysis indicators, visualization, etc. of the analysis report are described in detail to assist the analysis report model in outputting the final results. In the field of commodity retail, the indicator system, analysis methods and visualization display content of the analysis report in various fields such as warehousing, design, commodities, order transactions, distribution materials, customer relationships, etc. are gradually clarified to adjust the parameters and post-processing capabilities of the large model, so as to achieve one-click generation of efficient and professional analysis reports.

[0030] Through the above implementation methods, the method and method of intelligent generation of analysis reports based on large models of the present invention can effectively realize the automatic generation of intelligent analysis reports, improve analysis efficiency and accuracy, and provide strong support for data analysis and decision-making in various fields.

[0031] It should be noted that: indicator system module; Function: Define the indicator system and indicators. The definition of business indicators includes the business meaning, calculation formula, statistical period, statistical dimension, and other business limiting conditions. The business meaning of indicators is a description of the business rules and business significance of indicators in data statistics and analysis; the calculation formula of indicators is a definition of how to process and calculate indicator data in the form of calculation expressions; the statistical period of indicators is a limitation on the time conditions for indicator calculation; the statistical dimension of indicators is a definition of the data scope of business entity objects, geographical scope, business category types, etc. of indicator statistics; other business limiting conditions are a description of other statistical data filtering conditions outside of the statistical period and statistical dimension.

[0032] Implementation method: Describe the code from the perspective of numerical calculation, usually using database SQL language, or other code languages ​​such as python, java, etc. It is necessary to clearly describe the data tables, fields, and calculation logic involved in the code, and define the tables and fields.

[0033] Analysis report setting module; Function: Report template definition, including report type definition, report topic and sub-topic definition, to better target various specific business scenarios and analysis requirements. For example, the finance department may need a financial analysis report template, and the marketing department may need a market research report template, etc. Structural definition, clarify the overall structure of the analysis report, including the setting of title, table of contents, chapters, paragraphs, etc., so that the generated report has a clear logical framework and hierarchy, which is easy for readers to understand and read. Content framework construction, determine the content theme and approximate scope of each part, provide clear guidance and constraints for the generation of specific content for the large model, and ensure that the generated report content is complete and the key points are highlighted. Data indicator integration, embed the data indicators related to the analysis into the template, specify the source, calculation method and display method of the data, so that the report can accurately present the data-driven analysis results. Style and format setting, support users to set the style and format of the report, such as text style, chart style, typesetting layout, etc., to generate professional and beautiful analysis reports that meet the company's or personal style preferences; Implementation method: Data collection, obtaining analysis reports that can be used for large model tuning and training. In addition, in order to support higher frequency user usage, data interfaces and integration are provided, and interfaces with various data sources are established to obtain and integrate the required data. Through the data interface, the template setting module can extract data from data sources such as databases, file systems, and network services, and fill it into the corresponding positions in the template. In addition, it is also necessary to integrate with the large model to pass the template information and data to the large model so that it can generate report content that meets the template requirements. Logic and rules, define a series of logic and rules in the template setting module to ensure the correctness and validity of the template. For example, specify certain required fields, data format verification rules, logical relationships between chapters, etc., so as to improve the quality and accuracy of the generated report.

[0034] Among them, the data collection part is responsible for obtaining data related to the subject to be analyzed from various data sources. Data sources cover a wide range, including but not limited to databases, file systems, web page data obtained by web crawlers, real-time data collected by sensors, etc. It supports multiple data formats, such as structured database table data (such as data in SQL databases), semi-structured data (such as data in JSON and XML formats), and unstructured data (such as text files, pictures, audio, video, etc.). By configuring the data source management submodule, you can flexibly set the channel, frequency and scope of data collection to ensure that comprehensive and targeted data is obtained. For example, when analyzing the stock market in the financial field, data can be collected from multiple sources such as financial news websites and stock trading platform databases.

[0035] Implementation method of the data collection part: Use data collection tools and technologies to collect web page data through the web crawler framework, database connection tools to connect and obtain database data, file reading library to read local file data, etc., and obtain a large number of report-specific computing task-related files through the big data platform.

