Policy document analysis system and method based on big data technology

Abstract

The invention relates to the technical field of government affair data, in particular to a policy document analysis system and method based on the big data technology, and the system comprises a data collection module, a data preprocessing unit, a natural language processing NLP engine, a machine learning analyzer, and a data storage module. The policy document analysis system and method based on the big data technology have the beneficial effects that the policy document analysis system and method based on the big data technology are realized by adopting advanced natural language processing (NLP) and machine learning technologies. The system can accurately identify and interpret key information, including technical terms and complex policy contexts, in the policy document, so that a more accurate analysis result is provided.

Description

Technical Field

[0001] The present invention relates to the field of government data technology, and specifically to a policy document analysis system and method based on big data technology. Background Art

[0002] In the field of background technology of policy analysis systems, existing technical solutions mainly rely on traditional database management systems and manual analysis methods. These systems are generally capable of storing and retrieving policy documents, but they have limitations in processing large amounts of data, conducting in-depth analysis, and providing real-time insights. Traditional methods often require a lot of human resources to read, understand, and interpret policy documents, which is not only time-consuming but also susceptible to subjective bias. And with the surge in the number of policy documents and the increase in complexity, existing technical solutions are increasingly unable to meet the needs. Policy documents usually exist in unstructured or semi-structured formats, such as PDF, Word documents, etc., which makes automated processing and analysis complicated. In addition, the language and terminology in policy documents are often professional and complex, and traditional text analysis tools are difficult to accurately interpret.

[0003] Existing policy analysis systems also have deficiencies in data integration, real-time analysis, and predictive modeling. They are often unable to effectively integrate data from different sources and formats, lack the ability to update and analyze in real time, and have difficulty providing dynamic policy insights. In addition, these systems have limited capabilities in predicting policy changes and assessing policy impacts, and often rely on static reports and retrospective analysis. Summary of the invention

[0004] The purpose of the present invention is to provide a policy document analysis system and method based on big data technology to solve the problems raised in the above background technology.

[0005] To achieve the above-mentioned purpose, the present invention provides the following technical solution: a policy document analysis system based on big data technology, the system comprising:

[0006] The data collection module is used to automatically collect structured and unstructured policy documents from government websites, policy database websites and online database public data sources. It supports multiple file formats such as PDF, Word, Excel and HTML, and uses web crawler technology to automatically crawl updated policy documents on a regular basis.

[0007] The data preprocessing unit cleans the text, standardizes the data, and extracts key information from the collected policy documents to improve the accuracy and efficiency of subsequent analysis;

[0008] A natural language processing (NLP) engine to understand and analyze language patterns and terminology in policy documents, including part-of-speech tagging, entity recognition, and sentiment analysis capabilities;

[0009] Machine learning analyzer, which uses advanced algorithms to conduct in-depth analysis of pre-processed data to identify policy trends, patterns and potential impacts, including classification, clustering and prediction capabilities;

[0010] The data storage module uses a distributed storage architecture to safely and efficiently store and manage policy documents and analysis results, ensuring the integrity, accessibility and security of the data.

[0011] Preferably, the data preprocessing unit further includes:

[0012] Text cleaning function, used to remove irrelevant content in policy documents, such as headers, footers, and advertisements;

[0013] Data standardization capabilities to unify data formats from different sources, including dates, currencies, and units of measurement;

[0014] The key information extraction function uses regular expressions and text mining technology to extract key information such as policy number, release date, and responsible agency.

[0015] Preferably, the natural language processing (NLP) engine also includes:

[0016] Part-of-speech tagging function, used to identify nouns, verbs, and adjectives in text to help understand sentence structure and meaning;

[0017] Entity recognition function, which is used to identify key entities in the text, such as names of people, places, and organizations, to provide contextual information for further analysis;

[0018] Sentiment analysis function is used to evaluate the emotional tendency of policy documents, such as positive, negative or neutral, to identify the attitudes of the public and policymakers.

[0019] Preferably, a real-time update and feedback mechanism is also included, including:

[0020] Real-time data monitoring function, used to continuously monitor data sources to ensure timely update of policy documents;

[0021] User feedback system allows users to evaluate and make suggestions on analysis results, and the system adjusts analysis parameters and algorithms based on feedback;

[0022] Adaptive learning algorithms automatically adjust analysis strategies based on user feedback and behavior patterns to improve the relevance and accuracy of analysis.

