Intelligent risk early warning method and system based on multi-dimensional data analysis

Abstract

The invention relates to the field of enterprise risk early warning analysis, in particular to an intelligent risk early warning method and system based on multi-dimensional data analysis. The method comprises the following steps: acquiring a multi-dimensional enterprise data stream, performing heterogeneous index information analysis and logic hierarchy reconstruction, and constructing an enterprise running state sensing map; performing multi-index local fluctuation amplitude calculation on the enterprise operation state sensing map, and performing dynamic disturbance feature mining to construct a risk disturbance spectrogram; performing deep semantic analysis and node state abrupt change feature analysis on the risk disturbance spectrogram to generate a dynamic transition type of an abrupt change node; and performing full-period time sequence tracing on the risk disturbance spectrogram, performing potential risk node prediction based on the dynamic transition type, and identifying other potential risk propagation ports. Through accurate and efficient enterprise risk perception, the risk intervention decision is made in advance, and the anti-risk capability and the operation stability of the enterprise are improved.

Description

Technical Field

[0001] The present invention relates to the field of enterprise risk early warning analysis, and in particular to an intelligent risk early warning method and system based on multi-dimensional data analysis. Background Art

[0002] As enterprises continue to improve their informatization and digitization, the types and volumes of data they rely on for operations management, production operations, supply chain collaboration, and market expansion are exploding. Driven by cutting-edge technologies such as big data, cloud computing, and artificial intelligence, corporate decision-making processes are gradually shifting from a traditional experience-driven model to a data-driven one. Against this backdrop, the question of how to fully leverage multi-dimensional enterprise data for comprehensive analysis and mining, assist enterprises in scientifically identifying potential operational risks, and establish efficient and intelligent risk warning mechanisms has become a crucial topic in enterprise management and technology research.

[0003] In the actual operation of businesses, various operational risks are becoming more frequent, diverse, and complex, influenced by factors such as fluctuations in the external economic environment, policy changes, intensified market competition, and internal management errors. Supply chain disruptions can lead to production halts, capital constraints can cause credit crises, management turnover can lead to strategic uncertainty, and information security vulnerabilities can even cause serious data leaks. If these risks are not identified and addressed promptly, they often trigger a chain reaction, damaging a company's reputation, causing financial losses, and even facing a crisis of survival.

[0004] However, traditional enterprise risk early warning methods mostly rely on the establishment and periodic analysis of static indicators, such as financial statement analysis, credit scoring, and manual review. These methods not only suffer from delayed responses and coarse granularity, but also struggle to capture the dynamic, cross-cutting, and nonlinear risk characteristics hidden in enterprise operations. Furthermore, traditional methods often overlook the correlations between different types of data, making it difficult to integrate and analyze multi-source, heterogeneous data. This makes it difficult for risk early warning systems to accurately identify potential threats, achieve forward-looking predictions, and respond in real time. As enterprise business processes become increasingly complex and risk factors become increasingly diverse, single-dimensional data analysis methods can no longer support enterprises' needs for highly accurate and timely risk warnings. Therefore, a more intelligent and smart risk early warning method is urgently needed. Summary of the Invention

[0005] In order to solve the above technical problems, the present invention proposes an intelligent risk warning method and system based on multi-dimensional data analysis to solve at least one of the above technical problems.

[0006] To achieve the above objectives, the present invention provides an intelligent risk warning method based on multi-dimensional data analysis, comprising the following steps: Step S1: Obtain multi-dimensional enterprise data streams, perform heterogeneous metric information parsing and logical hierarchy reconstruction, and construct an enterprise operation status perception map; Step S2: Calculate the multi-metric local fluctuation amplitude of the enterprise operation status perception map, and conduct dynamic perturbation feature mining to construct a risk perturbation spectrogram; Step S3: Conduct in-depth semantic parsing and node state mutation feature parsing of the risk perturbation spectrogram to generate the dynamic transition types of mutation nodes; Step S4: Conduct full-cycle time-series tracing of the risk perturbation spectrogram, and predict potential risk nodes based on the dynamic transition types to identify other potential risk propagation ports; Step S5: Conduct multi-timepoint risk evolution simulation and risk perturbation diffusion coupling for other potential risk propagation ports to construct a multi-dimensional risk trajectory vector field; Step S6: Conduct multi-strategy anti-risk intervention deduction on the multi-dimensional risk trajectory vector field, and then optimize the anti-risk decision-making to construct an optimized anti-risk decision-making strategy.

[0007] The present invention obtains multi-dimensional data streams of an enterprise, including various types of data such as finance, operation, and market. The system can understand the operation status of the enterprise from all aspects. The parsing of heterogeneous data and the reconstruction of the logical hierarchy can make the data more standardized and comparable, and construct a perception map of the enterprise operation status, thereby providing comprehensive basic information for subsequent risk identification. This step can help the system deeply understand the dynamics of the enterprise and identify potential weaknesses and risks. Through multi-index fluctuation analysis, the local fluctuation amplitude of each index in the enterprise operation can be found, and then a risk perturbation spectrum diagram can be constructed. Local fluctuations are often early signals of potential risks. By mining dynamic perturbation characteristics, the system can identify the potential root causes of risks in the early stage, providing strong support for subsequent risk management. Through spectrum diagram analysis, the system can efficiently identify the periodicity and regularity of risk fluctuations for dynamic monitoring. By performing semantic parsing on the risk perturbation spectrum diagram, the essence of enterprise risks can be deeply understood from multiple dimensions. The mutation characteristics of node states can reflect the suddenness and severity of risk changes. This step can help the system discover and analyze the key nodes of risk mutations, thereby accurately identifying possible sudden risks and providing support for timely decision-making. The generation of dynamic transition types helps to understand the propagation path and change trend of risks in the enterprise system. Through full-cycle time series tracing, the key time points and their laws in the risk development process can be identified. This helps the system to give timely warnings when encountering similar risks in the future. Based on the prediction of dynamic transition types, potential risk nodes and their propagation paths can be identified, and other possible risk propagation ports that the enterprise may face can be predicted in advance, so as to take targeted preventive measures to reduce potential losses. Through multi-time-point risk evolution simulation, the system can simulate the propagation path and influence of potential risks in multiple time dimensions, and predict the possible risk diffusion situations in different time periods. The diffusion coupling of risk perturbations can help the enterprise analyze the mutual influence between different risk sources, providing a basis for formulating more comprehensive preventive measures. The construction of a multi-dimensional risk trajectory vector field enables the system to comprehensively analyze risks from multiple angles, forming a panoramic risk map. Based on the identified risk trajectories and paths, different anti-risk intervention strategies are simulated. Through strategy deduction, the system can identify the most effective countermeasures, improving the enterprise's ability to cope with complex risk situations. Finally, by optimizing anti-risk decisions, the system can provide customized anti-risk decision-making solutions for the enterprise, helping the enterprise to adopt the most suitable risk management strategies in a complex business environment, reducing the impact of potential risks, and enhancing the enterprise's anti-risk ability.

[0008] In this specification, an intelligent risk warning system based on multi-dimensional data analysis is provided for performing the intelligent risk warning method based on multi-dimensional data analysis as described above, including: The multi-index perception module is used to obtain multi-dimensional enterprise data streams, perform heterogeneous index information parsing and logical level reconstruction, and construct an enterprise operation status perception map; The risk perturbation spectrum module is used to calculate the multi-index local fluctuation amplitude of the enterprise operation status perception map and conduct dynamic perturbation feature mining to construct a risk perturbation spectrum diagram; The state mutation analysis module is used to perform in-depth semantic analysis and node state mutation feature analysis on the risk perturbation spectrum diagram to generate the dynamic transition types of mutation nodes; The risk node prediction module is used to conduct full-cycle time-series tracing on the risk perturbation spectrum diagram and predict potential risk nodes based on the dynamic transition types to identify other potential risk propagation ports; The risk trajectory module is used to perform multi-timepoint risk evolution simulation and risk perturbation diffusion coupling on other potential risk propagation ports to construct a multi-dimensional risk trajectory vector field; The anti-risk deduction module is used to conduct multi-strategy anti-risk intervention deduction on the multi-dimensional risk trajectory vector field, and then optimize the anti-risk decision-making to construct an optimized anti-risk decision-making strategy.

[0009] The present invention obtains multi-dimensional data streams (such as finance, operation, market, production, etc.). This module provides a comprehensive enterprise status perception map for the system, ensuring a comprehensive understanding of all aspects of the enterprise by the system. Heterogeneous index parsing and logical hierarchy reconstruction can effectively eliminate differences between data sources, enabling data to be effectively processed and analyzed on a unified platform. The reconstruction of the logical hierarchy helps the system better understand the relationships between different data dimensions, contributing to finding potential risks in complex enterprise systems. By constructing an enterprise operation status perception map, this module provides accurate background data for subsequent risk detection and prediction, ensuring that the system does not miss important upstream and downstream relationships when identifying risks. By calculating the local fluctuation amplitude of multiple indicators, this module can timely detect abnormal fluctuations in different business areas or operation links of the enterprise. Such fluctuations may be precursors of potential risks, thus enabling early warning. Through dynamic perturbation feature mining, the module can discover and distinguish different types of perturbation features, helping the system identify potential causes or trends of risk generation. This enables the system not only to identify current risks but also to predict their possible development directions. The risk perturbation spectrogram can visually present the fluctuation law of risk perturbations, thus helping decision-makers better understand the fluctuation of enterprise risks and quickly grasp the core issues of risks. By performing semantic analysis on the risk perturbation spectrogram, the module can convert complex data into easily understandable semantic information, enabling the system to understand and mine potential risk signals. The mutation of certain nodes may mean that a major risk event is about to occur. The mutation of node states is often closely related to the suddenness and severity of enterprise risks. This module can identify these mutations, helping decision-makers accurately understand which parts of the enterprise are undergoing rapid changes and may expose risks. According to the characteristics of node mutations, the module can identify and mark different types of dynamic transitions, helping the system accurately describe and predict the path of risk propagation and its impacts. This helps to timely identify and intervene in potential risks. By performing full-cycle time-series tracing on the risk perturbation spectrogram, this module can help the system understand the risk development trends in similar situations in history. This provides strong historical data support for identifying possible future risk events. Based on the type of dynamic transition for potential risk node prediction, it is possible to identify the future propagation direction of risks and the nodes that may be affected. This helps enterprises take preventive measures early to avoid unnecessary losses. By predicting potential risk nodes, the module can identify other potential risk propagation ports, helping the system effectively identify risks with multi-point propagation and enhance the overall prevention and control ability. Through multi-time-point simulation, the module can show the change path of risks and possible impacts at different time points, helping enterprises predict the evolution process of risks and make long-term preparations. The coupling analysis of risk perturbation diffusion enables the system to identify the mutual influences between risk sources, helping enterprises understand how risks in one area may affect other areas and enhancing the accuracy of overall risk prediction.The multi-dimensional risk trajectory vector field provides a visual risk propagation path, helping decision-makers quickly identify which businesses or nodes are most vulnerable to risk and take targeted measures. It simulates different risk mitigation intervention strategies and, by deducing the effects of different intervention measures, helps companies select the most appropriate response strategy. This can greatly improve risk mitigation effectiveness and ensure that companies can make optimal decisions when faced with a variety of complex scenarios. By optimizing risk mitigation decisions, the module can recommend the most effective risk mitigation measures based on the company's specific circumstances (such as resources and risk tolerance), ensuring the company's risk management strategy is maximally effective. Optimized risk mitigation decisions can help companies respond quickly to sudden risks, reduce potential losses and negative impacts, and enhance their emergency response capabilities and ongoing stability. BRIEF DESCRIPTION OF THE DRAWINGS

