Digital governance analysis method and system for digital economy

Abstract

The invention relates to the technical field of digital economy, in particular to a digital governance analysis method and system for digital economy. The method comprises the following steps: designing various digital economic governance decisions according to integrated digital economic data; executing digital economic governance operation based on various digital economic governance decisions; acquiring digital governance user feedback data and digital economic governance operation data based on the digital economic governance operation; constructing an iterative digital governance user emotion scoring model according to the digital governance user feedback data and the digital economic governance operation data; and performing real-time digital governance operation user emotion change graph analysis on the digital economic governance operation based on the iterative digital governance user emotion scoring model to generate a real-time digital governance operation user emotion change graph. According to the method, satisfaction feedback is performed by monitoring the emotion of the user during digital economic governance, so that more visual and clear digital economic governance effect analysis is realized.

Description

Technical Field

[0001] The present invention relates to the field of digital economy technology, and in particular to a digital governance analysis method and system for the digital economy. Background Art

[0002] With the rapid development of the digital economy, digital governance has become one of the important ways of economic governance for enterprises and governments. Digital governance solves problems such as data management and security, transaction fairness, identity authentication and privacy protection, cross-border data coordination, and government governance efficiency by applying technologies such as blockchain, the Internet of Things, big data analysis, smart contracts, and digital identity authentication. It promotes the healthy and sustainable development of the digital economy and has great benefits for the development of enterprises, individuals, and the entire social economy. However, the digital governance of the traditional digital economy has problems with mutual management and inconsistent standards in the process of data collection and integration, as well as the lagging user feedback mechanism that leads to a disconnect between governance strategies and user needs. In addition, the lack of intelligent and automated support leads to low efficiency and slow response in the governance process, and low user satisfaction. Summary of the invention

[0003] Based on this, the present invention provides a digital governance analysis method and system for the digital economy to solve at least one of the above-mentioned technical problems.

[0004] To achieve the above purpose, a digital governance analysis method for the digital economy includes the following steps:

[0005] Step S1: Obtain a multi-source digital economy platform interface protocol; perform integrated digital economy data collection and processing based on the multi-source digital economy platform interface protocol to generate integrated digital economy data;

[0006] Step S2: feature classifying the integrated digital economic data to obtain classified digital economic feature data; designing governance decisions based on the classified digital economic feature data to generate various types of digital economic governance decisions; and executing digital economic governance operations based on various types of digital economic governance decisions;

[0007] Step S3: Based on the digital economy governance operation, collect digital governance user feedback data to generate digital governance user feedback data; perform positive and negative classification analysis of user feedback emotions based on the digital governance user feedback data to generate positive and negative classification data of user feedback emotions;

[0008] Step S4: Conduct digital economic governance operation monitoring on digital economic governance operations to generate digital economic governance operation data; conduct user emotion valuation analysis through digital economic governance operation data and user feedback emotion positive and negative classification data to generate digital governance user emotion scoring indicators; establish a decision-making mathematical model for digital economic governance operations and digital governance user emotion scoring based on the digital governance user emotion scoring indicators to generate a digital governance user emotion scoring model; iteratively update the digital governance user emotion scoring model to obtain an iterative digital governance user emotion scoring model;

[0009] Step S5: Perform real-time digital governance user emotion score monitoring and evaluation on the digital economy governance operation through the iterative digital governance user emotion score model to generate real-time digital governance user emotion score monitoring data; draw a real-time digital governance operation user emotion change graph based on the digital economy governance operation and the real-time digital governance user emotion score monitoring data to generate a real-time digital governance operation user emotion change graph; feed back the real-time digital governance operation user emotion change graph to the terminal to execute the digital governance user satisfaction feedback operation.

[0010] The present invention realizes the effective integration and standardization of multi-source digital economic platform data through the multi-source digital economic platform interface protocol, and integrates and processes data from multiple sources. The generated integrated digital economic data has higher accuracy and consistency, providing a stable data source for subsequent digital governance decision analysis and policy formulation, improving the efficiency and value of data utilization, and the collected data can also more comprehensively reflect the overall picture of digital economic activities. The integrated digital economic data is characterized and classified. When designing governance decisions, the digital economic characteristic data is classified to realize the targeted decision design of various digital economic behaviors, realize the refined management of data, and improve the scientificity and accuracy of governance decisions. Through various digital economic governance decisions, digital economic governance operations are executed to promote the healthy and sustainable development of the digital economy and improve governance efficiency and effectiveness; through the digital governance user feedback data collection during the digital economic governance operation, user feedback information is comprehensively obtained. By classifying and analyzing the positive and negative user feedback emotions of the collected data, it is conducive to identifying the user satisfaction and needs in the governance process and improving the user experience in the governance process. Digital economic governance operation monitoring is carried out for digital economic governance operations, and real-time monitoring and analysis of operation data and user emotions are realized to fully and dynamically grasp the changes in user emotions. Establishing a digital governance user sentiment scoring model based on digital governance user sentiment scoring indicators and iterating and updating the model is conducive to improving the accuracy and reliability of sentiment scoring, and the estimated sentiment scoring can better respond to user needs, making the digital economy governance effect clearer and more transparent. By iterating the digital governance user sentiment scoring model, real-time digital governance user sentiment scoring monitoring and evaluation are carried out on the digital economy governance operation, and real-time digital governance user sentiment scoring monitoring data is generated, which is conducive to more rapid identification of user sentiment fluctuations and potential problems in the digital governance process. Drawing a real-time digital governance operation user sentiment change graph provides a dynamic display of user sentiment data, which is conducive to clearly viewing user sentiment fluctuations in the governance process, and facilitating timely adjustment and optimization of digital governance strategies. Feeding back the digital governance operation user sentiment change graph to the terminal user satisfaction operation is conducive to faster response to user needs, improving service quality and user satisfaction in the governance process, and enhancing the flexibility and effectiveness of digital economy governance.

[0011] Preferably, step S1 comprises the following steps:

[0012] Step S11: Obtaining the multi-source digital economy platform interface protocol;

[0013] Step S12: performing interface protocol parsing processing on the multi-source digital economic platform interface protocol to generate multi-source digital economic platform interface protocol parsing data;

[0014] Step S13: Constructing a digital economy data collection interface based on the multi-source digital economy platform interface protocol parsing data;

[0015] Step S14: performing multi-source interface feature analysis according to the multi-source digital economy platform interface protocol parsing data to generate multi-source interface feature data;

[0016] Step S15: performing preliminary digital economic data collection through the digital economic data collection interface to obtain preliminary digital economic data;

[0017] Step S16: performing data cleaning on the preliminary digital economic data to generate cleaned digital economic data;

[0018] Step S17: performing multi-source digital economic standardization conversion on the cleaned digital economic data according to the multi-source interface feature data to generate standard digital economic data;

[0019] Step S18: Perform digital economic integration processing on the standard digital economic data to generate integrated digital economic data.

[0020] The present invention acquires and analyzes the interface protocol of the multi-source digital economic platform, accurately constructs the data acquisition interface to realize preliminary data acquisition. By cleaning the preliminary digital economic data, it is helpful to reduce the noise and useless data in the data. The cleaned digital economic data is standardized and converted through the multi-source interface feature data, and data integration processing is performed, which provides a solid foundation for subsequent data analysis and application, and greatly improves the processing effect and efficiency of digital economic data.

[0021] Preferably, step S2 comprises the following steps:

[0022] Step S21: extracting digital economic features from the integrated digital economic data to generate digital economic feature data;

[0023] Step S22: classifying the digital economy characteristic data according to the preset digital economy classification rules to generate classified digital economy characteristic data;

[0024] Step S23: Design governance decisions based on the classified digital economy feature data to generate various types of digital economy governance decisions;

[0025] Step S24: Execute digital economy governance operations based on various digital economy governance decisions.

[0026] The present invention further improves the accuracy and scientificity of digital economic governance by extracting features, classifying and making governance decisions on integrated digital economic data. The feature extraction step enables the key characteristics of the data to be revealed and quantified, and the classification step reasonably groups the feature data according to preset rules, ensuring the orderliness and pertinence of the data. Based on the classified data, corresponding governance decisions are made so that each type of data can get the best processing solution. By executing these governance operations, efficient governance and optimization of the digital economy are achieved, providing strong support and guarantee for the development of the digital economy.

[0027] Preferably, step S23 includes the following steps:

[0028] Step S231: performing various types of digital economy clustering feature analysis on the classified digital economy feature data to generate various types of digital economy clustering feature data;

[0029] Step S232: performing dependency analysis on various types of digital economy clustering feature data to generate various types of digital economy dependency feature data;

[0030] Step S233: performing correlation analysis on various types of digital economy dependency feature data to generate various types of digital economy correlation feature data;

[0031] Step S234: performing preliminary governance decision analysis of various types of digital economy according to various types of digital economy correlation characteristic data, and generating various types of preliminary governance decision data of digital economy;

[0032] Step S235: Taking various types of preliminary digital economy governance decision data as digital governance objective functions, and taking classified digital economy characteristic data as digital governance demand functions, constructing various types of nonlinear digital economy governance decision models based on the digital governance objective functions and digital governance demand functions;

[0033] Step S236: Iteratively optimize various nonlinear digital economy governance decision models based on the interior point method to obtain various digital economy governance decision optimization models;

[0034] Step S237: Analyze various types of digital economy governance decision data through various digital economy governance decision optimization models to generate various types of digital economy governance decisions.

