Intelligent financial financing decision-making system of distributed data weaving framework

Abstract

The invention relates to the technical field of financial science and technology and data processing, in particular to an intelligent financial financing decision-making system of a distributed data weaving framework, which comprises the following modules connected in sequence: a distributed data weaving module comprising a multi-source heterogeneous data access unit; a dynamic knitting execution unit; the intelligent decision module comprises a dual-channel risk assessment unit; a financing scheme optimization unit; the dynamic risk control module comprises a model drift adaptive unit; and the anti-fact early warning unit is used for generating an associated virtual unfavorable scene set, and evaluating the robustness output early warning level of the financing scheme through the antagonism verification network. The application of transfer learning enables the model to automatically adapt to changes of enterprise and industry environments, thereby improving the stability and robustness of the risk control model, and reducing decision errors caused by outdated models.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of financial technology and data processing, and particularly relates to an intelligent financial financing decision system of a distributed data weaving framework. BACKGROUND

[0002] In the field of financial technology, enterprise financing decision systems rely on multi-source data integration and risk assessment models. The current mainstream technology has three structural defects, resulting in serious deficiencies in the accuracy and timeliness of financing decisions, which are specifically manifested as follows: 1. Cross-domain data islands cause evaluation distortion; Enterprise key data (financial flow, tax returns, and supply chain invoices) are scattered in bank core systems, government tax platforms, and third-party ERP systems, forming heterogeneous data barriers. Traditional ETL (Extract-Transform-Load) tools use a periodic batch synchronization mechanism, which has two major drawbacks: Timeliness is missing: data updates are delayed for more than 24 hours, and real-time changes in business operations (such as sudden large receivables defaults) cannot be captured; Semantic conflict cannot be resolved: the same entity is named ambiguously in different systems (such as "prepaid accounts" in the tax system as "unbilled income"), and simple field mapping leads to incorrect index calculation.

[0003] Industry analysis reports indicate that the misjudgment rate of enterprise solvency due to data source conflicts is as high as 18.7%.

[0004] 2. Risk control model lag amplifies risk exposure; The existing system generally uses a static scoring card model, which has the following defects: Risk factors are fixed: only historical financial indicators (asset-liability ratio, profit margin) are included, and market dynamic factors (such as the impact of commodity price fluctuations on manufacturing raw material costs) are ignored; Conduction mechanism is missing: unable to model the dynamic correlation between risk factors (such as interest rate increase → financing cost increase → cash flow pressure chain reaction).

[0005] Typical case: In 2023, a metal processing enterprise triggered a debt default due to a 20% single-month surge in copper prices, and the static model failed to timely warn of cash flow gaps; Therefore, there is an urgent need for an intelligent financial financing decision system of a distributed data weaving framework to solve the above problems. SUMMARY

[0006] Based on the above purpose, the present application provides an intelligent financial financing decision system of a distributed data weaving framework, which includes the following modules connected in turn: A distributed data weaving module, comprising: A multi-source heterogeneous data access unit is deployed in the semantic gateway agent of banks, tax authorities, and supply chain systems. The built-in domain ontology parser is used to map the original fields to a unified financial semantic model, and the bloodline tracking engine is used to record the data conversion path. The conversion step complexity and the historical error rate of the source system are used to generate a credibility weight; A dynamic weaving execution unit includes a federal query coordinator and an asynchronous stream merger. The federal query coordinator compiles the financing evaluation request into domain-specific language instructions and broadcasts them to each semantic gateway agent. The asynchronous stream merger aligns the sharded data stream through a dynamic sliding time window. The window size is adaptively adjusted according to the network delay variance. An intelligent decision-making module includes: A dual-channel risk assessment unit is composed of a static assessment channel and a dynamic deduction channel. The static assessment channel processes financial indicators through a rule engine to output a basic credit rating. The dynamic deduction channel constructs a time-dependent graph network. The nodes represent three types of risk factors, including interest rate fluctuations, commodity prices, and policy changes. The edge weights are dynamically calculated through a historical covariance matrix. External market events are loaded through an event injection simulator to deduce the cash flow gap probability. A financing scheme optimization unit generates the optimal combination of loan amount, term, and security method based on a constraint satisfaction problem solver. The constraint condition set consists of a cash flow coverage threshold and a financing cost upper limit. The objective function is to minimize the risk entropy value. A dynamic risk control module includes: A model drift adaptive unit calculates the feature distribution offset degree of real-time enterprise data and industry benchmark models through a transfer learning comparator. When the offset degree exceeds the adaptive threshold derived from industry volatility, incremental training of the model is triggered. A counterfactual early warning unit generates a set of associated virtual adverse scenarios and evaluates the robustness of the financing scheme through an adversarial verification network to output a warning level.

[0007] Preferably, the credibility weight generation process includes: The bloodline tracking engine analyzes the data conversion path and records the number of field-level conversion steps. The credibility base value decays by one conversion step. The decay coefficient is calculated based on the recent data verification error rate of the data source. The credibility weight is corrected based on the timestamp conflict detection result. If the timestamp deviation between tax reporting time and bank transaction records exceeds the industry reasonable threshold, the weight is reduced by the deviation proportion. The maximum weight is obtained by weighted fusion of the base value, timestamp correction coefficient, and source system authority rating. The proportion of each component in the weight fusion formula is dynamically set according to the data quality guidelines published by financial regulatory authorities.

[0008] Preferably, the dynamic window adjustment mechanism of the asynchronous stream merger includes: Real-time monitoring of the fluctuation amplitude of the transmission delay of each data source, which is quantified by calculating the variance of the delay within a continuous time window; When the variance exceeds the network stability baseline, increase the time window by a non-linear expansion strategy, and the expansion amplitude is a piecewise function of the variance value. The turning point of the function is obtained according to historical network congestion event statistics; After merging, an importance-based time series compression algorithm is used, and the compression granularity is dynamically configured according to the decision accuracy requirement: fine-grained retention for cash flow related data, and coarse-grained aggregation for auxiliary indicators.