[0010] Figure 1 This is a schematic diagram of the steps of an intelligent risk warning method based on multi-dimensional data analysis of the present invention; Figure 2 Detailed implementation flow chart of step S1; Figure 3 Detailed implementation flow chart of step S2; Figure 4 Schematic diagram of the detailed implementation steps of step S3. DETAILED DESCRIPTION

[0011] It should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention.

[0012] This application provides an intelligent risk warning method and system based on multi-dimensional data analysis. The execution entities of the intelligent risk warning method and system based on multi-dimensional data analysis include, but are not limited to, the following: mechanical equipment, data processing platforms, cloud server nodes, network upload devices, etc. equipped with the system, which can be regarded as general computing nodes of this application. The data processing platform includes, but is not limited to, at least one of an audio and image management system, an information management system, and a cloud data management system.

[0013] See also Figures 1 to 4 The present invention provides an intelligent risk warning method based on multi-dimensional data analysis, which includes the following steps: Step S1: Obtain multi-dimensional enterprise data streams, perform heterogeneous indicator information analysis and logical hierarchy reconstruction, and build an enterprise operation status perception map; Step S2: Calculate the local fluctuation amplitude of multiple indicators on the enterprise operation status perception map and conduct dynamic disturbance feature mining to construct a risk disturbance spectrum map; Step S3: Conduct in-depth semantic analysis and node state mutation feature analysis on the risk perturbation spectrogram to generate the dynamic transition types of mutation nodes; Step S4: Conduct full-cycle time series tracing on the risk perturbation spectrogram, and predict potential risk nodes based on the dynamic transition types to identify other potential risk propagation ports; Step S5: Conduct multi-timepoint risk evolution simulation and risk perturbation diffusion coupling on other potential risk propagation ports to construct a multi-dimensional risk trajectory vector field; Step S6: Conduct multi-strategy anti-risk intervention deduction on the multi-dimensional risk trajectory vector field, and then optimize the anti-risk decision-making to construct an optimized anti-risk decision-making strategy.

[0014] The present invention obtains multi-dimensional data streams of an enterprise, including various types of data such as finance, operation, and market. The system can understand the operating conditions of the enterprise from all aspects. The parsing of heterogeneous data and the reconstruction of the logical hierarchy can make the data more standardized and comparable, and construct a perception map of the enterprise's operating state, thereby providing comprehensive basic information for subsequent risk identification. This step can help the system deeply understand the dynamics of the enterprise and identify potential weaknesses and risks. Through multi-index fluctuation analysis, the local fluctuation amplitude of each index in the enterprise operation can be found, and then a risk perturbation spectrum diagram can be constructed. Local fluctuations are often early signals of potential risks. By mining dynamic perturbation characteristics, the system can identify the potential root causes of risks at an early stage, providing strong support for subsequent risk management. Through spectrum diagram analysis, the system can efficiently identify the periodicity and regularity of risk fluctuations and conduct dynamic monitoring. By performing semantic analysis on the risk perturbation spectrum diagram, the essence of enterprise risks can be deeply understood from multiple dimensions. The mutation characteristics of node states can reflect the suddenness and severity of risk changes. This step can help the system discover and analyze the key nodes of risk mutations, thereby accurately identifying possible sudden risks and providing support for timely decision-making. The generation of dynamic transition types helps to understand the propagation path and change trend of risks in the enterprise system. Through full-cycle time-series tracing, the key time points and their laws in the risk development process can be identified. This helps the system to give timely warnings when encountering similar risks in the future. Based on the prediction of dynamic transition types, potential risk nodes and their propagation paths can be identified, and other possible risk propagation ports that the enterprise may face can be predicted in advance, so as to take targeted preventive measures to reduce potential losses. Through multi-time-point risk evolution simulation, the system can simulate the propagation path and influence of potential risks in multiple time dimensions and predict the possible risk diffusion situations in different time periods. The diffusion coupling of risk perturbations can help the enterprise analyze the mutual influence between different risk sources and provide a basis for formulating more comprehensive preventive measures. The construction of a multi-dimensional risk trajectory vector field enables the system to comprehensively analyze risks from multiple angles and form a panoramic risk map. Based on the identified risk trajectories and paths, different anti-risk intervention strategies are simulated. Through strategy deduction, the system can identify the most effective countermeasures and improve the enterprise's ability to cope with complex risk situations. Finally, by optimizing anti-risk decisions, the system can provide customized anti-risk decision-making solutions for the enterprise, helping the enterprise adopt the most suitable risk management strategies in a complex business environment, reduce the impact of potential risks, and improve the enterprise's anti-risk ability.

[0015] In an embodiment of the present invention, refer to Figure 1 , which is a schematic diagram of the step flow of an intelligent risk warning method based on multi-dimensional data analysis according to the present invention. In this example, the steps of the intelligent risk warning method based on multi-dimensional data analysis include: Step S1: Obtain multi-dimensional enterprise data streams, perform heterogeneous metric information parsing and logical hierarchy reconstruction, and construct an enterprise operation status perception graph; In this embodiment, various internal and external data sources are identified, including ERP systems, CRM systems, financial systems, market research data, and supply chain management systems. These systems contain multi-dimensional information, such as sales data, inventory information, customer feedback, and financial statements. Inventory and sales data for the past six months are extracted from the ERP system, customer satisfaction survey results are obtained from the CRM system, and monthly financial statements are obtained from the financial system. ETL (Extract, Transform, Load) tools are used to collect and integrate the identified data. During the data extraction process, data quality must be ensured, duplication and erroneous information must be removed, and necessary format conversions must be performed to facilitate subsequent analysis. Data from each system is extracted and formatted uniformly, such as by standardizing dates to YYYY-MM-DD format, and missing values are imputed or removed to ensure data integrity. The collected multi-dimensional data is categorized, and the meaning and calculation methods of various indicators are defined. These indicators should cover key operational elements of the enterprise, such as financial indicators (profit margin, current ratio), operational indicators (inventory turnover, production efficiency), and market indicators (customer satisfaction, market share). Define "inventory turnover rate" as "the ratio of cost of goods sold to average inventory" and classify it as an operational metric for ease of subsequent analysis. Use data normalization methods to analyze metrics from different sources to ensure comparability. Common methods include Z-score normalization and Min-Max normalization to eliminate the impact of different scales. Min-Max normalization is performed on sales data, scaling it to a range of 0 to 1 to ensure reasonable comparisons between different metrics. Analyze the logical relationships between various metrics to identify influencing relationships and hierarchical structures. Causal analysis or graph theory can be used to construct a relationship diagram between metrics, identifying key indicators and influence chains. Analysis revealed a positive correlation between "inventory turnover rate" and "customer satisfaction," indicating that inventory management efficiency directly affects customer delivery timeliness, which in turn affects customer satisfaction. Based on the identified logical relationships, construct a hierarchical model of the enterprise's operational status. This model should clearly define the position and role of each metric within the overall operational status and reflect the relationship between high-level and low-level metrics. Build a three-tier model: the first tier represents the overall enterprise operational status; the second tier represents key financial, operational, and market indicators; and the third tier represents specific metrics (such as sales, inventory levels, and customer ratings). Use a graph database (such as Neo4j) or a data visualization tool (such as Tableau or Power BI) to design a graph that captures the enterprise's operational status. When constructing the graph, ensure a clear representation of nodes (indicators) and edges (relationships) to facilitate subsequent analysis and visualization. In the graph, represent each key indicator as a node, and edges represent the logical relationships between them, forming a comprehensive view of the enterprise's operational status. Based on the designed model, import the parsed data into the graph for visualization and preliminary verification.By comparing historical data with the actual operating status, the accuracy and effectiveness of the atlas are verified. Use the atlas visualization tool to display the changes in inventory turnover rate and customer satisfaction over different time periods to ensure that the atlas can accurately reflect the operating status of the enterprise.

