A data-assisted analysis, prediction, and judgment system based on indicator management

Abstract

This invention relates to the field of government data processing and intelligent decision-making technology, specifically disclosing a data-assisted analysis, prediction, and judgment system based on indicator management. The system constructs a multi-level indicator management system, defines the relationships between indicators, and configures dynamically updated thresholds. It collects raw operational data from multiple government data systems, performs quality verification and content integration to generate a standardized data set. Through correlation analysis and anomaly feature identification of the data set, a multi-indicator collaborative monitoring network is established. Based on state transition analysis of historical sequences, multiple development paths for future periods are deduced, generating prediction results including trend forecasts and risk levels. Finally, a decision scheme tree is constructed, and a visual analysis report is generated through multi-dimensional scoring. Based on implementation feedback, parameters are continuously optimized, forming a closed loop of analysis, prediction, and decision support. This invention achieves deep integration and intelligent judgment of cross-domain government data.

Description

Technical Field

[0001] This invention relates to the field of government data processing and intelligent decision-making technology, specifically to a data-assisted analysis, prediction and judgment system based on indicator management. Background Technology

[0002] With the rapid development of big data analytics, the construction of digital government has become an important way to improve government governance capabilities and service levels. In recent years, governments at all levels in my country have actively promoted the sharing and opening of government data and the construction of platforms, and government data has played an important role in economic regulation, government services, and business environment optimization.

[0003] Existing technologies lack solutions for how to systematically construct a unified indicator management system from scattered and heterogeneous government data, how to automatically discover and quantify the complex relationships between indicators, and how to conduct multi-scenario evolution simulations based on historical patterns and current states, thereby providing government decision-making with full-chain, closed-loop technical support from data integration and intelligent analysis to predictive judgment. Summary of the Invention

[0004] The purpose of this invention is to provide a data-assisted analysis, prediction, and judgment system based on indicator management to solve the problems mentioned above.

[0005] The objective of this invention can be achieved through the following technical solutions:

[0006] A data-assisted analysis, prediction, and judgment system based on indicator management includes:

[0007] The indicator management module is used to build a multi-level indicator management system, define multiple key indicators and their relationships, and configure threshold ranges and dynamic update rules for each indicator. The key indicators cover the fields of economic regulation, market supervision, social management, public services and ecological civilization construction.

[0008] The data processing module, based on the indicator management system, collects raw operational data from multiple independent government data systems. The raw operational data includes economic statistics, market entity registration data, social event data, public service processing data, and environmental monitoring data. The module performs quality verification, format unification, and content integration on the raw operational data to generate a standardized set of indicator data.

[0009] The analysis and prediction module is used to perform data correlation discovery and anomaly feature identification analysis on standardized indicator datasets, including historical trend tracking, horizontal comparison evaluation, and anomaly state identification, and outputs analysis results containing the indicator change patterns and internal correlations;

[0010] The decision application module, based on the analysis results, predicts the evolution of key indicators in the future through time-series evolution and risk transmission analysis, generating prediction results including development trend prediction and risk level classification.

[0011] The decision optimization module integrates analysis results and prediction results, performs data-driven comprehensive judgment through intelligent decision analysis process, generates decision solutions, supports multi-party collaborative discussion based on decision solutions, and optimizes indicator thresholds and analysis parameters to achieve a continuous improvement prediction and judgment cycle.

[0012] As a further aspect of the present invention: the construction of a multi-level indicator management system specifically includes:

[0013] Based on the division of business areas, a tree structure for classifying indicators is constructed, and key indicators are divided into multiple levels according to the fields of economic regulation, market supervision, social management, public services and ecological civilization construction, forming a hierarchical indicator catalog.

[0014] For each indicator in the hierarchical indicator catalog, the dependencies and influence weights between indicators are defined through a directed graph structure to generate an indicator association network.

[0015] An initial threshold range is configured for each indicator, and the threshold is dynamically adjusted through a rule matching process in conjunction with real-time data streams. The dynamic adjustment includes automatically triggering threshold updates based on changes in the indicator association network and applying priority rules to handle conflicting thresholds.

[0016] As a further aspect of the present invention: the data association discovery and anomaly feature identification and analysis specifically include:

[0017] Based on a standardized indicator dataset, a sliding window is used to extract the historical data sequence of each indicator, calculate the morphological similarity between different indicator sequences, and construct an indicator correlation matrix.

[0018] Based on the indicator correlation matrix, a multi-indicator collaborative monitoring network is established. By analyzing the propagation path between network nodes, the transmission process of local anomalies among related indicators is detected, and cross-domain anomaly propagation patterns are identified.

[0019] For the identified abnormal propagation patterns, an abnormal impact assessment system is established by combining the business attributes of the indicators. By tracking the spread path and impact range of the abnormal in the related network, an analysis report containing the root cause location and impact assessment of the abnormal is generated.

[0020] As a further aspect of the present invention: the construction of the index correlation matrix specifically includes:

[0021] The time series of each indicator in the standardized indicator dataset are segmented. An overlapping sliding window is used to divide each time series into multiple subsequence segments. Each subsequence segment contains a fixed number of data points and an overlapping area is set.