[0009] Preferably, the edge weight calculation method of the time series dependent graph network comprises: Obtain the historical time series data of each risk factor in the past preset period, and the period length is dynamically selected according to industry characteristics; Calculate the dynamic conditional correlation coefficient matrix, and the correlation coefficient significance is determined by hypothesis testing, and only the correlation edges with significance exceeding the confidence threshold are retained; Determine the edge direction according to the sign of the correlation coefficient: if the correlation is positive, the edge points from the dependent variable to the independent variable, and if the correlation is negative, the edge is connected in the opposite direction; The absolute value of the weight is mapped by the correlation coefficient normalization, and the mapping curve is calibrated according to the industry risk transmission intensity.

[0010] Preferably, the shock waveform generation method in the event injection simulator comprises: Grab the original text from the news API, identify the event type, impact subject and intensity level through the event extraction model, and the intensity level is scored by combining sentiment analysis results and keyword frequency; Convert the intensity level to the initial amplitude, and the amplitude size is positively correlated with the industry influence coefficient, which is obtained by consulting the pre-defined industry event sensitivity table; The shock waveform adopts a damped oscillation model, and the decay time constant is determined according to the median of the industry historical impact duration matched with the event type.

[0011] Preferably, the Monte Carlo verification process of the constraint satisfaction problem solver comprises: At each decision node of the branch and bound method, generate a number of cash flow simulation paths, and the number of paths is set according to the completeness of the historical data of the enterprise; Calculate the satisfaction of the constraint condition for each path, and count the satisfaction probability; The reliability threshold is inversely calculated by the historical operating volatility of the enterprise: the higher the volatility, the stricter the threshold setting, and the specific mapping relationship is calibrated by the industry risk preference model.

[0012] Preferably, the calculation and response mechanism of the feature distribution offset degree comprises: The distribution difference of real-time data and benchmark model in feature space is measured by Wasserstein distance, and the feature dimension is normalized by industry scale before distance calculation; The offset index is equal to the normalized distance divided by the baseline offset, and the baseline offset is taken from the historical offset spectrum of similar enterprises in the industry during the industry turbulence period; The incremental training adopts a federated learning framework, and each participant updates the model parameters locally, and updates the global model by aggregating encrypted gradients.

[0013] Preferably, the generation logic of the virtual adverse scenario set comprises: Identify enterprise vulnerabilities: calculate the elasticity coefficient of each risk factor variation on cash flow through sensitivity analysis, and select the factor with an elasticity coefficient exceeding the threshold value as the key exposure point; Generate a disturbance sequence for each exposure point, and set the disturbance step according to the quantile interval of the historical extreme value distribution of the factor; Establish a scenario correlation rule base: if two scenarios are logically exclusive, simultaneous activation is prohibited.

[0014] Preferably, it further comprises a federated verification feedback module: The financial institution verifies the financing scheme through a secure multi-party computation protocol: the bank inputs the cost of funds, the guarantor inputs the discount rate of the collateral, and each party locally calculates the risk value; A homomorphic encryption-based gradient aggregator generates a comprehensive verification result, and only when the verification deviation exceeds the tolerance threshold does it trigger scheme iteration, and the tolerance threshold is set according to the financing scale ladder; The credibility weight is introduced as a correction factor in the deviation calculation formula, and the tolerance of low credibility data sources is reduced.

[0015] Preferably, the calculation method of the risk entropy value comprises: Risk entropy value = Σ (risk exposure degree of each component of the financing scheme × diversification coefficient); The risk exposure degree is calculated by the product of the cash flow gap probability and the loss severity; The diversification coefficient is derived from the risk factor correlation matrix of each component, and the lower the correlation, the smaller the coefficient.

[0016] The present application has the following advantages: 1. The distributed data weaving module can realize real-time data integration through the multi-source heterogeneous data access unit and the built-in domain ontology parser, avoiding the delay problem of traditional ETL periodic synchronization. Data transformation paths are recorded through the bloodline tracking engine, ensuring real-time updating of data and real-time reflection of the financial situation of enterprises, and reducing the decision-making risk caused by data delay.

[0017] 2. Through the unified financial semantic model, the naming conflict of the same entity in different systems can be solved, and the index calculation error caused by simple field mapping is avoided, ensuring the consistency and accuracy of the data.

[0018] 3. The double-channel risk assessment unit in the intelligent decision-making module solves the limitations of the static scoring card model through the combination of static and dynamic deduction. The static assessment channel performs basic credit assessment based on financial data, while the dynamic deduction channel constructs a time-dependent graph network, dynamically tracks changes in external market factors such as interest rate fluctuations and commodity prices, and dynamically adjusts the relationship between risk factors through the historical covariance matrix.

[0019] 4. By introducing the relationship modeling between these dynamic market factors and risk factors, the system can timely capture the impact of market changes on enterprise cash flow, avoiding the problem that static models cannot respond to market sudden fluctuations.

[0020] 5. The time-dependent graph network simulates the time-dependent relationship of factors such as interest rate fluctuations, commodity price fluctuations, and policy changes by constructing a dynamic correlation graph between risk factors. The dynamic calculation method of edge weight uses the covariance matrix of historical data, based on correlation coefficients and significance tests, to accurately capture the transmission effects between risk factors. Each node represents a different risk factor, and the direction and weight of the edge represent the causal relationship between risk factors. The event injection simulator further enhances the sensitivity and prediction ability of the system to market changes by simulating external market events. BRIEF DESCRIPTION OF DRAWINGS

[0021] In order to more clearly illustrate the technical solutions in the present application or prior art, the following will briefly introduce the drawings needed to be used in the embodiments or prior art description. Obviously, for those skilled in the art, other drawings can also be obtained based on these drawings without creative labor.

