Macroeconomy multi-source heterogeneous analysis system

Abstract

The invention relates to a macroeconomic multi-source heterogeneous analysis system, in particular to the technical field of deep learning analysis, and the system comprises a data source acquisition module which is used for collecting data from a plurality of heterogeneous data sources, the plurality of heterogeneous data sources comprise but are not limited to government statistical data, financial market data, international trade data, social media data, industry report data and public database data, and the data source acquisition module can acquire data from online and offline channels in real time and support regular updating and incremental loading; in the invention, through integration of the data source acquisition module, data can be efficiently and comprehensively collected from a plurality of heterogeneous data sources, including government statistical data, financial market data, social media data and the like, the wide coverage of the data sources ensures that the system can comprehensively reflect various aspects of macroeconomy, and meanwhile, the data source acquisition module can effectively and comprehensively acquire the data. And the data acquisition module supports automatic acquisition and periodically performs incremental updating, so that the timeliness and the accuracy of the data are ensured.

Description

Technical Field

[0001] This invention relates to the field of deep learning analytics, and more particularly to a macroeconomic multi-source heterogeneous analysis system. Background Technology

[0002] The Macroeconomic Multi-Source Heterogeneous Analysis System is a comprehensive information analysis system designed to conduct macroeconomic analysis and provide decision support by integrating diverse data from different sources and formats. This system involves multiple stages, including data collection, data fusion, data processing, and analysis. It includes qualitative analysis, which in macroeconomic analysis typically refers to the analysis of economic phenomena, trends, and causal relationships based on non-quantitative data such as observations, expert judgments, historical experience, and policy background. Unlike quantitative analysis, which focuses on numbers and models, qualitative analysis relies more on subjective judgment and interpretation. It plays a crucial role in macroeconomic research, especially when facing complex and uncertain economic environments.

[0003] In practice, some problems still exist: 1. In traditional macroeconomic analysis methods, data collection and processing often rely on static data sources, and there are problems such as inconsistent formats and untimely updates between different data sources. Existing systems mainly collect information from standardized data sources such as government statistical departments, financial markets, and business reports, and most of them are updated periodically. This makes the timeliness and comprehensiveness of the data poor, making it difficult to respond quickly to economic changes and emergencies. In addition, data processing and integration usually rely on manual intervention, which makes the analysis process cumbersome and prone to errors. Under these circumstances, traditional systems cannot effectively handle unstructured data sources such as social media and news reports, and lack real-time insights into market sentiment and potential economic risks.

[0004] 2. Existing macroeconomic analysis systems also have certain limitations in integrating qualitative and quantitative analysis. Traditional qualitative analysis usually relies on experts' subjective judgments of policy changes and market dynamics. The analysis process lacks systematicity and automation, resulting in poor repeatability of results. Meanwhile, quantitative analysis relies more on static economic models. These models often fail to fully consider the rapid changes and uncertainties in the economic environment and are not sensitive enough to complex external shocks, such as financial crises and international trade fluctuations. Due to the lack of dynamic adjustment and real-time optimization capabilities, existing technologies are often unable to effectively cope with rapidly changing economic environments. In addition, many systems lack intelligent feedback mechanisms and cannot self-optimize based on historical data and real-time feedback, resulting in limited accuracy of analysis results and decision support capabilities. Summary of the Invention

[0005] (a) Technical problems to be solved To address the aforementioned problems in the prior art, this invention provides a macroeconomic multi-source heterogeneous analysis system, resolving the issues raised in the background section.

