Analysis model for metering economics and metering analysis method
By providing a system that integrates data acquisition, cleaning, model construction, optimization and risk assessment, the accuracy of data acquisition and model selection in econometric analysis is solved, and the accuracy of model and risk management capabilities are improved.
Patent Information
- Application Number
- CN202411781500.3
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2024-12-05
- Publication Date
- 2025-04-29
AI Technical Summary
In the prior art, data collection process has problems such as data loss and untimely failure, data cleaning and sorting are not perfect enough, which affects the accuracy of model selection, and there are problems with poor structural fit between model construction and selection.
It provides a system for econometric analysis models, including data acquisition and management module, model construction and selection module, parameter estimation and optimization module, result analysis and visualization module, policy simulation and prediction module, and risk assessment and early warning module. The system collects economic data through a variety of data sources, cleans and organizes it, supports users to customize the model structure, optimizes model parameters using advanced algorithms, and conducts detailed statistical analysis and visual display, and monitors and warnings for risks in real time.
Through high-quality data acquisition and cleaning, the accuracy and reliability of the model can be improved, the accuracy of model selection is ensured, the fitting effect and prediction ability of the model can be enhanced, and risks in the data and model can be detected and warned in a timely manner, ensuring the effectiveness of the analysis process.
Smart Images
Figure CN119904254A_ABST
Abstract
Description
Technical Field
[0001] The present invention relates to the technical field of econometric analysis models, and in particular to an econometric analysis model and an econometric analysis method. Background Art
[0002] Econometric analysis models are models that use statistical and mathematical tools to analyze economic phenomena. They are mainly used to study the relationship between economic variables, predict economic trends, evaluate policy effects, and support economic decision-making. Econometrics combines economic theory with actual data, relying on model construction, data collection, and the application of statistical methods to help economists, policymakers, and analysts make scientific judgments based on data.
[0003] Econometrics uses statistical and mathematical methods to transform abstract economic theories into operational and testable models. This method helps economists verify whether economic theories are consistent with reality or whether theoretical assumptions hold true in practice.
[0004] In the existing technology, faced with massive and complex data sources, the data collection process may suffer from data loss and untimely collection, which affects the integrity of the data available for subsequent modules. The rules and algorithms for data cleaning and sorting may not be perfect enough to completely eliminate all data quality issues, resulting in certain errors in the data passed to the model building and selection modules, which in turn affects the accuracy of model selection. Summary of the invention
[0005] In order to make up for the above shortcomings, the present invention provides an econometric analysis model and an econometric analysis method, which aims to improve the data collection problems and imperfect data cleaning in the data collection and management modules and the problems of improper model selection and poor structural fit in the model construction and selection modules.
[0006] In the first aspect, the present invention provides the following technical solutions: A model for econometric analysis, including: Data collection and management module, model building and selection module, parameter estimation and optimization module, result analysis and visualization module, policy simulation and prediction module and risk assessment and early warning module; The data collection and management module is used to collect economic data from various data sources and clean, organize and store the collected data; The model building and selection module is used to provide a variety of models and support users to customize the model structure according to research questions and data characteristics; The parameter estimation and optimization module uses advanced algorithms to estimate model parameters in order to improve the model's fitting effect and prediction ability; The result analysis and visualization module is used to perform detailed statistical analysis on the estimation results of the model and display the analysis results through charts, which are used for users to understand and interpret; The risk assessment and early warning module is used to assess the data and model risks in quantitative analysis, set indicators and thresholds for monitoring, and issue early warnings when risks exceed the limit; The policy simulation and prediction module simulates and predicts the policy effects based on the established econometric model. The simulation and prediction are used to provide a quantitative reference for policy formulation and decision-making.
[0007] Preferably, the data collection and management module includes a data acquisition unit, a data processing unit and a data storage unit. The data acquisition unit is used to collect economic data from multiple data sources, the data processing unit is used to clean and organize the collected data, and the data storage unit is used to store the processed data. The data sources include databases, statistical reports and web crawlers.
[0008] Preferably, the model building and selection module includes a model providing unit, a model customization unit and a model screening unit. The model providing unit is used to provide a variety of models, including linear regression, time series model and panel data model. The model customization unit is used to support users to customize model structure according to research questions and data characteristics. The model screening unit is used to help users compare and select models.