[0022] Based on multiple sub-sequence segments, the morphological features of each sub-sequence segment are extracted, including the distribution of local extreme points, trend direction and fluctuation amplitude. A morphological feature vector is constructed by combining multi-dimensional features.

[0023] Similarity matching is performed on the morphological feature vectors of different indicators, and the minimum cumulative distance is calculated by finding the optimal curved path to generate an indicator correlation matrix.

[0024] As a further aspect of the present invention: the identification of cross-domain abnormal propagation patterns specifically includes:

[0025] A directed network graph is constructed based on the correlation matrix of indicators, where nodes represent each indicator, edges represent the influence relationship between indicators, and the weight of the edges is set according to the correlation degree, forming a multi-indicator collaborative monitoring network.

[0026] In a multi-indicator collaborative monitoring network, by monitoring the data fluctuation status of each node in real time, when an abnormal state is detected in a node, the propagation path of the corresponding abnormal state in the network is tracked, and the affected related nodes are marked.

[0027] Based on the abnormal propagation path and combined with the business domain attributes of each node, cross-domain abnormal propagation patterns are identified.

[0028] As a further aspect of the present invention: the prediction of the evolution of key indicators in future periods through time-series evolution deduction and risk transmission analysis specifically includes:

[0029] Based on the indicator change patterns in the analysis results, the historical sequences of each key indicator are periodically decomposed and trend separated to identify the fluctuation characteristics at different time scales.

[0030] Based on the extracted fluctuation characteristics, an indicator state transition network is constructed. By analyzing the change path of indicator state in adjacent time periods, a state transition probability matrix is ​​established, forming a set of state transition paths.

[0031] Based on the obtained set of state transition paths, combined with the current indicator state, path matching and similarity calculation methods are used to deduce the possible evolution paths of indicators in future periods and generate multi-scenario development trend predictions.

[0032] Based on the predicted development trends of multiple scenarios, a risk heat map is constructed by analyzing the spread and impact of abnormal states in each evolution path, and risk levels are divided according to the heat distribution to form a risk level classification result.

[0033] As a further aspect of the present invention: the construction of the index state transition network specifically includes:

[0034] Based on the extracted fluctuation characteristics, the state discretization process is performed on each key indicator, dividing the continuously changing indicator values ​​into several state intervals. Each state interval corresponds to a specific numerical range and business meaning, forming a discrete state space.

[0035] Based on the obtained state space, the frequency of state transitions of each index between adjacent time segments is counted. By calculating the transition probability between states, a state transition probability matrix is ​​constructed, where each element represents the probability of transitioning from one state to another.

[0036] Based on the obtained state transition probability matrix, a directed weighted network structure is constructed, where nodes represent index states, directed edges represent state transition relationships, and the weights of the edges are determined by the transition probability values, thus forming a complete index state transition network.

[0037] In state transition networks, high-frequency transition paths are identified. By analyzing the importance of nodes and the distribution of edge weights in the paths, key state evolution patterns are extracted to form a set of state transition paths.

[0038] As a further aspect of the present invention: the method of using path matching and similarity calculation to deduce the possible evolution path of indicators in future time periods specifically includes:

[0039] Based on the set of state transition paths, multi-dimensional features of each historical path are extracted, including path length, state transition frequency, path stability index, and key node distribution characteristics, and a path feature vector is constructed.

[0040] The matching degree between the current indicator state and the path feature vector is calculated. By comparing the similarity between the current state and the historical path starting state, as well as the degree of conformity with the path evolution trend, several candidate evolution paths are selected.

[0041] A deep analysis of the candidate evolution paths is conducted, and the confidence index of each path is calculated, including the frequency of path occurrence, the time of most recent use, and the degree of matching with the current state. The optimal evolution paths are then selected.

[0042] Based on the selected optimal evolution path, and combined with the business meaning and impact of key nodes in the path, multi-scenario development trend predictions are generated, and each prediction path is labeled with a confidence level and key turning point.

[0043] As a further aspect of the present invention: the intelligent decision analysis process is as follows:

[0044] Based on the analysis and prediction results, a tree structure of decision-making schemes is constructed, and various analysis elements are hierarchically organized according to business logic relationships, establishing the dependencies and influence paths between decision-making elements;

[0045] The decision-making options are comprehensively evaluated, and a scoring standard is established from three dimensions: timeliness, feasibility, and scope of impact. The priority ranking of each option is obtained through weighted calculation.

[0046] Based on the priority ranking results, a visual decision analysis report is generated. The report includes a comparative analysis of the solutions, implementation path suggestions, and expected effect evaluation, and supports online collaborative review and solution optimization by multiple parties.

[0047] Based on feedback on the implementation effects of the decision-making plan, a parameter optimization knowledge base is established. By analyzing historical decision-making cases, indicator thresholds and analytical parameter configurations are adjusted.

[0048] The beneficial effects of this invention are:

[0049] (1) Traditional government data analysis often relies on isolated, static indicators, making it difficult to reveal complex relationships and future trends. This invention, by constructing a multi-level indicator association network and a state transition model, can automatically discover the intrinsic connections and dynamic evolution patterns among indicators from massive, multi-source government data. It uses a dynamic time warping algorithm to calculate morphological similarity and infers multi-scenario development paths based on state transition probabilities, enabling the analysis to move beyond historical summaries and achieve quantitative predictions and risk level classifications of future trends. This changes the previous experience-based decision-making model and provides data-driven, forward-looking scientific evidence for policy formulation.

