Financial management risk early warning system based on multi-dimensional machine learning algorithm

Abstract

The application relates to the technical field of financial risk management, and discloses a financial management risk early warning system based on a multidimensional machine learning algorithm. The system comprises a data acquisition and feature extraction module, a sequence coding module, a scene generation module, a risk simulation module and a signal processing and early warning module which are sequentially connected. A sequence encoder guided by an attention mechanism generates a risk semantic vector integrating causal relationships, and a risk interaction scene is constructed in combination with real-time data. A dynamic risk propagation simulation model is run based on the scene, the diffusion of risks in a financial network is simulated, and a risk field intensity distribution map is output. The distribution map and the original data trend are iteratively decoupled and signal calibrated, and a standardized early warning signal is synthesized. The application enhances the accuracy and foresight of risk early warning through reinforcement of causal inference and dynamic network simulation.

Description

Technical Field

[0001] This invention relates to the field of financial risk management technology, specifically to a financial management risk early warning system built based on multi-dimensional machine learning algorithms. Background Technology

[0002] Current financial risk early warning systems primarily rely on statistical models or traditional machine learning algorithms. These typically involve correlation analysis or pattern recognition of historical financial indicators to construct classification or regression models. However, the complex temporal dependencies and interactions between variables in financial data are often oversimplified, particularly neglecting in-depth analysis of the causal structure between risk causes and outcomes. Conventional feature engineering and sequence models struggle to distinguish between statistical correlations and genuine causal relationships, potentially leading to spurious associations in extracted features, which negatively impacts the accuracy and interpretability of subsequent risk assessments.

[0003] Existing financial risk early warning schemes mostly focus on risk assessment of a single enterprise or a static point in time, lacking dynamic simulation of the transmission and amplification of risks in complex business networks. Common methods often treat related entities as independent individuals or simply aggregate risk scores, failing to depict how risks propagate and evolve based on the real-time correlation strength between entities and their individual risk resistance. Static, isolated assessment models are unable to provide early warning of systemic risks triggered by chain reactions, nor can they offer a panoramic view of the risk's spread across time and space. Summary of the Invention

[0004] The purpose of this invention is to provide a financial management risk early warning system based on multi-dimensional machine learning algorithms to solve the problems mentioned in the background art.

[0005] To achieve the above objectives, this invention provides a financial management risk early warning system based on a multi-dimensional machine learning algorithm, the system comprising:

[0006] The data acquisition and feature extraction module is used to acquire raw financial data streams from the enterprise's multi-source heterogeneous databases in real time, and to acquire risk causal feature sets based on the raw financial data streams.

[0007] The sequence encoding module employs an attention-guided sequence encoder to encode the risk causal feature set, generating a risk semantic embedding vector that integrates long-range dependencies and causal relationships.

[0008] The scenario generation module is used to fill the interactive risk scenario template with real-time financial data and the risk semantic embedding vector to generate a risk interaction scenario with spatiotemporal context.

[0009] The risk simulation module is used to run a risk propagation simulation model based on the risk interaction scenario, simulate the diffusion process of risk in the financial entity network, and dynamically adjust the risk transmission path and intensity according to the entity association strength and risk resistance parameters, and output a risk field intensity distribution map.

[0010] The signal processing and early warning module is used to perform risk field-trend field iterative decoupling on the risk field intensity distribution map and the low-frequency trend component of the original financial data stream, separate the purified risk fluctuation signal, and perform amplitude normalization and time scale calibration on the purified risk fluctuation signal, and finally synthesize a standardized financial risk early warning indication signal.

[0011] Preferably, the step of obtaining raw financial data streams from multi-source heterogeneous databases of an enterprise in real time, and obtaining a risk causal feature set based on the raw financial data streams, includes:

[0012] Heterogeneous data fusion and time-series alignment are performed on the original financial data stream to generate a heterogeneous dataset containing structured report data and unstructured text data.

[0013] Entity recognition and relation extraction are performed on heterogeneous datasets. The identified financial entities and relations are constructed into a financial event knowledge graph with timestamps. A multidimensional temporal feature tensor composed of node attribute vectors, edge weights and temporal dependencies is extracted from the financial event knowledge graph.

[0014] Multidimensional temporal feature tensors are input into dynamic graph neural networks for spatiotemporal feature learning. The dynamic graph neural network generates a hidden state sequence that represents the dynamic evolution of financial status based on the node attributes and edge weights that evolve over time.

[0015] Based on the correlation strength and state transition probability between nodes in the hidden state sequence, a dynamic attribute graph reflecting the risk transmission path is constructed. By serializing the state evolution trajectory of high-risk nodes in the dynamic attribute graph, a risk evolution node sequence is formed.

[0016] Based on the risk evolution node sequence, risk path backtracking and risk event causal reasoning are performed to identify the potential causal chain of risk event occurrence, and risk causal feature set with clear causal logical relationship is extracted based on the causal chain.

[0017] Preferably, the step of performing entity recognition and relation extraction on the heterogeneous dataset, and constructing the identified financial entities and relations into a time-stamped financial event knowledge graph, includes:

[0018] The named entity recognition model is used to scan text data in a heterogeneous dataset to identify company entities, account entities, personnel entities, transaction entities, and amount entities;

[0019] The relation extraction model is used to analyze the context statements of entity co-occurrence, and to extract the equity relationship, transaction relationship, guarantee relationship and affiliation relationship between entities;

[0020] Append a timestamp of the source event data unit to each extracted entity relation triple, and store all timestamped triples in the graph database;

[0021] Based on the timestamp order of the triples, a financial event knowledge graph is dynamically constructed and updated in the graph database, with entities as nodes, relations as edges, and the edge attributes containing the time when the relations occurred.

[0022] Preferably, the step of extracting a multidimensional temporal feature tensor composed of node attribute vectors, edge weights, and temporal dependencies from the financial event knowledge graph includes:

[0023] Calculate a static attribute vector for each node in the financial event knowledge graph. The static attribute vector includes node type encoding and historical statistical features.

[0024] Calculate the dynamic edge weight for each edge. The dynamic edge weight is determined by the frequency of the relationship represented by the edge, the most recent occurrence time, and the associated amount.

[0025] Traverse the state of the financial event knowledge graph on consecutive time slices, analyze the attribute changes of the same node on different time slices, and form a temporal dependency matrix of node attributes.

[0026] The static attribute vectors of all nodes within the same time slice, the dynamic edge weights of all edges, and the temporal dependency matrix across time slices are concatenated and dimensionality reduced to form a multidimensional temporal feature tensor.

[0027] Preferably, the dynamic graph neural network generates a sequence of hidden states representing the dynamic evolution of the financial state based on the node attributes and edge weights that evolve over time, including:

[0028] The multidimensional temporal feature tensor is input into the graph convolutional layer of the dynamic graph neural network step by step. At each time step, the graph convolutional layer aggregates the attribute information of the adjacent nodes according to the current edge weight.

[0029] The nodes output by the graph convolutional layer are embedded into a sequence and input into a recurrent neural network layer, which captures the evolution pattern of the node state over time.

[0030] The recurrent neural network layer outputs the hidden states of all nodes at each time step, and performs pooling operation on the hidden states of all nodes at the same time step to obtain the global financial state representation of that time step.

[0031] Arrange the global financial state representations of all time steps in sequence to form a hidden state sequence that fully describes the dynamic evolution of the system's financial state.

