Big data driven financial management risk control prediction system and method

Abstract

The present application relates to the technical field of financial management, in particular to a big data driven financial management risk control prediction system and method, the system comprises a discrete mapping module, a node derivation module, a boundary construction module, a tendency derivation module and a risk control prediction module. In the present application, by discrete mapping of the relationship between the time difference of financial events and the business cycle, cross-period events are converted into comparable cycle labels, making the risk state have a unified cycle expression basis, and around the key time points formed by the budget expenditure growth and the execution delay, path nodes are constructed, unit time expenditure density difference and budget error correlation analysis are introduced, the inflection point boundary of risk evolution is determined, the risk transition probability is derived using the overlapping relationship of historical index intervals, the risk state transition has clear probability constraints and evolution trajectory description, the continuity of the prediction results is maintained under the condition of multi-cycle parallel, and the degree of influence of single cycle fluctuation on risk judgment is reduced.

Description

Technical Field

[0001] This invention relates to the field of financial management technology, and in particular to a big data-driven financial management risk control and prediction system and method. Background Technology

[0002] The field of financial management technology involves the systematic analysis and management of financial information of enterprises, institutions, or organizations, covering core matters such as financial statement preparation, budget control, cash flow monitoring, cost accounting, financial forecasting, internal auditing, and risk identification. This field supports managers in asset allocation, investment decisions, and compliance reviews by constructing mechanisms for financial data collection, processing, and analysis. With the development of information technology, financial management has gradually introduced computerized systems and algorithmic models to improve accounting efficiency, report accuracy, and risk response capabilities, forming an intelligent financial management system that integrates data acquisition, statistical analysis, and predictive modeling.

[0003] Traditional big data-driven financial management risk control and prediction systems refer to management solutions that build risk control and prediction functions based on large-scale heterogeneous financial data sources. These systems primarily model and assess credit risk, financial risk, tax risk, and investment risk faced by enterprises during operations. Traditional systems typically rely on rule bases, threshold judgment mechanisms, and historical data trend analysis, using statistical methods to classify risk levels by setting specific indicators such as accounts receivable turnover, debt ratio, and cash flow volatility. They then use regression analysis, time series analysis, or decision trees to predict the changing trends of financial indicators. Traditional systems often integrate functions such as historical financial data query, risk event trigger determination, and financial anomaly warnings to monitor and manage financial behavior. However, they rely heavily on fixed models and historical rules, lacking the ability to process high-dimensional, dynamic, and multi-period data, and are ill-suited to the multivariate interactions and risk evolution path analysis in complex business scenarios. Summary of the Invention

[0004] The purpose of this invention is to address the shortcomings of existing technologies by proposing a big data-driven financial management risk control prediction system and method.

[0005] To achieve the above objectives, the present invention adopts the following technical solution: a big data-driven financial management risk control prediction system, the system comprising: The discrete mapping module collects multi-source financial event records, calculates the difference in payment period, sets a jump tolerance value based on the business cycle, compares the remainder of the occurrence time field with the source event field corresponding to the jump tolerance value range based on the business cycle, and determines the generation of discrete label identifiers. The node derivation module constructs a budget sequence based on the discrete label identifier, selects three consecutive increasing points as fund-intensive nodes, calculates the unit time expenditure density difference of fund-intensive nodes, verifies the unit time expenditure density difference in conjunction with the budget application delay difference, and marks the budget path reversal node. The boundary construction module extracts the historical budget execution error rate and net cash flow difference of the budget path reversal node, calls the nonlinear regression model to fit the distribution, calculates the intersection point of the minimum upper bound and maximum lower bound of the interval, and constructs the boundary overlap set. The tendency derivation module monitors real-time indicators of the current financial cycle, counts the frequency of real-time indicators falling into the boundary overlap set, analyzes the coverage density of the boundary overlap set, and constructs a risk transition probability function. The risk control prediction module calls the Markov chain algorithm to analyze the risk transition probability function, deduce the evolution trajectory of financial risk status in the future, and generate financial management risk control prediction results.

[0006] As a further aspect of the present invention, the discrete label identifier includes a business event alignment index, a cycle jump tolerance marker, and a cross-source timestamp mapping code; the budget path reversal node includes the coordinates of the time of fund expenditure reversal, the unit time density mutation value, and the budget evolution direction identifier; the boundary overlap set includes the critical overlap region of budget error, the intersection of net cash flow fluctuations, and the resonance domain of indicator regression fitting; the risk transition probability function includes the risk boundary penetration frequency distribution, the financial cycle state transition gradient, and the multi-dimensional indicator space mapping matrix; and the financial management risk control prediction result includes the financial risk time series evolution trajectory, the future budget execution early warning sequence, and the capital chain state transition map.

[0007] As a further aspect of the present invention, the discrete mapping module includes: The tolerance parameter setting submodule collects multi-source financial event records, parses the start and end timestamps of each account period included in each record, performs a subtraction operation on the two to obtain the duration of the account period, calls the preset business cycle benchmark parameter, calculates the allowable fluctuation deviation range of the account period duration relative to the business cycle benchmark parameter, and establishes a jump tolerance value based on the business cycle. The time-domain remainder operation submodule calls the jump tolerance value based on the business cycle to construct an effective floating range for time matching, extracts the occurrence time field of the source data to be aligned and the time field of the target data, calculates the timestamp difference between the two, performs a modulo operation based on the jump tolerance value based on the business cycle on the timestamp difference, extracts the numerical part of the operation result, and obtains the time-domain deviation remainder sequence. The tag generation and verification submodule obtains the time-domain deviation remainder sequence, detects whether the value of the time-domain deviation remainder sequence is equal to an integer multiple of the minimum business cycle parameter according to the preset minimum business cycle parameter, compares in parallel whether the time-domain deviation remainder sequence is within the numerical envelope range limited by the jump tolerance value based on the business cycle, assigns a unified index code to event pairs that meet the matching logic, and generates discrete tag identifiers.

[0008] As a further aspect of the present invention, the node derivation module includes: The sequence feature extraction submodule, based on the discrete label identifier, reconstructs the multidimensional budget execution data in time series at the daily or weekly granularity, generates a continuous budget execution time series, monitors the slope of the expenditure amount change at each point in time in the budget execution time series, filters out time coordinates with three consecutive positive slopes and values ​​exceeding the preset growth benchmark, and marks the time coordinates as capital-intensive nodes. The density difference calculation submodule locks the capital-intensive node and its adjacent observation periods, extracts the business capital expenditure value and profit margin change index in each period, calculates the time delay data of budget application relative to actual expenditure, and calculates the expenditure density difference value per unit time. The reversal node calibration submodule obtains the unit time expenditure density difference value, collects the actual budget occupancy ratio and planned occupancy ratio data of the corresponding business node, calculates the difference between the two to obtain the budget occupancy ratio difference value, and determines whether the unit time expenditure density difference value is greater than the preset density anomaly judgment threshold and whether the budget occupancy ratio difference is greater than the preset occupancy deviation judgment threshold. When both conditions are met, the corresponding fund-intensive node is calibrated as the budget path reversal node.

[0009] As a further aspect of the present invention, the boundary construction module includes: The historical indicator extraction submodule, based on the budget path reversal node, locates the corresponding time index in the historical financial database, sets a backtracking sampling window, extracts the budget preparation amount and the actual execution amount within the window, calculates the deviation ratio, synchronously collects the cash inflow and outflow data of the same period, calculates the net change range, performs time-series alignment and normalization processing on the deviation ratio and the net change range, and generates a historical fluctuation feature vector. The regression interval fitting submodule calls a nonlinear regression model to map the historical fluctuation feature vector to a high-dimensional feature space, configures kernel function parameters, captures the nonlinear dependencies between data, performs least squares iterative calculations, fits the index distribution curve, calculates the fluctuation range of the fitted curve in the future period based on preset confidence parameters, extracts the upper limit sequence and lower limit sequence of the fluctuation range respectively, and establishes a nonlinear regression prediction interval. The overlapping boundary construction submodule obtains the nonlinear regression prediction interval, performs extreme value screening on the upper limit sequence within the interval to determine the minimum upper bound, performs extreme value screening on the lower limit sequence to determine the maximum lower bound, detects the crossover status of the minimum upper bound and the maximum lower bound in the numerical space, calculates the intersection coordinates of the two as a benchmark to delineate the critical resonance domain of the risk indicator, and constructs the boundary overlap set.