[0022] Fig. 1 is a structural block diagram of the system of the present application; Fig. 2 is a step flowchart of the shock waveform generation method in the event injection simulator in the system of the present application; Fig. 3 is a step flowchart of the Monte Carlo verification process of the constraint satisfaction problem solver in the system of the present application. DETAILED DESCRIPTION

[0023] The application will be described in detail below with reference to the drawings and specific embodiments. It should be noted that the following embodiments are the best, preferred embodiments, and other alternative embodiments can also be implemented by those skilled in the art for some known technologies; and the drawings are only used to more specifically describe the embodiments, and are not intended to specifically limit the application.

[0024] Please refer to Figs. 1-3 The embodiment of the application provides an intelligent financial financing decision system of a distributed data weaving framework, wherein a multi-source heterogeneous data access unit of a distributed data weaving module is deployed in a semantic gateway agent of a bank, a tax authority and a supply chain system, and the goal is to access data from various different source systems and integrate the data. Each semantic gateway agent is responsible for extracting original data from a local system and mapping the data to a unified financial semantic model. The mapping process relies on an embedded domain ontology parser to ensure that the same entity (such as “accounts receivable” and “unbilled revenue”) in different systems can be uniformly represented.

[0025] By establishing a unified financial semantic model, the semantic conflict problem (such as different naming of the same concept in different systems) in the traditional data integration process is solved. In addition, the bloodline tracking engine records the data conversion path, which enables each step of the data flow to be traced back, thereby improving the explainability and reliability of the data.

[0026] The dynamic weaving execution unit includes a federal query coordinator and an asynchronous stream merger. After receiving a financing evaluation request, the federal query coordinator compiles it into domain-specific language (DSL) instructions and broadcasts them to each semantic gateway agent. Each gateway executes the relevant query and returns the data. The asynchronous stream merger aligns the returned data through a dynamic sliding time window and adaptively adjusts the window size according to the variance of network delay, ensuring efficient docking of data streams between different systems.

[0027] Dynamic adjustment of the time window size can adapt to the data processing capacity of different systems, ensuring the real-time and accuracy of the data. This method significantly improves the throughput and timeliness of the data stream and reduces the delay in data integration.

[0028] 2. The dual-channel risk assessment unit of the intelligent decision-making module consists of a static assessment channel and a dynamic inference channel. The static assessment channel processes historical financial indicators (such as asset-liability ratio, profit margin, etc.) through a rule engine, thereby outputting a basic credit rating for the enterprise; while the dynamic inference channel models the dynamic relationships of market risk factors by constructing a time-dependent graph network. The nodes of the network represent three types of risk factors: interest rate fluctuations, commodity prices, and policy changes, each node is connected to other nodes through directed edges, and the weight of the edge represents the correlation strength between factors. The dynamic calculation of the weight relies on the historical covariance matrix, and external market events (such as policy changes or sharp fluctuations in commodity prices) are loaded through an event injection simulator to infer the probability of cash flow gap faced by the enterprise.

[0029] This module can introduce market dynamic factors on the basis of considering historical financial data, thereby improving the accuracy and flexibility of risk assessment, timely reflecting the influence of external markets, and making the risk control model no longer limited to static historical data.

[0030] The financing scheme optimization unit generates the optimal financing scheme based on a constraint satisfaction problem solver. The set of constraints includes a cash flow coverage rate threshold (i.e., the cash flow after financing must reach a certain percentage of coverage) and a financing cost upper limit (i.e., the financing cost cannot exceed a certain set threshold). The objective function is to minimize the risk entropy, i.e., under the premise of ensuring the feasibility of the financing scheme, to maximize the reduction of risk uncertainty.

[0031] By optimizing the financing scheme, the system can provide a tailor-made financing structure for the enterprise, so that the financing scheme can meet the enterprise's cash flow demand and financing cost control while maximizing risk reduction.

[0032] The model drift adaptive unit of the dynamic risk control module monitors the degree of deviation between the latest data of the enterprise and the feature distribution of the industry benchmark model in real time through a transfer learning comparator. When the distribution of the enterprise data deviates from the distribution of the industry benchmark model beyond a certain threshold (which is derived from the industry volatility), the system will automatically trigger incremental training of the model to update the risk control model to adapt to the new market and enterprise situation.

[0033] The application of transfer learning enables the model to automatically adapt to changes in the enterprise and industry environment, thereby improving the stability and robustness of the risk control model and reducing decision-making errors caused by outdated models.

[0034] The counterfactual early warning unit generates multiple sets of associated virtual adverse scenarios, conducts stress testing on the financing scheme through an adversarial verification network, evaluates the robustness of the financing scheme under different virtual scenarios, and outputs the corresponding early warning level. These virtual adverse scenarios simulate extreme market fluctuations, policy changes, and other potential risk scenarios.

[0035] The unit can provide a full range of risk assessment for financing solutions, effectively identify potential vulnerabilities through adversarial testing, provide early warning and help enterprises optimize financing solutions, and avoid financial risks due to delayed emergency response.

[0036] The present application provides significant innovations in data integration timeliness, real-time adaptability of risk control models, and optimization of financing solutions by introducing a distributed data weaving framework, an intelligent decision-making module, and a dynamic risk control mechanism. The technical implementation of each module not only cooperates with each other, but also independently plays a role, so that the system can quickly and accurately make financing decisions when facing multi-source heterogeneous data, and dynamically respond to changes in market and enterprise environment, thereby greatly improving the accuracy and timeliness of enterprise financing decisions.

[0037] In one possible implementation, after the data is accessed from the source system (such as bank flow, tax report, supply chain ERP) to the distributed data weaving module, the bloodline tracking engine automatically analyzes the conversion path at the field level, including field mapping, unit conversion, segment merging, classification aggregation, etc.

[0038] For each data record, the bloodline tracking engine maintains a conversion step linked list: For each added conversion step, the credibility base value is exponentially decayed according to the formula: ; Where, : initial credibility base value (such as 1); : the number of field-level conversion steps that have occurred; : decay coefficient.

[0039] Obtain the verification error rate of the source system in the last 30 days or 5000 records from the data verification module Such as missing values, inconsistencies, and checksum errors.