[0006] (II) Technical Solution To achieve the above objectives, the main technical solution adopted by the present invention is as follows: A macroeconomic multi-source heterogeneous analysis system, comprising: The data source acquisition module is used to collect data from multiple heterogeneous data sources, including but not limited to government statistics, financial market data, international trade data, social media data, industry report data, and public database data. The data source acquisition module can acquire data from online and offline channels in real time and supports regular updates and incremental loading. The data preprocessing module is used to clean, transform, and standardize the collected data, converting data of different formats into a unified format, and includes noise data removal, missing data imputation, and data redundancy deletion. The data fusion module is used to merge and unify data from multiple data sources. It can handle duplicate information and conflicting data between data sources and generate a consistent dataset through deduplication and merging mechanisms. The qualitative analysis module is used to process unstructured data, such as policy reports, news articles, and social media posts, including sentiment analysis, semantic understanding, and topic extraction of text, and to provide sentiment- and topic-based qualitative support for economic decision-making. The quantitative analysis module is used for statistical analysis, trend forecasting, causal relationship analysis, and economic model building based on structured data, and supports dynamic change analysis of various economic indicators. The results display module presents analysis results to users in various ways, such as charts, reports, heatmaps, and trend graphs. It supports interactive analysis, visualization, and data drill-down, allowing users to dynamically select analysis dimensions and set different parameters to view the results.

[0007] The data source acquisition module supports automated data collection and can access various economic data in real time through API interfaces, web scraping, sensors and external data providers, including real-time market prices, trade flows and financial transaction data. It also supports the retrospective loading of historical data for long-term trend analysis. The data source acquisition module further supports custom configuration of data sources, enabling users to select specific data sources and set acquisition frequency and data update strategies according to their needs.

[0008] The data preprocessing module includes multiple sub-modules: The text data processing unit is used for cleaning, word segmentation, noise reduction, sentiment analysis, and named entity recognition preprocessing of text data from social media, news articles, and policy documents; Numerical data standardization unit, which performs standardization processing on financial, trade and government statistical numerical data, including data normalization, missing value imputation and outlier detection; The time series processing unit is used to process time series data, perform time series interpolation, smoothing, and seasonal adjustment to support long-term and short-term trend forecasting. The data fusion and deduplication unit ensures that duplicate information, conflicting data, or erroneous data from multiple data sources are effectively cleaned to obtain accurate analytical input.

[0009] The data fusion module includes a multi-layered data fusion engine that can handle the conversion of heterogeneous data formats, the coordination of multiple data sources, and the resolution of data conflicts. Through the set data fusion rules, it generates a unique dataset to ensure the accuracy and consistency of subsequent analysis. The data fusion module further features an automated data quality assessment function, which can detect and report data quality issues such as missing data, outliers, and inconsistencies during each data fusion process and generate feedback reports.

[0010] The qualitative analysis module uses natural language processing technology to perform semantic understanding and sentiment analysis on unstructured text data, automatically identifying economic keywords, policy intentions, market sentiment and potential risks in the text, and providing qualitative support for economic decision-making through sentiment scores, topic popularity and potential risk assessment. The module can process various types of unstructured data, including news reports, policy documents, social media content, forum discussions, and more, and continuously improves its analytical accuracy and comprehension capabilities through model optimization.

[0011] The quantitative analysis module includes multi-dimensional economic trend analysis, regression analysis, causal inference, and model prediction based on historical data and real-time updated data, supporting multi-level prediction of key economic indicators such as GDP growth rate, price level, and unemployment rate. The quantitative analysis module further supports the simulation of the impact of different policies, such as monetary policy, fiscal policy, tax policy and trade policy, as well as external events, such as international economic fluctuations, natural disasters, technological revolutions, global pandemics and financial crises, on the macroeconomy through economic models.

[0012] The results display module supports real-time updates, which can automatically refresh reports and charts based on new data and analysis results in the system, ensuring that users receive the latest analysis views. Users can adjust visualization parameters through an interactive interface, such as selecting different time periods, regions, economic variables, or policy scenarios, to conduct multi-dimensional analysis and generate customized reports.

[0013] The qualitative module also includes an intelligent feedback mechanism, which can automatically adjust the data collection rules, data preprocessing process, analysis algorithm and result display method according to the deviation between the system analysis results and the actual economic data, so as to improve the accuracy and efficiency of subsequent analysis. The intelligent feedback mechanism includes a self-learning module based on historical data analysis, which can summarize errors, optimize the analysis process, and generate an optimization suggestion report after each analysis cycle.

[0014] The intelligent feedback mechanism further includes a prediction bias correction unit, which automatically adjusts the model parameters based on the model's historical prediction errors. The intelligent feedback mechanism also includes an adaptive update module, which automatically updates the analysis rules and data processing flow when new influencing factors are discovered during the analysis process.