[0016] Step S2: Calculate the multi-index local fluctuation amplitude of the enterprise operating status perception atlas and conduct dynamic perturbation feature mining to construct a risk perturbation spectrogram; In this embodiment, key indicators are selected from the enterprise operating status perception atlas for fluctuation amplitude calculation. These indicators may include sales volume, inventory level, customer satisfaction, production efficiency, etc. Ensure the integrity of the data time series of these indicators for fluctuation analysis. Select the sales volume and inventory level in the past six months as the calculation objects to ensure the consistency of data timestamps. Define the calculation method of the local fluctuation amplitude. Usually, indicators such as standard deviation, root mean square (RMS), or absolute change amplitude are used to quantify the fluctuation of indicators within each time period. The local fluctuation amplitude can be defined as the standard deviation of the change in sales volume within a time window (such as 7 days), reflecting the amplitude of sales fluctuations during this time period. Calculate the local fluctuation amplitude of each indicator within the selected time window. The window can be gradually moved through the sliding window method to calculate the fluctuation amplitude within each window. Calculate the standard deviation of the sales volume within each 7-day window and record it to form a time series of fluctuation amplitudes. Suppose the sales volume within a certain window is [1 million, 1.2 million, 1.15 million, 1.3 million, 1.25 million, 1.4 million, 1.35 million], then the calculated standard deviation is approximately 141,400. Define dynamic perturbation features, including the frequency, amplitude, duration, and related indicators of the fluctuations. These features will be used for subsequent construction of the risk perturbation spectrogram. The dynamic perturbation features can be defined as the maximum value, minimum value of the local fluctuation amplitude and their occurrence frequencies. Analyze the calculated local fluctuation amplitude data to extract dynamic perturbation features. Use time series analysis methods to identify the patterns and periodicities of the fluctuations. Use the autocorrelation function (ACF) to analyze the fluctuation amplitude data to identify the periodic characteristics of the fluctuations and record the fluctuation amplitude values with higher occurrence frequencies. Design a risk perturbation spectrogram model according to the extracted dynamic perturbation features. The spectrogram should be able to intuitively display the fluctuations of each indicator over different time periods and their impact on the enterprise operating status. Design the X-axis of the spectrogram as time and the Y-axis as the fluctuation amplitude. Different colors or shapes of points represent different indicators (such as sales volume, inventory level). Import the calculated dynamic perturbation feature data into a visualization tool (such as Tableau or Power BI) to generate a risk perturbation spectrogram. Ensure the readability and understandability of the spectrogram for management analysis. Plot the fluctuation spectrograms of sales volume and inventory level in the visualization tool to show the changes in fluctuation amplitude over different time periods and mark the time nodes with abnormally high fluctuations.

[0017] Step S3: Perform in-depth semantic analysis and node state mutation feature analysis on the risk perturbation spectrogram to generate the dynamic transition types of mutation nodes; In this embodiment, relevant data is extracted from the constructed risk perturbation spectrogram, including the fluctuation amplitude, time series, and the relationships between various indicators. These data will be used for subsequent in-depth semantic analysis. Extract the sales fluctuation data and corresponding customer satisfaction data for the past three months, ensuring that the timestamps and formats of the data are consistent for analysis. Use deep learning models (such as BERT or LSTM) in natural language processing (NLP) technology for semantic analysis to convert the numerical data in the spectrogram into interpretable semantic information. These information will help identify potential risk patterns and influencing factors. Use the BERT model to generate semantic embeddings for the fluctuation data, analyze the relationship between sales fluctuations and market feedback, and identify potential risk factors. Analyze the extracted data through the trained model to identify key fluctuation patterns and their semantic information, such as the market response or customer feedback corresponding to high fluctuations. Record this information for subsequent analysis. If the model identifies an increase in the customer complaint rate corresponding to "sharp sales fluctuations", record this information as a potential risk signal and mark its occurrence frequency and time period. Determine the definition of node state mutation, which generally refers to a node in the spectrogram where a certain indicator changes significantly in a short period of time. These mutations may be caused by factors such as external market changes and internal management decisions. Define "sales mutation" as a node where the sales change exceeds 15% within one month. Use statistical analysis methods (such as Z-score or control chart) to monitor the changes in node states and identify mutation nodes that exceed the normal fluctuation range. Capture abnormal changes by setting thresholds. For sales data, if the Z-score within a certain time period exceeds 3, mark the node as a mutation node and record its specific value and time. Extract the characteristics of the identified mutation nodes, analyze the amplitude, duration, and impact range of their changes. This will help understand the nature of the mutation and potential risks. If the sales of a certain mutation node jumps from 1 million to 1.5 million in a short period of time, record the amplitude of the node change as 500,000 and analyze its impact on inventory and customer satisfaction. Determine the classification criteria for dynamic transition types, including instantaneous transition, delayed transition, and frequency transition, etc. Each transition type should have clear characteristics and analysis methods. Define instantaneous transition as a drastic change that occurs in a short period of time, while delayed transition refers to a change that occurs after a certain period of time. Analyze the states of the extracted mutation nodes to identify their dynamic transition types. The characteristics can be extracted and classified by observing the state changes of the nodes in different time periods. If a sales mutation node experiences a large fluctuation in a short period of time (such as from 1 million to 1.5 million), mark it as an instantaneous transition; while if the fluctuation gradually appears within several months, mark it as a delayed transition. Organize the dynamic transition types of each mutation node into a report, describing their characteristics and the impact on enterprise risk management. Ensure that the report content is detailed and easy for management to understand and make decisions.The report indicates that the dynamic transition type of a certain node is an instantaneous transition, and analyzes its potential market reasons, such as the promotional activities of competitors, and suggests that the enterprise adjust its strategies in a timely manner to cope with market changes.

[0018] Step S4: Perform a full-cycle time series trace on the risk perturbation spectrogram, and predict potential risk nodes based on the dynamic transition type to identify other potential risk propagation ports; In this embodiment, relevant time series data are extracted from the risk perturbation spectrogram, including the changes of various risk indicators over a period of time. These data will be used for time series traceability analysis to ensure that the time range covers key fluctuation events. Extract the sales fluctuation data, inventory level changes, and customer feedback in the past year, and ensure that the timestamps of the data are consistent for subsequent analysis. Use time series analysis models (such as ARIMA model or dynamic time warping DTW) to perform full-cycle time series traceability on the extracted data. These models can identify trends, seasonality, and periodic fluctuations in the data to help analyze the risk evolution in different time periods. Use the ARIMA model to model the sales data to identify trend changes in the past six months and predict sales fluctuations in the future. Based on the established time series model, perform full-cycle time series traceability to analyze the fluctuation characteristics in different time periods and their impact on the enterprise operation status. Record the status changes at each key time node to form a complete trace record. If the analysis results show that abnormal fluctuations occur in both sales and inventory levels in a specific month, record the fluctuation amplitude and possible influencing factors at this time node. For the key nodes identified in the time series traceability, analyze their corresponding dynamic transition types. According to the previous definition, determine whether each node belongs to an instantaneous transition, a delayed transition, or a frequency transition to understand its nature. If a node experiences a large fluctuation in sales (such as from 1 million to 1.5 million) in a short period of time, it is marked as an instantaneous transition; if the fluctuation appears gradually over several months, it is marked as a delayed transition. Record the dynamic transition type and its characteristics of each node and analyze its possible impact on the enterprise. These characteristics will provide a basis for subsequent risk node prediction. Record that the transition type of a node is an instantaneous transition and analyze its possible market reasons, such as sudden market activities or price adjustments by competitors. Use machine learning algorithms (such as random forest, support vector machine, etc.) to construct a potential risk node prediction model. This model should be able to comprehensively consider the previously analyzed dynamic transition types and their characteristics to identify other potential risk propagation ports. Use historical data to train the model, and the input features include dynamic transition types, fluctuation amplitudes, and time nodes to predict future potential risk nodes. Train and validate the constructed prediction model to ensure its high accuracy and reliability. The cross-validation method can be used to evaluate the performance of the model on different data sets. Adopt an 80 / 20 training and test data division ratio to train the model and evaluate its accuracy on the test data to ensure the credibility of the prediction results. Based on the prediction results of the model, identify potential risk propagation ports. These ports may be due to the chain reaction caused by dynamic transitions, affecting other business processes or indicators. If the model predicts an increase in the risk of customer churn in a certain period, mark the customer service link as a potential risk propagation port and record its impact degree.

[0019] Step S5: Conduct multi-timepoint risk evolution simulation and risk perturbation diffusion coupling for other potential risk propagation ports, and construct a multi-dimensional risk trajectory vector field; In this example, in-depth analysis is conducted based on the potential risk propagation ports identified in the previous step. Each port may involve different indicators, such as customer service, supply chain management, or market feedback. The integrity of the relevant data is ensured for subsequent simulations. If customer service is identified as a potential risk propagation port, data such as customer feedback, complaint rates, and customer churn rates from the past six months are extracted, ensuring consistent timestamps. The extracted data is integrated, formatted, and cleaned as necessary to remove outliers and missing values. Data quality and consistency are ensured for risk evolution simulations. Outliers are identified and processed using the Z-score method, and outliers in customer complaint rates are removed to ensure the accuracy of subsequent data analysis. A system dynamics model or an agent-based model is used to construct a multi-time point risk evolution simulation. This model can simulate the dynamic characteristics of risk propagation over different time periods and account for the interactions of various factors. An agent-based model is constructed to simulate the dynamic impact of customer churn on sales, inventory, and customer satisfaction, reflecting the temporal evolution of risk. Model parameters, including the initial state, impact coefficient, and time step, are set based on historical data. Ensure reasonable parameter settings to enhance the reliability of simulation results. Set the initial customer churn rate to 5% with a monthly fluctuation of 1%. Simulate the risk evolution over the next six months. Execute the simulation model, observe the risk changes at different time points, and record the results. The simulation should cover multiple time points to comprehensively analyze the risk evolution. The simulation results indicate that the customer churn rate could rise to 10% over the next six months. Record changes in sales and inventory levels at each time point. Use a coupling analysis approach to construct a coupled risk perturbation diffusion model. This model should be able to integrate the mutual influences between different transmission ports and analyze how risks spread through different channels. Develop a multivariate regression model to analyze how customer churn affects sales and its potential impact on the supply chain, identifying the coupling relationships between the various ports. Set the coupling model parameters, including the influence coefficient and propagation speed of each transmission port. These parameters will help analyze the propagation characteristics of the risk perturbation between different ports. Set the customer service influence coefficient to 0.3 to represent the impact of customer service on sales, and calculate its contribution to the overall risk propagation. Execute the coupled model and observe the diffusion of risk disturbances, recording the risk status at different time points and the degree of impact across various ports. Simulation results show that when the customer churn rate reaches 10%, sales may decrease by 15%. Changes in the risk status of each port are recorded for subsequent analysis. Based on the results of the simulation and coupled analysis, design a multidimensional risk trajectory vector field. This vector field should be able to intuitively display different risk propagation paths and impact levels, helping management quickly identify potential risks. The vector field's X-axis represents time, the Y-axis represents risk status, and the Z-axis represents impact.Use different colors and arrow sizes to represent the intensity and direction of different risk propagation paths. Use data visualization tools (such as Tableau, Power BI) to visualize the constructed multi-dimensional risk trajectory vector field to ensure the clarity and understandability of the graph. Display the changes in risk status over different time periods in the visualization tool and mark the significant risk paths for the management to conduct decision-making analysis.

