An enterprise financial risk intelligent monitoring and analyzing method based on big data

Abstract

This invention discloses a big data-based intelligent monitoring and analysis method for enterprise financial risks, belonging to the field of enterprise financial risk management technology. The method includes constructing a multi-head attention network using a macro-cycle embedding vector as the query benchmark and a standardized financial indicator matrix as key-value pairs, and performing dynamic feature weighting to obtain a dynamic risk weight vector and a comprehensive risk index. Continuous intensity values ​​and the dynamic risk weight vector are input into a macro-gating function and subjected to nonlinear threshold evolution operations through a fractional-order generalized integral operator to output a dynamic risk threshold sequence. The comprehensive risk index is compared with the dynamic threshold sequence on a periodic basis, and a risk warning trigger signal is generated by combining the indicator with the largest weight value in the dynamic risk weight vector. This invention achieves multi-head context alignment and gradient stable propagation between macro-cycle latent state representation and standardized financial indicator matrix within a big data analysis framework.

Description

Technical Field

[0001] This invention relates to the field of enterprise financial risk management technology, and in particular to an intelligent monitoring and analysis method for enterprise financial risk based on big data. Background Technology

[0002] In recent years, with the exponential growth of corporate financial data and the increasing complexity of the macroeconomic environment, financial risk monitoring technology based on big data analytics has evolved from traditional static indicator analysis to dynamic time-series modeling and multimodal feature fusion. Existing technologies widely incorporate recurrent neural networks and attention mechanisms, aiming to achieve risk warnings by extracting the hidden states of historical financial sequences through big data analytics platforms. Meanwhile, the synergistic representation of macroeconomic cyclical fluctuations and microeconomic financial indicators has gradually become a research hotspot in industry. Some solutions attempt to use variational mode decomposition and phase synchronization measurements to remove cyclical features, and leverage information entropy optimization or functional integral theory within the framework of big data analytics to weighted aggregate multidimensional risk signals.

[0003] However, existing monitoring architectures still have significant limitations in complex market environments. On the one hand, traditional threshold generation mechanisms mostly rely on fixed quantile truncation or linear smoothing filtering, lacking a manifold characterization of nonlinear mutations and long-range memory coupling during macroeconomic phase transitions. This results in a severe lag in early warning triggering during big data analysis processes, making it difficult to adapt to the structural switching between cyclical expansion and recession. On the other hand, existing feature aggregation networks mostly use linear projection mapping in Euclidean space, failing to effectively eliminate cross-modal attitude quantity mismatch and high-frequency transient disturbances. This causes distortions in the risk evolution trajectory of multidimensional financial indicators under coherence decay or oscillatory divergence scenarios, significantly limiting the accuracy of comprehensive assessment. Summary of the Invention

[0004] In view of the aforementioned existing problems, the present invention is proposed.

[0005] Therefore, this invention provides a big data-based intelligent monitoring and analysis method for enterprise financial risks to solve the problems of untimely threshold warnings and cross-modal mapping risk signal distortion and insufficient assessment accuracy in existing financial risk monitoring.

[0006] To solve the above-mentioned technical problems, the present invention provides the following technical solution: This invention provides a method for intelligent monitoring and analysis of enterprise financial risks based on big data, comprising: This process involves collecting multidimensional financial data from enterprises and sequences of external macroeconomic factors. Outlier filtering and quantile standardization mapping are used to obtain a standardized financial indicator matrix and raw macroeconomic time-series data. The raw macroeconomic time-series data is then input into a time-series phase recognition algorithm to extract cyclical fluctuation features, outputting continuous intensity values ​​and a macroeconomic cycle embedding vector. Using the macroeconomic cycle embedding vector as the query benchmark and the standardized financial indicator matrix as key-value pairs, a multi-head attention network is constructed, and dynamic feature weighting is applied to obtain a dynamic risk weight vector and a comprehensive risk index. The continuous intensity values ​​and dynamic risk weight vector are input into a macroeconomic gating function and subjected to nonlinear threshold evolution operations using a fractional-order generalized integrator, outputting a dynamic risk threshold sequence. The comprehensive risk index is compared with the dynamic threshold sequence on a periodic basis, and combined with the indicator with the largest weight value in the dynamic risk weight vector, a risk warning trigger signal is generated. Warning level labels and dominant risk indicator identifiers are extracted from the risk warning trigger signal. An automated risk control instruction stream is generated using a tiered disposal strategy, and actual performance data is obtained.

[0007] As a preferred embodiment of the intelligent monitoring and analysis method for enterprise financial risks based on big data described in this invention, the specific steps for obtaining the standardized financial indicator matrix and the original macroeconomic time-series data are as follows: We collect multidimensional financial data of enterprises and external macroeconomic factor sequences, and eliminate disclosure time lag differences through cross-frequency dynamic alignment processing to obtain a time-axis synchronous data stream; we then perform outlier filtering on the time-axis synchronous data stream to obtain a clean time series sequence free of noise interference. Empirical quantile standardization mapping is performed on the pure time series, and the data dimensions are unified by inverse normal distribution transformation while retaining the characteristics of extreme risk distribution, resulting in a standardized financial indicator matrix and the original macroeconomic time series data.

[0008] As a preferred embodiment of the intelligent monitoring and analysis method for enterprise financial risks based on big data described in this invention, the specific steps for inputting the original macroeconomic time-series data into the time-series phase recognition algorithm and extracting periodic fluctuation features are as follows: The original macroscopic time series data is input into the time series phase recognition algorithm, and multi-scale intrinsic mode function sequence is output through multi-scale frequency band stripping; Perform Hilbert transform on the multi-scale intrinsic mode function sequence to obtain a complex analytic signal, and extract the instantaneous phase angle from the complex analytic signal; The delayed phase space trajectory is constructed using the instantaneous phase angle to generate the instantaneous phase trajectory matrix; the instantaneous phase trajectory matrix is ​​then subjected to geometric metric fusion mapping to obtain the periodic fluctuation characteristics that characterize the evolution of the macroeconomic cycle.

[0009] As a preferred embodiment of the intelligent monitoring and analysis method for enterprise financial risks based on big data described in this invention, the specific steps for outputting the continuous intensity value and the macroeconomic cycle embedding vector are as follows: Spatial dimensionality reduction and redundant mode stripping are performed on the periodic fluctuation characteristics to obtain an initial latent vector sequence; the phase coherence scalar is calculated using the initial latent vector sequence, and continuous intensity values ​​are generated by mapping through a nonlinear activation function; The continuous intensity values ​​and the initial latent vector sequence are concatenated by a gated tensor, and the magnitude fluctuations are eliminated by normalization using the hyperspherical norm to obtain the macroscopic periodic embedding vector.

[0010] As a preferred embodiment of the intelligent monitoring and analysis method for enterprise financial risks based on big data described in this invention, the specific steps for constructing the multi-head attention network are as follows: The macro-cycle embedding vector and the standardized financial indicator matrix are subjected to cross-modal linear mapping in the projection layer of the multi-head attention network. The macro-cycle embedding vector is mapped to a query tensor, and the standardized financial indicator matrix is ​​decomposed into key tensors and value tensors, generating a combination of query tensors, key tensors and value tensors corresponding to each attention head. The query tensor, key tensor, and value tensor are combined to perform dot product similarity operation and feature dimension scaling and normalization in the weight layer of a multi-head attention network. The causal temporal mask matrix is ​​then fused, and a probability distribution weight matrix is ​​generated through a probability distribution normalization operator. The probability distribution weight matrix is ​​used to perform matrix multiplication with the corresponding value tensor in the aggregation layer of the multi-head attention network, and the context features of each independent head are concatenated along the feature dimension to obtain the multi-head aggregation tensor; Global linear projection operations are performed on the recovery layer of the multi-head attention network using the multi-head aggregation tensor to restore the dimension of the target features. Then, by superimposing the identity residual mapping at the input end, layer normalization operations are performed to eliminate cross-modal gradient drift, generate a dynamic risk feature representation matrix, and obtain the multi-head attention network.