[0035] The present invention significantly improves the accuracy and efficiency of digital economic governance through systematic feature analysis and optimization decision models. By conducting a detailed analysis of the clustering characteristics, dependency characteristics, and correlation characteristics of classified digital economic feature data, the intrinsic connections and laws between data are deeply explored. Preliminary governance decisions are made based on these analysis results, providing a scientific basis for subsequent decisions. By constructing a nonlinear digital economic governance decision model and using the interior point method for iterative optimization, accurate optimization of governance decisions is achieved. By analyzing decision data through the optimization model, the generated governance decisions are more scientific and reasonable, ensuring the effectiveness and sustainability of digital economic governance, and providing solid decision support and optimization solutions for the digital governance of the digital economy.

[0036] Preferably, step S3 comprises the following steps:

[0037] Step S31: collecting digital governance user feedback data based on the digital economy governance operation to generate digital governance user feedback data;

[0038] Step S32: Using NLP technology to extract text features from digital governance user feedback data to generate digital governance user feedback feature data;

[0039] Step S33: Perform positive and negative classification processing on the digital governance user feedback feature data to obtain positive and negative classification data of user feedback emotions.

[0040] The present invention improves the user satisfaction and responsiveness of digital economic governance through the collection and sentiment analysis of user feedback data. By collecting user feedback data of digital governance operations, real user experience and feedback are obtained. NLP technology is used to extract text features from feedback data to effectively extract key information. Feedback data is classified into positive and negative emotions to clarify the tendency of user emotions, which can not only timely discover and solve problems in user feedback, but also continuously optimize digital governance decisions, enhance user participation and trust, and provide strong support for the healthy development of the digital economy.

[0041] Preferably, step S33 includes the following steps:

[0042] Step S331: using a preset text emotion recognition model to perform user feedback emotion analysis on the digital governance user feedback feature data to generate digital governance user feedback emotion data;

[0043] Step S332: Design a user emotion feature portrait for the digital governance user feedback emotion data to generate a digital governance user emotion feature portrait;

[0044] Step S333: Designing user emotion feature extraction rules and user positive and negative emotion classification rules based on the digital governance user emotion feature portrait;

[0045] Step S334: constructing a full-text index of digital governance user emotion features according to the user emotion feature extraction rules and the digital governance user feedback feature data;

[0046] Step S335: performing emotional feature data retrieval on the emotional data fed back by digital governance users according to the full-text index of the emotional features of digital governance users, and obtaining the emotional feature data of digital governance users;

[0047] Step S336: performing effective data conversion on the digital governance user emotion feature data based on the ETL technology to obtain effective digital governance user emotion feature data;

[0048] Step S337: Perform user feedback emotion positive and negative classification processing on the effective digital governance user emotion feature data according to the user positive and negative emotion classification rules to generate user feedback emotion positive and negative classification data.

[0049] The present invention improves the effectiveness and accuracy of digital governance user feedback through systematic and refined emotion analysis processing; uses text emotion recognition models to perform emotion analysis on user feedback and generate detailed emotion data. By designing user emotion feature portraits and constructing user emotion feature extraction rules and classification rules, emotion analysis is made more targeted and accurate. Emotion data retrieval is performed based on the full-text index of emotion features to ensure the comprehensiveness and accuracy of emotion feature data extraction. Emotion feature data is converted through ETL technology to improve the effectiveness and high quality of the data. Valid emotion feature data is classified and processed according to positive and negative classification rules to generate accurate positive and negative classification data of user feedback emotions, and the data structure is optimized to facilitate subsequent user emotion analysis.

[0050] Preferably, step S4 comprises the following steps:

[0051] Step S41: Perform digital economic governance operation monitoring on the digital economic governance operation and generate digital economic governance operation data;

[0052] Step S42: Time-series marking the digital economy governance operation data to obtain digital governance operation time-series marking data;

[0053] Step S43: dividing the positive and negative classification data of user feedback emotions into time series data of the digital governance stage according to the digital governance operation time series marking data, and obtaining the digital governance user emotion time series division data;

[0054] Step S44: Perform user emotion scoring analysis based on the digital governance user emotion time series division data and the corresponding digital governance operation time series marking data to generate a digital governance user emotion scoring index;

[0055] Step S45: establishing a decision mapping node between the digital economic governance operation data and the digital governance user sentiment scoring data based on the digital governance user sentiment scoring index, and integrating the decision mapping nodes through a decision tree algorithm to generate a digital governance user sentiment scoring model;

[0056] Step S46: Iteratively update the digital governance user sentiment scoring model to obtain an iterative digital governance user sentiment scoring model.

[0057] The present invention realizes efficient management and optimization of digital economic governance operations and user emotional responses through detailed monitoring and analysis. Monitor digital economic governance operations to obtain detailed governance operation data, and perform time series marking to ensure the timeliness and accuracy of the data. Use time series marking data to divide user feedback emotions into time series data of the digital governance stage, and accurately capture emotional changes in different time periods of digital governance. Perform user emotion scoring analysis through time series division data and corresponding governance operation data to generate reliable user emotion scoring indicators. Based on these indicators, decision mapping nodes for digital economic governance operation data and user emotion scoring data are established, and decision nodes are integrated through a decision tree algorithm to form a user emotion scoring model. Through iterative updates of the model, the accuracy and practicality of the model are continuously optimized and improved. This step not only improves the accuracy and responsiveness of digital governance, but also enhances user experience and satisfaction.

[0058] Preferably, the step S45 of establishing a decision mapping node between digital economic governance operation data and digital governance user emotion scoring data based on the digital governance user emotion scoring index includes the following steps:

[0059] Step S451: Encoding the digital governance user emotion scoring index to generate digital governance user emotion scoring index encoding data;

[0060] Step S452: Calculate the scoring index information entropy according to the digital governance user emotion scoring index encoding data to generate scoring index information entropy data;

[0061] Step S453: Perform information gain weighting processing on the digital governance user sentiment scoring index encoded data according to the scoring index information entropy data to obtain weighted scoring index encoded data, and establish a decision mapping node between the digital economic governance operation data and the digital governance user sentiment scoring data according to the weighted scoring index encoded data.

[0062] The present invention converts the digital governance user sentiment scoring indicators into a unified coding format through coding processing, which helps to simplify the data processing and analysis process and ensure the consistency and comparability of the scoring indicators. Calculating the information entropy of the scoring indicators helps to evaluate the uncertainty and information content of the indicator data, and provides a quantitative analysis of the quality and importance of the scoring indicator data, thereby helping to determine which indicators are more critical in the decision-making process. Through information gain weighting processing, the coded data of each scoring indicator is weighted to more accurately reflect the contribution of each indicator to the digital governance user sentiment score. This helps to establish more accurate and reliable decision mapping nodes between digital economic governance operation data and user sentiment scoring data, and supports more effective decision-making and execution processes.

[0063] Preferably, step S5 comprises the following steps:

[0064] Step S51: Establish a digital governance user emotion score monitoring engine by iterating the digital governance user emotion score model; perform real-time monitoring and processing of digital governance user emotion scores on digital economy governance operations through the digital governance user emotion score monitoring engine to generate real-time digital governance user emotion score monitoring data;

[0065] Step S52: Draw a real-time digital governance operation user emotion change graph based on the digital economy governance operation and the real-time digital governance user emotion score monitoring data to generate a real-time digital governance operation user emotion change graph;

[0066] Step S53: Feedback the real-time digital governance user emotion change graph to the terminal to execute the digital governance user satisfaction feedback task.

[0067] The present invention realizes real-time monitoring and feedback of user emotions in the process of digital economic governance by establishing and applying a user emotion scoring monitoring engine. An efficient user emotion scoring monitoring engine is established based on an iteratively optimized user emotion scoring model to realize real-time monitoring and processing of user emotion scores. A user emotion change graph is drawn based on real-time monitoring data to intuitively display the dynamic changes of user emotions. The real-time user emotion change graph is fed back to the terminal, and the user satisfaction feedback operation is executed to ensure timely response and adjustment of governance strategies. The overall process improves the transparency and response speed of digital governance and enhances user experience and satisfaction.