[0020] Step S6: Conduct multi-strategy anti-risk intervention deduction on the multi-dimensional risk trajectory vector field, and then optimize the anti-risk decision-making to construct an optimized anti-risk decision-making strategy.

[0021] In this embodiment, based on the analysis of the multi-dimensional risk trajectory vector field, multiple anti-risk intervention strategies are designed. These strategies should target the identified specific risk propagation paths and potential impacts, covering response measures for different business processes. For the risk of customer churn, multiple strategies can be designed, including improving customer service quality, increasing customer loyalty programs, and strengthening market promotion. Each strategy should clearly define its goals and expected effects. Set parameters for each intervention strategy, including implementation time, resource allocation, and expected effects. If the choice is to improve customer service quality, then it is necessary to set the number of additional customer service staff, training time, and invested funds. It is planned to increase 10 customer service staff within the next three months and conduct a two-week special training to improve customer satisfaction. Use simulation models (such as system dynamics models or agent-based models) to deduce the designed anti-risk intervention strategies and observe the changes in the enterprise operation status after the implementation of different strategies. The simulation should cover multiple time points and record the changes in various indicators. After the simulation is executed, it is found that implementing the strategy of improving customer service quality can reduce the customer churn rate from 10% to 6%, and the sales volume will increase by 10% after three months. Record these changes for subsequent analysis. Based on the deduction, set the goals for anti-risk decision optimization, including reducing the risk level, improving customer satisfaction, and increasing sales volume, etc. Ensure that the goals are clear and measurable to guide the subsequent optimization process. Set the goal to reduce the customer churn rate to 5% and increase the sales volume by 15% within six months after the implementation of the intervention. Construct a decision optimization model, using linear programming, mixed integer programming, or other optimization algorithms, comprehensively considering the effects of different intervention strategies, resource constraints, and objective functions. The model should be able to evaluate the effectiveness and feasibility of different strategy combinations. Set a linear programming model, define the decision variables as the implementation degree of each intervention strategy, and the constraint conditions include resource constraints (such as manpower, funds) and expected effects. Run the optimization model to solve the optimal anti-risk decision combination and generate the optimization results. The results should include the implementation degree of each intervention strategy and its contribution to the goals for decision support. The optimization results show that the optimal strategy combination is to improve customer service quality, increase market promotion efforts, and launch a customer loyalty program. The combination of these three strategies will reduce the customer churn rate to 4% and increase the sales volume by 18%. Integrate the optimization results into specific anti-risk decision optimization strategies to ensure that the strategy content is clear and operable. Record the implementation details, expected effects, and evaluation methods of each strategy. Construct an anti-risk decision optimization strategy report, detailing the specific implementation steps, resource requirements, and expected effects of each strategy, such as the specific action plan for the "improve customer service quality" strategy. Develop an implementation plan, including a specific schedule, responsibility assignment, and resource allocation. Ensure that each strategy can be effectively implemented within the scheduled time and monitor the implementation progress.

[0022] In this embodiment, refer to Figure 2, which is a schematic diagram of the detailed implementation steps of step S1. In this embodiment, the detailed implementation steps of step S1 include: Obtain multi-dimensional enterprise data streams based on the enterprise's internal operation system; Analyze the heterogeneous index information of the multi-dimensional enterprise data streams to generate heterogeneous index information, where the heterogeneous index information includes financial transaction information, upstream and downstream transaction chains, and supply chain logistics track information; Perform timestamp alignment processing on the heterogeneous index information and standardize the structure to construct multi-dimensional operation standardization indexes; Conduct multi-index causal relationship analysis on the multi-dimensional operation standardization indexes to extract the causal relationships between different indexes; Perform logical dimension mining on the multi-dimensional operation standardization indexes and reconstruct the logical hierarchy based on the causal relationships to construct an enterprise operation status perception map.

[0023] In this embodiment, various data sources within the enterprise are identified, including the financial system, ERP system, CRM system, and supply chain management system. Ensure that all relevant departments and business lines are covered to obtain a comprehensive data view. Extract sales revenue, cost, and profit data from the financial system; obtain inventory data and production progress from the ERP system; obtain customer transaction records from the CRM system. All data sources should be able to provide real-time or periodically updated data streams. Use ETL (Extract, Transform, Load) tools to extract the required data streams from each data source. Conduct preliminary cleaning on the extracted data, including removing duplicate data, filling in missing values, and format conversion, to ensure data quality. When extracting sales data, remove records of incomplete transactions and unify the date format to YYYY-MM-DD for subsequent processing. Integrate the processing results of each data source into a multi-dimensional data stream. The data stream should contain metrics in different dimensions, such as time, region, product category, etc., for subsequent analysis. Construct a multi-dimensional data table containing time, product ID, customer ID, sales amount, and logistics status to analyze the relationships between different dimensions. Identify heterogeneous metrics that need to be parsed, including financial transaction information (such as revenue and expenditure), upstream and downstream transaction chains (such as supplier and customer relationships), and supply chain logistics track information (such as shipping and arrival times). Financial transaction information may include the amount and date of each transaction, the upstream transaction chain includes the delivery situation of suppliers, and the logistics track information involves each link in the transportation process. Parse various types of data to extract heterogeneous metric information. Use data parsing tools (such as JSON parsers, XML parsers) to extract the required fields from the original data. Extract the amount and timestamp of each transaction from the financial data, and extract the supplier name and logistics status from the supply chain data. Integrate the parsed heterogeneous metric information into a unified database, using a suitable data model (such as a relational database or a time-series database) for subsequent querying and analysis. Design a database table containing fields such as transaction ID, amount, timestamp, supplier ID, logistics status, etc. to form a complete set of heterogeneous metric information. Align the timestamps of all heterogeneous metric information to ensure that the time formats of different data sources are consistent. This can be achieved through time format conversion and time interval standardization. Unify all timestamps to the UTC format for data comparison in different regions and systems. Standardize the structure of the heterogeneous metric information to ensure that all types of metrics follow a unified structure standard. A standardized template can be defined to ensure that all data conforms to this template. It is stipulated that financial transaction information should include fields such as "amount", "time", "transaction type", etc., and logistics information should include fields such as "shipping time", "arrival time", "status", etc. Based on the standardized data, construct multi-dimensional operation standard metrics. These metrics will be used for subsequent causal relationship analysis and logical dimension mining. Use statistical analysis methods (such as regression analysis, Granger causality test) to establish a causal relationship model between multiple metrics.Identify which indicators have significant causal relationships. Use regression analysis to determine how sales are affected by inventory levels and logistics timeliness. Analyze multidimensional standardized operational indicators to extract causal relationships between different indicators. Record the analysis results, including the strength and direction of the causal relationships. For example, if the analysis shows that a 10% increase in inventory levels leads to a 5% increase in sales, record this causal relationship. Define the logical dimensions, including the hierarchical relationships and interdependencies among the indicators. Identify which indicators are key and which are supporting. Set "sales" as the top-level indicator, "inventory level" and "logistics timeliness" as mid-level indicators, and bottom-level indicators may include specific sales data for individual products. Build a logical hierarchy based on causal relationships and logical dimensions. Categorize relevant indicators according to their importance and impact to form a clear logical hierarchy. Build a hierarchical structure diagram to show how top-level indicators (such as sales) are affected by mid-level indicators (such as inventory levels and logistics timeliness) and bottom-level indicators (such as product sales data). Based on the logical hierarchy, construct a perception map of the enterprise's operational status. This map should reflect the entirety of a company's operations, including the status and interrelationships of various indicators. A dynamic perception map should be generated to show real-time changes in sales, inventory levels, and logistics status, as well as the impact of these on each other.

[0024] In this embodiment, refer to Figure 3 , is a flowchart of the detailed implementation steps of step S2. In this embodiment, the detailed implementation steps of step S2 include: Decompose the enterprise operation status perception map into time series states and generate multiple state perception windows; Perform multi-index local fluctuation amplitude calculation on multiple state perception windows to obtain the state fluctuation amplitude value of each window; Performing transient high fluctuation distortion identification based on the state fluctuation amplitude value and marking the state fluctuation distortion point; Based on the preset risk sensitivity threshold, the abnormal risk sensitivity of the state fluctuation distortion point is evaluated to extract the potential risk perception factor; Dynamic disturbance feature mining is performed on potential risk perception factors to construct a risk disturbance spectrum diagram.

[0025] In this embodiment, the required operation index data is extracted from the enterprise operation state perception graph. These data will be used for subsequent time series state decomposition. Ensure the integrity of the timestamp information of the data to facilitate accurate time series analysis. Extract the time series data of indicators such as sales volume, inventory level, and logistics timeliness in the past month to form a time series data set containing multiple indicators. For the extracted time series data, state decomposition can be performed using methods such as wavelet transform or empirical mode decomposition (EMD). Through these methods, the time series data can be decomposed into multiple frequency components to identify different state fluctuation patterns. Use wavelet transform to process the sales volume data and decompose it into low-frequency trends and high-frequency fluctuations to help identify potential state changes. According to the results of the time series state decomposition, set the length of the time window (such as 5 days or 10 days), and divide the entire time series data into multiple state perception windows. Each window should contain the same time length for subsequent analysis. If 10 days is selected as a window, three state perception windows can be generated from the one-month data, and each window corresponds to a different time period. Determine the calculation method of the local fluctuation amplitude. Usually, indicators such as standard deviation, root mean square (RMS), or absolute change amplitude can be used to quantify the fluctuation of each state perception window. The local fluctuation amplitude can be defined as the standard deviation of all indicators within each window, reflecting the amplitude of the indicator fluctuations within that window. Calculate the fluctuation amplitude of the multi-indicator data within each state perception window.