[0015] The qualitative module also includes an event-driven analysis module, which can automatically trigger the collection, cleaning, processing, fusion and analysis of relevant data when specific economic events occur, such as policy releases, market fluctuations, major news and financial crises, and promptly assess the potential impact of the event on the macroeconomy. The event-driven analysis module includes a rule-based automated monitoring system that can monitor real-time economic events globally and regionally, and trigger data analysis and early warning mechanisms according to preset rules to provide decision-makers with impact analysis and risk assessment of events.

[0016] (III) Beneficial Effects The beneficial effects of this invention are: 1. In this invention, by integrating the data source acquisition module, data can be collected efficiently and comprehensively from multiple heterogeneous data sources, including government statistics, financial market data, and social media data. The broad coverage of these data sources ensures that the system can comprehensively reflect all aspects of the macroeconomy. At the same time, the data acquisition module supports automated collection and regular incremental updates, ensuring the timeliness and accuracy of the data. This function enables the system to obtain the latest economic data in a timely manner when the economic environment changes rapidly, and to conduct analysis based on this data, providing decision-makers with real-time and accurate economic data support, thereby better responding to economic fluctuations and risk management, and improving the emergency response capability of macroeconomic analysis.

[0017] 2. In this invention, the data preprocessing module enables the system to effectively process heterogeneous data from multiple data sources, ensuring data consistency and availability. This module, through the collaboration of multiple sub-modules, including a text data processing unit, a numerical data standardization unit, and a time series processing unit, can clean, transform, and standardize unstructured data, structured data, and time series data to ensure data quality. This processing can eliminate redundant information, fill in missing data, and repair outliers, ensuring the accuracy of subsequent system analysis. At the same time, this module supports efficient data processing workflows, enabling the system to maintain high efficiency when processing large-scale data, thereby greatly improving analysis speed and the reliability of results.

[0018] 3. In this invention, through the close cooperation of the qualitative analysis module and the quantitative analysis module, the system can conduct in-depth macroeconomic analysis. The qualitative analysis module uses natural language processing technology to perform sentiment analysis and semantic understanding on unstructured text data such as policy reports, news articles, and social media posts, extracting market sentiment, policy intentions, and potential risks to provide sentiment- and topic-based analytical support for economic decision-making. The quantitative analysis module, based on historical and real-time updated data, can predict key economic indicators and simulate the impact of policies and external events on the macroeconomy. The combination of the two enables the system to provide comprehensive economic analysis with both qualitative insights and quantitative support, improving the scientific nature and accuracy of decision-making.

[0019] 4. In this invention, by introducing an intelligent feedback mechanism, the system can continuously optimize and adjust itself during each analysis process. This mechanism can automatically adjust the data collection rules, data processing flow, and analysis model based on the deviation between the analysis results and actual economic data, thereby improving the accuracy and efficiency of subsequent analyses. The self-learning module in the intelligent feedback mechanism can summarize historical errors and optimize the system, ensuring that the system's performance continuously improves after each analysis cycle. At the same time, the prediction deviation correction unit and the adaptive update module can adjust the analysis strategy in real time, enabling the system to adapt to rapid changes in the economic environment. This mechanism greatly enhances the system's flexibility and adaptability, ensuring that the system can always provide accurate predictions and decision support in a complex and dynamic economic environment. Attached Figure Description

[0020] Figure 1 This is a schematic diagram illustrating the specific workflow of the present invention; Figure 2 This is a flowchart illustrating the logical judgment process of this invention. Detailed Implementation

[0021] To better explain and facilitate understanding of the present invention, the present invention will be described in detail below with reference to the accompanying drawings and specific embodiments.

