Industry Rotation Comprehensive Analysis System Based on Data Flow

Abstract

This invention provides a data flow-based industry rotation comprehensive analysis system, belonging to the field of intelligent financial technology. The system includes a control flow management module and functional modules. The functional modules include a data acquisition module, a streaming feature clustering and compression module, a multi-model collaborative optimization module, a dynamic hierarchical visualization module, a semantic-driven interaction module, and a transparent verification module. Data is transmitted between modules in JSON format via the high-performance message queue RabbitMQ. This invention achieves efficient data integration through real-time acquisition and standardized processing of multi-source financial data. The system can uniformly process time series of transaction data, periodic reports of financial data, and monthly indicators of macroeconomic data, generating standardized analytical factors and overcoming the problems of inconsistent data formats and misaligned timestamps. Compared with the low efficiency of existing technologies processing hundreds of data points per second, this invention supports processing thousands of data points per second, significantly improving processing speed.

Description

Technical Field

[0001] This invention relates to the field of intelligent financial technology, specifically to an industry rotation comprehensive analysis system based on data flow. Background Technology

[0002] Sector rotation analysis is a key technology in the financial investment field. It aims to identify high-potential sectors by comprehensively evaluating multi-dimensional information such as market transactions, capital flows, financial performance, and macroeconomic data, thereby assisting investment decisions and portfolio optimization. Existing technologies typically rely on financial data platforms, collecting price, trading volume, financial indicators, and economic data by subscribing to exchanges or third-party interfaces. This data generates analytical factors, such as price change rates, net capital inflows, and profitability indicators. These factors are integrated using statistical models or machine learning methods to generate industry scores or rankings. Analysis methods include single-factor analysis, ranking based on indicators such as price trends or capital flows, and multi-factor analysis, which integrates multiple indicators through weighted summaries. Results are visualized using tools such as bar charts, line charts, or heatmaps to display industry performance trends. Some systems allow users to query analysis results using fixed templates. Verification mechanisms typically store analysis results in a database or record them using encrypted hash values, which are then used for investment analysis and regulatory verification.

[0003] While existing technologies have achieved some success in industry rotation analysis, the following problems still exist in practical applications:

[0004] Problem 1: Existing technologies have limitations in data collection and integration. Current financial data sources include exchange trading data, corporate financial statements, and macroeconomic data from statistical agencies, and these data formats are inconsistent. Trading data generates high-frequency time series, financial data is in quarterly or annual reports, and macroeconomic data is in monthly or annual indicators, lacking a unified standardized processing mechanism. Data integration typically relies on manual screening or simple script merging, resulting in slow processing speeds, handling only a few hundred records per second, and prone to data gaps or inconsistencies, such as misaligned timestamps between price and funding data. Existing systems struggle to achieve real-time fusion of multi-source heterogeneous data, leading to fragmented comprehensive analysis results that fail to fully reflect industry trends.

[0005] The second issue is the inadequacy of existing technologies in model synergy and dynamic analysis. Traditional analysis systems rely on fixed-weight models, such as a 40% weighting for price factors and a 30% weighting for capital factors, lacking the ability to dynamically adjust according to market fluctuations. When market conditions change, such as sudden economic policies or industry events, fixed weights cannot reflect new trends in a timely manner, leading to biased industry scores and response times typically exceeding several minutes. Existing systems mostly use offline batch processing, updating at daily or weekly levels, making it difficult to capture industry rotation signals in real time. While a few real-time systems can process high-frequency data, they lack predictive models based on multi-dimensional factor synergy, failing to deeply analyze the causal relationships or interaction effects between factors, such as the linkage between capital inflows and price trends. This results in insufficient accuracy in comprehensive assessments, limiting the efficiency of investment decisions.

[0006] Therefore, a data flow-based industry rotation comprehensive analysis system is needed to solve the above problems. Summary of the Invention

[0007] Technical problems to be solved

[0008] To address the shortcomings of existing technologies, this invention provides a data flow-based industry rotation comprehensive analysis system, which solves the problems mentioned in the background section above.

[0009] Technical solution

[0010] To achieve the above objectives, the present invention provides the following technical solution: a data flow-based industry rotation comprehensive analysis system, comprising a control flow management module and functional modules. The functional modules include a data acquisition module, a streaming feature clustering and compression module, a multi-model collaborative optimization module, a dynamic hierarchical visualization module, a semantic-driven interaction module, and a transparent verification module. Data is transmitted between these modules in JSON format via a high-performance message queue, RabbitMQ. The control flow management module coordinates the execution order and error handling steps of the modules as follows:

[0011] The SP1 control flow management module employs a time-driven scheduling algorithm, checking the update status of the financial data interface every minute. This triggers the data acquisition module to collect multi-dimensional financial data, sequentially triggering the streaming feature clustering and compression module, the multi-model collaborative optimization module, the dynamic hierarchical visualization module, the semantic-driven interaction module, and the transparent verification module. When a module fails, a retry mechanism is initiated, retrying once every 5 seconds, up to a maximum of 3 times. The data is used for market-wide industry assessment and classification, generating investment decision support information.

[0012] The SP2 data acquisition module collects multidimensional financial data in real time from the financial data interface via the high-performance message queue RabbitMQ. This data includes 20 key factors from the following six models, updated every second. The collected data is then transmitted to the streaming feature clustering and compression module for full market industry evaluation.

[0013] The price-volume volatility model analyzes the volatility characteristics of price and volume, including intraday price increase and overnight gap increase factors, and is used to assess industry momentum and reversal effects.

[0014] The fund flow model analyzes the flow direction of institutional funds and retail investor funds, including factors for super-large and small weekly net inflows, to assess the attractiveness of industry funds.

[0015] Financial performance models analyze industry profitability and growth potential, including industry return on equity and net profit growth factors, to assess industry operating quality.

[0016] The chip distribution model analyzes market holding costs and sentiment, including holding returns and profit chip ratio factors, and is used to assess industry support and selling pressure.

[0017] Technical analysis models analyze industry technology trends and stability, including strength and stability factors, to assess the momentum of industry trends;

[0018] The macroeconomic driving model analyzes the impact of macroeconomic variables on industries, including the year-on-year factors of the manufacturing purchasing managers' index and the consumer price index, and is used to assess the economic cycle effect.

[0019] The Sp3 streaming feature clustering compression module receives multidimensional financial data, executes the streaming feature clustering compression algorithm, generates compressed data streams, and transmits them to the multi-model collaborative optimization module. The data is used for industry-level calculations and factor weight optimization.

[0020] The SP4 multi-model collaborative optimization module receives compressed data streams, executes the Shapley value multi-model collaborative optimization algorithm, quantifies the contribution of the six major models, dynamically allocates the weights of 20 key factors, generates industry classification results, and transmits them to the dynamic classification visualization module and the transparency verification module. The data is used for industry classification display and result verification.

[0021] The SP5 dynamic grading visualization module receives industry grading results, executes the dynamic grading visualization engine, divides the industry into bullish, neutral, and bearish groups, projects 20 key factors into a two-dimensional space through principal component analysis to generate a dynamic bubble chart, and displays the historical trajectory of industry grading in combination with an interactive timeline. The data is transmitted to the semantic-driven interactive module and applied to the user's investment decision interface display and portfolio optimization.

[0022] The SP6 semantic-driven interaction module receives bubble chart data, combines it with user natural language queries, generates structured queries through dependency parsing, and maps them to dynamic bubble charts or lists. The data is then applied to user interfaces, industry filtering, and portfolio adjustments.

[0023] The SP7 Transparent Verification Module receives industry classification results, generates encrypted verification values ​​through zero-knowledge proofs, and stores them in the distributed ledger IPFS. The data is used to verify the integrity and tamper-proof nature of the industry classification results, supporting regulatory compliance and user trust.

[0024] Preferably, the control flow management module includes the following steps:

[0025] Sp1 uses a time-driven scheduling algorithm to check the update status of the financial data interface every minute and trigger the data acquisition module to start acquisition. If the data interface does not respond, a retry mechanism is started, retrying once every 5 seconds, up to 3 times.

[0026] After SP2 confirms that the data acquisition module has completed the acquisition, it sequentially triggers the streaming feature clustering compression module to process the data, the multi-model collaborative optimization module to generate hierarchical results, the dynamic hierarchical visualization module to display the results, the semantic-driven interaction module to process user queries, and the transparent verification module to verify the results. The data is used to coordinate the execution order between modules and industry hierarchical analysis.

[0027] Preferably, the streaming feature clustering compression module includes the following steps:

[0028] Sp1 receives multidimensional financial data and uses an online K-means clustering algorithm to cluster 20 key factors in real time, generating highly correlated factor clusters and clustering intraday real price increases and overnight gap increases into one cluster.

[0029] Sp2 calculates the contribution of each factor using the information gain ratio, eliminates low-contribution factors, and prioritizes retaining factors from the price-volume volatility model in highly volatile markets.

[0030] Sp3 performs entropy encoding compression at edge nodes to generate compressed data streams with a compression ratio of 30% of the original volume. The compressed data is then transmitted to the multi-model collaborative optimization module for industry-level calculations and factor weight optimization.

