An economic and social comprehensive development index evaluation comparison method

Abstract

This invention discloses a method for evaluating and comparing comprehensive economic and social development indices, addressing the technical problems of inefficient data processing, rigid models, singular analysis, and static results in existing evaluation methods. The method sequentially completes five layers of operations: constructing and dynamically configuring a three-level evaluation indicator system to generate a configuration file containing comprehensive information; automatically collecting multi-source heterogeneous data and performing cleaning and standardization to generate a standard dataset; calculating and storing sub-domain indices and comprehensive development indices according to a bottom-up hierarchical aggregation logic; conducting longitudinal time-series comparisons, structural differentiation identification, and automated attribution analysis to generate a structured analysis report; and generating a multi-partition interactive visualization interface to achieve a step-by-step exploration of the index from macro to micro levels. This invention automates data processing, dynamically adapts the indicator system, completes in-depth index analysis and interactive display, improves the efficiency and practicality of evaluation, and provides precise decision-making support for the formulation of policies related to economic and social development.

Description

Technical Field

[0001] This invention relates to the field of economic and social development evaluation technology, and in particular to a method for evaluating and comparing comprehensive economic and social development indices. Background Technology

[0002] Currently, comprehensively evaluating the economic and social development levels of countries and regions is a crucial foundational task for formulating macroeconomic policies, comparing regional development, and measuring the effectiveness of high-quality development. Existing evaluation methods typically include the following steps: First, based on statistical principles and economic development theories, a set of evaluation indicators is constructed, encompassing multiple dimensions such as economic growth, industrial structure, people's well-being, and resources and environment. Second, raw data for each indicator is manually collected through channels such as statistical yearbooks, government gazettes, and industry reports. Third, evaluation models such as the analytic hierarchy process (AHP), the Delphi method, and principal component analysis are used to process the collected data and calculate the sub-indices and the comprehensive development index. Finally, the calculation results are summarized and presented in the form of tables or charts.

[0003] However, in practical applications, existing evaluation and comparison methods have the following technical shortcomings:

[0004] The data sources are heterogeneous and the processing efficiency is low: the indicator data come from a wide range of sources, including structured databases, semi-structured reports, and unstructured text files. Manual collection and sorting is not only time-consuming and labor-intensive, but also prone to problems such as inconsistent data formats, inconsistent definitions, and input errors, resulting in low efficiency and difficulty in ensuring accuracy in the data preprocessing stage.

[0005] Evaluation models are rigid and lack dynamic adaptability: Traditional methods often use fixed weights and evaluation models. When there are significant changes in the macroeconomic environment, policy orientation, or industrial structure, such as the shift from pursuing growth rate to pursuing high-quality development, the original indicator system weights and model structure are difficult to adjust in a timely and dynamic manner. This leads to deviations between the evaluation results and the actual situation, making it impossible to sensitively capture the optimization of economic structure and the improvement of development quality and efficiency.

[0006] The analysis is limited in scope and information mining is insufficient: Existing methods mostly focus on calculating the final composite index, lacking in-depth and automated mining and analysis of the intrinsic relationships between sub-domain indicators, structural differentiation characteristics, and the deep-seated driving factors behind index changes. Evaluation results often remain at the level of "quantities," failing to effectively reveal the "why" and "what" structural problems exist, thus offering limited support for policy making.

[0007] The results are static and lack interactivity: the final evaluation report is usually presented in the form of static tables and fixed charts. Users cannot perform interactive drill-down analysis according to their needs, nor can they flexibly compare the index change trends of different years and different fields, making it difficult to meet diverse decision analysis needs.

[0008] Therefore, we propose a method for evaluating and comparing the comprehensive economic and social development index. Summary of the Invention

[0009] The main objective of this invention is to propose a method for evaluating and comparing the comprehensive economic and social development index, which can effectively solve the problems in the background technology.