[0009] Preferably, the parameter estimation and optimization module includes an algorithm application unit, an optimization adjustment unit and an effect evaluation unit. The algorithm application unit is used to estimate the model parameters using an algorithm. The optimization adjustment unit is used to automatically optimize the model parameters. The optimized model parameters are used to improve fitting and prediction capabilities. The effect evaluation unit is used to evaluate parameter estimation and results. The parameter estimation includes maximum likelihood estimation and least squares method.
[0010] Preferably, the result analysis and visualization module includes a statistical analysis unit, a chart drawing unit and an interpretation assistance unit. The statistical analysis unit is used to perform coefficient tests and statistical analysis on the estimation results of the model, and the statistical analysis includes coefficient significance tests and goodness of fit assessments. The chart drawing unit is used to display the analysis results through icons, and the icons include line graphs, bar graphs and scatter graphs. The interpretation assistance unit is used to provide auxiliary explanations for users to understand and interpret the results.
[0011] Preferably, the policy simulation and prediction module includes a policy setting unit, a simulation operation unit and a prediction output unit, the policy setting unit is used to input and set policy parameters and conditions, the simulation operation unit is used to perform policy simulation calculations based on a econometric model, and the prediction output unit is used to output policy simulation and prediction results and analysis results.
[0012] Preferably, the risk assessment and early warning module includes a risk indicator setting unit, a risk monitoring unit and an early warning signal generating unit. The risk indicator setting unit is used to evaluate the indicators and thresholds of the risks of economic data and model results. The risk monitoring unit is used to monitor the risk factors in the operation of data and models in real time. The early warning signal generating unit is used to generate a corresponding early warning signal to remind the user to take measures when the monitoring unit detects that the risk indicator exceeds the threshold.
[0013] In a second aspect, the present invention provides the following technical solution, a method for econometric analysis, comprising the following steps: S1. Data collection and management: Economic data is collected from various data sources through the data acquisition unit, and stored by the data storage unit after being cleaned and sorted by the data processing unit; S2. Model construction and selection: Use the model providing unit to obtain multiple models, customize the model structure through the model customization unit, and compare and select through the model screening unit; S3, parameter estimation and optimization use the algorithm application unit to estimate parameters, optimize parameters with the help of the optimization adjustment unit, and evaluate the results through the effect evaluation unit; S4. Result analysis and visualization: Statistical analysis is performed by the statistical analysis unit, the results are presented by the chart drawing unit, and the interpretation is assisted by the interpretation assistance unit; S5. Policy simulation and prediction: Set policy parameters and conditions in the policy setting unit, simulate and calculate through the simulation operation unit, and output the results and analysis through the prediction output unit; S6. Risk assessment and early warning use the risk indicator setting unit to determine indicators and thresholds, monitor risks through the risk monitoring unit, and generate early warning signals by the early warning signal generation unit when the risk exceeds the limit.
[0014] The present invention has the following beneficial effects: 1. In the present invention, the data acquisition and management module provides a high-quality data foundation for other modules. The cleaned and organized data enables the model construction and selection module to select a suitable model based on accurate and standardized data, thereby improving the accuracy and reliability of the model. At the same time, the model structure and type determined by the model construction and selection module provide a clear object and direction for the parameter estimation and optimization module. The optimized parameters can present more valuable results in the result analysis and visualization module.
[0015] 2. In the present invention, the risk assessment and early warning module monitors and warns of risks in the processes of data collection, model building and parameter estimation, which helps to adjust the work of other modules in a timely manner and ensure the effectiveness of the entire analysis process. At the same time, the conclusions drawn and results displayed by the result analysis and visualization module can provide reference and basis for the policy simulation and prediction module, making its simulation and prediction more in line with the actual situation.