[0031] Preferably, the multi-model collaborative optimization module includes the following steps:

[0032] Sp1 receives compressed data streams and uses the Shapley value algorithm to quantify the contributions of the price-volume fluctuation model, capital flow model, financial performance model, chip distribution model, technical analysis model, and macroeconomic driving model, generating a dynamic weight combination with the price-volume fluctuation model weighting at 30% and the industry return on equity factor weighting at 15%.

[0033] Sp2 runs distributed backtesting in parallel on a multi-node cluster to simulate multi-cycle market environments, verify strategy stability, and generate industry-level results.

[0034] Sp3 uses dependency parsing to extract key entities from policy texts, namely manufacturing and interest rates, and maps them to factors in the macroeconomic driving model, such as the manufacturing purchasing managers' index, to enhance the accuracy of cross-validation. The industry classification results are transmitted to the dynamic classification visualization module and the transparency verification module, and the data is used for industry classification display and result verification.

[0035] Preferably, the price-volume fluctuation model includes the following steps:

[0036] Sp1 receives compressed data streams, executes time-series entropy analysis algorithms, and calculates the entropy values ​​of intraday price increases and overnight gap increases. High entropy values ​​indicate an increased probability of reversal, while low entropy values ​​indicate a continuation of the trend.

[0037] Sp2 dynamically adjusts industry percentiles based on entropy values, prioritizing industries with high trend strength into the long position group. Data is transmitted to the dynamic hierarchical visualization module for industry-level display and portfolio optimization to enhance predictive capabilities.

[0038] Preferably, the fund flow model includes the following steps:

[0039] Sp1 receives compressed data streams, executes capital flow network analysis algorithms, models the industry as graph nodes, uses ultra-large and small-to-medium weekly net inflows as edge weights, and uses the PageRank algorithm to calculate the industry's capital attractiveness.

[0040] Sp2 adjusts the edge weight thresholds based on market sentiment, filters out noisy capital flows, prioritizes highly attractive industries to enter the long position group, and transmits the data to the dynamic hierarchical visualization module for industry hierarchical display and portfolio optimization to improve predictive capabilities.

[0041] Preferably, the financial performance model includes the following steps:

[0042] Sp1 receives compressed data streams, executes financial factor standardized scoring algorithms, performs Z-score standardization on industry return on net assets, net profit growth rate and revenue growth rate, and generates a comprehensive score.

[0043] Sp2 dynamically adjusts factor weights based on the industry life cycle, prioritizing growth industries with a 40% weighting for revenue growth rate. Data is transmitted to a dynamic hierarchical visualization module for industry-level display and portfolio optimization to enhance predictive capabilities.

[0044] Preferably, the chip distribution model includes the following steps:

[0045] Sp1 receives compressed data streams, executes a chip distribution density analysis algorithm, and uses kernel density estimation to calculate the distribution density of holding profits and the proportion of profitable chips. High-density areas indicate strong support.

[0046] Sp2 adjusts the industry percentile based on the kernel density estimation results, strongly supporting industries to enter the long position group. The data is transmitted to the dynamic hierarchical visualization module for industry hierarchical display and portfolio optimization to improve forecasting capabilities.

[0047] Preferably, the technical analysis model includes the following steps:

[0048] Sp1 receives compressed data streams, executes technical factor spectrum analysis algorithms, performs Fourier transforms on strong and stable factors, extracts periodic features, and prioritizes industries with high periodic intensity to enter the long position group.

[0049] Sp2 dynamically adjusts the cycle threshold based on market volatility, and the data is transmitted to the dynamic hierarchical visualization module for industry-level display and portfolio optimization to improve predictive capabilities.

[0050] Preferably, the macroscopic driving model includes the following steps:

[0051] Sp1 receives compressed data streams, executes macroeconomic factor causal analysis algorithms, and uses causal testing algorithms to quantify the causal relationship between the year-on-year growth of the manufacturing purchasing managers' index and the consumer price index in the current month and the industry performance. The weight of high causal strength factors is increased to 35%.

[0052] Sp2 maps macroeconomic factors based on the industry life cycle, prioritizing economic growth factors for growth industries. The data is then transmitted to a dynamic hierarchical visualization module for industry-level display and portfolio optimization, thereby enhancing predictive capabilities.

[0053] Beneficial effects

[0054] This invention provides a data flow-based industry rotation comprehensive analysis system. It has the following beneficial effects:

[0055] This invention achieves efficient data integration through real-time acquisition and standardized processing of multi-source financial data. The system can uniformly process time series of transaction data, periodic reports of financial data, and monthly indicators of macroeconomic data, generating standardized analytical factors and overcoming problems of inconsistent data formats and misaligned timestamps. Compared to the low efficiency of existing technologies that process hundreds of data points per second, this invention supports processing thousands of data points per second, significantly improving processing speed. Furthermore, a data cleaning mechanism reduces data gaps and inconsistencies, ensuring that the comprehensive analysis results fully reflect industry trends.

[0056] This invention enhances the real-time performance and accuracy of industry rotation analysis through multi-model collaborative optimization and dynamic weight adjustment. The system integrates multi-dimensional models including price and volume fluctuations, capital flows, financial performance, share distribution, technical analysis, and macroeconomic drivers, dynamically allocating factor weights to replace the fixed-weight model of existing technologies. This allows for rapid adaptation to market fluctuations and industry events, with response times reduced to the second level, far superior to the minutes-long delays of existing technologies. The system supports real-time data stream processing, replacing offline batch processing. Combined with causal relationship analysis between factors, it deeply explores the interaction effects between capital inflows and price trends, improving the accuracy of comprehensive assessments and enhancing predictive capabilities. This invention achieves efficient integration and dynamic collaborative analysis of multi-dimensional data, significantly improving the real-time performance, accuracy, and reliability of comprehensive industry rotation analysis, providing strong technical support for investment decision optimization and regulatory compliance. Attached Figure Description

[0057] Figure 1 This is a system framework diagram of the present invention;

[0058] Figure 2 This is the core flowchart of the system of the present invention;

[0059] Figure 3 This is a bar chart showing the industry ratings for this invention. Detailed Implementation

[0060] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention. Specific Implementation Example 1:

[0062] like Figures 1 to 3 As shown, this system constructs a comprehensive market industry evaluation system by collecting and processing multidimensional financial data in real time, supporting investment decisions and portfolio optimization. The system comprises a control flow management module and six functional modules: a data acquisition module, a streaming feature clustering and compression module, a multi-model collaborative optimization module, a dynamic hierarchical visualization module, a semantic-driven interaction module, and a transparent verification module. Data is transmitted between modules in JSON format via the high-performance message queue RabbitMQ, and the control flow management module coordinates the execution order and error handling. The following details the complete operational flow of the system from data acquisition to result verification, covering three major mechanisms: multidimensional data scanning, in-depth model refinement, and a dynamic hierarchical system, ensuring a clear and implementable technical solution.

[0063] System operation process:

[0064] The control flow management module coordinates the operation of each module and monitors the status of the financial data interface to ensure real-time performance and robustness. The module employs a time-driven scheduling algorithm, checking the update status of the financial data interface every minute via a RESTful API or WebSocket protocol. Upon confirming data updates, it generates a trigger command to sequentially activate the data acquisition module, streaming feature clustering and compression module, multi-model collaborative optimization module, dynamic hierarchical visualization module, semantic-driven interaction module, and transparent verification module. The trigger command is sent via RabbitMQ and includes the module identifier and scheduling time. If a module fails, a retry mechanism is initiated, retrying every 5 seconds, up to a maximum of 3 times. If it still fails, an error log is recorded to the local database, triggering of that module is paused, and the next minute scheduling cycle begins.

[0065] The data acquisition module collects multi-dimensional financial data in real time, including 20 key factors from six models, for comprehensive market and industry assessment. The module subscribes to financial data interfaces via RabbitMQ, acquiring raw data every second. After cleaning and removing null and outlier values, the data is standardized to generate 20 factors, which are then transmitted to the streaming feature clustering and compression module. The composition and factor calculation methods of the six models are as follows: The price-volume volatility model analyzes price and volume volatility, including intraday price increase and overnight gap increase factors. It calculates intraday price trend strength using daily candlestick data and market expectation changes using continuous trading day data, used to assess industry momentum and reversal effects; The fund flow model analyzes the flow of institutional and retail funds, including ultra-large weekly net inflow and small-to-medium weekly net inflow factors, calculated by accumulating the buy and sell amounts of institutional and retail funds over five trading days, used to assess industry attractiveness; The financial performance model analyzes industry profitability and growth potential, including industry return on equity and net profit growth factors, calculated by weighted average annual return on equity of listed companies within the industry and year-on-year net profit growth in quarterly or annual financial reports. The system employs several analytical tools: a rate calculation to assess industry operating quality; a chip distribution model to analyze market holding costs and sentiment, including holding returns and the proportion of profitable chips, calculated using transaction details as the percentage difference between the average holding cost and the current price, and the proportion of profitable holdings to total holdings, to assess industry support and selling pressure; a technical analysis model to analyze industry technical trends and stability, including strength and stability factors, calculated using the 14-day Relative Strength Index (RSI) and the 20-day Bollinger Band width normalized value, to assess industry trend momentum; and a macroeconomic driver model to analyze the impact of macroeconomic variables on the industry, including the manufacturing purchasing managers' index (PMI) and the year-on-year CPI, calculated using monthly PMI and CPI data, to assess economic cycle effects.

