Financial risk protection method and system based on data mining

Abstract

The invention relates to the field of financial risk protection, in particular to a financial risk protection method and system based on data mining, and the method comprises the following steps: collecting enterprise multi-dimensional financial logs, carrying out the deep mining of transaction behaviors, and constructing an enterprise financial behavior map; performing multi-signal correlation degree calculation and liability structure change perception based on the enterprise financial behavior map, and marking potential fund chain vulnerabilities; upstream and downstream supply chain information of an enterprise is identified, supply chain ecological topology network analysis and risk effect calculation are carried out, and a key risk critical link is identified; carrying out multi-scene financial risk simulation on the potential fund chain vulnerability points and the critical links of the key risks, and constructing a financial risk simulation sand table; and carrying out impact bearing capability evaluation based on the financial risk simulation sand table. According to the method, the potential financial risk of the enterprise is accurately identified in advance, a protection decision is made in a targeted manner, the risk protection capability of the enterprise is improved, and enterprise assets are effectively protected.

Description

Technical Field

[0001] This invention relates to the field of financial risk protection, and in particular to a financial risk protection method and system based on data mining. Background Technology

[0002] Compared to traditional risk management methods, financial risk protection methods based on experience rules and manual monitoring have significant shortcomings. These methods typically rely on periodic report analysis, expert judgment, or simple early warning threshold settings. While they can identify some risk indicators, their response is lagging and they struggle to cope with complex and ever-changing risk environments. For example, when a company's cash flow is potentially disrupted due to abnormal upstream and downstream transactions or macroeconomic shocks, traditional methods often fail to achieve real-time monitoring and dynamic prediction, leading to untimely risk warnings and increasing the likelihood of financial imbalances or even bankruptcy. Furthermore, faced with the ever-emerging derivative instruments and multi-dimensional trading behaviors in the financial market, traditional protection methods struggle to accurately identify the non-linear risk evolution process under the superposition of multiple stages and factors, further increasing the uncertainty of corporate business decisions.

[0003] Furthermore, with the rapid development of digitalization and networking in financial services, enterprises also need to face multiple threats in the external environment, such as sudden macroeconomic shocks, cascading failures in the industrial chain, and capital outflows caused by international market fluctuations. These threats often manifest themselves in complex transaction behavior patterns or abnormal capital flow patterns, and are characterized by long incubation periods, rapid spread, and wide range of harm. Traditional risk protection mechanisms are inadequate in terms of accurate identification and real-time response. Summary of the Invention

[0004] To address the aforementioned technical problems, this invention proposes a financial risk protection method and system based on data mining, thereby resolving at least one of the aforementioned technical issues.

[0005] To achieve the above objectives, this invention provides a financial risk protection method based on data mining, comprising the following steps: Step S1: Collect multi-dimensional financial logs of enterprises, conduct in-depth analysis of transaction behavior, and construct a financial behavior map of enterprises; Step S2: Calculate the correlation of multiple signals and perceive changes in debt structure based on the corporate financial behavior map, and mark potential vulnerabilities in the capital chain; Step S3: Identify the upstream and downstream supply chain information of the enterprise, conduct supply chain ecosystem topology network analysis and risk effect calculation, and identify key risk-critical links; Step S4: Conduct multi-scenario financial risk simulations to identify potential vulnerabilities in the capital chain and critical risk thresholds, and construct a financial risk simulation sandbox. Step S5: Based on the financial risk simulation sandbox, conduct an impact tolerance assessment, calculate the risk mitigation interval, and carry out risk capital recovery and intelligent risk balance management.

[0006] This specification provides a data mining-based financial risk protection system for implementing the data mining-based financial risk protection method described above, including: The Behavior Graph module is used to collect multi-dimensional financial logs of enterprises, conduct in-depth mining of transaction behavior, and build enterprise financial behavior graphs. The structural change perception module is used to calculate the correlation of multiple signals and perceive changes in debt structure based on the enterprise's financial behavior map, and to mark potential vulnerabilities in the capital chain. The topology network analysis module is used to identify the upstream and downstream supply chain information of an enterprise, perform supply chain ecosystem topology network analysis and risk effect calculation, and identify key risk critical links. The risk simulation module is used to simulate financial risks in multiple scenarios, including potential vulnerabilities in the capital chain and critical risk-critical links, and to build a financial risk simulation sandbox. The dynamic protection module is used to assess shock tolerance based on a financial risk simulation sandbox, calculate the risk mitigation interval, and carry out risk capital recovery and intelligent risk balance management.

[0007] The beneficial effects of this invention are specifically as follows: By collecting and deeply analyzing multi-dimensional financial logs (such as income and expenditure flow, contract information, invoice records, tax declarations, financing activities, etc.), a temporal and correlational model of corporate capital activities is established. In-depth mining of transaction behavior characteristics helps to discover potential abnormal behaviors such as repeated borrowing, unreasonable payment terms, and frequent related-party transactions. The financial behavior graph serves as the knowledge carrier and risk mapping foundation for subsequent algorithms, supporting node analysis, path tracing, and event causal chain modeling. By real-time sensing of changes in the debt structure (such as the proportion of short-term debt, sudden increases in interest burden, etc.), early warnings of tight cash flow trends are provided. Through correlation analysis between signals (such as sudden liquidity contraction, abnormal fund transfers), potential risk paths and vulnerable nodes in the chain are identified. Input parameters are provided for subsequent financial simulations, effectively focusing on "high-risk segments" and improving risk management efficiency. Risks not only originate from a single enterprise but may also be transmitted through supply chain relationships. This step expands risk control from "points" to "chains" and "networks." Through the supply chain topology, the chain reactions that may be triggered by the financial distress of a key supplier are analyzed. This system facilitates the setting of risk buffering strategies and allows for pre-emptive intervention at key points, preventing localized risks from escalating into systemic risks. Simulation conditions are set based on historical crisis scenarios, macroeconomic disturbances, and industry cycle changes to test the stability of a company's cash flow. Through sandbox simulation, indicators such as the speed of risk spread, the scope of impact, and the duration of influence can be quantified, enhancing the foresight of decision-making. The simulation environment allows testing of the risk mitigation effects of different intervention measures (such as financing support, deferred payments, and supply chain adjustments), optimizing emergency response strategies. Analysis of how long it takes for a company to run out of funds under different intensities of shock, and which link will break first, provides a quantitative basis for risk capital allocation. It identifies how long "safe operation" can be maintained before taking action, providing a time-based decision-making basis for capital or policy intervention. The system can dynamically adjust risk control strategies based on sandbox feedback, such as adjusting credit limits, setting repayment plans, and triggering linked early warnings. After high-risk companies recover their risks, support strategies can be dynamically adjusted, forming a closed loop of "risk identification-intervention-assessment-recovery-redistribution." Attached Figure Description

[0008] Figure 1 This is a schematic diagram of the steps of a financial risk protection method based on data mining according to the present invention. Figure 2 This is a detailed flowchart illustrating the implementation steps of step S1. Figure 3 This is a flowchart illustrating the detailed implementation steps of step S2. Detailed Implementation

[0009] It should be understood that the specific embodiments described herein are for illustrative purposes only and are not intended to limit the scope of the invention.

[0010] This application provides a data mining-based financial risk protection method and system. The implementing entities of the data mining-based financial risk protection method and system include, but are not limited to, the following: mechanical equipment, data processing platforms, cloud server nodes, network upload devices, etc., which can be considered general computing nodes of this application. The data processing platform includes, but is not limited to, at least one of the following: an audio-visual management system, an information management system, and a cloud-based data management system.