[0023] Preferably, visualization and reporting tools are also included to present analysis results through intuitive charts and reports, support users to customize views and reports as needed, and provide interactive charts, customized reporting functions, data dashboards and user interfaces, while ensuring that all stored and transmitted data are encrypted, implementing strict access control mechanisms, and conducting regular compliance checks to ensure data security and user privacy.

[0024] A policy document analysis method based on big data technology, using a policy document analysis system based on big data technology, the method comprising the following steps:

[0025] The data collection module automatically collects policy documents from government websites, policy database websites and online database public data sources, supports multiple file formats such as PDF, Word, Excel, HTML, and has multi-source data integration capabilities;

[0026] The data preprocessing unit cleans the collected policy documents, standardizes the data, and extracts key information to improve the accuracy and efficiency of subsequent analysis;

[0027] Use Natural Language Processing (NLP) engines to understand and analyze language patterns and terminology in policy documents, including part-of-speech tagging, entity recognition, and sentiment analysis;

[0028] Apply machine learning analyzers to conduct in-depth analysis of pre-processed data to identify policy trends, patterns, and potential impacts, including classification, clustering, and prediction;

[0029] The analysis results are stored securely and efficiently in the data storage module to ensure data integrity, accessibility and security.

[0030] Preferably, the data preprocessing step further includes:

[0031] Remove irrelevant content from policy documents, such as headers, footers, and advertisements, to ensure data accuracy;

[0032] Standardize data formats from different sources, including dates, currencies, and units of measurement;

[0033] Use regular expressions and text mining techniques to extract key information such as policy number, issuance date, responsible agency.

[0034] Preferably, the natural language processing (NLP) step further includes:

[0035] Identify nouns, verbs, and adjectives in text to understand sentence structure and meaning;

[0036] Identify key entities in the text, such as names of people, places, and organizations, to provide contextual information for further analysis;

[0037] Assess the sentiment of policy documents, such as positive, negative, or neutral, to identify the attitudes of the public and policymakers.

[0038] Preferably, a real-time update and feedback mechanism is included, specifically:

[0039] Real-time monitoring of data sources to ensure timely updating of policy documents;

[0040] Allow users to evaluate and make suggestions on analysis results, and the system adjusts analysis parameters and algorithms based on feedback;

[0041] Use adaptive learning algorithms to automatically adjust analysis strategies based on user feedback and behavior patterns, improving the relevance and accuracy of analysis.

[0042] Preferably, visualization and reporting tools are also included, specifically:

[0043] Present analysis results through intuitive charts and reports, and support users to customize views and reports as needed;

[0044] Provide interactive charts, such as line charts, bar charts, and pie charts, to display policy trends and comparative analysis;

[0045] Allows users to generate customized reports based on specific needs, including key data, trend analysis and forecast results;

[0046] Provide a data dashboard to display real-time data, analysis results and user activities;

[0047] Ensure that all stored and transmitted data is encrypted, implement strict access control mechanisms, and conduct regular compliance checks to ensure data security and user privacy.

[0048] Compared with the prior art, the present invention has the following beneficial effects:

[0049] The policy document analysis system and method based on big data technology proposed in the present invention is realized by adopting advanced natural language processing (NLP) and machine learning technology. The system can accurately identify and interpret key information in policy documents, including professional terms and complex policy contexts, thereby providing more accurate analysis results.

[0050] The processing speed has been significantly improved through the automated data collection and analysis process. In addition, the system's analytical depth has also been significantly improved. Machine learning models can learn from historical data and predict the trends and potential impacts of policy changes, which is difficult to achieve with traditional analytical methods. This predictive analysis provides policymakers with valuable forward-looking information to help them make more informed decisions. BRIEF DESCRIPTION OF THE DRAWINGS

[0051] Figure 1 Schematic diagram of the data analysis system of the present invention;

[0052] Figure 2 The figure is a flow chart of the method of the present invention. DETAILED DESCRIPTION