[0066] The streaming feature clustering compression module receives data from 20 factors, executes the streaming feature clustering compression algorithm to generate highly correlated factor clusters and compress the data stream, optimizing transmission and computation efficiency. The module runs on an edge node, configured with 16GB of memory and a 4-core CPU, using Docker containers, and processes 10MB of data per second. The module first uses an online K-means clustering algorithm to group the 20 factors into highly correlated clusters, with an initial cluster size of 5. This is dynamically adjusted by minimizing the intra-cluster variance, and intraday price increases and overnight gap increases are clustered together based on cosine similarity. Next, the module calculates the contribution of factors to industry classification using the information gain ratio. In high-volatility markets, the volatility index is used to prioritize retaining price-volume volatility model factors and remove factors with a contribution below 0.1. Finally, entropy encoding compression is performed at the edge node, compressing the data stream to 30% of its original volume, including industry identifiers, factor clusters, and timestamps, before transmitting it to the multi-model collaborative optimization module.

[0067] The multi-model collaborative optimization module receives compressed data streams, integrates six major models, executes the Shapley value multi-model collaborative optimization algorithm, dynamically allocates 20 factor weights, generates industry-level classification results, and ensures the reliability of the strategy through distributed backtesting and macro-level cross-validation. The module decodes and compresses the data stream, extracts factor cluster data, and uses the Shapley value algorithm based on game theory to quantify the contribution of six major models and 20 factors. The initial weights are 30% for the price-volume volatility model, 15% for the industry return on equity factor, and 55% for other factors, dynamically adjusted according to market volatility. Distributed backtesting is run on a Kubernetes cluster with 8 nodes, each with 16GB of memory and 4 CPU cores. Five years of historical data are used to simulate daily, weekly, and monthly market environments to calculate strategy returns and Sharpe ratios, requiring a standard deviation below 0.05 to verify stability. Industry classification results are generated with scores from 0 to 1, 0.8 and above for the long group, 0.5 to 0.8 for the neutral group, and below 0.5 for the short group. Dependency parsing is used with HanLP to extract key entities from policy texts and map them to macroeconomic driving model factors to enhance classification accuracy. The classification results are transmitted to the dynamic classification visualization module and the transparency verification module via RabbitMQ.

[0068] The dynamic grading visualization module receives industry grading results, executes the dynamic grading visualization engine to divide the industry into bullish, neutral, and bearish groups, and generates dynamic bubble charts and interactive timelines to showcase opportunities in the sector. The module uses principal component analysis to reduce the dimensionality of 20 factors to a two-dimensional space, retaining 80% of the variance, generating coordinates. Bubble size represents scores, and color represents grading: green for bullish groups, yellow for neutral groups, and red for bearish groups. The bubble chart is drawn using D3.js, combined with the interactive timeline to display the 30-day grading trajectory, supporting adaptive resolution (minimum 1024x768). The output visualization data is then transmitted to the semantically driven interactive module.

[0069] The semantic-driven interaction module receives bubble chart data and allows users to query industry classification results via natural language. It generates an interactive interface to assist in industry filtering and portfolio adjustment. The module receives user queries, uses HanLP for dependency parsing to extract intent and entities, generates structured queries, maps them to bubble charts or lists, and filters multi-group industries or compares multi-industry trajectories. It uses React to render the interactive interface, supporting zoom, drag, and click operations, and outputs interactive interface data for industry filtering and portfolio adjustment.

[0070] The transparency verification module receives industry-level grading results, generates encrypted verification values ​​using zero-knowledge proofs, and stores them on the distributed ledger IPFS to ensure the integrity and tamper-proof nature of the results. The module calculates the SHA-256 hash value of the grading results, uses the zk-SNARK algorithm to generate a verification value, proving that the results have not been tampered with; the verification value is uploaded to IPFS v0.8.0, with nodes distributed across Asia, North America, and Europe, with a storage size of approximately 256 bytes, generating a content-addressable hash; a verification interface is provided, allowing users or regulatory agencies to query the verification value and confirm the credibility of the results.

[0071] The detailed structure and operation of the six models are as follows: The price-volume volatility model includes intraday price increase and overnight gap increase factors. It is calculated using daily candlestick data and continuous trading day data, and executes a time-series entropy analysis algorithm to analyze the entropy value of the factor's time series. High entropy values ​​indicate an increased probability of reversal, while low entropy values ​​indicate trend continuation. Industry rankings are adjusted based on entropy values, with industries exhibiting high trend strength prioritized for the bullish group. The resulting ranking is then transmitted to the dynamic ranking visualization module to enhance predictive capabilities. The fund flow model includes ultra-large weekly net inflow and small-to-medium weekly net inflow factors. It is calculated using five trading days' transaction details, and executes a fund flow network analysis algorithm. Industries are modeled as graphs, with industries as nodes and factor values ​​as edge weights. The PageRank algorithm is used to calculate attractiveness, and edge weights are adjusted based on the volatility index to filter noisy fund flows. Industries with high attractiveness are entered into the bullish group, and the resulting ranking is transmitted to the dynamic ranking visualization module to enhance predictive capabilities. The financial performance model includes industry return on equity (ROE), net profit growth rate, and revenue growth rate factors. Calculated using annual and quarterly financial reports, it employs a standardized scoring algorithm for financial factors. A weighted average of these standardized factors generates a comprehensive score. The initial weights are 40% for ROE, 30% for net profit growth rate, and 30% for revenue growth rate. These weights are adjusted based on the industry life cycle, with growth industries prioritized for a 40% weighting in revenue growth. The resulting tiered score is then transmitted to a dynamic tiered visualization module to enhance predictive capabilities. The share distribution model includes factors related to holding returns and the proportion of profitable shares. Calculated using transaction details, it employs a share distribution density analysis algorithm, using kernel density estimation to calculate distribution density. High-density areas indicate strong support. The industry percentile is adjusted, and industries with strong support are placed in the bullish group. The resulting tiered score is then transmitted to the dynamic tiered visualization module to enhance predictive capabilities. The technical analysis model includes factors of strength and stability. Calculated using the 14-day Relative Strength Index (RSI) and the 20-day Bollinger Band width, it employs a technical factor spectrum analysis algorithm. Fourier transform is used to extract cyclical characteristics, and industries with high cyclical strength are placed in the bullish group. The cyclical threshold is adjusted based on market volatility, and the resulting tiered score is transmitted to the dynamic tiered visualization module to enhance predictive capabilities. The macroeconomic driver model includes the year-on-year factors of the manufacturing purchasing managers' index and the consumer price index for the current month. It is calculated using monthly data, performs macroeconomic factor causal analysis algorithm, uses Granger causality test to quantify causal relationships, increases the weight of high causal strength factors to 35%, maps factors according to industry life cycle, prioritizes economic growth factors for growth industries, outputs graded results, and transmits them to the dynamic graded visualization module to improve forecasting capabilities.

[0072] The system operates by starting with the control flow management module triggering the data acquisition module every minute to collect 20 factors, which are then transmitted to the streaming feature clustering and compression module to generate a compressed data stream. The multi-model collaborative optimization module integrates six models to generate industry-specific classification results. The dynamic classification visualization module generates bubble charts and timelines. The semantic-driven interactive module supports user queries. The transparent verification module generates IPFS verification values. The output includes industry-specific classification results, an interactive interface, and verification values, applicable to investment decisions, portfolio optimization, and regulatory compliance. Specific Implementation Example 2:

[0074] like Figures 1 to 3 As shown, this system constructs a comprehensive market industry evaluation system by collecting and processing multidimensional financial data in real time, supporting investment decisions and portfolio optimization. The system comprises a control flow management module and six functional modules: a data acquisition module, a streaming feature clustering and compression module, a multi-model collaborative optimization module, a dynamic hierarchical visualization module, a semantic-driven interaction module, and a transparent verification module. Modules transmit data in JSON format via the high-performance message queue RabbitMQ, with the control flow management module coordinating execution order and error handling. The following details the algorithmic logic, input data, output results, and specific applications of each module and the six major models within the system, covering three major mechanisms: multidimensional data scanning, in-depth model refinement, and a dynamic hierarchical system, ensuring a clear and implementable technical solution. Market volatility indicators are used to measure market volatility levels. Calculated based on historical price fluctuations, they reflect investors' expectations of future market volatility and are used to assess high-volatility market environments.

[0075] Algorithm logic and application:

[0076] The control flow management module coordinates the operation of various modules through a time-driven scheduling algorithm to ensure system real-time performance and robustness. The algorithm logic involves periodically checking the status of the financial data interface, triggering subsequent modules, and handling operational errors. The operation steps include using a system timer to query the financial data interface every 60 seconds, obtaining the update status via a RESTful API or WebSocket protocol; after confirming data updates, generating a trigger command containing the module identifier and scheduling time, sequentially activating the data acquisition module, streaming feature clustering and compression module, multi-model collaborative optimization module, dynamic hierarchical visualization module, semantic-driven interaction module, and transparent verification module; if a module fails, a retry mechanism is initiated, retrying once every 5 seconds, up to a maximum of 3 times. Upon failure, an error log is recorded to the local database, triggering of that module is paused, and scheduling proceeds to the next minute. Input data is the interface status signal, and output is the trigger command. The application method ensures smooth data flow through scheduling, supports real-time assessment of the entire market industry, and enhances predictive capabilities.