[0036] Large model optimization training module: Function: Use the pre-trained big model to conduct in-depth analysis of the processed data. The big model is built on the neural network architecture of deep learning. The model based on the Transformer and Attention Mechanism architecture has strong semantic understanding and data mining capabilities. It can analyze the data in multiple dimensions, including but not limited to feature extraction, trend analysis, correlation analysis, classification prediction, etc. For example, in market research, when analyzing consumer purchasing behavior data, the big model can mine the correlation between different product attributes and purchasing intention, predict market trends, and classify consumers in order to formulate accurate marketing strategies. In addition, the big model has the ability to continuously learn, and can continuously update model parameters according to new data and task requirements to improve the accuracy and adaptability of the model.

[0037] Implementation method: Input the preprocessed data into the large model and obtain the analysis results through the forward propagation calculation of the model. The model can be pre-trained on a large-scale data set and then fine-tuned for specific analysis tasks. Use a distributed computing framework to accelerate the model training process and improve training efficiency.

[0038] Among them, the data preprocessing module: processes the collected raw data to meet the requirements of large model analysis. First, data cleaning is performed to remove noise, duplicate data, erroneous data, and missing data. For example, when processing text data, stop words are removed, spelling errors are corrected, etc.; for numerical data, the rationality and range of the data are checked. Then data conversion is performed to uniformly convert data of different formats and types into a format that can be processed by the large model, such as vectorizing text data and normalizing numerical data. Finally, data integration is performed to associate and merge data from multiple data sources to build a complete data set. For example, customer information, transaction records and other data obtained from different database tables are associated and integrated to conduct a comprehensive customer behavior analysis.

[0039] Data preprocessing implementation method: Use different algorithms and tools for different types of data. For data cleaning, use the data cleaning library (data processing functions in Pandas) to perform operations such as deduplication and missing value processing; for data conversion, use the feature engineering library (data conversion tools in Scikit-learn) to perform data standardization and normalization; for data integration, use database query statements (JOIN operations in SQL) or data fusion algorithms to merge multi-source data.

[0040] Report generation module: Function: Generate intelligent analysis reports based on the analysis results of the large model and the preset report template. The report template is customized according to different industries and analysis purposes, including the format and layout of the report title, summary, introduction, text, conclusion, suggestions and other parts. When generating a report, fill the data, conclusions and other information in the analysis results into the corresponding positions of the template, and generate data charts (such as bar charts, line charts, pie charts, etc.) as needed for visual display, making the report more intuitive and easy to understand. For example, when generating a financial analysis report, the stock price trend analysis results are displayed in the report in the form of a line chart, and investment advice is given in the conclusion section. The report can be exported to a variety of common formats, supporting PDF, PPT, WORD, etc., for users to view, share and archive.

[0041] Implementation method: Use report generation tools (ReportLab library in Python is used to generate PDF reports, Docx library is used to generate WORD reports), combined with data visualization libraries (Matplotlib, Seaborn, etc.) to generate report content containing text and charts, and then export it in the selected format.

[0042] Based on the above system, the one-key generation method of the intelligent analysis report based on the large model of the present invention includes the following steps: Step S1, data collection: According to the analysis requirements, users configure the data collection parameters in the data collection module, including specifying the data source, data type, collection time range, etc. The data collection module starts the data collection process according to the configuration information, obtains data from various data sources, and preliminarily organizes and classifies the collected data, and stores it in the temporary data storage area, waiting for the next step of processing.

[0043] Step S2, data preprocessing: The data preprocessing module reads data from the temporary data storage area and adopts corresponding cleaning strategies for different types of data. For structured data, field verification and data standardization operations are performed; for semi-structured data, key information is parsed and extracted; for unstructured data, text extraction and classification are performed. The processed data is then converted and integrated to form a unified format data set, which is stored in the analytical data storage area for use in large model analysis.

[0044] Step S3, large model intelligent analysis: The large model intelligent analysis module reads data from the analysis data storage area and inputs it into the pre-trained large model. The large model performs inference calculations through the internal neural network structure, analyzes the data in multiple dimensions, generates analysis results including data feature analysis, trend prediction, correlation judgment, etc., and stores the results in the result storage area.

[0045] Step S4, report generation: The report generation module reads the analysis results from the result storage area, and automatically fills in the corresponding text descriptions, data charts, etc. in the template according to the preset report template and the type and importance of the analysis results to generate the final intelligent analysis report. Users can choose to export the report to PDF, WORD and other formats for viewing, sharing and archiving.

[0046] Step S5, analysis report definition: The analysis report definition module assists in the generation of large model reports, and sets business-oriented and professional specifications for indicators and analysis reports, effectively ensuring the professionalism and standardization of analysis reports and indicators, and improving the efficiency and accuracy of analysis report generation. The analysis report definition is defined from two dimensions, qualitative and quantitative, and is defined and described using natural language and code language.

