Multi-dimensional financial pressure testing and financial early warning method and system

Abstract

The invention relates to the technical field of data processing, and discloses a multi-dimensional financial pressure testing and financial early warning method and system. The method comprises the following steps: forming a financial situation awareness data set through multi-dimensional data acquisition integration and standardization processing; adaptive weight distribution and data dimension reduction are carried out on the five dimension indexes, and a risk measurement standard spectrum is output; a third-level pressure scene is established according to the standard spectrum, a risk conduction path is analyzed, and a financial pressure fluctuation spectrum is generated; a deep learning early warning model is constructed based on the fluctuation pedigree, an early warning threshold value is determined, and a risk pulsation graph is formed; constructing a multi-level early warning system based on the pulsating diagram, and establishing a financial early warning intelligent chain; and matching historical cases based on the early warning intelligent chain, designing a prevention and control strategy, and constructing a risk solution scheme library. According to the invention, by constructing a multi-level risk assessment system, accurate identification, dynamic monitoring and intelligent disposal of financial risks are realized, and the accuracy and timeliness of risk early warning are improved.

Description

Technical Field

[0001] The present application relates to the field of data processing, and in particular to a multi-dimensional financial stress testing and financial early warning method and system. Background Art

[0002] At present, financial risk early warning has become an important part of risk management for financial institutions and enterprises. Existing financial risk early warning methods mainly include single indicator early warning method, comprehensive indicator early warning method and econometric model early warning method. Among them, the single indicator early warning method judges risks by monitoring the changes of certain key financial indicators, the comprehensive indicator early warning method conducts risk assessment by constructing a scoring system of multiple indicators, and the econometric model early warning method uses statistical methods to establish a prediction model. These methods have achieved certain results in practice, especially in the identification of risks in corporate financial status, operating efficiency and other aspects.

[0003] However, the existing technologies have the following shortcomings: First, most early warning methods only focus on static financial indicators and ignore the dynamic evolution of risks; second, the construction of the early warning indicator system lacks systematicity and fails to fully consider the correlation and transmission effect between indicators; third, the setting of early warning thresholds is often too fixed and subjective, making it difficult to adapt to the complex and changing financial environment; fourth, the formulation of risk disposal plans lacks data support and often relies on experience and judgment, making it difficult to guarantee the effectiveness of disposal; fifth, the existing early warning systems generally have lags and are unable to detect and respond to potential risks in a timely manner. Summary of the invention

[0004] The present application provides a multi-dimensional financial stress testing and financial early warning method and system, which is used to solve the technical problems in the existing financial risk early warning methods, such as single warning indicators, unclear risk transmission paths, fixed warning thresholds, and difficult evaluation of the effectiveness of disposal plans. By building a multi-level risk assessment system, it can achieve accurate identification, dynamic monitoring and intelligent disposal of financial risks, and improve the accuracy and timeliness of risk warnings.

[0005] In the first aspect, the present application provides a multi-dimensional financial stress test and financial early warning method, which includes: multi-dimensional collection and integration of corporate financial data, operating data, industry data and public opinion data, and outlier removal, missing value filling and numerical standardization to obtain a financial situation awareness data set; for the financial situation awareness data set, adaptive weight allocation is performed on indicators of five dimensions: debt repayment ability, operating ability, profitability, development ability and cash flow, and principal component extraction is combined to complete data dimensionality reduction, and a risk measurement standard spectrum is output; based on the risk measurement standard spectrum, a three-level stress test of mild, moderate and severe is established. Based on the financial risk scenario, a dynamic quantitative analysis of the risk transmission path between various indicators is conducted to generate a financial pressure fluctuation spectrum; based on the financial pressure fluctuation spectrum, an early warning model is established through time series feature extraction and deep learning, and the early warning threshold is determined by dynamic optimization to form a financial risk pulse diagram; based on the financial risk pulse diagram, a multi-level early warning system including blue warning, yellow warning, orange warning and red warning is constructed, and the risk level is determined through step-by-step transmission analysis, and a financial early warning intelligent chain is established; based on the financial early warning intelligent chain, the characteristics of historical risk disposal cases are extracted, similarity matching analysis is performed, differentiated prevention and control strategy combinations are designed, and a financial risk resolution solution library is constructed.

[0006] In a second aspect, the present application provides a multi-dimensional financial stress test and financial early warning system, the multi-dimensional financial stress test and financial early warning system comprising:

[0007] The collection module is used to collect and integrate enterprise financial data, operating data, industry data and public opinion data in multiple dimensions, and to remove outliers, fill in missing values ​​and standardize values ​​to obtain a financial situation awareness data set;

[0008] An allocation module is used to perform adaptive weight allocation on the indicators of five dimensions, namely, solvency, operating capacity, profitability, development capacity and cash flow, for the financial situation awareness data set, complete data dimensionality reduction by combining principal component extraction, and output a risk metric standard spectrum;

[0009] A quantitative module is used to establish three levels of stress scenarios, namely, mild, moderate and severe, based on the risk measurement standard spectrum, to dynamically quantify the transmission path of risks between various indicators and generate a spectrum of financial stress fluctuations;

[0010] Establish a module for establishing an early warning model based on the financial pressure fluctuation spectrum through time series feature extraction and deep learning, determining the early warning threshold by dynamic optimization, and forming a financial risk pulse diagram;

[0011] An early warning module is used to build a multi-level early warning system including blue warning, yellow warning, orange warning and red warning based on the financial risk pulse diagram, complete the risk level determination through step-by-step transmission analysis, and establish a financial early warning intelligent chain;

[0012] The matching module is used to extract the characteristics of historical risk disposal cases based on the financial early warning intelligent chain, conduct similarity matching analysis, design differentiated prevention and control strategy combinations, and construct a financial risk resolution solution library.

[0013] In the technical solution provided by this application, a comprehensive financial situation awareness data set is formed by multi-dimensional collection and integration of corporate financial data, operating data, industry data and public opinion data, combined with outlier removal, missing value filling and numerical standardization processing, which effectively improves the data quality and integrity and lays a reliable foundation for subsequent analysis. On this basis, adaptive weight allocation is performed for indicators in five dimensions of debt repayment ability, operating ability, profitability, development ability and cash flow, data dimension reduction is completed through principal component extraction, and risk metric standard spectrum is output, which significantly improves the accuracy and comprehensiveness of risk assessment. By establishing three levels of stress scenarios of mild, moderate and severe, the transmission path of risk between indicators is dynamically quantitatively analyzed, and a spectrum of financial stress fluctuations is generated, which accurately grasps the law of risk evolution and transmission mechanism. An early warning model is established based on time series feature extraction and deep learning, and the early warning threshold is determined by dynamic optimization to form a financial risk pulse diagram, which improves the timeliness and accuracy of risk warning. A multi-level early warning system including blue warning, yellow warning, orange warning and red warning is constructed, and the risk level determination is completed through step-by-step transmission analysis. A financial early warning intelligent chain is established to achieve accurate classification and effective transmission of risk warning. At the same time, by extracting the characteristics of historical risk disposal cases, conducting similarity matching analysis, designing differentiated prevention and control strategy combinations, and constructing a financial risk resolution solution library, the pertinence and effectiveness of risk disposal are improved. Overall, this method realizes the intelligence of the entire process from data collection, indicator construction, stress testing, early warning model to risk disposal, significantly improving the scientificity and efficiency of corporate financial risk management, and has important practical value for preventing and resolving corporate financial risks. While ensuring the accuracy of early warnings, the method of the present invention helps enterprises to timely discover and effectively respond to financial risks through a multi-level early warning system and differentiated prevention and control strategies, reducing risk losses and enhancing the risk resistance of enterprises. BRIEF DESCRIPTION OF THE DRAWINGS

[0014] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings required for use in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are some embodiments of the present invention. For ordinary technicians in this field, other accompanying drawings can be obtained based on these accompanying drawings without paying creative work.