[0050] (2) This invention achieves correlation analysis and comprehensive judgment of problems in different fields such as economy, society, and environment by constructing a unified and standardized set of indicator data and identifying cross-domain anomaly propagation patterns. The constructed decision-making scheme tree and multi-party online collaborative review mechanism support quantitative evaluation and optimization of schemes from multiple dimensions, ensuring the comprehensiveness and feasibility of decisions. More importantly, the scheme establishes a parameter optimization knowledge base based on effect feedback, which can automatically adjust indicator thresholds and analysis parameters using historical decision-making cases and algorithms such as gradient descent, forming a continuous improvement closed loop of "analysis-prediction-decision-feedback-optimization", thereby enabling the entire judgment system to have the ability to learn and evolve, and maintain its accuracy and adaptability in the long term. Attached Figure Description

[0051] The invention will now be further described with reference to the accompanying drawings.

[0052] Figure 1 This is a system block diagram of the present invention. Detailed Implementation

[0053] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0054] Please see Figure 1 As shown, this invention is a data-assisted analysis, prediction, and judgment system based on indicator management, comprising:

[0055] The indicator management module is used to build a multi-level indicator management system, define multiple key indicators and their relationships, and configure threshold ranges and dynamic update rules for each indicator. The key indicators cover the fields of economic regulation, market supervision, social management, public services and ecological civilization construction.

[0056] The data processing module, based on the indicator management system, collects raw operational data from multiple independent government data systems. The raw operational data includes economic statistics, market entity registration data, social event data, public service processing data, and environmental monitoring data. The module performs quality verification, format unification, and content integration on the raw operational data to generate a standardized set of indicator data.

[0057] The analysis and prediction module is used to perform data correlation discovery and anomaly feature identification analysis on standardized indicator datasets, including historical trend tracking, horizontal comparison evaluation, and anomaly state identification, and outputs analysis results containing the indicator change patterns and internal correlations;

[0058] The decision application module, based on the analysis results, predicts the evolution of key indicators in the future through time-series evolution and risk transmission analysis, generating prediction results including development trend prediction and risk level classification.

[0059] The decision optimization module integrates analysis results and prediction results, performs data-driven comprehensive judgment through intelligent decision analysis process, generates decision solutions, supports multi-party collaborative discussion based on decision solutions, and optimizes indicator thresholds and analysis parameters to achieve a continuous improvement prediction and judgment cycle.

[0060] In the indicator management module, the process of constructing a tree-like structure for indicator classification based on business domains is as follows: Key indicators are stratified into multiple levels according to the domains of economic regulation, market supervision, social management, public services, and ecological civilization construction. The economic regulation domain includes sub-domains such as economic trends, monitoring and analysis, industrial operation, industry prosperity, and business environment. Each sub-domain defines specific indicators; for example, the economic trends sub-domain includes indicators for overall trends, performance display, and thematic analysis. The market supervision domain includes indicators for business entities, regulatory trends, regulatory innovation, key areas, evaluation-driven management, and market effectiveness. The social management domain includes indicators for "one standard, three realities," grid governance, event analysis, and effectiveness analysis. The public services domain includes indicators for the overall trends of government services, service items, service processing, service recipients, service evaluation, service optimization, service support, data sharing, platform effectiveness, and service efficiency, as well as indicators for the overall trends of livelihood services, including early childhood education, schooling, employment, healthcare, housing, elderly care, and support for the vulnerable. The ecological civilization construction domain includes indicators for environmental monitoring, environmental governance, and ecological protection. The data for these indicators are collected from multiple data sources: economic regulation data is collected from the database servers of the Jiangxi Provincial Development and Reform Commission and the Jiangxi Provincial Bureau of Statistics, including economic statistics and industrial operation data; market supervision data is collected from the business system of the Jiangxi Provincial Market Supervision Administration, including market entity registration data and regulatory records; social management data is collected from the information platforms of the Jiangxi Provincial Political and Legal Affairs Commission and the General Office of the Jiangxi Provincial People's Government, including grid event data and population management data; public service data is collected from the government service platform of the General Office of the Jiangxi Provincial People's Government, including service processing records and public feedback data; and ecological civilization data is collected from environmental monitoring sensors of the Jiangxi Provincial Department of Ecology and Environment, including environmental parameters output by air quality sensors, water quality monitoring sensors, and ecological remote sensing equipment. The hierarchical indicator catalog is stored in database tables, with each indicator corresponding to a unique identifier, name, domain, and hierarchical position.

[0061] For each indicator in the hierarchical indicator catalog, a directed graph structure is used to define dependencies and influence weights between indicators, generating an indicator association network. Nodes in the directed graph represent individual indicators, edges represent dependencies between indicators, and the direction of the edges indicates the direction of influence. The influence weight is calculated using the Pearson correlation coefficient method: for historical data sequences of two indicators, their covariance is calculated and divided by the product of their respective standard deviations to obtain the correlation coefficient as the influence weight. Specifically, the time series data for each indicator is first extracted from the historical database and standardized to eliminate the influence of dimensions. Then, the Pearson correlation coefficient for each pair of indicator sequences is calculated using the formula: the correlation coefficient equals the covariance of the two indicator sequences divided by the product of their standard deviations. If the absolute value of the correlation coefficient is greater than 0.5, a dependency is considered to exist, and an edge is added to the directed graph with a weight equal to the correlation coefficient value. The indicator association network is stored in the database using an adjacency list structure, with each node recording its neighboring nodes and their weight values. Dependencies also include logical dependencies; for example, service processing indicators in the public service sector depend on service item indicators. This dependency is manually defined through business rules and added to the directed graph.