[0011] As a preferred embodiment of the intelligent monitoring and analysis method for enterprise financial risks based on big data described in this invention, the specific steps for obtaining the dynamic risk weight vector and the comprehensive risk index are as follows: The variational information entropy optimization operator is constructed by using the dynamic risk feature representation matrix to perform sparse regularization, resulting in a dynamic risk weight vector. The dynamic risk weight vector is synchronously loaded into the dynamic risk feature representation matrix, and the multidimensional risk signal weighted aggregation and trajectory reconstruction are performed through path integral functional operation to obtain the risk evolution sequence. The risk evolution sequence is coupled with the time-series decay kernel and the covariance regularization term, and the comprehensive risk index is calculated through deterministic nonlinear integral mapping.

[0012] As a preferred embodiment of the intelligent monitoring and analysis method for enterprise financial risks based on big data described in this invention, the specific steps for outputting the dynamic risk threshold sequence are as follows: A phase transition observation mapping operation is performed on the continuous intensity value and the dynamic risk weight vector. The continuous intensity value is projected onto the Riemann curvature space to obtain the state transition Jacobian matrix. The manifold coordinate transformation is performed on the state transition Jacobian matrix and the dynamic risk weight vector using the even tangent space mapping operation to obtain the conjugate gradient flow. The conjugate gradient flow is then subjected to a symplectic geometric outer product operation to fuse the macroscopic coherence and financial weight topological correlation information to generate a macroscopic gated kernel tensor. The macro-gating function calls the macro-gating kernel tensor and performs nonlinear threshold evolution operation through the fractional generalized integral operator. The macro-gating kernel tensor and the historical risk index sampling data are substituted into the integral kernel function for time-domain convolution to obtain the dynamic risk threshold benchmark sequence. The dynamic risk threshold benchmark sequence is filtered by first-order exponential moving average and subjected to tail correction operation based on extreme value theory. The forgetting coefficient moving average operator is applied to eliminate high-frequency macro data jitter and obtain a smooth benchmark sequence. The smooth benchmark sequence is superimposed with the generalized Pareto distribution inverse cumulative distribution function to calibrate the lower limit boundary of the threshold under extreme fluctuation scenarios, and then reorganized into a continuous time data stream to generate a dynamic risk threshold sequence.

[0013] As a preferred embodiment of the intelligent monitoring and analysis method for enterprise financial risks based on big data described in this invention, the specific steps for generating the risk warning trigger signal are as follows: The deviation of the comprehensive risk index and the dynamic threshold sequence is compared period by period, and the deviation ratio of each assessment time point is calculated. By using the superimposed index decay weight and the sliding window filtering, transient fluctuation interference is eliminated, and a normalized deviation sequence is generated. Based on the normalized deviation sequence, the dynamic risk weight vector is traversed to locate the financial indicator dimension with the largest weight value. The normalized deviation sequence is then subjected to the Hadamard product with the largest weight value, and the contribution of the core risk feature is amplified to obtain the dominant deviation feature vector. A nonlinear confidence mapping operator is constructed using the dominant deviation feature vector, and cumulative risk integration is performed. When the integral output value exceeds the preset graded warning threshold boundary, the state reversal logic is triggered to generate a high confidence risk warning trigger signal.

[0014] As a preferred embodiment of the intelligent monitoring and analysis method for enterprise financial risks based on big data described in this invention, the specific steps for extracting early warning level labels and dominant risk indicator identifiers are as follows: The risk warning trigger signal is mapped by geodesic curvature dimensionality reduction using a manifold state decoder to obtain the risk latent state representation vector; the risk latent state representation vector is coupled with a free energy functional to perform confidence integration to obtain the warning level label. Based on the warning level label and the risk latent characterization vector, a sparse inverse Jacobian sensitivity matrix is ​​constructed, and the dominant risk indicator is generated through orthogonal matching tracking.

[0015] As a preferred embodiment of the intelligent monitoring and analysis method for enterprise financial risks based on big data described in this invention, the specific steps for obtaining actual performance result data are as follows: The warning level labels and the dominant risk indicator identifiers are mapped to the graded disposal strategy library, and the risk potential representation vector is coupled to determine the dynamic safety boundary constraints and generate an automated risk control instruction flow. The automated risk control command stream is deployed to the event-driven execution bus to issue intervention actions, and the intervention response sequence fed back by distributed nodes is captured synchronously to obtain the original performance feedback data stream; Delay deviation correction and tail outlier removal are performed on the original performance feedback data stream, and the actual intervention effect trajectory is restored to obtain the actual performance result data.

[0016] The beneficial effects of this invention are as follows: By constructing a multi-head attention network to perform cross-modal linear mapping, causal temporal mask weighting, and identity residual layer normalization operations, multi-head context alignment and gradient stable propagation of macro-cycle latent state representation and standardized financial indicator matrix under the big data analysis framework are achieved, thus suppressing the oscillating divergence distortion of multi-dimensional financial indicators; By performing differential geometric fusion mapping on continuous intensity values ​​and dynamic risk weight vectors, driving fractional-order generalized integral operators to perform nonlinear threshold evolution and extreme tail correction, manifold-level coupling and long-range memory adaptive boundary characterization of macroeconomic phase transition characteristics driven by big data analysis and micro-financial weight topology are achieved, thus achieving the effect of dynamic risk threshold sequence strictly following the economic cycle structure switching adaptive convergence and divergence and eliminating early warning lag. Attached Figure Description

[0017] To more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings used in the following description of the embodiments will be briefly introduced. Obviously, the 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.

[0018] Figure 1 This is a flowchart of a big data-based intelligent monitoring and analysis method for corporate financial risks.

[0019] Figure 2A flowchart for obtaining the macroscopic periodic embedding vector.

[0020] Figure 3 This is a flowchart for calculating the comprehensive risk index.

[0021] Figure 4 A flowchart for generating risk warning trigger signals. Detailed Implementation

[0022] To make the above-mentioned objects, features and advantages of the present invention more apparent and understandable, the specific embodiments of the present invention will be described in detail below with reference to the accompanying drawings.

[0023] Many specific details are set forth in the following description in order to provide a full understanding of the invention. However, the invention may also be practiced in other ways different from those described herein, and those skilled in the art can make similar extensions without departing from the spirit of the invention. Therefore, the invention is not limited to the specific embodiments disclosed below.

[0024] Secondly, the term "one embodiment" or "embodiment" as used herein refers to a specific feature, structure, or characteristic that may be included in at least one implementation of the present invention. The phrase "in one embodiment" appearing in different places in this specification does not necessarily refer to the same embodiment, nor is it a single or selective embodiment that is mutually exclusive with other embodiments.

[0025] Reference Figures 1-4 This is one embodiment of the present invention, which provides a method for intelligent monitoring and analysis of enterprise financial risks based on big data, including the following steps: S1. Collect multidimensional financial data of enterprises and external macroeconomic factor sequences, and obtain standardized financial indicator matrix and original macroeconomic time series data through outlier filtering and quantile standardization mapping.

[0026] We collect multidimensional financial data of enterprises and external macroeconomic factor sequences, and eliminate disclosure time lag differences through cross-frequency dynamic alignment processing to obtain a time-axis synchronized data stream. We then perform outlier filtering on the time-axis synchronized data stream to obtain a pure time series sequence free of noise interference.

[0027] The specific process includes: collecting multidimensional financial data of enterprises and external macroeconomic factor sequences through the enterprise financial database interface and the data channel of the macroeconomic statistics platform; mapping multidimensional financial data of enterprises and external macroeconomic factor sequences of different release cycles to a unified time axis system through cross-frequency dynamic alignment processing; the quarterly update mechanism of financial reports and the monthly update mechanism of macroeconomic statistics may have time misalignment due to differences in disclosure time lag, which can be eliminated through cross-frequency dynamic alignment processing; and converting discrete data nodes into continuous time series data based on a unified time axis system to obtain a time axis synchronized data stream.

[0028] It should be noted that the quarterly financial report update mechanism is a time-series release rule for the compilation and disclosure of multidimensional financial data of enterprises according to the accounting quarter cycle. The multidimensional financial data of enterprises is constrained by the quarterly financial report update mechanism and exhibits a low-frequency discrete release characteristic. The monthly macroeconomic statistics update mechanism is a time-series statistical rule for the collection and release of external macroeconomic factor sequences according to the natural month cycle. The external macroeconomic factor sequences are constrained by the monthly macroeconomic statistics update mechanism and exhibit a high-frequency continuous release characteristic.