[0010] As a further aspect of the present invention, the process of configuring kernel function parameters to capture nonlinear dependencies between data specifically involves: selecting a Gaussian radial basis kernel function as the feature mapping operator; setting the initial search range for the kernel function's bandwidth parameter and regularization penalty coefficient; using cross-validation to divide the historical fluctuation feature vector into a training subset and a validation subset; performing a grid search on the parameter nodes within the initial search range; calculating the prediction mean square error of each set of parameter nodes on the validation subset; and selecting the bandwidth parameter and regularization penalty coefficient corresponding to the minimum mean square error as the optimal kernel function configuration. The process of performing least squares iterative calculations to fit the index distribution curve specifically involves: constructing a Lagrange dual function including slack variables to transform the nonlinear regression objective optimization problem into a quadratic programming solution process; iteratively updating the Lagrange multipliers using sequential minimum optimization logic until all sample points satisfy the Karouch-Kuntak condition; and constructing a nonlinear regression hyperplane to represent the index distribution curve based on the support vectors corresponding to the converged non-zero Lagrange multipliers. The process of calculating the fluctuation range of the fitted curve in the future period based on the preset confidence level parameters specifically involves: calculating the standard deviation of the training sample residuals and obtaining the critical value of the statistical distribution by combining the preset confidence level; using the predicted point of the fitted curve in the future period as the center, constructing a confidence interval by multiplying the standard deviation and the critical value; defining the upper edge of the confidence interval as the upper limit sequence and the lower edge of the confidence interval as the lower limit sequence.

[0011] As a further aspect of the present invention, the tendency deduction module includes: The real-time monitoring and statistics submodule monitors the real-time indicator values ​​of the current financial cycle, calls the boundary overlap set, performs the inclusion relationship determination between the real-time indicator values ​​and the numerical intervals defined by the boundary overlap set one by one, counts the number of event triggers that fall into the interval within the preset sampling time window, calculates the normalized proportion of the number of event triggers to the total number of sampling points, associates the proportion with the corresponding time index, and generates a real-time domain frequency vector. The spatial density analysis submodule obtains the real-time domain frequency vector, establishes a multi-dimensional mapping grid based on the spatial topology of the boundary value distribution, projects the real-time domain frequency vector to the corresponding nodes of the multi-dimensional mapping grid, calculates the index aggregation intensity within each node region, performs a local weighted average operation on the overlapping coverage area, and generates the boundary coverage density coefficient. The transition probability construction submodule calls the boundary coverage density coefficient to define the current risk level state space and the next risk level state space. Based on the boundary coverage density coefficient, it calculates the potential energy penetration flux between the two state spaces, introduces a time decay factor to dynamically correct the potential energy penetration flux, fits the conditional distribution law of state evolution over time, and constructs the risk transition probability function.

[0012] As a further aspect of the present invention, the process of performing a local weighted average operation on the overlapping coverage area to generate the boundary coverage density coefficient specifically involves: calculating the spatial Euclidean distance between the projection point of the real-time domain frequency vector in the multi-dimensional mapping grid and the geometric center of each grid node; calling a preset Gaussian kernel function to convert the spatial Euclidean distance into the corresponding spatial weight parameters, and determining the computational neighborhood centered on the projection point; extracting the index clustering intensity of each node in the computational neighborhood, performing a weighted summation operation on the index clustering intensity based on the spatial weight parameters, and normalizing the operation result to obtain the boundary coverage density coefficient; The process of calculating the potential energy penetration flux between two state spaces based on the boundary cover density coefficient specifically involves: calculating the gradient difference of the boundary cover density coefficients of the current risk level state space and the next risk level state space in the boundary region; obtaining the effective contact area data of the interface between the two state spaces; calculating the product of the gradient difference and the effective contact area data to obtain the basic flux value; setting the medium diffusion coefficient based on the historical fluctuation variance of the boundary cover density coefficient; and using the medium diffusion coefficient to perform linear gain correction on the basic flux value to obtain the potential energy penetration flux.

[0013] As a further aspect of the present invention, the risk control prediction module includes: The transition matrix construction submodule calls the risk transition probability function to define all discrete state nodes in the financial risk classification model, traverses all combinations of starting nodes and target nodes in the state space, calculates the conditional transition probability value under each combination path, maps the value to a square matrix structure according to the row and column indices, performs normalization accumulation verification on each row vector, and generates a state transition probability matrix. The evolution trajectory deduction submodule obtains the state transition probability matrix, collects real-time risk indicators of the current financial cycle, constructs an initial state distribution vector, performs continuous power-law iterative multiplication operations between the initial state distribution vector and the state transition probability matrix, derives the risk state probability distribution of each discrete time node within a future preset time window, extracts the state with the highest probability density at each time node as the key point of the evolution path, and establishes the risk time series evolution trajectory. The prediction result generation submodule identifies the time coordinates and residence time of entering the critical risk area in the trajectory based on the risk time series evolution trajectory, calculates the potential capital liquidity gap value in combination with the business budget cycle, maps the time coordinates and gap value to the preset control strategy library, matches the corresponding intervention measure index and early warning level code, and generates financial management risk control prediction results.

[0014] A big data-driven financial management risk control prediction method, the method being used to implement the aforementioned big data-driven financial management risk control prediction system, includes the following steps: S1: Collect multi-source financial event records, calculate the difference in payment period, set a jump tolerance value based on the business cycle, compare the remainder of the source event field corresponding to the occurrence time field and the jump tolerance value range based on the business cycle, and determine to generate discrete label identifiers. S2: Based on the discrete label identifier, construct the budget sequence, select three consecutive increase points as fund-intensive nodes, calculate the unit time expenditure density difference of fund-intensive nodes, verify the unit time expenditure density difference in combination with the budget application delay difference, and mark the budget path reversal node. S3: Extract the historical budget execution error rate and net cash flow difference of the budget path reversal node, call the nonlinear regression model to fit the distribution, calculate the intersection point of the minimum upper bound and the maximum lower bound of the interval, and construct the boundary overlap set; S4: Monitor real-time indicators of the current financial cycle, count the frequency of real-time indicators falling into the boundary overlap set, analyze the coverage density of the boundary overlap set, and construct a risk transition probability function. S5: Call the Markov chain algorithm to parse the risk transition probability function, deduce the evolution trajectory of financial risk status in the future period, and generate financial management risk control prediction results.

[0015] Compared with the prior art, the advantages and positive effects of the present invention are as follows: In this invention, by discretely mapping the relationship between the time difference of financial events and business cycles, intertemporal events are transformed into comparable cyclical labels, giving risk status a unified cyclical expression basis. Path nodes are constructed around key time points formed by budget expenditure growth and execution delays. Correlation analysis between unit time expenditure density difference and budget error is introduced to clarify the inflection point boundary of risk evolution. The probability of risk transition is derived by using the overlapping relationship of historical indicator intervals, giving risk status transitions clear probabilistic constraints and evolutionary trajectory descriptions. Under the condition of multiple parallel cycles, the continuity of prediction results is maintained, and the degree to which risk judgment is affected by single-cycle fluctuations is reduced. Attached Figure Description

[0016] To more clearly illustrate the technical solutions in the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0017] Figure 1 This is a system flowchart of the present invention; Figure 2 This is a schematic diagram of the system framework of the present invention; Figure 3 This is a flowchart of the discrete mapping module of the present invention; Figure 4 This is a flowchart of the node derivation module of the present invention; Figure 5 This is a flowchart of the boundary construction module of the present invention; Figure 6 This is a flowchart illustrating the derivation module of the present invention; Figure 7 This is a flowchart of the risk control prediction module of the present invention; Figure 8 This is a schematic diagram of the method steps of the present invention. Detailed Implementation

[0018] The technical solution of the present invention will now be described with reference to the accompanying drawings.

[0019] In embodiments of the present invention, words such as "exemplarily," "for example," etc., are used to indicate that something is an example, illustration, or description. Any embodiment or design described as "exemplary" in the present invention should not be construed as being more preferred or advantageous than other embodiments or designs. Specifically, the use of the word "exemplary" is intended to present the concept in a concrete manner. Furthermore, in embodiments of the present invention, the meaning expressed by "and / or" can be both, or either one.