[0010] To achieve the above objectives, the technical solution adopted by this invention is: a method for evaluating and comparing the comprehensive economic and social development index, comprising the following steps:

[0011] Step 1: Construct and dynamically configure the evaluation index system.

[0012] In the indicator system configuration module, a multi-level, multi-domain evaluation indicator system is preset. This system includes a first-level comprehensive index (such as the comprehensive economic and social development index), second-level domain indices (such as comprehensive, agriculture, industry, service industry, etc.), and third-level basic indicators (such as GDP, total labor productivity, pork, beef, mutton and poultry meat production, etc.).

[0013] Users can dynamically configure the indicator system through an interactive interface, based on policy guidance or analytical needs. Configuration operations include adding or deleting a third-level basic indicator, adjusting the weight of an indicator, and modifying the upper and lower thresholds of an indicator. After configuration, the system generates an indicator system configuration file containing information such as indicator ID, indicator name, hierarchical relationship, weight, and data source mapping relationship.

[0014] Step 2: Based on the configuration file, realize the automated collection and cleaning of multi-source heterogeneous data.

[0015] Based on the data source mapping relationship in the indicator system configuration file, the data acquisition module automatically retrieves raw data from multiple preset data sources (such as the National Bureau of Statistics database, the General Administration of Customs platform, industry research report database, unstructured documents, etc.).

[0016] For unstructured documents (such as PDF-formatted economic and social development reports), natural language processing technology is used, combined with indicator names in the indicator system, to perform entity recognition and key information extraction, and transform them into structured data.

[0017] The data cleaning and standardization module automates the processing of the collected raw data, including missing value imputation, outlier detection and correction, data format standardization, and unit normalization, ultimately generating a standardized dataset with a unified format and consistent definitions.

[0018] Step 3: Calculate the index based on the configured evaluation model.

[0019] The model calculation engine reads the weights and evaluation models from the indicator system configuration file, such as linear weighting, entropy method, and efficacy coefficient method.

[0020] Using the standard dataset obtained in step 2 as input, the data is aggregated and calculated level by level, from the third-level basic index to the second-level domain index, and then to the first-level comprehensive index.

[0021] For example, firstly, using the standardized three-level basic indicator values ​​and their corresponding weights, the indices for each secondary sector are calculated. Then, using the calculated secondary sector indices as input, combined with their weights in the primary comprehensive index, the final economic and social comprehensive development index is calculated. The calculation results are stored in the results database.

[0022] Step 4: Perform multi-dimensional structural comparison analysis on the index calculation results.

[0023] The analysis engine reads the comprehensive development index and sub-sector indices from the results database. First, it performs a longitudinal comparison, calculating the difference between each index this year and the previous year, i.e., the percentage increase or decrease.

[0024] Next, structural feature identification is performed. The analysis engine identifies all areas with positive growth and areas with negative growth, calculates their average growth or decline rate, and identifies features that highlight strengths and structural differentiation.

[0025] Next, attribution analysis is performed. When a significant change occurs in a secondary sector index, the analysis engine automatically drills down to compare the changes in the various tertiary basic indicators that make up that sector, and identifies the key drivers that caused the change in the sector index. For example, a decline in the fixed asset investment index is due to a sharp drop in investment in the secondary industry.

[0026] Finally, a structured analysis report is generated, including a list of areas of growth or decline, average magnitude of change, key driving factors, etc.

[0027] Step 5: Generate a visual comparison interface and display it interactively.

[0028] The visualization rendering engine reads the structured analysis report and index calculation results, and automatically generates a visual interactive interface. This interface includes at least:

[0029] A comprehensive display area shows the comprehensive development index and its annual changes in the form of dashboards or cards.

[0030] A sector-specific comparison chart area displays the annual comparison results of all secondary sector indices in the form of bar charts or line charts, and highlights sectors that have grown or declined with different colors.