[0029] By using a sliding window statistical mechanism to traverse the numerical distribution characteristics of each time node within the time axis synchronous data stream, the arithmetic mean and dispersion index of the time axis synchronous data stream within the sliding window are obtained. A standard deviation threshold is set for the outlier judgment boundary. Abnormal fluctuation nodes in the time axis synchronous data stream that exceed the judgment boundary are marked as invalid sampling points. The context numerical reconstruction of the marked invalid sampling points is performed using a linear interpolation algorithm to eliminate sudden jumps caused by transmission errors within the time axis synchronous data stream and remove high-frequency noise interference components from the time axis synchronous data stream, restoring the time axis synchronous data stream to a stable and continuous trend, and obtaining a pure time series sequence with noise interference removed.

[0030] It should be noted that the standard deviation threshold is pre-determined based on the local window dispersion distribution of the time axis synchronous data stream in historical samples, the cross-cycle fluctuation range of the enterprise's multidimensional financial data and external macroeconomic factor sequences, and the stability of outlier filtering. For example, after the time axis synchronous data stream is processed by the sliding window arithmetic mean and dispersion index to generate a standardized deviation sequence, the standard deviation threshold can be set to 2.0 to 4.0. If it is lower than this range, it means that data points within the normal fluctuation range are misjudged as outliers, resulting in the loss of effective risk signals. If it exceeds this range, it means that abnormal discrete nodes have not been effectively removed, causing residual noise interference risk.

[0031] Empirical quantile standardization mapping is performed on the pure time series, and the data dimensions are unified by inverse normal distribution transformation while retaining the characteristics of extreme risk distribution, resulting in a standardized financial indicator matrix and the original macroeconomic time series data.

[0032] The specific process includes: using empirical quantile standardization mapping analysis to determine the cumulative probability position of each time point value in the pure time series within the overall distribution, thus establishing quantile location; using inverse normal distribution transformation to map each quantile position to the corresponding quantile point in the standard normal distribution, generating a unified data dimension, and obtaining multidimensional financial data of enterprises and external macroeconomic factor sequences that conform to the standard normal distribution, while retaining the extreme risk distribution characteristics in the quantile mapping process; based on the pure time series, converting the multidimensional financial data of enterprises into a standardized financial indicator matrix, while keeping the external macroeconomic factor sequence as the original macroeconomic time series data, thus obtaining the standardized financial indicator matrix and the original macroeconomic time series data.

[0033] S2. Input the original macroscopic time series data into the time series phase recognition algorithm, extract the periodic fluctuation features, and output the continuous intensity value and macroscopic period embedding vector.

[0034] The original macroscopic time series data is input into the time series phase recognition algorithm, and multi-scale intrinsic mode function sequence is output through multi-scale frequency band stripping.

[0035] The specific process includes: using a time-series phase recognition algorithm to screen local extrema and fit envelopes in the original macroscopic time-series data; separating the intertwined high-frequency fluctuation components and low-frequency trend components in the original macroscopic time-series data layer by layer through multi-scale frequency band stripping; extracting oscillation components in different frequency bands and converting them into fluctuation functions that satisfy single-scale conditions; maintaining the phase continuity of each fluctuation function on the time axis; decoupling high-frequency interference and macroscopic periodic signals at the frequency domain level; reconstructing the superimposed waveforms in the original macroscopic time-series data into a set of independent components; and outputting a multi-scale intrinsic mode function sequence.

[0036] It should be noted that the time-series phase recognition algorithm refers to a nonlinear signal processing method that decomposes the original macroscopic time-series data according to the multi-scale frequency band stripping rule, and then extracts the periodic phase characteristics based on the evolution position of the instantaneous phase angle in the delayed phase space trajectory. The single-scale condition is a mathematical constraint standard in the signal decomposition process that each independent oscillation component contains only a single characteristic time scale or frequency band range. When a component satisfies that the difference between the number of local extrema and the number of zero crossings does not exceed one point and the local mean of the upper and lower envelopes approaches zero, it is determined that the component has eliminated the mode mixing phenomenon and completed the convergence judgment of the single-scale condition.

[0037] Perform a Hilbert transform on the multi-scale intrinsic mode function sequence to obtain a complex analytic signal, and extract the instantaneous phase angle from the complex analytic signal.

[0038] The specific process includes: mapping the multi-scale intrinsic mode function sequence to the complex domain space based on the orthogonal projection principle; constructing a conjugate imaginary part vector for each independent component in the multi-scale intrinsic mode function sequence through Hilbert transform; obtaining the complete complex expression by superimposing and combining the real part values ​​and conjugate imaginary part vectors of the multi-scale intrinsic mode function sequence, and generating a complex analytic signal with amplitude envelope information and phase evolution trajectory information; solving the ratio of the real part value and the imaginary part value of the complex analytic signal using the arctangent function operation to obtain the angular offset of the complex analytic signal at each sampling point on the time axis; quantifying the instantaneous position state of the oscillating component inside the macroscopic period through the angular offset, and extracting the instantaneous phase angle from the complex analytic signal.

[0039] The delayed phase space trajectory is constructed using the instantaneous phase angle to generate the instantaneous phase trajectory matrix; the instantaneous phase trajectory matrix is ​​then subjected to geometric metric fusion mapping to obtain the periodic fluctuation characteristics that characterize the evolution of the macroeconomic cycle.

[0040] The specific process includes: reorganizing the instantaneous phase angle into a multidimensional state through a time lag interval, and converting the single-dimensional time-series angle into a multidimensional spatial coordinate point set; retaining the phase offset relationship between adjacent time nodes in the multidimensional spatial coordinate point set; and arranging and combining the instantaneous phase angle state vectors of different lag dimensions according to the time series to generate an instantaneous phase trajectory matrix.

[0041] A geometric metric fusion mapping is performed on the instantaneous phase trajectory matrix, and the coordinate point set inside the instantaneous phase trajectory matrix is ​​extracted. By analyzing the curvature parameters and manifold distances between the tangent vectors of the trajectory inside the instantaneous phase trajectory matrix, a multidimensional phase correlation structure is established. Based on the multidimensional phase correlation structure, the core components of the principal variance direction of the instantaneous phase trajectory matrix are extracted using orthogonal transformation. High-frequency disturbance trajectories in the process of processing the instantaneous phase trajectory matrix by geometric metric fusion mapping are removed, and the phase evolution patterns of the expansion and contraction phases of the macroeconomic cycle are aligned to obtain the periodic fluctuation characteristics that characterize the evolution law of the macroeconomic cycle.

[0042] Spatial dimensionality reduction and redundant mode stripping are performed on the periodic fluctuation characteristics to obtain an initial latent vector sequence. The phase coherence scalar is calculated using the initial latent vector sequence, and continuous intensity values ​​are generated by mapping through a nonlinear activation function.

[0043] The specific process includes: obtaining the eigenvalues ​​and eigenvectors of the periodic fluctuation feature covariance matrix through principal component analysis; transforming the high-dimensional state vectors of the periodic fluctuation feature through orthogonal projection space to generate a set of mutually independent orthogonal principal components, thus achieving spatial dimensionality reduction of the periodic fluctuation feature; quantifying the variance contribution rate distribution by analyzing the energy contribution ratio of each component in the orthogonal principal component set to the overall macroscopic fluctuation, setting an energy retention threshold, and eliminating redundant oscillation modes with variance contribution rates lower than the energy retention threshold; compressing and recombining the tensor through a time evolution sequence to generate a low-dimensional state matrix, and removing cross-correlation interference through feature dimension alignment to obtain an initial latent vector sequence.

[0044] The instantaneous phase angles of each component are extracted from the initial latent vector sequence, and the vector magnitudes are superimposed and the ratios are normalized. The phase synchronization aggregation degree of the initial latent vector sequence is transformed into a phase coherence scalar within a standardized closed interval. The phase coherence scalar is smoothed by an exponential transformation through a nonlinear activation function mapping. The dynamic range of the phase coherence scalar is processed by nonlinear stretching to obtain continuous quantization of the macroscopic periodic resonance intensity and generate continuous intensity values.

[0045] It should be noted that the energy retention threshold is pre-determined based on the variance contribution distribution of the orthogonal principal component set in historical samples, the energy concentration of the macro-cycle core fluctuation trajectory, and the stability of redundant mode stripping. For example, after the orthogonal principal component set is generated into a cumulative variance explanation ratio sequence through eigenvalue decomposition, the energy retention threshold can be set to 0.85 to 0.95. If it is lower than this range, it indicates that the core cycle mode information is not retained enough, resulting in the loss of phase evolution characteristics. If it exceeds this range, it indicates that redundant noise modes have not been effectively removed, causing gradient drift risk.