[0026] Ensure that all relevant indicators are included in the calculation to obtain a comprehensive fluctuation amplitude value. If the sales volume, inventory, and logistics timeliness within a certain state perception window are [100, 120, 110], [200, 210, 195], and [5, 7, 6] respectively, then calculate the standard deviation of these indicators as the fluctuation amplitude of this window. Record the fluctuation amplitude values of each state perception window and conduct analysis to compare the fluctuations between different windows. This will help identify the windows with larger fluctuation amplitudes, which may have potential risks. According to historical data and industry standards, set the threshold for high-fluctuation distortion. This threshold should be able to effectively distinguish normal fluctuations from abnormal fluctuations. Set that the fluctuation amplitude exceeding 3 times the standard deviation is the identification of high-fluctuation distortion. Traverse the calculated state fluctuation amplitude values, identify the windows that exceed the set threshold, and mark these windows as state fluctuation distortion points. If the fluctuation amplitude of a certain window is 5 and higher than the set threshold, then mark this window as a distortion point and record its specific time period and fluctuation amplitude.

[0027] Set the risk sensitivity threshold according to historical data and business requirements. This threshold is used to evaluate the abnormal risk level of the state fluctuation distortion points. Set the risk sensitivity threshold to 2, indicating that distortion points with a fluctuation amplitude exceeding this value need further risk assessment. Conduct an abnormal risk sensitivity assessment on the marked state fluctuation distortion points. A risk scoring model can be used to evaluate based on factors such as the fluctuation amplitude, historical performance, and industry benchmarks. If the fluctuation amplitude of a certain distortion point is 6 and exceeds the sensitivity threshold, evaluate its potential risk as high and record it as a high-risk point. Extract potential risk perception factors based on the sensitivity assessment results. These factors will be used for subsequent risk analysis and the establishment of an early warning mechanism. If the assessment result shows that a certain distortion point is related to the delivery delay of a specific supplier, record the delivery status of this supplier as a potential risk factor. Define the definition of dynamic disturbance characteristics, including the frequency, amplitude, duration, and related indicators of the fluctuation, etc. These characteristics will be used to construct a risk disturbance spectrogram. Define the dynamic disturbance characteristics as the frequency of state fluctuation (such as several times a week), amplitude (such as the maximum fluctuation value), duration (such as lasting for several days), etc. Extract the dynamic disturbance characteristics of the marked state fluctuation distortion points. Record the characteristics such as the fluctuation frequency, amplitude, and duration of each distortion point for subsequent analysis. For a marked distortion point, extract that its occurrence frequency is 2 times a month, the maximum fluctuation amplitude is 7, and the duration is 5 days. Use the extracted dynamic disturbance characteristics to construct a risk disturbance spectrogram. This spectrogram should be able to reflect the characteristics of each disturbance and its impact on the system, facilitating subsequent monitoring and management. In the generated spectrogram, the X-axis is time, the Y-axis is the fluctuation amplitude, and each distortion point and its impact are marked in the figure to help management quickly identify potential risks.

[0028] In this embodiment, the specific steps for dynamically mining the disturbance characteristics of potential risk perception factors to construct a risk disturbance spectrogram are as follows: Conduct a periodic risk disturbance characteristic analysis on potential risk perception factors and extract periodic risk disturbance characteristics; Perform multi-point disturbance fitting based on the periodic risk disturbance characteristics to construct a risk disturbance waveform curve; Calculate the disturbance frequency intensity and trend slope of the risk disturbance waveform curve; Conduct disturbance frequency continuity mining based on the disturbance frequency intensity and trend slope to construct a risk disturbance spectrogram.

[0029] In this embodiment, the potential risk perception factors extracted from the previous analysis are used for periodic analysis. These factors may include the amplitude of fluctuations, frequency, duration, etc. Ensure that all relevant metrics are covered for a comprehensive analysis of periodic characteristics. Suppose the extracted potential risk factors include fluctuations in sales, changes in inventory levels, and delays in logistics timeliness. Record the historical data of these factors for subsequent analysis. Use Fourier transform or periodic analysis tools (such as autocorrelation function) to identify the periodic characteristics of potential risk factors. These tools can help identify the periodic patterns of factors within a certain time range. Analyze the sales fluctuation data using Fourier transform to identify its main periodic components, such as the frequency and amplitude of monthly fluctuations. Extract the periodic risk perturbation characteristics and record the amplitude and frequency of its periodic fluctuations. These characteristics will be used for subsequent fitting and waveform curve construction. If the sales fluctuation occurs once a month with a fluctuation amplitude of 10%, record this periodic characteristic as the basis for subsequent analysis. According to the extracted periodic risk perturbation characteristics, organize the relevant data to ensure the data is complete and the timestamps are consistent. The data should cover multiple time points for fitting analysis. Organize the sales, inventory, and logistics timeliness data for the past six months to form a time series dataset containing multiple time points. Select a suitable fitting model (such as sine wave model, linear regression model, or polynomial fitting) to fit the extracted periodic risk perturbation characteristics. The selected model should be able to effectively reflect the periodic changes in the data. Use the sine wave model to fit the sales fluctuations, and the model form is: y(t)=Asin(Bt+C)+D, where A is the amplitude, B is the frequency, C is the phase, and D is the baseline. Evaluate the fitting result and calculate the goodness of fit (such as R² value) to ensure that the model can effectively represent the periodic characteristics. Record the parameter values of the fitting for subsequent analysis. If the fitting result shows an R² value of 0.95, indicating that the model can fit the data well, record the fitting parameters (amplitude, frequency, and phase, etc.). Calculate the frequency intensity of the risk perturbation waveform curve, which can be achieved by analyzing the parameters of the fitting model. The frequency intensity can be defined as the product of the fluctuation amplitude and frequency. If the amplitude A in the fitting result is 10% and the frequency B is 0.2 (once a month), then the frequency intensity is: Frequency intensity = A×B = 10%×0.2 = 2%. Calculate the trend slope of the risk perturbation waveform curve, which can usually be achieved by performing linear regression analysis on the fitting result. The slope represents the rate of change of the waveform curve and can reflect the upward or downward trend of the risk perturbation. If the linear regression analysis result shows a slope of 0.05, indicating that the waveform curve increases by 5% per unit time, record this slope as the trend indicator. Select a suitable continuity mining method, such as sliding window analysis or time series decomposition, to identify the continuity of the perturbation frequency. Ensure that this method can effectively capture the patterns of frequency changes. Adopt sliding window analysis, set the window length to 30 days, and gradually move the window to calculate the perturbation frequency within each window.Analyze the frequency intensity and trend slope within each sliding window to identify the stability and continuity of the frequency. Record the time period of continuous frequency change. If the frequency intensity remains above 2% within a certain 30-day window, record the time period of this window and label it as a high-frequency continuity interval. Organize the mined frequency continuity results into a table or graph and construct a risk perturbation spectrogram. This spectrogram should be able to visually display the change in perturbation frequency over different time periods. Generate a spectrogram with the X-axis representing time and the Y-axis representing frequency intensity, and mark the high-frequency intervals in the graph to facilitate management in quickly identifying potential risks.

[0030] In this embodiment, refer to Figure 4 , which is a schematic diagram of the detailed implementation steps of step S3. In this embodiment, the detailed implementation steps of step S3 include: Perform in-depth semantic analysis on the risk perturbation spectrogram and perform semantic annotation nested encoding to obtain the risk perturbation semantic encoding; Perform association mapping and topological structure positioning on the enterprise operation state perception map according to the risk perturbation semantic encoding to obtain the map position information of the risk factors; Mine the risk behavior propagation path based on the map position information and extract the risk behavior propagation path; Perform key node analysis of path mutations on the risk behavior propagation path and extract the jump event nodes, delay diffusion nodes, and low-frequency activation nodes in the path; Analyze the node state mutation characteristics of the jump event nodes, delay diffusion nodes, and low-frequency activation nodes to generate the dynamic transition types of the mutation nodes.

[0031] In this embodiment, relevant data is extracted from the constructed risk perturbation spectrogram, including perturbation frequency, intensity, and their temporal variations. Ensure the integrity of the data for in-depth semantic parsing. Extract the frequency intensity variations within a specific time period. For example, within a certain time period, the frequency intensity is 2% - 5%, and record the corresponding perturbation events. Use deep learning models (such as BERT or LSTM) in natural language processing (NLP) technology for semantic parsing, converting the data in the spectrogram into understandable semantic information. This information will be used for subsequent encoding and annotation. Analyze the sudden high-frequency events in the spectrogram to identify their potential risk implications, such as market fluctuations, supply chain disruptions, etc. Annotate the parsed semantic information, and structure the semantic information using a specific encoding format (such as JSON or XML). Each annotation should include information such as event type, intensity, time, and potential impact. Annotate an event as "high-frequency risk" and record its frequency, occurrence time, and related impact factors, such as "a 10% decrease in sales". Extract relevant information from the enterprise operation state perception graph, including the time series data and mutual relationships of various state indicators. Ensure that the graph structure can support subsequent association mapping. Extract the key information of indicators such as sales volume, inventory level, and logistics timeliness from the state perception graph. Perform association mapping between the risk perturbation semantic encoding and the enterprise operation state perception graph. By identifying the relationships between the events in the semantic encoding and the indicators in the state graph, construct an association matrix. If a high-frequency risk corresponds to a decrease in sales, mark this relationship in the association matrix and record the relevant impact degree. According to the association mapping results, locate the graph position information of the risk factors. Through graph analysis tools (such as Gephi, Cytoscape, etc.), visually identify the distribution positions of the risk factors. In the generated topological graph, mark the relationships between sales, inventory, and logistics and the risk factors to facilitate the identification of key areas affected by risks. Use graph theory analysis methods to identify the propagation paths of risk factors in the enterprise operation state perception graph. By analyzing the connection relationships in the graph, determine the possible paths of risk propagation. Use the shortest path algorithm to identify the propagation path from the risk factor to the main operation indicators (such as sales). Record the identified risk behavior propagation paths, ensuring that all key nodes and connection relationships are covered. Each path should be annotated with its risk intensity and propagation time. If a path from supply chain delay to sales decline is identified, record the nodes of this path and their risk characteristics. Analyze the mined risk propagation paths to generate a report describing the characteristics of each path and its impact on enterprise operations. Ensure that the report content is detailed and easy to understand. Define the key nodes of path mutations, including jump event nodes, delay diffusion nodes, and low-frequency activation nodes. Clearly define the characteristics of each type of node and its role in risk propagation. Jump event nodes can be defined as high-risk events that frequently appear in the propagation path, while delay diffusion nodes are nodes with slower propagation speed but greater impact.Use statistical analysis or graph theory to identify key nodes in the path. Node importance can be assessed by calculating metrics such as node centrality and connectivity. Calculate the degree centrality of each node to identify key nodes that connect multiple important nodes. Record the characteristics of each key node, including the frequency of jump events, delayed diffusion time, and the intensity of low-frequency activation. Ensure data accuracy and completeness. If a jump event node frequently occurs and is associated with a sales decline, record the characteristics and relevant historical data for that node. Define the dynamic transition types of mutation nodes, including instantaneous, delayed, and frequent transitions. Each transition type should have clear characteristics and analysis methods. An instantaneous transition can be defined as a dramatic change that occurs in a short period of time, while a delayed transition refers to a change that occurs over a period of time. Use time series analysis to analyze the state changes of each mutation node and identify its dynamic transition type. Extract features by observing the node's state changes over different time periods. If a node experiences a dramatic fluctuation within a specific time period, label it as an instantaneous transition; if the fluctuation persists for a longer period, label it as a delayed transition. Compile a report on the dynamic transition types of each mutation node, describing its characteristics and impact on enterprise risk management. Ensure that the report is detailed to facilitate management decision-making. The report identifies the dynamic transition types experienced by a node in the past quarter and their potential impact on sales and inventory, recommending appropriate management measures.