[0016] 3. In the present invention, the model customization unit in the model construction and selection module allows users to adjust the model structure according to their own needs and data characteristics. The flexible architecture of the model construction and selection module and the dynamic adjustment capability of the parameter estimation and optimization module enable the model to be optimized as the data and research problems change, and to be able to adapt to a variety of scenarios during use. BRIEF DESCRIPTION OF THE DRAWINGS
[0017] Figure 1 A system architecture diagram for an econometric analysis model proposed by the present invention; Figure 2 This is a data collection and management module architecture diagram for an econometric analysis model and econometric analysis method proposed by the present invention; Figure 3 This is a model construction and selection module architecture diagram for an econometric analysis model and econometric analysis method proposed by the present invention; Figure 4 This is a parameter estimation and optimization module architecture diagram for an econometric analysis model and econometric analysis method proposed by the present invention; Figure 5 This is a result analysis and visualization module architecture diagram for an econometric analysis model and econometric analysis method proposed by the present invention; Figure 6 This is a policy simulation and forecasting module architecture diagram for an econometric analysis model and econometric analysis method proposed by the present invention; Figure 7 This is a risk assessment and early warning module architecture diagram for an econometric analysis model and econometric analysis method proposed by the present invention; Figure 8 The present invention provides a method flow chart for the econometric analysis method of econometrics. DETAILED DESCRIPTION
[0018] The technical solutions in the embodiments of the present invention will be described clearly and completely below in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.
[0019] Embodiment 1 Reference Figure 1-Figure 7 In a first embodiment of the present invention, the present invention provides a model for econometric analysis, comprising: Data collection and management module, model building and selection module, parameter estimation and optimization module, result analysis and visualization module, policy simulation and prediction module and risk assessment and early warning module; The data collection and management module is used to collect economic data from various data sources and clean, organize and store the collected data; The model building and selection module is used to provide a variety of models and support users to customize the model structure according to research questions and data characteristics; The parameter estimation and optimization module uses advanced algorithms to estimate model parameters in order to improve the model's fitting effect and prediction ability; The result analysis and visualization module is used to perform detailed statistical analysis on the estimation results of the model and display the analysis results through charts. The analysis results are used for users to understand and interpret; The risk assessment and early warning module is used to assess the data and model risks in quantitative analysis, set indicators and thresholds for monitoring, and issue early warnings when risks exceed the limit; The policy simulation and forecasting module simulates and forecasts the policy effects based on the established econometric model. The simulation and forecasting are used to provide a quantitative reference for policy formulation and decision-making.
[0020] The data collection and management module includes a data acquisition unit, a data processing unit and a data storage unit. The data acquisition unit is used to collect economic data from various data sources, the data processing unit is used to clean and organize the collected data, and the data storage unit is used to store the processed data. The data sources include databases, statistical reports and web crawlers.
[0021] Specifically, the data acquisition unit is responsible for collecting various economic-related data from diverse data sources, such as rich databases, authoritative statistical reports, and extensive web crawlers, etc., to cover a variety of potentially valuable economic data to the greatest extent possible, breaking the limitations of a single data source, and building a broad and diverse data foundation for subsequent in-depth analysis, thereby improving the comprehensiveness and accuracy of the analysis; The data processing unit performs fine processing operations on the raw data collected by the data acquisition unit. This includes identifying and eliminating noise data, erroneous data, and duplicate data, and standardizing and unifying the format and structure of the data. During use, the quality and consistency of the data can be significantly improved, making the data more accurate, complete, and standardized. The processed data can effectively reduce errors and uncertainties in the analysis process, providing a solid guarantee for subsequent accurate analysis and reliable conclusions; The data storage unit uses safe and reliable technologies and methods to properly store and manage the sorted data. It not only ensures the security and integrity of the data and prevents data loss or damage, but also enables efficient retrieval and rapid call of data. This greatly facilitates subsequent data analysis and application at any time, and effectively supports the long-term use of data and continuous value mining.
[0022] The model building and selection module includes a model providing unit, a model customization unit and a model screening unit. The model providing unit is used to provide a variety of models, including linear regression, time series model and panel data model. The model customization unit is used to support users to customize the model structure according to research questions and data characteristics. The model screening unit is used to help users compare and select models.
[0023] Specifically, the model providing unit prepares in advance and presents to users a variety of common and effective econometric models, such as linear regression, time series models, and panel data models, providing users with a wealth of choices covering different data types and research questions, saving users time in finding and understanding models on their own; The model customization unit allows users to independently adjust and create the structure of the model according to specific research questions, unique characteristics of the data, and personal analysis needs. It can meet personalized research needs, make the model more suitable for actual conditions, and improve the model's ability to explain and predict specific problems; The model screening unit assists users in making detailed comparisons of different models, comprehensively considering various evaluation indicators and performance, and helps users select the model that best suits the current research problem and data characteristics from a large number of models, thereby improving the accuracy and efficiency of the analysis.