[0046] The expression for calculating the phase coherence scalar is: ; in, A scalar representing the phase coherence degree within the latent characteristic space of macroeconomic cycles; This represents the total number of valid macroeconomic cycle sub-modes retained; Indicates the corresponding number of carriers Instantaneous state vector of instantaneous phase angle and amplitude evolution information of each macroscopic sub-band; An index representing the mode number of each macroscopic sub-band; This indicates a very small positive value.

[0047] The continuous intensity values ​​and the initial latent vector sequence are concatenated by a gated tensor, and the magnitude fluctuations are eliminated by normalization using the hyperspherical norm to obtain the macroscopic periodic embedding vector.

[0048] The specific process includes: broadcasting continuous intensity scalar values ​​as multidimensional feature vectors through tensor dimension expansion; concatenating the multidimensional feature vectors of continuous intensity values ​​with the initial latent vector sequence in the feature dimension axis to generate a composite tensor structure; based on the composite tensor structure, the macroscopic periodic energy amplitude and phase evolution information are normalized by hyperspheric norm and transformed by unit hyperspheric projection, and the lengths of each dimension vector of the composite tensor structure are forcibly constrained to a fixed radius boundary; the fixed radius boundary constraint eliminates the modulus fluctuation phenomenon caused by cross-modal dimensional differences in the composite tensor structure, and keeps the phase relative topological relationship of the composite tensor structure unchanged; the phase relative topological relationship is converted into a standardized multidimensional feature expression through tensor recombination, and macroscopic periodic embedding vectors are generated by aligning feature space coordinates to obtain the macroscopic periodic embedding vectors.

[0049] S3. Using the macro cycle embedding vector as the query benchmark and the standardized financial indicator matrix as the key-value pair, a multi-head attention network is constructed, and dynamic feature weighting is performed to obtain the dynamic risk weight vector and the comprehensive risk index.

[0050] The macro-cycle embedding vector and the standardized financial indicator matrix are subjected to cross-modal linear mapping in the projection layer of the multi-head attention network. The macro-cycle embedding vector is mapped to a query tensor, and the standardized financial indicator matrix is ​​decomposed into key tensors and value tensors, generating a combination of query tensors, key tensors and value tensors corresponding to each attention head.

[0051] The specific process includes: cross-modal linear mapping is performed by constructing a set of independent projection weight matrices and then performing a high-dimensional linear projection transformation on the macro-periodic embedding vector in the projection layer of the multi-head attention network to generate a query tensor; the set of independent projection weight matrices performs orthogonal feature space decomposition on the standardized financial indicator matrix and decomposes the standardized financial indicator matrix into key tensors representing modal matching relationships and value tensors carrying numerical distribution information; the query tensor, key tensor, and value tensor are dimensionally sliced ​​along the multi-head parallel channel, and the dimensional slice results are distributed according to the number of attention heads; the tensor routing distribution results are reorganized with local feature dimension alignment, and the reorganized tensor array is used for channel isolation and feature binding to generate a combination of query tensors, key tensors, and value tensors corresponding to each attention head.

[0052] The multi-head attention network comprises a projection layer, a weight layer, an aggregation layer, and a recovery layer. Trainable parameters include cross-modal mapping parameters, dimensionality scaling parameters, attention weight parameters, residual fusion parameters, and context aggregation parameters. The training process employs a step-by-step approach. The first training iteration inputs a macroeconomic cycle embedding vector and a standardized financial indicator matrix, performing a forward propagation operation to output a cross-modal representation tensor and learn the cross-modal spatial mapping between the macroeconomic phase representation and the standardized financial indicator matrix. The second training iteration inputs a causal time-series mask matrix and dynamic risk weight baseline labels, calls the mean squared error loss function, calculates the prediction bias, sets a minimum global error optimization objective, and uses an adaptive moment estimator optimizer to perform dynamic learning rate gradient updates, learning the macroeconomic cycle at different time steps. The high-order temporal causal relationship between the period-driven and financial characteristic responses is established. In the third training iteration, joint forward propagation is performed on the projection layer, weight layer, aggregation layer, and recovery layer. The mean squared error deviation between the dynamic risk feature representation matrix and the preset financial risk benchmark label is used to synchronously correct all trainable parameters. In the fourth training iteration, the feature dimension scaling and normalization boundary is corrected based on the correspondence between the probability distribution weight matrix and the actual risk weight distribution. The calculation is terminated when the mean squared error loss function value converges to the preset tolerance threshold or reaches the maximum number of iterations, resulting in a multi-head attention network. Step-by-step training gradually completes cross-modal feature alignment learning, temporal causal relationship learning, and global gradient optimization, effectively suppressing the oscillation and divergence distortion of multi-dimensional indicators and improving training stability and the robustness of risk warning representation.

[0053] By combining query tensors, key tensors, and value tensors in the weight layer of a multi-head attention network, dot product similarity operations and feature dimension scaling and normalization are performed. A causal temporal mask matrix is ​​then fused, and a probability distribution weight matrix is ​​generated through a probability distribution normalization operator.

[0054] The specific process includes: combining query tensors, key tensors, and value tensors to perform dot product similarity operations in the weight layer of a multi-head attention network to generate a similarity metric matrix; then scaling and normalizing the similarity metric matrix to eliminate gradient imbalance caused by overflow of inner product amplitude in high-dimensional space; fusing the scaled similarity matrix (which eliminates gradient imbalance) with the causal temporal masking matrix; and using historical temporal masking to force the values ​​corresponding to the similarity metric matrix at future time nodes to a negative infinity state to generate a temporal masking matrix; then using a probability distribution normalization operator to perform exponential mapping and row-wise summation and division to decay the negative infinity state values ​​to zero, and converting the effective attention score into a continuous probability distribution with a sum of one; finally, quantifying the cross-modal feature attention intensity through the continuous probability distribution to generate a probability distribution weight matrix.

[0055] The probability distribution weight matrix is ​​used to perform matrix multiplication with the corresponding value tensor in the aggregation layer of the multi-head attention network, and the context features of each independent head are concatenated along the feature dimension to obtain the multi-head aggregation tensor.

[0056] The specific process includes: performing matrix multiplication with the corresponding value tensor on the aggregation layer of the multi-head attention network using the probability distribution weight matrix; mapping the attention allocation ratio of the probability distribution weight matrix to the numerical space of the corresponding value tensor to generate a weighted feature representation; extracting local context information by grouping according to independent attention channels to obtain the context features of each independent head; performing tensor concatenation operation on the context features of each independent head along the feature dimension; arranging and combining the local representation results of the parallel computing channels in order into a complete feature sequence; and performing cross-channel information integration and dimension reorganization operations; eliminating the fragmentation of multi-channel data using dimension reorganization operations and mapping it uniformly to a shared representation space; and performing feature dimension alignment processing using the continuous tensor structure in the shared representation space to obtain the multi-head aggregation tensor.

[0057] Global linear projection operations are performed on the recovery layer of the multi-head attention network using the multi-head aggregation tensor to restore the dimension of the target features. Then, by superimposing the identity residual mapping at the input end, layer normalization operations are performed to eliminate cross-modal gradient drift, generate a dynamic risk feature representation matrix, and obtain the multi-head attention network.

[0058] The specific process includes: global linear projection operation using a projection transformation matrix to transform the feature space coordinates of the multi-head aggregation tensor, aligning the parallel channel dimensions of the multi-head aggregation tensor to a preset output size, and using the output size to recover the target feature dimension, resulting in a dimension-restored tensor; based on the identity residual mapping at the superimposed input end, element-wise addition is performed on the dimension-restored tensor and the initial data of the multi-head attention network to obtain a residual fusion tensor; layer normalization operation is used to correct the channel mean offset and scale the variance of the residual fusion tensor to eliminate the cross-modal gradient drift accumulated by the multi-layer linear mapping and converge the numerical distribution of the residual fusion tensor to a stable statistical interval; multi-dimensional risk context representation is reorganized on the feature vectors within the stable statistical interval to generate a dynamic risk feature representation matrix; parameters are solidified to solidify the complete mapping link from the projection layer to the recovery layer of the multi-head attention network, resulting in the multi-head attention network.

