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

Abstract

The application relates to the field of enterprise risk early warning analysis, in particular to an intelligent risk early warning method and system based on multidimensional data analysis. The method comprises the following steps: acquiring multidimensional enterprise data flow, performing heterogeneous index information analysis and logical level reconstruction, and constructing an enterprise operation state perception graph; performing multi-index local fluctuation amplitude calculation on the enterprise operation state perception graph, and performing dynamic disturbance feature mining to construct a risk disturbance frequency spectrum graph; performing deep semantic analysis and node state mutation feature analysis on the risk disturbance frequency spectrum graph to generate a dynamic transition type of a mutation node; performing full-cycle time sequence tracing on the risk disturbance frequency spectrum graph, and predicting potential risk nodes based on the dynamic transition type to identify other potential risk propagation ports. Through accurate and efficient enterprise risk perception, the application can make early risk intervention decisions, and improve the anti-risk ability and operation stability of enterprises.

Description

TECHNICAL FIELD

[0001] The present application 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

[0002] With the continuous improvement of enterprise informatization and digitization, the types and quantities of data relied on by enterprises in the aspects of business management, production operation, supply chain collaboration and market expansion are growing explosively. Under the promotion of cutting-edge technologies such as big data, cloud computing and artificial intelligence, the enterprise decision-making process is gradually transforming from the traditional experience-driven mode to the data-driven mode. Under this background, how to fully utilize multi-dimensional enterprise data for comprehensive analysis and mining to assist enterprises in scientifically identifying potential business risks and building an efficient and intelligent risk early warning mechanism has become an important issue in enterprise management and technical research.

[0003] In the actual operation of enterprises, various types of business risks are showing a trend of high frequency, diversification and complexity due to factors such as external economic environment fluctuations, policy changes, intensified market competition and internal management failures. Supply chain disruption may cause production to stop, tight capital chain may lead to credit crisis, management flow may bring strategic direction instability, and information security vulnerabilities may even cause serious data leakage problems. Once these risks are not identified and addressed in a timely manner, they often trigger a chain reaction, causing damage to the reputation of the enterprise, financial losses, and even survival crisis.

[0004] However, traditional enterprise risk early warning methods mostly rely on the setting and periodic analysis of static indicators, such as financial statement analysis, credit scoring, manual review, etc. This kind of method not only has a lagging response and a rough granularity, but also is difficult to capture the dynamic, cross and nonlinear risk characteristics hidden in the operation of the enterprise. In addition, traditional methods often ignore the relevance between different types of data, making it difficult to achieve integrated analysis of multi-source and heterogeneous data, resulting in the risk early warning system being unable to accurately identify potential threats and being unable to achieve forward-looking prediction and real-time response. With the increasing complexity of enterprise business processes and the increasing diversity of risk factors, single-dimensional data analysis methods have been unable to meet the needs of enterprises for high-precision and high-timeliness risk early warning. Therefore, there is an urgent need for a more intelligent intelligent risk early warning method. SUMMARY

[0005] The present application is to solve the above technical problems, and proposes an intelligent risk early 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 purpose, the present application provides an intelligent risk early warning method based on multi-dimensional data analysis, comprising the following steps:

[0007] Step S1: Obtain multi-dimensional enterprise data flow, analyze heterogeneous index information and reconstruct logical level, and construct enterprise operation state perception graph;

[0008] Step S2: Calculate multi-index local fluctuation amplitude of the enterprise operation state perception graph, and perform dynamic disturbance feature mining to construct a risk disturbance frequency spectrum graph;

[0009] Step S3: Perform deep semantic analysis and node state mutation feature analysis on the risk disturbance frequency spectrum graph to generate dynamic transition types of the mutation nodes;

[0010] Step S4: Perform full-cycle time sequence tracing on the risk disturbance frequency spectrum graph, and predict potential risk nodes based on the dynamic transition types to identify other potential risk propagation ports;

[0011] Step S5: Perform multi-time point risk evolution simulation and risk disturbance diffusion coupling on the other potential risk propagation ports to construct a multi-dimensional risk trajectory vector field;

[0012] Step S6: Perform multi-strategy anti-risk intervention deduction on the multi-dimensional risk trajectory vector field, and perform anti-risk decision optimization to construct an anti-risk decision optimization strategy.

[0013] The present application can help the system understand the dynamics of the enterprise, identify potential weaknesses and risks, and provide comprehensive information for subsequent risk identification. Through multi-index fluctuation analysis, the local fluctuation amplitude of each index in the enterprise operation can be found, and then a risk disturbance frequency spectrum graph can be constructed. Local fluctuations are often early signals of potential risks. By mining dynamic disturbance characteristics, the system can identify the potential source of risk at an early stage, providing strong support for subsequent risk management. Through frequency spectrum analysis, the system can efficiently identify the periodicity and regularity of risk fluctuations and conduct dynamic monitoring. Through semantic analysis of the risk disturbance frequency spectrum graph, the nature of enterprise risk can be understood in multiple dimensions. The mutation characteristics of node state can reflect the suddenness and severity of risk changes. This step can help the system find and analyze the key nodes of risk mutation, accurately identify possible sudden risks, and provide 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 sequence tracing, key points and their rules in the risk development process can be identified. This helps the system to provide timely warnings when similar risks are encountered in the future. Based on the prediction of dynamic transition types, potential risk nodes and their propagation paths can be identified, and other risk propagation ports that the enterprise may face can be predicted in advance, so that targeted preventive measures can be taken 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 risk diffusion that may occur in different time periods. The diffusion coupling of risk disturbance can help enterprises analyze the mutual influence between different risk sources, and provide a basis for developing more comprehensive preventive measures. The construction of 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 trajectory and path, different anti-risk intervention strategies are simulated. Through strategy deduction, the system can identify the most effective response measures to improve the enterprise's ability to cope with complex risk situations. Finally, through optimization of anti-risk decisions, the system can provide customized anti-risk decision-making solutions for enterprises, helping them adopt the most suitable risk management strategies in complex business environments, reduce the impact of potential risks, and improve the enterprise's anti-risk ability.

[0014] In the present specification, an intelligent risk early warning system based on multi-dimensional data analysis is provided for executing the intelligent risk early warning method based on multi-dimensional data analysis as described above, comprising:

[0015] A multi-index perception module is configured to acquire multi-dimensional enterprise data flow, perform heterogeneous index information analysis and logical level reconstruction, and construct an enterprise operation state perception graph.

[0016] A risk disturbance spectrum module is configured to calculate multi-index local fluctuation amplitude of the enterprise operation state perception graph, perform dynamic disturbance feature mining, and construct a risk disturbance spectrum graph.

[0017] A state mutation analysis module is configured to perform deep semantic analysis and node state mutation feature analysis on the risk disturbance spectrum graph, and generate a dynamic transition type of a mutation node.

[0018] A risk node prediction module is configured to perform full-cycle time sequence tracing on the risk disturbance spectrum graph, predict potential risk nodes based on the dynamic transition type, and identify other potential risk propagation ports.

[0019] A risk trajectory module is configured to perform multi-time-point risk evolution simulation and risk disturbance diffusion coupling on the other potential risk propagation ports, and construct a multi-dimensional risk trajectory vector field.

[0020] An anti-risk deduction module is configured to perform multi-strategy anti-risk intervention deduction on the multi-dimensional risk trajectory vector field, perform anti-risk decision optimization, and construct an anti-risk decision optimization strategy.

[0021] The application obtains multi-dimensional data streams (such as finance, operation, market, production, etc.), and the module provides a full range of enterprise state perception map for the system to ensure that the system has a comprehensive understanding of the situation of the enterprise. Heterogeneous index analysis and logical level reconstruction can effectively eliminate the differences between data sources, so that data can be effectively processed and analyzed on a unified platform. The reconstruction of the logical level helps the system to better understand the relationship between different data dimensions, which helps to find potential risks in complex enterprise systems. By constructing the enterprise running state perception map, the module provides accurate background data for subsequent risk detection and prediction, ensuring that the system will not miss important upstream and downstream relationships when identifying risks. By calculating the local fluctuation amplitude of multiple indicators, the module can timely discover abnormal fluctuations in different business fields or operation links of the enterprise. Such fluctuations may be a precursor to potential risks, so early warning can be made. Through dynamic disturbance feature mining, the module can discover and distinguish different types of disturbance features, helping the system to 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 direction. The risk disturbance frequency spectrum diagram can present the fluctuation law of risk disturbance through visualization, helping decision makers better understand the fluctuation of enterprise risks and quickly grasp the core problems of risks. Through semantic analysis of the risk disturbance frequency spectrum diagram, the module can convert complex data into easily understandable semantic information, so that the system can 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 state is often closely related to the suddenness and severity of enterprise risks. The module can identify these mutations to help decision makers accurately understand which parts of the enterprise are changing rapidly and may expose risks. According to the characteristics of node mutation, 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 identify and intervene in potential risks in a timely manner. By tracing the risk disturbance frequency spectrum diagram throughout the cycle, the module can help the system understand the risk development trend in similar situations in history. This provides strong historical data support for identifying possible future risk events. Based on the prediction of potential risk nodes based on dynamic transition types, the future propagation direction of risks and the nodes that may be affected can be identified. This helps enterprises to take preventive measures as soon as possible to avoid unnecessary losses. Through the prediction of potential risk nodes, the module can identify other potential risk propagation ports, helping the system to effectively identify multi-point risk propagation and improve the overall prevention and control capability. The module can show the change path and possible impact of risks at different time points through multi-time point simulation, helping enterprises to predict the evolution process of risks and make long-term preparations. The coupling analysis of risk disturbance diffusion enables the system to identify the mutual influence between risk sources, helping enterprises to understand how risks in one field may affect other fields and improve the accuracy of overall risk prediction.The multi-dimensional risk trajectory vector field provides a visualized risk propagation path, which can help decision-makers quickly identify which businesses or nodes are most vulnerable to risks and take targeted measures. By simulating different anti-risk intervention strategies and deducing the effects of different intervention measures, the module helps enterprises choose the most suitable response strategy. This can greatly improve the effectiveness of anti-risk and ensure that the enterprise can make the best decision when facing multiple complex situations. Through the optimization of anti-risk decisions, the module can recommend the most effective anti-risk measures based on the specific circumstances of the enterprise (such as resources, risk tolerance, etc.), ensuring that the enterprise's risk management strategy has the maximum benefit. Optimized anti-risk decisions can help enterprises quickly respond to unexpected risks, reduce potential losses and negative impacts, and improve the enterprise's emergency response capabilities and sustainability. BRIEF DESCRIPTION OF DRAWINGS