[0015] Figure 1 This is a schematic diagram of an embodiment of the multi-dimensional financial stress test and financial early warning method in the embodiment of the present application;

[0016] Figure 2 This is a schematic diagram of an embodiment of a multi-dimensional financial stress test and financial early warning system in an embodiment of the present application. DETAILED DESCRIPTION

[0017] The embodiment of the present application provides a multi-dimensional financial stress test and financial early warning method and system. The terms "first", "second", "third", "fourth", etc. (if any) in the specification and claims of the present application and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence. It should be understood that the data used in this way can be interchangeable where appropriate, so that the embodiments described here can be implemented in an order other than that illustrated or described here. In addition, the terms "including" or "having" and any variations thereof are intended to cover non-exclusive inclusions, for example, a process, method, system, product or device comprising a series of steps or units is not necessarily limited to those steps or units clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0018] For ease of understanding, the specific process of the embodiment of the present application is described below. Figure 1 , an embodiment of the multi-dimensional financial stress testing and financial early warning method in the embodiment of the present application includes:

[0019] Step S101: collect and integrate enterprise financial data, operating data, industry data and public opinion data in multiple dimensions, remove outliers, fill in missing values ​​and perform numerical standardization to obtain a financial situation awareness data set;

[0020] Step S102: for the financial situation awareness data set, adaptive weight allocation is performed on the indicators of the five dimensions of debt repayment ability, operating ability, profitability, development ability and cash flow, and data dimension reduction is completed in combination with principal component extraction to output the risk metric standard spectrum;

[0021] Step S103: Establish three levels of stress scenarios, namely, mild, moderate and severe, based on the risk measurement standard spectrum, conduct dynamic quantitative analysis on the transmission path of risk between various indicators, and generate a spectrum of financial stress fluctuations;

[0022] Step S104: According to the financial pressure fluctuation spectrum, an early warning model is established through time series feature extraction and deep learning, and the early warning threshold is determined by dynamic optimization to form a financial risk pulse diagram;

[0023] Step S105: Based on the financial risk pulse chart, a multi-level warning system including blue warning, yellow warning, orange warning and red warning is constructed, and risk level determination is completed through step-by-step transmission analysis to establish a financial warning intelligent chain;

[0024] Step S106: According to the financial early warning intelligent chain, extract the characteristics of historical risk disposal cases, conduct similarity matching analysis, design differentiated prevention and control strategy combinations, and construct a financial risk resolution solution library.

[0025] It is understandable that the execution subject of the present application may be a multi-dimensional financial stress test and financial early warning system, or a terminal or a server, which is not specifically limited here. The present application embodiment is described by taking a server as the execution subject as an example.

[0026] Specifically, it starts with multi-dimensional data collection and integration. Collect financial data such as quarterly balance sheets, income statements, cash flow statements, etc. from the enterprise financial system, obtain daily operating data such as sales orders, purchase records, inventory levels, etc. from the enterprise ERP system, obtain industry average indicators, market size and other data from industry regulatory departments and statistical agencies, and capture relevant public opinion information from news media and social platforms. For the collected raw data, the Mahalanobis distance method is used to detect outliers. The Mahalanobis distance identifies outliers by calculating the standardized distance between the data point and the sample mean. Taking the company's accounts receivable turnover rate as an example, when a quarter's data deviates significantly from the historical mean, it is marked as an outlier, and the moving window mean method is used to smooth the outliers. For missing data items, the K nearest neighbor algorithm is used to fill in by finding the most similar historical data points. Finally, the indicators of different dimensions are standardized to obtain the financial situation awareness data set. When conducting multi-dimensional indicator analysis on the financial situation awareness data set, the indicators are first divided into five dimensions: the solvency dimension includes indicators such as current ratio and quick ratio, the operating capacity dimension includes indicators such as accounts receivable turnover rate and inventory turnover rate, the profitability dimension includes indicators such as return on net assets and return on total assets, the development capacity dimension includes indicators such as operating income growth rate and profit growth rate, and the cash flow dimension includes indicators such as operating activity cash flow ratio. The hierarchical analysis method is used to compare the indicators of each dimension in pairs, and the eigenvalues ​​and eigenvectors are calculated after forming a judgment matrix to obtain the weight coefficients of each dimension. The principal component analysis method is used to reduce the dimension of high-dimensional data, extract the main features, and finally form a risk measurement standard spectrum.

[0027] In the stress test phase, three-level stress scenarios are constructed based on the risk metric spectrum. The mild stress scenario sets a 10% degradation of each indicator, the moderate stress scenario sets a 20% degradation, and the severe stress scenario sets a 30% degradation. The correlation strength between indicators is analyzed through the risk transmission spectrum, and the VAR analysis method is used to quantify the degree of loss on each risk transmission path. Taking the rise in interest rates as an example, the transmission chain of increased financing costs → decreased profits → increased cash flow pressure is analyzed, the impact of each link is quantified, and finally the financial stress fluctuation spectrum is generated. In the early warning model construction phase, the time series features of the financial stress fluctuation spectrum are extracted, and the time series data is decomposed into long-term trend items, periodic fluctuation items, and random disturbance items using wavelet decomposition. The long short-term memory network (LSTM) is used to learn the time series pattern, and the key time series nodes are identified through the attention mechanism. The ROC analysis is performed on the early warning probability, and the optimal threshold combination is selected through the F1 score to form a financial risk pulse diagram.

[0028] The warning signal classification stage divides the warning level into four levels: blue warning indicates that the risk is relatively minor and needs attention; yellow warning indicates that the risk is gradually emerging and needs vigilance; orange warning indicates that the risk is significantly aggravated and intervention is required; red warning indicates that the risk is serious and needs immediate disposal. The boundaries of warnings at all levels are determined by fuzzy cluster analysis, and the transfer probability between warning levels is calculated by Markov chain analysis. The risk transmission path diagram is established, and finally a financial warning intelligent chain is formed. In the risk disposal plan design stage, the disposal experience of similar risk events is extracted from the historical case library, and the cosine similarity algorithm is used to calculate the similarity between cases, and the reference cases with high matching degree are screened out. According to the current risk characteristics, differentiated disposal plans are designed, including short-term emergency measures and medium- and long-term prevention and control strategies, and the optimal combination of plans is determined through multi-objective planning.

[0029] For example, we conduct early warning analysis of financial risks for enterprises. First, we collect the quarterly financial data, monthly operating data, average data of the same industry and relevant public opinion information of the enterprise in the past three years. After data cleaning and standardization, we get a data set containing 60 basic indicators. Through the five-dimensional indicator analysis, we find that the enterprise performs poorly in terms of operating capacity and cash flow. The accounts receivable turnover rate is 2.3 times / year, which is significantly lower than the industry average of 3.5 times / year. The cash flow ratio from operating activities is 0.85, which is lower than the safety value of 1.0. The stress test shows that under moderate stress scenarios, the cash flow indicators of enterprises will fall below the warning line. Through time series analysis, it is predicted that there will be a risk of cash flow tension in the next three months. The system issues a yellow warning signal and matches similar cases from the historical case library. Based on this, differentiated disposal plans such as accounts receivable management optimization and supply chain financing are formulated to effectively resolve potential financial risks.

[0030] In the embodiment of the present application, a comprehensive financial situation awareness data set is formed by multi-dimensional collection and integration of corporate financial data, operating data, industry data and public opinion data, combined with outlier removal, missing value filling and numerical standardization processing, which effectively improves the data quality and integrity and lays a reliable foundation for subsequent analysis. On this basis, adaptive weight allocation is performed for indicators of five dimensions, namely, solvency, operating capacity, profitability, development capacity and cash flow, and data dimension reduction is completed through principal component extraction, and the risk metric standard spectrum is output, which significantly improves the accuracy and comprehensiveness of risk assessment. By establishing three levels of stress scenarios, namely, mild, moderate and severe, a dynamic quantitative analysis of the transmission path of risk between various indicators is performed, a spectrum of financial stress fluctuations is generated, and the law of risk evolution and transmission mechanism are accurately grasped. An early warning model is established based on time series feature extraction and deep learning, and the early warning threshold is determined by dynamic optimization to form a financial risk pulse diagram, which improves the timeliness and accuracy of risk early warning. A multi-level early warning system including blue warning, yellow warning, orange warning and red warning is constructed, and the risk level determination is completed through step-by-step transmission analysis, and a financial early warning intelligent chain is established, which realizes the accurate classification and effective transmission of risk early warning. At the same time, by extracting the characteristics of historical risk disposal cases, conducting similarity matching analysis, designing differentiated prevention and control strategy combinations, and constructing a financial risk resolution solution library, the pertinence and effectiveness of risk disposal are improved. Overall, this method realizes the intelligence of the entire process from data collection, indicator construction, stress testing, early warning model to risk disposal, significantly improving the scientificity and efficiency of corporate financial risk management, and has important practical value for preventing and resolving corporate financial risks. While ensuring the accuracy of early warnings, the method of the present invention helps enterprises to timely discover and effectively respond to financial risks through a multi-level early warning system and differentiated prevention and control strategies, reducing risk losses and enhancing the risk resistance of enterprises.