[0032] Preferably, the step of serializing the state evolution trajectories of high-risk nodes in the dynamic attribute graph to form a risk evolution node sequence includes:

[0033] Calculate the risk score for each node based on the hidden state sequence, and mark nodes whose risk scores are consistently higher than the threshold as high-risk nodes;

[0034] The state of each high-risk node is tracked in a dynamic attribute graph at multiple consecutive time steps. The state includes the changes in its attribute vector and the weights of the connected edges.

[0035] The state evolution trajectory of each high-risk node is transformed into a time-ordered sequence of node states.

[0036] The node state sequences of all high-risk nodes are sorted and spliced ​​according to the time logic of the occurrence of risk events to form a complete risk evolution node sequence that describes how the risk propagates across nodes and across time.

[0037] Preferably, the attention-guided sequence encoder is used to encode the risk causal feature set to generate a risk semantic embedding vector that integrates long-range dependencies and causal relationships, including:

[0038] Encode each causal logical relationship pair in the risk causal feature set into a feature vector;

[0039] The feature vectors of all causal logical relationship pairs are arranged in the inferred causal order and input into an attention-based sequence encoder;

[0040] The attention layer in the sequence encoder calculates the correlation weights between the current feature vector and all historical feature vectors in the sequence, and performs a weighted summation of different historical feature vectors based on the weights to fuse long-range dependency information;

[0041] The output layer of the sequence encoder performs nonlinear transformation and compression on the weighted summed context vector to generate a fixed-dimensional risk semantic embedding vector that incorporates complete causal chain information.

[0042] Preferably, the step of filling the interactive risk scenario template with real-time financial data and the risk semantic embedding vector to generate a risk interaction scenario with spatiotemporal context includes:

[0043] Map risk semantic embedding vectors to a high-dimensional risk semantic space;

[0044] In the high-dimensional risk semantic space, a clustering algorithm is used to divide the semantic regions corresponding to different risk patterns, and the distance between the real-time generated risk semantic embedding vector and the centroid of each semantic region is calculated to determine the risk pattern category to which it belongs.

[0045] Based on the determined risk pattern category, the corresponding interactive risk scenario template is matched from the pre-set risk scenario library; the pre-set risk scenario library stores the definitions of various typical risk patterns and their corresponding interactive risk scenario templates, and the interactive risk scenario templates define the roles, assets, actions and constraints of the risk event;

[0046] Calculate the similarity between the real-time generated risk semantic embedding vector and the semantic vector of each template in the risk scenario library;

[0047] Select the interactive risk scenario template with the highest similarity as the matching template;

[0048] The entities and relationships in the current financial event knowledge graph are instantiated into the role and asset slots of the matching template, and the causal and intensity information contained in the risk semantic embedding vector is filled into the action and constraint slots to generate specific risk interaction scenarios.

[0049] Preferably, the step of running the risk propagation simulation model based on the risk interaction scenario to simulate the diffusion process of risk in the financial entity network includes:

[0050] The risk propagation simulation model is initialized with the generated risk interaction scenario as the initial state. Each financial entity in the model is assigned an initial risk state and risk resistance parameters.

[0051] The risk propagation simulation model defines risk transmission rules based on the relationship type between entities and dynamic edge weights. The risk status is updated according to the transmission rules, resistance parameters, and random disturbances.

[0052] The simulation is advanced in discrete time steps. At each time step, the risk propagation simulation model calculates the amount of risk propagation along the network edges and updates the risk status of all entities.

[0053] When the simulation reaches the preset termination condition, the calculation stops and the risk status of all entities at the last time step is output, forming a risk field intensity distribution map.

[0054] Preferably, the step of performing risk field-trend field iterative decoupling on the risk field intensity distribution map and the low-frequency trend component of the original financial data stream includes:

[0055] The raw financial data stream is filtered to separate its low-frequency trend components.

[0056] The risk field intensity distribution map is regarded as a risk disturbance field superimposed on the low-frequency trend component;

[0057] A hybrid model of risk disturbance field and low-frequency trend component is established. The estimation of risk disturbance field is adjusted by iterative optimization algorithm so that the residual of the hybrid model after fitting the original data and removing the estimated risk disturbance field is minimized.

[0058] When the iteration converges, the final estimated risk disturbance field is purified to obtain a purified risk fluctuation signal.

[0059] The amplitude of the purified risk fluctuation signal is normalized and its time axis is aligned and calibrated with the standard financial reporting period to synthesize the final standardized financial risk early warning signal.

[0060] Compared with the prior art, the beneficial effects of the present invention are:

[0061] A sequence encoder guided by an attention mechanism is employed, incorporating a prior causal graph discovered from the data as a constraint in the calculation of attention weights. When learning feature representations, the model strengthens the connections between features with causal relationships while weakening statistical associations without causal basis. This ensures that the generated risk semantic embedding vectors not only contain long-term temporal patterns but also embed the causal logic of risk factors, thereby improving the stability and interpretability of feature representations. This makes the model more robust to confounding variables or spurious correlations, laying a more reliable feature foundation for subsequent accurate early warnings.

[0062] A risk propagation simulation model based on complex network dynamics was constructed, in which network nodes, edge weights, and node attributes are all set to be dynamically adjusted according to real-time financial data. The simulation process calculates the nonlinear transmission of risk flow using differential equations or surrogate models based on these dynamic parameters. It can simulate the diffusion process and evolution trajectory of risk along different paths and with varying intensities within the financial network. Its output is no longer a single risk value, but a dynamic, networked risk field intensity distribution map, thus achieving a leap from "point-based early warning" to "field-based perception," and can intuitively reveal risk aggregation areas, transmission links, and potential systemic weaknesses. Attached Figure Description

[0063] Figure 1 This is a schematic diagram illustrating the working principle of the financial management risk early warning system based on multi-dimensional machine learning algorithms described in this invention.

[0064] Figure 2 A flowchart for obtaining the risk causal feature set;

[0065] Figure 3 A flowchart for constructing a knowledge graph of financial events;

[0066] Figure 4 A time-series trend diagram of the causal intensity of each risk model;

[0067] Figure 5 A comparison chart of risk transmission coefficients corresponding to different types of financial entity relationships. Detailed Implementation

[0068] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0069] Please see Figure 1 This invention provides a financial management risk early warning system based on a multi-dimensional machine learning algorithm. The system includes: First, a data acquisition and feature extraction module continuously acquires raw financial data streams from multiple heterogeneous data sources within the enterprise. Based on this data stream, it performs in-depth analysis and feature construction to extract a feature set containing risk causal logic. Then, a sequence encoding module uses a sequence encoder with an attention mechanism at its core to encode this risk causal feature set, generating a low-dimensional, dense risk semantic embedding vector that integrates long-distance dependency and event causal relationship information. A scenario generation module then fills a predefined interactive risk scenario template with real-time financial data and this risk semantic embedding vector, thereby generating a specific risk interaction scenario rich in spatiotemporal context information. Based on this, a risk simulation module uses this risk interaction scenario as initial conditions to run a risk propagation simulation model, dynamically simulating the diffusion and evolution of risk in a network composed of financial entities and their relationships. During the simulation, the transmission path and impact intensity of the risk are adjusted in real time according to the dynamic correlation strength between entities and the risk resistance parameters of each entity, ultimately outputting a risk field intensity distribution map showing the distribution of risk on the network. Finally, the signal processing and early warning module separates and decouples the risk field from the long-term trend component in the original financial data stream by analyzing the risk field intensity distribution map obtained from the simulation. Through iterative optimization, it extracts the pure risk fluctuation signal and performs amplitude and time scale standardization processing on this signal. Finally, it synthesizes a standardized financial risk early warning indication signal that is easy to identify and interpret.