[0022] Figure 1 A step flow diagram of the intelligent risk early warning method based on multi-dimensional data analysis of the present application is shown in the figure.

[0023] Figure 2 A detailed implementation step flow diagram of step S1 is shown in the figure.

[0024] Figure 3 A detailed implementation step flow diagram of step S2 is shown in the figure.

[0025] Figure 4 A detailed implementation step flow diagram of step S3 is shown in the figure. DETAILED DESCRIPTION

[0026] It should be understood that the specific embodiments described herein are only used to explain the present application and do not limit the present application.

[0027] The present application provides an intelligent risk early warning method and system based on multi-dimensional data analysis. The execution subject of the intelligent risk early warning method based on multi-dimensional data analysis includes but is not limited to the following: mechanical equipment, data processing platform, cloud server node, network upload device, etc. which can be regarded as general computing nodes of the present application, and the data processing platform includes but is not limited to: audio image management system, information management system, cloud data management system at least one.

[0028] Please refer to Figures 1 to 4 The present application provides an intelligent risk early warning method based on multi-dimensional data analysis, which includes the following steps:

[0029] Step S1: Obtain multi-dimensional enterprise data flow, analyze heterogeneous index information and reconstruct logical level, and construct enterprise operation state perception map;

[0030] Step S2: Multi-index local fluctuation amplitude calculation is performed on the enterprise operation state perception graph, and dynamic disturbance feature mining is performed to construct a risk disturbance frequency spectrum graph;

[0031] Step S3: Deep semantic analysis and node state mutation feature analysis are performed on the risk disturbance frequency spectrum graph to generate dynamic transition types of the mutation nodes;

[0032] Step S4: Full-cycle time sequence tracing is performed on the risk disturbance frequency spectrum graph, and potential risk nodes are predicted based on the dynamic transition types to identify other potential risk propagation ports;

[0033] Step S5: Multi-time point risk evolution simulation and risk disturbance diffusion coupling are performed on the other potential risk propagation ports to construct a multi-dimensional risk trajectory vector field;

[0034] Step S6: Multi-strategy anti-risk intervention deduction is performed on the multi-dimensional risk trajectory vector field, and anti-risk decision optimization is performed to construct an anti-risk decision optimization strategy.

[0035] The present application can understand the running state of the enterprise from various aspects by obtaining the multi-dimensional data flow of the enterprise, including various data such as finance, operation and market. The analysis of heterogeneous data and the reconstruction of the logical level can make the data more standardized and comparable, and build an enterprise running state perception map, thereby providing comprehensive basic information for subsequent risk identification. This step can help the system to 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 disturbance frequency spectrum graph is constructed. Local fluctuation is often an early signal of potential risk. By mining dynamic disturbance characteristics, the system can identify the potential source of risk at an early stage, providing strong support for subsequent risk management. Through frequency spectrum analysis, the system can efficiently identify the periodicity and regularity of risk fluctuation and conduct dynamic monitoring. Through semantic analysis of the risk disturbance frequency spectrum graph, the nature of enterprise risk can be deeply understood from multiple dimensions. The mutation characteristics of node state can reflect the suddenness and severity of risk changes. This step can help the system to find and analyze the key nodes of risk mutation, 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 risk in the enterprise system. Through full-cycle time sequence tracing, the key points and their rules in the risk development process can be identified. This helps the system to provide timely warning 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 risk propagation ports that the enterprise may face can be predicted in advance, so that targeted preventive measures can be taken 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 risk diffusion that may occur in different time periods. The diffusion coupling of risk disturbance can help the enterprise to analyze the mutual influence between different risk sources and provide a basis for formulating more comprehensive prevention measures. The construction of 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 trajectory and path, different anti-risk intervention strategies are simulated. Through strategy deduction, the system can identify the most effective countermeasures to improve the enterprise's ability to cope with complex risk situations. Finally, through optimization of anti-risk decision-making, the system can provide customized anti-risk decision-making schemes for the enterprise, helping the enterprise to adopt the most suitable risk management strategy in complex business environment, reduce the impact of potential risks, and improve the enterprise's anti-risk ability.

[0036] In the embodiment of the present application, referring to Figure 1 The steps of the intelligent risk early warning method based on multi-dimensional data analysis according to the present application are shown in the flowchart, and in this example, the steps of the intelligent risk early warning method based on multi-dimensional data analysis include:

[0037] Step S1: acquire multi-dimensional enterprise data flow, perform heterogeneous index information analysis and logical level reconstruction, and construct enterprise operation state perception graph;

[0038] In this example, identify various data sources within and outside the enterprise, including ERP systems, CRM systems, financial systems, market research data, supply chain management systems, etc. These systems contain multi-dimensional information such as sales data, inventory information, customer feedback, financial statements, etc. Extract the past six months of inventory data and sales data from the ERP system, obtain customer satisfaction survey results from the CRM system, and obtain monthly financial statements from the financial system. Use ETL (Extract, Transform, Load) tools to collect and integrate the identified data. During the extraction process, ensure data quality, remove duplicates and errors, and perform necessary format conversions for subsequent analysis. Extract data from each system and unify the format, such as converting date formats to YYYY-MM-DD, and filling or removing missing values to ensure data integrity. Classify the collected multi-dimensional data and define the meaning and calculation method of each indicator. These indicators should cover the key operating elements of the enterprise, such as financial indicators (profit margin, liquidity ratio), operational indicators (inventory turnover rate, production efficiency), and market indicators (customer satisfaction, market share). Define "inventory turnover rate" as "the ratio of sales cost to average inventory" and classify it as an operational indicator to facilitate subsequent analysis. Use data standardization methods to analyze indicators from different sources to ensure comparability. Common methods include Z-score standardization, Min-Max standardization, etc. to eliminate the impact of different dimensions. Normalize the sales data to the range of 0 to 1 to ensure that the comparison between different indicators is reasonable. Analyze the logical relationship between each indicator and identify the influence relationship and hierarchical structure. Use causal relationship analysis or graph theory methods to build a relationship graph between indicators to identify key indicators and influence chains. Through analysis, it is found that there is a positive correlation between "inventory turnover rate" and "customer satisfaction", that is, the efficiency of inventory management directly affects the timeliness of customer delivery, thereby affecting satisfaction. According to the identified logical relationship, build a hierarchical model of enterprise operation status. This model should clearly define the position and role of each indicator in the overall operation status and reflect the relationship between high-level indicators and low-level indicators. Build a three-layer model: the first layer is the overall operation status of the enterprise, the second layer is the key financial, operational and market indicators, and the third layer is the specific measurement indicators (such as sales, inventory, customer ratings). Use graph databases (such as Neo4j) or data visualization tools (such as Tableau, Power BI) to design an enterprise operation status perception map. When building the map, consider clear representation of nodes (indicators) and edges (relationships) to facilitate subsequent analysis and visualization. In the map, nodes represent each key indicator and edges represent their logical relationships, forming a comprehensive view of enterprise operation status. According to the designed model, import the analyzed data into the map for visualization and preliminary verification.By comparing historical data and actual running state, the accuracy and effectiveness of the graph are tested. Using the graph visualization tool, the changes in inventory turnover rate and customer satisfaction in different time periods are displayed to ensure that the graph can accurately reflect the running state of the enterprise.