[0031] In a specific embodiment, the process of executing step S101 may specifically include the following steps:

[0032] (1) Collect the company's quarterly financial statements, monthly operating data, industry averages, and market sentiment information through the data source interface to form an original data sample set, and use the Mahalanobis distance method to perform outlier detection on the original data sample set to obtain an outlier labeled data set;

[0033] (2) Based on the outlier labeled data set, the outlier data points are smoothed by moving the window mean to generate a smoothed data set, and the missing data items are interpolated and filled using the K nearest neighbor algorithm to output a complete data set;

[0034] (3) Perform maximum and minimum normalization on the different dimensional indicators in the integrity data set, calculate the standardized parameter matrix, eliminate the dimensional influence through Z-score standardization transformation, and construct a standardized feature matrix;

[0035] (4) Reconstruct the standardized feature matrix into a three-dimensional tensor structure according to the time series, identify the time dimension related features, and perform multi-scale decomposition through wavelet transform to obtain multi-level feature combinations;

[0036] (5) Extracting periodic fluctuation patterns, trend change characteristics and sudden abnormal movement signals from the multi-level feature combination, constructing a feature vector set, and screening key feature indicators through the information gain criterion to form a feature indicator library;

[0037] (6) Compare and analyze the characteristic indicator library with the preset thresholds to identify potential risk characteristics, and determine the indicator warning level in combination with time series correlation analysis to output the financial situation awareness data set.

[0038] Specifically, the financial data, operating data, industry data and public opinion data of enterprises are obtained from multiple data sources through API interfaces, database connections and web crawlers. Enterprise financial data is obtained from quarterly financial statements, including balance sheet items (such as current assets, fixed assets, current liabilities, long-term liabilities, etc.), income statement items (such as operating income, operating costs, period expenses, net profit, etc.) and cash flow statement items (such as cash flow from operating activities, cash flow from investment activities, cash flow from financing activities, etc.). Daily operating data is collected from the enterprise ERP system, including monthly sales order quantity, amount, customer information, purchase order records, supplier information, inventory level, turnover status and other data. Industry data is obtained from industry associations, regulatory agencies and statistical departments, including indicators such as industry average profit margin, market size, growth rate, etc. Public opinion data is captured from news media, social platforms and other channels through web crawlers, including text information such as enterprise-related news reports, comments, analysis reports, etc. After preliminary sorting, these data form a sample set of original data.

[0039] The Mahalanobis distance method is used to detect outliers in the original data. Mahalanobis distance is a distance calculation method that considers the correlation between variables. It is different from the general Euclidean distance. Mahalanobis distance also considers the distribution characteristics of the data. The specific calculation process is: first calculate the mean vector and covariance matrix of each indicator, and then calculate the standardized distance from each data point to the mean point. When the Mahalanobis distance of a data point exceeds the set threshold (usually the critical value of the chi-square distribution), it is marked as an outlier. After outlier detection, the outlier marked data set is obtained. For the marked outliers, the moving window mean method is used for smoothing. The moving window mean is a time series smoothing technique that replaces outliers by calculating the mean within a certain time window before and after the data point. The window size is set according to the time frequency of the data. For monthly data, it is usually 36 months, and for quarterly data, it is 24 quarters. The smoothed data can better reflect the real change trend of the indicator.

[0040] For missing values ​​in the data set, the K nearest neighbor algorithm is used for interpolation filling. The K nearest neighbor algorithm is a data filling method based on similarity. It estimates missing values ​​by finding the K historical data points that are most similar to the missing data points and taking a weighted average based on the values ​​of these neighboring points. The calculation of similarity is based on the known values ​​of other indicators. The selection of the K value is usually determined according to the data scale, and generally 35 neighboring points are taken. This method maintains the temporal correlation of the data and the correlation between indicators. In order to eliminate the dimensional differences between different indicators, the integrity data set is standardized. First, the maximum and minimum value normalization method is used to uniformly map the value range of each indicator to the [0,1] interval, maintaining the relative distribution characteristics of the data. Then, the data is converted into a standard normal distribution with a mean of 0 and a standard deviation of 1 through Zscore standardization, so that the values ​​of different indicators are comparable. After two standardization processes, the standardized feature matrix is ​​obtained.

[0041] The standardized feature matrix is ​​reconstructed into a three-dimensional tensor structure, where the three dimensions represent time, indicators, and features respectively. This structure is more conducive to capturing the time series characteristics of the data. The time series is decomposed at multiple scales by wavelet transform, and the original signal is decomposed into sub-signals of different frequencies, which is convenient for analyzing the changing characteristics of the data at different time scales. Haar wavelet or db4 wavelet is used for wavelet transform, and the number of decomposition layers is determined according to the length of the data, usually 35 layers. Three types of features are extracted from the multi-scale decomposition results: the periodic fluctuation pattern reflects the regular changes of indicators, such as seasonal fluctuations; the trend change characteristics reflect the long-term development direction; and the sudden abnormal signal indicates short-term drastic fluctuations. The most representative feature indicators are selected by the information gain criterion. The information gain measures the contribution of the feature to risk warning, and the indicators with higher information gain values ​​are selected to form a feature indicator library.

[0042] Finally, the indicator values ​​in the characteristic indicator library are compared with the preset risk thresholds, and potential risk points are identified by combining time series correlation analysis. Time series correlation analysis includes autocorrelation analysis and cross-correlation analysis, which examine the time-dependent characteristics of indicators and the leading and lagging relationships between indicators. The warning level of each indicator is determined through comprehensive evaluation, and finally a financial situation awareness data set containing risk characteristics and warning levels is output.

[0043] For example, the data source interface collected the financial statement data of a certain enterprise for the past 12 quarters and the operating data of 36 months, as well as the industry average data for the same period. After the Mahalanobis distance outlier detection, it was found that the accounts receivable turnover rate of 5.8 times / year in the 8th quarter deviated significantly from the normal level (the industry average was 3.5 times / year) and was marked as an outlier. The moving window mean of the data of the two quarters before and after, 3.8 times / year, was used to replace the outlier. For the missing inventory turnover rate data in the 5th quarter, the K nearest neighbor algorithm was used to find the three most similar historical quarterly data (4.2, 4.0, and 4.3 times / year, respectively), and the weighted average of 4.15 times / year was taken to fill in. After standardization, the data of the past 12 quarters were subjected to wavelet decomposition, and it was identified that the operating capacity indicators of the enterprise had obvious seasonal fluctuation patterns, and the fourth quarter usually increased by 1520% compared with other quarters. Through information gain analysis, the 10 core indicators that contributed the most to risk warning were screened out, including current ratio, debt-to-asset ratio, accounts receivable turnover rate, etc. By comparing and analyzing these indicators with the risk threshold, it was found that the company's recent current ratio has continued to be below the safety line of 1.5, and the cash flow ratio from operating activities has also shown a downward trend. Based on this, it is determined that the company is currently at a medium risk level.

[0044] In a specific embodiment, the process of executing step S102 may specifically include the following steps:

[0045] (1) Extract debt repayment index data items, operation index data items, profit index data items, development index data items, and cash flow index data items from the financial situation awareness data set to generate a dimensional grouping data matrix, and use the hierarchical analysis method to calculate the relative importance of each dimensional index to obtain a judgment matrix;

[0046] (2) Compare and score the five dimensional indicators pairwise according to the judgment matrix, construct a pairwise comparison data table, and calculate the weight coefficient vector of each dimension through eigenvalue decomposition to output the dimensional weight matrix;

[0047] (3) Perform weighted combination operation on the dimension weight matrix and the dimension grouping data matrix to form a comprehensive evaluation matrix, and extract the principal component eigenvectors to generate a reduced-dimensional feature space;

[0048] (4) Selecting principal components whose eigenvalue cumulative contribution rate exceeds 85% from the reduced-dimensional feature space, constructing a core feature set, and performing orthogonal transformation to obtain a reduced-dimensional data matrix;

[0049] (5) Perform standardization and variance analysis on the dimension-reduced data matrix, calculate the fluctuation range of indicators in each dimension, and use the kernel density estimation method to determine the distribution characteristics of the indicators to form the indicator characteristic spectrum;

[0050] (6) Calculate the risk scores of each dimension and the comprehensive risk score based on the indicator characteristic spectrum, set the classification threshold range, and combine it with time series correlation analysis to output the risk metric standard spectrum.

[0051] Specifically, the indicators are classified according to their nature. Debt repayment dimension indicators include current ratio, quick ratio, cash ratio, and debt-to-asset ratio; operating dimension indicators include accounts receivable turnover, inventory turnover, and total asset turnover; profitability dimension indicators include gross profit margin, net profit margin, return on total assets, and return on net assets; development dimension indicators include operating income growth rate, net profit growth rate, and total asset growth rate; cash flow dimension indicators include operating cash flow ratio, cash flow debt ratio, etc. These indicators are organized into a matrix form according to the dimensions, where rows represent different time points and columns represent indicators of each dimension. In the hierarchical analysis process, the five dimensional indicators are compared pairwise based on the importance of the indicators to the financial risks of the enterprise, and a judgment matrix is ​​constructed. The comparison standard adopts the 19-scale method, where 1 means that the two indicators are equally important, 3 means that one indicator is slightly more important than the other, 5 means obviously important, 7 means strongly important, 9 means extremely important, and 2, 4, 6, and 8 are the corresponding intermediate values. Through expert evaluation and historical data analysis, each dimension is scored to form a judgment matrix. For example, in the current economic environment, the debt-paying ability and cash flow dimensions are more important than the operating ability dimension and are given corresponding higher scores.