[0024] The parameter estimation and optimization module includes an algorithm application unit, an optimization adjustment unit and an effect evaluation unit. The algorithm application unit is used to estimate the model parameters using the algorithm, the optimization adjustment unit is used to automatically optimize the model parameters, and the optimized model parameters are used to improve the fitting and prediction capabilities. The effect evaluation unit is used to evaluate parameter estimation and results. Parameter estimation includes maximum likelihood estimation and least squares method.
[0025] Specifically, the algorithm application unit uses algorithms such as maximum likelihood estimation and least squares method to make initial estimates of the model parameters, provide basic quantitative values for the model parameters, and lay the foundation for subsequent optimization work; The optimization adjustment unit automatically adjusts and improves the initially estimated model parameters, which can significantly improve the model's fitting effect and prediction ability, so that the model can more accurately reflect the inherent relationship in the data; The effect evaluation unit conducts a comprehensive and detailed evaluation of the process and results of parameter estimation. During use, it can help determine the accuracy and reliability of parameter estimation and provide direction and basis for further improving model parameters.
[0026] The result analysis and visualization module includes a statistical analysis unit, a chart drawing unit and an interpretation assistance unit. The statistical analysis unit is used to perform coefficient tests and statistical analysis on the estimation results of the model. The statistical analysis includes coefficient significance tests and goodness of fit assessments. The chart drawing unit is used to display the analysis results through icons. The icons include line charts, bar charts and scatter charts. The interpretation assistance unit is used to provide auxiliary instructions for users to understand and interpret the results.
[0027] Specifically, the statistical analysis unit uses coefficient test and other methods to conduct in-depth statistical analysis on the model estimation results, including coefficient significance test and goodness of fit assessment. In the process of use, it is able to accurately judge the importance of each variable in the model and the overall explanatory power of the model, providing a quantitative basis for the reliability of the model; The chart drawing unit displays complex analysis results in the form of intuitive charts such as line charts, bar charts, and scatter charts. It can help users quickly grasp the overall trend of the data, distribution characteristics, and the relationship between variables, and enhance the readability and intuitiveness of the results. The interpretation auxiliary unit provides users with detailed explanations and descriptions of the analysis results, helping users understand the meaning and potential economic significance behind the data. In the process of use, it enables users to have a deeper understanding of the analysis results, avoid misinterpretation and incorrect application of data, and make better decisions based on the results.
[0028] The policy simulation and prediction module includes a policy setting unit, a simulation operation unit and a prediction output unit. The policy setting unit is used to input and set policy parameters and conditions, the simulation operation unit is used to simulate and calculate the policy based on the econometric model, and the prediction output unit is used to output the results and analysis results of policy simulation and prediction.
[0029] Specifically, the policy setting unit provides users with an interface for inputting and clarifying policy-related parameters and specific conditions, ensuring that users can clearly and accurately describe the key elements of the policy when using it, laying the foundation for subsequent simulation and prediction work; The simulation operation unit simulates the set policy parameters and conditions based on the established measurement model. It can simulate the possible effects and impacts of policies under different circumstances, providing a basis for policy evaluation and optimization; The forecast output unit outputs the results of policy simulation and forecasting as well as detailed analysis conclusions in a clear and explicit form, enabling users to intuitively understand the potential impact of policies, providing valuable reference information for policy makers and decision makers, and supporting them to make more informed decisions.
[0030] The risk assessment and early warning module includes a risk indicator setting unit, a risk monitoring unit and an early warning signal generation unit. The risk indicator setting unit is used to evaluate the indicators and thresholds of economic data and model result risks. The risk monitoring unit is used to monitor the risk factors in data and model operation in real time. The early warning signal generation unit is used to generate corresponding early warning signals when the monitoring unit detects that the risk indicator exceeds the threshold, so as to remind the user to take measures.
[0031] Specifically, the risk indicator setting unit allows users to define specific indicators and corresponding thresholds for risk assessment based on the characteristics of economic data and models. It provides clear standards and references for accurately measuring and identifying risks, ensuring the scientificity and rationality of risk assessment; The risk monitoring unit conducts real-time and continuous tracking and observation of various risk factors in the operation of data and models, timely discovers potential risk changes and trends, and provides timely and accurate information support for early warning; When the risk monitoring unit detects that the risk index exceeds the set threshold, the early warning signal generation unit will quickly generate a corresponding early warning signal to promptly remind users of the risk status and prompt them to take effective response measures to reduce the losses and impacts caused by the risk.