[0039] Step S2: Multi-index local fluctuation amplitude calculation is performed on the enterprise running state perception graph, and dynamic disturbance feature mining is performed to construct a risk disturbance frequency spectrum graph;

[0040] In this embodiment, key indicators are selected from the enterprise operation state perception atlas for fluctuation amplitude calculation. These indicators may include sales, inventory level, customer satisfaction, production efficiency, etc. Ensure that the data time series of these indicators is complete for fluctuation analysis. Select sales and inventory level in the past six months as the calculation object, and ensure the consistency of the data timestamp. Define the calculation method of local fluctuation amplitude, usually use standard deviation, root mean square (RMS) or absolute change amplitude as an indicator to quantify the fluctuation of the indicator in each time period. The local fluctuation amplitude can be defined as the standard deviation of the sales change in a time window (such as 7 days), reflecting the amplitude of the sales fluctuation in that time period. In the selected time window, calculate the local fluctuation amplitude of each indicator. The fluctuation amplitude in each window can be calculated by moving the window step by step. In each 7-day window, calculate the standard deviation of sales and record it, forming a fluctuation amplitude time series. Assuming that the sales in a certain window are [1 million, 1.2 million, 1.15 million, 1.3 million, 1.25 million, 1.4 million, 1.35 million], the standard deviation calculation result is about 141.4 thousand. Define dynamic disturbance features, including fluctuation frequency, amplitude, duration and related indicators, etc. These features will be used to build the risk disturbance spectrum graph later. Dynamic disturbance features can be defined as the maximum value, minimum value and their occurrence frequency of local fluctuation amplitude. Analyze the calculated local fluctuation amplitude data and extract dynamic disturbance features. Use time series analysis method to identify the pattern and periodicity of fluctuation. Use autocorrelation function (ACF) to analyze the fluctuation amplitude data and identify the periodicity characteristics of the fluctuation, record the fluctuation amplitude values with higher frequency. According to the extracted dynamic disturbance features, design the risk disturbance spectrum graph model. The spectrum graph should be able to intuitively show the fluctuation of each indicator in different time periods and its impact on the enterprise operation state. Design the X-axis of the spectrum graph as time and the Y-axis as fluctuation amplitude, and different colors or shapes of points represent different indicators (such as sales, inventory level). Import the calculated dynamic disturbance feature data into a visualization tool (such as Tableau or Power BI) to generate a risk disturbance spectrum graph. Ensure the readability and ease of understanding of the atlas to facilitate analysis by management. Draw the fluctuation spectrum graph of sales and inventory level in the visualization tool, show the fluctuation amplitude change in different time periods, and mark the time nodes with abnormally high fluctuation.

[0041] Step S3: Deep semantic analysis and node state mutation feature analysis of the risk disturbance spectrum graph, generating dynamic transition types of the mutation nodes;

[0042] In this example, relevant data is extracted from the constructed risk disturbance spectrum, including fluctuation amplitude, time series, and relationships between various indicators. These data will be used for subsequent deep semantic analysis. Sales fluctuation data and corresponding customer satisfaction data from the past three months are extracted, ensuring that the data timestamps and formats are consistent for analysis. A deep learning model such as BERT or LSTM in natural language processing (NLP) technology is used for semantic analysis, converting numerical data in the spectrum into interpretable semantic information. This information will help identify potential risk patterns and influencing factors. BERT model is used to generate semantic embeddings for fluctuation data, analyzing the relationship between sales fluctuations and market feedback, and identifying potential risk factors. The trained model analyzes the extracted data to identify key fluctuation patterns and their semantic information, such as market reaction or customer feedback corresponding to high fluctuations. Record this information for subsequent analysis. If the model identifies that "sales fluctuation" corresponds to an increase in customer complaints, this information is recorded as a potential risk signal, along with its frequency and time period. Define the definition of node state mutation, which usually refers to a node in the spectrum that experiences a significant change in a short period of time. These mutations may be caused by external market changes, internal management decisions, or other factors. Define "sales mutation" as a node whose sales change more than 15% in a month. Use statistical analysis methods such as Z-score or control charts to monitor changes in node state and identify mutation nodes that exceed normal fluctuation range. By setting thresholds, abnormal changes can be captured. For sales data, if the Z-score exceeds 3 in a certain time period, mark the node as a mutation node and record its specific value and time. Feature extraction is performed on the identified mutation nodes to analyze the amplitude, duration, and impact range of the changes. This will help understand the nature of the mutation and potential risks. If a mutation node's sales jump from 1 million to 1.5 million in a short period of time, record the amplitude of the 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. Each transition type should have clear characteristics and analysis methods. Define instantaneous transition as a dramatic change that occurs in a short period of time, while delayed transition refers to changes that occur after a period of time. Analyze the extracted mutation node states to identify their dynamic transition types. By observing the state changes of nodes in different time periods, features can be extracted and classified. If a sales mutation node experiences a large fluctuation (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 gradually appears over several months, it is marked as a delayed transition. Compile the dynamic transition types of each mutation node into a report, describing their characteristics and impact on enterprise risk management. Ensure that the report is detailed and easy for management to understand and make decisions.The report points out that the dynamic transition type of a certain node is instantaneous transition, and analyzes the potential market reasons, such as the promotion activities of competitors, and suggests that the enterprise timely adjusts the strategy to cope with the market changes.

[0043] Step S4: full-cycle time sequence tracing is performed on the risk disturbance spectrum diagram, potential risk node prediction is performed based on the dynamic transition type, and other potential risk propagation ports are identified;

[0044] In this embodiment, relevant time series data is extracted from the risk disturbance spectrum, including the changes of each risk indicator over a certain period of time. These data will be used for time series traceability analysis, ensuring that the time range covers key fluctuation events. Extract sales fluctuation data, inventory level changes and customer feedback over the past year, ensuring consistent timestamps for subsequent analysis. Use time series analysis models (such as ARIMA 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, helping to analyze risk evolution in different time periods. Use the ARIMA model to model sales data and identify trends over the past six months, predicting sales fluctuations in the future. Based on the established time series model, perform full-cycle time series traceability to analyze fluctuation characteristics in different time periods and their impact on enterprise operation status. Record the state changes at each key time node to form a complete traceability record. If the analysis results show that sales and inventory levels have simultaneously experienced abnormal fluctuations in a certain month, record the fluctuation amplitude and possible influencing factors at that 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 transient transition, delayed transition or frequency transition to understand its nature. If a node experiences a large fluctuation in sales (e.g. from 1 million to 1.5 million) in a short period of time, it is marked as a transient transition; if the fluctuation gradually appears over several months, it is marked as a delayed transition. Record the dynamic transition type and its characteristics for each node, and analyze its possible impact on the enterprise. These characteristics will provide a basis for subsequent risk node prediction. Record the transition type of a node as a transient transition and analyze its possible market reasons, such as sudden market activities or competitor price adjustments. Use machine learning algorithms (such as random forests, support vector machines, etc.) to build a potential risk node prediction model. This model should be able to consider the dynamic transition types and their characteristics analyzed previously, and identify other potential risk propagation ports. Use historical data to train the model, input features including dynamic transition type, fluctuation amplitude and time node, and predict potential risk nodes in the future. Train and validate the constructed prediction model to ensure its high accuracy and reliability. Cross-validation methods can be used to evaluate the model's performance on different data sets. Use an 80 / 20 training and testing data division ratio to train the model and evaluate its accuracy on the test data, ensuring 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 transition, affecting other business segments or indicators. If the model predicts an increase in customer churn risk in a certain time period, identify the customer service segment as a potential risk propagation port and record its impact level.

[0045] Step S5: multi-time point risk evolution simulation and risk disturbance diffusion coupling are performed on other potential risk propagation ports, and a multi-dimensional risk trajectory vector field is constructed;

[0046] In this example, based on the potential risk propagation ports identified in the previous step, in-depth analysis is conducted. Each port may involve different indicators, such as customer service, supply chain management, or market feedback, etc. Ensure the integrity of the relevant data to facilitate subsequent simulation. If customer service is identified as a potential risk propagation port, extract data such as customer feedback, complaint rate, and customer churn rate within the past six months, ensuring consistent timestamps. Integrate the extracted data of various types, unify the format, and perform necessary cleaning to remove outliers and missing values. Ensure the quality and consistency of the data to facilitate risk evolution simulation. Use the Z-score method to identify and handle outliers, and remove outliers in customer complaint rate to ensure the accuracy of subsequent data analysis. Use a system dynamics model or an agent-based model (Agent-Based Model) to construct a multi-time point risk evolution simulation. This model can simulate the dynamic characteristics of risk propagation in different time periods, considering the interaction of various factors. Build an agent-based model to simulate the dynamic impact of customer churn on sales, inventory, and customer satisfaction, which can reflect the evolution of risk in the time dimension. Set model parameters based on historical data, including initial state, influence coefficient, and time step. Ensure reasonable parameter settings to improve the reliability of simulation results. Set the initial customer churn rate to 5%, with a monthly fluctuation range of 1%, and simulate the risk evolution process 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 evolution of risk. The simulation results show that the customer churn rate may rise to 10% in the next six months, and the changes in sales, inventory levels at each time node are recorded. Use coupling analysis to build a risk disturbance diffusion coupling model. This model should be able to integrate the mutual influence between different propagation ports and analyze how risk spreads through different channels. Establish a multivariate regression model to analyze how customer churn affects sales and its potential impact on the supply chain, and identify the coupling relationship between each port. Set the parameters of the coupling model, including the influence coefficient and propagation speed of each propagation port. These parameters will help analyze the propagation characteristics of risk disturbances between different ports. Set the influence coefficient of customer service to 0.3, indicating the degree of influence of customer service on sales, and calculate its contribution to overall risk propagation. Execute the coupling model and observe the diffusion process of risk disturbances, record the risk state at different time points and the influence degree between each port. The simulation results show that when the customer churn rate reaches 10%, the sales may decrease by 15%, and the risk state changes of each port are recorded for subsequent analysis. Based on the results of simulation and coupling analysis, design a multi-dimensional risk trajectory vector field. This vector field should be able to visually display different risk propagation paths and influence degrees, helping management quickly identify potential risks. Set the X-axis of the vector field as time, the Y-axis as risk state, and the Z-axis as influence degree.Different colors and arrow sizes are used to represent the strength and direction of different risk transmission paths. Use data visualization tools (such as Tableau, Power BI) to visualize the constructed multi-dimensional risk trajectory vector field, ensure the clarity and ease of understanding of the graphics. In the visualization tool, show the risk state changes in different time periods, mark out the significant risk paths, so as to facilitate the decision analysis of the management.