[0077] The data acquisition module uses a real-time acquisition algorithm to obtain multi-dimensional financial data and generate 20 key factors for market-wide industry assessment. The algorithm logic involves subscribing to a financial data interface, cleaning and standardizing the data, and generating factor data. The operational steps include connecting to the financial data interface via RabbitMQ to acquire raw data per second, including industry prices, trading volume, and financial indicators; cleaning the data, removing null and outlier values ​​(outliers are defined as data exceeding three standard deviations); standardizing the data, normalizing factor values ​​to the 0-1 range; and generating 20 factors covering: intraday price and volume fluctuations (intraday price increase and overnight gap increase); fund flow (large and small weekly net inflows); industry return on equity and net profit growth (financial performance); holding returns and profitable share ratio (share distribution); strength and stability (technical analysis); and the manufacturing purchasing managers' index and consumer price index (year-on-year). The input data is raw financial data, and the output is 20 standardized factor data, which is then transmitted to the streaming feature clustering and compression module. The application method involves providing basic data through multi-dimensional data scanning to support comprehensive industry rotation analysis and improve predictive capabilities.

[0078] The streaming feature clustering compression module processes 20 factor data points using a streaming feature clustering compression algorithm, generating highly correlated factor clusters and compressing the data stream to optimize transmission and computation efficiency. The algorithm logic involves clustering factors, filtering contribution, and compressing data. The operation steps include receiving 20 factor data points, running on an edge node with 16GB of memory and a 4-core CPU, using Docker containers to process 10MB of data per second; using K-means clustering to group factors into 5 highly correlated clusters, adjusting the number of clusters by minimizing intra-cluster variance, and grouping intraday price increases and overnight price gaps into one cluster based on cosine similarity; calculating the contribution of factors to industry classification, evaluating using information gain ratio, prioritizing price-volume volatility model factors in high-volatility markets with a market volatility index above 20, and removing factors with a contribution below 0.1; performing entropy encoding compression to compress the factor cluster data to 30% of its original volume, generating a compressed data stream containing industry identifiers, factor clusters, and timestamps, which is then transmitted to the multi-model collaborative optimization module. The input data is 20 factor data points, and the output is a compressed data stream. The application method achieves efficient data processing through clustering and compression, reduces bandwidth consumption, supports real-time multi-dimensional data scanning, and improves predictive capabilities.

[0079] The multi-model collaborative optimization module integrates six models using the Shapley value algorithm, dynamically assigns weights to 20 factors, generates industry-level classification results, and ensures reliability through distributed backtesting and macro-level cross-validation. The algorithm logic consists of weight optimization, strategy verification, and macro-level enhancement. The operational steps include receiving a compressed data stream and decoding it into factor cluster data; quantifying the contribution of six models and 20 factors using the Shapley value algorithm, with initial weights of 30% for the price-volume volatility model, 15% for industry return on equity, and the remaining factors evenly distributed at 55%, dynamically adjusted based on market volatility indicators; running distributed backtesting on a Kubernetes cluster, configured with 8 nodes, each with 16GB of memory and 4 CPU cores, using 5 years of historical data to simulate daily, weekly, and monthly market environments, calculating strategy returns and Sharpe ratios, requiring a standard deviation below 0.05, generating industry classification results with scores from 0 to 1, 0.8 and above for the long group, 0.5 to 0.8 for the neutral group, and below 0.5 for the short group; using dependency parsing with the HanLP tool to parse policy text, extract key entities, and map them to macroeconomic driving model factors to enhance classification accuracy. The input data is a compressed data stream, and the output is the industry classification results, which are transmitted to the dynamic classification visualization module and the transparency verification module. The application method involves refining the model through weight optimization and validation to improve the accuracy of industry classification and thus enhance predictive capabilities.

[0080] The dynamic grading visualization module uses a dynamic grading visualization engine to divide industry grading results into bullish, neutral, and bearish groups, generating dynamic bubble charts and interactive timelines to showcase sector opportunities. The algorithm logic involves dimensionality reduction and visualization processing. The operation steps include receiving industry grading results, including scores and factor data; using principal component analysis to reduce the dimensionality of 20 factors to a two-dimensional space, retaining 80% of the variance, generating coordinates, with bubble size representing scores and color representing grading (green for bullish groups, yellow for neutral groups, and red for bearish groups); using D3.js to draw bubble charts, combined with the interactive timeline to display the 30-day grading trajectory, supporting adaptive resolution (minimum 1024x768). The input data is the industry grading results, and the output is visualized data, transmitted to the semantically driven interactive module. The application method is to intuitively display industry grading through bubble charts and timelines, supporting the presentation of sector opportunities within the dynamic grading system to improve predictive capabilities.

[0081] The semantic-driven interaction module supports user natural language queries through a semantic-driven interaction algorithm, generating an interactive interface to assist in industry screening and portfolio adjustment. The algorithm logic involves parsing the query and mapping the results. The operation steps include receiving bubble chart data and user natural language queries; using HanLP for dependency parsing to extract intent and entities, generating structured queries; mapping the structured queries to bubble charts or lists to filter multi-group industries or compare multi-industry trajectories; and using React to render the interactive interface, supporting zooming, dragging, and clicking operations. The input data consists of bubble chart data and user queries, and the output is the interactive interface data. The application method is to enhance users' understanding of industry classification through the interactive interface, supporting investment decisions based on a dynamic classification system to improve predictive capabilities.

[0082] The transparent verification module generates encrypted verification values ​​using zk-SNARK (zero-knowledge concise non-interactive knowledge proof algorithm) and stores them on the distributed ledger IPFS to ensure the integrity and tamper-proof nature of industry classification results. The algorithm logic involves verification value generation and distributed storage. The operation steps include receiving industry classification results and calculating the SHA-256 hash value; generating a verification value using the zero-knowledge concise non-interactive knowledge proof algorithm to prove the result has not been tampered with; uploading the verification value to IPFS v0.8.0, with nodes distributed across Asia, North America, and Europe, storing approximately 256 bytes, and generating a content-addressed hash; and providing a verification interface to allow users or regulatory agencies to query the verification value. The input data is the industry classification result, and the output is the verification value and the IPFS hash. The application method ensures the reliability of classification results through trusted verification, supports regulatory compliance and user trust, and enhances predictive capabilities.

[0083] The algorithm logic and application methods of the six models are as follows. The price-volume fluctuation model includes intraday price increase and overnight gap increase factors. It is calculated using daily candlestick data and continuous trading day data, and executes a time-series entropy analysis algorithm to analyze the entropy value of the factor time series. A high entropy value indicates an increased probability of reversal, while a low entropy value indicates a continuation of the trend. The industry percentile is adjusted according to the entropy value, and industries with high trend strength are given priority to enter the bullish group. The algorithm logic is to calculate the entropy value and map it to the percentile. The operation steps include receiving the compressed data stream, extracting factors, analyzing the entropy value, setting the high entropy threshold to 1.5 and the low entropy threshold to 0.5, adjusting the percentile to the 0 to 1 range, outputting the graded results, and transmitting them to the dynamic graded visualization module. The application method is to evaluate industry momentum and reversal effect, support the screening of bullish groups, and improve predictive capabilities. The fund flow model includes ultra-large and small-to-medium-sized weekly net inflow factors. It calculates using transaction details from five trading days, executes a fund flow network analysis algorithm, models industries as graphs with industries as nodes and factor values ​​as edge weights, uses PageRank to calculate attractiveness, adjusts edge weights based on market volatility indicators, filters noisy fund flows, and identifies highly attractive industries as entering the bullish group. The algorithm logic involves constructing a graph and calculating attractiveness, with steps including factor extraction, setting edge weights, reducing the weight of small-to-medium-sized nodes when market volatility exceeds 20, setting a PageRank threshold of 0.7, outputting tiered results, and transmitting them to a dynamic tiered visualization module. The application is to assess industry fund attractiveness, support bullish group selection, and improve predictive capabilities. The financial performance model incorporates industry return on equity (ROE), net profit growth rate, and revenue growth rate factors. Calculated using annual and quarterly financial reports, it employs a standardized scoring algorithm for these financial factors. A weighted average is then used to generate a comprehensive score, with initial weights of 40% for ROE, 30% for net profit growth rate, and 30% for revenue growth rate. These weights are adjusted based on the industry's life cycle, prioritizing revenue growth rate at 40% for growth industries. The algorithm logic involves standardization and weighted scoring. The steps include factor extraction, standardization to a mean of 0 and a standard deviation of 1, weighted score calculation, weight adjustment, output of the tiered results, and transmission to a dynamic tiered visualization module. The application method is to assess industry profitability, supporting multi-group screening to enhance predictive capabilities. The chip distribution model incorporates holding returns and the proportion of profitable chips. It calculates chip distribution density using transaction details and executes a chip distribution density analysis algorithm. Kernel density estimation is used to calculate distribution density; high-density areas represent strong support. Industry percentiles are adjusted, and industries with strong support are added to the bullish group. The algorithm logic involves density estimation and percentile adjustment. Operational steps include factor extraction, setting the bandwidth to the Silverman rule, a density threshold of 0.6, outputting tiered results, and transmitting them to a dynamic tiered visualization module. Application methods include assessing industry support and selling pressure, supporting the screening of bullish groups, and improving predictive capabilities.The technical analysis model incorporates strength and stability factors, calculated using the 14-day Relative Strength Index (RSI) and 20-day Bollinger Band width. It employs a technical factor spectrum analysis algorithm, extracting cyclical features through Fourier transform. Industries with high cyclical strength are grouped into the bullish group, and the cycle threshold is adjusted based on market volatility. The algorithm logic involves feature extraction and threshold adjustment. Operational steps include factor extraction, setting the main frequency amplitude threshold to 0.5, adjusting the cycle based on the ATR indicator, outputting the grading results, and transmitting them to the dynamic grading visualization module. The application method is to assess industry trend momentum, support bullish group selection, and enhance predictive capabilities. The macroeconomic driver model includes the year-on-year factors of the Manufacturing Purchasing Managers' Index and the Consumer Price Index for the current month. It is calculated using monthly data and performs a macroeconomic factor causal analysis algorithm. The Granger causality test is used to quantify causal relationships, and the weight of factors with high causal strength is increased to 35%. Factors are mapped according to the industry life cycle, with growth industries taking priority in economic growth factors. The algorithm logic involves causal analysis and weight adjustment. The operation steps include extracting factors, setting a causal p-value threshold of 0.05, adjusting weights, mapping factors, outputting the classification results, and transmitting them to the dynamic classification visualization module. The application method is to assess the economic cycle effect and support multi-group screening to improve forecasting capabilities.