[0022] Please refer to Figures 1 to 2 As shown, the present invention provides a macroeconomic multi-source heterogeneous analysis system, including a data source acquisition module for collecting data from multiple heterogeneous data sources, including but not limited to government statistics, financial market data, international trade data, social media data, industry report data, and public database data. The data source acquisition module can acquire data from online and offline channels in real time and supports regular updates and incremental loading. The data preprocessing module is used to clean, transform, and standardize the collected data, converting data of different formats into a unified format, and includes noise data removal, missing data imputation, and data redundancy deletion. The data fusion module is used to merge and unify data from multiple data sources. It can handle duplicate information and conflicting data between data sources and generate a consistent dataset through deduplication and merging mechanisms. The qualitative analysis module is used to process unstructured data, such as policy reports, news articles, and social media posts, including sentiment analysis, semantic understanding, and topic extraction of text, and to provide sentiment- and topic-based qualitative support for economic decision-making. The quantitative analysis module is used for statistical analysis, trend forecasting, causal relationship analysis, and economic model building based on structured data, and supports dynamic change analysis of various economic indicators. The results display module presents analysis results to users in various ways, such as charts, reports, heatmaps, and trend graphs. It supports interactive analysis, visualization, and data drill-down, allowing users to dynamically select analysis dimensions and set different parameters to view the results.

[0023] Optionally, the data source acquisition module supports automated data collection and can access various economic data in real time through API interfaces, web crawling, sensors and external data providers, including real-time market prices, trade flows and financial transaction data. It also supports the retrospective loading of historical data for long-term trend analysis. The data source acquisition module further supports customized data source configuration, enabling users to select specific data sources and set acquisition frequency and data update strategies according to their needs. In practical implementation, the data source acquisition module's operation steps first include configuring different data sources according to requirements, including traditional government statistics, financial market data, and social media data. Data is acquired in real time through preset API interfaces, web scraping, and sensors. The module ensures that the collected data reflects the latest economic changes through a periodic update mechanism. By providing customized configuration, users can select and set data sources according to specific analytical needs, which provides flexibility to the system and ensures it can meet the needs of different decision-making scenarios. Through real-time access and historical data backloading, the system can support long-term trend analysis. The beneficial effect of this module is that it ensures the system can obtain relevant data in a timely manner in a volatile economic environment and provides efficient data collection support, ultimately providing accurate and timely basic data for economic forecasting.

[0024] Optionally, the data preprocessing module includes multiple sub-modules: The text data processing unit is used for cleaning, word segmentation, noise reduction, sentiment analysis, and named entity recognition preprocessing of text data from social media, news articles, and policy documents; Numerical data standardization unit, which performs standardization processing on financial, trade and government statistical numerical data, including data normalization, missing value imputation and outlier detection; The time series processing unit is used to process time series data, perform time series interpolation, smoothing, and seasonal adjustment to support long-term and short-term trend forecasting. The data fusion and deduplication unit ensures that duplicate, conflicting, or erroneous data from multiple data sources are effectively cleaned to obtain accurate analytical input. In practice, the data preprocessing module's steps include first cleaning and denoising the raw data to remove invalid data; then, the text data processing unit performs sentiment analysis and named entity recognition on social media and news articles to extract high-value information; next, the numerical data standardization unit performs data normalization and missing value imputation to ensure data consistency; and the time series processing unit smooths and seasonally adjusts time-dependent data to improve the accuracy of short-term and long-term economic trend forecasts. Through the data fusion and deduplication unit, the system automatically removes duplicate, conflicting, and erroneous data, ensuring the accuracy and consistency of the input data. The beneficial effect of this module is that it ensures the system can utilize clean, standardized, high-quality data when conducting macroeconomic analysis, improving the accuracy and reliability of subsequent analyses.

[0025] Optionally, the data fusion module includes a multi-layered data fusion engine that can handle the conversion of heterogeneous data formats, the coordination of multiple data sources, and the resolution of data conflicts. Through the set data fusion rules, it generates a unique dataset to ensure the accuracy and consistency of subsequent analysis. The data fusion module further features automated data quality assessment capabilities, enabling it to detect and report data quality issues such as missing data, outliers, and inconsistencies during each data fusion process, and generate feedback reports. In practical implementation, the data fusion module's steps include merging data from multiple heterogeneous data sources using the data fusion engine, converting it into a unified format. During this process, the system automatically resolves conflicts between different data sources through predefined data fusion rules, generating a consistent dataset. The module also supports automated data quality assessment, enabling real-time monitoring of data quality during each fusion process, promptly identifying and reporting issues such as missing data and outliers. If data inconsistencies exist, the module can correct them according to preset rules, ensuring the data ultimately meets the system's analytical requirements. In this way, the system not only improves data quality but also enhances the automation and accuracy of data processing, thus providing reliable data support for subsequent analysis.