[0047] Step S6: Multi-strategy anti-risk intervention deduction is carried out on the multi-dimensional risk trajectory vector field, and anti-risk decision optimization is carried out, and anti-risk decision optimization strategy is constructed.

[0048] In this embodiment, based on the analysis of the multi-dimensional risk trajectory vector field, various anti-risk intervention strategies are designed. These strategies should be targeted at the specific risk propagation paths and potential impacts identified, covering response measures for different business links. For customer churn risk, various strategies can be designed, including improving customer service quality, increasing customer loyalty programs, and strengthening market promotion. Each strategy should clearly define its target and expected effect. Parameters are set for each intervention strategy, including implementation time, resource allocation, and expected effect. If the strategy of improving customer service quality is chosen, the number of customer service personnel to be added, the training time, and the funds to be invested need to be set. It is planned to increase the number of customer service personnel by 10 in 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's operating state after the implementation of different strategies. The simulation should cover multiple time points and record the changes in various indicators. After executing the simulation, it is found that the implementation of the strategy of improving customer service quality can reduce the customer churn rate from 10% to 6% and increase sales by 10% after three months. Record these changes for subsequent analysis. Based on the deduction, set the goals of anti-risk decision optimization, including reducing risk level, improving customer satisfaction, and increasing sales, etc. Ensure that the goals are clear and measurable to guide the subsequent optimization process. The goal is to reduce the customer churn rate to 5% and increase sales by 15% within six months after the intervention. Build a decision optimization model using linear programming, mixed integer programming, or other optimization algorithms, 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 up a linear programming model, define the decision variables as the implementation degree of each intervention strategy, and set the constraints including resource constraints (such as manpower, funds) and expected effects. Run the optimization model to solve the best 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 target to support decision-making. The optimization results show that the best strategy combination is to improve customer service quality, increase market promotion, and launch customer loyalty programs, which will reduce the customer churn rate to 4% and increase sales 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. Build 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 timeline, responsibility allocation, and resource allocation. Ensure that each strategy can be effectively implemented within the scheduled time and monitor the implementation progress.

[0049] In this embodiment, refer to Figure 2For the detailed implementation step flowchart of step S1, in this embodiment, the detailed implementation step of step S1 includes:

[0050] Obtaining multi-dimensional enterprise data flow based on an internal operation system of an enterprise;

[0051] Performing heterogeneous index information analysis on the multi-dimensional enterprise data flow to generate heterogeneous index information, the heterogeneous index information including financial flow information, upstream and downstream transaction chain information, and supply chain logistics track information;

[0052] Performing timestamp alignment processing on the heterogeneous index information, and performing structure standardization, to construct multi-dimensional operation standardized indexes;

[0053] Performing multi-index causal relationship analysis on the multi-dimensional operation standardized indexes, to extract the causal relationship between different indexes;

[0054] Performing logic dimension mining on the multi-dimensional operation standardized indexes, and performing logic level reconstruction based on the causal relationship, to construct an enterprise operation state perception graph.

[0055] In this example, identify various data sources within the enterprise, including financial systems, ERP systems, CRM systems, and supply chain management systems. 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; and obtain customer transaction records from the CRM system. All data sources should provide real-time or regularly updated data streams. Use ETL (Extract, Transform, Load) tools to extract the required data streams from each data source. Perform preliminary data cleaning, 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 processed results from each data source into a multi-dimensional data stream. The data stream should include different dimensions such as time, region, product category, etc. to facilitate subsequent analysis. Construct a multi-dimensional data table containing time, product ID, customer ID, sales, and logistics status to analyze the relationships between different dimensions. Determine the heterogeneous indicators that need to be parsed, including financial transaction information (such as revenue, 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, upstream transaction chains include the delivery status of suppliers, and logistics track information involves each link in the transportation process. Parse various types of data to extract heterogeneous indicator information. Use data parsing tools (such as JSON parser, XML parser) to extract the required fields from the original data. Extract the amount and timestamp of each transaction from financial data, and extract the supplier name and logistics status from supply chain data. Integrate the parsed heterogeneous indicator information into a unified database using appropriate data models (such as relational databases or time series databases) to facilitate subsequent queries 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 indicator information. Align the timestamps of all heterogeneous indicator information to ensure consistency in time formats across different data sources. This can be achieved through time format conversion and time interval standardization. Unify all timestamps to UTC format to facilitate data comparison across different regions and systems. Standardize the structure of heterogeneous indicator information to ensure that all types of indicators follow a unified structure standard. Define a standardized template to ensure that all data conforms to this template. Specify that financial transaction information should include "amount", "time", "transaction type" fields, and logistics information should include "shipping time", "arrival time", "status" fields. Based on the standardized data, construct multi-dimensional operational standardized indicators. These indicators 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 causal relationship models between multiple indicators.Identify which indicators have significant causal relationships. Use regression analysis to determine how sales are affected by inventory levels and logistics timeliness. Analyze multidimensional operational standardized indicators to extract causal relationships between different indicators. Record the analysis results, including the strength and direction of the causal relationships. 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 of each indicator. Identify which indicators are key indicators and which are auxiliary indicators. 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 each product. Construct a logical hierarchy structure based on causal relationships and logical dimensions. Classify relevant indicators according to their importance and influence to form a clear logical hierarchy diagram. Construct a hierarchy diagram showing 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 structure, construct an enterprise operational status perception map. This graph should reflect the overall picture of the company's operations, including the status of various indicators and their interrelationships. It should generate a dynamic awareness graph that displays real-time changes in sales, inventory levels, and logistics status, as well as their mutual influences.

[0056] In this embodiment, see Figure 3 The diagram below illustrates the detailed implementation steps of step S2. In this embodiment, the detailed implementation steps of step S2 include:

[0057] The enterprise operation status perception map is decomposed into time-series states to generate multiple status perception windows;

[0058] Multi-index local fluctuation amplitude calculations are performed on multiple state perception windows to obtain the state fluctuation amplitude value for each window;

[0059] Based on the state fluctuation amplitude value, transient high fluctuation distortion is identified, and state fluctuation distortion points are marked.

[0060] An abnormal risk sensitivity assessment is performed on the state fluctuation distortion point based on the preset risk sensitivity threshold, and potential risk perception factors are extracted.

[0061] Dynamic perturbation features of potential risk perception factors are mined to construct a risk perturbation spectrum.

[0062] In this embodiment, the required operational indicator data is extracted from the enterprise operational state perception map. These data will be used for subsequent time series state decomposition. Ensure that the timestamp information of the data is complete in order to accurately perform time series analysis. Extract the time series data of indicators such as sales, inventory level, logistics timeliness, etc. in the past month to form a time series data set containing multiple indicators. State decomposition can be performed on the extracted time series data 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, and different state fluctuation patterns can be identified. Using wavelet transform to process sales data, it is decomposed into low-frequency trend and high-frequency fluctuation, which helps to identify potential state changes. According to the results of 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 length of time in order to facilitate subsequent analysis. If 10 days is chosen as a window, three state perception windows are generated from one month of data, each corresponding to a different time period. Determine the calculation method of local fluctuation amplitude, which can usually use indicators such as standard deviation, root mean square (RMS) or absolute change amplitude 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 indicator fluctuations within the window. Calculate the fluctuation amplitude for multi-indicator data within each state perception window.

[0063] Ensure that all relevant indicators are included in the calculation to obtain a comprehensive fluctuation amplitude value. If the sales, inventory and logistics timeliness in a certain state perception window are [100, 120, 110], [200, 210, 195] and [5, 7, 6] respectively, calculate the standard deviation of these indicators as the fluctuation amplitude of the window. Record the fluctuation amplitude value of each state perception window and analyze it to compare the fluctuations between different windows. This will help identify windows with larger fluctuation amplitudes, which may have potential risks. According to historical data and industry standards, set a threshold for high fluctuation distortion. This threshold should effectively distinguish between normal fluctuations and abnormal fluctuations. Set the fluctuation amplitude exceeding 3 times the standard deviation as 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 window is 5 and higher than the set threshold, mark the window as a distortion point and record its specific time period and fluctuation amplitude.