[0052] Based on the judgment matrix, the eigenvalues ​​and eigenvectors are calculated. The eigenvector corresponding to the maximum eigenvalue is the weight coefficient of each dimension. The weight calculation must meet the consistency test requirements, that is, the consistency ratio CR is less than 0.1 to ensure the rationality of the weight distribution. The obtained weight vectors constitute the dimension weight matrix, which reflects the relative importance of each dimension indicator in the comprehensive evaluation. The dimension weight matrix is ​​multiplied by the dimension grouping data matrix, and a weighted combination operation is performed to obtain a comprehensive evaluation matrix. This matrix not only retains the information of the original data, but also reflects the importance of each dimension. In order to reduce the data dimension, the principal component eigenvector is extracted. Principal component analysis is based on eigenvalue decomposition, which projects the original high-dimensional data into a low-dimensional feature space while maintaining the main information of the data.

[0053] Select principal components with a cumulative contribution rate of more than 85% from the reduced-dimensional feature space. These principal components can explain most of the information of the original data variation. Perform an orthogonal transformation on the selected principal components to make each principal component independent of each other, eliminate the correlation between indicators, and obtain a reduced-dimensional data matrix. This process effectively reduces the data dimension while maintaining key information. Standardize the reduced-dimensional data matrix so that each indicator has the same metric. Calculate the fluctuation range of indicators in each dimension through variance analysis to determine the range and stability of the indicator. Use kernel density estimation to analyze the distribution characteristics of the indicator. Kernel density estimation is a non-parametric estimation method that can reflect the probability distribution characteristics of the indicator and is not restricted by the normal distribution assumption. By setting appropriate bandwidth parameters, the density function of the indicator is estimated to form the characteristic spectrum of the indicator.

[0054] Finally, the risk score of each dimension is calculated based on the characteristic spectrum of the indicator. The risk score takes into account the current value, historical trend and fluctuation of the indicator, and sets the classification threshold interval in combination with industry standards. Through time series correlation analysis, the leading and lagging relationship and linkage effect between indicators are examined, and finally the risk metric spectrum containing the risk rating of each dimension is output.

[0055] For example, 20 key financial indicators were extracted from the financial situation awareness data set, which belong to five dimensions. The five dimensions were compared and scored in pairs by the hierarchical analysis method. Under the 9-point scoring system, the importance of the solvency dimension was scored as 7 relative to the operating ability dimension, and the score relative to the development ability dimension was 5. The final calculation obtained the weight of the solvency dimension as 0.35, the weight of the cash flow dimension as 0.25, the weight of the profitability dimension as 0.20, the weight of the operating ability dimension as 0.12, and the weight of the development ability dimension as 0.08. These weights were multiplied with the original data matrix to obtain a comprehensive evaluation matrix. Through principal component analysis, the first four principal components were selected, and the cumulative contribution rate reached 87%, which effectively reduced the data dimension. After kernel density estimation analysis, the distribution characteristics of each indicator were determined. For example, the kernel density curve of the current ratio showed a bimodal distribution, reflecting that there were two typical levels of corporate liquidity. The final risk metric spectrum showed that the company's risk score in the solvency dimension was 75 points (high risk), the risk score in the cash flow dimension was 68 points (medium risk), the risks in other dimensions were relatively low, and the comprehensive risk level was above medium.

[0056] In a specific embodiment, the process of executing step S103 may specifically include the following steps:

[0057] (1) Extract the indicator risk distribution characteristics and risk level thresholds from the risk metric spectrum, set the stress scenario parameter range, and identify key risk events through historical simulation to obtain the stress scenario benchmark set;

[0058] (2) Based on the stress scenario benchmark set, three levels of stress conditions, namely mild, moderate and severe, are constructed to form a scenario variable combination table. The risk transmission map is used to analyze the correlation strength between indicators and generate an indicator correlation matrix.

[0059] (3) Conduct network topology analysis on the indicator association matrix, extract the risk node transmission path, construct a pressure transmission network, and use the VAR analysis method to quantify the risk loss on each path and output a transmission intensity table;

[0060] (4) Sort the risk transmission nodes according to the transmission intensity table, construct the risk transmission sequence, and calculate the risk accumulation effect of each node using a sliding time window to obtain the risk evolution matrix;

[0061] (5) Superimpose the risk evolution matrix and the stress scenario variables to calculate the risk shock fluctuations under each scenario, and estimate the distribution of fluctuation amplitudes to form a fluctuation feature set;

[0062] (6) Perform time series decomposition and trend extraction on the volatility feature set to reveal the law of risk evolution, and combine it with the risk transmission effect analysis to generate the financial stress fluctuation spectrum.

[0063] Specifically, the risk distribution characteristics of each indicator are extracted from the risk metric spectrum, which reflect the fluctuation law of the indicator over time. By analyzing the historical data distribution of the indicator, including statistical characteristics such as mean, standard deviation, skewness, and kurtosis, the normal fluctuation range and risk threshold of the indicator are determined. For each risk indicator, the parameter value interval under different degrees of stress conditions is set. For example, for the asset-liability ratio indicator, according to industry characteristics and historical data, the mild stress interval is set to 60%-70%, the moderate stress interval is set to 70%-80%, and the severe stress interval is set to more than 80%. Through the historical simulation method, key risk events that significantly affect the financial status of the enterprise are identified from historical data, such as industry cyclical fluctuations, major policy changes, etc. These events constitute the benchmark set of stress scenarios. Based on the stress scenario benchmark set, three-level stress scenarios are constructed. Under the mild stress scenario, each risk indicator deteriorates by about 10% relative to the normal level; under the moderate stress scenario, the indicator deteriorates by about 20%; under the severe stress scenario, the indicator deteriorates by more than 30%. The correlation between indicators is analyzed through the risk transmission map, and the correlation coefficient between indicators is calculated to form an indicator correlation matrix. Each element in the matrix represents the correlation strength between two indicators, and the value range is [1,1]. The larger the absolute value, the stronger the correlation.

[0064] The indicator association matrix is ​​studied by network topology analysis method. Each risk indicator is regarded as a network node. The strength of association between indicators is used as the connection weight between nodes to extract the main risk transmission path. The vector autoregression (VAR) analysis method is used to quantitatively analyze these paths to measure the degree of loss of risk in the transmission process. The VAR model takes into account the dynamic relationship between variables and can describe how the change of one variable affects other variables. In actual operation, the appropriate lag order is first selected, then the model parameters are estimated, and finally the impact response function is calculated to obtain the quantitative relationship of risk transmission. For example, when the debt-to-asset ratio increases by 1 percentage point, the degree of impact and time lag effect on indicators such as the current ratio and working capital turnover rate. The final transmission intensity table contains the following key information: risk source indicators, affected indicators, transmission coefficients, time lags, cumulative effects, etc. According to the transmission intensity table, the importance of risk transmission nodes is ranked, and the out-degree (the degree of influence on other nodes) and in-degree (the degree of influence by other nodes) of the node are considered to construct the risk transmission sequence. A 12-month sliding time window is used to calculate the risk cumulative effect of each node in different time periods to obtain the risk evolution matrix. The matrix reflects the accumulation process and diffusion characteristics of risks in the time dimension.

[0065] The risk evolution matrix is ​​combined with the stress scenario variables to analyze the risk shock effects under different stress scenarios. The volatility of the indicators under each scenario is calculated, and the probability distribution characteristics of the volatility are estimated using statistical methods to form a volatility feature set. This feature set contains information such as the intensity, duration, and impact range of the risk shock. Finally, the volatility feature set is decomposed in time series to extract long-term trend components and cyclical volatility components to reveal the inherent laws of risk evolution. Combined with the previous risk transmission effect analysis, a financial stress volatility spectrum is finally generated, which comprehensively describes the risk status and evolution trend of enterprises under different stress scenarios.