[0026] Optionally, the qualitative analysis module uses natural language processing technology to perform semantic understanding and sentiment analysis on unstructured text data, automatically identify economic keywords, policy intentions, market sentiment and potential risks in the text, and provide qualitative support for economic decision-making through sentiment scores, topic popularity and potential risk assessment. The module can process various types of unstructured data, including news reports, policy documents, social media content, forum discussions, and more, and continuously improves its analytical accuracy and understanding capabilities through model optimization. In practical implementation, the qualitative analysis module's steps include preliminary preprocessing of text data from various sources, noise removal, and sentiment analysis and semantic understanding. Using natural language processing technology, the module can identify economic keywords, policy intentions, and potential market risks from social media, news reports, and policy documents. Through sentiment analysis, the system can assess the emotional bias in the text data and further determine the potential impact of policies or events on the economy through topic popularity analysis and risk assessment. As the model is continuously optimized, the system will improve its ability to understand different texts. Especially when dealing with unstructured data, the module's beneficial effect is ensuring that the system can promptly identify key information such as market dynamics and policy trends through automated text processing technology, providing decision-makers with real-time and accurate qualitative support.

[0027] Optionally, the quantitative analysis module includes multi-dimensional economic trend analysis, regression analysis, causal inference, and model prediction based on historical data and real-time updated data, supporting multi-level prediction of key economic indicators such as GDP growth rate, price level, and unemployment rate. The quantitative analysis module further supports the simulation of the impact of various policies, such as monetary policy, fiscal policy, tax policy, and trade policy, as well as external events, such as international economic fluctuations, natural disasters, technological revolutions, global pandemics, and financial crises, on the macroeconomy through economic models. In practical implementation, the quantitative analysis module's operational steps include in-depth analysis of historical data collected from multiple sources using methods such as regression analysis and causal inference. This module can automatically update real-time data and apply it to the economic model, thereby supporting multi-dimensional predictions of economic indicators such as GDP growth rate, price level, and unemployment rate. Through this data, the quantitative analysis module can simulate the implementation effects of different policies (such as monetary policy and fiscal policy) and assess the impact of external events (such as global pandemics and international economic fluctuations) on the macroeconomy. The model's predictions provide policymakers with sufficient evidence for policy adjustments. The module's beneficial effect lies in the fact that, through dynamic analysis and simulation, the quantitative analysis module can not only predict economic trends but also help governments and businesses cope with external shocks, improving the foresight and flexibility of decision-making.

[0028] Optionally, the results display module supports real-time updates, which can automatically refresh reports and charts based on new data and analysis results in the system, ensuring that users receive the latest analytical views; Users can adjust visualization parameters through an interactive interface, such as selecting different time periods, regions, economic variables, or policy scenarios, to conduct multi-dimensional analysis and generate customized reports. In practice, the results display module's operation involves presenting complex economic analysis results to users in the form of charts and reports through an interactive interface. Users can select different analysis dimensions, time periods, and economic variables, flexibly adjust the visualization content, and generate customized reports. As data and analysis results are updated, the display module automatically refreshes reports and charts to ensure users always receive the latest information. Through various display methods, the system provides decision-makers with an intuitive view of economic data, helping users quickly identify economic trends, policy effects, and market changes. The module's beneficial effect lies in greatly improving the user experience through enhanced interactivity and real-time updates, enabling decision-makers to more efficiently understand complex economic data and make corresponding decisions quickly.