[0032] Embodiment 2: Reference Figure 8 In a second embodiment of the present invention, the present invention provides a method for econometric analysis, comprising the following steps: S1. Data collection and management: Economic data is collected from various data sources through the data acquisition unit, and stored by the data storage unit after being cleaned and sorted by the data processing unit; S2. Model construction and selection: Use the model providing unit to obtain multiple models, customize the model structure through the model customization unit, and compare and select through the model screening unit; S3, parameter estimation and optimization use the algorithm application unit to estimate parameters, optimize parameters with the help of the optimization adjustment unit, and evaluate the results through the effect evaluation unit; S4. Result analysis and visualization: Statistical analysis is performed by the statistical analysis unit, the results are presented by the chart drawing unit, and the interpretation is assisted by the interpretation assistance unit; S5. Policy simulation and prediction: Set policy parameters and conditions in the policy setting unit, simulate and calculate through the simulation operation unit, and output the results and analysis through the prediction output unit; S6. Risk assessment and early warning use the risk indicator setting unit to determine indicators and thresholds, monitor risks through the risk monitoring unit, and generate early warning signals by the early warning signal generation unit when the risk exceeds the limit.
[0033] Specifically, data collection and management in S1 collects economic data from various data sources through the data acquisition unit, which is cleaned and sorted by the data processing unit to obtain comprehensive and accurate original data, which is stored by the data storage unit to provide data support for subsequent analysis at any time; In S2, model construction and selection utilize the model providing unit to obtain a variety of models, which enriches the range of model selection. The model customization unit customizes the model structure to meet personalized research needs. The model screening unit compares and selects to select the most suitable model. In S3, parameter estimation and optimization use the algorithm application unit to perform parameter estimation, which provides preliminary parameter values for the model. The optimization adjustment unit optimizes the parameters to improve the model performance. The effect evaluation unit evaluates the results to ensure the quality of parameter estimation. In S4, the result analysis and visualization are performed by the statistical analysis unit, which plays a role in deeply exploring the connotation of the model results. The results are displayed by the chart drawing unit, which plays a role in intuitively presenting the analysis results. The interpretation is assisted by the interpretation auxiliary unit, which helps users to clearly understand the results. In S5, policy simulation and prediction set policy parameters and conditions in the policy setting unit, which plays a role in clarifying the basis of policy simulation. Through simulation calculations in the simulation operation unit, it plays a role in evaluating the potential impact of the policy. The prediction output unit outputs results and analysis, which plays an effect of providing a reference basis for policy formulation. The risk assessment and early warning in S6 uses the risk indicator setting unit to determine indicators and thresholds, which plays a role in establishing risk measurement standards. The risk is monitored through the risk monitoring unit to play a role in real-time control of risk dynamics. The early warning signal generation unit generates an early warning signal when the risk exceeds the standard, which plays a role in promptly reminding users to take countermeasures.
[0034] It should be understood that the various parts of the present invention can be implemented by hardware, software, firmware or a combination thereof. In the above-mentioned embodiments, multiple steps or methods can be implemented by software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented by hardware, as in another embodiment, it can be implemented by any one of the following technologies known in the art or their combination: a discrete logic circuit having a logic gate circuit for implementing a logic function for a data signal, a dedicated integrated circuit having a suitable combination of logic gate circuits, a programmable gate array (PGA), a field programmable gate array (FPGA), etc.
[0035] Finally, it should be noted that the above is only a preferred embodiment of the present invention and is not intended to limit the present invention. Although the present invention has been described in detail with reference to the aforementioned embodiments, it is still possible for those skilled in the art to modify the technical solutions described in the aforementioned embodiments or to make equivalent substitutions for some of the technical features therein. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included in the protection scope of the present invention.
