Intelligent management method and platform based on real-time index prediction

Abstract

The invention relates to the field of index prediction, in particular to an intelligent management method and platform based on real-time index prediction. The method comprises the following steps: acquiring a multivariate index data source; performing deep semantic feature analysis and adaptive standardization processing on the multivariate index data source, and constructing a real-time index warehouse; performing multi-level index change analysis on the real-time index warehouse, and performing deep-layer change trend mining to generate a plurality of index deep-layer change trends; carrying out potential correlation analysis among indexes according to the real-time index resource library, and then carrying out intelligent causal relationship chain mining so as to construct a multivariate index causal relationship chain; and performing different time point index situation prediction on the multiple index deep change trends based on the multivariate index causal relationship chain so as to generate an index situation prediction sequence of multiple time points. Through accurate and real-time index prediction, the decision-making efficiency and accuracy of the index management platform are improved, and the management effect is optimized.

Description

Technical Field

[0001] The present invention relates to the field of index prediction, and in particular to an intelligent management method and platform based on real-time index prediction. Background Art

[0002] With the deepening of global economic integration and the continuous changes in market demand, commodities have played an important role in global trade. Commodity indexes, as an important tool to measure the overall volatility and performance of commodity markets, are widely used in investment, trade decision-making and economic analysis. The complexity and variability of commodity markets make their price fluctuations affected by a variety of factors, including the global economic situation, supply and demand, climate change, political risks, monetary policy, etc. The interaction of these factors makes the prediction of commodity indexes extremely difficult.

[0003] Traditional commodity index forecasting methods mostly rely on statistical analysis of historical data and simple mathematical models. Although these methods can reflect market trends to a certain extent, they are usually unable to cope with the complex nonlinear relationships and real-time changing factors in the commodity market, and have problems such as low forecasting accuracy, slow response, and poor adaptability. With the development of big data technology, artificial intelligence, and machine learning, traditional forecasting methods can no longer meet the needs of modern commodity index forecasting, especially when the market fluctuates violently and emergencies occur frequently. Traditional methods cannot adjust forecasting strategies in a timely manner, resulting in large deviations in forecast results.

[0004] In order to better adapt to market changes and improve forecast accuracy and response speed, an intelligent commodity index forecasting method is urgently needed. This method can not only process a large amount of historical data, but also monitor and analyze market dynamics in real time, identify potential influencing factors through advanced technologies such as machine learning and deep learning, and make automatic forecasts and adjustments. Intelligent management methods can adjust forecasting strategies in a timely manner under the influence of multiple factors such as the global economy, policy changes, and seasonal fluctuations, provide more accurate and reliable market trend forecasts, and provide scientific basis for investors, decision makers, and traders, so as to occupy a favorable position in the fiercely competitive commodity market.

[0005] Therefore, with the continuous advancement of big data, artificial intelligence and intelligent management technologies, the development of a commodity index prediction method based on intelligent analysis has important practical significance and application value for improving prediction accuracy, optimizing decision-making processes and reducing risks. Summary of the invention

[0006] In order to solve the above technical problems, the present invention proposes an intelligent management method and platform based on real-time index prediction to solve at least one of the above technical problems.

[0007] To achieve the above object, the present invention provides an intelligent management method based on real-time index prediction, comprising the following steps: Step S1: Obtain a multi-source index data source; perform in-depth semantic feature parsing and adaptive normalization processing on the multi-source index data source to construct a real-time index warehouse; Step S2: Conduct multi-level index change analysis on the real-time index warehouse and perform deep change trend mining to generate multiple deep index change trends; Step S3: Conduct potential correlation analysis between indexes based on the real-time index resource library, and then perform intelligent causal relationship chain mining to construct a multi-source index causal relationship chain; Step S4: Based on the multi-source index causal relationship chain, perform index situation prediction at different time points on multiple deep index change trends to generate an index situation prediction sequence at multiple time points; Step S5: Identify index prediction deviations based on the index situation prediction sequence at multiple time points and perform deviation fluctuation calculation to generate index situation prediction deviation characteristics; Step S6: Make immediate decision feedback adjustments according to the index situation prediction deviation characteristics and perform decision visualization to construct an intelligent decision management visualization model.

[0008] By obtaining multi-source exponential data, the present invention can comprehensively reflect various factors of the system and provide rich raw data for subsequent analysis. Through in-depth semantic parsing, hidden information and complex relationships in the data can be mined, providing more accurate features for analysis and helping to better understand the actual meaning behind the data. Data from different sources may vary, and standardization processing can eliminate these differences, making the data comparable on the same dimension, thereby enhancing the accuracy and consistency of subsequent analysis. The construction of a real-time warehouse means that data can be quickly obtained and processed, enabling the decision support system to respond in a timely manner in a dynamically changing environment. Through multi-level analysis, the changing rules of the index at different levels and dimensions can be discovered, avoiding the neglect of some potentially important signals. Deeply mining the trends in the data can not only reveal surface phenomena but also uncover potential changing factors, helping to predict future development trends. Through multiple different changing trends, diverse information is provided to decision-makers, contributing to a comprehensive assessment of the potential impacts of different factors on the future. By analyzing the correlation between different indices, it can be found which indices may interact with each other, thus more accurately understanding the systematic factors behind the index changes. Intelligent analysis can help identify more complex causal relationships, capture details that may be overlooked by traditional methods, and thus construct a more accurate causal chain. Constructing a multi-causal relationship chain helps to reveal the interaction between multiple factors and provides a theoretical basis for formulating more precise strategies. The causal relationship chain can serve as the basis for a prediction model, and the causal relationship it reveals is used for accurate prediction of index changes, improving the reliability of the prediction results. Generating prediction sequences at multiple time points can provide a comprehensive foresight of future changing trends and timely warning information for various decisions. Real-time index trend prediction enables decision-makers to quickly grasp future changing trends, make responses in advance, and reduce the risk of decision-making errors. Through real-time monitoring and deviation identification, the differences between predictions and actuals can be promptly discovered, and potential problems and abnormal fluctuations can be identified. Calculation of fluctuations can reveal the fluctuation range of prediction accuracy, helping to identify possible risk points and sources of uncertainty. Timely identification of prediction deviations and their fluctuations contributes to dynamic adjustment and optimization of future predictions, thereby continuously improving the accuracy of the system. Instant decision feedback can help managers make quick adjustments when problems are discovered, ensuring that decisions always meet real-time market demands. A visual decision support system can present data and analysis results in a graphical and interactive manner, helping decision-makers understand the analysis results more clearly and intuitively, and thus making more targeted decisions. Constructing an intelligent decision management system can improve the efficiency and accuracy of decision-making through automation and visualization, while reducing human errors and optimizing the overall management effect.

[0009] In this specification, an intelligent management platform based on real-time index prediction is provided for implementing the intelligent management method based on real-time index prediction as described above, including: A deep semantic analysis module for obtaining diverse exponential data sources, performing deep semantic feature analysis and adaptive normalization processing on the diverse exponential data sources, and constructing a real-time index repository; A deep trend mining module for performing multi-level index change analysis on the real-time index repository and conducting deep change trend mining to generate multiple index deep change trends; A potential association analysis module for performing potential association analysis between indexes based on the real-time index resource library and then conducting intelligent causal relationship chain mining to construct a multi-index causal relationship chain; An index situation prediction module for predicting index situations at different time points based on the multi-index causal relationship chain for multiple index deep change trends, thereby generating an index situation prediction sequence at multiple time points; A prediction deviation module for identifying index prediction deviations based on the index situation prediction sequence at multiple time points and calculating deviation fluctuations, thereby generating index situation prediction deviation characteristics; A decision feedback adjustment module for performing immediate decision feedback adjustment based on the index situation prediction deviation characteristics and conducting decision visualization to construct an intelligent decision management visualization model.

[0010] The present invention can extract effective information from various data sources through deep semantic analysis. Especially in complex and diverse data integration, it can ensure the accuracy and integrity of information. Adaptive standardization processing eliminates the differences between different data sources, making the data comparable on the same dimension and avoiding analysis biases caused by data inconsistency. The construction of a real-time index warehouse can effectively manage a large amount of data, providing fast and accurate real-time data support for subsequent prediction and decision-making. Through multi-level index change analysis, it can help discover multi-dimensional changes in the index, avoid one-sided prediction or analysis, and make trend prediction more comprehensive. Mining deep trends can reveal deeper reasons for changes and potential influencing factors, helping to improve the prediction accuracy and avoid staying only on the surface phenomena. Constructing deep change trends from multiple perspectives and dimensions enables managers to comprehensively understand market changes and make more accurate and timely decisions. Through correlation analysis, it can reveal the internal relationships between different indexes, thus helping decision-makers understand the correlations behind certain changes. Using intelligent methods to deeply mine the causal relationship chain, capture complex interactions, enhance the understanding of market or system dynamics, and then optimize the decision-making process. By establishing a causal relationship chain, it can identify which factors have a significant impact on other factors, thus optimizing resource allocation and decision-making. Through systematic analysis of the impact of the causal relationship chain on multi-dimensional index changes, the trends of multiple indexes at different time points can be accurately predicted. The generated prediction sequences at multiple time points can provide detailed pre-judgments for the short-term, medium-term, and long-term future, providing a panoramic prediction picture for decision-makers. Accurate trend prediction can help decision-makers plan and adjust strategies in advance, avoid being too late to react, and respond to market or environmental changes in a timely manner. By monitoring and identifying prediction biases, the gap between the model prediction and the actual situation can be discovered in a timely manner, providing a basis for model optimization. Calculating the fluctuation range of prediction biases helps to reveal prediction uncertainties, helps decision-makers evaluate the credibility of prediction results, and avoid decision-making risks caused by over-relying on a single prediction result. Bias identification and fluctuation calculation enable the system to dynamically adjust the prediction model according to the latest actual situation, improving the accuracy and real-time response ability of the prediction. It can perform real-time feedback adjustment based on prediction biases and actual changes, ensuring that the decision-making system can always maintain flexibility and accuracy in a dynamically changing environment. Decision visualization helps decision-makers understand complex data and analysis results through graphical and intuitive displays, improving the transparency and efficiency of decision-making. Through an intelligent decision management system, not only the efficiency of decision-making is improved, but also human errors and judgment biases can be reduced, realizing more scientific and efficient decision management. BRIEF DESCRIPTION OF THE DRAWINGS