[0053] In order to make the purpose and technical solution of the present invention clearly and completely described, and the advantages more clearly understood, the embodiments of the present invention are further described in detail with reference to the accompanying drawings. It should be understood that the specific embodiments described herein are part of the embodiments of the present invention, rather than all of the embodiments, and are only used to explain the embodiments of the present invention, and are not used to limit the embodiments of the present invention. All other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0054] For example, see Figure 1 The present invention provides a technical solution: a policy document analysis system based on big data technology, the system comprising:

[0055] 1. The data collection module is the foundation of the system. We collected public data sources such as policy database websites and online databases at the national and local levels from government websites. This module is responsible for automatically collecting policy documents from these data sources. This module has multi-source data integration capabilities and can handle data from different sources, including structured and unstructured data. It supports multiple file formats such as PDF, Word, Excel, HTML, etc. to ensure wide data compatibility. Using web crawler technology, this module can automatically capture updated policy documents regularly to reduce manual intervention.

[0056] 2. The data preprocessing unit performs preliminary processing on the collected policy documents to improve the accuracy and efficiency of subsequent analysis. This unit includes a text cleaning function to remove irrelevant content, such as headers, footers, advertisements, etc., to ensure the accuracy of data provided. The data standardization function unifies the data formats from different sources, including dates, currencies, and units of measurement. The key information extraction function uses regular expressions and text mining technology to extract key information, such as policy numbers, issuance dates, responsible agencies, etc.

[0057] 3. The Natural Language Processing (NLP) engine is the core of the system and is responsible for understanding and analyzing language patterns and terminology in policy documents. The engine includes a part-of-speech tagging function to identify nouns, verbs, adjectives, etc. in the text to help understand sentence structure and meaning. The entity recognition function identifies key entities in the text, such as names of people, places, and organizations, to provide contextual information for further analysis. The sentiment analysis function evaluates the emotional tendency of policy documents, such as positive, negative, or neutral, to identify the attitudes of the public and policymakers.

[0058] 4. The machine learning analyzer uses advanced algorithms to conduct in-depth analysis of pre-processed data to identify policy trends, patterns, and potential impacts. The analyzer includes classification algorithms to classify policy documents into different policy areas, such as education, health, and economy. Clustering algorithms find similarities and differences between policy documents to help identify emerging policy trends. Prediction models predict policy changes and their potential socioeconomic impacts based on historical data.

[0059] 5. The data storage module plays a vital role in the policy analysis system. It is responsible for storing and managing the policy documents and analysis results collected by the system in a secure and efficient manner. The design of this module fully considers the integrity, accessibility and security of the data to ensure the reliability and availability of the data throughout the analysis process. It adopts a distributed storage architecture, such as Hadoop Distributed File System (HDFS) or cloud storage solutions, which not only improves the reliability and scalability of data storage, but also can be seamlessly expanded as the amount of data increases.

[0060] 6. Real-time update and feedback mechanisms enable the system to monitor policy changes in real time and quickly update analysis results. Users can provide feedback through an interactive interface, and the system will learn user preferences to optimize future analysis. This mechanism includes real-time data monitoring capabilities, which continuously monitor data sources to ensure timely updates of policy documents. The user feedback system allows users to comment on and make suggestions on analysis results, and the system adjusts analysis parameters and algorithms based on feedback. Adaptive learning algorithms automatically adjust analysis strategies based on user feedback and behavior patterns to improve the relevance and accuracy of analysis.

[0061] 7. Visualization and reporting tools present analysis results through intuitive charts and reports, allowing users to easily understand complex policy information. These tools support users to customize views and reports according to their needs. Interactive charts provide dynamic charts, such as line charts, bar charts, pie charts, etc., to show policy trends and comparative analysis. The customized reporting function allows users to generate customized reports based on specific needs, including key data, trend analysis and forecast results. The data dashboard provides a centralized dashboard that displays real-time data, analysis results and user activities. The user interface provides intuitive navigation, powerful search functions and personalized notifications, making it easy for non-technical users to operate. Clear menus and navigation bars help users quickly find the required functions and information. Supports keyword search, advanced search and filtering so that users can quickly locate relevant policy documents. Users can subscribe to notifications for specific policy areas, and the system will automatically push relevant updates and analysis results.

[0062] 8. The system is designed with data security and user privacy in mind. All stored and transmitted data is encrypted to ensure data security. Strict access control mechanisms are implemented to ensure that only authorized users can access sensitive data. Compliance checks are performed regularly to ensure that the system complies with relevant data protection regulations and policies.