[0064] According to historical data and business needs, set the risk sensitivity threshold. This threshold is used to assess the abnormal risk level of the state fluctuation distortion point. Set the risk sensitivity threshold to 2, indicating that the distortion point with a fluctuation amplitude exceeding this value needs further risk assessment. Perform abnormal risk sensitivity assessment on the marked state fluctuation distortion points. Risk scoring models can be used to assess factors such as fluctuation amplitude, historical performance, and industry benchmarks. If the fluctuation amplitude of a certain distortion point is 6 and exceeds the sensitivity threshold, the potential risk is assessed as high, and it is recorded as a high-risk point. According to the sensitivity assessment results, extract potential risk perception factors. These factors will be used in subsequent risk analysis and the establishment of early warning mechanisms. If the assessment results show that a certain distortion point is related to the delivery delay of a specific supplier, record the delivery status of the supplier as a potential risk factor. Define dynamic disturbance characteristics, including fluctuation frequency, amplitude, duration, and related indicators. These characteristics will be used to construct a risk disturbance spectrum graph. Define dynamic disturbance characteristics as the frequency of state fluctuations (such as several times a week), amplitude (such as maximum fluctuation value), duration (such as lasting for several days), etc. Extract dynamic disturbance characteristics for marked state fluctuation distortion points. Record the fluctuation frequency, amplitude, and duration of each distortion point for subsequent analysis. For a marked distortion point, extract its occurrence frequency of 2 times per month, maximum fluctuation amplitude of 7, and duration of 5 days. Use the extracted dynamic disturbance characteristics to construct a risk disturbance spectrum graph. This graph should reflect the characteristics of each disturbance and its impact on the system, facilitating subsequent monitoring and management. In the generated spectrum graph, the X-axis represents time, and the Y-axis represents fluctuation amplitude. Each distortion point and its impact are marked on the graph to help management quickly identify potential risks.

[0065] In this embodiment, the specific steps for dynamically mining potential risk perception factors and constructing a risk disturbance spectrum graph are as follows:

[0066] Periodic risk disturbance characteristic analysis is performed on potential risk perception factors to extract periodic risk disturbance characteristics.

[0067] According to the periodic risk disturbance characteristics, perform multi-time point disturbance fitting to construct a risk disturbance waveform curve.

[0068] Calculate the disturbance frequency intensity and trend slope of the risk disturbance waveform curve.

[0069] According to the disturbance frequency intensity and trend slope, perform disturbance frequency continuity mining to construct a risk disturbance spectrum graph.

[0070] In this embodiment, the potential risk perception factors extracted from the previous analysis are used for periodic analysis. These factors can include amplitude, frequency, duration, etc. Ensure that all relevant indicators are covered to analyze the periodic characteristics comprehensively. Assume that 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 the main periodic components, such as the frequency and amplitude of monthly fluctuations. Extract the periodic risk disturbance characteristics and record their amplitude and frequency of 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 disturbance characteristics, organize the relevant data to ensure that the data is complete and time-stamped consistently. The data should cover multiple time points to facilitate fitting analysis. Organize the sales, inventory, and logistics timeliness data over the past six months to form a time series data set containing multiple time points. Choose an appropriate fitting model (such as a sine wave model, linear regression model, or polynomial fitting) to fit the extracted periodic risk disturbance characteristics. The selected model should effectively reflect the periodic changes in the data. Use the sine wave model to fit the sales fluctuation, with the model form: 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 results and calculate the goodness of fit (such as R² value) to ensure that the model can effectively represent the periodic characteristics. Record the fitted parameter values for subsequent analysis. If the fitting result shows an R² value of 0.95, indicating that the model can well fit the data, record the fitted parameters (amplitude, frequency, and phase, etc.). Calculate the frequency intensity of the risk disturbance waveform curve, which can be achieved by analyzing the parameters of the fitted 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), the frequency intensity is: frequency intensity = A × B = 10% × 0.2 = 2%, calculate the trend slope of the risk disturbance waveform curve, which can usually be achieved by linear regression analysis of the fitting results. The slope represents the rate of change of the waveform curve and can reflect the rising or falling trend of the risk disturbance. 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 a trend indicator. Choose a suitable continuous mining method, such as sliding window analysis or time series decomposition, to identify the continuity of the disturbance frequency. Ensure that this method can effectively capture the pattern of frequency changes. Use sliding window analysis with a window length of 30 days to calculate the disturbance frequency within each window by moving the window step by step.The frequency intensity and trend slope in each sliding window are analyzed to identify the stability and continuity of the frequency. The time period of the frequency continuity change is recorded. If the frequency intensity remains above 2% in a 30-day window, the time period of the window is recorded and marked as a high-frequency continuity interval. The mined frequency continuity results are organized into tables or graphs to construct a risk disturbance frequency spectrum graph. This graph should be able to visually display the disturbance frequency changes in different time periods. The spectrum graph is generated, with the X-axis representing time and the Y-axis representing frequency intensity. The high-frequency interval is marked in the graph to facilitate the quick identification of potential risks by management.

[0071] In this embodiment, referring to Figure 4 For the detailed implementation step flowchart of step S3, in this embodiment, the detailed implementation steps of step S3 include:

[0072] The risk disturbance frequency spectrum graph is subjected to deep semantic analysis and semantic annotation nested coding to obtain risk disturbance semantic coding;

[0073] According to the risk disturbance semantic coding, the enterprise operation state perception graph is associated and mapped and the topological structure is positioned to obtain the graph position information of the risk factor;

[0074] Based on the graph position information, the risk behavior propagation path is mined to extract the risk behavior propagation path;

[0075] The risk behavior propagation path is subjected to path mutation key node analysis to extract the jump event node, delay diffusion node and low-frequency activation node in the path;

[0076] The jump event node, delay diffusion node and low-frequency activation node are subjected to node state mutation feature analysis to generate the dynamic transition type of the mutation node.

[0077] In this embodiment, relevant data is extracted from the constructed risk disturbance spectrum, including disturbance frequency, intensity, and its time variation. The integrity of the data is ensured to facilitate deep semantic analysis. The frequency intensity variation within a certain time period is extracted, such as a frequency intensity of 2%-5% within a certain time period, and the corresponding disturbance event is recorded. A deep learning model (such as BERT or LSTM) in natural language processing (NLP) technology is used for semantic analysis to convert the data in the spectrum into understandable semantic information. These information will be used for subsequent encoding and labeling. The sudden high-frequency events in the spectrum are analyzed to identify their potential risk implications, such as market fluctuations, supply chain disruptions, etc. The semantic information parsed is labeled and structured using a specific encoding format (such as JSON or XML). Each label should include event type, intensity, time, and potential impact, etc. A certain event is labeled as "high-frequency risk" and its frequency, occurrence time, and related impact factors are recorded, such as "10% sales decline." Relevant information is extracted from the enterprise running state perception graph, including the time series data and mutual relationship of various state indicators. The graph structure is ensured to support subsequent correlation mapping. Key information of sales, inventory level, and logistics timeliness indicators is extracted from the state perception graph. The risk disturbance semantic encoding is correlated and mapped with the enterprise running state perception graph. By identifying the relationship between events in semantic encoding and indicators in the state graph, a correlation matrix is constructed. If a high-frequency risk corresponds to a decline in sales, the relationship is marked in the correlation matrix, and the related impact degree is recorded. According to the correlation mapping result, the graph location information of the risk factor is located. Through graph analysis tools (such as Gephi, Cytoscape, etc.), the distribution location of the risk factor is visualized. In the generated topology graph, the relationship between sales, inventory, and logistics and risk factors is marked to facilitate the identification of key areas of risk impact. Using graph theory analysis method, the propagation path of risk factor in enterprise running state perception graph is identified. By analyzing the connection relationship in the graph, the possible path of risk propagation is determined. Using the shortest path algorithm, the propagation path from the risk factor to the main operation indicator (such as sales) is identified. The identified risk behavior propagation path is recorded to ensure that all key nodes and connection relationships are covered. Each path should be labeled with its risk intensity and propagation time. If a path from supply chain delay to sales decline is identified, the nodes and risk characteristics of the path are recorded. The risk propagation path mined is analyzed 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. The definition of path mutation key nodes is determined, including jump event nodes, delay diffusion nodes, and low-frequency activation nodes. The characteristics of each node and its role in risk propagation are clearly defined. Jump event nodes can be defined as high-risk events that frequently appear in the propagation path, while delay diffusion nodes are nodes that propagate slowly but have a greater impact.Identify key nodes in the path using statistical analysis or graph theory analysis. Evaluate the importance of nodes by calculating their centrality, connectivity, and other indicators. 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, the time of delayed diffusion, and the strength of low-frequency activation. Ensure the accuracy and completeness of the data. If a jump event node frequently appears and is related to a decrease in sales, record the characteristics of the node and related historical data. Define the dynamic transition types of mutation nodes, including instantaneous transition, delayed transition, and frequency transition. Each transition type should have clear characteristics and analysis methods. Instantaneous transition can be defined as a dramatic change that occurs in a short period of time, while delayed transition refers to a change that is triggered after a period of time. Use time series analysis to analyze the state changes of each mutation node and identify its dynamic transition type. Observe the state changes of the node at different time periods and extract features. If a node experiences dramatic fluctuations within a specific time period, it is marked as an instantaneous transition, and if the fluctuations last for a long time, it is marked as a delayed transition. Compile the dynamic transition types of each mutation node into a report, describing its characteristics and impact on enterprise risk management. Ensure that the report is detailed and easy for management to make decisions. The report indicates the dynamic transition types experienced by a node in the past quarter and their potential impact on sales and inventory, and suggests appropriate management measures.