[0068] The present invention provides a digital governance system for digital economy, which is used to execute the digital governance analysis method for digital economy as described above, and the digital governance system for digital economy includes:

[0069] The digital economy data collection module is used to obtain the multi-source digital economy platform interface protocol; based on the multi-source digital economy platform interface protocol, the integrated digital economy data collection and processing are performed to generate integrated digital economy data;

[0070] The digital economy governance module is used to classify the characteristics of the integrated digital economy data to obtain classified digital economy characteristic data; design governance decisions based on the classified digital economy characteristic data to generate various types of digital economy governance decisions; and execute digital economy governance operations based on various types of digital economy governance decisions;

[0071] The digital governance user feedback analysis module is used to collect digital governance user feedback data based on digital economic governance operations and generate digital governance user feedback data; perform positive and negative classification analysis of user feedback emotions based on digital governance user feedback data and generate positive and negative classification data of user feedback emotions;

[0072] The user feedback sentiment analysis module is used to monitor the digital economic governance operations of digital economic governance operations and generate digital economic governance operation data; conduct user sentiment valuation analysis through digital economic governance operation data and user feedback sentiment positive and negative classification data to generate digital governance user sentiment scoring indicators; establish a decision-making mathematical model for digital economic governance operations and digital governance user sentiment scoring based on the digital governance user sentiment scoring indicators to generate a digital governance user sentiment scoring model; iteratively update the digital governance user sentiment scoring model to obtain an iterative digital governance user sentiment scoring model;

[0073] The digital governance user satisfaction feedback task is used to monitor and evaluate the real-time digital governance user sentiment score of the digital economy governance task through an iterative digital governance user sentiment score model, and generate real-time digital governance user sentiment score monitoring data; to draw a real-time digital governance task user sentiment change graph based on the digital economy governance task and the real-time digital governance user sentiment score monitoring data, and generate a real-time digital governance task user sentiment change graph; and to feed back the real-time digital governance task user sentiment change graph to the terminal to execute the digital governance user satisfaction feedback task.

[0074] The beneficial effect of the present application is that a digital governance analysis method of the digital economy of the present invention obtains and parses the interface protocol of the multi-source digital economy platform, and standardizes and integrates the data to improve the uniformity of the data, which is more conducive to subsequent data analysis. By collecting and analyzing user feedback data in real time, emotional classification processing is performed so that the governance strategy can be adjusted in time to respond to user needs. Through feature classification and decision design, emotional valuation analysis, iterative optimization of emotional scoring models, and real-time monitoring and drawing of user emotional change graphs, the intelligence, automation and response speed of the governance process are improved, and the governance efficiency and user satisfaction are greatly improved. BRIEF DESCRIPTION OF THE DRAWINGS

[0075] Figure 1A schematic diagram of the steps of a digital governance analysis method for digital economy according to the present invention;

[0076] Figure 2 for Figure 1 Detailed implementation steps of step S3 in FIG.

[0077] Figure 3 for Figure 1 Detailed implementation steps of step S4 in FIG.

[0078] Figure 4 for Figure 1 Detailed implementation steps of step S5;

[0079] The realization of the purpose, functional features and advantages of the present invention will be further explained in conjunction with embodiments and with reference to the accompanying drawings. DETAILED DESCRIPTION

[0080] The technical method of the present invention is described clearly and completely below in conjunction with the accompanying drawings. Obviously, the described embodiments are 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 technicians in this field without creative work are within the scope of protection of the present invention.

[0081] In addition, the accompanying drawings are only schematic illustrations of the present invention and are not necessarily drawn to scale. The same reference numerals in the figures represent the same or similar parts, and their repeated description will be omitted. Some of the block diagrams shown in the accompanying drawings are functional entities and do not necessarily correspond to physically or logically independent entities. The functional entities can be implemented in software form, or implemented in one or more hardware modules or integrated circuits, or implemented in different networks and / or processor methods and / or microcontroller methods.

[0082] It should be understood that, although the terms "first", "second", etc. may be used herein to describe various units, these units should not be limited by these terms. These terms are used only to distinguish one unit from another unit. For example, without departing from the scope of the exemplary embodiments, the first unit may be referred to as the second unit, and similarly the second unit may be referred to as the first unit. The term "and / or" used herein includes any and all combinations of one or more of the listed associated items.

[0083] To achieve this, please refer to Figures 1 to 4 The present invention provides a digital governance analysis method for digital economy, comprising the following steps:

[0084] Step S1: Obtain a multi-source digital economy platform interface protocol; perform integrated digital economy data collection and processing based on the multi-source digital economy platform interface protocol to generate integrated digital economy data;

[0085] Step S2: feature classifying the integrated digital economic data to obtain classified digital economic feature data; designing governance decisions based on the classified digital economic feature data to generate various types of digital economic governance decisions; and executing digital economic governance operations based on various types of digital economic governance decisions;

[0086] Step S3: Based on the digital economy governance operation, collect digital governance user feedback data to generate digital governance user feedback data; perform positive and negative classification analysis of user feedback emotions based on the digital governance user feedback data to generate positive and negative classification data of user feedback emotions;

[0087] Step S4: Conduct digital economic governance operation monitoring on digital economic governance operations to generate digital economic governance operation data; conduct user emotion valuation analysis through digital economic governance operation data and user feedback emotion positive and negative classification data to generate digital governance user emotion scoring indicators; establish a decision-making mathematical model for digital economic governance operations and digital governance user emotion scoring based on the digital governance user emotion scoring indicators to generate a digital governance user emotion scoring model; iteratively update the digital governance user emotion scoring model to obtain an iterative digital governance user emotion scoring model;

[0088] Step S5: Perform real-time digital governance user emotion score monitoring and evaluation on the digital economy governance operation through the iterative digital governance user emotion score model to generate real-time digital governance user emotion score monitoring data; draw a real-time digital governance operation user emotion change graph based on the digital economy governance operation and the real-time digital governance user emotion score monitoring data to generate a real-time digital governance operation user emotion change graph; feed back the real-time digital governance operation user emotion change graph to the terminal to execute the digital governance user satisfaction feedback operation.

[0089] The present invention realizes the effective integration and standardization of multi-source digital economic platform data through the multi-source digital economic platform interface protocol, and integrates and processes data from multiple sources. The generated integrated digital economic data has higher accuracy and consistency, providing a stable data source for subsequent digital governance decision analysis and policy formulation, improving the efficiency and value of data utilization, and the collected data can also more comprehensively reflect the overall picture of digital economic activities. The integrated digital economic data is characterized and classified. When designing governance decisions, the digital economic characteristic data is classified to realize the targeted decision design of various digital economic behaviors, realize the refined management of data, and improve the scientificity and accuracy of governance decisions. Through various digital economic governance decisions, digital economic governance operations are executed to promote the healthy and sustainable development of the digital economy and improve governance efficiency and effectiveness; through the digital governance user feedback data collection during the digital economic governance operation, user feedback information is comprehensively obtained. By classifying and analyzing the positive and negative user feedback emotions of the collected data, it is conducive to identifying the user satisfaction and needs in the governance process and improving the user experience in the governance process. Digital economic governance operation monitoring is carried out for digital economic governance operations, and real-time monitoring and analysis of operation data and user emotions are realized to fully and dynamically grasp the changes in user emotions. Establishing a digital governance user sentiment scoring model based on digital governance user sentiment scoring indicators and iterating and updating the model is conducive to improving the accuracy and reliability of sentiment scoring, and the estimated sentiment scoring can better respond to user needs, making the digital economy governance effect clearer and more transparent. By iterating the digital governance user sentiment scoring model, real-time digital governance user sentiment scoring monitoring and evaluation are carried out on the digital economy governance operation, and real-time digital governance user sentiment scoring monitoring data is generated, which is conducive to more rapid identification of user sentiment fluctuations and potential problems in the digital governance process. Drawing a real-time digital governance operation user sentiment change graph provides a dynamic display of user sentiment data, which is conducive to clearly viewing user sentiment fluctuations in the governance process, and facilitating timely adjustment and optimization of digital governance strategies. Feeding back the digital governance operation user sentiment change graph to the terminal user satisfaction operation is conducive to faster response to user needs, improving service quality and user satisfaction in the governance process, and enhancing the flexibility and effectiveness of digital economy governance.

[0090] As an embodiment of the present invention, refer to Figure 1 The above is a schematic diagram of the steps of a digital governance analysis method for digital economy of the present invention. In this embodiment, the digital governance analysis method for digital economy includes the following steps:

[0091] Step S1: Obtain a multi-source digital economy platform interface protocol; perform integrated digital economy data collection and processing based on the multi-source digital economy platform interface protocol to generate integrated digital economy data;

[0092] In an embodiment of the present invention, data from multiple digital economic platforms are established as data sources, such as government platforms, financial platforms, etc., API interface documents are obtained from each platform, and the data structure, request method, authentication method, etc. of each platform are understood, and the API information of each platform is organized into a unified format for unified data collection and processing. Programs are written based on programming languages ​​(such as Python) to collect data from various digital economic platforms, and data from different platforms are integrated to ensure data consistency and integrity. The integrated data is cleaned to handle missing values, format errors, and other issues to ensure data quality; the cleaned data is standardized and converted to unify the data format and unit to ensure data consistency; the data is integrated and stored in a database for subsequent analysis and decision-making.