[0059] It should be noted that the output size is determined by selecting the range of feature energy proportions that can distinguish the core risk transmission path from redundant noise interference, based on the contribution distribution between each dimension of the dynamic risk feature representation matrix and the comprehensive risk index in historical samples; the stable statistical interval is dynamically generated by performing channel mean offset correction and variance scaling operations on the residual fusion tensor through layer normalization operation.

[0060] By constructing a variational information entropy optimization operator using a dynamic risk feature representation matrix and performing sparse regularization, a dynamic risk weight vector is obtained.

[0061] The specific process includes: constructing a variational information entropy optimization operator through a dynamic risk feature representation matrix to perform multidimensional feature uncertainty measurement operations, obtaining the probability distribution divergence of each dimension of the dynamic risk feature representation matrix, generating an information entropy gradient tensor, and performing a norm penalty term superposition operation on the information entropy gradient tensor using sparse regularization processing to generate a sparsified feature mapping matrix; using the sparsified feature mapping matrix to remove redundant correlations and retain the core risk transmission path, and transforming the core risk transmission path into a sequence of importance scores for each dimension through normalized weight allocation operations to generate a dynamic risk weight vector.

[0062] The dynamic risk weight vector is synchronously loaded into the dynamic risk feature representation matrix, and the multidimensional risk signal weighted aggregation and trajectory reconstruction are performed through path integral functional operation to obtain the risk evolution sequence. The risk evolution sequence is coupled with the time-series decay kernel and the covariance regularization term, and the comprehensive risk index is calculated through deterministic nonlinear integral mapping.

[0063] The specific process includes mapping the importance scores of each dimension of the dynamic risk weight vector to the corresponding feature channels of the dynamic risk feature representation matrix to generate a weighted risk feature matrix; performing multi-dimensional risk signal superposition and integration operations in the continuous time domain through path integral functional operations; accumulating risk energy distribution along the observation time window to complete the smooth reconstruction of the state space trajectory; based on the smooth reconstruction of the state space trajectory, converting the discrete weighted risk feature matrix into a continuous evolution path and eliminating local measurement noise interference; mapping the intervention actions and actual feedback causal chains of the continuous evolution path to obtain the risk evolution sequence.

[0064] The risk evolution sequence is subjected to time-series decay kernel weight allocation operation through coupling processing, and continuous exponential decay weight is applied to the historical long-term risk energy along the time observation axis to generate a decay risk sequence. The decay risk sequence is collinearly corrected by using a covariance regularization term, and cross-index linear overlap interference is eliminated by matrix diagonalization to output a regularized risk trajectory. The regularized risk trajectory is transformed into a global convergent scalar by continuous time domain exponential transformation and quadratic norm integration through deterministic nonlinear integral mapping, and the cumulative fluctuation energy distribution of the regularized risk trajectory is converted into a global convergent scalar. The global convergent scalar is then subjected to a unified quantitative mapping operation of macro-cyclic risk intensity through feature dimension compression to obtain a comprehensive risk index.

[0065] The expression for calculating the comprehensive risk index is: ; in, This represents the comprehensive risk scalar; T represents the total monitoring duration. The decay rate parameter represents the weight allocation ratio in the current assessment, and its value ranges from 0.01 to 0.5. This represents the timestamp for risk status sampling from the start of monitoring to the assessment time; Indicates in The instantaneous state vector output at time t; This indicates the overall volatility intensity of the risk characteristic dimension obtained by using the trace operation of the covariance matrix; The dynamic risk feature represents the covariance regularization matrix between the features of each dimension in the matrix; This indicates a very small positive value.

[0066] It should be noted that the overall volatility intensity is a scalar indicator that quantifies the combined oscillation amplitude of a company's multidimensional financial data and external macroeconomic factor sequences within the observation window, serving as a dynamic volatility benchmark reference for setting outlier filtering and standard deviation thresholds.

[0067] S4. Input the continuous intensity value and dynamic risk weight vector into the macro-gating function and perform nonlinear threshold evolution operation through the fractional generalized integrator operator to output the dynamic risk threshold sequence. Compare the deviation between the comprehensive risk index and the dynamic threshold sequence period by period, and combine the index with the largest weight value in the dynamic risk weight vector to generate a risk warning trigger signal.

[0068] A phase transition observation mapping operation is performed on the continuous intensity value and the dynamic risk weight vector. The continuous intensity value is projected onto the Riemann curvature space to obtain the state transition Jacobian matrix. The state transition Jacobian matrix and the dynamic risk weight vector are transformed into manifold coordinates using the even tangent space mapping operation to obtain the conjugate gradient flow. The conjugate gradient flow is then subjected to a symplectic geometric outer product operation to fuse the macroscopic coherence and financial weight topological correlation information to generate a macroscopic gated kernel tensor.

[0069] The specific process includes: performing phase transition observation mapping operations on continuous intensity values ​​and dynamic risk weight vectors; extracting the instantaneous rate of change of continuous intensity values ​​in the time evolution dimension; constructing a differential geometric projection path; embedding the dynamic trajectory of continuous intensity values ​​into the Riemann curvature space; analyzing the local geometric deformation characteristics of continuous intensity values ​​in each orthogonal coordinate axis direction; generating a curvature gradient field; and performing first-order partial derivative matrix transformation operations to complete the quantization of state space transformation rate; performing dimension alignment and reorganization of the quantized state space transformation rate along the feature channel of the risk weight vector; and fully recording the dimension-aligned and reorganized partial derivative tensor array to obtain the cross-coupling relationship between macro-cycle coherence and financial weight topological correlation information; and obtaining the state transition Jacobian matrix through tensor dimension compression and linear space mapping.

[0070] The state transition Jacobian matrix and the dynamic risk weight vector are transformed into manifold coordinates using the even-tangent space mapping operation. The local rate of change of the state transition Jacobian matrix is ​​coupled with the characteristic gradient of the dynamic risk weight vector through an inner product to generate an initial gradient field. The covariant derivative of the initial gradient field is calculated through the symplectic geometric projection operation to generate a direction correction vector. The historical trajectory is orthogonally projected and culled using the conjugate orthogonalization operation to eliminate redundant search paths and generate a pure optimized trajectory. The pure optimized trajectory is aligned along the characteristic channel to complete the geometric locking of the risk transmission path. Finally, the conjugate gradient flow is generated through tensor flow field reorganization.

[0071] The conjugate gradient flow is expanded by vector antisymmetric multiplication through symplectic geometric outer product operation to analyze the directional characteristics of the conjugate gradient flow. A high-dimensional cross-product matrix is ​​constructed to achieve deep fusion of macroscopic coherence and financial weight topological correlation information, generating a topologically coupled feature field. The topologically coupled feature field is then aligned and mapped using tensor dimension reorganization operation to convert the fused correlation trajectory into a standardized multidimensional control parameter array. The coordinate axes are orthogonalized and calibrated through manifold space projection transformation to eliminate cross-modal dimensional bias and generate a macroscopic gated kernel tensor.

[0072] It should be noted that the symplectic geometric external product operation refers to a nonlinear tensor mapping method that, after performing manifold phase space analysis on the conjugate gradient flow, performs directional feature extraction and deep fusion of macroscopic coherence and financial weight topological correlation information during the construction of the high-dimensional cross-product matrix according to the antisymmetric multiplication rule of vectors, generating a topologically coupled feature field. The even-tangent space mapping operation refers to a differential geometric mapping method that, after performing manifold coordinate transformation on the state transition Jacobian matrix and the dynamic risk weight vector, performs covariant gradient form transformation operation during the covariant basis reconstruction according to the cotangent space metric tensor elevation rule, eliminating Euclidean linear projection distortion and generating the conjugate gradient flow.

[0073] The macro-gating function calls the macro-gating kernel tensor and performs nonlinear threshold evolution operations through a fractional-order generalized integral operator. The macro-gating kernel tensor and historical risk index sampling data are substituted into the integral kernel function for time-domain convolution to obtain the dynamic risk threshold benchmark sequence.

[0074] The specific process includes: the macro-gating function analyzes the multi-dimensional control parameters inside the macro-gating kernel tensor using a fractional-order generalized integral operator, generates a non-integer-order differential-integral mapping rule, and uses this rule to drive the historical risk index sampling data into the integral kernel function for continuous time-axis rolling superposition; the macro-gating kernel tensor and the historical risk index sampling data undergo weight sliding matching within the integral kernel function to generate a time-domain convolution result; the time-domain convolution result is used to perform nonlinear numerical fusion of the historical risk fluctuation trajectory and the current macro-control intensity to generate a fusion sequence; the fusion sequence is used to reconstruct the threshold benchmark shape, eliminate discrete jumps and smooth the safety boundary contour, and complete the continuous time-domain data stream reorganization operation to obtain a dynamic risk threshold benchmark sequence.