[0040] Decay coefficient: ; Where is an adjustment parameter (such as 0.5-1), so that data sources with high error rates will have faster credibility decay during the conversion process.

[0041] Perform timestamp consistency detection on key information in the data record (such as tax reporting time, bank flow time, and invoice generation time).

[0042] If the deviation exceeds the industry reasonable threshold (such as ±2 days), calculate the deviation ratio: ; And the credibility base value is: ; The greater the deviation, the greater the reduction in credibility.

[0043] According to the preset authority level table (such as the Financial Data Quality Guidelines issued by the financial regulatory authority): Bank core system: 0.95, tax system: 0.9, supply chain ERP: 0.7 as an important factor for final weighted fusion.

[0044] The maximum weight: ; Wherein, : the basis value after timestamp correction; : recent data quality verification results (such as missing rate compensation correction factor); : source system authority rating; : dynamic adjustment, according to the data quality guidelines issued by the financial regulatory authority or internal integration model to update regularly to ensure adaptability to regulatory requirements.

[0045] The final generated credibility weight is stored in the distributed weaving database with the original data, which is called by the subsequent risk assessment, financing scheme optimization module, and supports traceability. If future data errors are found, the specific conversion steps, error rate sources and timestamp conflict situations affecting the credibility can be quickly traced back.

[0046] In one possible implementation, the system monitors the data delay transmitted from different data sources (such as financial statements, bank data, etc.) to the stream merger in real time. In order to accurately capture delay fluctuations, the system will record the transmission delay of each data source regularly, usually setting a time window (such as every 100 milliseconds) to capture delay data.

[0047] The delay fluctuation is quantified by calculating the variance of the delay in each time window. The variance is an indicator to measure the delay fluctuation, and the calculation formula is as follows: ; Wherein, represents the delay at each time point, is the average value of the delay, is the number of delay records in the time window. The greater the variance, the more significant the delay fluctuation, reflecting the poor network stability.

[0048] The system sets a baseline variance value (network stability threshold) to determine whether the network is in a stable state. When the real-time calculated delay variance exceeds the baseline, the system considers that the network may be unstable and needs to adjust the processing strategy.

[0049] When the delay variance exceeds the stability baseline, the system increases the time window through a non-linear expansion strategy to cope with the instability of the data stream. The magnitude of the window increase is a piecewise function of the delay variance. Specifically, the system adopts the following strategy: If the delay variance is small, the system maintains the original time window.

[0050] When the delay variance is moderate, the time window will increase in a certain non-linear relationship, with a larger increase.

[0051] If the delay variance is large, the window is further expanded to ensure the integrity and reliability of the data stream.

[0052] The purpose of this strategy is to appropriately increase the size of the time window when the network delay fluctuates, thereby avoiding errors or omissions due to data not being timely merged.

[0053] The system determines the turning point of the piecewise function through statistical analysis of historical network congestion events. The calculation of the turning point is based on the distribution of delay variance in the past period of time to ensure that the time window is appropriately expanded when the delay fluctuates greatly.

[0054] After the data is merged, the system performs time series compression on the merged data. The granularity of compression is determined by the decision accuracy requirements of the data. The specific strategy is as follows: For cash flow data and other key decision-making data, a finer granularity is adopted, such as retaining data at the minute or second level, to ensure high accuracy in decision-making.

[0055] For auxiliary indicators and other less sensitive data, a coarser granularity is adopted, such as aggregating data at the hourly or daily level, to reduce storage and computing burden.

[0056] The granularity of time series compression can be dynamically adjusted according to actual needs. For example, when the system needs higher precision decision-making (such as financing decisions), finer granularity data is selected; when only trend analysis or long-term evaluation is needed, coarser granularity data is used.

[0057] In one possible implementation, the system first obtains historical time series data of multiple risk factors (such as interest rate fluctuations, market volatility, industry output, financing costs, exchange rates, etc.) from a distributed data platform.

[0058] The length of the historical period is not fixed but dynamically adjusted based on industry characteristics. For example, for the financial industry with frequent fluctuations, the system may select data for nearly one month; for manufacturing or infrastructure industries with periodic characteristics, data for three months or longer may be selected. This dynamic adjustment of the period is based on the optimization of volatility sensitivity analysis and decision delay tolerance models within the industry.

[0059] The dynamic conditional correlation method is used to establish the correlation structure between multiple risk factors. Unlike static correlation, DCC can depict the short-term and long-term dynamic relationship between time series.

[0060] To ensure the reliability of the graph structure, the system performs statistical hypothesis testing on the correlation coefficient of each pair of risk factors (such as based on t-test or Z-test).

[0061] When the p-value of the test statistic is less than the set confidence threshold (e.g. 0.05 or 0.01), the edge is considered significant.

[0062] The system only retains edges that pass the significance test, removing meaningless or noise relationships, thereby enhancing the sparsity and information density of the graph network.

[0063] If there is a positive correlation between two variables, the edge is directed from the dependent variable (explained variable) to the independent variable (explanatory variable); If there is a negative correlation, the connection is reversed, i.e. from the independent variable to the dependent variable.

[0064] The determination of direction depends on the causal graph reasoning principle in multi-factor modeling. Although pure correlation cannot directly infer causality, causal inference can be strengthened by embedding a structural VAR model (vector autoregressive model), which can improve the rationality and economic interpretation of direction determination.

[0065] The absolute weight of each retained edge is determined by the absolute value of its correlation coefficient.

[0066] To unify the scales of different variables and risk transmission, the correlation coefficient needs to be normalized and mapped. For example, map the correlation coefficient range of -1~1 to the weight value interval of 0~1.

[0067] The mapping function uses a nonlinear curve (such as a hyperbolic tangent function or a piecewise linear function) for mapping.

[0068] The shape parameters of the curve are trained and calibrated by the transmission effects between risk factors in historical industry risk events (such as financial crises and periods of liquidity stress), to fit the actual industry transmission rules.