[0093] Step S2: feature classifying the integrated digital economic data to obtain classified digital economic feature data; designing governance decisions based on the classified digital economic feature data to generate various types of digital economic governance decisions; and executing digital economic governance operations based on various types of digital economic governance decisions;

[0094] In the embodiment of the present invention, integrated digital economic data is loaded from the database, and features such as user behavior, transaction amount, and timestamp in the data are analyzed. Data classification is performed according to feature types such as user features, transaction features, and time features and business needs to improve the refined management of data. Behavioral analysis is performed on data of different feature types, and governance decision rules for various types of data are formulated based on the analysis results to clarify the actions to be taken in each situation. Specific operations and tasks are performed to implement governance decisions, such as sending manual review requests or automatically approving transactions, to ensure effective management and control of digital economic activities.

[0095] Step S3: Based on the digital economy governance operation, collect digital governance user feedback data to generate digital governance user feedback data; perform positive and negative classification analysis of user feedback emotions based on the digital governance user feedback data to generate positive and negative classification data of user feedback emotions;

[0096] In the embodiment of the present invention, a user feedback collection form is designed to collect user feedback information such as satisfaction and opinions. A data table is created in the database according to the user feedback collection form, and the user system is integrated through an API to automatically collect feedback data and store it in the data table to provide data support for subsequent sentiment analysis. The data in the data table is cleaned and the text is preprocessed to process invalid data and remove stop words and punctuation marks. The preprocessed feedback text is subjected to sentiment analysis and classified into positive or negative sentiments, and the data is stored in the database to facilitate subsequent analysis and decision-making.

[0097] Step S4: Conduct digital economic governance operation monitoring on digital economic governance operations to generate digital economic governance operation data; conduct user emotion valuation analysis through digital economic governance operation data and user feedback emotion positive and negative classification data to generate digital governance user emotion scoring indicators; establish a decision-making mathematical model for digital economic governance operations and digital governance user emotion scoring based on the digital governance user emotion scoring indicators to generate a digital governance user emotion scoring model; iteratively update the digital governance user emotion scoring model to obtain an iterative digital governance user emotion scoring model;

[0098] In the embodiment of the present invention, digital economy governance operation monitoring indicators are formulated, such as operation time, processing results, etc., and automated monitoring is developed through programming languages ​​(such as Python) to collect operation data of each monitoring indicator, select important features that affect the emotion score, such as governance operation type, operation time, and results, and extract important features from the operation monitoring data. Combined with the digital economy governance operation data and the user feedback data after emotion classification, statistical analysis is performed to generate user emotion score indicators such as average emotion score, emotion fluctuation degree, and emotion change trend within a specific time period. A decision tree model of governance operation and user emotion score is established based on the user emotion score indicator, and the emotion score is continuously predicted. The performance of the model is regularly evaluated, the error and accuracy are recorded, and feedback is collected for iterative training based on the difference between the model prediction results and the actual situation, and new features are introduced or existing features are adjusted to improve the accuracy and stability of the emotion score.

[0099] Step S5: Perform real-time digital governance user emotion score monitoring and evaluation on the digital economy governance operation through the iterative digital governance user emotion score model to generate real-time digital governance user emotion score monitoring data; draw a real-time digital governance operation user emotion change graph based on the digital economy governance operation and the real-time digital governance user emotion score monitoring data to generate a real-time digital governance operation user emotion change graph; feed back the real-time digital governance operation user emotion change graph to the terminal to execute the digital governance user satisfaction feedback operation.

[0100] In an embodiment of the present invention, through the iterative completion of the digital governance user emotion scoring model, the user emotion is dynamically evaluated and analyzed by the real-time collected data to capture the changes and trends of the user emotion. The digital economic governance operation and the real-time monitored user emotion scoring data are visualized, such as in the form of charts or graphs, to intuitively show the trends and changes of user emotions over time and operational changes. The drawn change graph is provided to the relevant end users or decision makers to help the end users or decision makers understand the impact of the current digital economic governance operation and user feedback, evaluate the effectiveness of the operation, identify potential improvement points, and support the adjustment and optimization strategies in the decision-making process to improve user satisfaction and operational efficiency.

[0101] Preferably, step S1 comprises the following steps:

[0102] Step S11: Obtaining the multi-source digital economy platform interface protocol;

[0103] Step S12: performing interface protocol parsing processing on the multi-source digital economic platform interface protocol to generate multi-source digital economic platform interface protocol parsing data;

[0104] Step S13: Constructing a digital economy data collection interface based on the multi-source digital economy platform interface protocol parsing data;

[0105] Step S14: performing multi-source interface feature analysis according to the multi-source digital economy platform interface protocol parsing data to generate multi-source interface feature data;

[0106] Step S15: performing preliminary digital economic data collection through the digital economic data collection interface to obtain preliminary digital economic data;

[0107] Step S16: performing data cleaning on the preliminary digital economic data to generate cleaned digital economic data;

[0108] Step S17: performing multi-source digital economic standardization conversion on the cleaned digital economic data according to the multi-source interface feature data to generate standard digital economic data;

[0109] Step S18: Perform digital economic integration processing on the standard digital economic data to generate integrated digital economic data.

[0110] The present invention acquires and analyzes the interface protocol of the multi-source digital economic platform, accurately constructs the data acquisition interface to realize preliminary data acquisition. By cleaning the preliminary digital economic data, it is helpful to reduce the noise and useless data in the data. The cleaned digital economic data is standardized and converted through the multi-source interface feature data, and data integration processing is performed, which provides a solid foundation for subsequent data analysis and application, and greatly improves the processing effect and efficiency of digital economic data.

[0111] In an embodiment of the present invention, the interface protocols of multiple digital economic platforms are obtained through channels such as public documents, API documents, technical support, etc. of each digital economic platform, and information such as interface URL, request method, parameters, response format, etc. are extracted to build a data collection interface for each platform, and each interface is tested and debugged to ensure that the collection interface can collect data on the platform normally. The characteristics of each platform interface, such as parameter type, response format, etc., are extracted for subsequent data standardization. Multiple data interfaces are merged to generate a unified collection interface for multiple data platforms, and the unified interface is used to collect data from each digital economic platform to obtain preliminary digital economic data. The preliminary digital economic data is cleaned to remove missing values ​​and duplicate data, and the data is converted to a standardized data format according to the characteristics of the platform interface extracted previously, and the field names and data types are unified. The standardized data of different platforms are merged, redundant data is removed, and a unified integrated data set is formed. Preferably, step S2 includes the following steps:

[0112] Step S21: extracting digital economic features from the integrated digital economic data to generate digital economic feature data;

[0113] Step S22: classifying the digital economy characteristic data according to the preset digital economy classification rules to generate classified digital economy characteristic data;

[0114] Step S23: Design governance decisions based on the classified digital economy feature data to generate various types of digital economy governance decisions;

[0115] Step S24: Execute digital economy governance operations based on various digital economy governance decisions.

[0116] The present invention further improves the accuracy and scientificity of digital economic governance by extracting features, classifying and making governance decisions on integrated digital economic data. The feature extraction step enables the key characteristics of the data to be revealed and quantified, and the classification step reasonably groups the feature data according to preset rules, ensuring the orderliness and pertinence of the data. Based on the classified data, corresponding governance decisions are made so that each type of data can get the best processing solution. By executing these governance operations, efficient governance and optimization of the digital economy are achieved, providing strong support and guarantee for the development of the digital economy.

[0117] In an embodiment of the present invention, digital economic characteristics such as transaction volume and transaction amount are defined, and representative and important feature data are extracted from the data set to provide a basis for subsequent classification and decision-making. Classification rules such as transaction amount and number of transactions are set, and the feature data is classified according to the classification rules to generate classified feature data. Different governance decision analyses are designed based on different classified feature data, such as strengthening supervision of the "High Value & High Volume" category and simplifying the "Low Value & Low Volume" category, and generating specific governance decisions based on the analysis results. Implement corresponding governance measures based on the decision content, such as strengthening monitoring, regular review, simplifying processes, etc., to ensure effective management of the digital economy.

[0118] Preferably, step S23 includes the following steps:

[0119] Step S231: performing various types of digital economy clustering feature analysis on the classified digital economy feature data to generate various types of digital economy clustering feature data;

[0120] Step S232: performing dependency analysis on various types of digital economy clustering feature data to generate various types of digital economy dependency feature data;

[0121] Step S233: performing correlation analysis on various types of digital economy dependency feature data to generate various types of digital economy correlation feature data;

[0122] Step S234: performing preliminary governance decision analysis of various types of digital economy according to various types of digital economy correlation characteristic data, and generating various types of preliminary governance decision data of digital economy;

[0123] Step S235: Taking various types of preliminary digital economy governance decision data as digital governance objective functions, and taking classified digital economy characteristic data as digital governance demand functions, constructing various types of nonlinear digital economy governance decision models based on the digital governance objective functions and digital governance demand functions;

[0124] Step S236: Iteratively optimize various nonlinear digital economy governance decision models based on the interior point method to obtain various digital economy governance decision optimization models;

[0125] Step S237: Analyze various types of digital economy governance decision data through various digital economy governance decision optimization models to generate various types of digital economy governance decisions.