[0062] An initial threshold range is configured for each indicator, and the threshold is dynamically adjusted through a rule-based matching process in conjunction with real-time data streams. The initial threshold is set based on the statistical quantiles of historical data: for example, for indicators in the economic regulation field, the initial threshold is set to the upper and lower quartiles of historical data. Real-time data streams are continuously input from various data sources, including economic indicators obtained from the real-time data interface of the Jiangxi Provincial Bureau of Statistics, market entity data obtained from the real-time monitoring system of the Jiangxi Provincial Market Supervision Administration, social event data obtained from the real-time event stream of the Jiangxi Provincial Political and Legal Affairs Commission, public service processing data obtained from the online service platform of the Jiangxi Provincial People's Government Office, and environmental monitoring data obtained from the sensor network of the Jiangxi Provincial Department of Ecology and Environment. The dynamic threshold adjustment process uses rule-based matching: the rule is defined as follows: if the value of an indicator exceeds the current threshold range, the status of its related indicators in the indicator association network is checked; if the change of a related indicator exceeds a specific proportion of its own threshold, a threshold update is triggered. The update formula is: the new threshold equals the current threshold plus an adjustment amount, which is calculated based on the influence weight and change of the related indicators, specifically, the adjustment amount equals the influence weight multiplied by the ratio of the change of the related indicator to the current threshold. Priority rules are used to handle conflicts when multiple rules trigger simultaneously: each rule is assigned a priority score, and rules with higher priority scores are applied first, based on their importance and timeliness; if multiple rules suggest different thresholds, the threshold suggested by the rule with the highest priority score is selected. After the threshold is updated, it is stored in the threshold configuration database for subsequent analysis.

[0063] In the data processing module, the data processing part, based on the established indicator management system, collects raw operational data from multiple independent government data sources. Economic statistics are collected through the business server deployed by the Jiangxi Provincial Bureau of Statistics, obtaining values ​​such as GDP and industrial added value. Market entity registration data is collected from the enterprise registration database of the Jiangxi Provincial Market Supervision Administration, extracting enterprise establishment, modification, and cancellation records through data interfaces. Social event data is collected through the social governance platform, which connects community grid workers' handheld smart terminals and the urban public safety monitoring network, recording event type, location, and handling status. Public service processing data is collected from the government service platform, obtaining the number of applications, completions, and processing times through service window terminals and the online government service platform database. Environmental monitoring data is collected through professional equipment deployed at monitoring stations, including particulate matter concentration measured by air quality monitors, chemical oxygen demand measured by water quality analyzers, and sound level data recorded by noise monitors.

[0064] The collected raw operational data undergoes quality verification. Quality verification includes integrity checks, numerical range verification, and logical consistency checks. Integrity checks confirm that all required data fields are complete; numerical range verification ensures that the data is within a reasonable range; and logical consistency checks verify that related data fields comply with business rules. Data that fails verification is recorded as an error and marked as invalid data, and is not included in subsequent processing.

[0065] Standardize the format of data that passes quality verification. Convert heterogeneous data from different data sources into a unified format, standardize date and time, retain two decimal places for numerical data, and standardize text data. Remap data fields according to a pre-defined standardized naming convention and establish a correspondence with the indicator directory.

[0066] The data, after being standardized in format, undergoes content integration. Content integration includes data association, deduplication, and aggregation. Data association connects data of the same entity from different sources by matching key fields; data deduplication identifies and removes duplicate records based on business primary keys; data aggregation summarizes and statistically analyzes fine-grained data according to indicator calculation requirements, generating a standardized data set that conforms to the indicator definition for subsequent analysis.

[0067] The analysis and prediction module involves specific implementation methods for data correlation discovery and anomaly identification analysis of standardized indicator datasets. This process includes historical trend tracking, horizontal comparative evaluation, and anomaly identification. By calculating the morphological similarity between indicator sequences and constructing a monitoring network, it outputs analytical results containing the patterns and inherent relationships of indicator changes. All processing is based on standardized indicator datasets collected from government data sources, covering indicators in the fields of economic regulation, market supervision, social management, public services, and ecological civilization construction.