[0020] In the embodiments of this invention, the terms "image" and "picture" may sometimes be used interchangeably. It should be noted that, without emphasizing the distinction between them, their intended meanings are consistent. Similarly, the terms "of," "corresponding (relevant)," and "corresponding" may sometimes be used interchangeably. It should be noted that, without emphasizing the distinction between them, their intended meanings are consistent.

[0021] In this embodiment of the invention, sometimes a subscript such as W1 may be written in a non-subscript form such as W1. When the difference is not emphasized, the meaning they express is the same.

[0022] To make the technical problems, technical solutions and advantages of the present invention clearer, a detailed description will be given below in conjunction with the accompanying drawings and specific embodiments.

[0023] Please see Figure 1 This invention provides a technical solution: a big data-driven financial management risk control prediction system. The system includes a discrete mapping module, a node derivation module, a boundary construction module, a tendency derivation module, and a risk control prediction module. The discrete mapping module collects multi-source financial event records, calculates the difference between the start and end times of the payment period, sets a jump tolerance value based on the business cycle, performs a remainder comparison operation between the occurrence time field and the source event field corresponding to the jump tolerance value range based on the business cycle, and generates a discrete label identifier when the minimum cycle integer multiple condition is met. The node derivation module constructs a budget execution time series based on discrete labels, selects the time points of three consecutive expenditure increases as fund-intensive nodes, calculates the unit time expenditure density difference centered on the fund-intensive nodes, verifies the unit time expenditure density difference by combining the delay difference of budget application time points, and marks the budget path reversal nodes. The boundary construction module extracts the historical budget execution error rate and net cash flow difference value corresponding to the budget path reversal node, calls the nonlinear regression model to fit the index distribution, calculates the intersection point of the minimum upper bound and the maximum lower bound of the nonlinear regression model output interval, and constructs the boundary overlap set. The tendency derivation module monitors the real-time indicator values ​​of the current financial cycle and counts the frequency of falling into the boundary overlap set. Based on the spatial overlap logic of the boundary value distribution, it analyzes the coverage density of the boundary overlap set and constructs the risk transition probability function. The risk control prediction module calls the Markov chain algorithm to analyze the risk transition probability function, deduce the evolution trajectory of financial risk status in the future period, and generate financial management risk control prediction results. Discrete label identifiers include business event alignment indexes, cycle jump tolerance markers, and cross-source timestamp mapping codes. Budget path reversal nodes include the coordinates of the time of fund expenditure reversal, the unit time density mutation value, and the budget evolution direction identifier. Boundary overlap sets include the critical overlap area of ​​budget errors, the intersection of net cash flow fluctuations, and the resonance domain of indicator regression fitting. Risk transition probability functions include the risk boundary penetration frequency distribution, the financial cycle state transition gradient, and the multi-dimensional indicator space mapping matrix. Financial management risk control prediction results include the time-series evolution trajectory of financial risks, the future budget execution early warning sequence, and the capital chain state transition map.

[0024] Please see Figure 2 and Figure 3 The discrete mapping module includes: The tolerance parameter setting submodule collects multi-source financial event records, parses the start and end timestamps of each account period included in each record, performs a subtraction operation on the two to obtain the duration of the account period, calls the preset business cycle benchmark parameter, calculates the allowable fluctuation deviation range of the account period duration relative to the business cycle benchmark parameter, and establishes a jump tolerance value based on the business cycle. The system integrates with the API interfaces of enterprise resource planning (ERP) systems and bank fund settlement systems to collect multi-source financial event records using a high-frequency polling mode. During the data access phase, the original data packets are first subjected to integrity verification, and data with a field missing rate exceeding a certain threshold is removed. Invalid records are identified, and timestamps from different sources (such as "YYYY-MM-DDHH:MM:SS" and Unix timestamps) are uniformly converted to the standard UTC time format with nanosecond precision. For each collected record, the start and end timestamps of the payment period are parsed. For example, when processing a cross-border supply chain procurement transaction, the start timetamp is marked as midnight on the contract effective date, and the end timetamp is marked as midnight on the final settlement date. The two are subtracted to obtain the duration of the payment period. At this point, a preset business cycle benchmark parameter is invoked. This parameter is not set based on empirical values, but rather on the company's past performance. Within the year A fast Fourier transform analysis was performed on the historical business transaction records to identify the period lengths corresponding to significant frequency domain peaks, for example... Daily (monthly settlement) or Daily (quarterly settlement). Calculate the allowable fluctuation range of the payment period duration relative to the business cycle baseline parameter. Specifically, calculate the standard deviation of the payment period duration for the same business type in historical data. ,Will Set the allowable fluctuation range to establish a jump tolerance value based on the business cycle.

[0025] The time-domain remainder operation submodule calls the jump tolerance value based on the business cycle to construct an effective floating range for time matching, extracts the occurrence time field of the source data to be aligned and the time field of the target data, calculates the timestamp difference between the two, performs a modulo operation based on the jump tolerance value based on the business cycle on the timestamp difference, extracts the numerical part of the operation result, and obtains the time-domain deviation remainder sequence. The process of performing modulo operation on the timestamp difference based on the jump tolerance value of the business cycle is as follows: subtract the value of the time field of the source data to be aligned from the value of the time field of the target data to obtain the original time difference; set the jump tolerance value based on the business cycle as the operation modulus; divide the original time difference by the operation modulus to obtain the quotient and the initial remainder; determine whether the initial remainder is a non-negative value; if the initial remainder is a negative value, add the initial remainder to the operation modulus until it is converted to a positive value, and determine the positive value obtained as an element in the time domain deviation remainder sequence. Extract the occurrence time field of the source data to be aligned (e.g., invoice issuance time) and the target data time field (e.g., actual transfer time), and calculate the timestamp difference between them. In actual processing, due to the existence of weekends or holidays, the timestamp difference may exhibit non-linear jumps. In this case, perform a modulo operation on the timestamp difference based on the jump tolerance value of the business cycle. The specific calculation logic is as follows: subtract the value of the target data time field from the value of the occurrence time field of the source data to be aligned to obtain the original timestamp difference value. Set the original timestamp difference value to be... The jump tolerance value based on the business cycle is .Will Set as the operation modulus, Divide by Obtain the quotient and initial remainder. The submodule determines whether the initial remainder is non-negative. If the initial remainder is negative (e.g., in a time backtracking calibration scenario), the modulus is accumulated and calculated until it is converted to a positive value. The resulting positive value is then used as an element in the time-domain deviation remainder sequence. This process eliminates time phase noise caused by asynchronous business cycles.

[0026] The tag generation and verification submodule obtains the time-domain deviation remainder sequence, and checks whether the value of the time-domain deviation remainder sequence is equal to an integer multiple of the minimum business cycle parameter according to the preset minimum business cycle parameter. It also compares in parallel whether the time-domain deviation remainder sequence is within the numerical envelope range limited by the jump tolerance value based on the business cycle. For event pairs that meet the matching logic, it assigns a unified index code and generates discrete tag identifiers. The process of detecting whether the value of the time-domain deviation remainder sequence is equal to an integer multiple of the minimum service cycle parameter is as follows: calculate the ratio of the element values ​​in the time-domain deviation remainder sequence to the minimum service cycle parameter to obtain the cycle multiplier coefficient; round the cycle multiplier coefficient to obtain the nearest integer factor; calculate the absolute value of the difference between the cycle multiplier coefficient and the integer factor, and mark the absolute value as the phase deviation; compare the phase deviation with the preset synchronization allowable error threshold. If the phase deviation is less than the synchronization allowable error threshold, it is determined that the integer multiple condition is met. The process of comparing the time-domain deviation remainder sequence in parallel to determine whether it falls within the numerical envelope range defined by the jump tolerance value based on the business cycle is as follows: read the maximum allowable positive deviation value and the maximum allowable negative deviation value based on the jump tolerance value based on the business cycle; construct a closed interval consisting of the maximum allowable negative deviation value to the maximum allowable positive deviation value; and detect whether the element values ​​in the time-domain deviation remainder sequence fall within the closed interval. If they do, it is determined that the numerical envelope range condition is met. Introduce preset minimum business cycle parameters (e.g., minimum cash flow unit) (Hours), checking whether the value of the time-domain deviation remainder sequence is equal to an integer multiple of the minimum business cycle parameter. Specifically, the ratio of the element values ​​in the time-domain deviation remainder sequence to the minimum business cycle parameter is calculated to obtain the cycle ratio coefficient. The cycle ratio coefficient is rounded to the nearest integer factor. Then, the absolute value of the difference between the cycle ratio coefficient and the integer factor is calculated, and this absolute value is marked as the phase deviation. The phase deviation is then compared with a preset synchronization tolerance threshold. This synchronization tolerance threshold is set to... That is, it is allowed to exist. The phase drift is considered. If the phase deviation is less than the synchronization tolerance threshold, it is determined that the integer multiple condition is met. In parallel, the submodule compares whether the time-domain deviation remainder sequence is within the numerical envelope range defined by the jump tolerance value based on the business cycle. Specifically, it reads the maximum allowable positive deviation value and the maximum allowable negative deviation value based on the jump tolerance value based on the business cycle, and constructs a closed interval consisting of the maximum allowable negative deviation value to the maximum allowable positive deviation value. It checks whether the element values ​​in the time-domain deviation remainder sequence fall within the closed interval. Only when both conditions are met, a unified index code is assigned to the event pair that conforms to the matching logic, and a discrete label is generated. Table 1 shows an example of data comparison before and after the module processing.