[0126] The present invention significantly improves the accuracy and efficiency of digital economic governance through systematic feature analysis and optimization decision models. By conducting a detailed analysis of the clustering characteristics, dependency characteristics, and correlation characteristics of classified digital economic feature data, the intrinsic connections and laws between data are deeply explored. Preliminary governance decisions are made based on these analysis results, providing a scientific basis for subsequent decisions. By constructing a nonlinear digital economic governance decision model and using the interior point method for iterative optimization, accurate optimization of governance decisions is achieved. By analyzing decision data through the optimization model, the generated governance decisions are more scientific and reasonable, ensuring the effectiveness and sustainability of digital economic governance, and providing solid decision support and optimization solutions for the digital governance of the digital economy.

[0127] In an embodiment of the present invention, the number of feature data clusters is determined, the feature data is clustered using the K-means clustering algorithm, the center and distribution characteristics of each cluster are extracted, and the dependencies between various types of cluster feature data are analyzed, such as the relationship between transaction amount and user activity. Statistical methods (such as correlation analysis) are used to determine the dependencies between features, and regression analysis or structural equation model (SEM) is applied to explore more complex dependencies. According to the results of the dependency analysis, various types of digital economic dependency feature data are generated to show the dependency strength and direction between features. Correlation coefficients (such as Pearson correlation coefficient and Spearman rank correlation coefficient) are used to analyze the correlation between various types of dependency feature data, and correlation feature data are generated based on the results of the correlation analysis to show the correlation strength and pattern between features. Digital governance goals such as improving user satisfaction and reducing transaction fraud are established. According to the correlation feature data, how to adjust the digital economic strategy to achieve the digital governance goals is analyzed, and decision analysis is performed using decision trees, AHP (hierarchical analysis method) and other methods. Based on the results of the decision analysis, various types of preliminary governance decision data for the digital economy are generated, and preliminary governance suggestions and strategies are proposed. Convert the target setting in the preliminary governance decision data into an objective function to describe the target to be optimized (such as maximizing user satisfaction), convert the classified digital economy feature data into a demand function to describe the demand for various features (such as user activity, transaction amount, etc.), and establish a nonlinear optimization model, in which the objective function and the demand function jointly determine the form and constraints of the optimization problem. Select a suitable interior point algorithm, such as the primal dual interior point method, as an optimization tool, initialize the nonlinear model, use the interior point method for iterative calculations, gradually optimize the objective function to meet the constraints, and obtain the optimal digital economy governance decision model through an iterative process. Apply the optimized model to actual data, analyze the effects of various digital economy governance decisions, and propose specific governance measures based on the model results, such as adjusting platform rules and optimizing user experience. Comprehensively analyze the results and generate various digital economy governance decisions, including implementation plans and strategy adjustment suggestions.

[0128] Preferably, step S3 comprises the following steps:

[0129] Step S31: collecting digital governance user feedback data based on the digital economy governance operation to generate digital governance user feedback data;

[0130] Step S32: Using NLP technology to extract text features from digital governance user feedback data to generate digital governance user feedback feature data;

[0131] Step S33: Perform positive and negative classification processing on the digital governance user feedback feature data to obtain positive and negative classification data of user feedback emotions.

[0132] The present invention improves the user satisfaction and responsiveness of digital economic governance through the collection and sentiment analysis of user feedback data. By collecting user feedback data of digital governance operations, real user experience and feedback are obtained. NLP technology is used to extract text features from feedback data to effectively extract key information. Feedback data is classified into positive and negative emotions to clarify the tendency of user emotions, which can not only timely discover and solve problems in user feedback, but also continuously optimize digital governance decisions, enhance user participation and trust, and provide strong support for the healthy development of the digital economy.

[0133] As an embodiment of the present invention, refer to Figure 2 As shown, Figure 1 FIG. 1 is a flow chart of detailed implementation steps of step S3 in FIG. 1 . In this embodiment, step S3 includes:

[0134] Step S31: collecting digital governance user feedback data based on the digital economy governance operation to generate digital governance user feedback data;

[0135] In an embodiment of the present invention, the type of user feedback data that needs to be collected is determined, such as user complaints, opinions and suggestions, satisfaction scores, etc. Key indicators for data collection are defined, such as feedback content, feedback time, user ID, feedback channel, etc. Social media monitoring tools (such as Hootsuite, Sprout Social) are used to collect user feedback and comments on social media in real time, and social media monitoring tools and customer service systems are integrated with the data collection platform through APIs to achieve automatic data collection. Automatic data collection tasks are set up to regularly obtain user feedback data from various channels to ensure the timeliness and completeness of the data. The collected data is cleaned to remove invalid and duplicate feedback information to ensure data quality. Unstructured feedback data (such as text comments) is structured and key fields such as feedback content, time, and user ID are extracted. The processed feedback data is stored in a database (such as MySQL, MongoDB) to facilitate subsequent analysis and processing.

[0136] Step S32: Using NLP technology to extract text features from digital governance user feedback data to generate digital governance user feedback feature data;

[0137] In an embodiment of the present invention, NLP tools (such as NLTK, spaCy) are used to perform word segmentation on the user feedback data in the database, the data is divided into single words or phrases, common stop words (such as "the", "is", "in") are removed, meaningful words are retained, and the words are restored to the root form (such as "running" is restored to "run"), and the TF-IDF value of the word is calculated to measure the importance of the word. A topic model (such as LDA) is used to extract the topic features of the feedback text. The various extracted features (word frequency, TF-IDF, sentiment score, topic distribution, etc.) are integrated into a feature vector, and the feature vector is standardized to ensure that the scale of the feature value is consistent. The generated user feedback feature data is stored in a database or data warehouse for subsequent analysis and use.

[0138] Step S33: Perform positive and negative classification processing on the digital governance user feedback feature data to obtain positive and negative classification data of user feedback emotions.

[0139] In an embodiment of the present invention, sentiment analysis tools (such as TextBlob and VADER) are integrated into the data processing flow to ensure that sentiment analysis can be performed on user feedback feature data. The previously generated user feedback feature data is read from the database, and each piece of user feedback feature data is subjected to sentiment positive and negative classification analysis to generate positive and negative classification results. The classification results are added to the user feedback feature data to generate user feedback sentiment positive and negative classification data containing sentiment classification labels. The generated user feedback sentiment positive and negative classification data is stored in a database or data warehouse for subsequent query and analysis.

[0140] Preferably, step S33 includes the following steps:

[0141] Step S331: using a preset text emotion recognition model to perform user feedback emotion analysis on the digital governance user feedback feature data to generate digital governance user feedback emotion data;

[0142] Step S332: Design a user emotion feature portrait for the digital governance user feedback emotion data to generate a digital governance user emotion feature portrait;

[0143] Step S333: Designing user emotion feature extraction rules and user positive and negative emotion classification rules based on the digital governance user emotion feature portrait;

[0144] Step S334: constructing a full-text index of digital governance user emotion features according to the user emotion feature extraction rules and the digital governance user feedback feature data;

[0145] Step S335: performing emotional feature data retrieval on the emotional data fed back by digital governance users according to the full-text index of the emotional features of digital governance users, and obtaining the emotional feature data of digital governance users;

[0146] Step S336: performing effective data conversion on the digital governance user emotion feature data based on the ETL technology to obtain effective digital governance user emotion feature data;

[0147] Step S337: Perform user feedback emotion positive and negative classification processing on the effective digital governance user emotion feature data according to the user positive and negative emotion classification rules to generate user feedback emotion positive and negative classification data.

[0148] The present invention improves the effectiveness and accuracy of digital governance user feedback through systematic and refined emotion analysis processing; uses text emotion recognition models to perform emotion analysis on user feedback and generate detailed emotion data. By designing user emotion feature portraits and constructing user emotion feature extraction rules and classification rules, emotion analysis is made more targeted and accurate. Emotion data retrieval is performed based on the full-text index of emotion features to ensure the comprehensiveness and accuracy of emotion feature data extraction. Emotion feature data is converted through ETL technology to improve the effectiveness and high quality of the data. Valid emotion feature data is classified and processed according to positive and negative classification rules to generate accurate positive and negative classification data of user feedback emotions, and the data structure is optimized to facilitate subsequent user emotion analysis.