[0047] The above technical solutions only reflect the preferred technical solutions of the technical solutions of the present invention. Some changes that may be made to certain parts thereof by technicians in this technical field all reflect the principles of the present invention and fall within the protection scope of the present invention.

Claims

1. A method for intelligently generating analysis reports based on a large model, characterized in that: include: Module 1: Create an indicator system module and define indicator semantics; Module 2: Set up the analysis report theme module, define the analysis data scope, indicator system, and visualization method; Module 3: Optimize the training of large models and continuously optimize the accuracy of the end-to-end conversion of intelligent analysis reports, data query summary codes and visualization; Module 4: By interacting with the big model; Indicator system module: users create indicator systems, define indicator semantics, and encapsulate the processing logic of business data into data indicators described by standard indicator semantics; The report theme module sets the analysis report theme, defines the analysis report theme and the corresponding data range, indicator system, and visualization method; in the report theme module, collects multi-source data related to the theme to be analyzed, and the multi-source data includes structured data, semi-structured data, and unstructured data; The intelligent analysis report large model tuning module optimizes the training of large models and continuously optimizes the accuracy of the intelligent analysis report from semantic interaction to data query summary code to visualization end-to-end. Clean, transform and integrate the collected training data into a standard format suitable for large model analysis; The big model intelligent analysis module uses the pre-trained big model to perform intelligent analysis on the processed data. By interacting with the big model, it automatically generates an intelligent analysis report based on the preset report template and the analysis results. The report includes a text description, data chart display, and conclusion and suggestion section.

2. The method for intelligently generating an analysis report based on a large model according to claim 1, wherein the indicator system module is characterized by an indicator semantic definition module and an indicator calculation description; Define the semantics of indicators, explain and modify the scope and restriction conditions of indicators with clear semantics. Two different types of indicators, atomic indicators and composite indicators, are defined in the indicator system. The calculation of atomic indicators is based on the attributes in the data model. When defining atomic indicators, first specify the source layer or integration layer data model on which it depends, then define the processing logic of atomic indicators for one or more data model attributes in the form of function expressions, and finally define the data filtering conditions of atomic indicators; the calculation of composite indicators is based on atomic indicators. When defining composite indicators, first define the processing logic of composite indicators for one or more atomic indicators in the form of function expressions, and then define the time window for composite indicator calculation, which is specifically divided into three types: the time window from the starting point of the data flow to the current time point, the time window from the cumulative statistics of indicator data of the data flow within the specified time length to the current time point, the instant statistics of indicator data flowing through the data at the current time point, the composite indicators are summarized according to the same time window, and the composite indicators with the same time window are used as data model attributes to create an indicator summary data model, that is, the composite indicators with the same time window are aggregated together in the form of a data model; Corresponding to different indicators, a clearer calculation description is given, usually using the database SQL language or other code languages. When describing the calculation, first determine the data source, use different calculation description methods according to different data sources, and then give the corresponding calculation code description based on the semantic definition of the indicator. Different languages ​​are adapted to different scenarios and can better cope with various situations. When actually connecting to csv and excel data files, using python language can make data calculation more convenient.

3. According to the method for intelligently generating analytical reports based on a large model as described in claim 1, the report theme module is characterized by report theme description and report data collection; To describe the report theme, first define the theme area of ​​the analysis report, then explain the indicator system and indicator content involved in the area. In the indicator theme area, semantic description is given for time and regional scope restrictions. Then, according to different indicators, semantic description is given for the visualization method of each indicator or multiple indicators. The visualization methods include bar charts, line charts, pie charts, and radar charts. Specific data visualization is displayed through month-on-month, year-on-year, and trend analysis methods. Report data collection includes report source management, which is used to configure and manage the source channels for data collection, and supports real-time data collection and historical data import. Report data collection obtains high-quality report data with clear structure, clear theme, and visual analysis through preliminary processing. High-quality and low-quality report data are scored through scoring, providing better support for the tuning of the subsequent report intelligent generation large model.