[0070] Example 1: See Figure 2The raw financial data stream from multi-source heterogeneous databases of enterprises undergoes heterogeneous data fusion and time-series alignment processing to form a unified heterogeneous dataset containing structured report data and unstructured text annotations. Entity recognition and relation extraction are then performed on this heterogeneous dataset. The named entity recognition model scans the text data to identify companies, accounts, personnel, transactions, and amounts, while the relation extraction model analyzes the context of entity co-occurrence to extract equity, transactions, guarantees, and affiliations. Each identified entity-relationship triple is appended with a timestamp of its source data unit and stored in a graph database. Based on this, a financial event knowledge graph is dynamically constructed and updated, with entities as nodes, relations as edges, and edge attributes including the occurrence time. From this financial event knowledge graph, a multi-dimensional time-series feature tensor is extracted, consisting of node attribute vectors, edge weights, and time-series dependencies. The node attribute vectors contain type encoding and historical statistical features, the edge weights are dynamically determined by relation frequency, relevance, and associated amount, and the time-series dependencies are obtained by analyzing the state changes of nodes across time slices. This multidimensional temporal feature tensor is input into a dynamic graph neural network step by step. The graph convolutional layer of the network aggregates the neighboring node information according to the current edge weight at each time step. The output node embedding sequence is then processed by a recurrent neural network layer to capture the temporal evolution pattern. The recurrent neural network layer outputs the hidden state of all nodes at each time step. After pooling, the global financial state representation of each time step is obtained, thus forming a hidden state sequence that describes the dynamic evolution process of the system state.

[0071] Based on this hidden state sequence, a risk score is calculated for each node. Nodes with risk scores consistently above a preset threshold are marked as high-risk nodes. In a dynamic attribute graph constructed based on node associations and state transition probabilities, the state evolution trajectory of each high-risk node is tracked over consecutive time steps and transformed into a time-ordered node state sequence. All high-risk node state sequences are sorted and concatenated according to the temporal logic of risk event occurrence, forming a risk evolution node sequence. Based on this risk evolution node sequence, risk path backtracking and causal reasoning of risk events are performed to identify potential causal chains leading to risk events. A risk causal feature set with a clear causal logical relationship is extracted based on this causal chain. In specific implementation, the data acquisition and feature extraction module obtains raw financial data streams in real time from the enterprise's multi-source heterogeneous databases. These raw financial data streams include structured transaction records from the enterprise resource planning system, budget data from semi-structured spreadsheets, and unstructured text data from internal communications and reports. In practice, the original financial data stream undergoes heterogeneous data fusion and time-series alignment. Data from different sources are aligned based on a unified timestamp benchmark to generate a heterogeneous dataset containing structured report data and unstructured text data.

[0072] In some embodiments, entity recognition and relation extraction are performed on heterogeneous datasets. Named entity recognition models scan text data to identify company entities, account entities, personnel entities, transaction entities, and monetary entities. In some embodiments, relation extraction models analyze the contextual statements of co-occurring entities to extract equity relationships, transaction relationships, guarantee relationships, and affiliation relationships between entities. A timestamp of the source event data unit is appended to each extracted entity-relationship triple, and all timestamped triples are stored in a graph database. Based on the timestamp order of the triples, a financial event knowledge graph is dynamically constructed and updated in the graph database, with entities as nodes, relations as edges, and edge attributes including the time of relation occurrence.

[0073] In practice, a multidimensional temporal feature tensor, composed of node attribute vectors, edge weights, and temporal dependencies, is extracted from the financial event knowledge graph. A static attribute vector is calculated for each node in the financial event knowledge graph, including node type encoding and historical statistical features. A dynamic edge weight is calculated for each edge, determined by the frequency of the relationship represented by the edge, the most recent occurrence time, and the associated amount. The state of the financial event knowledge graph is traversed across consecutive time slices, analyzing the attribute changes of the same node across different time slices to form a temporal dependency matrix of node attributes. The static attribute vectors of all nodes within the same time slice, the dynamic edge weights of all edges, and the temporal dependency matrix across time slices are concatenated and dimensionality reduced to integrate into a multidimensional temporal feature tensor.

[0074] A multidimensional temporal feature tensor is input into a dynamic graph neural network (Graph Neural Network) at time steps for spatiotemporal feature learning. The Graph Neural Network generates a sequence of hidden states representing the dynamic evolution of the financial state based on the node attributes and edge weights that evolve over time. In practice, the graph convolutional layer of the Graph Neural Network aggregates the attribute information of neighboring nodes at each time step based on the current edge weights. The node embedding sequence output by the graph convolutional layer is input into a recurrent neural network (RNN) layer, which captures the evolution pattern of node states over time. The RNN layer outputs the hidden states of all nodes at each time step, and pooling the hidden states of all nodes at the same time step yields the global financial state representation for that time step.

[0075] Based on the correlation strength and state transition probability between nodes in the hidden state sequence, a dynamic attribute graph reflecting the risk propagation path is constructed. By serializing the state evolution trajectories of high-risk nodes in the dynamic attribute graph, a risk evolution node sequence is formed. In specific implementation, a risk score is calculated for each node based on the hidden state sequence, and nodes with risk scores consistently above a threshold are marked as high-risk nodes. The state of each high-risk node is tracked in the dynamic attribute graph across multiple consecutive time steps, including changes in attribute vectors and the weights of connected edges. The state evolution trajectory of each high-risk node is transformed into a time-ordered node state sequence. The node state sequences of all high-risk nodes are sorted and concatenated according to the temporal logic of the risk event occurrence, forming a complete risk evolution node sequence describing how risk propagates across nodes and across time.

[0076] Based on the risk evolution node sequence, risk path backtracking and risk event causal reasoning are performed to identify potential causal chains of risk events and extract a risk causal feature set with clear causal logical relationships based on these chains. In practice, risk path backtracking starts from the high-risk node at the end of the risk evolution node sequence and traces backward along the edges of the dynamic attribute graph to identify the previous hop node sequence that influenced the state of the end node. Risk event causal reasoning analyzes the order and correlation strength of state changes of adjacent nodes to infer the causal direction of state transmission. It can be understood that the causal chain consists of a series of time-ordered node state transition pairs, each containing a cause node state, a result node state, and a causal strength weight between them. In practice, the process of extracting the risk causal feature set based on the causal chain encodes each node state transition pair into a feature vector. The dimensions of the feature vector include the cause node type, result node type, causal strength weight, state transition time interval, and the associated transaction amount. The extracted feature vector set constitutes the risk causal feature set.

[0077] It is understandable that an alternative mathematical expression for risk path backtracking and causal reasoning of risk events is the construction of a causal relationship matrix. The dimension of the causal relationship matrix is ​​equal to the number of nodes in the financial event knowledge graph, and the elements of the causal relationship matrix represent the strength of causal influence between nodes within a specific time window. The calculation of the causal relationship matrix can be based on Granger causality tests or transfer entropy analysis of hidden state sequences. For each pair of nodes, the Granger causality test is applied, and the goodness of fit of the model with and without predictor variables is compared by constructing an autoregressive model. The F-test is used to determine whether the hidden state sequence of one node has a significant predictive ability for the sequence of another node. The p-value or test statistic of the test can be converted into causal strength weights after thresholding. As an alternative, transfer entropy analysis is based on information theory principles, calculating the information flow from the source node to the target node. It measures the degree to which one sequence reduces the uncertainty of another sequence by estimating the conditional probability distribution. In specific implementations, histograms or kernel density estimation methods are used to calculate joint probabilities and conditional probabilities. The transfer entropy value directly reflects the strength of causal influence and does not require linear assumptions. The non-zero elements of the causal relationship matrix and their corresponding node pairs constitute the identified causal chain. The corresponding row vectors and column vectors extracted from the causal relationship matrix can be combined to form the risk causal feature set.