[0011] Figure 1 It is a schematic flow chart of the steps of an intelligent management method based on real-time index prediction according to the present invention; Figure 2It is a schematic diagram of the detailed implementation steps of step S1; Figure 3 It is a schematic diagram of the detailed implementation steps of step S2; Figure 4 It is a schematic diagram of the detailed implementation steps of step S3. Detailed implementation manner

[0012] It should be understood that the specific embodiments described herein are only used to explain the present invention and are not used to limit the present invention.

[0013] The embodiments of the present application provide an intelligent management method and platform based on real-time index prediction. The execution subjects of the intelligent management method and platform based on real-time index prediction include, but are not limited to, the following general computing nodes that carry the system: mechanical equipment, data processing platforms, cloud server nodes, network upload devices, etc. The data processing platform includes, but is not limited to, at least one of an audio and image management system, an information management system, and a cloud data management system.

[0014] Please refer to Figures 1 to 4 , the present invention provides an intelligent management method based on real-time index prediction. The intelligent management method based on real-time index prediction includes the following steps: Step S1: Obtain a multi-source index data source; perform in-depth semantic feature parsing and adaptive standardization processing on the multi-source index data source to construct a real-time index warehouse; Step S2: Perform multi-level index change analysis on the real-time index warehouse and conduct deep change trend mining to generate multiple index deep change trends; Step S3: Conduct potential correlation analysis between indexes based on the real-time index resource library, and then conduct intelligent causal relationship chain mining to construct a multi-source index causal relationship chain; Step S4: Based on the multi-source index causal relationship chain, perform index trend prediction at different time points on multiple index deep change trends to generate an index trend prediction sequence at multiple time points; Step S5: Identify index prediction deviations based on the index trend prediction sequence at multiple time points and calculate deviation fluctuations to generate index trend prediction deviation characteristics; Step S6: Perform immediate decision feedback adjustment based on the index trend prediction deviation characteristics and conduct decision visualization to construct an intelligent decision management visualization model.

[0015] By obtaining multi-source exponential data, the present invention can comprehensively reflect various factors of the system, providing rich raw data for subsequent analysis. Through in-depth semantic parsing, it can mine hidden information and complex relationships in the data, providing more accurate features for analysis and helping to better understand the actual meaning behind the data. Data from different sources may vary, and standardization processing can eliminate these differences, making the data comparable on the same dimension, thereby enhancing the accuracy and consistency of subsequent analysis. The construction of a real-time warehouse means that data can be quickly obtained and processed, enabling the decision support system to respond in a timely manner in a dynamically changing environment. Through multi-level analysis, the changing patterns of the index at different levels and dimensions can be discovered, avoiding the neglect of some potentially important signals. Deeply mining the trends in the data can not only reveal surface phenomena but also uncover potential changing factors, helping to predict future development trends. Through multiple different changing trends, diverse information is provided to decision-makers, helping to comprehensively evaluate the potential impacts of different factors on the future. By analyzing the correlation between different indices, it can be found which indices may influence each other, thus more accurately understanding the systematic factors behind the index changes. Intelligent analysis can help identify more complex causal relationships, capturing details that traditional methods may overlook, thereby constructing a more accurate causal chain. Constructing a multi-causal relationship chain helps to reveal the interaction between multiple factors, providing a theoretical basis for formulating more precise strategies. The causal relationship chain can serve as the basis for a prediction model, using the revealed causal relationship to accurately predict index changes and improving the reliability of the prediction results. Generating prediction sequences at multiple time points can comprehensively foresee future changing trends, providing timely warning information for various decisions. Real-time index trend prediction enables decision-makers to quickly grasp future changing trends, make responses in advance, and reduce the risk of decision-making errors. Through real-time monitoring and deviation identification, the differences between predictions and actual situations can be promptly discovered, identifying potential problems and abnormal fluctuations. The calculation of fluctuations can reveal the fluctuation range of prediction accuracy, helping to identify possible risk points and sources of uncertainty. Timely identification of prediction deviations and their fluctuations helps to dynamically adjust and optimize future predictions, thereby continuously improving the accuracy of the system. Immediate decision feedback can help managers make quick adjustments when problems are discovered, ensuring that decisions always meet real-time market demands. A visual decision support system can present data and analysis results in a graphical and interactive manner, helping decision-makers to more clearly and intuitively understand the analysis results and then make more targeted decisions. Constructing an intelligent decision management system, through automation and visualization, improves the efficiency and accuracy of decision-making, reduces human errors at the same time, and optimizes the overall management effect.

[0016] In an embodiment of the present invention, refer to Figure 1, is a schematic diagram of the step process of an intelligent management method based on real-time index prediction according to the present invention. In this example, the steps of the intelligent management method based on real-time index prediction include: Step S1: Obtain a multi-source index data source; perform in-depth semantic feature parsing and adaptive normalization processing on the multi-source index data source to construct a real-time index warehouse; In this embodiment, appropriate multiple-index data sources are identified and selected. These data sources can include financial market data, economic indicators, industry reports, social media data, etc. According to the research objectives, ensure the diversity and representativeness of the data sources. Set the acquisition criteria for the data sources, such as the update frequency of the data, the integrity of historical data, and the credibility of the data. Prioritize data provided by authoritative institutions, such as national statistical bureaus, financial regulatory agencies, and large data providers. Select suitable data collection methods, such as API interface calls, web crawlers, data imports, etc. If the API method is adopted, ensure that the corresponding access rights and keys are available. For each data source, configure the corresponding collection scripts or tools, and set the collection time frequency (such as hourly, daily, or weekly) to ensure the timeliness of the data. During the data acquisition process, design a reasonable storage scheme, select a suitable database (such as a relational database, NoSQL database, or data warehouse), and set the storage structure of the data. Conduct a preliminary collation of the acquired data, record the source, timestamp, and relevant metadata of each piece of data for subsequent query and analysis. For the acquired multiple-index data, identify the key features that need to be parsed. These features may include time features (such as date, time), numerical features (such as growth rate, volatility), categorical features (such as industry category, region), etc. Determine the objectives of feature parsing, such as extracting features that are of great significance for market analysis to support subsequent analysis and modeling. Select suitable deep semantic parsing techniques, such as natural language processing (NLP) methods, machine learning algorithms, or deep learning models (such as LSTM, BERT, etc.). These techniques can extract potential semantic features from the original data. Set the algorithm parameters for parsing, such as the dimension of word vectors, the number of training epochs, etc., to ensure the effectiveness of the parsing process. Conduct a deep semantic feature parsing of the multiple-index data. Through the selected algorithm, extract the semantic features of each data item and transform them into structured data. Record the key findings during the parsing process, including the types of features extracted, the distribution of feature values, etc., for subsequent analysis and verification. Determine a suitable standardization method, such as Z-score standardization, Min-Max standardization, etc. When selecting a standardization method, consider the distribution characteristics of the data and the requirements of subsequent analysis. Set the standardization parameters, such as the mean and standard deviation (for Z-score standardization), or the maximum and minimum values (for Min-Max standardization). Conduct an adaptive standardization process on the parsed multiple-index features. According to the selected standardization method, process each feature to meet the standardization requirements. During the standardization process, ensure that the standardization process of each feature is recorded, including the values before and after processing, standardization parameters, etc., for subsequent analysis and verification. Store the standardized multiple-index features in a real-time index warehouse. Design a reasonable database structure to support efficient query and analysis.Regularly update the real-time index repository to ensure it contains the latest data and feature information, providing support for subsequent analysis and decision-making.