[0027] Table 1. Time Alignment Processing Table for Financial Event Records; As shown in Table 1, although EVT_002 is in the same service flow, it was judged as non-periodic noise data and no label was generated because the remainder sequence value of 6.5 failed to meet the integer multiple matching of the minimum period (the minimum period is set to 24h, the tolerance is 24h, and the remainder should be close to 0).

[0028] Please see Figure 2 and Figure 4 The node derivation module includes: The sequence feature extraction submodule, based on discrete label identification, reconstructs the multidimensional budget execution data at a daily or weekly granularity to generate a continuous budget execution time series. It monitors the slope of the expenditure amount change at each point in time in the budget execution time series, filters out time coordinates with three consecutive positive slopes and values ​​exceeding the preset growth benchmark, and marks the time coordinates as capital-intensive nodes. Discrete financial events are mapped onto a continuous timeline, and blank data for non-trading days is filled in using linear interpolation to generate a continuous budget execution time series. Subsequently, the slope of expenditure changes at each point in time within the budget execution time series is monitored. Specifically, the current point in time is calculated... Compared with the previous time point The difference in expenditure amount is divided by the time step. The submodule filters time points where the slope is positive for three consecutive periods and the value exceeds a preset growth benchmark. This preset growth benchmark is set to the historical average growth rate. For example, the historical average weekly growth rate is [number] times. The benchmark is The time coordinates that meet this condition are marked as capital-intensive nodes.

[0029] The density difference calculation submodule identifies capital-intensive nodes and their adjacent observation periods, extracts the business capital expenditure values ​​and profit margin changes within each period, and calculates the time lag between budget requests and actual expenditures using the following formula: ; The expenditure density difference value per unit time is calculated and obtained. in, The score represents the difference in expenditure density per unit time. Represents the first in the next adjacent period The normalized value of capital expenditure for this type of business is obtained by collecting the data for the first time within the period. The actual expenditure amount for this type of business is divided by the total budget for that period. Represents the first in the previous adjacent period The normalized value of capital expenditure for this type of business is obtained by collecting the data for the first time within the period. The actual expenditure amount for this type of business is divided by the total budget for that period. The normalization coefficient representing the unit time base is obtained by setting it as the ratio of the unit time step to the total observation period. This represents the total number of business categories, which is obtained by counting the actual number of business types that occur. This represents the normalized value of the rate of change in profit margin over the same period. It is obtained by calculating the first difference of the profit margin and dividing it by the historical maximum fluctuation range of the profit margin. This represents a smoothing constant to prevent the denominator from being zero; it is obtained by pre-setting a very small positive floating-point number. Representing the The time delay difference between the budget application date and the actual expenditure date is obtained by calculating the difference between the budget application timestamp and the fund disbursement timestamp. This represents the number of budget applications, which is obtained by counting the application records within the assessment period. The maximum allowable delay period is determined by setting the longest delay record in historical business data. The density anomaly judgment threshold is set based on the 95th percentile of the historical density distribution, and the occupancy deviation judgment threshold is set based on the upper limit of the allowable budget deviation. Identify key funding nodes and their adjacent observation periods (e.g., each preceding and following period) (Days). Extract the business cash expenditure figures and profit margin changes for each period, and calculate the time lag between budget requests and actual expenditures. To accurately quantify the abnormal density of cash flow, the following formula is used to calculate the expenditure density difference per unit time: ; In the formula, The difference in expenditure density per unit of time represents the value of the expenditure density. Represents the first in the next adjacent period Normalized figures for fund expenditures in this type of business. These figures are obtained by collecting data from the first [number]th [period] of the current period. The actual expenditure for a business category (such as raw material procurement) is divided by the total budget for that period. For example, the actual expenditure is... Yuan, total budget is Yuan, then . Represents the first in the previous adjacent period The normalized figures for capital expenditures of similar business types are obtained in the same way as above. The normalization coefficient representing the unit time base is set as the ratio of the unit time step (e.g., 1 day) to the total observation period (e.g., 14 days). . The total number of business categories is represented by the number of actual business types that occur. For example... . This represents the normalized value of the rate of change in profit margin over the same period, obtained by calculating the first difference of the profit margin (e.g., the current period). Subtract the previous period equal ), and divide by the historical maximum profit margin fluctuation range (e.g. ),Right now . This represents a smoothing constant to prevent the denominator from being zero, and is preset to 0. . Representing the The time delay difference between the budget application date and the actual expenditure date is obtained by calculating the difference between the budget application timestamp and the fund disbursement timestamp, in days. This represents the number of budget applications. This represents the maximum allowed delay period, set based on the longest delay record in historical business data, for example... The advantage of this formula is that it introduces... As a denominator, this amplifies the change in the density of capital expenditures when profit margin fluctuations are small (i.e., operating conditions are stable), thus more acutely capturing "abnormal expenditures in a stable environment"; simultaneously, through the delayed correction factor... For funding stages with severely delayed approval processes, their authority is reduced to avoid false risk alerts caused by administrative procedures. Experimental data shows that when... When the overall size is large, The value decreased significantly, effectively filtering out... Non-substantive risk false alarms.

[0030] In the specific example, the following is set: , The absolute value of the difference is . The result of the delay term calculation is as follows: That is, the correction factor is .but .

[0031] The reversal node calibration submodule obtains the expenditure density difference value per unit time, collects the actual budget occupancy ratio and planned occupancy ratio data of the corresponding business node, calculates the difference between the two to obtain the budget occupancy ratio difference, and determines whether the expenditure density difference value per unit time is greater than the preset density anomaly judgment threshold and whether the budget occupancy ratio difference is greater than the preset occupancy deviation judgment threshold. When both conditions are met, the corresponding fund-intensive node is calibrated as the budget path reversal node.

[0032] Collect data on the actual and planned budget occupancy rates for the corresponding business nodes, and calculate the difference between the two to obtain the budget occupancy rate difference. Set the actual occupancy rate. Planned occupation The difference is Judge separately Whether it exceeds the preset density anomaly detection threshold (set as the 95th percentile of the historical distribution, for example) ), and whether the difference in budget occupancy ratio is greater than the preset occupancy deviation judgment threshold (set to ), and whether the difference in budget occupancy ratio is greater than the preset occupancy deviation judgment threshold. When both conditions are met ( and When the corresponding capital-intensive node is marked as the budget path reversal node, the corresponding node is marked as the budget path reversal node.