[0011] Please see Figures 1 to 3 This invention provides a financial risk protection method based on data mining, comprising the following steps: Step S1: Collect multi-dimensional financial logs of enterprises, conduct in-depth analysis of transaction behavior, and construct a financial behavior map of enterprises; Step S2: Calculate the correlation of multiple signals and perceive changes in debt structure based on the corporate financial behavior map, and mark potential vulnerabilities in the capital chain; Step S3: Identify the upstream and downstream supply chain information of the enterprise, conduct supply chain ecosystem topology network analysis and risk effect calculation, and identify key risk-critical links; Step S4: Conduct multi-scenario financial risk simulations to identify potential vulnerabilities in the capital chain and critical risk thresholds, and construct a financial risk simulation sandbox. Step S5: Based on the financial risk simulation sandbox, conduct an impact tolerance assessment, calculate the risk mitigation interval, and carry out risk capital recovery and intelligent risk balance management.

[0012] In the embodiments of the present invention, see Figure 1 The diagram below illustrates the steps of a data mining-based financial risk protection method according to the present invention. In this example, the steps of the data mining-based financial risk protection method include: Step S1: Collect multi-dimensional financial logs of enterprises, conduct in-depth analysis of transaction behavior, and construct a financial behavior map of enterprises; In this embodiment, complete financial log data from the past 36 months of the enterprise is collected, including daily cash inflow and outflow records, bank statement details, changes in accounts receivable and payable, purchase and redemption of investment and wealth management products, loan repayment details, and comprehensive transaction information such as bill acceptance and discounting. The collected financial data undergoes standardized preprocessing, with time windows set at three levels: 7 days, 30 days, and 90 days. A sliding window technique is used to extract transaction pattern features at different time scales. A graph neural network algorithm is used to construct a graph of the enterprise's financial behavior, where nodes represent different types of financial items (cash, bank deposits, accounts receivable, short-term loans, etc.), and edge weights reflect the intensity and frequency of cash flows between items. The PageRank algorithm is used to calculate the importance score of each financial item node, identifying the core paths and key nodes of cash flows. A temporal clustering analysis method is used to classify the enterprise's financial transaction behavior into four basic modes: routine operations, investment expansion, debt adjustment, and crisis response. Each mode has a corresponding feature vector with 128 dimensions, including key indicators such as transaction amount variance, transaction frequency standard deviation, cash stay time, and concentration of transaction counterparties. By using a deep autoencoder to perform dimensionality reduction mapping on financial behavior characteristics, a 32-dimensional corporate financial behavior gene code is generated. This code can reflect the financial decision-making preferences and risk tolerance characteristics of enterprises at different stages of operation.

[0013] Step S2: Calculate the correlation of multiple signals and perceive changes in debt structure based on the corporate financial behavior map, and mark potential vulnerabilities in the capital chain; In this embodiment, based on the corporate financial behavior graph constructed in step S1, the linear and nonlinear correlations between different financial signals are calculated using Pearson correlation coefficient and mutual information theory. A correlation threshold of 0.75 is set to identify highly correlated combinations of financial indicators. Principal component analysis is used to extract the main directions of change in the financial behavior graph, retaining the top N principal components with a cumulative contribution rate of 85% as key monitoring dimensions. The time-series change patterns of the corporate debt structure are analyzed using a dynamic time warping algorithm, focusing on monitoring the fluctuations of key financial ratios such as the proportion of short-term borrowings, current ratio, quick ratio, and debt-to-equity ratio. An anomaly detection mechanism is triggered when the monthly change of any indicator exceeds 15%. The isolated forest algorithm is used to identify abnormal patterns in the financial behavior graph, setting an anomaly scoring threshold of 0.6, and marking time periods with scores exceeding the threshold as potential risk periods. The Markov chain Monte Carlo method is used to simulate the probability distribution of a broken capital chain, and Bayesian inference is used to calculate the vulnerability probability of the corporate capital chain under different external shock intensities. Establish a health assessment model for the capital chain, comprehensively considering multiple dimensions such as cash flow adequacy (target value > 1.5), capital turnover efficiency (target cycle < 45 days), and financing channel diversity (at least 3 different types of channels), to generate a dynamic marking map of the enterprise's capital chain vulnerabilities, and classify and mark them according to three levels: high risk, medium risk, and low risk.

[0014] Step S3: Identify the upstream and downstream supply chain information of the enterprise, conduct supply chain ecosystem topology network analysis and risk effect calculation, and identify key risk-critical links; In this embodiment, information on the enterprise's upstream and downstream partners is collected through multi-source data interfaces such as the enterprise's ERP system, procurement management system, and sales management system. This includes key parameters such as supplier payment cycles, customer payment terms, contract amount distribution, and performance stability scores. A supply chain ecosystem network topology is constructed, with the enterprise as the central node and upstream suppliers and downstream customers as related nodes. The edge weights are calculated by weighting the transaction amount, cooperation frequency, and dependence. The importance of each node in the supply chain network is assessed using three indicators from complex network theory: degree centrality, betweenness centrality, and proximity centrality, identifying the key partners with the greatest impact on the enterprise's operations. The SIR infectious disease model is used to simulate the transmission mechanism of supply chain risks, setting the risk transmission rate β=0.3 and the recovery rate γ=0.1. Numerical simulation analysis is used to analyze the diffusion path and impact range of risks in the supply chain network. A network resilience analysis method is employed, testing the supply chain network's resistance to shocks by randomly deleting nodes and edges. The minimum number of nodes required to remove when network connectivity decreases by 50% is calculated as a network vulnerability indicator. A supply chain risk transmission matrix was established to quantify the impact coefficient of changes in the financial status of upstream and downstream enterprises on the core enterprise, with the supplier risk impact weight set at 0.4 and the customer risk impact weight set at 0.6. 10,000 risk scenarios were generated using the Monte Carlo simulation method to identify critical risk thresholds that would lead to a supply chain disruption probability exceeding 20%, and a corresponding risk level classification system was established.

[0015] Step S4: Conduct multi-scenario financial risk simulations to identify potential vulnerabilities in the capital chain and critical risk thresholds, and construct a financial risk simulation sandbox. In this embodiment, based on the potential vulnerabilities in the capital chain identified in step S2 and the key risk thresholds determined in step S3, a multi-scenario financial risk simulation sandbox is constructed, covering three levels: macroeconomic shocks, industry cycle fluctuations, and enterprise-specific risks. Macroeconomic shock scenarios include various combinations such as interest rate increases of 100-300 basis points, exchange rate fluctuations of ±10%-30%, and GDP growth rate declines of 1-3 percentage points. At the industry level, scenarios include raw material price fluctuations of ±15%-50%, downstream demand contraction of 10%-40%, and the entry of new competitors. Monte Carlo simulation technology is used to generate 50,000 random scenarios, each containing 200 time steps, with each time step set to 1 day, and the simulation period covering the next 12 months. A vector autoregression (VAR) model is used to establish the dynamic response relationship between enterprise financial indicators and external shock factors. The model includes 12 endogenous variables and 8 exogenous shock variables, and the model parameters are obtained by fitting historical data. The simulation sandbox sets up real-time monitoring indicators for corporate financial status, including 24 key financial indicators such as net present value of cash flow, debt-to-equity ratio, current ratio, and return on equity. When any indicator reaches a warning threshold, the system automatically records and analyzes the cause. A corporate bankruptcy probability prediction model is established, and the relationship between corporate financial indicators and bankruptcy probability is fitted using the Logistic regression method, with the model achieving an accuracy rate of over 87%. Through stress testing, the survival probability and recovery time of enterprises under different intensities of shock are analyzed, generating a three-dimensional distribution map of corporate financial risk tolerance.

[0016] Step S5: Based on the financial risk simulation sandbox, conduct an impact tolerance assessment, calculate the risk mitigation interval, and carry out risk capital recovery and intelligent risk balance management.