[0078] Example 2: See Figure 3This study employs a named entity recognition model to scan text data in a heterogeneous dataset, identifying various financially related entities, including company entities, account entities, personnel entities, transaction entities, and monetary entities. A relation extraction model analyzes the contextual statements of entity co-occurrence, extracting equity relationships, transaction relationships, guarantee relationships, and affiliation relationships between entities. For each relation triple extracted from entity recognition and relation extraction, a timestamp corresponding to its source event data unit is appended, and all timestamped triples are stored in a graph database. Based on the timestamp order of the stored triples, a financial event knowledge graph is dynamically constructed and continuously updated in the graph database, with entities as nodes, relations as edges, and edge attributes including the time of relation occurrence. A static attribute vector is calculated for each node in the financial event knowledge graph, which integrates node type encoding and its historical behavioral statistical features. A dynamic edge weight is calculated for each edge in the knowledge graph; the weight value is determined by the frequency of the relation represented by the edge within a specific time window, the time of its most recent occurrence, and the average or total amount associated with the relation. The snapshot states of the financial event knowledge graph are traversed across a series of consecutive time slices. The attribute change patterns of the same node across different time slices are analyzed to form a temporal dependency matrix describing the temporal evolution of node attributes. The static attribute vectors of all nodes within the same time slice, the dynamic edge weights of all edges, and the temporal dependency matrix extracted from adjacent time slices are concatenated and dimensionality reduced to integrate them into a unified multidimensional temporal feature tensor, which is used to represent the comprehensive state of the financial knowledge graph at that moment.

[0079] In its implementation, the Named Entity Recognition (NAME) model, based on a bidirectional Long Short-Term Memory (LSTM) network and a Conditional Random Field (CRF) architecture, scans text data in heterogeneous datasets to identify company entities, account entities, personnel entities, transaction entities, and monetary entities. For example, processing the text "Board Secretary Zhang San approved a purchase order of 5 million yuan from Morgan Equipment Co., Ltd.", the NAME model identifies the personnel entity "Zhang San," the company entity "Morgan Equipment Co., Ltd.", the transaction entity "purchase order," and the monetary entity "5 million yuan." The Relationship Extraction (REE) model, based on dependency parsing and graph neural networks, analyzes the contextual statements of co-occurring entities to extract equity relationships, transaction relationships, guarantee relationships, and hierarchical relationships between entities. For instance, analyzing the statement "Subsidiary Sunshine Technology provides a guarantee for the bond issuance of its parent company Xinghe Group," the REE model extracts the guarantee relationship connecting "Sunshine Technology" and "Xinghe Group."

[0080] In practice, each entity-relation triple obtained from named entity recognition and relation extraction is appended with a timestamp of its source event data unit, and all timestamped triples are stored in a graph database. The entity-relation triple "Zhang San is...the board secretary of Pioneer Software Company" originates from an announcement document with an appointment date of "2023-09-01," therefore, this triple is timestamped "2023-09-01" before being stored in the graph database. Based on the timestamp order of the stored triples, a financial event knowledge graph is dynamically constructed and continuously updated in the graph database, with entities as nodes, relations as edges, and edge attributes including the time of relation occurrence. With the addition of the triple "Pioneer Software Company, sells software to...Hongda Manufacturing Company" with a timestamp of "2023-11-15," the financial event knowledge graph will add a "Hongda Manufacturing Company" node and an edge pointing from "Pioneer Software Company" to "Hongda Manufacturing Company" with the label "sells software" and a time attribute.

[0081] In some embodiments, a static attribute vector is calculated for each node in the financial event knowledge graph. The static attribute vector includes a node type code and historical statistical features. For the "Pioneer Software Company" node, its node type code is "Listed Company," and its historical statistical features include its average debt-to-asset ratio over the past three years, R&D expenses as a percentage of revenue, and the number of changes in senior management. A dynamic edge weight is calculated for each edge in the financial event knowledge graph. The dynamic edge weight is determined by the frequency of the relationship represented by the edge, the most recent occurrence time, and the associated amount, used to quantify the strength of the relationship within a specific time window. For the "Selling Software" edge connecting "Pioneer Software Company" and "Hongda Manufacturing Company," the dynamic edge weight calculation considers the number of transactions between the two parties in the past quarter, the number of days since the most recent transaction, and the total quarterly transaction amount. It is understood that the design of dynamic edge weights needs to comprehensively consider the activity, freshness, and economic scale of the relationship. An optional, dimensionally consistent calculation method is based on the transition strength according to conditional probability, expressed by the formula:

[0082]

[0083] in: Indicates the statistical time window Inside, from the node To the node The dynamic edge weights of the edges. Indicates within the time window Inside, from the node To the node The number of relational events that occurred. Indicates within the time window Inside, from the node Departure to all associated nodes Total number of relational events that occurred. Indicates within the time window Inside, from the node To the node The total amount of money involved in the relationship events that occurred. Indicates within the time window Inside, from the node Departure to all associated nodes The total amount involved in the relationship events that occurred. Indicates the current time. Indicates from node To the node The timestamp of the most recent occurrence of the relationship event. It is a time decay factor used to adjust the degree of influence of the most recent occurrence time on the dynamic edge weights.

[0084] The financial event knowledge graph is traversed across a series of consecutive time slices, and the attribute changes of the same node across different time slices are analyzed to form a temporal dependency matrix of node attributes. For the "short-term loans" attribute, the temporal dependency matrix records the short-term loan balances of the "Hongda Manufacturing Company" node at the end of the past eight quarters, forming an eight-dimensional temporal vector. In some embodiments, the static attribute vectors of all nodes within the same time slice, the dynamic edge weights of all edges, and the temporal dependency matrix across time slices are concatenated and dimensionality reduced to integrate a multi-dimensional temporal feature tensor. This multi-dimensional temporal feature tensor serves as the fundamental input for subsequent spatiotemporal feature learning by the dynamic graph neural network. For the time slice at the end of the fourth quarter of 2023, the static attribute vectors of all companies are stacked to form a matrix, the dynamic edge weights of the transaction relationships between all companies are organized into a weighted adjacency matrix, and the temporal dependency matrices of the "gross profit margin" of the nodes in the most recent four quarters are flattened and concatenated.