[0069] Different industries can set different mapping curves to enhance the adaptability of the graph network to industry characteristics.

[0070] This method has significant advantages in modeling rationality, real-time performance, industry adaptability, and economic interpretation, providing a solid graph structure foundation for risk modeling and decision optimization in intelligent financial systems.

[0071] In one possible implementation, the system automatically scrapes real-time news texts by accessing news APIs (such as news aggregation platforms, social media APIs, etc.). These news sources include financial news, industry reports, social platform information, etc., covering various events that may affect financial markets or industries.

[0072] Using natural language processing (NLP) techniques, especially event extraction models (such as BERT-based named entity recognition, relation extraction, etc.), the system identifies the following information from the scraped news: Event type: such as political events, natural disasters, economic policy changes, corporate mergers, product releases, etc.

[0073] Influenced subject: the main entity affected by the event, such as a company, industry, market, etc.

[0074] Influence intensity: assessed based on sentiment analysis and keyword frequency of the text. Sentiment analysis uses sentiment dictionaries and deep learning models (such as sentiment classification models) to evaluate the positive or negative sentiment polarity of the news content.

[0075] By weighting the sentiment analysis results and keyword frequency, the event impact intensity score is calculated. For example, if the sentiment analysis is negative and involves important companies or industries affected, the score is higher.

[0076] Convert the intensity level to amplitude, with the amplitude size positively correlated with the industry impact coefficient. The industry impact coefficient is looked up according to a predefined industry event sensitivity table. The sensitivity value of each industry to different types of events has been obtained through historical data statistics and expert evaluation.

[0077] For example, the financial industry has a higher amplitude coefficient for financial events such as interest rate changes and stock market fluctuations, while the manufacturing industry may have a higher sensitivity to natural disasters.

[0078] The event impact waveform uses a damped oscillation model to simulate the impact of events on financial markets or industries. The basic form of the model is: ; where, is the amplitude of the waveform changing over time, is the initial amplitude, is the damping coefficient, is the oscillation frequency, is time.

[0079] The system determines the damping coefficient based on the historical impact duration of different event types. For example, political events usually have a more lasting impact, with a smaller damping coefficient; while sudden market changes (such as natural disasters, stock market crashes) may decay faster, with a larger damping coefficient.

[0080] Through the analysis of historical data, the system finds the median impact time of each event type on each industry and matches the corresponding decay time constant.

[0081] In one possible implementation, during the constraint satisfaction problem solving process, the branch and bound method is a commonly used optimization algorithm for solving problems by constructing a decision tree. At each decision node, the algorithm evaluates the current decision and makes a judgment based on the constraints, generating multiple candidate solutions (i.e., different cash flow paths).

[0082] At each decision node, the system needs to generate several sets of cash flow simulation paths based on historical data and the current state. The number of these paths is determined according to the completeness of the enterprise's historical data. Specifically, if the enterprise's historical data is relatively complete, the model can generate more paths, and vice versa, the number of paths is correspondingly reduced.

[0083] Cash flow path simulation is usually achieved through stochastic processes or time series analysis, for example, using Monte Carlo simulation to generate multiple possible cash flow paths to ensure that various possible operating scenarios are covered.

[0084] For each set of simulation paths, the system checks whether it meets the pre-set constraints. These constraints may involve liquidity constraints, debt servicing ability, capital adequacy ratio, etc. Each path is evaluated according to these constraints to determine whether it meets the requirements.

[0085] After evaluating all simulation paths, the number of paths that meet the constraints is counted, and the probability of meeting the constraints is calculated. This step is calculated by a simple ratio: ; The higher the probability of meeting, the more likely the decision scheme will meet the actual operating conditions and the lower the risk.

[0086] According to the historical operating volatility of the enterprise, the system will deduce the reliability threshold. Specifically, the higher the volatility, the greater the operating risk faced by the enterprise, and therefore, the system needs to set a more stringent reliability threshold, i.e., the proportion of paths that meet the constraints is required to be higher. Conversely, for enterprises with low volatility, the threshold can be set relatively loose.

[0087] The system derives the mapping relationship between volatility and reliability threshold based on the historical operating volatility of the enterprise. Volatility can usually be obtained by calculating the standard deviation of the enterprise's historical financial data, while the reliability threshold is calibrated through the industry's risk preference model.

[0088] Industry Risk Appetite Model: This model adjusts the reliability threshold of a company based on the general risk tolerance and preferences within the industry. For example, the financial industry may tend to set stricter thresholds, while the industry of start-ups may allow for some tolerance for error.

[0089] In one possible implementation, the Wasserstein distance is used to measure the distributional difference between real-time data and the benchmark model in the feature space. The Wasserstein distance is a distance measure that measures the difference between two probability distributions, especially suitable for comparing continuous distributions, and can capture the degree of change between distributions. It not only focuses on statistical characteristics such as mean and variance, but also measures the global difference of data, including the shape and position change of distribution.

[0090] Before calculating the Wasserstein distance, the feature dimension of the data needs to be normalized by the industry scale. Industry scale normalization is to standardize each feature value to the industry average level, so that the dimension and scale of the feature are consistent. Normalized data can effectively reduce the error caused by the difference between different enterprises due to industry characteristics, so that the calculation of the Wasserstein distance is more accurate.

[0091] Industry scale normalization is achieved by performing standardization on each feature dimension, such as Z-score standardization (subtracting the mean and dividing by the standard deviation) or Min-Max normalization (scaling the value to a specified range).

[0092] The offset degree refers to the degree of deviation between the real-time data distribution and the benchmark model, reflecting the changes in the operating environment of the enterprise. After the Wasserstein distance is calculated, the normalized Wasserstein distance can be compared with the baseline offset to calculate the offset degree index: ; The baseline offset is taken from the historical offset spectrum of similar enterprises during the industry turbulence period. Specifically, the baseline offset is calculated from historical data (such as industry crises, market fluctuations, etc.), which represents the average offset of the enterprise feature distribution within a specific historical period.