[0068] Based on a standardized indicator dataset, a sliding window is used to extract historical data sequences for each indicator, calculate the morphological similarity between different indicator sequences, and construct an indicator correlation matrix. First, the time series of each indicator is segmented: an overlapping sliding window is used to divide the sequence into multiple sub-sequence segments, each containing a fixed number of data points, e.g., 30 data points, with an overlap of 50%, meaning adjacent sub-sequence segments overlap by 15 data points. Next, the morphological features of each sub-sequence segment are extracted: the distribution of local extreme points is identified by calculating the change in the first derivative, i.e., points in the sequence that satisfy the change in the sign of the derivative; the trend is represented by the slope of a linear regression, calculated using the slope formula. ;in This represents the number of data points in the subsequence segment. For time indexing, For indicator values, Indicates the first Each subsequence segment contains data points; the fluctuation amplitude is calculated using the standard deviation. Based on these features, a multi-dimensional morphological feature vector is constructed, for example, a vector in the form of [number of extreme points, slope, standard deviation] or [number of extreme points, slope, standard deviation]. Then, similarity matching is performed on the morphological feature vectors of different indicators: the minimum cumulative distance is calculated using the dynamic time warping algorithm, given two sequences X and Y with lengths of respectively... and Cumulative distance matrix The calculation formula is:

[0069] ;in It is a point and The Euclidean distance between them, and the final dynamic time-warped distance is Based on the dynamic time-warped distance, it is converted into a correlation value, and the formula is: Correlation Degree. Generate an index correlation matrix, where the matrix elements... Indicates the degree of correlation.

[0070] Based on the indicator correlation matrix, a multi-indicator collaborative monitoring network is established. By analyzing the propagation path between network nodes, the transmission process of local anomalies among related indicators is detected, and cross-domain anomaly propagation patterns are identified.

[0071] First, a directed network graph is constructed: nodes represent various indicators, edges represent the influence relationships between indicators, and the weights of the edges are set according to the correlation values ​​in the correlation matrix, forming a multi-indicator collaborative monitoring network. In this network, the data fluctuation status of each node is monitored in real time: abnormal states are detected using the Z-score method, the Z-score calculation formula is: ,in This is the current indicator value. It is the arithmetic mean of historical data. The standard deviation of historical data; if | If |>2, then the node is determined to be in an abnormal state. When an abnormal state is detected in a node, the propagation path of the corresponding abnormal state in the network is traced: a breadth-first search algorithm is used, starting from the abnormal node, traversing the nodes connected by its outgoing edges, and calculating the propagation probability based on the edge weights, for example, the propagation probability. ,in Indicates edge weight, if If the value is greater than 0.5, the associated node is marked as an affected node. Based on the abnormal propagation path and combined with the business domain attributes of each node (such as economic regulation, market supervision, etc.), cross-domain abnormal propagation patterns are identified: for example, if the propagation path involves nodes in different business domains, it is recorded as a cross-domain pattern, and the cross-domain impact coefficient is calculated using the formula: ,in This represents the number of nodes on the cross-domain path. For edge weights, The node anomaly strength (i.e., the absolute value of the Z-score).

[0072] For the identified anomaly propagation patterns, an anomaly impact assessment system is established by combining indicator business attributes. This system tracks the spread and impact range of the anomaly within the related network, generating an analysis report that includes root cause localization and impact assessment. First, the anomaly impact assessment system is established: the degree of impact is determined by calculating the number and severity of affected nodes; the severity formula is as follows: ,in The number of affected nodes. For nodes Z-score For nodes The network distance to the root cause of the anomaly (i.e., the number of edges on the shortest path) is calculated. Then, the propagation path of the anomaly in the associated network is traced: a depth-first search algorithm is used to traverse backwards from the affected nodes to find the root cause node of the anomaly, which is the node with the largest Z-score.

[0073] Finally, an analysis report is generated, including the identification of the root cause of the anomaly (listing the names of the root cause node indicators and business areas), the impact assessment (outputting the impact degree value s and the cross-domain impact coefficient c), and recommended measures based on business attributes. For example, for anomalies in economic indicators, it is recommended to adjust policy parameters.

[0074] In the decision-making application module, based on the indicator change patterns in the analysis results, the historical sequences of each key indicator are decomposed periodically and separated for trends to identify fluctuation characteristics at different time scales. A seasonal decomposition method is used to split the historical sequence into trend components, seasonal components, and residual components. The trend component is extracted using the moving average method, calculated as follows: the trend value at each time point equals the arithmetic mean of the values ​​at the k time points before and after that point, where k is half the period length. The seasonal component is obtained by calculating the average of each seasonal cycle, and the residual component is obtained by subtracting the trend and seasonal components from the original sequence. This process identifies fluctuation characteristics at different time scales, such as annual, quarterly, and monthly.

[0075] Based on the extracted fluctuation characteristics, the key indicators are discretized, dividing the continuously changing indicator values ​​into several state intervals. The division of state intervals adopts a segmentation method based on business rules; for example, the regional GDP growth rate is divided into negative growth (less than 0%), low-speed growth (0% to 5%), medium-speed growth (5% to 8%), and high-speed growth (greater than 8%). Each state interval corresponds to a specific numerical range and business meaning, forming a discrete state space encompassing all possible states.

[0076] Based on the obtained state space, the frequency of state transitions for each indicator between adjacent time segments is statistically analyzed. The number of times each state transitions to other states is recorded. A state transition probability matrix is ​​constructed by calculating the transition probabilities between states, where each element P_hz represents the probability of transitioning from state h to state z. The formula is: P_hz equals the number of times transitioning from state h to state z divided by the total number of times transitioning from state h. Each element of this matrix takes a value between 0 and 1, and the sum of the elements in each row equals 1.

