Precise policy decision matching system based on big data driving
By introducing data quality management, strengthening interpretability and security modules into the big data policy accurate decision matching system, the problems of data quality, security and transparency are solved, the accuracy and transparency of decisions are improved, and the security of the system is ensured. User trust.
Patent Information
- Application Number
- CN202411117459.X
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2024-08-15
- Publication Date
- 2024-12-13
- Estimated Expiration
- Not applicable · inactive patent
AI Technical Summary
There are problems in the accurate decision-making matching system of big data policies, such as uneven data quality, privacy and security risks, as well as insufficient interpretation and transparency, which affect the accuracy and trustworthiness of decision-making results.
A system including data quality management module, enhanced interpretive module and security module was designed. Through modules such as data collection, cleaning, verification, encryption, interpretation and privacy protection, data quality, security and interpretation are ensured. Form a closed-loop connection to improve the accuracy and transparency of decision-making.
It improves data quality and decision-making accuracy, enhances the security and interpretability of the system, ensures the reliability and transparency of decision-making results, and enhances users' understanding and trust in the decision-making process.
Smart Images
Figure CN119127851A_ABST
Abstract
Description
Technical Field
[0001] The present invention relates to the technical field of data matching, and in particular to a matching system for accurate policy decision-making driven by big data. Background Art
[0002] The policy precision decision-making matching system is a system based on computer technology and artificial intelligence technology. It can help decision makers quickly and accurately select the most appropriate policy solutions to solve problems by analyzing large amounts of data and information. This system usually includes multiple modules such as data collection, data analysis, model building, and policy recommendations. It can help decision makers better understand problems and situations, improve the scientificity and accuracy of policy making, thereby better meeting the needs of the people and promoting social and economic development.
[0003] The matching system for big data policy precise decision-making may have the following disadvantages in actual application: 1. Data quality issues: Big data systems rely on massive data for decision analysis, but the data quality is uneven, and there may be missing, erroneous or inaccurate data, which affects the accuracy of decision results; 2. Privacy and security risks: Big data systems need to process a large amount of sensitive data and may face privacy leaks and security threats, especially in the process of data exchange, storage and processing. There are certain risks; 3. Lack of explainability and transparency: Some big data algorithms and models have strong predictive capabilities, but their internal operating mechanisms may be more complex and lack explainability and transparency, making decision results difficult to understand and trust. Summary of the invention
[0004] The purpose of the present invention is to provide a matching system for accurate policy decision-making based on big data drive to solve the problems raised in the above background technology.
[0005] To achieve the above-mentioned purpose, the present invention provides the following technical solutions: including, a policy database, an enterprise management platform, a data quality management module, a computing model module, an enhanced interpretative module, a matching push module, a user interface and a security module, a shared collaboration platform is provided between the data quality management module and the computing model module, the policy database, the enterprise management platform, the data quality management module, the computing model module, the enhanced interpretative module, the matching push module and the shared collaboration platform form a closed-loop connection, wherein the matching push module is connected to the user interface.
[0006] Preferably, the user interface is connected to a feedback information collection module.
[0007] Preferably, the data quality management module includes a data acquisition and cleaning module, a data verification and calibration module, a data conversion module, a data quality measurement and monitoring module, and a data audit and traceability module.
[0008] Preferably, the enhanced explanatory module includes a data feature selection and extraction module, a data visualization module, a model interpretation module, a policy knowledge base module and a user feedback evaluation module.
[0009] Preferably, the user feedback evaluation module is connected to the user interface signal.
[0010] Preferably, the security module includes a data encryption and decryption module, a data protection and authority management module, a security audit and monitoring module, a data anonymization and desensitization module, a security vulnerability management module and a privacy protection rule engine module.