[0017] In this embodiment, based on the stress test results of the financial risk simulation sandbox in step S4, the maximum shock threshold that the enterprise can withstand under different risk scenarios is calculated. The upper limit of risk tolerance is set at 15% of net assets. Exceeding this threshold requires the initiation of emergency risk mitigation measures. A dynamic programming algorithm is used to optimize the enterprise's risk mitigation strategy combination, including multiple options such as increasing bank credit lines, adjusting debt structure, selling non-core assets, and introducing strategic investors. Each strategy is configured with corresponding cost-benefit parameters and execution time windows. A risk capital allocation model is established to allocate risk capital according to the risk contribution of different business lines, setting a regulatory requirement that the risk capital adequacy ratio should not be less than 8%. A genetic algorithm is used to optimize the dynamic allocation ratio of risk capital at different times. The objective function is to maximize the risk-adjusted return while maintaining the capital adequacy ratio constraint. The risk mitigation interval is calculated, that is, the time required from the occurrence of a risk event to the return of the risk level to normal. Through historical data statistical analysis, the average mitigation cycle is found to be 90-180 days. An intelligent risk balancing management system is established, which uses reinforcement learning algorithms to continuously optimize the execution effect of risk mitigation strategies. The system includes three core components: a state perception module, a strategy decision-making module, and an execution monitoring module. A risk capital recovery mechanism is established so that once the enterprise has weathered the risk period, the system automatically assesses the timing and proportion of recovery of excess risk capital to ensure maximum capital utilization efficiency. A risk management dashboard is built to provide visualized monitoring of the risk situation, including multiple monitoring interfaces such as risk level indicators, capital adequacy ratio meters, and stress test results displays, providing management with real-time risk management decision support.

[0018] In this embodiment, see Figure 2 The diagram below illustrates the detailed implementation steps of step S1. In this embodiment, the detailed implementation steps of step S1 include: Collect multi-dimensional financial logs from enterprises; define the length of financial behavior cycles; and conduct in-depth analysis of transaction behavior in multi-dimensional financial logs to extract cash flow behavior data and timestamp nodes. Based on the aforementioned capital flow behavior data analysis, the company's multi-dimensional transaction trajectory, asset restructuring history, strategic investment decisions, and regulatory compliance response records are analyzed to construct multi-dimensional business behavior characteristics; High-order tensor decomposition and topological anomaly detection are performed on multi-dimensional business behavior characteristics to extract nonlinear evolution trajectories of business behavior across multiple time scales; Based on the timestamp nodes, multi-period time-series deep fitting and time point mapping marking are performed on the nonlinear evolution trajectory of the business behavior to construct a corporate financial behavior map.

[0019] In this embodiment, after obtaining authorization from the enterprise and the platform, a high-quality, comprehensive financial log collection mechanism is established. The so-called "multi-dimensional financial logs" not only include the enterprise's basic financial statements (balance sheet, income statement, cash flow statement), but also more granular financial transaction records, internal accounting vouchers, invoice data, contract payment term execution status, and cross-border payment settlement data. During the collection process, a unified log data standard needs to be established, such as a unified timestamp format (ISO 8601), currency units (primarily in yuan or US dollars), and identification codes of the transaction entities (such as legal person codes, bank account numbers, internal department codes, etc.). Simultaneously, to ensure the traceability of the collected data, a log chain storage structure or a blockchain-based log evidence mechanism is typically adopted, ensuring that each financial transaction record has an immutable timestamp and signature verification. Data collection is not limited to internal enterprise systems but also includes external data sources, such as bank statements obtained through direct bank-enterprise interfaces, credit data from credit reporting agencies, regulatory disclosures, and financial-related reports from news and public opinion monitoring. In practical deployments, data is typically accessed and integrated through distributed log collection systems (such as Kafka or Flume). The collection frequency can be set according to the actual scenario, such as real-time collection (second-level), end-of-day batch collection (daily-level), or periodic updates (weekly / monthly-level). Multi-dimensional financial log collection provides comprehensive, dynamic, and quantifiable raw data input for subsequent analysis, laying a solid data foundation for modeling corporate financial behavior. The definition of the cycle is the core of the entire analytical logic because financial behavior usually exhibits temporal regularity and repetitiveness. The cycle length can be set based on the natural patterns of corporate financial activities, such as daily cycles (suitable for e-commerce and financial institutions with frequent transactions), monthly cycles (suitable for corporate settlements and payroll), quarterly cycles (highly correlated with quarterly report disclosures and financing activities), and annual cycles (involving annual reports, tax audits, and strategic decisions). When defining the cycle, external events, such as policy regulatory windows and market fluctuation cycles, should also be considered to ensure that the cycle division has business interpretability. After completing the cycle definition, in-depth analysis of transaction behavior is required on the collected financial logs. This stage typically includes transaction classification (such as revenue, costs, investments, financing, and expenditures), fund destination labeling (internal transfers, external payments, and cross-border fund flows), counterparty profiling (suppliers, customers, banks, and investment institutions), and precise timestamp positioning. Deep analysis requires using Natural Language Processing (NLP) to semantically parse unstructured contract terms, and combining rule matching with machine learning models to classify and label transaction logs. The analysis results are not only attribute labels for individual log entries but also need to form a transaction event chain, linking a payment, its corresponding contract execution, invoice issuance, and goods delivery together to form a traceable business logic chain.

[0020] Multi-dimensional transaction trajectories can be characterized through counterparty network analysis: the fund flows between a company and different customers, suppliers, and financial institutions can be modeled as a dynamic weighted graph, where the edge weights represent transaction amounts, the directions represent fund flow directions, and timestamps serve as the driving factor for graph updates. Secondly, the asset restructuring process of a company can be identified through historical cash flows and accounting data. For example, by analyzing the fund paths of large internal transfers, asset disposals, and equity acquisitions, combined with public announcements, key steps and motivations for asset restructuring can be reconstructed. Furthermore, strategic investment decision analysis can identify fund inflows into long-term equity investments, venture capital fund contributions, and mergers and acquisitions, and combine this with investment returns and industry comparison indicators to characterize the company's strategic layout. For regulatory compliance response records, it is crucial to focus on the company's fund adjustment behavior when facing regulatory penalties, audit rectification, or the introduction of new regulations, such as temporary tax payments and large reserve provisions. Combining these dimensions, multi-dimensional operational behavior characteristics can be constructed. These characteristics include not only quantitative indicators such as transaction intensity, transaction concentration, and transaction path complexity, but also qualitative labels for strategic corporate behaviors (expansion, contraction, restructuring, and compliance adjustments). After obtaining high-dimensional operational behavior characteristics, higher-order tensor decomposition methods are needed to reduce the dimensionality of the data and extract patterns. Commonly used methods include Tucker decomposition and CP decomposition (CANDECOMP / PARAFAC), which can decompose complex multidimensional data tensors into multiple latent factors, thereby extracting the core evolutionary patterns of enterprises at different time scales and transaction dimensions. For example, a company may exhibit significant cash flow problems at the quarterly scale but show characteristics of capital expansion at the annual scale; this multi-scale difference can be effectively identified through tensor decomposition. Simultaneously, for the nonlinear characteristics of the evolutionary trajectory, persistent cohomology methods in Topological Data Analysis (TDA) are needed for topological anomaly detection. This method can identify abnormal patterns in enterprise operations, such as isolated high-value transactions in the cash flow network, suddenly broken chains in the investment path, or discontinuous behavior during asset restructuring. These anomalies are often highly correlated with potential financial risks, such as money laundering, concealed related-party transactions, or abnormal financing channels. Through this process, the nonlinear evolutionary trajectory of operational behavior can be extracted at multiple time scales, i.e., the complex evolutionary path of enterprise operations over time. These trajectories include both regular long-term trends (such as continuous expansion) and sudden abnormal fluctuations (such as short-term cash flow disruptions).