[0066] For example: First, from the quarterly data of a certain company in the past three years, two key risk events were identified: one was the cost pressure caused by the sharp rise in upstream raw material prices (causing the gross profit margin to drop by 8 percentage points), and the other was the revenue decline caused by the shrinking industry demand (a decrease of 15% month-on-month). Based on these historical data, the specific parameters of the three-level stress scenario were set: in the mild stress scenario, the operating income dropped by 10% and the gross profit margin dropped by 5 percentage points; in the moderate stress scenario, the respective decreases were 20% and 10 percentage points; in the severe stress scenario, the respective decreases were 30% and 15 percentage points. For example, the relevant transmission intensity table is as follows:

[0067] Conductivity Table

[0068]

[0069]

[0070] Through VAR analysis, it was found that every 1 percentage point decrease in gross profit margin will lead to a 0.85 percentage point decrease in net profit margin within 1 month, and the cumulative effect will reach 0.92; every 1 percentage point decrease in net profit margin will lead to a 0.72% decrease in operating cash flow within 2 months, and the cumulative effect will be 0.65.

[0071] In a specific embodiment, the process of executing step S104 may specifically include the following steps:

[0072] (1) Extract the time series variation characteristics, fluctuation cycle characteristics and intensity characteristics from the financial pressure fluctuation spectrum, build a time series feature database, and perform multi-scale analysis on the time series data through wavelet decomposition method to obtain the feature decomposition sequence;

[0073] (2) Based on the characteristic decomposition sequence, long-term trend terms, periodic fluctuation terms and random disturbance terms are extracted to generate a time series component matrix, and the long short-term memory network is used to learn the time series pattern to obtain the risk evolution law matrix;

[0074] (3) Input the risk evolution law matrix into the attention mechanism layer, extract the key time series node features, construct the attention weight vector, and calculate the risk warning probability in combination with the historical warning data, and output the warning probability sequence;

[0075] (4) Perform probability distribution analysis on the warning probability sequence, calculate the risk density of different probability intervals, construct the risk distribution curve, and determine the candidate threshold set through ROC analysis to obtain the threshold evaluation matrix;

[0076] (5) Based on the threshold evaluation matrix, the underreporting rate and false alarm rate indicators are counted, a threshold evaluation table is constructed, and the best threshold combination is screened by the F1 score to form a dynamic threshold sequence;

[0077] (6) Compare and analyze the dynamic threshold sequence and warning probability, mark the risk warning points, draw the risk pulse curve, and generate a financial risk pulse chart.

[0078] Specifically, the risk evolution law matrix is ​​input into the attention mechanism layer for processing. The attention mechanism automatically identifies key time points by calculating the importance weights of different time series nodes. For each time step, its correlation score with other time steps is calculated to form an attention weight vector. These weights reflect the degree of influence of different time points on the current warning status. The larger the value, the more significant the influence. Combined with historical warning data, the risk warning probability at each time point is calculated based on weighted voting to generate a warning probability sequence.

[0079] When performing distribution analysis on the warning probability sequence, the kernel density estimation method is used to calculate the risk density of different probability intervals. The calculation formula is:

[0080]

[0081] Where: R(x) is the density value at the risk probability x, m is the number of risk probability intervals, β k is the weight coefficient of the kth probability interval, θ k is the standard deviation parameter of the probability distribution, γ k is the mean parameter of the probability distribution, λ k is the interval importance factor, ψ(v k ) is the timing correction factor, x is the probability value, v k is the interval index.

[0082] Based on the calculated risk distribution curve, the candidate threshold set is determined by ROC (receiver operating characteristic) analysis. ROC analysis screens out a set of candidate thresholds that balance the accuracy and timeliness of early warning by drawing the relationship curve between the true positive rate and the false positive rate under different thresholds, forming a threshold evaluation matrix. The threshold evaluation matrix records the evaluation indicators corresponding to each candidate threshold. Based on the threshold evaluation matrix, the false negative rate (the proportion of early warnings that fail to be issued in time) and the false positive rate (the proportion of early warnings issued in error) of each candidate threshold are calculated to construct a threshold evaluation table. The optimal threshold combination is screened by the F1 score (the harmonic mean of the precision and recall rate). The higher the F1 score, the more reasonable the threshold setting. The threshold combination with the highest F1 score is selected to form a dynamic threshold sequence. These thresholds will be dynamically adjusted with the input of new data to maintain the accuracy of the early warning.

[0083] Compare the dynamic threshold sequence with the real-time calculated warning probability, and mark it as a risk warning point when the warning probability exceeds the corresponding threshold. Connect these warning points and draw a risk pulse curve that reflects the change in risk intensity, and finally generate a financial risk pulse diagram that includes the distribution of risk warning points, risk intensity changes, and warning levels.

[0084] For example, we conduct a financial risk early warning analysis: first, we extract time series features from the company's 36-month financial data, and through wavelet decomposition, we find that its working capital turnover rate has obvious seasonal fluctuations (significantly increased by about 25% in the fourth quarter of each year) and a long-term downward trend (an average annual decline of 5%). The LSTM network automatically captures this composite pattern during the training process and predicts the trend of change in the next three months. The attention mechanism analysis shows that the first and third months are key time nodes, with weights of 0.4 and 0.3 respectively. Combined with historical data, the early warning probabilities of these two time points are calculated to be 0.75 and 0.82 respectively. The risk distribution is calculated by kernel density estimation, and the risk density is highest in the two probability intervals of 0.70.8 and 0.80.9, which are 0.35 and 0.42 respectively. The optimal threshold obtained by ROC analysis is 0.78 (F1 score 0.86). Comparing this threshold with the early warning probability, it is found that a high-risk early warning signal appears in the third month. Finally, the early warning point is marked on the financial risk pulse chart, and a specific risk warning of "the turnover rate of accounts receivable continues to deteriorate and the cash flow pressure increases" is given. This early warning signal prompted the company to adjust its credit policy in a timely manner and avoid potential capital chain risks.

[0085] In a specific embodiment, the process of executing step S105 may specifically include the following steps:

[0086] (1) Extract the risk intensity characteristics, risk frequency characteristics and risk persistence characteristics from the financial risk pulse diagram, construct a three-dimensional matrix of risk characteristics, and determine the boundaries of the four-level warning through fuzzy cluster analysis to obtain the warning level zoning map;

[0087] (2) According to the warning level zoning map, the blue warning interval, yellow warning interval, orange warning interval and red warning interval are divided, and the cross-interval migration rules are set. The transition probability between each interval is calculated through Markov chain analysis to form a warning level migration matrix;

[0088] (3) Compare and verify the warning level migration matrix with historical risk evolution data, extract the critical features of risk upgrade and downgrade, build a warning level determination rule base, and establish a risk transmission path diagram in combination with time series correlation analysis to output the risk transmission network;

[0089] (4) Decompose the risk transmission network hierarchically, identify the key nodes of risk transmission, calculate the node impact intensity and transmission speed, construct a transmission effect evaluation table, and determine the risk diffusion scope through complex network analysis to generate a risk spread map;

[0090] (5) Establish a hierarchical response mechanism for early warning signals based on the risk spread map, calculate the risk accumulation effect under different warning levels, construct a risk accumulation intensity curve, and identify risk resonance points through spatiotemporal correlation analysis to form an early warning signal transmission chain;

[0091] (6) Design a multi-level signal confirmation process for the early warning signal transmission chain, extract the characteristics of the risk evolution pattern, integrate multi-dimensional early warning information, establish a risk early warning linkage mechanism, and generate a financial early warning intelligent chain.

[0092] Specifically, when extracting feature data from the financial risk pulse chart, it is necessary to analyze risk indicators in three dimensions: risk intensity features include the size and variation of risk values, risk frequency features include the frequency and interval of risk events, and risk persistence features include the duration and impact of a single risk event. These features are organized into a three-dimensional matrix structure, in which the three dimensions of the matrix correspond to risk intensity, frequency, and persistence, and each dimension contains multiple subdivision indicators. The fuzzy clustering analysis method is used to analyze the three-dimensional feature matrix, and the risk status is divided into four levels by calculating the membership between sample points. Fuzzy clustering uses an improved FCM algorithm to determine the cluster center through iterative optimization, and finally obtains the boundary thresholds of the four risk levels to form a warning level partition map.

[0093] Based on the warning level zoning map, the risk state space is divided into four intervals: blue warning interval (relatively mild risk), yellow warning interval (risk gradually emerging), orange warning interval (risk significantly aggravated) and red warning interval (serious risk). Each interval has a clear boundary value and feature description. At the same time, the migration rules between intervals are formulated, and the conditions for risk level upgrade and downgrade are stipulated. Using the Markov chain analysis method, the transition probability between any two warning levels is calculated based on historical data to form a 4×4 warning level migration matrix. Each element in the matrix represents the probability of transferring from one risk level to another. The warning level migration matrix is ​​compared and analyzed with the historical risk evolution data to identify the key features when the risk level changes. The acceleration factors and triggering conditions in the risk upgrade process, as well as the mitigation measures and effect characteristics in the risk downgrade process, are extracted to establish a warning level determination rule base. Combined with the time series correlation analysis, a path diagram reflecting the risk transmission process between indicators of different dimensions is constructed to form a complete risk transmission network.