[0149] In an embodiment of the present invention, a suitable text emotion recognition model is selected, such as VADER, TextBlob, BERT, DistilBERT, etc., and the model is trained and optimized using the labeled user feedback data. The digital governance user feedback feature data is input into the emotion recognition model, the emotion of each feedback is analyzed, the emotion score and emotion category (such as positive, negative, neutral) are obtained, and a digital governance user feedback emotion data table containing feedback content, emotion score, and emotion category is generated. Determine the key indicators of user emotion characteristics, such as emotion score, emotion category, feedback frequency, common keywords, etc., perform aggregation analysis on user feedback emotion data, summarize emotion characteristics by user ID, design a user emotion feature portrait template, including the above-mentioned emotion feature indicators, and generate an emotion feature portrait for each user based on the aggregated data. According to the emotion portrait, determine the emotion feature extraction rules such as extracting emotion score, keyword frequency, etc., and design the positive and negative classification rules of user emotions, such as emotion score>0.5 is positive emotion, emotion score<-0.5 is negative emotion, and the rest are neutral emotions. Based on the emotion feature extraction rules, a full-text search engine (such as Elasticsearch) is used to build a full-text index of emotion features, including emotion scores, keywords, emotion categories, and other information. Search conditions are set according to the keywords, emotion scores, emotion categories, etc. of the emotion features, and the full-text search engine is used to perform the search to obtain the emotion feature data that meets the conditions. The extracted data is cleaned and converted, missing values ​​and duplicate data are processed, and data is standardized. The converted valid data is loaded into the data warehouse or database to form effective digital governance user emotion feature data. The effective digital governance user emotion feature data is input into the positive and negative classification rules. According to the emotion score and the preset classification rules, the data is classified into positive emotion, negative emotion, and neutral emotion, and a user feedback emotion positive and negative classification data table containing user ID, emotion score, and emotion category (positive, negative, neutral) is generated.

[0150] Preferably, step S4 comprises the following steps:

[0151] Step S41: Perform digital economic governance operation monitoring on the digital economic governance operation and generate digital economic governance operation data;

[0152] Step S42: Time-series marking the digital economy governance operation data to obtain digital governance operation time-series marking data;

[0153] Step S43: dividing the positive and negative classification data of user feedback emotions into time series data of the digital governance stage according to the digital governance operation time series marking data, and obtaining the digital governance user emotion time series division data;

[0154] Step S44: Perform user emotion scoring analysis based on the digital governance user emotion time series division data and the corresponding digital governance operation time series marking data to generate a digital governance user emotion scoring index;

[0155] Step S45: establishing a decision mapping node between the digital economic governance operation data and the digital governance user sentiment scoring data based on the digital governance user sentiment scoring index, and integrating the decision mapping nodes through a decision tree algorithm to generate a digital governance user sentiment scoring model;

[0156] Step S46: Iteratively update the digital governance user sentiment scoring model to obtain an iterative digital governance user sentiment scoring model.

[0157] The present invention realizes efficient management and optimization of digital economic governance operations and user emotional responses through detailed monitoring and analysis. Monitor digital economic governance operations to obtain detailed governance operation data, and perform time series marking to ensure the timeliness and accuracy of the data. Use time series marking data to divide user feedback emotions into time series data of the digital governance stage, and accurately capture emotional changes in different time periods of digital governance. Perform user emotion scoring analysis through time series division data and corresponding governance operation data to generate reliable user emotion scoring indicators. Based on these indicators, decision mapping nodes for digital economic governance operation data and user emotion scoring data are established, and decision nodes are integrated through a decision tree algorithm to form a user emotion scoring model. Through iterative updates of the model, the accuracy and practicality of the model are continuously optimized and improved. This step not only improves the accuracy and responsiveness of digital governance, but also enhances user experience and satisfaction.

[0158] As an embodiment of the present invention, refer to Figure 3 As shown, Figure 1 FIG. 4 is a flow chart of detailed implementation steps of step S4 in FIG. 4 . In this embodiment, step S4 includes:

[0159] Step S41: Perform digital economic governance operation monitoring on the digital economic governance operation and generate digital economic governance operation data;

[0160] In the embodiment of the present invention, the digital economy governance operation objectives that need to be monitored, such as user operations, system performance, and service responses, are determined, key monitoring indicators (KPIs) such as the number of user logins, page loading time, transaction success rate, and error rate are defined, and monitoring programs are developed through programming languages ​​(such as Python). Data collection points are set at key operation points of the system (such as user login, page access, transaction operations, etc.) to ensure comprehensive coverage of governance operations. The system operation is monitored in real time, and the collected data is stored in the database to ensure data integrity and traceability.

[0161] Step S42: Time-series marking the digital economy governance operation data to obtain digital governance operation time-series marking data;

[0162] In an embodiment of the present invention, the granularity of the time series mark is determined, such as marking by minutes, hours, days, etc., and the time series mark field is set, such as using the timestamp field to represent the time. The previously generated digital economic governance operation data is read from the database or data storage system, the data is grouped according to the preset time series granularity, the time series mark data is generated, and the generated time series mark data is stored in the database for subsequent query and analysis.

[0163] Step S43: dividing the positive and negative classification data of user feedback emotions into time series data of the digital governance stage according to the digital governance operation time series marking data, and obtaining the digital governance user emotion time series division data;

[0164] In the embodiment of the present invention, the previously generated digital governance operation time series marking data and user feedback emotion classification data are read from the database, and the timestamp fields of the digital governance operation time series marking data and the user feedback emotion classification data are converted to a standard date and time format to ensure that the timestamp field format between the data is consistent. The user feedback emotion data and the digital governance operation time series marking data are grouped according to the same digital governance stage to generate emotion time series division data, and the generated emotion time series division data is stored in the database for subsequent query and analysis.

[0165] Step S44: Perform user emotion scoring analysis based on the digital governance user emotion time series division data and the corresponding digital governance operation time series marking data to generate a digital governance user emotion scoring index;

[0166] In an embodiment of the present invention, the previously generated emotion timing division data and operation timing marking data are read from the database, and the emotion timing division data and the operation timing marking data are merged according to the timing segment to ensure that the data in each timing segment can correspond. The emotion scoring rules are defined according to the emotion data, and weights are set using certain indicators in the operation data (such as response time, error rate, etc.) to affect the final emotion score, and the user emotion score is calculated for the data in each timing segment. According to certain indicators in the operation timing marking data (such as average response time, success rate, etc.), the operation influence factor is defined, and the emotion score is adjusted using the operation influence factor. The adjusted emotion scores are summarized to generate emotion scoring indicators, and the generated emotion scoring indicators are stored in the database for subsequent query and analysis.

[0167] Step S45: establishing a decision mapping node between the digital economic governance operation data and the digital governance user sentiment scoring data based on the digital governance user sentiment scoring index, and integrating the decision mapping nodes through a decision tree algorithm to generate a digital governance user sentiment scoring model;

[0168] In an embodiment of the present invention, a previously generated emotion scoring index is read from a database, and based on the emotion scoring index, an emotion scoring node corresponding to each governance operation is determined. A decision tree algorithm (such as CART, ID3, C4.5) is used to analyze the decision mapping nodes, and key operations that affect the emotion scoring are found. The results of the decision tree are integrated to form a complete decision tree model that describes the relationship between digital governance operations and user emotion scores, and generates a digital governance user emotion scoring model containing decision rules and nodes.

[0169] Step S46: Iteratively update the digital governance user sentiment scoring model to obtain an iterative digital governance user sentiment scoring model.

[0170] In the embodiment of the present invention, the accuracy and stability of the initial model are evaluated, and the model is verified using methods such as cross-validation. New governance operation data and user feedback sentiment data are regularly collected to update the database, and the model is retrained using the new data to adjust the model parameters and structure and improve model performance. Based on the evaluation results and new data, multiple iterations are performed to optimize the prediction accuracy of the model, and finally a digital governance user sentiment scoring model that has been iteratively updated is generated to ensure that the model can accurately reflect the latest user sentiment and governance operation relationship.

[0171] Preferably, the step S45 of establishing a decision mapping node between digital economic governance operation data and digital governance user emotion scoring data based on the digital governance user emotion scoring index includes the following steps:

[0172] Step S451: Encoding the digital governance user emotion scoring index to generate digital governance user emotion scoring index encoding data;

[0173] Step S452: Calculate the scoring index information entropy according to the digital governance user emotion scoring index encoding data to generate scoring index information entropy data;

[0174] Step S453: Perform information gain weighting processing on the digital governance user sentiment scoring index encoded data according to the scoring index information entropy data to obtain weighted scoring index encoded data, and establish a decision mapping node between the digital economic governance operation data and the digital governance user sentiment scoring data according to the weighted scoring index encoded data.

[0175] The present invention converts the digital governance user sentiment scoring indicators into a unified coding format through coding processing, simplifies the data processing and analysis process, and ensures the consistency and comparability of the scoring indicators. The information entropy of the scoring indicators is calculated to evaluate the uncertainty and information content of the indicator data, and a quantitative analysis of the quality and importance of the scoring indicator data is provided, thereby helping to determine which indicators are more critical in the decision-making process. Through information gain weighting processing, the coding data of each scoring indicator is weighted to more accurately reflect the contribution of each indicator to the digital governance user sentiment score. It is conducive to establishing more accurate and reliable decision-making mapping nodes between digital economic governance operation data and user sentiment scoring data, and supports more effective decision-making and execution processes.