[0063] Through the collection of these technical features, this policy analysis system provides a comprehensive, efficient and user-friendly solution to support policymakers, researchers and the public to better understand and respond to policy changes. The system not only improves the speed and accuracy of policy analysis, but also ensures the relevance and practicality of the analysis results through real-time updates and user feedback mechanisms. Through this technical solution, users can gain in-depth policy insights and make more informed decisions.

[0064] Embodiment 2, refer to the attached Figure 2 As shown, based on the first embodiment, a policy document analysis method based on big data technology is proposed, and a policy document analysis system based on big data technology is adopted. The method includes the following steps:

[0065] The data collection module automatically collects policy documents from government websites, policy database websites and online database public data sources, supports multiple file formats such as PDF, Word, Excel, HTML, and has multi-source data integration capabilities;

[0066] The data preprocessing unit cleans the collected policy documents, standardizes the data, and extracts key information to improve the accuracy and efficiency of subsequent analysis. It also includes: removing irrelevant content in policy documents, such as headers, footers, and advertisements to ensure data accuracy; unifying data formats from different sources, including dates, currencies, and units of measurement, to achieve data standardization; using regular expressions and text mining technology to extract key information, such as policy numbers, issuance dates, and responsible agencies.

[0067] Use natural language processing (NLP) engines to understand and analyze language patterns and terminology in policy documents, including part-of-speech tagging, entity recognition, and sentiment analysis. It also includes: identifying nouns, verbs, and adjectives in the text to understand sentence structure and meaning; identifying key entities in the text, such as names of people, places, and organizations, to provide contextual information for further analysis; and evaluating the sentiment of policy documents, such as positive, negative, or neutral, to identify the attitudes of the public and policymakers.

[0068] Apply machine learning analyzers to conduct in-depth analysis of pre-processed data to identify policy trends, patterns, and potential impacts, including classification, clustering, and prediction;

[0069] The analysis results are stored securely and efficiently in the data storage module to ensure data integrity, accessibility and security.

[0070] It includes real-time update and feedback mechanisms, specifically: real-time monitoring of data sources to ensure timely updating of policy documents; allowing users to evaluate and make suggestions on analysis results, and the system adjusts analysis parameters and algorithms based on feedback; using adaptive learning algorithms to automatically adjust analysis strategies based on user feedback and behavior patterns to improve the relevance and accuracy of the analysis.

[0071] It also includes visualization and reporting tools, specifically: presenting analysis results through intuitive charts and reports, supporting users to customize views and reports as needed; providing interactive charts, such as line charts, bar charts, and pie charts, to display policy trends and comparative analysis; allowing users to generate customized reports based on specific needs, including key data, trend analysis, and forecast results; providing a data dashboard to display real-time data, analysis results, and user activities; ensuring that all stored and transmitted data is encrypted, implementing strict access control mechanisms, and conducting regular compliance checks to ensure data security and user privacy.

[0072] Although embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions and variations may be made to the embodiments without departing from the principles and spirit of the present invention, and that the scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. A policy document analysis system based on big data technology, characterized by: The system comprises: The data collection module is used to automatically collect structured and unstructured policy documents from government websites, policy database websites and online database public data sources. It supports multiple file formats such as PDF, Word, Excel and HTML, and uses web crawler technology to automatically crawl updated policy documents on a regular basis. The data preprocessing unit cleans the text, standardizes the data, and extracts key information from the collected policy documents to improve the accuracy and efficiency of subsequent analysis; A natural language processing (NLP) engine to understand and analyze language patterns and terminology in policy documents, including part-of-speech tagging, entity recognition, and sentiment analysis capabilities; Machine learning analyzer, which uses advanced algorithms to conduct in-depth analysis of pre-processed data to identify policy trends, patterns and potential impacts, including classification, clustering and prediction capabilities; The data storage module uses a distributed storage architecture to safely and efficiently store and manage policy documents and analysis results, ensuring the integrity, accessibility and security of the data.

2. According to claim 1, a policy document analysis system based on big data technology is characterized by: The data preprocessing unit also includes: Text cleaning function, used to remove irrelevant content in policy documents, such as headers, footers, and advertisements; Data standardization capabilities to unify data formats from different sources, including dates, currencies, and units of measurement; The key information extraction function uses regular expressions and text mining technology to extract key information such as policy number, release date, and responsible agency.