[0093] The historical offset spectrum of the industry turbulence period can provide a set of reference values to help the system understand the normal fluctuation range of enterprise features during historical industry fluctuations. This benchmark value calibrates the offset degree of real-time data, avoiding excessive reaction due to short-term fluctuations or accidental events.

[0094] To ensure data privacy and security, the system employs a federated learning framework for incremental training. Federated learning is a distributed learning method that allows different data owners (such as multiple enterprises, financial institutions, etc.) to train models locally and then send encrypted gradients or model parameters to a central server for aggregation. This approach avoids centralized storage and processing of data, ensuring data privacy protection.

[0095] Each participant trains the model locally and updates the model parameters. The updated gradients are encrypted and transmitted to the global server, which aggregates encrypted gradients from multiple participants to update the global model. This mechanism effectively improves training efficiency while ensuring that data does not leave the local environment, meeting privacy protection requirements.

[0096] By measuring feature distribution differences using the Wasserstein distance, combining the bias shift index calculation with the baseline bias shift, and using the federated learning framework for incremental training, the intelligent financial financing decision-making system can better handle real-time data changes and optimize in a safe and privacy-protected manner. This mechanism not only improves the accuracy and responsiveness of data analysis but also ensures that the system has strong robustness and adaptability when facing uncertain environments.

[0097] In one possible implementation, sensitivity analysis is used to assess the degree of influence of different risk factors on enterprise cash flow. The specific method is to calculate the elasticity coefficient of cash flow with respect to each risk factor. The elasticity coefficient reflects the sensitivity of cash flow to fluctuations in risk factors. If the elasticity coefficient of a certain factor is large, it indicates that the enterprise is sensitive to changes in that factor and may have greater vulnerability.

[0098] In implementation, the elasticity coefficient of each risk factor can be determined through historical data and regression analysis. The goal of sensitivity analysis is to identify risk factors that have a significant impact on enterprise cash flow, which become key exposure points and objects that need to be focused on when simulating adverse scenarios.

[0099] Factors with elasticity coefficients exceeding a certain threshold are selected as key exposure points. For example, if a certain risk factor (such as changes in raw material prices, fluctuations in market demand, etc.) has a significant impact on enterprise cash flow, it should be considered as a key exposure point for tracking and simulation.

[0100] For each key exposure point, the system will generate a perturbation sequence to simulate possible extreme changes in that factor. The perturbation sequence is generated through the following steps: First, collect historical extreme value data for the factor, such as the maximum and minimum values of the factor's changes in history.

[0101] Then, the disturbance step size is determined based on the quantile distribution of historical data. Specifically, the disturbance step size can be set with reference to the quantile intervals (e.g., the 5th percentile and 95th percentile quantile values) of historical data, which represent the range of extreme fluctuations that the factor may experience.

[0102] The disturbance sequence is generated by referencing these extreme values and quantile intervals, ensuring that the simulated disturbances can cover the worst-case scenarios that may occur in real-world environments.

[0103] Based on the historical data of each exposure point, the system dynamically adjusts the disturbance step size to ensure that the generated disturbance sequence is realistic and extreme. These disturbance sequences will be used for subsequent virtual adverse scenario set generation.

[0104] When generating virtual adverse scenarios, the interrelationships between different scenarios must be considered. By establishing a scenario association rule library, the system can automatically determine which scenarios can occur simultaneously and which scenarios should be mutually exclusive based on the logical relationships between different scenarios. For example, if there is a logical mutual exclusion relationship between "raw material price increase" and "overcapacity," these two events cannot be activated simultaneously at the same time point.

[0105] Through the combination of historical data and industry knowledge, the system can construct a scenario association rule library, classifying scenarios according to their logical relationships. If two scenarios have a mutual exclusion relationship (e.g., "market demand sharply declines" and "capacity expansion"), the system will prohibit the simultaneous activation of these two scenarios during simulation.

[0106] The association rule library not only handles simple mutual exclusion relationships but also identifies more complex conditional dependencies. For example, if the occurrence of certain scenarios requires specific prerequisites, the system can also automatically constrain based on these rules.

[0107] By accurately identifying the vulnerabilities of enterprises, generating extreme scenario disturbance sequences, and combining the logical judgments of the scenario association rule library, a highly reliable virtual adverse scenario set generation mechanism is provided for enterprises. This mechanism can provide accurate risk assessment and decision support in a volatile market environment, helping enterprises maintain competitiveness in complex financial environments.

[0108] In one possible implementation, in the financing decision-making process, multiple financial participants (such as banks, guarantee institutions, credit rating companies, etc.) need to jointly verify the feasibility and risk reasonableness of a financing scheme.

[0109] To protect sensitive data of each party (such as the bank's funding cost, the guarantee party's mortgage discount rate), the system uses secure multi-party computation protocol (SMPC) to ensure that each party completes joint computation without revealing private data.

[0110] Each financial institution deploys a data security agent locally, and inputs its internal parameters (such as the cost of funds, guarantee discount factor, liquidity adjustment factor, etc.) according to the system's uniformly defined risk assessment model.

[0111] Each participant calculates its own "partial risk value" for the financing scheme locally, and transmits it to the homomorphic encryption gradient aggregator in the system through an encrypted channel.

[0112] All locally calculated risk values will be encrypted by homomorphic encryption algorithm. The characteristics of homomorphic encryption allow the system to perform addition, multiplication and other operations without decrypting the data, thus realizing the weighted aggregation of multiple financial institution risk inputs.

[0113] The aggregator generates a comprehensive risk verification result, reflecting the overall risk assessment level of the current financing scheme under the cognition of multiple institutions.

[0114] The federal verification system sets a tolerance threshold gradient table related to the financing scale, for example, for small financing, the tolerance threshold can be higher, while for large financing, a lower deviation is required.

[0115] The system compares the risk value calculated locally by each participant with the comprehensive result. If the deviation exceeds the threshold of this step, it is considered that there is a significant divergence in the current risk consensus, and the parameter adjustment or re-evaluation of the financing scheme needs to be triggered.