[0031] A correlation analysis chart shows the correlation between indices in various fields or their relationship with changes in key basic indicators.

[0032] Users can trigger a drill-down operation by clicking on a specific area on the interactive interface, such as fixed asset investment. In response to this interaction, the system retrieves the three-level basic indicator data and its changes for that area from the results database and dynamically refreshes and displays it on the current interface, enabling a step-by-step exploration from macro indices to micro indicators.

[0033] Compared with the prior art, the present invention has the following beneficial effects:

[0034] 1. This invention achieves automated collection of multi-source heterogeneous data through data source mapping relationships, combines NLP technology to complete the structured transformation of unstructured data, and then replaces the traditional manual collection, entry, and organization work through fully automated cleaning and standardization processing, shortening the data preprocessing cycle from several days to several hours, and significantly reducing labor costs; at the same time, it completely solves the problems of inconsistent formats, inconsistent standards, and entry errors caused by manual operation, ensuring the consistency and accuracy of data, and supporting the high-frequency and routine conduct of evaluation work.

[0035] 2. This invention designs a three-level dynamic indicator system. Users can complete operations such as adding, deleting, and adjusting the weight of indicators through an interactive interface without modifying the underlying code. It can quickly adapt to scenarios such as changes in macroeconomic policy orientation, changes in regional development stages, and adjustments in evaluation needs. It solves the problem of the rigidity of existing technical evaluation systems, ensures that the evaluation results are consistent with the actual regional development, and improves the universality and timeliness of the evaluation method.

[0036] 3. This invention breaks through the limitations of existing technologies that only focus on the calculation of comprehensive indices. It constructs a three-layer analysis logic of longitudinal time series comparison, structural differentiation identification, and automated attribution analysis. It can not only present the index numerical results, but also automatically identify the structural highlights and shortcomings of regional development, accurately locate the key driving indicators of index changes, and realize in-depth analysis from what to why. It provides systematic decision support for policy formulation and precise regulation, and fully explores the intrinsic value of evaluation data.

[0037] 4. This invention, through a multi-partition, interactive visual interface, enables a full-link exploration of comprehensive index overview, sub-domain comparison, and detailed indicator drill-down. It also supports user-defined cross-period or cross-regional comparisons, replacing traditional static reports, lowering the threshold for data analysis, meeting the diverse and personalized decision-making analysis needs of government departments, research institutions, and other entities, and significantly improving the practical application value of evaluation results. Attached Figure Description

[0038] Figure 1 This is a flowchart of a method for evaluating and comparing the comprehensive economic and social development index according to the present invention. Detailed Implementation

[0039] To make the technical means, creative features, and achieved objectives of this invention easier to understand, the invention will be further described below in conjunction with specific embodiments.

[0040] Please see Figure 1 As shown, this invention provides a technical solution: a method for evaluating and comparing the comprehensive economic and social development index. It designs five core modules: indicator system configuration, multi-source data collection and cleaning, hierarchical index calculation, multi-dimensional structural analysis, and interactive visualization. Through the synergistic linkage between these modules, a closed-loop method for evaluating and comparing the comprehensive economic and social development index is formed, specifically including the following steps:

[0041] Step 1: Construct and dynamically configure a multi-level evaluation index system, and generate a standardized configuration file.

[0042] In the indicator system configuration module, a three-level tree-structured evaluation indicator system for comprehensive economic and social development is built, and users can configure it dynamically and visually. Finally, an indicator system configuration file containing full-dimensional information of the indicators is generated, providing a unified benchmark for subsequent data collection and index calculation.

[0043] The three-tiered indicator system is structured from top to bottom as follows: a first-level comprehensive index, second-level sector indices, and third-level basic indicators. The first-level comprehensive index is the overall economic and social development index, which is a quantitative result of the overall regional development level. The second-level sector indices are sector-specific indices, which can be configured according to evaluation needs to cover core areas such as economic development, social welfare, ecological environment, innovation-driven development, and urban-rural integration. The third-level basic indicators are the smallest data units for index calculation and form the basis for each second-level sector index. Each second-level sector index corresponds to several third-level basic indicators.