[0078] In this embodiment, step S4 includes the following steps:

[0079] Perform multi-factor correlation mining on the risk disturbance spectrum diagram to identify associated risk factor groups;

[0080] Perform deep deconstruction of the risk transmission logic on the associated risk factor groups to generate a risk transmission logic;

[0081] Based on the risk transmission logic, perform full-cycle time sequence tracing on the risk behavior propagation path to obtain a full-cycle risk propagation chain;

[0082] Based on the dynamic transition type and the full-cycle risk propagation chain, predict potential risk nodes and identify other potential risk propagation ports.

[0083] In this example, data for each risk factor is collected from the constructed risk disturbance spectrum graph. These factors may include sales fluctuations, inventory levels, supply chain delays, etc. Ensure that the data is clear and time-stamped consistently for subsequent analysis. Extract data on sales, inventory status, and order fulfillment times over the past six months to identify 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, and if the correlation coefficient is close to 1, it indicates a strong correlation between the 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, they are grouped together. Record all identified groups of associated factors, including their correlation coefficients and possible impact levels, for subsequent analysis. Determine the components of the risk transmission logic, including risk factors, transmission paths, and impact mechanisms. Clearly define how each factor affects other factors through different paths. Define how sales fluctuations affect production planning through changes in inventory levels, which further affect the stability of the supply chain. Use system dynamics or causal diagram analysis methods to deeply deconstruct the identified groups of associated risk factors. By constructing a causal relationship diagram, identify the mutual influence relationships between factors. Construct a causal relationship diagram with nodes representing risk factors and edges representing the influence relationships between factors, such as "sales fluctuations → inventory levels → logistics delays." Based on the deconstruction results, form a detailed risk transmission logic. Record the transmission path and impact level of each factor for subsequent analysis and application. The generated transmission logic shows that sales fluctuations lead to a decrease in inventory levels, which in turn affects order fulfillment times, ultimately leading to a decrease in customer satisfaction. Collect full-cycle data related to risk factors to ensure coverage of all time periods of risk transmission. Data should include historical changes in 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 transmission chain. By analyzing the historical changes in factors, identify the time nodes of risk transmission and their impact. Use DTW to analyze the changes in sales fluctuations and inventory levels to identify their corresponding relationship in time. Based on the time series tracing results, generate a full-cycle risk transmission chain, recording each key time node and its corresponding risk state. Ensure the integrity and accuracy of the chain. Record the nodes and times of inventory decline, logistics delay, etc. caused by sales fluctuations to form a complete risk transmission chain. Analyze the identified dynamic transition types to determine their impact on the risk transmission chain. Identify the relationship between transition types and risk transmission paths to make potential risk predictions.If the transition type of a node is "delayed transition", it may trigger greater fluctuations in subsequent risk propagation. Machine learning algorithms such as random forests or support vector machines 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. Historical data is used to train the model to predict which nodes are likely to become potential risk propagation ports under certain conditions.

[0084] In this embodiment, step S5 includes the following steps:

[0085] Based on multi-dimensional enterprise data flow, the current running state of the enterprise is identified;

[0086] Real-time dynamic enterprise behavior analysis is performed on the current running state of the enterprise to extract key enterprise behaviors;

[0087] According to the key enterprise behaviors, multi-time point risk evolution simulation is performed on other potential risk propagation ports to generate risk evolution simulation data;

[0088] Risk trajectory evolution analysis is performed on the risk evolution simulation data to generate a risk evolution trajectory;

[0089] The direction, speed, potential energy, and risk outbreak probability of the risk evolution trajectory are calculated;

[0090] Risk disturbance diffusion coupling is performed according to the direction, speed, potential energy, and risk outbreak probability to construct a multi-dimensional risk trajectory vector field.

[0091] In this example, we integrate multi-dimensional data streams from various departments of the enterprise, including financial, operational, market, and supply chain data. These data should be updated in real-time to ensure accurate reflection of the current state of the enterprise. Extract recent sales data and cost data from the financial system, obtain inventory levels 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 operational state of the enterprise, including sales, inventory turnover rate, customer satisfaction, and supply chain stability. These indicators will be used to comprehensively assess 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 real-time monitoring systems. Use real-time data monitoring tools to regularly assess the above key indicators to obtain the current operational state of the enterprise. Display the latest changes in each indicator through dashboards or data visualization tools. Use data visualization dashboards to display the dynamic changes in sales, inventory, and customer satisfaction to facilitate management's quick identification of the enterprise's operational status. Build a real-time dynamic enterprise behavior analysis framework, including data collection, data processing, and behavior identification. Ensure that the analysis process can respond to changes in the operational state of the enterprise in real time. Set up hourly analysis of sales data, inventory data, and customer feedback to identify potential key behaviors. Use machine learning algorithms (such as clustering analysis or anomaly detection) to analyze real-time data and identify key enterprise behaviors. Identify behaviors such as rapid sales growth and sharp inventory decline. Identify behaviors that have rapidly increased sales in a specific time period through clustering analysis and mark them as key behaviors. Record the identified key enterprise behaviors and generate reports describing the characteristics of these behaviors, the time of occurrence, and their impact on enterprise operations. Identify other potential risk transmission ports based on key enterprise behaviors. These ports may be related to key behaviors, such as supplier delivery delays, customer churn, etc. If sales growth is accompanied by an increase in customer complaints, identify the customer service link as a potential risk transmission port.

[0092] Based on the existing risk propagation model, a multi-time point risk evolution simulation model is constructed. This model should be able to simulate the risk propagation process under different conditions to generate corresponding evolution data. A simulation model based on stochastic process is constructed to simulate the change of 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. Using the dynamic time warping method, compare the risk states at different time points to identify the change pattern of the risk. According to the analysis results, generate the risk evolution trajectory and record the risk state and its change path at each time node. This will help identify potential risk trends. The generated trajectory graph shows the change process from sales growth to customer complaint increase, which facilitates visual analysis.

[0093] By analyzing the change trend of the risk trajectory, the direction of the risk evolution trajectory is calculated. Linear regression or trend analysis can be used to identify the upward or downward direction of the trajectory. If the sales growth and the increase of customer complaints are positively correlated, the direction is upward. Calculate the change rate of the risk evolution trajectory, usually by observing the change amount of the risk state per unit time. If the customer complaints increased by 10 in the past month, the rate is 10 per month. Calculate the potential energy and the probability of explosion of the risk. Potential energy can be defined as the potential energy of the change of risk state, and the probability of explosion is estimated based on historical data and current state. If the historical data shows that the customer churn rate has a 20% probability under certain conditions, record this probability as the risk explosion probability. According to the direction, rate, potential energy and risk explosion probability, construct a risk disturbance diffusion coupling model. This model should be able to consider the influence of each factor on risk propagation. Set a coupling model to combine the growth rate of customer complaints, the change of sales and the inventory level to simulate their impact on the overall risk. Generate a multi-dimensional risk trajectory through the coupling model to form a risk trajectory vector field. This field should be able to reflect the strength and direction of different risk propagation paths. The generated vector field shows the dynamic changes of each potential risk propagation path, which facilitates decision-making by management. Visualize the generated multi-dimensional risk trajectory vector field to help management identify the key path of risk propagation and potential intervention points. Use visualization tools to display the dynamic changes of the risk trajectory to facilitate the management to quickly identify and respond to potential risks.

[0094] In this embodiment, the specific steps of step S6 are:

[0095] Based on multi-dimensional enterprise data flow, identify enterprise callable resources;

[0096] According to the enterprise-callable resource, a multi-strategy anti-risk intervention deduction is performed on a multi-dimensional risk trajectory vector field to generate anti-risk simulation data of a plurality of intervention schemes;

[0097] The anti-risk simulation data is calculated to obtain a back-to-stable speed of an enterprise operation index after anti-risk intervention and a multi-index state fluctuation residual amount;

[0098] A secondary risk probability is predicted based on the back-to-stable speed and the multi-index state fluctuation residual amount to generate a secondary risk probability;

[0099] An adaptive risk early warning is performed based on the secondary risk probability to generate an adaptive risk early warning signal;

[0100] A secondary risk density is calculated based on the anti-risk simulation data to generate a secondary residual risk density;

[0101] A risk diffusion trend analysis is performed on the secondary residual risk density to obtain a secondary risk diffusion trend graph;

[0102] An anti-risk decision optimization is performed based on the secondary risk diffusion trend graph to construct an anti-risk decision optimization strategy;

[0103] An intelligent risk perception and early warning operation is performed based on the anti-risk decision optimization strategy and the adaptive risk early warning signal.