[0075] It should be noted that nonlinear threshold evolution operation refers to an adaptive threshold calculation method that inputs the macroscopic gate kernel tensor and historical risk index sampling data into a fractional-order generalized integral operator, and then performs temporal convolution operations in the continuous time domain according to non-integer-order differential-integral mapping rules to output a dynamic risk threshold benchmark sequence. The macroscopic gate function takes the received continuous intensity values ​​and dynamic risk weight vector as input signals, uses the geometric modulation basis and cross-modal coupling parameters provided by the macroscopic gate kernel tensor as the core operational basis, performs tensor inner product convolution and nonlinear threshold calibration operations, and constructs a dynamic signal modulation mapping rule based on the macroscopic gate kernel tensor.

[0076] The dynamic risk threshold benchmark sequence is filtered by first-order exponential moving average and subjected to tail correction operation based on extreme value theory. The forgetting coefficient moving average operator is applied to eliminate high-frequency macro data jitter and obtain a smooth benchmark sequence. The smooth benchmark sequence is superimposed with the generalized Pareto distribution inverse cumulative distribution function to calibrate the lower limit boundary of the threshold under extreme fluctuation scenarios, and then reorganized into a continuous time data stream to generate a dynamic risk threshold sequence.

[0077] The specific process includes: performing a weighted attenuation operation on the dynamic risk threshold benchmark sequence using historical observations through a first-order exponential moving average filter to generate a preliminary smoothed sequence; then using the tail correction operation based on extreme value theory to redistribute the probability of extreme points exceeding the threshold to obtain a tail-corrected sequence; using the forgetting coefficient moving average operator to perform high-weighted iterative accumulation of recent data on the tail-corrected sequence to generate a weighted average trajectory; completing the high-frequency macro data jitter stripping operation to obtain the local fluctuation suppression result; and finally generating a smoothed benchmark sequence through continuous time domain data stream recombination.

[0078] The inverse cumulative distribution function of the generalized Pareto distribution is used to perform an inverse mapping operation on the extreme quantiles of the smoothed benchmark sequence, converting the numerical distribution of the smoothed benchmark sequence into a tail risk extreme value probability scalar, completing the lower limit boundary calibration of the threshold under extreme fluctuation scenarios, and generating calibration boundary parameters. The calibration boundary parameters are continuously spliced ​​and reassembled in time axis order to construct a complete time domain data chain, and the discrete node intervals are eliminated through time series index alignment operation. The continuous data stream after eliminating discrete node intervals is uniformly mapped in feature dimensions and reassembled into a continuous time data stream to generate a dynamic risk threshold sequence.

[0079] It should be noted that the forgetting coefficient moving average operator is obtained by jointly calibrating the attenuation factor parameter and the sliding window length parameter. The attenuation factor parameter is pre-determined based on the autocorrelation attenuation rate and noise frequency band distribution of historical data, and the sliding window length parameter is calculated based on the ratio of the average duration of the macroscopic period to the sampling frequency.

[0080] The comprehensive risk index and the dynamic threshold sequence are compared period by period to determine the deviation ratio at each assessment time point. By using superimposed exponential decay weights and performing sliding window filtering, transient fluctuation interference is eliminated to generate a normalized deviation sequence.

[0081] The specific process includes: calculating the algebraic difference between the instantaneous sampled value of the comprehensive risk index and the benchmark value of the dynamic threshold sequence during the same period through a period-by-period deviation comparison operation to generate a deviation difference sequence; performing dynamic threshold sequence denominator division and relative proportion conversion operations on the deviation difference sequence along each assessment time node to generate a period-by-period deviation ratio array; using superimposed exponential decay weights to exponentially decrease the weights of historical nodes in the period-by-period deviation ratio array to generate a weighted deviation matrix, and performing sliding window filtering on the weighted deviation matrix to complete the mean replacement operation within the local time window to generate a preliminary filtered sequence; removing transient fluctuation interference from the preliminary filtered sequence through high-frequency component stripping to obtain a pure deviation trajectory, and numerically distributing the pure deviation trajectory through extreme value interval scaling mapping to generate a normalized deviation sequence.

[0082] It should be noted that the exponential decay weight is pre-determined based on the autocorrelation decay characteristics of the period-by-period deviation ratio sequence in historical samples, the statistical index of the half-life of macro-cyclic fluctuations, and the frequency band distribution of transient noise. For example, after the period-by-period deviation ratio sequence is fitted with an autocorrelation function to generate the slope parameter of the decay curve, the exponential decay weight can be set to the range of 0.01 to 0.5. If it is lower than this range, it means that the long-term deviation residual has not been effectively suppressed, resulting in the normalized deviation sequence being interfered with by historical noise. If it is higher than this range, it means that the recent key deviation signal has been excessively decayed, weakening the sensitivity of risk warning triggering.

[0083] The expression for calculating the period-by-period deviation ratio is: ; in, Indicates the current assessment time The period-by-period deviation ratio of the comprehensive risk index relative to the dynamic risk threshold sequence; This represents the timestamp index for risk status sampling from the start of macro-monitoring to the completion of the current risk assessment; Indicates at time The instantaneous sampled value of the comprehensive risk index output after calculation; Indicates at time Input the continuous intensity value and the dynamic risk weight vector into the dynamic risk threshold benchmark instantaneous value output by the macro gating function; This indicates a very small positive value.

[0084] Based on the normalized deviation sequence, the dynamic risk weight vector is traversed to locate the financial indicator dimension with the largest weight value. The normalized deviation sequence is then subjected to the Hadamard product with the largest weight value, and the contribution of the core risk feature is amplified to obtain the dominant deviation feature vector.

[0085] The specific process includes: aligning the offset ratios of each time point in the normalized deviation sequence with the importance scores of each dimension within the dynamic risk weight vector through a dimension-by-dimensional cross-comparison mapping; extracting the channel corresponding to the highest value within the dynamic risk weight vector using extreme value retrieval operations to generate the financial indicator dimension identifier with the largest weight value; extracting the corresponding scalar value as the maximum weight value using the financial indicator dimension identifier with the largest weight value, and performing an element-by-element Hadamard product operation with the normalized deviation sequence to generate a weighted deviation product matrix; amplifying the contribution of core risk features in the weighted deviation product matrix through risk channel signal strength multiplication processing; and recombining the amplified weighted deviation product matrix through dimension compression to generate a single dominant risk trajectory, thus obtaining the dominant deviation feature vector.

[0086] It should be noted that the contribution of core risk features is a scalar indicator that quantifies the relative influence weight of each monitoring dimension on the evolution of the current risk state. It is composed of a standardized numerical sequence generated by tensor dot product operation and energy normalization of the non-zero sensitivity coefficient matrix elements in the sparse inverse Jacobian sensitivity matrix and the dynamic risk weight vector.

[0087] A nonlinear confidence mapping operator is constructed using the dominant deviation feature vector, and cumulative risk integration is performed. When the integral output value exceeds the preset graded warning threshold boundary, the state reversal logic is triggered to generate a high confidence risk warning trigger signal.

[0088] The specific process includes: using a nonlinear confidence mapping operator to perform a continuously differentiable exponential transformation operation on the dominant deviation feature vector to generate a confidence distribution curve; continuously superimposing risk energy within the observation window through cumulative risk integral operation to generate a cumulative risk integral value; comparing the cumulative risk integral value with the preset graded warning threshold boundary through threshold crossing judgment operation; triggering state reversal logic when the cumulative risk integral value breaks through the preset graded warning threshold boundary through threshold crossing judgment operation; transitioning the continuous accumulated value to a discrete warning level output through the state reversal logic; and deterministically marking the risk event through the discrete warning level output to generate a high-confidence risk warning trigger signal.

[0089] It should be noted that the tiered warning threshold boundary is selected based on the deviation distribution between the comprehensive risk index and the dynamic threshold sequence in historical samples, and the range of deviation ratio intensity that can distinguish between normal risk status and abnormal risk status is selected as the preset judgment condition for the tiered warning threshold.

[0090] S5. Extract the warning level label and the main risk indicator identifier from the risk warning trigger signal, generate an automated risk control instruction flow using the hierarchical handling strategy, and obtain actual performance result data.