[0044] Visualized dynamic configuration: Users can add, delete, and adjust the weights of indicators through the module interaction interface, as well as modify the statistical scope, upper and lower thresholds, and data source mapping relationships of indicators. The system responds to user operations and updates the corresponding information in the indicator system configuration file in real time without modifying the underlying code, enabling flexible iteration of the indicator system.

[0045] Configuration file generation: After configuration, the system automatically generates a standardized indicator system configuration file. The file contains core information such as the unique ID, name, hierarchy, parent indicator relationship, weight value, data source mapping address, and indicator type (positive or negative) for each indicator. The file format uses Extensible Markup Language (XML) to ensure its readability and compatibility.

[0046] Step 2: Automated collection and standardization of multi-source heterogeneous data to generate a standard dataset.

[0047] The data acquisition module automatically captures raw data from preset multi-source heterogeneous data sources based on the data source mapping relationship in the configuration file generated in step 1. Then, the data cleaning and standardization submodule performs fully automated processing on the raw data, and finally generates a standard dataset with uniform format, consistent caliber, no noise, and dimensionless, which serves as the input data for index calculation.

[0048] Automated collection of multi-source heterogeneous data: Preset data sources include structured databases from national or local statistical bureaus, databases from government platforms such as the General Administration of Customs or the Department of Ecology and Environment, industry research report libraries, and unstructured government reports in PDF or text format. For structured data sources, data is automatically captured through API interfaces or database connection protocols. For unstructured data sources, Natural Language Processing (NLP) technology is used to perform named entity recognition and key value extraction by combining indicator names and statistical caliber keywords, transforming unstructured data into structured data and achieving automated collection of multi-source data in a unified manner.

[0049] Raw data standardization processing: The collected raw data undergoes fully automated cleaning and standardization. Specific operations include:

[0050] Missing value imputation: Based on data characteristics, the system automatically selects linear interpolation, adjacent period mean method, or same level region mean method to impute missing data and generates a missing value processing log to ensure data integrity.

[0051] Outlier detection and correction: Outliers are identified using the 3σ principle and box plot method. They are then corrected by combining historical trends of indicators with reasonable ranges within the same level region to eliminate data noise.

[0052] Standardize format and statistical standards: Standardize the units of measurement, statistical time, and statistical standards for data from different sources to eliminate format differences between data sources;

[0053] Dimensional normalization: Based on the indicator type (positive or negative), the extreme value method is used to map all indicator data to the [0,1] interval to eliminate the influence of dimensional differences on index calculation. Positive indicators use the positive normalization formula, such as GDP per capita, where a larger value is better. Negative indicators use the negative normalization formula, such as energy consumption per unit of GDP, where a smaller value is better.

[0054] Step 3: Calculate the index based on hierarchical aggregation logic and store the calculation results.

[0055] The model calculation engine reads the indicator system configuration file (weights, evaluation model) from step 1 and the standard dataset from step 2. Following a bottom-up hierarchical aggregation logic, it calculates the secondary domain index and the primary comprehensive development index level by level, and stores all calculation results in the results database to achieve data traceability and subsequent analysis callability.

[0056] Preset evaluation models: The engine has built-in a variety of mainstream evaluation models, including linear weighted summation model, entropy value method model and efficacy coefficient method model. Users can choose according to their evaluation needs. The default is to use the linear weighted summation model, and the weights meet the normalization requirements, that is, the sum of the weights of all indicators at the same level is 1.

[0057] Hierarchical aggregation calculation:

[0058] First, using the standardized values ​​of the three basic indicators as input, and combining them with their corresponding weights, the indices of each secondary domain are calculated.