[0104] In this embodiment, multi-dimensional data streams within the enterprise are collected, including information on human resources, material inventory, equipment availability, and financial flows, to gain a comprehensive understanding of the enterprise's callable resources. The current number of employees and 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 enterprise's currently callable resources. This can be achieved by setting resource utilization indicators to assess the actual availability of each resource. The utilization rate of various human resources (such as employee attendance rate, workload, etc.) is calculated, and resources available for risk resistance intervention are marked. Based on the analysis results, a list of enterprise callable resources is generated, clearly indicating the available quantity, status, and potential contribution of each resource. This list will provide a basis for subsequent risk resistance interventions. According to the identified callable resources, multiple risk resistance intervention strategies are designed. These strategies should consider different resource combinations and usage scenarios to meet the enterprise's current risk response needs. Design includes a variety of intervention strategies such as supplementing inventory, increasing employee training, and mobilizing funds to ensure that each strategy can effectively address potential risks. A risk resistance simulation model is constructed to simulate the impact of different intervention strategies on the enterprise's operating state. The model should consider the interaction of various resources and their impact on enterprise operating indicators. Using a system dynamics model, simulate the changes in sales, inventory levels, and customer satisfaction under different intervention strategies. Run the simulation model to generate risk resistance simulation data for multiple intervention scenarios. These data should include changes in enterprise operating indicators after implementing each strategy. Simulate the changes in sales growth, inventory turnover rate, and other indicators after implementing different strategies, and record the specific values of each indicator. By analyzing the risk resistance simulation data, calculate the recovery speed of each enterprise operating indicator. This can be achieved by observing the recovery time of the indicator after implementing the intervention. If the sales volume recovers from 1 million to the normal level of 1.5 million after intervention, and the recovery time is two weeks, then the recovery speed is recorded as 750,000 per week.

[0105] Calculate the multi-indicator state volatility residual amount to assess the volatility of each indicator after the 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 after the intervention is 50,000, the residual amount is 100,000, indicating that there is still some volatility. Define the secondary risk, which is usually the chain reaction and potential new risks that may be caused by the primary risk. These risks should be closely related to the operating state of the enterprise. If inventory shortages cause production delays, which in turn affect sales, then the loss of customers due to delayed delivery can be considered a secondary risk. Use statistical analysis methods (such as logistic regression or time series analysis) to predict the probability of secondary risk. The model should consider the speed of recovery, volatility residual amount, and their impact on secondary risk. Establish a logistic regression model, input the recovery speed and volatility residual amount data, and predict the probability of customer loss. Run the probability prediction model to generate the probability of secondary risk. These results will help management understand the severity of potential risks and their possible impact. The model output shows that the probability of secondary risk is 20%, indicating that there is a certain risk of customer loss under the current circumstances. Determine the criteria for adaptive risk warning signals, including the probability of secondary risk, the change range of key indicators, etc., to monitor the operating state of the enterprise in real time.

[0106] Set the risk warning signal to trigger when the probability of secondary risk exceeds 15%. Build a real-time monitoring system to continuously track changes in the probability of secondary risk and other key indicators. Once the warning condition is triggered, the system should immediately issue an alarm and provide corresponding decision recommendations. Set the monitoring system to update the risk probability every hour, and if it is found to exceed the warning threshold, automatically send a risk alert. Generate adaptive risk warning signals based on monitoring results, and record the time, type, and impact of the warning. Ensure that the signal is clear and easy to understand so that management can respond quickly. The generated signal shows "high risk of customer loss, please adjust sales strategy in time", and records the timestamp of the signal. Choose an appropriate risk density calculation method (such as kernel density estimation) to perform density analysis on the probability of secondary risk. This will help identify the distribution of risk at different times. Use the kernel density estimation method to analyze the changes in customer loss risk over the past six months, and find out the peak risk period. Run the calculation model to generate the density of secondary residual risk. These density values will help the enterprise identify potential risk concentration areas and times. The generated density chart shows that the risk of customer loss reaches the highest in a certain month, with a density value of 0.3, indicating that attention should be paid to this period.

[0107] The calculation results of the secondary residual risk density are recorded, and a report is generated to describe the risk distribution and its potential impact on the operation of the enterprise. The report indicates that the customer churn risk density is as high as 0.3 in a certain period of time, and suggests taking measures to reduce the risk. Time series analysis or regression analysis method is used to analyze the diffusion trend of the secondary residual risk density. This will help identify the propagation mode and speed of the risk. Using simple linear regression analysis, the trend of risk density change in different time periods is identified. According to the analysis results, a secondary risk diffusion trend chart is generated to show the change of risk density in different time periods and its diffusion trend. The chart shows that the risk density rises in a certain period of time and is predicted to further expand in the next few months. According to the analysis results, an anti-risk decision optimization framework is constructed. The framework should consider various factors, including secondary risk probability, risk density and diffusion trend, etc. A decision model is set up to input different risk factors to evaluate the effectiveness of different anti-risk strategies. Through optimization algorithms such as linear programming, genetic algorithm, etc., anti-risk decision optimization strategies are generated. These strategies should be able to effectively reduce secondary risk and its impact. An optimization strategy is developed to suggest increasing market promotion efforts during high-risk periods to improve customer loyalty and satisfaction.

[0108] The generated anti-risk decision optimization strategies are evaluated to ensure their effectiveness and operability. Necessary adjustments are made based on feedback to improve the execution effect of the strategies. The anti-risk decision optimization strategies are integrated with the adaptive risk early warning signals to build an intelligent risk perception system. The system should be able to monitor and respond to potential risks in real time. A comprehensive platform is built to collect risk early warning, decision strategies and key indicators in real time to facilitate management to quickly obtain information. Early warning operations are implemented in the intelligent risk perception system. Once the risk signal is triggered, the system should quickly send an alarm and provide corresponding decision suggestions. If the customer churn risk is rising, the system will immediately send an alarm and suggest starting specific customer maintenance strategies. A feedback mechanism is established to continuously optimize risk perception and early warning operations. By analyzing the differences between early warning results and actual situations, system parameters are adjusted in a timely manner. The accuracy of the early warning system is evaluated regularly, and the risk early warning threshold is adjusted according to the feedback to improve the accuracy and response speed of future early warning.

[0109] In this embodiment, an intelligent risk early warning system based on multi-dimensional data analysis is provided for implementing the intelligent risk early warning method based on multi-dimensional data analysis as described above, comprising:

[0110] A multi-index perception module is used to acquire multi-dimensional enterprise data streams, analyze heterogeneous index information and reconstruct logical levels, and build an enterprise operation state perception map.

[0111] a risk disturbance spectrum module for calculating multi-index local fluctuation amplitude of enterprise operation state perception graph and performing dynamic disturbance feature mining to construct a risk disturbance spectrum graph;

[0112] a state mutation analysis module for performing deep semantic analysis and node state mutation feature analysis on the risk disturbance spectrum graph to generate dynamic transition types of mutation nodes;

[0113] a risk node prediction module for performing full-cycle time sequence tracing on the risk disturbance spectrum graph and predicting potential risk nodes based on the dynamic transition types to identify other potential risk propagation ports;

[0114] a risk trajectory module for performing multi-time-point risk evolution simulation and risk disturbance diffusion coupling on the other potential risk propagation ports to construct a multi-dimensional risk trajectory vector field;

[0115] an anti-risk deduction module for performing multi-strategy anti-risk intervention deduction on the multi-dimensional risk trajectory vector field, performing anti-risk decision optimization, and constructing an anti-risk decision optimization strategy.

[0116] The application obtains multi-dimensional data streams (such as finance, operation, market, production, etc.), and the module provides a full range of enterprise state perception map for the system to ensure that the system has a comprehensive understanding of the situation of the enterprise. Heterogeneous index analysis and logical level reconstruction can effectively eliminate the differences between data sources, so that data can be effectively processed and analyzed on a unified platform. The reconstruction of the logical level helps the system to better understand the relationship between different data dimensions, which helps to find potential risks in complex enterprise systems. By constructing the enterprise running state perception map, the module provides accurate background data for subsequent risk detection and prediction, ensuring that the system will not miss important upstream and downstream relationships when identifying risks. By calculating the local fluctuation amplitude of multiple indicators, the module can timely discover abnormal fluctuations in different business fields or operation links of the enterprise. Such fluctuations may be a precursor to potential risks, so early warning can be made. Through dynamic disturbance feature mining, the module can discover and distinguish different types of disturbance features, helping the system to 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 direction. The risk disturbance frequency spectrum diagram can present the fluctuation law of risk disturbance through visualization, helping decision makers better understand the fluctuation of enterprise risks and quickly grasp the core problems of risks. Through semantic analysis of the risk disturbance frequency spectrum diagram, the module can convert complex data into easily understandable semantic information, so that the system can 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 state is often closely related to the suddenness and severity of enterprise risks. The module can identify these mutations to help decision makers accurately understand which parts of the enterprise are changing rapidly and may expose risks. According to the characteristics of node mutation, 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 identify and intervene in potential risks in a timely manner. By tracing the risk disturbance frequency spectrum diagram throughout the cycle, the module can help the system understand the risk development trend in similar situations in history. This provides strong historical data support for identifying possible future risk events. Based on the prediction of potential risk nodes based on dynamic transition types, the future propagation direction of risks and the nodes that may be affected can be identified. This helps enterprises to take preventive measures as soon as possible to avoid unnecessary losses. Through the prediction of potential risk nodes, the module can identify other potential risk propagation ports, helping the system to effectively identify multi-point risk propagation and improve the overall prevention and control capability. The module can show the change path and possible impact of risks at different time points through multi-time point simulation, helping enterprises to predict the evolution process of risks and make long-term preparations. The coupling analysis of risk disturbance diffusion enables the system to identify the mutual influence between risk sources, helping enterprises to understand how risks in one field may affect other fields and improve the accuracy of overall risk prediction.The multi-dimensional risk trajectory vector field provides a visualized risk propagation path, which can help decision-makers quickly identify which businesses or nodes are most vulnerable to risks and take targeted measures. By simulating different anti-risk intervention strategies and deducing the effects of different intervention measures, the module helps enterprises choose the most suitable response strategy. This can greatly improve the effectiveness of anti-risk and ensure that enterprises can make optimal decisions when facing multiple complex situations. Through the optimization of anti-risk decisions, the module can recommend the most effective anti-risk measures based on the specific circumstances of the enterprise (such as resources, risk tolerance, etc.), ensuring that the enterprise's risk management strategy has the maximum benefit. Optimized anti-risk decisions can help enterprises respond quickly when facing unexpected risks, reducing potential losses and negative impacts, and improving the enterprise's emergency response capabilities and sustainability.