[0084] The system operates by starting with the control flow management module triggering the data acquisition module every minute to collect 20 factors, which are then transmitted to the streaming feature clustering and compression module to generate a compressed data stream. The multi-model collaborative optimization module integrates six models to generate industry-specific classification results. The dynamic classification visualization module generates bubble charts and timelines. The semantic-driven interactive module supports user queries. The transparent verification module generates IPFS verification values. The output includes industry-specific classification results, an interactive interface, and verification values, applicable to investment decisions, portfolio optimization, and regulatory compliance. Specific Implementation Example 3:

[0086] like Figures 1 to 3 As shown, the following are the core hardware components and supplementary hardware descriptions:

[0087] The control flow management module's hardware consists of a central server and network interface devices, responsible for coordinating module operation and monitoring data interfaces. The central server runs a time-driven scheduling algorithm, is configured with 16GB of memory, a quad-core CPU, a Linux operating system, and RabbitMQ message queue software, handling minute-by-minute interface status queries and trigger command generation. The network interface devices support RESTful APIs and WebSocket protocols, connecting to financial data interfaces to ensure a data transfer rate of 10MB per second. The hardware's role is to execute scheduling tasks, generate trigger commands, and process error logs, ensuring system real-time performance and robustness. The application involves scheduling data streams through the central server to support real-time market-wide industry assessments, thereby improving predictive capabilities.

[0088] The data acquisition module's hardware consists of a data acquisition server and storage devices, responsible for acquiring multidimensional financial data and generating 20 key factors. The data acquisition server is equipped with 16GB of RAM, a quad-core CPU, runs a Linux operating system, and has data cleaning and standardization software installed. It connects to RabbitMQ to subscribe to financial data interfaces, processing raw data per second, including industry prices, trading volumes, and financial indicators. The storage device is a 1TB solid-state drive, storing the cleaned factor data, with a read / write speed of 500MB / s. The hardware's function is to collect, clean, and standardize data, generate factor data, and transmit it to the streaming feature clustering and compression module. The application method is to provide the foundational data for multidimensional data scanning, supporting comprehensive industry rotation analysis to improve predictive capabilities.

[0089] The hardware components of the streaming feature clustering compression module include edge computing nodes and network transmission equipment, responsible for processing factor data and generating compressed data streams. The edge computing nodes are deployed on servers close to the data source, configured with 16GB of memory and a 4-core CPU. They use Docker containers to run K-means clustering and entropy coding compression algorithms, processing 10MB of data per second and generating 5 highly correlated factor clusters. The network transmission equipment supports Gigabit Ethernet, ensuring the compressed data stream is transmitted in JSON format to the multi-model collaborative optimization module via RabbitMQ. The hardware's role is to perform clustering and compression, reducing bandwidth consumption. The application approach is to support real-time multi-dimensional data scanning through efficient data processing, thereby improving predictive capabilities.

[0090] The hardware components of the multi-model collaborative optimization module include a distributed computing cluster and a storage cluster, responsible for integrating six major models and generating industry-specific ranking results. The distributed computing cluster adopts a Kubernetes architecture, configured with 8 nodes, each with 16GB of memory and 4 CPU cores. It runs the Shapley value (Shapley value algorithm) multi-model collaborative optimization algorithm, processes factor cluster data, and dynamically allocates weights. The cluster also runs distributed backtesting, simulating 5 years of historical data to calculate strategy returns and Sharpe ratios. The storage cluster is a distributed file system with a capacity of 10TB, storing historical data and ranking results, with read / write speeds reaching 1GB per second. The hardware's role is to perform weight optimization, backtesting, and dependency parsing, generating ranking results and transmitting them to the dynamic ranking visualization module and the transparent verification module. The application method involves using distributed computing to deeply refine the models, improving the accuracy of industry ranking, and thus enhancing predictive capabilities.

[0091] The hardware components of the dynamic grading visualization module include a graphics processing server and a display device, responsible for generating dynamic bubble charts and interactive timelines. The graphics processing server is configured with 32GB of RAM, an 8-core CPU, and a GPU (graphics card model supporting CUDA, 8GB of VRAM), running principal component analysis and D3.js visualization software to process industry grading results, generate two-dimensional coordinate bubble charts, and display the 30-day grading trajectory. The display device is a high-resolution monitor, supporting a minimum resolution of 1024x768, presenting green bullish groups, yellow neutral groups, and red bearish groups. The hardware's role is to perform dimensionality reduction and visualization, generate visualized data, and transmit it to the semantically driven interactive module. The application method is to intuitively display industry grading through bubble charts and timelines, supporting the presentation of track opportunities within the dynamic grading system to improve predictive capabilities.

[0092] The semantic-driven interaction module's hardware consists of a front-end server and an interaction device, responsible for processing user natural language queries and generating the interactive interface. The front-end server is configured with 16GB of memory and a quad-core CPU, running the React framework and HanLP dependency parsing software to parse user queries, generate structured queries, and map them to bubble charts or lists. The interaction device is a touchscreen or keyboard and mouse terminal, supporting zoom, drag, and click operations, displaying filtered multi-group industry or multi-industry trajectories. The hardware's role is to perform query parsing and interface rendering, generating interactive interface data. The application method is to enhance users' understanding of industry classification through the interactive interface, supporting investment decisions based on a dynamic classification system to improve predictive capabilities.

[0093] The hardware components of the transparent verification module include a verification server and distributed storage nodes, responsible for generating encrypted verification values ​​to ensure the integrity of the results. The verification server is configured with 16GB of memory and a 4-core CPU, running zk-SNARK (a zero-knowledge concise non-interactive knowledge proof algorithm) to calculate SHA-256 hash values ​​and generate verification values. The distributed storage nodes run IPFS v0.8.0, distributed across Asia, North America, and Europe, storing the verification values. Each node has a capacity of 1TB, with a storage size of approximately 256 bytes, and generates content-addressed hashes. The hardware's role is to perform verification value generation and storage, and provide the verification interface. The application method is to ensure the reliability of the classification results through trusted verification, supporting regulatory compliance and user trust, and improving predictive capabilities.