[0017] Step S2: Conduct multi-level index change analysis on the real-time index repository and perform in-depth change trend mining to generate multiple in-depth change trends of the index. In this embodiment, construct a multi-level analysis framework to determine the hierarchical structure of the analysis. For example, the first layer can be analyzed according to the time dimension (such as daily, weekly, monthly), the second layer can be analyzed according to the industry or geographical dimension, and the third layer can be analyzed according to specific indicators (such as growth rate, volatility, etc.). Determine the index system for analysis, and select the indexes and their change characteristics that need to be focused on. These indexes may include economic growth index, consumer confidence index, market volatility index, etc. Extract relevant index data from the real-time index repository and integrate the data to ensure that data at different levels can be effectively compared and analyzed. For each level, set an appropriate time window. For example, for daily-level analysis, the data of the past 30 days can be selected; for weekly-level analysis, the data of the past 6 weeks can be selected. Use statistical analysis methods (such as descriptive statistics, time series analysis, etc.) to analyze the data at each level. Record the key indicators and their changes at each level. Use visualization tools (such as line charts, bar charts) to display the index changes at different levels for intuitive understanding and trend identification. For example, by visually showing the change of the consumer confidence index in the past three months, it helps to analyze its fluctuation trend. Select suitable trend mining methods, such as time series decomposition, moving average, exponential smoothing, etc. These methods can help identify the potential trends and their change patterns in the data. Set mining parameters, such as the window size of the moving average, smoothing factor, etc., to ensure the accuracy of the analysis. Conduct trend mining on the real-time index data, use the selected method to decompose the data, and extract the trend component, seasonal component, and random component. For example, use the seasonal decomposition method to decompose the consumer confidence index into trend, seasonal, and residual components. Record the trend changes in each time period, such as whether there is an upward or downward trend and the rate of trend change. Based on the mined trend component, conduct in-depth change trend analysis to identify the main factors affecting the index change. These factors may include market events, economic cycles, etc. Generate an in-depth change trend report, which should include the change trend of each index, factor analysis, and prediction results.

[0018] Step S3: Conduct potential association analysis between indexes based on the real-time index resource library, and then perform intelligent causal relationship chain mining to construct a multi-index causal relationship chain. In this embodiment, a framework for potential association analysis is established to determine the objectives and scope of the analysis. The objective is to identify the potential associations between different indices, and the scope covers all available real-time index data. Determine the set of indices to be analyzed, such as economic growth index, inflation rate, unemployment rate, etc., ensuring that the selected indices have potential mutual influence relationships in economic theory. Extract the data of relevant indices from the real-time index repository and perform data cleaning. The cleaning process includes handling missing values, removing outliers, and standardizing the data format to ensure the quality and consistency of the data. To conduct association analysis, set an appropriate time window, such as data for the past year or the past few quarters, to capture the long-term and short-term relationships between indices. Select suitable analysis methods, such as Pearson correlation coefficient, Spearman rank correlation coefficient, and mutual information, etc., to evaluate the associations between different indices. Set the significance level of the analysis, usually choosing 0.05 as the statistical significance level to determine the reliability of the associations. Apply the selected statistical method to calculate the associations between each index, and record the correlation coefficient and its significance level between each pair of indices. Generate an association analysis report, which should include the correlation matrix between each index and its interpretation, indicating which indices have significant association relationships. Select suitable causal relationship mining methods, such as Granger causality test, structural equation model (SEM), Bayesian network, etc. These methods can effectively identify the causal relationships between variables. Determine the parameters of the analysis, such as the number of lags (in Granger test) and model complexity (in Bayesian network), to ensure the accuracy of the analysis. Based on the previous potential association analysis results, select the significantly correlated indices for causal relationship modeling. Use the Granger test method, set different numbers of lags, and conduct causal tests on each pair of indices to determine whether one index has predictive ability for the changes of another index. When using the structural equation model, establish the model structure, define the relationships between latent variables and observed variables, and conduct model estimation and testing. For the tested causal relationships, record the directionality and strength of each causal chain. For example, record the causal relationship of "economic growth index → unemployment rate" and evaluate its significance. Generate a causal relationship chain report, which should include all identified causal relationship chains, correlation coefficients, and their statistical significance, providing a basis for subsequent analysis. Integrate the identified causal relationships into a multi-index causal relationship chain. According to the directionality and strength of the causal relationships, construct a causal network to show the mutual influences between each index. Use a graphical tool (such as a network diagram) to display the causal relationship chain, facilitating the understanding and analysis of the complex relationships between each index. Conduct dynamic causal analysis to evaluate the stability and changes of the causal relationships between indices over different time periods. It may be found that some causal relationships are more significant during specific time periods. Record the changes in dynamic causal relationships and analyze the possible driving factors, such as market events, etc. Organize the analysis results of the multi-index causal relationship chain and generate a comprehensive report.The construction process, significance analysis and practical implications of each causal relationship chain should be described in detail in the report. Provide decision-making suggestions to help decision-makers understand market dynamics and potential risks based on the findings of the causal relationship chain.

[0019] Step S4: Based on the multivariate exponential causal relationship chain, conduct index trend predictions for the deep changes of multiple indexes at different time points, so as to generate an index trend prediction sequence at multiple time points; In this embodiment, the objectives of exponential trend prediction are clarified, that is, based on the multivariate exponential causal relationship chain, predict the exponential changes at different future time points. These objectives may include the change trends of specific economic indicators, market fluctuations, etc. Determine the indices to be predicted and their correlations to ensure that the prediction model can comprehensively reflect the dynamic relationships between the indices. Set the time points for prediction, such as the next 30 days, 60 days, and 90 days, etc., to ensure that the selected time points can cover the short-term and medium-term change trends. According to historical data, select an appropriate time interval, such as daily, weekly, or monthly, for systematic prediction analysis. Select a suitable prediction model, such as time series models (ARIMA, SARIMA), machine learning models (random forest, support vector machine), or deep learning models (LSTM, GRU). Determine the parameter settings of the model, such as the p, d, q parameters of ARIMA, or the number of layers, number of nodes, and learning rate of LSTM, etc., to ensure the effectiveness of the model. Extract the historical data for prediction from the real-time index repository, including all input features related to the target index. Perform data cleaning to handle missing values and outliers to ensure the quality of the data. Standardize the data to eliminate the influence of different dimensions and make the model input more consistent. According to the multivariate exponential causal relationship chain, construct input features, including historical index values, lag values, moving averages, volatilities, etc. Ensure that the features can effectively capture the relationships between the indices. Add time-related features, such as seasonal and periodic indicators, to help the model identify time trends. Divide the processed dataset into a training set and a test set. Usually, 70%-80% of the data is used for model training, and 20%-30% of the data is used for model testing. Ensure that the time order is preserved during the division to avoid data leakage and ensure the generalization ability of the model. Use the training set data to train the selected prediction model. According to the characteristics of the model, adjust the hyperparameters and optimize the model performance through cross-validation. Record the key metrics during the model training process, such as training error, validation error, etc., to ensure the accuracy of the model. Use the trained model to predict the indices at different future time points. For each selected time point, generate the corresponding prediction results, and record the predicted values and their confidence intervals at each time point. For example, predict the economic growth index within the next 30 days and record the predicted values and their corresponding error ranges for each day. Organize the prediction results in a time series format for subsequent analysis and visualization. Record the predicted values, actual values (if any), and their deviations at each time point. Analyze the prediction results, identify important change trends, and evaluate the prediction performance of the model, such as using metrics like root mean square error (RMSE) or mean absolute percentage error (MAPE) for evaluation. Generate exponential trend prediction sequences for multiple time points based on the prediction results. These sequences should include the predicted values, trend changes, and their confidence intervals at each time point. For example, generate an exponential trend prediction sequence for the next 90 days and record the predicted values of economic indicators at each time point.Visualize the generated exponential trend prediction sequence, and use line charts or bar charts to display the prediction results and their changing trends for intuitive understanding by decision-makers. In the visualization, mark important prediction time points and their corresponding trend changes for analysis and decision support. Regularly verify the accuracy of the prediction sequence, compare the deviation between the actual exponential value and the predicted value, and identify the deficiencies of the model. According to the verification results, adjust the model parameters or select a different model for retraining to improve subsequent prediction accuracy.

[0020] Step S5: Identify the exponential prediction deviation based on the exponential trend prediction sequences at multiple time points, and calculate the deviation fluctuation to generate the exponential trend prediction deviation characteristics; In this embodiment, the actual index values corresponding to the exponential trend prediction sequences at multiple time points are collected. Ensure that the source of the actual data is reliable and the time stamps of the data are consistent with the predicted values for effective comparison. For each time point, calculate the deviation between the predicted value and the actual value. The formula for calculating the deviation is: Deviation = Actual Value - Predicted Value. Record the deviation values at each time point for subsequent analysis. The positive or negative sign of the deviation value represents whether the prediction is too high or too low. Conduct statistical analysis on the calculated deviation values, and calculate key statistical features such as mean, standard deviation, maximum value, and minimum value. These statistical features can help identify the overall trend and fluctuation degree of the prediction deviation. Set a significance level. For example, use a t-test to test the statistical significance of the deviation to determine whether the prediction results systematically deviate from the actual values. Use visualization tools (such as line charts or bar charts) to display the deviation values and their changes at each time point. Through visualization, decision-makers can intuitively see which time points have larger prediction deviations. In the visualization, mark the significance level of the deviation to help analysts quickly identify outliers and systematic problems. Define the volatility of the deviation. Usually, the standard deviation is used to measure the fluctuation degree of the deviation. The larger the standard deviation, the stronger the volatility of the prediction deviation and the higher the uncertainty of the prediction. Set the calculation time window, such as the deviation values of the past 30 time points, to capture the fluctuation trend within a certain time range. Calculate the collected deviation values and record the standard deviation values within each time window for subsequent analysis and comparison. Analyze the calculated deviation volatility results, identify the time periods with large fluctuations, and explore their possible reasons, such as market events. Generate a deviation volatility report. The report should include the volatility analysis results, changes in key indicators, and their impact on the prediction results. Organize the identified deviations and their volatility characteristics into a structured format, including time points, predicted values, actual values, deviation values, and their statistical features. Store these features in a database to ensure data traceability and queryability for subsequent analysis and decision support. Based on the generated prediction deviation characteristics, provide references for decision-makers to help identify potential risks and opportunities. For example, if a certain index continuously shows prediction deviations, it may mean that the prediction model or strategy needs to be adjusted. Provide a real-time monitoring and feedback mechanism to continuously update the deviation characteristics according to new actual data to maintain the accuracy and relevance of the prediction. Regularly evaluate the effectiveness of the deviation characteristics and optimize the prediction model and analysis methods according to the feedback results. For example, use machine learning methods to retrain the model to reduce future prediction deviations. Record the changes in key indicators during the optimization process to summarize experience and lessons and improve the overall accuracy of the prediction.