[0033] Please see Figure 2 and Figure 5 The boundary construction module includes: The historical indicator extraction submodule, based on the budget path reversal node, locates the corresponding time index in the historical financial database, sets a backtracking sampling window, extracts the budget preparation amount and the actual execution amount within the window, calculates the deviation ratio, synchronously collects the cash inflow and outflow data of the same period, calculates the net change magnitude, and performs time-series alignment and normalization processing on the deviation ratio and net change magnitude to generate a historical fluctuation feature vector. Establish a high-bandwidth connection to the enterprise data warehouse and perform time-indexed location in the historical financial database based on budget path reversal nodes (i.e., time coordinates marked as abnormal fund flows). First, set a backtracking sampling window; the size of this window is not fixed but dynamically set to the business cycle in which the current reversal node is located. Double the duration (for example, if the business cycle is monthly, then the window is the past). (Days). During the extraction phase, two data retrieval commands are executed in parallel: one is to extract the budget preparation amount sequence and the actual execution amount sequence within the extraction window; the other is to simultaneously collect the cash inflow and cash outflow data streams for the same period. For the extracted raw data, data cleaning is first performed to remove null records caused by system maintenance (the null value rate is controlled within a certain range). (The following). Then, the deviation ratio is calculated, specifically based on the following logic: (Actual execution amount) Budgetary Amount Budgetary budget allocation. Simultaneously, calculate the net change rate, i.e., (cash inflows) The absolute value of cash outflows. To address the issue of inconsistent data magnitudes across different dimensions (e.g., a deviation ratio of percentages versus net changes in millions), the submodule performs time-series alignment and normalization on the two sets of derived data. Time-series alignment employs zero-order hold-and-resample technique to uniformly map non-uniform financial event time points to daily data. The standard time axis. Normalization is performed using the Z-Score standardization method, the formula is... ,in The mean of the data within the window. The standard deviation is denoted as . The processed data is encapsulated into a historical volatility feature vector, which contains dual-dimensional information reflecting budget execution deviation and capital turnover volatility, providing a standardized input tensor for subsequent nonlinear regression.

[0034] The regression interval fitting submodule calls a nonlinear regression model to map historical fluctuation feature vectors to a high-dimensional feature space, configures kernel function parameters, captures nonlinear dependencies between data, performs least squares iterative calculations, fits the index distribution curve, calculates the fluctuation range of the fitted curve in the future period based on preset confidence parameters, extracts the upper limit sequence and lower limit sequence of the fluctuation range respectively, and establishes a nonlinear regression prediction interval. The process of configuring kernel function parameters to capture nonlinear dependencies between data is as follows: a Gaussian radial basis kernel function is selected as the feature mapping operator, and the initial search range of the kernel function's bandwidth parameter and regularization penalty coefficient is set; the historical fluctuation feature vector is divided into a training subset and a validation subset using cross-validation, and a grid search is performed on the parameter nodes within the initial search range; the prediction mean square error of each set of parameter nodes on the validation subset is calculated, and the bandwidth parameter and regularization penalty coefficient corresponding to the minimum mean square error are selected as the optimal kernel function configuration. The process of performing least squares iterative calculations to fit the index distribution curve is as follows: constructing a Lagrange dual function including slack variables, transforming the nonlinear regression objective optimization problem into a quadratic programming solution process; iteratively updating the Lagrange multipliers using sequential minimum optimization logic until all sample points satisfy the Karouch-Kuntak condition; and constructing a nonlinear regression hyperplane to represent the index distribution curve based on the support vectors corresponding to the converged non-zero Lagrange multipliers. The process of calculating the fluctuation range of the fitted curve in the future period based on the preset confidence level parameters is as follows: calculate the standard deviation of the training sample residuals, and obtain the critical value of the statistical distribution by combining the preset confidence level; take the predicted point of the fitted curve in the future period as the center, and construct the confidence interval by multiplying the standard deviation and the critical value; define the upper edge of the confidence interval as the upper limit sequence and the lower edge of the confidence interval as the lower limit sequence. Support vector regression is used to map historical fluctuation feature vectors to a high-dimensional feature space. Kernel function parameters are configured to capture nonlinear dependencies between data. A Gaussian radial basis function is selected as the feature mapping operator, and the formula is as follows: Set the bandwidth parameter of the kernel function. With regularization penalty coefficient The initial search range (e.g.) , ).use Cross-validation divides the historical fluctuation feature vector into training and validation subsets, and performs a grid search on the parameter nodes within the initial search range. It calculates the mean squared error of the predictions for each set of parameter nodes on the validation subset, and selects the bandwidth parameter (e.g., the one with the smallest mean squared error) that corresponds to this minimum. ) and regularization penalty coefficient (e.g. The optimal kernel function configuration is then determined. Subsequently, least squares iterative calculations are performed to fit the index distribution curve. This process includes constructing a Lagrange dual function including slack variables, transforming the nonlinear regression objective optimization problem into a quadratic programming solution. The Lagrange multipliers are then iteratively updated using sequential minimum optimization logic. and This process continues until all sample points satisfy the Karouch-Kun-Tucker condition. Based on the support vectors corresponding to the converged non-zero Lagrange multipliers, a nonlinear regression hyperplane is constructed to represent the distribution curve of the index. Finally, based on preset confidence parameters (e.g., ...), ... Calculate the range of fluctuation of the fitted curve over future periods. Specifically, calculate the standard deviation of the training sample residuals. The critical value of the statistical distribution is obtained by combining the preset confidence level. The predicted points of the fitted curve in future time periods. Confidence intervals are constructed using the product of the standard deviation and the critical value, centered at the critical value. The upper edge of the confidence interval is defined as the upper bound sequence, and the lower edge of the confidence interval is defined as the lower bound sequence, thus establishing the nonlinear regression prediction interval. Table 2 lists the key parameters and results of model training.

[0035] Table 2. Nonlinear regression model configuration and training results; As shown in Table 2, the model has an extremely low MSE under optimal parameters, indicating that the fitted curve can accurately reflect the historical fluctuation pattern.

[0036] The overlapping boundary construction submodule obtains the nonlinear regression prediction interval, performs extreme value screening on the upper limit sequence within the interval to determine the minimum upper bound, performs extreme value screening on the lower limit sequence to determine the maximum lower bound, detects the crossover of the minimum upper bound and the maximum lower bound in the numerical space, calculates the intersection coordinates of the two as the benchmark to delineate the critical resonance domain of the risk indicator, and constructs the boundary overlap set. Perform sliding window extreme value filtering on the upper limit sequence within the interval, with the window length set to . For each time step, the minimum value is selected within each window to form a smoothed minimum upper bound sequence (Min-Upper). Simultaneously, the same sliding window filtering is applied to the lower bound sequence, selecting the maximum value to form a smoothed maximum lower bound sequence (Max-Lower). Then, the overlap between the minimum upper bound and the maximum lower bound in the numerical space is detected. Point-by-point comparison is performed. and The magnitude of the value. If If the condition is met, then a valid overlapping region exists at the current moment. This region represents the common steady-state space under the worst-case (highest lower bound) and most optimistic (lowest upper bound) historical fluctuations. The geometric center coordinates of both are calculated as reference points, and then... The resulting numerical range is defined as the critical resonance domain of the risk indicator. The set of critical resonance domains at all time points together constructs the boundary overlap set. This set physically eliminates the divergence edges of the prediction interval and retains the core fluctuation channel with high confidence, serving as a rigid reference system for subsequent real-time monitoring.

[0037] Please see Figure 2 and Figure 6 The tendency derivation module includes: The real-time monitoring and statistics submodule monitors the real-time indicator values ​​of the current financial cycle, calls the boundary overlap set, performs the inclusion relationship determination between the real-time indicator values ​​and the numerical intervals defined by the boundary overlap set one by one, counts the number of event triggers that fall into the interval within the preset sampling time window, calculates the normalized proportion of the number of event triggers to the total number of sampling points, associates the proportion with the corresponding time index, and generates a real-time domain frequency vector. A high-speed circular buffer, resident in built-in memory, is used to monitor real-time metrics for the current financial cycle (such as real-time cash flow rate and real-time budget expenditure rate). This is done at millisecond-level frequencies (e.g.,...). Data is collected and stored in a buffer. Boundary overlap sets are called to determine inclusion relationships one by one. The specific logic gates or software judgment logic are as follows: Input the current real-time indicator value. Read the boundary overlap interval corresponding to the current time. ,judge Whether it holds true. The statistical logic is based on a preset sampling time window (e.g., past). (hours), the time window covers There are 10 sampling points. A counter is used to count the number of events triggered within the time window that fall within the interval (i.e., satisfy the above inequality). The counter is set to return a value of 1. This indicates that the system is in a steady state most of the time. Subsequently, the normalized proportion of the number of event triggers to the total number of sampling points is calculated (…). If the proportion is lower than the preset steady-state threshold (e.g.) If the calculated proportion is not found, it indicates that the system has experienced frequent out-of-bounds behavior. Finally, the calculated proportion values ​​are associated with the corresponding time index (Unix timestamp) to generate a real-time domain frequency vector. This vector is a time series data that intuitively reflects the frequency distribution of deviations from the "secure kernel" on the time axis.