[0077] Based on the obtained state transition probability matrix, a directed weighted network structure is constructed. Nodes in the network represent various index states, directed edges represent state transition relationships, and the weight of each edge is determined by its corresponding transition probability value. When the transition probability is greater than a preset threshold of 0.1, directed edges are established between the corresponding nodes, forming a complete index state transition network. In this network, the size of the node reflects the stability of the state, and the thickness of the edge represents the magnitude of the transition probability.

[0078] Identify high-frequency transition paths in state transition networks. A depth-first search algorithm is used to traverse the network, identifying all transition paths with a length not exceeding 5. The weighted probability of each path is calculated as the product of the weights of each edge along the path. Paths with a weighted probability greater than 0.01 are selected as high-frequency transition paths, and key state evolution patterns are extracted from them to form a set of state transition paths.

[0079] Based on the set of state transition paths, multi-dimensional features are extracted for each historical path. Path length refers to the number of nodes contained in the path; state transition frequency refers to the number of times the path appears in historical data; path stability index is obtained by calculating the geometric mean of the transition probabilities in the path; key node distribution features are determined by identifying the locations of nodes in the path with a transition probability less than 0.3. Based on these features, a path feature vector is constructed, with the vector form being [length, frequency, stability, key node location].

[0080] The matching degree between the current indicator state and the path feature vector is calculated. First, the similarity between the current state and the historical path starting state is compared using the state distance metric, calculated as the absolute value of the ordinal difference between the two states in the state space. Then, the degree of consistency between the path evolution trend and the current data changes is evaluated, and the trend correlation coefficient is calculated. Based on the conditions that the state distance is less than 2 and the trend correlation coefficient is greater than 0.6, several candidate evolution paths are selected.

[0081] A deep analysis was conducted on the candidate evolution paths, and a confidence index was calculated for each path. The confidence index consists of three dimensions: path occurrence frequency (weighted at 0.4), most recent usage time (weighted at 0.3), and matching degree with the current state (weighted at 0.3). Path occurrence frequency was processed using max-min normalization; most recent usage time was calculated by subtracting the time difference between the last occurrence time of the path and the current time; and matching degree with the current state was a weighted combination of state distance and trend correlation coefficient. The overall confidence score was calculated as: 0.4 × frequency score + 0.3 × time score + 0.3 × matching degree score. The five paths with the highest confidence scores were selected as the optimal evolution paths.

[0082] Based on the selected optimal evolution paths, multi-scenario development trend predictions are generated. Each predicted path contains a state transition sequence for the next three months, labeled with a confidence level: greater than 0.8 is high-level, 0.6 to 0.8 is medium-level, and less than 0.6 is low-level. States with a transition probability less than 0.3 in the path are identified as key turning points, representing periods where the development trend may change significantly.

[0083] Based on the predicted development trends across multiple scenarios, the propagation range and impact of anomalous states in each evolution path are analyzed. An anomalous state is defined as a state with an absolute Z-score greater than 2. The propagation range is determined by calculating the number of affected related indicators, and the impact is measured by the weighted average of the Z-scores of the affected indicators, with the weights derived from the edge weights in the indicator association network. A risk heatmap is constructed based on these parameters, dividing the indicator space into grids. The heat value of each grid is the arithmetic mean of the impact of all anomalous states within that region.

[0084] Risk levels are determined based on heat intensity distribution. Areas with a heat intensity value greater than 0.8 are classified as high-risk, 0.5 to 0.8 as medium-risk, 0.2 to 0.5 as low-risk, and less than 0.2 as no-risk. The risk level classification results are presented visually, annotating the spatial distribution and risk intensity of each level, forming a complete predictive analysis report.

[0085] Based on the comprehensive analysis and forecast results, a tree-like structure for decision-making solutions is constructed. Various analytical elements are organized hierarchically according to business logic relationships, with the top layer representing the decision objective, the middle layer representing the decision direction, and the bottom layer representing specific measures. For example, in response to abnormal economic indicators, the decision objective is "stable economic growth," the decision direction includes "adjusting fiscal policy" and "adjusting monetary policy," and the specific measures include "increasing infrastructure investment" and "reducing the reserve requirement ratio." The dependencies and influence paths between decision elements are established, and directed edges are used to connect related elements, forming a complete tree-like decision graph.

[0086] The decision-making options are comprehensively evaluated, with scoring criteria established from three dimensions: timeliness, feasibility, and scope of impact. Timeliness is scored based on the expected time to effect: 3 points for effect within 30 days, 2 points for effect within 30-90 days, and 1 point for effect within 90 days. Feasibility is based on the adequacy of implementation resources: 3 points for sufficient resources, 2 points for average resources, and 1 point for insufficient resources. Scope of impact is calculated based on the proportion of the affected population: 3 points for a proportion greater than 30%, 2 points for 10%-30%, and 1 point for less than 10%. The priority ranking of each option is determined through a weighted calculation: the comprehensive score equals the timeliness score multiplied by a weight of 0.4, plus the feasibility score multiplied by a weight of 0.3, plus the scope of impact score multiplied by a weight of 0.3. Options are then ranked from highest to lowest based on their comprehensive scores.

[0087] Based on the priority ranking results, a visual decision analysis report is generated. The report includes a comparative analysis of the proposed solutions, displaying detailed scores and ranking results for each solution in tabular form; implementation path suggestions detailing the specific execution steps and timelines for each solution; and an expected effect assessment using quantitative indicators to predict the changes in key performance indicators after implementation. The report supports collaborative online review by multiple parties via a web interface. Participants can provide feedback using annotation tools and engage in discussions through real-time chat tools to optimize the solutions.