[0021] Step S6: Make immediate decision feedback adjustments based on the exponential trend prediction deviation characteristics, perform decision visualization, and construct an intelligent decision-making management visualization model.

[0022] In this embodiment, an immediate feedback mechanism based on prediction deviation characteristics is established. The core of this mechanism is to monitor the deviation between the actual data and the predicted data, and set a threshold to judge the significance of the deviation. Usually, the threshold can be set at 5% or 10%, and when the deviation exceeds this range, feedback adjustment is triggered. Determine the process of feedback adjustment, including data monitoring, deviation calculation, execution and recording of adjustment decisions. Ensure the transparency and traceability of the process for subsequent analysis. Use a real-time data monitoring system to continuously track the changes in the actual index value. Whenever new actual data is available, immediately calculate the new prediction deviation and compare it with the historical deviation data. If the deviation exceeds the set threshold, automatically trigger the adjustment mechanism to generate adjustment suggestions. For example, if the deviation between the predicted value and the actual value of a certain economic indicator is significant, the parameters or methods of the relevant prediction model should be considered for adjustment. Implement corresponding adjustment strategies according to the results of the feedback mechanism. This may include adjusting model parameters, updating input features, selecting different prediction methods, etc. Ensure that the adjustment strategy can effectively handle the current prediction deviation. Record the decision-making process of each adjustment, including the reasons for adjustment, the adjusted model parameters and prediction results, etc., to form a systematic decision record for subsequent review and analysis. Determine the visualization objectives, which usually include displaying real-time prediction results, deviation analysis, the impact of adjustment decisions, etc. The clarity of the visualization objectives helps to select appropriate tools and methods. Select suitable data visualization tools, such as Tableau, Power BI or D3.js, and decide whether to use static charts or dynamic dashboards according to the requirements. Organize the data to be displayed, including real-time prediction results, actual values, deviation values and adjustment strategies, etc. Ensure the integrity and consistency of the data for easy visualization. Design visualization reports and dashboards, using various chart forms such as line charts, bar charts and heat maps to facilitate the display of data in different dimensions. For example, use a line chart to display the change trends of actual values and predicted values, and mark the significant points of deviation on the chart. Visualize the organized data through the selected visualization tool. Ensure that the visualization results can be updated in real time to reflect the latest predictions and adjustments. Introduce interactive functions in the visualization, such as users can select different time ranges, view detailed information of specific indexes, etc., to enhance the user experience and the effectiveness of decision-making support. Design the overall architecture of the intelligent decision-making management visualization model, and determine the functional modules of the model, such as data input module, deviation analysis module, decision adjustment module and visualization display module, etc. Ensure the efficient connection and data flow between each module to facilitate the realization of automated decision feedback and visualization display. Integrate the real-time data source with the decision-making management model to ensure that the model can obtain the latest actual index data and prediction results in real time. Based on the deviation characteristics, update the decision adjustment suggestions in real time and display them to the decision-makers through the visualization interface to ensure the timeliness and effectiveness of the decision-making process. Test the constructed intelligent decision-making management visualization model to verify its performance in actual applications.By simulating different market scenarios, evaluate the response speed and adjustment effect of the model. According to the test results, optimize the parameters and design of the model to ensure that it can effectively support decision-making in practical applications.

[0023] In this embodiment, refer to Figure 2 , which is a schematic diagram of the detailed implementation steps of step S1. In this embodiment, the detailed implementation steps of step S1 include: Step S11: Obtain a multi-source index data source; detect abnormal data in the multi-source index data source and mark abnormal data points; Step S12: Filter abnormal data points to obtain a filtered and optimized index data source; Step S13: Perform in-depth semantic feature analysis on the filtered and optimized index data source to generate index semantic features; Step S14: Classify the business types according to the filtered and optimized index data source to obtain index types; Step S15: Perform adaptive standardization processing on the multi-source index data source according to the index semantic features and index types to obtain a multi-source standardized index; Step S16: Perform data integration processing on the multi-source standardized index to build a real-time index warehouse.

[0024] In this embodiment, the types of multi-source exponential data sources that need to be obtained are clarified, such as economic indicators, market indices, climate change indices, etc. These data sources usually come from public databases, government statistical bureaus, financial markets, or websites of other relevant institutions. Set the frequency of data acquisition, such as daily, weekly, or monthly updates, to ensure the freshness and relevance of the data. At the same time, select a suitable acquisition method, such as API calls, data scraping, or manual downloads, etc. Use data scraping tools (such as Beautiful Soup, Scrapy, etc.) to extract relevant data from the specified web pages or databases. This process requires handling the conversion of data formats to ensure that the data can be correctly parsed. Store the acquired data in a database, such as using a relational database (such as MySQL, PostgreSQL) or a non-relational database (such as MongoDB) for storage, for subsequent processing and analysis. Perform preliminary data cleaning to handle issues such as missing values, duplicate values, and inconsistent data formats. This process ensures the integrity and consistency of the data and avoids affecting subsequent analysis. Record the relevant information of the data source, including the data source, acquisition time, data field descriptions, etc., for subsequent traceability and verification. Select a suitable anomaly detection algorithm, such as Z-score, IQR (Interquartile Range), or machine learning methods (such as Isolation Forest, Support Vector Machine, etc.). These methods can effectively identify anomaly points in the data. Set the detection threshold. For example, when using the Z-score method, the threshold is usually set to 3, indicating that data points exceeding 3 standard deviations are considered anomalies. Perform anomaly detection on the acquired multi-source exponential data sources, execute the selected detection algorithm, and mark all anomaly data points. Use visualization tools (such as Matplotlib, Seaborn) to display the data distribution to help identify anomaly points. Record the information of each anomaly data point, including its specific value, location, and detection reason, etc., to provide a basis for subsequent filtering and analysis. In the dataset, mark all detected anomaly data points. This can be done by adding a new column "Anomaly Mark" to indicate the status (normal or abnormal) of the data points. Ensure the accuracy of the anomaly mark, and regularly review and verify the effectiveness of the mark for the effective implementation of subsequent steps. For the filtered data source, select suitable features for in-depth semantic feature parsing. These features can include time series features, trend features, and other data features that may affect the index. Use natural language processing (NLP) techniques to extract semantic features such as sentiment and topic related to the data, especially for index data with text descriptions (such as market reviews, analysis reports, etc.). Apply deep learning models (such as LSTM, GRU, etc.) for feature parsing, especially when dealing with time series data. These models can capture complex patterns and long-term dependencies in the data. Set the hyperparameters of the model, such as the learning rate, batch size, etc., and use historical data for model training to generate accurate semantic features. Generate the semantic features of the index based on the results of in-depth parsing.These features will be used for subsequent classification and standardization processes. Record the generated feature information, including feature types, feature values, and their corresponding original data, for subsequent analysis and verification. Based on the characteristics of the index and business requirements, formulate classification criteria. For example, data can be classified into economic indices, financial indices, market indices, etc. Set feature indicators for each category for reference and verification during subsequent classification processes. Use machine learning algorithms (such as decision trees, random forests, support vector machines, etc.) to classify the filtered and optimized index data sources by business type. When training the model, use the labeled dataset for supervised learning. Set evaluation metrics for the model, such as accuracy, recall rate, and F1-score, to ensure the effectiveness and accuracy of the classification model. Add a new column "business type" to the dataset to record the classification results of each data point. This will facilitate subsequent analysis and processing. Regularly evaluate the performance of the classification model to ensure it can adapt to data changes and update the model as needed. Select appropriate standardization methods, such as Z-score standardization, Min-Max standardization, etc. These methods can scale data with different features to the same range for subsequent analysis. Set standardization parameters, for example, use different standardization strategies for different types of indices to ensure the flexibility of processing. Standardize the multi-variate index data sources according to the semantic features of the indices and their business types. Process each feature according to the preset standardization method to ensure that the data can be compared on the same scale. Record the data distributions before and after the standardization process to evaluate the effect of standardization. Add a new column "standardized index" to the dataset to record the data values after the standardization process. This will provide basic data for subsequent analysis and decision-making. Regularly check the effect of the standardization process to ensure the applicability and effectiveness of the standardization method. According to business requirements, determine the standards and processes for data integration. For example, factors such as timestamps, data sources, and their quality may need to be considered when integrating data. Set the data format and storage structure after integration for subsequent real-time query and analysis. Integrate the multi-variate standardized indices and use database management tools (such as ETL tools) to merge data from different sources into a centralized data warehouse. Record the problems and solutions that occur during the integration process for future reference and optimization. After the integration is completed, build a real-time index warehouse to support real-time query and analysis. The warehouse should have efficient data indexing and retrieval capabilities to handle large-scale data access requirements. Regularly maintain and update the real-time index warehouse to ensure the accuracy and timeliness of its data and provide support for subsequent decision-making and analysis.