[0038] The spatial density analysis submodule obtains the real-time domain frequency vector, establishes a multi-dimensional mapping grid based on the spatial topology of the boundary value distribution, projects the real-time domain frequency vector to the corresponding nodes of the multi-dimensional mapping grid, calculates the index aggregation intensity within each node region, performs local weighted average calculation for overlapping coverage areas, quantifies the compactness of the distribution and eliminates the influence of outliers, and generates the boundary coverage density coefficient. The process of generating the boundary coverage density coefficient by performing a local weighted average operation on the overlapping coverage area is as follows: First, the spatial Euclidean distance between the projection point of the real-time domain frequency vector in the multi-dimensional mapping grid and the geometric center of each grid node is calculated. Second, a preset Gaussian kernel function is called to convert the spatial Euclidean distance into the corresponding spatial weight parameters, and the computational neighborhood centered on the projection point is determined. Third, the index clustering intensity of each node in the computational neighborhood is extracted, and a weighted summation operation is performed on the index clustering intensity based on the spatial weight parameters. Finally, the computation result is normalized to obtain the boundary coverage density coefficient. The real-time frequency vector of the boundary value distribution is obtained, and a multi-dimensional mapping grid is established based on the spatial topology of the boundary value distribution. The grid is designed as follows: The two-dimensional discrete plane is used, with the horizontal axis representing "time urgency" (normalized remaining budget period) and the vertical axis representing "deviation severity" (the value of the real-time frequency vector). The submodule projects each data point in the real-time frequency vector to the corresponding node of the multi-dimensional mapping grid, using nearest-neighbor interpolation as the projection rule. After projection, a discrete point cloud distribution is formed on the grid. To quantify the continuity and compactness of the distribution, the index clustering intensity within each node region is calculated, i.e., the sum of the number of points falling into that grid cell and its 8 neighbors. For overlapping areas (i.e., areas with dense point clouds), a local weighted average operation is performed to eliminate random noise (outliers). Specifically, the Euclidean distance between the projection point of the real-time frequency vector in the grid and the geometric center of each grid node is measured. Call the preset Gaussian kernel function. (in To smooth bandwidth, set to (Number of grid units) converts the distance into a spatial weight parameter. A radius is determined with the projection point as the center. The computational neighborhood is calculated. The clustering intensity of the index for each node within the neighborhood is extracted, a weighted sum is performed based on the spatial weight parameters, and the result is normalized (divided by the sum of the weights) to generate a boundary cover density coefficient. This coefficient is a value between... arrive The higher the value of the scalar field between them, the more solidified the risk state of the region is, and the more difficult it is to return to normal through natural fluctuations.

[0039] The transition probability construction submodule calls the boundary coverage density coefficient to define the current risk level state space and the next risk level state space. Based on the boundary coverage density coefficient, it calculates the potential energy penetration flux between the two state spaces, introduces a time decay factor to dynamically correct the potential energy penetration flux, fits the conditional distribution law of state evolution over time, and constructs the risk transition probability function. The process of calculating the potential energy penetration flux between two state spaces based on the boundary cover density coefficient is as follows: Calculate the gradient difference of the boundary cover density coefficient between the current risk level state space and the next risk level state space in the boundary region; obtain the effective contact area data of the interface between the two state spaces; calculate the product of the gradient difference and the effective contact area data to obtain the basic flux value; set the medium diffusion coefficient based on the historical fluctuation variance of the boundary cover density coefficient; and use the medium diffusion coefficient to perform linear gain correction on the basic flux value to obtain the potential energy penetration flux. Define the current risk level state space (state) ) and the state space of the next risk level (state) The partitioning of the state space is based on contour lines of density coefficients, such as density... This is a high-risk area. The potential energy penetration flux between the two state spaces is calculated based on the boundary cover density coefficient, and the current state is then determined. Next state The gradient difference of the boundary cover density coefficient in the boundary region (boundary line) Obtain the effective contact area data of the interface. (i.e., the number of grid points on the boundary line). Calculate the product of the gradient difference and the contact area. The baseline flux value is obtained. This simulates the tendency of risk to permeate from high-density areas to low-density areas (or vice versa) like a fluid. To reflect historical inertia, the medium diffusion coefficient is set based on the historical fluctuation variance of the boundary cover density coefficient. (The larger the variance, the better) The larger the value, the more unstable the system and the more prone it is to transitions. Linear gain correction is applied to the base flux value: Finally, a time decay factor is introduced. Dynamic correction is applied to the potential energy penetration flux, where The memory decay constant (values) This is to reflect that recent data has a greater weight in influencing state transitions than long-term data. By fitting the conditional distribution of state evolution over time (using a logistic regression function to map flux to probability), a probability function is constructed for the transition from a risk state to the next level.

[0040] Table 3 shows a snapshot of the data processing in a real-world risk assessment.

[0041] Table 3. Calculation of risk state space density and transition probability; As shown in Table 3, the density coefficient at coordinates (46, 61) is extremely high and accompanied by a large positive gradient difference. The calculated transition probability increases significantly, indicating that the system is very likely to slide to a higher risk level from this state point.

[0042] Please see Figure 2 and Figure 7 The risk control prediction module includes: The transition matrix construction submodule calls the risk transition probability function to define all discrete state nodes in the financial risk classification model, traverses all combinations of starting nodes and target nodes in the state space, calculates the conditional transition probability value under each combination path, maps the value to a square matrix structure according to the row and column indices, performs normalization accumulation verification on each row vector, and generates a state transition probability matrix. Define all discrete state nodes in the financial risk grading model (e.g.) :Safety, Warning (Crisis). Traverse all combinations of starting and target nodes in the state space (e.g., ... , ), calculate the conditional transition probability value for each combined path. Map the values ​​to a square matrix structure according to row and column indices, perform normalized accumulation verification on each row vector (ensuring the probability sum of each row is 1), and generate a state transition probability matrix. For example, matrix Possible: .

[0043] The evolution trajectory deduction submodule obtains the state transition probability matrix, collects real-time risk indicators of the current financial cycle, constructs the initial state distribution vector, performs continuous power-law iterative multiplication operations between the initial state distribution vector and the state transition probability matrix, derives the risk state probability distribution of each discrete time node within the future preset time window, extracts the state with the highest probability density at each time node as the key point of the evolution path, and establishes the risk time series evolution trajectory. The process of performing continuous power-law iterative multiplication of the initial state distribution vector and the state transition probability matrix is ​​as follows: the initial state distribution vector is set as the state input at the starting point of the time series; within each discrete prediction time step, the state distribution vector output at the previous time step is multiplied by the state transition probability matrix to obtain the predicted state distribution vector at the current time step; the predicted state distribution vector at the current time step is used as the input vector for the next time step step to continue participating in the calculation, and the matrix multiplication is performed cyclically until all time nodes within the preset future time window are traversed. Collect real-time risk indicators for the current financial cycle and construct an initial state distribution vector (e.g., if the current state is confirmed as safe, the vector is...). Perform a series of power-law iterative multiplications of the initial state distribution vector and the state transition probability matrix, setting the initial state distribution vector as the state input at the start of the time series. At each discrete prediction time step (e.g., ... Within this step, the state distribution vector output at the previous time step is multiplied by the state transition probability matrix to obtain the predicted state distribution vector at the current time step. For example, The predicted state distribution vector at the current moment is used as the input vector for the next time step to continue the calculation. Matrix multiplication is performed iteratively until all time nodes within the preset future time window are traversed. The state with the highest probability density at each time node is extracted as the key point of the evolution path, establishing the risk time series evolution trajectory.