4. According to the method for intelligent generation of analysis reports based on large models as described in claim 1, the large model in the large model optimization training module adopts a neural network architecture based on deep learning, and has continuous learning and optimization capabilities, and continuously optimizes the accuracy of the intelligent analysis report semantic interaction to the data query summary code to the visualization of the entire end-to-end, and updates the model according to new data and task requirements; Training data preprocessing: the data cleaning methods used include removing duplicate data, correcting erroneous data, processing missing data, and filtering noise data. The collected training data is cleaned, converted, and integrated to convert it into a standard format suitable for large model analysis. The analysis report data used to train the model needs to evaluate the quality of the analysis report. Training the model with high-quality analysis reports can produce better model parameters. Intelligent analysis report large model training and tuning, the input data is the overall analysis report, and the local indicator visualization data; the output is the large model parameters; the model basic framework adopts Transformer, the attention mechanism deep learning architecture, which performs well in natural language processing tasks; it consists of multiple encoder and decoder layers to process complex language structures and semantic information; Text encoding uses word embedding technology, including Word2Vec, GloVe, and BERT pre-trained language models to convert each word in the input text into a vector representation and adopt a hierarchical encoding method to capture semantic information at different levels; Structured data processing: For tabular data, it is converted into text descriptions and then processed using text encoding technology. In addition, a specialized recurrent neural network is used to process the tabular data. Chart data processing: For chart data, we use image recognition technology to extract the features of the chart, and then convert it into text description for processing. For analytical charts, there are two processing methods: one is direct image processing, and the other is conversion into structured data description. We use a special chart analysis algorithm to process the chart data. Analysis and reasoning: Use multi-layer neural networks for analysis and reasoning to extract key information and patterns from input data, adopt attention mechanisms to focus on important parts of input data to improve the accuracy of analysis, and use reinforcement learning methods to continuously optimize the quality of analysis reports based on user feedback.

5. The method for intelligently generating analysis reports based on a large model according to claim 1, wherein the large model intelligent analysis module is characterized by: report generation and report templateization; Use the pre-trained big model to perform intelligent analysis on the processed data. By interacting with the big model, an intelligent analysis report is automatically generated according to the preset report template and the analysis results, and an analysis report text in natural language is generated; based on the analysis report text, post-processing optimization is performed to generate a part including text description, data chart display, and conclusion and suggestion; The report template is customized according to user needs and industry characteristics, and can automatically adapt to different analysis results to ensure the integrity and professionalism of the report; the generated report is post-processed.

6. A method for intelligently generating an analysis report based on a large model, the steps comprising: S1: Use the data collection part in the indicator system module and the report theme module to collect and analyze multi-source data related to the report theme and analysis indicators; S2: Clean, convert and integrate the collected data through the data preprocessing module in the large model intelligent analysis module; S3: Use the large model optimization training module to perform intelligent analysis and large model optimization training on the processed data to obtain the model tuning results; S4: Based on the preset templates and analysis results in the large model intelligent analysis module, through interaction with customers, intelligent analysis reports are efficiently generated; S5: Based on the definition modules of indicators and analysis reports, the business and calculation definitions of analysis reports and indicators are continuously improved and modified through interactive feedback from customers through intelligently generated analysis reports, providing support for data collection, large model tuning, and post-processing of report generation.

7. According to the large model-based intelligent analysis report generation method of claim 6, in step S1, the data acquisition module automatically acquires data from multiple data sources according to the acquisition parameters and data source configuration set by the user, and preliminarily organizes and classifies the data.

8. According to the large model-based intelligent analysis report generation method described in claim 6, in step S2, the data preprocessing module adopts different cleaning strategies for different types of data, performs field verification and data standardization on structured data, parses and extracts key information on semi-structured data, and performs text extraction and classification on unstructured data.

9. According to the intelligent generation method of analysis reports based on big models as described in claim 6, in step S3, the big model intelligent analysis module inputs the preprocessed data into the pre-trained big model, and outputs analysis results including multi-dimensional data feature analysis, trend prediction, and correlation judgment through the model's reasoning calculation.

10. According to the large model-based intelligent analysis report generation method of claim 6, in step S4, the report generation module automatically fills in the corresponding text description and data chart in the report template according to the type and importance of the analysis result, and generates a final intelligent analysis report, which can be exported to multiple formats, including PDF and WORD; In step S5, the analysis report definition module sets business-oriented and professional specifications for indicators and analysis reports based on business analysis and user feedback, effectively ensuring the professionalism and standardization of analysis reports and indicators, and improving the efficiency and accuracy of analysis report generation. The analysis report definition is defined from both qualitative and quantitative dimensions, and is defined and described using natural language and code language.

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