[0176] In an embodiment of the present invention, a set of scoring index coding rules is designed, such as numerical coding or symbolic coding, to ensure that each emotion scoring index can be uniquely and clearly defined. The emotion scoring index data is read from the database and coded according to the defined scoring index coding rules to ensure that each indicator is accurately mapped to the corresponding coding value for subsequent information entropy calculation and information gain weighting processing. The scoring index coded data is imported into the computing environment, and information entropy calculation is performed using computing tools (such as Python or R) to ensure the accuracy and completeness of the data during the calculation process to obtain reliable information entropy data. According to the information gain calculation results, the scoring index coded data is weighted. According to the weighted scoring index coded data, a decision mapping node between digital economic governance operation data and digital governance user emotion scoring data is established. These nodes will guide the subsequent decision tree algorithm integration to generate a digital governance user emotion scoring model, thereby optimizing the decision-making process and effect of digital economic governance operations.

[0177] Preferably, step S5 comprises the following steps:

[0178] Step S51: Establish a digital governance user emotion score monitoring engine by iterating the digital governance user emotion score model; perform real-time monitoring and processing of digital governance user emotion scores on digital economy governance operations through the digital governance user emotion score monitoring engine to generate real-time digital governance user emotion score monitoring data;

[0179] Step S52: Draw a real-time digital governance operation user emotion change graph based on the digital economy governance operation and the real-time digital governance user emotion score monitoring data to generate a real-time digital governance operation user emotion change graph;

[0180] Step S53: Feedback the real-time digital governance user emotion change graph to the terminal to execute the digital governance user satisfaction feedback task.

[0181] The present invention realizes real-time monitoring and feedback of user emotions in the process of digital economic governance by establishing and applying a user emotion scoring monitoring engine. An efficient user emotion scoring monitoring engine is established based on an iteratively optimized user emotion scoring model to realize real-time monitoring and processing of user emotion scores. A user emotion change graph is drawn based on real-time monitoring data to intuitively display the dynamic changes of user emotions. The real-time user emotion change graph is fed back to the terminal, and the user satisfaction feedback operation is executed to ensure timely response and adjustment of governance strategies. The overall process improves the transparency and response speed of digital governance and enhances user experience and satisfaction.

[0182] As an embodiment of the present invention, refer to Figure 4 As shown, Figure 1 FIG. 5 is a flow chart of detailed implementation steps of step S5, in this embodiment, step S5 includes:

[0183] Step S51: Establish a digital governance user emotion score monitoring engine by iterating the digital governance user emotion score model; perform real-time monitoring and processing of digital governance user emotion scores on digital economy governance operations through the digital governance user emotion score monitoring engine to generate real-time digital governance user emotion score monitoring data;

[0184] In an embodiment of the present invention, the architecture of the digital governance user sentiment score monitoring engine, such as data flow, data processing flow and real-time monitoring strategy, is designed by iterating the digital governance user sentiment score model. Data access points such as governance operation data and system performance data are set to ensure that the engine can obtain real-time digital economy governance operation data from the system. The engine receives real-time governance operation data and user feedback data, and transmits them to the aforementioned optimized digital governance user sentiment score model for processing. This process generates real-time digital governance user sentiment score monitoring data, and each score data contains a timestamp and the identifier of the relevant operation for subsequent analysis and decision-making.

[0185] Step S52: Draw a real-time digital governance operation user emotion change graph based on the digital economy governance operation and the real-time digital governance user emotion score monitoring data to generate a real-time digital governance operation user emotion change graph;

[0186] In the embodiment of the present invention, the real-time digital governance user emotion score data is summarized and sorted, and the aggregation processing is performed by time period (such as minutes and hours), and the change trend of user emotions in each time period is analyzed by combining the digital economy governance operation data (such as user operation records, system events, etc.). Use data visualization tools (such as Matplotlib, D3.js, Tableau) to draw a user emotion change graph. The graph should show the change trend of user emotion scores in different time periods, and mark key governance events and operations at the same time. Output a real-time digital governance operation user emotion change graph containing a timeline, emotion score change curve, and key event annotations, and publish it to the monitoring panel or data report.

[0187] Step S53: Feedback the real-time digital governance user emotion change graph to the terminal to execute the digital governance user satisfaction feedback task.

[0188] In the embodiment of the present invention, a data feedback mechanism is established to transmit the generated user emotion change graph to the relevant terminal device or user interface, and the user emotion change graph is displayed on the terminal device for relevant personnel to monitor and evaluate user satisfaction in real time. Based on the real-time monitoring results of the user emotion change graph, digital governance user satisfaction feedback operations are performed, such as sending satisfaction questionnaires to users, adjusting governance strategies and operating procedures to improve user experience, etc., recording user feedback results and satisfaction evaluation data, tracking subsequent changes in user emotions, and continuously optimizing digital governance operations.

[0189] The present invention provides a digital governance system for digital economy, which is used to execute the digital governance analysis method for digital economy as described above, and the digital governance system for digital economy includes:

[0190] The digital economy data collection module is used to obtain the multi-source digital economy platform interface protocol; based on the multi-source digital economy platform interface protocol, the integrated digital economy data collection and processing are performed to generate integrated digital economy data;

[0191] The digital economy governance module is used to classify the characteristics of the integrated digital economy data to obtain classified digital economy characteristic data; design governance decisions based on the classified digital economy characteristic data to generate various types of digital economy governance decisions; and execute digital economy governance operations based on various types of digital economy governance decisions;

[0192] The digital governance user feedback analysis module is used to collect digital governance user feedback data based on digital economic governance operations and generate digital governance user feedback data; perform positive and negative classification analysis of user feedback emotions based on digital governance user feedback data and generate positive and negative classification data of user feedback emotions;

[0193] The user feedback sentiment analysis module is used to monitor the digital economic governance operations of digital economic governance operations and generate digital economic governance operation data; conduct user sentiment valuation analysis through digital economic governance operation data and user feedback sentiment positive and negative classification data to generate digital governance user sentiment scoring indicators; establish a decision-making mathematical model for digital economic governance operations and digital governance user sentiment scoring based on the digital governance user sentiment scoring indicators to generate a digital governance user sentiment scoring model; iteratively update the digital governance user sentiment scoring model to obtain an iterative digital governance user sentiment scoring model;

[0194] The digital governance user satisfaction feedback task is used to monitor and evaluate the real-time digital governance user sentiment score of the digital economy governance task through an iterative digital governance user sentiment score model, and generate real-time digital governance user sentiment score monitoring data; to draw a real-time digital governance task user sentiment change graph based on the digital economy governance task and the real-time digital governance user sentiment score monitoring data, and generate a real-time digital governance task user sentiment change graph; and to feed back the real-time digital governance task user sentiment change graph to the terminal to execute the digital governance user satisfaction feedback task.

[0195] The beneficial effect of the present application is that a digital governance analysis method of the digital economy of the present invention obtains and parses the interface protocol of the multi-source digital economy platform, and standardizes and integrates the data to improve the uniformity of the data, which is more conducive to subsequent data analysis. By collecting and analyzing user feedback data in real time, emotional classification processing is performed so that the governance strategy can be adjusted in time to respond to user needs. Through feature classification and decision design, emotional valuation analysis, iterative optimization of emotional scoring models, and real-time monitoring and drawing of user emotional change graphs, the intelligence, automation and response speed of the governance process are improved, and the governance efficiency and user satisfaction are greatly improved.

[0196] Therefore, the embodiments should be regarded as illustrative and non-restrictive from all points, and the scope of the present invention is limited by the appended claims rather than the above description, and it is therefore intended that all changes falling within the meaning and range of equivalent elements of the application documents are included in the present invention.

[0197] The above description is only a specific embodiment of the present invention, so that those skilled in the art can understand or implement the present invention. Various modifications to these embodiments will be apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the present invention. Therefore, the present invention will not be limited to the embodiments shown herein, but should conform to the widest scope consistent with the principles and novel features invented herein.

Claims

1. A digital governance analysis method for digital economy, characterized in that: The following steps are involved: Step S1: Obtain a multi-source digital economy platform interface protocol; perform integrated digital economy data collection and processing based on the multi-source digital economy platform interface protocol to generate integrated digital economy data; Step S2: feature classifying the integrated digital economic data to obtain classified digital economic feature data; designing governance decisions based on the classified digital economic feature data to generate various types of digital economic governance decisions; and executing digital economic governance operations based on various types of digital economic governance decisions; Step S3: Collect digital governance user feedback data based on the digital economy governance operation to generate digital governance user feedback data; Perform positive and negative classification analysis of user feedback emotions based on digitally governed user feedback data to generate positive and negative classification data of user feedback emotions; Step S4: Conduct digital economic governance operation monitoring on digital economic governance operations and generate digital economic governance operation data; Through the digital economic governance operation data and the positive and negative classification data of user feedback emotions, user emotion valuation analysis is performed to generate a digital governance user emotion scoring index; based on the digital governance user emotion scoring index, a decision-making mathematical model for digital economic governance operations and digital governance user emotion scoring is established to generate a digital governance user emotion scoring model; the digital governance user emotion scoring model is iteratively updated to obtain an iterative digital governance user emotion scoring model; Step S5: Perform real-time digital governance user sentiment score monitoring and evaluation processing on the digital economy governance operation by iterating the digital governance user sentiment score model to generate real-time digital governance user sentiment score monitoring data; Based on the digital economy governance operations and real-time digital governance user emotion score monitoring data, a real-time digital governance operation user emotion change graph is drawn to generate a real-time digital governance operation user emotion change graph; the real-time digital governance operation user emotion change graph is fed back to the terminal to execute the digital governance user satisfaction feedback operation.