[0025] In this embodiment, refer to Figure 3 , which is a schematic diagram of the detailed implementation steps of step S2. In this embodiment, the detailed implementation steps of step S2 include: Step S21: Divide the real-time index warehouse into multi-period sliding windows to generate index windows for multiple periods; Step S22: Calculate the change amplitude of each index one by one based on the index windows for multiple periods to generate the change amplitude of each index; Step S23: Statistically analyze the index time series mutation rate based on the index windows for multiple periods to obtain the index time series mutation rate; Step S24: Conduct multi-level index change analysis based on the index time series mutation rate and the change amplitude of each index, thereby generating the multi-level change characteristics of each index; Step S25: Mine the deep change trend of the multi-level change characteristics of each index, thereby generating the deep change trends of multiple indexes.

[0026] In this embodiment, determine the time length and step size of the sliding window. For example, select 1 hour as the window length and slide every 15 minutes. Such a setting can capture the exponential changes within a short period in detail. Set the number of windows, which is determined according to the time span of the real-time index data. For example, if there is one week of data and one window is generated per hour, a total of 112 windows can be generated finally. Use a data processing tool (such as Pandas) to slice the data in the real-time index repository according to the set window length and step size, so as to generate index windows for multiple time periods. During the slicing process, ensure that each window contains complete timestamp information so that the data source can be accurately located during subsequent analysis. Attach metadata to each generated index window, including the start time, end time, index values within the window, etc. This information will facilitate subsequent data analysis and visualization. Regularly check the accuracy of the window division to ensure that each window can correctly reflect the index changes within the corresponding time period. Determine the calculation method of the change amplitude. Commonly used ones are simple absolute change (current value minus previous value) or relative change (ratio of absolute change to previous value). Select a method suitable for business requirements. Set the calculation formula for the change amplitude, such as absolute change amplitude = index value of the current window - index value of the previous window. For each generated index window, calculate the change amplitude one by one. You can use a loop to iterate through each window, gradually calculate and record the change amplitude. Record the change amplitude of each window, including its corresponding time period, for subsequent analysis and visualization. Store the calculated change amplitude in the database, adding a new column "change amplitude" for subsequent query and analysis. Use a visualization tool (such as Matplotlib) to plot the change amplitude graph to help visually display the index changes in each time period and facilitate the identification of potential abnormal fluctuations. Define the mutation rate, that is, within a certain time period, the ratio of the number of times the index change amplitude exceeds the set threshold to the total number of windows. Set the mutation threshold. For example, when the change amplitude exceeds 2%, it is regarded as a mutation. Set the calculation formula: mutation rate = number of windows with mutations / total number of windows. Traverse the generated index windows for multiple time periods, and count the number of windows whose change amplitude exceeds the set threshold. During the traversal process, record the time period and change amplitude of each mutation event. Generate a statistical table of the mutation rate, including information such as time period, number of mutations, and mutation rate. Store the statistical results in the database and generate a visualization chart of the mutation rate to facilitate the analysis and display of the index fluctuations. Regularly evaluate the change of the mutation rate to judge whether it is necessary to adjust the mutation threshold or analysis method to better adapt to the actual data changes. Build a multi-level index change analysis framework. First, define the levels of analysis, such as short-term changes (such as daily changes), medium-term changes (such as weekly changes), and long-term changes (such as monthly changes). Set the analysis indicators for each level, including change amplitude, mutation rate, fluctuation amplitude, etc. For different levels of time periods, combine the mutation rate and change amplitude to analyze the change characteristics of each index one by one.For example, analyze the relationship between the change amplitude and the mutation rate in the short term to identify the potential causes of short-term fluctuations. Record the results of each level of analysis, including the discovered change patterns and potential influencing factors. Integrate the results of the multi-level analysis to generate the multi-level change characteristic data for each index. This will help provide the basic data for subsequent in-depth change trend mining. Regularly update and maintain the multi-level change characteristics to ensure that they reflect the latest data trends. Select appropriate in-depth change trend mining methods, such as time series analysis, principal component analysis (PCA), or clustering analysis, etc. The selection method should be based on the characteristics of the data and the analysis objectives. Set the mining objectives, such as identifying potential trends, periodic changes, or abnormal patterns, etc. Conduct in-depth analysis on the generated multi-level change characteristic data, and extract the in-depth change trends through the selected methods. For example, use a time series model to predict the future index changes. Record the trend characteristics discovered during the mining process, including the trend direction, intensity, and their corresponding time periods. Generate an in-depth change trend report for each index based on the results of the in-depth analysis. This will provide a basis for decision-making and help identify potential market opportunities or risks. Regularly update and evaluate the in-depth change trends to ensure that they are consistent with the actual data changes, so as to facilitate timely adjustment of strategies.

[0027] In this embodiment, refer to Figure 4 , which is a schematic diagram of the detailed implementation steps of step S3. In this embodiment, the detailed implementation steps of step S3 include: Step S31: Analyze the market activity requirements based on the real-time index repository to generate real-time market activity requirement characteristics; Step S32: Mine the changes in user requirements from the real-time index repository to generate user requirement change data; Step S33: Statistically count the resource consumption frequency based on the real-time index repository to generate the resource consumption frequency; Step S34: Conduct potential correlation analysis among the indexes based on the real-time market activity requirement characteristics, user requirement change data, and resource consumption frequency to obtain the potential correlation characteristics among the indexes; Step S35: Mine the intelligent causal relationship chain of the potential correlation characteristics among the indexes to construct a multi-index causal relationship chain.

[0028] In this embodiment, key indicators for clarifying market activity requirements are identified, such as the number of participants, activity frequency, user feedback, etc. These indicators will be used to analyze the effectiveness and attractiveness of market activities. Set the time range for data collection, such as daily, weekly, or monthly, to facilitate capturing the dynamic changes of market activities. Extract data related to market activities from the real-time index repository, including historical activity records, user participation, and market responses. Use data processing tools (such as ETL tools) for data integration to ensure data consistency and integrity. During the integration process, record the source and timestamp of each piece of data for subsequent analysis and verification. Extract features from the collected data to generate real-time market activity requirement features. For example, calculate indicators such as the participation rate and user satisfaction of each activity, and analyze their impact on market demand. Store the generated feature data in the database for convenient subsequent query and analysis. Define key indicators for changes in user requirements, such as purchase frequency, user activity, product preference, etc. These indicators will help identify the changing trends of user requirements. Set the time period for data analysis, such as daily, weekly, or monthly analysis, to capture the subtle fluctuations in demand changes. Use data mining techniques (such as clustering analysis, time series analysis, etc.) to deeply analyze the user behavior data in the real-time index repository. By analyzing the user's purchase history, activity participation, etc., identify the changing patterns of user requirements. Record the important findings in the analysis process, including changes in user groups, trends of demand increase or decrease, etc., for subsequent use. Organize the mined user requirement change information into structured data to generate a user requirement change dataset. The dataset should contain information such as timestamps, change amplitudes, and change trends. Regularly update the user requirement change data to ensure that it can reflect the latest market dynamics and user behavior. Determine the types of resources to be counted, such as advertising expenses, product inventory, market activity manpower, etc. Set key indicators for various resource consumptions for frequency statistics. Set the time range for statistics, such as counting the resource consumption in the past month, to analyze the efficiency and effectiveness of resource use. Extract relevant resource consumption data from the real-time index repository and perform frequency statistics. Aggregation functions (such as SUM, COUNT, etc.) can be used to summarize the consumption of different resources. Record the consumption frequency of each resource, including the timestamp and information on relevant market activities, for subsequent analysis. Organize the statistically obtained resource consumption frequencies into a report. The report should include the consumption of various resources and their changing trends, facilitating decision-makers to evaluate. Regularly review and update the resource consumption frequency data to ensure that it can reflect the real-time resource usage and market demand changes. Select suitable association analysis methods, such as correlation coefficient analysis, regression analysis, or association rule learning (such as the Apriori algorithm). These methods can reveal the potential association relationships between different indices.Set the goals of the analysis, such as identifying the relationship between market activity requirements and changes in user requirements, and the impact of resource consumption frequency on these requirements. Use statistical analysis software (such as R, Python, etc.) to analyze the characteristics of real-time market activity requirements, data on changes in user requirements, and resource consumption frequency. Generate a correlation matrix to identify the correlations between various indicators. Record the analysis results, including correlation coefficients, p-values, etc., to help judge the significance and strength of the associations. Organize the potential association characteristics obtained from the analysis into structured data and generate an association characteristics report. The report should include the association relationships between various indices and their possible business impacts. Regularly update the association analysis results to ensure that they reflect the latest market dynamics and changes in user requirements. Select a suitable causal relationship analysis method, such as structural equation modeling (SEM), Granger causality test, or Bayesian network, etc., to determine the mining path of the causal relationship chain. Set the analysis goals of the causal relationship, such as identifying which market activities have a significant impact on changes in user requirements, and how resource consumption affects the effectiveness of these activities. Mine the causal relationship chain for potential association characteristics and analyze the causal relationships between various indices. Statistical software can be used for model fitting to judge the goodness of fit of the model and the significance of the causal relationship. Record the causal relationship chains discovered during the mining process, including information such as the direction, strength of the relationship, and the corresponding time delay. Based on the mined causal relationships, construct a multi-index causal relationship chain diagram to show the causal relationships and mutual influences between various indices. The diagram should include information such as key indices, relationship directions, and strengths. Regularly update the causal relationship chain to ensure that it can reflect the latest market dynamics and changes in user behavior, providing support for decision-making.