[0117] Therefore, from any point of view, the embodiments should be regarded as exemplary and non-limiting, the scope of the invention being defined by the attached claims and not by the above description, therefore all variations falling within the meaning and scope of the equivalent elements of the application file are intended to be included in the invention.

[0118] The above description is merely that of specific embodiments of the present application, enabling a person skilled in the art to understand or implement the present application. Various modifications to these embodiments will be apparent to those skilled in the art, and the general principles defined herein can be implemented in other embodiments without departing from the spirit or scope of the present application. Therefore, the present application will not be limited to these embodiments shown herein, but will conform to the widest scope 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, The method comprises the following steps: Step S1: obtaining multi-dimensional enterprise data flow, performing heterogeneous index information analysis and logical level reconstruction, and constructing enterprise operation state perception graph; Step S2: calculating multi-index local fluctuation amplitude of the enterprise operation state perception graph, and performing dynamic disturbance feature mining to construct a risk disturbance frequency spectrum graph; Step S3: performing deep semantic analysis and node state mutation feature analysis on the risk disturbance frequency spectrum graph, and generating dynamic transition types of the mutation nodes; Step S4: performing full-cycle time sequence tracing on the risk disturbance frequency spectrum graph, and predicting potential risk nodes based on the dynamic transition types to identify other potential risk propagation ports; Step S5: performing multi-time point risk evolution simulation and risk disturbance diffusion coupling on the other potential risk propagation ports to construct a multi-dimensional risk trajectory vector field; Step S6: performing multi-strategy anti-risk intervention deduction on the multi-dimensional risk trajectory vector field, and then performing anti-risk decision optimization to construct an anti-risk decision optimization strategy; In step S1, the specific steps are as follows: Obtaining multi-dimensional enterprise data flow based on an enterprise internal operation system; Performing heterogeneous index information analysis on the multi-dimensional enterprise data flow to generate heterogeneous index information, wherein the heterogeneous index information includes financial flow information, upstream and downstream transaction chains, and supply chain logistics track information; Performing timestamp alignment processing and structure standardization on the heterogeneous index information to construct multi-dimensional operation standardized indexes; Performing multi-index causal relationship analysis on the multi-dimensional operation standardized indexes to extract the causal relationship between different indexes; Performing logical dimension mining on the multi-dimensional operation standardized indexes, and performing logical level reconstruction based on the causal relationship to construct an enterprise operation state perception graph; In step S2, the specific steps are as follows: Performing time sequence state decomposition on the enterprise operation state perception graph to generate a plurality of state perception windows; Performing multi-index local fluctuation amplitude calculation on the plurality of state perception windows to obtain state fluctuation amplitude values of each window; Performing transient high fluctuation distortion identification based on the state fluctuation amplitude values to mark state fluctuation distortion points; Performing abnormal risk sensitivity evaluation on the state fluctuation distortion points based on a preset risk sensitivity threshold to extract potential risk perception factors; Performing dynamic disturbance feature mining on the potential risk perception factors to construct a risk disturbance frequency spectrum graph; The specific steps of performing dynamic disturbance feature mining on the potential risk perception factors to construct a risk disturbance frequency spectrum graph are as follows: Performing periodic risk disturbance feature analysis on the potential risk perception factors to extract periodic risk disturbance features; Performing multi-time point disturbance fitting according to the periodic risk disturbance features to construct a risk disturbance waveform curve; Calculating the disturbance frequency intensity and trend slope of the risk disturbance waveform curve; Performing disturbance frequency continuity mining according to the disturbance frequency intensity and trend slope to construct a risk disturbance frequency spectrum graph; In step S3, the specific steps are as follows: Performing deep semantic analysis on the risk disturbance frequency spectrum graph, and performing semantic annotation nested coding to obtain risk disturbance semantic coding; Performing correlation mapping and topological structure positioning on the enterprise operation state perception graph according to the risk disturbance semantic coding to obtain graph position information of risk factors; Based on the atlas position information, a risk behavior propagation path is mined, and a risk behavior propagation path is extracted; The path mutation key node analysis is performed on the risk behavior propagation path, and the jump event node, delay diffusion node and low-frequency activation node in the path are extracted; The node state mutation feature analysis is performed on the jump event node, delay diffusion node and low-frequency activation node, and the dynamic transition type of the mutation node is generated. 2.The intelligent risk early warning method based on multi-dimensional data analysis of claim 1, wherein, The specific steps of step S4 are: The multi-factor associated mining is performed on the risk disturbance spectrum diagram, and the associated risk factor group is identified; The risk transmission logic is generated by performing deep deconstruction on the associated risk factor group; Based on the risk transmission logic, the full-cycle time sequence of the risk behavior propagation path is traced, and the full-cycle risk propagation chain is obtained; Based on the dynamic transition type and the full-cycle risk propagation chain, the potential risk node is predicted, and other potential risk propagation ports are identified. 3.The intelligent risk early warning method based on multi-dimensional data analysis of claim 1, characterized in that, The specific steps of step S5 are: Based on the multi-dimensional enterprise data flow, the current running state of the enterprise is identified; The real-time dynamic enterprise behavior analysis is performed on the current running state of the enterprise, and the key enterprise behavior is extracted; According to the key enterprise behavior, the multi-time-point risk evolution simulation is performed on the other potential risk propagation ports, and the risk evolution simulation data is generated; The risk trajectory evolution analysis is performed on the risk evolution simulation data, and the risk evolution trajectory is generated; The direction, rate, potential and risk outbreak probability of the risk evolution trajectory are calculated; The risk disturbance diffusion coupling is performed according to the direction, rate, potential and risk outbreak probability, and the multi-dimensional risk trajectory vector field is constructed. 4.The intelligent risk early warning method based on multi-dimensional data analysis of claim 1, wherein, The specific steps of step S6 are: Based on the multi-dimensional enterprise data flow, the callable resources of the enterprise are identified; According to the callable resources of the enterprise, the multi-strategy anti-risk intervention deduction is performed on the multi-dimensional risk trajectory vector field, and the anti-risk simulation data of multiple intervention schemes is generated; The back-to-stable speed and multi-index state fluctuation residual amount of the enterprise running index after the anti-risk intervention of the anti-risk simulation data are calculated; The back-to-stable speed and multi-index state fluctuation residual amount are subjected to secondary risk probability prediction to generate secondary risk probability; Based on the secondary risk probability, the adaptive risk early warning is performed, and an adaptive risk early warning signal is generated; The secondary residual risk density is calculated based on the anti-risk simulation data, and the secondary residual risk density is generated; The risk diffusion trend analysis is performed on the secondary residual risk density, and a secondary risk diffusion trend diagram is obtained; Based on the secondary risk diffusion trend diagram, the anti-risk decision optimization strategy is constructed; Based on the anti-risk decision optimization strategy and the adaptive risk early warning signal, the intelligent risk perception and early warning operation are performed.

5. An intelligent risk early warning system based on multi-dimensional data analysis, characterized in that, The intelligent risk early warning method based on multi-dimensional data analysis comprises: A multi-index perception module is configured to acquire multi-dimensional enterprise data flow, perform heterogeneous index information analysis and logical level reconstruction, and construct an enterprise running state perception atlas; A risk disturbance spectrum module is configured to calculate the multi-index local fluctuation amplitude of the enterprise running state perception atlas, and perform dynamic disturbance feature mining to construct a risk disturbance spectrum diagram. The state mutation analysis module is configured to perform deep semantic analysis and node state mutation feature analysis on the risk disturbance frequency spectrum diagram, and generate a dynamic transition type of a mutation node. The risk node prediction module is configured to perform full-cycle time sequence tracing on the risk disturbance frequency spectrum diagram, and predict potential risk nodes based on the dynamic transition type, and identify other potential risk propagation ports. The risk trajectory module is configured to perform multi-time-point risk evolution simulation and risk disturbance diffusion coupling on the other potential risk propagation ports, and construct a multi-dimensional risk trajectory vector field. The anti-risk deduction module is configured to perform multi-strategy anti-risk intervention deduction on the multi-dimensional risk trajectory vector field, and perform anti-risk decision optimization, and construct an anti-risk decision optimization strategy.

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