[0029] Optionally, the qualitative module also includes an intelligent feedback mechanism, which can automatically adjust the data collection rules, data preprocessing process, analysis algorithm and result display method according to the deviation between the system analysis results and the actual economic data, so as to improve the accuracy and efficiency of subsequent analysis; The intelligent feedback mechanism includes a self-learning module based on historical data analysis. This module summarizes errors, optimizes the analysis process, and generates an optimization suggestion report after each analysis cycle. In practical implementation, the intelligent feedback mechanism operates by analyzing the differences between the system's output and actual economic data. It automatically adjusts data collection rules, data preprocessing procedures, analysis algorithms, and result presentation methods. Through historical data analysis, the self-learning module automatically summarizes prediction errors and optimizes the analysis process after each analysis cycle, generating an optimization report that provides improvement directions for subsequent analyses. Simultaneously, the prediction deviation correction unit adjusts the parameters of the analysis model based on historical prediction errors to ensure the accuracy of future predictions. When data or the environment changes, the adaptive update module automatically updates the analysis rules and data processing procedures, ensuring the system adapts to changes in the economic environment. The beneficial effect of this module is that through continuous self-optimization, it ensures the system consistently provides high-quality analysis results in the face of a changing economic environment.

[0030] Optionally, the intelligent feedback mechanism further includes a prediction bias correction unit that automatically adjusts the model parameters based on the model's historical prediction errors; The intelligent feedback mechanism also includes an adaptive update module, which automatically updates the analysis rules and data processing flow when new influencing factors are discovered during the analysis process. In actual implementation, the prediction deviation correction unit's operation steps include comparing historical data with predicted data, identifying deviations, and adjusting model parameters based on these deviations. Each time a prediction is made, the system automatically adjusts the model's predictive ability by comparing it with actual economic data. This unit optimizes model parameters by providing real-time feedback on historical errors, thereby improving the accuracy of subsequent predictions. Furthermore, this unit can monitor changes in economic trends and quickly adapt to market dynamics, promptly correcting prediction errors. This function ensures that the system maintains high prediction accuracy in the face of complex and unpredictable economic environments, helping decision-makers make more accurate economic decisions.

[0031] Optionally, the qualitative module also includes an event-driven analysis module, which can automatically trigger the collection, cleaning, processing, fusion and analysis of relevant data when specific economic events occur, such as policy releases, market fluctuations, major news and financial crises, and promptly assess the potential impact of the event on the macroeconomy. The event-driven analysis module includes a rule-based automated monitoring system capable of monitoring real-time economic events globally and regionally. It triggers data analysis and early warning mechanisms based on preset rules, providing decision-makers with impact analysis and risk assessments of events. In practical implementation, the event-driven analysis module tracks significant global and regional economic events in real time through the automated monitoring system. This ensures that the system can rapidly trigger data collection, cleaning, and analysis when specific economic events (such as policy announcements, market fluctuations, major news events, and financial crises) occur. The module processes the data in real time according to preset rules and assesses the potential impact of the event on the macroeconomy. In this way, the system can quickly respond to emergencies, promptly assess their impact on the economy, and provide decision-makers with rapid and accurate risk assessments and response recommendations. The beneficial effect of this module is that the system can efficiently respond to sudden economic events, adjust economic analysis results in a timely manner, and provide decision-makers with timely and reliable decision-making basis.