[0085] Example 3: This example illustrates a method for generating a hidden state sequence and forming a risk evolution node sequence. A multidimensional temporal feature tensor is input sequentially into a dynamic graph neural network (GNN). At each time step, the graph convolutional layer of this network aggregates the attribute information of neighboring nodes based on the current edge weights, updating the embedding vector of each node. The node embedding sequence output by the graph convolutional layer is fed into a recurrent neural network (RNN) layer. The RNN layer, through its internal state memory and update mechanism, captures the evolution pattern and long-term dependencies of each node's state over time. At each time step, the RNN layer outputs the hidden states of all nodes. By pooling the hidden states of all nodes at that time step, a single vector representing the global financial state at that moment is obtained. The global financial state representations of all consecutive time steps are arranged chronologically to form a hidden state sequence that fully describes the dynamic evolution of the entire system's financial state. Based on this hidden state sequence, the risk score of each node at multiple time steps is calculated, and nodes with risk scores consistently higher than a set threshold are marked as high-risk nodes. In a dynamic attribute graph reflecting the relationships and state transitions between nodes, the state of each high-risk node is tracked across multiple consecutive time steps. The tracked states include changes in its attribute vectors and the evolution of the weights of its connected edges. The state evolution trajectory of each high-risk node from the emergence of risk to the current moment is transformed into a time-ordered sequence of node states, recording snapshots of the node's state at various key time points. The node state sequences generated by all high-risk nodes are then sorted and concatenated according to the temporal logic of risk event occurrence and propagation, forming a coherent and complete sequence describing how risk propagates and evolves between different nodes across different time periods.

[0086] In practice, multidimensional temporal feature tensors are input into a dynamic graph neural network (GNN) step-by-step for spatiotemporal feature learning. At each time step, the graph convolutional layer of the GNN aggregates the attribute information of neighboring nodes based on the current edge weights. For a slice of the multidimensional temporal feature tensor corresponding to time step t, the GNN's operation is based on the topological structure of the financial event knowledge graph. It performs a weighted summation and nonlinear transformation on the feature vectors of each node and its neighboring nodes according to dynamic edge weights to update the node's embedding representation. The node embedding sequence output by the GNN is then input into a recurrent neural network (RNN) layer. The RNN layer employs gated recurrent units (GRUs) or long short-term memory (LSTM) network structures to capture the evolution pattern and long-term dependencies of each node's embedding vector over time.

[0087] The recurrent neural network layer outputs the hidden states of all nodes at each time step. Pooling is then performed on the hidden states of all nodes at the same time step to obtain a global financial state representation for that time step. The pooling operation can be max pooling, average pooling, or attention-based weighted pooling. Average pooling averages the element-wise hidden state vectors of all nodes at the same time step, generating a global vector that comprehensively represents the state of the entire financial network at that moment. Arranging the global financial state representations of all consecutive time steps in chronological order forms a sequence of hidden states that fully describes the dynamic evolution of the entire system's financial state. The mathematical expression of the hidden state sequence can be described as follows: for a time step sequence... The hidden state sequence is the global financial state representation vector. ordered set .

[0088] In some embodiments, a dynamic attribute graph reflecting the risk transmission path is constructed based on the correlation strength and state transition probability between nodes in the hidden state sequence. The node set and edge set of the dynamic attribute graph are consistent with those of the financial event knowledge graph, but the weight of each edge in the dynamic attribute graph needs to be recalculated using the hidden state sequence to reflect the potential strength of risk transmission. The correlation strength between nodes can be obtained by calculating the correlation coefficient or mutual information between the hidden state sequences of two nodes. The state transition probability can be estimated by analyzing the conditional probability that the state of one node changes after the state of another node changes. The edge weights of the dynamic attribute graph... One calculation method combines association strength and state transition probability, expressed by the formula:

[0089]

[0090] in: Indicates from node To the node The edge weights are used to quantify risk from the node. Transmission to nodes The tendency. Represents a node Hidden state sequence With nodes Hidden state sequence Measures of the strength of the association between them, such as the Pearson correlation coefficient. Indicates the observation of the node The state has changed significantly Under the condition, node The state subsequently changed significantly. The conditional probability.

[0091] In practice, the state evolution trajectories of high-risk nodes in the dynamic attribute graph are serialized to form a risk evolution node sequence. A risk score for each node is calculated based on the hidden state sequence, and nodes with risk scores consistently above a threshold are marked as high-risk nodes. It can be understood that a node's risk score can be calculated based on a specific dimension of its hidden state vector, the volatility of its historical hidden states, or its matching degree with a known risk pattern template. Within a quarter, if the risk score of the "Supplier Y Company" node exceeds the threshold of 0.7 for twelve consecutive weeks, then the "Supplier Y Company" node is marked as a high-risk node. The state of each high-risk node is tracked in the dynamic attribute graph over multiple consecutive time steps, including changes in the attribute vector and the weights of connected edges. The "Supplier Y Company" node is tracked, recording its cash flow attribute vector values ​​for twenty time steps before and after being marked as a high-risk node, as well as the dynamic weight changes of the edge between it and its core customer, "Manufacturer Z Company."

[0092] The state evolution trajectory of each high-risk node is transformed into a time-ordered sequence of node states. Each element of the node state sequence is a tuple containing a timestamp, a node identifier, a snapshot of the node's attribute vector at that moment, and a snapshot of the weights of the main edges connected to it. The node state sequences of all high-risk nodes are sorted and concatenated according to the temporal logic of the risk event, forming a complete risk evolution node sequence describing how the risk propagates across nodes and across time. Optionally, the temporal logic can be based on the time point when the state anomaly begins or the time point when the risk propagation event is detected. If the risk first appears at the "Supplier Y Company" node and then propagates to the "Manufacturer Z Company" node, then the risk evolution node sequence will first contain the state sequence fragment of the "Supplier Y Company" node at the initial stage of the anomaly, and then concatenate the state sequence fragment of the "Manufacturer Z Company" node after it is affected. In some embodiments, the final form of the risk evolution node sequence is a list containing state snapshots of m nodes at n key time points, which clearly shows the timeline and topology of the risk origin, propagation path, and scope of impact.

[0093] Example 4: Each feature pair reflecting causal logic in the risk causal feature set is encoded into a numerical feature vector. The feature vectors of all causal logic relationship pairs are arranged according to the logical order derived from causal reasoning and input into an attention-based sequence encoder. The attention layer in the sequence encoder calculates the correlation weights between the current feature vector and all historical feature vectors in the sequence, and performs a weighted summation of different historical feature vectors based on these weights, thereby fusing long-range dependency information. The output layer of the sequence encoder performs a nonlinear transformation and compression on the context vector obtained after weighted summation, generating a fixed-dimensional risk semantic embedding vector that incorporates complete causal chain information. The generated risk semantic embedding vector is projected onto a high-dimensional risk semantic space through a mapping function. In this high-dimensional risk semantic space, a clustering algorithm is used to divide the semantic regions corresponding to different risk patterns. The Euclidean distance or cosine distance between the real-time generated risk semantic embedding vector and the centroid of each semantic region is calculated, and the risk pattern category is determined based on the minimum distance. Based on the determined risk pattern category, a corresponding interactive risk scenario template is matched from a pre-built risk scenario library. This library stores definitions of various typical risk patterns and their corresponding scenario templates. Each template defines the roles, assets, actions, and constraints involved in the risk event. The similarity between the real-time generated risk semantic embedding vector and the semantic vector of each template in the risk scenario library is calculated. The interactive risk scenario template with the highest similarity is selected as the matching template. Specific entities and relationships in the current financial event knowledge graph are instantiated into the role and asset slots of the matching template. Simultaneously, the causal logic and strength information contained in the risk semantic embedding vector are filled into the action and constraint slots of the template, thereby generating a specific, executable risk interaction scenario with spatiotemporal context.