2. The digital governance analysis method for the digital economy according to claim 1 is characterized in that: Step S1 includes the following steps: Step S11: Obtaining the multi-source digital economy platform interface protocol; Step S12: performing interface protocol parsing processing on the multi-source digital economic platform interface protocol to generate multi-source digital economic platform interface protocol parsing data; Step S13: Constructing a digital economy data collection interface based on the multi-source digital economy platform interface protocol parsing data; Step S14: performing multi-source interface feature analysis according to the multi-source digital economy platform interface protocol parsing data to generate multi-source interface feature data; Step S15: performing preliminary digital economic data collection through the digital economic data collection interface to obtain preliminary digital economic data; Step S16: performing data cleaning on the preliminary digital economic data to generate cleaned digital economic data; Step S17: performing multi-source digital economic standardization conversion on the cleaned digital economic data according to the multi-source interface feature data to generate standard digital economic data; Step S18: Perform digital economic integration processing on the standard digital economic data to generate integrated digital economic data.

3. The digital governance analysis method for the digital economy according to claim 1 is characterized in that: Step S2 includes the following steps: Step S21: extracting digital economic features from the integrated digital economic data to generate digital economic feature data; Step S22: classifying the digital economy characteristic data according to the preset digital economy classification rules to generate classified digital economy characteristic data; Step S23: Design governance decisions based on the classified digital economy feature data to generate various types of digital economy governance decisions; Step S24: Execute digital economy governance operations based on various digital economy governance decisions.

4. The digital governance analysis method for the digital economy according to claim 3 is characterized in that: Step S23 includes the following steps: Step S231: performing various types of digital economy clustering feature analysis on the classified digital economy feature data to generate various types of digital economy clustering feature data; Step S232: performing dependency analysis on various types of digital economy clustering feature data to generate various types of digital economy dependency feature data; Step S233: performing correlation analysis on various types of digital economy dependency feature data to generate various types of digital economy correlation feature data; Step S234: performing preliminary governance decision analysis of various types of digital economy according to various types of digital economy correlation characteristic data, and generating various types of preliminary governance decision data of digital economy; Step S235: Taking various types of preliminary digital economy governance decision data as digital governance objective functions, and taking classified digital economy characteristic data as digital governance demand functions, constructing various types of nonlinear digital economy governance decision models based on the digital governance objective functions and digital governance demand functions; Step S236: Iteratively optimize various nonlinear digital economy governance decision models based on the interior point method to obtain various digital economy governance decision optimization models; Step S237: Analyze various types of digital economy governance decision data through various digital economy governance decision optimization models to generate various types of digital economy governance decisions.

5. The digital governance analysis method for the digital economy according to claim 1 is characterized in that: Step S3 includes the following steps: Step S31: collecting digital governance user feedback data based on the digital economy governance operation to generate digital governance user feedback data; Step S32: Using NLP technology to extract text features from digital governance user feedback data to generate digital governance user feedback feature data; Step S33: Perform positive and negative classification processing on the digital governance user feedback feature data to obtain positive and negative classification data of user feedback emotions.

6. The digital governance analysis method for the digital economy according to claim 5 is characterized in that: Step S33 includes the following steps: Step S331: using a preset text emotion recognition model to perform user feedback emotion analysis on the digital governance user feedback feature data to generate digital governance user feedback emotion data; Step S332: Design a user emotion feature portrait for the digital governance user feedback emotion data to generate a digital governance user emotion feature portrait; Step S333: Designing rules for extracting user emotion features and classifying user positive and negative emotions based on the digital governance user emotion feature portrait; Step S334: constructing a full-text index of digital governance user emotion features according to the user emotion feature extraction rules and the digital governance user feedback feature data; Step S335: performing emotional feature data retrieval on the emotional data fed back by digital governance users according to the full-text index of the emotional features of digital governance users, and obtaining the emotional feature data of digital governance users; Step S336: performing effective data conversion on the digital governance user emotion feature data based on the ETL technology to obtain effective digital governance user emotion feature data; Step S337: Perform user feedback emotion positive and negative classification processing on the effective digital governance user emotion feature data according to the user positive and negative emotion classification rules to generate user feedback emotion positive and negative classification data.

7. The digital governance analysis method for the digital economy according to claim 1 is characterized in that: Step S4 includes the following steps: Step S41: Perform digital economic governance operation monitoring on the digital economic governance operation and generate digital economic governance operation data; Step S42: Time-series marking the digital economy governance operation data to obtain digital governance operation time-series marking data; Step S43: dividing the positive and negative classification data of user feedback emotions into time series data of the digital governance stage according to the digital governance operation time series marking data, and obtaining the digital governance user emotion time series division data; Step S44: Perform user emotion scoring analysis based on the digital governance user emotion time series division data and the corresponding digital governance operation time series marking data to generate a digital governance user emotion scoring index; Step S45: establishing a decision mapping node between the digital economic governance operation data and the digital governance user sentiment scoring data based on the digital governance user sentiment scoring index, and integrating the decision mapping nodes through a decision tree algorithm to generate a digital governance user sentiment scoring model; Step S46: Iteratively update the digital governance user sentiment scoring model to obtain an iterative digital governance user sentiment scoring model.

8. The digital governance analysis method for the digital economy according to claim 7 is characterized in that: The step S45 of establishing a decision mapping node between digital economic governance operation data and digital governance user emotion scoring data based on the digital governance user emotion scoring index includes the following steps: Step S451: Encoding the digital governance user emotion scoring index to generate digital governance user emotion scoring index encoding data; Step S452: Calculate the scoring index information entropy according to the digital governance user emotion scoring index encoding data to generate scoring index information entropy data; Step S453: Perform information gain weighting processing on the digital governance user sentiment scoring index encoded data according to the scoring index information entropy data to obtain weighted scoring index encoded data, and establish a decision mapping node between the digital economic governance operation data and the digital governance user sentiment scoring data according to the weighted scoring index encoded data.

9. The digital governance analysis method for the digital economy according to claim 1 is characterized in that: Step S5 includes the following steps: Step S51: Establish a digital governance user emotion score monitoring engine by iterating the digital governance user emotion score model; perform real-time monitoring and processing of digital governance user emotion scores on digital economy governance operations through the digital governance user emotion score monitoring engine to generate real-time digital governance user emotion score monitoring data; Step S52: Draw a real-time digital governance operation user emotion change graph based on the digital economy governance operation and the real-time digital governance user emotion score monitoring data to generate a real-time digital governance operation user emotion change graph; Step S53: Feedback the real-time digital governance user emotion change graph to the terminal to execute the digital governance user satisfaction feedback task.

10. A digital governance analysis system for digital economy, characterized in that: For executing the digital governance analysis method of the digital economy as claimed in claim 1, the digital governance analysis system of the digital economy comprises: The digital economy data collection module is used to obtain the multi-source digital economy platform interface protocol; based on the multi-source digital economy platform interface protocol, the integrated digital economy data collection and processing are performed to generate integrated digital economy data; The digital economy governance module is used to classify the characteristics of the integrated digital economy data to obtain classified digital economy characteristic data; design governance decisions based on the classified digital economy characteristic data to generate various types of digital economy governance decisions; and execute digital economy governance operations based on various types of digital economy governance decisions; The digital governance user feedback analysis module is used to collect digital governance user feedback data based on digital economic governance operations and generate digital governance user feedback data; perform positive and negative classification analysis of user feedback emotions based on digital governance user feedback data and generate positive and negative classification data of user feedback emotions; The user feedback sentiment analysis module is used to monitor the digital economic governance operations of digital economic governance operations and generate digital economic governance operation data; conduct user sentiment valuation analysis through digital economic governance operation data and user feedback sentiment positive and negative classification data to generate digital governance user sentiment scoring indicators; establish a decision-making mathematical model for digital economic governance operations and digital governance user sentiment scoring based on the digital governance user sentiment scoring indicators to generate a digital governance user sentiment scoring model; iteratively update the digital governance user sentiment scoring model to obtain an iterative digital governance user sentiment scoring model; The digital governance user satisfaction feedback task is used to monitor and evaluate the real-time digital governance user sentiment score of the digital economy governance task through an iterative digital governance user sentiment score model, and generate real-time digital governance user sentiment score monitoring data; to draw a real-time digital governance task user sentiment change graph based on the digital economy governance task and the real-time digital governance user sentiment score monitoring data, and generate a real-time digital governance task user sentiment change graph; and to feed back the real-time digital governance task user sentiment change graph to the terminal to execute the digital governance user satisfaction feedback task.

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