[0032] Working Principle: This macroeconomic multi-source heterogeneous analysis system integrates multiple core modules, aiming to achieve the entire process from real-time data collection from different data sources to macroeconomic trend forecasting and decision support. The system first collects data in real-time from multiple heterogeneous data sources (including government statistics, financial market data, international trade data, social media data, industry report data, etc.) through the data source acquisition module. This module supports automated data acquisition from online and offline channels and can periodically update and incrementally load data to ensure the timeliness and accuracy of the data used. The collected raw data is cleaned, transformed, and standardized through the data preprocessing module. This module includes several sub-units: a text data processing unit cleans and performs sentiment analysis on unstructured text data (such as social media, news articles, and policy documents); and a numerical data standardization unit processes structured data such as financial and trade data, performing normalization, missing value imputation, and outlier detection.The time series processing unit interpolates and seasonally adjusts data involving time variations to ensure data consistency and adapt to trend forecasting needs. Furthermore, the data fusion and deduplication unit eliminates duplicate information and resolves data conflicts from multiple data sources, ensuring the accuracy and consistency of the data used. The preprocessed data then enters the data fusion module. This module uses a multi-layered data fusion engine to coordinate format differences between different data sources, resolve data conflicts, and generate a unique, consistent dataset, ensuring the accuracy and reliability of subsequent analysis. This module also features automated data quality assessment capabilities, detecting and reporting problems in the data during the fusion process to guarantee the high quality of the final analysis results. For the processed data, the system conducts in-depth analysis through qualitative and quantitative analysis modules. The qualitative analysis module uses natural language processing technology to analyze unstructured text data, extracting economic keywords, policy intentions, market sentiment, and potential risks to assist economic decision-making. The quantitative analysis module, based on historical and real-time updated data, performs economic trend forecasting, regression analysis, and causal relationship analysis, supporting multi-dimensional forecasting of key economic indicators such as GDP growth rate, price level, and unemployment rate. It also simulates policies (such as monetary policy, fiscal policy, tax policy, and trade policy) and external events (such as international economic fluctuations, natural disasters, and technological revolutions) through economic models. The system analyzes the impact of global pandemics and financial crises on the macroeconomy. The results presentation module visually presents the analysis results to users through charts, reports, heatmaps, and trend graphs. It supports interactive analysis, visualization, and data drill-down, allowing users to select analysis dimensions, time periods, regions, and economic variables according to different needs to generate customized reports. The system updates the presentation content in real time, ensuring decision-makers can quickly access the latest analysis results. The system's core advantage also lies in its intelligent feedback mechanism. Through this mechanism, the system can automatically adjust data collection, preprocessing, analysis models, and result presentation methods based on the deviation between the results of each analysis and actual economic data. This intelligent feedback mechanism utilizes a self-learning model... The system summarizes errors and optimizes the analysis process, automatically generating optimization reports to ensure continuous improvement after each cycle, enhancing analytical accuracy and adaptability. Furthermore, the prediction deviation correction unit and adaptive update module automatically adjust relevant parameters and processing rules when prediction errors or new influencing factors are detected, enabling the system to quickly adapt to changes in the economic environment. Finally, the event-driven analysis module automatically monitors important global and regional economic events, automatically triggering the collection and analysis of relevant data when policy releases, market fluctuations, or major news events occur, promptly assessing the potential impact of these events on the macroeconomy and providing decision-makers with timely and accurate early warnings and risk assessments.

[0033] The above description is merely an embodiment of the present invention and does not limit the patent scope of the present invention. Any equivalent modifications made based on the content of the present invention specification and drawings, or direct or indirect applications in related technical fields, are similarly included within the patent protection scope of the present invention.

Claims

1. A macroeconomic multi-source heterogeneous analysis system, characterized in that, Comprise: Data source acquisition module, for collecting data from multiple heterogeneous data sources, including but not limited to government statistics, financial market data, international trade data, social media data, industry report data and public database data, the data source acquisition module can acquire data from online and offline channels in real time, and support periodic update and incremental loading; Data preprocessing module, for cleaning, converting and standardizing the collected data, converting different formats of data into unified format, and including noise data elimination, missing data filling and data redundancy deletion; Data fusion module, for merging and unified processing of data from multiple data sources, capable of processing repeated information and conflict data between data sources, generating consistent data set through deduplication and merging mechanism; Qualitative analysis module, for processing unstructured data such as policy reports, news articles and social media posts, including text sentiment analysis, semantic understanding and topic extraction, and providing qualitative support based on sentiment and topic for economic decision-making; Quantitative analysis module, for statistical analysis, trend prediction, causal relationship analysis and economic model establishment according to structured data, supporting dynamic change analysis of multiple economic indicators; Result display module, for displaying analysis results to users through various display methods such as charts, reports, heat maps and trend charts, supporting interactive analysis, visualization and data drilling, users can dynamically select analysis dimensions and set different parameters to view results.

2. The macroeconomic multi-source heterogeneous analysis system according to claim 1, characterized in that: The data source acquisition module supports automated data collection, which can access various economic data in real time through API interface, web scraping, sensors and external data providers, including real-time market prices, trade flows and financial transaction data, while supporting historical data backtracking loading for long-term trend analysis; The data source acquisition module further supports custom configuration of data sources, allowing users to select specific data sources and set acquisition frequency and data update strategy according to needs.