[0032] In this embodiment, step S4 includes the following steps: Conduct multi-factor correlation mining on the risk disturbance spectrum to identify associated risk factor groups; Deeply deconstruct the risk transmission logic of the associated risk factor group to generate the risk transmission logic; Based on the risk transmission logic, the full cycle time sequence of the risk behavior transmission path is traced to obtain the full cycle risk transmission chain; Based on the dynamic transition type and the full-cycle risk propagation chain, potential risk nodes are predicted and other potential risk propagation ports are identified.

[0033] In this embodiment, data of each risk factor are collected from the constructed risk perturbation spectrogram. These factors may include sales fluctuations, inventory levels, supply chain delays, etc. Ensure that the data is clear and the timestamps are consistent for subsequent analysis. Extract data on sales, inventory status, and order fulfillment time over the past six months to identify the correlations between factors. Use association rule learning (such as the Apriori algorithm) or correlation analysis (such as the Pearson correlation coefficient) to identify groups of associated risk factors. By analyzing the relationships between multiple factors, identify significantly correlated factors. Calculate the correlation between sales and inventory levels using the Pearson correlation coefficient. If the correlation coefficient is close to 1, it indicates a strong association between these two factors. Based on the analysis results, identify significant groups of associated risk factors. If a significant correlation is found between sales fluctuations and inventory shortages and logistics delays, group them together. Record all identified groups of associated factors, including their correlation coefficients and possible degrees of impact, for subsequent analysis. Determine the components of the risk conduction logic, including risk factors, conduction paths, and impact mechanisms. Clearly define how each factor affects other factors through different paths. Define how sales fluctuations affect production plans through changes in inventory levels, thereby further affecting the stability of the supply chain. Use system dynamics or causal graph analysis methods to deeply deconstruct the identified groups of associated risk factors. By constructing a causal relationship graph, identify the mutual influence relationships between factors. Construct a causal relationship graph where the nodes are risk factors and the edges represent the influence relationships between factors, such as "sales fluctuations → inventory levels → logistics delays". Based on the deconstruction results, form a detailed risk conduction logic. Record the conduction paths of each factor and their degrees of impact for subsequent analysis and application. The generated conduction logic shows that sales fluctuations lead to a decrease in inventory levels, which in turn affects the order fulfillment time and ultimately results in a decrease in customer satisfaction. Collect full-cycle data related to risk factors to ensure coverage of all time periods of risk propagation. The data should include the historical changes of each factor for time-series tracing. Collect sales, inventory, and logistics data over the past year to ensure that the time range of the data is comprehensive enough. Use time-series analysis methods such as dynamic time warping (DTW) or time series decomposition to trace the full-cycle risk propagation chain. By analyzing the historical changes of factors, identify the time nodes of risk propagation and their impacts. Use DTW to analyze the changes in sales fluctuations and inventory levels to identify their corresponding relationships in time. According to the results of time-series tracing, generate a full-cycle risk propagation chain, recording each key time node and its corresponding risk status. Ensure the integrity and accuracy of the chain. Record each node and its time, such as the inventory decrease and logistics delay caused by sales fluctuations, to form a complete risk propagation chain. Analyze the identified types of dynamic transitions to determine their impacts in the risk propagation chain. Identify the relationships between the types of transitions and the risk propagation paths for potential risk prediction.If the transition type of a certain node is "delayed transition", it may cause greater fluctuations in subsequent risk propagation. Machine learning algorithms (such as random forest or support vector machine) are used to train the risk propagation chain to predict potential risk nodes. These nodes can be potential risk propagation ports that may appear in the future. The model is trained using historical data to predict which nodes may become potential risk propagation ports under specific conditions.

[0034] In this embodiment, step S5 includes the following steps: Identify the current operating state of the enterprise based on multi-dimensional enterprise data streams; Conduct real-time dynamic enterprise behavior analysis on the current operating state of the enterprise to extract key enterprise behaviors; Perform multi-point risk evolution simulation on other potential risk propagation ports according to the key enterprise behaviors to generate risk evolution simulation data; Conduct risk trajectory evolution analysis on the risk evolution simulation data to generate a risk evolution trajectory; Calculate the direction, rate, potential energy, and risk outbreak probability of the risk evolution trajectory; Perform risk perturbation diffusion coupling according to the direction, rate, potential energy, and risk outbreak probability to construct a multi-dimensional risk trajectory vector field.

[0035] In this embodiment, multi-dimensional data streams from various departments of the enterprise are integrated, including data such as finance, operations, marketing, and supply chain. These data should be updated in real time to ensure that they can accurately reflect the current state of the enterprise. Extract the latest sales data and cost data from the financial system, obtain the inventory level and production progress from the ERP system, and collect customer feedback and market demand data from the CRM system. Define key indicators for evaluating the current operating state of the enterprise, including sales volume, inventory turnover rate, customer satisfaction, and supply chain stability, etc. These indicators will be used to comprehensively evaluate the health of the enterprise. Set indicators such as sales growth rate, inventory turnover days, and customer satisfaction score to ensure that these data can be obtained in the real-time monitoring system. Use real-time data monitoring tools to regularly evaluate the above key indicators to obtain the current operating state of the enterprise. Through dashboards or data visualization tools, display the latest changes in each indicator. Use a data visualization dashboard to display the dynamic changes in sales volume, inventory, and customer satisfaction, facilitating management to quickly identify the operating state of the enterprise. Build a real-time dynamic enterprise behavior analysis framework, including three main links: data collection, data processing, and behavior recognition. Ensure that the analysis process can respond in real time to changes in the operating state of the enterprise. Set to analyze sales data, inventory data, and customer feedback every hour to identify potential key behaviors. Use machine learning algorithms (such as clustering analysis or anomaly detection) to analyze real-time data to identify key enterprise behaviors. Discover behaviors such as abnormal growth in sales volume and sharp decline in inventory. Through clustering analysis, identify behaviors with rapid sales growth during a specific period and mark them as key behaviors. Record the identified key enterprise behaviors and generate reports describing the characteristics, occurrence time, and their impact on enterprise operations of these behaviors. Based on the key enterprise behaviors, identify other potential risk propagation ports. These ports may be links related to key behaviors, such as delayed supplier deliveries, customer churn, etc. If it is found that the increase in sales volume is accompanied by an increase in customer complaints, then the customer service link is identified as a potential risk propagation port.

[0036] Based on the existing risk propagation model, construct a multi-timepoint risk evolution simulation model. This model should be able to simulate the risk propagation process under different conditions to generate corresponding evolution data. Construct a simulation model based on a stochastic process to simulate the changes in customer complaints under different sales growth rates. Run the evolution simulation model to generate risk evolution simulation data. These data should include the state changes of each potential risk propagation port at different time nodes. Select appropriate analysis methods (such as dynamic time warping or state space model) to analyze the risk evolution simulation data and identify the evolution of the risk trajectory. Use the dynamic time warping method to compare the risk states at different time points and identify the change patterns of the risks. According to the analysis results, generate a risk evolution trajectory, recording the risk states and their change paths at each time node. This will help identify potential risk trends. The generated trajectory graph shows the change process from sales growth to an increase in customer complaints, facilitating visual analysis.

[0037] By analyzing the change trend of the risk trajectory, calculate the direction of the risk evolution trajectory. Linear regression or trend analysis can be used to identify the upward or downward direction of the trajectory. If there is a positive correlation between sales growth and an increase in customer complaints, the direction is upward. Calculate the change rate of the risk evolution trajectory, usually by observing the change amount of the risk state within a unit of time. If the number of customer complaints has increased by 10 in the past month, the rate is 10 complaints per month. Calculate the potential energy and outbreak probability of the risk. The potential energy can be defined as the potential energy of the risk state change, and the outbreak probability is estimated based on historical data and the current state. If under specific conditions, historical data shows that there is a 20% probability of customer churn rate, record this probability as the risk outbreak probability. Based on the direction, rate, potential energy, and risk outbreak probability, construct a risk perturbation diffusion coupling model. This model should be able to comprehensively consider the impact of various factors on risk propagation. Set a coupling model that combines the growth rate of customer complaints, changes in sales, and inventory levels to simulate their impact on the overall risk. Generate multi-dimensional risk trajectories through the coupling model to form a risk trajectory vector field. This field should be able to reflect the intensity and direction of different risk propagation paths. The generated vector field shows the dynamic changes of each potential risk propagation path, facilitating decision-making by management. Visualize the generated multi-dimensional risk trajectory vector field to help management identify the key paths of risk propagation and potential intervention points. Use visualization tools to display the dynamic changes of the risk trajectory to facilitate management's quick identification and response to potential risks.