3. According to claim 1, a policy document analysis system based on big data technology is characterized by: The Natural Language Processing (NLP) engine also includes: Part-of-speech tagging function, used to identify nouns, verbs, and adjectives in text to help understand sentence structure and meaning; Entity recognition function, which is used to identify key entities in the text, such as names of people, places, and organizations, to provide contextual information for further analysis; Sentiment analysis function is used to evaluate the emotional tendency of policy documents, such as positive, negative or neutral, to identify the attitudes of the public and policymakers.

4. According to claim 1, a policy document analysis system based on big data technology is characterized by: Also included are real-time update and feedback mechanisms, including: Real-time data monitoring function, used to continuously monitor data sources to ensure timely update of policy documents; User feedback system allows users to evaluate and make suggestions on analysis results, and the system adjusts analysis parameters and algorithms based on feedback; Adaptive learning algorithms automatically adjust analysis strategies based on user feedback and behavior patterns to improve the relevance and accuracy of analysis.

5. According to claim 1, a policy document analysis system based on big data technology is characterized by: It also includes visualization and reporting tools for presenting analysis results through intuitive charts and reports, supporting users to customize views and reports as needed, and providing interactive charts, customized reporting functions, data dashboards and user interfaces, while ensuring that all stored and transmitted data is encrypted, implementing strict access control mechanisms, and conducting regular compliance checks to ensure data security and user privacy.

6. A policy document analysis method based on big data technology, using a policy document analysis system based on big data technology as described in any one of claims 1 to 5, characterized in that: The method comprises the following steps: The data collection module automatically collects policy documents from government websites, policy database websites and online database public data sources, supports multiple file formats such as PDF, Word, Excel, HTML, and has multi-source data integration capabilities; The data preprocessing unit cleans the collected policy documents, standardizes the data, and extracts key information to improve the accuracy and efficiency of subsequent analysis; Use Natural Language Processing (NLP) engines to understand and analyze language patterns and terminology in policy documents, including part-of-speech tagging, entity recognition, and sentiment analysis; Apply machine learning analyzers to conduct in-depth analysis of pre-processed data to identify policy trends, patterns, and potential impacts, including classification, clustering, and prediction; The analysis results are stored securely and efficiently in the data storage module to ensure data integrity, accessibility and security.

7. The policy document analysis method based on big data technology according to claim 6 is characterized by: The data preprocessing steps also include: Remove irrelevant content from policy documents, such as headers, footers, and advertisements, to ensure data accuracy; Standardize data formats from different sources, including dates, currencies, and units of measurement; Use regular expressions and text mining techniques to extract key information such as policy number, issuance date, responsible agency.

8. The policy document analysis method based on big data technology according to claim 6 is characterized by: The Natural Language Processing (NLP) step also includes: Identify nouns, verbs, and adjectives in text to understand sentence structure and meaning; Identify key entities in the text, such as names of people, places, and organizations, to provide contextual information for further analysis; Assess the sentiment of policy documents, such as positive, negative, or neutral, to identify the attitudes of the public and policymakers.

9. The policy document analysis method based on big data technology according to claim 6 is characterized by: Includes real-time update and feedback mechanisms, specifically: Real-time monitoring of data sources to ensure timely updating of policy documents; Allow users to evaluate and make suggestions on analysis results, and the system adjusts analysis parameters and algorithms based on feedback; Use adaptive learning algorithms to automatically adjust analysis strategies based on user feedback and behavior patterns, improving the relevance and accuracy of analysis.

10. The policy document analysis method based on big data technology according to claim 6 is characterized by: Also included are visualization and reporting tools, specifically: Present analysis results through intuitive charts and reports, and support users to customize views and reports as needed; Provide interactive charts, such as line charts, bar charts, and pie charts, to display policy trends and comparative analysis; Allows users to generate customized reports based on specific needs, including key data, trend analysis and forecast results; Provide a data dashboard to display real-time data, analysis results and user activities; Ensure that all stored and transmitted data is encrypted, implement strict access control mechanisms, and conduct regular compliance checks to ensure data security and user privacy.

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