[0029] In this embodiment, step S4 includes the following steps: Step S41: Identify multiple index application scenarios based on the multi-index data source; Step S42: Speculate on the actual market demand for each of the multiple index application scenarios one by one, so as to obtain the actual market demand for each scenario; Step S43: Perform multi-path evolution based on the actual market demand for each scenario to generate multiple index evolution paths; Step S44: Based on the multiple index evolution paths and the multi-index causal relationship chain, predict the index trend at different time points for the deep changes in multiple indices, so as to generate an index trend prediction sequence for multiple time points.

[0030] In this embodiment, determine the types of application scenarios to be recognized. These scenarios may include economic analysis, market prediction, etc. Each scenario should be able to utilize the characteristics provided by the multi-source index data. Set the criteria for scenario recognition. For example, each scenario should include a clear goal, relevant indices, and the industry background of their application. Conduct an in-depth analysis of the multi-source index data to identify the key metrics that can be used for different scenarios. For example, indices such as economic growth rate, inflation rate, and stock market volatility can be used in the economic analysis scenario. Use data mining techniques to analyze the correlation and application potential between different indices to help identify the most valuable scenarios. Based on the data analysis results, identify multiple actual application scenarios and record the description, relevant indices, and potential value of each scenario. For example, identify the application value of the "Consumer Confidence Index" in the "Market Demand Prediction" scenario. Ensure that the recorded format is unified for subsequent analysis and reference. Select a suitable market demand prediction model, such as time series analysis, regression analysis, or machine learning algorithms (such as random forest, neural network, etc.). The selection of the model should be based on the scenario characteristics and available data. Set the input parameters of the prediction model, such as historical market demand data, relevant index data, etc., to ensure the effective operation of the model. Collect the historical market demand data related to each application scenario and preprocess the data, including missing value handling, outlier detection, and data standardization, etc. For example, clean the demand data of the "online retail market" to ensure its accuracy and integrity. For each identified application scenario, apply the selected market demand prediction model to calculate the actual market demand one by one. Record the output results of the model, including the predicted demand value and its confidence interval. For example, speculate on the impact of the "Consumer Confidence Index" on the "retail market demand", obtain the corresponding demand prediction results, and analyze its trend changes. Determine the definition and criteria for multi-path evolution. The evolution path should be able to reflect the changing trend of market demand and potential external influencing factors. Set the time frame of the evolution path, such as short-term (1 month), medium-term (3 months), and long-term (6 months), for comprehensive analysis. Use a dynamic system model (such as a system dynamics model) or a process modeling tool to model the market demand changes in each scenario and identify the key factors affecting market demand. Set the parameters of the model, such as the market response speed, to ensure that the model can reflect the real market dynamics. Based on the established model, simulate the market demand evolution under different paths to generate multiple index evolution paths. Record the changes in the key metrics of each path and their corresponding time points. Select a suitable trend prediction model, such as ARIMA (Autoregressive Integrated Moving Average Model), LSTM (Long Short-Term Memory Network), etc., which can handle the complexity of time series data. Set the parameters of the model, such as the lag order, learning rate, etc., for effective prediction. According to the generated multiple index evolution paths, prepare the corresponding time series data. These data should include the values of the key indices at different time points and the influencing factors (such as market events, etc.).For example, prepare data for the past 12 months for "GDP growth rate" for use in predicting the situation in the next three months. Based on the selected prediction model, conduct a situation prediction for each time point, and record the prediction results, including the predicted index value and its confidence interval. For example, predict the change trend of "inflation rate", generate a situation prediction sequence for the next three months, and analyze its potential impact.

[0031] In this embodiment, the specific steps of step S44 are as follows: Define multiple index prediction time points; Conduct an exponential interaction evolution analysis on the multivariate exponential causal relationship chain to generate the interaction evolution law between different indexes; Conduct an exponential evolution matching on multiple index evolution paths to obtain the matching evolution path of each index; Based on the multiple index prediction time points, conduct a rolling time point situation prediction on the deep change trend of multiple indexes through the interaction evolution law between different indexes and the matching evolution path of each index, so as to obtain the index situation prediction data at multiple time points; Conduct a chronological fitting on the index situation prediction data at multiple time points to construct an index situation prediction sequence at multiple time points.

[0032] In this embodiment, the selection criteria for determining the predicted time points are defined. These time points may include the release dates of key economic reports, the end of quarters, annual summaries, etc., ensuring that these time points can reflect the real changes in the market. Set the intervals of the time points, such as the first day of each month, the first day of each quarter, etc., for systematic analysis of the data. Collect historical data related to the selected time points, including the values of various indices at these time points and their background information. This data will be used for subsequent construction and validation of the prediction model. Clean and preprocess the collected data to ensure the accuracy and consistency of the data, and avoid the impact of missing values and outliers on the analysis results. Organize the selected multiple prediction time points into a list or data frame for subsequent analysis and application. Each time point should include specific dates and information on relevant indices. Regularly review and update the prediction time points to adapt to market changes and new data releases. Select suitable causal relationship analysis methods, such as Granger causality test, structural equation model (SEM), etc., to identify the causal relationships and their evolution among various indices. Set the analysis objectives, such as identifying which indices have the greatest impact on other indices within a specific time period. Collect multivariate index data related to the analysis, including historical data and real-time data, ensuring that the time range of the data covers the required analysis period. Preprocess the data, handle missing values and outliers, and standardize it to ensure data quality and comparability. Use the selected causal relationship analysis method to conduct an evolution analysis of the interactions among multivariate indices. Record the influence intensity and direction of each index on other indices. Generate an interaction evolution law report, which should include the causal relationships among the indices and their trends over time. Define the evolution path of each index, including its historical change trend and influencing factors. Determine the key indicators of the path, which may include growth rate, fluctuation range, etc. Select a suitable model (such as a dynamic system model, Markov process, etc.) to establish the index evolution path for subsequent matching analysis. Select a suitable path matching algorithm, such as dynamic time warping (DTW), least squares fitting, etc., which can effectively compare and match the evolution paths of different indices. Set the matching criteria, such as the similarity threshold for matching, to screen out similar evolution paths. Match the evolution paths of multiple indices one by one, and record the matching paths and matching degrees of each index. Ensure that each matching result can reflect the similarities and differences among the indices. Generate a matching result report, including the matching evolution paths and matching degrees of each index, for subsequent analysis. According to the identified interaction evolution laws and the matched evolution paths, select a suitable rolling prediction model, such as ARIMA model, LSTM, etc. These models can handle the dynamic characteristics of time series data. Set the model parameters, including the prediction time span, step size, etc., to ensure the effectiveness of the model. Prepare multi-time point input data, including the historical data and prediction inputs of various indices at different time points. Ensure that the input data can cover all the time points required for prediction.By performing data standardization and normalization processes, improve the training effect of the model. Use the constructed prediction model to conduct trend predictions for multiple indices at rolling time points. Record the prediction results at each time point, including the predicted index values and confidence intervals. Generate a prediction result report, detailing the predicted values and their changing trends at each time point to assist decision-makers in understanding the future market situation. Select a suitable time-series fitting method, such as curve fitting, smoothing techniques (such as moving average, exponential smoothing, etc.), or polynomial regression, to better present the trend of the predicted data. Set the fitting objective, such as minimizing the prediction error or maximizing the goodness of fit, to ensure the optimization of the fitting effect. Organize the rolling prediction results into a time-series data format for easy time-series fitting. Ensure that the timestamps of the data correspond accurately to the predicted values. Remove outliers and noise to improve the accuracy of the fitting. Perform time-series fitting on the organized prediction data, recording the key parameters and results during the fitting process. Ensure that the fitted curve can effectively reflect the changing trend of the predicted data. Generate a fitting result chart to show the relationship between the fitted curve and the actual predicted data for easy visual analysis and decision support.

[0033] In this embodiment, the specific steps of step S5 are as follows: Step S51: Extract multiple prediction timestamps based on the index trend prediction sequences at multiple time points; Step S52: Obtain the actual index change data according to the multiple prediction timestamps; Step S53: Identify the exponential prediction deviation of the actual index change data according to the index trend prediction sequences at multiple time points to obtain the exponential prediction deviation value at each time point; Step S54: Calculate the deviation fluctuation of the exponential prediction deviation value at each time point to generate the exponential trend prediction deviation feature.