[0038] In this embodiment, the specific steps of step S6 are as follows: Identify the enterprise's available resources based on multi-dimensional enterprise data streams; Perform multi-strategy anti-risk intervention deduction on the multi-dimensional risk trajectory vector field according to the enterprise's available resources to generate anti-risk simulation data for multiple intervention plans; Calculate the recovery speed of the enterprise operation indicators after anti-risk intervention and the residual amount of multi-indicator state fluctuations of the anti-risk simulation data; Conduct a secondary risk probability prediction on the recovery speed and the residual amount of multi-indicator state fluctuations to generate a secondary risk probability; Based on the secondary risk probability, conduct an adaptive risk warning to generate an adaptive risk warning signal; Calculate the secondary risk density of the anti-risk simulation data to generate a secondary residual risk density; Analyze the risk diffusion trend of the secondary residual risk density to obtain a secondary risk diffusion trend diagram; Optimize the anti-risk decision based on the secondary risk diffusion trend diagram and construct an optimized anti-risk decision-making strategy; Carry out intelligent risk perception and warning operations based on the optimized anti-risk decision-making strategy and the adaptive risk warning signal.

[0039] In this embodiment, multi-dimensional data streams within the enterprise are collected, including information such as human resources, material inventory, equipment availability, and capital flow, to comprehensively understand the enterprise's available resources. The current number of employees and their skill levels are extracted from the human resources system, raw material inventory data is obtained from the ERP system, and available funds and liquidity are understood from the financial system. The collected data is analyzed to identify the resources available to the enterprise at present. This can be achieved by setting resource utilization indicators to evaluate the actual availability of each resource. Calculate the utilization rates of various types of human resources (such as employee attendance rate, workload, etc.), and mark the resources available for risk mitigation interventions. Based on the analysis results, generate a list of the enterprise's available resources, specifying the available quantity, status, and potential contributions of each resource. This list will provide a basis for subsequent risk mitigation interventions. According to the identified available resources, design multiple risk mitigation intervention strategies. These strategies should consider different resource combinations and usage scenarios to meet the enterprise's current risk response needs. Design various intervention strategies including replenishing inventory, increasing employee training, and mobilizing funds to ensure that each strategy can effectively address potential risks. Construct a risk mitigation simulation model to simulate the impact of different intervention strategies on the enterprise's operating status. The model should consider the interactions of various types of resources and their impact on the enterprise's operating indicators. Use a system dynamics model to simulate the changes in sales, inventory levels, and customer satisfaction under different intervention strategies. Run the simulation model to generate risk mitigation simulation data for multiple intervention scenarios. This data should include the changes in the enterprise's operating indicators after the implementation of each strategy. Simulate the changes in indicators such as sales growth and inventory turnover rate after the implementation of different strategies, and record the specific values of each indicator. By analyzing the risk mitigation simulation data, calculate the stabilization speed of each enterprise operating indicator. This can be achieved by observing the recovery time of the indicator after the implementation of the intervention. If the sales amount recovers from 1 million to 1.5 million at the normal level after the intervention and the observed recovery time is two weeks, then record the stabilization speed as 750,000 per week.

[0040] Calculate the residual amount of multi-index state fluctuations to evaluate the fluctuations of various indicators after anti-risk intervention. The residual amount can be measured by calculating the standard deviation of the indicators before and after the intervention. If the standard deviation of sales before the intervention is 150,000 and it is 50,000 after the intervention, then the residual amount is 100,000, indicating that there are still certain fluctuations. Define the definition of secondary risks, usually the chain reactions and potential new risks that may be caused under the influence of primary risks. These risks should be closely related to the operating status of the enterprise. If inventory shortages lead to production delays, which in turn affect sales, then the loss of customers due to delayed delivery can be regarded as a secondary risk. Use statistical analysis methods (such as logistic regression or time series analysis) to predict the probability of secondary risks. The model should consider the recovery speed, the residual amount of fluctuations, and their impact on secondary risks. Establish a logistic regression model, input the data of the recovery speed and the residual amount of fluctuations, and predict the probability of customer loss. Run the probability prediction model to generate the probability of secondary risks. These results will help management understand the severity of potential risks and their possible impacts. The model output shows that the probability of secondary risks is 20%, indicating that there is a certain risk of customer loss in the current situation. Determine the criteria for adaptive risk warning signals, including the probability of secondary risks, the change range of key indicators, etc., in order to monitor the operating status of the enterprise in real time.

[0041] Set that when the probability of secondary risks exceeds 15%, a risk warning signal is triggered. Build a real-time monitoring system to continuously track the changes in the probability of secondary risks and other key indicators. Once the warning conditions are triggered, the system should immediately issue an alarm and provide corresponding decision-making suggestions. Set the monitoring system to update the risk probability every hour. If it is found that the threshold is exceeded, a risk alarm is automatically sent. Generate adaptive risk warning signals based on the monitoring results, and record the time, type, and impact of the warning. Ensure that the signals are clear and easy to understand for management to react quickly. The generated signal shows "High risk of customer loss, please adjust the sales strategy in a timely manner", and record the timestamp when the signal is issued. Select a suitable risk density calculation method (such as kernel density estimation) to perform density analysis on the probability of secondary risks. This will help identify the distribution of risks in different time periods. Use the kernel density estimation method to analyze the changes in the customer loss risk in the past six months and find the peak risk period. Run the calculation model to generate the density of secondary residual risks. These density values will help the enterprise identify potential risk concentration areas and times. The generated density map shows that the customer loss risk reaches the highest in a specific month, and the density value is 0.3, indicating that special attention is needed during this period.

[0042] Record the calculation results of the secondary residual risk density and generate a report to describe the risk distribution and its potential impact on enterprise operations. The report indicates that the density of customer churn risk is as high as 0.3 during a certain period, and it is recommended to take measures to reduce the risk. Use time series analysis or regression analysis methods to analyze the diffusion trend of the secondary residual risk density. This will help identify the propagation pattern and speed of the risk. Use simple linear regression analysis to identify the change trend of the risk density in different time periods. According to the analysis results, generate a secondary risk diffusion trend chart to show the change of the risk density and its diffusion trend in different time periods. The chart shows that the risk density rises during a certain period and is predicted to further expand in the coming months. According to the above analysis results, construct an anti-risk decision optimization framework. This framework should consider various factors, including secondary risk probability, risk density, and diffusion trend, etc. Set up a decision model, input different risk factors to evaluate the effectiveness of different anti-risk strategies. Through optimization algorithms (such as linear programming, genetic algorithms, etc.), generate anti-risk decision optimization strategies. These strategies should be able to effectively reduce the secondary risk and its impact. Develop an optimization strategy that recommends increasing market promotion efforts during high-risk periods to enhance customer stickiness and satisfaction.

[0043] Evaluate the generated anti-risk decision optimization strategies to ensure their effectiveness and operability. Make necessary adjustments according to the feedback results to improve the implementation effect of the strategies. Integrate the anti-risk decision optimization strategies with adaptive risk warning signals to construct an intelligent risk perception system. This system should be able to monitor and respond to potential risks in real time. Build a comprehensive platform to collect risk warnings, decision-making strategies, and key indicators in real time for the management to quickly obtain information. Implement warning operations in the intelligent risk perception system. Once the risk signal is triggered, the system should quickly send an alarm and provide corresponding decision-making suggestions. If it is detected that the customer churn risk rises, the system immediately issues an alarm and recommends starting specific customer maintenance strategies. Establish a feedback mechanism to continuously optimize risk perception and warning operations. By analyzing the differences between the warning results and the actual situation, adjust the system parameters in a timely manner. Regularly evaluate the accuracy of the warning system and adjust the risk warning threshold according to the feedback results to improve the accuracy and response speed of future warnings.

[0044] In this embodiment, an intelligent risk warning system based on multi-dimensional data analysis is provided for implementing the intelligent risk warning method based on multi-dimensional data analysis as described above, including: A multi-index perception module for obtaining multi-dimensional enterprise data streams, parsing heterogeneous index information, and reconstructing the logical hierarchy to construct an enterprise operation state perception map; A risk perturbation spectrum module for calculating the local fluctuation amplitude of multiple indicators of the enterprise operation state perception map and mining dynamic perturbation characteristics to construct a risk perturbation spectrum diagram; A state mutation analysis module for performing in-depth semantic analysis and node state mutation feature analysis on the risk perturbation spectrogram to generate the dynamic transition types of the mutated nodes; A risk node prediction module for performing full-cycle time series tracing on the risk perturbation spectrogram and predicting potential risk nodes based on the dynamic transition types to identify other potential risk propagation ports; A risk trajectory module for performing multi-timepoint risk evolution simulation and risk perturbation diffusion coupling on other potential risk propagation ports to construct a multi-dimensional risk trajectory vector field; A risk resistance deduction module for performing multi-strategy risk resistance intervention deduction on the multi-dimensional risk trajectory vector field, and then optimizing the risk resistance decision-making to construct an optimized risk resistance decision-making strategy.