[0094] The risk transmission network is decomposed at multiple levels, and the risk nodes are divided into primary transmission nodes, secondary transmission nodes, and terminal impact nodes according to the transmission level. The key nodes in each level are identified, and the influence intensity (based on out-degree centrality) and transmission speed (based on path length) of the nodes are calculated to form a transmission effect evaluation table. Through complex network analysis methods, the topological characteristics of the network such as clustering coefficient and average path length are calculated to determine the spread range and speed of the risk, and generate an intuitive risk spread map. On the basis of the risk spread map, a hierarchical response mechanism is established. Corresponding response measures and disposal processes are set for different warning levels, and the cumulative effects of risks at all levels are calculated. A risk accumulation intensity curve is constructed to reflect the changing trend of risk accumulation over time. Through spatiotemporal correlation analysis, risk resonance points are identified, that is, the superposition points of multiple risk factors in time and space. These points are often the key nodes for concentrated risk outbreaks. Finally, a complete warning signal transmission chain is formed, which clearly shows the transmission path and evolution process of risks.

[0095] Design a multi-level signal confirmation process around the early warning signal transmission chain, including three levels: initial signal judgment, cross-validation, and comprehensive judgment. Extract typical pattern characteristics of risk evolution, including different types such as sudden, gradual, and cyclical. Integrate early warning information from different dimensions, establish a cross-validation mechanism and linkage response mechanism for early warning signals, and finally generate a complete financial early warning intelligent chain.

[0096] For example, firstly, feature data was extracted from the risk pulse chart of the enterprise in the past three years, and it was found that the average value of its risk intensity index was 0.65 (full score 1.0), risk events occurred 2.3 times per quarter on average, and the average duration of a single risk was 18 days. Through fuzzy cluster analysis, the risk status was divided into four levels: risk values ​​of 00.3 were blue warnings, 0.30.6 were yellow warnings, 0.60.8 were orange warnings, and 0.8 and above were red warnings. Through Markov chain analysis, it was found that the probability of upgrading from yellow warning to orange warning was 0.25, and the probability of downgrading from orange to yellow was 0.15.

[0097] In the risk transmission network analysis, it was found that the debt-to-asset ratio is a first-level transmission node, and its changes will be transmitted to the current ratio (impact intensity 0.8) and interest coverage ratio (impact intensity 0.6) within 2 months. The key path of risk diffusion is determined through complex network analysis: the debt-to-asset ratio increases → financing costs increase → profits decrease → cash flow pressure increases → debt repayment capacity decreases. In a certain risk event, the debt-to-asset ratio increased from 65% to 75%, triggering an orange warning. It is calculated that the cumulative effect of the risk will cause the operating cash flow to decrease by 20% within 3 months. Based on this early warning signal, the company adjusted its debt structure in a timely manner and successfully reduced the risk level to the yellow warning range by optimizing working capital management and other measures. This case demonstrates the effective application of this method in actual risk warning.

[0098] In a specific embodiment, the process of executing step S106 may specifically include the following steps:

[0099] (1) Obtain historical financial risk case data from the financial early warning intelligent chain, extract risk occurrence scenarios, transmission characteristics, disposal methods, and effect evaluation factors, construct case feature vector sets, and identify key risk description information through semantic analysis to obtain a risk case knowledge base;

[0100] (2) Based on the risk case knowledge base, a case similarity calculation matrix is ​​constructed to quantitatively characterize risk characteristics, statistically analyze risk treatment effect indicators, and calculate the similarity between cases using the cosine similarity algorithm to form a case matching sequence;

[0101] (3) Sort the case matching sequence by similarity, select the candidate case set with a matching degree exceeding the threshold, establish a disposal plan association map, and perform scenario mapping based on the current risk characteristics to output a strategy reference plan;

[0102] (4) Conduct scenario adaptability analysis on the strategic reference schemes, evaluate the feasibility of the schemes and the degree of risk mitigation, build a scheme evaluation index system, and determine the optimal scheme combination through multi-objective planning to generate a prevention and control strategy library;

[0103] (5) Design differentiated implementation paths based on the prevention and control strategy library, formulate short-term emergency measures and medium- and long-term prevention and control measures, construct a prevention and control strategy deployment map, and establish a feedback correction mechanism through effect tracking and analysis to form a dynamic adjustment sequence;

[0104] (6) Conduct continuous monitoring and regular evaluation of the dynamic adjustment sequence, optimize the implementation plan of the prevention and control strategy, update the risk management experience database, and build an intelligent risk resolution solution database.

[0105] Specifically, in the multi-dimensional financial stress testing and financial early warning method, the financial early warning intelligent chain obtains historical financial risk case data through automated data collection and processing technology. These data contain key elements such as risk occurrence scenarios, transmission characteristics, disposal methods and effect evaluation, which are converted into structured data through text feature extraction technology to form a case feature vector set. Specifically, for each historical case, natural language processing technology is used to perform word segmentation, part-of-speech tagging and syntactic analysis on the case description, extract keywords and phrases, and construct a semantic network. The TFIDF algorithm is used to calculate the importance weight of words, identify risk key description information, such as "tight cash flow" and "decline in accounts receivable turnover", and integrate these features into the knowledge graph to establish a risk case knowledge base. Based on the constructed risk case knowledge base, the cosine similarity algorithm is used to calculate the similarity between different cases. First, each case is represented as a feature vector, and the vector dimensions include financial indicators, risk types, disposal methods and other features. The qualitative description is converted into quantitative indicators through numerical processing. Standardization is used to eliminate the impact of dimensions for financial indicators, and unique hot encoding is used to represent qualitative features. Then calculate the cosine similarity between any two case vectors to obtain the case similarity matrix. At the same time, count the treatment effect indicators of each historical case, including risk relief time, cost input, impact on business operations and other dimensions to form an effect evaluation vector. Combine the similarity matrix with the effect evaluation vector to generate a case matching sequence. Arrange the case matching sequence in descending order, set the similarity threshold to 0.8, and filter out the candidate case set whose similarity exceeds the threshold. Use graph database technology to construct a treatment plan association map. The nodes in the map represent different treatment plans, the edges represent the association between the plans, and the weights of the edges reflect the strength of the association. Combined with the specific risk characteristics currently faced by the enterprise, a path search is performed in the map to find the treatment plan combination adopted by the most similar case as a strategic reference plan.

[0106] A multi-dimensional evaluation method is used to analyze the scenario adaptability of the strategic reference solution. First, the feasibility dimension is evaluated, including technical feasibility, resource feasibility and time feasibility, and several specific indicators are set for each aspect. The technical feasibility evaluation determines whether there are technical barriers to the implementation of the solution, the resource feasibility evaluation determines whether the enterprise has the required human, material and financial support, and the time feasibility evaluation determines whether the solution can be implemented before the risk ferments. Secondly, the risk elimination degree dimension is evaluated, including the thoroughness of risk elimination, side effect control, and sustainability. Finally, the cost-benefit dimension is evaluated to calculate the various costs and expected benefits of the solution implementation. These evaluation dimensions are integrated into a unified solution evaluation index system, the hierarchical analysis method is used to determine the weight of each indicator, and the optimal solution combination is solved through a multi-objective programming model.

[0107] Based on the optimal combination of solutions, formulate differentiated implementation paths. Short-term emergency measures focus on rapid risk mitigation, such as replenishing liquidity through emergency financing and accelerating the recovery of accounts receivable; medium- and long-term prevention and control measures focus on fundamentally solving problems, such as optimizing business structure and improving management mechanisms. Decompose these measures according to the time dimension and functional dimension, draw a prevention and control strategy deployment map, and clarify the responsible parties, implementation steps and time nodes of each measure. Establish an implementation effect tracking mechanism, regularly collect key indicator data, analyze the implementation of measures and risk change trends, and form a dynamic adjustment sequence. Continuously monitor and regularly evaluate the dynamic adjustment sequence, and track risk changes and measure implementation effects in real time. Design evaluation indicators including the degree of improvement of risk indicators, progress of measure implementation, and resource investment. Timely warn of deviations from expectations, analyze the causes and adjust the optimization plan. Summarize and refine successful experiences and lessons from failures, update the risk disposal experience library, and continuously improve and enrich the intelligent risk resolution solution library.