[0094] The hardware components and application methods of the six models are as follows. The price-volume fluctuation model uses a distributed computing cluster (8 nodes, each with 16GB of memory and 4 CPU cores) and a storage cluster (10TB). It runs an entropy analysis algorithm to process intraday price increases and overnight gap price increases, analyze entropy values ​​(high entropy threshold 1.5, low entropy threshold 0.5), and adjust industry percentiles. The algorithm logic is to calculate entropy values ​​and map them to percentiles. The operation steps include receiving compressed data streams, extracting factors, analyzing entropy values, adjusting percentiles to the 0 to 1 range, outputting grading results, and transmitting them to the dynamic grading visualization module. The application method is to evaluate industry momentum and reversal effects, support long-only group screening, and improve predictive capabilities. The fund flow model uses a distributed computing and storage cluster to run a fund flow network analysis algorithm, processing factors for very large and small weekly net inflows. It constructs an industry graph, executes the PageRank algorithm, sets edge weights, and reduces the weight of small and medium-sized funds when the market volatility index exceeds 20. The PageRank algorithm threshold is 0.7. The resulting classification is then transmitted to a dynamic classification visualization module. Its application is to assess the attractiveness of industries to funds, supporting multi-group screening to improve predictive capabilities. The financial performance model uses a distributed computing and storage cluster to run a standardized scoring algorithm for financial factors, processing industry return on equity (ROE), net profit growth rate, and revenue growth rate. After standardization, a weighted average is applied (initial weights: ROE 40%, net profit growth rate 30%, revenue growth rate 30%). The weights are adjusted according to the industry life cycle, with growth industries given a priority of 40% for revenue growth. The resulting classification is then transmitted to a dynamic classification visualization module. Its application is to assess industry profitability, supporting multi-group screening to improve predictive capabilities. The chip distribution model uses a distributed computing and storage cluster to run a chip distribution density analysis algorithm, process holding returns and profitable chip ratio factors, perform kernel density estimation, set the bandwidth to the Silverman rule, set the density threshold to 0.6, and output graded results, which are then transmitted to the dynamic graded visualization module. Its application is to assess industry support and selling pressure, support bullish group screening, and improve predictive capabilities. The technical analysis model uses a distributed computing and storage cluster to run a technical factor spectrum analysis algorithm, process strong and stable factors, perform Fourier transform, set the main frequency amplitude threshold to 0.5, adjust the period according to the ATR indicator, output graded results, and transmit them to the dynamic graded visualization module. Its application is to assess industry trend momentum, support bullish group screening, and improve predictive capabilities. The macroeconomic driving model uses a distributed computing and storage cluster to run a macroeconomic factor causal analysis algorithm. It processes the year-on-year factors of the manufacturing purchasing managers' index and the consumer price index for the current month, performs Granger causality tests, sets a causal p-value threshold of 0.05, adjusts the weights to 35%, maps the factors, outputs the hierarchical results, and transmits them to the dynamic hierarchical visualization module. The application method is to assess the economic cycle effect and support multi-group screening to improve forecasting capabilities.

[0095] Additional hardware notes:

[0096] The core hardware is deployed in data centers, including a central server, data acquisition servers, edge computing nodes, a distributed computing cluster, graphics processing servers, front-end servers, verification servers, and distributed storage nodes. The central server and data acquisition servers are deployed in the main data center, close to the financial data interface, configured with gigabit Ethernet to ensure data transmission latency of less than 10 milliseconds. Edge computing nodes are deployed in regions close to the data source (such as near exchanges), using Docker containers to run lightweight algorithms, and nodes are connected via VPN to ensure data security. The distributed computing cluster adopts a Kubernetes architecture, with 8 nodes distributed across multiple data centers, supporting failover, with a cluster network bandwidth of 10Gbps, processing 5 years of historical data. The storage cluster uses a distributed file system, supporting high availability and data redundancy. The graphics processing server and front-end server are deployed in the user access center, supporting high-concurrency user queries. The GPU supports CUDA-accelerated principal component analysis, and the display supports touch interaction. The verification server connects to IPFS distributed storage nodes, distributed in Asia, North America, and Europe, storing verification values, with a network bandwidth of 1Gbps, ensuring global access latency of less than 100 milliseconds. The operating environment is a Linux operating system (Ubuntu 20.04 or later), with software such as Python 3.8, RabbitMQ 3.8, HanLP 2.0, D3.js 7.0, and React 17.0 installed. Memory usage is kept below 80%, and CPU usage below 90% to ensure system stability. Hardware maintenance includes daily monitoring of node status, regular firmware updates, monthly data backups, and a fault recovery time of less than 30 minutes. The hardware configuration supports expansion; the number of edge computing nodes can be increased to 16, and the distributed computing cluster can be expanded to 16 nodes to meet high-load requirements. Specific Implementation Example 4:

[0098] like Figures 1 to 3 As shown, the following are specific use cases of the content described in the above embodiments:

[0099] This system collects and processes multi-dimensional financial data in real time to construct a comprehensive market industry assessment system, supporting investment decisions, portfolio optimization, and regulatory compliance. The following provides three use cases in different scenarios, including scenario descriptions and application processes, demonstrating the system's specific applications in portfolio optimization for investment institutions, compliance verification for regulatory agencies, and industry screening for individual investors.

[0100] Use cases:

[0101] Scenario 1: Investment institutions optimize their industry portfolios:

[0102] Scenario Description: An investment institution manages multiple funds and needs to regularly adjust its industry portfolio. The goal is to identify high-potential industries, optimize asset allocation, reduce risk, and increase returns. The institution faces challenges from frequent market volatility and rapid industry rotation, requiring it to quickly analyze overall market trends and screen for industries with high momentum and attractive capital flows.

[0103] Application Process: Investment institutions input their analysis needs through the system's front-end interface, specifying sectors such as new energy, pharmaceuticals, and technology, with a timeframe of the most recent 30 days. The control flow management module triggers the data acquisition module every minute, obtaining raw data such as prices, trading volumes, and financial indicators through a financial data interface. This data generates 20 standardized factors, covering intraday price increases, large weekly net inflows, and industry return on equity, among others. The streaming feature clustering compression module receives the factor data, runs the K-means clustering algorithm, generates five highly correlated factor clusters, compresses the data stream to 30% of its original volume, and then transmits it to the multi-model collaborative optimization module. The multi-model collaborative optimization module runs the Shapley score algorithm, integrating price-volume volatility models, capital flow models, financial performance models, share distribution models, technical analysis models, and macroeconomic driving models. Weights are dynamically allocated, with the price-volume volatility model accounting for 30%. Strategy stability is verified through distributed backtesting, generating industry-specific ranking results with scores ranging from 0 to 1. The new energy industry scores 0.85, placing it in the bullish group; the pharmaceutical industry scores 0.65, placing it in the neutral group; and the technology industry scores 0.45, placing it in the bearish group. The dynamic ranking visualization module generates a bubble chart, displaying the new energy industry as large green dots, the pharmaceutical industry as medium yellow dots, and the technology industry as small red dots, combined with a 30-day ranking trajectory to show trends. The semantic-driven interaction module receives natural language queries from institutions, parses the intent using HanLP, filters for bullish industries, generates an interactive interface, and displays the capital inflow and profitability trends of the new energy industry. The transparent verification module generates SHA-256 hash values ​​and zero-knowledge concise non-interactive knowledge proof algorithm (zk-SNARK) verification values ​​for the tiered results, storing them in IPFS to ensure the reliability of the results. The output includes recommendations for prioritizing investment in the new energy sector, an interactive interface, and verification values. Investment institutions adjust their portfolios based on the results, increasing allocations to the new energy sector and decreasing the weighting of the technology sector to optimize fund returns.

[0104] Application Value: The system identifies high-potential industries through multi-dimensional data scanning, and the model is refined in depth to ensure accurate classification. The dynamic classification system provides intuitive trends, assisting investment institutions in optimizing their portfolios and improving their predictive capabilities.

[0105] Scenario 2: Regulatory agencies verify industry-specific compliance:

[0106] Scenario Description: A regulatory agency needs to verify the reliability of industry classification results provided by a market analysis platform, prevent data tampering, and ensure fairness. The goal is to check the completeness and transparency of the classification results to ensure compliance with regulatory requirements.

[0107] Application Process: Regulatory agencies input their query requirements through the system verification interface, specifying the need to verify the integrity of the industry classification results for a specific day. The control flow management module triggers the data acquisition module to obtain historical financial data, generate 20 factors, and transmit them to the streaming feature clustering compression module. The streaming feature clustering compression module runs the K-means clustering algorithm to generate a compressed data stream, which is then transmitted to the multi-model collaborative optimization module. The multi-model collaborative optimization module runs the Shapley value algorithm, integrates six models, and generates classification results with scores ranging from 0 to 1. The financial industry receives 0.75 and is classified as a wait-and-see group. The transparency verification module receives the classification results, calculates the SHA-256 hash value, generates a verification value using a zero-knowledge concise non-interactive knowledge proof algorithm, uploads it to IPFS v0.8.0, and generates a content-addressed hash. Regulatory agencies query the IPFS hash through the verification interface to confirm that the classification results have not been tampered with. The dynamic classification visualization module generates a bubble chart, displaying the financial industry as a yellow dot, and combines it with a 30-day trajectory to verify the stability of the results. The output includes the verification value, IPFS hash, and bubble chart. The regulatory agency confirms that the classification results are credible and comply with compliance requirements.

[0108] Application value: The system ensures that the grading results are tamper-proof through a transparent verification module, and the dynamic grading system provides visual verification evidence to assist regulatory agencies in confirming compliance and improve predictive capabilities.

[0109] Scenario 3: Individual investors screening high-potential industries:

[0110] Scenario Description: An individual investor wants to screen high-potential industries for long-term investment. The goal is to identify industries with strong trends and stable capital inflows. With a limited budget, they need intuitive tools to assist in decision-making.