[0021] By aligning nonlinear evolution trajectories with timestamp nodes, a comprehensive corporate financial behavior map is constructed. Deep learning methods, such as LSTM (Long Short-Term Memory), GRU (Gated Recurrent Unit), and even Transformer-based time series modeling frameworks, are introduced at the time series modeling level to perform multi-period fitting of the company's financial trajectories across different periods (weeks, months, quarters, and years). The goal of the fitting is not only to predict future financial behavior but also to extract key change nodes at different points in time and map them as risk events or behavioral labels. For example, if a high concentration of funds flowing to a single counterparty is detected in the cash flow trajectory during a certain quarter, this point in time can be marked as a potential risk node. If a large cross-industry investment is detected in the strategic investment trajectory at a certain point in time, it can be marked as a strategic transformation node. Through this time point mapping and labeling, discrete timestamp nodes can be organically combined with nonlinear evolution trajectories to ultimately form a corporate financial behavior map. This map uses time as the main axis and multi-dimensional business behaviors as branches, combining cyclical trends and abnormal fluctuations to intuitively depict the financial operation logic and potential risk points of a company at different stages.

[0022] 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: Based on the corporate financial behavior graph, real-time financial flow, cash inflow and outflow rhythm, debt interest rate fluctuations and bill splitting frequency are calculated to obtain multimodal financial signals; Based on multimodal financial signals, analyze the distribution of corporate asset liquidity to generate asset liquidity distribution characteristics; Based on the characteristics of asset liquidity distribution, multi-signal correlation is calculated and changes in liability structure are perceived to construct a holographic perception map of asset structure. Based on the holographic perception map of the asset structure, calculate the frequency of cash inflows and outflows, capital turnover efficiency, and financial leverage strength, and perform capital transaction sensitivity calculation to mark potential vulnerabilities in the capital chain.

[0023] In this embodiment, after obtaining authorization from the enterprise and the platform, information on fund inflows and outflows is collected by accessing the enterprise's financial system, bank transaction interface, and bill clearing system. Here, "real-time" typically refers to data latency ranging from seconds to minutes to meet the timeliness requirements of risk monitoring. The rhythm of cash inflows and outflows can be achieved using a sliding window statistical method: for example, using a 1-hour window to count the frequency of fund inflows and outflows, and combining this with daily, weekly, and monthly levels to calculate flow patterns, resulting in a multi-scale representation of cash flow rhythm. For fluctuations in debt interest rates, dynamic interest rate data needs to be extracted from enterprise loan contracts, financing bills, and benchmark interest rates in the financial market, and compared with the enterprise's own cost of capital to form an interest rate fluctuation curve. The frequency of bill splitting is calculated by tracking the amount of bills issued, the splitting ratio, and the repayment period of enterprise bill financing, with a focus on abnormally frequent splitting behavior, which is often related to tight cash flow or compliance risks. Combining the above multi-dimensional indicators, a multimodal financial signal set composed of time series, statistical characteristics, and topological changes can be obtained. These signals comprehensively reflect a company's financial status, including not only quantitative data (such as net cash inflows) but also structural signals (such as the complexity of bill splitting chains). Corporate assets are categorized into several levels: highly liquid assets (such as cash, demand deposits, and trading financial assets), moderately liquid assets (such as accounts receivable, inventory, and short-term bills), and low-liquidity assets (such as long-term equity investments, fixed assets, and intangible assets). For each category, weighted calculations are performed using cash flow rhythm, bill splitting frequency, and changes in debt interest rates from multimodal financial signals. For example, the characteristics of highly liquid assets can be defined by the frequency of cash inflows and outflows and the volatility of short-term funding costs; moderately liquid assets are characterized by a focus on the accounts receivable collection cycle and bill splitting frequency; and low-liquidity assets require attention to the time of capital accumulation and changes in market valuation. To more intuitively express the distribution of asset liquidity, kernel density estimation (KDE) or quantile distribution modeling can be used to obtain the probability distribution characteristics of assets in different liquidity ranges. In terms of parameter settings, three time windows—30 days, 90 days, and 360 days—can be used as benchmarks to assess liquidity at different time scales. The resulting asset liquidity distribution characteristics are not only a static proportional relationship, but also a dynamically updated distribution curve over time, which can reveal the flexibility of an enterprise's asset structure and its ability to convert funds into cash under different economic cycles.

[0024] Nonlinear dependencies between different signals are calculated using correlation matrices and mutual information. For example, a high correlation between cash outflow rhythm and short-term debt interest rate fluctuations indicates that the company's financing costs are significantly constrained by short-term cash flow; a high correlation between the frequency of bill splitting and the proportion of medium-liquid assets may mean that the company is adjusting accounts receivable liquidity through bill instruments. In addition to linear correlation coefficients (such as the Pearson correlation coefficient), nonlinear indicators, such as dynamic time warping (DTW) distance, need to be introduced to measure the similarity of time-series signals. Through these calculations, the coupling relationship between a company's asset and liability structure can be identified. Next, a "holographic perception map of asset structure" is constructed using network modeling methods. In this map, nodes represent different categories of assets and liabilities, edges represent the cash flow or correlation strength between them, and edge weights are defined by correlation values. To reflect the dynamic nature of structural changes, a time slicing method can be introduced to overlay the graphs under different time windows to form a dynamic hologram. After the holographic perception map is constructed, the signals within it need to be quantitatively calculated to extract risk-sensitive indicators. First, the frequency of cash inflows and outflows is calculated, which can be achieved through the time series changes of the weights of capital flows in the graph. For example, the frequency of cash flows can be statistically analyzed on a daily basis, and its periodic fluctuation characteristics can be analyzed through Fourier transform or wavelet transform. Second, the capital turnover efficiency is calculated. The commonly used formula is: Capital Turnover Rate = (Operating Revenue ÷ Average Working Capital), which can also be estimated by the speed of capital flow between asset nodes in the hologram. The financial leverage strength is reflected by weighting the ratio of total debt to shareholders' equity (debt-to-equity ratio) in combination with interest rate fluctuations of different debt types, reflecting the sensitivity of leverage use. In the calculation of capital transaction sensitivity, a sensitivity coefficient can be defined: when the cash flow frequency is highly coupled with the fluctuation of debt interest rates, the sensitivity increases; when the frequency of bill splitting exceeds a set threshold (e.g., more than 50 splits per month), the sensitivity increases again. Through this sensitivity coefficient, potential vulnerabilities in the capital chain can be automatically marked in the hologram. These vulnerabilities are usually manifested by certain nodes (such as short-term debt) having excessively high weights, or certain edges (such as bill financing chains) being abnormally active. Once the system identifies a vulnerability, it can generate an early warning signal to alert the company to potential liquidity risks or cash flow disruptions.

[0025] In this embodiment, step S3 includes the following steps: Identify the upstream and downstream supply chain information of enterprises; Based on the upstream and downstream supply chain information, the supply chain transaction process is analyzed to obtain the multi-stage transaction logic. Based on the upstream and downstream supply chain information, performance stability, contract dependence intensity, and price transmission sensitivity are calculated to generate supply chain stability assessment indicators; Based on supply chain stability assessment indicators, the transaction logic of multiple links is analyzed to perform supply chain ecosystem topology network analysis, and the evolution of risk cascading failure scenarios is carried out to generate supply chain ecosystem risk cascading scenarios. Based on the risk cascading scenario of the supply chain ecosystem, the nonlinear amplification effect of risks is calculated, and key risk-critical links are identified.