[0011] Compared with the prior art, the present invention has the following beneficial effects:
[0012] 1. The present invention can improve the level of data quality management through the data quality management module, ensure the accuracy, completeness and consistency of data, provide reliable data support for accurate decision-making of big data policies, and introduce data of more dimensions, so as to have a more comprehensive understanding of problems and influencing factors and improve the accuracy of decision-making;
[0013] 2. The present invention can also improve the interpretability of the enhanced algorithm of the decision matching system through the enhanced interpretative module, help decision makers better understand and accept the decision results, improve the reliability and transparency of the decision, and allow users to have a deeper understanding of the decision results and the algorithm operation process, and ensure the security of the system operation through the security module to avoid information leakage. BRIEF DESCRIPTION OF THE DRAWINGS
[0014] Figure 1 This is a schematic diagram of the overall module structure of a matching system for accurate policy decision-making driven by big data in the present invention;
[0015] Figure 2 This is a schematic diagram of the module structure of a data quality management module in a matching system for accurate policy decision-making driven by big data according to the present invention;
[0016] Figure 3 It is a schematic diagram of the module structure of an enhanced explanatory module in a matching system for accurate policy decision-making driven by big data according to the present invention;
[0017] Figure 4 This is a schematic diagram of the module structure of a security module in a matching system for accurate policy decision-making driven by big data in the present invention. DETAILED DESCRIPTION
[0018] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on 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.
[0019] See also Figure 1-4 The present invention provides a technical solution: including a policy database, an enterprise management platform, a data quality management module, a calculation model module, an enhanced explanatory module, a matching push module, a user interface and a security module. A shared collaboration platform is provided between the data quality management module and the calculation model module to promote cross-departmental cooperation, promote data sharing and collaboration among government departments, build an integrated big data platform, and enhance the synergy of decision-making. The policy database, the enterprise management platform, the data quality management module, the calculation model module, the enhanced explanatory module, the matching push module and the shared collaboration platform form a closed-loop module, wherein the matching push module is connected to the user interface, and the security module is connected to the closed-loop module. The policy database contains historical data, trend data, internal and external enterprise data, social media platform data and policy data itself, and can introduce data of more dimensions, so that problems and influencing factors can be understood more comprehensively and the accuracy of decision-making can be improved.
[0020] The user interface is connected to the feedback information collection module, which allows users to participate in the update of the system, utilize the feedback mechanism, establish a user demand feedback channel, fully consider the opinions and feedback of decision makers and stakeholders, and continuously optimize the system.
[0021] The data quality management module includes a data collection and cleaning module. During the data collection and input stage, data verification and validation rules can be set, such as checks on data format, scope, integrity, etc., to ensure the accuracy and completeness of the data. During data cleaning and preprocessing, various technologies and algorithms can be used to deduplicate, correct errors, fill in missing values, and process outliers to improve data consistency and accuracy. The data in the policy database can be collected; the data verification and calibration module verifies and validates the collected data, compares data sources, standards and specifications to ensure data accuracy and consistency, and uses data verification rules and algorithms to identify abnormal and erroneous data; the data conversion module uses unified data standards and specifications , perform unified naming, unit conversion, coding standardization and other processing on the data to ensure the consistency and comparability of the data; data quality measurement and monitoring module, establish a data quality measurement indicator system, including evaluation standards in terms of completeness, accuracy, consistency, timeliness, etc., regularly monitor data quality, and promptly discover and handle data quality issues; data audit and traceability module, record the source, flow and modification history of data, ensure the traceability and credibility of data, and audit and monitor data modification operations. Through the application of the above system modules, data quality management can be effectively improved to ensure the accuracy, completeness, consistency and credibility of data, provide enterprises and organizations with a high-quality data foundation, and support the accuracy and efficiency of decision-making and business operations.