[0111] Application Process: Individual investors input natural language queries through the system's front-end interface, specifying the consumer goods and industrial sectors to watch, with a time range of the most recent 60 days. The control flow management module triggers the data acquisition module, obtaining raw data such as prices, trading volumes, and financial indicators through a financial data interface, generating 20 standardized factors covering intraday price increases, large single-week net inflows, and industry return on equity. The streaming feature clustering compression module runs the K-means clustering algorithm to generate compressed data streams, which are then transmitted to the multi-model collaborative optimization module. The multi-model collaborative optimization module runs the Shapley value algorithm, integrating six models and dynamically allocating weights. The fund flow model has a weight of 25%. Distributed backtesting verifies the strategy's stability, generating industry classification results with scores ranging from 0 to 1. The consumer goods sector scores 0.82, belonging to the bullish group, while the industrial sector scores 0.55, belonging to the neutral group. The dynamic classification visualization module generates a bubble chart, displaying the consumer goods sector as large green dots and the industrial sector as medium yellow dots, combined with the 60-day classification trajectory to show the trend. The semantic-driven interaction module parses investor queries using the HanLP tool, maps them to a bubble chart, and generates an interactive interface that supports zooming and selecting consumer goods industry data. The transparency verification module generates SHA-256 hash values ​​and zero-knowledge concise non-interactive knowledge proof algorithm verification values ​​for tiered results, storing them in IPFS to ensure the reliability of the results. The output includes recommendations for prioritizing investments in the consumer goods industry, an interactive interface, and verification values. Investors can then prioritize long-term investments in the consumer goods industry based on these results.

[0112] Application Value: The system identifies high-potential industries through multi-dimensional data scanning, and the model is refined in depth to ensure accurate classification. The dynamic classification system provides intuitive visualization to assist individual investors in decision-making and improve predictive capabilities. Specific Implementation Example 5:

[0114] like Figures 1 to 3 As shown, the following are the specific experimental details provided based on the above content:

[0115] Individual investors select high-potential industries:

[0116] Experimental Objective: To help individual investors screen sectors with strong trends and stable capital inflows, generate investment recommendations, and assist in long-term investment decisions. The experiment will verify how the system, through multi-dimensional data analysis, model integration, and interactive visualization, recommends suitable investment sectors to meet the needs of investors with limited budgets.

[0117] Experiment Overview: Individual investors seeking to invest in consumer goods and industrial sectors, focusing on 60-day trends, use the system to screen for high-potential industries. The control flow management module triggers the data acquisition module every minute to acquire multi-dimensional financial data, generating 20 key factors covering price and volume fluctuations, fund flows, financial performance, share distribution, technical analysis, and macroeconomic drivers. The streaming feature clustering and compression module clusters and compresses the factors, generating an efficient data stream. The multi-model collaborative optimization module integrates six models, dynamically allocating factor weights to generate industry-level ranking results. The dynamic ranking visualization module generates bubble charts and timelines to display industry trends. The semantic-driven interaction module parses user queries and provides an interactive interface. The transparency verification module generates verification values ​​to ensure the reliability of the results. The experiment outputs investment advice and visualization results to assist investors in decision-making.

[0118] Experimental steps:

[0119] Individual investors can input natural language queries through the system's front-end interface, specifying the consumer goods and industrial sectors for analysis, with a time range of 60 days, and querying "which sectors are suitable for long-term investment".

[0120] The control flow management module uses a runtime-driven scheduling algorithm to check the status of the financial data interface via a RESTful API. Once an update is confirmed, a trigger command is generated and sent to the data acquisition module via RabbitMQ.

[0121] The data acquisition module subscribes to the financial data interface, acquiring raw data such as price, trading volume, and financial indicators every second. It cleans up null values ​​and outliers (exceeding 3 times the standard deviation), standardizes them to the range of 0 to 1, and generates 20 factors, including intraday real estate price increase, super-large single-week net inflow, industry return on equity, holding returns, strong forces, and manufacturing purchasing managers' index, which are then transmitted to the streaming feature clustering and compression module.

[0122] The streaming feature clustering compression module runs the K-means clustering algorithm on the edge node (16GB memory, 4-core CPU, Docker container), grouping 20 factors into 5 highly correlated clusters. Based on cosine similarity, the intraday price increase and overnight gap increase are clustered into one cluster. The factor contribution is calculated by the information gain ratio. When the market volatility index is higher than 20, the price-volume volatility model factors are retained first, and the factors with a contribution of less than 0.1 are removed. Entropy encoding compression is performed to generate a compressed data stream (30% of the original volume), which includes industry identifiers and timestamps, and is transmitted to the multi-model collaborative optimization module.

[0123] The multi-model collaborative optimization module decodes and compresses the data stream on a Kubernetes cluster (8 nodes, 16GB memory and 4 CPU cores per node), runs the Shapley value algorithm, and quantifies the contribution of six major models. The initial weights are: 25% for the capital flow model, 20% for the technical analysis model, 15% for the macro driving model, and the rest are evenly distributed. The price-volume fluctuation model calculates entropy values ​​using a time-series entropy analysis algorithm (high entropy of 1.5 indicates reversal, and low entropy of 0.5 indicates trend continuation). The capital flow model runs the PageRank algorithm, using the largest single-week net inflow as the edge weight to calculate industry attractiveness. The financial performance model uses a standardized scoring algorithm for financial factors, with a weight of 40% for the revenue growth rate of the consumer goods industry. The chip distribution model analyzes holding returns through kernel density estimation. The technical analysis model extracts cyclical features through Fourier transform. The macro driving model quantifies causal relationships through Granger causality testing, with a weight of 35% for the manufacturing purchasing managers' index. The module backtests the strategy's stability (standard deviation below 0.05) using 5 years of historical data, generates a grading result with a score of 0 to 1. A score above 0.8 indicates a long position group, 0.5 to 0.8 indicates a neutral position group, and a score below 0.5 indicates a short position group. The results are then transmitted to the dynamic grading visualization module and the transparent verification module.

[0124] The dynamic hierarchical visualization module performs principal component analysis, reducing the 20 factors to a two-dimensional space while retaining 80% of the variance. It generates a bubble chart, with large green dots representing the consumer goods industry and medium yellow dots representing the industrial industry. Combined with a 60-day interactive timeline to display trends, the visualized data is output to the semantically driven interactive module.

[0125] The semantic-driven interaction module runs the HanLP tool to parse query intent, generate structured queries, map them to a bubble chart, filter multi-headed industries, render a React interactive interface, support zooming and clicking, and display the capital inflow and trend strength of the consumer goods industry.

[0126] The transparent verification module generates zero-knowledge concise non-interactive knowledge proof algorithm (zk-SNARK) verification values ​​for the hierarchical results, which are stored in IPFS v0.8.0 (nodes are distributed in Asia, North America, and Europe, 256 bytes) to ensure the credibility of the results.

[0127] The output includes recommendations for prioritizing investments in the consumer goods sector, an interactive interface, and a bubble chart, allowing investors to select sectors within the consumer goods industry based on the results.

[0128] Input: Financial data (prices, trading volume, financial indicators, etc.), user queries (consumer goods, industrial sectors, 60 days).

[0129] Output: Industry classification results, bubble chart, interactive interface, and verification values.

[0130] Experimental data:

[0131] industry Grading score Grading results Key factor weights (fund flows / technical analysis / macroeconomic drivers) consumer goods 0.85 Bullish group 25% / 20% / 15% industry 0.57 Observation Group 20% / 15% / 20%

[0132] Experimental data source: The experimental data is simulated data, generated based on the system's algorithm logic, and is not real market data. The generation steps include: a data acquisition module simulating financial data input to generate 20 factors; a streaming feature clustering compression module running the K-means clustering algorithm to generate 5 factor clusters; a multi-model collaborative optimization module running the Shapley value algorithm, allocating weights (25% for the capital flow model, 20% for the technical analysis model, and 15% for the macro-driven model), and generating graded scores (0-1) through 5 years of historical data backtesting; the weights are adjusted based on the market volatility indicator (threshold 20), reflecting the logic of the capital flow model, page ranking algorithm, etc. The data is used to demonstrate the system's ability to screen high-potential industries and complies with patent disclosure requirements.

[0133] Application Value: The system analyzes 20 factors through multi-dimensional data scanning, and the model is deeply refined and integrated with six major models to ensure accurate grading. The dynamic grading system provides bubble charts and an interactive interface to help individual investors select high-potential industries and improve their predictive capabilities.

[0134] Appendix Figure 3 This is a bar chart showing the industry scores (industry 1 to industry 10), ranging from 0 to 1, reflecting the overall performance of the multi-model collaborative optimization module. The horizontal axis represents the industry name, the vertical axis represents the standardized score, and the bar height represents the score for each industry.

[0135] As shown in the chart, industries 9 and 10 have the highest scores (approximately 1.0), followed by industry 1 (approximately 0.9). These industries may have received high weightings (30% and 15% respectively) due to the superior performance of price-volume fluctuation models (such as intraday price increases) and return on equity factors, and are recommended as priority investment targets for the long-only group (score ≥ 0.8). Industries 3 and 7 have scores of approximately 0.5, while industries 2 and 5 are close to 0.5, belonging to the neutral group (0.5-0.8). Attention should be paid to subsequent changes in macroeconomic driving factors (such as PMI) or technical analysis factors (such as strong forces). Industries 4, 6, and 8 have the lowest scores (approximately 0.1), belonging to the short-only group (< 0.5). They may be negatively affected by financial performance models (such as low net profit growth) or share distribution models (such as low proportion of profitable shares), and cautious investment is advised.

[0136] It should be noted that, in this document, relational terms such as "first" and "second" are used merely to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitations, an element defined by the phrase "comprising a reference structure" does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes the element.