[0108] For example, a high-tech manufacturing company had a financial risk warning in the third quarter of 2023. Through the analysis of the financial warning intelligent chain, it was found that the company had problems such as a continuous decline in accounts receivable turnover, negative operating cash flow, and rising debt-to-asset ratio. The system retrieved similar cases from the risk case knowledge base and extracted three historical cases with a similarity of more than 0.8, involving manufacturing companies that encountered similar problems between 2019 and 2022. By analyzing the measures taken and the effects of these cases, initial strategic recommendations were generated. After scenario adaptability analysis, the feasibility of each recommendation was evaluated, including factors such as the company's existing financial situation, customer structure characteristics, and industry development trends, and a combination of "strengthening accounts receivable management + optimizing inventory levels + adjusting financing structure" was screened and formed. Specific measures include: (1) implementing a special action to collect accounts receivable in the short term, focusing on collecting accounts receivable with a payment period of more than 90 days, and at the same time giving appropriate concessions to cash and spot customers to improve operating cash flow; (2) conducting refined management of inventory, disposing of overstocked inventory, and reducing the funds occupied by inventory; (3) carrying out bank-enterprise cooperation, replacing some short-term loans with medium- and long-term loans, and optimizing the debt structure. Through continuous tracking and analysis, it was found that after taking the above measures, the company's accounts receivable turnover rate increased by 15% in the fourth quarter, operating cash flow turned from negative to positive, and the asset-liability ratio decreased by 3 percentage points, confirming the effectiveness of the plan. These practical experiences were timely summarized and refined, updated to the risk disposal experience library, and further improved the risk resolution solution library.

[0109] The above describes the multi-dimensional financial stress test and financial early warning method in the embodiment of the present application. The following describes the multi-dimensional financial stress test and financial early warning system in the embodiment of the present application. Figure 2 In the embodiment of the present application, an embodiment of the multi-dimensional financial stress test and financial early warning system includes:

[0110] The collection module is used to collect and integrate enterprise financial data, operating data, industry data and public opinion data in multiple dimensions, and to remove outliers, fill in missing values ​​and standardize values ​​to obtain a financial situation awareness data set;

[0111] An allocation module is used to perform adaptive weight allocation on the indicators of five dimensions, namely, solvency, operating capacity, profitability, development capacity and cash flow, for the financial situation awareness data set, complete data dimensionality reduction by combining principal component extraction, and output a risk metric standard spectrum;

[0112] A quantitative module is used to establish three levels of stress scenarios, namely, mild, moderate and severe, based on the risk measurement standard spectrum, to dynamically quantify the transmission path of risks between various indicators and generate a spectrum of financial stress fluctuations;

[0113] Establish a module for establishing an early warning model based on the financial pressure fluctuation spectrum through time series feature extraction and deep learning, determining the early warning threshold by dynamic optimization, and forming a financial risk pulse diagram;

[0114] An early warning module is used to build a multi-level early warning system including blue warning, yellow warning, orange warning and red warning based on the financial risk pulse diagram, complete the risk level determination through step-by-step transmission analysis, and establish a financial early warning intelligent chain;

[0115] The matching module is used to extract the characteristics of historical risk disposal cases based on the financial early warning intelligent chain, conduct similarity matching analysis, design differentiated prevention and control strategy combinations, and construct a financial risk resolution solution library.

[0116] Through the collaboration of the above components, through multi-dimensional collection and integration of corporate financial data, operating data, industry data and public opinion data, combined with outlier removal, missing value filling and numerical standardization, a comprehensive financial situation awareness data set is formed, which effectively improves the data quality and integrity, and lays a solid foundation for subsequent analysis. On this basis, adaptive weight allocation is performed for indicators in five dimensions: debt repayment ability, operating ability, profitability, development ability and cash flow, and data dimension reduction is completed through principal component extraction. The risk metric standard spectrum is output, which significantly improves the accuracy and comprehensiveness of risk assessment. By establishing three levels of stress scenarios of mild, moderate and severe, the transmission path of risk between various indicators is dynamically quantitatively analyzed, and the spectrum of financial stress fluctuations is generated, which accurately grasps the law of risk evolution and transmission mechanism. Based on time series feature extraction and deep learning, an early warning model is established, and the early warning threshold is determined by dynamic optimization to form a financial risk pulse diagram, which improves the timeliness and accuracy of risk warning. A multi-level early warning system including blue warning, yellow warning, orange warning and red warning is constructed, and the risk level is determined through step-by-step transmission analysis. A financial early warning intelligent chain is established to achieve accurate classification and effective transmission of risk warning. At the same time, by extracting the characteristics of historical risk disposal cases, conducting similarity matching analysis, designing differentiated prevention and control strategy combinations, and constructing a financial risk resolution solution library, the pertinence and effectiveness of risk disposal are improved. Overall, this method realizes the intelligence of the entire process from data collection, indicator construction, stress testing, early warning model to risk disposal, significantly improving the scientificity and efficiency of corporate financial risk management, and has important practical value for preventing and resolving corporate financial risks. While ensuring the accuracy of early warnings, the method of the present invention helps enterprises to timely discover and effectively respond to financial risks through a multi-level early warning system and differentiated prevention and control strategies, reducing risk losses and enhancing the risk resistance of enterprises.

[0117] As described above, the above embodiments are only used to illustrate the technical solutions of the present application, rather than to limit them. Although the present application has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present application.

Claims

1. A multi-dimensional financial stress testing and financial early warning method, characterized in that: The multi-dimensional financial stress testing and financial early warning method includes: Collect and integrate corporate financial data, operating data, industry data and public opinion data in multiple dimensions, remove outliers, fill in missing values ​​and standardize values ​​to obtain a financial situation awareness data set; For the financial situation awareness data set, adaptive weight allocation is performed on the indicators of five dimensions, namely, solvency, operating capacity, profitability, development capacity and cash flow, and principal component extraction is combined to complete data dimensionality reduction, and a risk metric standard spectrum is output; Based on the risk measurement standard spectrum, three levels of stress scenarios, namely mild, moderate and severe, are established, and the transmission paths of risks between various indicators are dynamically quantitatively analyzed to generate a spectrum of financial stress fluctuations; According to the financial pressure fluctuation spectrum, an early warning model is established through time series feature extraction and deep learning, and the early warning threshold is determined by dynamic optimization to form a financial risk pulse diagram; Based on the financial risk pulse chart, a multi-level warning system including blue warning, yellow warning, orange warning and red warning is constructed, and the risk level is determined through step-by-step transmission analysis to establish a financial warning intelligent chain; Based on the financial early warning intelligent chain, the characteristics of historical risk disposal cases are extracted, similarity matching analysis is performed, differentiated prevention and control strategy combinations are designed, and a financial risk resolution solution library is constructed.

2. The multi-dimensional financial stress testing and financial early warning method according to claim 1 is characterized in that: The multi-dimensional collection and integration of corporate financial data, operating data, industry data and public opinion data, as well as outlier removal, missing value filling and numerical standardization processing are performed to obtain a financial situation awareness data set, including: Collect corporate quarterly financial statements, monthly operating data, industry averages, and market sentiment information through the data source interface to form an original data sample set, and use the Mahalanobis distance method to perform outlier detection on the original data sample set to obtain an outlier labeled data set; According to the outlier labeled data set, the outlier data points are smoothed by moving the window mean to generate a smoothed data set, and the missing data items are interpolated and filled by using the K nearest neighbor algorithm to output a complete data set; Performing maximum and minimum value normalization processing on the different dimensional indicators in the integrity data set, calculating the standardized parameter matrix, and eliminating the dimensional influence through Z-score standardized transformation to construct a standardized feature matrix; Reconstructing the standardized feature matrix into a three-dimensional tensor structure according to the time series, identifying the time dimension related features, and performing multi-scale decomposition through wavelet transform to obtain a multi-level feature combination; Extracting periodic fluctuation patterns, trend change characteristics and sudden abnormal movement signals from the multi-level feature combination, constructing a feature vector set, and screening key feature indicators through the information gain criterion to form a feature indicator library; The characteristic indicator library is compared and analyzed with the preset threshold value to identify potential risk characteristics, and the indicator warning level is determined in combination with time series correlation analysis to output the financial situation awareness data set.

3. The multi-dimensional financial stress testing and financial early warning method according to claim 1 is characterized in that: For the financial situation awareness data set, adaptive weight allocation is performed on the indicators of the five dimensions of debt repayment ability, operating ability, profitability, development ability and cash flow, and data dimension reduction is completed in combination with principal component extraction to output the risk metric standard spectrum, including: Extracting debt repayment index data items, operation index data items, profit index data items, development index data items and cash flow index data items from the financial situation awareness data set, generating a dimensional grouping data matrix, and using a hierarchical analysis method to calculate the relative importance between the dimensional indicators to obtain a judgment matrix; According to the judgment matrix, the five dimensional indicators are compared and scored in pairs, a pairwise comparison data table is constructed, and the weight coefficient vector of each dimension is calculated by eigenvalue decomposition, and the dimensional weight matrix is ​​output; Performing a weighted combination operation on the dimension weight matrix and the dimension grouping data matrix to form a comprehensive evaluation matrix, and extracting the principal component eigenvectors to generate a dimensionality reduction feature space; Selecting principal components whose eigenvalue cumulative contribution rate exceeds 85% from the reduced-dimensional feature space, constructing a core feature set, and performing orthogonal transformation processing to obtain a reduced-dimensional data matrix; The dimension-reduced data matrix is ​​subjected to standardization and variance analysis, the fluctuation range of each dimension indicator is calculated, and the distribution characteristics of the indicator are determined by using the kernel density estimation method to form an indicator characteristic spectrum; The risk scores of each dimension and the comprehensive risk score are calculated for the indicator characteristic spectrum, the classification threshold interval is set, and the risk metric standard spectrum is output in combination with time series correlation analysis.