[0091] The risk warning trigger signal is mapped by geodesic curvature dimensionality reduction using a manifold state decoder to obtain the risk latent state representation vector; the confidence integral operation is then performed using the risk latent state representation vector coupled with the free energy functional to obtain the warning level label.

[0092] The specific process includes: the risk warning trigger signal is mapped to geodesic curvature using a manifold state decoder, and the shortest path geometric features are extracted along the high-dimensional risk trajectory. Orthogonal projection compression is then performed to remove redundant dimensions of discrete warning levels, retain the core risk evolution direction, and align the coordinates in the low-dimensional manifold space to generate a risk latent state representation vector. This risk latent state representation vector is coupled with a free energy functional, and the energy distribution of the risk latent state representation vector is accumulated along the observation time axis through confidence integral calculation to obtain a probability mass convergence value. The probability mass convergence value is used to divide the discrete risk interval, map it to a preset classification judgment standard, and obtain a warning level label through the corresponding risk level identifier.

[0093] It should be noted that the classification criteria are based on the confidence distribution between the probability quality convergence value and the actual risk event level label in historical samples, and select the range of risk intensity quantiles that can distinguish between low-risk incubation period, medium-risk accumulation period and high-risk outbreak period as the preset classification conditions for the classification criteria.

[0094] Based on the warning level label and the risk latent characterization vector, a sparse inverse Jacobian sensitivity matrix is ​​constructed, and the dominant risk indicator is generated through orthogonal matching tracking.

[0095] The specific process includes: First, gradient partial derivative inversion operations are performed on the warning level label and the risk latent characterization vector through matrix construction. The warning level label provides discrete risk interval classification weight parameters, and the risk latent characterization vector provides low-dimensional manifold space state coordinate data. Second, the local gradient response relationship is jointly processed using the classification weight parameters and state coordinate data, and an inverse mapping operation is performed to eliminate weakly correlated channels, generating a sparse inverse Jacobian sensitivity matrix. Third, the sparse inverse Jacobian sensitivity matrix is ​​iteratively matched with atomic dictionary operations using an orthogonal matching pursuit algorithm. Within the matrix, the maximum inner product projection dimension is selected round by round, and the residual vector is updated. The accumulated residual vector update results form a support set index sequence. Fourth, the support set index sequence is mapped to the risk indicator mapping space through dimension alignment and recombination. Finally, the feature channel locking operation is performed using the mapping space index sequence to obtain the core risk factor encoding and generate the dominant risk indicator identifier.

[0096] It should be noted that the sparse inverse Jacobian sensitivity matrix is ​​a sparse data carrier composed of non-zero sensitivity coefficient matrix elements representing the driving strength of the core index and zero-value placeholders formed by filtering out weak correlation channels through sparsification; the orthogonal matching pursuit algorithm refers to a sparse signal reconstruction method that sorts the sparse inverse Jacobian sensitivity matrix according to the iterative inner product similarity rule, performs orthogonal projection in the residual vector space according to the maximum projection amplitude condition, and selects the corresponding feature support set index.

[0097] The warning level labels and dominant risk indicator identifiers are mapped to the graded disposal strategy library, and the risk potential representation vector is coupled to determine the dynamic safety boundary constraints and generate an automated risk control instruction flow.

[0098] The specific process includes: First, the warning level label and the dominant risk indicator identifier are matched and calculated using a tiered response strategy library. Then, the library retrieves the corresponding risk level's response plan set and indicator intervention rule set. Second, the response plan set and indicator intervention rule set are aligned to obtain an initial intervention strategy array. Third, the initial intervention strategy array is coupled with a risk latent representation vector, and a multi-dimensional state space constraint mapping is performed. Fourth, the low-dimensional manifold coordinates of the risk latent representation vector are projected onto the response strategy control plane through multi-dimensional state space constraint mapping to generate boundary adjustment parameters. These parameters are then integrated into the safety interval judgment logic for adaptive control range determination, establishing dynamic safety boundary constraints. Fifth, the dynamic safety boundary constraints are used for spatial limitation, generating a control instruction encoding sequence. Finally, operational actions are parsed and time-series arranged to obtain a standardized operational instruction set, generating an automated risk control instruction flow.

[0099] It should be noted that the tiered response strategy library is a structured set of decision-making knowledge that maps historical risk warning cases and corresponding intervention measures according to risk level and indicator dimension rules, performs routing matching operations in the strategy index space based on the warning level label and the dominant risk indicator identifier, and selects the corresponding response plan set and indicator intervention rule set.

[0100] The automated risk control command stream is deployed to the event-driven execution bus to issue intervention actions, and the intervention response sequence fed back by distributed nodes is captured synchronously to obtain the original performance feedback data stream.

[0101] The specific process includes: the automated risk control command flow undergoes routing parsing and channel allocation operations through an event-driven execution bus; the event-driven execution bus matches the corresponding communication link based on the command encoding characteristics, triggers the issuance of intervention actions, and converts standardized control strategies into executable control messages, which are then pushed to the target execution terminal. Status receipt information is generated by parsing the control messages and executing the strategies on the target execution terminal; the intervention response sequence fed back by distributed nodes is captured synchronously, and the status receipt information returned by each target execution terminal is received in real time through the bus listening interface; the status receipt information is sorted by timestamp sequence and parsed using communication protocols to generate an intervention response sequence, fully recording the action execution status and response delay parameters; the intervention response sequence is packaged and transmitted to the parsing node through an aggregation channel, where field mapping and format reorganization operations are performed to obtain the original performance feedback data stream.

[0102] Delay deviation correction and tail outlier removal are performed on the original performance feedback data stream, and the actual intervention effect trajectory is restored to obtain the actual performance result data.

[0103] The specific process includes: First, the original performance feedback data stream undergoes timestamp alignment and transmission lag compensation through delay deviation correction to obtain the response time difference of each node. Then, a phase shift operation is performed to generate a deviation-corrected data sequence. Next, outlier removal is performed on the deviation-corrected data sequence, followed by quantile boundary determination and extreme value truncation to remove discrete nodes that deviate from the main body at both ends of the distribution, generating a clean response data set. Then, continuous spatial interpolation and trend smoothing fitting are performed using trajectory restoration to connect discrete sampling points into a complete evolutionary path and eliminate local measurement noise interference. Finally, the complete evolutionary path is used to map the intervention action and the actual feedback causal chain, generating a true intervention effect trajectory. Finally, through data field reorganization and state alignment processing, the actual performance result data is obtained.

[0104] In summary, this invention achieves multi-head context alignment and gradient stable propagation of macro-cycle latent state representation and standardized financial indicator matrix under the big data analysis framework by constructing a multi-head attention network to perform cross-modal linear mapping, causal temporal mask weighting, and identity residual layer normalization operations. This suppresses the oscillating divergence distortion of multi-dimensional financial indicators. Furthermore, by performing differential geometric fusion mapping on continuous intensity values ​​and dynamic risk weight vectors, driving fractional-order generalized integral operators to perform nonlinear threshold evolution and extreme tail correction, this invention achieves manifold-level coupling and long-range memory adaptive boundary characterization of macroeconomic phase transition characteristics driven by big data analysis and micro-financial weight topology. This ensures that the dynamic risk threshold sequence strictly follows the economic cycle structure switching, adaptively converges and diverges, and eliminates the early warning lag phenomenon.

[0105] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention, and all such modifications or substitutions should be covered within the scope of the claims of the present invention.

Claims

1. A method for intelligent monitoring and analysis of enterprise financial risks based on big data, characterized in that, include: We collect multidimensional financial data of enterprises and external macroeconomic factor sequences, and through outlier filtering and quantile standardization mapping, we obtain a standardized financial indicator matrix and the original macroeconomic time series data. The original macroscopic time series data is input into the time series phase recognition algorithm, and the periodic fluctuation features are extracted. The continuous intensity value and the macroscopic period embedding vector are output. A multi-head attention network is constructed using the macro-cycle embedding vector as the query benchmark and the standardized financial indicator matrix as the key-value pair. Dynamic feature weighting is then applied to obtain the dynamic risk weight vector and the comprehensive risk index. The continuous intensity value and dynamic risk weight vector are input into the macro-gating function and nonlinear threshold evolution operation is performed through the fractional generalized integrator operator to output the dynamic risk threshold sequence. The comprehensive risk index is compared with the dynamic threshold sequence period by period, and the risk warning trigger signal is generated by combining the index with the largest weight value in the dynamic risk weight vector. The system extracts warning level labels and key risk indicator identifiers from risk warning trigger signals, generates automated risk control instruction streams using a tiered handling strategy, and obtains actual performance data.