[0045] The present invention obtains multi-dimensional data streams (such as finance, operation, market, production, etc.). This module provides a comprehensive enterprise status perception map for the system to ensure a comprehensive understanding of all aspects of the enterprise by the system. Heterogeneous index parsing and logical level reconstruction can effectively eliminate the differences between data sources, enabling data to be effectively processed and analyzed on a unified platform. The reconstruction of the logical level helps the system better understand the relationships between different data dimensions and is conducive to finding potential risks in complex enterprise systems. By constructing an enterprise operation status perception map, this module provides accurate background data for subsequent risk detection and prediction, ensuring that the system does not miss important upstream and downstream relationships when identifying risks. By calculating the local fluctuation amplitude of multiple indicators, this module can timely detect abnormal fluctuations in different business areas or operation links of the enterprise. Such fluctuations may be precursors of potential risks, so early warning can be given. Through dynamic perturbation feature mining, the module can discover and distinguish different types of perturbation features, helping the system identify the potential causes or trends of risk generation. This enables the system not only to identify current risks but also to predict their possible development directions. The risk perturbation spectrogram can visually present the fluctuation rules of risk perturbations, thus helping decision-makers better understand the fluctuation of enterprise risks and quickly grasp the core issues of risks. Through semantic parsing of the risk perturbation spectrogram, the module can convert complex data into easily understandable semantic information, enabling the system to understand and mine potential risk signals. The mutation of certain nodes may mean that a major risk event is about to occur. The mutation of node states is often closely related to the suddenness and severity of enterprise risks. This module can identify these mutations, helping decision-makers accurately understand which parts of the enterprise are undergoing rapid changes and may expose risks. According to the characteristics of node mutations, the module can identify and mark different types of dynamic transitions, helping the system accurately describe and predict the path of risk propagation and its impact. This helps to timely identify and intervene in potential risks. Through full-cycle time-series tracing of the risk perturbation spectrogram, this module can help the system understand the risk development trends in similar situations in history. This provides strong historical data support for identifying potential risk events in the future. Based on the prediction of potential risk nodes according to the type of dynamic transition, the propagation direction of risks in the future and the nodes that may be affected can be identified. This helps enterprises take preventive measures early to avoid unnecessary losses. Through the prediction of potential risk nodes, the module can identify other potential risk propagation ports, helping the system effectively identify risks with multi-point propagation and enhance the overall prevention and control ability. Through multi-time-point simulation, the module can show the change path of risks and their possible impacts at different time points, helping enterprises predict the evolution process of risks and make long-term preparations. The coupling analysis of risk perturbation diffusion enables the system to identify the mutual influence between risk sources, helping enterprises understand how the risks in one area may affect other areas and improve the accuracy of overall risk prediction.The multi-dimensional risk trajectory vector field provides a visual risk propagation path, helping decision-makers quickly identify which businesses or nodes are most vulnerable to risk and take targeted measures. It simulates different risk mitigation intervention strategies and, by deducing the effects of different intervention measures, helps companies select the most appropriate response strategy. This can greatly improve risk mitigation effectiveness and ensure that companies can make optimal decisions when faced with a variety of complex scenarios. By optimizing risk mitigation decisions, the module can recommend the most effective risk mitigation measures based on the company's specific circumstances (such as resources and risk tolerance), ensuring the company's risk management strategy is maximally effective. Optimized risk mitigation decisions can help companies respond quickly to sudden risks, reduce potential losses and negative impacts, and enhance their emergency response capabilities and ongoing stability.

[0046] The present invention is therefore intended to be illustrative and non-restrictive in all respects, with the scope of the invention being defined by the appended claims rather than the foregoing description, and all changes that come within the meaning and range of equivalents of the application documents are intended to be embraced therein.

[0047] The foregoing description is intended only to provide specific embodiments of the present invention, which will enable those skilled in the art to understand and implement the present invention. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the present invention. Therefore, the present invention is not intended to be limited to the embodiments shown herein, but is to be construed in the widest possible manner consistent with the principles and novel features disclosed herein.

Claims

1. An intelligent risk early warning method based on multi-dimensional data analysis, characterized in that, It includes the following steps: Step S1: Obtain multi-dimensional enterprise data streams, perform heterogeneous index information parsing and logical level reconstruction, and construct an enterprise operation status perception map; Step S2: Calculate the multi-index local fluctuation amplitude of the enterprise operation status perception map, and conduct dynamic disturbance feature mining to construct a risk disturbance spectrum map; Step S3: Perform in-depth semantic analysis and node state mutation feature analysis on the risk disturbance spectrum map to generate the dynamic transition types of mutation nodes; Step S4: Conduct full-cycle time-series tracing on the risk disturbance spectrum map, and predict potential risk nodes based on the dynamic transition types to identify other potential risk propagation ports; Step S5: Conduct multi-timepoint risk evolution simulation and risk disturbance diffusion coupling on other potential risk propagation ports to construct a multi-dimensional risk trajectory vector field; Step S6: Conduct multi-strategy anti-risk intervention deduction on the multi-dimensional risk trajectory vector field, and then optimize the anti-risk decision-making to construct an optimized anti-risk decision-making strategy.

2. The intelligent risk early warning method based on multi-dimensional data analysis according to claim 1, wherein The specific steps of Step S1 are as follows: Obtain multi-dimensional enterprise data streams based on the enterprise internal operation system; Perform heterogeneous index information parsing on the multi-dimensional enterprise data streams to generate heterogeneous index information, where the heterogeneous index information includes financial flow information, upstream and downstream transaction chains, and supply chain logistics trajectory information; Perform timestamp alignment processing on the heterogeneous index information, and conduct structure standardization to construct multi-dimensional operation standardized indexes; Conduct multi-index causal relationship analysis on the multi-dimensional operation standardized indexes to extract the causal relationships between different indexes; Conduct logical dimension mining on the multi-dimensional operation standardized indexes, and perform logical level reconstruction based on the causal relationships to construct an enterprise operation status perception map.

3. The intelligent risk warning method based on multi-dimensional data analysis according to claim 1, wherein, The specific steps of Step S2 are as follows: Perform time-series state decomposition on the enterprise operation status perception map to generate multiple state perception windows; Calculate the multi-index local fluctuation amplitude of the multiple state perception windows to obtain the state fluctuation amplitude value of each window; Perform transient high-fluctuation distortion identification based on the state fluctuation amplitude value, and mark the state fluctuation distortion points; Conduct abnormal risk sensitivity assessment on the state fluctuation distortion points based on a preset risk sensitivity threshold to extract potential risk perception factors; Conduct dynamic disturbance feature mining on the potential risk perception factors to construct a risk disturbance spectrum map.

4. The intelligent risk early warning method based on multi-dimensional data analysis according to claim 3, characterized in that The specific steps of conducting dynamic disturbance feature mining on the potential risk perception factors to construct a risk disturbance spectrum map are as follows: Conduct periodic risk disturbance feature analysis on the potential risk perception factors to extract periodic risk disturbance features; [[ID= ​ ​ 5. The intelligent risk early warning method based on multi-dimensional data analysis according to claim 1, wherein ​ ​ ​ Mine the risk behavior propagation path based on the map position information and extract the risk behavior propagation path; Perform critical node analysis of path mutations on the risk behavior propagation path, and extract jump event nodes, delayed diffusion nodes, and low-frequency activation nodes in the path; Analyze the node state mutation characteristics of the jump event nodes, delayed diffusion nodes, and low-frequency activation nodes to generate the dynamic transition types of the mutation nodes.

6. The intelligent risk early warning method based on multi-dimensional data analysis according to claim 1, characterized in that The specific steps of step S4 are as follows: Perform multi-factor correlation mining on the risk perturbation spectrogram to identify the associated risk factor groups; Deeply deconstruct the risk conduction logic of the associated risk factor groups to generate the risk conduction logic; Perform full-cycle time-series tracing on the risk behavior propagation path based on the risk conduction logic to obtain the full-cycle risk propagation chain; Predict potential risk nodes based on the dynamic transition type and the full-cycle risk propagation chain, and identify other potential risk propagation ports.

7. The intelligent risk early warning method based on multi-dimensional data analysis according to claim 1, characterized in that The specific steps of step S5 are as follows: Identify the current operating state of the enterprise based on multi-dimensional enterprise data streams; Perform real-time dynamic enterprise behavior analysis on the current operating state of the enterprise to extract key enterprise behaviors; Perform multi-point risk evolution simulation on other potential risk propagation ports according to the key enterprise behaviors to generate risk evolution simulation data; Perform risk trajectory evolution analysis on the risk evolution simulation data to generate a risk evolution trajectory; Calculate the direction, rate, potential energy, and risk outbreak probability of the risk evolution trajectory; Perform risk perturbation diffusion coupling according to the direction, rate, potential energy, and risk outbreak probability to construct a multi-dimensional risk trajectory vector field.

8. The intelligent risk early warning method based on multi-dimensional data analysis according to claim 1, characterized in that The specific steps of step S6 are as follows: Identify the enterprise's available resources based on multi-dimensional enterprise data streams; Perform multi-strategy anti-risk intervention deduction on the multi-dimensional risk trajectory vector field according to the enterprise's available resources to generate anti-risk simulation data for multiple intervention plans; Calculate the recovery speed of the enterprise operation indicators after anti-risk intervention and the residual amount of multi-index state fluctuations of the anti-risk simulation data; Perform secondary risk probability prediction on the recovery speed and the residual amount of multi-index state fluctuations to generate a secondary risk probability; Perform adaptive risk warning based on the secondary risk probability to generate an adaptive risk warning signal; Calculate the secondary residual risk density of the anti-risk simulation data to generate a secondary residual risk density; Perform risk diffusion trend analysis on the secondary residual risk density to obtain a secondary risk diffusion trend diagram; Optimize the anti-risk decision based on the secondary risk diffusion trend diagram to construct an anti-risk decision optimization strategy; Perform intelligent risk perception and warning operations based on the anti-risk decision optimization strategy and the adaptive risk warning signal.

9. An intelligent risk early warning system based on multi-dimensional data analysis, characterized in that, For executing the intelligent risk warning method based on multi-dimensional data analysis as described in claim 1, including: A multi-index perception module, configured to obtain multi-dimensional enterprise data streams, perform heterogeneous index information parsing and logical level reconstruction, and construct an enterprise operation state perception map; A risk perturbation spectrum module, configured to calculate the multi-index local fluctuation amplitude of the enterprise operation state perception map, and perform dynamic perturbation feature mining to construct a risk perturbation spectrogram; The state mutation analysis module is used to perform in-depth semantic analysis and node state mutation feature analysis on the risk perturbation spectrogram, and generate the dynamic transition types of the mutated nodes; The risk node prediction module is used to perform full-cycle time series tracing on the risk perturbation spectrogram, and predict potential risk nodes based on the dynamic transition types, and identify other potential risk propagation ports; The risk trajectory module is used to perform multi-timepoint risk evolution simulation and risk perturbation diffusion coupling on other potential risk propagation ports, and construct a multi-dimensional risk trajectory vector field; The anti-risk deduction module is used to perform multi-strategy anti-risk intervention deduction on the multi-dimensional risk trajectory vector field, and then optimize the anti-risk decision-making, and construct an optimized anti-risk decision-making strategy.

Images