Claims
1. A model for econometric analysis, characterized in that: include: Data collection and management module, model building and selection module, parameter estimation and optimization module, result analysis and visualization module, policy simulation and prediction module and risk assessment and early warning module; The data collection and management module is used to collect economic data from various data sources and clean, organize and store the collected data; The model building and selection module is used to provide a variety of models and support users to customize the model structure according to research questions and data characteristics; The parameter estimation and optimization module uses advanced algorithms to estimate model parameters in order to improve the model's fitting effect and prediction ability; The result analysis and visualization module is used to perform detailed statistical analysis on the estimation results of the model and display the analysis results through charts, which are used for users to understand and interpret; The risk assessment and early warning module is used to assess the data and model risks in quantitative analysis, set indicators and thresholds for monitoring, and issue early warnings when risks exceed the limit; The policy simulation and prediction module simulates and predicts the policy effects based on the established econometric model. The simulation and prediction are used to provide a quantitative reference for policy formulation and decision-making.
2. The econometric analysis model according to claim 1, characterized in that: The data acquisition module and management module include a data acquisition unit, a data processing unit and a data storage unit. The data acquisition unit is used to collect economic data from multiple data sources, the data processing unit is used to clean and organize the collected data, and the data storage unit is used to store the processed data. The data sources include databases, statistical reports and web crawlers.
3. The econometric analysis model according to claim 1, characterized in that: The model building and selection module includes a model providing unit, a model customization unit and a model screening unit. The model providing unit is used to provide a variety of models, including linear regression, time series model and panel data model. The model customization unit is used to support users to customize model structure according to research questions and data characteristics. The model screening unit is used to help users compare and select models.
4. The econometric analysis model according to claim 1, characterized in that: The parameter estimation and optimization module includes an algorithm application unit, an optimization adjustment unit and an effect evaluation unit. The algorithm application unit is used to estimate the model parameters using an algorithm, the optimization adjustment unit is used to automatically optimize the model parameters, and the optimized model parameters are used to improve the fitting and prediction capabilities. The effect evaluation unit is used to evaluate parameter estimation and results, and the parameter estimation includes maximum likelihood estimation and least squares method.
5. The econometric analysis model according to claim 1, characterized in that: The result analysis and visualization module includes a statistical analysis unit, a chart drawing unit and an interpretation assistance unit. The statistical analysis unit is used to perform coefficient tests and statistical analysis on the estimation results of the model. The statistical analysis includes coefficient significance tests and goodness of fit assessments. The chart drawing unit is used to display the analysis results through icons. The icons include line graphs, bar graphs and scatter graphs. The interpretation assistance unit is used to provide auxiliary explanations for users to understand and interpret the results.
6. The econometric analysis model according to claim 1, characterized in that: The policy simulation and prediction module includes a policy setting unit, a simulation operation unit and a prediction output unit. The policy setting unit is used to input and set policy parameters and conditions, the simulation operation unit is used to perform policy simulation calculations based on a econometric model, and the prediction output unit is used to output policy simulation and prediction results and analysis results.
7. The econometric analysis model according to claim 1, characterized in that: The risk assessment and early warning module includes a risk indicator setting unit, a risk monitoring unit and an early warning signal generating unit. The risk indicator setting unit is used to evaluate the indicators and thresholds of the risks of economic data and model results. The risk monitoring unit is used to monitor the risk factors in the operation of data and models in real time. The early warning signal generating unit is used to generate a corresponding early warning signal when the monitoring unit detects that the risk indicator exceeds the threshold, so as to remind the user to take measures.
8. A method for econometric analysis, characterized in that: A model for econometric analysis as claimed in any one of claims 1 to 7, comprising the following steps: S1. Data collection and management: Economic data are collected from various data sources through the data acquisition unit, and stored by the data storage unit after being cleaned and sorted by the data processing unit; S2. Model construction and selection: Use the model providing unit to obtain multiple models, customize the model structure through the model customization unit, and compare and select through the model screening unit; S3, parameter estimation and optimization use the algorithm application unit to estimate parameters, optimize parameters with the help of the optimization adjustment unit, and evaluate the results through the effect evaluation unit; S4. Result analysis and visualization: Statistical analysis is performed by the statistical analysis unit, the results are presented by the chart drawing unit, and the interpretation is assisted by the interpretation assistance unit; S5. Policy simulation and prediction: Set policy parameters and conditions in the policy setting unit, simulate and calculate through the simulation operation unit, and output the results and analysis through the prediction output unit; S6. Risk assessment and early warning use the risk indicator setting unit to determine indicators and thresholds, monitor risks through the risk monitoring unit, and generate early warning signals by the early warning signal generation unit when the risk exceeds the limit.