[0059] Then, the calculated secondary sector indices are used as new inputs, and combined with their corresponding weights in the primary comprehensive index, the final primary economic and social comprehensive development index is calculated.

[0060] Results storage: Standardized data of the three-level basic indicators, the second-level field indices, and the first-level comprehensive development index, along with information such as the evaluation year, evaluation region, and indicator system version, are stored together in the results database to form a structured index calculation results library, providing data support for subsequent multi-dimensional analysis.

[0061] Step 4: Multi-dimensional structural comparison analysis to generate a structured analysis report.

[0062] The analysis engine retrieves comprehensive development index and sub-domain index data from the results database, and conducts a three-layer multi-dimensional structural comparison analysis, including longitudinal time series comparison, structural differentiation identification, and automated attribution analysis, to identify the core characteristics and driving factors of index changes and generate a standardized structured analysis report.

[0063] Longitudinal time series comparison: Calculate the difference (growth or decline percentage points) between the current year's secondary field index and the primary composite index and the corresponding index in the base period (previous year) to complete the basic inter-period change comparison.

[0064] Structural differentiation feature identification: Based on the longitudinal comparison results, the system automatically identifies areas with positive and negative index growth, calculates the proportion of each type of area, and calculates the average growth rate of positive growth areas and the average decline rate of negative growth areas. This generates the structural differentiation features of regional development in terms of area growth and decline, and clarifies the core highlights and shortcomings of regional development.

[0065] Automated attribution analysis: A preset threshold for index change magnitude is set. When the change magnitude of a certain secondary domain index exceeds the threshold, the engine automatically drills down to compare the changes of all tertiary basic indicators that constitute the secondary domain index, calculates the contribution of each tertiary basic indicator to the change of the domain index, and sorts them from high to low contribution to determine the key driving indicators that cause the change of the domain index, accurately locating the deep-seated reasons for the index change.

[0066] Structured report generation: The engine generates a structured analysis report based on the above three-layer analysis results according to a preset template. The report includes core content such as the cross-period changes of the comprehensive index, a list of changes in sub-domain indices, structural differentiation characteristics analysis, attribution results of key driving indicators, and highlights and shortcomings of development. The report is in structured text format and can be directly connected to subsequent visualization modules.

[0067] Step 5: Generate an interactive visual interface to enable interactive display and exploration of index results.

[0068] The visualization rendering engine reads the structured analysis report from step 4 and the index calculation results from step 3, and automatically generates a multi-regional, interactive, drill-down visualization interface to achieve full-link exploration from macro-comprehensive indices to micro-basic indicators, meeting the diverse analysis needs of users.

[0069] Interface Layout: The visual interface adopts a functional partition design, with three main display areas:

[0070] The first exhibition area (general overview area) uses digital cards, dashboards and line graphs to display the current value of the first-level comprehensive development index, the range of changes over time, and the historical trend of changes over the past 5-10 years, intuitively presenting the overall level and long-term trend of regional development.

[0071] The second display area (sectoral comparison area): The current period and base period values ​​of all secondary sector indices are displayed in the form of grouped bar charts or horizontal comparison charts, and positive growth sectors and negative growth sectors are highlighted with different colors to clearly present the development differences of each sector;

[0072] The third display area (detailed exploration area): is a dynamic display area that responds to user interactions.

[0073] Interactive drill-down exploration: When a user clicks on a secondary domain index in the second display area, the engine automatically retrieves detailed data (including standardized values, weights, inter-period changes, contribution, etc.) of all tertiary basic indicators in that domain from the results database and updates and displays them in real time in the third display area. This enables step-by-step drill-down from macro-level first-level comprehensive index to meso-level second-level domain index to micro-level tertiary basic indicator, meeting the user's detailed exploration needs.

[0074] Customizable comparison function: The interface allows users to customize the evaluation period, base period, and evaluation region, enabling cross-period or cross-regional benchmarking analysis across different years and regions, thereby improving the practicality of the evaluation results.