[0116] The historical assessment accuracy, data update frequency, model transparency, etc. of each financial participant can be used as the basis for calculating its "data credibility weight".

[0117] The risk assessment result of a high-credibility data source has a relatively high tolerance for deviation; while the result of a low-credibility institution may trigger verification feedback even if the deviation is small.

[0118] ; Where: represents the weighted deviation value of the party; is the credibility weight of the data source (the lower the weight, the smaller the tolerance); is the risk value calculated locally by the party; is the overall risk value generated by the aggregator.

[0119] The system compares the weighted deviation value of all participants with the corresponding tolerance threshold. If the of any party exceeds its threshold, the system will automatically rollback the financing parameter configuration and regenerate the recommended scheme.

[0120] By introducing multi-party participation, privacy protection, trusted weight correction, and bias dynamic triggering mechanism, a powerful and compliant risk verification engine is provided for the intelligent financial financing system, significantly improving the rationality, compliance, and risk resistance of the financing scheme.

[0121] In one possible implementation, the risk entropy value (REV) is a comprehensive indicator that measures the risk uncertainty of each component in the financing scheme. Its calculation formula is: Risk Entropy Value = Σ (Risk Exposure of Each Component of the Financing Scheme × Dispersion Coefficient); where the risk entropy value is a measure of the overall risk uncertainty of the financing scheme, and the risk entropy value of each component is determined by two important factors: Risk Exposure: refers to the risk exposure degree of each financing component; Dispersion Coefficient: measures the risk dispersion effect between components.

[0122] Risk exposure is a measure of the risk exposure degree that each component of the financing scheme may face. Its calculation method is obtained by the product of cash flow gap probability and loss severity: ; Cash flow gap probability: refers to the probability that the cash flow of a component in the financing scheme may have a gap at a certain point in time. For example, a project may not be able to repay the debt on time in the future, resulting in a cash flow gap.

[0123] Loss severity: refers to the potential impact of the component's gap on the entire financing scheme. Usually, this is evaluated based on historical data, financial models, and macroeconomic factors.

[0124] For example, in a loan financing scheme, both interest income gap and principal repayment gap of the loan will affect cash flow, and the severity of each gap will affect the risk exposure.

[0125] The dispersion coefficient represents the risk relationship between each component in the financing scheme, especially the influence of risk factor correlation. This coefficient can be calculated by the following steps: By collecting historical data, financial indicators, market changes, etc., calculate the correlation between risk factors (such as market volatility, interest rate risk, credit risk, etc.) of each financing component.

[0126] Use correlation matrix (e.g. Pearson correlation coefficient) to measure the degree of correlation between risk factors of each component.

[0127] The lower the correlation, the higher the risk independence between components, and the smaller the dispersion coefficient, indicating better dispersion benefit.

[0128] The higher the correlation, the stronger the association between the risk of each part, and the greater the dispersion coefficient, meaning the higher the concentration of risk.

[0129] Specifically, the formula for calculating the dispersion coefficient can be expressed as: ; Where the element in the correlation matrix represents the correlation between the risk factors of the th and the th financing component.

[0130] Once the risk exposure of each component and the dispersion coefficient are calculated, they can be substituted into the formula to calculate the risk entropy value of the entire financing plan.

[0131] This step will integrate the risk characteristics of all financing components to provide a quantitative risk assessment result for financing decisions.

[0132] By considering the risk exposure and dispersion effect of the financing plan, the intelligent financial financing decision system provides an effective risk assessment method, helping decision-makers to quantify and optimize financing risks, so as to better cope with complex financial market environment.

[0133] The present application encompasses any substitutions, modifications, equivalent methods and schemes made on the essence and scope of the present application. In order to make the public have a thorough understanding of the present application, specific details are described in the following preferred embodiments of the present application, and the present application can also be fully understood without the description of these details to those skilled in the art. In addition, in order to avoid unnecessary confusion to the essence of the present application, well-known methods, processes, procedures, elements and circuits, etc. are not described in detail.

[0134] The above is only the preferred embodiment of the present application, it should be pointed out that for ordinary skilled in the art, without departing from the principles of the present application, can also make a number of improvements and refinements, these improvements and refinements should also be considered as the protection scope of the present application.

Claims

1. Intelligent financial financing decision-making system based on distributed data weaving framework, characterized by: It includes the following modules connected in sequence: Distributed data weaving module, including: The multi-source heterogeneous data access unit is a semantic gateway agent deployed in banking, tax, and supply chain systems. It has a built-in domain ontology parser for mapping raw fields to a unified financial semantic model. It also records the data conversion path through a lineage tracking engine and generates credibility weights based on the complexity of the conversion steps and the historical error rate of the source system. Dynamically weaving execution units, including a federated query coordinator and an asynchronous streaming merger. The federated query coordinator compiles financing assessment requests into domain-specific language instructions and broadcasts them to each semantic gateway agent. The asynchronous streaming merger aligns sharded data streams through a dynamic sliding time window, with the window size adaptively adjusted based on network delay variance. Intelligent decision-making module, including: The dual-channel risk assessment unit consists of a static assessment channel and a dynamic deduction channel. The static assessment channel processes financial indicators through a rule engine to output a basic credit rating. The dynamic deduction channel constructs a time-dependent graph network, with nodes representing three types of risk factors: interest rate fluctuations, commodity prices, and policy changes. Edge weights are dynamically calculated using the historical covariance matrix, and external market events are loaded through an event injection simulator to deduce the probability of a cash flow gap. The financing plan optimization unit generates the optimal combination of loan amount, term, and collateral method based on the constraint satisfaction problem solver. Its constraint set consists of a cash flow coverage ratio threshold and a financing cost upper limit, and the objective function is to minimize the risk entropy value; Dynamic risk control module, including: The model drift adaptation unit uses a transfer learning comparator to calculate the deviation of the feature distribution between real-time enterprise data and the industry benchmark model. When the deviation exceeds the adaptive threshold derived from the industry volatility, it triggers incremental model training. The counterfactual warning unit generates a set of associated virtual adverse scenarios, evaluates the robustness of the financing plan through an adversarial verification network, and outputs a warning level.