[0026] In this embodiment, within the context of financial risk protection, identifying upstream and downstream supply chain information is the first step in establishing a supply chain risk monitoring system. Supply chain information includes not only the company's direct supplier and customer lists, but also contract terms, payment cycles, goods flow nodes, invoice and document records, logistics documents, and publicly disclosed information. The main sources of data collection include the company's internal ERP system, procurement and sales contract databases, accounts payable and receivable records in the financial system, and third-party industry databases (such as Dun & Bradstreet, customs import and export databases, and industry association directories). During data integration, the company's unique identifier (such as a unified social credit code or global legal entity identifier) ​​is typically used as the primary key to perform entity parsing and association matching of multi-source heterogeneous data, thereby forming a complete upstream and downstream relationship diagram. In practice, the timeliness of information must also be considered; for example, supplier directories may be updated quarterly, while transaction records need to be updated daily or even in real-time. For cross-border supply chains, attention also needs to be paid to the data interfaces of currency exchange and international clearing systems. The core of transaction link analysis is to model the links such as raw material procurement, component supply, manufacturing, wholesale distribution, and end-user sales in a chain-like manner. Each link includes not only the flow of funds (payment, financing, settlement), but also the flow of logistics (goods transportation, warehousing) and information (orders, invoices, contract terms). By establishing a transaction event chain, the timestamps of a single enterprise's procurement payment, supply delivery, invoice generation, and contract execution are linked, thereby identifying the dependencies and transaction sequence between upstream and downstream. In implementation, process mining methods can be used to reconstruct the logic of the supply chain links by analyzing transaction logs and contract execution. For example, for a supplier with a 90-day contract fulfillment cycle, its delivery time and payment settlement cycle can be mapped to fixed logical nodes in the transaction chain, while abnormal delays will expose potential risks. To ensure the accuracy of the logical chain, a graph database (such as Neo4j) is needed to represent complex multi-link relationships, with enterprises as nodes and transaction behaviors as edges, and edge weights defined by amount, period, or transaction frequency. Through structured modeling, a complete multi-link transaction logic is obtained, clearly presenting the position and role of enterprises in the supply chain network.

[0027] Performance stability is primarily measured by contract execution rate, which is the proportion of deliveries and payments made on time within the contractually agreed period. Stability can be characterized by calculating the contract performance rate, delay rate, and default rate over the past 12 months, combined with volatility parameters (such as standard deviation). Secondly, contract dependence strength reflects the degree of a company's reliance on a particular supplier or customer. A concentration index, such as the Herfindahl-Hirschman Index (HHI), can be used for specific calculations. When a particular supplier accounts for a high proportion of procurement costs (e.g., exceeding 40%), the dependence strength is high, indicating concentrated risk. Finally, price transmission sensitivity needs to be calculated by combining commodity or industry raw material price indices with the correlation between the company's procurement and sales prices. If a 1% increase in upstream prices results in a change in the company's downstream selling price or profit margin exceeding a certain threshold (e.g., 0.8%), it indicates high price transmission sensitivity. Through these three dimensions, a supply chain stability indicator system can be generated, where performance stability is a time-dimensional indicator, contract dependence strength is a structural-dimensional indicator, and price transmission sensitivity is a market externality indicator. This indicator system provides a quantitative foundation for risk modeling, revealing whether enterprises have single-point dependencies, weak cost transfer capabilities, or high contract execution risks in the supply chain. It maps multi-stage transaction logic to a complex network, classifying enterprise nodes according to industry roles (suppliers, manufacturers, distributors, retailers), and weighting edge weights based on indicators such as transaction amount, performance stability, and dependence strength. Key nodes—enterprises occupying important positions in the supply chain—can be identified through network centrality indicators (such as degree centrality, betweenness centrality, and PageRank). When these nodes experience risks, it can trigger a global shock. Next, risk cascading failure modeling is used to simulate scenario evolution. A specific method can refer to the power grid cascading failure model: when a node fails (e.g., default, bankruptcy, supply disruption), its upstream and downstream nodes will be impacted based on dependence strength and performance stability, triggering a chain reaction. For example, if a key supplier defaults, 70% of its customers who depend on that supplier will be severely affected, and their downstream customers may also experience secondary shocks. During the modeling process, transmission parameters (such as failure probability thresholds of 0.3, 0.6, and 0.9) can be set to simulate the scenario evolution under different risk transmission intensities.

[0028] Further calculations of the nonlinear amplification effect are performed to identify critical risk points. Nonlinear amplification refers to the fact that supply chain risks do not simply accumulate linearly, but rather diffuse exponentially due to complex dependencies and feedback mechanisms. For example, if the price of a raw material increases by 10%, a single company's cost may increase by 15%, but through the transmission effect of the supply chain, the profit of the entire industry chain may ultimately decrease by more than 30%. To quantify this effect, complex network dynamics models (such as an improved version of the SIR contagion model) can be used, treating risk as a "propagation factor" that spreads through the network with different probabilities and intensities. Through multiple Monte Carlo simulations, the overall loss range and concentration of the system under different risk scenarios can be estimated. Key nonlinear characteristics include: risk exhibits a critical threshold effect at certain nodes; that is, when the dependence intensity exceeds a certain proportion (e.g., 60%), the system risk no longer increases smoothly but suddenly accelerates its spread. The calculation results can identify critical risk points, such as certain highly dependent suppliers, customers with low fulfillment stability, or raw material nodes with extremely high price sensitivity. Once problems occur at these critical points, the systemic risk of the entire supply chain ecosystem will be greatly amplified.

[0029] In this embodiment, step S4 includes the following steps: Collect macroeconomic feedback information; calculate inflation expectations, monetary policy tendencies, and exchange rate fluctuation trends based on macroeconomic feedback information to obtain macroeconomic environment parameter characteristics; The macroeconomic environment parameters are aligned in multiple dimensions and heterogeneous data are fused to construct a macroeconomic risk field perception network. We mine corporate financial coherence perturbations in the macro risk field perception network and generate a multidimensional dynamic perturbation tensor. Multi-scenario financial risk simulation is conducted on multi-dimensional dynamic disturbance tensors, key risk critical links, and potential vulnerabilities in the capital chain to construct a financial risk simulation sandbox.

[0030] In this embodiment, a mechanism for collecting macroeconomic feedback information is established. Macroeconomic feedback information mainly includes three categories: First, macroeconomic statistical indicators, such as Gross Domestic Product (GDP), Consumer Price Index (CPI), Producer Price Index (PPI), unemployment rate, and total social financing, which are typically released regularly by the National Bureau of Statistics, the central bank, and international organizations. Second, policy information, including minutes of the central bank's monetary policy committee meetings, fiscal policy adjustments, industrial support and regulation policies, and international trade policies, which have strong guiding significance. Third, market data, such as interest rate curves, exchange rates, commodity price indices, and capital market volatility indices (such as the VIX index). Data collection methods can be divided into two categories: periodic capture and real-time updates. Periodic capture is suitable for official statistical data, with an update cycle of monthly or quarterly; real-time updates are suitable for financial market indicators, and can obtain minute-level or even second-level data through exchanges and interbank market interfaces. During the data collection process, it is also necessary to pay attention to the inclusion of cross-border data, such as the US dollar index, international oil prices, and the CPI and interest rate decisions of major global economies, because risk transmission in a globalized context often crosses national borders. To ensure data quality, data cleaning mechanisms are needed, such as missing value imputation, outlier detection, and timestamp-based standardized processing, to ensure comparability of data from different sources across time. Inflation expectations can be calculated through time series modeling, such as using ARIMA, VAR (Vector Autoregression) models, or machine learning-based LSTM models to predict historical CPI and PPI data. Simultaneously, market expectation indicators, such as the term spread of the government bond yield curve (long-term interest rate minus short-term interest rate), should be introduced as a market-based reflection of inflation expectations. Secondly, the extraction of monetary policy stance needs to combine the policy texts released by the central bank with actual market operations. Sentiment analysis and keyword frequency statistics can be performed on policy announcements using text mining (NLP) to determine whether they are tight or loose, while also quantifying this by combining money supply (M2), policy interest rate adjustments (such as the reserve requirement ratio and benchmark interest rate), and the scale of open market operations. Thirdly, exchange rate fluctuation trends can be calculated by using historical volatility (GARCH model) and implied volatility (option market data) of major currency pairs (such as RMB / USD and EUR), and then corrected by combining international balance of payments and capital flow trends. The resulting macroeconomic environment parameters are not just single values, but a multi-dimensional indicator system. For example, inflation expectations can be represented by the CPI forecast and confidence interval for the next three months, the monetary policy stance can be represented by a continuous quantitative range of "tightening-neutral-easing", and the exchange rate fluctuation trend can be represented by the expected volatility curve.