[0094] In practical implementation, each causal logical relationship pair in the risk causal feature set is encoded as a feature vector. These causal logical relationship pairs originate from the analysis of the risk evolution node sequence and are represented as the state of the cause node, the state of the result node, causal strength, and time lag. The construction of the feature vector includes multiple dimensions, numerically representing various aspects of the causal logical relationship pair. An exemplary feature vector can be represented as [cause node type encoding, result node type encoding, causal strength weight, time lag days, related transaction amount, relationship type encoding]. The feature vectors of all causal logical relationship pairs are arranged according to the inferred causal order, forming an ordered sequence that is input into an attention-based sequence encoder. The sequence encoder adopts a Transformer encoder structure, including a multi-head self-attention layer and a feedforward neural network layer.

[0095] The attention layer in the sequence encoder calculates the relevance weights between the current feature vector and all historical feature vectors in the sequence. This attention mechanism allows the model to dynamically focus on historical causal events most relevant to the current risk state. For a feature vector at position `pos` in the sequence, its attention weight with the feature vector at historical position `k` is calculated using a query-key dot product and then processed by the Softmax function. Based on the calculated relevance weights, different historical feature vectors are weighted and summed to fuse long-range dependency information. The weighted context vector contains a summary of historical causal information related to the current position. The output layer of the sequence encoder performs a non-linear transformation and compression on the weighted context vector to generate a fixed-dimensional risk semantic embedding vector that incorporates complete causal chain information. The dimension of the risk semantic embedding vector is predefined, such as 256 or 512 dimensions, and its numerical distribution encodes the semantics of a specific risk pattern.

[0096] In practice, the generated risk semantic embedding vectors are mapped to a high-dimensional risk semantic space via a fully connected neural network. The dimension of this high-dimensional risk semantic space is typically higher than the original dimension of the risk semantic embedding vectors, for example, mapping from 256 dimensions to 1024 dimensions. Within this high-dimensional risk semantic space, a clustering algorithm is used to divide the semantic regions corresponding to different risk patterns. K-Means or DBSCAN clustering algorithms are employed to perform unsupervised clustering on a large number of risk semantic embedding vectors generated from historical risk events. The distance between the real-time generated risk semantic embedding vectors and the centroids of each semantic region is calculated to determine the risk pattern category to which it belongs. Euclidean or cosine distance is used for distance calculation, assigning the risk semantic embedding vector to the category represented by the nearest centroid. Based on the determined risk pattern category, a corresponding interactive risk scenario template is matched from a pre-set risk scenario library. This risk scenario library is a database or knowledge base that stores various typical risk pattern definitions and their corresponding interactive risk scenario templates. A pre-built risk scenario library stores definitions of various typical risk patterns and their corresponding interactive risk scenario templates. These templates define the roles, assets, actions, and constraints of risk events and exist in the form of structured scripts or configuration files. The similarity between the real-time generated risk semantic embedding vector and the semantic vector of each template in the risk scenario library is calculated. The semantic vector of each template is pre-encoded using standard causal feature sequences for that type of risk. Cosine similarity can be used for similarity calculation, and the formula can be expressed as:

[0097]

[0098] in: Represents the real-time generated risk semantic embedding vector With the risk scenario library semantic vectors of templates Cosine similarity between them. Representing vectors with vector The dot product. Representing vectors The L2 norm. Representing vectors The L2 norm of the model is used. The interactive risk scenario template with the highest similarity is selected as the matching template. The matching process involves iterating through comparisons and selecting the template with the largest similarity. The template corresponding to the value is completed.

[0099] Entities and relationships from the current financial event knowledge graph are instantiated into the role and asset slots of the matching template, and causal and intensity information contained in the risk semantic embedding vector is filled into the action and constraint slots, thereby generating specific risk interaction scenarios. In some embodiments, the interactive risk scenario template is defined in JSON format, which includes roles, assets, actions, and constraints. The matching process selects the most suitable template from the risk scenario library based on the similarity between the risk semantic embedding vector and the template semantic vector. Refer to Table 1, which shows a simplified risk pattern category and preset template matching table.

[0100] Table 1: Matching Table of Risk Model Categories and Pre-set Templates

[0101]

[0102] The template matching process is understandable; it involves retrieval and comparison. The real-time generated risk semantic embedding vector is classified as "debt default chain reaction," and the system retrieves and selects the "supplier-customer chain default template" from the database. Generating a specific risk interaction scenario is understood to be a process of filling and instantiation. The "Company Alpha" entity identified from the financial event knowledge graph is instantiated into the "core debtor" role slot of the template, and the "Company Beta" entity is instantiated into the "upstream supplier" role slot. The causal strength value decoded from the risk semantic embedding vector is filled into the "default transmission probability" field of the template action slot, thereby generating a specific risk interaction scenario that can be executed by the risk simulation module.

[0103] See Figure 4In the multi-dimensional analysis of risk causal feature vectors, the temporal variation patterns of the causal intensity weights of three risk models—"debt default chain reaction," "rapid depreciation of collateral value," and "related-party transaction benefit transfer"—are presented. Specifically, the causal intensity weights characterize the degree of correlation between causal relationships under each risk model. The horizontal axis represents the time series from November 2024 to September 2025, and the vertical axis represents the causal intensity weights (values ​​ranging from 0 to 0.9). The variation characteristics of different risk models exhibit differentiated fluctuations: "related-party transaction benefit transfer" (green curve) reached a peak (approximately 0.84) in January 2025 and then fluctuated downwards; "debt default chain reaction" (blue curve) showed a significant peak (approximately 0.88) in June 2025, and had the largest overall fluctuation range; "rapid depreciation of collateral value" (red curve) showed multi-peak fluctuations, with local highs at time points such as February 2025 and June 2025. In terms of parameters, the differences in the intensity weights of the three types of risks at different time points reflect the dynamic evolution characteristics of risk transmission paths and causal relationships, which can serve as the core input basis for subsequent risk scenario generation and simulation modules.

[0104] Example 5: Using the generated risk interaction scenario as the initial state, the risk propagation simulation model is initialized. Each financial entity in the model is assigned an initial risk state value based on the scenario and a risk resistance parameter characterizing its ability to withstand risks. The risk propagation simulation model defines the rules for risk propagation along network edges based on the relationship type between entities and the dynamic edge weights obtained from the knowledge graph. The risk state of each entity is updated according to the propagation rules, its own resistance parameter, and certain random disturbance factors. The simulation process is advanced in discrete time steps. At each time step, the risk propagation simulation model calculates the propagation amount of risk along each edge in the network and updates the risk state values ​​of all related entities accordingly. When the simulation reaches the preset termination condition, the calculation stops, and the set of risk state values ​​of all entities in the last simulation time step is output. This set is presented graphically as the risk field intensity distribution map. The original financial data stream is low-pass filtered to separate its low-frequency trend components. The risk field intensity distribution map is considered as a risk disturbance field superimposed on the low-frequency trend components. A hybrid model of risk disturbance field and low-frequency trend component is established. The estimation of risk disturbance field is adjusted by an iterative optimization algorithm to minimize the difference between the hybrid model fitting the original data and the residual obtained after removing the estimated risk disturbance field.