[0044] The prediction result generation submodule identifies the time coordinates and residence time of entering the critical risk area based on the risk time series evolution trajectory, calculates the potential cash flow gap value in combination with the business budget cycle, maps the time coordinates and gap value to the preset control strategy library, matches the corresponding intervention measure index and early warning level code, and generates financial management risk control prediction results. The process of calculating the potential liquidity gap in conjunction with the business budget cycle is as follows: extract the risk level weight coefficients corresponding to the key points of the evolution path, perform weighted reduction on the expected cash inflow amount within the business budget cycle based on the risk level weight coefficients to obtain the risk-adjusted available funds amount; obtain the total amount of rigid expenditure plan within the business budget cycle, calculate the numerical difference between the total amount of rigid expenditure plan and the risk-adjusted available funds amount, and determine the numerical difference as the potential liquidity gap amount. The process of mapping time coordinates and gap values ​​to a pre-defined management strategy library is as follows: a two-dimensional strategy index matrix is ​​constructed, which includes the dimensions of time urgency and funding gap severity. The time coordinates are used to locate the corresponding intervals in the dimension of time urgency, and the potential funding liquidity gap values ​​are used to locate the corresponding intervals in the dimension of funding gap severity. The grid cell data locked by the two intervals in the two-dimensional strategy index matrix is ​​extracted. Extract the risk level weight coefficients corresponding to key points in the evolution path (e.g., the weight of a crisis state is...). Based on risk level weighting coefficients, the expected cash inflows within the business budget period are weighted and reduced to obtain the risk-adjusted available funds. The total amount of rigid expenditure plans within the business budget period is obtained, and the difference between this total and the risk-adjusted available funds is calculated. This difference is identified as the potential liquidity gap. Finally, the time coordinates and the gap value are mapped to a pre-defined control strategy library. A two-dimensional strategy index matrix is ​​constructed, including dimensions of time urgency and funding gap severity. The time coordinates are used to locate the corresponding intervals in the time urgency dimension (e.g., "next 3 days"), and the potential liquidity gap value is used to locate the corresponding intervals in the funding gap severity dimension (e.g., ">1 million"). The grid cell data locked by both intervals in the two-dimensional strategy index matrix (e.g., "activate emergency credit line") is extracted to generate financial management risk control prediction results.

[0045] Please see Figure 8 A big data-driven financial management risk control prediction method, used to implement the aforementioned big data-driven financial management risk control prediction system, includes the following steps: S1: Collect multi-source financial event records, calculate the difference in payment period, set a jump tolerance value based on the business cycle, compare the remainder of the source event field corresponding to the occurrence time field and the jump tolerance value range based on the business cycle, and determine to generate discrete label identifiers. S2: Based on discrete label identification, construct budget sequence, select three consecutive increase points as fund-intensive nodes, calculate the unit time expenditure density difference of fund-intensive nodes, verify the unit time expenditure density difference in combination with budget application delay difference, and mark budget path reversal nodes. S3: Extract the historical budget execution error rate and net cash flow difference of the budget path reversal node, call the nonlinear regression model to fit the distribution, calculate the intersection point of the minimum upper bound and maximum lower bound of the interval, and construct the boundary overlap set; S4: Monitor real-time indicators of the current financial cycle, count the frequency of real-time indicators falling into the boundary overlap set, analyze the coverage density of the boundary overlap set, and construct the risk transition probability function. S5: Call the Markov chain algorithm to analyze the risk transition probability function, deduce the evolution trajectory of financial risk status in the future, and generate financial management risk control prediction results.

[0046] It should be understood that the term "and / or" in this article is merely a description of the relationship between related objects, indicating that three relationships can exist. For example, A and / or B can represent: A alone, A and B simultaneously, or B alone. A and B can be singular or plural. Additionally, the character " / " in this article generally indicates an "or" relationship between the preceding and following related objects, but it can also represent an "and / or" relationship. Please refer to the context for a more accurate understanding.

[0047] In this invention, "at least one" means one or more, and "more than one" means two or more. "At least one of the following" or similar expressions refer to any combination of these items, including any combination of a single item or a plurality of items. For example, at least one of a, b, or c can represent: a, b, c, ab, ac, bc, or abc, where a, b, and c can be a single item or multiple items.

[0048] It should be understood that, in various embodiments of the present invention, the order of the above-mentioned process numbers does not imply the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present invention.

[0049] Those skilled in the art will recognize that the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementations should not be considered beyond the scope of this invention.

[0050] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the specific working processes of the devices, apparatuses, and units described above can be referred to the corresponding processes in the foregoing method embodiments, and will not be repeated here.

[0051] In the several embodiments provided by this invention, it should be understood that the disclosed devices, apparatuses, and methods can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative; for instance, the division of units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another device, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be through some interfaces; the indirect coupling or communication connection between devices or units may be electrical, mechanical, or other forms.

[0052] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0053] In addition, the functional units in the various embodiments of the present invention can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit.

[0054] If the aforementioned functions are implemented as software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art, or a part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of the present invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0055] The above description is merely a specific embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the technical scope disclosed in the present invention should be included within the scope of protection of the present invention. Therefore, the scope of protection of the present invention should be determined by the scope of protection of the technical solution.

Claims

1. A big data-driven financial management risk control and prediction system, characterized in that: The system includes: The discrete mapping module collects multi-source financial event records, calculates the difference in payment period, sets a jump tolerance value based on the business cycle, compares the remainder of the occurrence time field with the source event field corresponding to the jump tolerance value range based on the business cycle, and determines the generation of discrete label identifiers. The node derivation module constructs a budget sequence based on the discrete label identifier, selects three consecutive increasing points as fund-intensive nodes, calculates the unit time expenditure density difference of fund-intensive nodes, verifies the unit time expenditure density difference in conjunction with the budget application delay difference, and marks the budget path reversal node. The boundary construction module extracts the historical budget execution error rate and net cash flow difference of the budget path reversal node, calls the nonlinear regression model to fit the distribution, calculates the intersection point of the minimum upper bound and maximum lower bound of the interval, and constructs the boundary overlap set. The tendency derivation module monitors real-time indicators of the current financial cycle, counts the frequency of real-time indicators falling into the boundary overlap set, analyzes the coverage density of the boundary overlap set, and constructs a risk transition probability function. The risk control prediction module calls the Markov chain algorithm to analyze the risk transition probability function, deduce the evolution trajectory of financial risk status in the future, and generate financial management risk control prediction results.

2. The big data-driven financial management risk control prediction system according to claim 1, characterized in that, The discrete label identifier includes a business event alignment index, a cycle jump tolerance marker, and a cross-source timestamp mapping code. The budget path reversal node includes the coordinates of the time of fund expenditure reversal, the unit time density mutation value, and the budget evolution direction identifier. The boundary overlap set includes the critical overlap area of ​​budget error, the intersection of net cash flow fluctuations, and the resonance domain of indicator regression fitting. The risk transition probability function includes the risk boundary penetration frequency distribution, the financial cycle state transition gradient, and the multi-dimensional indicator space mapping matrix. The financial management risk control prediction results include the financial risk time series evolution trajectory, the future budget execution early warning sequence, and the capital chain state transition map.

3. The big data-driven financial management risk control prediction system according to claim 1, characterized in that, The discrete mapping module includes: The tolerance parameter setting submodule collects multi-source financial event records, parses the start and end timestamps of each account period included in each record, performs a subtraction operation on the two to obtain the duration of the account period, calls the preset business cycle benchmark parameter, calculates the allowable fluctuation deviation range of the account period duration relative to the business cycle benchmark parameter, and establishes a jump tolerance value based on the business cycle. The time-domain remainder operation submodule calls the jump tolerance value based on the business cycle to construct an effective floating range for time matching, extracts the occurrence time field of the source data to be aligned and the time field of the target data, calculates the timestamp difference between the two, performs a modulo operation based on the jump tolerance value based on the business cycle on the timestamp difference, extracts the numerical part of the operation result, and obtains the time-domain deviation remainder sequence. The tag generation and verification submodule obtains the time-domain deviation remainder sequence, detects whether the value of the time-domain deviation remainder sequence is equal to an integer multiple of the minimum business cycle parameter according to the preset minimum business cycle parameter, compares in parallel whether the time-domain deviation remainder sequence is within the numerical envelope range limited by the jump tolerance value based on the business cycle, assigns a unified index code to event pairs that meet the matching logic, and generates discrete tag identifiers.

4. The big data-driven financial management risk control prediction system according to claim 3, characterized in that, The node derivation module includes: The sequence feature extraction submodule, based on the discrete label identifier, reconstructs the multidimensional budget execution data in time series at the daily or weekly granularity, generates a continuous budget execution time series, monitors the slope of the expenditure amount change at each point in time in the budget execution time series, filters out time coordinates with three consecutive positive slopes and values ​​exceeding the preset growth benchmark, and marks the time coordinates as capital-intensive nodes. The density difference calculation submodule locks the capital-intensive node and its adjacent observation periods, extracts the business capital expenditure value and profit margin change index in each period, calculates the time delay data of budget application relative to actual expenditure, and calculates the expenditure density difference value per unit time. The reversal node calibration submodule obtains the unit time expenditure density difference value, collects the actual budget occupancy ratio and planned occupancy ratio data of the corresponding business node, calculates the difference between the two to obtain the budget occupancy ratio difference value, and determines whether the unit time expenditure density difference value is greater than the preset density anomaly judgment threshold and whether the budget occupancy ratio difference is greater than the preset occupancy deviation judgment threshold. When both conditions are met, the corresponding fund-intensive node is calibrated as the budget path reversal node.