[0031] In terms of time dimension, macroeconomic parameters are typically updated monthly or quarterly, while corporate financial and transaction data may be daily or real-time. Therefore, time granularity alignment is necessary. Common methods include downsampling or interpolation, such as converting daily corporate data to monthly data through rolling averages, or pushing monthly macroeconomic data down to the daily level using decomposition models (such as Kalman filtering). Secondly, in terms of spatial dimension, the statistical calibers of different macroeconomic indicators may differ. For example, national CPI data and the price index of the company's region need to be aligned using weighted averages to better reflect the economic environment in which the company operates. Thirdly, in terms of semantic dimension, macroeconomic parameters and corporate characteristics need semantic mapping. For example, inflation expectations can be mapped to a company's raw material cost sensitivity, and exchange rate fluctuation trends can be mapped to the risk exposure of cross-border income or foreign debt. After alignment, heterogeneous data fusion is required. Fusion methods can employ multimodal fusion frameworks, such as feature-level fusion (concatenating features of different parameters into a high-dimensional vector), decision-level fusion (integrating results through weighted voting or Bayesian methods), or representation learning based on deep learning (such as multimodal autoencoders). During the fusion process, weight parameters can be set, for example, a weight ratio of 0.4:0.6 for macro data and enterprise micro data, to ensure the comprehensiveness of risk field modeling. Through alignment and fusion, a "macro risk field perception network" is formed. This network is essentially a multidimensional dynamic relationship graph, where nodes represent different macro parameters, and edges represent their correlations and mapping relationships at the enterprise level. Further analysis of its potential impact paths on enterprise finance involves coherence perturbation mining. "Coherence perturbation" refers to the correlation and coupling effect between macro parameter fluctuations and enterprise financial indicators (such as cash flow, debt-to-equity ratio, and revenue growth rate). Specifically, mutual information and dynamic correlation coefficients are first used to calculate the correlation between macro parameters and enterprise indicators at different time windows. For example, the probability distribution of increased raw material costs for businesses when inflation expectations rise by 1%; and the magnitude of increased financing costs for businesses when monetary policy tightens. Secondly, it is necessary to utilize perturbation propagation models to simulate changes in macroeconomic parameters as external shock factors, injecting them into the corporate financial network to observe their transmission path. To achieve this, simulation methods based on complex network dynamics can be used, such as using weighted directed graphs, injecting changes in inflation expectations as external nodes, with edge weights representing transmission sensitivity. Through repeated iterations and Monte Carlo simulations, the dynamic financial responses of businesses under different shocks can be generated.

[0032] This paper integrates perturbation tensors with previously identified key risk thresholds (such as highly dependent suppliers and price transmission-sensitive nodes) and vulnerabilities in the capital chain (such as concentrated short-term debt repayments and unusually frequent bill splitting) to construct a global simulation model encompassing both macro and micro factors. Secondly, a multi-scenario simulation framework is designed, including combined scenarios such as "rapid inflation + monetary policy tightening" and "sharp currency depreciation + concentrated foreign debt maturities." Different perturbation parameters are input for each scenario, and the dynamic response of the enterprise's financial system is calculated using tensors. During the simulation, system dynamics or agent-based modeling methods can be employed to simulate the behavioral adjustments and chain reactions of different enterprise nodes under shocks. To capture nonlinear amplification effects, a transmission threshold needs to be set; when certain indicators (such as leverage exceeding 70%) are triggered, the risk diffusion speed significantly increases. The final result is a "financial risk simulation sandbox" that not only displays the evolution path of the enterprise's financial system under different scenarios but also uses visualization to mark potential vulnerabilities and critical risk points. The sand table can be divided into three types of outputs: first, quantitative indicators, such as loss rate, cash flow gap, and probability of default; second, structural maps, such as risk diffusion paths and network vulnerability locations; and third, strategic recommendations, such as priority ranking of mitigation measures.

[0033] In this embodiment, step S5 includes the following steps: Based on a financial risk simulation sandbox, black swan and gray rhino events are simulated to generate simulation data of extreme risk scenarios. Environmental shock tolerance assessment is conducted based on simulation data of extreme risk scenarios, and the maximum loss under risk scenarios is calculated to generate the maximum tolerable risk parameters for the scenarios. Identify the enterprise's risk tolerance boundary surface and critical failure threshold based on the maximum tolerable risk parameter of the scenario; Dynamic risk protection planning is carried out based on the enterprise risk tolerance boundary surface and critical failure threshold to construct a full-cycle risk protection intelligent agent.

[0034] In this embodiment, extreme event simulation is introduced to capture the impact of "black swan" and "gray rhino" events on corporate financial systems. "Black swan" events refer to sudden risks with extremely low probability but significant impact, such as global financial crises, pandemics, and market freezes caused by geopolitical conflicts. "Gray rhino" events, on the other hand, are high-probability and already manifested systemic risks, such as real estate debt crises, prolonged declines in commodity prices, or prolonged increases in interest rates. When simulating these events, it is first necessary to set the event triggering conditions and impact parameters. For example, in a black swan scenario, the exchange rate could fluctuate by more than 30% within a week, or a short-term liquidity freeze could lead to a sharp increase in financing rates by 500 basis points; in a gray rhino scenario, the price of raw materials could fall by 20% within a year and remain at a low level. Secondly, these impact parameters are injected into the macroeconomic disturbance tensor and the corporate micro-vulnerability model in the financial risk simulation sandbox to observe the dynamic response process under extreme situations. To ensure the diversity and representativeness of the simulation, Monte Carlo simulation can be used to repeatedly and randomly sample extreme events, adjusting the impact intensity and duration in each iteration to generate simulation data covering different extreme scenarios. The final result is a set of "risk extreme scenario simulation data," which includes not only time-series risk transmission trajectories but also the failure distribution of enterprise nodes and the extreme loss level of the overall system. This further assesses the enterprise's resilience under different shocks and calculates the maximum loss level. First, resilience assessment can be achieved by setting safety thresholds for key financial indicators. For example, a cash flow coverage ratio (operating cash flow ÷ short-term debt) below 1.0 indicates excessive liquidity pressure, a debt-to-equity ratio above 80% indicates excessive financial leverage, and an interest coverage ratio (EBIT ÷ interest expense) below 1.5 indicates that debt repayment pressure is approaching a critical point. In the extreme scenario data, the dynamic changes of these indicators are gradually tracked to determine the enterprise's critical performance under shocks of different intensities. Second, the maximum loss calculation needs to be quantified using the Value at Risk (VaR) and Conditional Value at Risk (CVaR). For example, at a 99% confidence interval, the maximum loss amount for the enterprise in the event of a currency crash or interest rate surge can be calculated. Simultaneously, the cumulative loss range at 3, 6, and 12 months of risk transmission can also be calculated by combining the results of system dynamics simulations. By comparing affordability with loss calculations, we can extract the "maximum tolerable risk parameter for a given scenario." This parameter includes both quantitative values ​​(such as a company's ability to withstand a cash flow gap of 1 billion RMB in the worst-case scenario) and structural indicators (such as maintaining short-term debt repayment capacity at 70%).