[0075] The foregoing has shown and described the basic principles, main features, and advantages of the present invention. Those skilled in the art should understand that the present invention is not limited to the above embodiments. The embodiments and descriptions in the specification are merely illustrative of the principles of the invention. Various changes and modifications can be made to the invention without departing from its spirit and scope, and all such changes and modifications fall within the scope of the present invention as claimed. The scope of protection of the present invention is defined by the appended claims and their equivalents.

Claims

1. A method for evaluating and comparing a comprehensive economic and social development index, characterized in that, This includes the following steps: Step 1: In the indicator system configuration module, construct and dynamically configure a multi-level economic and social comprehensive development evaluation indicator system, and generate an indicator system configuration file containing indicator hierarchy relationships, weights, and data source mapping relationships; Step 2: The data acquisition module automatically captures the raw data of each indicator from multiple preset heterogeneous data sources according to the data source mapping relationship in the indicator system configuration file, and cleans and standardizes the raw data to generate a standard dataset. Step 3: The model calculation engine reads the weights and preset evaluation models in the indicator system configuration file, uses the standard dataset as input, calculates the sub-domain index and comprehensive development index step by step, and stores the calculation results in the results database; Step 4: The analysis engine reads the comprehensive development index and the sub-domain index from the results database, performs multi-dimensional structural comparison analysis, identifies the structural characteristics of index changes, and generates a structured analysis report; Step 5: The visualization rendering engine reads the structured analysis report and the index calculation results, and generates and displays an interactive visualization interface.

2. The method for evaluating and comparing the comprehensive economic and social development index according to claim 1, characterized in that, The construction and dynamic configuration of the evaluation index system in step 1 specifically includes: In response to user actions such as adding, deleting, or adjusting the weights of indicators on the interactive interface, the indicator list and corresponding weight values ​​in the indicator system configuration file are updated.

3. The method for evaluating and comparing the comprehensive economic and social development index according to claim 1, characterized in that, Step 2 involves cleaning and standardizing the raw data, specifically including: The original data is processed by filling in missing values, detecting and correcting outliers, and standardizing data formats from different sources.

4. The method for evaluating and comparing the comprehensive economic and social development index according to claim 1, characterized in that, Step 3 involves calculating the sub-domain indices and the comprehensive development index level by level, specifically including: First, based on the three basic indicators and their corresponding weights in the standard dataset, the second-level domain index is calculated; Then, using the calculated secondary domain index as input, and based on its corresponding weight in the primary comprehensive index, the primary comprehensive development index is calculated.

5. The method for evaluating and comparing the comprehensive economic and social development index according to claim 1, characterized in that, Step 4 involves multi-dimensional structural comparison analysis, specifically including: Calculate the difference between the current year's sub-sector index and the corresponding index of the previous year to identify sectors with positive and negative index growth. The average change magnitude of the positive and negative growth areas of the index is calculated separately to generate structural differentiation characteristics of area growth and decline.

6. The method for evaluating and comparing the comprehensive economic and social development index according to claim 5, characterized in that, Step 4 involves multi-dimensional structural comparison analysis, which also includes attribution analysis: When the change in a certain secondary domain index exceeds a preset threshold, drill down and compare the changes in the various tertiary basic indicators that constitute the secondary domain index to determine the key driving indicators that cause the change in the secondary domain index.

7. The method for evaluating and comparing the comprehensive economic and social development index according to claim 1, characterized in that, Step 5 involves generating and displaying an interactive visual interface, specifically including: The comprehensive development index and its annual changes are displayed in the first display area of ​​the visualization interface; The second display area presents the annual comparison results of all the aforementioned sub-field indices in the form of comparative charts; In response to a user's click on a sub-domain index on the visualization interface, detailed data of the three-level basic indicators that constitute the sub-domain index are retrieved from the results database and updated and displayed on the visualization interface.

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