2. The intelligent financial financing decision-making system based on the distributed data weaving framework according to claim 1 is characterized in that: The process of generating the credibility weight includes: The lineage tracking engine parses the data conversion path and records the number of field-level conversion steps. The credibility base value decays with each additional conversion step. The decay coefficient is calculated based on the recent data verification error rate of the data source. Correct the credibility weight based on the timestamp conflict detection results: if the timestamp deviation between the tax declaration time and the bank transaction record exceeds the industry reasonable threshold, the weight will be reduced in proportion to the deviation; The final weight is a weighted fusion of the base value, timestamp correction coefficient, and source system authority rating. The proportion of each component in the weight fusion formula is dynamically set based on the data quality guidelines issued by financial regulatory authorities.

3. The intelligent financial financing decision-making system based on the distributed data weaving framework according to claim 1 is characterized in that: The dynamic window adjustment mechanism of the asynchronous streaming combiner includes: Real-time monitoring of the fluctuation of transmission delay of each data source. The fluctuation is quantified by calculating the delay variance within a continuous time window. When the variance exceeds the network stability baseline, the time window is increased according to a nonlinear expansion strategy. The expansion amplitude and the variance value have a piecewise functional relationship, and the turning point of the function is obtained based on historical network congestion event statistics. After the merging is completed, a time series compression algorithm based on importance is adopted, and the compression granularity is dynamically configured according to the decision accuracy requirements: fine-grained retention is used for cash flow related data, and coarse-grained aggregation is used for auxiliary indicators.

4. The intelligent financial financing decision-making system based on the distributed data weaving framework according to claim 1 is characterized in that: The edge weight calculation method of the temporal dependency graph network includes: Obtain historical time series data for each risk factor over the past preset period, with the period length dynamically selected based on industry characteristics; Calculate the dynamic conditional correlation coefficient matrix, determine the significance of the correlation coefficient through hypothesis testing, and only retain the correlation edges whose significance exceeds the confidence threshold; The direction of the edge is determined by the sign of the correlation coefficient: if the correlation is positive, the edge is connected from the dependent variable to the result variable, and if the correlation is negative, the edge is connected in the opposite direction; The absolute value of the weight is mapped through normalization of the correlation coefficient, and the mapping curve is calibrated according to the industry risk transmission intensity.

5. The intelligent financial financing decision-making system based on the distributed data weaving framework according to claim 4 is characterized in that: The shock waveform generation method in the event injection simulator includes: We crawl raw text from the news API and use the event extraction model to identify event types, impact entities, and intensity levels. The intensity level is scored based on sentiment analysis results and keyword frequency. Convert the intensity level into the initial amplitude. The amplitude is positively correlated with the industry impact coefficient. The industry impact coefficient is referenced from the predefined industry event sensitivity comparison table. The shock waveform adopts a damped oscillation model, and the decay time constant is determined by matching the event type with the median duration of the industry's historical impact.

6. The intelligent financial financing decision-making system based on the distributed data weaving framework according to claim 1 is characterized in that: The Monte Carlo verification process of the constraint satisfaction problem solver includes: At each decision node of the branch and bound method, several sets of cash flow simulation paths are generated, and the number of paths is set according to the level of completeness of the company's historical data; Calculate the constraint satisfaction for each set of paths and calculate the probability of satisfaction; The reliability threshold is inferred through the company's historical operating volatility: the higher the volatility, the stricter the threshold setting, and the specific mapping relationship is calibrated using the industry risk preference model.

7. The intelligent financial financing decision-making system based on the distributed data weaving framework according to claim 1 is characterized in that: The calculation and response mechanism of the characteristic distribution deviation degree includes: The Wasserstein distance is used to measure the distribution difference between real-time data and the benchmark model in the feature space. The feature dimensions are normalized to industry scale before distance calculation. Deviation index = normalized distance / baseline deviation, where baseline deviation is taken from the historical deviation spectrum of similar companies during industry turbulence. Incremental training adopts a federated learning framework, where each participant updates the model parameters locally and updates the global model through encrypted gradient aggregation.

8. The intelligent financial financing decision-making system based on the distributed data weaving framework according to claim 1 is characterized in that: The generation logic of the virtual adverse scenario set includes: Identify enterprise vulnerabilities: Calculate the elasticity of each risk factor to cash flow through sensitivity analysis, and select factors with elasticity exceeding the threshold as key exposure points. Generate a perturbation sequence for each exposure point, and the perturbation step size is set according to the quantile interval of the historical extreme value distribution of the factor; Establish a scenario association rule base: If two scenarios are logically mutually exclusive, they cannot be activated at the same time.

9. The intelligent financial financing decision-making system based on the distributed data weaving framework according to claim 1 is characterized in that: Also includes a federated verification feedback module: Financial institutions verify financing plans through a secure multi-party computing protocol: the bank inputs the cost of funds, the guarantor inputs the collateral discount rate, and each party locally calculates the risk value; A homomorphically encrypted gradient aggregator generates comprehensive verification results, triggering solution iteration only when the verification deviation exceeds a tolerance threshold, which is set in stages based on the financing scale. The credibility weight is introduced as a correction factor in the deviation calculation formula, and the deviation tolerance of low-credibility data sources is reduced.

10. The intelligent financial financing decision-making system based on the distributed data weaving framework according to claim 1 is characterized in that: The method for calculating the risk entropy value includes: Risk entropy = Σ(risk exposure of each component of the financing plan × diversification coefficient); Risk exposure is calculated by multiplying the probability of a cash flow shortfall by the severity of the loss; The diversification coefficient is derived from the risk factor correlation matrix of each component. The lower the correlation, the smaller the coefficient.

Images