[0137] Although embodiments of the invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the appended claims and their equivalents.

Claims

1. A data flow-based industry rotation comprehensive analysis system, comprising a control flow management module and a functional module, the functional module including a data acquisition module, a streaming feature clustering compression module, a multi-model collaborative optimization module, a dynamic classification visualization module, a semantic-driven interaction module, and a transparent verification module, the modules transmit data in JSON format through a high-performance message queue RabbitMQ, and the control flow management module coordinates the execution sequence and error handling steps of the functional module as follows: The Sp1 control flow management module adopts a time-driven scheduling algorithm, checks the financial data interface update state every minute, triggers the multi-dimensional financial data collection module to collect data, and sequentially triggers the stream feature clustering compression module, the multi-model collaborative optimization module, the dynamic hierarchical visualization module, the semantic-driven interaction module and the transparent verification module; When module execution fails, a retry mechanism is started, with a retry every 5 seconds for a maximum of 3 times, and the data is used for full market industry assessment and classification to generate investment decision support information; Sp2 Data acquisition module, which acquires multi-dimensional financial data in real time from financial data interfaces through a high-performance message queue RabbitMQ, including 20 key factors of the following six models, updated every second, and the collected data is transmitted to the streaming feature clustering compression module for full market industry assessment: Price and volume fluctuation model, which analyzes the fluctuation characteristics of price and trading volume, including intraday entity price increase and overnight price increase factors, for assessing industry momentum and reversal effect; Fund flow model, which analyzes the flow direction of main force funds and retail funds, including super-large single-week net inflow and small single-week net inflow factors, for judging industry fund attractiveness; Financial performance model, which analyzes industry profitability and growth potential, including industry net asset yield and net profit growth factors, for assessing industry operating quality; Chips distribution model, which analyzes market holding cost and sentiment, including holding yield and profit chip ratio factors, for assessing industry support and selling pressure; Technical analysis model, which analyzes industry technical trends and stability, including strength and stability factors, for assessing industry trend momentum; Macro-driven model, which analyzes the impact of macroeconomic variables on the industry, including manufacturing purchasing managers' index and consumer price index month-on-month factors, for assessing economic cycle effect; Sp3 Streaming feature clustering compression module, which receives multi-dimensional financial data, executes streaming feature clustering compression algorithm, generates compressed data stream, and transmits it to the multi-model collaborative optimization module, which is used for industry classification calculation and factor weight optimization; Sp4 Multi-model collaborative optimization module, which receives compressed data stream, executes Shapley value multi-model collaborative optimization algorithm, quantifies the contribution of six models, dynamically allocates the weights of 20 key factors, generates industry classification results, and transmits them to the dynamic classification visualization module and the transparent verification module, which are used for industry classification display and result verification; Sp5 Dynamic classification visualization module, which receives industry classification results, executes dynamic classification visualization engine, classifies industries into long, neutral, and short groups, projects 20 key factors into two-dimensional space through principal component analysis to generate dynamic bubble chart, and displays industry classification history track through interactive timeline, data is transmitted to the semantic-driven interaction module, which is applied to user investment decision interface display and portfolio optimization; Sp6 semantic-driven interactive module, receiving bubble chart data, combining with user natural language query, generating structured query through dependency syntax analysis, mapping to dynamic bubble chart and list, data applied to user interactive interface, industry screening and portfolio adjustment; Sp7 transparent verification module, receiving industry grading results, generating encrypted check value through zero-knowledge proof, stored in distributed ledger IPFS, data applied to verify the integrity and tamper resistance of industry grading results, supporting regulatory compliance and user trust.

2. The data stream based industry rotation comprehensive analysis system according to claim 1, wherein: The control flow management module comprises the following steps: Sp1 adopts time-driven scheduling algorithm, checks the update state of financial data interface every minute, triggers the data collection module to start collection, if the data interface does not respond, starts the retry mechanism, retries every 5 seconds, at most 3 times; Sp2 confirms that the data collection module completes the collection, triggers the flow feature clustering compression module to process data, the multi-model collaborative optimization module to generate grading results, the dynamic grading visualization module to display results, the semantic-driven interactive module to process user query, and the transparent verification module to verify results in turn, data used to coordinate the execution order between modules and industry grading analysis.

3. The data stream based industry rotation comprehensive analysis system according to claim 1, wherein: The flow feature clustering compression module comprises the following steps: Sp1 receives multi-dimensional financial data, uses online K-means clustering algorithm to perform real-time clustering on 20 key factors, generates high-correlation factor clusters, and clusters intraday entity price increase and overnight price jump increase into one cluster; Sp2 calculates the contribution of each factor through information gain rate, removes low-contribution factors, and preferentially retains factors of price and quantity volatility model in high-volatility market; Sp3 performs entropy encoding compression on the edge node to generate compressed data stream, the compression ratio reaches 30% of the original volume, and the compressed data stream is transmitted to the multi-model collaborative optimization module, data used for industry grading calculation and factor weight optimization.

4. The data stream based industry rotation comprehensive analysis system according to claim 1, wherein: The multi-model collaborative optimization module comprises the following steps: Sp1 receives compressed data stream, uses Shapley value algorithm to quantify the contribution of price and quantity volatility model, fund flow model, financial performance model, share distribution model, technical analysis model and macro-driven model, generates dynamic weight combination, price and quantity volatility model weight 30%, industry net asset return rate factor weight 15%; Sp2 runs distributed backtest in parallel on multi-node cluster, simulates multi-period market environment, verifies strategy stability, and generates industry grading results; Sp3 uses dependency syntax analysis to extract key entities in policy text, manufacturing and interest rate, maps to factors of macro-driven model, manufacturing purchasing managers index, enhances cross-validation accuracy, industry grading results transmitted to dynamic grading visualization module and transparent verification module, data used for industry grading display and result verification.

5. The data stream based industry rotation comprehensive analysis system according to claim 1, wherein: The price and quantity volatility model comprises the following steps: Sp1 receives compressed data stream, performs time series entropy analysis algorithm, calculates the entropy value of intraday entity price increase and overnight price jump increase, high entropy value indicates increased possibility of reversal, low entropy value indicates trend continuation; Sp2 dynamically adjusts industry percentiles based on entropy values, prioritizing industries with high trend strength into the long position group. Data is transmitted to the dynamic hierarchical visualization module for industry-level display and portfolio optimization to enhance predictive capabilities.

6. The data stream based industry rotation comprehensive analysis system according to claim 1, wherein: The fund flow model includes the following steps: Sp1 receives compressed data streams, executes capital flow network analysis algorithms, models the industry as graph nodes, uses ultra-large and small-to-medium weekly net inflows as edge weights, and uses the PageRank algorithm to calculate the industry's capital attractiveness. Sp2 adjusts the edge weight thresholds based on market sentiment, filters out noisy capital flows, prioritizes highly attractive industries to enter the long position group, and transmits the data to the dynamic hierarchical visualization module for industry hierarchical display and portfolio optimization to improve predictive capabilities.

7. The data stream based industry rotation comprehensive analysis system according to claim 1, wherein: The financial performance model includes the following steps: Sp1 receives compressed data streams, executes financial factor standardized scoring algorithms, performs Z-score standardization on industry return on net assets, net profit growth rate and revenue growth rate, and generates a comprehensive score. Sp2 dynamically adjusts factor weights based on the industry life cycle, prioritizing growth industries with a 40% weighting for revenue growth rate. Data is transmitted to a dynamic hierarchical visualization module for industry-level display and portfolio optimization to enhance predictive capabilities.

8. The data stream based industry rotation comprehensive analysis system according to claim 1, wherein: The chip distribution model includes the following steps: Sp1 receives compressed data streams, executes a chip distribution density analysis algorithm, and uses kernel density estimation to calculate the distribution density of holding profits and the proportion of profitable chips. High-density areas indicate strong support. Sp2 adjusts the industry percentile based on the kernel density estimation results, strongly supporting industries to enter the long position group. The data is transmitted to the dynamic hierarchical visualization module for industry hierarchical display and portfolio optimization to improve forecasting capabilities.

9. The data stream based industry rotation comprehensive analysis system according to claim 1, wherein: The technical analysis model includes the following steps: Sp1 receives compressed data streams, executes technical factor spectrum analysis algorithms, performs Fourier transforms on strong and stable factors, extracts periodic features, and prioritizes industries with high periodic intensity to enter the long position group. Sp2 dynamically adjusts the cycle threshold based on market volatility, and the data is transmitted to the dynamic hierarchical visualization module for industry-level display and portfolio optimization to improve predictive capabilities.

10. The data stream based industry rotation syndicated analysis system of claim 1, wherein: The macro-driving model includes the following steps: Sp1 receives compressed data streams, executes macroeconomic factor causal analysis algorithms, and uses causal testing algorithms to quantify the causal relationship between the year-on-year growth of the manufacturing purchasing managers' index and the consumer price index in the current month and the industry performance. The weight of high causal strength factors is increased to 35%. Sp2 maps macroeconomic factors based on the industry life cycle, prioritizing economic growth factors for growth industries. The data is then transmitted to a dynamic hierarchical visualization module for industry-level display and portfolio optimization, thereby enhancing predictive capabilities.

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