[0035] By using different risk factors (such as exchange rate fluctuations, interest rate increases, and raw material price declines) as independent variables and the corresponding maximum tolerable loss or financial stress indicators as dependent variables, a multidimensional functional relationship is constructed. Through interpolation and fitting (such as polynomial fitting, support vector regression, or neural network fitting), a smooth boundary surface can be generated, representing the company's capacity limit under different combinations of shocks. Secondly, to identify the critical failure threshold, it is necessary to find the nonlinear inflection point on the surface; that is, when a certain risk factor exceeds a certain threshold, the company's financial status rapidly changes from "tolerable" to "unbearable." For example, when the interest rate increase exceeds 300 basis points, the company's interest coverage ratio rapidly drops from 2.0 to below 1.0; this is the critical failure point. During implementation, sensitivity analysis and marginal contribution analysis can be used to calculate the impact of each risk factor on the boundary surface, thereby identifying which variables are most likely to push the company across the critical point. The final result is a boundary model of a company's risk tolerance capacity, which includes a multidimensional surface plot, key threshold point coordinates, and a risk sensitivity ranking. A three-stage, full-cycle risk protection mechanism should be established, encompassing risk warning, response, and recovery. In the risk warning stage, the intelligent agent monitors macroeconomic indicators (such as exchange rates, interest rates, and CPI), corporate financial data (cash flow, debt ratio), and supply chain dynamics in real time, comparing these against the boundary surface. Once the observed data approaches a critical threshold (e.g., reaching 80% of the risk tolerance boundary), the system automatically triggers an early warning. In the response stage, the intelligent agent prioritizes risk mitigation measures based on risk sensitivity, such as increasing liquidity reserves, extending debt maturities, hedging exchange rate risks, or adjusting procurement structures. In the recovery stage, after the risk shock, the boundary model and the agent's decision-making logic are revised by comparing actual and expected losses, achieving adaptive optimization. To ensure the system's dynamism, the intelligent agent needs reinforcement learning capabilities, continuously iterating through simulations and real-world feedback to optimize risk protection strategies. Simultaneously, the agent's operating cycle needs to cover multiple scales, including daily, weekly, monthly, and quarterly, ensuring it can respond to both sudden risks and manage long-term structural risks.

[0036] In this embodiment, the specific steps for constructing a full-cycle risk protection intelligent agent based on the enterprise risk-bearing capacity boundary surface and critical failure threshold for dynamic risk protection planning are as follows: Dynamic risk protection planning is performed based on the enterprise risk tolerance capacity boundary surface and critical failure threshold to generate risk protection strategies. Based on risk protection strategies, make enterprise resource reallocation decisions and build intelligent resource reallocation solutions; Calculate the risk mitigation interval based on the intelligent resource reconfiguration scheme; Risk capital recovery and intelligent risk balancing management are carried out during the risk mitigation interval to construct a full-cycle risk protection intelligent agent.

[0037] In this embodiment, key variables on the boundary surface are ranked by sensitivity. For example, the cash flow coverage ratio is sensitive to interest rate fluctuations by 0.8, which is higher than its sensitivity to exchange rate fluctuations by 0.5, so interest rate hedging tools are prioritized. Secondly, a multi-timescale protection plan is constructed: at the short-term level (e.g., 7-30 days), the focus is on liquidity protection, such as establishing a daily average cash flow warning threshold; at the medium-term level (quarterly-semi-annual), debt structure adjustments and bill splitting controls are used to alleviate cash flow tensions; at the long-term level (1-3 years), strategic balancing is achieved through capital expenditure optimization and equity financing planning. Thirdly, multi-scenario simulations are introduced to verify the effectiveness of the strategy. For example, in a composite scenario of a 15% exchange rate fluctuation, a 10% decrease in raw material prices, and a 200 basis point increase in loan interest rates, the proposed protection strategy is tested to ensure that the indicators do not exceed the critical failure threshold. Through this method, the final risk protection strategy has both quantitative objectives (e.g., stabilizing the debt-to-equity ratio below 65%) and operational measures (e.g., prioritizing maintaining liquidity reserves at 1.5 times the average monthly expenditure), constituting an executable risk management plan. These strategies are then implemented at the enterprise's resource allocation level, i.e., intelligent resource reallocation decisions. Resources here include not only capital, but also human resources, asset structure, and supply chain resources. The implementation process consists of three steps: First, resource pool identification and classification. Existing enterprise resources are divided according to liquidity, profitability, and risk sensitivity, such as working capital, fixed assets, strategic investment assets, and key supply chain contracts. Second, reallocation optimization based on the objective function of risk protection strategies. For example, when short-term cash flow risk increases, the proportion of low-liquidity long-term investments should be reduced, and the allocation of cash and high-liquidity assets should be increased. This optimization process can employ multi-objective optimization methods, with objectives including: maximizing risk mitigation effects, minimizing resource reallocation costs, and maintaining basic production and operational stability. Third, establishing a dynamic intelligent model for resource allocation. This model, based on reinforcement learning or game theory optimization frameworks, can continuously adjust resource allocation schemes in a simulated environment until an optimal balance is reached under different risk scenarios. For example, within a market interest rate fluctuation range of ±200 basis points, the system repeatedly tests the allocation ratio of funds between debt repayment and working capital, ultimately forming a dynamic optimal solution. The final intelligent resource reconfiguration solution includes both a resource category and proportion adjustment table (e.g., cash increases by 15%, short-term notes decrease by 10%) and an emergency mechanism (e.g., rapid redemption of some investment products under trigger conditions) to ensure that the company maintains dynamic stability amidst risks.

[0038] The risk mitigation interval refers to the time window required for an enterprise to recover from a risk shock to a relatively safe range after resource reallocation. This time parameter is a crucial indicator in dynamic risk protection because it determines the responsiveness and sustained effectiveness of protective measures. Key financial indicators of the enterprise (cash flow coverage ratio, debt-to-equity ratio, interest coverage ratio, etc.) are used as state variables, and resource reallocation measures are used as control variables. Secondly, the time required for enterprise indicators to recover from the critical point to the safe threshold under different shock scenarios is simulated. For example, if a liquidity shock causes the cash flow coverage ratio to fall to 0.8, and the reallocation plan increases liquidity by 20%, the model calculates that it will take 45 days to recover to 1.2. Furthermore, calculations are considered across multiple time scales: short-term intervals may be achieved within weeks, while medium- to long-term intervals may require months or even longer. In this case, a recovery function can be introduced, such as an exponential decay model, to describe the rate of decline in risk exposure over time, and Monte Carlo simulations can be used to estimate the probability distribution of the interval. The final result is a "risk mitigation interval matrix," which not only provides recovery time estimates for different risk types but also identifies the optimal mitigation duration under different resource allocation combinations. Risk capital recovery refers to the gradual return of emergency liquidity to the company's capital pool after the risk shock has subsided. For example, when short-term debt repayment leads to increased liquidity usage, emergency funds should be returned within 2-3 financial cycles after risk mitigation, through increased operating cash flow accumulation or refinancing. Secondly, there is intelligent risk balancing management. This management involves not only capital recovery but also the rebalancing of overall risk exposure. For example, when external macroeconomic risks decrease while internal operational risks increase, the agent automatically reduces the frequency of hedging tool use while increasing the intensity of supply chain performance monitoring. To achieve full-cycle management, the agent needs adaptive learning capabilities: by continuously recording the complete cycle of risk shock—mitigation—recovery, updating model parameters, enabling faster entry into the mitigation interval and shorter recovery time under similar risk conditions in the future. The final intelligent risk protection system is a cyclical system that includes six key components: real-time monitoring, dynamic planning, resource reallocation, recovery during downtime, capital recovery, and risk balancing, forming a closed-loop control.