3. The macroeconomic multi-source heterogeneous analysis system according to claim 1, characterized in that: The data preprocessing module includes multiple sub-modules: Text data processing unit, for cleaning, tokenization, denoising, sentiment analysis and named entity recognition preprocessing of social media, news article and policy document text data; Numerical data standardization unit, for standardizing financial and trade and government statistics numerical data, including data normalization, missing value filling and outlier detection; Time series processing unit, for processing time series data, performing time series interpolation, smoothing and seasonal adjustment to support long-term and short-term trend prediction; Data fusion and deduplication unit, to ensure that repeated information, conflict data or error data from multiple data sources are effectively cleaned up to obtain accurate analysis input.

4. The macroeconomic multi-source heterogeneous analysis system according to claim 1, characterized in that: The data fusion module includes a multi-level data fusion engine, which can handle heterogeneous data format conversion, multi-source data coordination and data conflict resolution, generate unique data set through set data fusion rules, ensure accuracy and consistency of subsequent analysis; The data fusion module further has an automated data quality assessment function, which can detect and report data quality issues such as missing data, outliers, and inconsistencies during each data fusion process and generate a feedback report.

5. The macroeconomic multi-source heterogeneous analysis system according to claim 1, characterized in that: The qualitative analysis module uses natural language processing techniques to perform semantic understanding and sentiment analysis on unstructured text data, automatically identifying economic keywords, policy intentions, market sentiment, and potential risks in the text, and providing qualitative support for economic decision-making through sentiment scores, topic popularity, and potential risk assessment. The module can process news reports, policy documents, social media content, forum discussions, and various types of unstructured data, and continuously improve analysis accuracy and understanding through model optimization.

6. The macroeconomic multi-source heterogeneous analysis system according to claim 1, wherein: The quantitative analysis module includes historical data and real-time updates for multi-dimensional economic trend analysis, regression analysis, causal inference, and model prediction, supporting multi-level prediction of key economic indicators such as GDP growth rate, price level, and unemployment rate. The quantitative analysis module further supports the simulation of the impact of different policies, such as monetary policy, fiscal policy, tax policy, and trade policy, and external events, such as international economic fluctuations, natural disasters, technological revolutions, global epidemics, and financial crises, on the macro economy.

7. The macroeconomic multi-source heterogeneous analysis system according to claim 1, wherein: The results display module supports real-time updating, which can automatically refresh reports and charts based on new data and analysis results in the system, ensuring that users have the latest analysis view. Users can adjust visualization parameters such as selecting different time periods, regions, economic variables, or policy scenarios through an interactive interface to perform multi-dimensional analysis and generate customized reports.

8. The macroeconomic multi-source heterogeneous analysis system according to claim 1, characterized in that: The qualitative module also includes an intelligent feedback mechanism that can automatically adjust data collection rules, data preprocessing processes, analysis algorithms, and result display methods based on deviations between system analysis results and actual economic data to improve the accuracy and efficiency of subsequent analysis. The intelligent feedback mechanism includes a self-learning module based on historical data analysis that can summarize errors after each analysis cycle, optimize analysis processes, and generate optimization recommendation reports.

9. The macroeconomic multi-source heterogeneous analysis system according to claim 8, characterized in that: The intelligent feedback mechanism further includes a prediction bias correction unit that automatically adjusts model parameters based on historical prediction errors of the model. The intelligent feedback mechanism also includes an adaptive update module that automatically updates analysis rules and data processing processes when new influencing factors are discovered during the analysis process.

10. The macroeconomic multi-source heterogeneous analysis system of claim 9, wherein: The qualitative module also includes an event-driven analysis module that can automatically trigger the collection, cleaning, processing, fusion, and analysis of relevant data when specific economic events such as policy releases, market fluctuations, major news, and financial crises occur, and assess the potential impact of the event on the macro economy in a timely manner. The event-driven analysis module includes a rule-based automated monitoring system that can monitor real-time economic events globally and regionally, trigger data analysis and early warning mechanisms based on pre-set rules, and provide impact analysis and risk assessment of the event to decision-makers.

Images