4. The multi-dimensional financial stress testing and financial early warning method according to claim 1 is characterized in that: According to the risk measurement standard spectrum, three levels of stress scenarios, namely, mild, moderate and severe, are established, and the transmission paths of risks between various indicators are dynamically quantitatively analyzed to generate a spectrum of financial stress fluctuations, including: Extracting the indicator risk distribution characteristics and risk level thresholds from the risk metric spectrum, setting the stress scenario parameter interval, and identifying key risk events through historical simulation to obtain a stress scenario benchmark set; According to the stress scenario benchmark set, three levels of stress conditions, namely, mild, moderate and severe, are constructed to form a scenario variable combination table, and the correlation strength between indicators is analyzed using the risk conduction map to generate an indicator correlation matrix; Conduct network topology analysis on the indicator association matrix, extract risk node transmission paths, construct a pressure transmission network, and quantify the risk losses on each path using the VAR analysis method to output a transmission intensity table; The risk transmission nodes are sorted according to the transmission intensity table, a risk transmission sequence is constructed, and the risk accumulation effect of each node is calculated using a sliding time window to obtain a risk evolution matrix; The risk evolution matrix is ​​superimposed and analyzed with the stress scenario variables to calculate the risk shock fluctuations under each scenario, and the fluctuation amplitude distribution is estimated to form a fluctuation feature set; Time series decomposition and trend extraction are performed on the volatility feature set to reveal the law of risk evolution, and combined with the risk transmission effect analysis to generate a financial stress fluctuation spectrum.

5. The multi-dimensional financial stress testing and financial early warning method according to claim 1 is characterized in that: According to the financial pressure fluctuation spectrum, an early warning model is established through time series feature extraction and deep learning, and a warning threshold is determined by dynamic optimization to form a financial risk pulse diagram, including: Extracting time series variation characteristics, fluctuation cycle characteristics and intensity characteristics from the financial pressure fluctuation spectrum, constructing a time series characteristic database, and performing multi-scale analysis on the time series data by wavelet decomposition method to obtain a characteristic decomposition sequence; According to the characteristic decomposition sequence, long-term trend terms, periodic fluctuation terms and random disturbance terms are extracted to generate a time series component matrix, and a long short-term memory network is used to learn time series patterns to obtain a risk evolution law matrix; Input the risk evolution law matrix into the attention mechanism layer, extract key time sequence node features, construct an attention weight vector, calculate the risk warning probability in combination with historical warning data, and output a warning probability sequence; Perform probability distribution analysis on the warning probability sequence, calculate the risk density of different probability intervals, construct a risk distribution curve, and determine the candidate threshold set through ROC analysis to obtain a threshold evaluation matrix; Based on the threshold evaluation matrix, the underreporting rate and false alarm rate indicators are counted, a threshold evaluation table is constructed, and the best threshold combination is screened by the F1 score to form a dynamic threshold sequence; A comparative analysis is performed on the dynamic threshold sequence and the warning probability, risk warning points are marked, a risk pulse curve is drawn, and a financial risk pulse diagram is generated.

6. The multi-dimensional financial stress testing and financial early warning method according to claim 1 is characterized in that: Based on the financial risk pulse chart, a multi-level warning system including blue warning, yellow warning, orange warning and red warning is constructed, risk level determination is completed through step-by-step transmission analysis, and a financial warning intelligent chain is established, including: Extract risk intensity characteristics, risk frequency characteristics and risk persistence characteristics from the financial risk pulse diagram, construct a risk characteristic three-dimensional matrix, and determine the division boundaries of the four-level warning through fuzzy cluster analysis to obtain a warning level zoning map; According to the warning level zoning map, a blue warning interval, a yellow warning interval, an orange warning interval and a red warning interval are divided, and cross-interval migration rules are set. The transition probability between each interval is calculated through Markov chain analysis to form a warning level migration matrix; Compare and verify the warning level migration matrix with historical risk evolution data, extract the critical features of risk upgrade and downgrade, build a warning level determination rule base, and establish a risk transmission path diagram in combination with time series correlation analysis to output a risk transmission network; Decompose the risk transmission network hierarchically, identify the key nodes of risk transmission, calculate the node impact intensity and transmission speed, construct a transmission effect evaluation table, and determine the risk diffusion scope through complex network analysis to generate a risk spread map; Based on the risk spread map, a warning signal hierarchical response mechanism is established to calculate the risk accumulation effect under different warning levels, construct a risk accumulation intensity curve, and identify risk resonance points through spatiotemporal correlation analysis to form a warning signal transmission chain; A multi-level signal confirmation process is designed for the warning signal transmission chain, the risk evolution pattern characteristics are extracted, multi-dimensional warning information is integrated, a risk warning linkage mechanism is established, and a financial warning intelligent chain is generated.

7. The multi-dimensional financial stress testing and financial early warning method according to claim 6 is characterized in that: According to the financial early warning intelligent chain, the historical risk disposal case characteristics are extracted, similarity matching analysis is performed, a differentiated prevention and control strategy combination is designed, and a financial risk resolution solution library is constructed, including: Acquire historical financial risk case data from the financial early warning smart chain, extract risk occurrence scenarios, transmission characteristics, disposal methods, and effect evaluation factors, construct a case feature vector set, and identify key risk description information through semantic analysis to obtain a risk case knowledge base; Constructing a case similarity calculation matrix based on the risk case knowledge base, quantitatively characterizing risk characteristics, statistically analyzing risk treatment effect indicators, and calculating the similarity between cases through a cosine similarity algorithm to form a case matching sequence; Sort the case matching sequence by similarity, select a set of candidate cases whose matching exceeds a threshold, establish a disposal plan association map, perform scenario mapping in combination with current risk characteristics, and output a strategy reference plan; Conduct scenario adaptability analysis on the strategy reference scheme, evaluate the feasibility of the scheme and the degree of risk mitigation, build a scheme evaluation index system, determine the optimal scheme combination through multi-objective planning, and generate a prevention and control strategy library; Design differentiated implementation paths based on the prevention and control strategy library, formulate short-term emergency measures and medium- and long-term prevention and control measures, construct a prevention and control strategy deployment map, and establish a feedback correction mechanism through effect tracking and analysis to form a dynamic adjustment sequence; Conduct continuous monitoring and regular evaluation of the dynamic adjustment sequence, optimize the prevention and control strategy implementation plan, update the risk management experience database, and build an intelligent risk resolution solution database.

8. A multi-dimensional financial stress test and financial early warning system, used to implement the multi-dimensional financial stress test and financial early warning method according to any one of claims 1 to 7, characterized in that: The multi-dimensional financial stress test and financial early warning system includes: The collection module is used to collect and integrate enterprise financial data, operating data, industry data and public opinion data in multiple dimensions, and to remove outliers, fill in missing values ​​and perform numerical standardization to obtain a financial situation awareness data set; An allocation module is used to perform adaptive weight allocation on the indicators of five dimensions, namely, solvency, operating capacity, profitability, development capacity and cash flow, for the financial situation awareness data set, complete data dimensionality reduction by combining principal component extraction, and output a risk metric standard spectrum; A quantitative module is used to establish three levels of stress scenarios, namely, mild, moderate and severe, based on the risk measurement standard spectrum, to dynamically quantify the transmission path of risks between various indicators and generate a spectrum of financial stress fluctuations; Establish a module for establishing an early warning model based on the financial pressure fluctuation spectrum through time series feature extraction and deep learning, determining the early warning threshold by dynamic optimization, and forming a financial risk pulse diagram; An early warning module is used to build a multi-level early warning system including blue warning, yellow warning, orange warning and red warning based on the financial risk pulse diagram, complete the risk level determination through step-by-step transmission analysis, and establish a financial early warning intelligent chain; The matching module is used to extract the characteristics of historical risk disposal cases based on the financial early warning intelligent chain, conduct similarity matching analysis, design differentiated prevention and control strategy combinations, and construct a financial risk resolution solution library.

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