[0034] In this embodiment, the definition of the predicted timestamps to be extracted is clarified. These timestamps should correspond to the key time points in the exponential trend prediction sequence, usually including important moments such as expected market fluctuations, the end of a quarter, the end of a month, etc. Determine the frequency and interval of the timestamps, such as daily, weekly, or monthly, to ensure that the extracted timestamps can cover the overall trend of the prediction sequence. Extract the prediction data corresponding to the key time points from the existing exponential trend prediction sequence. Data processing tools (such as Pandas) can be used for data screening to ensure that only relevant predicted timestamps are extracted. During the extraction process, ensure that the specific date of each timestamp, the corresponding predicted value, and its change trend are recorded for subsequent analysis. Organize the multiple extracted predicted timestamps into a structured format, such as a list or a data frame, for subsequent operations and analysis. For example, record the timestamp as "2025-02-01" and attach the corresponding predicted value and other relevant information to ensure the integrity and accuracy of the information. Determine the source of the actual index change data. This data may come from market databases, industry reports, real-time data released by statistical bureaus, etc. Set the time range for data collection to ensure that the obtained data corresponds to the extracted predicted timestamps. For each extracted predicted timestamp, obtain the actual index change data through API calls, web scraping, or manual downloads. When obtaining the data, pay attention to the format and unit of the data to ensure consistency with the prediction data. For example, ensure that the unit of the actual index change data is the same as that of the prediction data. Organize the obtained actual index change data into a structured format and attach timestamp information. Ensure that the actual change data corresponding to each timestamp can be found. Verify the obtained data to ensure its integrity and accuracy, and avoid affecting subsequent analysis due to data errors. Determine the criteria for deviation identification, usually using the difference between the predicted value and the actual value as an indicator. Set the calculation formula for the deviation: deviation value = actual value - predicted value. For each predicted timestamp, calculate the deviation between the actual change data and the prediction sequence. Use a loop structure to traverse the predicted timestamps and the actual data, and calculate the deviation one by one. Record the deviation values at each time point, including the absolute value and relative value of the deviation, for subsequent analysis. Organize the calculated deviation values into a table, recording the timestamp, predicted value, actual value, and their corresponding deviation values. This will provide the basic data for subsequent deviation analysis. Regularly update the deviation identification results to ensure that they reflect the latest data changes and prediction effects to support decision-making. Determine the calculation method for deviation volatility. Usually, statistical indicators such as standard deviation and coefficient of variation are used to measure the volatility of the deviation. Set the time range for calculation to capture the volatility of the deviation, such as the deviation values in the past three months. Calculate the volatility of the deviation value at each recorded time point. The sliding window method can be selected, and the standard deviation is calculated within each window to evaluate the change of the deviation. Record the process of volatility calculation, including the standard deviation values of each time window and their corresponding time periods.Organize the results of deviation fluctuation calculation into a report, which should include the deviation fluctuation characteristics, fluctuation amplitude and its change trend at each time point. For example, generate a chart to show the change of deviation fluctuation over time to help decision-makers understand the stability and reliability of the prediction.

[0035] In this embodiment, the specific steps of step S6 are as follows: Step S61: Perform intelligent decision analysis based on the exponential trend prediction data at multiple time points to generate an exponential prediction decision result; Step S62: Use the exponential situation prediction deviation characteristics to perform intelligent instant feedback adjustment on the exponential prediction decision result, so as to obtain an instant feedback adjustment decision result; Step S63: Mine the decision paths of the instant feedback adjustment decision result to obtain multiple decision paths; Step S64: Perform end-to-end learning optimization on multiple decision paths to generate an end-to-end optimized decision path; Step S65: Visualize the decisions of the end-to-end optimized decision path and the instant feedback adjustment decision result to construct an intelligent decision management visualization model.

[0036] In this embodiment, determining a suitable decision analysis model generally includes decision trees, random forests, support vector machines, etc. These models can process multi-dimensional data and help identify the impact of different factors on index prediction. Set the model inputs, including index trend prediction data, market conditions, user behavior, etc. at multiple time points to ensure that the model can comprehensively reflect the decision-making environment. Preprocess the index trend prediction data at multiple time points, including missing value filling, outlier detection, and data standardization, to ensure data quality. Organize the data structure to ensure that each data point contains necessary features for subsequent model training and analysis. Use the selected decision analysis model to analyze the organized data and identify key factors and their impact levels. Generate index prediction decision results, including recommended action plans, expected results, and their confidence levels. Record the findings during the analysis process and generate a decision report, which should include the detailed background of each recommendation, prediction results, and potential risks. Determine the criteria for the feedback adjustment mechanism, usually based on the characteristics of the index situation prediction deviation to judge the effectiveness of the decision results. Set a threshold, for example, adjust when the deviation exceeds 10%. Set the feedback adjustment process, including data monitoring, deviation calculation, and formulation of adjustment strategies. Use the previously generated deviation characteristics to monitor the index prediction decision results in real time. Record the deviation value at each time point and compare it with the decision results. When it is found that the deviation exceeds the set threshold, automatically trigger the adjustment mechanism and generate suggestions for instantaneously feedback-adjusting the decision results. Adjust the original decision results according to the feedback information. Record the adjusted decision plan and its basis to ensure the transparency and traceability of the adjustment process. Organize the adjustment results into a report for subsequent analysis and decision-making reference. Define the concept of decision paths, usually referring to the decision-making steps and strategies taken under specific conditions. Set the mining criteria, such as the chronological order of decisions, decision types, and their results. Select a suitable path mining method, such as sequential pattern mining, association rule learning, etc., to identify key nodes and turning points in the decision-making process. Organize the decision results after instantaneously feedback-adjusting, record each step of the decision, its corresponding timestamp, decision type, and result. Clean the data to ensure that there are no missing values and outliers in the dataset to improve the accuracy of mining. Through the selected method, perform path mining on the organized decision data to identify multiple decision paths and their interrelationships. Record the key decision points and execution results of each path. Generate a path mining analysis report, which should include descriptions of each path, key decisions, and their success rates. Determine the goals of end-to-end learning optimization, such as improving decision-making efficiency, reducing deviations, and enhancing decision-making success rates, etc. Set optimization metrics for subsequent evaluation. Select a suitable learning algorithm, such as deep learning models, reinforcement learning, etc., to optimize multiple decision paths. Prepare historical data of multiple decision paths, including decision steps, timestamps, and results, for model training. Ensure the integrity and consistency of the data.Use the selected learning algorithm to train on historical data and optimize the model parameters to improve the predictive ability of the decision-making path. Based on the trained model, optimize multiple decision-making paths, evaluate the effectiveness of each path, and generate optimization suggestions. Record the optimized paths and their expected results. Generate an optimization result report, which should include descriptions of each optimized path, key improvement points, and potential risks. Select suitable visualization tools and technologies, such as Tableau, Power BI, D3.js, etc., which can display complex decision-making data and path relationships. Determine the visualization objectives, such as showing the optimized decision-making path, immediate feedback adjustment results, and their impacts. Organize the optimized decision-making paths and immediate feedback adjustment results to ensure that the data structure is suitable for visualization. Design visualization charts, including decision flowcharts, path diagrams, trend charts, etc., to facilitate the intuitive display of the decision-making process and results. Use the selected visualization tools to visualize the organized data. Generate a decision-making visualization model to enable decision-makers to quickly understand complex decision-making information. Regularly update the visualization content to ensure that it reflects the latest decision-making dynamics and market changes and provides real-time information for decision-making support.

[0037] In this embodiment, an intelligent management platform based on real-time index prediction is provided for implementing the intelligent management method based on real-time index prediction as described above, including: A deep semantic parsing module for obtaining a multi-source index data source; performing deep semantic feature parsing and adaptive normalization processing on the multi-source index data source to construct a real-time index warehouse; A deep trend mining module for performing multi-level index change analysis on the real-time index warehouse and conducting deep change trend mining to generate multiple index deep change trends; A potential association analysis module for performing potential association analysis between indexes based on the real-time index resource library and then conducting intelligent causal relationship chain mining to construct a multi-source index causal relationship chain; An index situation prediction module for performing index situation prediction at different time points on multiple index deep change trends based on the multi-source index causal relationship chain to generate an index situation prediction sequence at multiple time points; A prediction deviation module for identifying index prediction deviations based on the index situation prediction sequence at multiple time points and calculating deviation fluctuations to generate index situation prediction deviation characteristics; A decision feedback adjustment module for performing immediate decision feedback adjustment based on the index situation prediction deviation characteristics and conducting decision visualization to construct an intelligent decision management visualization model.