[0105] When the iterative optimization process converges, the final estimated risk disturbance field undergoes signal purification processing to remove potential noise and estimation errors, thereby obtaining a purified risk fluctuation signal. The amplitude of the purified risk fluctuation signal is normalized to fall within a uniform dimensional range, and its time axis is aligned and calibrated with the standard financial reporting cycle, ultimately synthesizing a standardized financial risk early warning indicator signal. In specific implementation, the risk propagation simulation model is initialized with the generated risk interaction scenario as the initial state. The generated risk interaction scenario defines the entity roles, asset relationships, and action constraints in specific risk events. Each financial entity in the risk propagation simulation model is assigned an initial risk state and risk resistance parameter based on the scenario. The initial risk state is calculated based on the entity's role in the risk interaction scenario and its current financial indicators. For example, an entity with the role of a "core debtor" may receive a higher initial risk state value. The risk resistance parameter is calculated based on the entity's debt-to-equity ratio, current ratio, and credit rating, resulting in a value between 0 and 1; the lower the value, the weaker the resistance. The risk propagation simulation model defines risk transmission rules based on the relationship types between entities and dynamic edge weights obtained from the financial event knowledge graph. Relationship types include equity relationships, transaction relationships, and guarantee relationships. Different types of relationships correspond to different risk transmission coefficients and thresholds, and the dynamic edge weights provide real-time measurements of relationship strength. The risk status is updated based on the transmission rules, resistance parameters, and random disturbances. Random disturbances are used to simulate external random factors not modeled in the explicit rules, such as market conditions and policy changes.

[0106] The simulation is performed in discrete time steps. At each time step, the risk propagation simulation model calculates the transmission amount of risk along the network edges and updates the risk state of all entities. For the edge connecting entity i and entity j, the transmission amount of risk from entity i to entity j is calculated based on the current risk state value of entity i, the dynamic weight of the edge, the risk resistance parameter of entity j, and the transmission coefficient corresponding to the relationship type. The simulation stops when a preset termination condition is reached. The preset termination condition can be reaching a set maximum number of simulation steps or the average rate of change of the risk state of all entities in the entire network falling below a set threshold. The simulation outputs the risk state set of all entities at the last time step, forming a risk field intensity distribution map. The risk field intensity distribution map is presented in a node-color depth mapping manner, where the color depth of each financial entity node is proportional to its final risk state value.

[0107] In practice, the raw financial data stream is filtered to separate its low-frequency trend components. This filtering is achieved using a digital low-pass filter, with the cutoff frequency set based on the main cyclical components of the financial data to preserve quarterly or annual trends. The risk field intensity distribution map is considered as a risk disturbance field superimposed on the low-frequency trend components. The risk field intensity distribution map provides spatial intensity information of risk within the financial entity network, while the low-frequency trend components describe the long-term baseline of financial indicators over time. A hybrid model of the risk disturbance field and the low-frequency trend components is established. An iterative optimization algorithm is used to adjust the estimate of the risk disturbance field, minimizing the residual between the hybrid model fitting the original data and the residual after removing the estimated risk disturbance field. The hybrid model is expressed as the original observed financial data sequence equal to the sum of the low-frequency trend component sequence, the risk disturbance field sequence, and a noise sequence.

[0108] In some embodiments, the iterative optimization process employs an alternating projection method, alternately estimating the risk perturbation field and the low-frequency trend component. An optional, dimensionlessly consistent iterative update formula is as follows:

[0109]

[0110] in: Indicates the first The estimation of the risk perturbation field sequence during the next iteration. Indicates the first The estimation of the low-frequency trend component sequence during the next iteration. This represents the original sequence of observed financial data. This indicates a low-pass filtering operation. During iterative initialization, it is set... That is, the initial trend estimate is the result of direct filtering of the original data, and the initial risk disturbance field estimate is... Then it is a zero sequence. In the... In this iteration, the original observation sequence is used first. Subtract the current low-frequency trend estimate The updated risk perturbation field estimate is obtained. Then, the original observation sequence Subtract the updated risk perturbation field estimate The result is low-pass filtered The updated low-frequency trend estimate is obtained. It is understandable that this iterative process aims to progressively decompose the original signal into high-frequency risk disturbance components and low-frequency trend components.

[0111] When the iteration converges, the final estimated risk perturbation field is subjected to signal purification processing to obtain a purified risk fluctuation signal. The signal purification processing is applied to the signal obtained after the iteration converges. The sequence undergoes wavelet thresholding denoising to suppress any potentially residual high-frequency noise. The amplitude of the purified risk volatility signal is normalized, and its time axis is aligned and calibrated with a standard financial reporting period to synthesize the final standardized financial risk early warning indicator signal. Amplitude normalization linearly scales the values ​​of the purified risk volatility signal sequence to [a specific value]. Within the specified interval, time axis calibration aligns the signal's timestamps to standard quarter-end or year-end reporting dates. In some embodiments, the standardized financial risk warning indicator signal is represented as a time series, where each data point corresponds to a standard financial reporting period, and its numerical value represents the intensity of systemic financial risk fluctuations during that period.

[0112] See Figure 5 In the risk transmission rules definition of the financial risk propagation simulation model, different entity relationship types correspond to differentiated risk transmission coefficients. Specifically, the risk propagation simulation model configures corresponding risk transmission coefficients based on the entity relationship types (guarantee relationship, transaction relationship, equity relationship) extracted from the financial event knowledge graph: the transmission coefficient for guarantee relationships is 0.8, for equity relationships it is 0.7, and for transaction relationships it is 0.5. These coefficients serve as core weight parameters for risk transmission along different relationship edges, directly participating in the calculation of the risk transmission volume between entities (the transmission volume needs to be determined in conjunction with the entity's current risk status, dynamic edge weights, and resistance parameters). During parameter configuration, the differences in transmission coefficients among different relationship types reflect the strong correlation of guarantee relationships in risk transmission, the relatively weak transmission of transaction relationships, and the moderate transmission characteristics of equity relationships.

[0113] Although embodiments of the invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the appended claims and their equivalents.

Claims

1. A financial management risk early warning system constructed based on multi-dimensional machine learning algorithms, characterized in that: The system includes: The data acquisition and feature extraction module is used to acquire raw financial data streams from the enterprise's multi-source heterogeneous databases in real time, and to acquire risk causal feature sets based on the raw financial data streams. The sequence encoding module employs an attention-guided sequence encoder to encode the risk causal feature set, generating a risk semantic embedding vector that integrates long-range dependencies and causal relationships. The scenario generation module is used to fill the interactive risk scenario template with real-time financial data and the risk semantic embedding vector to generate a risk interaction scenario with spatiotemporal context. The risk simulation module is used to run a risk propagation simulation model based on the risk interaction scenario, simulate the diffusion process of risk in the financial entity network, and dynamically adjust the risk transmission path and intensity according to the entity association strength and risk resistance parameters, and output a risk field intensity distribution map. The signal processing and early warning module is used to perform risk field-trend field iterative decoupling on the risk field intensity distribution map and the low-frequency trend component of the original financial data stream, separate the purified risk fluctuation signal, and perform amplitude normalization and time scale calibration on the purified risk fluctuation signal, and finally synthesize a standardized financial risk early warning indication signal. The process of acquiring raw financial data streams in real time from multi-source heterogeneous databases of an enterprise, and obtaining a risk causal feature set based on the raw financial data streams, includes: Heterogeneous data fusion and time-series alignment are performed on the original financial data stream to generate a heterogeneous dataset containing structured report data and unstructured text data. Entity recognition and relation extraction are performed on heterogeneous datasets. The identified financial entities and relations are constructed into a financial event knowledge graph with timestamps. A multidimensional temporal feature tensor composed of node attribute vectors, edge weights and temporal dependencies is extracted from the financial event knowledge graph. Multidimensional temporal feature tensors are input into dynamic graph neural networks for spatiotemporal feature learning. The dynamic graph neural network generates a hidden state sequence that represents the dynamic evolution of financial status based on the node attributes and edge weights that evolve over time. Based on the correlation strength and state transition probability between nodes in the hidden state sequence, a dynamic attribute graph reflecting the risk transmission path is constructed. By serializing the state evolution trajectory of high-risk nodes in the dynamic attribute graph, a risk evolution node sequence is formed. Based on the risk evolution node sequence, risk path backtracking and risk event causal reasoning are performed to identify the potential causal chain of risk event occurrence, and risk causal feature set with clear causal logical relationship is extracted based on the causal chain; The risk propagation simulation model based on the aforementioned risk interaction scenario simulates the diffusion process of risk in the financial entity network, including: The risk propagation simulation model is initialized with the generated risk interaction scenario as the initial state. Each financial entity in the model is assigned an initial risk state and risk resistance parameters. The risk propagation simulation model defines risk transmission rules based on the relationship type between entities and dynamic edge weights. The risk status is updated according to the transmission rules, resistance parameters, and random disturbances. The simulation is advanced in discrete time steps. At each time step, the risk propagation simulation model calculates the amount of risk propagation along the network edges and updates the risk status of all entities. When the simulation reaches the preset termination condition, the calculation stops and the risk status of all entities at the last time step is output, forming a risk field intensity distribution map.