2. The intelligent monitoring and analysis method for enterprise financial risks based on big data as described in claim 1, characterized in that, The specific steps for obtaining the standardized financial indicator matrix and the original macroeconomic time-series data are as follows: We collect multidimensional financial data of enterprises and external macroeconomic factor sequences, and eliminate disclosure time lag differences through cross-frequency dynamic alignment processing to obtain a time-axis synchronous data stream; we then perform outlier filtering on the time-axis synchronous data stream to obtain a clean time series sequence free of noise interference. Empirical quantile standardization mapping is performed on the pure time series, and the data dimensions are unified by inverse normal distribution transformation while retaining the characteristics of extreme risk distribution, resulting in a standardized financial indicator matrix and the original macroeconomic time series data.

3. The intelligent monitoring and analysis method for enterprise financial risks based on big data as described in claim 1, characterized in that, The specific steps for inputting the raw macroscopic time series data into the time series phase recognition algorithm and extracting periodic fluctuation features are as follows: The original macroscopic time series data is input into the time series phase recognition algorithm, and multi-scale intrinsic mode function sequence is output through multi-scale frequency band stripping; Perform Hilbert transform on the multi-scale intrinsic mode function sequence to obtain a complex analytic signal, and extract the instantaneous phase angle from the complex analytic signal; The delayed phase space trajectory is constructed using the instantaneous phase angle to generate the instantaneous phase trajectory matrix; the instantaneous phase trajectory matrix is ​​then subjected to geometric metric fusion mapping to obtain the periodic fluctuation characteristics that characterize the evolution of the macroeconomic cycle.

4. The intelligent monitoring and analysis method for enterprise financial risks based on big data as described in claim 1, characterized in that, The specific steps for generating the continuous intensity value and the macroscopic period embedding vector are as follows: Spatial dimensionality reduction and redundant mode stripping are performed on the periodic fluctuation characteristics to obtain an initial latent vector sequence; the phase coherence scalar is calculated using the initial latent vector sequence, and continuous intensity values ​​are generated by mapping through a nonlinear activation function; The continuous intensity values ​​and the initial latent vector sequence are concatenated by a gated tensor, and the magnitude fluctuations are eliminated by normalization using the hyperspherical norm to obtain the macroscopic periodic embedding vector.

5. The intelligent monitoring and analysis method for enterprise financial risks based on big data as described in claim 1, characterized in that, The specific steps for constructing the multi-head attention network are as follows: The macro-cycle embedding vector and the standardized financial indicator matrix are subjected to cross-modal linear mapping in the projection layer of the multi-head attention network. The macro-cycle embedding vector is mapped to a query tensor, and the standardized financial indicator matrix is ​​decomposed into key tensors and value tensors, generating a combination of query tensors, key tensors and value tensors corresponding to each attention head. The query tensor, key tensor, and value tensor are combined to perform dot product similarity operation and feature dimension scaling and normalization in the weight layer of a multi-head attention network. The causal temporal mask matrix is ​​then fused, and a probability distribution weight matrix is ​​generated through a probability distribution normalization operator. The probability distribution weight matrix is ​​used to perform matrix multiplication with the corresponding value tensor in the aggregation layer of the multi-head attention network, and the context features of each independent head are concatenated along the feature dimension to obtain the multi-head aggregation tensor; Global linear projection operations are performed on the recovery layer of the multi-head attention network using the multi-head aggregation tensor to restore the dimension of the target features. Then, by superimposing the identity residual mapping at the input end, layer normalization operations are performed to eliminate cross-modal gradient drift, generate a dynamic risk feature representation matrix, and obtain the multi-head attention network.

6. The intelligent monitoring and analysis method for enterprise financial risks based on big data as described in claim 1, characterized in that, The specific steps to obtain the dynamic risk weight vector and the comprehensive risk index are as follows: The variational information entropy optimization operator is constructed by using the dynamic risk feature representation matrix to perform sparse regularization, resulting in a dynamic risk weight vector. The dynamic risk weight vector is synchronously loaded into the dynamic risk feature representation matrix, and the multidimensional risk signal weighted aggregation and trajectory reconstruction are performed through path integral functional operation to obtain the risk evolution sequence. The risk evolution sequence is coupled with the time-series decay kernel and the covariance regularization term, and the comprehensive risk index is calculated through deterministic nonlinear integral mapping.

7. The intelligent monitoring and analysis method for enterprise financial risks based on big data as described in claim 1, characterized in that, The specific steps for outputting the dynamic risk threshold sequence are as follows: A phase transition observation mapping operation is performed on the continuous intensity value and the dynamic risk weight vector. The continuous intensity value is projected onto the Riemann curvature space to obtain the state transition Jacobian matrix. The manifold coordinate transformation is performed on the state transition Jacobian matrix and the dynamic risk weight vector using the even tangent space mapping operation to obtain the conjugate gradient flow. The conjugate gradient flow is then subjected to a symplectic geometric outer product operation to fuse the macroscopic coherence and financial weight topological correlation information to generate a macroscopic gated kernel tensor. The macro-gating function calls the macro-gating kernel tensor and performs nonlinear threshold evolution operation through the fractional generalized integral operator. The macro-gating kernel tensor and the historical risk index sampling data are substituted into the integral kernel function for time-domain convolution to obtain the dynamic risk threshold benchmark sequence. The dynamic risk threshold benchmark sequence is filtered by first-order exponential moving average and subjected to tail correction operation based on extreme value theory. The forgetting coefficient moving average operator is applied to eliminate high-frequency macro data jitter and obtain a smooth benchmark sequence. The smooth benchmark sequence is superimposed with the generalized Pareto distribution inverse cumulative distribution function to calibrate the lower limit boundary of the threshold under extreme fluctuation scenarios, and then reorganized into a continuous time data stream to generate a dynamic risk threshold sequence.

8. The intelligent monitoring and analysis method for enterprise financial risks based on big data as described in claim 1, characterized in that, The specific steps for generating the risk warning trigger signal are as follows: The deviation of the comprehensive risk index and the dynamic threshold sequence is compared period by period, and the deviation ratio of each assessment time point is calculated. By using the superimposed index decay weight and the sliding window filtering, transient fluctuation interference is eliminated, and a normalized deviation sequence is generated. Based on the normalized deviation sequence, the dynamic risk weight vector is traversed to locate the financial indicator dimension with the largest weight value. The normalized deviation sequence is then subjected to the Hadamard product with the largest weight value, and the contribution of the core risk feature is amplified to obtain the dominant deviation feature vector. A nonlinear confidence mapping operator is constructed using the dominant deviation feature vector, and cumulative risk integration is performed. When the integral output value exceeds the preset graded warning threshold boundary, the state reversal logic is triggered to generate a high confidence risk warning trigger signal.

9. The intelligent monitoring and analysis method for enterprise financial risks based on big data as described in claim 1, characterized in that, The specific steps for extracting the early warning level label and the dominant risk indicator identifier from the risk warning trigger signal are as follows: The risk warning trigger signal is mapped by geodesic curvature dimensionality reduction using a manifold state decoder to obtain the risk latent state representation vector; the risk latent state representation vector is coupled with a free energy functional to perform confidence integration to obtain the warning level label. Based on the warning level label and the risk latent characterization vector, a sparse inverse Jacobian sensitivity matrix is ​​constructed, and the dominant risk indicator is generated through orthogonal matching tracking.

10. The intelligent monitoring and analysis method for enterprise financial risks based on big data as described in claim 1, characterized in that, The specific steps for obtaining actual performance result data are as follows: The warning level labels and the dominant risk indicator identifiers are mapped to the graded disposal strategy library, and the risk potential representation vector is coupled to determine the dynamic safety boundary constraints and generate an automated risk control instruction flow. The automated risk control command stream is deployed to the event-driven execution bus to issue intervention actions, and the intervention response sequence fed back by distributed nodes is captured synchronously to obtain the original performance feedback data stream; Delay deviation correction and tail outlier removal are performed on the original performance feedback data stream, and the actual intervention effect trajectory is restored to obtain the actual performance result data.

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