[0038] The present invention can extract effective information from various data sources through in-depth semantic analysis. Especially in complex and diverse data integration, it can ensure the accuracy and integrity of information. Adaptive standardization processing eliminates the differences between different data sources, making the data comparable on the same dimension and avoiding analysis biases caused by data inconsistency. The construction of a real-time index warehouse can effectively manage a large amount of data, providing fast and accurate real-time data support for subsequent prediction and decision-making. Through multi-level index change analysis, it can help discover multi-dimensional changes in the index, avoid one-sided prediction or analysis, and make trend prediction more comprehensive. Mining deep trends can reveal deeper reasons for changes and potential influencing factors, helping to improve the prediction accuracy and avoid staying only on the surface phenomena. Constructing deep change trends from multiple perspectives and dimensions enables managers to comprehensively understand market changes and make more accurate and timely decisions. Through correlation analysis, it can reveal the internal relationships between different indexes, thus helping decision-makers understand the correlations behind certain changes. Using intelligent methods to deeply mine the causal relationship chain, capture complex interactions, enhance the understanding of market or system dynamics, and then optimize the decision-making process. By establishing a causal relationship chain, it can identify which factors have a significant impact on other factors, thereby optimizing resource allocation and decision-making. Through systematic analysis of the impact of the causal relationship chain on multi-dimensional index changes, the trends of multiple indexes at different time points can be accurately predicted. The generated prediction sequences at multiple time points can provide detailed pre-judgments for the short-term, medium-term, and long-term future, providing a panoramic prediction picture for decision-makers. Accurate trend prediction can help decision-makers plan and adjust strategies in advance, avoid being belated, and respond to market or environmental changes in a timely manner. By monitoring and identifying prediction biases, the gap between model prediction and actual situation can be discovered in a timely manner, providing a basis for model optimization. Calculating the fluctuation range of prediction biases helps to reveal prediction uncertainties, helps decision-makers evaluate the credibility of prediction results, and avoids decision-making risks caused by over-relying on a single prediction result. Bias identification and fluctuation calculation enable the system to dynamically adjust the prediction model according to the latest actual situation, improving the prediction accuracy and real-time response ability. It can perform real-time feedback adjustment based on prediction biases and actual changes, ensuring that the decision-making system can always maintain flexibility and accuracy in a dynamically changing environment. Decision visualization helps decision-makers understand complex data and analysis results through graphical and intuitive displays, improving the transparency and efficiency of decision-making. Through an intelligent decision management system, not only the efficiency of decision-making is improved, but also human errors and judgment biases can be reduced, realizing more scientific and efficient decision management.

[0039] Therefore, from any perspective, the embodiments should be regarded as exemplary and non-limiting. The scope of the present invention is defined by the appended claims rather than the above description. Thus, all changes falling within the meaning and scope of the equivalent elements of the application documents are intended to be encompassed within the present invention.

[0040] As described above, these are merely specific embodiments of the present invention, enabling those skilled in the art to understand or implement the present invention. Various modifications to these embodiments will be obvious to those skilled in the art. The general principles defined herein can be implemented in other embodiments without departing from the spirit or scope of the present invention. Therefore, the present invention will not be limited to these embodiments shown herein, but rather will conform to the broadest scope consistent with the principles and novel features invented herein.

Claims

1. An intelligent management method based on real-time index prediction, characterized in that: The following steps are involved: Step S1: Acquire a multivariate index data source; perform deep semantic feature analysis and adaptive standardization on the multivariate index data source to build a real-time index warehouse; Step S2: Perform multi-level index change analysis on the real-time index warehouse and conduct deep change trend mining to generate multiple deep change trends of the index; Step S3: Perform potential correlation analysis between indexes based on the real-time index resource library, and then perform intelligent causal chain mining to construct a multivariate index causal chain; Step S4: Based on the multivariate index causal chain, the index situation prediction at different time points is performed on the deep change trends of multiple indexes, thereby generating an index situation prediction sequence at multiple time points; Step S5: performing index prediction deviation identification based on the index situation prediction sequence at multiple time points, and performing deviation fluctuation calculation, thereby generating index situation prediction deviation features; Step S6: Make instant decision feedback adjustments based on the index situation prediction deviation characteristics, visualize the decisions, and build an intelligent decision management visualization model.

2. The intelligent management method based on real-time index prediction according to claim 1 is characterized in that: The specific steps of step S1 are: Step S11: Acquire a multivariate index data source; perform abnormal data detection on the multivariate index data source and mark abnormal data points; Step S12: filtering abnormal data points to obtain a filtering optimization index data source; Step S13: performing deep semantic feature analysis on the filtering optimization index data source to generate index semantic features; Step S14: classify the business type according to the filtered optimized index data source to obtain the index type; Step S15: Adaptively normalize the multivariate index data source according to the index semantic features and index type to obtain a multivariate normalized index; Step S16: Perform data integration processing on the multivariate standardized index and build a real-time index warehouse.

3. The intelligent management method based on real-time index prediction according to claim 1 is characterized in that: The specific steps of step S2 are: Step S21: Divide the real-time index warehouse into multiple time period sliding windows to generate index windows of multiple time periods; Step S22: Calculating the index change range one by one based on the index windows of multiple time periods to generate the change range of each index; Step S23: performing exponential time series mutation rate statistics according to the exponential windows of multiple time periods to obtain the exponential time series mutation rate; Step S24: performing a multi-level index change analysis based on the index time series mutation rate and the change amplitude of each index, thereby generating a multi-level change feature of each index; Step S25: Perform deep change trend mining on the multi-level change characteristics of each index, thereby generating multiple deep change trends of the indexes.

4. The intelligent management method based on real-time index prediction according to claim 1 is characterized in that: The specific steps of step S3 are: Step S31: performing market activity demand analysis according to the real-time index resource library to generate real-time market activity demand characteristics; Step S32: mining the user demand changes in the real-time index resource library to generate user demand change data; Step S33: performing resource consumption frequency statistics based on the real-time index resource library to generate resource consumption frequency; Step S34: Based on the real-time market activity demand characteristics, user demand change data and resource consumption frequency, potential correlation analysis is performed between indexes to obtain potential correlation characteristics between indexes; Step S35: Perform intelligent causal chain mining on the potential correlation features between indexes, so as to construct a multivariate index causal chain.

5. The intelligent management method based on real-time index prediction according to claim 1 is characterized in that: The specific steps of step S4 are: Step S41: identifying multiple index application scenarios based on the multivariate index data source; Step S42: Speculating the actual market demand for each of the multiple index application scenarios, thereby obtaining the actual market demand for each scenario; Step S43: performing multi-path evolution according to actual market demand of each scenario to generate multiple exponential evolution paths; Step S44: Based on multiple index evolution paths and multivariate index causal relationship chains, index situation predictions are performed at different time points on the deep change trends of multiple indexes, thereby generating index situation prediction sequences at multiple time points.

6. The intelligent management method based on real-time index prediction according to claim 5 is characterized in that: The specific steps of step S44 are: Define multiple index prediction time points; Conduct exponential interaction evolution analysis on multivariate exponential causal chain to generate interaction evolution laws between different exponents; Perform exponential evolution matching on multiple exponential evolution paths to obtain the matching evolution path of each index; Based on the multiple index prediction time points, a rolling time point situation forecast is performed on the deep change trends of multiple indexes through the interaction evolution law between different indexes and the matching evolution path of each index, so as to obtain index situation forecast data at multiple time points; The exponential trend prediction data at multiple time points are time-series fitted to construct the exponential trend prediction sequence at multiple time points.

7. The intelligent management method based on real-time index prediction according to claim 1 is characterized in that: The specific steps of step S5 are: Step S51: extracting multiple prediction timestamps based on the index situation prediction sequence of multiple time points; Step S52: obtaining actual exponential change data according to multiple prediction timestamps; Step S53: performing index prediction deviation identification on the actual index change data according to the index situation prediction sequence of multiple time points to obtain the index prediction deviation value at each time point; Step S54: Calculate the deviation fluctuation of the index prediction deviation value at each time point to generate the index situation prediction deviation feature.

8. The intelligent management method based on real-time index prediction according to claim 1 is characterized in that: The specific steps of step S6 are: Step S61: Perform intelligent decision analysis based on the index trend prediction data at multiple time points to generate index prediction decision results; Step S62: using the index situation prediction deviation feature to perform intelligent instant feedback adjustment on the index prediction decision result, thereby obtaining an instant feedback adjustment decision result; Step S63: mining decision paths for the instant feedback adjustment decision results, thereby obtaining multiple decision paths; Step S64: performing end-to-end learning optimization on multiple decision paths to generate an end-to-end optimized decision path; Step S65: Visualize the end-to-end optimized decision path and the instant feedback adjustment decision results, and build an intelligent decision management visualization model.

9. An intelligent management platform based on real-time index prediction, characterized in that: The method for executing the intelligent management method based on real-time index prediction according to claim 1 comprises: The deep semantic analysis module is used to obtain multiple index data sources; perform deep semantic feature analysis and adaptive standardization on multiple index data sources to build a real-time index warehouse; The deep trend mining module is used to perform multi-level index change analysis on the real-time index warehouse and perform deep change trend mining, thereby generating multiple index deep change trends; Potential correlation analysis module, which is used to analyze the potential correlation between indexes based on the real-time index resource library, and then conduct intelligent causal chain mining to build a multivariate index causal chain; The index situation prediction module is used to predict the index situation at different time points based on the multi-index causal relationship chain for the deep change trends of multiple indexes, thereby generating an index situation prediction sequence at multiple time points; A prediction deviation module is used to identify index prediction deviations based on the index situation prediction sequences at multiple time points and calculate the deviation fluctuations, thereby generating index situation prediction deviation features; The decision feedback adjustment module is used to make instant decision feedback adjustments based on the index situation prediction deviation characteristics, visualize the decisions, and build an intelligent decision management visualization model.

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