5. The big data-driven financial management risk control prediction system according to claim 4, characterized in that, The boundary construction module includes: The historical indicator extraction submodule, based on the budget path reversal node, locates the corresponding time index in the historical financial database, sets a backtracking sampling window, extracts the budget preparation amount and the actual execution amount within the window, calculates the deviation ratio, synchronously collects the cash inflow and outflow data of the same period, calculates the net change range, performs time-series alignment and normalization processing on the deviation ratio and the net change range, and generates a historical fluctuation feature vector. The regression interval fitting submodule calls a nonlinear regression model to map the historical fluctuation feature vector to a high-dimensional feature space, configures kernel function parameters, captures the nonlinear dependencies between data, performs least squares iterative calculations, fits the index distribution curve, calculates the fluctuation range of the fitted curve in the future period based on preset confidence parameters, extracts the upper limit sequence and lower limit sequence of the fluctuation range respectively, and establishes a nonlinear regression prediction interval. The overlapping boundary construction submodule obtains the nonlinear regression prediction interval, performs extreme value screening on the upper limit sequence within the interval to determine the minimum upper bound, performs extreme value screening on the lower limit sequence to determine the maximum lower bound, detects the crossover status of the minimum upper bound and the maximum lower bound in the numerical space, calculates the intersection coordinates of the two as a benchmark to delineate the critical resonance domain of the risk indicator, and constructs the boundary overlap set.

6. The big data-driven financial management risk control prediction system according to claim 5, characterized in that, The process of configuring kernel function parameters to capture nonlinear dependencies between data specifically involves: selecting a Gaussian radial basis kernel function as the feature mapping operator; setting the initial search range for the kernel function's bandwidth parameter and regularization penalty coefficient; using cross-validation to divide the historical fluctuation feature vector into a training subset and a validation subset; performing a grid search on the parameter nodes within the initial search range; calculating the prediction mean square error of each set of parameter nodes on the validation subset; and selecting the bandwidth parameter and regularization penalty coefficient corresponding to the minimum mean square error as the optimal kernel function configuration. The process of performing least squares iterative calculations to fit the index distribution curve specifically involves constructing a Lagrange dual function including slack variables, transforming the nonlinear regression objective optimization problem into a quadratic programming solution process, and iteratively updating the Lagrange multipliers using sequential minimum optimization logic until all sample points satisfy the Karouch-Kuntak condition. Based on the support vectors corresponding to the converged non-zero Lagrange multipliers, a nonlinear regression hyperplane is constructed to represent the index distribution curve. The process of calculating the fluctuation range of the fitted curve in the future period based on the preset confidence level parameters specifically involves: calculating the standard deviation of the training sample residuals and obtaining the critical value of the statistical distribution by combining the preset confidence level; using the predicted point of the fitted curve in the future period as the center, constructing a confidence interval by multiplying the standard deviation and the critical value; defining the upper edge of the confidence interval as the upper limit sequence and the lower edge of the confidence interval as the lower limit sequence.

7. The big data-driven financial management risk control prediction system according to claim 5, characterized in that, The tendency derivation module includes: The real-time monitoring and statistics submodule monitors the real-time indicator values ​​of the current financial cycle, calls the boundary overlap set, performs the inclusion relationship determination between the real-time indicator values ​​and the numerical intervals defined by the boundary overlap set one by one, counts the number of event triggers that fall into the interval within the preset sampling time window, calculates the normalized proportion of the number of event triggers to the total number of sampling points, associates the proportion with the corresponding time index, and generates a real-time domain frequency vector. The spatial density analysis submodule obtains the real-time domain frequency vector, establishes a multi-dimensional mapping grid based on the spatial topology of the boundary value distribution, projects the real-time domain frequency vector to the corresponding nodes of the multi-dimensional mapping grid, calculates the index aggregation intensity within each node region, performs a local weighted average operation on the overlapping coverage area, and generates the boundary coverage density coefficient. The transition probability construction submodule calls the boundary coverage density coefficient to define the current risk level state space and the next risk level state space. Based on the boundary coverage density coefficient, it calculates the potential energy penetration flux between the two state spaces, introduces a time decay factor to dynamically correct the potential energy penetration flux, fits the conditional distribution law of the state evolution over time, and constructs the risk transition probability function.

8. The big data-driven financial management risk control prediction system according to claim 7, characterized in that, The process of performing a local weighted average operation on the overlapping coverage area to generate the boundary coverage density coefficient specifically involves: calculating the spatial Euclidean distance between the projection point of the real-time domain frequency vector in the multi-dimensional mapping grid and the geometric center of each grid node; calling a preset Gaussian kernel function to convert the spatial Euclidean distance into the corresponding spatial weight parameters, and determining the computational neighborhood centered on the projection point; extracting the index clustering intensity of each node in the computational neighborhood, performing a weighted summation operation on the index clustering intensity based on the spatial weight parameters, and normalizing the operation result to obtain the boundary coverage density coefficient; The process of calculating the potential energy penetration flux between two state spaces based on the boundary cover density coefficient specifically involves: calculating the gradient difference of the boundary cover density coefficients of the current risk level state space and the next risk level state space in the boundary region; obtaining the effective contact area data of the interface between the two state spaces; calculating the product of the gradient difference and the effective contact area data to obtain the basic flux value; setting the medium diffusion coefficient based on the historical fluctuation variance of the boundary cover density coefficient; and using the medium diffusion coefficient to perform linear gain correction on the basic flux value to obtain the potential energy penetration flux.

9. The big data-driven financial management risk control prediction system according to claim 7, characterized in that, The risk control prediction module includes: The transition matrix construction submodule calls the risk transition probability function to define all discrete state nodes in the financial risk classification model, traverses all combinations of starting nodes and target nodes in the state space, calculates the conditional transition probability value under each combination path, maps the value to a square matrix structure according to the row and column indices, performs normalization accumulation verification on each row vector, and generates a state transition probability matrix. The evolution trajectory deduction submodule obtains the state transition probability matrix, collects real-time risk indicators of the current financial cycle, constructs an initial state distribution vector, performs continuous power-law iterative multiplication operations between the initial state distribution vector and the state transition probability matrix, derives the risk state probability distribution of each discrete time node within a future preset time window, extracts the state with the highest probability density at each time node as the key point of the evolution path, and establishes the risk time series evolution trajectory. The prediction result generation submodule identifies the time coordinates and residence time of entering the critical risk area in the trajectory based on the risk time series evolution trajectory, calculates the potential capital liquidity gap value in combination with the business budget cycle, maps the time coordinates and gap value to the preset control strategy library, matches the corresponding intervention measure index and early warning level code, and generates financial management risk control prediction results.

10. A big data-driven financial management risk control prediction method, characterized in that, The method is used to implement the big data-driven financial management risk control prediction system according to any one of claims 1-9, and includes the following steps: S1: Collect multi-source financial event records, calculate the difference in payment period, set a jump tolerance value based on the business cycle, compare the remainder of the source event field corresponding to the occurrence time field and the jump tolerance value range based on the business cycle, and determine to generate discrete label identifiers. S2: Based on the discrete label identifier, construct the budget sequence, select three consecutive increase points as fund-intensive nodes, calculate the unit time expenditure density difference of fund-intensive nodes, verify the unit time expenditure density difference in combination with the budget application delay difference, and mark the budget path reversal node. S3: Extract the historical budget execution error rate and net cash flow difference of the budget path reversal node, call the nonlinear regression model to fit the distribution, calculate the intersection point of the minimum upper bound and the maximum lower bound of the interval, and construct the boundary overlap set; S4: Monitor real-time indicators of the current financial cycle, count the frequency of real-time indicators falling into the boundary overlap set, analyze the coverage density of the boundary overlap set, and construct a risk transition probability function. S5: Call the Markov chain algorithm to parse the risk transition probability function, deduce the evolution trajectory of financial risk status in the future period, and generate financial management risk control prediction results.

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