[0088] Based on feedback from the implementation effects of decision-making schemes, a parameter optimization knowledge base is established. Data before and after the implementation of historical decision-making cases is collected, recording elements such as scheme type, implementation duration, and actual effects. By analyzing historical cases, the adjustment amount for indicator thresholds is calculated. The adjustment amount equals the difference between the arithmetic mean of the historical best values ​​and the current threshold multiplied by the learning rate of 0.1. The optimization of parameter configuration is performed using the gradient descent method, adjusting parameter values ​​according to the direction of the partial derivative of the prediction error, with each adjustment not exceeding 5% of the original value. A parameter update rule is established: when the prediction accuracy is below 85% for three consecutive periods, the parameter optimization process is automatically triggered to ensure continuous improvement in the prediction and judgment cycle.

[0089] The working principle of this invention is as follows: First, a multi-level indicator management system covering economic regulation, market supervision, social management, public services, and ecological civilization construction is constructed by defining the correlation between indicators and configuring dynamically updatable thresholds. Then, raw operational data, including economic statistics, market entity registration, social events, public service processing, and environmental monitoring, is collected from multiple independent government data systems. After quality verification, format standardization, and content integration, a standardized indicator data set is generated. Next, data correlation discovery and anomaly identification analysis are performed on this data set. By calculating the morphological similarity of indicator sequences, a correlation matrix is ​​constructed, establishing a multi-indicator collaborative monitoring system. The system uses a network to identify cross-domain abnormal propagation patterns and outputs analytical results containing the patterns of indicator changes. Based on the analytical results, the historical sequences of each key indicator are periodically decomposed and their states discretized. A state transition network is constructed, and a path matching method is used to deduce multiple possible evolution paths of the indicators in future periods, generating prediction results that include development trend forecasts and risk level classifications. Finally, the system integrates the analysis and prediction results to construct a tree structure of decision-making schemes. The schemes are prioritized through multi-dimensional comprehensive scoring, and a visual decision analysis report is generated to support multi-party collaborative review. Based on the feedback of implementation effects, the indicator thresholds and analysis parameters are optimized to form a continuously improving prediction and judgment closed loop.

[0090] The foregoing has provided a detailed description of one embodiment of the present invention, but this description is merely a preferred embodiment and should not be construed as limiting the scope of the invention. All equivalent variations and modifications made within the scope of the claims of this invention should still fall within the patent coverage of this invention.

Claims

1. A data-assisted analysis, prediction, and judgment system based on indicator management, characterized in that, include: The indicator management module is used to build a multi-level indicator management system, define multiple key indicators and their relationships, and configure threshold ranges and dynamic update rules for each indicator. The key indicators cover the fields of economic regulation, market supervision, social management, public services and ecological civilization construction. The data processing module, based on the indicator management system, collects raw operational data from multiple independent government data systems. The raw operational data includes economic statistics, market entity registration data, social event data, public service processing data, and environmental monitoring data. The module performs quality verification, format unification, and content integration on the raw operational data to generate a standardized set of indicator data. The analysis and prediction module is used to perform data correlation discovery and anomaly identification analysis on a standardized indicator dataset, including historical trend tracking, horizontal comparative evaluation, and anomaly state identification. It outputs analysis results containing the patterns and inherent relationships of indicator changes. Specifically, the data correlation discovery and anomaly identification analysis include: Based on a standardized indicator dataset, a sliding window is used to extract the historical data sequence of each indicator, calculate the morphological similarity between different indicator sequences, and construct an indicator correlation matrix. Based on the indicator correlation matrix, a multi-indicator collaborative monitoring network is established. By analyzing the propagation path between network nodes, the transmission process of local anomalies among related indicators is detected, and cross-domain anomaly propagation patterns are identified. For the identified abnormal propagation patterns, an abnormal impact assessment system is established by combining the business attributes of the indicators. By tracking the spread path and impact range of the abnormal in the related network, an analysis report containing the root cause location and impact assessment of the abnormal is generated. The decision application module, based on the analysis results, predicts the future evolution of key indicators through time-series evolution deduction and risk transmission analysis, generating prediction results including development trend forecasts and risk level classifications. Specifically, the time-series evolution deduction and risk transmission analysis process for predicting the future evolution of key indicators includes: Based on the indicator change patterns in the analysis results, the historical sequences of each key indicator are periodically decomposed and trend separated to identify the fluctuation characteristics at different time scales. Based on the extracted fluctuation characteristics, an indicator state transition network is constructed. By analyzing the change path of indicator state in adjacent time periods, a state transition probability matrix is ​​established, forming a set of state transition paths. Based on the obtained set of state transition paths, combined with the current indicator state, path matching and similarity calculation methods are used to deduce the possible evolution paths of indicators in future periods and generate multi-scenario development trend predictions. Based on the obtained multi-scenario development trend predictions, a risk heat map is constructed by analyzing the propagation range and impact of abnormal states in each evolution path, and risk levels are divided according to the heat distribution to form a risk level classification result. The decision optimization module integrates analysis results and prediction results, performs data-driven comprehensive judgment through intelligent decision analysis process, generates decision solutions, supports multi-party collaborative discussion based on decision solutions, and optimizes indicator thresholds and analysis parameters to achieve a continuous improvement prediction and judgment cycle.