[0022] The enhanced explanatory module includes a data feature selection and extraction module, which uses highly interpretable features such as topics, keywords, text length, text similarity, etc. These features can intuitively reflect policy content and related factors, making it easier for users to deeply understand decision results; a data visualization module, which uses various charts and interactive interfaces to display decision matching results and algorithm operation processes, such as heat maps, scatter plots, decision trees, etc., so that users can intuitively understand decision results and algorithm decision basis; a model interpretation module, which uses various technologies and algorithms to interpret models, such as local interpretability models, machine learning, SHAP, etc. These technologies can help users understand algorithms The decision-making rules and weight distribution of the decision-making system can provide an in-depth understanding of the reasons and factors behind the decision-making results; the policy knowledge base module combines policy knowledge with algorithm explanations by establishing a complete policy knowledge base, including policy themes, scope of application, key elements and other information, so that users can understand the influence and role of policy factors in the algorithm decision-making process; the user feedback evaluation module collects user feedback and opinions on the algorithm interpretability, continuously optimizes the algorithm interpretability, and improves user trust in and user experience of the decision matching system. Through the comprehensive application of the above system modules, the enhanced algorithm interpretability of the decision matching system can be improved, allowing users to have a deeper understanding of the decision results and the algorithm operation process.
[0023] The user feedback evaluation module is connected with the user interface signal to facilitate the collection of user feedback information.
[0024] The security module includes a data encryption and decryption module, which uses various encryption algorithms to encrypt, store and transmit sensitive data to ensure the security of data during transmission and storage within and outside the system. Common encryption algorithms include symmetric encryption algorithms and asymmetric encryption algorithms. Here, a hybrid encryption algorithm can be used, that is, first use an asymmetric encryption algorithm to encrypt and transmit data, and then use a symmetric encryption algorithm to encrypt, store and transmit the encrypted data; a data protection and permission management module, which controls the access and operation of internal and external users of the system, restricts unauthorized personnel from accessing and modifying the system and data, and prevents data leakage and abuse; a security audit and monitoring module, which records the access and operation history of the system and data, monitors the system operation status and security events, and promptly discovers and responds to security threats and risks; a data anonymization and desensitization module, which uses data desensitization technology to process personal identity information and sensitive data to protect user privacy and ensure the anonymity and security of data during use; a security vulnerability management module, which regularly performs security vulnerability scanning and vulnerability repair to ensure the security and stability of the system; and a privacy protection rule engine module, which checks and executes privacy protection rules for the internal and external data flow process of the system to ensure the privacy and security of data throughout its life cycle.
[0025] It should be noted that, in this article, relational terms such as first and second, etc. are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the terms "include", "comprise" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device.
[0026] 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 matching system for accurate policy decision-making based on big data, including a policy database, an enterprise management platform, a data quality management module, a calculation model module, an enhanced explanation module, a matching push module, a user interface and a security module, characterized in that: A shared collaboration platform is provided between the data quality management module and the computing model module. The policy database, enterprise management platform, data quality management module, computing model module, enhanced interpretative module, matching push module and the shared collaboration platform form a closed-loop connection, wherein the matching push module is connected to the user interface.
2. According to claim 1, a matching system for accurate policy decision-making based on big data drive, characterized in that: The user interface is connected to the feedback information collection module information.
3. According to claim 1, a matching system for accurate policy decision-making based on big data drive, characterized in that: The data quality management module includes a data acquisition and cleaning module, a data verification and calibration module, a data conversion module, a data quality measurement and monitoring module, and a data audit and traceability module.
4. According to claim 1, a matching system for accurate policy decision-making based on big data drive, characterized in that: The enhanced explanatory module includes a data feature selection and extraction module, a data visualization module, a model interpretation module, a policy knowledge base module and a user feedback evaluation module.
5. According to claim 4, a matching system for accurate policy decision-making based on big data drive is characterized by: The user feedback evaluation module is connected to the user interface signal.
6. According to claim 1, a matching system for accurate policy decision-making based on big data drive, characterized in that: The security module includes a data encryption and decryption module, a data protection and authority management module, a security audit and monitoring module, a data anonymization and desensitization module, a security vulnerability management module and a privacy protection rule engine module.