[0039] In this embodiment, a data mining-based financial risk protection system is provided for executing the data mining-based financial risk protection method described above, including: The Behavior Graph module is used to collect multi-dimensional financial logs of enterprises, conduct in-depth mining of transaction behavior, and build enterprise financial behavior graphs. The structural change perception module is used to calculate the correlation of multiple signals and perceive changes in debt structure based on the enterprise's financial behavior map, and to mark potential vulnerabilities in the capital chain. The topology network analysis module is used to identify the upstream and downstream supply chain information of an enterprise, perform supply chain ecosystem topology network analysis and risk effect calculation, and identify key risk critical links. The risk simulation module is used to simulate financial risks in multiple scenarios, including potential vulnerabilities in the capital chain and critical risk-critical links, and to build a financial risk simulation sandbox. The dynamic protection module is used to assess shock tolerance based on a financial risk simulation sandbox, calculate the risk mitigation interval, and carry out risk capital recovery and intelligent risk balance management.

[0040] Therefore, the embodiments should be considered as exemplary and non-limiting in all respects, and the scope of the invention is defined by the appended claims rather than the foregoing description. Thus, all variations falling within the meaning and scope of the equivalents of the application are intended to be included within the invention.

[0041] The above description is merely a specific embodiment of the present invention, enabling those skilled in the art to understand or implement it. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the general principles defined herein are implemented in other embodiments without departing from the spirit or scope of the invention. Therefore, the present invention is not to be limited to the embodiments shown herein, but is to be accorded the widest scope consistent with the principles and novel features of the invention herein.

Claims

1. A data mining-based financial risk prevention method, characterized by, The method comprises the following steps: Step S1: Collecting enterprise multi-dimensional financial logs, performing deep mining of transaction behavior, and constructing an enterprise financial behavior graph; Step S2: Based on the enterprise financial behavior graph, calculating the multi-signal correlation degree and perceiving the change of the liability structure, and marking the potential financial chain vulnerability point; Step S3: Identifying the upstream and downstream supply chain information of the enterprise, performing supply chain ecological topology network analysis and risk effect calculation, and identifying the key risk critical link; Step S4: Multi-scenario financial risk simulation is performed on the potential financial chain vulnerability point and the key risk critical link, and a financial risk simulation sand table is constructed; Step S5: Based on the financial risk simulation sand table, impact bearing capacity assessment is performed, the risk relief interval is calculated, and risk capital recovery and intelligent risk balance management are performed. 2.The data mining based financial risk prevention method according to claim 1, characterized in that, The specific steps of step S1 are: Collecting enterprise multi-dimensional financial logs; defining the length of the financial behavior cycle, and performing deep mining of transaction behavior on the enterprise multi-dimensional financial logs to extract fund flow behavior data and timestamp nodes; According to the fund flow behavior data, the multi-dimensional transaction track, asset restructuring history, strategic investment decision and regulatory compliance response record of the enterprise are mined, and multi-dimensional operating behavior characteristics are constructed; The multi-dimensional operating behavior characteristics are subjected to high-order tensor decomposition and topological anomaly detection, and the operating behavior nonlinear evolution track of multiple time scales is extracted; According to the timestamp nodes, the operating behavior nonlinear evolution track is subjected to multi-cycle time series deep fitting and time point mapping marking, and an enterprise financial behavior graph is constructed. 3.The data mining based financial risk prevention method according to claim 1, wherein, The specific steps of step S2 are: Based on the enterprise financial behavior graph, real-time financial flow, cash inflow and outflow rhythm, liability interest rate fluctuation and bill splitting frequency are calculated to obtain multi-modal financial signals; According to the multi-modal financial signals, the asset liquidity distribution of the enterprise is analyzed, and the asset liquidity distribution characteristics are generated; According to the asset liquidity distribution characteristics, multi-signal correlation degree calculation and liability structure change perception are performed, and an asset structure holographic perception graph is constructed; Based on the asset structure holographic perception graph, cash inflow and outflow frequency, fund turnover efficiency and financial leverage strength are calculated, and fund transaction sensitivity is calculated to mark the potential financial chain vulnerability point.

4. The data mining based financial risk guarding method of claim 1, wherein, The specific steps of step S3 are: Identify the upstream and downstream supply chain information of the enterprise; Based on the upstream and downstream supply chain information, the multi-link transaction logic is obtained by analyzing the supply chain transaction links; According to the upstream and downstream supply chain information, the contract dependence strength and price transmission sensitivity are calculated to generate supply chain stability evaluation indexes; According to the supply chain stability evaluation indexes, the supply chain ecological topology network analysis is performed on the multi-link transaction logic, and the risk cascade failure scenario evolution is performed to generate the supply chain ecological risk cascade scenario; According to the supply chain ecological risk cascade scenario, the risk nonlinear amplification effect is calculated, and the key risk critical link is identified.

5. The data mining based financial risk guarding method of claim 1, wherein, The specific steps of step S4 are: Collect macroeconomic feedback information; according to the macroeconomic feedback information, calculate the inflation expectation, monetary policy tendency and exchange rate fluctuation trend to obtain macroeconomic environment parameter characteristics; Align the macroeconomic environment parameter characteristics in multiple dimensions, perform heterogeneous data fusion, and construct a macro risk field perception network; The macro risk field perception network is disturbed by enterprise financial coherence to generate a multi-dimensional dynamic disturbance tensor; The multi-dimensional dynamic disturbance tensor, key risk critical links and potential financial chain vulnerability points are simulated in multiple scenarios to build a financial risk simulation sand table.

6. The data mining based financial risk guarding method of claim 1, wherein, The specific steps of step S5 are: Based on the financial risk simulation sand table, black swan and gray rhino events are simulated to generate risk extreme scenario simulation data; Based on the risk extreme scenario simulation data, environmental impact bearing capacity is evaluated, and maximum loss under risk scenarios is calculated to generate scenario maximum bearable risk parameters; According to the scenario maximum bearable risk parameters, the enterprise risk bearing capacity boundary surface and critical failure threshold are identified; Based on the enterprise risk bearing capacity boundary surface and critical failure threshold, dynamic risk protection planning is carried out to build a full-cycle risk protection intelligent agent.

7. The data mining based financial risk guarding method of claim 6, wherein, The specific steps of the dynamic risk protection planning based on the enterprise risk bearing capacity boundary surface and critical failure threshold to build a full-cycle risk protection intelligent agent are: Based on the enterprise risk bearing capacity boundary surface and critical failure threshold, dynamic risk protection planning is carried out to generate risk protection strategies; Based on the risk protection strategies, enterprise resource reconfiguration decisions are made to build intelligent resource reconfiguration schemes; Based on the intelligent resource reconfiguration scheme, the risk relief interval is calculated; Risk capital recovery and intelligent risk balance management are carried out on the risk relief interval to build a full-cycle risk protection intelligent agent.

8. A data mining based financial risk protection system, characterized by, The method for performing the data mining-based financial risk protection method of claim 1 comprises: A behavior graph module for collecting enterprise multi-dimensional financial logs, performing transaction behavior deep mining, and building an enterprise financial behavior graph; A structure change perception module for multi-signal correlation calculation and liability structure change perception based on the enterprise financial behavior graph to mark potential financial chain vulnerability points; A topological network analysis module for identifying enterprise upstream and downstream supply chain information, performing supply chain ecological topological network analysis and risk effect calculation, and identifying key risk critical links; A risk simulation module for multi-scenario financial risk simulation of potential financial chain vulnerability points and key risk critical links to build a financial risk simulation sand table; A dynamic protection module for impact bearing capacity evaluation based on the financial risk simulation sand table, risk relief interval calculation, risk capital recovery and intelligent risk balance management.

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