2. The data-assisted analysis, prediction, and judgment system based on indicator management according to claim 1, characterized in that, The construction of a multi-level indicator management system specifically includes: Based on the division of business areas, a tree structure for classifying indicators is constructed, and key indicators are divided into multiple levels according to the fields of economic regulation, market supervision, social management, public services and ecological civilization construction, forming a hierarchical indicator catalog. For each indicator in the hierarchical indicator catalog, the dependencies and influence weights between indicators are defined through a directed graph structure to generate an indicator association network. An initial threshold range is configured for each indicator, and the threshold is dynamically adjusted through a rule matching process in conjunction with real-time data streams. The dynamic adjustment includes automatically triggering threshold updates based on changes in the indicator association network and applying priority rules to handle conflicting thresholds.

3. The data-assisted analysis, prediction, and judgment system based on indicator management according to claim 1, characterized in that, The construction of the indicator correlation matrix specifically includes: The time series of each indicator in the standardized indicator dataset are segmented. An overlapping sliding window is used to divide each time series into multiple subsequence segments. Each subsequence segment contains a fixed number of data points and an overlapping area is set. Based on multiple sub-sequence segments, the morphological features of each sub-sequence segment are extracted, including the distribution of local extreme points, trend direction and fluctuation amplitude. A morphological feature vector is constructed by combining multi-dimensional features. Similarity matching is performed on the morphological feature vectors of different indicators, and the minimum cumulative distance is calculated by finding the optimal curved path to generate an indicator correlation matrix.

4. The data-assisted analysis, prediction, and judgment system based on indicator management according to claim 1, characterized in that, The identification of cross-domain abnormal propagation patterns specifically includes: A directed network graph is constructed based on the correlation matrix of indicators, where nodes represent each indicator, edges represent the influence relationship between indicators, and the weight of the edges is set according to the correlation degree, forming a multi-indicator collaborative monitoring network. In a multi-indicator collaborative monitoring network, by monitoring the data fluctuation status of each node in real time, when an abnormal state is detected in a node, the propagation path of the corresponding abnormal state in the network is tracked, and the affected related nodes are marked. Based on the abnormal propagation path and combined with the business domain attributes of each node, cross-domain abnormal propagation patterns are identified.

5. The data-assisted analysis, prediction, and judgment system based on indicator management according to claim 1, characterized in that, The construction of the indicator state transition network specifically includes: Based on the extracted fluctuation characteristics, the state discretization process is performed on each key indicator, dividing the continuously changing indicator values ​​into several state intervals. Each state interval corresponds to a specific numerical range and business meaning, forming a discrete state space. Based on the obtained state space, the frequency of state transitions of each index between adjacent time segments is counted. By calculating the transition probability between states, a state transition probability matrix is ​​constructed, where each element represents the probability of transitioning from one state to another. Based on the obtained state transition probability matrix, a directed weighted network structure is constructed, where nodes represent index states, directed edges represent state transition relationships, and the weights of the edges are determined by the transition probability values, thus forming a complete index state transition network. In state transition networks, high-frequency transition paths are identified. By analyzing the importance of nodes and the distribution of edge weights in the paths, key state evolution patterns are extracted to form a set of state transition paths.

6. The data-assisted analysis, prediction, and judgment system based on indicator management according to claim 1, characterized in that, The method of using path matching and similarity calculation to predict the possible evolution path of indicators in future time periods specifically includes: Based on the set of state transition paths, multi-dimensional features of each historical path are extracted, including path length, state transition frequency, path stability index, and key node distribution characteristics, and a path feature vector is constructed. The matching degree between the current indicator state and the path feature vector is calculated. By comparing the similarity between the current state and the historical path starting state, as well as the degree of conformity with the path evolution trend, several candidate evolution paths are selected. A deep analysis of the candidate evolution paths is conducted, and the confidence index of each path is calculated, including the frequency of path occurrence, the most recent usage time, and the degree of matching with the current state, to select the best evolution paths. Based on the selected optimal evolution path, and combined with the business meaning and impact of key nodes in the path, multi-scenario development trend predictions are generated, and each prediction path is labeled with a confidence level and key turning point.

7. The data-assisted analysis, prediction, and judgment system based on indicator management according to claim 1, characterized in that, The intelligent decision analysis process is as follows: Based on the analysis and prediction results, a tree structure of decision-making schemes is constructed, and various analysis elements are hierarchically organized according to business logic relationships, establishing the dependencies and influence paths between decision-making elements; The decision-making options are comprehensively evaluated, and a scoring standard is established from three dimensions: timeliness, feasibility, and scope of impact. The priority ranking of each option is obtained through weighted calculation. Based on the priority ranking results, a visual decision analysis report is generated. The report includes a comparative analysis of the solutions, implementation path suggestions, and expected effect evaluation, and supports online collaborative review and solution optimization by multiple parties. Based on feedback on the implementation effects of the decision-making plan, a parameter optimization knowledge base is established, and by analyzing historical decision-making cases, indicator thresholds and analytical parameter configurations are adjusted.

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