2. The financial management risk early warning system based on multi-dimensional machine learning algorithm as described in claim 1, characterized in that, The process of performing entity recognition and relation extraction on heterogeneous datasets, and constructing a timestamped financial event knowledge graph from the identified financial entities and relations, includes: The named entity recognition model is used to scan text data in a heterogeneous dataset to identify company entities, account entities, personnel entities, transaction entities, and amount entities; The relation extraction model is used to analyze the context statements of entity co-occurrence, and to extract the equity relationship, transaction relationship, guarantee relationship and affiliation relationship between entities; Append a timestamp of the source event data unit to each extracted entity relation triple, and store all timestamped triples in the graph database; Based on the timestamp order of the triples, a financial event knowledge graph is dynamically constructed and updated in the graph database, with entities as nodes, relations as edges, and the edge attributes containing the time when the relations occurred.

3. The financial management risk early warning system based on multi-dimensional machine learning algorithm as described in claim 2, characterized in that, The extraction of a multidimensional temporal feature tensor from the financial event knowledge graph, consisting of node attribute vectors, edge weights, and temporal dependencies, includes: Calculate a static attribute vector for each node in the financial event knowledge graph. The static attribute vector includes node type encoding and historical statistical features. Calculate the dynamic edge weight for each edge. The dynamic edge weight is determined by the frequency of the relationship represented by the edge, the most recent occurrence time, and the associated amount. Traverse the state of the financial event knowledge graph on consecutive time slices, analyze the attribute changes of the same node on different time slices, and form a temporal dependency matrix of node attributes. The static attribute vectors of all nodes within the same time slice, the dynamic edge weights of all edges, and the temporal dependency matrix across time slices are concatenated and dimensionality reduced to form a multidimensional temporal feature tensor.

4. The financial management risk early warning system based on multi-dimensional machine learning algorithm as described in claim 3, characterized in that, The dynamic graph neural network generates a sequence of hidden states representing the dynamic evolution of financial status based on the node attributes and edge weights that evolve over time, including: The multidimensional temporal feature tensor is input into the graph convolutional layer of the dynamic graph neural network step by step. At each time step, the graph convolutional layer aggregates the attribute information of the adjacent nodes according to the current edge weight. The nodes output by the graph convolutional layer are embedded into a sequence and input into a recurrent neural network layer, which captures the evolution pattern of the node state over time. The recurrent neural network layer outputs the hidden states of all nodes at each time step, and performs pooling operation on the hidden states of all nodes at the same time step to obtain the global financial state representation of that time step. Arrange the global financial state representations of all time steps in sequence to form a hidden state sequence that fully describes the dynamic evolution of the system's financial state.

5. The financial management risk early warning system based on a multi-dimensional machine learning algorithm as described in claim 4, characterized in that, The step of serializing the state evolution trajectories of high-risk nodes in the dynamic attribute graph to form a risk evolution node sequence includes: Calculate the risk score for each node based on the hidden state sequence, and mark nodes whose risk scores are consistently higher than the threshold as high-risk nodes; The state of each high-risk node is tracked in a dynamic attribute graph at multiple consecutive time steps. The state includes the changes in its attribute vector and the weights of the connected edges. The state evolution trajectory of each high-risk node is transformed into a time-ordered sequence of node states. The node state sequences of all high-risk nodes are sorted and spliced ​​according to the time logic of the occurrence of risk events to form a complete risk evolution node sequence that describes how the risk propagates across nodes and across time.

6. The financial management risk early warning system based on a multi-dimensional machine learning algorithm as described in claim 5, characterized in that, The attention-guided sequence encoder is used to encode the risk causal feature set to generate a risk semantic embedding vector that integrates long-range dependencies and causal relationships, including: Encode each causal logical relationship pair in the risk causal feature set into a feature vector; The feature vectors of all causal logical relationship pairs are arranged in the inferred causal order and input into an attention-based sequence encoder; The attention layer in the sequence encoder calculates the correlation weights between the current feature vector and all historical feature vectors in the sequence, and performs a weighted summation of different historical feature vectors based on the weights to fuse long-range dependency information; The output layer of the sequence encoder performs nonlinear transformation and compression on the weighted summed context vector to generate a fixed-dimensional risk semantic embedding vector that incorporates complete causal chain information.

7. The financial management risk early warning system based on a multi-dimensional machine learning algorithm as described in claim 6, characterized in that, The step of filling the interactive risk scenario template with real-time financial data and the risk semantic embedding vector to generate a risk interaction scenario with spatiotemporal context includes: Map risk semantic embedding vectors to a high-dimensional risk semantic space; In the high-dimensional risk semantic space, a clustering algorithm is used to divide the semantic regions corresponding to different risk patterns, and the distance between the real-time generated risk semantic embedding vector and the centroid of each semantic region is calculated to determine the risk pattern category to which it belongs. Based on the determined risk pattern category, the corresponding interactive risk scenario template is matched from the pre-set risk scenario library; the pre-set risk scenario library stores the definitions of various typical risk patterns and their corresponding interactive risk scenario templates, and the interactive risk scenario templates define the roles, assets, actions and constraints of the risk event; Calculate the similarity between the real-time generated risk semantic embedding vector and the semantic vector of each template in the risk scenario library; Select the interactive risk scenario template with the highest similarity as the matching template; The entities and relationships in the current financial event knowledge graph are instantiated into the role and asset slots of the matching template, and the causal and intensity information contained in the risk semantic embedding vector is filled into the action and constraint slots to generate specific risk interaction scenarios.

8. The financial management risk early warning system based on multi-dimensional machine learning algorithm as described in claim 1, characterized in that, The step of performing risk field-trend field iterative decoupling on the risk field intensity distribution map and the low-frequency trend component of the original financial data stream includes: The raw financial data stream is filtered to separate its low-frequency trend components. The risk field intensity distribution map is regarded as a risk disturbance field superimposed on the low-frequency trend component; A hybrid model of risk disturbance field and low-frequency trend component is established. The estimation of risk disturbance field is adjusted by iterative optimization algorithm so that the residual of the hybrid model after fitting the original data and removing the estimated risk disturbance field is minimized. When the iteration converges, the final estimated risk disturbance field is purified to obtain a purified risk fluctuation signal. The amplitude of the purified risk fluctuation signal is normalized and its time axis is aligned and calibrated with the standard financial reporting period to synthesize the final standardized financial risk early warning signal.

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